Operation and maintenance management method and platform based on digital twinning

Abstract

The invention relates to an operation and maintenance management method and platform based on digital twinning, which are applied to the field of data processing, and the method comprises the steps: obtaining various key values and data of power grid equipment in an edge substation, determining first power grid equipment and equipment strongly associated with the first power grid equipment as key equipment according to the key values of the equipment, for the second power grid equipment except the first power grid equipment in the multiple pieces of power grid equipment, the key equipment and the non-key equipment are determined from the second power grid equipment according to the operation duration, the fault frequency, the service life and the equipment key value of each piece of second power grid equipment, so that classification of the key equipment and the non-key equipment is accurately realized; according to the method, digital twinborn models with different precisions are constructed for the key equipment and the non-key equipment, so that fine management of the key equipment is ensured, resource waste on the non-key equipment is avoided, the construction efficiency of the digital twinborn models is improved, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to an operation and maintenance management method and platform based on digital twins. Background Art

[0002] With the rapid development of science and technology, as an important part of the modern power system, the intelligent and efficient operation and maintenance of smart grid has become an important development direction of the power industry. By integrating advanced sensing technology, communication technology, information processing technology and automatic control technology, smart grid realizes comprehensive monitoring and control of power transmission and distribution systems, greatly improving the reliability and operation efficiency of the power grid.

[0003] At present, a common practice for the operation and maintenance management of smart grids is to deploy data collection and monitoring equipment at key nodes of the power grid to collect equipment data of power equipment in real time, and transmit it to the central processing center for centralized processing via wired or wireless means. The central processing center uses cloud computing and big data technology to conduct in-depth mining and analysis of massive data, and build a digital twin model for fault warning and other functions.

[0004] However, in the huge network of smart grids, edge substations are key nodes for power transmission. Since edge substations have a wide variety of equipment and varying ages, and the construction of digital twin models for each grid device requires a large amount of data, the construction efficiency of digital twin models for edge substations is low and the maintenance cost is high. Summary of the invention

[0005] The purpose of this application is to provide an operation and maintenance management method and platform based on digital twins, which can improve the construction efficiency of digital twin models and reduce maintenance costs.

[0006] First, a digital twin-based operation and maintenance management method is provided, including: Obtaining key values ​​and equipment data corresponding to each of multiple power grid equipment in the edge substation, wherein the equipment data includes operation time, number of failures, and service life; According to the device key values ​​corresponding to the plurality of power grid devices, a plurality of first power grid devices belonging to the key devices are determined from the plurality of power grid devices; the plurality of first power grid devices include: power grid devices whose device key values ​​are greater than a preset device criticality threshold and power grid devices that are strongly associated with power grid devices whose device key values ​​are greater than the preset device criticality threshold; For second power grid devices other than the first power grid device among the plurality of power grid devices, determining key devices and non-key devices from the second power grid devices according to the operating time, number of failures, service life and key value of each second power grid device; Constructing digital twin models corresponding to key equipment and non-key equipment respectively, and obtaining a digital twin model corresponding to the edge substation; wherein the modeling accuracy of the first digital twin model corresponding to the key equipment is higher than the modeling accuracy of the second digital twin model corresponding to the non-key equipment; Operation and maintenance management is carried out based on the digital twin model of the edge substation.

[0007] In a preferred example, the present application may be further configured as follows: the process of determining strongly associated power grid devices includes: Determine the corresponding associated devices according to the connection directed graph of the power grid devices whose device criticality values ​​are greater than the preset device criticality threshold; Obtaining the abnormal time sequence, abnormal degree and abnormal frequency of the associated devices when the power grid device with a device critical value greater than a preset device criticality threshold is abnormal, wherein the abnormal time sequence is the order in which the abnormalities of the power grid devices occur in the abnormal event; Determine the impact value of the associated equipment on the power grid equipment whose equipment criticality value is greater than a preset equipment criticality threshold value according to the abnormal time sequence, abnormality degree and abnormality frequency of the associated equipment; A strongly associated device is determined from the associated devices based on the influence value.

[0008] In a preferred example, the present application may be further configured as follows: the operation and maintenance management based on the digital twin model of the edge substation includes: When it is determined that the operating state of the power grid equipment is abnormal, the relevant information of the power grid equipment with abnormal operating state is determined through the digital twin model of the edge substation, and the relevant information includes at least: equipment location information, location information of abnormal components, and important values ​​of abnormal components; Predict abnormal causes of abnormal components; According to the cause of the abnormality, several maintenance suggestions corresponding to the abnormal components are matched from the operation and maintenance dictionary; Determine an inspection trajectory according to relevant information of the power grid equipment with abnormal operating status, wherein the inspection trajectory includes: an inspection order of the power grid equipment with abnormal operating status, and an inspection duration of the power grid equipment with abnormal operating status, wherein the inspection duration includes a display component duration and a display several maintenance suggestions duration; When the inspection start command is detected, navigation guidance is provided to the user in the virtual scene according to the inspection trajectory. When an abnormal component is inspected, several corresponding maintenance suggestions are displayed in the virtual scene.

[0009] In a preferred example, the present application may be further configured as follows: determining the inspection trajectory according to the relevant information of the power grid equipment with abnormal operation status includes: Clustering the power grid equipment according to the abnormal causes of the abnormal components of the power grid equipment with abnormal operating status to obtain a number of equipment groups; For each device group, determining a first order of the power grid devices in the device group according to an important average value corresponding to the important values ​​of the abnormal components of the power grid devices in the device group; Determine a second order of the equipment groups according to the number of key equipment in each equipment group and the importance average value corresponding to the importance value of the abnormal components of the power grid equipment in the equipment group; Determine the inspection order based on the first order, the second order, the equipment location information, and the location information of the abnormal component; Determine the duration of displaying the several maintenance suggestions according to the content length of the several maintenance suggestions; The duration for displaying the abnormal component is determined according to the importance value and complexity of the abnormal component.

[0010] In a preferred example, the present application may be further configured as follows: the power grid device is a device having a multi-level structural hierarchy, and the non-root structural hierarchy is composed of at least two sub-level structures; After constructing digital twin models corresponding to key equipment and non-key equipment, and obtaining the digital twin model corresponding to the edge substation, the method further includes: Obtaining a first preset verification parameter and first actual operation and maintenance information corresponding to the first preset verification parameter, wherein the first preset verification parameter includes verification parameters corresponding to multiple structural levels of power grid equipment; and obtaining a second preset verification parameter and second actual operation and maintenance information corresponding to the second preset verification parameter; According to the verification parameters corresponding to the preset structural level in the first preset verification parameters of the power grid equipment, the first operation and maintenance information corresponding to the preset structural level is input into the digital twin model; according to the first operation and maintenance information and the actual operation and maintenance information corresponding to the preset structural level in the first actual operation and maintenance information of the power grid equipment, the validity of the preset structural level of the digital twin model corresponding to the edge substation is verified; when the verification of the preset structural level fails, the model parameters of the preset structural level are adjusted, and the verification is performed again until the verification is successful, and then the verification of the preset structural level of the next same level is performed; When all preset structural levels are successfully verified, the correlation between the components of the preset structural level is determined based on the correlation map of the power grid equipment, and the components are sub-level structural levels of the preset structural level; the components with correlation are verified again using the verification parameters about the preset structural level in the second preset verification parameters and the actual operation and maintenance information corresponding to the preset structural level in the second actual operation and maintenance information of the power grid equipment; if the verification of the component with correlation fails, the model parameters of the target component are adjusted until the verification is successful, and the target component is the component with correlation and the verification fails; If the verification of the associated components is successful, the verification of the next level of structural level is carried out until the verification and adjustment of all structural levels are completed to obtain the final digital twin model corresponding to the edge substation.

