CN113379033A - Cable health state intelligent early warning method based on time-space network enhanced deep learning - Google Patents
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Abstract
The invention relates to a cable health state intelligent early warning method based on space-time network enhanced deep learning, which comprises the following steps: 1) collecting various cable monitoring data of the cable through an intelligent data collection system; 2) inputting high-dimensional cable monitoring data in a (t-n, t) time period into an LSTM prediction model, respectively controlling the discarding, updating and outputting of information through input control, forgetting control and output control, and then outputting a predicted value in the (t, t + m) time period; 3) converting the multi-feature quantity into a few main feature components by utilizing the idea of PCA principal component analysis dimension reduction, and packaging the principal component structure into a model; 4) performing classification prediction on the principal component characteristics of the new sample based on a CNN convolutional neural network, extracting characteristics through convolution, performing pooling dimension reduction, and performing normalization to obtain probability; 5) obtaining the final cable health state classification according to the probability value; the invention realizes the prediction of the running state of the cable and carries out early warning prompt according to the prediction result.
Description
Technical Field
The invention relates to the technical field of monitoring of cable running health states, in particular to an intelligent early warning method for cable health states based on space-time network enhanced deep learning.
Background
With the great annual increase of the number of the cables in the tunnel, under the coupling action of a plurality of factors such as the service time of the cables, the tunnel environment, the working condition parameters and the like, the service safety problem of the cables is increasingly prominent, the health state of the cables is accurately and efficiently pre-warned, the pre-informed maintenance is realized, and the cable service safety warning device becomes an important means for guaranteeing the service safety of the cables, and the cable service safety warning device becomes a wide consensus.
At present, the early warning of the health state of the cable mainly comprises two modes: 1) the multi-stage early warning is realized by setting one or more thresholds, and the threshold with universality is very difficult to set, so that the alarm is difficult and the false alarm is more; 2) the traditional machine learning prediction method cannot perform associated storage on features in a long time span, and is limited by gradient disappearance in a parameter optimization process.
Disclosure of Invention
The invention aims to solve the problems of the existing cable health state early warning technology, provides an intelligent cable health state early warning method based on space-time network enhanced deep learning, and can be used for early warning potential operation faults of cables and optimizing early warning accuracy.
The specific scheme of the invention is as follows: the cable health state intelligent early warning method based on the space-time network enhanced deep learning adopts an intelligent early warning system to carry out early warning, the intelligent early warning system comprises a data acquisition system, an intelligent analysis server and a fault early warning server which are sequentially connected, the intelligent analysis server is further connected with a data storage server, and the intelligent early warning comprises the following steps:
1) collecting various cable monitoring data of the cable through an intelligent data collection system;
2) inputting high-dimensional cable monitoring data in a (t-n, t) time period into an LSTM prediction model, respectively controlling the discarding, updating and outputting of information through input control, forgetting control and output control, and then outputting a predicted value in the (t, t + m) time period;
3) converting the multi-feature quantity into a few main feature components by utilizing the idea of PCA principal component analysis dimension reduction, and packaging the principal component structure into a model;
4) performing classification prediction on the principal component characteristics of the new sample based on a CNN convolutional neural network, extracting characteristics through convolution, performing pooling dimension reduction, and performing normalization to obtain probability;
5) and obtaining the final cable health state classification according to the probability value.
In the step 1), the various cable monitoring data comprise parameters with obvious time correlation characteristics and high-dimensional characteristics, such as partial discharge, temperature, load, circulation and the like.
The cable monitoring data are periodically collected according to a set collection period and uploaded to an intelligent analysis server.
The invention can also obtain the cable monitoring data cached in the data acquisition cluster by periodically sending data acquisition requests through the intelligent analysis server.
The health state in the step 5) is classified into a health mode, a sub-health mode, an early warning mode and an alarm mode.
The data acquisition of the invention comprises data of each measuring point and a plurality of different measuring points in front and at back which are mutually related, and also comprises measuring point data which are mutually related at the same position on A, B, C three-phase cables.
