視覺計數推理過程中基於置（zhì）信度自動觸發優化程序，可以構建一個自適應的智能係統

public class ConfidenceBasedOptimizer
{
/// <summary>
/// 基於置信度的自動優（yōu）化觸發器（qì）
/// </summary>
public class ConfidenceMonitor
{
private Queue<float> _recentConfidences = new Queue<float>();
private DateTime _lastOptimization = DateTime.MinValue;
private bool _optimizationInProgress = false;

// 配置（zhì）參（cān）數
private class OptimizationConfig
{
public float LowConfidenceThreshold { get; set; } = 0.4f; // 低置信度閾（yù）值
public float ConfidenceDropThreshold { get; set; } = 0.15f; // 置信度下降閾（yù）值
public int SampleWindowSize { get; set; } = 100; // 監控窗口（kǒu）大小
public TimeSpan MinOptimizationInterval { get; set; } = TimeSpan.FromHours(1);
public int MinLowConfidenceSamples { get; set; } = 20; // 觸發優化（huà）的最小低置信度樣本數
}

public bool ShouldTriggerOptimization(List<ZxiaResult> currentResults, Mat image = null)
{
if (_optimizationInProgress) return false;

// 檢查時間間隔
if (DateTime.Now - _lastOptimization < _config.MinOptimizationInterval)
return false;

// 分析當前推理結果（guǒ）的置信度
var confidenceAnalysis = AnalyzeConfidence(currentResults);

// 更新曆史記錄
UpdateConfidenceHistory(confidenceAnalysis.MeanConfidence);

// 檢查各種觸發條（tiáo）件
return CheckTriggerConditions(confidenceAnalysis, currentResults, image);
}

private ConfidenceAnalysis AnalyzeConfidence(List<ZxiaResult> results)
{
var analysis = new ConfidenceAnalysis();

if (results == null || results.Count == 0)
{
analysis.MeanConfidence = 0f;
analysis.LowConfidenceRatio = 1.0f;
analysis.HighConfidenceCount = 0;
return analysis;
}

var confidences = results.Select(r => r.Score).ToArray();
analysis.MeanConfidence = confidences.Average();
analysis.LowConfidenceRatio = confidences.Count(c => c < _config.LowConfidenceThreshold) / (float)confidences.Length;
analysis.HighConfidenceCount = confidences.Count(c => c > 0.7f);

return analysis;
}

private bool CheckTriggerConditions(ConfidenceAnalysis analysis, List<ZxiaResult> results, Mat image)
{
// 條件1: 平均（jun1）置信度過低
if (analysis.MeanConfidence < _config.LowConfidenceThreshold)
return true;

// 條件2: 低置（zhì）信度樣本比例過高
if (analysis.LowConfidenceRatio > 0.6f)
return true;

// 條件3: 檢（jiǎn）測（cè）到置信度持續下降趨勢
if (CheckConfidenceDeclineTrend())
return true;

// 條件4: 連續多個樣本都是（shì）低置信度
if (CheckConsecutiveLowConfidenceSamples())
return true;

// 條（tiáo）件5: 檢測到新的未知類別模式
if (image != null && DetectNovelPattern(image, results))
return true;

return false;
}

private bool CheckConfidenceDeclineTrend()
{
if (_recentConfidences.Count < 20) return false;

var recent = _recentConfidences.Skip(_recentConfidences.Count - 10).Average();
var previous = _recentConfidences.Take(10).Average();

return (previous - recent) > _config.ConfidenceDropThreshold;
}
}
}

public class RealTimeOptimizationExecutor
{
/// <summary>
/// 實時優化執行器 - 在後台執行模型優化
/// </summary>
public class OptimizationExecutor
{
private ZxiaTask _currentModel;
private ConcurrentQueue<OptimizationSample> _optimizationQueue = new ConcurrentQueue<OptimizationSample>();
private CancellationTokenSource _cancellationTokenSource = new CancellationTokenSource();

public void StartOptimizationService()
{
// 啟動後台優化服務
Task.Run(async () => await OptimizationServiceLoop(_cancellationTokenSource.Token));
}

public void EnqueueLowConfidenceSample(Mat image, List<ZxiaResult> results, float confidenceScore)
{
if (_optimizationQueue.Count < 1000) // 防止內存溢出
{
var sample = new OptimizationSample
{
Image = image.Clone(),
Predictions = results,
ConfidenceScore = confidenceScore,
Timestamp = DateTime.Now,
IsLowConfidence = confidenceScore < 0.4f
};

