Resource scheduling method for power equipment fault processing in extreme weather

Abstract

The invention discloses a resource scheduling method for power equipment fault processing in extreme weather, and relates to the technical field of data processing, and the method comprises the steps: carrying out the embedding of a power equipment topological point cloud model and a power equipment fault analysis function network through employing a digital twin technology, a power equipment operation twin model is generated to analyze the power equipment operation data flow, and power equipment fault feature information is obtained; and carrying out influence analysis on the regional weather information of the power equipment to obtain a multi-dimensional weather resource influence factor, transmitting the fault feature information of the power equipment and the multi-dimensional weather resource influence factor to an operation and maintenance resource cloud to carry out scheduling analysis, and obtaining power equipment resource collaborative scheduling parameters to carry out resource scheduling and equipment fault processing. The technical effects of realizing power equipment fault analysis and operation and maintenance resource intelligent scheduling by using the digital twinning technology, improving resource scheduling accuracy and scheduling efficiency, reducing equipment operation and maintenance cost and further ensuring power equipment fault processing timeliness are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a resource scheduling method for power equipment fault processing under extreme weather conditions. Background Art

[0002] As climate change intensifies, the frequency and intensity of extreme weather events are increasing, posing a huge challenge to the stable operation of global power equipment. In extreme weather, power equipment is prone to failure. In order to ensure the stability and reliability of power system operation, timely resource scheduling is required to effectively deal with power equipment failures. However, the existing resource scheduling is not intelligent enough, and the scheduling measures are inaccurate, resulting in untimely handling of power equipment failures. Summary of the invention

[0003] This application solves the technical problems of low intelligence level of resource scheduling and low accuracy of scheduling measures in the prior art, which lead to untimely handling of power equipment faults, by providing a resource scheduling method for power equipment fault handling under extreme weather conditions. This application achieves the technical effect of using digital twin technology to realize power equipment fault analysis and intelligent scheduling of operation and maintenance resources, improve resource scheduling accuracy and scheduling efficiency, reduce equipment operation and maintenance costs, and thereby ensure timeliness of power equipment fault handling.

[0004] In view of the above problems, the present invention provides a resource scheduling method for handling power equipment failures under extreme weather conditions.

[0005] In the first aspect, the present application provides a resource scheduling method for power equipment fault handling under extreme weather conditions, the method comprising: using a laser radar device to perform sensing scanning and alignment modeling on the equipment distribution information of the target power equipment area to generate a power equipment topological point cloud model; based on the power equipment operation standard, quantitatively extracting and training and fitting fault indicators to obtain a power equipment fault analysis function network; using digital twin technology to embed the power equipment topological point cloud model and the power equipment fault analysis function network to generate a power equipment operation twin model; using meteorological sensors and power monitoring equipment to respectively collect and obtain power equipment regional weather information and power equipment operation data streams; based on the power equipment operation twin model, predicting and analyzing the power equipment operation data stream to obtain power equipment fault feature information; extracting and analyzing the influencing factors of the power equipment regional weather information to obtain multidimensional weather resource influencing factors; integrating the power equipment fault feature information and the multidimensional weather resource influencing factors and transmitting them to the operation and maintenance resource cloud for scheduling analysis to obtain power equipment resource collaborative scheduling parameters, and performing resource scheduling and equipment fault handling based on the power equipment resource collaborative scheduling parameters.

[0006] On the other hand, the present application also provides a resource scheduling system for power equipment fault handling under extreme weather conditions, the system comprising: an induction registration modeling module, which is used to perform induction scanning and registration modeling of the equipment distribution information of the target power equipment area through a lidar device, and generate a topological point cloud model of the power equipment; a fault analysis network acquisition module, which is used to perform quantitative extraction and training and fitting of fault indicators based on the power equipment operation standard, and obtain a power equipment fault analysis function network; a twin model generation module, which is used to use digital twin technology to integrate the power equipment topological point cloud model and the power equipment fault analysis function network to generate a power equipment operation twin model; a data stream acquisition module, which is used to obtain a data stream through meteorological sensors and power The power monitoring equipment collects and obtains the regional weather information of the power equipment and the power equipment operation data stream respectively; the fault prediction and analysis module is used to predict and analyze the power equipment operation data stream based on the power equipment operation twin model to obtain the power equipment fault feature information; the influencing factor extraction and analysis module is used to extract and analyze the influencing factors of the regional weather information of the power equipment to obtain the multi-dimensional weather resource influencing factors; the equipment fault processing module is used to integrate and transmit the power equipment fault feature information and the multi-dimensional weather resource influencing factors to the operation and maintenance resource cloud for scheduling analysis, obtain the power equipment resource collaborative scheduling parameters, and perform resource scheduling and equipment fault processing based on the power equipment resource collaborative scheduling parameters.

