From 4092854b0bea9e1fc02f29222c6f7cd876e565a5 Mon Sep 17 00:00:00 2001
From: shuishen <1109946754@qq.com>
Date: Mon, 17 Aug 2026 09:43:20 +0800
Subject: [PATCH] feat:项目基础整理
---
.env.example | 2
capabilities/15-trajectory-analysis/run_trajectory_analysis.py | 57 ++
apps/workbench-console/README.md | 29 +
apps/workbench-console/src/components/TrajectoryAnalysisPanel.vue | 36
capabilities/15-trajectory-analysis/prepare_real_flight.py | 419 ++++++++++++++++++
apps/workbench-console/src/components/TrajectoryMap.vue | 93 +++
apps/workbench-console/.env.example | 2
scripts/serve_workbench_console.py | 256 ++++++++++
capabilities/15-trajectory-analysis/tests/test_prepare_real_flight.py | 78 +++
/dev/null | 0
apps/workbench-console/src/components/ObjectDetectionPanel.vue | 51 +-
capabilities/01-object-detection/README.md | 5
apps/workbench-console/src/api/artifacts.ts | 133 +----
apps/workbench-console/src/views/OverviewView.vue | 5
capabilities/15-trajectory-analysis/requirements.txt | 1
apps/workbench-console/vite.config.ts | 3
apps/workbench-console/src/stores/artifacts.ts | 64 --
capabilities/15-trajectory-analysis/README.md | 26 +
capabilities/15-trajectory-analysis/tests/test_demo.py | 2
PROJECT_CONTEXT.md | 20
apps/workbench-console/src/vite-env.d.ts | 1
apps/workbench-console/src/styles.css | 82 +++
22 files changed, 1,137 insertions(+), 228 deletions(-)
diff --git a/.env.example b/.env.example
index 24da9ff..d269229 100644
--- a/.env.example
+++ b/.env.example
@@ -8,3 +8,5 @@
MODEL_API_BASE_URL=
MODEL_API_KEY=
+# Optional public client-side map service token for the local console
+VITE_TIANDITU_TOKEN=
diff --git a/PROJECT_CONTEXT.md b/PROJECT_CONTEXT.md
index 2d3b4c9..3d6fd78 100644
--- a/PROJECT_CONTEXT.md
+++ b/PROJECT_CONTEXT.md
@@ -54,18 +54,19 @@
| pyproj | 3.7.2 |
| scikit-learn | 1.9.0 |
| matplotlib | 3.11.1 |
+| openpyxl | 3.1.5 |
`geoai-py` is intentionally not installed in this environment because version 0.42.0 has no trajectory, tracking, or behavior-recognition API. The capability consumes timestamped track results that may originate from an upstream GeoAI detection/export workflow. `pip check` passes.
## Local Experiment Console
- Location: `apps/workbench-console/`
-- Frontend: Vue 3 + Vite + Ant Design Vue + Pinia. Trajectory maps use Cesium `1.126.0` with a local grid base layer.
+- Frontend: Vue 3 + Vite + Ant Design Vue + Pinia. Trajectory maps use Cesium `1.126.0`, public ArcGIS World Imagery, and optional TianDiTu image/label layers configured by `apps/workbench-console/.env.local` `VITE_TIANDITU_TOKEN`.
- Build: `Set-Location .\apps\workbench-console; npm install; npm run build`
- Start command: `py -3.12 .\scripts\serve_workbench_console.py`
- URL: `http://127.0.0.1:6173` (built console); Vite development is `http://127.0.0.1:6174/apps/workbench-console/`. Only `6xxx` ports are accepted.
-- Scope: independent, read-only local UI for this workbench. It has no code, API, account, data-upload, or task-execution link to the two drone-product repositories.
-- First release: overview plus live result pages for `01-object-detection` and `15-trajectory-analysis`; other capability pages show their registered status only.
+- Scope: independent, read-only local UI for this workbench. It has no code, account, data-upload, or task-execution link to the two drone-product repositories. The map client directly requests public ArcGIS tiles and, only when configured, TianDiTu tiles.
+- First release: overview plus live result pages for `01-object-detection` and `15-trajectory-analysis`; the trajectory page can switch between the real Tian Dun flight and synthetic validation cases. Other capability pages show their registered status only.
- File exposure: the local server permits only console assets, `shared/outputs`, and source images required for the object-detection comparison. It does not expose the rest of the repository.
## Capability Status
@@ -118,6 +119,7 @@
- Input contract: WGS84 timestamped CSV observations plus GeoJSON reference routes and zones, connected by a `*.case.json` manifest.
- Generated representative inputs: `shared/data/raw/15-trajectory-analysis`
- Inspectable outputs: `shared/outputs/15-trajectory-analysis`
+- User-provided real source data is staged, unchanged, in `shared/data/raw/15-trajectory-analysis/tian-dun-demo-20260814/`: no-fly GeoJSON, flyable-area Gzip binary source, DJI WPMZ KMZ, and an actual-flight XLSX log. `prepare_real_flight.py` produces a single WGS84 case from all four sources at `shared/data/processed/15-trajectory-analysis/tian-dun-flight-19578/`. The Gzip was verified as 46,518 closed `int32 / 1e7` coordinate rings and yields two local flyable polygons for display only; the zone rule still recognizes only `zone_type=restricted`.
Measured validation:
@@ -125,13 +127,16 @@
- The difficult synthetic case has 3 tracks, drops 1 duplicate timestamp, and finds exactly one stop, one route deviation, one restricted-zone event, and one gathering event.
- Directory processing handled 5 tracks and 78 cleaned observations in about 2.8 seconds on CPU.
- Missing required CSV columns fail cleanly with exit code 2. Both 1400 x 980 PNG results were visually inspected.
+- Real Tian Dun flight 19578: 233 observations became one WGS84 drone track; the 10-waypoint WPMZ route, one locally selected no-fly source area and two local flyable polygons were prepared and analyzed in 0.362 seconds. It produced two route-deviation runs (77 and 175 seconds) and one 464-second source-zone intersection, with no stop or gathering event. The static result map was visually inspected.
Current decisions and limitations:
- Keep the boundary explicit: the current Demo is C because it does not call `geoai-py`; using geospatial ecosystem libraries alone does not make it B. `geoai-py` is only a possible upstream source.
- This is rule-based behavior detection, not learned video action recognition, and it assumes track IDs already exist.
- Synthetic data verifies logic only. Do not claim real-world accuracy or batch-run operational data yet.
-- Next decision: obtain one normal and one difficult anonymized real track set, manually label events, then measure false positives, misses, GPS sensitivity, and per-entity thresholds.
+- The Tian Dun zone intersection is a spatial result against an unverified third-party source, not a violation conclusion. The reference route does not encode takeoff, return-to-home or landing, so its two deviation runs require flight-phase-aware validation.
+- The flyable-area binary structure has been decoded, but its authority, publication time and operational semantics remain unverified; it is visual-only until that validation is complete.
+- Next decision: obtain manual truth labels for this real flight and another difficult real flight, then measure false positives, misses, GPS sensitivity and phase-aware thresholds.
## Starting a New Capability
@@ -143,3 +148,10 @@
```
After completing verified work, update this snapshot and the capability README. Change `AGENTS.md` only when a long-lived rule changes.
+## 2026-08-14 工作台更新
+
+- 本地控制台已从只读结果页升级为独立实验工作台：能力页包含新建运行入口、服务端自动发现的案例库和结果工作区，仍不连接任何无人机产品。
+- `scripts/serve_workbench_console.py` 仅监听 `127.0.0.1` 等回环地址，支持轨迹与目标检测的受限 JSON/Base64 上传 API；每次生成唯一运行目录，不覆盖原始数据或既有输出。
+- 轨迹上传入口接受 XLSX、KMZ、禁飞区 GeoJSON 和可选适飞区 Gzip，预处理器已支持缺少适飞区时继续生成可运行 case。
+- 目标检测页面默认并列展示原图与标注图；案例数据从输出目录扫描，历史多模型检测结果可以在同一案例库中选择。
+- 轨迹汇总的 `normal`、`stop`、`route_deviation`、`restricted_zone`、`gathering` 标签在 UI 显示中文，原始英文值保留在 CSV/GeoJSON。
diff --git a/apps/workbench-console/.env.example b/apps/workbench-console/.env.example
new file mode 100644
index 0000000..bee952c
--- /dev/null
+++ b/apps/workbench-console/.env.example
@@ -0,0 +1,2 @@
+# Optional public client-side token for TianDiTu image and label layers.
+VITE_TIANDITU_TOKEN='e110584a27d506da2740edca951683f4'
diff --git a/apps/workbench-console/README.md b/apps/workbench-console/README.md
index dd0d4ad..552bcfd 100644
--- a/apps/workbench-console/README.md
+++ b/apps/workbench-console/README.md
@@ -1,8 +1,9 @@
# GeoAI Workbench 本地实验控制台
这是一个完全独立于无人机产品的本地只读控制台。它只展示本工作区已有能力的
-输入说明、运行元数据和结果工件，不上传数据、不调用外部 API，也不提供删除、
-覆盖或启动算法的操作。
+输入说明、运行元数据和结果工件，不上传数据，也不提供删除、覆盖或启动算法的
+操作。地图会直接请求公开 ArcGIS 影像切片；配置天地图 token 后还会直接请求
+天地图影像和注记切片。
## 启动
@@ -37,6 +38,18 @@
Vite 开发服务固定使用 `http://127.0.0.1:6174/apps/workbench-console/`，会通过本机代理读取 `6173` 的结果工件。
+## 地图底图
+
+Cesium 默认加载公开 ArcGIS World Imagery 底图。要叠加天地图影像和注记，在
+`apps/workbench-console/.env.local` 中填写下列值后重新执行 `npm run build`：
+
+```text
+VITE_TIANDITU_TOKEN=<your-token>
+```
+
+可先参考同目录的 `.env.example`。`VITE_` 变量会被打包进本地浏览器代码，因此
+只应填入适合客户端使用的天地图访问 token，不要填入其他服务密钥。
+
## 目录职责
- `apps/workbench-console/src/`：Vue 3 组件、路由、Pinia 状态与能力适配层。
@@ -44,5 +57,13 @@
- `scripts/serve_workbench_console.py`：只读本地文件服务；仅暴露构建后的控制台、已有输出和目标检测原图，不暴露仓库其余文件。
- `shared/outputs/`：能力原始输出；控制台不复制、不修改这些文件。
-首版已接入 `01-object-detection` 和 `15-trajectory-analysis`。其他能力保留目录和
-边界状态，待其首个 Demo 产出可检查工件后再接入。
+首版已接入 `01-object-detection` 和 `15-trajectory-analysis`。田墩实飞案例会显示
+实飞轨迹、计划航线、禁飞区与适飞区，并提供轨迹和区域范围的聚焦按钮。其他能力
+保留目录和边界状态，待其首个 Demo 产出可检查工件后再接入。
+## 本地运行工作台
+
+控制台现支持可操作的本地实验运行，并保持与无人机产品完全独立。能力页面提供新建运行入口和可搜索扩展的案例库，案例由本地服务从运行目录自动发现，不再依赖前端硬编码 Tabs。
+
+- 轨迹分析上传 `XLSX`、`KMZ`、禁飞区 `GeoJSON` 和可选适飞区 `Gzip`，原始、处理和输出分别归档在 `shared/data/raw`、`shared/data/processed`、`shared/outputs`。
+- 目标检测上传最多 12 张 `JPG/JPEG/PNG`，原图和标注图默认并列对比，每次生成独立运行编号。
+- 本地 API 为 `GET/POST /api/trajectory/runs` 和 `GET/POST /api/object-detection/runs`，仅监听回环地址，端口必须是 `6xxx`；服务只调用固定虚拟环境和能力脚本，不接受任意命令或任意路径。
diff --git a/apps/workbench-console/src/api/artifacts.ts b/apps/workbench-console/src/api/artifacts.ts
index 47de970..21202bc 100644
--- a/apps/workbench-console/src/api/artifacts.ts
+++ b/apps/workbench-console/src/api/artifacts.ts
@@ -1,101 +1,46 @@
-export interface Detection {
- class_id: number;
- class_name: string;
- confidence: number;
- bbox_xyxy: number[];
-}
-
-export interface DetectionImage {
- file: string;
- annotated_file: string;
- width: number;
- height: number;
- detections: Detection[];
-}
-
-export interface DetectionRun {
- detection_count: number;
- processed_images: number;
- elapsed_seconds: number;
- device: string;
- model: string;
- confidence: number;
- input_dir: string;
- notes: string[];
-}
-
-export interface TrajectoryEvent {
- event_id: string;
- event_type: string;
- track_ids: string[];
- start_time: string;
- end_time: string;
- duration_seconds: number;
-}
-
-export interface TrajectoryRun {
- case_count: number;
- track_count: number;
- event_count: number;
- dropped_duplicate_observations: number;
- elapsed_seconds: number;
- device: string;
- input: string;
- thresholds_by_case: Record<string, Record<string, number>>;
-}
-
-export interface TrajectorySummary {
- track_id: string;
- entity_type: string;
- point_count: string;
- distance_m: string;
- average_speed_mps: string;
- behavior_labels: string;
-}
+export interface Detection { class_id: number; class_name: string; confidence: number; bbox_xyxy: number[]; }
+export interface DetectionImage { file: string; annotated_file: string; width: number; height: number; detections: Detection[]; }
+export interface DetectionRun { detection_count: number; processed_images: number; elapsed_seconds: number; device: string; model: string; confidence: number; input_dir: string; notes: string[]; created_at?: string; }
+export interface DetectionCase { id: string; label: string; note: string; artifactRoot: string; inputRoot: string; createdAt: string; run: DetectionRun; images: DetectionImage[]; }
+export interface TrajectoryEvent { event_id: string; event_type: string; track_ids: string[]; start_time: string; end_time: string; duration_seconds: number; }
+export interface TrajectoryCaseRun { case_id: string; input_count: number; track_count: number; event_count: number; dropped_duplicate_observations: number; elapsed_seconds: number; device: string; input: string; thresholds: Record<string, number>; created_at?: string; }
+export interface TrajectorySummary { track_id: string; entity_type: string; point_count: string; distance_m: string; average_speed_mps: string; behavior_labels: string; }
+export interface TrajectoryCase { id: string; label: string; note: string; artifactRoot: string; showSpatialContext: boolean; showFlyableZones: boolean; createdAt: string; run: TrajectoryCaseRun; events: TrajectoryEvent[]; summary: TrajectorySummary[]; }
+interface CaseDefinition { id: string; label: string; note: string; artifactRoot: string; createdAt: string; }
+interface TrajectoryDefinition extends CaseDefinition { showSpatialContext: boolean; showFlyableZones: boolean; }
+interface DetectionDefinition extends CaseDefinition { inputRoot: string; }
+export interface UploadFilePayload { name: string; content: string; }
export const artifactUrl = (path: string) => `/${path.replace(/\\/g, "/").split("/").map(encodeURIComponent).join("/")}`;
-async function getJson<T>(path: string): Promise<T> {
- const response = await fetch(artifactUrl(path), { cache: "no-store" });
- if (!response.ok) throw new Error(`无法读取 ${path} (${response.status})`);
- return response.json() as Promise<T>;
+async function responseJson<T>(response: Response): Promise<T> {
+ const payload = await response.json().catch(() => ({})) as { error?: string } & T;
+ if (!response.ok) throw new Error(payload.error || `本地服务请求失败 (${response.status})`);
+ return payload;
+}
+async function getJson<T>(path: string): Promise<T> { return responseJson<T>(await fetch(artifactUrl(path), { cache: "no-store" })); }
+async function getText(path: string): Promise<string> { const response = await fetch(artifactUrl(path), { cache: "no-store" }); if (!response.ok) throw new Error(`无法读取 ${path} (${response.status})`); return response.text(); }
+function parseCsv(text: string): TrajectorySummary[] { const lines = text.trim().split(/\r?\n/); const headers = lines.shift()?.replace(/^\uFEFF/, "").split(",") ?? []; return lines.filter(Boolean).map((line) => { const values = line.split(","); return Object.fromEntries(headers.map((header, index) => [header, values[index] ?? ""])) as unknown as TrajectorySummary; }); }
+
+export async function loadTrajectoryArtifacts(): Promise<Record<string, TrajectoryCase>> {
+ const { runs } = await getJson<{ runs: TrajectoryDefinition[] }>("api/trajectory/runs");
+ const cases = await Promise.all(runs.map(async (definition) => {
+ const [run, eventPayload, summary] = await Promise.all([getJson<TrajectoryCaseRun>(`${definition.artifactRoot}/run_metadata.json`), getJson<{ events: TrajectoryEvent[] }>(`${definition.artifactRoot}/events.json`), getText(`${definition.artifactRoot}/trajectory_summary.csv`)]);
+ return { ...definition, run, events: eventPayload.events, summary: parseCsv(summary) } satisfies TrajectoryCase;
+ }));
+ return Object.fromEntries(cases.map((item) => [item.id, item]));
