A power data management method, system and device based on a digital twin architecture

Abstract

The application provides a power data management method, system and device based on a digital twin architecture, the method comprising: constructing a digital twin system suitable for a power system network structure based on the digital twin architecture according to the requirements of system computing resources, storage resources and network resources under different power grid scales; setting data access standards suitable for power grid business requirements for the digital twin system according to a data acquisition, transmission and storage standard system and technical requirements of a power grid enterprise; obtaining digital twin system data by using the data access standards, and processing the digital twin system data by using a data processing tool to realize relationship mapping and logical binding of models and structured data. The application uses digital twin technology to improve the enhanced perception, enhanced cognition and enhanced decision-making capabilities of a new power system, and solves the common demand problem of comprehensive acquisition, real-time simulation calculation and intelligent optimization collaboration of the new power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power data management, and more particularly to an electric power data management method, system and device based on a digital twin architecture. BACKGROUND

[0002] Improving the digital level of the power grid: it is the inevitable requirement to promote the deep integration of digital technology and energy technology, and to build a new type of power system; it is an important means to improve the power grid's ability to observe, measure, adjust and control, and to build a digital smart grid; it is an important support to realize the coordination and interaction of source, grid, load and storage, and to upgrade to an energy internet enterprise.

[0003] Digital twin technology has the advantages of panoramic visualization, intelligent diagnosis, deep analysis and efficient decision-making, and integrates sensing, modeling simulation, Internet of Things, cloud-edge collaboration, big data and other technologies. It has become an important technical means in manufacturing, medical care, transportation, smart cities and other fields, and is gradually improving and penetrating into various industries. In the future, digital twin will fully empower the digital transformation of the power grid, combined with artificial intelligence, 5G and other new generation technologies, to realize the application in the power grid generation, transmission, transformation, distribution and use, thereby supporting the digital transformation of the power grid.

[0004] At present, the digital twin technology of the power system is still in the early stage of development, and the following problems need to be solved: lack of efficient model-driven data management method; insufficient research on the correlation and interaction between data and models: the power system has large amount of data, multiple types and different structures, and there is a lack of practical tools for data verification, analysis and cleaning; there is no efficient automatic implementation method for the mapping relationship between data and models; in the new power system, there are various data types such as new energy, microgrid and energy storage, but due to different professional needs of companies, data is scattered in various business systems, making it difficult to retrieve and having different structures, forming data barriers and information islands. This brings obstacles to data utilization and business development, and cannot effectively utilize existing data. Digital twin technology can integrate and utilize the large amount of multi-source heterogeneous data in various business systems, maximize the value of data, realize data visualization, mapping and correlation between data and equipment, and coupled logical analysis between data, but at present, most of the research and application of digital twin technology is carried out by Internet companies, which do not have a deep understanding of the power system business, and there is little relevant research. This is an important problem that needs to be solved in the application of digital twin technology in the power system, thereby failing to solve the common demand problem of the new power system for comprehensive collection and control, real-time simulation calculation and intelligent optimization collaboration. SUMMARY

[0005] In view of the above problems, the present application aims to provide an electric power data management method, system and device based on a digital twin architecture.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A power data management method based on a digital twin architecture, comprising the following steps:

[0007] S1: Based on the requirements of system computing resources, storage resources, and network resources under different power grid scales, a digital twin system adapted to the power system network structure is constructed based on the digital twin architecture;

[0008] S2: Based on the data collection, transmission, and storage standard system and technical requirements of the power grid enterprise, set data access standards for the digital twin system that are adapted to the power grid business needs;

[0009] S3: Use the data access standard to obtain digital twin system data, and process the digital twin system data through data processing tools to achieve relationship mapping and logical binding between the model and structured data.

[0010] Further, step S1 includes:

[0011] Based on the digital twin architecture, the server-side program is deployed on the cloud platform, and workstations are deployed locally as edge devices. The data center, service business system, and on-site terminals are used as the system data sources of the digital twin system, and a digital twin system is constructed.

[0012] Configure planning and construction, equipment operation and maintenance, dispatching and operation, emergency repair command, power supply access, charging pile management, and customer service functions for the digital twin system, and set up data collection and access methods;

[0013] Configure multi-system data deep mining and fusion functions for the digital twin system;

[0014] By integrating multi-system data, a mapping relationship between data from different systems and digital twin models is established.

