Unity HDRP + Azure IoT 的 Python 后端實現與集成方案

雖然Unity HDRP本身使用C#開發，但我們可以構建Python后端服務支持物聯網系統，并與Unity引擎深度集成。以下是完整的實現方案：

系統架構

一、Python 后端服務實現

1. 設備數據接收與處理 (`iot_processor.py`)

import asyncio
from azure.iot.hub.aio import IoTHubRegistryManager
from azure.iot.hub import DigitalTwinClient
import json
import websockets
from collections import deque# 設備狀態存儲
device_states = {}
history_data = deque(maxlen=1000)  # 存儲最近1000條數據async def device_twin_listener():"""監聽Azure IoT Hub設備狀態變化"""connection_string = "HostName=your-iot-hub.azure-devices.net;SharedAccessKeyName=service;SharedAccessKey=your-key"device_id = "your-device-id"async with IoTHubRegistryManager(connection_string) as registry_manager:while True:twin = await registry_manager.get_twin(device_id)properties = twin.propertiesif properties.reported:device_states[device_id] = properties.reported# 存儲歷史數據history_data.append({"timestamp": time.time(),"device_id": device_id,"data": properties.reported})await asyncio.sleep(1)  # 每秒更新async def send_to_unity(websocket):"""將數據發送到Unity HDRP應用"""while True:if device_states:# 準備發送給Unity的數據payload = {"type": "device_update","data": device_states}await websocket.send(json.dumps(payload))await asyncio.sleep(0.1)  # 10Hz更新頻率async def command_handler(websocket):"""處理來自Unity的控制命令"""async for message in websocket:data = json.loads(message)if data["type"] == "control_command":device_id = data["device_id"]command = data["command"]# 更新設備數字孿生twin_client = DigitalTwinClient.from_connection_string("HostName=your-iot-hub.azure-devices.net;SharedAccessKeyName=service;SharedAccessKey=your-key")patch = {"properties": {"desired": {"command": command}}}twin_client.update_digital_twin(device_id, patch)print(f"Sent command {command} to device {device_id}")async def main():"""主函數"""# 啟動設備監聽asyncio.create_task(device_twin_listener())# WebSocket服務器async with websockets.serve(lambda ws, path: asyncio.gather(send_to_unity(ws),command_handler(ws)), "localhost", 8765):print("Python backend service running on ws://localhost:8765")await asyncio.Future()  # 永久運行if __name__ == "__main__":asyncio.run(main())

2. 預測性維護分析 (`predictive_maintenance.py`)

import joblib
import numpy as np
from sklearn.ensemble import IsolationForest
from tensorflow import kerasclass PredictiveMaintenance:def __init__(self):# 加載預訓練的模型self.anomaly_detector = joblib.load('models/anomaly_detector.pkl')self.failure_predictor = keras.models.load_model('models/failure_predictor.h5')def detect_anomaly(self, sensor_data):"""檢測傳感器數據異常"""# 轉換為模型輸入格式features = np.array([sensor_data['temperature'],sensor_data['vibration'],sensor_data['current'],sensor_data['pressure']]).reshape(1, -1)prediction = self.anomaly_detector.predict(features)return prediction[0] == -1  # -1表示異常def predict_failure_probability(self, sensor_data, history):"""預測設備故障概率"""# 創建時間序列數據sequence = []for data in history[-10:]:  # 使用最近10個時間點sequence.extend([data['temperature'],data['vibration'],data['current'],data['pressure']])# 如果歷史數據不足，用0填充if len(sequence) < 40:sequence = sequence + [0] * (40 - len(sequence))# 預測sequence = np.array(sequence).reshape(1, 10, 4)probability = self.failure_predictor.predict(sequence)[0][0]return float(probability)# 在main.py中使用
# pm = PredictiveMaintenance()
# if pm.detect_anomaly(device_data):
#     print("Anomaly detected!")

