Data management method and system based on GIM model

Abstract

The invention relates to the technical field of power system power grid engineering management, and discloses a GIM model-based data management method and system, and the method comprises the steps: obtaining GIM model data; importing the GIM model data into a database to obtain database data; performing data identification judgment operation on the database data to obtain an identification result, and performing attribute relation matching operation according to the identification result to obtain structured data; extracting attribute conditions and relation conditions of the structured data related to the current business demand type, and determining the attribute conditions and the relation conditions as query conditions; according to the query condition, performing index query classification operation on the structured data to obtain a final main data set; and performing initialization and data loading operation according to the final main data set to obtain a basic data structure and a complete data set which support visual display of the power grid engineering business. According to the method, the problems of visual expression and unified management of three-dimensional data of the power grid project can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system grid engineering management, and in particular to a data management method and system based on a GIM model. Background Art

[0002] Currently, my country's power grid project planning and design has gradually shifted from traditional 2D to 3D design methods. Grid project displays based on 3D models can more intuitively display various attributes and 3D geometric information of power grid projects. Based on this foundation, the power grid project data management platform has become a key tool for effectively inheriting and transferring model data across all phases, disciplines, and equipment of power grid projects, as well as for full lifecycle management.

[0003] In one existing technology, power grid engineering data management is mainly carried out through databases. These databases are based on the data tables of various power grid systems. No three-dimensional data model of the power grid is established. In addition, the subsystem databases are not integrated and uniformly managed. The data formats are different and cannot be shared. The exchange and inheritance between databases are greatly restricted.

[0004] The existing technology fails to effectively construct a three-dimensional data model of the power grid project, and it is difficult to intuitively and vividly reflect the three-dimensional data of the power grid project. Summary of the Invention

[0005] The present invention provides a data management method and system based on the GIM model to solve the problems of intuitive expression and unified management of three-dimensional data of power grid projects.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a data management method based on the GIM model, comprising:

[0007] Get GIM model data;

[0008] Importing the GIM model data into a database to obtain database data;

[0009] Performing a data recognition and judgment operation on the database data to obtain a recognition result, and performing an attribute relationship matching operation based on the recognition result to obtain structured data;

[0010] Extracting attribute conditions and relationship conditions related to the structured data and the current business requirement type, and determining the attribute conditions and the relationship conditions as query conditions;

[0011] According to the query conditions, index query and classification operations are performed on the structured data to obtain a final master data set;

[0012] Initialization and data loading operations are performed based on the final master data set to obtain a basic data structure and a complete data set that supports the visualization of power grid engineering services.

[0013] Preferably, the step of importing the GIM model data into the database to obtain database data includes:

[0014] Establish the data interface of GIM model and connect it with the database;

[0015] Performing a data parsing operation on the GIM model data to obtain parsed data;

[0016] Converting the parsed data into structural feature data, and storing the structural feature data in a buffer;

[0017] Determine whether the data table in the database exists; if the data table in the database exists, obtain the structural feature data from the buffer, and input the structural feature data into the database through the data interface to obtain data table data;

[0018] If the data table does not exist in the database, create a data table, obtain the structural feature data from the buffer, and input the structural feature data into the database through the data interface to obtain data table data;

[0019] Perform a data index creation operation on the data table data to obtain database data.

[0020] Preferably, performing a data identification and judgment operation on the database data to obtain an identification result, and performing an attribute relationship matching operation based on the identification result to obtain structured data includes:

[0021] Performing an existence check operation on the database data; if the database data does not exist, re-performing the step of importing the GIM model data into the database to obtain the database data;

[0022] If the database data exists, performing a data identification status judgment operation on the database data;

[0023] If the database data has been identified, determining the database data as structured data;

[0024] If the database data is not identified, the database data is subjected to master data matching and feature extraction operations to obtain attribute feature information and associated feature information; based on the attribute feature information and the associated feature information, the database data is subjected to attribute relationship matching operations to obtain structured data.

[0025] Preferably, performing an attribute relationship matching operation on the database data according to the attribute feature information and the association feature information to obtain structured data includes:

[0026] Based on a predefined attribute rule set and according to the attribute feature information, an attribute matching degree is calculated to obtain an attribute matching degree;

[0027] When the attribute matching degree is greater than or equal to a preset attribute matching degree threshold, a relationship matching degree is obtained by performing calculation based on the associated feature information based on a predefined relationship rule set;

[0028] When the relationship matching degree is greater than or equal to a preset relationship matching degree threshold, determining the database data corresponding to the relationship matching degree as structured data;

[0029] When the relationship matching degree is less than a preset relationship matching degree threshold, a relationship splitting operation is performed to obtain a sub-relationship set matching degree; a splitting calculation is performed based on the sub-relationship matching degree to obtain a split relationship matching degree;

[0030] When the split relationship matching degree is greater than or equal to a preset relationship matching degree threshold, the database data corresponding to the split relationship matching degree is determined as structured data;

[0031] When the attribute matching degree is less than a preset attribute matching degree threshold, an attribute optimization operation is performed according to the attribute matching degree and the relationship matching degree.

[0032] Preferably, the calculation formula for the attribute matching degree is:

[0033]

[0034] Where A is the attribute matching degree; M attr is a set of predefined attribute rules; S attr is a set of attribute feature information; |S attr ∩M attr | is the Jaccard similarity coefficient of the intersection of the predefined attribute rule set and the attribute feature information set; |S attr ∪M attr | is the Jaccard similarity coefficient of the union of the predefined attribute rule set and the attribute feature information set;

[0035] The calculation formula of the relationship matching degree is:

[0036]

[0037] Where A rel is the relationship matching degree; M rel is a set of predefined association rules; S relis a set of associated feature information; |S rel ∩M rel | is the Jaccard similarity coefficient of the intersection of the predefined association rule set and the association feature information set; |S rel ∪M rel | is the Jaccard similarity coefficient of the union of the predefined association rule set and the association feature information set;

[0038] The calculation formula of the split relationship matching degree is:

[0039]

[0040] Where A' rel A is the split relationship matching degree; rel,i is the matching degree of the sub-relationship set; n is the number of matching degrees of the sub-relationship set.

[0041] Preferably, when the attribute matching degree is less than a preset attribute matching degree threshold, performing an attribute optimization operation according to the attribute matching degree and the relationship matching degree includes:

[0042] Calculating the attribute matching degree and the relationship matching degree to obtain an adjusted matching score;

[0043] When the adjusted matching score is greater than or equal to a preset adjusted matching score threshold, returning to the step of calculating, based on the association feature information and a predefined relationship rule set, when the attribute matching degree is greater than or equal to a preset attribute matching degree threshold, to obtain the relationship matching degree;

[0044] When the adjusted matching score is less than a preset adjusted matching score threshold, an attribute splitting operation is performed to obtain a sub-attribute matching degree;

[0045] Perform split calculation based on the sub-attribute matching degree to obtain the split attribute matching degree;

[0046] When the split attribute matching degree is greater than or equal to the preset attribute matching degree threshold, return to execute the step of calculating the relationship matching degree based on the association feature information based on the predefined relationship rule set when the attribute matching degree is greater than or equal to the preset attribute matching degree threshold to obtain the relationship matching degree.

