Dynamic updating method, system, equipment and medium for digital twin model of power grid

Abstract

The invention provides a dynamic updating method, system and device for a power grid digital twin model and a medium, and the method comprises the steps: determining whether there is corresponding call data in a cache region of the power grid digital twin model based on a call request; when calling data exists in the cache region of the digital twin model of the power grid, returning the calling data to a requester of the calling request, and completing dynamic updating of the digital twin model of the power grid; when no calling data exists in the cache region of the power grid digital twinborn model, dynamic updating of the power grid digital twinborn model is completed by adopting a demand data calling rule through power grid digital twinborn model resources; according to the method, the demand data corresponding to the request needing to be called is quickly determined according to the clear service type, so that the problem that model updating is difficult to realize in an active request mode due to the fact that the service data is fuzzy and cannot be called according to the calling request in the prior art is solved, and the immediate updating of the digital twin model of the power grid is realized.

Description

Technical Field

[0001] The patent application of this invention belongs to the field of power information technology, and specifically relates to a dynamic update method, system, equipment and medium for a digital twin model of a power grid. Background Art

[0002] With the advancement of the construction of new power systems, the digital resources of power grids are constantly accumulating and enriching, and the business analysis applications of power grids based on digital resources are gradually diversified and complex. Digital twin technology can abstract entities such as equipment, facilities, personnel, and models in the power grid into virtual space, and express and describe them with digital objects. It has been widely used in scenarios such as power grid operation status monitoring and equipment fault analysis. However, the increasing richness of digital twin objects has brought huge bottleneck problems to the construction and analysis of digital twin models. At present, most digital twin technology applications remain in the visual monitoring stage, and are restricted by bottlenecks such as data update performance and data redundancy, making it difficult to carry out business analysis applications.

[0003] At present, the actual application of digital twin technology in the power grid has the following main bottlenecks: data synchronization within the digital twin model is difficult. Although the update speed of the digital twin model is not high due to constraints such as transmission and security, some data can still be synchronized through active requests. However, due to the fuzzy data requests in the business application process, it is impossible to identify clear data requirements, and therefore it is impossible to actively initiate data call requests to the data source. The above are the main problems that plague the large-scale application of digital twin technology in power grid business scenarios. The deepening and development of power grid business urgently needs to overcome the problem of digital twin model data update. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the present invention patent application proposes a method for dynamically updating a power grid digital twin model, including:

[0005] Based on the call request, determine whether there is corresponding call data in the pre-built power grid digital twin model cache area;

[0006] When the call data is in the cache area of ​​the power grid digital twin model, the call data is returned to the requester of the call request to complete the dynamic update of the power grid digital twin model;

[0007] When the call data is not present in the cache area of ​​the power grid digital twin model or part of the call data is missing, complete call data is obtained by using the pre-built power grid digital twin model resources and the demand data call rule, and the complete call data is returned to the requester of the call request to complete the dynamic update of the power grid digital twin model;

[0008] Among them, the power grid digital twin model includes power grid digital twin model resources and power grid digital twin model cache area; the power grid digital twin model resources are constructed based on the power grid equipment data in the target area, and the power grid digital twin model cache area is constructed based on the business type.

[0009] Preferably, the process of constructing the power grid digital twin model resources includes:

[0010] According to the power grid equipment data of the target area, the power grid equipment data is divided into relational power grid equipment data and unstructured power grid equipment data;

[0011] Taking the equipment facility name in each of the relational power grid equipment data as a unit, constructing a data block corresponding to the relational power grid equipment data in the power grid digital twin model resource;

[0012] Taking each data file of the unstructured power grid device data as a unit, constructing a data block corresponding to the unstructured power grid device data in the power grid digital twin model resource;

[0013] After adding an identifier to each of the data blocks, the construction of the power grid digital twin model resources is completed;

[0014] Among them, each of the power grid equipment data includes at least one or more of the following: equipment facility name, equipment facility number, equipment facility type, equipment facility three-dimensional point cloud data, equipment facility basic file information, equipment facility operating status, equipment facility timing collection, equipment facility text and equipment facility video; the identification includes: data request type number and request number; each of the data blocks corresponds to the type data of a power grid equipment.

[0015] Preferably, the process of constructing the cache area of ​​the power grid digital twin model includes:

[0016] According to the predetermined business type, a cache area corresponding to each business type is constructed;

[0017] Determine the type data of the power grid equipment stored in each buffer area according to the business data corresponding to the business type, and complete the construction of the buffer area of ​​the power grid digital twin model;

[0018] Among them, the business types include at least one or more of the following: load forecasting, equipment failure analysis and power outage scope analysis; the type data includes one or more of the following: time-series power grid equipment data, basic archive power grid equipment data, text-type power grid equipment data and video-type power grid equipment data, and also includes operating status-type power grid equipment data.

[0019] Preferably, a data synchronization interface is used to periodically extract power grid equipment data in the target area, and perform data synchronization in the power grid digital twin model resources.

[0020] Preferably, when the call data is not present in the cache area of ​​the power grid digital twin model or part of the call data is missing, complete call data is obtained by using the pre-built power grid digital twin model resources and the demand data call rule, and the complete call data is returned to the requester of the call request to complete the dynamic update of the power grid digital twin model, including:

[0021] When the demand data does not exist in the target cache area in the power grid digital twin model cache area or part of the demand data is missing, the demand data calling rules are adopted to call the demand data from the data block in the power grid digital twin model resource to the target cache area through the power grid digital twin model calling interface, and then the complete demand data in the target cache area is returned to the requester of the calling request to complete the dynamic update of the power grid digital twin model.