[0007] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0008] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The equipment distribution information of the target power equipment area is scanned and aligned to model the equipment, a power equipment topology point cloud model is generated, and fault index training and fitting are performed based on the power equipment operation standard to obtain the power equipment fault analysis function network. The power equipment topology point cloud model and the power equipment fault analysis function network are integrated using digital twin technology to generate a power equipment operation twin model to predict and analyze the power equipment operation data stream, obtain power equipment fault feature information, extract and analyze the influencing factors of the power equipment regional weather information, and obtain multi-dimensional weather resource influencing factors; the power equipment fault feature information and the multi-dimensional weather resource influencing factors are integrated and transmitted to the operation and maintenance resource cloud for scheduling analysis, and the power equipment resource collaborative scheduling parameters are obtained for resource scheduling and equipment fault processing. The technical solution is achieved by using digital twin technology to realize power equipment fault analysis and intelligent scheduling of operation and maintenance resources, improve resource scheduling accuracy and scheduling efficiency, reduce equipment operation and maintenance costs, and ensure the timeliness of power equipment fault processing.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A flow chart of a resource scheduling method for handling power equipment failures in extreme weather conditions in this application;

[0013] Figure 2 A schematic diagram of a flow chart of a power equipment fault analysis function network obtained in a resource scheduling method for power equipment fault processing under extreme weather conditions in this application;

[0014] Figure 3 This is a schematic diagram of the structure of a resource scheduling system for power equipment failure handling in extreme weather conditions in this application;

[0015] Figure 4 This is a schematic diagram of the structure of an exemplary electronic device of the present application.

[0016] Explanation of the accompanying drawings: induction alignment modeling module 11, fault analysis network acquisition module 12, twin model generation module 13, data flow acquisition module 14, fault prediction analysis module 15, influencing factor extraction and analysis module 16, equipment fault processing module 17, bus 1110, processor 1120, transceiver 1130, bus interface 1140, memory 1150, operating system 1151, application 1152 and user interface 1160. DETAILED DESCRIPTION

[0017] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0018] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0019] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0020] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0021] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0022] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0023] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0024] The present application is described below in conjunction with the drawings in the present application.

[0025] Embodiment 1

[0026] like Figure 1 As shown, the present application provides a resource scheduling method for handling power equipment failures in extreme weather, the method comprising:

[0027] Step S1: using a laser radar device to sense and scan the equipment distribution information of the target power equipment area, perform registration modeling, and generate a topological point cloud model of the power equipment;

[0028] Furthermore, the generation of the power equipment topology point cloud model, the present application also includes the following steps:

[0029] The equipment distribution information is sensed and scanned by a laser radar device to obtain the distribution point cloud data information of the source power equipment;

[0030] Using a Gaussian filter to perform abnormal point filtering preprocessing on the source power equipment distribution point cloud data information to obtain standard power equipment distribution point cloud data information;

[0031] Performing multi-dimensional feature extraction on the standard electric power equipment distribution point cloud data information to obtain a multi-dimensional point cloud feature descriptor set of electric power equipment;

[0032] The multi-dimensional point cloud feature descriptor set of the power equipment is registered and fused, and point cloud modeling is performed to generate the topological point cloud model of the power equipment.

[0033] Specifically, in order to realize intelligent analysis of power equipment failure, the equipment distribution information of the target power equipment area is firstly sensed and scanned by the laser radar equipment, wherein the target power equipment area is the distribution area of ​​the power equipment to be monitored and maintained, and the radar emits laser and receives the reflected echo to obtain the source power equipment distribution point cloud data information, which is unprocessed high-precision, high-resolution three-dimensional power equipment distribution point cloud data, including the regional distribution spatial position of the power equipment, equipment size, etc. Since the laser beam of the laser radar equipment may hit other objects or the reflection intensity changes during scanning, which may cause noise in the point cloud data, the Gaussian filter is used to perform abnormal point filtering preprocessing on the source power equipment distribution point cloud data information, wherein the Gaussian filter is a linear smoothing filter, which eliminates the abnormal point cloud data through filtering, and obtains the standard power equipment distribution point cloud data information after abnormal point filtering preprocessing, to ensure the availability of the point cloud data.

[0034] The multi-dimensional feature extraction is performed on the standard power equipment distribution point cloud data information to obtain the corresponding power equipment multi-dimensional point cloud feature description subset, which is the multi-dimensional description feature information of the point cloud data, including feature information such as spatial position, color, and texture. The power equipment multi-dimensional point cloud feature description subset is registered and fused, and the similarity calculation and matching of the power equipment multi-dimensional point cloud feature description subset is performed by similarity algorithm or ICP registration, and the point cloud data of different distribution areas from different perspectives are registered and fused to obtain more accurate and complete power equipment data information of the power equipment area. Then, the fused point cloud data is visualized and modeled using three-dimensional modeling technology to generate a power equipment topology point cloud model, which is a three-dimensional point cloud model of the target power equipment area, and visualizes the power equipment structure distribution information. The power equipment distribution visualization modeling is realized to serve as a reference model for power equipment fault identification and operation and maintenance resource scheduling, thereby improving the accuracy of power equipment fault analysis.

[0035] Step S2: quantify and extract fault indicators based on the power equipment operation standard, perform training and fitting, and obtain a power equipment fault analysis function network;

[0036] like Figure 2 As shown, further, the steps of obtaining the power equipment fault analysis function network in this application also include:

[0037] Quantitatively extract fault indicators based on power equipment operation standards to obtain a set of evaluation indicators for power equipment fault factors;

[0038] Quantifying the correlation of each indicator information in the electric power equipment fault factor evaluation indicator set to obtain fault evaluation indicator correlation information;

[0039] Screening the electric power equipment fault factor evaluation index set according to the fault evaluation index correlation information to determine a multi-dimensional fault evaluation function index set;

[0040] The multi-dimensional fault evaluation function indicator set is trained and fitted to obtain the power equipment fault analysis function network.