_optimizationQueue.Enqueue(sample);
}
}

private async Task OptimizationServiceLoop(CancellationToken cancellationToken)
{
while (!cancellationToken.IsCancellationRequested)
{
try
{
// 檢查是否有足夠的樣（yàng）本（běn）進行優化
if (_optimizationQueue.Count >= 50)
{
await PerformIncrementalOptimization();
}

await Task.Delay(TimeSpan.FromMinutes(1), cancellationToken);
}
catch (OperationCanceledException)
{
break;
}
catch (Exception ex)
{
Console.WriteLine($"優化服務錯（cuò）誤: {ex.Message}");
await Task.Delay(TimeSpan.FromMinutes(5), cancellationToken);
}
}
}

private async Task PerformIncrementalOptimization()
{
Console.WriteLine("開始（shǐ）增（zēng）量（liàng）優化...");

// 收集優化樣本（běn）
var samples = CollectOptimizationSamples();

if (samples.Count < 10) return;

// 創建模型（xíng）副本進（jìn）行優化
var optimizedModel = CloneModel(_currentModel);

// 執行快速增（zēng）量學（xué）習（xí）
await Task.Run(() => QuickFineTune(optimizedModel, samples));

// 驗證優化效果
if (ValidateOptimization(optimizedModel, samples))
{
// 切換到（dào）優化後的模型
SwapToOptimizedModel(optimizedModel);
Console.WriteLine("✅ 模型優（yōu）化完成並已切換（huàn）");
}
else
{
optimizedModel.Dispose();
Console.WriteLine("❌ 優化驗證失敗，保持原模型");
}
}
}
}

public class SelfOptimizingInferenceEngine
{
/// <summary>
/// 具備自動優化能（néng）力的推理引擎
/// </summary>
public class AdaptiveInferenceEngine : IDisposable
{
private ZxiaTask _model;
private ConfidenceMonitor _confidenceMonitor;
private OptimizationExecutor _optimizationExecutor;
private ModelVersionManager _versionManager;

private int _totalInferences = 0;
private int _lowConfidenceInferences = 0;

public AdaptiveInferenceEngine(string modelPath)
{
_model = LoadModel(modelPath);
_confidenceMonitor = new ConfidenceMonitor();
_optimizationExecutor = new OptimizationExecutor();
_versionManager = new ModelVersionManager();

// 啟動優化服務
_optimizationExecutor.StartOptimizationService();
}

public List<ZxiaResult> PredictWithAutoOptimization(Mat image, string contextId = null)
{
_totalInferences++;

// 1. 執行推理
var results = _model.ImagePredict(image, 0.3f, 0.7f);

// 2. 分析置信度
var confidenceAnalysis = AnalyzeResultsConfidence(results);

// 3. 記錄低置（zhì）信度樣本
if (confidenceAnalysis.IsLowConfidence)
{
_lowConfidenceInferences++;
_optimizationExecutor.EnqueueLowConfidenceSample(image, results, confidenceAnalysis.MeanConfidence);
}

// 4. 檢查是否觸發優化
if (_confidenceMonitor.ShouldTriggerOptimization(results, image))
{
TriggerBackgroundOptimization();
}

// 5. 記錄推理（lǐ）統計
LogInferenceStatistics(contextId, confidenceAnalysis);

return results;
}

private ConfidenceAnalysis AnalyzeResultsConfidence(List<ZxiaResult> results)
{
var analysis = new ConfidenceAnalysis();

if (results == null || results.Count == 0)
{
analysis.MeanConfidence = 0f;
analysis.IsLowConfidence = true;
return analysis;
}

analysis.MeanConfidence = results.Average(r => r.Score);
analysis.IsLowConfidence = analysis.MeanConfidence < 0.4f ||
results.Count(r => r.Score < 0.3f) > results.Count / 2;

return analysis;
}

private void TriggerBackgroundOptimization()
{
Task.Run(async () =>
{
try
{
Console.WriteLine("🚀 觸發自動優化程序...");