The LSTM time sequence prediction comprises an LSTM Internet of things time sequence prediction model, is a time recursive neural network, is suitable for processing and predicting important events with relatively long intervals and relatively long delays in time sequences, and can well predict long-term, short-term and even weak time-dependent Internet of things time sequence data.
The principal component analysis of PCA is a most widely used data dimension reduction algorithm, and maps n-dimensional features to k-dimensional features, wherein the k-dimensional features are brand new orthogonal features and are also called principal components, and are k-dimensional features reconstructed on the basis of original n-dimensional features. The PCA works by sequentially finding a group of mutually orthogonal coordinate axes from an original space, and the selection of a new coordinate axis is closely related to the data, so that the data structure can be effectively simplified.
The CNN convolutional neural network classification is a feedforward neural network which comprises convolutional calculation and has a deep structure, has the characteristic learning capacity, and can perform translation invariant classification on input information according to the hierarchical structure of the input information. On one hand, the number of weights can be reduced to make the network easy to optimize, and on the other hand, the complexity of the model can be reduced, namely, the risk of overfitting is reduced.
The invention has the following beneficial effects: 1. the invention provides an intelligent cable early warning method based on space-time network enhanced deep learning, which is characterized in that real-time analysis is carried out based on operation monitoring data of an intelligent cable acquired in real time, prediction of the operation state of the intelligent cable is carried out through the space-time network enhanced deep learning, and early warning prompt is carried out according to the prediction result; 2. the intelligent cable early warning platform can early warn potential operation faults of the intelligent cable, and the operation management of the intelligent cable is optimized; 3. compared with the traditional intelligent cable management system, the general prediction method can not perform associated storage on the characteristics in a longer time span, and the limitation of gradient disappearance is faced in the parameter optimization process.
Drawings
FIG. 1 is a schematic diagram of an early warning process of the intelligent early warning system for cables according to the present invention;
FIG. 2 is a flow chart of an algorithm of the cable intelligent warning system of the present invention;
FIG. 3 is a schematic diagram of the intelligent early warning system of the present invention;
FIG. 4 is a spatiotemporal enhancement deep learning framework diagram of the present invention.
Detailed Description
The embodiment provides an intelligent cable early warning system based on spatio-temporal network enhanced deep learning, referring to fig. 3, specifically including: the system comprises a data acquisition cluster, an intelligent analysis server, a data storage server and a fault early warning server. The data acquisition cluster is arranged at the front end and used for acquiring operation monitoring data of each intelligent cable line, wherein the operation monitoring data can be periodically acquired by the data acquisition cluster according to a set data acquisition period and uploaded to the intelligent analysis server; or the intelligent analysis server periodically issues a data acquisition request to acquire the operation monitoring data cached in the data acquisition cluster. It should be noted that, data correlation between different monitoring points on each cable, that is, data correlation exists between each measuring point and several different measuring points before and after the measuring point, and historical data of each measuring point are also correlated; and the other side is that the measuring point data on the same position on the A, B, C three-phase cable has correlation. Meanwhile, the cable monitoring data has obvious time correlation characteristics and high-dimensional characteristics (parameters such as partial discharge, temperature, load, circulation and the like).
Referring to fig. 2, in the cable intelligent early warning method based on spatio-temporal network enhanced deep learning provided in this embodiment, the intelligent analysis server performs operation failure prediction analysis based on the received operation monitoring data in real time. And performing prediction analysis by using a preset big data analysis model to obtain a corresponding prediction result. The prediction result is sent to a fault early warning server, and the fault early warning server carries out early warning prompt on the operation fault of the intelligent cable based on the prediction result, so that the operation fault risk of the potential risk can be avoided better, and the operation management of the intelligent cable is optimized.
Referring to fig. 3, the algorithm of the cable intelligent early warning system based on spatio-temporal network enhanced deep learning is to input high-dimensional cable monitoring data in a (t-n, t) Time period into an lstm (long Short Time memory) prediction model, respectively control the discarding, updating and outputting of information through input control, forgetting control and output control, and then output a predicted value in the (t, t + m) Time period; then, converting multiple characteristic quantities into a few main characteristic components by utilizing the idea of PCA (principal Component analysis) dimension reduction, packaging the main Component structure into a model, performing classification prediction based on a CNN (convolutional Neural networks) convolutional Neural network model on the main Component characteristics of the new sample, respectively extracting characteristics through convolution, performing pooling dimension reduction and normalization to obtain probability, and obtaining the final cable health state classification according to the probability value.