[0015] Furthermore, the data access standards adapted to the grid service requirements include:

[0016] Real-time perception information of the operating status of physical entities is obtained through sensors, edge computing terminals, and power distribution monitoring terminals. The real-time perception information is connected to the system according to the digital system data access specifications and converted into standardized protocol text.

[0017] Further, step S3 includes:

[0018] The digital twin system data is obtained using the data access standard, and data extraction, data cleaning and data binding of the digital twin system data are performed through data processing tools, data fusion tools and data visualization tools to achieve relationship mapping and logical binding between the model and structured data.

[0019] Furthermore, the data extraction process includes:

[0020] Extract point cloud data from the digital twin system data, and extract the coordinate data of the corresponding equipment and facilities from the point cloud data to determine the precise spatial distribution of the elements;

[0021] The ledger data obtained from the digital twin system data is used to calibrate the basic attributes of equipment and facilities, and to mark, read and locate target objects;

[0022] Business data is extracted from the digital twin system data. The business data includes the power status of the equipment, linkage logic, and subordinate relationships, which are used to improve the data dimensions of the digital twin substation.

[0023] Furthermore, the data cleaning process includes:

[0024] Filter the discrete noise in the point cloud data file and perform data sparse abstraction;

[0025] Fill in missing items, correct and delete duplicate items, verify and modify erroneous items in ledger data to generate standardized standard ledger data that can be bound and called;

[0026] Effectively aggregate discrete business data, structure the unstructured data, and filter and shield meaningless data.

[0027] Further, the data binding process includes:

[0028] Based on the cleaned digital twin system data and combined with the subordinate hierarchical structure of the 3D model, the corresponding data is bound to the model to establish a mapping relationship between the data and the model;

[0029] Bind the logic, topology relationships and information interfaces between discrete models.

[0030] Furthermore, multi-system data includes: real-time operation data of the dispatching automation system, ledger data of the control cloud system, ledger data of the PMS system, GIS system topology model, topological relationship and coordinate data, low-voltage ledger and operation data of the procurement system, SG186 system business expansion registration data, marketing audit system overcapacity data, marketing basic data platform customer files and station-line relationship data, Internet of Vehicles system ledger and operation data, meteorological data of the provincial power forecasting system, power supply service command system work order and fault defect data.

[0031] Accordingly, the present invention also discloses a power data management system based on a digital twin architecture, comprising:

[0032] A system construction module is configured to build a digital twin system adapted to the power system network structure based on the digital twin architecture according to the system computing resources, storage resources, and network resource requirements under different power grid scales; a data access module is configured to set data access standards for the digital twin system adapted to the power grid business needs based on the data collection, transmission, and storage standard system and technical requirements of the power grid enterprise;

[0033] The data processing module is configured to obtain digital twin system data using the data access standard and process the digital twin system data through a data processing tool to achieve relationship mapping and logical binding between the model and structured data.

[0034] Accordingly, the present invention discloses a power data management device based on a digital twin architecture, comprising:

[0035] Memory for storing power data management programs based on the digital twin architecture;

[0036] A processor is used to implement the steps of the power data management method based on the digital twin architecture as described in any of the above items when executing the power data management program based on the digital twin architecture.

[0037] Compared with the existing technology, the beneficial effects of the present invention are as follows: the present invention discloses a power data management method, system and device based on the digital twin architecture, which uses digital twin technology to improve the enhanced perception, enhanced cognition and enhanced decision-making capabilities of the new power system, solves the common demand problems of the new power system for comprehensive acquisition, measurement and control, real-time simulation calculation and intelligent optimization collaboration, and improves economic benefits. The present invention can effectively promote the integrated application of digital twin technology and power systems, so that the future power grid will develop in the direction of intelligence, flexibility and interactivity. Its large-scale application and promotion can provide support for the digital transformation of the power grid and improve the power supply reliability and efficiency of the power grid.

[0038] It can be seen that compared with the prior art, the present invention has outstanding substantial features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0040] Figure 1 It is a method flow chart of a specific embodiment of the present invention.

[0041] Figure 2 It is a system structure diagram of a specific implementation method of the present invention.

