本项目旨在构建一个地理空间感知系统，帮助智能体（如自动驾驶车辆、机器人、无人机等）感知和理解其周围的地理环境。该系统支持从多种数据源（包括手动输入、GeoJSON 文件、Shapefile 文件）加载地理数据，并提供路径规划、可视化、感知等功能。

组件	技术选型	版本要求
核心框架	Python 3.10+	≥3.10.6
地理处理	GeoPandas 0.12+	≥0.12.2
空间索引	Rtree 0.9+	≥0.9.7
路径规划	NetworkX 3.0+	≥3.0
可视化	Folium/Matplotlib	可选
并发处理	asyncio/Ray	可选

class ResearchArea:
    def __init__(self, name: str, crs: str = "EPSG:4326"):
        self.name = name
        self.crs = crs  # 强制统一坐标系
        self.road_network = RoadNetwork()
        self.poi_collection = POICollection()
        self.agent_manager = AgentManager()
        self.sim_engine = SimulationEngine()
        self.graph_editor = GraphEditor()
        
    def load_data(self, config: dict):
        """支持多数据源加载
        config示例：
        {
            "roads": {
                "type": "shp",
                "path": "data/roads.shp",
                "topo_check": True
            },
            "pois": {
                "type": "geojson",
                "path": "data/pois.geojson"
            }
        }
        """

class RoadNode(TypedDict):
    node_id: str  # "lat_lon_uuid"
    lon: float
    lat: float
    elevation: Optional[float]
    connectivity: int  # 连接边数

class RoadEdge(TypedDict):
    edge_id: str
    start_node: str
    end_node: str
    length: float  # 米
    geometry: LineString
    attrs: dict  # 道路等级/车道数等

class HybridIndex:
    def __init__(self):
        self.rtree = index.Index()  # 精确索引
        self.grid = SpatialGrid(100)  # 100m网格粗索引
        
    def query(self, bbox: tuple, zoom_level: int):
        """混合查询策略：
        - zoom_level > 12: 使用RTree精确查询
        - zoom_level ≤ 12: 使用网格快速过滤
        """

class MovementStrategy(ABC):
    @abstractmethod
    def calculate_move(self, agent: Agent, dt: float) -> MovementCommand:
        pass

class NetworkMovement(MovementStrategy):
    def calculate_move(self, agent, dt):
        """沿路网移动逻辑"""
        next_node = agent.current_edge.end_node
        return MoveToNodeCommand(next_node)

class FreeMovement(MovementStrategy):
    def calculate_move(self, agent, dt):
        """自由空间移动"""
        return MoveByVectorCommand(agent.velocity * dt)

def find_nearest_edge(point: Point) -> Tuple[Edge, float]:
    """混合查询算法流程：
    1. 使用网格索引快速筛选候选边（10-50条）
    2. 使用RTree进行精确距离计算
    3. 应用Shapely的nearest_points优化
    4. 返回最近边及投影点
    时间复杂度：O(logN + M) M为候选边数
    """

class RoutePlanner:
    def __init__(self, network: RoadNetwork):
        self.ch = ContractionHierarchies(network)  # 预计算层次
        
    def shortest_path(self, start: Point, end: Point) -> Route:
        """使用CH加速查询：
        1. 将起点/终点投影到路网
        2. 在高层级图上快速路由
        3. 逐步细化到底层路径
        比传统A*快5-10倍
        """

端点	方法	描述
/v1/spatial/query	POST	空间查询接口
/v1/routing/calculate	GET	路径规划接口
/v1/agents/{id}/position	GET	获取Agent实时位置
/v1/simulation/step	PUT	推进模拟时间步长

路径规划请求：

{
  "origin": {
    "lon": 121.4737,
    "lat": 31.2304,
    "node_id": "optional" 
  },
  "destination": {
    "lon": 121.4788,
    "lat": 31.2355
  },
  "profile": {
    "type": "car",
    "options": {
      "avoid_tolls": true,
      "max_speed": 100
    }
  }
}

成功响应：

{
  "status": "success",
  "data": {
    "path": [
      [121.4737, 31.2304],
      [121.4740, 31.2306],
      ...
    ],
    "cost": 125.6,
    "edges": ["E4512", "E4513"],
    "geometry": "LINESTRING(...)"
  },
  "metadata": {
    "processing_time": 23.5
  }
}

class ResearchAreaData:
    road_network: nx.MultiDiGraph
    poi_kd_tree: KDTree  # 快速POI查询
    agent_states: Dict[str, AgentState]
    
    class AgentState(NamedTuple):
        position: Tuple[float, float]
        velocity: float
        current_edge: Optional[str]
        path: Optional[List[Tuple]]

def save_snapshot(path: str, format: str = "parquet"):
    """支持保存为：
    - Parquet（推荐，压缩率高）
    - GeoJSON（可读性好）
    - Protocol Buffers（高性能）
    """
    
def load_snapshot(path: str):
    """自动检测格式并加载"""

