Method and apparatus for predicting appliance failure based on neuron model

By constructing a neuron model and a weighted regression model, determining the business weight identifier and the neural node identifier, and training IoT appliances, the system solves the intelligent sensing errors caused by high-dimensional information, achieves accurate prediction of appliance faults, and improves the accuracy of intelligent sensing.

CN116227325BActive Publication Date: 2026-05-19CHINA TELECOM CORP LTD
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Patent Information

Application Number
CN202211580975.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-12-09
Publication Date
2026-05-19
Estimated Expiration
2042-12-09

AI Technical Summary

Technical Problem

In existing technologies for predicting appliance faults in the Internet of Things (IoT), the high-dimensional information leads to errors in intelligent sensing, making it impossible to accurately predict appliance faults.

Method used

A neuron model and a weighted regression model are constructed. By determining the business weight identifier and the neural node identifier, the model is trained on the fault diagnosis sample data of the specified electrical appliances. The weighted regression model is used to determine the fitted value, and fault prediction is performed when the preset threshold is met.

Benefits of technology

It improves the accuracy of intelligent sensing, avoids erroneous perception caused by faulty appliance information, and realizes accurate fault prediction for IoT appliances.

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Abstract

Embodiments of the present application provide a kind of electric appliance fault prediction method and device based on neuron model, electronic equipment and readable storage medium, the method comprises: constructing neuron model and weight regression model, determine the business weight mark and nerve node mark of the neuron model;Obtain the fault diagnosis sample data of specified electric appliance, and according to the fault diagnosis sample data of the specified electric appliance, the business weight mark and the nerve node mark, the neuron model is trained;Using the weight regression model determines the fitting value of the neuron model after training;When the fitting value meets preset threshold value, the neuron model after training is determined as the neuron model of training completion, to carry out fault prediction according to the specified electric appliance of the neuron model of training completion.This embodiment of the present application can avoid the information collected by the electric appliance of fault to cause intelligent perception error, improve the accuracy of intelligent perception.
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