[0011] In a preferred example, the present application may be further configured as follows: if the preset structural level is a non-root structural level, verification of the next preset structural level of the same level is performed, including: According to the number of sub-level structural levels that overlap with the verified preset structural level, other preset structural levels at the same level are sorted to obtain a first sequence; According to the first sequence, other preset structural levels of the same level are verified in turn; When verifying other preset structural hierarchies of the target, if the verification fails, the model parameters of the non-overlapping sub-level structural hierarchies are adjusted to verify and adjust the model parameters of other preset structural hierarchies, and the target other preset structural hierarchies are any structural hierarchies among other preset structural hierarchies of the same level.

[0012] In a preferred example, the present application can be further configured as follows: performing operation and maintenance management based on the digital twin model of the edge substation, including: Acquire currently running compressed data corresponding to each of the plurality of power grid devices in the edge substation, wherein the currently running compressed data includes multi-dimensional compressed data, and each dimension of compressed data is compressed data of a varying dimension; Decompressing the currently running compressed data to obtain currently running data; The digital twin model of the edge substation is used to monitor the operating status of multiple power grid equipment in the edge substation based on the current operating data.

[0013] Secondly, an operation and maintenance management platform based on digital twins is provided, including: An acquisition module is used to acquire the key values ​​and equipment data corresponding to each of the plurality of power grid equipment in the edge substation, wherein the keyness of the equipment is determined based on the role and importance of the power grid equipment in the power grid, and the equipment data includes the operation time, the number of failures, and the service life; The device classification module is used to determine, from the plurality of power grid devices, a plurality of first power grid devices belonging to key devices according to the device key values ​​corresponding to the plurality of power grid devices; the plurality of first power grid devices include: power grid devices whose device key values ​​are greater than a preset device key threshold and power grid devices that are strongly associated with the power grid devices whose device key values ​​are greater than the preset device key threshold; And, for second power grid devices other than the first power grid device among the plurality of power grid devices, determining critical devices and non-critical devices from the second power grid devices according to the operating time, number of failures, service life and critical value of each second power grid device; A construction module is used to construct digital twin models corresponding to key equipment and non-key equipment respectively, and obtain a digital twin model corresponding to the edge substation; wherein the modeling accuracy of the first digital twin model corresponding to the key equipment is higher than the modeling accuracy of the second digital twin model corresponding to the non-key equipment; Management module, used for operation and maintenance management based on the digital twin model of the edge substation.

[0014] In a third aspect, an electronic device is provided, comprising: one or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to: execute the steps of the method described in any one of the first aspects.

[0015] According to a fourth aspect, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method is implemented as shown in any possible implementation manner of the first aspect.

[0016] To summarize, the present application includes at least one of the following beneficial technical effects: obtaining multiple key values ​​and data of power grid equipment in the edge substation, grading the equipment according to the criticality of the equipment in the power grid, determining the first power grid equipment and the equipment strongly associated with it according to the key values ​​of the equipment as key equipment, and for the second power grid equipment other than the first power grid equipment among multiple power grid equipment, determining the key equipment and non-key equipment from the second power grid equipment according to the operating time, number of failures, service life and equipment key values ​​of each second power grid equipment, accurately realizing the classification of key equipment and non-key equipment, and building digital twin models of different precisions for key equipment and non-key equipment, which not only ensures the fine management of key equipment, but also avoids the waste of resources on non-key equipment, improves the construction efficiency of digital twin models and reduces maintenance costs, and then, performs operation and maintenance management based on these refined digital twin models. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of an operation and maintenance management method based on digital twins provided in an embodiment of the present application; Figure 2 is a schematic diagram of a multi-level structure provided in an embodiment of the present application; Figure 3 It is a structural diagram of an operation and maintenance management platform based on digital twins provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0021] Specifically, the embodiment of the present application provides an operation and maintenance management method based on digital twins. The method provided in the embodiment of the present application can be executed by an electronic device, and the electronic device is a server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited to this. The terminal device and the electronic device can be directly or indirectly connected via wired or wireless communication. The embodiment of the present application does not limit this. Figure 1 As shown, the method includes: S101, obtaining device key values ​​and device data corresponding to each of multiple power grid devices in the edge substation; The key value of the equipment is determined based on the role and importance of the power grid equipment in the power grid. The equipment data includes the operating time, number of failures, and service life; Edge substations are usually located in remote areas, with a wide variety of grid equipment and varying ages. The construction and maintenance of digital twin models face huge challenges, and the accuracy of the model is difficult to ensure. Grid equipment refers to various types of equipment used in edge substations to achieve functions such as power conversion, distribution, monitoring and protection, including but not limited to transformers, circuit breakers, switch cabinets, capacitor banks, power supply equipment, etc. The key value of the equipment is determined based on the role and importance of the grid equipment in the grid, and is specifically an evaluation value set by technicians based on experience. Equipment data refers to various parameters and records that describe the operating status of grid equipment, including but not limited to operating time (indicating the total working time of the equipment since it was put into operation), number of failures (recording the number of failures of the equipment in a specific time period, reflecting the reliability of the equipment), and service life (the time from the date of commissioning of the equipment to the present, reflecting the degree of aging of the equipment).

[0022] In an embodiment of the present application, the automated monitoring system of the substation is utilized to summarize and extract the operation records and maintenance reports of the power grid equipment, from which information such as the operation time and number of failures of the power grid equipment is extracted, and combined with the service life data in the equipment archives, the structure of the power grid equipment, the equipment size, and the equipment performance, etc., to construct a complete equipment data set.

[0023] S102, determining a plurality of first power grid devices belonging to key devices from the plurality of power grid devices according to the device key values ​​corresponding to the plurality of power grid devices; The plurality of first power grid devices include: power grid devices having a device critical value greater than a preset device criticality threshold and power grid devices having a strong association with the power grid devices having a device critical value greater than the preset device criticality threshold; The preset equipment criticality threshold is a pre-set numerical standard. When the critical value of the power grid equipment exceeds this threshold, it means that the equipment has an important impact on the safe and stable operation of the power grid and is regarded as a critical equipment.

