Automatic updating method and device of power equipment data model, equipment and medium

Abstract

The invention provides an automatic updating method and device for a power equipment data model, equipment and a medium. The method comprises the following steps: acquiring state parameter data of stage operation of power equipment, and removing abnormal data in the state parameter data; determining the similarity between the state parameter data and the characteristic data of the operation of the power equipment; storing the state parameter data of the threshold values with relatively small similarity in an equipment characteristic database; and constructing or updating the power equipment data model based on the data stored in the equipment characteristic database. In the mode, the data stored in the equipment characteristic database can be adaptively adjusted, and the power equipment data model can be automatically adjusted along with the change of the running state of the equipment through a dynamic updating mechanism, so that model deviation caused by data aging is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power equipment, and in particular to a method, apparatus, equipment and medium for automatically updating an electric power equipment data model. Background Art

[0002] Currently, dynamic updates of power equipment data models are a core requirement of smart grids and the Industrial Internet of Things. As the amount of equipment operating data surges, traditional approaches that rely on mechanism-based models or static data modeling struggle to adapt to performance drift caused by equipment aging and environmental changes.

[0003] However, although the data-driven model in the existing technology can be automatically updated through real-time data, it has the following problems: 1. Model deviation from physical mechanism: Pure data-driven models may deviate from the actual physical characteristics of the equipment due to noisy data or abnormal operating conditions, leading to misjudgment.

[0004] 2. Low update efficiency: Existing solutions (such as dynamically adjusting encoding capabilities based on system load) are not optimized for the characteristics of power equipment data, and the update process is computationally redundant.

[0005] 3. Insufficient data screening: Relying on system performance thresholds, abnormal data from power equipment is not actively cleaned, affecting model accuracy. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for automatically updating the data model of power equipment, which is suitable for the intelligent operation and maintenance and status assessment of power equipment. It can be widely used in scenarios such as power grids and new energy power stations to provide reliable data support for equipment health management.

[0007] In the first aspect, an embodiment of the present invention provides a method for automatically updating a data model of an electric power equipment, the method comprising: obtaining state parameter data of a stage operation of the electric power equipment, and eliminating abnormal data in the state parameter data; determining the similarity between the state parameter data and characteristic data of the operation of the electric power equipment; storing the state parameter data with a smaller similarity threshold in a device characteristic database; and constructing or updating the electric power equipment data model based on the data stored in the device characteristic database.

[0008] In an optional embodiment of the present application, the above-mentioned step of eliminating abnormal data in the state parameter data includes: determining data points under abnormal operating conditions based on the state parameter data, and determining abnormal time periods based on the time of the data points; and eliminating the state parameter data corresponding to the abnormal time period from the state parameter data.

[0009] In an optional embodiment of the present application, the above-mentioned characteristic data of the operation of the power equipment includes: factory characteristic parameters of the power equipment and historical operation characteristic data of the power equipment.

[0010] In an optional embodiment of the present application, the step of determining the similarity between the state parameter data and the characteristic data of the power equipment operation includes: performing singular value decomposition on the state parameter data and the characteristic data of the power equipment operation to obtain singular values; wherein the singular value decomposition method includes: complete SVD, truncated SVD, randomized SVD, block SVD, power iteration method, or Lanczos algorithm; the specific singular value method selected for processing the data is selected based on the specific characteristics of the power equipment data. If the singular value is greater than a preset threshold, the similarity is determined to be low; if the singular value is less than or equal to the preset threshold, the similarity is determined to be high.

[0011] In an optional embodiment of the present application, the above method also includes: dividing the data stored in the equipment feature database into standard data and characteristic operation data; wherein the standard data includes factory parameters and new equipment operation parameters, and the characteristic operation data includes dynamically updated status parameter data; based on the preset ratio of standard data and characteristic operation data, determining the amount of standard data and characteristic operation data in the data stored in the equipment feature database.