[0022] Preferably, when the demand data does not exist in the target cache area in the power grid digital twin model cache area or part of the demand data is missing, the demand data calling rule is adopted, and the demand data is called from the data block in the power grid digital twin model resource to the target cache area through the power grid digital twin model calling interface, and then the complete demand data in the target cache area is returned to the requester of the calling request, so as to complete the dynamic update of the power grid digital twin model, including:

[0023] When the demand data does not exist in the target cache area or part of the demand data is missing, the demand data is called from the power grid digital twin model resources to the corresponding type data in the target cache area through the power grid digital twin model calling interface;

[0024] Then, the required data in the target buffer area is returned to the requester of the call request, and the number of each type of data in the current target buffer area is counted;

[0025] When the number of types of each type of data does not exceed the type number threshold corresponding to each type of data, the dynamic update of the power grid digital twin model is completed;

[0026] When the number of types of existing type data exceeds the type number threshold corresponding to the type data, part of the data in the type data is deleted so that the number of types of the type data is within the type number threshold, and the dynamic update of the power grid digital twin model is completed.

[0027] Preferably, when the number of types of existing type data exceeds a type number threshold corresponding to the type data, deleting part of the type data so that the number of types of the type data is within the type number threshold, the dynamic update of the power grid digital twin model is completed, including:

[0028] When the number of types of type data exceeds the type number threshold corresponding to the type data, the count fields of the type data are arranged in ascending order to generate an ascending set;

[0029] The smallest count field in the ascending set is used as the count field with the lowest call frequency, and the count field with the lowest call frequency is extracted to determine the data with the lowest call frequency;

[0030] When the data with the lowest call frequency is a single piece of data, the single piece of data is deleted until the number of types of the type of data is within the type number threshold, thereby completing the dynamic update of the power grid digital twin model;

[0031] When the data with the lowest call frequency is a data set, the data set is sorted in chronological order to obtain a time series, and the earliest data is found from the time series, taken out and deleted, until the number of types of the type data is within the type number threshold, and the dynamic update of the power grid digital twin model is completed.

[0032] Based on the same inventive concept, the patent application of the present invention also provides a dynamic update system of a power grid digital twin model, including: a call data confirmation module, a first dynamic update module and a second dynamic update module;

[0033] The call data confirmation module is used to determine whether there is corresponding call data in a pre-built power grid digital twin model cache area based on the call request;

[0034] The first dynamic update module is used to return the call data to the requester of the call request when there is the call data in the cache area of ​​the power grid digital twin model, so as to complete the dynamic update of the power grid digital twin model;

[0035] The second dynamic update module is used to obtain complete call data by using the pre-built power grid digital twin model resources and the demand data call rules when there is no call data or part of the call data is missing in the power grid digital twin model cache area, and return the complete call data to the requester of the call request to complete the dynamic update of the power grid digital twin model;

[0036] Among them, the power grid digital twin model includes power grid digital twin model resources and power grid digital twin model cache area; the power grid digital twin model resources are constructed based on the power grid equipment data in the target area, and the power grid digital twin model cache area is constructed based on the business type.

[0037] Preferably, it also includes: a power grid digital twin model resource construction module, which is used to:

[0038] According to the power grid equipment data of the target area, the power grid equipment data is divided into relational power grid equipment data and unstructured power grid equipment data;

[0039] Taking the equipment facility name in each of the relational power grid equipment data as a unit, constructing a data block corresponding to the relational power grid equipment data in the power grid digital twin model resource;

[0040] Taking each data file of the unstructured power grid device data as a unit, constructing a data block corresponding to the unstructured power grid device data in the power grid digital twin model resource;

[0041] After adding an identifier to each of the data blocks, the construction of the power grid digital twin model resources is completed;

[0042] Among them, each of the power grid equipment data includes at least one or more of the following: equipment facility name, equipment facility number, equipment facility type, equipment facility three-dimensional point cloud data, equipment facility basic file information, equipment facility operating status, equipment facility timing collection, equipment facility text and equipment facility video; the identification includes: data request type number and request number; each of the data blocks corresponds to the type data of a power grid equipment.

[0043] Preferably, it also includes: a power grid digital twin model cache area construction module, which is used to:

[0044] According to the predetermined business type, a cache area corresponding to each business type is constructed;

[0045] Determine the type data of the power grid equipment stored in each buffer area according to the business data corresponding to the business type, and complete the construction of the buffer area of ​​the power grid digital twin model;

[0046] Among them, the business types include at least one or more of the following: load forecasting, equipment failure analysis and power outage scope analysis; the type data includes one or more of the following: time-series power grid equipment data, basic archive power grid equipment data, text-type power grid equipment data and then video-type power grid equipment data.

[0047] Preferably, it also includes: a data synchronization interface module, used for:

[0048] A data synchronization interface is used to periodically extract power grid equipment data in the target area, and data synchronization is performed in the power grid digital twin model resources.

[0049] Preferably, the second dynamic update module includes:

[0050] The calling interface is combined with the calling rules to complete the update sub-module, which is used to use the demand data calling rules to call the demand data from the data block in the power grid digital twin model resource to the target cache area through the power grid digital twin model calling interface when the demand data does not exist in the target cache area in the power grid digital twin model cache area or part of the demand data is missing, and then return the complete demand data in the target cache area to the requester of the calling request to complete the dynamic update of the power grid digital twin model.

