HC Cross-Scale Diagnostic Service - High-Performance API 集成缓存、异步批处理、Prometheus监控 import json import numpy as np from scipy.stats import gaussian_kde, entropy from functools import lru_cache import asyncio from concurrent.futures import ThreadPoolExecutor import threading import time from typing import Optional, List, Dict, Any from fastapi import FastAPI, HTTPException, BackgroundTasks from fastapi.responses import JSONResponse from pydantic import BaseModel, Field import redis.asyncio as aioredis import pickle from prometheus_client import Counter, Histogram, Gauge, generate_latest, CONTENT_TYPE_LATEST from fastapi.middleware.cors import CORSMiddleware # # Prometheus监控指标 # REQUEST_COUNT Counter(hc_diagnostic_requests_total, Total diagnostic requests) REQUEST_DURATION Histogram(hc_diagnostic_duration_seconds, Request duration in seconds) CACHE_HIT_COUNT Counter(hc_diagnostic_cache_hits_total, Cache hit count) CACHE_MISS_COUNT Counter(hc_diagnostic_cache_misses_total, Cache miss count) ACTIVE_REQUESTS Gauge(hc_diagnostic_active_requests, Active requests count) KL_DIVERGENCE Gauge(hc_diagnostic_kl_divergence, KL divergence value) LNK_TOTAL Gauge(hc_diagnostic_lnk_total, Total log Bayes factor) # # Pydantic数据模型 # class DiagnosticRequest(BaseModel): gw_summary_path: str cmb_summary_path: str output_path: Optional[str] None grid_points: Optional[int] 200 cache_ttl: Optional[int] 300 class DiagnosticResponse(BaseModel): status: str dashboard: Dict[str, Any] cache_hit: bool processing_time_ms: float version: str 2.0.0 # # 核心诊断引擎优化版 # class OptimizedConsistencyDashboard: 高并发优化的跨尺度一致性诊断引擎 VERSION 2.0.0 def __init__(self, max_workers: int 4, grid_points: int 200, redis_url: Optional[str] None, cache_ttl: int 300): self.executor ThreadPoolExecutor(max_workersmax_workers) self.grid_points grid_points self.cache_ttl cache_ttl self._kde_cache {} self._cache_lock threading.Lock() self._redis None if redis_url: self._redis aioredis.from_url(redis_url, decode_responsesTrue) async def _get_cached_kde(self, data_id: str) - Optional[tuple]: 从Redis获取缓存的KDE对象 if not self._redis: return None try: key fkde:{data_id} cached await self._redis.get(key) if cached: gw_kde, cmb_kde pickle.loads(cached) CACHE_HIT_COUNT.inc() return gw_kde, cmb_kde except Exception: pass CACHE_MISS_COUNT.inc() return None async def _cache_kde(self, data_id: str, gw_kde, cmb_kde): 缓存KDE对象到Redis if not self._redis: return try: key fkde:{data_id} await self._redis.setex(key, self.cache_ttl, pickle.dumps((gw_kde, cmb_kde))) except Exception: pass async def _calculate_kl_divergence_async(self, gw_data_id: str, cmb_data_id: str, gw_samples: List[float], cmb_samples: List[float], grid_points: int None) - float: 异步计算KL散度带双层缓存 grid_points grid_points or self.grid_points # 1. 尝试从内存缓存获取 cache_key (gw_data_id, cmb_data_id, grid_points) with self._cache_lock: if cache_key in self._kde_cache: kl_div, timestamp self._kde_cache[cache_key] if time.time() - timestamp self.cache_ttl: return kl_div # 2. 尝试从Redis获取KDE gw_omega np.array(gw_samples) cmb_omega np.array(cmb_samples) combined_id f{gw_data_id}:{cmb_data_id} kde_cached await self._get_cached_kde(combined_id) if kde_cached: gw_kde, cmb_kde kde_cached else: # 3. 计算KDECPU密集型在线程池中执行 loop asyncio.get_event_loop() gw_kde, cmb_kde await asyncio.gather( loop.run_in_executor(self.executor, gaussian_kde, gw_omega), loop.run_in_executor(self.executor, gaussian_kde, cmb_omega) ) await self._cache_kde(combined_id, gw_kde, cmb_kde) # 4. 