方钰, 朱静波, 许学, 秦瑞英, 郭书普, 张立平. 基于地理位置解析的种子溯源双向动态交互模型及实现[J]. 农业工程学报, 2017, 33(24): 207-214. DOI: 10.11975/j.issn.1002-6819.2017.24.027
    引用本文: 方钰, 朱静波, 许学, 秦瑞英, 郭书普, 张立平. 基于地理位置解析的种子溯源双向动态交互模型及实现[J]. 农业工程学报, 2017, 33(24): 207-214. DOI: 10.11975/j.issn.1002-6819.2017.24.027
    Fang Yu, Zhu Jingbo, Xu Xue, Qin Ruiying, Guo Shupu, Zhang Liping. Bidirectional dynamic interaction model of seed traceability based on location analysis and its implementation[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(24): 207-214. DOI: 10.11975/j.issn.1002-6819.2017.24.027
    Citation: Fang Yu, Zhu Jingbo, Xu Xue, Qin Ruiying, Guo Shupu, Zhang Liping. Bidirectional dynamic interaction model of seed traceability based on location analysis and its implementation[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(24): 207-214. DOI: 10.11975/j.issn.1002-6819.2017.24.027

    基于地理位置解析的种子溯源双向动态交互模型及实现

    Bidirectional dynamic interaction model of seed traceability based on location analysis and its implementation

    • 摘要: 为更好解决农作物种子溯源问题,帮助生产企业统计分析经营状况,实现交互式营销,该文利用种子电子代码,通过分开录入售前阶段各级分销信息,分层写入地理代码集合,在用户验证最小包装单元时动态解析用户位置信息,逐层匹配地理代码集合,构建了种子溯源双向动态交互模型。用户通过该模型获得溯源信息时可选择互动交流,企业可采集用户行为数据,推送营销信息,实现了溯源结果对企业、用户双向推送。通过对小麦品种华成3366销售和反馈2组数据进行相关性分析,其拟合优度为0.997 8,说明企业通过扫码次数的反馈能较好地促进销售,有效防止窜货发生。该模型分层独立的流通信息,保证了溯源的可靠性;双向交互性有效地帮助企业指导生产实际,也为监管部门提供了可靠的管理手段。

       

      Abstract: Abstract: Agricultural product safety concerns the national economy and the people's livelihood. From the starting point of agriculture production, the quality and safety of products, i.e. seeds, are especially important and need to be traced. To realize the traceability of crop seeds and help the production enterprise to analyze the operation status and realize the interactive marketing, in this paper, we constructed a bidirectional dynamic model of seed traceability based on location analysis. Through the seed electronic code, the model divided the traditional logistics information chain data into different sets of geographic codes, which were nested according to the geographical location of the distribution. The commodity seed from the factory to the user will experience a lot of path node, each path node dealer was required to writes its own geographic code. The geographic code contained the company information of the dealers at all levels and the place name information of the sale’s area. In the pre-sale stage of the seed, the geographic code set information was independent of each other. When the end user queried the seed electronic code, the flag was activated and the model started to dynamically analyze the location of the user. By converting the user's latitude and longitude information, this model received the detailed address information, started match the geographical code set of information from small to large, and ultimately found the dealer information or returned warning information. The query action of the user connected the geographical code sets of all levels, and the whole traceability data chain provided the traceability result to both the enterprise and the user at this time. The enterprise-side model consisted of a collection layer, a data sharing analysis layer, and an application presentation layer. When the enterprise obtained the traceability information, it can communicate with users, collecting user’s behavior data, and pushing enterprise marketing information. The enterprise-side model can be combined with supply chain data for data analysis to help improve and guide production. By selecting the data of sales and feedback of Huacheng 3366 wheat in ten counties, the goodness-of-fit was 0.997 8, which showed that the feedback from the code scanning could promote the sales. Among all the 14 092 barcode scanning position information, only 21 of them cannot be converted into the appropriate place name fields, and the model geo-code position conversion rate was around 99.851%, which can basically meet the business requirements of collecting and analyzing feedback information, helped enterprise effectively avoid the risk of transregional behavior. During the 36 months of trial operation, the monthly average number of server downtime was about 0.27 times, and the safe running days ratio was 99.087%, which can meet the requirements of enterprise traceability stability. The model was layered and independent of circulation information, which ensured the integrity, authenticity and security of traceability. The model added the bidirectional interactive data link on the traditional traceability model, so that the enterprise can interact with the user when scanning and obtain the user behavior data and push the marketing information. The model’s commissioning of the geocode location conversion rate, the number of days, the site safe operation and other technical indicators can effectively help companies master the actual operation, effectively prevent the occurrence of the transregional behavior, optimize the enterprise distribution channels, it also provides a more reliable means of information for the regulatory department to manage the seed market.

       

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