The invention discloses an elevator real-time monitoring and grading early warning method and system based on big data. The method comprises the steps that A, a BP neural network is established; b, creating an initial database; c, BP neural network learning; d, elevator operation data are obtained; e, running data of the elevator are input; f, the risk level of the elevator operation data is judged; g, the risk level of elevator operation is output; through statistics and analysis of fault data such as people trapped in an elevator, a BP neural network algorithm is introduced to learn and train various fault data, the weight of an input quantity is continuously adjusted until output layer data meets error requirements compared with a true value, and then a learned algorithm model is applied to carry out operation calculation analysis on key data in the operation process of the elevator, so that the operation accuracy of the elevator is improved. According to the method, the corresponding risk level is obtained, risk early warning is achieved, potential safety hazards can be eliminated in time, and in the follow-up operation process, the accuracy is further improved through learning and parameter adjustment.
本发明公开了一种基于大数据的电梯实时监测及分级预警方法与系统,方法包括:包括:A.建立BP神经网络;B.创建初始数据库;C.BP神经网络学习;D.获得电梯运行数据;E.输入电梯的运行数据;F.判定电梯运行数据的风险等级;G.输出电梯运行的风险等级;通过对电梯困人等故障数据的统计和分析,引入BP神经网络算法对各类故障数据进行学习训练,不断调整输入量的权值,直到输出层数据与真实值相比满足误差要求,然后运用学习后的算法模型,对电梯运行过程中的关键数据运行计算分析,得出相应的风险等级,实现风险预警,便于及时消除安全隐患,且在后续运行过程中,通过学习以及参数调整,进一步提高准确率。
Elevator real-time monitoring and grading early warning method and system based on big data
基于大数据的电梯实时监测及分级预警方法与系统
2024-10-11
Patent
Electronic Resource
Chinese
IPC: | B66B Aufzüge , ELEVATORS |
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