[0012] In an optional embodiment of the present application, the above-mentioned step of storing the status parameter data in the device feature database includes: if the data stored in the device feature database exceeds a preset threshold, determining the priority of the data stored in the device feature database based on the data retention time and similarity; and replacing the data with the lowest priority in the device feature database with the status parameter data.

[0013] In an optional embodiment of the present application, the above-mentioned step of constructing or updating the power equipment data model based on the data stored in the equipment feature database includes: determining the modeling data from the data stored in the equipment feature database based on the ratio of standard data and characteristic operation data; and constructing or updating the power equipment data model based on the modeling data.

[0014] In the second aspect, an embodiment of the present invention also provides an automatic updating device for an electric power equipment data model, the device including: a state parameter data acquisition module, used to obtain the state parameter data of the stage operation of the electric power equipment and eliminate abnormal data in the state parameter data; a state parameter data storage module, used to determine the similarity between the state parameter data and the characteristic data of the electric power equipment operation; store the state parameter data with a smaller similarity threshold in the equipment characteristic database; and a data model construction or update module, used to construct or update the electric power equipment data model based on the data stored in the equipment characteristic database.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the above-mentioned method for automatically updating the power equipment data model.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned method for automatically updating the power equipment data model.

[0017] The embodiments of the present invention bring the following beneficial effects: Embodiments of the present invention provide a method, apparatus, device, and medium for automatically updating a power equipment data model. These methods include obtaining state parameter data of power equipment during operation, removing abnormal data from the state parameter data, determining the similarity between the state parameter data and characteristic data of the power equipment operation, storing state parameter data with a lower threshold of similarity in a device characteristic database, and constructing or updating a power equipment data model based on the data stored in the device characteristic database. This method allows for adaptive adjustment of the data stored in the device characteristic database. Through a dynamic update mechanism, the power equipment data model can automatically adjust as the equipment's operating status changes, avoiding model deviations caused by data aging.

[0018] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by practicing the above-mentioned technology of the present disclosure.

[0019] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A flowchart of a method for automatically updating a power equipment data model provided by an embodiment of the present invention; Figure 2 A schematic diagram of a method for automatically updating a power equipment data model provided by an embodiment of the present invention; Figure 3A flowchart of another method for automatically updating a power equipment data model provided by an embodiment of the present invention; Figure 4 A schematic structural diagram of an automatic updating device for a power equipment data model provided by an embodiment of the present invention; Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] Based on this, an embodiment of the present invention provides an automatic update method, device, equipment and medium for the data model of power equipment, which relates to the field of power equipment modeling and power equipment intelligence, and aims to: realize the dynamic fusion of mechanism model and data-driven model, and constrain the physical rationality of the data model through factory parameters; design an automatic data screening mechanism based on SVD (Singular Value Decomposition) to eliminate abnormal data and retain high-value feature data; establish priority replacement rules to optimize the update efficiency of the feature database and reduce computing overhead.

[0024] To facilitate understanding of this embodiment, a method for automatically updating a power equipment data model disclosed in an embodiment of the present invention is first introduced in detail.

[0025] Example 1: An embodiment of the present invention provides a method for automatically updating a data model for power equipment, aiming to achieve dynamic modeling and self-correction of power equipment operating status data. The data model in this embodiment refers to a mathematical or data-driven model for describing and predicting the operating status and performance of power equipment, established by collecting and analyzing historical data on power equipment operation.

[0026] Based on the above description, see Figure 1 The flowchart of a method for automatically updating a data model of an electric power device is shown. The method for automatically updating a data model of an electric power device comprises the following steps: Step S102: obtaining state parameter data of the power equipment during operation, and removing abnormal data from the state parameter data.

[0027] See also Figure 2The diagram shows a method for automatically updating a data model of an electric power device. In this embodiment, data preprocessing and screening can be performed first to eliminate abnormal data in the state parameter data.

