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Research on Building Police Intelligent Patrol Command and Dispatch System under Big Data Technology

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DOI: 10.23977/cpcs.2023.070110 | Downloads: 23 | Views: 584

Author(s)

Chang Liu 1

Affiliation(s)

1 Chaoyang District Public Security Bureau, 645 West Minzhu Street, Changchun, Jilin, 130000, China

Corresponding Author

Chang Liu

ABSTRACT

In recent years, public security safety has been one of the core issues of high concern to the whole society. Building a smart patrol command and dispatch system has become one of the development directions for public security organs in response to the increasingly complex and ever-changing public security situation. This article was based on big data technology, analyzing the characteristics and value of patrol data, and using methods such as machine learning and deep learning to construct a smart patrol command and scheduling system for public security, in order to improve the efficiency and level of public security work. By applying experimental testing methods and comparing with traditional methods, performance data of the public security intelligent patrol command and dispatch system can be obtained. Experimental data showed that the stability of the intelligent patrol command and scheduling system based on big data technology reached 86%, accuracy reached 88%, security reached 84%, and work efficiency reached 85%. After comprehensive testing, the performance, safety, and stability of the public security intelligent patrol command and dispatch system have been effectively verified.

KEYWORDS

Big Data Technology, Public Security Security Systems, Intelligent Patrols, Command and Dispatch

CITE THIS PAPER

Chang Liu, Research on Building Police Intelligent Patrol Command and Dispatch System under Big Data Technology. Computing, Performance and Communication Systems (2023) Vol. 7: 82-91. DOI: http://dx.doi.org/10.23977/cpcs.2023.070110.

REFERENCES

[1] Damminda Alahakoon, Rashmika Nawaratne, Yan Xu, Daswin De Silva, Uthayasankar Sivarajah, Bhumika Gupta: Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities. Inf. Syst. Frontiers 25(1): 221-240 (2023)
[2] Ulrich Rendtel, Willi Seidel, Christine Müller, Florian Meinfelder, Joachim Wagner, Jürgen Chlumsky, Markus Zwick: Statistik zwischen Data Science, Artificial Intelligence und Big Data: Beiträge aus dem Kolloquium "Make Statistics great again". AStA Wirtschafts und Sozialstatistisches Arch. 16(2): 97-147 (2022)
[3] Giuseppe Riva, Brenda K. Wiederhold, Sauro Succi: Zero Sales Resistance: The Dark Side of Big Data and Artificial Intelligence. Cyberpsychology Behav. Soc. Netw. 25(3): 169-173 (2022)
[4] Rosa Lombardi, Raffaele Trequattrini, Benedetta Cuozzo, Alberto Manzari: Big data, artificial intelligence and epidemic disasters. A primary structured literature review. Int. J. Appl. Decis. Sci. 15(2): 156-180 (2022)
[5] Ayman Wael Al-Khatib, T. Ramayah: Big data analytics capabilities and supply chain performance: testing a moderated mediation model using partial least squares approach. Bus. Process. Manag. J. 29(2): 393-412 (2023)
[6] Tariq Ahamed Ahanger, Abdullah Alqahtani, Meshal Alharbi, Abdullah Algashami: Cognitive decision-making in smart police industry. J. Supercomput. 78(10): 12834-12860 (2022)
[7] Sukanya Samanta, Goutam Sen, Soumya Kanti Ghosh: A literature review on police patrolling problems. Ann. Oper. Res. 316(2): 1063-1106 (2022)
[8] Michael Saint-Guillain, Celia Paquay, Sabine Limbourg: Time-dependent stochastic vehicle routing problem with random requests: Application to online police patrol management in Brussels. Eur. J. Oper. Res. 292(3): 869-885 (2021)
[9] Seiya Nuta, Jacob C. N. Schuldt, Takashi Nishide: PoS Blockchain-Based Forward-Secure Public Key Encryption with Immutable Keys and Post-Compromise Security Guarantees. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 106(3): 212-227 (2023)
[10] Mohammad Wazid, Basudeb Bera, Ashok Kumar Das, Saraju P. Mohanty, Minho Jo: Fortifying Smart Transportation Security through Public Blockchain. IEEE Internet Things J. 9(17): 16532-16545 (2022)
[11] Johanna Leigh, Sarah Dunnett, Lisa M. Jackson: Predictive police patrolling to target hotspots and cover response demand. Ann. Oper. Res. 283(1-2): 395-410 (2019)
[12] Jurgen Scherer, Bernhard Rinner: Multi-Robot Patrolling with Sensing Idleness and Data Delay Objectives. J. Intell. Robotic Syst. 99(3): 949-967 (2020)
[13] Luis Castro, Maria Santos-Corrada, Jose A. Flecha-Ortiz, Evelyn Lopez, Jose Gomez, Brunilda Aponte: Knowledge management and innovative behavior: police reform efforts in Puerto Rico. J. Knowl. Manag. 26(5): 1262-1279 (2022)
[14] Danil Yurievich Pimenov, Andrés Bustillo, Szymon Wojciechowski, Vishal S. Sharma, Munish Kumar Gupta, Mustafa Kuntoglu: Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review. J. Intell. Manuf. 34(5): 2079-2121 (2023)
[15] Swagatika Sahoo, Raju Halder: Traceability and ownership claim of data on big data marketplace using blockchain technology. J. Inf. Telecommun. 5(1): 35-61 (2021)   
[16] Muhammad Saleem Sumbal, Murad Ali, Umar Farooq Sahibzada, Faisal Nawaz, Adeel Tariq, Hina Munir: Big Data Based Knowledge Management vs. Traditional Knowledge Management: A People, Process and Technology Perspective. J. Inf. Sci. Eng. 37(5): 1053-1065 (2021)
[17] Ohsung Kwon, Sangmin Lim, Duk Hee Lee: Innovation patterns of big data technology in large companies and start-ups: an empirical analysis. Technol. Anal. Strateg. Manag. 33(9): 1052-1067 (2021)
[18] Zhao Zhang, Kyle Barbary, Frank Austin Nothaft, Evan R. Sparks, Oliver Zahn, Michael J. Franklin, David A. Patterson, Saul Perlmutter: Kira: Processing Astronomy Imagery Using Big Data Technology. IEEE Trans. Big Data 6(2): 369-381 (2020)
[19] S. C. M. S. De Sirisuriya, T. G. I. Fernando, M. K. A. Ariyaratne: Algorithms for path optimizations: a short survey. Computing 105(2): 293-319 (2023)
[20] Pouria Arsalani, Mohammad Reisi-Nafchi, Vahid Dardashti, Ghasem Moslehi: Two new mixed-integer programming models for the integrated train formation and shipment path optimization problem. Networks 81(3): 359-377 (2023)

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