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

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.