[0024] Strongly associated power grid equipment refers to equipment that is strongly associated with power grid equipment that has a significant impact on the safe and stable operation of the power grid. This strong association includes power grid equipment that is closely connected in terms of physical connection, functional coordination, or fault impact. When a key device fails or is abnormal, these strongly associated devices may also be directly or indirectly affected.

[0025] In an embodiment of the present application, in one implementation, a database of power grid equipment is established to store data such as equipment key values, function descriptions, location information, and association relationships of each power grid equipment. Then, the system has a preset equipment criticality threshold built in, and automatically compares and analyzes the equipment key values ​​in the database. For devices whose equipment key values ​​are greater than the threshold, other devices that are strongly associated with these devices are searched to obtain a list of the first power grid equipment.

[0026] S103, for the second power grid devices other than the first power grid device among the plurality of power grid devices, determine the key devices and non-key devices from the second power grid devices according to the operation time, number of failures, service life and key value of each second power grid device; In the embodiment of the present application, the power grid devices are classified based on the relevant information of each second power grid device to obtain key devices and non-key devices.

[0027] Among them, the operating time refers to the cumulative working time of the power grid equipment since it was put into operation, which is an important indicator for evaluating the aging degree and usage status of the equipment. Number of failures: records the number of failures that occurred in the power grid equipment during operation, reflecting the reliability and stability of the equipment. Service life: refers to the longest time that the power grid equipment has been put into use.

[0028] Optionally, an implementation method includes: determining a first numerical value corresponding to each second power grid device based on the operating time, number of failures, and service life of each second power grid device; obtaining a second numerical value for each second power grid device by performing a weighted calculation based on the device critical value of each second power grid device and the first numerical value, and determining critical devices and non-critical devices from the second power grid devices according to the second numerical value.

[0029] Among them, the first value = , where α is a weight factor used to adjust the relative importance of the usage rate in the comprehensive performance evaluation. Users can set the size according to actual conditions. The first value represents the comprehensive performance index of the power grid equipment. The larger the first value, the higher the comprehensive performance index and the better the equipment performance. The equipment with excellent performance has fewer abnormalities and is more stable. The second value = (1-first value) * first weight + equipment key value * second weight / equipment key value limit. Users can set the first weight value and the second weight value according to actual conditions. Set a threshold value, and treat the second power grid equipment greater than the threshold as key equipment, and other second power grid equipment as non-key equipment.

[0030] Of course, the embodiments of the present application are no longer limited according to user specifications, as long as the purpose of the embodiments of the present application can be achieved.

[0031] S104. Construct digital twin models corresponding to key equipment and non-key equipment respectively, and obtain digital twin models corresponding to edge substations; Among them, the modeling accuracy of the first digital twin model corresponding to the key equipment is higher than the modeling accuracy of the second digital twin model corresponding to the non-key equipment; The higher the modeling accuracy, the more accurately the model can reflect the actual situation of the physical entity, thereby providing more valuable prediction and decision support. Among them, the digital twin models built for key equipment and non-key equipment differ in modeling accuracy. The first digital twin model corresponding to the key equipment needs to have higher modeling accuracy due to its importance to the safe and stable operation of the power grid to ensure accurate simulation and prediction of the behavior of the key equipment. Although the second digital twin model corresponding to the non-key equipment has a relatively low modeling accuracy, it still needs to meet the accurate description of the basic status and behavior of the equipment.

[0032] For key equipment, complex and precise modeling techniques are used, such as physics-based simulation models, deep learning models, etc., to ensure the accuracy and reliability of the model. For non-key equipment, relatively simplified modeling methods, such as statistical models or rule-based reasoning models, can be used to reduce modeling costs and complexity.

[0033] S105. Perform operation and maintenance management based on the digital twin model of the edge substation.

[0034] The constructed digital twin model of the edge substation can be used to carry out intelligent management of the substation's operation and maintenance, including but not limited to equipment status monitoring, fault warning, performance evaluation, and operation and maintenance decision support.

[0035] It can be seen that in the embodiment of the present application, multiple key values ​​and data of the power grid equipment in the edge substation are obtained, and the equipment is graded according to its criticality in the power grid. According to the key values ​​of the equipment, the first power grid equipment and the equipment strongly associated with it are determined as key equipment. For the second power grid equipment other than the first power grid equipment among multiple power grid equipment, according to the operating time, number of failures, service life and equipment key values ​​of each second power grid equipment, the key equipment and non-key equipment are determined from the second power grid equipment, and the classification of key equipment and non-key equipment is accurately realized. Digital twin models of different precisions are constructed for key equipment and non-key equipment, which not only ensures the fine management of key equipment, but also avoids the waste of resources on non-key equipment, improves the construction efficiency of digital twin models and reduces maintenance costs, and then, operation and maintenance management is carried out based on these refined digital twin models.

[0036] Further, the process of determining the strongly associated power grid equipment includes: S201-S204 (not shown in the drawings), wherein: S201, determining corresponding associated devices according to a connection directed graph of power grid devices whose device criticality values ​​are greater than a preset device criticality threshold; The directed graph of power grid equipment connections is used to represent the connection relationship between various devices in the power grid. In the directed graph, each device is represented as a node, and the connection between devices is represented as a directed edge, which can intuitively understand the structure of the power grid and the mutual influence between devices. The directed graph of power grid equipment connections is stored in the graph database, and key value attributes are set for each device node. The query statement is used to filter out device nodes with key values ​​greater than the preset threshold, and the graph traversal function of the graph database is used to find the associated devices of these key devices.

[0037] S202, when a power grid device with a critical value greater than a preset device criticality threshold is abnormal, the abnormal time sequence, abnormal degree and abnormal frequency of the associated devices are obtained, wherein the abnormal time sequence is the order in which the abnormalities of the power grid devices occur in the abnormal event; Obtain the historical operation information of the power grid equipment, which includes multiple abnormal events. Each abnormal event includes the abnormal time sequence, abnormal degree and abnormal frequency of all abnormal devices in the abnormal state or fault situation. Among them, abnormal time sequence: when the power grid equipment is abnormal, the time sequence of abnormalities in different devices. This time sequence is of great significance for analyzing the propagation path of abnormal events, determining the source of faults and formulating emergency response strategies. The degree of abnormality indicates the degree to which the power grid equipment deviates from the normal operating state under abnormal conditions. Abnormal frequency refers to the number of times the power grid equipment has abnormalities within a certain period of time. The high or low abnormal frequency can reflect the stability and reliability of the equipment.

[0038] S203, determining the impact value of the associated device on the power grid device whose device critical value is greater than a preset device criticality threshold according to the abnormal time sequence, abnormal degree and abnormal frequency of the associated device; The impact value is a quantitative value that represents the degree or scope of the impact of the associated equipment on the key equipment.

