import pyarrow as pa
from rtree import index
import geopandas as gpd
from geohash import encode, decodeclass HybridSpatialIndex:def __init__(self, persist_path="index_data"):self.mem_index = index.Index()self.geohash_map = {}self.persist_path = persist_pathself._init_disk_storage()def _init_disk_storage(self):"""初始化磁盘存储结构"""import osos.makedirs(self.persist_path, exist_ok=True)self.disk_index = pa.RecordBatchFileWriter(pa.OSFile(f"{self.persist_path}/data.arrow", "wb"),pa.schema([("geohash", pa.string()),("geometry", pa.binary())]))def insert_batch(self, geometries):"""批量插入几何对象"""# 内存索引更新for idx, geom in enumerate(geometries):self.mem_index.insert(idx, geom.bounds)self.geohash_map[idx] = encode(geom.centroid.y, geom.centroid.x, precision=9)# 定期持久化if len(self.geohash_map) > 1e6:self._flush_to_disk()def _flush_to_disk(self):"""数据刷写到磁盘"""# 构建Arrow格式数据geohashes = list(self.geohash_map.values())geometries = [geom.wkb for geom in self._get_geometries()]batch = pa.RecordBatch.from_arrays([pa.array(geohashes),pa.array(geometries)], ["geohash", "geometry"])self.disk_index.write_batch(batch)self.mem_index = index.Index()self.geohash_map.clear()def query(self, bbox, level="all"):"""空间范围查询"""# 内存索引查询mem_results = list(self.mem_index.intersection(bbox))# 磁盘索引查询disk_results = []if level == "all":with pa.OSFile(f"{self.persist_path}/data.arrow", "rb") as f:reader = pa.RecordBatchFileReader(f)for batch in reader:# 使用PyArrow计算引擎过滤import pyarrow.compute as pcmask = pc.geo.bbox_intersects(batch["geometry"],bbox[0], bbox[1], bbox[2], bbox[3])disk_results.extend(batch.filter(mask)["geohash"].to_pylist())return mem_results + disk_results

from pyspark.sql import SparkSession
from pyspark.sql.functions import pandas_udf
from pyspark.sql.types import *
import pandas as pdclass TrajectoryProcessor:def __init__(self):self.spark = SparkSession.builder \.appName("TrajectoryProcessing") \.config("spark.sql.execution.arrow.pyspark.enabled", "true") \.getOrCreate()@staticmethod@pandas_udf(ArrayType(StructType([StructField("timestamp", TimestampType()),StructField("longitude", DoubleType()),StructField("latitude", DoubleType()),StructField("speed", DoubleType()),StructField("segment_type", StringType())])))def process_trajectory(traj_df: pd.DataFrame) -> pd.DataFrame:"""轨迹处理UDF"""from trajectorytools import (compress_trajectory,detect_movement_states,calculate_features)# 轨迹压缩compressed = compress_trajectory(traj_df, tolerance=50)# 运动状态识别labeled = detect_movement_states(compressed, stop_speed_thresh=0.5, min_stop_duration=300)# 特征提取features = calculate_features(labeled)return pd.DataFrame([features])def run_pipeline(self, input_path, output_path):"""执行处理流水线"""# 读取原始轨迹数据df = self.spark.read.parquet(input_path)# 按设备分组处理results = df.groupby("device_id").applyInPandas(self.process_trajectory,schema=StructType([StructField("device_id", StringType()),StructField("features", ArrayType(StructType([StructField("timestamp", TimestampType()),StructField("longitude", DoubleType()),StructField("latitude", DoubleType()),StructField("speed", DoubleType()),StructField("segment_type", StringType())])))]))# 写入处理结果results.write.parquet(output_path)