[0041] Furthermore, the steps of obtaining the power equipment fault analysis function network in this application also include:

[0042] Fault data crawling is performed on the multi-dimensional fault evaluation function indicator set by using data mining technology to build a power equipment operation fault database;

[0043] Acquire power equipment fault attribute classification factors, wherein the power equipment fault attribute classification factors include fault type, impact range, and fault level;

[0044] Based on the power equipment fault attribute classification factors, clustering and partitioning training is performed on the power equipment operation fault database to obtain a power equipment fault factor analysis network set;

[0045] Each factor analysis network in the electric power equipment fault factor analysis network set is equally weighted and fused to generate an electric power equipment fault analysis function network.

[0046] Specifically, based on the power equipment operation standard, the fault index is quantitatively extracted to obtain a set of evaluation indexes for power equipment fault factors. The set of evaluation indexes for power equipment fault factors is an evaluation index for fault identification of power equipment operation, including operating temperature, vibration, current and voltage, operating gas, etc. The correlation information of each indicator in the set of evaluation indexes for power equipment fault factors is quantified, and the correlation degree of each evaluation indicator and equipment fault can be quantitatively analyzed through the experience of power equipment operation fault data to obtain the corresponding fault evaluation indicator correlation information of each evaluation indicator. The greater the correlation, the greater the impact of the evaluation indicator on the equipment fault. Then, the set of evaluation indexes for power equipment fault factors is screened according to the fault evaluation indicator correlation information, and the set of power equipment fault evaluation function indicators can be screened according to the correlation sorting according to a preset proportion to determine a multi-dimensional fault evaluation function indicator set for multi-dimensional fault identification evaluation of the equipment operation data stream.

[0047] The multi-dimensional fault evaluation function indicator set is trained and fitted. First, the fault data of the multi-dimensional fault evaluation function indicator set is crawled through data mining technology to construct a power equipment operation fault database. The power equipment operation fault database is the historical power equipment operation fault data information associated with the multi-dimensional fault evaluation function indicator set. Formulate and obtain power equipment fault attribute classification factors. The power equipment fault attribute classification factors are attribute classification indicators of power equipment operation faults, including fault type, impact range, and fault level. Based on the power equipment fault attribute classification factors, clustering and division training is performed on the power equipment operation fault database. The division hierarchy table of each power equipment fault attribute classification factor can be set by the power equipment operation standard, and then the fault data in the power equipment operation fault database is hierarchically divided and marked in turn through the division hierarchy table.

[0048] Then, the power equipment operation fault data after attribute labeling is trained by using a deep neural network structure according to the power equipment fault attribute classification factors, and a power equipment fault factor analysis network set with converged accuracy is obtained. The power equipment fault factor analysis network set corresponds to the power equipment fault attribute classification factors, and is used to analyze the fault type, impact range, and fault level, respectively. Each factor analysis network in the power equipment fault factor analysis network set is equally weighted and fused, that is, each factor analysis network is fused according to the same weight ratio to generate a power equipment fault analysis function network. The power equipment fault analysis function network is a three-dimensional neural network, which is used to perform multi-dimensional fault factor analysis on the power equipment operation data stream. The comprehensiveness of the training and analysis of the power equipment fault analysis function network is achieved, the accuracy and efficiency of the power equipment fault analysis are improved, and the timeliness of the power equipment fault handling is ensured.

[0049] Step S3: using digital twin technology to embed the power equipment topology point cloud model and the power equipment fault analysis function network to generate a power equipment operation twin model;

[0050] Specifically, the digital twin technology is used to integrate the power equipment topology point cloud model and the power equipment fault analysis functional network, the digital twin technology is used to map the power equipment entity to the digital model, and the functional model is used to perform fault simulation analysis on the equipment entity, so as to generate a power equipment operation twin model. The power equipment operation twin model is used to visualize the distribution of power equipment and perform multi-dimensional fault identification and analysis of power equipment.

[0051] Step S4: respectively collecting and acquiring regional weather information of power equipment and power equipment operation data stream through meteorological sensors and power monitoring equipment;

[0052] Step S5: performing prediction analysis on the power equipment operation data stream based on the power equipment operation twin model to obtain power equipment fault feature information;

[0053] Step S6: extracting and analyzing the influencing factors of the weather information in the power equipment area to obtain multi-dimensional weather resource influencing factors;

[0054] Specifically, the meteorological sensor sends an ultrasonic signal to collect and acquire the weather information of the power equipment area, and the weather information of the power equipment area includes the weather wind speed, wind direction, temperature, humidity, atmospheric pressure, etc. At the same time, the power equipment operation data stream is collected and acquired through the power monitoring equipment, and the power equipment operation data stream includes the operating temperature, vibration, current and voltage, operating gas and other information of the power equipment. In extreme weather, the difficulty of scheduling operation and maintenance resources increases. In order to ensure the timely scheduling of operation and maintenance resources, the influencing factors of the weather information of the power equipment area are extracted and analyzed to obtain multi-dimensional weather resource influencing factors. The multi-dimensional weather resource influencing factors are the impact information of extreme weather on the scheduling of operation and maintenance resources for power equipment fault handling, including resource scheduling time, scheduling route, scheduling demand, etc.