[0039] In a feasible scheme, based on Ii=α / (Ti+ )+β·Si+γ·log(Fi+1) determines the impact value, where: Ti: the quantitative value of the abnormal time sequence of the i-th associated device (for example, the reverse time difference from the current time, the closer the time, the greater the impact); is a small positive number used to prevent the denominator from being zero; Si: abnormality degree of the i-th associated device, |actual value - normal value| / normal value; Fi: abnormal frequency of the i-th associated device within a period of time; α, β, γ: are the weight coefficients of time sequence, abnormality degree and abnormality frequency respectively, and α+β+γ=1.

[0040] Ii: The impact value of the i-th associated device on the power grid equipment.

[0041] S204: Determine strongly associated devices from the associated devices based on the influence value.

[0042] Based on the impact value, the associated devices with the greatest impact on the key devices are further screened out, namely, strongly associated devices. Strongly associated devices can be determined from the associated devices by setting an impact value threshold, or by using a sorting and screening method to sort all associated devices from large to small according to the impact value, and select several associated devices with the highest ranking as strongly associated devices according to actual needs.

[0043] It can be seen that in the embodiment of the present application, in the process of determining strongly associated power grid equipment, potential associated devices are identified based on the directed graph of device connections, and then the abnormal performance (time sequence, degree and frequency) of the associated devices when the first power grid equipment is abnormal is analyzed, and their impact on key equipment is quantified to screen out strongly associated devices.

[0044] Furthermore, S105 performs operation and maintenance management based on the digital twin model of the edge substation, including: S1051-S1055 (not shown in the figure), where: S1051. When it is determined that the operation state of the power grid equipment is abnormal, determine the relevant information of the power grid equipment with abnormal operation state through the digital twin model of the edge substation, and the relevant information includes at least: equipment location information, location information of abnormal components, and important values ​​of abnormal components; When it is determined that the operating state of the power grid device deviates from a normal or expected range, it is determined that the operating state of the power grid device is abnormal.

[0045] In the embodiments of the present application, the abnormal component refers to the specific component or area in the power grid equipment that causes the abnormal operation status. The location information refers to the specific location of the abnormal component in the power grid equipment. The importance value is a value used to quantify the degree of impact of the abnormal component on the operation of the power grid equipment, using a comprehensive evaluation of factors including fault severity, impact range, and repair difficulty.

[0046] Specifically, when the power grid monitoring system issues an abnormal alarm, the detailed data and simulation functions in the digital twin model are used to further determine the specific location information of the equipment, including its layout position in the substation and its relative position to other equipment. At the same time, the model can also analyze the specific components that cause the equipment abnormality and provide the specific location information of these components inside or outside the equipment. Finally, based on the evaluation function of the digital twin model, the importance value of the abnormal component is calculated.

[0047] By utilizing the digital twin model of the edge substation, relevant information can be quickly determined when an abnormality occurs in the power grid equipment, including the location of the equipment, the location of the abnormal component, and important values, which can improve the fault response speed.

[0048] S1052. predicting the abnormal cause of the abnormal component; Abnormal component: refers to the specific component or area in the power grid equipment whose operating status deviates from the normal or expected range.

[0049] By analyzing the current status, historical data, and environmental factors of the abnormal component, the root cause of the abnormality can be inferred.

[0050] Specifically, obtain a historical data set containing the status data of abnormal components and their corresponding abnormal causes, obtain a machine learning model (such as decision tree, random forest, support vector machine, etc.), and use the historical data set to train the model; after the training is completed, input the status data of the current abnormal component into the model, and the model will output the predicted abnormal cause.

[0051] S1053. According to the cause of the abnormality, a number of maintenance suggestions corresponding to the abnormal components are matched from the operation and maintenance dictionary; The operation and maintenance dictionary stores a database or knowledge base about various problems that may be encountered during system operation and maintenance and the corresponding solution strategies.

[0052] Based on the known abnormal causes and components, search and match in the dictionary to find maintenance suggestions. In some embodiments, the operation and maintenance dictionary can be designed as a database that supports keyword search. The operation and maintenance personnel can enter the abnormal causes and components as keywords, and the system quickly locates the relevant maintenance suggestions through full-text search technology. In order to improve the accuracy of matching, the operation and maintenance dictionary can also introduce a weight distribution mechanism to assign different importance to different abnormal components, thereby giving them higher priority in the matching process. The system can also provide an interactive interface, allowing operation and maintenance personnel to further filter or adjust the search conditions based on the matching results to obtain more accurate maintenance suggestions.

[0053] By quickly matching the maintenance suggestions for abnormal components from the operation and maintenance dictionary, operation and maintenance personnel can locate problems more accurately, reduce diagnosis time, and avoid misjudgment or omissions due to lack of experience or knowledge. In addition, the operation and maintenance dictionary also plays a role in knowledge inheritance and standardization, allowing operation and maintenance personnel of different levels to handle problems according to unified processes and standards, improving the standardization and consistency of operation and maintenance work.

[0054] S1054. Determine an inspection trajectory based on the relevant information of the power grid equipment with abnormal operation status, where the inspection trajectory includes: the inspection order of the power grid equipment with abnormal operation status, and the inspection duration of the power grid equipment with abnormal operation status, where the inspection duration includes the duration of displaying components and the duration of displaying several maintenance suggestions; In one achievable manner, S1054, according to the relevant information of the power grid equipment with abnormal operation status, determines the inspection trajectory, including: SA1-SA6 (not shown in the drawings), wherein: SA1. Clustering the power grid equipment according to the abnormal causes of the abnormal components of the power grid equipment with abnormal operating status to obtain several equipment groups; After an abnormality is detected in the operation status monitoring system of the power grid equipment, it is necessary to quickly locate the abnormal equipment and analyze the cause of the abnormality so as to take appropriate measures to restore the normal operation of the equipment or prevent the further expansion of the fault.

[0055] In the process of cluster analysis, you can use the clustering algorithm and set reasonable parameters (such as the number of clusters, similarity threshold, etc.).

[0056] Specifically, the abnormal causes and cluster numbers of multiple power grid devices are obtained; the initial cluster centers of the number of clusters are randomly determined from the multiple power grid devices; for the abnormal causes of the multiple power grid devices, the weights of the multiple power grid devices corresponding to the initial cluster centers are determined using the Euclidean distance; according to the weights of the multiple power grid devices corresponding to the initial cluster centers, duplicate removal is performed to obtain the power grid devices corresponding to each initial cluster center; and a number of equipment groups are obtained.

[0057] SA2. For each device group, determine the first order of the power grid devices in the device group according to the importance average values ​​corresponding to the importance values ​​of the abnormal components of the power grid devices in the device group; The first order is the order obtained after sorting the power grid devices in the device group.

[0058] In this step, by determining the first-order device in the device group, the system can prioritize those faults that have the greatest impact on the operation of the power grid or are the most urgent, thereby quickly restoring the stability and reliability of the power grid.