[0051] Preferably, the calling interface completes the updating submodule in combination with the calling rules, including:

[0052] A demand data calling unit, configured to call the demand data from the power grid digital twin model resources to the corresponding type of data in the target cache area through the power grid digital twin model calling interface when the demand data does not exist in the target cache area or part of the demand data is missing;

[0053] A type quantity counting unit, used to return the required data in the target buffer area to the requester of the call request, and count the type quantity of each type of data currently in the target buffer area;

[0054] A dynamic update unit whose type quantity does not exceed a threshold value is used to complete the dynamic update of the power grid digital twin model when the type quantity of each type of data does not exceed the type quantity threshold value corresponding to each type of data;

[0055] The dynamic update unit for type quantity exceeding a threshold is used to delete part of the data in the type data when the type quantity of the existing type data exceeds the type quantity threshold corresponding to the type data, so as to complete the dynamic update of the power grid digital twin model when the type quantity of the type data is within the type quantity threshold.

[0056] Preferably, the dynamic update unit whose number of types exceeds a threshold is specifically used for:

[0057] When the number of types of type data exceeds the type number threshold corresponding to the type data, the count fields of the type data are arranged in ascending order to generate an ascending set;

[0058] The smallest count field in the ascending set is used as the count field with the lowest call frequency, and the count field with the lowest call frequency is extracted to determine the data with the lowest call frequency;

[0059] When the data with the lowest call frequency is a single piece of data, the single piece of data is deleted until the number of types of the type of data is within the type number threshold, thereby completing the dynamic update of the power grid digital twin model;

[0060] When the data with the lowest call frequency is a data set, the data set is sorted in chronological order to obtain a time series, and the earliest data is found from the time series, taken out and deleted, until the number of types of the type data is within the type number threshold, and the dynamic update of the power grid digital twin model is completed.

[0061] Based on the same inventive concept, the present patent application also provides a computer device, including: one or more processors;

[0062] A memory for storing one or more programs;

[0063] When the one or more programs are executed by the one or more processors, a dynamic update method of a power grid digital twin model as described above is implemented.

[0064] Based on the same inventive concept, the patent application of the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed, a dynamic update method of a power grid digital twin model as described above is implemented.

[0065] Compared with the closest prior art, the patent application of this invention has the following beneficial effects:

[0066] The patent application of the present invention provides a method, system, device and medium for dynamically updating a digital twin model of a power grid, including: based on a call request, determining whether there is corresponding call data in a pre-built cache area of ​​a digital twin model of a power grid; when there is the call data in the cache area of ​​the digital twin model of the power grid, returning the call data to the requester of the call request to complete the dynamic update of the digital twin model of the power grid; when there is no call data or part of the call data is missing in the cache area of ​​the digital twin model of the power grid, using the pre-built digital twin model resources of the power grid and the demand data call rules to obtain complete call data, returning the complete call data to the requester of the call request to complete the dynamic update of the digital twin model of the power grid; wherein the digital twin model of the power grid includes the digital twin model resources of the power grid and the power grid digital twin model. The digital twin model cache area of ​​the power grid is constructed based on the power grid equipment data of the target area, and the digital twin model cache area of ​​the power grid is constructed based on the business type; the present invention adopts a method of calling demand data from the digital twin model cache area of ​​the power grid or the digital twin model resource according to the call request to update the digital twin model of the power grid. The digital twin model cache area of ​​the power grid is constructed according to a clear business type. Therefore, when responding to the call request, the demand data corresponding to the call request can be quickly determined in the digital twin model of the power grid according to the clear business type, thereby solving the problem that the prior art cannot identify clear data requirements according to the call request due to fuzzy business data, which makes it difficult to update the model by active request, thereby realizing the immediate update of the digital twin model of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic flow chart of a method for dynamically updating a digital twin model of a power grid provided in the patent application of the present invention;

[0068] Figure 2 A data structure diagram of the power grid digital twin model provided for the patent application of the present invention;

[0069] Figure 3 A schematic diagram of cache space data identification for a power grid digital twin model provided for the patent application of the present invention;

[0070] Figure 4 A schematic diagram of adding call type and access frequency identification to the text and video files provided in the patent application of the present invention;

[0071] Figure 5 A schematic diagram of calling digital twin model data resources according to access requirements provided for the patent application of the present invention;

[0072] Figure 6A specific implementation flow chart of the present invention provided for the present invention patent application;

[0073] Figure 7 A schematic diagram of a dynamic update system of a power grid digital twin model provided for the patent application of the present invention;

[0074] Figure 8 A schematic structural diagram of a computer device provided for the patent application of the present invention. DETAILED DESCRIPTION

[0075] The specific implementation methods of the patent application of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0076] Embodiment 1:

[0077] The present invention patent application provides a method for dynamically updating a power grid digital twin model. Figure 1 As shown, including:

[0078] Step 1: Based on the call request, determine whether there is corresponding call data in the pre-built power grid digital twin model cache area;

[0079] Step 2: When the call data is in the cache area of ​​the power grid digital twin model, the call data is returned to the requester of the call request to complete the dynamic update of the power grid digital twin model;

[0080] Step 3: When there is no call data or part of the call data is missing in the cache area of ​​the power grid digital twin model, complete call data is obtained by using the pre-built power grid digital twin model resources and the demand data call rule, and the complete call data is returned to the requester of the call request to complete the dynamic update of the power grid digital twin model;

[0081] Among them, the power grid digital twin model includes power grid digital twin model resources and power grid digital twin model cache area; the power grid digital twin model resources are constructed based on the power grid equipment data in the target area, and the power grid digital twin model cache area is constructed based on the business type.