[0055] Step S7: Integrate the power equipment fault feature information and the multi-dimensional weather resource influencing factors and transmit them to the operation and maintenance resource cloud for scheduling analysis, obtain power equipment resource collaborative scheduling parameters, and perform resource scheduling and equipment fault processing based on the power equipment resource collaborative scheduling parameters.

[0056] Furthermore, the steps of obtaining the coordinated dispatching parameters of power equipment resources in this application also include:

[0057] Calling the power operation and maintenance resource scheduling topology network through the operation and maintenance resource cloud, the power operation and maintenance resource scheduling topology network includes a resource scheduling type network, a resource scheduling location network, a resource scheduling quantity network and a resource scheduling route network;

[0058] Based on the multi-dimensional weather resource influencing factors, the power operation and maintenance resource scheduling topology network is optimized to obtain a power operation and maintenance resource scheduling optimization topology network;

[0059] Using the power operation and maintenance resource scheduling optimization topology network to dynamically schedule and analyze the fault feature information of the power equipment, and output the power operation and maintenance resource scheduling type, power operation and maintenance resource scheduling location, power operation and maintenance resource scheduling quantity and power operation and maintenance resource scheduling route;

[0060] The coordinated dispatching parameters of the electric power equipment resources are determined based on the dispatching type of the electric power operation and maintenance resources, the dispatching location of the electric power operation and maintenance resources, the dispatching quantity of the electric power operation and maintenance resources, and the dispatching route of the electric power operation and maintenance resources.

[0061] Furthermore, the application steps also include:

[0062] Real-time monitoring of power equipment operation and maintenance resources is performed through the operation and maintenance resource cloud to obtain equipment failure operation and maintenance progress information and operation and maintenance resource usage status feedback information;

[0063] Determine resource allocation correction parameters according to the equipment failure operation and maintenance progress information and operation and maintenance resource usage status feedback information;

[0064] Based on the resource allocation correction parameter, the power operation and maintenance resource scheduling optimization topology network is updated to obtain a power operation and maintenance resource scheduling update topology network;

[0065] The power equipment resource collaborative scheduling parameters are updated and corrected based on the power operation and maintenance resource scheduling update topology network.

[0066] Furthermore, the application steps also include:

[0067] Determine the weather influencing factors for fault handling according to the equipment fault handling process, including fault handling time, handling safety hazards, and impact on power equipment;

[0068] Performing a multi-source impact analysis on the multi-dimensional weather resource impact factor based on the fault handling weather impact factor to obtain a fault handling weather impact parameter;

[0069] The coordinated dispatching parameters of the power equipment resources are adjusted and supplemented based on the fault handling weather impact parameters.

[0070] Specifically, the power equipment fault feature information and the multi-dimensional weather resource influencing factors are integrated and transmitted to the operation and maintenance resource cloud for scheduling analysis, wherein the operation and maintenance resource cloud is a cloud processing center for scheduling and analyzing the operation and maintenance resources of power equipment, and has the advantages of large computing processing capacity and high computing rate. The power operation and maintenance resource scheduling topology network is called through the operation and maintenance resource cloud. The power operation and maintenance resource scheduling topology network is obtained through historical resource scheduling data, including a dispatchable resource database and operation and maintenance resource scheduling data, and the corresponding resource scheduling optimal effect data fitting training. It is a three-dimensional neural network model used for scheduling and analyzing power operation and maintenance resources, including a resource scheduling type network, such as scheduling personnel, tools, spare parts, etc.; a resource scheduling location network, that is, the target scheduling location of the operation and maintenance resources; a resource scheduling quantity network, that is, the target scheduling quantity of the operation and maintenance resources; and a resource scheduling route network, that is, the optimal scheduling route of the operation and maintenance resources.

[0071] Based on the multi-dimensional weather resource impact factors, the power operation and maintenance resource dispatching topology network is optimized, that is, the dispatchable resource database is updated through weather resource impact factors such as resource dispatching time, dispatching route, and dispatching demand, and then the power operation and maintenance resource dispatching topology network is updated with loss data through the updated dispatchable resource database, so as to obtain the power operation and maintenance resource dispatching optimization topology network after considering the weather impact factors. The power operation and maintenance resource dispatching optimization topology network is used to dynamically dispatch and analyze the fault characteristic information of the power equipment, and the power operation and maintenance resource dispatching type, power operation and maintenance resource dispatching location, power operation and maintenance resource dispatching quantity, and power operation and maintenance resource dispatching route corresponding to the resource dispatching type network, resource dispatching location network, resource dispatching quantity network, and resource dispatching route network are output. Based on the power operation and maintenance resource dispatching type, power operation and maintenance resource dispatching location, power operation and maintenance resource dispatching quantity, and power operation and maintenance resource dispatching route, the integrated composition determines the power equipment resource collaborative dispatching parameters. And based on the power equipment resource collaborative dispatching parameters, resource dispatching and equipment fault processing are carried out to realize intelligent dispatching of operation and maintenance resources, improve resource dispatching accuracy and dispatching efficiency, and thus ensure the timeliness of power equipment fault processing.

