Speaker
摘要
人工智能和大数据技术已成功应用于众多领域的科学问题分析中，其中也包括犯罪分析领域。犯罪案件侦查一直是犯罪分析工作的重点和难点。犯罪案件在侦查阶段，主要包括证据收集和证据推理等工作。证据的全面高效收集对案件推理和快速侦破具有重要意义。与此同时，不同证据在案件中的重要程度不同，如果具有高重要度的证据在侦查早期阶段被收集，可有助于提高案件侦破效率。然而，现有的人工智能方法应用于犯罪侦查决策支持的研究较少。鉴于此，该文提出了一种基于加权信息熵的证据重要度的计算方法，并在此基础上构建了基于Bayes网络的犯罪侦查决策支持模型，然后利用420例犯罪案例对模型的准确率进行了验证，并用一例实际案例的分析过程对模型应用进行了阐述。分析结果表明：提出的模型能够输出有效的侦查建议，为侦查阶段的证据收集和推理工作提供决策支持。
Abstract
Artificial intelligence and big data technologies have been used to solve many scientific problems, including crime analysis. The investigation of criminal cases has always been a critical and difficult point in the domain of crime analysis. The investigation stage of criminal cases primarily consists of evidence collection and evidence reasoning, and comprehensive and efficient collection and reasoning of evidence are critical to the rapid detection of cases. Simultaneously, the significance of the various pieces of evidence in the case varies. Evidence of high importance gathered during the investigation stage is critical for the accurate and efficient resolution of crime cases. However, existing research lacks the application of artificial intelligence methods to crime investigation decision support. This article proposes a method for calculating the importance of evidence based on weighted information entropy, and constructs a criminal investigation decision support model based on Bayes network. The accuracy of the model is verified using 420 criminal cases, and the application of the model is explained through the analysis process of an actual case. The analysis results indicate that the proposed model can output effective investigation recommendations, providing decision support for evidence collection and inference work in the investigation stage.
| 关键词 | 社会安全；犯罪侦查；证据重要度；Bayes网络；决策支持 |
|---|---|
| Keywords | Social Security; Crime Investigation; Evidence Importance; Bayes Network; Decision Support |
