CN116129247A - Sensing method and related device of multi-mode high-voltage switch - Google Patents

Sensing method and related device of multi-mode high-voltage switch Download PDF

Info

Publication number
CN116129247A
CN116129247A CN202310188281.7A CN202310188281A CN116129247A CN 116129247 A CN116129247 A CN 116129247A CN 202310188281 A CN202310188281 A CN 202310188281A CN 116129247 A CN116129247 A CN 116129247A
Authority
CN
China
Prior art keywords
sequence
voltage switch
prediction
time
network model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310188281.7A
Other languages
Chinese (zh)
Inventor
张杰明
高宜凡
李波
刘洋
陈显超
陈展尘
陈益哲
梁妍陟
陈金成
陈忠颖
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Power Grid Co Ltd
Zhaoqing Power Supply Bureau of Guangdong Power Grid Co Ltd
Original Assignee
Guangdong Power Grid Co Ltd
Zhaoqing Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Power Grid Co Ltd, Zhaoqing Power Supply Bureau of Guangdong Power Grid Co Ltd filed Critical Guangdong Power Grid Co Ltd
Priority to CN202310188281.7A priority Critical patent/CN116129247A/en
Publication of CN116129247A publication Critical patent/CN116129247A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Mathematical Optimization (AREA)
  • Medical Informatics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Molecular Biology (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Multimedia (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Algebra (AREA)
  • Measurement Of Current Or Voltage (AREA)

Abstract

The application discloses a sensing method and a related device of a multi-mode high-voltage switch, wherein the sensing method comprises the following steps: acquiring detection data and image data of a high-voltage switch state detection sensor, combining the detection data and the image data, and judging the state of the high-voltage switch by introducing a switch state weight matrix; binary coding is carried out on the switch weight matrix to obtain a binary time sequence and an environmental factor weight matrix, and the binary time sequence is decomposed into a plurality of stable time subsequences through wavelet decomposition technology; training the prediction network model through the stable time subsequence, and inputting the binary time sequence into the trained prediction network model to obtain a prediction time subsequence; and carrying out sequence reconstruction on the predicted time subsequence to obtain a predicted time reconstruction sequence, and introducing the predicted time reconstruction sequence into an environmental factor weight matrix to obtain a switch state prediction sequence, thereby solving the problem of poor detection and prediction precision of the high-voltage switch state in the prior art.