[0072] In order to ensure the real-time dispatch of power equipment operation and maintenance resources, the power equipment operation and maintenance resources are monitored in real time through the operation and maintenance resource cloud, and their dispatch application is monitored to obtain the corresponding equipment fault operation and maintenance progress information, that is, the equipment fault operation and maintenance processing progress; the operation and maintenance resource usage status feedback information, that is, the remaining usage status of the dispatch resource database. According to the equipment fault operation and maintenance progress information and the operation and maintenance resource usage status feedback information, a comparison and analysis is performed to determine whether the current fault operation and maintenance progress has reached the preset value. If it has not reached, the remaining dispatch resources need to be supplemented and deployed, so as to determine the resource allocation correction parameters, and the resource allocation correction parameters are parameters such as the type and quantity of the operation and maintenance resources to be supplemented and deployed. Based on the resource allocation correction parameters, the power operation and maintenance resource scheduling optimization topology network is updated to obtain the power operation and maintenance resource scheduling update topology network after considering the resources to be supplemented and deployed. Then, based on the power operation and maintenance resource scheduling update topology network, the power equipment resource collaborative scheduling parameters are updated and corrected to ensure the real-time update and scheduling timeliness of the operation and maintenance resource scheduling.

[0073] Extreme weather can also affect the fault operation and maintenance of power equipment. Therefore, according to the equipment fault handling process, the fault handling weather influencing factors are determined. The fault handling weather influencing factors include fault handling time. In extreme weather, the difficulty and time of power equipment fault handling may be extended. For example, heavy rain may require longer equipment drainage and cleaning; handling safety hazards. In extreme weather, the operation of power equipment will face safety hazards. For example, heavy rain may cause equipment leakage and equipment damage; power equipment impact. In extreme weather, it will have a fault impact on equipment operation. For example, high temperature may cause overload, overheating, aging and other problems of power equipment such as transformers, cables, and circuit breakers. Based on the fault handling weather influencing factors, a multi-source impact analysis is performed on the multi-dimensional weather resource influencing factors to obtain corresponding fault handling weather impact parameters. The fault handling weather impact parameters are the impact type and impact degree of the current extreme weather on power fault handling. Then, based on the fault handling weather impact parameters, the power equipment resource collaborative scheduling parameters are deployed and supplemented. For example, according to the fault operation and maintenance impact caused by the current weather, such as heavy rain, the corresponding rainwater removal equipment can be added accordingly. Improve the real-time update and applicability of fault operation and maintenance resource scheduling, reduce the impact of extreme weather on power equipment, and ensure the stable operation of the power system.

[0074] In summary, the resource scheduling method for power equipment fault handling in extreme weather provided by this application has the following technical effects:

[0075] The equipment distribution information of the target power equipment area is scanned and aligned to model the equipment, a power equipment topology point cloud model is generated, and fault index training and fitting are performed based on the power equipment operation standard to obtain the power equipment fault analysis function network. The power equipment topology point cloud model and the power equipment fault analysis function network are integrated using digital twin technology to generate a power equipment operation twin model to predict and analyze the power equipment operation data stream, obtain power equipment fault feature information, extract and analyze the influencing factors of the power equipment regional weather information, and obtain multi-dimensional weather resource influencing factors; the power equipment fault feature information and the multi-dimensional weather resource influencing factors are integrated and transmitted to the operation and maintenance resource cloud for scheduling analysis, and the power equipment resource collaborative scheduling parameters are obtained for resource scheduling and equipment fault processing. The technical solution is achieved by using digital twin technology to realize power equipment fault analysis and intelligent scheduling of operation and maintenance resources, improve resource scheduling accuracy and scheduling efficiency, reduce equipment operation and maintenance costs, and ensure the timeliness of power equipment fault processing.

[0076] Embodiment 2

[0077] Based on the same inventive concept as the resource scheduling method for handling power equipment failures in extreme weather in the aforementioned embodiment, the present invention also provides a resource scheduling system for handling power equipment failures in extreme weather, such as Figure 3 As shown, the system comprises:

[0078] The sensing registration modeling module 11 is used to perform sensing scanning and registration modeling on the equipment distribution information of the target power equipment area through the laser radar equipment to generate a topological point cloud model of the power equipment;

[0079] The fault analysis network acquisition module 12 is used to perform quantitative extraction and training fitting of fault indicators based on the power equipment operation standard to obtain a power equipment fault analysis function network;

[0080] A twin model generation module 13 is used to use digital twin technology to embed the power equipment topology point cloud model and the power equipment fault analysis function network to generate a power equipment operation twin model;

[0081] The data stream acquisition module 14 is used to collect and acquire the regional weather information of the power equipment and the power equipment operation data stream through the meteorological sensor and the power monitoring equipment respectively;

[0082] A fault prediction and analysis module 15 is used to perform prediction and analysis on the power equipment operation data stream based on the power equipment operation twin model to obtain power equipment fault feature information;

[0083] An influencing factor extraction and analysis module 16 is used to extract and analyze influencing factors of the weather information in the power equipment area to obtain multi-dimensional weather resource influencing factors;

[0084] The equipment fault processing module 17 is used to integrate the power equipment fault characteristic information and the multi-dimensional weather resource influencing factors and transmit them to the operation and maintenance resource cloud for scheduling analysis, obtain the power equipment resource collaborative scheduling parameters, and perform resource scheduling and equipment fault processing based on the power equipment resource collaborative scheduling parameters.