[0082] In one implementation, the process of constructing the power grid digital twin model resources includes:

[0083] According to the power grid equipment data of the target area, the power grid equipment data is divided into relational power grid equipment data and unstructured power grid equipment data;

[0084] For example, first obtain the 3D point cloud data of the equipment and facilities in the target area power grid, then extract the operating status parameter data of these equipment, unstructured data such as text and video, and analysis process and result data from various business information systems of the power grid, and generate Figure 2 The schematic diagram of the data structure of the power grid digital twin model is shown.

[0085] Taking the equipment facility name in each of the relational power grid equipment data as a unit, constructing a data block corresponding to the relational power grid equipment data in the power grid digital twin model resource;

[0086] Taking each data file of the unstructured power grid device data as a unit, constructing a data block corresponding to the unstructured power grid device data in the power grid digital twin model resource;

[0087] For example, the data shown above is divided. For relational power grid equipment data, each row of data corresponds to a data block; for unstructured power grid equipment data such as text and video, the data file is used as a unit, and one data file corresponds to one data block.

[0088] After adding an identifier to each of the data blocks, the construction of the power grid digital twin model resources is completed;

[0089] For example, the data resource identifiers in the power grid digital twin model resources are initialized. For relational power grid equipment data, two identifiers are added after each row of data: data request type number and request count. After initialization, the data structure is as follows: Figure 3 As shown in the figure, for unstructured data such as text and video in the power grid digital twin model resources, two identifiers are added to the data file: data request type number and request number, as shown in the figure. Figure 4 As shown in the figure, the business type is the data request type number, and the access frequency is the number of requests.

[0090] Among them, each of the power grid equipment data includes at least one or more of the following: equipment facility name, equipment facility number, equipment facility type, equipment facility three-dimensional point cloud data, equipment facility basic file information, equipment facility operating status, equipment facility timing collection, equipment facility text and equipment facility video; the identification includes: data request type number and request number; each of the data blocks corresponds to the type data of a power grid equipment.

[0091] In one implementation, the process of constructing the cache area of ​​the power grid digital twin model includes:

[0092] According to the predetermined business type, a cache area corresponding to each business type is constructed;

[0093] Determine the type data of the power grid equipment stored in each buffer area according to the business data corresponding to the business type, and complete the construction of the buffer area of ​​the power grid digital twin model;

[0094] Among them, the business types include at least one or more of the following: load forecasting, equipment failure analysis and power outage scope analysis; the type data includes one or more of the following: time-series power grid equipment data, basic archive power grid equipment data, text-type power grid equipment data and then video-type power grid equipment data.

[0095] For example, a digital twin model calling interface is defined, and a digital twin model cache area is established, with each business type corresponding to a cache area. The present invention can create a corresponding cache area based on a clear business type, and each cache area contains different types of data. Therefore, according to the subsequent calling request, the required data can be quickly extracted from the digital twin model resources of the power grid, and the request can be responded to quickly, thereby completing the update of the digital twin model in a timely manner.

[0096] In one implementation, it also includes: using a data synchronization interface to periodically extract power grid equipment data in the target area, and performing data synchronization in the power grid digital twin model resources.

[0097] For example, a data synchronization interface is established to regularly extract real-time operating status parameters of equipment, updated text, video and other unstructured data, as well as analysis process and result data from various business information systems of the power grid, and store them as incremental data in the relevant data structure corresponding to the digital twin model resource; At present, the actual application of digital twin technology in the power grid still has the following bottlenecks: It is difficult to update the digital twin model. A large number of data acquisition devices are deployed in the power grid with a wide collection range and high frequency. However, due to the limitations of transmission and other conditions, the update speed of the digital twin model of the power grid is difficult to keep up with the actual collection speed. The real-time performance of the data in the digital twin model is not high, and it is difficult to meet the real-time analysis business needs; The present invention adopts a data synchronization interface, which can regularly synchronize power grid data and solve the problem of difficult data updating of the digital twin model.

[0098] In the above step 1, each call request is parsed to obtain the call type of the call request, one call type corresponds to one of the above business types, and according to the call type, a search is performed in the cache area of ​​the digital twin model of the power grid to determine whether the data required for the call request exists; the present invention can quickly determine the corresponding target cache area in the cache area of ​​the digital twin model of the power grid through the correspondence between the call type of the call request and the business type.

[0099] In the above step 2, if the target cache area of ​​the power grid digital twin model cache area has all the required call data, all the required data in the target cache area will be returned to the requester of the call request; the present invention returns all the required call data in the target cache area to the requester, indicating that the power grid digital twin model cache area itself has the call data required by the requester, and by responding to this call request, the update status of the power grid digital twin model is promptly fed back to the requester.

[0100] The above step 3 includes, when there is no call data or part of the call data is missing in the cache area of ​​the power grid digital twin model, using the pre-built power grid digital twin model resources and the demand data call rule to obtain complete call data, returning the complete call data to the requester of the call request, and completing the dynamic update of the power grid digital twin model, including:

[0101] When the demand data does not exist in the target cache area in the power grid digital twin model cache area or part of the demand data is missing, the demand data calling rules are adopted to call the demand data from the data block in the power grid digital twin model resource to the target cache area through the power grid digital twin model calling interface, and then the complete demand data in the target cache area is returned to the requester of the calling request to complete the dynamic update of the power grid digital twin model.

[0102] For example, Figure 5 The figure shows a schematic diagram of calling digital twin model resources based on access requests.

