A Dynamic Source Tracing Method for Food Supply Chain Quality and Safety Based on Big Data
The data of food quality tracing information have a few features, such as wide coverage range, many circulation links, complex data sources, low authenticity, and difficult information sharing. The continuous development of big data technology provides infinite possibilities for the construction of...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/6385201 |
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| _version_ | 1832553591659823104 |
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| author | Jun Song Hong Huo Teng Li Lingyun Chu |
| author_facet | Jun Song Hong Huo Teng Li Lingyun Chu |
| author_sort | Jun Song |
| collection | DOAJ |
| description | The data of food quality tracing information have a few features, such as wide coverage range, many circulation links, complex data sources, low authenticity, and difficult information sharing. The continuous development of big data technology provides infinite possibilities for the construction of food quality source tracing systems. Currently, there are many studies on the application of food quality source tracing systems; however, most of them are in the field of food quality databases, and few have concerned about its application in the field of big data. Therefore, to fill in this research gap, this paper aimed to study a dynamic source tracing method for food supply chain quality and safety based on big data. At first, this paper summarized the variables of food supply chain quality and safety, constructed a Petri net model and a Bayesian network model for food quality prediction and source tracing, and realized the prediction of food quality features. Then, this paper applied two data analysis and processing methods—the density-based clustering algorithm and the cosine similarity algorithm—to preliminarily process the collected quality tracing information of each link in the food supply chain and analyzed the influencing factors of food quality. Finally, experimental results proved the effectiveness of the constructed model. Relying on the real-timeliness and authenticity of big data, this paper guarantees the credibility of the traceable information in the tracking process and improves the accuracy through real-time stream processing of the updated data, providing unlimited possibilities for the comprehensive tracking of food sources. |
| format | Article |
| id | doaj-art-1f4beb0ff8be4cf9bddc0926ff838138 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-1f4beb0ff8be4cf9bddc0926ff8381382025-02-03T05:53:36ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/6385201A Dynamic Source Tracing Method for Food Supply Chain Quality and Safety Based on Big DataJun Song0Hong Huo1Teng Li2Lingyun Chu3School of ManagementSchool of ManagementSchool of ManagementSchool of Foreign LanguagesThe data of food quality tracing information have a few features, such as wide coverage range, many circulation links, complex data sources, low authenticity, and difficult information sharing. The continuous development of big data technology provides infinite possibilities for the construction of food quality source tracing systems. Currently, there are many studies on the application of food quality source tracing systems; however, most of them are in the field of food quality databases, and few have concerned about its application in the field of big data. Therefore, to fill in this research gap, this paper aimed to study a dynamic source tracing method for food supply chain quality and safety based on big data. At first, this paper summarized the variables of food supply chain quality and safety, constructed a Petri net model and a Bayesian network model for food quality prediction and source tracing, and realized the prediction of food quality features. Then, this paper applied two data analysis and processing methods—the density-based clustering algorithm and the cosine similarity algorithm—to preliminarily process the collected quality tracing information of each link in the food supply chain and analyzed the influencing factors of food quality. Finally, experimental results proved the effectiveness of the constructed model. Relying on the real-timeliness and authenticity of big data, this paper guarantees the credibility of the traceable information in the tracking process and improves the accuracy through real-time stream processing of the updated data, providing unlimited possibilities for the comprehensive tracking of food sources.http://dx.doi.org/10.1155/2022/6385201 |
| spellingShingle | Jun Song Hong Huo Teng Li Lingyun Chu A Dynamic Source Tracing Method for Food Supply Chain Quality and Safety Based on Big Data Discrete Dynamics in Nature and Society |
| title | A Dynamic Source Tracing Method for Food Supply Chain Quality and Safety Based on Big Data |
| title_full | A Dynamic Source Tracing Method for Food Supply Chain Quality and Safety Based on Big Data |
| title_fullStr | A Dynamic Source Tracing Method for Food Supply Chain Quality and Safety Based on Big Data |
| title_full_unstemmed | A Dynamic Source Tracing Method for Food Supply Chain Quality and Safety Based on Big Data |
| title_short | A Dynamic Source Tracing Method for Food Supply Chain Quality and Safety Based on Big Data |
| title_sort | dynamic source tracing method for food supply chain quality and safety based on big data |
| url | http://dx.doi.org/10.1155/2022/6385201 |
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