自主高性能三维GIS平台架构：从"能看"到"能战"

// 瓦片数据源基类（参考课程中的TileSource设计）
class TileSource {
public:
    virtual ~TileSource() = default;
    virtual std::shared_ptr<TileData> loadTile(int level, int row, int col) = 0;
    virtual TileInfo getTileInfo(int level, int row, int col) = 0;
};

// 本地文件数据源
class LocalFileTileSource : public TileSource {
public:
    std::shared_ptr<TileData> loadTile(int level, int row, int col) override {
        std::string path = buildTilePath(level, row, col);
        // 从本地文件读取瓦片数据
        return readTileFromFile(path);
    }
};

// WMTS网络服务数据源
class WmtsTileSource : public TileSource {
public:
    std::shared_ptr<TileData> loadTile(int level, int row, int col) override {
        std::string url = buildWmtsUrl(level, row, col);
        // 异步HTTP请求获取瓦片
        return httpGet(url);
    }
};

# 事件总线（Python风格示意，解耦模块通信）
class EventBus:
    def __init__(self):
        self._subscribers = {}  # event_type -> list of callbacks
    
    def subscribe(self, event_type, callback):
        if event_type not in self._subscribers:
            self._subscribers[event_type] = []
        self._subscribers[event_type].append(callback)
    
    def publish(self, event):
        for callback in self._subscribers.get(event.type, []):
            callback(event)

# 使用场景：相机视角变化时，所有关心的模块自动响应
bus = EventBus()

# 标注模块订阅视角变化事件
bus.subscribe("camera_changed", on_camera_changed)
# LOD调度模块也订阅同一个事件
bus.subscribe("camera_changed", on_lod_update)

# 相机模块只需发布事件，不需要知道谁在监听
bus.publish(CameraChangedEvent(zoom=15, center=(116.4, 39.9)))

import random

class RenderParameterOptimizer:
    def __init__(self, population_size=20, generations=50):
        self.population = []
        # 参数范围：屏幕空间误差、最大瓦片数、LOD切换阈值等
        self.param_bounds = [
            (1.0, 20.0),   # 屏幕空间误差
            (50, 500),     # 最大并发加载数
            (0.3, 0.9),    # LOD切换阈值
            # ... 共20个参数
        ]
    
    def evaluate(self, params):
        # 将参数应用到引擎，测量渲染时间
        apply_params(params)
        render_time = measure_render_time()
        return 1.0 / render_time  # 适应度：渲染时间越短越好
    
    def optimize(self):
        # 差分演化：变异 -> 交叉 -> 选择
        for gen in range(self.generations):
            for i, individual in enumerate(self.population):
                # 变异操作（从种群中选三个不同个体）
                a, b, c = random.sample(self.population, 3)
                mutant = self.mutate(a, b, c)
                # 交叉操作
                trial = self.crossover(individual, mutant)
                # 选择：保留适应度更高的个体
                if self.evaluate(trial) > self.evaluate(individual):
                    self.population[i] = trial
        return self.population[0]  # 返回最优参数组合