[0047] Preferably, the calculation formula for adjusting the matching score is:

[0048] C=α×A+β×A rel

[0049] Where C is the adjusted matching score; A is the attribute matching degree; A rel is the relationship matching degree; α, β are weight coefficients;

[0050] The calculation formula for the split attribute matching degree is:

[0051]

[0052] Where A' is the attribute relationship matching degree; Ai is the sub-attribute set matching degree; m is the number of sub-attribute set matching degrees.

[0053] Preferably, the index query classification operation is performed on the structured data according to the query conditions to obtain a final master data set, including:

[0054] According to the query condition, performing an index-based screening operation on the structured data to obtain a database data set that preliminarily meets the query condition;

[0055] According to the database data set and the query condition, further identification operations are performed on the attribute features, hierarchical features and associated features of the data to obtain a feature database data set;

[0056] Based on the current business requirement type, a master data range determination operation is performed on the feature database data set to obtain a master data set that meets the current business requirement type;

[0057] A data classification operation is performed on the master data set to obtain a final master data set.

[0058] Preferably, the initialization and data loading operations are performed based on the final master data set to obtain a basic data structure and a complete data set supporting the visualization of power grid engineering services, including:

[0059] Extracting grid characteristic information of the final master data set, and performing data definition operations based on the grid characteristic information to obtain an initial master data table, an initial attribute data table, and an initial relationship data table;

[0060] Performing a shortest path calculation based on the Floyd-Warshall algorithm according to the final master data set to obtain a shortest path, and storing the shortest path in the initial relationship data table to obtain an initial topology relationship data table;

[0061] Performing an index table creation operation according to the initial master data table, the initial attribute data table, and the initial topology relationship data table to obtain a power grid master data table, a power grid attribute data table, and a power grid topology relationship data table;

[0062] According to the power grid master data table, the power grid attribute data table and the power grid topology relationship data table, the final master data set is loaded into the database to obtain a basic data structure and a complete data set that supports visualization of power grid engineering services;

[0063] The shortest path is calculated according to the following formula:

[0064]

[0065] Where, is the shortest path from node i to node j in the final master data set considering k intermediate nodes; d ij is the existing path distance between node i and node j in the final master data set; d ik is the path distance between node i and the kth intermediate node in the final master data set; d kj is the path distance between node j and the kth intermediate node in the final master data set; d ik +d kj is the path distance passing through the kth intermediate node in the final main data set.

[0066] In a second aspect, the present invention provides a data management system based on the GIM model, comprising:

[0067] Data acquisition module, used to obtain GIM model data;

[0068] A data import module is used to import the GIM model data into a database to obtain database data;

[0069] A data identification module is used to perform a data identification and judgment operation on the database data to obtain an identification result, and perform an attribute relationship matching operation based on the identification result to obtain structured data;

[0070] A query establishment module, configured to extract attribute conditions and relationship conditions related to the structured data and the current business requirement type, and determine the attribute conditions and the relationship conditions as query conditions;

[0071] A data screening module is used to perform index query and classification operations on the structured data according to the query conditions to obtain a final master data set;

[0072] The data loading module is used to perform initialization and data loading operations based on the final master data set to obtain a basic data structure and a complete data set that supports the visual display of power grid engineering business.

[0073] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned GIM model-based data management methods.

[0074] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned GIM model-based data management methods.

[0075] Compared with the prior art, the present invention discloses a data management method based on the GIM model, which aims to solve the problem of intuitive expression and unified management of three-dimensional data of power grid projects. The present invention obtains GIM model data, imports it into the database, and performs data identification, attribute and relationship matching, query condition extraction, index query classification, and initialization and loading operations, and finally constructs a basic data structure and a complete data set that supports the visualization display of power grid project business. By establishing a GIM model data interface and connecting it to the database, parsing the GIM model data and performing structural feature data conversion operations, the effective management of the GIM model data is achieved. In the process of database data identification, by judging the existence and identification status of the data, combining the master data matching and feature extraction operations, attribute feature information and associated feature information are generated, and the matching degree of attributes and relationships is calculated based on the predefined rule set to ensure the accuracy and integrity of the data structure.

[0076] The present invention has a beneficial effect in the data management of power grid projects. First, through the structured data construction method based on attributes and associated features, the problem that three-dimensional data cannot be expressed intuitively and vividly in the existing technology is solved, and the combination of the three-dimensional model of the power grid project and business needs is enhanced. Secondly, by using the Jaccard similarity coefficient formula and the matching threshold mechanism, combined with the attribute splitting and optimization strategy, the accuracy and processing efficiency of data matching are improved. At the same time, the present invention realizes index-based dynamic data query and classification by extracting query conditions related to the current business demand type, ensuring the response of the power grid project in different stages and scenarios. In addition, the present invention constructs a topological relationship table through the Floyd-Warshall algorithm, calculates the shortest path and association relationship between devices, and provides efficient topological analysis capabilities for the scheduling, operation and maintenance of the power grid project. At the same time, the present invention covers the whole process management from data parsing, identification, matching, classification to initialization and loading, forming a full life cycle data management system for each stage of power grid project planning, design, construction, operation and maintenance.

[0077] In summary, by achieving structured and dynamic management of three-dimensional data, the present invention improves the visual management efficiency of power grid projects throughout their entire life cycle and solves the problems of intuitive expression and unified management of three-dimensional data of power grid projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 11 is a flow chart of a data management method based on a GIM model provided by a first embodiment of the present invention;

[0079] Figure 2 It is a structural diagram of a data management system based on the GIM model provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0080] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0081] Reference Figure 1 The first embodiment of the present invention provides a data management method based on the GIM model, comprising the following steps:

[0082] S11, obtaining GIM model data;

[0083] S12, importing the GIM model data into a database to obtain database data;

[0084] S13, performing a data recognition and judgment operation on the database data to obtain a recognition result, and performing an attribute relationship matching operation based on the recognition result to obtain structured data;

[0085] S14, extracting attribute conditions and relationship conditions related to the structured data and the current business requirement type, and determining the attribute conditions and the relationship conditions as query conditions;

[0086] S15, performing index query and classification operations on the structured data according to the query conditions to obtain a final master data set;

[0087] S16, performing initialization and data loading operations based on the final master data set to obtain a basic data structure and a complete data set that supports visualization of power grid engineering services.