[0059] SA3. Determine the second order of the equipment groups according to the number of key equipment in each equipment group and the importance average value corresponding to the importance value of the abnormal components of the power grid equipment in the equipment group; Specifically, according to Scorei=w1Ni+w2Yi, Ni is the number of key devices in device group i, Yi is the important average value corresponding to the important value of the abnormal component of the power grid equipment in device group i, w1 is the weight of the number of key devices, and w2 is the weight of the important average value; for example, Assume that there are three device groups with the following data: Equipment group 1: N1=5, Y1=10; Equipment group 2: N2=3, Y2=7.5; Equipment group 3: N3=4, Y3=8; If we choose w1=0.6 and w2=0.4, then: The score of device group 1: Score1=0.6*5+0.4*10=3+4=7; The score of equipment group 2: Score2=0.6*3+0.4*7.5=1.8+3=4.8; The score of equipment group 3: Score3=0.6*4+0.4*8=2.4+3.2=5.6.

[0060] After obtaining Scorei, all device groups are sorted to obtain the second order, namely, device group 3 - device group 2 - device group 1.

[0061] SA4. Determine the inspection order based on the first order, the second order, the equipment location information, and the location information of the abnormal component; SA5. Determine the duration of displaying the maintenance suggestions according to the length of the maintenance suggestions; When the content length is the text length, the duration of displaying several maintenance suggestions is determined according to the content length and the preset reading speed; When the content length is the same as the audio or video playback length, the duration of displaying several maintenance suggestions is determined based on the content length and the preset reading speed.

[0062] SA6. Determine the duration of displaying the abnormal component based on the importance value of the abnormal component and the complexity of the abnormal component.

[0063] The higher the importance value, the greater the impact of the abnormal component on the equipment. Complexity refers to the complexity of the abnormal component in terms of structure, function or repair difficulty. The higher the complexity, the more difficult it is to analyze, diagnose or repair the part.

[0064] The component display duration refers to the length of time that the abnormal component structure is displayed in the display interface or report. This duration may vary depending on the importance and complexity so that users can prioritize and handle more important or more complex abnormal components.

[0065] Furthermore, a dynamic adjustment strategy can be adopted. After determining the duration of displaying several maintenance suggestions and the duration of displaying components, the display duration can be dynamically adjusted according to the user's interactive behavior (such as clicking, zooming in, viewing detailed information, etc.) during the actual inspection process and real-time data updates. For example, if the user frequently views the detailed information of an abnormal component, the system can increase the display duration of the component; if the status of an abnormal component is alleviated or repaired, the system can reduce its display duration.

[0066] It can be seen that in the embodiment of the present application, in the process of determining the inspection trajectory, clustering of equipment according to the cause of the abnormality helps to centrally process equipment with similar problems; through the dual consideration of important values ​​and the number of key equipment, it is ensured that equipment and areas with greater impact on the power grid are given priority, combined with the refined adjustment of location information and display duration, the efficiency and effectiveness of the inspection work are further improved.

[0067] S1055. When a patrol start instruction is detected, navigation guidance is provided to the user in the virtual scene according to the patrol track. When an abnormal component is found during patrol, corresponding maintenance suggestions are displayed in the virtual scene.

[0068] After a user operation, scheduled task or external system call is triggered, a patrol start instruction is generated; patrol is based on the patrol track.

[0069] Furthermore, augmented reality (AR) technology can be used to display virtual scenes and inspection tracks in the AR device worn by the user, and the real-world images can be captured through the camera to overlay virtual information on the real world. When the user inspects an abnormal component, the system will display maintenance suggestions in real time in the AR device and remind the user through visual, auditory and other methods. This method can provide a more intuitive and immersive inspection experience.

[0070] It can be seen that in the embodiment of the present application, in the operation and maintenance management stage, the method uses the digital twin model to quickly respond to the abnormal status of the power grid equipment. Once an abnormality is detected, detailed equipment information and abnormal details are immediately obtained through the model, and then the cause of the abnormality is predicted and the corresponding maintenance suggestions are matched; it can also plan inspection trajectories based on equipment status and abnormal information, including the optimal inspection sequence and time allocation, to ensure the efficiency and pertinence of the inspection work, and then, combined with the navigation guidance and maintenance suggestion display in the virtual scene, it greatly improves the operating convenience and response speed of the operation and maintenance personnel, reduces the operation and maintenance costs, and improves the operation and maintenance efficiency.

[0071] Furthermore, for power grid equipment, there may be complex structures. After the digital twin model is generated, the digital twin model of the power grid equipment can be verified. For complex structures, it may be divided into multiple levels for verification to improve the accuracy of the verification.

[0072] For details, see Figure 2 , the power grid device is a device with a multi-level structure hierarchy, and the non-root level structure hierarchy is composed of at least two sub-level structures; After constructing the digital twin models corresponding to the key equipment and non-key equipment, and obtaining the digital twin model corresponding to the edge substation, it also includes: SC1-SC4 (not shown in the figure), where: SC1. Obtain a first preset verification parameter and first actual operation and maintenance information corresponding to the first preset verification parameter, where the first preset verification parameter includes verification parameters corresponding to multiple structural levels of the power grid equipment; and obtain a second preset verification parameter and second actual operation and maintenance information corresponding to the second preset verification parameter; The preset verification parameters involve different structural levels of the power grid equipment, such as the input data of the equipment as a whole, subsystems, and components. The actual operation and maintenance information refers to the actual operation data or status information corresponding to the preset verification parameters during the actual operation of the power grid equipment.

[0073] SC2. According to the verification parameters corresponding to the preset structural level in the first preset verification parameters of the power grid equipment, the digital twin model is input to obtain the first operation and maintenance information corresponding to the preset structural level; according to the first operation and maintenance information and the actual operation and maintenance information corresponding to the preset structural level in the first actual operation and maintenance information of the power grid equipment, the validity of the preset structural level of the digital twin model corresponding to the edge substation is verified; when the verification of the preset structural level fails, the model parameters of the preset structural level are adjusted, and the verification is performed again until the verification is successful, and then the verification of the preset structural level of the next same level is performed; Reference Figure 2 ,Specifically, the steps are as follows: First, select the verification parameter corresponding to the preset structural level that needs to be verified from the first preset verification parameter. Taking the second level 1 as an example, first obtain the verification parameter corresponding to the second level 1; These verification parameters are used as input conditions into the established digital twin model, and the model is run to obtain the first operation and maintenance information corresponding to the structural level; The actual operation and maintenance information corresponding to the same preset structural level is extracted from the first actual operation and maintenance information of the power grid equipment.

[0074] The first operation and maintenance information is compared with the first actual operation and maintenance information to evaluate the consistency between the simulation result of the digital twin model at this structural level and the actual operation status; If the verification result shows that the simulation result is inconsistent with the actual operating status, that is, the verification fails, it is necessary to adjust the model parameters of the preset structural level to improve the accuracy of the model; After adjustment, run the digital twin model again and verify it, and repeat this process until the verification is successful.

[0075] When the verification of the current preset structure level is successful, the verification of the next preset structure level of the same level (such as the second level 2) will continue until all the structure levels (second levels) that need to be verified have completed the verification.