import networkx as nx
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.problems.functional import FunctionalProblem
from pymoo.optimize import minimizeclass MultiConstraintRouter:def __init__(self, road_network):self.network = road_networkself.time_matrix = self._compute_time_matrix()self.cost_matrix = self._compute_cost_matrix()self._build_hierarchical_graph()def _build_hierarchical_graph(self):"""构建分层路网"""self.hierarchical_graph = {"continental": self._extract_highway_network(),"national": self._extract_primary_network(),"local": self.network}def plan_route(self, origin, destination, constraints):"""多约束路径规划"""# 生成候选路径集candidates = self._generate_candidate_paths(origin, destination)# 定义多目标优化问题problem = FunctionalProblem(len(candidates),self._time_objective,self._cost_objective,constr_ieq=[self._build_constraints(constraints)],xl=0, xu=1)# 配置优化算法algorithm = NSGA2(pop_size=50,eliminate_duplicates=True)# 执行优化res = minimize(problem, algorithm, ('n_gen', 100))# 解析结果return self._decode_solutions(res.X, candidates)def _time_objective(self, x):"""时间目标函数"""return [self.time_matrix[path] for path in x]def _cost_objective(self, x):"""成本目标函数"""return [self.cost_matrix[path] for path in x]def _build_constraints(self, constraints):"""构建约束条件"""def constraint(x):# 时间约束time_violation = max(0, self.time_matrix[x] - constraints["max_time"])# 成本约束cost_violation = max(0, self.cost_matrix[x] - constraints["max_cost"])return max(time_violation, cost_violation)return constraint

import cupy as cp
from numba import cuda@cuda.jit
def gpu_point_in_polygon(points, polygons, results):"""GPU并行点在多边形判断"""i = cuda.grid(1)if i < len(points):x, y = points[i]for j in range(len(polygons)):if _contains(polygons[j], x, y):results[i] = jbreakclass GPUTopologyAnalyzer:def __init__(self, polygons):"""初始化GPU分析器"""self.polygons_gpu = cp.array([p.bounds for p in polygons])self.polygon_data = cp.array([(p.area, p.length) for p in polygons])def batch_contains(self, points):"""批量点在多边形分析"""points_gpu = cp.array([[p.x, p.y] for p in points])results = cp.zeros(len(points), dtype=cp.int32)# 配置CUDA核函数threads_per_block = 256blocks_per_grid = (len(points) + threads_per_block - 1) // threads_per_block# 执行核函数gpu_point_in_polygon[blocks_per_grid, threads_per_block](points_gpu, self.polygons_gpu, results)return results.get()def spatial_join(self, geometries, predicate="intersects"):"""空间连接分析"""# 转换输入数据geoms_gpu = cp.array([g.bounds for g in geometries])# 分配结果内存results = cp.zeros((len(geometries), len(self.polygons_gpu)), dtype=bool)# 执行批量空间关系计算self._compute_relations(geoms_gpu, results, predicate)return results.get()def _compute_relations(self, geometries, results, predicate):"""计算空间关系矩阵"""# 实现不同谓词的空间关系计算pass

from fastapi import FastAPI
import torch
import redis
from kubernetes import client, configapp = FastAPI()
r = redis.Redis(host='redis', port=6379, decode_responses=True)class ModelService:def __init__(self):self.model = Noneself.load_model("/models/current.pt")def load_model(self, model_path):"""加载TorchScript模型"""try:new_model = torch.jit.load(model_path)self.model = new_modelr.set("model:version", model_path.split("/")[-1])return Trueexcept Exception as e:print(f"模型加载失败: {e}")return Falsedef predict(self, features):"""执行预测"""try:with torch.no_grad():inputs = self._preprocess(features)return self.model(inputs).numpy().tolist()except Exception as e:print(f"预测失败: {e}")return Nonemodel_service = ModelService()@app.post("/predict")
async def predict(features: dict):"""预测端点"""# 检查特征缓存cache_key = f"pred:{hash(str(features))}"cached = r.get(cache_key)if cached:return {"result": cached, "source": "cache"}# 执行预测result = model_service.predict(features)if result:# 更新缓存r.setex(cache_key, 3600, str(result))return {"result": result, "source": "model"}return {"error": "Prediction failed"}, 500@app.post("/update_model")
async def update_model(model_url: str):"""模型更新端点"""# 下载新模型if not download_model(model_url):return {"status": "download failed"}, 500# 加载新模型if model_service.load_model("/models/new_model.pt"):# 触发服务扩展scale_deployment(replicas=2)return {"status": "success"}return {"status": "load failed"}, 500def scale_deployment(replicas: int):"""调整K8s部署规模"""config.load_incluster_config()apps_v1 = client.AppsV1Api()patch = {"spec": {"replicas": replicas}}apps_v1.patch_namespaced_deployment_scale(name="model-service",namespace="default",body=patch)