feat: 拆出 FastAPI 独立后端 + MQTT 控制 + 阿里云 AI
后端（backend/，FastAPI :10085）： - 全部数据 REST 接口：dashboard 快照 / 企业分区 / 播放列表 / 媒体 / 设置 - MQTT 控制通道：opc/display/command（切页/播放/卡片/通知）， 管理端 POST /api/display/command → MQTT 广播 → 所有大屏同步响应 - 阿里云 DashScope：通义千问 LLM + 函数调用（工具经 MQTT 广播）， paraformer-realtime-v2 语音识别 /api/ai/asr - 媒体资源统一由后端存储返回（上传/列表/静态服务） - SSE /api/events 保留作 MQTT 不可用时的兼容回退 前端： - config.js + .env.local 配置后端地址与 MQTT 账号（前端 dpm / 服务端 dpmserver） - mqtt.js 客户端 + useMqttControl（MQTT 驱动切页/媒体/卡片/通知） - DpmOverlays 全局覆盖层（通知 toast + 企业/分区/总览卡片） - useParkSim 改为后端 API 数据源（离线回退本地模拟） - AiChatPanel：对话走后端 LLM（工具调用），语音走本地录音 + 后端 ASR - MediaScreen：媒体控制走 MQTT（保留 SSE 回退），修复 useEffect TDZ Rust：lib.rs 移除内嵌 HTTP 服务器，只保留薄壳（窗口/权限/自启/npc 隧道） 安全：backend/.env、.env.local、media、data.json 已 gitignore
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# -*- coding: utf-8 -*-
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"""园区数据模拟引擎 —— 由前端 parkData.js 移植，后端统一产生数据
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前端通过 GET /api/dashboard/snapshot 获取，或订阅 MQTT opc/dashboard/tick
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"""
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import random
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import threading
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import time
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MODEL_NAMES = ["DeepSeek-V3", "通义千问", "智谱 GLM-4", "豆包", "讯飞星火"]
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TOOL_NAMES = ["文档生成", "数据查询", "图像创作", "代码执行", "语音合成"]
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COMPANY_NAMES = [
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"云南派音人工智能科技", "米勒克尔蓝宝石珠宝", "中泰研学合作", "云南宸中低空经济",
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"昆明智海银高文化科技", "云南廷秀文旅康养", "瀚颖AI+教育信息咨询",
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"仰光客厅", "云南上古绝学文化", "中越生物医疗", "酷享野农AI农业",
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"滇缅国际设计", "昆明舒诺生物科技", "达岸教育管理", "花仙子园艺肥料",
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"研X同行者网络", "鬼才明AI创意工作室", "朵哈·玫瑰特色产业链", "昆明云韵体育",
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"南菌优培食用菌", "五华区丽裳文化", "云南星瑞航空", "综合直播私域平台",
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"昆明屿澈电商", "蓝智科技", "云品出滇·纸享万家", "启元人工智能科技",
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]
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def _pick(arr):
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return arr[random.randrange(len(arr))]
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def _now_time():
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return time.strftime("%H:%M:%S")
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def make_series(base, growth, noise, n=30):
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out = []
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v = base
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for _ in range(n):
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v = v * (1 + growth) + (random.random() - 0.5) * noise
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out.append(round(max(1, v * 100) / 100, 2))
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return out
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def gen_event():
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r = random.random
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pool = [
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{"icon": "bolt", "text": f"AI 推理任务完成 · 消耗 {round(800 + r() * 9000):,} tokens（{_pick(MODEL_NAMES)}）"},
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{"icon": "wrench", "text": f"工具「{_pick(TOOL_NAMES)}」被调用 {round(10 + r() * 90)} 次"},
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{"icon": "building", "text": f"「{_pick(COMPANY_NAMES)}」提交入驻申请 · 进入评审流程"},
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{"icon": "users", "text": f"「{_pick(COMPANY_NAMES)}」新增招聘岗位 {round(1 + r() * 5)} 个"},
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{"icon": "coin", "text": f"园区企业完成一笔 ¥{(0.5 + r() * 9):.1f}万 交易"},
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{"icon": "robot", "text": f"「{_pick(MODEL_NAMES)}」模型完成一次微调任务"},
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]
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e = _pick(pool)
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return {"id": f"{int(time.time()*1000)}-{random.random()}", "icon": e["icon"], "text": e["text"], "time": _now_time()}
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def init_feed():
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return [
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{"id": 1, "icon": "bolt", "text": "云南派音AI 完成音频向量嵌入任务 · 消耗 12,480 tokens", "time": _now_time()},
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{"id": 2, "icon": "building", "text": "「启元人工智能科技」通过评审 · 正式入驻 OPC 创业空间", "time": _now_time()},
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{"id": 3, "icon": "users", "text": "「昆明舒护安养老服务」新增招聘岗位 2 个", "time": _now_time()},
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{"id": 4, "icon": "coin", "text": "园区企业完成一笔 ¥3.6万 交易", "time": _now_time()},
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]
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def init_snapshot():
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return {
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"t": 0,
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"token": {"today": 128.64, "total": 12840, "rate": 84.6, "series": make_series(82, 0.012, 9)},
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"tools": {"today": 3568, "total": 365204, "success": 98.7},
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"projects": {"inPark": 158, "cum": 208, "todayNew": 2},
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"jobs": {"total": 2186, "todayNew": 3},