[0088] In step S11, it is necessary to obtain GIM model data, including:

[0089] First, it is necessary to analyze the sources of power grid project data. These data come from multiple business lines, relevant departments, partners and external units, and involve engineering projects such as new construction, reconstruction, expansion and technological transformation. The data types are diverse and the structure is complex. In response to these multi-source data, the present invention adopts Microsoft MSSQLServer database as a management platform, and realizes data import and integration by developing data interfaces linked to various data sources. These data are mainly in the format based on DITA standard (such as GIDX files) and XML format (such as files defined by GIDX-2012 standard), which can describe the geometric information, scene configuration and topological relationship of power grid equipment. In order to ensure the uniformity and standardization of data, the imported data files need to be strictly formatted and standardized to ensure that they comply with the GIM data format standard.

[0090] To acquire data, a data interface must first be established to connect to the database of the data source. This data interface, developed in the VC6 language, enables data transfer from the data source to the platform. Furthermore, to prevent non-standard data files from interfering with database management, the GIM model data management platform provides a format validation function, checking that the file's field names, field lengths, and data values ​​meet predefined standards (for example, equipment capacity must be within the range [10kW, 1000kW]). This process selects data files that meet the requirements and stores them in a local buffer. The buffer is used to temporarily store validated data files, ensuring that the data meets standardization requirements before entering the database.

[0091] Ultimately, the acquired data files will include validated GIM model data in DITA and XML formats that accurately describe the device's geometry, attributes, and associations. All files are assigned unique identifiers in the buffer for subsequent parsing and processing.

[0092] In step S12, the GIM model data needs to be imported into the database to obtain database data, including:

[0093] First, the GIM model's data interface must be established to connect to the database. The data interface is a key component for establishing a communication channel between the data source and the database. This article uses the VC6 programming language to develop the interface and connect to the Microsoft MSSQL Server database. The data interface must be designed to support data read and write operations while ensuring data transmission security.

[0094] Specifically, you can encrypt the transmission channel by setting up an encryption protocol (such as SSL / TLS) to prevent data tampering or leakage during transmission. After the interface is established, you can test the database's connection status and access rights to confirm that the database is ready to receive GIM model data.

[0095] Next, the GIM model data is parsed to extract the key content from the original file and convert it into basic data units. The core of data parsing is to extract and map fields for different data formats (such as DITA standard GIDX files or XML format files). For example, for GIDX files, it is necessary to extract the geometric properties of the device (such as shape, size, position), association relationships (such as the connection between the device and sub-devices), and scene configuration data (such as topological relationships). The parsing tool can scan the file structure line by line through the parser module, and extract the field content according to the predefined GIM data format standard, and verify whether the field meets the specified length, type and format (for example, the device ID must be a digital type with a length of 10 bits). After the parsing is completed, all extracted fields are organized into standardized basic data units and validated to ensure that there are no missing fields and format errors.

[0096] In a specific embodiment, after the parsing is completed, the basic data unit is further converted into structural feature data and stored in a buffer. Structural feature data is a logical abstraction of the basic data, including the hierarchical relationship, attribute characteristics, and topological association information of the device. As a temporary storage area, the buffer needs to assign a unique identifier (such as a UUID) to each piece of data for subsequent tracking and management. In addition, the buffer needs to organize the data in a unified format. For example, each data unit contains fields such as device ID, device type, and associated parent node ID.

[0097] In a specific embodiment, it is necessary to determine whether the corresponding data table structure already exists in the database. If the data table already exists, the structural feature data is directly extracted from the buffer and imported into the database in batches through the data interface. The batch import operation requires the use of SQL batch insert statements (such as INSERT INTO) to improve the efficiency of data writing. At the same time, during the import process, check whether the field values ​​​​conform to the definition of the database table to avoid import failures due to field type or length mismatches. If the data table does not exist, a new data table must be created according to the hierarchical structure and field definition of the GIM model data. For example, the field definition of the device table includes device ID (primary key), device name (string type), geometric data (string type) and parent node ID (foreign key). After creation is completed, the structural feature data in the buffer is written to the newly created data table.

[0098] Finally, to improve database query efficiency, data tables need to be indexed. Index design should take into account the query characteristics of device data, creating primary key indexes, attribute indexes, and association indexes. Primary key indexes are used to quickly locate device data, for example, by directly searching for device information via the device ID. Attribute indexes are optimized for commonly used query fields (such as device type and operating status) to accelerate conditional queries. Association indexes are designed for topological relationship fields (such as parent and child node IDs) to support fast traversal of hierarchical structures.

[0099] For example, an index can be created using a SQL command, such as:

[0100] CREATE INDEX idx_DeviceID ON GIM_Model(DeviceID);

[0101] CREATE INDEX idx_DeviceType ON GIM_Model(DeviceType);

[0102] CREATE INDEX idx_ParentID ON GIM_Model(ParentID);

[0103] These indexes improve database query performance, especially when processing large amounts of data. The result is standardized database data storage, including complete GIM model data tables and optimized index structures. The data stored in the database includes device attributes, geometry, topological relationships, and other business-related information.

[0104] In step S13, it is necessary to perform a data recognition and judgment operation on the database data to obtain a recognition result, and perform an attribute relationship matching operation based on the recognition result to obtain structured data, including:

[0105] First, the existence of the data needs to be checked to confirm whether the target data is included in the database. If the data does not exist, it is necessary to roll back to step S12 and re-execute the import operation of the GIM model data to ensure data integrity.

[0106] Specifically, the existence of the target table can be verified by querying the database metadata table (such as information_schema.tables), for example, by confirming whether the table exists through SQL query. If the table does not exist, stop the current operation and re-import the data; if the table exists, enter the data recognition state. The purpose of this operation is to confirm whether the data in the database has completed structured recognition according to established rules. The specific method is to check the value of the flag field (such as IsIdentified) in the database to confirm whether it is in the "recognized" state. If the data has been recognized, it is directly marked as structured data for subsequent use; if the recognition is not completed, further master data matching and feature extraction operations are required.

[0107] In a specific embodiment, for database data that has not been fully identified, master data matching and feature extraction operations are required to generate attribute feature information and associated feature information for matching. Attribute feature information and associated feature information are the data basis, which are used to evaluate and match whether the data in the database complies with predefined business rules. Attribute feature information specifically describes the basic characteristics of the equipment, mainly including equipment type, equipment status, geometric attributes and operating parameters. For example, the equipment type can be "transformer" or "circuit breaker", and the geometric attributes include the location information (such as coordinates) and physical dimensions (such as length, width and height) of the equipment. The operating parameters describe the dynamic state of the equipment, such as "capacity: 100kW" or "voltage: 220V". These feature information reflects the static and dynamic properties of the equipment itself, and is the basis for data identification and matching.