Description

Sensing method and related device of multi-mode high-voltage switch
Technical Field
The application relates to the technical field of high-voltage switch state analysis, in particular to a sensing method and a related device of a multi-mode high-voltage switch.
Background
The high-voltage switch state prediction is an extremely important field for safety in smart grid construction, and is a foundation for constructing a safe and stable smart grid. The accurate prediction of the state of the high-voltage switch can predict the on-off state of the next state of each switch, through the prediction analysis of the state of the switch, a worker can observe abnormal changes of some data, and the worker can check where problems, in particular what problems, occur according to the abnormal states of the switches, and can maintain or repair the whole power grid system or a local power grid system according to the problems. The high-voltage switch state prediction not only can improve the safety performance of the whole intelligent power grid, but also can improve economic benefit to a certain extent, reduce workload of workers and loss caused by disasters, and has more positive significance for power grid planning and construction and important significance for improving the safety of a power system.
In terms of detecting the state of the high-voltage switch, the currently mainstream methods are mainly divided into two methods, namely, a sensor is adopted for detection, and a hardware circuit, such as a voltage sampling circuit, is adopted for detection. Because the power grid structure is more complicated, has stronger electromagnetic interference, and the sensor is digital signal, receives the influence greatly, and possible collection data can be unstable, uses voltage sampling circuit to convert high voltage into low pressure, then carries out the method of sampling the closed state of recognition switch, because the conversion between the multistage is required, the circuit is comparatively complicated to the sampling data also can be comparatively unstable, need carry out filter processing on software or the hardware, improve the detection accuracy.
In the aspect of predicting the state of a high-voltage switch, there are few papers or patents for predicting the state of the switch, the aspect of predicting the switch mainly predicts time series data, an LSTM model or branches thereof are adopted in the predicting mode, and recently, a learner uses a transducer to combine with the time series to predict, but the time complexity of the transducer is high, and the accuracy is not particularly high.
Disclosure of Invention
The application provides a sensing method and a related device of a multi-mode high-voltage switch, which are used for solving the technical problem that the state detection and prediction precision of the high-voltage switch are poor in the prior art.
In view of this, a first aspect of the present application provides a sensing method of a multi-mode high voltage switch, the method comprising:
acquiring detection data of a plurality of high-voltage switch state detection sensors and image data of high-voltage switch state image recognition, combining the detection data with the image data, and judging the state of a high-voltage switch by introducing a switch state weight matrix;
binary coding is carried out on the switch weight matrix to obtain a binary time sequence and an environmental factor weight matrix, and the binary time sequence is decomposed into a plurality of stable time subsequences through a wavelet decomposition technology;
training a prediction network model through the stable time subsequence, and inputting the binary time sequence into the trained prediction network model to obtain a prediction time subsequence;
and carrying out sequence reconstruction on the predicted time subsequence to obtain a predicted time reconstruction sequence, and introducing the predicted time reconstruction sequence into the environmental factor weight matrix to obtain a switch state predicted sequence.
Optionally, the training of the prediction network model through the stable time subsequence, and inputting the binary time sequence into the trained prediction network model, so as to obtain a prediction time subsequence, which specifically includes:
dividing each stable time sub-sequence into a training set and a testing set, training a prediction network model constructed by a residual convolution network and an Informir layer to obtain a trained prediction network model, and inputting the binary time sequence into the prediction network model to obtain a prediction time sub-sequence.
Optionally, the prediction network model is composed of a residual convolution layer, an infomer layer, a flattening layer and a full-connection layer in the LSTM, wherein the residual convolution layer is composed of a plurality of convolution layers through multiple transforms.
Optionally, the frame of the Informir layer is composed of an encoder Decoder and a Decoder, and is composed of a plurality of convolution layers, an embedding layer and a pooling layer, and has a plurality of attention mechanisms.
A second aspect of the present application provides a sensing system for a multi-mode high voltage switch, the system comprising:
the detection module is used for acquiring detection data of a plurality of high-voltage switch state detection sensors and image data of high-voltage switch state image recognition, combining the detection data with the image data and judging the state of the high-voltage switch by introducing a switch state weight matrix;
the preprocessing module is used for binary coding the switch weight matrix to obtain a binary time sequence and an environmental factor weight matrix, and decomposing the binary time sequence into a plurality of stable time subsequences through a wavelet decomposition technology;
the training module is used for training the prediction network model through the stable time subsequence, and inputting the binary time sequence into the trained prediction network model to obtain a prediction time subsequence;
the prediction module is used for carrying out sequence reconstruction on the prediction time subsequence to obtain a prediction time reconstruction sequence, and introducing the prediction time reconstruction sequence into the environmental factor weight matrix to obtain a switch state prediction sequence.
Optionally, the training module is specifically configured to:
dividing each stable time sub-sequence into a training set and a testing set, training a prediction network model constructed by a residual convolution network and an Informir layer to obtain a trained prediction network model, and inputting the binary time sequence into the prediction network model to obtain a prediction time sub-sequence.
Optionally, the prediction network model is composed of a residual convolution layer, an infomer layer, a flattening layer and a full-connection layer in the LSTM, wherein the residual convolution layer is composed of a plurality of convolution layers through multiple transforms.
Optionally, the frame of the Informir layer is composed of an encoder Decoder and a Decoder, and is composed of a plurality of convolution layers, an embedding layer and a pooling layer, and has a plurality of attention mechanisms.
A third aspect of the present application provides a sensing device for a multi-mode high voltage switch, the device comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the steps of the sensing method of the multi-mode high voltage switch according to the first aspect according to the instructions in the program code.
A fourth aspect of the present application provides a computer readable storage medium for storing program code for executing the sensing method of the multi-mode high voltage switch according to the first aspect.
From the above technical scheme, the application has the following advantages:
the application provides a sensing method of a multi-mode high-voltage switch, which comprises the following steps: acquiring detection data of a plurality of high-voltage switch state detection sensors and image data of high-voltage switch state image recognition, combining the detection data and the image data, and judging the state of the high-voltage switch by introducing a switch state weight matrix; binary coding is carried out on the switch weight matrix to obtain a binary time sequence and an environmental factor weight matrix, and the binary time sequence is decomposed into a plurality of stable time subsequences through a wavelet decomposition technology; training the prediction network model through the stable time subsequence, and inputting the binary time sequence into the trained prediction network model to obtain a prediction time subsequence; and carrying out sequence reconstruction on the predicted time subsequence to obtain a predicted time reconstruction sequence, and introducing the predicted time reconstruction sequence into an environmental factor weight matrix to obtain a switch state predicted sequence.
Compared with the prior art:
1) In the detection aspect, in order to alleviate the problem of accuracy in the detection aspect, the invention combines image detection and sensor detection in the detection aspect, uses a weight network to judge the state of the switch together, and improves the detection accuracy and efficiency.
In the aspect of prediction, in order to alleviate the problem of lower accuracy in the aspect of prediction, the invention adopts the latest Informir layer to perform time sequence prediction processing, combines the residual convolution network structure of the LSTM to perform model building, and has lower time complexity and higher accuracy. And the preprocessing part adopts a wavelet decomposition mode to process the original time sequence, so that the precision can be further improved. And a plurality of environmental factors are also considered, and an environmental factor weight matrix is adopted, so that the prediction result is more close to the real situation.
Drawings
Fig. 1 is a schematic flow chart of an embodiment of a sensing method of a multi-mode high-voltage switch provided in an embodiment of the present application;
fig. 2 is a schematic structural diagram of an embodiment of a sensing system of a multi-mode high-voltage switch according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will clearly and completely describe the technical solution in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Referring to fig. 1, a sensing method of a multi-mode high voltage switch provided in an embodiment of the present application includes:
step 101, acquiring detection data of a plurality of high-voltage switch state detection sensors and image data of high-voltage switch state image recognition, combining the detection data and the image data, and judging the state of the high-voltage switch by introducing a switch state weight matrix;
in this embodiment, the image data of the plurality of high-voltage switch state detection sensors and the high-voltage switch state image recognition are collected, the switch positions are recognized by using an image processing method, the state of the high-voltage switch is judged by the data of the sensors, and the data is sampled once in a short time, so that the influence of noise or jitter is eliminated. And introducing a switch state weight matrix to obtain the state of the high-voltage switch.
102, binary coding is carried out on the switch weight matrix to obtain a binary time sequence and an environmental factor weight matrix, and the binary time sequence is decomposed into a plurality of stable time subsequences through a wavelet decomposition technology;
it should be noted that, binary encoding is performed on the data of the switching state in step 101 to obtain a binary time sequence and an environmental factor weight matrix, and the time sequence is decomposed into a plurality of stable time sub-sequences by wavelet.
Step 103, training a prediction network model through the stable time subsequence, and inputting a binary time sequence into the trained prediction network model to obtain a prediction time subsequence;
it should be noted that, the stationary time sub-sequence is divided into a training set and a testing set, and the training set and the testing set are trained in a network constructed by a residual convolution network and an Informir layer to obtain a trained network model, and the binary time sequence at each moment is passed through the network model to obtain a predicted time sub-sequence.
The prediction network model is composed of a residual convolution layer, an Informir layer, a flattening layer and a full-connection layer in the LSTM, wherein the residual convolution layer is formed by a plurality of convolution layers through multiple transformations.
The framework of the Informir layer consists of an encoder Decoder and a Decoder, and is composed of a plurality of convolution layers, an embedding layer and a pooling layer, and has a plurality of attention mechanisms.
And 104, carrying out sequence reconstruction on the predicted time subsequence to obtain a predicted time reconstruction sequence, and introducing the predicted time reconstruction sequence into an environmental factor weight matrix to obtain a switch state predicted sequence.
Finally, a new predicted time sequence is obtained through a sequence reconstruction method of the predicted time sub-sequence, and then the predicted time sequence is introduced into an environmental factor weight matrix obtained before to obtain a final switch state predicted sequence.
The above is a sensing method of a multi-mode high-voltage switch provided in the embodiments of the present application, and the following is a sensing system of a multi-mode high-voltage switch provided in the embodiments of the present application.
Referring to fig. 2, a sensing system of a multi-mode high voltage switch provided in an embodiment of the present application includes:
the detection module 201 is configured to obtain detection data of a plurality of high-voltage switch state detection sensors and image data of high-voltage switch state image recognition, combine the detection data and the image data, and determine a state of the high-voltage switch by introducing a switch state weight matrix;
the preprocessing module 202 is configured to binary encode the switch weight matrix to obtain a binary time sequence and an environmental factor weight matrix, and decompose the binary time sequence into a plurality of stable time subsequences by using a wavelet decomposition technique;
the training module 203 is configured to train the prediction network model through the stable time subsequence, and input the binary time sequence into the trained prediction network model to obtain a prediction time subsequence;
the prediction module 204 is configured to perform sequence reconstruction on the predicted time subsequence to obtain a predicted time reconstruction sequence, and introduce the predicted time reconstruction sequence into the environmental factor weight matrix to obtain a switch state prediction sequence.
Further, in an embodiment of the present application, there is further provided a sensing device of a multi-mode high voltage switch, where the sensing device includes a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the steps of the sensing method of the multi-mode high voltage switch according to the instruction in the program code.
Further, in the embodiment of the present application, there is further provided a computer readable storage medium, where the computer readable storage medium is used to store program code, where the program code is used to execute the sensing method of the multi-mode high voltage switch described in the foregoing method embodiment.
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working procedures of the above-described system and unit may refer to the corresponding procedures in the foregoing method embodiments, which are not repeated here.
The terms "first," "second," "third," "fourth," and the like in the description of the present application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be capable of operation in sequences other than those illustrated or described herein, for example. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in this application, "at least one" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (10)