[0028] In some embodiments, data points under abnormal operating conditions can be determined based on the state parameter data, and abnormal time periods can be determined based on the time of the data points; and the state parameter data corresponding to the abnormal time period can be eliminated from the state parameter data.

[0029] like Figure 2 As shown, this embodiment can filter the status parameter data of the power equipment stage operation, identify and extract data points under non-normal operation (i.e., abnormal operation) conditions, and eliminate the entire data before and after the abnormal data point (i.e., abnormal time period) to ensure data quality.

[0030] For example, assuming that the time of the data point under abnormal operating conditions is x seconds, the 5 seconds before and after x seconds can be used as the abnormal time period, and the entire data of the 5 seconds before and after x seconds can be eliminated.

[0031] Step S104 , determining the similarity between the state parameter data and the characteristic data of the power equipment operation; and storing the state parameter data with a smaller similarity threshold in the equipment characteristic database.

[0032] In this embodiment, the similarity between the state parameter data and the characteristic data of the power equipment operation can also be determined. If the similarity is small, it means that the state parameter data and the characteristic data of the power equipment operation are quite different, and the state parameter data is stored in the equipment characteristic database.

[0033] The characteristic data of the operation of the power equipment includes: the factory characteristic parameters of the power equipment and the historical operation characteristic data of the power equipment.

[0034] In some embodiments, the state parameter data and the characteristic data of the power equipment operation can be subjected to singular value decomposition to obtain singular values; wherein, the singular value decomposition methods include: complete SVD, truncated SVD, randomized SVD, block SVD, power iteration method or Lanczos algorithm; if the singular value is greater than a preset threshold, it is determined that the similarity is small; if the singular value is less than or equal to the preset threshold, it is determined that the similarity is large.

[0035] Among them, the complete SVD performs a complete singular value decomposition of the matrix; the truncated SVD only retains the first few largest singular values ​​and their corresponding vectors; the randomized SVD constructs a low-dimensional approximation space through random projection, and then accurately decomposes the small matrix; the block SVD recursively divides the matrix into blocks and uses parallel computing to accelerate the bidiagonalization process; the power iteration method iteratively approximates the main eigenvector of the matrix, and combined with the SVD, the maximum singular value can be obtained; the Lanczos algorithm transforms the symmetric matrix into a tridiagonal form through Krylov subspace iteration, and then calculates its eigenvalues.

[0036] like Figure 2 As shown, this embodiment can perform similarity determination and feature extraction. SVD similarity determination is performed on the preprocessed state parameter data and the characteristic data of the power equipment operation (for example, the factory characteristic parameters of the power equipment and the historical operation characteristic data of the power equipment). If the resulting singular value is greater than a preset threshold, it can be considered that the similarity between the state parameter data and the characteristic data of the power equipment operation is low. The state parameter data can be stored in the equipment feature database for subsequent construction or update of the power equipment data model.

[0037] SVD refers to the process of screening or evaluating the singular values ​​obtained by decomposition. The calculated singular values ​​represent the differences in feature intensity of the original data in different dimensions.

[0038] This embodiment can perform efficient screening of similarities, and the SVD-based similarity determination method can effectively screen key state parameter data, thereby improving the modeling accuracy of the power equipment data model.

[0039] Step S106: construct or update the power equipment data model based on the data stored in the equipment feature database.

[0040] In this embodiment, the power equipment data model can be constructed or updated based on the data stored in the equipment feature database, and the data stored in the equipment feature database can be adaptively adjusted. Through the dynamic update mechanism, the power equipment data model can be automatically adjusted as the equipment operating status changes, avoiding model deviations caused by data aging.

[0041] An embodiment of the present invention provides a method for automatically updating a power equipment data model. The method obtains state parameter data of power equipment during its phased operation, removes abnormal data from the state parameter data, determines the similarity between the state parameter data and characteristic data of the power equipment operation, stores state parameter data with a lower threshold of similarity in a device characteristic database, and constructs or updates the power equipment data model based on the data stored in the device characteristic database. This method allows for adaptive adjustment of the data stored in the device characteristic database. Through a dynamic update mechanism, the power equipment data model can automatically adjust as the equipment's operating status changes, avoiding model deviations caused by data aging.