[0085] Furthermore, the system also includes:

[0086] A point cloud sensing scanning unit is used to sense and scan the equipment distribution information through a laser radar device to obtain the source power equipment distribution point cloud data information;

[0087] A filtering preprocessing unit, used for performing abnormal point filtering preprocessing on the source power equipment distribution point cloud data information by using a Gaussian filter to obtain standard power equipment distribution point cloud data information;

[0088] A multi-dimensional feature extraction unit, used to extract multi-dimensional features from the standard electric power equipment distribution point cloud data information to obtain a multi-dimensional point cloud feature descriptor set of electric power equipment;

[0089] The point cloud modeling unit is used to perform registration and fusion of the multi-dimensional point cloud feature description sub-set of the power equipment and point cloud modeling to generate a topological point cloud model of the power equipment.

[0090] Furthermore, the system also includes:

[0091] A fault index quantitative extraction unit is used to perform fault index quantitative extraction based on the power equipment operation standard to obtain a set of evaluation indexes for power equipment fault factors;

[0092] A correlation quantification unit, used to perform correlation quantification on each indicator information in the electric power equipment fault factor evaluation indicator set to obtain fault evaluation indicator correlation information;

[0093] A function indicator screening unit, used to screen the electric power equipment fault factor evaluation indicator set according to the fault evaluation indicator correlation information, and determine a multi-dimensional fault evaluation function indicator set;

[0094] The indicator training and fitting unit is used to train and fit the multi-dimensional fault evaluation function indicator set to obtain the power equipment fault analysis function network.

[0095] Furthermore, the system also includes:

[0096] A fault data crawling unit, used to crawl fault data of the multi-dimensional fault evaluation function indicator set by using data mining technology to build a power equipment operation fault database;

[0097] A classification factor acquisition unit, used to acquire the classification factors of the fault attributes of the electric equipment, wherein the classification factors of the fault attributes of the electric equipment include the fault type, the impact range and the fault level;

[0098] A clustering training unit, used to perform clustering training on the power equipment operation fault database based on the power equipment fault attribute classification factors to obtain a power equipment fault factor analysis network set;

[0099] The equally weighted fusion unit is used to equally weight each factor analysis network in the electric power equipment fault factor analysis network set to generate an electric power equipment fault analysis function network.

[0100] Furthermore, the system also includes:

[0101] A topology network calling unit, used to call the power operation and maintenance resource scheduling topology network through the operation and maintenance resource cloud, wherein the power operation and maintenance resource scheduling topology network includes a resource scheduling type network, a resource scheduling location network, a resource scheduling quantity network, and a resource scheduling route network;

[0102] A network impact optimization unit, configured to optimize the impact of the power operation and maintenance resource scheduling topology network based on the multi-dimensional weather resource impact factor to obtain a power operation and maintenance resource scheduling optimization topology network;

[0103] A dynamic scheduling analysis unit is used to perform dynamic scheduling analysis on the fault characteristic information of the power equipment using the power operation and maintenance resource scheduling optimization topology network, and output the power operation and maintenance resource scheduling type, power operation and maintenance resource scheduling location, power operation and maintenance resource scheduling quantity and power operation and maintenance resource scheduling route;

[0104] A collaborative scheduling parameter determination unit is used to determine the collaborative scheduling parameters of the power equipment resources based on the power operation and maintenance resource scheduling type, the power operation and maintenance resource scheduling location, the power operation and maintenance resource scheduling quantity and the power operation and maintenance resource scheduling route.

[0105] Furthermore, the system also includes:

[0106] A real-time resource monitoring unit, used to monitor the operation and maintenance resources of the power equipment in real time through the operation and maintenance resource cloud, and obtain equipment failure operation and maintenance progress information and operation and maintenance resource usage status feedback information;

[0107] A correction parameter determination unit, used to determine the resource allocation correction parameter according to the equipment failure operation and maintenance progress information and the operation and maintenance resource usage status feedback information;

[0108] A topology network updating unit, configured to update the power operation and maintenance resource scheduling optimization topology network based on the resource allocation correction parameter to obtain a power operation and maintenance resource scheduling update topology network;

[0109] A resource update and correction unit is used to update and correct the power equipment resource collaborative scheduling parameters based on the power operation and maintenance resource scheduling update topology network.

[0110] Furthermore, the system also includes:

[0111] A weather influencing factor determination unit, used to determine the weather influencing factors for fault handling according to the equipment fault handling process, wherein the weather influencing factors for fault handling include fault handling time, handling safety hazards, and power equipment impact;

[0112] A multi-source impact analysis unit, configured to perform a multi-source impact analysis on the multi-dimensional weather resource impact factor based on the fault handling weather impact factor to obtain a fault handling weather impact parameter;

[0113] A resource allocation and supplement unit is used to allocate and supplement the power equipment resource collaborative scheduling parameters based on the fault handling weather impact parameters.

[0114] The foregoing Figure 1 The various variations and specific examples of the resource scheduling method for handling power equipment faults in extreme weather in Example 1 are also applicable to the resource scheduling system for handling power equipment faults in extreme weather in this embodiment. Through the above detailed description of the resource scheduling method for handling power equipment faults in extreme weather, those skilled in the art can clearly understand the implementation method of the resource scheduling system for handling power equipment faults in extreme weather in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0115] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and run on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method embodiment for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0116] Exemplary Electronic Devices

[0117] For details, see Figure 4 As shown, the present application also provides an electronic device, which includes a bus 1110 , a processor 1120 , a transceiver 1130 , a bus interface 1140 , a memory 1150 and a user interface 1160 .