// 執行（háng）優化
await _optimizationExecutor.ExecuteFullOptimization();

// 更新模型版本
_versionManager.CreateNewVersion(_model, "auto_optimized");

Console.WriteLine("✅ 自動優化完成");
}
catch (Exception ex)
{
Console.WriteLine($"❌ 自動（dòng）優化失敗: {ex.Message}");
}
});
}

public void PrintOptimizationStatistics()
{
double lowConfidenceRate = (double)_lowConfidenceInferences / _totalInferences * 100;

Console.WriteLine("=== 優化統計 ===");
Console.WriteLine($"總推理次數: {_totalInferences}");
Console.WriteLine($"低置信（xìn）度推理: {_lowConfidenceInferences} ({lowConfidenceRate:F1}%)");
Console.WriteLine($"模型版本: {_versionManager.CurrentVersion}");
Console.WriteLine($"最（zuì）後優（yōu）化: {_versionManager.LastOptimizationTime}");
}
}
}

public class ScenarioAwareOptimization
{
/// <summary>
/// 根據不同的低置信度場景采（cǎi）用不同的優化策略
/// </summary>
public class ScenarioOptimizer
{
public OptimizationStrategy SelectOptimizationStrategy(ConfidenceAnalysis analysis, List<ZxiaResult> results)
{
var scenario = IdentifyScenario(analysis, results);

return scenario switch
{
LowConfidenceScenario.NewObjectTypes => new OptimizationStrategy
{
Method = OptimizationMethod.ContinualLearning,
LearningRate = 0.0001f,
FocusOnFeatureLearning = true,
AugmentationType = AugmentationType.Diverse
},

LowConfidenceScenario.LightingConditions => new OptimizationStrategy
{
Method = OptimizationMethod.DomainAdaptation,
LearningRate = 0.0005f,
FocusOnFeatureLearning = false,
AugmentationType = AugmentationType.LightingVariation
},

LowConfidenceScenario.Occlusions => new OptimizationStrategy
{
Method = OptimizationMethod.RobustnessTraining,
LearningRate = 0.0002f,
FocusOnFeatureLearning = true,
AugmentationType = AugmentationType.Occlusion
},

LowConfidenceScenario.ScaleVariation => new OptimizationStrategy
{
Method = OptimizationMethod.MultiScaleTraining,
LearningRate = 0.0003f,
FocusOnFeatureLearning = false,
AugmentationType = AugmentationType.MultiScale
},

_ => new OptimizationStrategy
{
Method = OptimizationStrategy.GeneralFineTuning,
LearningRate = 0.001f,
FocusOnFeatureLearning = true,
AugmentationType = AugmentationType.Standard
}
};
}

private LowConfidenceScenario IdentifyScenario(ConfidenceAnalysis analysis, List<ZxiaResult> results)
{
// 分析低置信度的原因
if (ContainsNovelObjects(results))
return LowConfidenceScenario.NewObjectTypes;

if (HasLightingIssues(results))
return LowConfidenceScenario.LightingConditions;

if (HasOcclusionPatterns(results))
return LowConfidenceScenario.Occlusions;

if (HasScaleIssues(results))
return LowConfidenceScenario.ScaleVariation;

return LowConfidenceScenario.General;
}
}

public enum LowConfidenceScenario
{
NewObjectTypes, // 新的物體類型
LightingConditions, // 光照條件變化
Occlusions, // 遮擋情況
ScaleVariation, // 尺度變（biàn）化
General // 一般性低（dī）置信度
}
}

實施優（yōu）勢

✅ 實時適應性

• 自（zì）動檢測模型（xíng）性能下降

• 實時響應數據分布變化（huà）

• 無需人工幹預的持（chí）續優化

✅ 資源效率

• 隻在需（xū）要（yào）時觸發優化

• 後台執行不影響主推理流程

• 增量學習（xí）減少計算（suàn）開銷

✅ 業務價值

• 保持模型在變化環境中的性能

• 減少人工模型維護成本

• 提升係統（tǒng）整體魯棒（bàng）性

✅ 安（ān）全機製

• 優化（huà）前驗證（zhèng）確保不會降（jiàng）低性能（néng）

• 版本管理支持快速（sù）回滾

• 資源監控防止係統過載

觸發條件建議