[0042] Example 2: This embodiment provides another automatic update method for the data model of power equipment. This method is implemented on the basis of the above embodiment, focusing on the description of data hierarchical management and proportional control and dynamic data update mechanism. Figure 3 A flowchart of another method for automatically updating a data model of an electric power device is shown, and the method for automatically updating a data model of an electric power device includes the following steps: Step S302, obtaining state parameter data of the power equipment during operation, and removing abnormal data from the state parameter data; Step S304, determining the similarity between the state parameter data and the characteristic data of the power equipment operation; storing the state parameter data with a smaller similarity threshold in the equipment characteristic database; Step S306, divide the data stored in the equipment feature database into standard data and characteristic operation data; wherein, the standard data includes factory parameters and new equipment operation parameters, and the characteristic operation data includes dynamically updated status parameter data; based on the preset ratio of standard data and characteristic operation data, determine the amount of standard data and characteristic operation data in the data stored in the equipment feature database.

[0043] like Figure 2 As shown, this embodiment can perform data hierarchical management and proportional control: the data stored in the device feature database is divided into standard data (including factory parameters and new device operating parameters) and characteristic operating data (including dynamically updated status parameter data), and the amount of standard data and characteristic operating data in the data stored in the device feature database is determined through proportional control (such as the ratio of standard data to characteristic operating data is 2:3).

[0044] In some embodiments, if the data stored in the device feature database exceeds a preset threshold, the priority of the data stored in the device feature database is determined based on data retention time and similarity; the state parameter data replaces the data with the lowest priority in the device feature database.

[0045] like Figure 2As shown, this embodiment also provides a dynamic data update mechanism. When the data stored in the device feature database exceeds a set threshold, the priority of each data can be evaluated based on data retention time and similarity calculations, and the lowest priority data can be replaced to maintain the timeliness and representativeness of the database.

[0046] Step S308: construct or update the power equipment data model based on the data stored in the equipment feature database.

[0047] In some embodiments, modeling data is determined from data stored in a device feature database based on a ratio of standard data to characteristic operation data; and a power device data model is constructed or updated based on the modeling data.

[0048] In this embodiment, the ratio of standard data to characteristic operation data in the modeling data can be determined by proportional control (such as the ratio of standard data to characteristic operation data is 2:3), ensuring that the power equipment data model constructed or updated based on the modeling data not only conforms to the equipment mechanism, but also has the ability to adapt as the operating time increases.

[0049] This embodiment can perform mechanism fusion and ensure that the power equipment data model not only conforms to the theoretical characteristics of the equipment but also reflects the actual operation trend by controlling the ratio of standard data to characteristic operation data.

[0050] In summary, the above method provided in this embodiment can be applied to the intelligent operation and maintenance and status assessment of power equipment, and can be widely used in scenarios such as power grids and new energy power stations, providing reliable data support for equipment health management. It mainly includes the following contents: 1. Hybrid data modeling: Standard data and characteristic operation data are mixed in proportion to ensure that the power equipment data model has both physical interpretability and self-correction capabilities.

[0051] 2. SVD similarity determination: Perform singular value decomposition on the pre-processed state parameter data and the characteristic data of the power equipment operation, and only retain the state parameter data that meets the similarity standard for updating the power equipment data model.

[0052] 3. Dynamic priority replacement: The priority of each data is evaluated based on data retention time and similarity calculation, and low-priority and low-value feature data are automatically eliminated.

[0053] The above method provided in this embodiment mainly has the following advantages: 1. Improved accuracy and reliability: Through SVD screening and mechanism data blending, the power equipment data model is prevented from deviating from physical reality, improving the accuracy of fault diagnosis; abnormal data is automatically eliminated to reduce noise interference.