[0076] SC3. When all preset structural levels are successfully verified, the correlation between the components of the preset structural level is determined based on the correlation map of the power grid equipment; the components with correlation are verified again using the verification parameters about the preset structural level in the second preset verification parameters and the actual operation and maintenance information corresponding to the preset structural level in the second actual operation and maintenance information of the power grid equipment; if the verification of the component with correlation fails, the model parameters of the target component are adjusted until the verification is successful, and the target component is the component with correlation and the verification fails; The association diagram of power grid equipment is a network structure diagram used to describe the relationship between the components of power grid equipment, such as Figure 2 As shown. The association map can show the interrelationship and dependency between the components, subsystems or functional modules of the equipment. For example, the multiple components of the second level 1 are: root level 1 and root level 2.

[0077] Specifically, the execution process is as follows: Determine the correlation: Analyze and determine the correlation between the components in the preset structure level according to the correlation map of the power grid equipment; Preparing verification parameters and information: selecting verification parameters related to the preset structural level or components currently requiring verification from the second preset verification parameters; extracting actual operation and maintenance information corresponding to these components from the second actual operation and maintenance information of the power grid equipment; Verification: Using the selected verification parameters and the extracted actual operation and maintenance information, the components with correlation are verified one by one. The verification process may involve comparing the simulated values ​​with the actual values, evaluating whether the performance indicators meet the standards, etc. Processing verification results: If the verification results show that the associated components fail to meet the predetermined verification criteria or conditions, the verification fails; Adjust model parameters: For components that failed verification, adjust their model parameters to optimize the accuracy and reliability of the model. The adjustment process may need to be repeated until the verification is successful.

[0078] SC4. If the verification of the associated components is successful, the verification of the next level of structure is carried out until the verification and adjustment of all structure levels are completed to obtain the final digital twin model corresponding to the edge substation.

[0079] It can be seen that in the embodiment of the present application, after constructing the digital twin model of the edge substation, a multi-level verification mechanism is introduced for the complex structure of the power grid equipment. Specifically, by comparing the preset verification parameters with the actual operation and maintenance information, the validity of each preset structural level in the digital twin model is ensured; when the verification of a certain level fails, the model parameters of the level can be adjusted in a targeted manner, and the verification is repeated until success; after all the preset structural levels are successfully verified, the correlation between the components is determined through the association map, and the components with correlation are verified for the second time, which further refines the verification process and ensures the consistency and accuracy of the model in the complex structural levels; through step-by-step verification and adjustment, a comprehensive, accurate and reliable digital twin model of the edge substation is obtained.

[0080] Furthermore, if the preset structure level is a non-root structure level, SC2 performs verification of the next preset structure level of the same level, including: According to the number of sub-level structural levels that overlap with the verified preset structural level, other preset structural levels at the same level are sorted to obtain a first sequence; According to the first sequence, other preset structural levels of the same level are verified in turn; When verifying other preset structural hierarchies of the target, if the verification fails, the model parameters of the non-overlapping sub-level structural hierarchies are adjusted to verify and adjust the model parameters of other preset structural hierarchies. The target other preset structural hierarchies are any structural hierarchies among the other preset structural hierarchies at the same level.

[0081] Among them, the sub-level structure is the component. When the second level 1 is verified, the number of sub-level structure levels overlapping with the second level 2 is 2, the number of sub-level structure levels overlapping with the second level 3 is 1, the number of sub-level structure levels overlapping with the second level 4 is 0, and the number of sub-level structure levels overlapping with the second level 5 is 0. The first sequence obtained is second level 2-second level 3-second level 4 / second level 5. Then verify once according to the first sequence and adjust with the model.

[0082] It can be seen that in the embodiment of the present application, when verifying the preset structural hierarchy of the non-root structural hierarchy, by comparing the number of overlapping sub-level structural hierarchies in different preset structural hierarchies, other preset structural hierarchies at the same level are sorted, so as to give priority to verifying those structural hierarchies with high similarity to the verified hierarchies, which not only improves the efficiency of verification, but also can more accurately locate the problem when the verification fails, that is, the non-overlapping sub-level structural hierarchies, and then make targeted model parameter adjustments.

[0083] Furthermore, S105 performs operation and maintenance management based on the digital twin model of the edge substation, including: Obtaining currently running compressed data corresponding to each of the plurality of power grid devices in the edge substation, wherein the currently running compressed data includes multi-dimensional compressed data, and each dimension of compressed data is compressed data of a varying dimension; Decompress the currently running compressed data to obtain the currently running data; The digital twin model of the edge substation is used to monitor the operating status of multiple power grid equipment in the edge substation based on the current operating data.

[0084] In the embodiments of the present application, when the power grid equipment operates normally, a large amount of real-time data will be continuously generated. The direct storage and transmission of this data not only takes up a lot of resources, but may also lead to low processing efficiency due to the large amount of data. Therefore, in practical applications, these real-time data are usually compressed to reduce the space requirements for storage and transmission.

[0085] Specifically, pre-operation compressed data refers to the real-time data of power grid equipment. The data set obtained after compression processing is designed to reduce the space requirements for data storage and transmission while retaining the main characteristics and change information of the data. Multi-dimensional compressed data means that the compressed data contains information in multiple dimensions, each of which corresponds to a specific aspect or parameter in the operation of the power grid equipment. These dimensions can be voltage, current, temperature, power factor, etc. Each dimension of compressed data is a dimensional data that changes greatly over time or conditions. This compression of changing dimensions is intended to more accurately retain the changing trends and abnormal information of the data. After obtaining the current running compressed data, the detailed operation data of the power grid equipment can be decompressed for subsequent analysis or monitoring.

[0086] It can be seen that in the embodiment of the present application, when performing operation and maintenance management based on the digital twin model of the edge substation, a multi-dimensional compressed data decompression technology is introduced; the data is efficiently compressed, reducing the burden of storage and transmission, which not only improves the efficiency and accuracy of data processing, but also enables real-time and accurate monitoring of the operating status of power grid equipment without sacrificing data integrity.

[0087] In the embodiment of the present application, an operation and maintenance management platform 300 based on digital twin is provided, such as Figure 3 As shown, including: The acquisition module 310 is used to obtain the key values ​​and equipment data corresponding to each of the multiple power grid equipment in the edge substation. The keyness of the equipment is determined based on the role and importance of the power grid equipment in the power grid. The equipment data includes the operation time, the number of failures, and the service life; The device classification module 320 is used to determine, from the plurality of power grid devices, a plurality of first power grid devices belonging to key devices according to the device key values ​​corresponding to the plurality of power grid devices; the plurality of first power grid devices include: power grid devices having device key values ​​greater than a preset device key threshold and power grid devices having a strong association with the power grid devices having device key values ​​greater than the preset device key threshold; And, for second power grid devices other than the first power grid device among the plurality of power grid devices, determining critical devices and non-critical devices from the second power grid devices according to the operating time, number of failures, service life and critical value of each second power grid device; A construction module 330 is used to construct digital twin models corresponding to key equipment and non-key equipment, respectively, to obtain a digital twin model corresponding to the edge substation; wherein the modeling accuracy of the first digital twin model corresponding to the key equipment is higher than the modeling accuracy of the second digital twin model corresponding to the non-key equipment; The management module 340 is used to perform operation and maintenance management based on the digital twin model of the edge substation.

