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基于ACP行为动力学的犯罪主体行为平行建模分析

刘烁 王帅 孟庆振 叶佩军 王涛 黄文林 王飞跃

刘烁, 王帅, 孟庆振, 叶佩军, 王涛, 黄文林, 王飞跃. 基于ACP行为动力学的犯罪主体行为平行建模分析. 自动化学报, 2018, 44(2): 251-261. doi: 10.16383/j.aas.2018.c160824
引用本文: 刘烁, 王帅, 孟庆振, 叶佩军, 王涛, 黄文林, 王飞跃. 基于ACP行为动力学的犯罪主体行为平行建模分析. 自动化学报, 2018, 44(2): 251-261. doi: 10.16383/j.aas.2018.c160824
LIU Shuo, WANG Shuai, MENG Qing-Zhen, YE Pei-Jun, WANG Tao, HUANG Wen-Lin, WANG Fei-Yue. Parallel Modeling of Criminal Subjects Behavior Based on ACP Behavioral Dynamics. ACTA AUTOMATICA SINICA, 2018, 44(2): 251-261. doi: 10.16383/j.aas.2018.c160824
Citation: LIU Shuo, WANG Shuai, MENG Qing-Zhen, YE Pei-Jun, WANG Tao, HUANG Wen-Lin, WANG Fei-Yue. Parallel Modeling of Criminal Subjects Behavior Based on ACP Behavioral Dynamics. ACTA AUTOMATICA SINICA, 2018, 44(2): 251-261. doi: 10.16383/j.aas.2018.c160824

基于ACP行为动力学的犯罪主体行为平行建模分析

doi: 10.16383/j.aas.2018.c160824
基金项目: 

国家自然科学基金 61603381

公安理论及软科学研究计划 2015LLYJGAES037

详细信息
    作者简介:

    刘烁  中国科学院大学经济与管理学院博士研究生.主要研究方向为管理科学与工程.E-mail:lskjj04@sina.com

    王帅  中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生.主要研究方向为社会计算, 平行管理, 区块链技术及应用.E-mail:wangshuai2015@ia.ac.cn

    孟庆振  公安部物证鉴定中心助理研究员.2011年于北京大学获得硕士学位.主要研究方向为刑事技术政策规划, 法医遗传学.E-mail:mengqz2011@126.com

    叶佩军  中国科学院自动化研究所复杂系统管理与控制国家重点实验室助理研究员, 青岛智能产业技术研究院助理研究员.2013年于中国科学院大学获博士学位.主要研究方向为多代理系统建模, 社会计算, 交通行为分析与交通流优化, 分布式计算.E-mail:peijun_ye@hotmail.com

    王涛  国防科技大学系统工程学院讲师.2015年于国防科技大学获博士学位.主要研究方向为社会计算, 大规模在线协作, 人力资源分析.E-mail:wangtao@nudt.edu.cn

    黄文林  公安部物证鉴定中心副研究员.2015年于中国人民公安大学获得博士学位.主要研究方向为刑事科学技术, 言语识别.E-mail:huangwenlin2004@163.com

    通讯作者:

    王飞跃  中国科学院自动化研究所复杂系统管理与控制国家重点实验室研究员, 国防科技大学军事计算实验与平行系统技术中心教授.主要研究方向为智能系统和复杂系统的建模, 分析与控制.本文通信作者.E-mail:feiyue.wang@ia.ac.cn

Parallel Modeling of Criminal Subjects Behavior Based on ACP Behavioral Dynamics

Funds: 

National Natural Science Foundation of China 61603381

Public Security Theory and Soft Science Research Program 2015LLYJGAES037

More Information
    Author Bio:

     Ph.D. candidate at the School of Economics and Management, University of Chinese Academy of Sciences. His research interest covers management science and engineering

     Ph.D. candidate at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers social computing, parallel management, and blockchain and its application

     Assistant professor at the Institute of Forensic Science, Ministry of Public Security. He received his master degree from Peking University in 2011. His research interest covers forensic science policy and planning, forensic genetics

