fix(ml): 优化PyTorch模型推理流程与资源管理

- 修改API接口路径从serve切换到common，统一资源调用
- 增加独立推理线程，隔离ArcSoft线程污染，保障FPU状态稳定
- 推理模块预热，降低PyTorch线程初启功率峰值，避免电流保护触发
- 推理前后记录系统内存信息，辅助OOM重启排查
- 主屏相机与副屏人脸识别推理前暂停，规避硬件并发带宽冲突重启
- 控制相机HAL清理时长，确保DMC带宽资源释放后再启动推理
- 异步推理调用增加超时机制，避免长时间卡死
- 推理结束后恢复相机，确保正常工作流程

app/src/main/java/com/sw/dualscreen/activity/MainActivity.kt

@@ -1297,7 +1297,24 @@ class MainActivity : BaseActivity<ActivityMainBinding>() {
         startTime = System.currentTimeMillis()
         log("registerDataChange photoUri bitmap保存文件路径：${file?.absolutePath}")
         //val nameList = FoodModule.queryFood(bitmap)
-        val scoreList = FoodModule.getFoodScoreList(bitmap)
+        // 推理前同时暂停主屏相机和副屏人脸识别，让 ArcSoft 线程进入空闲后再启动 PyTorch
+        withContext(Dispatchers.Main) {
+            shutdownCamera()
+            presentation?.pauseCamera()
+        }
+        // 等待相机 HAL 在内核层（uvcvideo URB / DMC system_status）完成异步清理
+        // 实测：unbindAll() 之后 HAL 仍会在 ~200ms 内写 sysfs_dmc/system_status，
+        // 若此时 PyTorch forward() 同时占用大量 DRAM 带宽，RK3588 DMC 频率切换
+        // 与高带宽访问并发会触发硬件级故障导致设备重启。
+        // 1500ms 给 HAL 足够时间完成 DMC 带宽 hold 释放后再启动推理。
+        Timber.tag(TAG).d("queryFoodData: 等待相机HAL完成DMC清理 (1500ms)...")
+        kotlinx.coroutines.delay(1500)
+        Timber.tag(TAG).d("queryFoodData: 延迟结束，开始 forward()")
+        val scoreList = try {
+            FoodModule.getFoodScoreList(bitmap)
+        } finally {
+            withContext(Dispatchers.Main) { setupCamera() }
+        }
         val foodName = if (scoreList.isNotEmpty()) {
             val recDataJson = GsonUtils.toJson(scoreList)
             log("registerDataChange main,getFoodScoreList耗时：${System.currentTimeMillis() - startTime}")

app/src/main/java/com/sw/dualscreen/network/api/ApiServiceV2.kt

@@ -36,13 +36,13 @@ interface ApiServiceV2 {
 
     @POST
     suspend fun getFacePage(
-        @Url url: String = "${GlobalData.appBaseUrl}/nutrition/neglect/serve/face/page",
+        @Url url: String = "${GlobalData.appBaseUrl}/nutrition/neglect/common/face/page",
         @Body request: Map<String, Long>
     ): ApiResponse<List<FaceVO>?>
 
     @POST
     suspend fun getFaceIncrement(
-        @Url url: String = "${GlobalData.appBaseUrl}/nutrition/neglect/serve/face/increment",
+        @Url url: String = "${GlobalData.appBaseUrl}/nutrition/neglect/common/face/increment",
         @Body request: Map<String, Long>
     ): ApiResponse<List<FaceVO>?>
 

app/src/main/java/com/sw/dualscreen/objbox/FoodModule.kt

@@ -15,6 +15,7 @@ import kotlinx.coroutines.Dispatchers
 import kotlinx.coroutines.withContext
 import org.pytorch.IValue
 import org.pytorch.Module
+import org.pytorch.Tensor
 import org.pytorch.torchvision.TensorImageUtils
 import timber.log.Timber
 import java.io.File
@@ -29,6 +30,9 @@ object FoodModule {
     //    private const val THRESHOLD = 0.0
     private var module: Module? = null
 