}
-async function getText(path: string): Promise<string> {
- const response = await fetch(artifactUrl(path), { cache: "no-store" });
- if (!response.ok) throw new Error(`无法读取 ${path} (${response.status})`);
- return response.text();
+export async function loadDetectionArtifacts(): Promise<Record<string, DetectionCase>> {
+ const { runs } = await getJson<{ runs: DetectionDefinition[] }>("api/object-detection/runs");
+ const cases = await Promise.all(runs.map(async (definition) => {
+ const [run, payload] = await Promise.all([getJson<DetectionRun>(`${definition.artifactRoot}/run_metadata.json`), getJson<{ images: DetectionImage[] }>(`${definition.artifactRoot}/detections.json`)]);
+ return { ...definition, run, images: payload.images } satisfies DetectionCase;
+ }));
+ return Object.fromEntries(cases.map((item) => [item.id, item]));
}
-function parseCsv(text: string): TrajectorySummary[] {
- const lines = text.trim().split(/\r?\n/);
- const headers = lines.shift()?.replace(/^\uFEFF/, "").split(",") ?? [];
- return lines.filter(Boolean).map((line) => {
- const values = line.split(",");
- return Object.fromEntries(headers.map((header, index) => [header, values[index] ?? ""])) as unknown as TrajectorySummary;
- });
-}
-
-export async function loadDetectionArtifacts() {
- const [run, payload] = await Promise.all([
- getJson<DetectionRun>("shared/outputs/01-object-detection/run_metadata.json"),
- getJson<{ images: DetectionImage[] }>("shared/outputs/01-object-detection/detections.json")
- ]);
- return { run, images: payload.images };
-}
-
-export async function loadTrajectoryArtifacts() {
- const [run, difficultEvents, difficultSummary, normalSummary] = await Promise.all([
- getJson<TrajectoryRun>("shared/outputs/15-trajectory-analysis/run_metadata.json"),
- getJson<{ events: TrajectoryEvent[] }>("shared/outputs/15-trajectory-analysis/difficult/events.json"),
- getText("shared/outputs/15-trajectory-analysis/difficult/trajectory_summary.csv"),
- getText("shared/outputs/15-trajectory-analysis/normal/trajectory_summary.csv")
- ]);
- return {
- run,
- cases: {
- difficult: { events: difficultEvents.events, summary: parseCsv(difficultSummary) },
- normal: { events: [], summary: parseCsv(normalSummary) }
- }
- };
-}
+async function postRun<T>(path: string, body: object): Promise<T> { return responseJson<T>(await fetch(artifactUrl(path), { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify(body) })); }
+export async function createTrajectoryRun(files: { flight: UploadFilePayload; route: UploadFilePayload; restricted: UploadFilePayload; flyable?: UploadFilePayload }) { return postRun<{ run: TrajectoryDefinition }>("api/trajectory/runs", { files }); }
+export async function createDetectionRun(images: UploadFilePayload[]) { return postRun<{ run: DetectionDefinition }>("api/object-detection/runs", { images }); }
+export function readFileAsPayload(file: File): Promise<UploadFilePayload> { return new Promise((resolve, reject) => { const reader = new FileReader(); reader.onerror = () => reject(new Error(`无法读取 ${file.name}`)); reader.onload = () => { const value = String(reader.result ?? ""); resolve({ name: file.name, content: value.slice(value.indexOf(",") + 1) }); }; reader.readAsDataURL(file); }); }
diff --git a/apps/workbench-console/src/components/ObjectDetectionPanel.vue b/apps/workbench-console/src/components/ObjectDetectionPanel.vue
index 6959579..a9ba213 100644
--- a/apps/workbench-console/src/components/ObjectDetectionPanel.vue
+++ b/apps/workbench-console/src/components/ObjectDetectionPanel.vue
@@ -1,37 +1,40 @@
<script setup lang="ts">
import { computed, onMounted, ref } from "vue";
-import { FileImageOutlined, InfoCircleOutlined } from "@ant-design/icons-vue";
-
-import { artifactUrl } from "@/api/artifacts";
+import { FileImageOutlined, InfoCircleOutlined, PlayCircleOutlined, UploadOutlined } from "@ant-design/icons-vue";
+import { createDetectionRun, artifactUrl, readFileAsPayload } from "@/api/artifacts";
import ArtifactState from "@/components/ArtifactState.vue";
import { useArtifactStore } from "@/stores/artifacts";
const store = useArtifactStore();
-const mode = ref<"annotated" | "original">("annotated");
+const caseId = ref("");
const selectedName = ref("");
-const selectedImage = computed(() => store.detectionImages.find((item) => item.file === selectedName.value) ?? store.detectionImages[0]);
-const previewSource = computed(() => {
- if (!selectedImage.value) return "";
- const path = mode.value === "annotated"
- ? `shared/outputs/01-object-detection/${selectedImage.value.annotated_file}`
- : `shared/data/raw/01-object-detection/${selectedImage.value.file}`;
- return artifactUrl(path);
-});
-
-onMounted(async () => {
- await store.loadDetection();
- const mostDetections = [...store.detectionImages].sort((left, right) => right.detections.length - left.detections.length)[0];
- selectedName.value = mostDetections?.file ?? "";
-});
+const showRunForm = ref(false);
+const files = ref<File[]>([]);
+const running = ref(false);
+const runError = ref<string | null>(null);
+const searchText = ref("");
+const currentCase = computed(() => store.detectionCases[caseId.value] ?? Object.values(store.detectionCases)[0]);
+const selectedImage = computed(() => currentCase.value?.images.find((item) => item.file === selectedName.value) ?? currentCase.value?.images[0]);
+const caseOptions = computed(() => Object.values(store.detectionCases).map((item) => ({ value: item.id, label: item.label })));
+const filteredCaseOptions = computed(() => caseOptions.value.filter((item) => item.label.toLowerCase().includes(searchText.value.trim().toLowerCase())));
+function syncSelection() { const candidate = currentCase.value?.images.reduce((best, item) => item.detections.length > (best?.detections.length ?? -1) ? item : best, undefined as typeof currentCase.value.images[number] | undefined); selectedName.value = candidate?.file ?? ""; }
+function beforeUpload(file: File) { files.value = [...files.value, file]; return false; }
+function removeFile(file: { name: string }) { files.value = files.value.filter((item) => item.name !== file.name); }
+async function submitRun() { if (!files.value.length) { runError.value = "请至少选择一张 JPG、JPEG 或 PNG 图像。"; return; } running.value = true; runError.value = null; try { const images = await Promise.all(files.value.map(readFileAsPayload)); const { run } = await createDetectionRun(images); await store.loadDetection(true); caseId.value = run.id; syncSelection(); files.value = []; showRunForm.value = false; } catch (error) { runError.value = error instanceof Error ? error.message : "目标检测运行失败"; } finally { running.value = false; } }
+onMounted(async () => { await store.loadDetection(); caseId.value = Object.keys(store.detectionCases)[0] ?? ""; syncSelection(); });
</script>
<template>
<ArtifactState :loading="store.loading" :error="store.error" />
- <template v-if="store.detectionRun && selectedImage">
- <a-row :gutter="[18, 18]">
- <a-col :xs="24" :xl="17"><section class="surface-section"><div class="section-toolbar"><a-select v-model:value="selectedName" :options="store.detectionImages.map((item) => ({ value: item.file, label: `${item.file} · ${item.detections.length} 个候选` }))" /><a-segmented v-model:value="mode" :options="[{ label: '标注结果', value: 'annotated' }, { label: '原始影像', value: 'original' }]" /></div><a-image class="detection-image" :src="previewSource" :alt="`${selectedImage.file} 预览`" /><p class="image-caption">{{ selectedImage.width }} x {{ selectedImage.height }} 像素 · {{ selectedImage.detections.length }} 个候选</p></section></a-col>
- <a-col :xs="24" :xl="7"><section class="surface-section"><h2>本次运行</h2><a-descriptions size="small" :column="1"><a-descriptions-item label="处理影像">{{ store.detectionRun.processed_images }} 张</a-descriptions-item><a-descriptions-item label="检测候选">{{ store.detectionRun.detection_count }} 个</a-descriptions-item><a-descriptions-item label="耗时">{{ store.detectionRun.elapsed_seconds }} 秒</a-descriptions-item><a-descriptions-item label="设备">{{ store.detectionRun.device }}</a-descriptions-item><a-descriptions-item label="模型">{{ store.detectionRun.model }}</a-descriptions-item><a-descriptions-item label="阈值">{{ store.detectionRun.confidence }}</a-descriptions-item></a-descriptions><a-divider /><a-space direction="vertical"><a-button type="link" :href="artifactUrl('shared/outputs/01-object-detection/detections.json')" target="_blank"><FileImageOutlined />检测 JSON</a-button><a-button type="link" :href="artifactUrl('shared/outputs/01-object-detection/run_metadata.json')" target="_blank"><InfoCircleOutlined />运行元数据</a-button></a-space></section></a-col>
- </a-row>
- <section class="surface-section"><div class="section-heading"><div><h2>当前影像检测明细</h2><p>边界框是 JPEG 像素坐标，不含可用地理坐标。</p></div></div><a-table :data-source="selectedImage.detections" :pagination="false" row-key="bbox_xyxy" size="small" :scroll="{ x: 680 }"><a-table-column title="类别" data-index="class_name" key="class_name" /><a-table-column title="置信度" key="confidence" align="right"><template #default="{ record }">{{ (record.confidence * 100).toFixed(1) }}%</template></a-table-column><a-table-column title="像素框 x1, y1, x2, y2" key="bbox"><template #default="{ record }">{{ record.bbox_xyxy.map((value: number) => value.toFixed(1)).join(', ') }}</template></a-table-column><template #emptyText>该影像没有达到当前阈值的候选目标。</template></a-table></section>
+ <section class="workspace-command">
+ <div><h2>新建检测运行</h2><p>上传少量代表性影像，CPU 基线会生成独立结果，不覆盖既有案例。</p></div>
+ <a-button type="primary" @click="showRunForm = !showRunForm"><PlayCircleOutlined />{{ showRunForm ? "收起运行表单" : "上传并运行" }}</a-button>
+ </section>
+ <section v-if="showRunForm" class="surface-section run-form"><a-alert type="info" show-icon message="单次最多 12 张图像；当前基线识别人员和常见车辆，树木不在有效类别内。" /><a-upload class="run-upload" multiple accept=".jpg,.jpeg,.png" :file-list="files.map((file) => ({ uid: file.name, name: file.name, status: 'done' as const }))" :before-upload="beforeUpload" @remove="removeFile"><a-button><UploadOutlined />选择图像</a-button></a-upload><a-alert v-if="runError" type="error" show-icon :message="runError" /><a-button type="primary" :loading="running" :disabled="!files.length" @click="submitRun"><PlayCircleOutlined />开始检测</a-button></section>
+ <template v-if="currentCase && selectedImage">
+ <div class="detection-workspace">
+ <a-row :gutter="[18, 18]"><a-col :xs="24" :xl="5"><section class="surface-section run-library"><h2>案例库</h2><a-input-search v-model:value="searchText" placeholder="搜索运行" allow-clear /><a-list size="small" :data-source="filteredCaseOptions"><template #renderItem="{ item }"><a-list-item class="run-item" :class="{ active: item.value === currentCase.id }" @click="caseId = item.value; syncSelection()">{{ item.label }}</a-list-item></template></a-list></section></a-col><a-col :xs="24" :xl="19"><section class="surface-section"><div class="section-toolbar"><a-select v-model:value="selectedName" :options="currentCase.images.map((item) => ({ value: item.file, label: `${item.file} · ${item.detections.length} 个候选` }))" /><span class="toolbar-note">{{ currentCase.note }}</span></div><div class="comparison-grid"><figure><figcaption>原始影像</figcaption><a-image class="detection-image" :src="artifactUrl(`${currentCase.inputRoot}/${selectedImage.file}`)" :alt="`${selectedImage.file} 原始影像`" /></figure><figure><figcaption>标注结果</figcaption><a-image class="detection-image" :src="artifactUrl(`${currentCase.artifactRoot}/${selectedImage.annotated_file}`)" :alt="`${selectedImage.file} 标注结果`" /></figure></div><p class="image-caption">{{ selectedImage.width }} x {{ selectedImage.height }} 像素 · {{ selectedImage.detections.length }} 个候选</p></section></a-col></a-row>
+ <a-row :gutter="[18, 18]"><a-col :xs="24" :xl="8"><section class="surface-section"><h2>本次运行</h2><a-descriptions size="small" :column="1"><a-descriptions-item label="处理影像">{{ currentCase.run.processed_images }} 张</a-descriptions-item><a-descriptions-item label="检测候选">{{ currentCase.run.detection_count }} 个</a-descriptions-item><a-descriptions-item label="耗时">{{ currentCase.run.elapsed_seconds }} 秒</a-descriptions-item><a-descriptions-item label="设备">{{ currentCase.run.device }}</a-descriptions-item><a-descriptions-item label="模型">{{ currentCase.run.model }}</a-descriptions-item></a-descriptions><a-divider /><a-space direction="vertical"><a-button type="link" :href="artifactUrl(`${currentCase.artifactRoot}/detections.json`)" target="_blank"><FileImageOutlined />检测 JSON</a-button><a-button type="link" :href="artifactUrl(`${currentCase.artifactRoot}/run_metadata.json`)" target="_blank"><InfoCircleOutlined />运行元数据</a-button></a-space></section></a-col><a-col :xs="24" :xl="16"><section class="surface-section"><div class="section-heading"><div><h2>当前影像检测明细</h2><p>边界框为 JPEG 像素坐标，不含可用地理坐标。</p></div></div><a-table :data-source="selectedImage.detections" :pagination="false" row-key="bbox_xyxy" size="small" :scroll="{ x: 680 }"><a-table-column title="类别" data-index="class_name" key="class_name" /><a-table-column title="置信度" key="confidence" align="right"><template #default="{ record }">{{ (record.confidence * 100).toFixed(1) }}%</template></a-table-column><a-table-column title="像素框 x1, y1, x2, y2" key="bbox"><template #default="{ record }">{{ record.bbox_xyxy.map((value: number) => value.toFixed(1)).join(', ') }}</template></a-table-column><template #emptyText>该影像没有达到当前阈值的候选目标。</template></a-table></section></a-col></a-row>
+ </div>
</template>
</template>
diff --git a/apps/workbench-console/src/components/TrajectoryAnalysisPanel.vue b/apps/workbench-console/src/components/TrajectoryAnalysisPanel.vue
index 8544d8d..d61dff2 100644
--- a/apps/workbench-console/src/components/TrajectoryAnalysisPanel.vue
+++ b/apps/workbench-console/src/components/TrajectoryAnalysisPanel.vue
@@ -1,28 +1,34 @@
<script setup lang="ts">
import { computed, defineAsyncComponent, onMounted, ref } from "vue";
-import { DownloadOutlined } from "@ant-design/icons-vue";
-
-import { artifactUrl } from "@/api/artifacts";
+import { DownloadOutlined, PlayCircleOutlined, UploadOutlined } from "@ant-design/icons-vue";
+import { artifactUrl, createTrajectoryRun, readFileAsPayload } from "@/api/artifacts";
import ArtifactState from "@/components/ArtifactState.vue";
import { useArtifactStore } from "@/stores/artifacts";
const TrajectoryMap = defineAsyncComponent(() => import("@/components/TrajectoryMap.vue"));
-
const store = useArtifactStore();
-const caseId = ref<"difficult" | "normal">("difficult");
-const currentCase = computed(() => store.trajectoryCases[caseId.value] ?? { events: [], summary: [] });
-const eventNames: Record<string, string> = { stop: "停留", route_deviation: "偏航", restricted_zone: "进入禁入区", gathering: "聚集" };
-const caseNote = computed(() => caseId.value === "difficult" ? "含停留、偏航、禁入区、聚集与重复观测。" : "连续移动且贴合参考路线，预期无事件。" );
