azure-ai-anomalydetector-java

Azure AI Anomaly Detector SDK for Java

使用 Azure AI Anomaly Detector SDK for Java 构建异常检测应用程序。

服务状态

Microsoft 的退役指南指出，已于 2023 年 9 月禁用新资源，并计划于 2026 年 10 月退役该服务。请将这些 SDK 片段视为历史/迁移参考，而非用于新云部署的方案。实施前，请核实实际资源生命周期，并评估文档中提到的 Fabric/开源替代方案。

代码片段省略了外围应用程序代码和导入语句，并非经过测试的完整程序。对于异步训练/推理，请在截止时间前轮询终止状态，并在访问结果前处理失败情况。请使用经批准的数据、有效期短且遵循最小权限原则的存储访问方式，并保护报告的私密性；切勿泄露 SAS 令牌。

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");

// Multivariate client for multiple correlated signals
MultivariateClient multivariateClient = new AnomalyDetectorClientBuilder()
    .credential(new AzureKeyCredential(key))
    .endpoint(endpoint)
    .buildMultivariateClient();

// Univariate client for single variable analysis
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：实时检测最新数据点
• 变点检测：检测时间序列中的趋势变化

多变量异常检测

• 检测 300 多个相关信号中的异常
• 使用图注意力网络处理信号间的相关性
• 三步流程：训练 → 推理 → 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)
    // ... more data points (minimum 12 points required)
);

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

UnivariateEntireDetectionResult result = univariateClient.detectUnivariateEntireSeries(options);

// Check for anomalies
for (int i = 0; i < result.getIsAnomaly().size(); i++) {
    if (result.getIsAnomaly().get(i)) {
        System.out.printf("Anomaly detected at index %d with value %.2f%n",
            i, series.get(i).getValue());
    }
}

单变量最新数据点检测（流式）

UnivariateLastDetectionResult lastResult = univariateClient.detectUnivariateLastPoint(options);

if (lastResult.isAnomaly()) {
    System.out.println("Latest point is an anomaly!");
    System.out.printf("Expected: %.2f, Upper: %.2f, Lower: %.2f%n",
        lastResult.getExpectedValue(),
        lastResult.getUpperMargin(),
        lastResult.getLowerMargin());
}

变点检测

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("Change point at index %d with confidence %.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;

// Prepare training request with blob storage data
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");

// Train model (long-running operation)
AnomalyDetectionModel trainedModel = multivariateClient.trainMultivariateModel(modelInfo);

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

// Check training status
AnomalyDetectionModel model = multivariateClient.getMultivariateModel(modelId);
System.out.println("Status: " + model.getModelInfo().getStatus());

多变量批量推理

MultivariateBatchDetectionOptions detectionOptions = new MultivariateBatchDetectionOptions()
    .setDataSource("https://storage.blob.core.windows.net/container/inference-data.zip?sasToken")
    .setStartTime(OffsetDateTime.parse("2023-07-01T00:00:00Z"))
    .setEndTime(OffsetDateTime.parse("2023-07-31T00:00:00Z"))
    .setTopContributorCount(10);

MultivariateDetectionResult detectionResult = 
    multivariateClient.detectMultivariateBatchAnomaly(modelId, detectionOptions);

String resultId = detectionResult.getResultId();

// Poll for results
MultivariateDetectionResult result = multivariateClient.getBatchDetectionResult(resultId);
for (AnomalyState state : result.getResults()) {
    if (state.getValue().isAnomaly()) {
        System.out.printf("Anomaly at %s, severity: %.2f%n",
            state.getTimestamp(),
            state.getValue().getSeverity());
    }
}

多变量最新数据点检测

MultivariateLastDetectionOptions lastOptions = new MultivariateLastDetectionOptions()
    .setVariables(List.of(
        new VariableValues("variable1", List.of("timestamp1"), List.of(1.0f)),
        new VariableValues("variable2", List.of("timestamp1"), List.of(2.5f))
    ))
    .setTopContributorCount(5);

MultivariateLastDetectionResult lastResult = 
    multivariateClient.detectMultivariateLastAnomaly(modelId, lastOptions);

if (lastResult.getValue().isAnomaly()) {
    System.out.println("Anomaly detected!");
    // Check contributing variables
    for (AnomalyContributor contributor : lastResult.getValue().getInterpretation()) {
        System.out.printf("Variable: %s, Contribution: %.2f%n",
            contributor.getVariable(),
            contributor.getContributionScore());
    }
}

模型管理

// List all models
PagedIterable<AnomalyDetectionModel> models = multivariateClient.listMultivariateModels();
for (AnomalyDetectionModel m : models) {
    System.out.printf("Model: %s, Status: %s%n",
        m.getModelId(),
        m.getModelInfo().getStatus());
}

// Delete a model
multivariateClient.deleteMultivariateModel(modelId);

错误处理

import com.azure.core.exception.HttpResponseException;

try {
    univariateClient.detectUnivariateEntireSeries(options);
} catch (HttpResponseException e) {
    System.out.println("Status code: " + e.getResponse().getStatusCode());
    System.out.println("Error: " + e.getMessage());
}

环境变量

AZURE_ANOMALY_DETECTOR_ENDPOINT=https://<resource>.cognitiveservices.azure.com/
AZURE_ANOMALY_DETECTOR_API_KEY=<your-api-key>

最佳实践

1. 最少数据点：单变量检测至少需要 12 个数据点；更多数据可提高准确性
2. 粒度匹配：让 TimeGranularity 与实际数据频率保持一致
3. 灵敏度调优：较高的值（0-99）可检测到更多异常
4. 多变量训练：根据模式复杂度使用 200-1000 的滑动窗口
5. 错误处理：始终处理 API 错误的 HttpResponseException

触发短语

• “Java 异常检测”
• “检测时间序列异常”
• “Java 多变量异常”
• “单变量异常检测”
• “流式异常检测”
• “变点检测”
• “Azure AI Anomaly Detector”

适用场景

当任务涉及执行概述中描述的工作流或操作时，可使用此技能。

限制

• 仅当任务与上述范围明确匹配时，才使用此技能。
• 请勿将输出视为环境特定验证、测试或专家审查的替代品。
• 如果缺少必需的输入、权限、安全边界或成功标准，请暂停并请求澄清。