[0042] In the figure, 1. System construction module; 2. Data access module; 3. Data processing module. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0044] Example 1:

[0045] like Figure 1 As shown, this embodiment provides a power data management method based on a digital twin architecture, comprising the following steps:

[0046] S1: Based on the requirements of system computing resources, storage resources, and network resources under different power grid scales, a digital twin system that adapts to the power system network structure is constructed based on the digital twin architecture.

[0047] In the specific implementation, based on the digital twin architecture, the server-side program is first deployed on the cloud platform, and a local workstation is deployed as an edge device. The data center, service business system, and on-site terminals are used as the system data sources for the digital twin system, and the digital twin system is constructed. Then, the digital twin system is configured with planning and construction, equipment operation and maintenance, scheduling and operation, emergency repair command, power access, charging pile management, and customer service functions, and data collection and access methods are set. At the same time, the digital twin system is configured with multi-system data deep mining and fusion functions. Finally, by fusing multi-system data, a mapping relationship between data from different systems and the digital twin model is established.

[0048] As an example, in order to construct a digital twin system, by collecting and accessing power-related data, while further deepening and fusing system data, to realize the observation, measurement and control of various business data. The method is based on cloud platform to carry out digital twin architecture design, determine the deployment mode of digital twin system in information intranet and data collection form, form a typical cloud edge collaborative architecture design scheme suitable for power system; The scheme fully considers the advancement, ease of use, low maintenance and flexible expansion of the system architecture under the general conditions of meeting the requirements of power grid information system construction, access requirements, security requirements, etc. Through the study of the regular requirements of system computing resources, storage resources, network resources, etc. under different power grid scales, provide basis for similar system construction. Realize the multi-level, multi-source technology fusion analysis of digital twin system architecture from the bottom layer of data collection, to the near ground edge side of data instant disposal, to the cloud end of fusion processing, to the cloud upper layer of data visualization display.

[0049] Specifically, according to the digital twin architecture, a system architecture suitable for the network structure of the power system is constructed. Including: deploying the server program on the cloud platform, deploying the local workstation as the edge device, and the system data sources include data center, service business system, terminal direct sampling in the station, etc. The digital twin system collects and accesses data around the functions of planning construction, equipment operation and maintenance, dispatching operation, repair command, power supply access, charging pile management, customer service, etc. Deeply excavate and fuse each system data, mainly including real-time operation data of dispatching automation system, account data of dispatching control cloud system, account data of PMS system, GIS system topology model, topology relationship and coordinate data, low-voltage account and operation data of use and collection system, SG186 system industry expansion data, marketing inspection system super capacity data, marketing basic data platform customer file and station line relationship data, vehicle networking system account and operation data, meteorological data of provincial dispatching power prediction system, work order and fault defect data of power supply service command system. Through the fusion of multi-system data, the mapping relationship between data and digital twin model in different systems is established, and finally the deepening application of various business data is realized from the perspective of the whole regional power grid system. Data processing plays an especially important role in the process of twin modeling, which mainly realizes the relationship mapping and logical binding of three-dimensional data and structured data, so as to realize the parameterization driving of data to the model, thereby supporting the subsequent development of corresponding digital twin business.

[0050] S2: According to the data collection, transmission and storage standard system and technical requirements of the power grid enterprise, set the data access standard of the digital twin system to adapt to the business demand of the power grid.

[0051] In the specific implementation method, under the existing power grid enterprise data collection, transmission, and storage standard system and technical requirements, the standardized data access method of the digital twin system is studied; data interaction between the digital twin system and the enterprise data middle platform is realized, and real-time perception information of the operating status of physical entities by various sensors, edge computing terminals, distribution monitoring terminals, etc. is obtained to form a data access standard that can adapt to the business needs of the power grid.

[0052] Digital twin system data access, through data extraction and integration, creates a data model for the circuit system digital twin and combines it with digital twin system data access methods. According to the digital system data access specification, real-time sensor information is connected to the system and converted into standardized protocol text, thus providing a foundation and basis for the subsequent construction of similar systems.

[0053] S3: Use the data access standard to obtain digital twin system data, and process the digital twin system data through data processing tools to achieve relationship mapping and logical binding between the model and structured data.

[0054] In a specific implementation method, data extraction, data cleaning, and data binding are performed through data processing tools, data fusion tools, and data visualization tools to achieve relationship mapping and logical binding between the model and structured data, supporting the corresponding digital twin business development.