1. A method of sensing a multi-mode high voltage switch, comprising:
acquiring detection data of a plurality of high-voltage switch state detection sensors and image data of high-voltage switch state image recognition, combining the detection data with the image data, and judging the state of a high-voltage switch by introducing a switch state weight matrix;
binary coding is carried out on the switch weight matrix to obtain a binary time sequence and an environmental factor weight matrix, and the binary time sequence is decomposed into a plurality of stable time subsequences through a wavelet decomposition technology;
training a prediction network model through the stable time subsequence, and inputting the binary time sequence into the trained prediction network model to obtain a prediction time subsequence;
and carrying out sequence reconstruction on the predicted time subsequence to obtain a predicted time reconstruction sequence, and introducing the predicted time reconstruction sequence into the environmental factor weight matrix to obtain a switch state predicted sequence.
2. The method for sensing the multi-mode high-voltage switch according to claim 1, wherein the training of the prediction network model through the stable time subsequence and the inputting of the binary time sequence into the trained prediction network model obtain the prediction time subsequence specifically comprise:
dividing each stable time sub-sequence into a training set and a testing set, training a prediction network model constructed by a residual convolution network and an Informir layer to obtain a trained prediction network model, and inputting the binary time sequence into the prediction network model to obtain a prediction time sub-sequence.
3. The method of claim 2, wherein the predictive network model is composed of a residual convolution layer, an infomer layer, a flattening layer, and a full connection layer in LSTM, wherein the residual convolution layer is composed of a plurality of convolution layers transformed multiple times.
4. A method of sensing a multi-mode high voltage switch according to claim 3, wherein the frame of the infomer layer is composed of an encoder Incoder and a Decoder and is composed of a plurality of convolutional layers, an embedded layer and a pooled layer, and has a plurality of attention mechanisms.
5. A sensing system for a multi-mode high voltage switch, comprising:
the detection module is used for acquiring detection data of a plurality of high-voltage switch state detection sensors and image data of high-voltage switch state image recognition, combining the detection data with the image data and judging the state of the high-voltage switch by introducing a switch state weight matrix;
the preprocessing module is used for binary coding the switch weight matrix to obtain a binary time sequence and an environmental factor weight matrix, and decomposing the binary time sequence into a plurality of stable time subsequences through a wavelet decomposition technology;
the training module is used for training the prediction network model through the stable time subsequence, and inputting the binary time sequence into the trained prediction network model to obtain a prediction time subsequence;
the prediction module is used for carrying out sequence reconstruction on the prediction time subsequence to obtain a prediction time reconstruction sequence, and introducing the prediction time reconstruction sequence into the environmental factor weight matrix to obtain a switch state prediction sequence.
6. The sensing system of a multi-mode high voltage switch of claim 5, wherein the training module is specifically configured to:
dividing each stable time sub-sequence into a training set and a testing set, training a prediction network model constructed by a residual convolution network and an Informir layer to obtain a trained prediction network model, and inputting the binary time sequence into the prediction network model to obtain a prediction time sub-sequence.
7. The perception system of a multi-mode high voltage switch according to claim 6, wherein the predictive network model is composed of a residual convolution layer, an infomer layer, a flattening layer, a full connection layer in LSTM, wherein the residual convolution layer is composed of a plurality of convolution layers transformed multiple times.
8. The system of claim 7, wherein the frame of the infomer layer is comprised of an encoder and a Decoder, and is comprised of a plurality of convolutional layers, embedded layers, and pooled layers, and has a plurality of attention mechanisms.
9. A sensing device for a multi-mode high voltage switch, the device comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the sensing method of the multi-mode high voltage switch of any one of claims 1-4 according to instructions in the program code.
10. A computer readable storage medium for storing program code for performing the sensing method of the multi-mode high voltage switch of any one of claims 1-4.
CN202310188281.7A 2023-02-28 2023-02-28 Sensing method and related device of multi-mode high-voltage switch Pending CN116129247A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310188281.7A CN116129247A (en) 2023-02-28 2023-02-28 Sensing method and related device of multi-mode high-voltage switch

