面向警务业务的数据可视化处理平台设计与实现

Design and Implementation of a Data Visualization and Processing Platform for Policing Operations

摘要: 针对警务业务中多源异构数据难以快速理解、检索分析与直观呈现的问题，本文面向警务数据分析与教学科研场景，设计并实现了一套桌面端数据分析可视化处理平台。平台以Python为主要开发语言，采用PyQt5构建图形交互界面，并通过QtWebEngine将pyecharts生成的交互式HTML图表嵌入客户端，实现“数据处理–分析建模–交互可视化展示”的一体化流程。系统围绕三类典型任务构建模块：1) 网络舆情文本分析与多视图可视化，基于中文分词与停用词过滤完成词频统计，采用TF-IDF评估关键词重要性，生成词云、词频/权重表、评论长度分布图，并结合IP/地域信息进行二维地图展示；2) 警情文本信息检索与统计展示，对案件类别进行聚合统计并生成饼图/玫瑰图，支持按类别条件过滤与表格化结果回显；3) 基于机器学习的人脸识别流程，采用PCA进行特征降维并训练SVM分类器，通过准确率、F1值与混淆矩阵对模型性能进行评估，同时以可视化方式展示推理结果。实验结果表明，该平台具有较好的交互性、模块化与可扩展性，可为警务数据可视化处理系统原型构建及教学实训提供工程化参考。

Abstract: To address the difficulties in rapidly understanding, retrieving, analyzing, and intuitively presenting multi-source heterogeneous data in police operations, this paper designs and implements a desktop-based data analysis and visualization processing platform oriented toward police data analysis as well as teaching and research scenarios. The platform is primarily developed using Python, with PyQt5 employed to construct the graphical interactive interface. Interactive HTML charts generated by pyecharts are embedded into the client through QtWebEngine, enabling an integrated workflow of “data processing - analytical modeling - interactive visualization.” The system consists of three task-oriented modules: 1) Online public opinion text analysis and multi-view visualization, where Chinese word segmentation and stop-word filtering are applied to perform word frequency statistics, TF-IDF is used to evaluate keyword importance, and visualizations such as word clouds, word frequency/weight tables, and comment length distribution charts are generated. Additionally, IP and geographic information are combined to display results on a two-dimensional map; 2) Police incident text information retrieval and statistical visualization, which aggregates statistics by case category and generates pie charts or rose charts, while supporting conditional filtering by category and tabular result display; 3) A machine learning–based face recognition workflow, where PCA is applied for feature dimensionality reduction and an SVM classifier is trained. Model performance is evaluated using accuracy, F1-score, and confusion matrix, while inference results are presented through visualization. Experimental results demonstrate that the platform exhibits good interactivity, modularity, and scalability, providing an engineering reference for the development of police data visualization system prototypes as well as teaching and training applications.