[0108] In a specific embodiment, association feature information is used to describe the logical, topological, and hierarchical relationships between devices. For example, a parent-child relationship describes the hierarchical subordination of devices (e.g., "Device A is the parent node, Device B is the child node"). Topological relationships reflect the physical connections between devices (e.g., "Transformer A is connected to Circuit Breaker B"). Whole-part relationships represent the inclusion of a device and its components (e.g., "Device C includes Component D"). This feature information is used to establish association logic between devices, ensuring that data can be effectively organized and matched.

[0109] In a specific embodiment, the purpose of attribute feature information extraction is to extract key information such as geometric attributes and logical attributes of the device, which includes fields such as device type, capacity, and status. For example, the device ID, device type, capacity, and geometric information fields can be extracted through SQL query to construct the attribute feature set S attr , such as "Device type: transformer", "Capacity: 100kW". The extraction of associated feature information is to obtain the relationship information between devices, such as parent-child relationship or topological relationship. By querying the foreign key field or relationship table, the association relationship between devices is extracted to generate the associated feature set Srel , such as "Device A is the parent node and Device B is the child node".

[0110] In a specific embodiment, after the feature extraction is completed, the attribute relationship matching operation needs to be performed on the database data based on the extracted attribute feature information and associated feature information. First, the attribute matching degree is calculated to evaluate the attribute characteristics of the data and the predefined attribute rule set M. attr degree of similarity.

[0111] The calculation formula of the attribute matching degree is:

[0112]

[0113] Where A is the attribute matching degree; M attr is a set of predefined attribute rules; S attr is a set of attribute feature information; |S attr ∩M attr | is the Jaccard similarity coefficient of the intersection of the predefined attribute rule set and the attribute feature information set; |S attr ∪M attr | is the Jaccard similarity coefficient of the union of the predefined attribute rule set and the attribute feature information set;

[0114] Next, calculate the relationship matching degree A rel , the purpose is to evaluate the association characteristics of the data with the predefined rule set M rel The calculation formula of the relationship matching degree is:

[0115]

[0116] Where A rel is the relationship matching degree; M rel is a set of predefined association rules; S rel is a set of associated feature information; |S rel ∩M rel | is the Jaccard similarity coefficient of the intersection of the predefined association rule set and the association feature information set; |S rel ∪M rel | is the Jaccard similarity coefficient of the union of the predefined association rule set and the association feature information set;

[0117] In a specific embodiment, if both the attribute matching degree and the relationship matching degree are greater than or equal to a preset threshold, the data is marked as structured data; otherwise, optimization or splitting processing is required.

[0118] When the relationship matching degree is less than the preset relationship matching degree threshold, it is necessary to split the complex association relationship to gradually improve the matching accuracy and meet the matching requirements. First, the complex relationship set that failed to match is decomposed into simpler sub-relationship sets (for example, decomposing a many-to-many relationship into a one-to-one or partial relationship). Then, the matching degree is calculated for each sub-relationship set after the split. The calculation formula for the split relationship matching degree is:

[0119]

[0120] Where A' rel A is the split relationship matching degree; rel,i is the matching degree of the sub-relationship set; n is the number of matching degrees of the sub-relationship set.

[0121] If the split relationship matching degree is greater than or equal to the preset relationship matching degree threshold, the corresponding data will be marked as structured data; if it still does not reach the threshold, it will be marked as unmatched data, requiring further manual analysis or rule adjustment.

[0122] In a specific embodiment, when the attribute matching is less than the preset attribute matching threshold, an attribute optimization operation is performed by combining the attribute matching and the relationship matching to improve the overall matching accuracy of the data. First, the adjusted comprehensive matching score is calculated. The calculation formula for the adjusted matching score is:

[0123] C=α×A+β×A rel

[0124] Where C is the adjusted matching score; A is the attribute matching degree; A rel is the relationship matching degree; α, β are weight coefficients;

[0125] Specifically, if the comprehensive matching score is greater than or equal to the adjusted matching score threshold, the process returns to the relationship matching degree calculation step to re-judge whether the matching requirements are met; if the requirements are still not met, the process enters the attribute splitting operation. The attribute splitting operation parses complex attributes into more basic sub-attributes, such as splitting "capacity: 100kW~500kW" into smaller attribute values ​​such as "capacity: 100kW" and "capacity: 200kW". Subsequently, the split attribute matching degree is calculated for each sub-attribute. The calculation formula for the split attribute matching degree is:

[0126]

[0127] Where A' is the attribute relationship matching degree; A i is the sub-attribute set matching degree; m is the number of sub-attribute set matching degrees.

[0128] Specifically, if the split attribute matching degree is greater than or equal to the preset attribute matching degree threshold, the process returns to the relationship matching degree calculation step; if the requirement is still not met, the data is marked as unmatched data and requires further manual analysis or rule adjustment.

[0129] It's important to note that the attribute matching threshold is set to assess the degree of match between a device's attributes and the rule set. The default approach involves analyzing key attributes in business requirements, compiling historical matching data, and dynamically adjusting the matching criteria. A recommended threshold is 0.75, meaning that an attribute matching degree of at least 75% is required to meet the requirement.

[0130] It should be noted that the relationship matching threshold is used to assess whether the association characteristics between devices conform to the set of rules. The preset method includes analyzing the device topology, assigning weights to the association characteristics, and calculating the distribution of historical matches. For clear parent-child relationships or topological relationships, the matching threshold is set to 0.8, and a relationship matching degree of at least 80% is considered satisfactory.

[0131] It should be noted that adjusting the matching score threshold is to evaluate the overall matching degree of the data through weighted calculation of attribute matching and relationship matching. The weight coefficients α and β represent the importance of attribute matching and relationship matching, and it is recommended that α + β = 1. For example, after setting α = 0.6 and β = 0.4, the comprehensive threshold T adjust =0.6·A+0.4·A rel , which is 0.77, where A is the attribute matching degree, A rel This means that the adjusted overall match score must be above 77% to meet the requirements.

[0132] It should be noted that the predefined attribute rule set represents standardized requirements for device attributes. It includes a set of attribute fields and their corresponding value ranges, defined by business requirements or industry specifications. These rules cover the device's type, state, geometric attributes, and operating parameters. For example, the device type might be "transformer" or "circuit breaker," the device state might be "normal" or "abnormal," and the operating parameters might include numerical ranges for device capacity, voltage, and other parameters. Methods for obtaining the predefined attribute rule set include business requirements analysis, reference to industry standards, and historical data statistics. By analyzing the actual requirements of power grid projects, key attribute fields, such as device type and capacity, can be determined. By combining national or industry standards (such as power grid design specifications), the legal range of attributes can be further clarified. For example, the capacity range for transformers should be 50kW to 500kW, and the voltage level should be 220V or 380V. Furthermore, by analyzing the distribution of device attributes in historical databases, common feature ranges can be extracted. For example, 90% of the equipment capacities in the statistical historical data are distributed between 100kW and 500kW, thereby generating a rule set {equipment capacity: 100kW-500kW, voltage level: 220V, 380V}.