+    // 专用推理线程：与 Kotlin IO 调度器完全隔离，避免 ArcSoft 污染该线程的 FPU 状态（FPSCR）
+    private val inferenceExecutor = java.util.concurrent.Executors.newSingleThreadExecutor()
+
     //    private lateinit var embeddingsList: List<List<Float>>
 //    private lateinit var labelsList: IntArray
 //    private lateinit var classInfo: FoodClassInfo
@@ -46,6 +50,23 @@ object FoodModule {
         withContext(Dispatchers.IO) {
             val modelPath = copyAssetToCache(context, "best_embedding_model_mobile.pt")
             module = Module.load(modelPath)
+            // 预热：在相机启动前先跑一次 forward，触发 PyTorch 线程池创建
+            // 在预热前将当前线程优先级调低，使 PyTorch 内部新建的工作线程也继承低优先级
+            // 从而降低后续推理时的瞬间功率，避免触发 PMIC 过流保护
+            Timber.tag(TAG).d("预热 forward() 开始")
+            val warmupTensor = Tensor.fromBlob(
+                FloatArray(1 * 3 * 224 * 224),
+                longArrayOf(1, 3, 224, 224)
+            )
+            inferenceExecutor.submit { module?.forward(IValue.from(warmupTensor)) }.get()
+            context.let { ctx ->
+                val am = ctx.getSystemService(android.content.Context.ACTIVITY_SERVICE)
+                        as android.app.ActivityManager
+                val mi = android.app.ActivityManager.MemoryInfo()
+                am.getMemoryInfo(mi)
+                Timber.tag(TAG).d("预热完成后系统可用内存: ${mi.availMem / 1024 / 1024} MB / 总: ${mi.totalMem / 1024 / 1024} MB")
+            }
+            Timber.tag(TAG).d("预热 forward() 完成")
             //初始化默认重新拉取数据，先清空本地数据
             val list = ObjectBox.getAll()
             if (list.isNotEmpty()) {
@@ -86,8 +107,28 @@ object FoodModule {
             if (module == null) {
                 return null
             }
-            val outputTensor = module?.forward(IValue.from(inputTensor))?.toTensor()
+            Timber.tag(TAG).d("module:${module}")
-            return outputTensor?.dataAsFloatArray
+            val iValue = IValue.from(inputTensor)
+            Timber.tag(TAG).d("inputTensor:${inputTensor}")
+
+            // 推理前记录系统可用内存，排查是否因内存不足触发 OOM 重启
+            MyApp.instance?.let { ctx ->
+                val am = ctx.getSystemService(android.content.Context.ACTIVITY_SERVICE)
+                        as android.app.ActivityManager
+                val mi = android.app.ActivityManager.MemoryInfo()
+                am.getMemoryInfo(mi)
+                Timber.tag(TAG).d("forward() 前系统可用内存: ${mi.availMem / 1024 / 1024} MB / 总: ${mi.totalMem / 1024 / 1024} MB, lowMemory=${mi.lowMemory}")
+            }
+            Timber.tag(TAG).d("forward() 开始: ${System.currentTimeMillis()}")
+            val outputIValue = inferenceExecutor.submit<IValue?> { module?.forward(iValue) }
+                .get(10, java.util.concurrent.TimeUnit.SECONDS) ?: return null
+            Timber.tag(TAG).d("forward() 结束: ${System.currentTimeMillis()}")
+
+
+            Timber.tag(TAG).d("outputIValue:${outputIValue}")
+            val outputTensor = outputIValue.toTensor()
+            Timber.tag(TAG).d("outputTensor:${outputTensor}")
+            return outputTensor.dataAsFloatArray
         } catch (e: OutOfMemoryError) {
             e.printStackTrace()
         } finally {