-const difficultRules = computed(() => store.trajectoryRun?.thresholds_by_case.difficult ?? {});
-
-onMounted(() => store.loadTrajectory());
+const caseId = ref(""); const showRunForm = ref(false); const running = ref(false); const runError = ref<string | null>(null);
+const searchText = ref("");
+const inputs = ref<{ flight?: File; route?: File; restricted?: File; flyable?: File }>({});
+const currentCase = computed(() => store.trajectoryCases[caseId.value] ?? Object.values(store.trajectoryCases)[0]);
+const caseOptions = computed(() => Object.values(store.trajectoryCases).map((item) => ({ label: item.label, value: item.id })));
+const filteredCaseOptions = computed(() => caseOptions.value.filter((item) => item.label.toLowerCase().includes(searchText.value.trim().toLowerCase())));
+const eventNames: Record<string, string> = { normal: "正常", stop: "停留", route_deviation: "偏航", restricted_zone: "进入禁入区", gathering: "聚集" };
+const currentRules = computed(() => currentCase.value?.run.thresholds ?? {});
+const outputFiles = computed(() => { const base = ["events.json", "events.geojson", "trajectories.geojson", "reference_routes.geojson", "zones.geojson", "run_metadata.json"]; return currentCase.value?.showFlyableZones ? [...base.slice(0, 5), "flyable_zones.geojson", "run_metadata.json"] : base; });
+function beforeUpload(key: keyof typeof inputs.value) { return (file: File) => { inputs.value = { ...inputs.value, [key]: file }; return false; }; }
+function clearInput(key: keyof typeof inputs.value) { return () => { const copy = { ...inputs.value }; delete copy[key]; inputs.value = copy; }; }
+function behaviorTags(value: string) { return (value || "normal").split("|").filter(Boolean).map((item) => ({ raw: item, label: eventNames[item] || item })); }
+async function submitRun() { const { flight, route, restricted, flyable } = inputs.value; if (!flight || !route || !restricted) { runError.value = "请提供实际飞行 XLSX、规划航线 KMZ 和禁飞区 GeoJSON。"; return; } running.value = true; runError.value = null; try { const [flightPayload, routePayload, restrictedPayload, flyablePayload] = await Promise.all([readFileAsPayload(flight), readFileAsPayload(route), readFileAsPayload(restricted), flyable ? readFileAsPayload(flyable) : Promise.resolve(undefined)]); const { run } = await createTrajectoryRun({ flight: flightPayload, route: routePayload, restricted: restrictedPayload, ...(flyablePayload ? { flyable: flyablePayload } : {}) }); await store.loadTrajectory(true); caseId.value = run.id; inputs.value = {}; showRunForm.value = false; } catch (error) { runError.value = error instanceof Error ? error.message : "轨迹分析运行失败"; } finally { running.value = false; } }
+onMounted(async () => { await store.loadTrajectory(); caseId.value = Object.keys(store.trajectoryCases)[0] ?? ""; });
</script>
<template>
<ArtifactState :loading="store.loading" :error="store.error" />
- <template v-if="store.trajectoryRun">
- <a-row :gutter="[18, 18]"><a-col :xs="24" :xl="17"><section class="surface-section"><div class="section-toolbar"><a-segmented v-model:value="caseId" :options="[{ label: '困难样本', value: 'difficult' }, { label: '正常样本', value: 'normal' }]" /><span class="toolbar-note">{{ caseNote }}</span></div><TrajectoryMap :case-id="caseId" /></section></a-col><a-col :xs="24" :xl="7"><section class="surface-section"><h2>本次运行</h2><a-descriptions size="small" :column="1"><a-descriptions-item label="案例">{{ store.trajectoryRun.case_count }} 个</a-descriptions-item><a-descriptions-item label="轨迹">{{ store.trajectoryRun.track_count }} 条</a-descriptions-item><a-descriptions-item label="事件">{{ store.trajectoryRun.event_count }} 个</a-descriptions-item><a-descriptions-item label="重复清洗">{{ store.trajectoryRun.dropped_duplicate_observations }} 条</a-descriptions-item><a-descriptions-item label="耗时">{{ store.trajectoryRun.elapsed_seconds }} 秒</a-descriptions-item><a-descriptions-item label="设备">{{ store.trajectoryRun.device }}</a-descriptions-item></a-descriptions><a-divider /><h3>默认规则</h3><a-descriptions size="small" :column="1"><a-descriptions-item label="停留速度">{{ difficultRules.stop_speed_mps }} m/s</a-descriptions-item><a-descriptions-item label="偏航距离">{{ difficultRules.route_deviation_m }} m</a-descriptions-item><a-descriptions-item label="聚集距离">{{ difficultRules.gathering_radius_m }} m</a-descriptions-item></a-descriptions></section></a-col></a-row>
- <a-row :gutter="[18, 18]" class="result-row"><a-col :xs="24" :xl="10"><section class="surface-section"><div class="section-heading"><div><h2>事件记录</h2><p>{{ currentCase.events.length }} 个规则事件</p></div></div><a-empty v-if="!currentCase.events.length" description="正常样本未触发规则事件" /><a-timeline v-else><a-timeline-item v-for="event in currentCase.events" :key="event.event_id" :color="event.event_type === 'gathering' ? 'green' : event.event_type === 'restricted_zone' ? 'gold' : event.event_type === 'route_deviation' ? 'purple' : 'red'"><strong>{{ eventNames[event.event_type] || event.event_type }}</strong><p>{{ event.track_ids.join('、') }}</p><small>{{ new Date(event.start_time).toLocaleString('zh-CN', { hour12: false }) }} · {{ event.duration_seconds }} 秒</small></a-timeline-item></a-timeline></section></a-col><a-col :xs="24" :xl="14"><section class="surface-section"><div class="section-heading"><div><h2>轨迹汇总</h2><p>聚类 ID 仅用于探索相似轨迹，不代表确认异常。</p></div></div><a-table :data-source="currentCase.summary" :pagination="false" row-key="track_id" size="small" :scroll="{ x: 760 }"><a-table-column title="轨迹" data-index="track_id" key="track_id" /><a-table-column title="对象" data-index="entity_type" key="entity_type" /><a-table-column title="点数" data-index="point_count" key="point_count" align="right" /><a-table-column title="距离" key="distance" align="right"><template #default="{ record }">{{ Number(record.distance_m).toFixed(1) }} m</template></a-table-column><a-table-column title="均速" key="speed" align="right"><template #default="{ record }">{{ Number(record.average_speed_mps).toFixed(2) }} m/s</template></a-table-column><a-table-column title="行为标签" data-index="behavior_labels" key="behavior_labels" /></a-table></section></a-col></a-row>
- <section class="surface-section result-files"><a-space wrap><a-button v-for="file in ['events.json', 'events.geojson', 'trajectories.geojson', 'run_metadata.json']" :key="file" :href="artifactUrl(`shared/outputs/15-trajectory-analysis/${caseId}/${file}`)" target="_blank"><DownloadOutlined />{{ file }}</a-button></a-space></section>
+ <section class="workspace-command"><div><h2>新建轨迹分析</h2><p>上传原始航线与飞行日志后，控制台会自动转换为分析输入并保存为新的本地运行。</p></div><a-button type="primary" @click="showRunForm = !showRunForm"><PlayCircleOutlined />{{ showRunForm ? "收起运行表单" : "上传并分析" }}</a-button></section>
+ <section v-if="showRunForm" class="surface-section run-form"><a-alert type="info" show-icon message="适飞区为可选图层，只用于地图展示；禁飞区相交是技术空间结果，不是违规结论。" /><div class="upload-grid"><div><label>实际飞行轨迹（XLSX）</label><a-upload accept=".xlsx" :max-count="1" :before-upload="beforeUpload('flight')" @remove="clearInput('flight')"><a-button><UploadOutlined />{{ inputs.flight?.name || "选择 XLSX" }}</a-button></a-upload></div><div><label>规划航线（KMZ）</label><a-upload accept=".kmz" :max-count="1" :before-upload="beforeUpload('route')" @remove="clearInput('route')"><a-button><UploadOutlined />{{ inputs.route?.name || "选择 KMZ" }}</a-button></a-upload></div><div><label>禁飞区（GeoJSON）</label><a-upload accept=".geojson,.json" :max-count="1" :before-upload="beforeUpload('restricted')" @remove="clearInput('restricted')"><a-button><UploadOutlined />{{ inputs.restricted?.name || "选择 GeoJSON" }}</a-button></a-upload></div><div><label>适飞区（可选 Gzip）</label><a-upload accept=".gzip,.gz" :max-count="1" :before-upload="beforeUpload('flyable')" @remove="clearInput('flyable')"><a-button><UploadOutlined />{{ inputs.flyable?.name || "选择 Gzip" }}</a-button></a-upload></div></div><a-alert v-if="runError" type="error" show-icon :message="runError" /><a-button type="primary" :loading="running" @click="submitRun"><PlayCircleOutlined />开始轨迹分析</a-button></section>
+ <template v-if="currentCase"><a-row :gutter="[18, 18]"><a-col :xs="24" :xl="5"><section class="surface-section run-library"><h2>案例库</h2><a-input-search v-model:value="searchText" placeholder="搜索运行" allow-clear /><a-list size="small" :data-source="filteredCaseOptions"><template #renderItem="{ item }"><a-list-item class="run-item" :class="{ active: item.value === currentCase.id }" @click="caseId = item.value">{{ item.label }}</a-list-item></template></a-list></section></a-col><a-col :xs="24" :xl="19"><section class="surface-section"><div class="section-toolbar"><span class="toolbar-note">{{ currentCase.note }}</span></div><TrajectoryMap :artifact-root="currentCase.artifactRoot" :show-spatial-context="currentCase.showSpatialContext" :show-flyable-zones="currentCase.showFlyableZones" /></section></a-col></a-row>
+ <a-row :gutter="[18, 18]" class="result-row"><a-col :xs="24" :xl="8"><section class="surface-section"><h2>本次运行</h2><a-descriptions size="small" :column="1"><a-descriptions-item label="轨迹">{{ currentCase.run.track_count }} 条</a-descriptions-item><a-descriptions-item label="事件">{{ currentCase.run.event_count }} 个</a-descriptions-item><a-descriptions-item label="重复清洗">{{ currentCase.run.dropped_duplicate_observations }} 条</a-descriptions-item><a-descriptions-item label="耗时">{{ currentCase.run.elapsed_seconds }} 秒</a-descriptions-item><a-descriptions-item label="设备">{{ currentCase.run.device }}</a-descriptions-item></a-descriptions><a-divider /><h3>默认规则</h3><a-descriptions size="small" :column="1"><a-descriptions-item label="停留速度">{{ currentRules.stop_speed_mps }} m/s</a-descriptions-item><a-descriptions-item label="偏航距离">{{ currentRules.route_deviation_m }} m</a-descriptions-item><a-descriptions-item label="聚集距离">{{ currentRules.gathering_radius_m }} m</a-descriptions-item></a-descriptions></section></a-col><a-col :xs="24" :xl="7"><section class="surface-section"><div class="section-heading"><div><h2>事件记录</h2><p>{{ currentCase.events.length }} 个规则事件</p></div></div><a-empty v-if="!currentCase.events.length" description="当前案例未触发规则事件" /><a-timeline v-else><a-timeline-item v-for="event in currentCase.events" :key="event.event_id" :color="event.event_type === 'gathering' ? 'green' : event.event_type === 'restricted_zone' ? 'gold' : event.event_type === 'route_deviation' ? 'purple' : 'red'"><strong>{{ eventNames[event.event_type] || event.event_type }}</strong><p>{{ event.track_ids.join('、') }}</p><small>{{ new Date(event.start_time).toLocaleString('zh-CN', { hour12: false }) }} · {{ event.duration_seconds }} 秒</small></a-timeline-item></a-timeline></section></a-col><a-col :xs="24" :xl="9"><section class="surface-section"><div class="section-heading"><div><h2>轨迹汇总</h2><p>行为标签以中文展示；英文值仍保留在结构化输出中。</p></div></div><a-table :data-source="currentCase.summary" :pagination="false" row-key="track_id" size="small" :scroll="{ x: 720 }"><a-table-column title="轨迹" data-index="track_id" key="track_id" /><a-table-column title="点数" data-index="point_count" key="point_count" align="right" /><a-table-column title="距离" key="distance" align="right"><template #default="{ record }">{{ Number(record.distance_m).toFixed(1) }} m</template></a-table-column><a-table-column title="行为标签" key="behavior_labels"><template #default="{ record }"><a-space wrap><a-tag v-for="tag in behaviorTags(record.behavior_labels)" :key="tag.raw" :color="tag.raw === 'normal' ? 'green' : 'gold'">{{ tag.label }}</a-tag></a-space></template></a-table-column></a-table></section></a-col></a-row>
+ <section class="surface-section result-files"><a-space wrap><a-button v-for="file in outputFiles" :key="file" :href="artifactUrl(`${currentCase.artifactRoot}/${file}`)" target="_blank"><DownloadOutlined />{{ file }}</a-button></a-space></section>
</template>
</template>
diff --git a/apps/workbench-console/src/components/TrajectoryMap.vue b/apps/workbench-console/src/components/TrajectoryMap.vue
index 33c8f78..86c5558 100644
--- a/apps/workbench-console/src/components/TrajectoryMap.vue
+++ b/apps/workbench-console/src/components/TrajectoryMap.vue
@@ -1,6 +1,8 @@
<script setup lang="ts">
import { onBeforeUnmount, onMounted, ref, watch } from "vue";
+import { AimOutlined, BorderOutlined } from "@ant-design/icons-vue";
import {
+ ArcGisMapServerImageryProvider,
Color,
ColorMaterialProperty,
ConstantProperty,
@@ -8,17 +10,21 @@
GridImageryProvider,
HeightReference,
PointGraphics,
+ UrlTemplateImageryProvider,
Viewer
} from "cesium";
import { artifactUrl } from "@/api/artifacts";
-const props = defineProps<{ caseId: "difficult" | "normal" }>();
+const props = defineProps<{ artifactRoot: string; showSpatialContext: boolean; showFlyableZones: boolean }>();
const host = ref<HTMLElement>();
const mapError = ref<string | null>(null);
let viewer: Viewer | null = null;
let trajectorySource: GeoJsonDataSource | null = null;
let eventSource: GeoJsonDataSource | null = null;
+let routeSource: GeoJsonDataSource | null = null;
+let zoneSource: GeoJsonDataSource | null = null;
+let flyableZoneSource: GeoJsonDataSource | null = null;
function eventColor(type: string) {
if (type === "route_deviation") return Color.fromCssColorString("#9a5da8");
@@ -33,7 +39,15 @@
mapError.value = null;
if (trajectorySource) viewer.dataSources.remove(trajectorySource, true);
if (eventSource) viewer.dataSources.remove(eventSource, true);
- const root = `shared/outputs/15-trajectory-analysis/${props.caseId}`;
+ if (routeSource) viewer.dataSources.remove(routeSource, true);
+ if (zoneSource) viewer.dataSources.remove(zoneSource, true);
+ if (flyableZoneSource) viewer.dataSources.remove(flyableZoneSource, true);
+ trajectorySource = null;
+ eventSource = null;
+ routeSource = null;
+ zoneSource = null;
+ flyableZoneSource = null;
+ const root = props.artifactRoot;
trajectorySource = await GeoJsonDataSource.load(artifactUrl(`${root}/trajectories.geojson`), { clampToGround: false });
trajectorySource.entities.values.forEach((entity) => {
if (entity.polyline) {
@@ -52,12 +66,78 @@
heightReference: new ConstantProperty(HeightReference.NONE)
});
});
+ if (props.showSpatialContext) {
+ routeSource = await GeoJsonDataSource.load(artifactUrl(`${root}/reference_routes.geojson`), { clampToGround: false });
+ routeSource.entities.values.forEach((entity) => {
+ if (entity.polyline) {
+ entity.polyline.width = new ConstantProperty(3);
+ entity.polyline.material = new ColorMaterialProperty(Color.fromCssColorString("#444444"));
+ }
+ });
+ zoneSource = await GeoJsonDataSource.load(artifactUrl(`${root}/zones.geojson`), { clampToGround: false });
+ zoneSource.entities.values.forEach((entity) => {
+ if (entity.polygon) {
+ entity.polygon.material = new ColorMaterialProperty(Color.fromCssColorString("#e5c951").withAlpha(0.16));
+ entity.polygon.outline = new ConstantProperty(true);