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"revenue": {"today": 38.6, "total": 20800, "growth": 8.2, "series": make_series(30, 0.006, 4)},
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"park": {"devices": 98.6, "energy": 386, "people": 127, "desk": 76, "meeting": 3, "nodes": 12},
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"feed": init_feed(),
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}
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def next_snapshot(s):
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r = random.random
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t_delta = round(0.26 + r() * 0.34, 2)
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token = {
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"today": round(s["token"]["today"] + t_delta, 2),
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"total": round(s["token"]["total"] + t_delta, 2),
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"rate": round(76 + r() * 20, 1),
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}
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tool_delta = round(13 + r() * 22)
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tools = {
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"today": s["tools"]["today"] + tool_delta,
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"total": s["tools"]["total"] + tool_delta,
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"success": round(98.1 + r() * 1.2, 1),
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}
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rev_delta = round(0.6 + r() * 1.7, 1)
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revenue = {
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"today": round(s["revenue"]["today"] + rev_delta, 1),
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"total": round(s["revenue"]["total"] + rev_delta, 1),
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"growth": round(7.2 + r() * 2.2, 1),
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}
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park = {
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"devices": round(97.6 + r() * 1.6, 1),
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"energy": round(320 + r() * 130),
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"people": round(80 + r() * 95),
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"desk": round(62 + r() * 24),
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"meeting": round(2 + r() * 4),
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"nodes": 12,
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}
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projects = dict(s["projects"])
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if r() < 0.055:
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projects["todayNew"] += 1
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if r() < 0.035:
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projects["inPark"] += 1
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projects["cum"] += 1
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jobs = dict(s["jobs"])
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if r() < 0.08:
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jobs["todayNew"] += 1
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if r() < 0.05:
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jobs["total"] += 1
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t_series = list(s["token"]["series"])
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t_series[-1] = round(t_series[-1] + t_delta, 2)
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if s["t"] % 12 == 11:
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t_series = t_series[1:] + [round(72 + r() * 30, 2)]
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token["series"] = t_series
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r_series = list(s["revenue"]["series"])
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r_series[-1] = round(r_series[-1] + rev_delta, 1)
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if s["t"] % 12 == 11:
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r_series = r_series[1:] + [round(26 + r() * 9, 1)]
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revenue["series"] = r_series
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feed = [gen_event()] + s["feed"][:6] if s["t"] % 3 == 2 else s["feed"]
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return {
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"t": s["t"] + 1,
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"token": token, "tools": tools, "revenue": revenue,
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"park": park, "projects": projects, "jobs": jobs, "feed": feed,
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}
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class SimEngine:
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"""带锁的快照引擎：单例供 API 与 MQTT tick 共用"""
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def __init__(self):
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self._lock = threading.RLock()
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self._snap = init_snapshot()
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def snapshot(self):
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with self._lock:
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return dict(self._snap)
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def tick(self):
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with self._lock:
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self._snap = next_snapshot(self._snap)
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return dict(self._snap)
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sim_engine = SimEngine()
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