[0133] It should be noted that the set of predefined relationship rules describes the logical relationships, topological relationships, or hierarchical structures between devices. These rules are used to constrain the association logic between devices, such as parent-child relationships, topological connection relationships, and whole-part relationships. The method for obtaining the set of predefined relationship rules mainly includes engineering topology model analysis, device relationship standard reference, and business scenario logic requirements. For example, based on the actual topology of the power grid project, the connection relationship between the main equipment and the auxiliary equipment can be clarified, such as the direct connection between the transformer and the circuit breaker; combined with the industry standards for device relationships, the inclusion relationship of the equipment can be further standardized, such as the whole-part relationship between the transformer and the cooling system; in addition, common inspection or maintenance tasks in business scenarios will also place specific requirements on the relationship between devices. For example, in inspection tasks, a direct topological connection relationship is required between the transformer and the circuit breaker, such as {parent-child relationship: transformer-circuit breaker, inclusion relationship: transformer-cooling system, topological relationship: distribution cabinet-switch}.

[0134] In step S14, it is necessary to extract the attribute conditions and relationship conditions related to the structured data and the current business requirement type, and determine the attribute conditions and the relationship conditions as query conditions, including:

[0135] In one specific embodiment, by analyzing the type of business requirement, the key attribute conditions and relationship conditions required for the requirement are identified. For example, in a power grid project, an equipment maintenance requirement involves a transformer with an "abnormal" status and a directly connected circuit breaker. Therefore, the extracted attribute conditions are "Equipment Status: Abnormal" and "Equipment Type: Transformer," and the relationship condition is "Direct Connection: Transformer - Circuit Breaker." These conditions are then used as query conditions for further filtering and classification of structured data.

[0136] In step S15, it is necessary to perform index query and classification operations on the structured data according to the query conditions to obtain the final master data set, including:

[0137] In a specific embodiment, based on the generated query conditions, the system performs an index-based screening operation on the structured data. By querying the data index table, it quickly locates data entries that meet the conditions, thereby initially generating a database data set that meets the query conditions. For example, by querying the device type index table, all devices of the "transformer" type can be quickly found. The status index table then filters out devices in an "abnormal" state. These results are then aggregated to form a preliminary data set that meets the conditions. This process shortens screening time. Based on this preliminary screening, the database data set and the query conditions are further analyzed and processed, including the identification of attribute features, hierarchical features, and association features. Attribute feature identification further verifies data compliance, for example, checking whether the device capacity is within the specified range; hierarchical feature identification determines the device's position in the topology, such as whether it is a primary device or an auxiliary device; and association feature identification verifies the logical relationship between devices, such as whether a transformer is directly connected to a circuit breaker. Through this series of identification operations, the system generates a feature database data set that ensures that the data meets the multi-dimensional requirements of the query conditions.

[0138] In a specific embodiment, based on the current business requirement type, the master data scope of the feature database data set is determined. This determination of the master data scope involves filtering out the most core and relevant data based on the specific scenario. For example, in an equipment maintenance scenario, only the maintenance object and its directly related equipment are required as master data, without any other irrelevant data.

[0139] Finally, the master data set is further classified by data type, level, or purpose. For example, transformers and circuit breakers can be categorized into different equipment categories, or equipment status can be divided into "abnormal equipment" and "normal equipment." This ultimately creates the final master data set required by business needs.

[0140] In step S16, initialization and data loading operations need to be performed based on the final master data set to obtain a basic data structure and a complete data set that supports the visualization of power grid engineering services, including:

[0141] First, by traversing each device node in the final master data set, reading its predefined attribute fields (such as device type, capacity, status, geometric coordinates) and associated fields (such as parent node ID, connected device ID), the grid characteristic information in the final master data set is extracted. Based on this characteristic information, the field structure, data type, and constraint relationship of the data table are defined. Data definition operations are performed based on the characteristic information to generate the initial master data table, initial attribute data table, and initial relationship data table. Among them, the initial master data table: stores the core identification information of the device (such as device ID, name, type), and its structure is defined by the device identification class feature; the initial attribute data table: stores the dynamic parameters and status of the device (such as capacity, voltage, operating status), and its structure is defined by the device attribute class feature; the initial relationship data table: stores the logical associations between devices (such as parent-child relationships, connection relationships), and its structure is defined by the device association class feature.

[0142] In a specific embodiment, after generating the initial data table, the system uses the Floyd-Warshall algorithm to perform the shortest path calculation on the final master data set, generates the optimal topological path between the power grid devices, and stores these paths as new relationship fields in the initial relationship data table to form an initial topological relationship data table.

[0143] The shortest path is calculated according to the following formula:

[0144]

[0145] Where, is the shortest path from node i to node j in the final master data set considering k intermediate nodes; d ij is the existing path distance between node i and node j in the final master data set; d ik is the path distance between node i and the kth intermediate node in the final master data set; d kj is the path distance between node j and the kth intermediate node in the final master data set; d ik +d kj is the path distance passing through the kth intermediate node in the final main data set.

[0146] For example, in a simple three-node network, if the direct path distance from node 1 to node 2 is 5, and the path distance from node 1 to node 2 via node 3 is 4, the shortest path distance is updated to 4.

[0147] Next, the system establishes an index table based on the initial master data table, the initial attribute data table, and the initial topology relationship data table, including the power grid master data table, the power grid attribute data table, and the power grid topology relationship data table. The purpose of establishing the index table is to improve the efficiency of data query and management. The power grid master data table is used to store basic information of the device, such as device ID and device type; the power grid attribute data table records the operating parameters and dynamic status of the device, such as capacity, voltage, etc.; the power grid topology relationship data table stores the connection information and shortest path information between devices. For example, through the index table, the directly connected device or path distance of a certain device can be quickly queried, which improves data query efficiency and operational performance. The power grid master data table, the power grid attribute data table, and the power grid topology relationship data table constitute the basic data structure.

[0148] Finally, the system completes initialization and data loading operations based on the final master data set, loads the data into the database, and generates a complete data set that supports the visualization of power grid engineering business.

[0149] Specifically, before data is loaded, the system must validate and cleanse the extracted and converted data to ensure its integrity and consistency. This validation process includes checking the uniqueness of master data, checking foreign key constraints, and verifying data integrity. For example, the system checks the uniqueness of each device ID and confirms that all attribute data and topological relationship data can be found in the master data table. Any data that does not meet the requirements is marked as an anomaly and cleaned up, such as deleting duplicate data or correcting data format errors. This validation and cleaning process ensures the quality of the data ultimately loaded into the database.