+ entity.polygon.outlineColor = new ConstantProperty(Color.fromCssColorString("#9a7a00"));
+ }
+ });
+ if (props.showFlyableZones) {
+ flyableZoneSource = await GeoJsonDataSource.load(artifactUrl(`${root}/flyable_zones.geojson`), { clampToGround: false });
+ flyableZoneSource.entities.values.forEach((entity) => {
+ if (entity.polygon) {
+ entity.polygon.material = new ColorMaterialProperty(Color.fromCssColorString("#36a269").withAlpha(0.22));
+ entity.polygon.outline = new ConstantProperty(true);
+ entity.polygon.outlineColor = new ConstantProperty(Color.fromCssColorString("#167344"));
+ }
+ });
+ }
+ }
+ if (zoneSource) await viewer.dataSources.add(zoneSource);
+ if (flyableZoneSource) await viewer.dataSources.add(flyableZoneSource);
+ if (routeSource) await viewer.dataSources.add(routeSource);
await viewer.dataSources.add(trajectorySource);
await viewer.dataSources.add(eventSource);
- await viewer.flyTo(trajectorySource, { duration: 0 });
+ await viewer.flyTo(props.showSpatialContext && zoneSource ? zoneSource : trajectorySource, { duration: 0 });
} catch (error) {
mapError.value = error instanceof Error ? error.message : "Cesium 地图加载失败";
}
+}
+
+async function focusTrajectory() {
+ if (viewer && trajectorySource) await viewer.flyTo(trajectorySource, { duration: 0.4 });
+}
+
+async function focusZones() {
+ if (viewer && zoneSource) await viewer.flyTo(zoneSource, { duration: 0.4 });
+}
+
+async function addBaseLayers() {
+ if (!viewer) return;
+ viewer.imageryLayers.removeAll();
+ try {
+ const arcgis = await ArcGisMapServerImageryProvider.fromUrl(
+ "https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer"
+ );
+ viewer.imageryLayers.addImageryProvider(arcgis);
+ } catch {
+ viewer.imageryLayers.addImageryProvider(new GridImageryProvider({ cells: 8, glowWidth: 0 }));
+ }
+ const token = (import.meta.env.VITE_TIANDITU_TOKEN ?? "").trim();
+ if (!token) return;
+ const subdomains = ["0", "1", "2", "3", "4", "5", "6", "7"];
+ viewer.imageryLayers.addImageryProvider(new UrlTemplateImageryProvider({
+ url: `https://t{s}.tianditu.gov.cn/DataServer?T=img_w&x={x}&y={y}&l={z}&tk=${token}`,
+ subdomains,
+ maximumLevel: 18,
+ credit: "Tianditu imagery"
+ }));
+ viewer.imageryLayers.addImageryProvider(new UrlTemplateImageryProvider({
+ url: `https://t{s}.tianditu.gov.cn/DataServer?T=cva_w&x={x}&y={y}&l={z}&tk=${token}`,
+ subdomains,
+ maximumLevel: 18,
+ credit: "Tianditu labels"
+ }));
}
onMounted(async () => {
@@ -74,15 +154,14 @@
selectionIndicator: false,
timeline: false
});
- viewer.imageryLayers.removeAll();
- viewer.imageryLayers.addImageryProvider(new GridImageryProvider({ cells: 8, glowWidth: 0 }));
+ await addBaseLayers();
await drawCase();
});
-watch(() => props.caseId, drawCase);
+watch(() => [props.artifactRoot, props.showSpatialContext, props.showFlyableZones], drawCase);
onBeforeUnmount(() => viewer?.destroy());
</script>
<template>
- <div class="trajectory-map"><div ref="host" class="cesium-host"></div><a-alert v-if="mapError" class="map-error" type="warning" show-icon :message="mapError" /></div>
+ <div class="trajectory-map"><div ref="host" class="cesium-host"></div><div v-if="showSpatialContext" class="map-legend"><span><i class="legend-track"></i>实飞轨迹</span><span><i class="legend-route"></i>计划航线</span><span><i class="legend-restricted"></i>禁飞区</span><span v-if="showFlyableZones"><i class="legend-flyable"></i>适飞区</span></div><div v-if="showSpatialContext" class="map-tools"><a-tooltip title="聚焦轨迹"><a-button shape="circle" aria-label="聚焦轨迹" @click="focusTrajectory"><AimOutlined /></a-button></a-tooltip><a-tooltip title="查看禁飞区范围"><a-button shape="circle" aria-label="查看禁飞区范围" @click="focusZones"><BorderOutlined /></a-button></a-tooltip></div><a-alert v-if="mapError" class="map-error" type="warning" show-icon :message="mapError" /></div>
</template>
diff --git a/apps/workbench-console/src/stores/artifacts.ts b/apps/workbench-console/src/stores/artifacts.ts
index 879183a..2f662a7 100644
--- a/apps/workbench-console/src/stores/artifacts.ts
+++ b/apps/workbench-console/src/stores/artifacts.ts
@@ -1,61 +1,15 @@
import { defineStore } from "pinia";
+import { loadDetectionArtifacts, loadTrajectoryArtifacts, type DetectionCase, type TrajectoryCase } from "@/api/artifacts";
-import {
- loadDetectionArtifacts,
- loadTrajectoryArtifacts,
- type DetectionImage,
- type DetectionRun,
- type TrajectoryRun,
- type TrajectorySummary,
- type TrajectoryEvent
-} from "@/api/artifacts";
-
-interface ArtifactState {
- detectionRun: DetectionRun | null;
- detectionImages: DetectionImage[];
- trajectoryRun: TrajectoryRun | null;
- trajectoryCases: Record<string, { events: TrajectoryEvent[]; summary: TrajectorySummary[] }>;
- loading: boolean;
- error: string | null;
-}
-
+interface ArtifactState { detectionCases: Record<string, DetectionCase>; trajectoryCases: Record<string, TrajectoryCase>; loading: boolean; error: string | null; }
export const useArtifactStore = defineStore("artifacts", {
- state: (): ArtifactState => ({
- detectionRun: null,
- detectionImages: [],
- trajectoryRun: null,
- trajectoryCases: {},
- loading: false,
- error: null
- }),
+ getters: {
+ detectionRun: (state) => Object.values(state.detectionCases)[0]?.run ?? null,
+ detectionImages: (state) => Object.values(state.detectionCases)[0]?.images ?? []
+ },
+ state: (): ArtifactState => ({ detectionCases: {}, trajectoryCases: {}, loading: false, error: null }),
actions: {
- async loadDetection() {
- if (this.detectionRun) return;
- this.loading = true;
- this.error = null;
- try {
- const result = await loadDetectionArtifacts();
- this.detectionRun = result.run;
- this.detectionImages = result.images;
- } catch (error) {
- this.error = error instanceof Error ? error.message : "目标检测结果读取失败";
- } finally {
- this.loading = false;
- }
- },
- async loadTrajectory() {
- if (this.trajectoryRun) return;
- this.loading = true;
- this.error = null;
- try {
- const result = await loadTrajectoryArtifacts();
- this.trajectoryRun = result.run;
- this.trajectoryCases = result.cases;
- } catch (error) {
- this.error = error instanceof Error ? error.message : "轨迹分析结果读取失败";
- } finally {
- this.loading = false;
- }
- }
+ async loadDetection(force = false) { if (!force && Object.keys(this.detectionCases).length) return; this.loading = true; this.error = null; try { this.detectionCases = await loadDetectionArtifacts(); } catch (error) { this.error = error instanceof Error ? error.message : "目标检测结果读取失败"; } finally { this.loading = false; } },
+ async loadTrajectory(force = false) { if (!force && Object.keys(this.trajectoryCases).length) return; this.loading = true; this.error = null; try { this.trajectoryCases = await loadTrajectoryArtifacts(); } catch (error) { this.error = error instanceof Error ? error.message : "轨迹分析结果读取失败"; } finally { this.loading = false; } }
}
});
diff --git a/apps/workbench-console/src/styles.css b/apps/workbench-console/src/styles.css
index ac01e94..d385e06 100644
--- a/apps/workbench-console/src/styles.css
+++ b/apps/workbench-console/src/styles.css
@@ -12,6 +12,82 @@
.loading-block { display: flex; gap: 9px; align-items: center; justify-content: center; min-height: 150px; color: #66716b; }.metric-row { margin-bottom: 30px; }.metric-row .ant-statistic { min-height: 106px; padding: 17px; background: #ffffff; border: 1px solid #d9e0da; box-shadow: 0 9px 25px rgba(20, 43, 30, 0.06); }.metric-row .ant-statistic-content { color: #1c392b; font-size: 29px; }.view-section { margin-top: 32px; }.section-heading { display: flex; justify-content: space-between; gap: 16px; margin-bottom: 14px; }.section-heading h2, .surface-section h2 { margin: 0 0 4px; color: #202b25; font-size: 18px; letter-spacing: 0; }.section-heading p, .surface-section p { margin: 0; color: #66716b; font-size: 12px; }
.capability-card { overflow: hidden; border-radius: 4px !important; border-color: #d9e0da !important; box-shadow: 0 9px 25px rgba(20, 43, 30, 0.07) !important; }.card-media { display: block; width: 100%; height: 260px; object-fit: cover; background: #e0e6e1; }.card-copy { padding: 18px; }.card-topline { display: flex; align-items: flex-start; justify-content: space-between; gap: 12px; }.card-topline h3 { margin: 0 0 6px; color: #202b25; font-size: 16px; letter-spacing: 0; }.card-topline p, .planned-card p { color: #66716b; }.card-copy .ant-space { display: flex; min-height: 40px; align-content: flex-start; margin-top: 14px; }.card-copy .ant-btn { margin-top: 8px; }.planned-card { height: 100%; border-radius: 4px !important; border-color: #d9e0da !important; }.planned-card p { min-height: 38px; margin: 7px 0 8px; font-size: 12px; }
.surface-section { margin-bottom: 18px; padding: 20px; background: #ffffff; border: 1px solid #d9e0da; box-shadow: 0 9px 25px rgba(20, 43, 30, 0.06); }.section-toolbar { display: flex; align-items: center; justify-content: space-between; gap: 12px; margin-bottom: 13px; }.section-toolbar .ant-select { width: min(65%, 540px); }.detection-image { display: block; width: 100%; min-height: 390px; max-height: 660px; overflow: hidden; background: #232e28; text-align: center; }.detection-image .ant-image-img { width: 100%; max-height: 660px; object-fit: contain; }.image-caption { padding-top: 9px; }.surface-section .ant-descriptions { font-size: 12px; }.surface-section .ant-descriptions-item-label { color: #66716b; }.surface-section h3 { margin: 0 0 8px; font-size: 14px; }.result-row { margin-top: 0; }.result-files { margin-bottom: 0; }.toolbar-note { color: #66716b; font-size: 12px; text-align: right; }
-.trajectory-map { position: relative; height: 510px; overflow: hidden; background: #1b2620; }.cesium-host { width: 100%; height: 100%; }.map-error { position: absolute; right: 14px; bottom: 14px; left: 14px; }.cesium-viewer-bottom { display: none !important; }.cesium-viewer-toolbar { top: 10px; right: 10px; }.ant-empty { margin: 70px 0; }.ant-empty-description p { color: #66716b; }
-@media (max-width: 991px) { .application-sider { position: static !important; height: auto; }.navigation-menu { height: auto; max-height: 290px; }.sider-status { display: none; }.view-container { padding: 25px 22px 42px; }.detection-image { min-height: 270px; }.trajectory-map { height: 440px; } }
-@media (max-width: 640px) { .view-container { padding: 20px 14px 34px; }.page-header, .section-toolbar { align-items: stretch; flex-direction: column; }.page-header h1 { font-size: 24px; }.local-tag { align-self: flex-start; }.section-toolbar .ant-select { width: 100%; }.toolbar-note { text-align: left; }.surface-section { padding: 14px; }.trajectory-map { height: 380px; }.card-media { height: 205px; } }
+.workspace-command { display: flex; align-items: center; justify-content: space-between; gap: 18px; margin: 0 0 18px; padding: 16px 20px; background: #eaf3ed; border: 1px solid #c9ddcf; }.workspace-command h2 { margin: 0 0 4px; font-size: 16px; }.workspace-command p { margin: 0; color: #66716b; font-size: 12px; }.run-form { display: grid; gap: 16px; }.run-form .ant-alert { margin: 0; }.run-upload { display: block; }.upload-grid { display: grid; grid-template-columns: repeat(4, minmax(0, 1fr)); gap: 14px; }.upload-grid label { display: block; margin-bottom: 7px; color: #66716b; font-size: 12px; }.run-library { min-height: 100%; }.run-library h2 { margin-bottom: 12px; }.run-item { display: block !important; padding: 9px 10px !important; color: #425148; cursor: pointer; border: 0 !important; }.run-item.active { color: #176b50; background: #e6f2ea; font-weight: 700; }.comparison-grid { display: grid; grid-template-columns: repeat(2, minmax(0, 1fr)); gap: 14px; }.comparison-grid figure { min-width: 0; margin: 0; }.comparison-grid figcaption { margin-bottom: 7px; color: #425148; font-size: 12px; font-weight: 700; }.comparison-grid .detection-image { min-height: 280px; height: 430px; }.comparison-grid .detection-image .ant-image-img { height: 430px; }
+.trajectory-map { position: relative; height: 510px; overflow: hidden; background: #1b2620; }.cesium-host { width: 100%; height: 100%; }.map-error { position: absolute; right: 14px; bottom: 14px; left: 14px; }.map-legend { position: absolute; bottom: 14px; left: 14px; display: flex; gap: 10px; flex-wrap: wrap; max-width: calc(100% - 28px); padding: 7px 9px; color: #f8fbf9; background: rgba(20, 32, 25, 0.82); border: 1px solid rgba(224, 239, 229, 0.45); font-size: 12px; }.map-legend span { display: inline-flex; gap: 5px; align-items: center; white-space: nowrap; }.map-legend i { display: inline-block; width: 12px; height: 12px; border: 1px solid rgba(255, 255, 255, 0.85); }.legend-track { background: #176b50; }.legend-route { background: #444444; }.legend-restricted { background: #e5c951; }.legend-flyable { background: #36a269; }.map-tools { position: absolute; top: 12px; left: 12px; display: flex; gap: 6px; }.map-tools .ant-btn { border-color: #b8c9bd; color: #1f4834; }.cesium-viewer-bottom { display: none !important; }.cesium-viewer-toolbar { top: 10px; right: 10px; }.ant-empty { margin: 70px 0; }.ant-empty-description p { color: #66716b; }
+@media (max-width: 991px) { .application-sider { position: static !important; height: auto; }.navigation-menu { height: auto; max-height: 290px; }.sider-status { display: none; }.view-container { padding: 25px 22px 42px; }.detection-image { min-height: 270px; }.trajectory-map { height: 440px; }.upload-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); } }
+@media (max-width: 640px) { .view-container { padding: 20px 14px 34px; }.page-header, .section-toolbar, .workspace-command { align-items: stretch; flex-direction: column; }.page-header h1 { font-size: 24px; }.local-tag { align-self: flex-start; }.section-toolbar .ant-select { width: 100%; }.toolbar-note { text-align: left; }.surface-section { padding: 14px; }.trajectory-map { height: 380px; }.card-media { height: 205px; }.upload-grid, .comparison-grid { grid-template-columns: 1fr; }.comparison-grid .detection-image, .comparison-grid .detection-image .ant-image-img { height: 300px; } }
+/* Layout refinement: keep the workbench dense, aligned, and readable at desktop widths. */
+.content-layout { min-width: 0; }
+.view-container { width: 100%; max-width: 1680px; padding: 28px 32px 48px; }
+.page-header { margin-bottom: 22px; }
+.page-header h1 { font-size: 27px; }
+.metric-row { margin-bottom: 24px; }
+.metric-row .ant-statistic { min-height: 96px; padding: 15px 16px; }
+.metric-row .ant-statistic-title { margin-bottom: 8px; font-size: 12px; }
+.metric-row .ant-statistic-content { font-size: 26px; }
+.view-section { margin-top: 26px; }
+.section-heading { margin-bottom: 12px; }
+.surface-section { margin-bottom: 16px; padding: 17px 18px; }
+.surface-section h2 { font-size: 17px; }
+.section-toolbar { min-height: 32px; margin-bottom: 10px; }
+.run-library { min-height: 0; height: auto; align-self: flex-start; }
