fix: 修复3D地球坐标映射多个严重bug
## Bug修复详情 ### 1. 致命错误：球面距离计算 (calculateDistance) - 问题：使用勾股定理计算经纬度距离，在球体表面完全错误 - 修复：改用Haversine公式计算球面大圆距离 - 影响：赤道1度=111km，极地1度=19km，原计算误差巨大 ### 2. 经度范围规范化 (vector3ToLatLon) - 问题：Math.atan2返回[-180°,180°]，转换后可能超出标准范围 - 修复：添加while循环规范化到[-180, 180]区间 - 影响：避免本初子午线附近返回360°的异常值 ### 3. 屏幕坐标转换支持非全屏 (screenToEarthCoords) - 问题：假设Canvas永远全屏，非全屏时点击偏移严重 - 修复：新增domElement参数，使用getBoundingClientRect()计算相对坐标 - 影响：嵌入式3D地球组件也能精准拾取 ### 4. 地球旋转时经纬度映射错误 - 问题：Raycaster返回世界坐标，未考虑地球自转 - 修复：使用earth.worldToLocal()转换到本地坐标空间 - 影响：地球旋转时经纬度显示正确跟随 ## 新增功能 - CelesTrak卫星数据采集器 - Space-Track卫星数据采集器 - 卫星可视化模块（500颗，实时SGP4轨道计算） - 海底光缆悬停显示info-card - 统一info-card组件 - 工具栏按钮（Stellarium风格） - 缩放控制（百分比显示） - Docker volume映射（代码热更新） ## 文件变更 - utils.js: 坐标转换核心逻辑修复 - satellites.js: 新增卫星可视化 - cables.js: 悬停交互支持 - main.js: 悬停/锁定逻辑 - controls.js: 工具栏UI - info-card.js: 统一卡片组件 - docker-compose.yml: volume映射 - restart.sh: 简化重启脚本
This commit is contained in:
rayd1o
@@ -1,8 +1,13 @@
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"""Visualization API - GeoJSON endpoints for 3D Earth display"""
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"""Visualization API - GeoJSON endpoints for 3D Earth display
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Unified API for all visualization data sources.
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Returns GeoJSON format compatible with Three.js, CesiumJS, and Unreal Cesium.
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"""
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from datetime import datetime
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from fastapi import APIRouter, HTTPException, Depends
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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from sqlalchemy import select, func
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from typing import List, Dict, Any, Optional
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from app.db.session import get_db
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@@ -12,6 +17,9 @@ from app.services.cable_graph import build_graph_from_data, CableGraph
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router = APIRouter()
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# ============== Converter Functions ==============
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def convert_cable_to_geojson(records: List[CollectedData]) -> Dict[str, Any]:
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"""Convert cable records to GeoJSON FeatureCollection"""
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features = []
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@@ -122,6 +130,117 @@ def convert_landing_point_to_geojson(records: List[CollectedData]) -> Dict[str,
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return {"type": "FeatureCollection", "features": features}
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def convert_satellite_to_geojson(records: List[CollectedData]) -> Dict[str, Any]:
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"""Convert satellite TLE records to GeoJSON"""
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features = []
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for record in records:
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metadata = record.extra_data or {}
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norad_id = metadata.get("norad_cat_id")
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if not norad_id:
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continue
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features.append(
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{
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"type": "Feature",
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"id": norad_id,
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"geometry": {"type": "Point", "coordinates": [0, 0, 0]},
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"properties": {
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"id": record.id,
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"norad_cat_id": norad_id,
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"name": record.name,
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"international_designator": metadata.get("international_designator"),
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"epoch": metadata.get("epoch"),
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"inclination": metadata.get("inclination"),
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"raan": metadata.get("raan"),
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"eccentricity": metadata.get("eccentricity"),
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"arg_of_perigee": metadata.get("arg_of_perigee"),
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"mean_anomaly": metadata.get("mean_anomaly"),
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"mean_motion": metadata.get("mean_motion"),
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"bstar": metadata.get("bstar"),
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"classification_type": metadata.get("classification_type"),
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"data_type": "satellite_tle",
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},
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}
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)
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return {"type": "FeatureCollection", "features": features}
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def convert_supercomputer_to_geojson(records: List[CollectedData]) -> Dict[str, Any]:
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"""Convert TOP500 supercomputer records to GeoJSON"""
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features = []
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for record in records:
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try:
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lat = float(record.latitude) if record.latitude and record.latitude != "0.0" else None
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lon = (
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float(record.longitude) if record.longitude and record.longitude != "0.0" else None
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)
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except (ValueError, TypeError):
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lat, lon = None, None
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metadata = record.extra_data or {}
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features.append(
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{
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"type": "Feature",
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"id": record.id,
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"geometry": {"type": "Point", "coordinates": [lon or 0, lat or 0]},
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"properties": {
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"id": record.id,
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"name": record.name,
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"rank": metadata.get("rank"),
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"r_max": record.value,
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"r_peak": metadata.get("r_peak"),
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"cores": metadata.get("cores"),
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"power": metadata.get("power"),
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"country": record.country,
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"city": record.city,