场景	目标性能	实现手段
10k节点路网加载	<1.5s	并行化Shp解析
100 Agent并发移动	<50ms/step	向量化位置更新
1km²区域POI查询	<10ms	分层空间索引
10km路径规划	<100ms	预计算Contraction Hierarchy

# 使用Numba加速距离计算
@njit
def calculate_distances(points: np.ndarray) -> np.ndarray:
    """计算点阵间距离矩阵"""
    n = points.shape[0]
    dists = np.zeros((n, n))
    for i in range(n):
        for j in range(n):
            dists[i,j] = haversine(points[i], points[j])
    return dists

# plugins/template.py
class GISensePlugin:
    def __init__(self, research_area: ResearchArea):
        self.area = research_area
        
    def on_load(self):
        """插件加载时调用"""
        
    def on_sim_step(self, dt: float):
        """模拟步进时调用"""

def register(research_area: ResearchArea) -> GISensePlugin:
    return MyPlugin(research_area)

1. 天气模拟：影响移动速度
2. 动态障碍物：实时修改路网
3. 三维扩展：添加高程维度
4. 视觉渲染：集成Cesium/Deck.gl

class TestRoadNetwork(unittest.TestCase):
    def test_node_connectivity(self):
        """测试节点连接性"""
        self.assertEqual(network.get_node_degree('n123'), 3)
        
    def test_path_continuity(self):
        """测试路径连续性"""
        path = network.shortest_path('A', 'B')
        self.assertTrue(check_path_connected(path))

# 使用Locust模拟并发
$ locust -f tests/load_test.py --users 100 --spawn-rate 10

FROM python:3.10-slim
RUN pip install gisense geopandas==0.12.2

# 启用JIT加速
ENV NUMBA_DISABLE_JIT=0
EXPOSE 8000

CMD ["gunicorn", "gisense.api:app", "-b :8000"]

版本	重点功能	预计周期
v1.0	基础路网建模+Agent移动	6周
v1.5	高性能路径规划+空间索引	4周
v2.0	分布式模拟+三维扩展	8周

以下是 GISense 基础实现的详细伪代码，包含关键设计细节和可扩展的接口定义：

---

import uuid
from typing import NamedTuple

class GeoCoordinate(NamedTuple):
    lon: float  # 经度 [-180, 180]
    lat: float  # 纬度 [-90, 90]
    alt: float = 0.0  # 高程（可选）

class Node:
    def __init__(self, coord: GeoCoordinate, osm_id: str = None):
        self.coord = coord
        self.osm_id = osm_id  # 保留原始ID
        self.uid = self._generate_uid(coord)
        
    @staticmethod
    def _generate_uid(coord: GeoCoordinate) -> str:
        """生成唯一节点ID：base36(经纬度哈希)_随机后缀"""
        lat_lon_hash = hash(f"{coord.lat:.6f}_{coord.lon:.6f}")
        return (
            base36_encode(abs(lat_lon_hash))[:6] 
            + "_" 
            + str(uuid.uuid4())[:4]
        )
        
    @property
    def as_shapely(self) -> Point:
        return Point(self.coord.lon, self.coord.lat)

class RoadEdge:
    def __init__(self, 
                 edge_id: str,
                 start_node: Node,
                 end_node: Node,
                 geometry: LineString,
                 attrs: dict = None):
        self.edge_id = edge_id
        self.nodes = (start_node, end_node)
        self.geometry = geometry  # Shapely LineString
        self.length = geometry.length  # 单位：米
        self.attrs = attrs or {}
        
        # 动态计算字段
        self._bbox = geometry.bounds  # 缓存边界框
        
    def split(self, ratio: float) -> Tuple['RoadEdge', 'RoadEdge']:
        """在比例位置分割边（0.3表示30%位置）"""
        split_point = self.geometry.interpolate(ratio, normalized=True)
        new_node = Node(GeoCoordinate(*split_point.coords[0]))
        
        # 创建两条新边
        edge1 = RoadEdge(
            edge_id=f"{self.edge_id}_a",
            start_node=self.nodes[0],
            end_node=new_node,
            geometry=LineString(list(self.geometry.coords)[:int(ratio*100)])
        
        edge2 = RoadEdge(
            edge_id=f"{self.edge_id}_b",
            start_node=new_node,
            end_node=self.nodes[1],
            geometry=LineString(list(self.geometry.coords)[int(ratio*100):]))
            
        return edge1, edge2

---

class HybridIndex:
    def __init__(self, grid_size: float = 100.0):
        self.rtree = index.Index()
        self.grid = {}
        self.grid_size = grid_size
        
    def _get_grid_key(self, point: Point) -> Tuple[int, int]:
        """计算点所属网格坐标"""
        return (
            int(point.x // self.grid_size),
            int(point.y // self.grid_size)
        )
        
    def insert(self, obj: Union[Node, RoadEdge]):
        """插入对象到索引"""
        # RTree插入
        self.rtree.insert(id(obj), obj.bbox)
        
