Manuscript received September 2, 2025; accepted December 7, 2025; published December 24, 2025.
Abstract—Under the strategic backdrop of “Digital China” and “Smart Policing,” the traditional policing dispatch model, which relies on manual experience, faces challenges such as information lag, protracted decision-making, and uneven resource allocation when coping with complex and dynamic social security risks. To address this, this paper proposes a construction scheme for a “Police Case–Personnel–Equipment” precise matching and dispatch intelligent agent based on the DeepSeek large language model [1]. Centered around a “Perception–Decision–Execution” core architecture, this intelligent agent integrates multi-source heterogeneous policing data and leverages knowledge graph and semantic understanding technologies to achieve in-depth analysis of police cases and intelligent resource allocation. The system employs a collaborative “Cloud Brain + Edge Terminal” mechanism, balancing global coordination with real-time on-scene response. Research demonstrates that the proposed model holds significant advantages in information fusion, knowledge representation, and semantic reasoning, offering public security organs a replicable and scalable intelligent decision-making framework, thereby promoting the evolution of police dispatch towards a more scientific, precise, and proactive paradigm.
Keywords—police intelligence agent, DeepSeek, knowledge graph, precise dispatch
Cite: Yuekai Ma, Yuehua Zhu, Shuifeng Zhang, Qi Liu, Junquan Zhou, Bo Li, and Enhao Yu, "Research on the Construction of an Intelligent Agent for Precise Matching and Dispatch of 'Police Cases, Personnel, and Equipment'," International Journal of Engineering and Technology, vol. 17, no. 4, pp. 231-234, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (
CC BY 4.0).