3. 數字孿生同步 (`digital_twin_sync.py`)

from azure.iot.hub import DigitalTwinClient
import timeclass DigitalTwinManager:def __init__(self):self.connection_string = "HostName=your-iot-hub.azure-devices.net;SharedAccessKeyName=service;SharedAccessKey=your-key"self.client = DigitalTwinClient.from_connection_string(self.connection_string)def update_digital_twin(self, device_id, properties):"""更新數字孿生體屬性"""patch = {"properties": {"desired": properties}}self.client.update_digital_twin(device_id, patch)def get_digital_twin(self, device_id):"""獲取數字孿生體狀態"""return self.client.get_digital_twin(device_id)def create_virtual_device(self, device_id, model_id):"""創建虛擬設備孿生體"""digital_twin = {"$dtId": device_id,"$metadata": {"$model": model_id},"properties": {"desired": {},"reported": {}}}self.client.create_digital_twin(device_id, digital_twin)

二、Unity HDRP 集成實現 (C#)

1. WebSocket 客戶端 (`WebSocketClient.cs`)

using UnityEngine;
using NativeWebSocket;
using System.Collections.Generic;public class WebSocketClient : MonoBehaviour
{public string serverUrl = "ws://localhost:8765";private WebSocket websocket;private Queue<string> messageQueue = new Queue<string>();async void Start(){websocket = new WebSocket(serverUrl);websocket.OnOpen += () => Debug.Log("Connected to Python backend");websocket.OnError += (e) => Debug.LogError($"WebSocket Error: {e}");websocket.OnClose += (e) => Debug.Log("Connection closed");websocket.OnMessage += (bytes) =>{var message = System.Text.Encoding.UTF8.GetString(bytes);lock (messageQueue) messageQueue.Enqueue(message);};await websocket.Connect();}void Update(){#if !UNITY_WEBGL || UNITY_EDITORif (websocket != null) websocket.DispatchMessageQueue();#endif// 處理接收到的消息lock (messageQueue){while (messageQueue.Count > 0){ProcessMessage(messageQueue.Dequeue());}}}private void ProcessMessage(string message){var data = JsonUtility.FromJson<DeviceUpdateMessage>(message);if (data.type == "device_update"){DeviceManager.Instance.UpdateDeviceStates(data.data);}}public async void SendCommand(string deviceId, string command){if (websocket.State == WebSocketState.Open){var message = new ControlCommand{type = "control_command",device_id = deviceId,command = command};await websocket.SendText(JsonUtility.ToJson(message));}}async void OnApplicationQuit(){if (websocket != null) await websocket.Close();}[System.Serializable]private class DeviceUpdateMessage{public string type;public Dictionary<string, DeviceState> data;}[System.Serializable]private class ControlCommand{public string type;public string device_id;public string command;}
}

2. 設備狀態可視化 (`DeviceVisualization.cs`)

using UnityEngine;
using System.Collections.Generic;public class DeviceVisualization : MonoBehaviour
{[System.Serializable]public class DeviceModel{public string deviceId;public GameObject modelPrefab;public Material normalMaterial;public Material warningMaterial;public Material criticalMaterial;}public List<DeviceModel> deviceModels = new List<DeviceModel>();private Dictionary<string, GameObject> deviceInstances = new Dictionary<string, GameObject>();private Dictionary<string, Renderer> deviceRenderers = new Dictionary<string, Renderer>();void Start(){// 初始化設備實例foreach (var model in deviceModels){var instance = Instantiate(model.modelPrefab, Vector3.zero, Quaternion.identity);instance.SetActive(false);deviceInstances[model.deviceId] = instance;deviceRenderers[model.deviceId] = instance.GetComponent<Renderer>();}}public void UpdateDeviceState(string deviceId, DeviceState state){if (!deviceInstances.ContainsKey(deviceId)) return;var instance = deviceInstances[deviceId];if (!instance.activeSelf) instance.SetActive(true);// 更新位置instance.transform.position = new Vector3(state.position_x, state.position_y, state.position_z);// 更新材質顏色UpdateMaterial(deviceId, state);// 更新動畫狀態UpdateAnimation(instance, state);}private void UpdateMaterial(string deviceId, DeviceState state){var renderer = deviceRenderers[deviceId];DeviceModel model = deviceModels.Find(m => m.deviceId == deviceId);if (state.temperature > 80f){renderer.material = model.criticalMaterial;}else if (state.temperature > 60f){renderer.material = model.warningMaterial;}else{renderer.material = model.normalMaterial;}}private void UpdateAnimation(GameObject device, DeviceState state){var animator = device.GetComponent<Animator>();if (animator != null){animator.speed = state.speed;animator.SetBool("IsRunning", state.status == "running");animator.SetBool("IsFault", state.status == "fault");}}
}