[0055] In this step, data extraction includes the extraction of point cloud data, ledger data, business data, and environmental data to improve the data dimensions of the digital twin system, thereby achieving accurate support for in-depth business.

[0056] Data extraction includes multiple channels and multiple types of data extraction. The specific process is as follows:

[0057] 1. Point cloud data. The most basic data extracted from point cloud data is the coordinate data of the corresponding equipment and facilities (including CGCS2000 geodetic Gaussian coordinates and 84 longitude and latitude coordinates). Based on this, we have the ability to understand the layout and size of each element in the environment, thereby achieving the grasp and implementation of the precise spatial distribution of elements.

[0058] 2. Inventory data: The inventory data obtained from the PMS system or other power information systems can be used to calibrate the basic attributes of equipment and facilities, and then used for accurate marking, reading and positioning of target objects in the later stage.

[0059] 3. Business data, thus the business data extracted from the business system, such as the power status of the equipment, linkage logic, subordinate relationships, etc., can better improve the data dimensions of the digital twin substation, thereby achieving accurate support for in-depth business.

[0060] In this step, the data cleaning process includes the verification, analysis, and selection of multi-source data. Point cloud data cleaning includes filtering discrete noise in point cloud files and abstracting overly dense data. Ledger data cleaning includes filling in missing items, correcting and deleting duplicates, and verifying and modifying erroneous items. Business data cleaning includes effectively aggregating originally discrete business data (multi-system data), structuring unstructured data, and filtering and shielding meaningless data, thereby improving the precision and accuracy of business data.

[0061] As an example, the specific process of data cleaning is as follows:

[0062] 1. Filter discrete noise in point cloud files, extract and abstract overly dense data, and optimize the scale and size of data as much as possible while ensuring accuracy;

[0063] 2. Ledger data cleaning includes filling in missing items (model and ledger quantity discrepancies, missing names, missing asset IDs, missing equipment IDs, etc.), correcting and deleting duplicate items (duplicate equipment IDs, duplicate names, duplicate asset IDs, etc.), and verifying and modifying erroneous items (abnormal truncation, garbled characters, special symbols, etc.). Ultimately, a standardized ledger data set is generated that can be bound and called.

[0064] 3. Clean and process business data, effectively aggregate originally discrete data (multi-system data), structure unstructured data (primary wiring diagram, etc.), filter and shield meaningless data (data not related to business), thereby improving the precision and accuracy of business data.

[0065] In this step, data binding is based on the cleaned and organized data, combined with the subordinate hierarchical structure of the three-dimensional model, to bind the corresponding data to the model, thereby building a mapping relationship between the data and the model; at the same time, the logical, topological associations and information interfaces between the discrete models are bound to achieve the ability to build a data-driven model in the later stage. Specifically, data binding is based on the above-mentioned cleaned and organized data, combined with the subordinate hierarchical structure of the three-dimensional model, to bind the corresponding data to the model, thereby building a mapping relationship between the data and the model, thereby achieving the ability to build a data-driven model in the later stage. Ultimately, a four-in-one comprehensive digital twin model is created, namely: the three-dimensional model determines the shape, the data model gives parameters, the mechanism model anchors the action, and the logical model connects the relationship.

[0066] This embodiment provides a power data management method based on a digital twin architecture. By utilizing digital twin technology, it improves the capabilities of enhanced perception, enhanced cognition, and enhanced decision-making of new power systems, and solves the common needs of new power systems for comprehensive data collection, measurement and control, real-time simulation calculation, and intelligent optimization collaboration.

[0067] Example 2:

[0068] Based on Example 1, Figure 2 As shown, the present invention also discloses a power data management system with a digital twin architecture, including: a system construction module 1, a data access module 2 and a data processing module 3.

[0069] System construction module 1 is configured to build a digital twin system adapted to the power system network structure based on the digital twin architecture according to the requirements of system computing resources, storage resources, and network resources under different power grid scales.

[0070] The data access module 2 is configured to set data access standards for the digital twin system that are adapted to the power grid business needs based on the data collection, transmission, and storage standard system and technical requirements of the power grid enterprise.

