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Design and Test of the Intelligent Rubber Tapping Technology Evaluation Equipment Based on Cloud Model

机译：基于云模型的智能橡胶攻丝技术评估设备的设计与测试

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摘要

The level of rubber workers' tapping technology is the key to the impact of rubber production. Accurate rubber tapping level evaluation plays an important role in improving the level of rubber tapping teams and rubber production. We have designed and implemented an intelligent-tapping-technology learning system based on the cloud model. This paper introduces the mechanical structure of the intelligent-tapping-technology learning instrument. To evaluate the level of rubber tapping, the system combines the Delphi method and the gray correlation to determine the 10 evaluation. By using entropy weight method, the system obtains rubber tapping level quantitative function, and through combining with the inverse cloud generator, it transforms the score into the qualitative evaluation of the level of tapping. At last, it uses the k-means clustering method to determine the center points of different tapping level and completes the classification of tapping level by minimizing the European distance. The experimental results show that the accuracy of the rubber tapping level evaluation by model-based intelligent tapping technology auxiliary learning system is higher than 90%, and time cost of the real-time update performance is less than 3 s. The system is conducive to the rubber workers to improve the level of tapping, thereby advancing the rubber production and therefore it can be used and further promoted.
机译：橡胶工人的攻丝技术水平是影响橡胶生产的关键。准确的橡胶攻丝水平评估在提高橡胶攻丝队伍和橡胶生产水平方面起着重要作用。我们已经设计并实现了基于云模型的智能点击技术学习系统。本文介绍了智能敲击技术学习仪器的机械结构。为了评估橡胶攻丝的水平，系统结合了Delphi方法和灰色关联度来确定10个评估值。该系统采用熵权法获得橡胶攻丝水平定量功能，并与逆云生成器相结合，将得分转化为攻丝水平的定性评价。最后，采用k-means聚类方法确定不同攻丝等级的中心点，并通过最小化欧洲距离来完成攻丝等级的分类。实验结果表明，基于模型的智能攻丝技术辅助学习系统对橡胶攻丝水平评估的准确性高于90％，实时更新性能的时间成本小于3 s。该系统有利于橡胶工人提高出胶水平，从而促进了橡胶产量，因此可以使用和进一步推广。

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