[0103] In one implementation, when the demand data does not exist in the target cache area in the power grid digital twin model cache area or part of the demand data is missing, the demand data calling rule is adopted, and the demand data is called from the data block in the power grid digital twin model resource to the target cache area through the power grid digital twin model calling interface, and then the complete demand data in the target cache area is returned to the requester of the calling request, so as to complete the dynamic update of the power grid digital twin model, including:

[0104] When the demand data does not exist in the target cache area or part of the demand data is missing, the demand data is called from the power grid digital twin model resources to the corresponding type data in the target cache area through the power grid digital twin model calling interface;

[0105] For example, if it does not exist or partially does not exist, the required data resources are extracted from the digital twin model resources into the target cache area.

[0106] Then, the required data in the target buffer area is returned to the requester of the call request, and the number of each type of data in the current target buffer area is counted;

[0107] For example, all required data are returned from the cache to the requester, and the business call classification corresponding to the data resource is assigned a business identifier, and the call type count is increased by 1 in the count field; at the same time, the number of time series data in the data resource newly transferred into the target cache is Se_count, then the time series data statistics value in the cache is Total_count=Total_count+k, where k is the number of time series data in the data resource newly transferred into the target cache;

[0108] When the number of types of each type of data does not exceed the type number threshold corresponding to each type of data, the dynamic update of the power grid digital twin model is completed;

[0109] For example, the type quantity threshold corresponding to each type of data in each buffer area is defined, as shown in Table 1:

[0110] Table 1 Threshold settings

[0111]

[0112] The data in the digital twin model resources are divided into multiple types of data, such as time series, basic archives, text, and video. The number of types in each cache area is recorded as sort_count, and data scale thresholds are defined for time series data, text, video and other types of data, that is, the type quantity threshold corresponding to each type of data.

[0113] When the number of types of existing type data exceeds the type number threshold corresponding to the type data, part of the data in the type data is deleted so that the number of types of the type data is within the type number threshold, and the dynamic update of the power grid digital twin model is completed.

[0114] In one implementation, when the number of types of existing type data exceeds a type number threshold corresponding to the type data, deleting part of the type data so that the number of types of the type data is within the type number threshold, the dynamic update of the power grid digital twin model is completed, including:

[0115] When the number of types of type data exceeds the type number threshold corresponding to the type data, the count fields of the type data are arranged in ascending order to generate an ascending set;

[0116] The smallest count field in the ascending set is used as the count field with the lowest call frequency, and the count field with the lowest call frequency is extracted to determine the data with the lowest call frequency;

[0117] When the data with the lowest call frequency is a single piece of data, the single piece of data is deleted until the number of types of the type of data is within the type number threshold, thereby completing the dynamic update of the power grid digital twin model;

[0118] When the data with the lowest call frequency is a data set, the data set is sorted in chronological order to obtain a time series, and the earliest data is found from the time series, taken out and deleted, until the number of types of the type data is within the type number threshold, and the dynamic update of the power grid digital twin model is completed.

[0119] For example, the relationship between the statistical values ​​and thresholds of all types of data in the target cache interval is compared. There are a total of sort_count types of data. Taking time series data as an example, if the statistical value of the time series data, recorded as Total_count, is less than or equal to the time series data quantity threshold, recorded as Se_threshold, then the access frequency of the counting data block is reduced by one, recorded as sort_count_tmp=sort_count-1. If the access frequency of the counting data block is reduced to less than or equal to 0, that is, sort_count_tmp≤0, the call ends and the update of the power grid digital twin model is completed. If sort_count_tmp>0, continue to determine whether the statistical values ​​of other types of data exceed the threshold.

[0120] If the count field value Total_count of the time series data is greater than the threshold Se_threshold, the count fields of all the time series data in the cache interval are arranged in ascending order. The ascending queue is as follows:

[0121] Map_asc(Se1,Se2,...,Sen)

[0122] Wherein, Map_asc is the ascending queue, and Sen is the count field value of the nth time series data;

[0123] Find the data with the lowest frequency of calls from the ascending queue. If the data with the lowest frequency is a single piece of data, take it out and destroy it. If it is not a single piece of data but a data set, as follows:

[0124] [Sei, Sej, .., Sev]

[0125] Among them, Sei is the i-th data in the data set, Sej is the j-th data in the data set, and Sev is the v-th data in the data set;

[0126] Sort the data set again in chronological order and find the earliest data Se from the data set. k , take out and destroy the data, continue to compare the statistical value Total_count with the threshold Se_threshold, if the statistical value Total_count>Se_threshold, continue to sort the count fields of all the time series data in the cache interval in ascending order as mentioned above, find the data with the lowest call frequency and destroy it, until Total_count≤Se_threshold, perform sort_count_tmp=sort_count-1, if the access frequency of the counting data block is reduced to less than or equal to 0, that is, sort_count_tmp≤0, the call ends, and the update of the power grid digital twin model is completed; for the dynamic update of the power grid digital twin model, there is a low efficiency in calling data resources, resulting in business The application has performance bottlenecks and poor application effects. The present invention introduces a method based on data resource call frequency analysis to optimize the scheduling of digital twin data resources; the present invention uses a method of calling demand data from the power grid digital twin model cache area or the power grid digital twin model resource according to the call request to update the power grid digital twin model. The power grid digital twin model cache area is constructed according to a clear business type. Therefore, when responding to the call request, the power grid digital twin model can quickly determine the demand data corresponding to the call request according to the clear business type, thereby solving the problem of the prior art that due to the fuzzy business data, it is impossible to identify clear data requirements according to the call request, which makes it difficult to update the model by active request, thereby realizing the immediate update of the power grid digital twin model; such as Figure 6 As shown, the present invention first integrates and fuses the three-dimensional point cloud data of equipment and facilities in the power grid, the equipment operating status parameters in each business system of the power grid, unstructured data such as text and video, the results in the business analysis model, and the intermediate process data to construct a digital twin model resource for the power grid, and then establishes a digital twin cache area to store the data resources required for business calls in the cache area. Data swapping rules are formulated according to the access frequency. When the cache space is insufficient, the data with lower access frequency is swapped out and replaced with high-frequency data to improve the accuracy of the cache data resources in responding to data call requests. The above method for constructing and dynamically updating a digital twin model for a power grid based on comprehensive considerations of update cost and accuracy can solve the data access bottleneck problem for power grid business applications and optimize the data base for power grid digital twin applications.