[0150] After verification, the system batch-loads the data into the database. Using efficient batch operation tools (such as LOAD DATA INFILE), the system quickly imports the extracted data into the appropriate data tables. Once loaded, the three primary data tables—the grid master data table, the grid attribute data table, and the grid topology relationship data table—form a complete data set.

[0151] Specifically, these data sets are presented in a structured format, visually demonstrating the attributes, logical relationships, and topological structure of power grid equipment, supporting the planning, operation, and maintenance of power grid projects. For example, through a visual interface, users can view the operating status of power grid equipment, the topological connections between devices, and the shortest power transmission lines.

[0152] In summary, the present invention discloses a data management method based on the GIM model, which aims to solve the problem of intuitive expression and unified management of three-dimensional data of power grid engineering. The present invention obtains GIM model data, imports it into the database, and performs data identification, attribute and relationship matching, query condition extraction, index query classification, and initialization and loading operations, and finally constructs a basic data structure and a complete data set that supports the visualization display of power grid engineering business. By establishing a GIM model data interface and connecting it to the database, the GIM model data is parsed and the structural feature data conversion operations are performed on the GIM model data, thereby achieving effective management of the GIM model data. In the process of database data identification, by judging the existence and identification status of the data, combining the master data matching and feature extraction operations, attribute feature information and associated feature information are generated, and the matching degree of attributes and relationships is calculated based on the predefined rule set to ensure the accuracy and integrity of the data structure.

[0153] The present invention has a beneficial effect in the data management of power grid projects. First, through the structured data construction method based on attributes and associated features, the problem that three-dimensional data cannot be expressed intuitively and vividly in the existing technology is solved, and the combination of the three-dimensional model of the power grid project and business needs is enhanced. Secondly, by using the Jaccard similarity coefficient formula and the matching threshold mechanism, combined with the attribute splitting and optimization strategy, the accuracy and processing efficiency of data matching are improved. At the same time, the present invention realizes index-based dynamic data query and classification by extracting query conditions related to the current business demand type, ensuring the response of the power grid project in different stages and scenarios. In addition, the present invention constructs a topological relationship table through the Floyd-Warshall algorithm, calculates the shortest path and association relationship between devices, and provides efficient topological analysis capabilities for the scheduling, operation and maintenance of the power grid project. At the same time, the present invention covers the whole process management from data parsing, identification, matching, classification to initialization and loading, forming a full life cycle data management system for each stage of power grid project planning, design, construction, operation and maintenance.

[0154] By achieving structured and dynamic management of three-dimensional data, the present invention improves the visual management efficiency of power grid projects throughout their entire life cycle and solves the problems of intuitive expression and unified management of three-dimensional data of power grid projects.

[0155] Reference Figure 2 The second embodiment of the present invention provides a data management system based on the GIM model, including:

[0156] Data acquisition module, used to obtain GIM model data;

[0157] A data import module is used to import the GIM model data into a database to obtain database data;

[0158] A data identification module is used to perform a data identification and judgment operation on the database data to obtain an identification result, and perform an attribute relationship matching operation based on the identification result to obtain structured data;

[0159] A query establishment module, configured to extract attribute conditions and relationship conditions related to the structured data and the current business requirement type, and determine the attribute conditions and the relationship conditions as query conditions;

[0160] A data screening module is used to perform index query and classification operations on the structured data according to the query conditions to obtain a final master data set;

[0161] The data loading module is used to perform initialization and data loading operations based on the final master data set to obtain a basic data structure and a complete data set that supports the visual display of power grid engineering business.

[0162] Preferably, the data acquisition module is used to acquire GIM model data;

[0163] Preferably, the data import module is used to import the GIM model data into the database to obtain database data, including:

[0164] The step of importing the GIM model data into the database to obtain database data includes:

[0165] Establish the data interface of GIM model and connect it with the database;

[0166] Performing a data parsing operation on the GIM model data to obtain parsed data;

[0167] Converting the parsed data into structural feature data, and storing the structural feature data in a buffer;

[0168] Determine whether the data table in the database exists; if the data table in the database exists, obtain the structural feature data from the buffer, and input the structural feature data into the database through the data interface to obtain data table data;

[0169] If the data table does not exist in the database, create a data table, obtain the structural feature data from the buffer, and input the structural feature data into the database through the data interface to obtain data table data;

[0170] Perform a data index creation operation on the data table data to obtain database data.

[0171] Preferably, the data identification module is used to perform a data identification and judgment operation on the database data to obtain an identification result, and perform an attribute relationship matching operation based on the identification result to obtain structured data, including:

[0172] The performing of a data identification and judgment operation on the database data to obtain an identification result, and performing an attribute relationship matching operation based on the identification result to obtain structured data, includes:

[0173] Performing an existence check operation on the database data; if the database data does not exist, re-performing the step of importing the GIM model data into the database to obtain the database data;

[0174] If the database data exists, performing a data identification status judgment operation on the database data;

[0175] If the database data has been identified, determining the database data as structured data;

[0176] If the database data is not identified, the database data is subjected to master data matching and feature extraction operations to obtain attribute feature information and associated feature information; based on the attribute feature information and the associated feature information, the database data is subjected to attribute relationship matching operations to obtain structured data.

[0177] The performing an attribute relationship matching operation on the database data according to the attribute feature information and the association feature information to obtain structured data includes:

[0178] Based on a predefined attribute rule set and according to the attribute feature information, an attribute matching degree is calculated to obtain an attribute matching degree;

[0179] When the attribute matching degree is greater than or equal to a preset attribute matching degree threshold, a relationship matching degree is obtained by performing calculation based on the associated feature information based on a predefined relationship rule set;

[0180] When the relationship matching degree is greater than or equal to a preset relationship matching degree threshold, determining the database data corresponding to the relationship matching degree as structured data;

[0181] When the relationship matching degree is less than a preset relationship matching degree threshold, a relationship splitting operation is performed to obtain a sub-relationship set matching degree; a splitting calculation is performed based on the sub-relationship matching degree to obtain a split relationship matching degree;

[0182] When the split relationship matching degree is greater than or equal to a preset relationship matching degree threshold, the database data corresponding to the split relationship matching degree is determined as structured data;

[0183] When the attribute matching degree is less than a preset attribute matching degree threshold, an attribute optimization operation is performed according to the attribute matching degree and the relationship matching degree.