+.run-library .ant-input-search { margin-bottom: 8px; }
+.run-library .ant-list-item { min-height: 34px; }
+.run-item { padding: 7px 9px !important; }
+.trajectory-map { height: 500px; }
+.result-row { align-items: flex-start; }
+.result-row > .ant-col-xl-7,
+.result-row > .ant-col-xl-8,
+.result-row > .ant-col-xl-9 { flex: 0 0 33.333333% !important; max-width: 33.333333% !important; }
+.result-row .surface-section { height: auto; min-height: 100%; }
+.result-files { display: flex; align-items: center; min-height: 54px; padding: 10px 14px; }
+.result-files .ant-space { row-gap: 8px !important; column-gap: 8px !important; }
+.result-files .ant-btn { height: 31px; padding-inline: 10px; font-size: 12px; }
+.surface-section .ant-divider { margin: 16px 0; }
+.surface-section .ant-table-wrapper { margin-top: 8px; }
+.surface-section .ant-table-thead > tr > th { padding: 9px 10px; font-size: 12px; }
+.surface-section .ant-table-tbody > tr > td { padding: 8px 10px; font-size: 12px; }
+.surface-section .ant-timeline { margin-top: 12px; }
+.surface-section .ant-timeline-item { padding-bottom: 17px; }
+.comparison-grid { gap: 12px; }
+.comparison-grid figcaption { padding: 0 2px; }
+.comparison-grid .detection-image, .comparison-grid .detection-image .ant-image-img { height: 400px; }
+.workspace-command { margin-bottom: 16px; padding: 14px 17px; }
+.run-form { gap: 13px; }
+.upload-grid { gap: 12px; }
+
+@media (max-width: 1199px) {
+ .view-container { max-width: 100%; padding-inline: 26px; }
+}
+@media (max-width: 640px) {
+ .view-container { padding: 20px 14px 34px; }
+ .surface-section { padding: 14px; }
+ .trajectory-map { height: 390px; }
+ .result-files { align-items: flex-start; }
+}
+
+/* Equal-height composition within each result band. */
+.run-library { height: 100%; min-height: 100%; align-self: stretch; display: flex; flex-direction: column; }
+.run-library .ant-list { flex: 1 1 auto; min-height: 0; overflow-y: auto; }
+.result-row { align-items: stretch !important; }
+.result-row > .ant-col { display: flex; }
+.result-row > .ant-col > .surface-section { width: 100%; min-height: 100%; margin-bottom: 0; }
+.result-files { margin-top: 24px !important; }
+
+/* Object detection has an explicit two-row rhythm: comparison first, details second. */
+.detection-workspace > .ant-row:first-child > .ant-col > .surface-section { margin-bottom: 0; }
+.detection-workspace > .ant-row + .ant-row { align-items: stretch !important; margin-top: 24px; }
+.detection-workspace > .ant-row + .ant-row > .ant-col { display: flex; }
+.detection-workspace > .ant-row + .ant-row > .ant-col > .surface-section { width: 100%; height: 100%; min-height: 0; margin-bottom: 0; }
+.result-row { margin-top: 8px !important; }
+
+/* The trajectory sidebar must match the visible map card, not the row's trailing margin. */
+@media (min-width: 1200px) {
+ .ant-row:has(.trajectory-map) .run-library { height: 578px; min-height: 578px; }
+}
+
+@media (max-width: 1199px) {
+ .run-library { height: auto; min-height: 0; }
+ .run-library .ant-list { overflow: visible; }
+ .result-row { align-items: stretch !important; }
+}
diff --git a/apps/workbench-console/src/views/OverviewView.vue b/apps/workbench-console/src/views/OverviewView.vue
index 0a2dc9f..07d2961 100644
--- a/apps/workbench-console/src/views/OverviewView.vue
+++ b/apps/workbench-console/src/views/OverviewView.vue
@@ -12,6 +12,7 @@
const store = useArtifactStore();
const router = useRouter();
const planned = computed(() => capabilities.filter((item) => item.status === "planned"));
+const realTrajectory = computed(() => store.trajectoryCases["tian-dun-flight-19578"]);
onMounted(async () => Promise.all([store.loadDetection(), store.loadTrajectory()]));
function openCapability(id: string) { router.push({ name: "capability", params: { id } }); }
@@ -19,8 +20,8 @@
<template>
<div class="view-container"><PageHeader eyebrow="WORKBENCH / OVERVIEW" title="实验结果一眼可见" description="独立于无人机产品的本地控制台。它只读取当前工作区已有结果，不上传数据，也不会触发算法运行。" /><ArtifactState :loading="store.loading" :error="store.error" />
- <a-row :gutter="[14, 14]" class="metric-row"><a-col :xs="12" :lg="6"><a-statistic title="能力目录" :value="capabilities.length" /></a-col><a-col :xs="12" :lg="6"><a-statistic title="已验证 Demo" :value="verifiedCapabilities.length" /></a-col><a-col :xs="12" :lg="6"><a-statistic title="最近检测结果" :value="store.detectionRun?.detection_count ?? 0" suffix="个" /></a-col><a-col :xs="12" :lg="6"><a-statistic title="最近轨迹事件" :value="store.trajectoryRun?.event_count ?? 0" suffix="个" /></a-col></a-row>
- <section class="view-section"><div class="section-heading"><div><h2>已验证能力</h2><p>从实验结果进入，不需要再查找输出目录。</p></div></div><a-row :gutter="[18, 18]"><a-col :xs="24" :xl="12"><a-card class="capability-card" :body-style="{ padding: '0' }"><img class="card-media" :src="artifactUrl('shared/outputs/01-object-detection/annotated/DJI_20260810092727_0001_V_10.jpeg')" alt="目标检测标注图" /><div class="card-copy"><div class="card-topline"><div><h3>地物目标检测</h3><p>原图与标注图切换、检测框明细与运行参数。</p></div><a-tag color="green">已验证</a-tag></div><a-space wrap><a-tag color="gold">A：直接能力</a-tag><a-tag>{{ store.detectionRun?.processed_images ?? 0 }} 张影像</a-tag><a-tag>{{ store.detectionRun?.detection_count ?? 0 }} 个候选</a-tag></a-space><a-button type="primary" @click="openCapability('01-object-detection')"><EyeOutlined />查看实验结果</a-button></div></a-card></a-col><a-col :xs="24" :xl="12"><a-card class="capability-card" :body-style="{ padding: '0' }"><img class="card-media" :src="artifactUrl('shared/outputs/15-trajectory-analysis/difficult/analysis.png')" alt="轨迹分析图" /><div class="card-copy"><div class="card-topline"><div><h3>轨迹分析与行为识别</h3><p>Cesium 轨迹地图、规则事件和轨迹汇总。</p></div><a-tag color="green">已验证</a-tag></div><a-space wrap><a-tag color="gold">C：产品能力</a-tag><a-tag>{{ store.trajectoryRun?.track_count ?? 0 }} 条轨迹</a-tag><a-tag>{{ store.trajectoryRun?.event_count ?? 0 }} 个事件</a-tag></a-space><a-button type="primary" @click="openCapability('15-trajectory-analysis')"><EyeOutlined />查看实验结果</a-button></div></a-card></a-col></a-row></section>
+ <a-row :gutter="[14, 14]" class="metric-row"><a-col :xs="12" :lg="6"><a-statistic title="能力目录" :value="capabilities.length" /></a-col><a-col :xs="12" :lg="6"><a-statistic title="已验证 Demo" :value="verifiedCapabilities.length" /></a-col><a-col :xs="12" :lg="6"><a-statistic title="最近检测结果" :value="store.detectionRun?.detection_count ?? 0" suffix="个" /></a-col><a-col :xs="12" :lg="6"><a-statistic title="田墩轨迹事件" :value="realTrajectory?.run.event_count ?? 0" suffix="个" /></a-col></a-row>
+ <section class="view-section"><div class="section-heading"><div><h2>已验证能力</h2><p>从实验结果进入，不需要再查找输出目录。</p></div></div><a-row :gutter="[18, 18]"><a-col :xs="24" :xl="12"><a-card class="capability-card" :body-style="{ padding: '0' }"><img class="card-media" :src="artifactUrl('shared/outputs/01-object-detection/annotated/DJI_20260810092727_0001_V_10.jpeg')" alt="目标检测标注图" /><div class="card-copy"><div class="card-topline"><div><h3>地物目标检测</h3><p>原图与标注图切换、检测框明细与运行参数。</p></div><a-tag color="green">已验证</a-tag></div><a-space wrap><a-tag color="gold">A：直接能力</a-tag><a-tag>{{ store.detectionRun?.processed_images ?? 0 }} 张影像</a-tag><a-tag>{{ store.detectionRun?.detection_count ?? 0 }} 个候选</a-tag></a-space><a-button type="primary" @click="openCapability('01-object-detection')"><EyeOutlined />查看实验结果</a-button></div></a-card></a-col><a-col :xs="24" :xl="12"><a-card class="capability-card" :body-style="{ padding: '0' }"><img class="card-media" :src="artifactUrl('shared/outputs/15-trajectory-analysis/tian-dun-flight-19578/tian-dun-flight-19578/analysis.png')" alt="田墩实飞轨迹分析图" /><div class="card-copy"><div class="card-topline"><div><h3>轨迹分析与行为识别</h3><p>田墩实飞、计划航线、区域图层和规则事件。</p></div><a-tag color="green">已验证</a-tag></div><a-space wrap><a-tag color="gold">C：产品能力</a-tag><a-tag>{{ realTrajectory?.run.track_count ?? 0 }} 条轨迹</a-tag><a-tag>{{ realTrajectory?.run.event_count ?? 0 }} 个事件</a-tag></a-space><a-button type="primary" @click="openCapability('15-trajectory-analysis')"><EyeOutlined />查看实验结果</a-button></div></a-card></a-col></a-row></section>
<section class="view-section"><div class="section-heading"><div><h2>待实现能力</h2><p>首个 Demo 产生可检查工件后，可以按相同方式接入控制台。</p></div></div><a-row :gutter="[14, 14]"><a-col v-for="capability in planned.slice(0, 8)" :key="capability.id" :xs="24" :md="12" :xl="6"><a-card size="small" class="planned-card"><div class="card-topline"><h3>{{ capability.title }}</h3><a-tag>{{ capability.level }}</a-tag></div><p>{{ capability.note }}</p><a-button type="link" size="small" @click="openCapability(capability.id)"><ExperimentOutlined />查看状态</a-button></a-card></a-col></a-row></section>
</div>
</template>
diff --git a/apps/workbench-console/src/vite-env.d.ts b/apps/workbench-console/src/vite-env.d.ts
new file mode 100644
index 0000000..11f02fe
--- /dev/null
+++ b/apps/workbench-console/src/vite-env.d.ts
@@ -0,0 +1 @@
+/// <reference types="vite/client" />
diff --git a/apps/workbench-console/vite.config.ts b/apps/workbench-console/vite.config.ts
index c690248..53172b2 100644
--- a/apps/workbench-console/vite.config.ts
+++ b/apps/workbench-console/vite.config.ts
@@ -33,7 +33,8 @@
port: 6174,
strictPort: true,
proxy: {
- "/shared": "http://127.0.0.1:6173"
+ "/shared": "http://127.0.0.1:6173",
+ "/api": "http://127.0.0.1:6173"
}
}
});
diff --git a/baseData/no-fly-zone-Chn_xS2X.geojson b/baseData/no-fly-zone-Chn_xS2X.geojson
deleted file mode 100644
index 1db3278..0000000
--- a/baseData/no-fly-zone-Chn_xS2X.geojson
+++ /dev/null
@@ -1,203348 +0,0 @@
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\ No newline at end of file
diff --git a/baseData/uom-fly-zone.bin-B8O-S8Cs.gzip b/baseData/uom-fly-zone.bin-B8O-S8Cs.gzip
deleted file mode 100644
index 3beb081..0000000
--- a/baseData/uom-fly-zone.bin-B8O-S8Cs.gzip
+++ /dev/null
Binary files differ
diff --git "a/baseData/\346\274\224\347\244\272.xlsx" "b/baseData/\346\274\224\347\244\272.xlsx"
deleted file mode 100644
index 5aa5230..0000000
--- "a/baseData/\346\274\224\347\244\272.xlsx"
+++ /dev/null
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diff --git "a/baseData/\347\224\260\345\242\251-\347\226\253\346\234\250\350\257\206\345\210\253\357\274\210\345\213\277\345\210\240\357\274\211.kmz" "b/baseData/\347\224\260\345\242\251-\347\226\253\346\234\250\350\257\206\345\210\253\357\274\210\345\213\277\345\210\240\357\274\211.kmz"
deleted file mode 100644
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--- "a/baseData/\347\224\260\345\242\251-\347\226\253\346\234\250\350\257\206\345\210\253\357\274\210\345\213\277\345\210\240\357\274\211.kmz"
+++ /dev/null
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diff --git a/capabilities/01-object-detection/README.md b/capabilities/01-object-detection/README.md
index 4bb47f7..5cbe346 100644
--- a/capabilities/01-object-detection/README.md
+++ b/capabilities/01-object-detection/README.md
@@ -99,3 +99,8 @@
- `DJI_20260713102047_0001_V_19.jpeg`：切片 YOLO 检出多个人员，但两种模型均未检出右上角红色汽车；该近景车顶外观仍需要更匹配的航拍数据或本项目样本微调。
- NWPU 会产生少量其他航拍类别误检，正式 Demo 只保留 `vehicle`；脚本还会过滤被高置信度整车框大部分包含的重复局部框。
- 树木不属于当前两个模型的有效类别，必须单独建设树冠检测/分割分支。
+## 控制台运行入口
+
+本地实验控制台可以直接上传最多 12 张 `JPG/JPEG/PNG` 进行一次 CPU 基线检测。每次运行会把原图保存到 `shared/data/raw/01-object-detection/runs/<run-id>/`，结果保存到 `shared/outputs/01-object-detection/runs/<run-id>/`，不会覆盖既有基线或用户原图。页面默认将同一影像的原图与标注图并列展示，并可在案例库中回看历史运行。
+
+该入口调用固定的 `run_detection.py` 与 `01-object-detection` 虚拟环境，仍然只适用于人员和常见车辆的基线验证；树木、真实准确率、许可审查和生产批处理不在此入口的承诺范围内。
diff --git a/capabilities/15-trajectory-analysis/README.md b/capabilities/15-trajectory-analysis/README.md
index a206d9a..5d565f1 100644
--- a/capabilities/15-trajectory-analysis/README.md
+++ b/capabilities/15-trajectory-analysis/README.md
@@ -106,15 +106,41 @@
| pandas / GeoPandas / Shapely / scikit-learn | BSD 3-Clause 系列许可证 |
| PyProj | MIT；同时需遵守其随附 PROJ 数据/组件许可 |
| Matplotlib | Matplotlib License；随附字体等资产有各自许可证 |
+| openpyxl 3.1.5 | MIT；仅用于读取用户提供的 XLSX 飞行日志 |
| Demo 数据 | 由本仓库脚本生成，无第三方数据集或模型权重 |
| 预训练模型/权重 | 无 |
以上是开发阶段记录，不等于已完成产品商用法务审查。
+## 真实单例：田墩飞行任务 19578
+
+用户提供的原始 XLSX、DJI WPMZ KMZ 和禁飞区 GeoJSON 保持在 `shared/data/raw/15-trajectory-analysis/tian-dun-demo-20260814/`，不会被脚本改写。使用以下命令生成可审计的处理输入，再运行单例分析：
+
+```powershell
+$py = '.\.venvs\15-trajectory-analysis\Scripts\python.exe'
+& $py .\capabilities\15-trajectory-analysis\prepare_real_flight.py
+& $py .\capabilities\15-trajectory-analysis\run_trajectory_analysis.py `
+ --input .\shared\data\processed\15-trajectory-analysis\tian-dun-flight-19578\tian-dun-flight-19578.case.json `
+ --output .\shared\outputs\15-trajectory-analysis\tian-dun-flight-19578
+```
+
+预处理器会将 Excel 的本地无时区时间按 `Asia/Shanghai` 解释后转换为 UTC，提取 `waylines.wpml` 中的 10 个 WGS84 航点，并从 2,300 个禁飞区源要素中按飞行/航线周边 5 km 筛选、合并为一个局部区域。点状区域按原始 `radius` 转为米制缓冲区；源数据的 `no-fly` 被映射为规则输入 `restricted`，只用于技术空间评估。适飞区 Gzip 已验证为 46,518 个闭合坐标环的 `int32 / 1e7` 流，田墩周边筛出 2 个适飞面；它只用于可视化，不参与禁入规则判定。
+
+已验证的真实单例有 233 个观测点、1 条无人机轨迹，运行耗时 0.338 秒。输出报告 2 段偏航（77 秒、175 秒）和 1 个持续 464 秒的区域相交事件；没有停留或聚集事件。实飞点到计划线的最小/平均/最大距离为 0.0 / 221.0 / 540.8 m。两段偏航分别覆盖起飞接近计划线之前和自动返航/降落阶段，计划航线未表达这些飞行阶段，因此不能直接将其作为异常结论。区域相交仅代表与名为“三清山”的源区域（`no-fly-40001494`）发生空间相交，不是法规或运营违规认定。
+
+处理输入位于 `shared/data/processed/15-trajectory-analysis/tian-dun-flight-19578/`；结果位于 `shared/outputs/15-trajectory-analysis/tian-dun-flight-19578/tian-dun-flight-19578/`，包括轨迹、计划航线、禁飞区、适飞区和事件 GeoJSON，以及 `analysis.png`。本地控制台的“田墩实飞”案例可直接查看这些结果；地图以 ArcGIS 影像为底图，并可在 `apps/workbench-console/.env.local` 配置 `VITE_TIANDITU_TOKEN` 叠加天地图影像和注记。
+
## 已知限制与下一步
+- 2026-08-14 已归档一组用户提供的田墩真实原始资料至 `shared/data/raw/15-trajectory-analysis/tian-dun-demo-20260814/`：禁飞区 GeoJSON、适飞区 Gzip 二进制源文件、DJI WPMZ 航线 KMZ 与实际飞行 Excel。原文件保持未改名、未转换状态，当前尚未纳入分析结果。
+- `prepare_real_flight.py` 已支持此批 XLSX、KMZ、禁飞 GeoJSON 与适飞区 Gzip 的单例准备。适飞区的二进制几何已验证为闭合坐标环，但其发布日期、授权方和运营语义仍未独立核验，当前只用于可视化，不参与规则判断。当前区域规则只识别 `zone_type=restricted`，不能把 `no_fly` 标签直接等同为已验证的禁入事件。
- 合成样本只能验证程序逻辑，不能证明真实场景准确率；目前没有可报告的真实误报率和漏报率。
- 聚集要求时间戳对齐，GPS 漂移、采样频率和轨迹断点会直接影响结果。
- 偏航依赖可信参考路线；禁入区依赖有效区域数据；不同人员、车辆、船舶和无人机需要分别标定阈值。
- 下一步应先选 1 个正常、1 个困难的脱敏实测轨迹，人工标注事件，再评估误报、漏报和阈值，而不是直接跑大目录。
- 若输入来自无人机视频，还需在此能力之前引入独立的多目标跟踪器，并验证 ID 切换问题。
+## 控制台输入转换
+
+本地实验控制台支持直接上传用户常见的 `XLSX` 飞行日志、`KMZ` DJI WPMZ 航线、禁飞区 `GeoJSON`，以及可选适飞区 `Gzip`。服务端会在固定的轨迹虚拟环境中调用 `prepare_real_flight.py` 和 `run_trajectory_analysis.py`，把文件转换成 Demo 的 `case.json`、WGS84 轨迹和 GeoJSON 输出。适飞区缺省时仍可分析，地图只是不显示适飞区图层。
+
+每次运行使用新的 `trajectory-<UTC时间>-<随机串>` 编号，原始输入不会被覆盖；控制台案例库会自动扫描 `shared/outputs/15-trajectory-analysis`，行为标签在界面显示中文释义，结构化文件继续保存英文键值，便于程序处理。
diff --git a/capabilities/15-trajectory-analysis/prepare_real_flight.py b/capabilities/15-trajectory-analysis/prepare_real_flight.py
new file mode 100644
index 0000000..27108c8
--- /dev/null
+++ b/capabilities/15-trajectory-analysis/prepare_real_flight.py
@@ -0,0 +1,419 @@
+"""Prepare one DJI flight log, WPMZ route and no-fly dataset for analysis."""