[0071] The data processing module 3 is configured to obtain digital twin system data using the data access standard, and process the digital twin system data through a data processing tool to achieve relationship mapping and logical binding between the model and structured data.

[0072] The specific implementation of the power data management system based on the digital twin architecture of this embodiment is basically the same as the specific implementation of the power data management method based on the digital twin architecture mentioned above, and will not be repeated here.

[0073] Example 3:

[0074] This embodiment discloses a power data management device based on a digital twin architecture, comprising a processor and a memory; wherein, when the processor executes a power data management program based on a digital twin architecture stored in the memory, it implements the steps of the power data management method based on the digital twin architecture as described in any one of the above items.

[0075] Furthermore, the power data management device based on the digital twin architecture in this embodiment may further include:

[0076] The input interface is used to obtain an externally imported power data management program based on a digital twin architecture and save the obtained power data management program based on a digital twin architecture to the memory. It can also be used to obtain various instructions and parameters transmitted by external terminal devices and transmit them to the processor so that the processor can use these various instructions and parameters to carry out corresponding processing. In this embodiment, the input interface can specifically include but is not limited to a USB interface, a serial interface, a voice input interface, a fingerprint input interface, a hard disk read interface, etc.

[0077] The output interface is used to output various data generated by the processor to the terminal device connected to it, so that other terminal devices connected to the output interface can obtain various data generated by the processor. In this embodiment, the output interface can specifically include but is not limited to a USB interface, a serial interface, etc.

[0078] A communication unit is configured to establish a remote communication connection between the power data management device based on the digital twin architecture and an external server, so that the power data management device based on the digital twin architecture can mount the image file to the external server. In this embodiment, the communication unit may specifically include, but is not limited to, a remote communication unit based on wireless or wired communication technology.

[0079] The keyboard is used to obtain various parameter data or instructions input by the user by tapping the keycaps in real time.

[0080] A display is used to display relevant information of the power data management process based on the digital twin architecture in real time.

[0081] The mouse can be used to assist users in inputting data and simplify user operations.

[0082] In summary, the present invention can effectively promote the integrated application of digital twin technology and power systems, enabling future power grids to develop in the direction of intelligence, flexibility, and interactivity.

[0083] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments are sufficient. The methods disclosed in the embodiments are described briefly because they correspond to the systems disclosed in the embodiments. For relevant details, refer to the method description.

[0084] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0085] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0086] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0087] In addition, the functional modules in the various embodiments of the present invention may be integrated into one processing unit, or each module may exist physically separately, or two or more modules may be integrated into one unit.

[0088] Similarly, each processing unit in each embodiment of the present invention may be integrated into one functional module, or each processing unit may exist physically, or two or more processing units may be integrated into one functional module.

[0089] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0090] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0091] The above is a detailed introduction to the power data management method, system and device based on the digital twin architecture provided by the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified in several ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A power data management method based on digital twin architecture, characterized in that: The steps include: S1: Based on the requirements of system computing resources, storage resources, and network resources under different power grid scales, a digital twin system adapted to the power system network structure is constructed based on the digital twin architecture. Step S1 includes: deploying the server program on the cloud platform based on the digital twin architecture, deploying workstations locally as edge devices, using the data center, supply and service business system, and in-station terminals as the system data sources of the digital twin system, and constructing the digital twin system. Configure planning and construction, equipment operation and maintenance, dispatching and operation, emergency repair command, power supply access, charging pile management, and customer service functions for the digital twin system, and set up data collection and access methods; Configure multi-system data deep mining and fusion functions for the digital twin system; By integrating multi-system data, a mapping relationship between data from different systems and digital twin models is established; S2: Based on the data collection, transmission, and storage standard system and technical requirements of the power grid enterprise, set data access standards for the digital twin system that adapt to the power grid business needs. The data access standards that adapt to the power grid business needs include: obtaining real-time perception information of the operating status of physical entities through sensors, edge computing terminals, and distribution monitoring terminals, accessing the real-time perception information into the system according to the digital system data access specification, and converting it into a standardized protocol text; S3: Acquire digital twin system data using the data access standard, and process the digital twin system data using a data processing tool to achieve relationship mapping and logical binding between the model and structured data; Step S3 includes: Acquire digital twin system data using the data access standard, and extract, clean, and bind digital twin system data using data processing tools, data fusion tools, and data visualization tools; The data extraction process includes: Extract point cloud data from the digital twin system data, and extract the coordinate data of the corresponding equipment and facilities from the point cloud data to determine the precise spatial distribution of the elements; The ledger data obtained from the digital twin system data is used to calibrate the basic attributes of equipment and facilities, and to mark, read and locate target objects; Extract business data from the digital twin system data. Business data includes equipment power status, linkage logic, and subordinate relationships, which is used to improve the data dimensions of the digital twin substation. The data cleaning process includes: Filter the discrete noise in the point cloud data file and perform data sparse abstraction; Fill in missing items, correct and delete duplicate items, verify and modify erroneous items in ledger data to generate standardized standard ledger data that can be bound and called; Effectively aggregate discrete business data, structure the unstructured data, and filter and shield meaningless data.