[0127] Embodiment 2:

[0128] Based on the same inventive concept, the present invention patent application also provides a dynamic update system for a power grid digital twin model. Figure 7As shown, it includes: a calling data confirmation module, a first dynamic update module and a second dynamic update module;

[0129] The call data confirmation module is used to determine whether there is corresponding call data in a pre-built power grid digital twin model cache area based on the call request;

[0130] The first dynamic update module is used to return the call data to the requester of the call request when there is the call data in the cache area of ​​the power grid digital twin model, so as to complete the dynamic update of the power grid digital twin model;

[0131] The second dynamic update module is used to obtain complete call data by using the pre-built power grid digital twin model resources and the demand data call rules when there is no call data or part of the call data is missing in the power grid digital twin model cache area, and return the complete call data to the requester of the call request to complete the dynamic update of the power grid digital twin model;

[0132] Among them, the power grid digital twin model includes power grid digital twin model resources and power grid digital twin model cache area; the power grid digital twin model resources are constructed based on the power grid equipment data in the target area, and the power grid digital twin model cache area is constructed based on the business type.

[0133] Preferably, it also includes: a power grid digital twin model resource construction module, which is used to:

[0134] According to the power grid equipment data of the target area, the power grid equipment data is divided into relational power grid equipment data and unstructured power grid equipment data;

[0135] Taking the equipment facility name in each of the relational power grid equipment data as a unit, constructing a data block corresponding to the relational power grid equipment data in the power grid digital twin model resource;

[0136] Taking each data file of the unstructured power grid device data as a unit, constructing a data block corresponding to the unstructured power grid device data in the power grid digital twin model resource;

[0137] After adding an identifier to each of the data blocks, the construction of the power grid digital twin model resources is completed;

[0138] Among them, each of the power grid equipment data includes at least one or more of the following: equipment facility name, equipment facility number, equipment facility type, equipment facility three-dimensional point cloud data, equipment facility basic file information, equipment facility operating status, equipment facility timing collection, equipment facility text and equipment facility video; the identification includes: data request type number and request number; each of the data blocks corresponds to the type data of a power grid equipment.

[0139] Preferably, it also includes: a power grid digital twin model cache area construction module, which is used to:

[0140] According to the predetermined business type, a cache area corresponding to each business type is constructed;

[0141] Determine the type data of the power grid equipment stored in each buffer area according to the business data corresponding to the business type, and complete the construction of the buffer area of ​​the power grid digital twin model;

[0142] Among them, the business types include at least one or more of the following: load forecasting, equipment failure analysis and power outage scope analysis; the type data includes one or more of the following: time-series power grid equipment data, basic archive power grid equipment data, text-type power grid equipment data and then video-type power grid equipment data.

[0143] Preferably, it also includes: a data synchronization interface module, used for:

[0144] A data synchronization interface is used to periodically extract power grid equipment data in the target area, and data synchronization is performed in the power grid digital twin model resources.

[0145] Preferably, the second dynamic update module includes:

[0146] The calling interface is combined with the calling rules to complete the update sub-module, which is used to use the demand data calling rules to call the demand data from the data block in the power grid digital twin model resource to the target cache area through the power grid digital twin model calling interface when the demand data does not exist in the target cache area in the power grid digital twin model cache area or part of the demand data is missing, and then return the complete demand data in the target cache area to the requester of the calling request to complete the dynamic update of the power grid digital twin model.

[0147] Preferably, the calling interface completes the updating submodule in combination with the calling rules, including:

[0148] A demand data calling unit, configured to call the demand data from the power grid digital twin model resources to the corresponding type of data in the target cache area through the power grid digital twin model calling interface when the demand data does not exist in the target cache area or part of the demand data is missing;

[0149] A type quantity counting unit, used to return the required data in the target buffer area to the requester of the call request, and count the type quantity of each type of data currently in the target buffer area;

[0150] A dynamic update unit whose type quantity does not exceed a threshold value is used to complete the dynamic update of the power grid digital twin model when the type quantity of each type of data does not exceed the type quantity threshold value corresponding to each type of data;

[0151] The dynamic update unit for type quantity exceeding a threshold is used to delete part of the data in the type data when the type quantity of the existing type data exceeds the type quantity threshold corresponding to the type data, so as to complete the dynamic update of the power grid digital twin model when the type quantity of the type data is within the type quantity threshold.

[0152] Preferably, the dynamic update unit whose number of types exceeds a threshold is specifically used for:

[0153] When the number of types of type data exceeds the type number threshold corresponding to the type data, the count fields of the type data are arranged in ascending order to generate an ascending set;

[0154] The smallest count field in the ascending set is used as the count field with the lowest call frequency, and the count field with the lowest call frequency is extracted to determine the data with the lowest call frequency;

[0155] When the data with the lowest call frequency is a single piece of data, the single piece of data is deleted until the number of types of the type of data is within the type number threshold, thereby completing the dynamic update of the power grid digital twin model;

[0156] When the data with the lowest call frequency is a data set, the data set is sorted in chronological order to obtain a time series, and the earliest data is found from the time series, taken out and deleted, until the number of types of the type data is within the type number threshold, and the dynamic update of the power grid digital twin model is completed.