[0088] In an achievable embodiment, the device classification module 320 is further configured to: Determine the corresponding associated devices according to the connection directed graph of the power grid devices whose device criticality values ​​are greater than the preset device criticality threshold; Obtaining the abnormal time sequence, abnormal degree and abnormal frequency of the associated equipment when the power grid equipment with the equipment key value greater than the preset equipment criticality threshold is abnormal, wherein the abnormal time sequence is the order in which the abnormality of the power grid equipment occurs in the abnormal event; Determine the impact value of the associated equipment on the power grid equipment whose equipment criticality value is greater than a preset equipment criticality threshold value according to the abnormal time sequence, abnormality degree and abnormality frequency of the associated equipment; A strongly associated device is determined from the associated devices based on the influence value.

[0089] In an achievable embodiment, the management module 340 is further configured to: When it is determined that the operating state of the power grid equipment is abnormal, the relevant information of the power grid equipment with abnormal operating state is determined through the digital twin model of the edge substation, and the relevant information includes at least: equipment location information, location information of abnormal components, and important values ​​of abnormal components; Predict abnormal causes of abnormal components; According to the cause of the abnormality, several maintenance suggestions corresponding to the abnormal components are matched from the operation and maintenance dictionary; Determine the inspection trajectory according to the relevant information of the power grid equipment with abnormal operation status, the inspection trajectory includes: the inspection order of the power grid equipment with abnormal operation status, and the inspection time of the power grid equipment with abnormal operation status, the inspection time includes the display time of components and the display time of several maintenance suggestions; When the inspection start command is detected, navigation guidance is provided to the user in the virtual scene according to the inspection trajectory. When an abnormal component is inspected, several corresponding maintenance suggestions are displayed in the virtual scene.

[0090] In an achievable embodiment, the management module 340 is further used to: cluster the power grid devices according to the abnormal causes of the abnormal components of the power grid devices with abnormal operating states to obtain a plurality of device groups; For each device group, determining a first order of the power grid devices in the device group according to an important average value corresponding to the important values ​​of the abnormal components of the power grid devices in the device group; Determine a second order of the equipment groups according to the number of key equipment in each equipment group and the importance average value corresponding to the importance value of the abnormal components of the power grid equipment in the equipment group; Determine the inspection order based on the first order, the second order, the equipment location information, and the location information of the abnormal component; Determine the duration of displaying the several maintenance suggestions according to the content length of the several maintenance suggestions; The duration for displaying the abnormal component is determined according to the importance value and complexity of the abnormal component.

[0091] In an achievable embodiment, the power grid device is a device having a multi-level structural hierarchy, and the non-root structural hierarchy is composed of at least two sub-level structures; The platform further includes: a verification module 350, which is used to: Obtaining a first preset verification parameter and first actual operation and maintenance information corresponding to the first preset verification parameter, wherein the first preset verification parameter includes verification parameters corresponding to multiple structural levels of the power grid equipment; and obtaining a second preset verification parameter and second actual operation and maintenance information corresponding to the second preset verification parameter; According to the verification parameters corresponding to the preset structural level in the first preset verification parameters of the power grid equipment, the first operation and maintenance information corresponding to the preset structural level is input into the digital twin model; according to the first operation and maintenance information and the actual operation and maintenance information corresponding to the preset structural level in the first actual operation and maintenance information of the power grid equipment, the validity of the preset structural level of the digital twin model corresponding to the edge substation is verified; when the verification of the preset structural level fails, the model parameters of the preset structural level are adjusted, and the verification is performed again until the verification is successful, and then the verification of the preset structural level of the next same level is performed; When all preset structural levels are successfully verified, the correlation between the components of the preset structural level is determined based on the correlation map of the power grid equipment, and the components are sub-level structural levels of the preset structural level; the components with correlation are verified again using the verification parameters about the preset structural level in the second preset verification parameters and the actual operation and maintenance information corresponding to the preset structural level in the second actual operation and maintenance information of the power grid equipment; if the verification of the component with correlation fails, the model parameters of the target component are adjusted until the verification is successful, and the target component is the component with correlation and the verification fails; If the verification of the associated components is successful, the verification of the next level of structure is carried out until the verification and adjustment of all structure levels are completed to obtain the final digital twin model corresponding to the edge substation.

[0092] In an achievable embodiment, the verification module 350 is further configured to: According to the number of sub-level structural levels that overlap with the verified preset structural level, other preset structural levels at the same level are sorted to obtain a first sequence; According to the first sequence, other preset structural levels of the same level are verified in turn; When verifying other preset structural hierarchies of the target, if the verification fails, the model parameters of the non-overlapping sub-level structural hierarchies are adjusted to verify and adjust the model parameters of other preset structural hierarchies. The target other preset structural hierarchies are any structural hierarchies among the other preset structural hierarchies at the same level.

[0093] In an achievable embodiment, the acquisition module 310 is used to: Obtaining currently running compressed data corresponding to each of the plurality of power grid devices in the edge substation, wherein the currently running compressed data includes multi-dimensional compressed data, and each dimension of compressed data is compressed data of a varying dimension; Decompress the currently running compressed data to obtain the currently running data; The digital twin model of the edge substation is used to monitor the operating status of multiple power grid equipment in the edge substation based on the current operating data.

[0094] An electronic device is provided in an embodiment of the present application, such as Figure 4 As shown, Figure 4 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0095] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0096] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0097] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0098] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0099] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0100] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0101] An embodiment of the present application provides a computer program product, including a computer program, which implements the corresponding contents of the aforementioned method embodiment when the computer program is executed by a processor.

[0102] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0103] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An operation and maintenance management method based on digital twins, characterized in that: include: Obtaining key values ​​and equipment data corresponding to each of multiple power grid equipment in the edge substation, wherein the equipment data includes operation time, number of failures, and service life; According to the device key values ​​corresponding to the plurality of power grid devices, determining a plurality of first power grid devices belonging to the key devices from the plurality of power grid devices; The plurality of first power grid devices include: power grid devices having a device critical value greater than a preset device criticality threshold and power grid devices having a strong association with the power grid devices having a device critical value greater than the preset device criticality threshold; For second power grid devices other than the first power grid device among the plurality of power grid devices, determining key devices and non-key devices from the second power grid devices according to the operating time, number of failures, service life and key value of each second power grid device; Constructing digital twin models corresponding to key equipment and non-key equipment respectively, and obtaining a digital twin model corresponding to the edge substation; wherein the modeling accuracy of the first digital twin model corresponding to the key equipment is higher than the modeling accuracy of the second digital twin model corresponding to the non-key equipment; Operation and maintenance management is carried out based on the digital twin model of the edge substation.