2. The power data management method based on digital twin architecture according to claim 1 is characterized in that: The data binding process includes: Based on the cleaned digital twin system data and combined with the subordinate hierarchical structure of the 3D model, the corresponding data is bound to the model to establish a mapping relationship between the data and the model; Bind the logic, topology relationships and information interfaces between discrete models.

3. The power data management method based on digital twin architecture according to claim 1 is characterized in that: The multi-system data include: real-time operation data of the dispatching automation system, ledger data of the control cloud system, ledger data of the PMS system, GIS system topology model, topological relationship and coordinate data, low-voltage ledger and operation data of the procurement system, SG186 system business expansion registration data, marketing audit system overcapacity data, marketing basic data platform customer files and station-line relationship data, vehicle network system ledger and operation data, meteorological data of the provincial power forecasting system, power supply service command system work order and fault defect data.

4. A power data management system based on digital twin architecture, characterized by: include: The system construction module is configured to build a digital twin system adapted to the power system network structure based on the digital twin architecture according to the requirements of system computing resources, storage resources, and network resources under different power grid scales; including: based on the digital twin architecture, deploying the server program on the cloud platform, deploying workstations locally as edge devices, using the data middle platform, supply and service business system, and in-station terminals as the system data sources of the digital twin system, and building a digital twin system; configuring planning and construction, equipment operation and maintenance, dispatching and operation, emergency repair command, power supply access, charging pile management, and customer service functions for the digital twin system, and setting up data collection and access methods; configuring multi-system data deep mining and fusion functions for the digital twin system; and establishing a mapping relationship between data and digital twin models between different systems by fusing multi-system data; A data access module is configured to set data access standards for the digital twin system that are adapted to the business needs of the power grid based on the data collection, transmission, and storage standards and technical requirements of the power grid enterprise. The data access standards that are adapted to the business needs of the power grid include: obtaining real-time perception information of the operating status of physical entities through sensors, edge computing terminals, and distribution monitoring terminals, accessing the real-time perception information into the system according to the digital system data access specification, and converting it into a standardized protocol text; The data processing module is configured to obtain digital twin system data using the data access standard, and process the digital twin system data through data processing tools to achieve relationship mapping and logical binding between the model and structured data; including: obtaining digital twin system data using the data access standard, and performing data extraction, data cleaning and data binding of digital twin system data through data processing tools, data fusion tools and data visualization tools; the data extraction process includes: extracting point cloud data from digital twin system data, extracting coordinate data of corresponding equipment and facilities from point cloud data to determine the precise spatial distribution of elements; the ledger data obtained from the digital twin system data is used for labeling Determine the basic attributes of equipment and facilities, and mark, read and locate target objects; extract business data from the digital twin system data, which includes the power status of the equipment, linkage logic, and subordinate relationships, and is used to improve the data dimensions of the digital twin substation; the data cleaning process includes: filtering the discrete noise in the point cloud data file and performing data rarefaction and abstraction; filling in missing items, correcting and deleting duplicate items, and verifying and modifying erroneous items in the ledger data to generate standardized standard ledger data that can be bound and called; effectively aggregate discrete business data, structure the unstructured data, and filter and shield meaningless data.

5. A power data management device based on digital twin architecture, characterized in that: include: Memory for storing power data management programs based on the digital twin architecture; A processor is used to implement the steps of the power data management method based on the digital twin architecture as described in any one of claims 1 to 3 when executing the power data management program based on the digital twin architecture.

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