+
+from __future__ import annotations
+
+import argparse
+import gzip
+import hashlib
+import json
+import sys
+import warnings
+import xml.etree.ElementTree as element_tree
+from datetime import UTC
+from pathlib import Path
+from zipfile import ZipFile
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+from shapely import make_valid
+from shapely.geometry import LineString, Polygon, mapping
+from shapely.ops import unary_union
+
+
+WGS84 = "EPSG:4326"
+LOCAL_TIMEZONE = "Asia/Shanghai"
+KML_NAMESPACE = "http://www.opengis.net/kml/2.2"
+WPML_NAMESPACE = "http://www.dji.com/wpmz/1.0.5"
+EXCEL_COLUMNS = {
+ "mission_id": "\u98de\u884c\u4efb\u52a1ID",
+ "latitude": "\u7eac\u5ea6",
+ "longitude": "\u7ecf\u5ea6",
+ "absolute_height_m": "\u7edd\u5bf9\u9ad8\u5ea6(m)",
+ "relative_height_m": "\u5b9e\u65f6\u771f\u9ad8(m)",
+ "timestamp": "\u521b\u5efa\u65f6\u95f4",
+ "flight_phase": "\u98de\u884c\u7c7b\u578b",
+}
+
+
+def parse_args() -> argparse.Namespace:
+ root = Path(__file__).resolve().parents[2]
+ parser = argparse.ArgumentParser(description="Prepare one real DJI flight for trajectory analysis.")
+ parser.add_argument(
+ "--raw-dir",
+ type=Path,
+ default=root / "shared" / "data" / "raw" / "15-trajectory-analysis" / "tian-dun-demo-20260814",
+ )
+ parser.add_argument(
+ "--output",
+ type=Path,
+ default=root / "shared" / "data" / "processed" / "15-trajectory-analysis" / "tian-dun-flight-19578",
+ )
+ parser.add_argument("--case-id", default="tian-dun-flight-19578")
+ parser.add_argument("--overwrite", action="store_true", help="Update derived files in an existing output directory.")
+ parser.add_argument(
+ "--zone-search-radius-m",
+ type=float,
+ default=5000.0,
+ help="Keep source no-fly areas intersecting this buffer around the flight and route.",
+ )
+ return parser.parse_args()
+
+
+def discover_single(directory: Path, suffix: str) -> Path:
+ matches = sorted(directory.glob(f"*{suffix}"))
+ if len(matches) != 1:
+ raise ValueError(f"Expected exactly one {suffix} file in {directory}, found {len(matches)}.")
+ return matches[0]
+
+
+def sha256(path: Path) -> str:
+ digest = hashlib.sha256()
+ with path.open("rb") as stream:
+ for chunk in iter(lambda: stream.read(1024 * 1024), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def choose_metric_crs(longitude: float, latitude: float) -> str:
+ zone = int((longitude + 180) // 6) + 1
+ return f"EPSG:{(32600 if latitude >= 0 else 32700) + zone}"
+
+
+def load_observations(path: Path) -> tuple[pd.DataFrame, dict[str, object]]:
+ warnings.filterwarnings("ignore", message="Workbook contains no default style")
+ source = pd.read_excel(path)
+ missing = [column for column in EXCEL_COLUMNS.values() if column not in source.columns]
+ if missing:
+ raise ValueError(f"Flight workbook is missing columns: {', '.join(missing)}")
+ missions = source[EXCEL_COLUMNS["mission_id"]].dropna().unique().tolist()
+ if len(missions) != 1:
+ raise ValueError(f"This single-flight preparer requires one mission ID, found: {missions}")
+ mission_id = str(missions[0]).strip()
+ timestamps = pd.to_datetime(source[EXCEL_COLUMNS["timestamp"]], errors="raise")
+ if getattr(timestamps.dt, "tz", None) is None:
+ timestamps = timestamps.dt.tz_localize(LOCAL_TIMEZONE, ambiguous="raise", nonexistent="raise")
+ timestamps = timestamps.dt.tz_convert(UTC)
+ longitude = pd.to_numeric(source[EXCEL_COLUMNS["longitude"]], errors="raise")
+ latitude = pd.to_numeric(source[EXCEL_COLUMNS["latitude"]], errors="raise")
+ valid = longitude.between(-180, 180) & latitude.between(-90, 90)
+ if not valid.all():
+ raise ValueError("Flight workbook contains longitude/latitude values outside WGS84 bounds.")
+ track_id = f"drone-{mission_id}"
+ observations = pd.DataFrame(
+ {
+ "track_id": track_id,
+ "entity_type": "drone",
+ "timestamp": timestamps.map(lambda value: value.strftime("%Y-%m-%dT%H:%M:%SZ")),
+ "longitude": longitude.astype(float),
+ "latitude": latitude.astype(float),
+ "flight_phase": source[EXCEL_COLUMNS["flight_phase"]].astype(str),
+ "absolute_height_m": pd.to_numeric(source[EXCEL_COLUMNS["absolute_height_m"]], errors="coerce"),
+ "relative_height_m": pd.to_numeric(source[EXCEL_COLUMNS["relative_height_m"]], errors="coerce"),
+ "source_row": range(2, len(source) + 2),
+ }
+ ).sort_values("timestamp", kind="stable")
+ duplicates = int(observations.duplicated(["track_id", "timestamp"], keep="last").sum())
+ return observations, {
+ "source_row_count": int(len(source)),
+ "prepared_row_count": int(len(observations)),
+ "duplicate_timestamps_for_analyzer": duplicates,
+ "mission_id": mission_id,
+ "track_id": track_id,
+ "input_time_interpretation": f"Naive timestamps interpreted as {LOCAL_TIMEZONE}, then converted to UTC.",
+ "start_time_utc": observations["timestamp"].iloc[0],
+ "end_time_utc": observations["timestamp"].iloc[-1],
+ }
+
+
+def extract_route(path: Path, track_id: str) -> tuple[dict[str, object], LineString]:
+ with ZipFile(path) as archive:
+ try:
+ payload = archive.read("wpmz/waylines.wpml")
+ except KeyError as exc:
+ raise ValueError("KMZ does not contain wpmz/waylines.wpml.") from exc
+ root = element_tree.fromstring(payload)
+ namespaces = {"k": KML_NAMESPACE, "w": WPML_NAMESPACE}
+ waypoints: list[tuple[int, list[float]]] = []
+ for placemark in root.findall(".//k:Placemark", namespaces):
+ coordinates = placemark.findtext("k:Point/k:coordinates", namespaces=namespaces)
+ index = placemark.findtext("w:index", namespaces=namespaces)
+ if coordinates is None or index is None:
+ continue
+ values = coordinates.strip().split(",")
+ longitude, latitude = float(values[0]), float(values[1])
+ if not (-180 <= longitude <= 180 and -90 <= latitude <= 90):
+ raise ValueError("KMZ waypoint is outside WGS84 bounds.")
+ waypoints.append((int(index), [longitude, latitude]))
+ waypoints.sort(key=lambda item: item[0])
+ coordinates = [item[1] for item in waypoints]
+ if len(coordinates) < 2:
+ raise ValueError("KMZ must contain at least two ordered waypoints.")
+ line = LineString(coordinates)
+ feature = {
+ "type": "Feature",
+ "properties": {
+ "track_id": track_id,
+ "source_format": "DJI WPMZ waylines.wpml",
+ "waypoint_count": len(coordinates),
+ },
+ "geometry": mapping(line),
+ }
+ return feature, line
+
+
+def build_zones(
+ path: Path,
+ context_wgs84: LineString,
+ metric_crs: str,
+ search_radius_m: float,
+) -> tuple[list[dict[str, object]], dict[str, object]]:
+ source = gpd.read_file(path)
+ if source.crs is None:
+ raise ValueError("No-fly GeoJSON has no declared CRS; it cannot be prepared safely.")
+ metric = source.to_crs(metric_crs)
+ context = gpd.GeoSeries([context_wgs84], crs=WGS84).to_crs(metric_crs).iloc[0].buffer(search_radius_m)
+ grouped: dict[str, dict[str, object]] = {}
+ skipped = 0
+ selected_source_features = 0
+ for row in metric.itertuples():
+ geometry = row.geometry
+ if geometry.geom_type == "Point":
+ radius_value = pd.to_numeric(getattr(row, "radius", None), errors="coerce")
+ radius = 0.0 if pd.isna(radius_value) else float(radius_value)
+ if radius <= 0:
+ skipped += 1
+ continue
+ effective_geometry = geometry.buffer(radius)
+ elif geometry.geom_type in {"Polygon", "MultiPolygon"}:
+ effective_geometry = geometry
+ else:
+ skipped += 1
+ continue
+ if not effective_geometry.intersects(context):
+ continue
+ selected_source_features += 1
+ area_id = str(getattr(row, "area_id"))
+ entry = grouped.setdefault(
+ area_id,
+ {
+ "geometry": [],
+ "name": str(getattr(row, "name", "")),
+ "city": str(getattr(row, "city", "")),
+ "level": getattr(row, "level", None),
+ "height": getattr(row, "height", None),
+ "source_feature_count": 0,
+ },
+ )
+ entry["geometry"].append(effective_geometry)
+ entry["source_feature_count"] = int(entry["source_feature_count"]) + 1
+ features: list[dict[str, object]] = []
+ for area_id, entry in sorted(grouped.items()):
+ geometry = unary_union(entry["geometry"])
+ geometry_wgs84 = gpd.GeoSeries([geometry], crs=metric_crs).to_crs(WGS84).iloc[0]
+ features.append(
+ {
+ "type": "Feature",
+ "properties": {
+ "zone_id": f"no-fly-{area_id}",
+ "zone_type": "restricted",
+ "source_area_id": area_id,
+ "source_name": entry["name"],
+ "source_city": entry["city"],
+ "source_level": entry["level"],
+ "source_height_m": entry["height"],
+ "source_feature_count": entry["source_feature_count"],
+ "mapping_note": "Source no-fly area mapped to restricted for this technical rule evaluation.",
+ },
+ "geometry": mapping(geometry_wgs84),
+ }
+ )
+ return features, {
+ "source_feature_count": int(len(source)),
+ "selected_source_feature_count": selected_source_features,
+ "selected_zone_count": len(features),
+ "skipped_source_feature_count": skipped,
+ "source_crs": str(source.crs),
+ "zone_search_radius_m": search_radius_m,
+ "mapping": "Polygon areas are retained; point areas are converted to metric-radius buffers; no-fly is mapped to restricted only for this technical rule evaluation.",
+ }
+
+
+def build_flyable_zones(
+ path: Path,
+ context_wgs84: LineString,
+ metric_crs: str,
+ search_radius_m: float,
+) -> tuple[list[dict[str, object]], dict[str, object]]:
+ """Decode the supplied closed-ring UOM coordinate stream near one flight."""
+ values = np.frombuffer(gzip.decompress(path.read_bytes()), dtype="<i4")
+ if values.size < 3:
+ raise ValueError("Flyable-area Gzip is too small to contain a coordinate stream.")
+ ring_count = int(values[-1])
+ coordinate_values = values[:-1]
+ if ring_count <= 0 or coordinate_values.size % 2:
+ raise ValueError("Flyable-area Gzip has an invalid ring count or coordinate alignment.")
+ coordinates = coordinate_values.reshape(-1, 2)
+ context_metric = gpd.GeoSeries([context_wgs84], crs=WGS84).to_crs(metric_crs).iloc[0].buffer(search_radius_m)
+ context = gpd.GeoSeries([context_metric], crs=metric_crs).to_crs(WGS84).iloc[0]
+ minx, miny, maxx, maxy = context.bounds
+ features: list[dict[str, object]] = []
+ start = 0
+ skipped = 0
+ for ring_index in range(ring_count):
+ end = start + 4
+ while end <= len(coordinates) and not np.array_equal(coordinates[end - 1], coordinates[start]):
+ end += 1
+ if end > len(coordinates):
+ raise ValueError(f"Flyable-area ring {ring_index} is not closed within the coordinate stream.")
+ ring = coordinates[start:end].astype(np.float64) / 1e7
+ start = end
+ ring_minx, ring_miny = ring.min(axis=0)
+ ring_maxx, ring_maxy = ring.max(axis=0)
+ if ring_maxx < minx or ring_minx > maxx or ring_maxy < miny or ring_miny > maxy:
+ continue
+ polygon = Polygon(ring)
+ if not polygon.is_valid:
+ polygon = make_valid(polygon)
+ if polygon.is_empty or polygon.geom_type not in {"Polygon", "MultiPolygon"} or not polygon.intersects(context):
+ skipped += 1
+ continue
+ features.append(
+ {
+ "type": "Feature",
+ "properties": {
+ "zone_id": f"flyable-bin-{ring_index:05d}",
+ "zone_type": "flyable",
+ "source_format": "UOM closed-ring coordinate stream (int32 / 1e7)",
+ "source_ring_index": ring_index,
+ "vertex_count": len(ring),
+ },
+ "geometry": mapping(polygon),
+ }
+ )
+ if start > len(coordinates):
+ raise ValueError("Flyable-area coordinate parser exceeded the available stream.")
+ return features, {
+ "source_ring_count": ring_count,
+ "consumed_coordinate_pair_count": start,
+ "selected_zone_count": len(features),
+ "skipped_candidate_count": skipped,
+ "zone_search_radius_m": search_radius_m,
+ "decoder": "Closed rings parsed from little-endian int32 longitude/latitude values scaled by 1e7.",
+ "limitation": "The supplied file contains geometry only; its publication time, authority and operational semantics remain unverified.",
+ }
+
+
+def write_json(path: Path, payload: dict[str, object]) -> None:
+ path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
+
+
+def main() -> int:
+ args = parse_args()
+ raw_dir = args.raw_dir.resolve()
+ output_dir = args.output.resolve()
+ if args.zone_search_radius_m <= 0:
+ print("Preparation failed: --zone-search-radius-m must be greater than zero.", file=sys.stderr)
+ return 2
+ if output_dir.exists() and not args.overwrite:
+ print(f"Preparation failed: output already exists: {output_dir}", file=sys.stderr)
+ return 2
+ try:
+ flight_path = discover_single(raw_dir / "tracks", ".xlsx")
+ route_path = discover_single(raw_dir / "routes", ".kmz")
+ zone_path = discover_single(raw_dir / "areas", ".geojson")
+ flyable_matches = sorted((raw_dir / "areas").glob("*.gzip"))
+ if len(flyable_matches) > 1:
+ raise ValueError(f"Expected zero or one .gzip file in {raw_dir / 'areas'}, found {len(flyable_matches)}.")