[0157] Example 3

[0158] like Figure 8As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0159] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to realize the steps of a dynamic update method of a power grid digital twin model in the above-mentioned embodiment.

[0160] Example 4

[0161] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a dynamic update method of a power grid digital twin model in the above embodiment.

[0162] It should be understood by those skilled in the art that the embodiments of the present invention patent application can be provided as methods, systems, or computer program products. Therefore, the present invention patent application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention patent application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0163] The present invention patent application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present invention patent application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the patent application of the present invention rather than to limit its protection scope. Although the patent application of the present invention is described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the patent application of the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A dynamic update method for a power grid digital twin model, It is characterized in that include: Based on the call request, determine whether there is corresponding call data in the pre-built power grid digital twin model cache area; When the call data is in the cache area of ​​the power grid digital twin model, the call data is returned to the requester of the call request to complete the dynamic update of the power grid digital twin model; When the call data is not present in the cache area of ​​the power grid digital twin model or part of the call data is missing, complete call data is obtained by using the pre-built power grid digital twin model resources and the demand data call rule, and the complete call data is returned to the requester of the call request to complete the dynamic update of the power grid digital twin model; Among them, the power grid digital twin model includes power grid digital twin model resources and power grid digital twin model cache area; the power grid digital twin model resources are constructed based on the power grid equipment data in the target area, and the power grid digital twin model cache area is constructed based on the business type.

2. The method according to claim 1, It is characterized in that The construction process of the power grid digital twin model resource includes: According to the power grid equipment data of the target area, the power grid equipment data is divided into relational power grid equipment data and unstructured power grid equipment data; Taking the equipment facility name in each of the relational power grid equipment data as a unit, constructing a data block corresponding to the relational power grid equipment data in the power grid digital twin model resource; Taking each data file of the unstructured power grid device data as a unit, constructing a data block corresponding to the unstructured power grid device data in the power grid digital twin model resource; After adding an identifier to each of the data blocks, the construction of the power grid digital twin model resources is completed; Among them, each of the power grid equipment data includes at least one or more of the following: equipment facility name, equipment facility number, equipment facility type, equipment facility three-dimensional point cloud data, equipment facility basic file information, equipment facility operating status, equipment facility timing collection, equipment facility text and equipment facility video; the identification includes: data request type number and request number; each of the data blocks corresponds to the type data of a power grid equipment.

3. The method according to claim 1, It is characterized in that The construction process of the power grid digital twin model cache area includes: According to the predetermined business type, a cache area corresponding to each business type is constructed; Determine the type data of the power grid equipment stored in each buffer area according to the business data corresponding to the business type, and complete the construction of the buffer area of ​​the power grid digital twin model; Among them, the business types include at least one or more of the following: load forecasting, equipment failure analysis and power outage scope analysis; the type data includes one or more of the following: time-series power grid equipment data, basic archive power grid equipment data, text-type power grid equipment data and then video-type power grid equipment data.

4. The method according to claim 1, It is characterized in that Also includes: A data synchronization interface is used to periodically extract power grid equipment data in the target area, and data synchronization is performed in the power grid digital twin model resources.

5. The method according to claim 1, It is characterized in that When the power grid digital twin model cache area does not contain the call data or lacks part of the call data, the pre-built power grid digital twin model resources are used to adopt the demand data call rule to obtain complete call data, and the complete call data is returned to the requester of the call request to complete the dynamic update of the power grid digital twin model, including: When the demand data does not exist in the target cache area in the power grid digital twin model cache area or part of the demand data is missing, the demand data calling rules are adopted to call the demand data from the data block in the power grid digital twin model resource to the target cache area through the power grid digital twin model calling interface, and then the complete demand data in the target cache area is returned to the requester of the calling request to complete the dynamic update of the power grid digital twin model.

6. The method according to claim 5, It is characterized in that When the demand data does not exist in the target cache area in the power grid digital twin model cache area or part of the demand data is missing, the demand data calling rule is adopted, and the demand data is called from the data block in the power grid digital twin model resource to the target cache area through the power grid digital twin model calling interface, and then the complete demand data in the target cache area is returned to the requester of the calling request, so as to complete the dynamic update of the power grid digital twin model, including: When the demand data does not exist in the target cache area or part of the demand data is missing, the demand data is called from the power grid digital twin model resources to the corresponding type data in the target cache area through the power grid digital twin model calling interface; Then, the required data in the target buffer area is returned to the requester of the call request, and the number of each type of data in the current target buffer area is counted; When the number of types of each type of data does not exceed the type number threshold corresponding to each type of data, the dynamic update of the power grid digital twin model is completed; When the number of types of existing type data exceeds the type number threshold corresponding to the type data, part of the data in the type data is deleted so that the number of types of the type data is within the type number threshold, and the dynamic update of the power grid digital twin model is completed.

7. The method according to claim 6, It is characterized in that When the number of types of existing type data exceeds a type number threshold corresponding to the type data, deleting part of the type data so that the number of types of the type data is within the type number threshold, the dynamic update of the power grid digital twin model is completed, including: When the number of types of type data exceeds the type number threshold corresponding to the type data, the count fields of the type data are arranged in ascending order to generate an ascending set; The smallest count field in the ascending set is used as the count field with the lowest call frequency, and the count field with the lowest call frequency is extracted to determine the data with the lowest call frequency; When the data with the lowest call frequency is a single piece of data, the single piece of data is deleted until the number of types of the type of data is within the type number threshold, thereby completing the dynamic update of the power grid digital twin model; When the data with the lowest call frequency is a data set, the data set is sorted in chronological order to obtain a time series, and the earliest data is found from the time series, taken out and deleted, until the number of types of data of the type is within the type number threshold, and the dynamic update of the power grid digital twin model is completed.