2. The method according to claim 1, characterized in that The process of identifying strongly associated grid devices includes: Determine the corresponding associated devices according to the connection directed graph of the power grid devices whose device criticality values ​​are greater than the preset device criticality threshold; Obtaining the abnormal time sequence, abnormal degree and abnormal frequency of the associated devices when the power grid device with a device critical value greater than a preset device criticality threshold is abnormal, wherein the abnormal time sequence is the order in which the abnormalities of the power grid devices occur in the abnormal event; Determine the impact value of the associated equipment on the power grid equipment whose equipment criticality value is greater than a preset equipment criticality threshold value according to the abnormal time sequence, abnormality degree and abnormality frequency of the associated equipment; A strongly associated device is determined from the associated devices based on the influence value.

3. The method according to claim 1, characterized in that The operation and maintenance management based on the digital twin model of the edge substation includes: When it is determined that the operating state of the power grid equipment is abnormal, the relevant information of the power grid equipment with abnormal operating state is determined through the digital twin model of the edge substation, and the relevant information includes at least: equipment location information, location information of abnormal components, and important values ​​of abnormal components; Predict abnormal causes of abnormal components; According to the cause of the abnormality, several maintenance suggestions corresponding to the abnormal components are matched from the operation and maintenance dictionary; Determine an inspection trajectory according to relevant information of the power grid equipment with abnormal operating status, wherein the inspection trajectory includes: an inspection order of the power grid equipment with abnormal operating status, and an inspection duration of the power grid equipment with abnormal operating status, wherein the inspection duration includes a display component duration and a display several maintenance suggestions duration; When the inspection start command is detected, navigation guidance is provided to the user in the virtual scene according to the inspection trajectory. When an abnormal component is inspected, several corresponding maintenance suggestions are displayed in the virtual scene.

4. The method according to claim 3, characterized in that Determining the inspection trajectory according to the relevant information of the power grid equipment with abnormal operation status includes: Clustering the power grid equipment according to the abnormal causes of the abnormal components of the power grid equipment with abnormal operating status to obtain a number of equipment groups; For each device group, determining a first order of the power grid devices in the device group according to an important average value corresponding to the important values ​​of the abnormal components of the power grid devices in the device group; Determine a second order of the equipment groups according to the number of key equipment in each equipment group and the importance average value corresponding to the importance value of the abnormal components of the power grid equipment in the equipment group; Determine the inspection order based on the first order, the second order, the equipment location information, and the location information of the abnormal component; Determine the duration of displaying the several maintenance suggestions according to the content length of the several maintenance suggestions; The duration of displaying the abnormal component is determined according to the importance value and complexity of the abnormal component.

5. The method according to claim 1, characterized in that The power grid device is a device with a multi-level structure hierarchy, and the non-root level structure hierarchy is composed of at least two sub-level structures; After constructing digital twin models corresponding to key equipment and non-key equipment, and obtaining the digital twin model corresponding to the edge substation, the method further includes: Obtaining a first preset verification parameter and first actual operation and maintenance information corresponding to the first preset verification parameter, wherein the first preset verification parameter includes verification parameters corresponding to multiple structural levels of power grid equipment; and obtaining a second preset verification parameter and second actual operation and maintenance information corresponding to the second preset verification parameter; According to the verification parameters corresponding to the preset structural level in the first preset verification parameters of the power grid equipment, the first operation and maintenance information corresponding to the preset structural level is input into the digital twin model; according to the first operation and maintenance information and the actual operation and maintenance information corresponding to the preset structural level in the first actual operation and maintenance information of the power grid equipment, the validity of the preset structural level of the digital twin model corresponding to the edge substation is verified; when the verification of the preset structural level fails, the model parameters of the preset structural level are adjusted, and the verification is performed again until the verification is successful, and then the verification of the preset structural level of the next same level is performed; When all preset structural levels are successfully verified, the correlation between the components of the preset structural level is determined based on the correlation map of the power grid equipment, and the components are sub-level structural levels of the preset structural level; the components with correlation are verified again using the verification parameters about the preset structural level in the second preset verification parameters and the actual operation and maintenance information corresponding to the preset structural level in the second actual operation and maintenance information of the power grid equipment; if the verification of the component with correlation fails, the model parameters of the target component are adjusted until the verification is successful, and the target component is the component with correlation and the verification fails; If the verification of the associated components is successful, the verification of the next level of structural level is carried out until the verification and adjustment of all structural levels are completed to obtain the final digital twin model corresponding to the edge substation.

6. The method according to claim 5, characterized in that If the preset structure level is a non-root structure level, the next preset structure level of the same level is verified, including: According to the number of sub-level structural levels that overlap with the verified preset structural level, other preset structural levels at the same level are sorted to obtain a first sequence; According to the first sequence, other preset structural levels of the same level are verified in turn; When verifying other preset structural hierarchies of the target, if the verification fails, the model parameters of the non-overlapping sub-level structural hierarchies are adjusted to verify and adjust the model parameters of other preset structural hierarchies, and the target other preset structural hierarchies are any structural hierarchies among other preset structural hierarchies of the same level.

7. The method according to any one of claims 1 to 6, characterized in that: Operation and maintenance management based on the digital twin model of the edge substation includes: Acquire currently running compressed data corresponding to each of the plurality of power grid devices in the edge substation, wherein the currently running compressed data includes multi-dimensional compressed data, and each dimension of compressed data is compressed data of a varying dimension; Decompressing the currently running compressed data to obtain currently running data; The digital twin model of the edge substation is used to monitor the operating status of multiple power grid equipment in the edge substation based on the current operating data.

8. An operation and maintenance management platform based on digital twins, characterized in that: include: An acquisition module is used to acquire the key values ​​and equipment data corresponding to each of the plurality of power grid equipment in the edge substation, wherein the keyness of the equipment is determined based on the role and importance of the power grid equipment in the power grid, and the equipment data includes the operation time, the number of failures, and the service life; The device classification module is used to determine a plurality of first power grid devices belonging to key devices from the plurality of power grid devices according to the device key values ​​corresponding to the plurality of power grid devices respectively; The plurality of first power grid devices include: power grid devices having a device critical value greater than a preset device criticality threshold and power grid devices having a strong association with the power grid devices having a device critical value greater than the preset device criticality threshold; And, for second power grid devices other than the first power grid device among the plurality of power grid devices, determining critical devices and non-critical devices from the second power grid devices according to the operating time, number of failures, service life and critical value of each second power grid device; A construction module is used to construct digital twin models corresponding to key equipment and non-key equipment respectively, and obtain a digital twin model corresponding to the edge substation; wherein the modeling accuracy of the first digital twin model corresponding to the key equipment is higher than the modeling accuracy of the second digital twin model corresponding to the non-key equipment; Management module, used for operation and maintenance management based on the digital twin model of the edge substation.

9. An electronic device, characterized in that: include: one or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to: execute the steps of the digital twin-based operation and maintenance management method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded by a processor and executes the steps of the digital twin-based operation and maintenance management method according to any one of claims 1 to 7.

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