+ flyable_path = flyable_matches[0] if flyable_matches else None
+ observations, observation_metadata = load_observations(flight_path)
+ metric_crs = choose_metric_crs(float(observations["longitude"].mean()), float(observations["latitude"].mean()))
+ route_feature, route = extract_route(route_path, str(observation_metadata["track_id"]))
+ track_line = LineString(observations[["longitude", "latitude"]].to_numpy().tolist())
+ context = unary_union([track_line, route])
+ zones, zone_metadata = build_zones(zone_path, context, metric_crs, args.zone_search_radius_m)
+ flyable_zones: list[dict[str, object]] = []
+ flyable_metadata: dict[str, object] | None = None
+ if flyable_path is not None:
+ flyable_zones, flyable_metadata = build_flyable_zones(
+ flyable_path, context, metric_crs, args.zone_search_radius_m
+ )
+ if not zones:
+ raise ValueError("No source no-fly areas intersect the configured flight context buffer.")
+ observations_metric = gpd.GeoSeries(
+ gpd.points_from_xy(observations["longitude"], observations["latitude"]), crs=WGS84
+ ).to_crs(metric_crs)
+ route_metric = gpd.GeoSeries([route], crs=WGS84).to_crs(metric_crs).iloc[0]
+ output_dir.mkdir(parents=True, exist_ok=args.overwrite)
+ observations.to_csv(output_dir / "observations.csv", index=False, encoding="utf-8-sig")
+ write_json(
+ output_dir / "reference_routes.geojson",
+ {
+ "type": "FeatureCollection",
+ "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}},
+ "features": [route_feature],
+ },
+ )
+ write_json(
+ output_dir / "zones.geojson",
+ {
+ "type": "FeatureCollection",
+ "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}},
+ "features": zones,
+ },
+ )
+ if flyable_path is not None:
+ write_json(
+ output_dir / "flyable_zones.geojson",
+ {
+ "type": "FeatureCollection",
+ "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}},
+ "features": flyable_zones,
+ },
+ )
+ manifest = {
+ "case_id": args.case_id,
+ "description": "One user-provided DJI flight, prepared from an XLSX log, WPMZ route and local no-fly-zone subset.",
+ "crs": WGS84,
+ "observations": "observations.csv",
+ "reference_routes": "reference_routes.geojson",
+ "zones": "zones.geojson",
+ }
+ if flyable_path is not None:
+ manifest["flyable_zones"] = "flyable_zones.geojson"
+ write_json(output_dir / f"{args.case_id}.case.json", manifest)
+ metadata = {
+ "case_id": args.case_id,
+ "prepared_at": pd.Timestamp.now(tz=UTC).isoformat(),
+ "raw_inputs": [
+ {"path": str(path), "sha256": sha256(path)}
+ for path in (flight_path, route_path, zone_path)
+ if path is not None
+ ] + ([{"path": str(flyable_path), "sha256": sha256(flyable_path)}] if flyable_path is not None else []),
+ "observation_preparation": observation_metadata,
+ "route_preparation": {
+ "source_format": "DJI WPMZ waylines.wpml",
+ "waypoint_count": route_feature["properties"]["waypoint_count"],
+ "track_to_route_min_m": round(float(observations_metric.distance(route_metric).min()), 3),
+ "track_to_route_mean_m": round(float(observations_metric.distance(route_metric).mean()), 3),
+ "track_to_route_max_m": round(float(observations_metric.distance(route_metric).max()), 3),
+ },
+ "zone_preparation": zone_metadata,
+ "flyable_zone_preparation": flyable_metadata,
+ "crs": {"output": WGS84, "metric": metric_crs},
+ "limitations": [
+ "No-fly source semantics, publication time and operational authority have not been independently verified.",
+ "Spatial intersection with a source no-fly area is a technical result, not a legal or operational violation conclusion.",
+ "The reference route does not encode flight-phase semantics such as takeoff, return-to-home or landing.",
+ ],
+ }
+ write_json(output_dir / "preparation_metadata.json", metadata)
+ except (OSError, ValueError, KeyError, element_tree.ParseError, pd.errors.ParserError) as exc:
+ print(f"Preparation failed: {exc}", file=sys.stderr)
+ return 2
+ print(f"Prepared 1 real flight case in {output_dir}")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/capabilities/15-trajectory-analysis/requirements.txt b/capabilities/15-trajectory-analysis/requirements.txt
index a989bce..1e39c9f 100644
--- a/capabilities/15-trajectory-analysis/requirements.txt
+++ b/capabilities/15-trajectory-analysis/requirements.txt
@@ -2,3 +2,4 @@
geopandas>=1,<2
scikit-learn>=1.5,<2
matplotlib>=3.9,<4
+openpyxl>=3.1,<4
diff --git a/capabilities/15-trajectory-analysis/run_trajectory_analysis.py b/capabilities/15-trajectory-analysis/run_trajectory_analysis.py
index 947c0f8..1afb80d 100644
--- a/capabilities/15-trajectory-analysis/run_trajectory_analysis.py
+++ b/capabilities/15-trajectory-analysis/run_trajectory_analysis.py
@@ -25,6 +25,7 @@
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
+from matplotlib.patches import Patch
DEFAULT_THRESHOLDS = {
@@ -105,7 +106,7 @@
return CRS.from_epsg(epsg)
-def load_case(manifest_path: Path) -> tuple[dict[str, Any], pd.DataFrame, gpd.GeoDataFrame, gpd.GeoDataFrame, int, CRS]:
+def load_case(manifest_path: Path) -> tuple[dict[str, Any], pd.DataFrame, gpd.GeoDataFrame, gpd.GeoDataFrame, gpd.GeoDataFrame | None, int, CRS]:
manifest = load_json(manifest_path)
for field in ("case_id", "crs", "observations", "reference_routes", "zones"):
if not manifest.get(field):
@@ -142,6 +143,12 @@
raise ValueError("Every track must contain at least two distinct timestamps.")
routes = gpd.read_file(routes_path)
zones = gpd.read_file(zones_path)
+ flyable_zones: gpd.GeoDataFrame | None = None
+ if manifest.get("flyable_zones"):
+ flyable_path = resolve_input_path(manifest_path, str(manifest["flyable_zones"]))
+ flyable_zones = gpd.read_file(flyable_path)
+ if flyable_zones.crs is None:
+ raise ValueError("Flyable-zone GeoJSON must declare a CRS.")
if "track_id" not in routes.columns:
raise ValueError("Reference route GeoJSON must contain a track_id property.")
if "zone_id" not in zones.columns or "zone_type" not in zones.columns:
@@ -153,7 +160,15 @@
if missing_routes:
raise ValueError(f"Missing reference routes for: {', '.join(sorted(missing_routes))}")
metric_crs = choose_metric_crs(float(frame["longitude"].mean()), float(frame["latitude"].mean()))
- return manifest, frame, routes.to_crs(metric_crs), zones.to_crs(metric_crs), duplicate_count, metric_crs
+ return (
+ manifest,
+ frame,
+ routes.to_crs(metric_crs),
+ zones.to_crs(metric_crs),
+ flyable_zones.to_crs(metric_crs) if flyable_zones is not None else None,
+ duplicate_count,
+ metric_crs,
+ )
def true_runs(flags: list[bool], times: list[pd.Timestamp], max_gap: float) -> Iterable[tuple[int, int]]:
@@ -410,6 +425,7 @@
tracks: dict[str, gpd.GeoDataFrame],
routes_wgs84: gpd.GeoDataFrame,
zones_wgs84: gpd.GeoDataFrame,
+ flyable_zones_wgs84: gpd.GeoDataFrame | None,
metadata: dict[str, Any],
) -> None:
output_dir.mkdir(parents=True, exist_ok=True)
@@ -435,7 +451,22 @@
for event in events
]
write_geojson(output_dir / "events.geojson", event_features)
- render_map(output_dir / "analysis.png", tracks, routes_wgs84, zones_wgs84, events, manifest["case_id"])
+ route_features = json.loads(routes_wgs84.to_json())["features"]
+ write_geojson(output_dir / "reference_routes.geojson", route_features)
+ zone_features = json.loads(zones_wgs84.to_json())["features"]
+ write_geojson(output_dir / "zones.geojson", zone_features)
+ if flyable_zones_wgs84 is not None:
+ flyable_features = json.loads(flyable_zones_wgs84.to_json())["features"]
+ write_geojson(output_dir / "flyable_zones.geojson", flyable_features)
+ render_map(
+ output_dir / "analysis.png",
+ tracks,
+ routes_wgs84,
+ zones_wgs84,
+ flyable_zones_wgs84,
+ events,
+ manifest["case_id"],
+ )
(output_dir / "run_metadata.json").write_text(
json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8"
)
@@ -457,17 +488,20 @@
tracks: dict[str, gpd.GeoDataFrame],
routes: gpd.GeoDataFrame,
zones: gpd.GeoDataFrame,
+ flyable_zones: gpd.GeoDataFrame | None,
events: list[dict[str, Any]],
case_id: str,
) -> None:
figure, axis = plt.subplots(figsize=(10, 7), dpi=140)
zones.plot(ax=axis, facecolor="#f0e442", edgecolor="#8c6d1f", alpha=0.28, linewidth=1.5)
- routes.plot(ax=axis, color="#666666", linestyle="--", linewidth=1.2)
+ if flyable_zones is not None and not flyable_zones.empty:
+ flyable_zones.plot(ax=axis, facecolor="#36a269", edgecolor="#167344", alpha=0.22, linewidth=1.3)
palette = ["#0072b2", "#009e73", "#d55e00", "#cc79a7", "#56b4e9"]
for index, (track_id, track) in enumerate(sorted(tracks.items())):
color = palette[index % len(palette)]
axis.plot(track.geometry.x, track.geometry.y, color=color, linewidth=2.2, marker="o", markersize=2.8, label=track_id)
axis.annotate(track_id, (track.geometry.x.iloc[-1], track.geometry.y.iloc[-1]), fontsize=7, color=color)
+ routes.plot(ax=axis, color="#444444", linestyle="--", linewidth=1.5, zorder=4, label="reference route")
for event in events:
event_type = event["event_type"]
axis.scatter(
@@ -481,6 +515,11 @@
zorder=5,
)
handles, labels = axis.get_legend_handles_labels()
+ handles.append(Patch(facecolor="#f0e442", edgecolor="#8c6d1f", alpha=0.45, label="restricted area"))
+ labels.append("restricted area")
+ if flyable_zones is not None and not flyable_zones.empty:
+ handles.append(Patch(facecolor="#36a269", edgecolor="#167344", alpha=0.45, label="flyable area"))
+ labels.append("flyable area")
present_event_types = {event["event_type"] for event in events}
handles.extend(
Line2D(
@@ -503,6 +542,13 @@
axis.set_ylabel("Latitude (WGS84)")
axis.grid(alpha=0.18)
axis.ticklabel_format(useOffset=False)
+ visible_geometries = [geometry for track in tracks.values() for geometry in track.geometry]
+ visible_geometries.extend(geometry for geometry in routes.geometry if geometry is not None)
+ bounds = gpd.GeoSeries(visible_geometries, crs="EPSG:4326").total_bounds
+ width = max(bounds[2] - bounds[0], 0.0005)
+ height = max(bounds[3] - bounds[1], 0.0005)
+ axis.set_xlim(bounds[0] - width * 0.08, bounds[2] + width * 0.08)
+ axis.set_ylim(bounds[1] - height * 0.08, bounds[3] + height * 0.08)
figure.tight_layout()
figure.savefig(path)
plt.close(figure)
@@ -510,7 +556,7 @@
def process_manifest(manifest_path: Path, output_dir: Path) -> dict[str, Any]:
started = time.perf_counter()
- manifest, frame, routes_metric, zones_metric, duplicates, metric_crs = load_case(manifest_path)
+ manifest, frame, routes_metric, zones_metric, flyable_zones_metric, duplicates, metric_crs = load_case(manifest_path)
thresholds = dict(DEFAULT_THRESHOLDS)
for key, value in manifest.get("thresholds", {}).items():
if key not in thresholds:
@@ -566,6 +612,7 @@
tracks,
routes_metric.to_crs("EPSG:4326"),
zones_metric.to_crs("EPSG:4326"),
+ flyable_zones_metric.to_crs("EPSG:4326") if flyable_zones_metric is not None else None,
metadata,
)
return metadata
diff --git a/capabilities/15-trajectory-analysis/tests/test_demo.py b/capabilities/15-trajectory-analysis/tests/test_demo.py
index a71fa40..f4cf6d6 100644
--- a/capabilities/15-trajectory-analysis/tests/test_demo.py
+++ b/capabilities/15-trajectory-analysis/tests/test_demo.py
@@ -46,6 +46,8 @@
"events.json",
"trajectories.geojson",
"events.geojson",
+ "reference_routes.geojson",
+ "zones.geojson",
"analysis.png",
"run_metadata.json",
):
diff --git a/capabilities/15-trajectory-analysis/tests/test_prepare_real_flight.py b/capabilities/15-trajectory-analysis/tests/test_prepare_real_flight.py
new file mode 100644
index 0000000..4754e3a
--- /dev/null
+++ b/capabilities/15-trajectory-analysis/tests/test_prepare_real_flight.py
@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+import json
+import gzip
+import subprocess
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+from zipfile import ZipFile
+
+from openpyxl import Workbook
+
+
+CAPABILITY_DIR = Path(__file__).resolve().parents[1]
+PREPARER = CAPABILITY_DIR / "prepare_real_flight.py"
+ANALYZER = CAPABILITY_DIR / "run_trajectory_analysis.py"
+
+
+class PrepareRealFlightTests(unittest.TestCase):
+ def test_prepares_a_runnable_single_flight_case(self) -> None:
+ with tempfile.TemporaryDirectory() as temporary:
+ root = Path(temporary)
+ raw = root / "raw"
+ (raw / "tracks").mkdir(parents=True)
+ (raw / "routes").mkdir()
+ (raw / "areas").mkdir()
+ workbook = Workbook()
+ sheet = workbook.active
+ sheet.append([
+ "\u98de\u884c\u7c7b\u578b", "\u98de\u884c\u4efb\u52a1ID", "\u7eac\u5ea6", "\u7ecf\u5ea6", "\u7edd\u5bf9\u9ad8\u5ea6(m)", "\u5b9e\u65f6\u771f\u9ad8(m)", "\u521b\u5efa\u65f6\u95f4"
+ ])
+ sheet.append(["\u822a\u7ebf\u98de\u884c", 42, 28.0, 118.0, 100.0, 30.0, "2026-01-01 08:00:00"])
+ sheet.append(["\u822a\u7ebf\u98de\u884c", 42, 28.0001, 118.0001, 100.0, 30.0, "2026-01-01 08:00:10"])
+ workbook.save(raw / "tracks" / "flight.xlsx")
+ kmz = raw / "routes" / "route.kmz"
+ wpml = """<?xml version='1.0' encoding='UTF-8'?><kml xmlns='http://www.opengis.net/kml/2.2' xmlns:wpml='http://www.dji.com/wpmz/1.0.5'><Document><Folder><Placemark><Point><coordinates>118.0,28.0</coordinates></Point><wpml:index>0</wpml:index></Placemark><Placemark><Point><coordinates>118.0001,28.0001</coordinates></Point><wpml:index>1</wpml:index></Placemark></Folder></Document></kml>"""
+ with ZipFile(kmz, "w") as archive:
+ archive.writestr("wpmz/waylines.wpml", wpml)
+ zones = {
+ "type": "FeatureCollection",
+ "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}},
+ "features": [{"type": "Feature", "properties": {"area_id": 1, "name": "test", "city": "test", "level": 2, "height": 120, "radius": 1000}, "geometry": {"type": "Point", "coordinates": [118.0, 28.0]}}],
+ }
+ (raw / "areas" / "zones.geojson").write_text(json.dumps(zones), encoding="utf-8")
+ ring = [(118.0, 28.0), (118.001, 28.0), (118.001, 28.001), (118.0, 28.001), (118.0, 28.0)]
+ encoded = [value for point in ring for value in (round(point[0] * 1e7), round(point[1] * 1e7))] + [1]
+ (raw / "areas" / "flyable.gzip").write_bytes(gzip.compress(__import__("array").array("i", encoded).tobytes()))
+ prepared = root / "prepared"
+ result = subprocess.run(
+ [sys.executable, str(PREPARER), "--raw-dir", str(raw), "--output", str(prepared), "--case-id", "sample"],
+ capture_output=True,
+ encoding="utf-8",
+ text=True,
+ check=False,
+ )
+ self.assertEqual(result.returncode, 0, result.stderr)
+ observations = (prepared / "observations.csv").read_text(encoding="utf-8-sig")
+ self.assertIn("flight_phase", observations)
+ self.assertIn("2026-01-01T00:00:00Z", observations)
+ zone_payload = json.loads((prepared / "zones.geojson").read_text(encoding="utf-8"))
+ self.assertEqual(zone_payload["features"][0]["properties"]["zone_type"], "restricted")
+ flyable_payload = json.loads((prepared / "flyable_zones.geojson").read_text(encoding="utf-8"))
+ self.assertEqual(flyable_payload["features"][0]["properties"]["zone_type"], "flyable")
+ analyzed = subprocess.run(
+ [sys.executable, str(ANALYZER), "--input", str(prepared / "sample.case.json"), "--output", str(root / "outputs")],
+ capture_output=True,
+ encoding="utf-8",
+ text=True,
+ check=False,
+ )
+ self.assertEqual(analyzed.returncode, 0, analyzed.stderr)
+ self.assertTrue((root / "outputs" / "sample" / "zones.geojson").is_file())
+ self.assertTrue((root / "outputs" / "sample" / "flyable_zones.geojson").is_file())
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/scripts/serve_workbench_console.py b/scripts/serve_workbench_console.py
index 83126a9..302eb37 100644
--- a/scripts/serve_workbench_console.py
+++ b/scripts/serve_workbench_console.py
@@ -1,44 +1,275 @@
-"""Serve the local GeoAI Workbench console from the repository root."""
