In order to improve the utilization rate of agricultural big data and solve the security issues problem of multisource and heterogeneous agricultural big data, an improved agricultural big data ant colony optimization algorithm (BigDataACO) is proposed to complete the multisource agricultural big data information in the feature layer and decision-making, and the problem of multisource data fusion was solved. The swarm intelligence algorithm is a process of simulating the complex problem of populations in nature through the mutual cooperation between individuals. The algorithm has potential parallelism and strong robustness, and the algorithm does not depend on specific problems. The definition, principle, and implementation method of agricultural big data fusion problem are studied. Then, the insufficiency of big data fusion modeling algorithm is analyzed. Finally, the source and core steps of the ant colony big data fusion algorithm are studied. The experimental results show that the improved BigDataACO algorithm is verified by the measured data. Compared with K-means, D-S evidence theory, and Bayesian algorithm, the uncertainty of data fusion is greatly reduced by the improved algorithm proposed in this paper.

中文翻译：

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基于物联网的农业大数据挖掘与区块链安全方法

为了提高农业大数据的利用率，解决多源和异构农业大数据的安全问题，提出了一种改进的农业大数据蚁群优化算法（BigDataACO）来完成多源农业大数据信息的功能。层次和决策，解决了多源数据融合的问题。群智能算法是通过个体之间的相互合作来模拟自然界中复杂问题的过程。该算法具有潜在的并行性和强大的鲁棒性，并且不依赖于特定问题。研究了农业大数据融合问题的定义，原理和实现方法。然后，分析了大数据融合建模算法的不足。最后，研究了蚁群大数据融合算法的来源和核心步骤。实验结果表明，改进的BigDataACO算法得到了实测数据的验证。与K-means，DS证据理论和贝叶斯算法相比，本文提出的改进算法大大降低了数据融合的不确定性。