Intelligent Security System in Food Supply Chain Based on Big Data Analysis

Authors

  • Xin Zhang College of Business, Jiaxing University, Jiaxing 314001, Zhejiang, China

Abstract

Traditional security management systems established and applied in the food supply chain mostly use relational databases and parallel data warehouses to store, manage and analyze data. With the continuous development of technology, today’s data sources are more diverse, and traditional methods no longer meet the requirements for data processing efficiency and availability. Therefore, big data analysis can be used to establish an intelligent security system in the food supply chain, which segments massive data, decomposes tasks, and summarizes results. This facilitates intelligent security monitoring and the management of every link in the food supply chain specialization on food supply chain platform. Firstly, a large amount of security-related data in the food supply chain is collected through Internet of Things (IoT) devices and radio frequency identification (RFID) technology, including sensor data, monitoring data, etc. Then, the Hadoop Distributed File System (HDFS) is selected to store the collected raw data. At the same time, the data is preprocessed for subsequent analysis and processing. The preprocessing involves data cleaning, deduplication, conversion, and standardization. Various big data analysis techniques such as Spark and k-means algorithms are utilized to analyze and mine stored data in order to detect abnormal patterns, identify risk events and behaviors, and predict potential threats. Data visualization tools are used to present the analysis results in a way that is easy to understand and visualize. Displaying sensor data, food source information, etc., can help users understand security trends and key indicators in real time. Based on the analysis results, some security responses and decision-making processes are automated by, for instance, automatically invoking security measures and emergency response processes to quickly handle and mitigate security incidents. By continuously optimizing and analyzing models, algorithms, and strategies, as well as learning and training new data, the accuracy and response efficiency of intelligent security systems can be improved. In this study, when using big data analysis technology to process data, the average response time for reading data is 3.647 seconds, and the average response time for writing data is 6.139 seconds, with an average throughput of 896MB/s. Compared with traditional methods, this is a significant improvement of data processing speed, reflecting its stronger parallel processing ability. Big data analysis has good scalability in data processing, and can therefore handle a mixture of multiple types of data. It can also comprehensively and efficiently control security throughout the entire platform for food supply chain specialization to build an intelligent security system. The application of intelligent security systems based on big data analysis in the food industry chain can improve the safety, quality, and traceability of food.

Keywords: Internet of Things Devices, Hadoop Distributed File System, Intelligent Security System, Food Supply chain, Radio
Frequency Identification

Cite As

X. Zhang, "Intelligent Security System in Food Supply Chain Based on Big Data Analysis", Engineering Intelligent Systems,
vol. 32 no. 5, pp. 507-520, 2024.

 

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Published

2024-09-01

Issue

Vol. 32 No. 5 (2024): International Journal of Engineering Intelligent Systems

Section

Article

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