     Assistant researcher at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, and Qingdao Academy of Intelligent Industries. He received his Ph.D. degree from University of Chinese Academy of Sciences in 2013. His research interest covers multi-agent system modeling, social computing, travel behavior analysis and traffic flow optimization, distributed computing

     Lecturer at the College of Systems Engineering, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 2015. His research interest covers social computing, large-scale online collaboration, and human resource analytics

     Associate research fellow at the Institute of Forensic Science, Ministry of Public Security. He received his Ph.D. degree from People's Public Security University of China in 2015. His research interest covers forensic science and forensic linguistic analysis

    Corresponding author: WANG Fei-Yue  Professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, and professor at the Research Center of Military Computational Experiments and Parallel System, National University of Defense Technology. His research interest covers modeling, analysis, and control of intelligent systems and complex systems. Corresponding author of this paper
  • 摘要: 犯罪行为分析是侦查破案的重要参考,也是学界长期以来关注的热点.目前,犯罪行为分析主要采用现场证据-行为推断的思路,忽略了犯罪过程中犯罪主体和客体之间的复杂互动.本文在基于ACP(Artificial societies(人工社会)+Computational experiments(计算实验)+Parallel execution(平行执行))方法的犯罪现场平行系统框架下,从行为动力学角度提出了故意杀人行为的犯罪主体时间和空间互动模型,并采用真实案例数据对模型参数进行了标定.计算实验结果表明,本文提出的互动模型能较好地模拟真实数据,从而为分析犯罪过程中的复杂互动提供了一个可靠的基础.
    1)  本文责任编委 张敏灵
  • 图  1  犯罪现场平行系统

    Fig.  1  Parallel system of crime scene

    图  2  犯罪不同阶段时间互动关系简化

    Fig.  2  Simplified temporal interactive relationship in different stages of crime

    图  3  犯罪现场空间关系及其简化赋值

    Fig.  3  Spatial relationship of crime scene and its simplification

    图  4  犯罪发生前双方互动时间关系

    Fig.  4  Temporal interactive relationship before the crime

    图  5  犯罪进行中双方互动时间关系:现场激烈程度分析

    Fig.  5  Temporal interactive relationship in criminal process: analysis of scene fierce degree

    图  6  犯罪发生前双方空间互动关系

    Fig.  6  Spacial interactive relationship before the crime

    图  7  犯罪进行中双方空间互动关系

    Fig.  7  Spacial interactive relationship in criminal process

    图  8  犯罪发生后犯罪人逃逸轨迹:距犯罪现场

    Fig.  8  Criminal escape trajectory after the crime: distance from the crime scene

    图  9  犯罪发生后犯罪人逃逸轨迹:距犯罪人住处

    Fig.  9  Criminal escape trajectory after the crime: distance from the criminal residence

    图  10  犯罪行为时间模型计算实验结果

    Fig.  10  Results of computational experiment for the temporal criminal model

    图  11  犯罪行为空间模型计算实验结果

    Fig.  11  Results of computational experiment for the spatial criminal model

    表  1  犯罪主体行为模型参数值

    Table  1  Parameter values of criminal subject behavior model

    类别表达式犯罪阶段 Ⅰ类案例Ⅱ类案例
    $ \alpha_{t} $或$ \alpha_{s} $ $ \beta_{t} $或$ \beta_{s} $ $ \alpha_{t} $或$ \alpha_{s} $ $ \beta_{t} $或$ \beta_{s} $
    犯罪主体行为时间模型 $ y= \alpha_{t} \times x_{t}^{ \beta_{t}}$犯罪发生前7.76520.12326.42690.2165
    犯罪进行中5.9480.18712.65640.4113
    犯罪主体行为空间模型 $ y= \alpha_{s} \times x_{s}^{- \beta_{s}}$犯罪发生前14.9420.21516.6680.261
    犯罪进行中13.3070.1316.1510.16
    犯罪后逃逸(与犯罪现场距离)11.7290.13612.0490.124
    犯罪后逃逸(与原住处距离)13.1970.21711.8860.111
    下载: 导出CSV
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