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"data_type": "supercomputer",
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},
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}
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)
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return {"type": "FeatureCollection", "features": features}
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def convert_gpu_cluster_to_geojson(records: List[CollectedData]) -> Dict[str, Any]:
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"""Convert GPU cluster records to GeoJSON"""
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features = []
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for record in records:
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try:
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lat = float(record.latitude) if record.latitude else None
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lon = float(record.longitude) if record.longitude else None
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except (ValueError, TypeError):
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lat, lon = None, None
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metadata = record.extra_data or {}
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features.append(
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{
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"type": "Feature",
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"id": record.id,
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"geometry": {"type": "Point", "coordinates": [lon or 0, lat or 0]},
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"properties": {
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"id": record.id,
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"name": record.name,
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"country": record.country,
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"city": record.city,
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"metadata": metadata,
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"data_type": "gpu_cluster",
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},
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}
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)
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return {"type": "FeatureCollection", "features": features}
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# ============== API Endpoints ==============
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@router.get("/geo/cables")
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async def get_cables_geojson(db: AsyncSession = Depends(get_db)):
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"""获取海底电缆 GeoJSON 数据 (LineString)"""
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@@ -196,6 +315,178 @@ async def get_all_geojson(db: AsyncSession = Depends(get_db)):
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}
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@router.get("/geo/satellites")
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async def get_satellites_geojson(
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limit: int = 10000,
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db: AsyncSession = Depends(get_db),
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):
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"""获取卫星 TLE GeoJSON 数据"""
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stmt = (
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select(CollectedData)
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.where(CollectedData.source == "celestrak_tle")
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.where(CollectedData.name != "Unknown")
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.order_by(CollectedData.id.desc())
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.limit(limit)
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)
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result = await db.execute(stmt)
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records = result.scalars().all()
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if not records:
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return {"type": "FeatureCollection", "features": [], "count": 0}
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geojson = convert_satellite_to_geojson(list(records))
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return {
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**geojson,
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"count": len(geojson.get("features", [])),
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}
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@router.get("/geo/supercomputers")
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async def get_supercomputers_geojson(
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limit: int = 500,
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db: AsyncSession = Depends(get_db),
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):
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"""获取 TOP500 超算中心 GeoJSON 数据"""
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stmt = (
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select(CollectedData)
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.where(CollectedData.source == "top500")
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.where(CollectedData.name != "Unknown")
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.limit(limit)
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)
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result = await db.execute(stmt)
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records = result.scalars().all()
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if not records:
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return {"type": "FeatureCollection", "features": [], "count": 0}
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geojson = convert_supercomputer_to_geojson(list(records))
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return {
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**geojson,
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"count": len(geojson.get("features", [])),
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}
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@router.get("/geo/gpu-clusters")
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async def get_gpu_clusters_geojson(
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limit: int = 100,
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db: AsyncSession = Depends(get_db),
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):
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"""获取 GPU 集群 GeoJSON 数据"""
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stmt = (
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select(CollectedData)
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.where(CollectedData.source == "epoch_ai_gpu")
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.where(CollectedData.name != "Unknown")
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.limit(limit)
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)
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result = await db.execute(stmt)
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records = result.scalars().all()
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if not records:
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return {"type": "FeatureCollection", "features": [], "count": 0}
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geojson = convert_gpu_cluster_to_geojson(list(records))
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return {
