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And the work flow design of the intelligent building control system structure based on artificial intelligence and CAN algorithm is discussed, and finally the application of the intelligent building control system based on artificial intelligence and CAN algorithm is experimentally tested. The correct number of switch control by artificial intelligence and CAN algorithm in the intelligent building control system is relatively high. The correct number of switch control in the interference environment is less than that in the humid environment, and the number of switch operations for the control system is the least in the humid environment. 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摘要

随着网络通信、计算机技术和人工智能的发展,推广以智能、舒适、安全为特点的“智能建筑”已成为必然的发展趋势。为了解决现有智能楼宇控制系统研究的不足,本文讨论了CAN算法、人工智能模糊控制器和智能楼宇的泛函方程,针对基于人工智能和CAN算法的智能楼宇控制系统进行了研究。简要介绍了硬件设置和参数设置。讨论了基于人工智能和CAN算法的智能楼宇控制系统结构的工作流程设计,最后对基于人工智能和CAN算法的智能楼宇控制系统的应用进行了实验测试。在智能楼宇控制系统中,采用人工智能和CAN算法控制开关的正确率较高。干扰环境下开关控制的正确次数比潮湿环境下少,潮湿环境下控制系统的开关操作次数最少。500和高达2500的误差率均小于5%,从而验证了基于人工智能和CAN算法的智能楼宇控制系统应用的优越性。
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Intelligent Building Control System Based on Artificial Intelligence and CAN Algorithm
With the development of network communication, computer technology and artificial intelligence, it has become an inevitable development trend to promote “intelligent buildings” characterized by intelligence, comfort and safety. In order to solve the shortcomings of the existing research on intelligent building control system, this paper discusses the functional equation of CAN algorithm, artificial intelligence fuzzy controller and intelligent building, and aims at the intelligent building control system based on artificial intelligence and CAN algorithm. Hardware settings and parameter settings are briefly introduced. And the work flow design of the intelligent building control system structure based on artificial intelligence and CAN algorithm is discussed, and finally the application of the intelligent building control system based on artificial intelligence and CAN algorithm is experimentally tested. The correct number of switch control by artificial intelligence and CAN algorithm in the intelligent building control system is relatively high. The correct number of switch control in the interference environment is less than that in the humid environment, and the number of switch operations for the control system is the least in the humid environment. The error rate of 500 and up to 2500 is less than 5%, thus verifying the superiority of the application of intelligent building control system based on artificial intelligence and CAN algorithm.
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