        # 网格索引插入
        if isinstance(obj, Node):
            grid_key = self._get_grid_key(obj.as_shapely)
            self.grid.setdefault(grid_key, []).append(obj)
        else:  # RoadEdge
            for segment in self._split_edge_to_grid(obj):
                grid_key = self._get_grid_key(segment.centroid)
                self.grid.setdefault(grid_key, []).append(obj)
    
    def query_radius(self, center: Point, radius: float) -> List:
        """圆形区域查询优化流程"""
        # 第一步：网格快速过滤
        affected_grids = self._get_grids_in_radius(center, radius)
        candidates = []
        for grid in affected_grids:
            candidates.extend(self.grid.get(grid, []))
            
        # 第二步：RTree精确筛选
        precise_matches = [
            obj for obj in candidates 
            if self.rtree.intersection(obj.bbox) 
            and center.distance(obj.geometry) <= radius
        ]
        
        return precise_matches

---

class LinearReference:
    @staticmethod
    def encode_position(edge: RoadEdge, point: Point) -> dict:
        """将点编码为线性参考位置"""
        projected = edge.geometry.project(point)
        return {
            "edge_id": edge.edge_id,
            "offset": projected,  # 距起点的米数
            "ratio": projected / edge.length,
            "node_a": edge.nodes[0].uid,
            "node_b": edge.nodes[1].uid
        }
        
    @staticmethod
    def decode_position(ref: dict) -> Point:
        """从线性参考解码为坐标"""
        edge = get_edge_by_id(ref["edge_id"])
        return edge.geometry.interpolate(ref["offset"])

---

class MovementStrategy(ABC):
    @abstractmethod
    def get_next_position(self, 
                        agent: 'Agent',
                        current_pos: GeoCoordinate,
                        dt: float) -> GeoCoordinate:
        pass

class NetworkStrategy(MovementStrategy):
    def get_next_position(self, agent, current_pos, dt):
        """沿路网移动策略"""
        if not agent.current_path:
            agent.current_path = self._find_path(agent.destination)
            
        next_node = agent.current_path.pop(0)
        move_vector = self._calculate_move_vector(
            current_pos, 
            next_node.coord,
            agent.speed * dt
        )
        return current_pos + move_vector
        
class FreeMoveStrategy(MovementStrategy):
    def get_next_position(self, agent, current_pos, dt):
        """自由空间移动策略"""
        return GeoCoordinate(
            current_pos.lon + agent.velocity.lon * dt,
            current_pos.lat + agent.velocity.lat * dt
        )

---

class GraphEncoder:
    @staticmethod
    def to_geojson(nodes: List[Node], edges: List[RoadEdge]) -> dict:
        """转换为GeoJSON格式"""
        return {
            "type": "FeatureCollection",
            "features": [
                *[{
                    "type": "Feature",
                    "geometry": node.as_shapely.__geo_interface__,
                    "properties": {"uid": node.uid}
                } for node in nodes],
                *[{
                    "type": "Feature",
                    "geometry": edge.geometry.__geo_interface__,
                    "properties": {
                        "edge_id": edge.edge_id,
                        "length": edge.length,
                        **edge.attrs
                    }
                } for edge in edges]
            ]
        }
        
    @staticmethod
    def from_geojson(data: dict) -> Tuple[List[Node], List[RoadEdge]]:
        """从GeoJSON解析"""
        # 实现逆解析逻辑

---

class GISenseError(Exception):
    """基础异常类"""
    
class TopologyError(GISenseError):
    """拓扑关系异常"""
    def __init__(self, edge1: str, edge2: str):
        super().__init__(f"Edge {edge1} and {edge2} are not connected")

class SpatialQueryError(GISenseError):
    """空间查询异常"""
    def __init__(self, point: GeoCoordinate, radius: float):
        super().__init__(
            f"No results found around {point} within {radius}m")

---

1. 节点ID生成：

   • 采用base36(经纬度哈希)_4位UUID的混合方案
   • 优点：相同坐标生成相同前缀，便于调试；UUID后缀保证唯一性
   • 示例：k5g9f3_1a2b

2. 边分割策略：

   • 保留原始边ID并添加后缀（如E123_a和E123_b）
   • 自动计算分割后的几何对象长度

3. 混合索引优化：

   • 网格索引用于快速筛选
   • RTree用于精确计算
   • 对长边进行网格分段索引

4. 线性参考系统：

   • 同时存储offset和ratio两种表示法
   • 支持通过任意一种方式还原坐标

5. 策略模式应用：

   • 移动策略可运行时切换
   • 易于扩展新的移动方式（如三维飞行）

---

1. 先实现Node和RoadEdge基础类
2. 构建HybridIndex并测试空间查询
3. 实现LinearReference位置编码
4. 开发MovementStrategy及其子类
5. 最后完成持久化模块