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**geojson,
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"count": len(geojson.get("features", [])),
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}
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@router.get("/all")
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async def get_all_visualization_data(db: AsyncSession = Depends(get_db)):
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"""获取所有可视化数据的统一端点
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Returns GeoJSON FeatureCollections for all data types:
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- satellites: 卫星 TLE 数据
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- cables: 海底电缆
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- landing_points: 登陆点
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- supercomputers: TOP500 超算
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- gpu_clusters: GPU 集群
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"""
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cables_stmt = select(CollectedData).where(CollectedData.source == "arcgis_cables")
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cables_result = await db.execute(cables_stmt)
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cables_records = list(cables_result.scalars().all())
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points_stmt = select(CollectedData).where(CollectedData.source == "arcgis_landing_points")
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points_result = await db.execute(points_stmt)
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points_records = list(points_result.scalars().all())
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satellites_stmt = (
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select(CollectedData)
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.where(CollectedData.source == "celestrak_tle")
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.where(CollectedData.name != "Unknown")
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)
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satellites_result = await db.execute(satellites_stmt)
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satellites_records = list(satellites_result.scalars().all())
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supercomputers_stmt = (
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select(CollectedData)
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.where(CollectedData.source == "top500")
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.where(CollectedData.name != "Unknown")
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)
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supercomputers_result = await db.execute(supercomputers_stmt)
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supercomputers_records = list(supercomputers_result.scalars().all())
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gpu_stmt = (
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select(CollectedData)
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.where(CollectedData.source == "epoch_ai_gpu")
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.where(CollectedData.name != "Unknown")
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)
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gpu_result = await db.execute(gpu_stmt)
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gpu_records = list(gpu_result.scalars().all())
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cables = (
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convert_cable_to_geojson(cables_records)
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if cables_records
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else {"type": "FeatureCollection", "features": []}
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)
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landing_points = (
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convert_landing_point_to_geojson(points_records)
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if points_records
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else {"type": "FeatureCollection", "features": []}
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)
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satellites = (
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convert_satellite_to_geojson(satellites_records)
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if satellites_records
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else {"type": "FeatureCollection", "features": []}
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)
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supercomputers = (
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convert_supercomputer_to_geojson(supercomputers_records)
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if supercomputers_records
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else {"type": "FeatureCollection", "features": []}
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)
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gpu_clusters = (
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convert_gpu_cluster_to_geojson(gpu_records)
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if gpu_records
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else {"type": "FeatureCollection", "features": []}
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)
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return {
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"generated_at": datetime.utcnow().isoformat() + "Z",
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"version": "1.0",
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"data": {
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"satellites": satellites,
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"cables": cables,
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"landing_points": landing_points,
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"supercomputers": supercomputers,
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"gpu_clusters": gpu_clusters,
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},
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"stats": {
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"total_features": (
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len(satellites.get("features", []))
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+ len(cables.get("features", []))
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+ len(landing_points.get("features", []))
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+ len(supercomputers.get("features", []))
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+ len(gpu_clusters.get("features", []))
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),
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"satellites": len(satellites.get("features", [])),
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"cables": len(cables.get("features", [])),
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"landing_points": len(landing_points.get("features", [])),
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"supercomputers": len(supercomputers.get("features", [])),
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"gpu_clusters": len(gpu_clusters.get("features", [])),
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},
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}
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# Cache for cable graph
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_cable_graph: Optional[CableGraph] = None
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