Referring to fig. 4, the LSTM time series prediction includes an LSTM internet of things time series prediction model, which is a time recursive neural network, is suitable for processing and predicting important events with relatively long intervals and delays in time series, and can well predict long-term, short-term, and even weak time-dependent internet of things time series data.
The PCA principal component analysis is a most widely used data dimension reduction algorithm, and maps n-dimensional features to k dimensions, wherein the k dimensions are brand new orthogonal features and are also called principal components, and the k-dimensional features are reconstructed on the basis of original n-dimensional features. The PCA works by sequentially finding a group of mutually orthogonal coordinate axes from an original space, and the selection of a new coordinate axis is closely related to the data, so that the data structure can be effectively simplified.
The CNN convolutional neural network classification is a feedforward neural network which comprises convolutional calculation and has a depth structure, has the characteristic learning capacity and can perform translation invariant classification on input information according to a hierarchical structure. On one hand, the number of weights can be reduced to make the network easy to optimize, and on the other hand, the complexity of the model can be reduced, namely, the risk of overfitting is reduced.
Claims (6)
1. The cable health state intelligent early warning method based on the time-space network enhanced deep learning is characterized by comprising the following steps: adopt intelligent early warning system to carry out the early warning, intelligent early warning system is including the data acquisition system, intelligent analysis server, the trouble early warning server that connect gradually, intelligent analysis server still is connected with data storage server, intelligent early warning includes following step:
1) collecting various cable monitoring data of the cable through an intelligent data collection system;
2) inputting high-dimensional cable monitoring data in a certain past time period into an LSTM prediction model, respectively controlling the discarding, updating and outputting of information through input control, forgetting control and output control, and then outputting a predicted value in a future time period;
3) converting the multi-feature quantity into a few main feature components by utilizing the idea of PCA principal component analysis dimension reduction, and packaging the principal component structure into a model;
4) performing classification prediction on the principal component characteristics of the new sample based on a CNN convolutional neural network, extracting characteristics through convolution, performing pooling dimension reduction, and performing normalization to obtain probability;
5) and obtaining the final cable health state classification according to the probability value.
2. The cable health state intelligent early warning method based on spatio-temporal network enhanced deep learning of claim 1, which is characterized in that: the monitoring data of the plurality of cables in the step 1) comprise body temperature, partial discharge, temperature, load and circulation.
3. The cable health state intelligent early warning method based on spatio-temporal network enhanced deep learning of claim 1, which is characterized in that: and the cable monitoring data are periodically acquired according to a set acquisition period and are uploaded to an intelligent analysis server.
4. The cable health state intelligent early warning method based on spatio-temporal network enhanced deep learning of claim 1, which is characterized in that: the intelligent analysis server periodically issues data acquisition requests to acquire cable monitoring data cached in the data acquisition cluster.
5. The cable health state intelligent early warning method based on spatio-temporal network enhanced deep learning of claim 1, which is characterized in that: the data acquisition comprises data of each measuring point and a plurality of different measuring points in front and at back which are mutually related, and also comprises measuring point data which are mutually related at the same position on A, B, C three-phase cables.
6. The cable health state intelligent early warning method based on spatio-temporal network enhanced deep learning of claim 1, which is characterized in that: the health state in the step 5) is classified into a health mode, a sub-health mode, an early warning mode and an alarm mode.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114722130A (en) * | 2022-03-09 | 2022-07-08 | 大连东软信息学院 | Digital ocean submarine cable state monitoring method based on GIS system |
| CN117215766A (en) * | 2023-06-26 | 2023-12-12 | 杭州喜倍科技有限公司 | Multimedia intelligent dispatcher and method thereof |
| CN118091331A (en) * | 2024-04-26 | 2024-05-28 | 国网辽宁省电力有限公司抚顺供电公司 | Cable fault sensing method and system |
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