+"""Serve the local GeoAI Workbench console and its narrow local-run APIs."""
from __future__ import annotations
import argparse
+import base64
+import binascii
+import json
import os
-from pathlib import PurePosixPath
+import re
+import subprocess
+import threading
+from datetime import UTC, datetime
from http import HTTPStatus
from http.server import SimpleHTTPRequestHandler, ThreadingHTTPServer
-from pathlib import Path
+from pathlib import Path, PurePosixPath
+from typing import Any
from urllib.parse import unquote, urlsplit
+from uuid import uuid4
DEFAULT_HOST = "127.0.0.1"
DEFAULT_PORT = 6173
+MAX_REQUEST_BYTES = 128 * 1024 * 1024
+MAX_FILE_BYTES = 96 * 1024 * 1024
+MAX_IMAGES_PER_RUN = 12
ALLOWED_PATH_PREFIXES = (
"apps/workbench-console",
"shared/outputs",
"shared/data/raw/01-object-detection",
)
+SAFE_FILE_NAME = re.compile(r"[^A-Za-z0-9._-]+")
+RUN_LOCK = threading.Lock()
+
+
+class ApiError(ValueError):
+ """A request error that can be shown to the local console user."""
+
+
+def safe_file_name(value: str, expected_suffixes: set[str]) -> str:
+ name = Path(value).name
+ suffix = Path(name).suffix.lower()
+ if suffix not in expected_suffixes:
+ raise ApiError(f"Unsupported file type: {suffix or '(none)'}.")
+ stem = SAFE_FILE_NAME.sub("_", Path(name).stem).strip("._") or "upload"
+ return f"{stem[:80]}{suffix}"
+
+
+def decode_upload(payload: dict[str, Any], expected_suffixes: set[str]) -> tuple[str, bytes]:
+ if not isinstance(payload, dict) or not isinstance(payload.get("name"), str) or not isinstance(payload.get("content"), str):
+ raise ApiError("Each uploaded file must include name and Base64 content.")
+ name = safe_file_name(payload["name"], expected_suffixes)
+ try:
+ content = base64.b64decode(payload["content"], validate=True)
+ except (binascii.Error, ValueError) as exc:
+ raise ApiError(f"Invalid Base64 file content for {name}.") from exc
+ if not content:
+ raise ApiError(f"Uploaded file is empty: {name}.")
+ if len(content) > MAX_FILE_BYTES:
+ raise ApiError(f"Uploaded file exceeds {MAX_FILE_BYTES // (1024 * 1024)} MB: {name}.")
+ return name, content
+
+
+def make_run_id(prefix: str) -> str:
+ return f"{prefix}-{datetime.now(UTC):%Y%m%d-%H%M%S}-{uuid4().hex[:6]}"
+
+
+def relative_path(root: Path, path: Path) -> str:
+ return path.relative_to(root).as_posix()
+
+
+def load_json(path: Path) -> dict[str, Any]:
+ try:
+ payload = json.loads(path.read_text(encoding="utf-8"))
+ except (OSError, json.JSONDecodeError):
+ return {}
+ return payload if isinstance(payload, dict) else {}
+
+
+def trajectory_runs(root: Path) -> list[dict[str, Any]]:
+ output_root = root / "shared" / "outputs" / "15-trajectory-analysis"
+ records: list[dict[str, Any]] = []
+ for metadata_path in output_root.rglob("run_metadata.json"):
+ artifact = metadata_path.parent
+ if not (artifact / "trajectory_summary.csv").is_file() or not (artifact / "events.json").is_file():
+ continue
+ metadata = load_json(metadata_path)
+ case_id = str(metadata.get("case_id") or artifact.name)
+ is_real = case_id == "tian-dun-flight-19578"
+ records.append(
+ {
+ "id": case_id,
+ "label": "田墩实飞" if is_real else case_id,
+ "note": "区域相交是来源数据的空间结果，不是违规结论。" if is_real else "本地实验运行结果，可继续查看结构化输出。",
+ "artifactRoot": relative_path(root, artifact),
+ "showSpatialContext": (artifact / "zones.geojson").is_file() and (artifact / "reference_routes.geojson").is_file(),
+ "showFlyableZones": (artifact / "flyable_zones.geojson").is_file(),
+ "createdAt": str(metadata.get("created_at") or ""),
+ }
+ )
+ return sorted(records, key=lambda item: (item["createdAt"], item["id"]), reverse=True)
+
+
+def detection_runs(root: Path) -> list[dict[str, Any]]:
+ output_root = root / "shared" / "outputs" / "01-object-detection"
+ records: list[dict[str, Any]] = []
+ for metadata_path in output_root.rglob("run_metadata.json"):
+ artifact = metadata_path.parent
+ if not (artifact / "detections.json").is_file():
+ continue
+ metadata = load_json(metadata_path)
+ input_value = str(metadata.get("input_dir") or "")
+ input_dir = Path(input_value) if input_value else root / "shared" / "data" / "raw" / "01-object-detection"
+ try:
+ input_root = relative_path(root, input_dir.resolve())
+ except ValueError:
+ continue
+ run_id = artifact.name if artifact != output_root else "baseline"
+ records.append(
+ {
+ "id": run_id,
+ "label": "既有基线结果" if run_id == "baseline" else run_id,
+ "note": "CPU 基线：人员与常见车辆；树木不在当前模型有效类别内。",
+ "artifactRoot": relative_path(root, artifact),
+ "inputRoot": input_root,
+ "createdAt": str(metadata.get("created_at") or ""),
+ }
+ )
+ return sorted(records, key=lambda item: (item["createdAt"], item["id"]), reverse=True)
class WorkbenchConsoleHandler(SimpleHTTPRequestHandler):
- """Read-only static handler rooted at the workbench repository."""
+ """Static UI plus fixed, local-only ingestion and experiment commands."""
+
+ server_version = "GeoAIWorkbench/1.0"
+
+ @property
+ def root(self) -> Path:
+ return Path(self.directory).resolve()
def do_GET(self) -> None: # noqa: N802 - inherited standard-library method name
- if urlsplit(self.path).path == "/":
+ path = urlsplit(self.path).path
+ if path == "/api/trajectory/runs":
+ self.send_json(HTTPStatus.OK, {"runs": trajectory_runs(self.root)})
+ return
+ if path == "/api/object-detection/runs":
+ self.send_json(HTTPStatus.OK, {"runs": detection_runs(self.root)})
+ return
+ if path == "/":
self.send_response(HTTPStatus.FOUND)
self.send_header("Location", "/apps/workbench-console/")
self.end_headers()
return
super().do_GET()
+ def do_POST(self) -> None: # noqa: N802 - inherited standard-library method name
+ path = urlsplit(self.path).path
+ try:
+ payload = self.read_json_body()
+ if path == "/api/trajectory/runs":
+ self.send_json(HTTPStatus.CREATED, {"run": self.create_trajectory_run(payload)})
+ return
+ if path == "/api/object-detection/runs":
+ self.send_json(HTTPStatus.CREATED, {"run": self.create_detection_run(payload)})
+ return
+ self.send_json(HTTPStatus.NOT_FOUND, {"error": "Unknown local API endpoint."})
+ except ApiError as exc:
+ self.send_json(HTTPStatus.BAD_REQUEST, {"error": str(exc)})
+ except subprocess.TimeoutExpired:
+ self.send_json(HTTPStatus.GATEWAY_TIMEOUT, {"error": "The local run exceeded its time limit; no existing result was overwritten."})
+ except Exception as exc: # pragma: no cover - defensive server boundary
+ self.log_error("local run failed: %s", exc)
+ self.send_json(HTTPStatus.INTERNAL_SERVER_ERROR, {"error": "Local run failed. Check the console terminal for details."})
+
+ def do_OPTIONS(self) -> None: # noqa: N802
+ self.send_response(HTTPStatus.NO_CONTENT)
+ self.send_header("Allow", "GET, POST, OPTIONS")
+ self.end_headers()
+
+ def read_json_body(self) -> dict[str, Any]:
+ content_length = self.headers.get("Content-Length")
+ if content_length is None or not content_length.isdigit():
+ raise ApiError("A JSON request body with Content-Length is required.")
+ size = int(content_length)
+ if size <= 0 or size > MAX_REQUEST_BYTES:
+ raise ApiError(f"Request must be between 1 byte and {MAX_REQUEST_BYTES // (1024 * 1024)} MB.")
+ if "application/json" not in self.headers.get("Content-Type", ""):
+ raise ApiError("Content-Type must be application/json.")
+ try:
+ payload = json.loads(self.rfile.read(size).decode("utf-8"))
+ except (UnicodeDecodeError, json.JSONDecodeError) as exc:
+ raise ApiError("Request body is not valid UTF-8 JSON.") from exc
+ if not isinstance(payload, dict):
+ raise ApiError("JSON request body must be an object.")
+ return payload
+
+ def run_command(self, command: list[str], timeout: int) -> None:
+ completed = subprocess.run(command, cwd=self.root, capture_output=True, text=True, timeout=timeout, check=False)
+ if completed.returncode:
+ message = (completed.stderr or completed.stdout or "Unknown script error.").strip().splitlines()[-1]
+ raise ApiError(f"Processing failed: {message[:600]}")
+
+ def create_trajectory_run(self, payload: dict[str, Any]) -> dict[str, Any]:
+ files = payload.get("files")
+ if not isinstance(files, dict):
+ raise ApiError("Trajectory request must contain a files object.")
+ required = {
+ "flight": {".xlsx"},
+ "route": {".kmz"},
+ "restricted": {".geojson"},
+ }
+ decoded = {key: decode_upload(files.get(key), suffixes) for key, suffixes in required.items()}
+ flyable = decode_upload(files["flyable"], {".gzip"}) if files.get("flyable") else None
+ run_id = make_run_id("trajectory")
+ raw_root = self.root / "shared" / "data" / "raw" / "15-trajectory-analysis" / "runs" / run_id
+ paths = {"flight": raw_root / "tracks" / decoded["flight"][0], "route": raw_root / "routes" / decoded["route"][0], "restricted": raw_root / "areas" / decoded["restricted"][0]}
+ for key, path in paths.items():
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_bytes(decoded[key][1])
+ if flyable:
+ flyable_path = raw_root / "areas" / flyable[0]
+ flyable_path.write_bytes(flyable[1])
+ processed = self.root / "shared" / "data" / "processed" / "15-trajectory-analysis" / run_id
+ output_parent = self.root / "shared" / "outputs" / "15-trajectory-analysis" / "runs" / run_id
+ python = self.root / ".venvs" / "15-trajectory-analysis" / "Scripts" / "python.exe"
+ if not python.is_file():
+ raise ApiError("Trajectory virtual environment is unavailable. Run the capability setup first.")
+ with RUN_LOCK:
+ self.run_command([str(python), str(self.root / "capabilities" / "15-trajectory-analysis" / "prepare_real_flight.py"), "--raw-dir", str(raw_root), "--output", str(processed), "--case-id", run_id], 300)
+ self.run_command([str(python), str(self.root / "capabilities" / "15-trajectory-analysis" / "run_trajectory_analysis.py"), "--input", str(processed / f"{run_id}.case.json"), "--output", str(output_parent)], 300)
+ artifact = output_parent / run_id
+ if not (artifact / "run_metadata.json").is_file():
+ raise ApiError("Trajectory script finished without the expected result metadata.")
+ return next(item for item in trajectory_runs(self.root) if item["id"] == run_id)
+
+ def create_detection_run(self, payload: dict[str, Any]) -> dict[str, Any]:
+ uploads = payload.get("images")
+ if not isinstance(uploads, list) or not uploads:
+ raise ApiError("Object-detection request must include at least one image.")
+ if len(uploads) > MAX_IMAGES_PER_RUN:
+ raise ApiError(f"A local run accepts at most {MAX_IMAGES_PER_RUN} images.")
+ decoded = [decode_upload(item, {".jpg", ".jpeg", ".png"}) for item in uploads]
+ if len({name.casefold() for name, _ in decoded}) != len(decoded):
+ raise ApiError("Uploaded image names must be unique within one run.")
+ run_id = make_run_id("detection")
+ raw_root = self.root / "shared" / "data" / "raw" / "01-object-detection" / "runs" / run_id
+ raw_root.mkdir(parents=True, exist_ok=False)
+ for name, content in decoded:
+ (raw_root / name).write_bytes(content)
+ output = self.root / "shared" / "outputs" / "01-object-detection" / "runs" / run_id
+ python = self.root / ".venvs" / "01-object-detection" / "Scripts" / "python.exe"
+ if not python.is_file():
+ raise ApiError("Object-detection virtual environment is unavailable. Run the capability setup first.")
+ with RUN_LOCK:
+ self.run_command([str(python), str(self.root / "capabilities" / "01-object-detection" / "run_detection.py"), "--input", str(raw_root), "--output", str(output)], 1200)
+ if not (output / "run_metadata.json").is_file():
+ raise ApiError("Detection script finished without the expected result metadata.")
+ return next(item for item in detection_runs(self.root) if item["id"] == run_id)
+
+ def send_json(self, status: HTTPStatus, payload: dict[str, Any]) -> None:
+ body = json.dumps(payload, ensure_ascii=False).encode("utf-8")
+ self.send_response(status)
+ self.send_header("Content-Type", "application/json; charset=utf-8")
+ self.send_header("Content-Length", str(len(body)))
+ self.end_headers()
+ self.wfile.write(body)
+
def translate_path(self, path: str) -> str:
"""Expose only the static UI and artifacts required by the local console."""
decoded_path = unquote(urlsplit(path).path).lstrip("/")
requested = PurePosixPath(decoded_path)
- is_allowed = any(
- decoded_path == prefix or decoded_path.startswith(f"{prefix}/")
- for prefix in ALLOWED_PATH_PREFIXES
- )
+ is_allowed = any(decoded_path == prefix or decoded_path.startswith(f"{prefix}/") for prefix in ALLOWED_PATH_PREFIXES)
if ".." in requested.parts or not is_allowed:
return os.fspath(Path(self.directory) / ".console-forbidden")
if decoded_path == "apps/workbench-console" or decoded_path.startswith("apps/workbench-console/"):
@@ -71,13 +302,10 @@
app_dir = root / "apps" / "workbench-console"
if not (root / "shared").is_dir() or not (app_dir / "dist" / "index.html").is_file():
raise SystemExit(f"Not a GeoAI Workbench root: {root}")
-
- handler = lambda *handler_args, **handler_kwargs: WorkbenchConsoleHandler( # noqa: E731
- *handler_args, directory=os.fspath(root), **handler_kwargs
- )
+ handler = lambda *handler_args, **handler_kwargs: WorkbenchConsoleHandler(*handler_args, directory=os.fspath(root), **handler_kwargs) # noqa: E731
server = ThreadingHTTPServer((args.host, args.port), handler)
print(f"GeoAI Workbench console: http://{args.host}:{args.port}")
- print(f"Serving built console and read-only artifacts from: {root}")
+ print("Local runs use fixed capability scripts and create a new run directory.")
try:
server.serve_forever()
except KeyboardInterrupt:
--
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