3. 數字孿生交互 (`DigitalTwinInteraction.cs`)

using UnityEngine;
using UnityEngine.EventSystems;public class DigitalTwinInteraction : MonoBehaviour, IPointerClickHandler
{public string deviceId;public GameObject infoPanelPrefab;private GameObject infoPanel;public void OnPointerClick(PointerEventData eventData){if (infoPanel == null){infoPanel = Instantiate(infoPanelPrefab);infoPanel.GetComponent<DeviceInfoPanel>().Initialize(deviceId);}else{Destroy(infoPanel);infoPanel = null;}}void Update(){if (infoPanel != null){// 讓信息面板跟隨設備位置Vector3 screenPos = Camera.main.WorldToScreenPoint(transform.position);infoPanel.transform.position = screenPos + new Vector3(150, 0, 0);}}
}

三、Python 與 Unity 的混合現實集成

手勢控制命令 (`gesture_controller.py`)

import cv2
import mediapipe as mp
import numpy as np
import asyncio
import websockets# 手勢識別模型
mp_hands = mp.solutions.hands
hands = mp_hands.Hands(max_num_hands=1)async def gesture_control(websocket):"""通過手勢控制Unity場景"""cap = cv2.VideoCapture(0)while cap.isOpened():success, image = cap.read()if not success:continue# 手勢識別image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)results = hands.process(image)if results.multi_hand_landmarks:hand_landmarks = results.multi_hand_landmarks[0]# 檢測手勢命令command = detect_gesture(hand_landmarks)if command:await websocket.send(json.dumps({"type": "gesture_command","command": command}))# 降低處理頻率await asyncio.sleep(0.1)cap.release()def detect_gesture(hand_landmarks):"""檢測特定手勢"""# 獲取關鍵點坐標landmarks = hand_landmarks.landmarkthumb_tip = landmarks[mp_hands.HandLandmark.THUMB_TIP]index_tip = landmarks[mp_hands.HandLandmark.INDEX_FINGER_TIP]# 1. 捏合手勢（選擇對象）distance = np.sqrt((thumb_tip.x - index_tip.x)**2 + (thumb_tip.y - index_tip.y)**2)if distance < 0.05:return "select"# 2. 握拳手勢（停止命令）fingers_folded = all(landmarks[i].y > landmarks[i-2].y for i in [mp_hands.HandLandmark.INDEX_FINGER_TIP,mp_hands.HandLandmark.MIDDLE_FINGER_TIP,mp_hands.HandLandmark.RING_FINGER_TIP,mp_hands.HandLandmark.PINKY_TIP])if fingers_folded:return "stop"# 3. 手掌張開（開始命令）fingers_extended = all(landmarks[i].y < landmarks[i-2].y for i in [mp_hands.HandLandmark.INDEX_FINGER_TIP,mp_hands.HandLandmark.MIDDLE_FINGER_TIP,mp_hands.HandLandmark.RING_FINGER_TIP,mp_hands.HandLandmark.PINKY_TIP])if fingers_extended:return "start"return None

四、部署架構與優化

性能優化策略

部署配置

組件	技術棧	云服務	說明
數據采集層	Python + Azure IoT SDK	Azure IoT Hub	設備數據接收與預處理
數據處理層	Python + FastAPI	Azure Functions	實時數據處理與分析
數字孿生層	Python + Azure Digital Twins SDK	Azure Digital Twins	設備狀態同步與管理
可視化層	Unity HDRP + C#	Azure Virtual Machines	高性能GPU渲染
混合現實層	Python + MediaPipe + Unity MRTK	HoloLens 2	手勢交互與AR可視化
存儲層	Python + Cosmos DB SDK	Azure Cosmos DB	歷史數據存儲與分析

性能指標優化

數據延遲優化：
- 邊緣計算預處理：減少云端傳輸量
- WebSocket二進制傳輸：使用MessagePack替代JSON
- Unity Job System：多線程處理數據

渲染性能優化：

// Unity HDRP 實例化渲染
Graphics.DrawMeshInstancedProcedural(mesh, 0, material, bounds, instanceCount,properties
);

預測分析加速：

# ONNX模型加速
import onnxruntime as ortsession = ort.InferenceSession("model.onnx")
inputs = {"input": sensor_data.astype(np.float32)}
results = session.run(None, inputs)