[0054] 2. Efficiency optimization: The dynamic priority replacement mechanism reduces storage and computing costs and is suitable for long-term power equipment. The dedicated process based on the characteristics of power data is more efficient than the general data model update method.

[0055] 3. Field adaptability: Designed specifically for power equipment, it supports the coordinated update of mechanisms and data, filling the gaps in existing technologies in this field.

[0056] 4. Fully automated: No human intervention is required from data cleaning to model updating, which is superior to semi-automated solutions that require setting information entropy thresholds.

[0057] Example 3: Corresponding to the above method embodiment, the embodiment of the present invention provides an automatic updating device for the data model of the power equipment, see Figure 4 The structure diagram of an automatic updating device for an electric power equipment data model shown in FIG. 1 includes: The state parameter data acquisition module 41 is used to obtain the state parameter data of the power equipment during operation and eliminate abnormal data in the state parameter data; The state parameter data storage module 42 is used to determine the similarity between the state parameter data and the characteristic data of the power equipment operation; and store the state parameter data with a smaller similarity threshold in the equipment characteristic database; The data model building or updating module 43 is used to build or update the power equipment data model based on the data stored in the equipment feature database.

[0058] An embodiment of the present invention provides an automatic updating device for an electric power equipment data model. The device obtains state parameter data of the electric power equipment during its operation phase, removes abnormal data from the state parameter data, determines the similarity between the state parameter data and characteristic data of the electric power equipment operation, stores the state parameter data with a lower threshold of similarity in a device characteristic database, and constructs or updates the electric power equipment data model based on the data stored in the device characteristic database. This method allows for adaptive adjustment of the data stored in the device characteristic database. Through a dynamic update mechanism, the electric power equipment data model can automatically adjust as the equipment's operating status changes, avoiding model deviations caused by data aging.

[0059] The above-mentioned state parameter data acquisition module is used to determine the data points under abnormal operating conditions based on the state parameter data, determine the abnormal time period based on the time of the data points; and eliminate the state parameter data corresponding to the abnormal time period from the state parameter data.

[0060] The above-mentioned characteristic data of the operation of the power equipment include: factory characteristic parameters of the power equipment and historical operation characteristic data of the power equipment.

[0061] The above-mentioned state parameter data storage module is used to perform singular value decomposition on the state parameter data and the characteristic data of the power equipment operation to obtain singular values; wherein the singular value decomposition methods include: complete SVD, truncated SVD, randomized SVD, block SVD, power iteration method or Lanczos algorithm; if the singular value is greater than a preset threshold, it is determined that the similarity is small; if the singular value is less than or equal to the preset threshold, it is determined that the similarity is large.

[0062] The above-mentioned device also includes: a data division module, which is used to divide the data stored in the equipment feature database into standard data and characteristic operation data; wherein, the standard data includes factory parameters and new equipment operation parameters, and the characteristic operation data includes dynamically updated status parameter data; based on the preset ratio of standard data and characteristic operation data, the amount of standard data and characteristic operation data in the data stored in the equipment feature database is determined.

[0063] The above-mentioned device also includes: a priority processing module, which is used to determine the priority of the data stored in the device feature database based on data retention time and similarity if the data stored in the device feature database exceeds a preset threshold; and replace the data with the lowest priority in the device feature database with the status parameter data.

[0064] The data model building or updating module is used to determine modeling data from the data stored in the equipment feature database based on the ratio of standard data and characteristic operation data; and to build or update the power equipment data model based on the modeling data.

[0065] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the automatic update device of the power equipment data model described above can refer to the corresponding process in the aforementioned embodiment of the automatic update method of the power equipment data model, and will not be repeated here.

[0066] Example 4: The embodiment of the present invention further provides an electronic device for executing the above-mentioned automatic updating method of the power equipment data model; Figure 5 A structural schematic diagram of an electronic device is shown, which includes a memory 100 and a processor 101, wherein the memory 100 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 101 to implement the above-mentioned automatic update method of the power equipment data model.