[0118] In the present application, the electronic device further includes: a computer program stored in the memory 1150 and executable on the processor 1120, and when the computer program is executed by the processor 1120, each process of the above-mentioned method embodiment for controlling output data is implemented.

[0119] The transceiver 1130 is configured to receive and send data under the control of the processor 1120 .

[0120] In the present application, a bus architecture (represented by bus 1110 ) may include any number of interconnected buses and bridges, and bus 1110 connects various circuits including one or more processors represented by processor 1120 and a memory represented by memory 1150 .

[0121] Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. Such architectures include, by way of example and not limitation, an Industry Standard Architecture bus, a Micro Channel Architecture bus, an expansion bus, a Video Electronics Standards Association, a Peripheral Component Interconnect bus.

[0122] The processor 1120 may be an integrated circuit chip having signal processing capabilities. In the implementation process, each step of the above method embodiment may be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processors include: a general-purpose processor, a central processing unit, a network processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, a complex programmable logic device, a programmable logic array, a microcontroller unit or other programmable logic device, a discrete gate, a transistor logic device, and a discrete hardware component. The various methods, steps, and logic block diagrams disclosed in this application may be implemented or executed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated into a single chip or located on multiple different chips.

[0123] Processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in the present application may be directly executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a readable storage medium known in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, a register, etc. The readable storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0124] The bus 1110 may also connect various other circuits such as peripheral devices, voltage regulators or power management circuits, and the bus interface 1140 provides an interface between the bus 1110 and the transceiver 1130, which are well known in the art. Therefore, this application will not further describe them.

[0125] The transceiver 1130 may be one element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on a transmission medium. For example, the transceiver 1130 receives external data from other devices, and the transceiver 1130 is used to send data processed by the processor 1120 to other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, such as a touch screen, a physical keyboard, a display, a mouse, a speaker, a microphone, a trackball, a joystick, or a stylus.

[0126] It should be understood that in the present application, the memory 1150 may further include a memory remotely arranged relative to the processor 1120, and these remotely arranged memories may be connected to the server through a network. One or more parts of the above network may be a self-organizing network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the Internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless network, a wireless fidelity network, and a combination of two or more of the above networks. For example, the cellular telephone network and the wireless network may be a global mobile communication device, a code division multiple access device, a global microwave interconnection access device, a general packet radio service device, a wideband code division multiple access device, a long term evolution device, a LTE frequency division duplex device, a LTE time division duplex device, an advanced long term evolution device, a universal mobile communication device, an enhanced mobile broadband device, a massive machine type communication device, an ultra-reliable low latency communication device, and the like.

[0127] It should be understood that the memory 1150 in the present application may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. Among them, the non-volatile memory includes: a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, or a flash memory.

[0128] Volatile memory includes random access memory, which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronously linked dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in the present application includes, but is not limited to, the above and any other suitable types of memory.

[0129] In the present application, the memory 1150 stores the following elements of the operating system 1151 and the application program 1152: executable modules, data structures, or subsets thereof, or extended sets thereof.

[0130] Specifically, the operating system 1151 includes various device programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 1152 includes various application programs, such as a media player and a browser, which are used to implement various application services. The program implementing the method of the present application may be included in the application 1152. The application 1152 includes applets, objects, components, logic, data structures, and other computer device executable instructions that perform specific tasks or implement specific abstract data types.

[0131] In addition, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned method embodiment for controlling output data are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0132] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A resource scheduling method for handling power equipment failures in extreme weather, characterized in that: The method comprises: Use laser radar equipment to sense, scan, and align the equipment distribution information in the target power equipment area to generate a topological point cloud model of the power equipment; Based on the power equipment operation standards, the fault indicators are quantitatively extracted, trained and fitted to obtain the power equipment fault analysis function network; Using digital twin technology to embed the power equipment topology point cloud model and the power equipment fault analysis function network to generate a power equipment operation twin model; The weather information of the power equipment area and the power equipment operation data stream are collected and obtained through meteorological sensors and power monitoring equipment respectively; Based on the power equipment operation twin model, predictive analysis is performed on the power equipment operation data stream to obtain power equipment fault feature information; Extract and analyze the influencing factors of the weather information in the power equipment area to obtain multi-dimensional weather resource influencing factors; The power equipment fault characteristic information and the multi-dimensional weather resource influencing factors are integrated and transmitted to the operation and maintenance resource cloud for scheduling analysis, to obtain power equipment resource collaborative scheduling parameters, and to perform resource scheduling and equipment fault processing based on the power equipment resource collaborative scheduling parameters.

2. The method according to claim 1, characterized in that The generating of the power equipment topology point cloud model comprises: The equipment distribution information is sensed and scanned by a laser radar device to obtain the distribution point cloud data information of the source power equipment; Using a Gaussian filter to perform abnormal point filtering preprocessing on the source power equipment distribution point cloud data information to obtain standard power equipment distribution point cloud data information; Performing multi-dimensional feature extraction on the standard electric power equipment distribution point cloud data information to obtain a multi-dimensional point cloud feature descriptor set of electric power equipment; The multi-dimensional point cloud feature descriptor set of the power equipment is registered and fused, and point cloud modeling is performed to generate the topological point cloud model of the power equipment.