8. A dynamic update system for power grid digital twin model, It is characterized in that include: Calling a data confirmation module, a first dynamic update module, and a second dynamic update module; The call data confirmation module is used to determine whether there is corresponding call data in a pre-built power grid digital twin model cache area based on the call request; The first dynamic update module is used to return the call data to the requester of the call request when there is the call data in the cache area of ​​the power grid digital twin model, so as to complete the dynamic update of the power grid digital twin model; The second dynamic update module is used to obtain complete call data by using the pre-built power grid digital twin model resources and the demand data call rules when there is no call data or part of the call data is missing in the power grid digital twin model cache area, and return the complete call data to the requester of the call request to complete the dynamic update of the power grid digital twin model; Among them, the power grid digital twin model includes power grid digital twin model resources and power grid digital twin model cache area; the power grid digital twin model resources are constructed based on the power grid equipment data in the target area, and the power grid digital twin model cache area is constructed based on the business type.

9. The system of claim 8, It is characterized in that Also includes: Power grid digital twin model resource building blocks for: According to the power grid equipment data of the target area, the power grid equipment data is divided into relational power grid equipment data and unstructured power grid equipment data; Taking the equipment facility name in each of the relational power grid equipment data as a unit, constructing a data block corresponding to the relational power grid equipment data in the power grid digital twin model resource; Taking each data file of the unstructured power grid device data as a unit, constructing a data block corresponding to the unstructured power grid device data in the power grid digital twin model resource; After adding an identifier to each of the data blocks, the construction of the power grid digital twin model resources is completed; Among them, each of the power grid equipment data includes at least one or more of the following: equipment facility name, equipment facility number, equipment facility type, equipment facility three-dimensional point cloud data, equipment facility basic file information, equipment facility operating status, equipment facility timing collection, equipment facility text and equipment facility video; the identification includes: data request type number and request number; each of the data blocks corresponds to the type data of a power grid equipment.

10. The system according to claim 8, It is characterized in that Also includes: The power grid digital twin model buffer area building module is used to: According to the predetermined business type, a cache area corresponding to each business type is constructed; Determine the type data of the power grid equipment stored in each buffer area according to the business data corresponding to the business type, and complete the construction of the buffer area of ​​the power grid digital twin model; Among them, the business types include at least one or more of the following: load forecasting, equipment failure analysis and power outage scope analysis; the type data includes one or more of the following: time-series power grid equipment data, basic archive power grid equipment data, text-type power grid equipment data and then video-type power grid equipment data.

11. The system of claim 8, It is characterized in that Also includes: Data synchronization interface module, used for: A data synchronization interface is used to periodically extract power grid equipment data in the target area, and data synchronization is performed in the power grid digital twin model resources.

12. The system of claim 8, It is characterized in that The second dynamic update module includes: The calling interface is combined with the calling rules to complete the update sub-module, which is used to use the demand data calling rules to call the demand data from the data block in the power grid digital twin model resource to the target cache area through the power grid digital twin model calling interface when the demand data does not exist in the target cache area in the power grid digital twin model cache area or part of the demand data is missing, and then return the complete demand data in the target cache area to the requester of the calling request to complete the dynamic update of the power grid digital twin model.

13. The system of claim 12, It is characterized in that The calling interface combines the calling rules to complete the updating submodule, including: A demand data calling unit, configured to call the demand data to corresponding type data of the target cache area from the power grid digital twin model resources through a power grid digital twin model calling interface when the demand data does not exist in the target cache area or part of the demand data is missing; A type quantity counting unit, used to return the required data in the target buffer area to the requester of the call request, and count the type quantity of each type of data currently in the target buffer area; A dynamic update unit whose type quantity does not exceed a threshold value is used to complete the dynamic update of the power grid digital twin model when the type quantity of each type of data does not exceed the type quantity threshold value corresponding to each type of data; The dynamic update unit for type quantity exceeding a threshold is used to delete part of the data in the type data when the type quantity of the existing type data exceeds the type quantity threshold corresponding to the type data, so as to complete the dynamic update of the power grid digital twin model when the type quantity of the type data is within the type quantity threshold.

14. The system of claim 13, It is characterized in that The dynamic update unit whose number of types exceeds a threshold is specifically used for: When the number of types of type data exceeds the type number threshold corresponding to the type data, the count fields of the type data are arranged in ascending order to generate an ascending set; The smallest count field in the ascending set is used as the count field with the lowest call frequency, and the count field with the lowest call frequency is extracted to determine the data with the lowest call frequency; When the data with the lowest call frequency is a single piece of data, the single piece of data is deleted until the number of types of the type of data is within the type number threshold, thereby completing the dynamic update of the power grid digital twin model; When the data with the lowest call frequency is a data set, the data set is sorted in chronological order to obtain a time series, and the earliest data is found from the time series, taken out and deleted, until the number of types of data of the type is within the type number threshold, and the dynamic update of the power grid digital twin model is completed.

15. An electronic device, It is characterized in that include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a dynamic update method of a power grid digital twin model as described in any one of claims 1 to 7 is implemented.

16. A readable storage medium, It is characterized in that An execution program is stored thereon, and when the execution program is executed, a dynamic update method of a power grid digital twin model as described in any one of claims 1 to 7 is implemented.

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