[0067] Furthermore, Figure 5 The electronic device shown further includes a bus 102 and a communication interface 103 , and the processor 101 , the communication interface 103 and the memory 100 are connected via the bus 102 .

[0068] The memory 100 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is achieved through at least one communication interface 103 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 102 may be an ISA bus, a PCI bus, or an EISA bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0069] The processor 101 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 101 or software instructions. The above processor 101 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as a random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or register. The storage medium is located in the memory 100, and the processor 101 reads the information in the memory 100 and, in conjunction with its hardware, completes the steps of the method of the aforementioned embodiment.

[0070] An embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned method for automatically updating the power equipment data model. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0071] The computer program product of the method, apparatus, device and medium for automatically updating the data model of electric power equipment provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method in the previous method embodiment. For specific implementation, please refer to the method embodiment and will not be repeated here.

[0072] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the system and / or device described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0073] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0074] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0075] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0076] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for automatically updating a power equipment data model, characterized in that: The method comprises: Acquire state parameter data of the power equipment during operation, and remove abnormal data from the state parameter data; Determining the similarity between the state parameter data and the characteristic data of the operation of the power equipment; storing the state parameter data with a smaller threshold value of the similarity in the equipment characteristic database; An electric power equipment data model is constructed or updated based on the data stored in the equipment feature database.

2. The method according to claim 1, characterized in that The step of eliminating abnormal data in the state parameter data includes: Determine a data point under an abnormal operating condition based on the state parameter data, and determine an abnormal time period based on the time of the data point; The state parameter data corresponding to the abnormal time period is removed from the state parameter data.

3. The method according to claim 1, characterized in that The characteristic data of the operation of the electric equipment includes: the factory characteristic parameters of the electric equipment and the historical operation characteristic data of the electric equipment.

4. The method according to claim 1, wherein The step of determining the similarity between the state parameter data and the characteristic data of the operation of the power equipment comprises: Performing singular value decomposition on the state parameter data and the characteristic data of the power equipment operation to obtain singular values; wherein the singular value decomposition method includes: full SVD, truncated SVD, randomized SVD, block SVD, power iteration method or Lanczos algorithm; If the singular value is greater than a preset threshold, determining that the similarity is small; If the singular value is less than or equal to a preset threshold, it is determined that the similarity is large.

5. The method according to claim 1, wherein The method further comprises: Dividing the data stored in the equipment feature database into standard data and feature operation data; wherein the standard data includes factory parameters and new equipment operation parameters, and the feature operation data includes dynamically updated status parameter data; Based on a preset ratio of the standard data to the characteristic operation data, the amount of the standard data and the characteristic operation data in the data stored in the device characteristic database is determined.

6. The method according to claim 1, characterized in that The step of storing the state parameter data in a device feature database comprises: If the data stored in the device feature database exceeds a preset threshold, determining the priority of the data stored in the device feature database based on the data retention time and the similarity; The state parameter data replaces the data with the lowest priority in the device feature database.

7. The method according to claim 5, characterized in that The step of constructing or updating the power equipment data model based on the data stored in the equipment feature database includes: determining modeling data from data stored in the device feature database based on a ratio of the standard data to the feature operation data; An electric equipment data model is constructed or updated based on the modeling data.

8. An automatic updating device for a power equipment data model, characterized in that: The device comprises: A state parameter data acquisition module is used to acquire state parameter data of the power equipment during operation and eliminate abnormal data in the state parameter data; A state parameter data storage module is configured to determine the similarity between the state parameter data and the characteristic data of the operation of the power equipment; and store the state parameter data with a smaller similarity threshold in a device characteristic database; A data model building or updating module is used to build or update the power equipment data model based on the data stored in the equipment feature database.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the automatic updating method of the power equipment data model according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the automatic updating method of the power equipment data model according to any one of claims 1 to 7.

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