[0184] The calculation formula of the attribute matching degree is:

[0185]

[0186] Where A is the attribute matching degree; M attr is a set of predefined attribute rules; S attr is a set of attribute feature information; |S attr ∩M attr | is the Jaccard similarity coefficient of the intersection of the predefined attribute rule set and the attribute feature information set; |S attr ∪M attr | is the Jaccard similarity coefficient of the union of the predefined attribute rule set and the attribute feature information set;

[0187] The calculation formula of the relationship matching degree is:

[0188]

[0189] Where A rel is the relationship matching degree; M rel is a set of predefined association rules; S rel is a set of associated feature information; |S rel ∩M rel | is the Jaccard similarity coefficient of the intersection of the predefined association rule set and the association feature information set; |S rel ∪M rel | is the Jaccard similarity coefficient of the union of the predefined association rule set and the association feature information set;

[0190] The calculation formula of the split relationship matching degree is:

[0191]

[0192] Where A' rel A is the split relationship matching degree; rel,i is the matching degree of the sub-relationship set; n is the number of matching degrees of the sub-relationship set.

[0193] When the attribute matching degree is less than a preset attribute matching degree threshold, performing an attribute optimization operation according to the attribute matching degree and the relationship matching degree includes:

[0194] Calculating the attribute matching degree and the relationship matching degree to obtain an adjusted matching score;

[0195] When the adjusted matching score is greater than or equal to a preset adjusted matching score threshold, returning to the step of calculating, based on the association feature information and a predefined relationship rule set, when the attribute matching degree is greater than or equal to a preset attribute matching degree threshold, to obtain the relationship matching degree;

[0196] When the adjusted matching score is less than a preset adjusted matching score threshold, an attribute splitting operation is performed to obtain a sub-attribute matching degree;

[0197] Perform split calculation based on the sub-attribute matching degree to obtain the split attribute matching degree;

[0198] When the split attribute matching degree is greater than or equal to the preset attribute matching degree threshold, return to execute the step of calculating the relationship matching degree based on the association feature information based on the predefined relationship rule set when the attribute matching degree is greater than or equal to the preset attribute matching degree threshold to obtain the relationship matching degree.

[0199] The calculation formula for adjusting the matching score is:

[0200] C=α×A+β×A rel

[0201] Where C is the adjusted matching score; A is the attribute matching degree; A rel is the relationship matching degree; α, β are weight coefficients;

[0202] The calculation formula for the split attribute matching degree is:

[0203]

[0204] Where A' is the attribute relationship matching degree; A i is the sub-attribute set matching degree; m is the number of sub-attribute set matching degrees.

[0205] Preferably, the query establishment module is used to extract attribute conditions and relationship conditions related to the structured data and the current business requirement type, and determine the attribute conditions and the relationship conditions as query conditions;

[0206] Preferably, the data screening module is used to perform index query and classification operations on the structured data according to the query conditions to obtain a final master data set, including:

[0207] According to the query conditions, the structured data is indexed, queried and classified to obtain a final master data set, including:

[0208] According to the query condition, performing an index-based screening operation on the structured data to obtain a database data set that preliminarily meets the query condition;

[0209] According to the database data set and the query condition, further identification operations are performed on the attribute features, hierarchical features and associated features of the data to obtain a feature database data set;

[0210] Based on the current business requirement type, a master data range determination operation is performed on the feature database data set to obtain a master data set that meets the current business requirement type;

[0211] A data classification operation is performed on the master data set to obtain a final master data set.

[0212] Preferably, the data loading module is configured to perform initialization and data loading operations based on the final master data set to obtain a basic data structure and a complete data set supporting visualization of power grid engineering services, including:

[0213] The initialization and data loading operations are performed based on the final master data set to obtain a basic data structure and a complete data set that supports the visualization of power grid engineering business, including:

[0214] Extracting grid characteristic information of the final master data set, and performing data definition operations based on the grid characteristic information to obtain an initial master data table, an initial attribute data table, and an initial relationship data table;

[0215] Performing a shortest path calculation based on the Floyd-Warshall algorithm according to the final master data set to obtain a shortest path, and storing the shortest path in the initial relationship data table to obtain an initial topology relationship data table;

[0216] Performing an index table creation operation according to the initial master data table, the initial attribute data table, and the initial topology relationship data table to obtain a power grid master data table, a power grid attribute data table, and a power grid topology relationship data table;

[0217] According to the power grid master data table, the power grid attribute data table and the power grid topology relationship data table, the final master data set is loaded into the database to obtain a basic data structure and a complete data set that supports visualization of power grid engineering services;

[0218] The shortest path is calculated according to the following formula:

[0219]

[0220] Where, is the shortest path from node i to node j in the final master data set considering k intermediate nodes; d ij is the existing path distance between node i and node j in the final master data set; d ikis the path distance between node i and the kth intermediate node in the final master data set; d kj is the path distance between node j and the kth intermediate node in the final master data set; d ik +d kj is the path distance passing through the kth intermediate node in the final main data set.

[0221] It should be noted that the data management system based on the GIM model provided in an embodiment of the present invention is used to execute all the process steps of the data management method based on the GIM model in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0222] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a shortest path calculation program. When the processor executes the computer program, the steps in the above-mentioned embodiments of the data management method based on the GIM model are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data identification module.

[0223] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0224] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0225] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, connecting various parts of the entire electronic device using various interfaces and lines.

[0226] The memory can be used to store the computer programs and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0227] Wherein, if the module / unit integrated in the electronic device is implemented in the form of 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 present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of each of the above-mentioned method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0228] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0229] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A data management method based on the GIM model, characterized in that include: Get GIM model data; Importing the GIM model data into a database to obtain database data; Performing a data recognition and judgment operation on the database data to obtain a recognition result, and performing an attribute relationship matching operation based on the recognition result to obtain structured data; Extracting attribute conditions and relationship conditions related to the structured data and the current business requirement type, and determining the attribute conditions and the relationship conditions as query conditions; According to the query conditions, index query and classification operations are performed on the structured data to obtain a final master data set; Initialization and data loading operations are performed based on the final master data set to obtain a basic data structure and a complete data set that supports the visualization of power grid engineering services.

2. The data management method based on the GIM model according to claim 1, characterized in that: The step of importing the GIM model data into the database to obtain database data includes: Establish the data interface of GIM model and connect it with the database; Performing a data parsing operation on the GIM model data to obtain parsed data; Converting the parsed data into structural feature data, and storing the structural feature data in a buffer; Determine whether the data table in the database exists; if the data table in the database exists, obtain the structural feature data from the buffer, and input the structural feature data into the database through the data interface to obtain data table data; If the data table does not exist in the database, create a data table, obtain the structural feature data from the buffer, and input the structural feature data into the database through the data interface to obtain data table data; Perform a data index creation operation on the data table data to obtain database data.