在精简网格上评估 sample_min min(gw_omega.min(), cmb_omega.min()) sample_max max(gw_omega.max(), cmb_omega.max()) x_grid np.linspace(sample_min, sample_max, grid_points) gw_vals gw_kde(x_grid) 1e-12 cmb_vals cmb_kde(x_grid) 1e-12 gw_vals / np.sum(gw_vals) cmb_vals / np.sum(cmb_vals) kl_div entropy(gw_vals, cmb_vals) kl_div float(kl_div) # 5. 更新内存缓存 with self._cache_lock: self._kde_cache[cache_key] (kl_div, time.time()) # LRU清理保留最近128个 if len(self._kde_cache) 128: oldest min(self._kde_cache.keys(), keylambda k: self._kde_cache[k][1]) del self._kde_cache[oldest] return kl_div async def generate_dashboard_async(self, gw_summary_path: str, cmb_summary_path: str, output_path: Optional[str] None) - Dict: 异步生成诊断看板主要入口 start_time time.time() ACTIVE_REQUESTS.inc() try: loop asyncio.get_event_loop() # 1. 异步加载数据IO密集 gw_data, cmb_data await asyncio.gather( loop.run_in_executor(self.executor, self._load_json, gw_summary_path), loop.run_in_executor(self.executor, self._load_json, cmb_summary_path) ) # 2. 生成数据ID用于缓存 gw_samples gw_data.get(omega0_posterior, []) cmb_samples cmb_data.get(omega0_posterior, []) gw_data_id fgw_{hash(str(gw_samples[:10]))} cmb_data_id fcmb_{hash(str(cmb_samples[:10]))} # 3. 并行计算核心指标 lnK_future loop.run_in_executor( None, self._compute_lnK_total, gw_data, cmb_data ) kl_future self._calculate_kl_divergence_async( gw_data_id, cmb_data_id, gw_samples, cmb_samples ) lambda0_future loop.run_in_executor( None, self._compute_lambda0_lock, gw_data, cmb_data ) lnK_total, kl_div, lambda0_combined await asyncio.gather( lnK_future, kl_future, lambda0_future ) # 4. 更新Prometheus指标 KL_DIVERGENCE.set(kl_div) LNK_TOTAL.set(lnK_total) # 5. 构建看板 dashboard self._build_dashboard( gw_data, cmb_data, lnK_total, kl_div, lambda0_combined ) # 6. 可选保存结果 if output_path: await loop.run_in_executor( self.executor, self._save_json, output_path, dashboard ) processing_time (time.time() - start_time) * 1000 REQUEST_COUNT.inc() REQUEST_DURATION.observe(processing_time / 1000) return { dashboard: dashboard, cache_hit: False, # 可进一步细化 processing_time_ms: processing_time } finally: ACTIVE_REQUESTS.dec() def _load_json(self, filepath: str) - Dict: with open(filepath) as f: return json.load(f) def _save_json(self, filepath: str, data: Dict): with open(filepath, w) as f: json.dump(data, f, indent2) def _compute_lnK_total(self, gw_data: Dict, cmb_data: Dict) - float: return gw_data.get(lnK, 0) cmb_data.get(lnK, 0) def _compute_lambda0_lock(self, gw_data: Dict, cmb_data: Dict) - float: gw_omega np.array(gw_data.get(omega0_posterior, [16.5])) cmb_omega np.array(cmb_data.get(omega0_posterior, [16.5])) combined np.concatenate([gw_omega, cmb_omega]) return float(np.exp(2 * np.pi / np.mean(combined))) def _build_dashboard(self, gw_data: Dict, cmb_data: Dict, lnK_total: float, kl_div: float, lambda0_combined: float) - Dict: verdicts [] if lnK_total 10: verdicts.append( Decisive Evidence (lnK 10)) elif lnK_total 5: verdicts.append(⭐ Very Strong Evidence (lnK 5)) else: verdicts.append(f Evidence Level: lnK {lnK_total:.2f}) if kl_div 0.1: verdicts.append(✅ Cross-Scale Consistency (KL 0.1)) elif kl_div 0.5: verdicts.append( Moderate Consistency (KL 0.5)) else: verdicts.append(⚠️ Tension Detected (KL 0.5)) if 1.45 lambda0_combined 1.48: verdicts.append(f λ₀ Locked: {lambda0_combined:.4f} (within 1% of 1.464)) else: verdicts.append(f λ₀ {lambda0_combined:.4f}) return { version: self.VERSION, timestamp: time.strftime(%Y-%m-%dT%H:%M:%SZ, time.gmtime()), bayesian_evidence: { lnK_GW: gw_data.get(lnK, 0), lnK_CMB: cmb_data.get(lnK, 0), lnK_total: lnK_total, K_total: float(np.exp(lnK_total)) }, omega0_consistency: { GW_median: float(np.median(gw_data.get(omega0_posterior, [16.5]))), CMB_median: float(np.median(cmb_data.get(omega0_posterior, [16.5]))), KL_divergence: kl_div }, lambda0_lock: { lambda0_combined: lambda0_combined, deviation_from_1.464: float(abs(lambda0_combined - 1.464) / 1.464 * 100) }, verdict: verdicts, paradigm_shift: CONFIRMED if (lnK_total 10 and kl_div 0.1) else PENDING } # # FastAPI应用 # app FastAPI( titleHC Cross-Scale Diagnostic Service, descriptionHelio-Core框架跨尺度一致性诊断API, version2.0.0 ) app.add_middleware( CORSMiddleware, allow_origins[*], allow_methods[*], allow_headers[*], ) # 全局引擎实例依赖注入 engine OptimizedConsistencyDashboard( max_workersint(os.getenv(MAX_WORKERS, 4)), grid_pointsint(os.getenv(GRID_POINTS, 200)), redis_urlos.getenv(REDIS_URL, None), cache_ttlint(os.getenv(CACHE_TTL, 300)) ) app.get(/health) async def health_check(): return {status: healthy, version: engine.VERSION} app.get(/metrics) async def metrics(): return Response(contentgenerate_latest(), media_typeCONTENT_TYPE_LATEST) app.post(/diagnose, response_modelDiagnosticResponse) async def diagnose(request: DiagnosticRequest, background_tasks: BackgroundTasks): 执行跨尺度一致性诊断 try: result await engine.generate_dashboard_async( request.gw_summary_path, request.cmb_summary_path, request.output_path ) return DiagnosticResponse( statussuccess, dashboardresult[dashboard], cache_hitresult.get(cache_hit, False), processing_time_msresult[processing_time_ms] ) except Exception as e: raise HTTPException(status_code500, detailstr(e)) app.post(/diagnose/batch) async def diagnose_batch(requests: List[DiagnosticRequest]): 批量诊断高并发批处理 tasks [ engine.generate_dashboard_async( req.gw_summary_path, req.cmb_summary_path, req.output_path ) for req in requests ] results await asyncio.gather(*tasks) return { status: success, count: len(results), results: results } app.on_event(shutdown) async def shutdown(): await engine._redis.close() if engine._redis else None该高性能API服务通过异步架构、双层缓存、并行计算和监控集成实现了高并发场景下的跨尺度一致性诊断。其核心优化策略如下表所示优化维度具体实现技术要点性能收益异步并发处理使用asyncioThreadPoolExecutorIO密集型任务文件加载与CPU密集型任务KDE计算分离通过asyncio.gather实现并行计算提升吞吐量避免请求阻塞双层缓存机制内存LRU缓存 Redis分布式缓存内存缓存最近128个KL散度结果Redis持久化KDE对象通过CACHE_HIT_COUNT/CACHE_MISS_COUNT监控命中率减少重复计算降低延迟计算轻量化动态网格采样默认200点根据数据范围动态生成评估网格平衡计算精度与速度降低KL散度计算复杂度监控与可观测性Prometheus指标集成实时监控请求量、延迟、缓存命中率、核心指标KL散度、lnK_total快速定位性能瓶颈批量处理支持/diagnose/batch端点支持批量请求处理利用asyncio.gather并发执行多个诊断任务提升批量任务处理效率关键代码解析异步KL散度计算(_calculate_kl_divergence_async)缓存优先首先检查内存缓存其次查询Redis缓存避免重复的KDE计算。并行KDE估计使用asyncio.gather并发执行gaussian_kde计算充分利用多核CPU。动态网格根据输入数据的实际范围(sample_min,sample_max)生成评估网格避免固定范围带来的冗余计算。服务端点设计/diagnose单次诊断入口返回结构化响应(DiagnosticResponse)。/diagnose/batch批量诊断入口适用于需要同时处理多个数据对的场景通过并发提升整体吞吐量。/metrics暴露Prometheus格式的监控指标便于集成到Grafana等监控系统进行可视化。/health健康检查端点用于服务探活。配置与扩展通过环境变量(MAX_WORKERS,GRID_POINTS,REDIS_URL,CACHE_TTL)动态配置引擎参数增强部署灵活性。OptimizedConsistencyDashboard类设计为可独立实例化便于单元测试或集成到其他服务中。部署与运行# 1. 安装依赖 pip install fastapi uvicorn numpy scipy redis prometheus-client # 2. 设置环境变量示例 export MAX_WORKERS8 export GRID_POINTS200 export REDIS_URLredis://localhost:6379 export CACHE_TTL600 # 3. 启动服务 uvicorn main:app --host 0.0.0.0 --port 8000 --workers 4 # 4. 调用APIcurl -X POST http://localhost:8000/diagnose \ -H Content-Type: application/json \ d { gw_summary_path: ./data/gw_summary.json, cmb_summary_path: ./data/cmb_summary.json }该服务通过将计算密集型诊断任务封装为异步API并辅以缓存和监控能够有效应对高并发请求同时保持对lnK_total、KL散度和lambda_0锁定精度三个核心指标的高效、稳定计算。参考来源GPT-5.5 Instant实时智能体架构与程序员工作流重构7个高级诊断技巧快速定位分布式AI代理系统瓶颈终极车辆诊断利器OpenVehicleDiag如何用Rust重定义汽车ECU诊断Cocos Engine跨平台技术栈深度解构从架构抽象到多端适配的实现路径AI数学推理跃迁从IMO解题到工程落地的四重突破