Design and Application of Police Comprehensive Service Platform Based on B/S Architecture

The design and application of a Police Comprehensive Service Platform (PCSP) based on B/S architecture aim to enhance law enforcement efficiency by integrating digital tools for crime analysis, public service delivery, and real-time data processing. Traditional policing systems often suffer from fragmented data management, slow response times, and limited interoperability, hindering operational effectiveness. To address these challenges, this study proposes an intelligent platform leveraging machine learning algorithms such as Random Forest for crime prediction and kNearest Neighbours $(k-\text{NN})$ for case classification, while introducing advanced techniques like Graph Neural Networks (GNNs) for relational crime analysis and Transformer-based NLP models for automated incident reporting. The system is evaluated using the “Indian Crimes Dataset”, comprising historical crime records, demographic data, and real-time incident logs. Performance metrics demonstrate 98.7% prediction accuracy, 15 % improvement in response efficiency, 20 % faster case resolution, and 90 % system reliability, significantly outperforming conventional policing software. The novelty of the proposed system lies in its adaptive crime pattern recognition, real-time multi-agency data synchronization, and AI-driven predictive policing, establishing a new standard for intelligent law enforcement platforms.