3. The data management method based on the GIM model according to claim 1, characterized in that: The performing of a data identification and judgment operation on the database data to obtain an identification result, and performing an attribute relationship matching operation based on the identification result to obtain structured data, includes: Performing an existence check operation on the database data; if the database data does not exist, re-performing the step of importing the GIM model data into the database to obtain the database data; If the database data exists, performing a data identification status judgment operation on the database data; If the database data has been identified, determining the database data as structured data; If the database data is not identified, the database data is subjected to master data matching and feature extraction operations to obtain attribute feature information and associated feature information; based on the attribute feature information and the associated feature information, the database data is subjected to attribute relationship matching operations to obtain structured data.

4. The data management method based on the GIM model according to claim 3, characterized in that: The performing an attribute relationship matching operation on the database data according to the attribute feature information and the association feature information to obtain structured data includes: Based on a predefined attribute rule set and according to the attribute feature information, an attribute matching degree is calculated to obtain an attribute matching degree; When the attribute matching degree is greater than or equal to a preset attribute matching degree threshold, a relationship matching degree is obtained by performing calculation based on the associated feature information based on a predefined relationship rule set; When the relationship matching degree is greater than or equal to a preset relationship matching degree threshold, determining the database data corresponding to the relationship matching degree as structured data; When the relationship matching degree is less than a preset relationship matching degree threshold, a relationship splitting operation is performed to obtain a sub-relationship set matching degree; a splitting calculation is performed based on the sub-relationship matching degree to obtain a split relationship matching degree; When the split relationship matching degree is greater than or equal to a preset relationship matching degree threshold, the database data corresponding to the split relationship matching degree is determined as structured data; When the attribute matching degree is less than a preset attribute matching degree threshold, an attribute optimization operation is performed according to the attribute matching degree and the relationship matching degree.

5. The data management method based on the GIM model according to claim 4, characterized in that: The calculation formula of the attribute matching degree is: Where A is the attribute matching degree; M attr is a set of predefined attribute rules; S attr is a set of attribute feature information; |S attr ∩M attr | is the Jaccard similarity coefficient of the intersection of the predefined attribute rule set and the attribute feature information set; |S attr ∪M attr | is the Jaccard similarity coefficient of the union of the predefined attribute rule set and the attribute feature information set; The calculation formula of the relationship matching degree is: Where A rel is the relationship matching degree; M rel is a set of predefined association rules; S rel is a set of associated feature information; |S rel ∩M rel | is the Jaccard similarity coefficient of the intersection of the predefined association rule set and the association feature information set; |S rel ∪M rel | is the Jaccard similarity coefficient of the union of the predefined association rule set and the association feature information set; The calculation formula of the split relationship matching degree is: Where A' rel A is the split relationship matching degree; rel,i is the matching degree of the sub-relationship set; n is the number of matching degrees of the sub-relationship set.

6. The data management method based on the GIM model according to claim 4, characterized in that: When the attribute matching degree is less than a preset attribute matching degree threshold, performing an attribute optimization operation according to the attribute matching degree and the relationship matching degree includes: Calculating the attribute matching degree and the relationship matching degree to obtain an adjusted matching score; When the adjusted matching score is greater than or equal to a preset adjusted matching score threshold, returning to the step of calculating, based on the association feature information and a predefined relationship rule set, when the attribute matching degree is greater than or equal to a preset attribute matching degree threshold, to obtain the relationship matching degree; When the adjusted matching score is less than a preset adjusted matching score threshold, an attribute splitting operation is performed to obtain a sub-attribute matching degree; Perform split calculation based on the sub-attribute matching degree to obtain the split attribute matching degree; When the split attribute matching degree is greater than or equal to the preset attribute matching degree threshold, return to execute the step of calculating the relationship matching degree based on the association feature information based on the predefined relationship rule set when the attribute matching degree is greater than or equal to the preset attribute matching degree threshold to obtain the relationship matching degree.

7. The data management method based on the GIM model according to claim 6, characterized in that: The calculation formula for adjusting the matching score is: C=α×A+β×A rel Where C is the adjusted matching score; A is the attribute matching degree; A rel is the relationship matching degree; α, β are weight coefficients; The calculation formula for the split attribute matching degree is: Where A' is the attribute relationship matching degree; A i is the sub-attribute set matching degree; m is the number of sub-attribute set matching degrees.

8. The data management method based on the GIM model according to claim 1, characterized in that: According to the query conditions, the structured data is indexed, queried and classified to obtain a final master data set, including: According to the query condition, performing an index-based screening operation on the structured data to obtain a database data set that preliminarily meets the query condition; According to the database data set and the query condition, further identification operations are performed on the attribute features, hierarchical features and associated features of the data to obtain a feature database data set; Based on the current business requirement type, a master data range determination operation is performed on the feature database data set to obtain a master data set that meets the current business requirement type; A data classification operation is performed on the master data set to obtain a final master data set.

9. The data management method based on the GIM model according to claim 1, characterized in that: The initialization and data loading operations are performed based on the final master data set to obtain a basic data structure and a complete data set that supports the visualization of power grid engineering business, including: Extracting grid characteristic information of the final master data set, and performing data definition operations based on the grid characteristic information to obtain an initial master data table, an initial attribute data table, and an initial relationship data table; Performing a shortest path calculation based on the Floyd-Warshall algorithm according to the final master data set to obtain a shortest path, and storing the shortest path in the initial relationship data table to obtain an initial topology relationship data table; Performing an index table creation operation according to the initial master data table, the initial attribute data table, and the initial topology relationship data table to obtain a power grid master data table, a power grid attribute data table, and a power grid topology relationship data table; According to the power grid master data table, the power grid attribute data table and the power grid topology relationship data table, the final master data set is loaded into the database to obtain a basic data structure and a complete data set that supports visualization of power grid engineering services; The shortest path is calculated according to the following formula: Where, is the shortest path from node i to node j in the final master data set considering k intermediate nodes; d ij is the existing path distance between node i and node j in the final master data set; d ik is the path distance between node i and the kth intermediate node in the final master data set; d kj is the path distance between node j and the kth intermediate node in the final master data set; d ik +d kj is the path distance passing through the kth intermediate node in the final main data set.

10. A data management system based on the GIM model, characterized in that: include: Data acquisition module, used to obtain GIM model data; A data import module is used to import the GIM model data into a database to obtain database data; A data identification module is used to perform a data identification and judgment operation on the database data to obtain an identification result, and perform an attribute relationship matching operation based on the identification result to obtain structured data; A query establishment module, configured to extract attribute conditions and relationship conditions related to the structured data and the current business requirement type, and determine the attribute conditions and the relationship conditions as query conditions; A data screening module is used to perform index query and classification operations on the structured data according to the query conditions to obtain a final master data set; The data loading module is used to perform initialization and data loading operations based on the final master data set to obtain a basic data structure and a complete data set that supports the visual display of power grid engineering business.

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