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Instrument analysis method and system based on TextCNN-BiLSTM, equipment and medium

An instrument analysis and instrument technology, applied in the field of instrument analysis based on TextCNN-BiLSTM, can solve the problems of high cost, unintelligent instrument efficiency, etc., and achieve the effect of reducing equipment loss and labor cost

Pending Publication Date: 2021-04-27
CHONGQING CHUANYI AUTOMATION
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Problems solved by technology

[0003] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a TextCNN-BiLSTM-based instrument analysis method, system, equipment and medium, which are used to solve the problem of low efficiency and high cost caused by unintelligent instruments in the prior art. question

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  • Instrument analysis method and system based on TextCNN-BiLSTM, equipment and medium
  • Instrument analysis method and system based on TextCNN-BiLSTM, equipment and medium
  • Instrument analysis method and system based on TextCNN-BiLSTM, equipment and medium

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[0037] Divide the sample training set into a test subset and a training subset, and input the convolutional neural network for training; the process of inputting the convolutional neural network for training is: the first convolution layer, the input data passes through 5 convolution kernels, The size of the convolution kernel is 3*3*5, and the step size is 1; the second layer of pooling layer max pool, the pooling window is 2*2, and the step size is 2 to downsample the data; the third layer of convolution layer, after 5 convolution kernels, the size of the convolution kernel is 3*3*5, and the step size is 1; the fourth layer of pooling layer max pool, the pooling window is 2*2, and the step size is 2; obtained after convolution Increase the deviation on the result, the output result is activated using the activation function ReLU, and the loss function uses cross entropy; the fifth layer and the sixth layer are fully connected layers, and the data obtained by the fourth layer ...

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Abstract

The invention provides an instrument analysis method and system based on TextCNN-BiLSTM, equipment and a medium. The method comprises the steps: collecting instrument data images of instruments corresponding to a water pump, a welding device and a hydraulic machine; processing the instrument data image to obtain a training set; constructing a TextCNN-BiLSTM model used for predicting instrument health according to the training set; utilizing the TextCNN-BiLSTM model to identify an instrument data image to be tested to obtain probability data; inputting the probability data into a Softmax activation function to obtain an output sequence; and decoding the output sequence according to a connection time sequence classifier, and predicting the health state of the instrument according to the reading of the instrument data image. By collecting instrument data images of instruments corresponding to the water pump, the welding equipment and the hydraulic machine, the use conditions of all kinds of equipment can be observed in real time, the use conditions of the equipment are intelligently analyzed through the deep learning combination model TextCNNBiLSTM, and whether the equipment breaks down or is about to have an accident or not is judged; not only can equipment loss be reduced, but also labor cost can be reduced.

Description

technical field [0001] The invention relates to the technical field of smart meters, in particular to a TextCNN-BiLSTM-based meter analysis method, system, equipment and media. Background technique [0002] In industrial production, both water equipment and electrical equipment are connected to a water meter and an electricity meter. However, the traditional method needs to manually check the data of the instruments one by one to record the water or electricity consumption status of the equipment, which undoubtedly increases the labor cost; at the same time, the troubleshooting of water and electricity equipment also requires manual on-site observation one by one to find equipment problems. , which not only increases the labor cost, but also causes the equipment to stop production due to failure once the best inspection time is missed. Contents of the invention [0003] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/044G06N3/045G06F18/2415G06F18/214
Inventor 邱洪姚杰王玉军
Owner CHONGQING CHUANYI AUTOMATION
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