---

name: azure-ai-anomalydetector-java description: "使用 Azure AI 异常检测器 SDK for Java 构建异常检测应用。适用于实现单变量/多变量异常检测、时间序列分析或 AI 驱动的监控。" risk: unknown source: community date_added: "2026-02-27"

Azure AI 异常检测器 SDK for Java

使用 Azure AI 异常检测器 SDK for Java 构建异常检测应用。

安装

<dependency>
  <groupId>com.azure</groupId>
  <artifactId>azure-ai-anomalydetector</artifactId>
  <version>3.0.0-beta.6</version>
</dependency>

创建客户端

同步和异步客户端

import com.azure.ai.anomalydetector.AnomalyDetectorClientBuilder;
import com.azure.ai.anomalydetector.MultivariateClient;
import com.azure.ai.anomalydetector.UnivariateClient;
import com.azure.core.credential.AzureKeyCredential;

String endpoint = System.getenv("AZURE_ANOMALY_DETECTOR_ENDPOINT");
String key = System.getenv("AZURE_ANOMALY_DETECTOR_API_KEY");

// 多变量客户端，用于多个相关信号
MultivariateClient multivariateClient = new AnomalyDetectorClientBuilder()
    .credential(new AzureKeyCredential(key))
    .endpoint(endpoint)
    .buildMultivariateClient();

// 单变量客户端，用于单变量分析
UnivariateClient univariateClient = new AnomalyDetectorClientBuilder()
    .credential(new AzureKeyCredential(key))
    .endpoint(endpoint)
    .buildUnivariateClient();

使用 DefaultAzureCredential

import com.azure.identity.DefaultAzureCredentialBuilder;

MultivariateClient client = new AnomalyDetectorClientBuilder()
    .credential(new DefaultAzureCredentialBuilder().build())
    .endpoint(endpoint)
    .buildMultivariateClient();

核心概念

单变量异常检测

• 批量检测：一次性分析整个时间序列
• 流式检测：对最新数据点进行实时检测
• 变化点检测：检测时间序列中的趋势变化

多变量异常检测

• 可检测 300+ 个相关信号中的异常
• 使用图注意力网络（Graph Attention Network）处理变量间关联
• 三步流程：训练 → 推理 → 获取结果

核心模式

单变量批量检测

import com.azure.ai.anomalydetector.models.*;
import java.time.OffsetDateTime;
import java.util.List;

List<TimeSeriesPoint> series = List.of(
    new TimeSeriesPoint(OffsetDateTime.parse("2023-01-01T00:00:00Z"), 1.0),
    new TimeSeriesPoint(OffsetDateTime.parse("2023-01-02T00:00:00Z"), 2.5),
    // ... 更多数据点（最少需要 12 个点）
);

UnivariateDetectionOptions options = new UnivariateDetectionOptions(series)
    .setGranularity(TimeGranularity.DAILY)
    .setSensitivity(95);

UnivariateEntireDetectionResult result = univariateClient.detectUnivariateEntireSeries(options);

// 检查异常
for (int i = 0; i < result.getIsAnomaly().size(); i++) {
    if (result.getIsAnomaly().get(i)) {
        System.out.printf("在索引 %d 处检测到异常，值为 %.2f%n",
            i, series.get(i).getValue());
    }
}

单变量最新点检测（流式）

UnivariateLastDetectionResult lastResult = univariateClient.detectUnivariateLastPoint(options);

if (lastResult.isAnomaly()) {
    System.out.println("最新数据点为异常！");
    System.out.printf("期望值: %.2f, 上界: %.2f, 下界: %.2f%n",
        lastResult.getExpectedValue(),
        lastResult.getUpperMargin(),
        lastResult.getLowerMargin());
}

变化点检测

UnivariateChangePointDetectionOptions changeOptions = 
    new UnivariateChangePointDetectionOptions(series, TimeGranularity.DAILY);

UnivariateChangePointDetectionResult changeResult = 
    univariateClient.detectUnivariateChangePoint(changeOptions);

for (int i = 0; i < changeResult.getIsChangePoint().size(); i++) {
    if (changeResult.getIsChangePoint().get(i)) {
        System.out.printf("在索引 %d 处检测到变化点，置信度 %.2f%n",
            i, changeResult.getConfidenceScores().get(i));
    }
}

多变量模型训练

import com.azure.ai.anomalydetector.models.*;
import com.azure.core.util.polling.SyncPoller;

// 使用 Blob 存储数据准备训练请求
ModelInfo modelInfo = new ModelInfo()
    .setDataSource("https://storage.blob.core.windows.net/container/data.zip?sasToken")
    .setStartTime(OffsetDateTime.parse("2023-01-01T00:00:00Z"))
    .setEndTime(OffsetDateTime.parse("2023-06-01T00:00:00Z"))
    .setSlidingWindow(200)
    .setDisplayName("MyMultivariateModel");

// 训练模型（长时间运行操作）
AnomalyDetectionModel trainedModel = multivariateClient.trainMultivariateModel(modelInfo);

String modelId = trainedModel.getModelId();
System.out.println("模型 ID: " + modelId);

// 检查训练状态
AnomalyDetectionModel model = multivariateClient.getMultivariateModel(modelId);
System.out.println("状态: " + model.getModelInfo().getStatus());