| 应用自检机器人与关联规则的大数据全链路追溯算法 |
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| 引用本文：李明霞1，王鹤森2，赵鑫2，刘佳音2.应用自检机器人与关联规则的大数据全链路追溯算法[J].计算技术与自动化,2025,(1):113-117 |
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| 中文摘要:针对大数据维度高、数据量庞大的特点，实际应用中大数据全链路追溯算法常面临性能不佳的挑战。为此，提出了一种结合自检机器人与关联规则的优化算法。对采集的大数据实施数据归约与降维操作，随后分析其特征。通过计算特征的支持度、置信度及提升度，确立特征间的关联规则。计算节点综合度量值，识别关键节点，初步勾勒大数据全链路追溯路径。引入自检机器人，实时监控追溯过程，并评估追溯路径节点与实际节点的匹配度。若匹配度不足，及时修正，确保最终追溯路径的准确性。实验结果显示，该算法在实际应用中表现出优越的追溯性能。 |
| 中文关键词:自检机器人 关联规则 大数据 全链路 数据追溯 追溯算法 算法设计 |
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| A Big Data Full Link Traceability Algorithm Using Self Checking Robots and Association Rules |
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| Abstract:Due to the high dimensionality and large amount of data in big data, the full chain traceability algorithm of big data often faces the challenge of poor performance in practical applications. Therefore, this article proposes an optimization algorithm that combines self checking robots with association rules. Implement data reduction and dimensionality reduction operations on the collected big data, and then analyze its characteristics. By calculating the support, confidence, and enhancement of features, establish association rules between features. Calculate the comprehensive measurement value of nodes, identify key nodes, and preliminarily outline the traceability path of the entire big data chain. Introduce self checking robots to monitor the traceability process in real time and evaluate the matching degree between the traceability path node and the actual node. If the matching degree is insufficient, make timely corrections to ensure the accuracy of the final traceability path. The experimental results show that the algorithm exhibits superior traceability performance in practical applications. |
| keywords:self checking robot association rules big data full link data traceability traceability algorithm algorithm design |
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