3. The method according to claim 1, characterized in that The method of obtaining a power equipment fault analysis function network includes: Quantitatively extract fault indicators based on power equipment operation standards to obtain a set of evaluation indicators for power equipment fault factors; Quantifying the correlation of each indicator information in the electric power equipment fault factor evaluation indicator set to obtain fault evaluation indicator correlation information; Screening the electric power equipment fault factor evaluation index set according to the fault evaluation index correlation information to determine a multi-dimensional fault evaluation function index set; The multi-dimensional fault evaluation function indicator set is trained and fitted to obtain the power equipment fault analysis function network.

4. The method according to claim 3, characterized in that The step of obtaining the power equipment fault analysis function network comprises: Fault data crawling is performed on the multi-dimensional fault evaluation function indicator set by using data mining technology to build a power equipment operation fault database; Acquire power equipment fault attribute classification factors, wherein the power equipment fault attribute classification factors include fault type, impact range, and fault level; Based on the power equipment fault attribute classification factors, clustering and partitioning training is performed on the power equipment operation fault database to obtain a power equipment fault factor analysis network set; Each factor analysis network in the power equipment fault factor analysis network set is equally weighted and fused to generate a power equipment fault analysis function network.

5. The method according to claim 1, characterized in that The obtaining of the coordinated dispatching parameters of power equipment resources includes: Calling the power operation and maintenance resource scheduling topology network through the operation and maintenance resource cloud, the power operation and maintenance resource scheduling topology network includes a resource scheduling type network, a resource scheduling location network, a resource scheduling quantity network and a resource scheduling route network; Based on the multi-dimensional weather resource influencing factors, the power operation and maintenance resource scheduling topology network is optimized to obtain a power operation and maintenance resource scheduling optimization topology network; Using the power operation and maintenance resource scheduling optimization topology network to dynamically schedule and analyze the fault feature information of the power equipment, and output the power operation and maintenance resource scheduling type, power operation and maintenance resource scheduling location, power operation and maintenance resource scheduling quantity and power operation and maintenance resource scheduling route; The coordinated dispatching parameters of the electric power equipment resources are determined based on the dispatching type of the electric power operation and maintenance resources, the dispatching location of the electric power operation and maintenance resources, the dispatching quantity of the electric power operation and maintenance resources, and the dispatching route of the electric power operation and maintenance resources.

6. The method according to claim 5, characterized in that, The method comprises: Real-time monitoring of power equipment operation and maintenance resources is performed through the operation and maintenance resource cloud to obtain equipment failure operation and maintenance progress information and operation and maintenance resource usage status feedback information; Determine resource allocation correction parameters according to the equipment failure operation and maintenance progress information and operation and maintenance resource usage status feedback information; Based on the resource allocation correction parameter, the power operation and maintenance resource scheduling optimization topology network is updated to obtain a power operation and maintenance resource scheduling update topology network; The power equipment resource collaborative scheduling parameters are updated and corrected based on the power operation and maintenance resource scheduling update topology network.

7. The method according to claim 1, characterized in that The method comprises: Determine the weather influencing factors for fault handling according to the equipment fault handling process, including fault handling time, handling safety hazards, and impact on power equipment; Performing a multi-source impact analysis on the multi-dimensional weather resource impact factor based on the fault handling weather impact factor to obtain a fault handling weather impact parameter; The coordinated dispatching parameters of the power equipment resources are adjusted and supplemented based on the fault handling weather impact parameters.

8. Resource dispatching system for power equipment fault handling in extreme weather conditions, characterized in that: The system comprises: The induction registration modeling module is used to perform induction scanning and registration modeling of the equipment distribution information of the target power equipment area through the laser radar equipment to generate a topological point cloud model of the power equipment; The fault analysis network acquisition module is used to perform quantitative extraction and training fitting of fault indicators based on the power equipment operation standards to obtain the power equipment fault analysis function network; A twin model generation module, used to use digital twin technology to embed the power equipment topology point cloud model and the power equipment fault analysis function network to generate a power equipment operation twin model; A data stream acquisition module is used to collect and acquire regional weather information of power equipment and power equipment operation data streams through meteorological sensors and power monitoring equipment respectively; A fault prediction and analysis module, used to perform prediction and analysis on the power equipment operation data stream based on the power equipment operation twin model to obtain power equipment fault feature information; An influencing factor extraction and analysis module is used to extract and analyze influencing factors of the weather information in the power equipment area to obtain multi-dimensional weather resource influencing factors; The equipment fault processing module is used to integrate the power equipment fault characteristic information and the multi-dimensional weather resource influencing factors and transmit them to the operation and maintenance resource cloud for scheduling analysis, obtain the power equipment resource collaborative scheduling parameters, and perform resource scheduling and equipment fault processing based on the power equipment resource collaborative scheduling parameters.

9. An electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps in the resource scheduling method for handling power equipment failures in extreme weather as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the resource scheduling method for handling power equipment failures in extreme weather as described in any one of claims 1 to 7 are implemented.

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