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310188281.7A CN116129247A (en) 2023-02-28 2023-02-28 Sensing method and related device of multi-mode high-voltage switch

Publications (1)

Publication Number Publication Date
CN116129247A true CN116129247A (en) 2023-05-16

Family

ID=86306357

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310188281.7A Pending CN116129247A (en) 2023-02-28 2023-02-28 Sensing method and related device of multi-mode high-voltage switch

Country Status (1)

Country Link
CN (1) CN116129247A (en)

Similar Documents

Publication Publication Date Title
CN111914873A (en) Two-stage cloud server unsupervised anomaly prediction method
CN111368890A (en) Fault detection method and device and information physical fusion system
CN112462261B (en) Motor abnormality detection method and device, electronic equipment and storage medium
CN112199252B (en) Abnormality monitoring method and device and electronic equipment
Ren et al. A $ T^{2} $-tensor-aided multiscale transformer for remaining useful life prediction in IIoT
CN115587335A (en) Training method of abnormal value detection model, abnormal value detection method and system
CN114860542A (en) Trend prediction model optimization method, trend prediction model optimization device, electronic device, and medium
CN114970717A (en) Time series data abnormity detection method, electronic equipment and computer storage medium
CN113076545A (en) Deep learning-based kernel fuzzy test sequence generation method
CN116433223A (en) Substation equipment fault early warning method and equipment based on double-domain sparse transducer model
CN115130232A (en) Method, device, apparatus, storage medium, and program product for predicting life of part
CN117892921A (en) Intelligent water affair comprehensive management system and method based on big data
CN117540136A (en) Time sequence signal prediction method, device, equipment and storage medium
CN117833468A (en) Maintenance method for circuit breaker unit transmission control part of power distribution ring main unit in operation
CN117374913A (en) Wave energy prediction method and device based on STL decomposition and multilayer seq2seq model
CN116129247A (en) Sensing method and related device of multi-mode high-voltage switch
CN115952928A (en) Short-term power load prediction method, device, equipment and storage medium
CN112686330B (en) KPI abnormal data detection method and device, storage medium and electronic equipment
GB2623358A (en) Method and system for fault diagnosis of nuclear power circulating water pump based on optimized capsule network
CN115758273A (en) Method, device, equipment and medium for detecting time sequence data abnormity
CN115328753A (en) Fault prediction method and device, electronic equipment and storage medium
CN114997292A (en) Digital twin escalator service life prediction method based on spatial reconstruction attention model
CN114792026A (en) Method and system for predicting residual life of aircraft engine equipment
CN116361673B (en) Quasi-periodic time sequence unsupervised anomaly detection method, system and terminal
CN112199765A (en) Partitioning method and device for concrete dam, electronic equipment and readable medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination