A data management method and system based on a GIM model

Abstract

The application relates to the technical field of power system grid engineering management, and discloses a data management method and system based on a GIM model, which comprises the following steps: acquiring GIM model data; importing the GIM model data into a database to obtain database data; performing data recognition and judgment operations on the database data to obtain a recognition result, and performing attribute relationship matching operations according to the recognition result to obtain structured data; extracting attribute conditions and relationship conditions related to a current business demand type of the structured data, and determining the attribute conditions and the relationship conditions as query conditions; performing index query classification operations on the structured data according to the query conditions to obtain a final main data set; and performing initialization and data loading operations according to the final main data set to obtain a basic data structure and a complete data set supporting visual display of power grid engineering business. The method can solve the problems of intuitive expression and unified management of three-dimensional data of power grid engineering.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system grid engineering management, and particularly relates to a data management method and system based on a GIM model. BACKGROUND

[0002] At present, the planning and design work of power grid engineering in China has gradually changed from the traditional two-dimensional design method to the three-dimensional design method, and the power grid engineering display based on the three-dimensional model can more intuitively show various attributes and three-dimensional geometric information of the power grid engineering. On this basis, the power grid engineering data management platform becomes an important tool for realizing the effective inheritance, transmission and full life cycle management of model data between various stages, various professions and various devices of the power grid engineering.

[0003] In one prior art, power grid engineering data management is mainly managed through databases, these databases are based on data tables of various systems of the power grid, no three-dimensional data model of the power grid is established, and the subsystem databases are not integrated and uniformly managed, the data formats are different, cannot be shared, and the exchange and inheritance between the databases are greatly limited.

[0004] The three-dimensional data model of the power grid engineering cannot be effectively constructed in the prior art, and it is difficult to intuitively and visually reflect the three-dimensional data of the power grid engineering. SUMMARY

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

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

[0007] obtaining GIM model data;

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

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

[0010] extracting attribute conditions and relationship conditions related to a current business demand type of the structured data, and determining the attribute conditions and the relationship conditions as query conditions;

[0011] performing an index query classification operation on the structured data according to the query conditions to obtain a final main data set;

[0012] According to the final master data set, initialization and data loading operations are performed to obtain a basic data structure and complete data set supporting visual display of power grid engineering business.

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

[0014] The data interface of the GIM model is connected to the database;

[0015] The GIM model data is subjected to data analysis to obtain analysis data;

[0016] The analysis data is converted into structural feature data, and the structural feature data is stored in a buffer;

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

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

[0019] The data table data is subjected to data index establishment to obtain database data.

[0020] Preferably, the data recognition and judgment operation on the database data is performed to obtain a recognition result, and an attribute relationship matching operation is performed according to the recognition result to obtain structured data, which comprises:

[0021] The database data is subjected to existence judgment, if the database data does not exist, the step of importing the GIM model data into the database to obtain database data is re-executed;

[0022] If the database data exists, a data recognition state judgment operation is performed on the database data;

[0023] If the database data has completed recognition, the database data is determined as structured data;

[0024] If the database data has not completed recognition, a master data matching and feature extraction operation is performed on the database data to obtain attribute feature information and associated feature information, and an attribute relationship matching operation is performed on the database data according to the attribute feature information and the associated feature information to obtain structured data.

[0025] Preferably, the attribute relationship matching operation is performed on the database data according to the attribute feature information and the association feature information to obtain the structured data, including:

[0026] According to the attribute feature information, attribute matching degree calculation is performed based on a predefined attribute rule set 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 calculated based on a predefined relationship rule set according to the association feature information;

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

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

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

[0031] When the attribute matching degree is less than the 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 of the attribute matching degree is as follows:

[0033]

[0034] In the formula, A represents the attribute matching degree; M represents the predefined attribute rule set; S represents the attribute feature information set; |S represents the Jaccard similarity coefficient of the intersection of the predefined attribute rule set and the attribute feature information set; |S represents the Jaccard similarity coefficient of the union of the predefined attribute rule set and the attribute feature information set. attr attr attr attr attr attr

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

[0036]

[0037] In the formula, A represents the relationship matching degree; M represents the predefined association rule set; S represents the association feature information set. rel rel rel ​​​​​​​​is a Jaccard similarity coefficient of an intersection of the predefined association rule set and the association characteristic information set; |S rel ∩M rel is a Jaccard similarity coefficient of an intersection of the predefined association rule set and the association characteristic information set; |S rel ∪M rel is a Jaccard similarity coefficient of an intersection of the predefined association rule set and the association characteristic information set; |S

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

[0039]

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

[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, comprising:

[0042] performing a calculation according to the attribute matching degree and the relationship matching degree to obtain an adjustment matching score;

[0043] when the adjustment matching score is greater than or equal to a preset adjustment matching score threshold, returning to perform the step of calculating a relationship matching degree according to the association characteristic information based on the predefined relationship rule set when the attribute matching degree is greater than or equal to a preset attribute matching degree threshold;

[0044] when the adjustment matching score is less than a preset adjustment matching score threshold, performing an attribute split operation to obtain a sub-attribute matching degree;

[0045] performing a split calculation according to the sub-attribute matching degree to obtain a split attribute matching degree;

[0046] when the split attribute matching degree is greater than or equal to a preset attribute matching degree threshold, returning to perform the step of calculating a relationship matching degree according to the association characteristic information based on the predefined relationship rule set when the attribute matching degree is greater than or equal to a preset attribute matching degree threshold.

[0047] Preferably, the calculation formula of the adjustment matching score is:

[0048] C = α × A + β × A rel

[0049] In the formula, C is an adjustment matching score; A is an attribute matching degree; A rel is a relationship matching degree; α, β are weight coefficients;

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

[0051]

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

[0053] Preferably, the index query classification operation on the structured data according to the query condition to obtain the final master data set comprises:

[0054] performing an index-based screening operation on the structured data according to the query condition to obtain a database data set preliminarily meeting the query condition;

[0055] performing further identification operations on attribute features, hierarchical features and correlation features of data according to the database data set and the query condition to obtain a feature database data set;

[0056] performing a master data range determination operation on the feature database data set based on a current business requirement type to obtain a master data set meeting the current business requirement type;

[0057] performing a data classification operation on the master data set to obtain a final master data set.

[0058] Preferably, the initialization and data loading operation according to the final master data set to obtain the basic data structure and complete data set supporting the visual display of the power grid engineering business comprises:

[0059] extracting power grid feature information of the final master data set and performing a data definition operation according to the power grid feature 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 topological relationship data table;

[0061] performing an index table establishment operation according to the initial master data table, the initial attribute data table and the initial topological relationship data table to obtain a power grid master data table, a power grid attribute data table and a power grid topological relationship data table;

[0062] loading the final master data set into the database according to the power grid master data table, the power grid attribute data table and the power grid topological relationship data table to obtain the basic data structure and complete data set supporting the visual display of the power grid engineering business;

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

[0064]

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

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

[0067] a data acquisition module configured to acquire GIM model data;

[0068] a data import module configured to import the GIM model data into a database to obtain database data;

[0069] a data recognition module configured to perform a data recognition judgment operation on the database data to obtain a recognition result, and perform an attribute relationship matching operation according to the recognition result to obtain structured data;

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

[0071] a data screening module configured to perform an index query classification operation on the structured data according to the query conditions to obtain a final main data set;

[0072] a data loading module configured to perform initialization and data loading operations according to the final main data set to obtain a basic data structure and a complete data set supporting power grid engineering business visual display.

[0073] In a third aspect, the present application further 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 the processor implements the data management method based on the GIM model according to any one of the above aspects when executing the computer program.

[0074] In a fourth aspect, the present application also provides a computer readable storage medium comprising a stored computer program, wherein the computer readable storage medium controls a device in which the computer readable storage medium is located to execute the GIM model-based data management method according to any one of the preceding aspects when the computer program is run.

[0075] Compared with the prior art, the present application discloses a GIM model-based data management method, aiming to solve the problems of intuitive expression and unified management of power grid engineering three-dimensional data. The present application obtains GIM model data, imports it into a database, and then performs data recognition, 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 supporting visual display of power grid engineering business. By establishing a GIM model data interface and connecting it with a database, the GIM model data is parsed and structure feature data conversion operations are performed, realizing effective management of the GIM model data. In the database data recognition process, by judging the existence and recognition state of the data, combining the main data matching and feature extraction operations, generating attribute feature information and associated feature information, and based on a pre-defined rule set, the matching degree of attributes and relationships is calculated, ensuring the accuracy and integrity of the data structure.

[0076] The present application has beneficial effects in power grid engineering data management. First, by using the structured data construction method based on attribute and associated features, the problem of three-dimensional data being unable to be intuitively and visually expressed in the prior art is solved, and the combination of power grid engineering three-dimensional model and business demand is enhanced. Second, by using the Jaccard similarity coefficient formula and the matching threshold mechanism, combined with attribute splitting and optimization strategies, the accuracy and processing efficiency of data matching are improved. At the same time, the present application extracts the query conditions related to the current business demand type, realizes dynamic data query and classification based on index, and ensures the response of power grid engineering in different stages and scenarios. In addition, the present application constructs a topological relationship table by using the Floyd-Warshall algorithm, calculates the shortest path and associated relationship between devices, and provides efficient topological analysis capability for power grid engineering scheduling, operation and maintenance. At the same time, the present application covers the whole process management from data parsing, recognition, matching, classification to initialization and loading, forming a whole life cycle data management system for power grid engineering planning, design, construction, operation and maintenance.

[0077] In summary, by realizing the structuring and dynamic management of three-dimensional data, the present application improves the visual management efficiency of the whole life cycle of power grid engineering, and solves the problems of intuitive expression and unified management of power grid engineering three-dimensional data. BRIEF DESCRIPTION OF DRAWINGS

[0078] Figure 1is a GIM model-based data management method flow diagram provided by the first embodiment of the present application;

[0079] Figure 2 is a GIM model-based data management system structure diagram provided by the second embodiment of the present application. DETAILED DESCRIPTION

[0080] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0081] Referring to Figure 1 The first embodiment of the present application provides a GIM model-based data management method, including 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 data recognition and judgment operation on the database data to obtain a recognition result, and performing attribute relationship matching operation according to the recognition result to obtain structured data;

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

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

[0087] S16, performing initialization and data loading operation according to the final main data set to obtain a basic data structure and a complete data set supporting power grid engineering business visual display.

[0088] In step S11, GIM model data needs to be obtained, including:

[0089] Firstly, the source of the power grid engineering project data needs to be analyzed. The data comes from multiple business lines, related departments, partners and external units, and involves new, reconstruction, expansion and technical transformation projects. The data is diverse in type and complex in structure. For these multi-source data, the application uses Microsoft MSSQL Server database as the management platform, and realizes data import and integration through the development of data interfaces linked with each data source. These data are mainly in the format based on DITA standard (such as GIDX file) and XML format (such as file 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 the data, the imported data files need to be strictly checked for format and standardized, so as to ensure that they meet the GIM data format standard.

[0090] When acquiring data, the data interface needs to be established to connect with the database of the data source. The data interface is developed by VC6 language, which can realize the transmission from the data source to the platform. At the same time, in order to avoid the interference of data files that do not meet the standard to the database management, the GIM model data management platform provides format checking function to check whether the field name, field length and data value of the file meet the predefined standard (for example, the equipment capacity needs to be in the range of [10kW, 1000kW]). Through this process, the data files that meet the requirements can be screened out and stored in the local buffer area. The buffer area is used to temporarily store the checked data files, ensuring that the data meets the standardization requirements before entering the database.

[0091] Finally, the acquired data files will include the DITA and XML format GIM model data that pass the verification, which can accurately describe the geometric information, attribute information and associated information of the equipment. All the files are assigned a unique identifier in the buffer area for subsequent parsing and processing.

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

[0093] Firstly, the connection between the data interface of the GIM model and the database needs to be established. The data interface is a key component for establishing communication channel between the data source and the database. The application develops the interface by VC6 programming language to realize the connection with Microsoft MSSQL Server database. The design of the data interface needs to ensure that it can support the read and write operations of the data, and at the same time, guarantee the security of data transmission.

[0094] Specifically, the transmission channel can be encrypted by setting an encryption protocol (such as SSL / TLS) to avoid tampering or leakage of data during transmission. After the interface is established, the connection status and access rights of the test database are tested to confirm that the database is ready to receive GIM model data.

[0095] Next, data parsing operations are performed on the GIM model data to extract 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 GIDX files or XML format files in DITA standard). For example, for GIDX files, the geometric properties of devices (such as shape, size, and position), associated relationships (such as the connection between devices and sub-devices), and scene configuration data (such as topological relationships) need to be extracted. The parsing tool can scan the file structure line by line through the parser module and extract field content according to the predefined GIM data format standard, verifying whether the fields meet the specified length, type, and format (for example, device ID needs to be a numeric type with a length of 10 bits). After parsing is completed, all extracted fields are organized into standardized basic data units and subjected to validity verification to ensure that there are no missing fields or format errors.

[0096] In one specific embodiment, after parsing is completed, the basic data units are further converted into structure feature data and stored in a buffer. Structure feature data is a logical abstraction of basic data, containing the hierarchical relationship of devices, attribute characteristics, and topological association information. The buffer, as a temporary storage area, needs to assign a unique identifier (such as UUID) to each data for subsequent tracking and management. In addition, the buffer needs to organize data in a unified format, such as each data unit containing device ID, device type, associated parent node ID, etc.

[0097] In one 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 structure feature data is directly extracted from the buffer and imported into the database through the data interface in batches. Batch import operations need to use SQL batch insert statements (such as INSERT INTO) to improve the efficiency of data writing. At the same time, check whether the field values match the definition of the database table during the import process to avoid import failures due to field type or length mismatches. If the data table does not exist, a new data table needs to 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, the structure feature data in the buffer is written to the newly created data table.

[0098] Finally, to improve the query efficiency of the database, indexes need to be established for the data tables. The design of the indexes needs to be combined with the query characteristics of the device data, and primary key indexes, attribute indexes, and association indexes are created respectively. The primary key index is used to quickly locate the device data, such as directly looking up the device information through the device ID; the attribute index is optimized for commonly used query fields (such as device type, running state) to speed up conditional queries; the association index is designed for topological relationship fields (such as parent node ID and child node ID) to support fast traversal of the hierarchical structure.

[0099] Exemplarily, indexes can be created through SQL commands, for example:

[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] Through these indexes, the query performance of the database can be improved, especially when a large amount of data needs to be processed. Finally, the database data stored in a normalized manner is obtained, including the complete GIM model data table and the optimized index structure. The data stored in the database includes the attributes, geometric information, topological relationships, and other business-related information of the devices.

[0104] In step S13, a data recognition judgment operation needs to be performed on the database data to obtain a recognition result, and an attribute relationship matching operation is performed according to the recognition result to obtain structured data, including:

[0105] First, the existence of the data needs to be judged, which is to confirm whether the target data is contained in the database. If the data does not exist, it needs to be rolled back to step S12 to 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, confirming whether the table exists through an SQL query. If the table does not exist, the current operation is stopped and the data is re-imported; if the table exists, the data recognition state is entered, and the purpose of this operation is to confirm whether the data in the database has been completed structured recognition according to the predetermined 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 "identified" state. If the data has completed recognition, it is directly marked as structured data for subsequent use; if it has not completed recognition, further primary data matching and feature extraction operations need to be performed.

[0107] In a specific embodiment, for the database data that has not completed recognition, primary data matching and feature extraction operations need to be performed to generate attribute feature information and associated feature information for matching. Attribute feature information and associated feature information are the basis for evaluating and matching whether the data in the database meets the predefined business rules. Attribute feature information specifically describes the basic characteristics of the equipment, mainly including equipment type, equipment state, geometric properties, and operating parameters. For example, the equipment type can be "transformer" or "circuit breaker", the geometric properties include the position 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 attributes of the equipment itself and is the basis for data recognition and matching.

[0108] In a specific embodiment, associated feature information is used to describe the logical relationship, topological relationship, and hierarchical structure between devices. For example, the parent-child relationship is used to describe the hierarchical affiliation of the equipment (such as "device A is the parent node and device B is the child node"), the topological relationship reflects the physical connection between devices (such as "transformer A connects circuit breaker B"), and the whole-part relationship represents the inclusion relationship between the equipment and its components (such as "device C contains component D"). These feature information is used to establish the associated logic between devices to ensure that the data can be effectively organized and matched.

[0109] In a specific embodiment, the purpose of attribute feature information extraction is to extract the geometric properties, logical properties, and other key information of the equipment, including equipment type, capacity, state, and other fields. For example, the equipment ID, equipment type, capacity, and geometric information fields can be extracted through an SQL query to construct the attribute feature set S attr For example, "equipment 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 associated relationship between devices is extracted to generate the associated feature set Srel For example, "device A is a parent node and device B is a child node".

[0110] In one embodiment, after the attribute extraction is completed, the attribute relationship matching operation is performed on the database data according to the extracted attribute feature information and the associated feature information. First, the attribute matching degree is calculated, and the purpose is to evaluate the similarity between the attribute features of the data and the pre-defined attribute rule set M attr .

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

[0112]

[0113] In the formula, A is the attribute matching degree; M attr is the pre-defined attribute rule set; S attr is the attribute feature information set; |S attr ∩M attr | is the Jaccard similarity coefficient of the intersection of the pre-defined attribute rule set and the attribute feature information set; |S attr ∪M attr | is the Jaccard similarity coefficient of the union of the pre-defined attribute rule set and the attribute feature information set.

[0114] Then, the relationship matching degree A rel is calculated, and the purpose is to evaluate the matching degree between the associated features of the data and the pre-defined rule set M rel . The calculation formula of the relationship matching degree is as follows:

[0115]

[0116] In the formula, A rel is the relationship matching degree; M rel is the pre-defined association rule set; S rel is the association feature information set; |S rel ∩M rel | is the Jaccard similarity coefficient of the intersection of the pre-defined association rule set and the association feature information set; |S rel ∪M rel | is the Jaccard similarity coefficient of the union of the pre-defined association rule set and the association feature information set.

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

[0118] When the relationship matching degree is less than a preset relationship matching degree threshold, it is necessary to split the complex relationships to gradually improve the matching accuracy and meet the matching requirements. First, the complex relationship set that fails to match is decomposed into simpler sub-relationship sets (e.g., many-to-many relationships are decomposed into one-to-one or local relationships). Then, the matching degree is calculated for each of the split sub-relationship sets. The formula for calculating the split relationship matching degree is:

[0119]

[0120] In the formula, A' rel To split the relationship matching degree; A rel,i represents the matching degree of the sub-relation set; n represents the number of sub-relation sets with matching degrees.

[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 one specific embodiment, when the attribute matching score is less than a preset attribute matching score threshold, attribute optimization is performed by combining the attribute matching score and the relationship matching score to improve the overall matching accuracy of the data. First, the adjusted comprehensive matching score is calculated, and the formula for calculating the adjusted matching score is:

[0123] C = α × A + β × A rel

[0124] In the formula, C represents the adjusted matching score; A represents the attribute matching degree; A rel α represents the relationship matching degree; β represents the weighting coefficients.

[0125] Specifically, if the overall matching score is greater than or equal to the adjusted matching score threshold, the process returns to the relationship matching degree calculation step to reassess whether the matching requirements are met. If the requirements are still not met, the process proceeds to attribute splitting. Attribute splitting breaks down complex attributes into more basic sub-attributes; for example, it splits "capacity: 100kW~500kW" into smaller ranges of attribute values ​​such as "capacity: 100kW" and "capacity: 200kW". Subsequently, the split attribute matching degree is calculated for each sub-attribute. The formula for calculating the split attribute matching degree is:

[0126]

[0127] In the formula, A' represents the attribute relationship matching degree; A i is the matching degree of the sub-attribute set; 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, return to the relationship matching degree calculation step; if it still does not meet the requirements, mark it as unmatched data, which needs further manual analysis or rule adjustment.

[0129] It should be noted that the setting of the attribute matching degree threshold is to evaluate the matching degree between the attribute features of the device and the rule set. The preset method includes analyzing the key attributes in the business requirements, counting the historical data matching distribution, and dynamically adjusting the matching standard. It is recommended to set the threshold to 0.75, which means that the attribute feature matching degree needs to reach 75% or more to meet the requirements.

[0130] It should be noted that the relationship matching degree threshold is used to evaluate whether the associated features between devices meet the rule set. The preset method includes analyzing the topology of the device, assigning weights to the associated features, and counting the historical matching distribution. For clear parent-child relationships or topological relationships, the matching degree threshold is set to 0.8, which requires the relationship matching degree to reach 80% or more to meet the requirements.

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

[0132] It should be noted that the predefined attribute rule set is a standardized requirement for device attributes, which contains a set of attribute fields and their corresponding value ranges defined by business requirements or industry standards. These rules include the type, state, geometric properties and operating parameters of the device. For example, the device type can be "transformer" or "circuit breaker", the device state includes "normal" or "abnormal", and the operating parameters involve the device capacity, voltage range, etc. The method of obtaining the predefined attribute rule set includes business requirement analysis, industry standard reference and historical data statistics. By analyzing the actual requirements of power grid engineering, key attribute fields such as device type and capacity can be determined; combined with national or industry standards (such as power grid design specifications), the legal range of attributes can be further clarified, for example, the capacity range of transformer is 50kW to 500kW, and the voltage level is 220V or 380V; in addition, the common feature range can also be extracted by statistical distribution of device attributes in historical database. For example, 90% of the device capacity in the historical data is distributed between 100kW and 500kW, and the rule set {device capacity: 100kW-500kW, voltage level: 220V, 380V} is generated.

[0133] It should be noted that the predefined relationship rule set describes the logical relationship, topological relationship or hierarchy between devices. These rules are used to constrain the associated logic between devices, such as parent-child relationship, topological connection relationship, whole-part relationship, etc. The method of obtaining the predefined relationship rule set mainly includes engineering topological model analysis, device relationship standard reference and business scenario logic requirements. For example, according to the actual topological structure of power grid engineering, the connection relationship between main devices and auxiliary devices can be determined, such as the direct connection between transformer and circuit breaker; combined with the industry standard of device relationship, the inclusion relationship of devices can be further specified, such as the whole-part relationship between transformer and cooling system; in addition, the common maintenance or maintenance tasks in business scenarios also have specific requirements for the relationship between devices, for example, in the maintenance task, it is required that the transformer and circuit breaker have a direct topological connection relationship, such as {parent-child relationship: transformer-circuit breaker, inclusion relationship: transformer-cooling system, topological relationship: distribution cabinet-switch}.

[0134] In step S14, the attribute conditions and relationship conditions related to the current business demand type of the structured data need to be extracted, and the attribute conditions and the relationship conditions are determined as query conditions, including:

[0135] In one specific embodiment, by analyzing the type of current business requirement, the key attribute conditions and relationship conditions required by the requirement are determined. For example, in power grid engineering, the equipment maintenance business requirement involves transformers with an equipment state of “abnormal” and circuit breakers directly connected thereto, so the extracted attribute conditions are “equipment state: abnormal” and “equipment type: transformer”, and the relationship condition is “direct connection: transformer-circuit breaker”. After these conditions are determined as query conditions, they are used for further filtering and classification operations on the structured data.

[0136] In step S15, an index query classification operation needs to be performed on the structured data according to the query conditions to obtain a final main data set, including:

[0137] In one specific embodiment, according to the generated query conditions, the system performs an index-based filtering operation on the structured data, quickly locates the data entries that meet the conditions by querying the data index table, and thus preliminarily generates a database data set that meets the query conditions. For example, by querying the equipment type index table, all “transformer” type equipment can be quickly found, and then by querying the state index table, the equipment in the “abnormal” state can be filtered out, and these result sets are combined to form a preliminary data set that meets the conditions. This process shortens the filtering time. On the basis of the preliminary filtering, further analysis and processing of the database data set and the query conditions are performed, including attribute feature identification, hierarchical feature identification, and association feature identification. The attribute feature identification further verifies whether the data meets the conditions, for example, checks whether the equipment capacity is within the range specified by the requirement; the hierarchical feature identification is used to determine the position of the equipment in the topology, for example, whether it is a main equipment or an auxiliary equipment; and the association feature identification is used to verify the logical relationship between the equipment, for example, whether the transformer is directly connected to the circuit breaker. Through this series of identification operations, the system generates a feature database data set to ensure that the data meets the multi-dimensional requirements of the query conditions.

[0138] In one specific embodiment, based on the type of current business requirement, a main data range determination operation is performed on the feature database data set. The main data range determination is to filter out the most core and most relevant data according to the specific scenario of the requirement. For example, in the equipment maintenance scenario, only the maintenance object and its directly related equipment are needed as the main data, and other irrelevant data are not needed.

[0139] Finally, further data classification operations are performed on the main data set to classify it according to the type, level, or purpose of the data. For example, the transformer and the circuit breaker can be classified into different equipment categories, or they can be divided into “abnormal equipment” and “normal equipment” according to the equipment state, and finally the final main data set required by the business requirement is formed.

[0140] In step S16, initialization and data loading operations need to be performed according to the final main data set to obtain a basic data structure and a complete data set supporting visual display of power grid engineering business, including:

[0141] First, by traversing each device node in the final main data set, the pre-defined attribute fields (such as device type, capacity, state, geometric coordinates) and associated fields (such as parent node ID, connected device ID) thereof are read, the power grid feature information in the final main data set is extracted, and the field structure, data type and constraint relationship of the data table are defined based on the feature information. Data definition operations are performed according to the feature information to generate an initial main data table, an initial attribute data table and an initial relationship data table. The initial main data table stores device core identification information (such as device ID, name, type), and its structure is defined by device identification features. The initial attribute data table stores device dynamic parameters and states (such as capacity, voltage, operating state), and its structure is defined by device attribute features. The initial relationship data table stores the logical association between devices (such as parent-child relationship, connection relationship), and its structure is defined by device association features.

[0142] In one specific embodiment, after the initial data table is generated, the system performs shortest path calculation on the final main data set using the Floyd-Warshall algorithm to generate optimal topological paths between 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] In the formula, is the shortest path from node i to node j in the final main data set considering k intermediate nodes; d ij is the existing path distance between node i and node j in the final main data set; d ik is the path distance between node i and the kth intermediate node in the final main data set; d kj is the path distance between node j and the kth intermediate node in the final main data set; d ik +d kj is the path distance through the kth intermediate node in the final main data set.

[0146] Exemplarily, 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 through node 3 is 4, then the shortest path distance is updated to 4.

[0147] Next, the system establishes index tables, including the power grid main data table, the power grid attribute data table, and the power grid topology relationship data table, based on the initial main data table, the initial attribute data table, and the initial topology relationship data table. The purpose of establishing the index table is to improve the efficiency of data query and management. The power grid main data table is used to store the basic information of the equipment, such as equipment ID and equipment type; the power grid attribute data table records the operating parameters and dynamic state of the equipment, such as capacity and voltage; and the power grid topology relationship data table stores the connection information and shortest path information between equipment. For example, through the index table, the directly connected equipment or path distance of a certain equipment can be quickly queried, which improves the data query efficiency and operation performance. The power grid main 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 the initialization and data loading operation based on the final main data set, loads the data into the database, and generates a complete data set that supports the visual display of power grid engineering business.

[0149] Specifically, before data loading, the system needs to verify and clean the extracted and converted data to ensure the integrity and consistency of the data. The verification process includes uniqueness check of main data, foreign key constraint check, and data integrity check. For example, the system checks whether each equipment ID is unique and confirms that all attribute data and topology relationship data can find the corresponding equipment ID in the main data table. For data that does not meet the requirements, the system will be marked as abnormal and cleaned up, such as deleting duplicate data or correcting data format inconsistencies. This verification and cleaning process ensures the quality of the data finally loaded into the database.

[0150] After completing the verification, the system loads the data into the database in batches. Through efficient batch operation tools (such as LOAD DATA INFILE), the system quickly imports the extracted data into the corresponding data table. After loading is completed, the three main data tables - the power grid main data table, the power grid attribute data table, and the power grid topology relationship data table - form a complete data set.

[0151] Specifically, these data sets are presented in a structured form, which can visually display the attributes, logical relationships, and topology structure of the power grid equipment, supporting the planning, operation, and maintenance of power grid engineering. For example, through the visual interface, users can view the operating state of the power grid equipment, the topology connection between equipment, and the shortest path of the power grid transmission line.

[0152] In summary, the application discloses a data management method based on a GIM model, aiming to solve the problems of intuitive expression and unified management of three-dimensional data of power grid engineering.

[0153] The application has beneficial effects in power grid engineering data management. First, by using the structured data construction method based on attribute and association characteristics, the problem of three-dimensional data being unable to be intuitively and visually expressed in the prior art is solved, and the combination of three-dimensional models of power grid engineering and business requirements is enhanced. Second, by using the Jaccard similarity coefficient formula and the matching threshold mechanism, combined with attribute splitting and optimization strategies, the accuracy and processing efficiency of data matching are improved. At the same time, the application realizes dynamic data query and classification based on indexes by extracting query conditions related to the current business requirement type, ensuring the response of power grid engineering in different stages and scenarios. In addition, the application constructs a topological relationship table by using the Floyd-Warshall algorithm, calculates the shortest path and association relationship between devices, and provides efficient topological analysis capability for scheduling, operation and maintenance of power grid engineering. At the same time, the application covers the whole process management from data analysis, identification, matching, classification to initialization and loading, forming a whole life cycle data management system for each stage of power grid engineering planning, design, construction, operation and maintenance.

[0154] By realizing the structuring and dynamic management of three-dimensional data, the application improves the visual management efficiency of the whole life cycle of power grid engineering, and solves the problems of intuitive expression and unified management of three-dimensional data of power grid engineering.

[0155] With reference to Figure 2 The second embodiment of the application provides a data management system based on a GIM model, comprising:

[0156] A data acquisition module is configured to acquire GIM model data.

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

[0158] The data recognition module is configured to perform a data recognition judgment operation on the database data to obtain a recognition result, and perform an attribute relationship matching operation according to the recognition result to obtain structured data.

[0159] The query establishment module is configured to extract attribute conditions and relationship conditions related to a current service demand type from the structured data, and determine the attribute conditions and the relationship conditions as query conditions.

[0160] The data screening module is configured to perform an index query classification operation on the structured data according to the query conditions to obtain a final main data set.

[0161] The data loading module is configured to perform initialization and data loading operations according to the final main data set to obtain a basic data structure and a complete data set supporting power grid engineering service visualization display.

[0162] Preferably, the data acquisition module is configured to acquire GIM model data.

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

[0164] The GIM model data is imported into the database to obtain the database data, including:

[0165] A data interface of the GIM model is connected with the database.

[0166] The GIM model data is subjected to a data analysis operation to obtain analysis data.

[0167] The analysis data is converted into structure feature data, and the structure feature data is stored in a buffer.

[0168] It is judged whether a data table exists in the database, if the data table exists in the database, the structure feature data is acquired from the buffer, and the structure feature data is input into the database through the data interface to obtain data table data.

[0169] If the data table does not exist in the database, a data table creation operation is performed, the structure feature data is acquired from the buffer, and the structure feature data is input into the database through the data interface to obtain the data table data.

[0170] The data table data is subjected to a data index establishment operation to obtain database data.

[0171] Preferably, the data recognition module is configured to perform a data recognition operation on the database data to obtain a recognition result, and perform an attribute relationship matching operation according to the recognition result to obtain the structured data, including:

[0172] The data recognition operation on the database data to obtain a recognition result, and the attribute relationship matching operation according to the recognition result to obtain the structured data, include:

[0173] The existence judgment operation is performed on the database data, and if the database data does not exist, the step of importing the GIM model data into the database to obtain the database data is re-executed;

[0174] If the database data exists, a data recognition state judgment operation is performed on the database data;

[0175] If the database data has completed recognition, the database data is determined as the structured data;

[0176] If the database data has not completed recognition, a main data matching and characteristic extraction operation is performed on the database data to obtain attribute characteristic information and associated characteristic information; and an attribute relationship matching operation is performed on the database data according to the attribute characteristic information and the associated characteristic information to obtain the structured data.

[0177] The attribute relationship matching operation on the database data according to the attribute characteristic information and the associated characteristic information to obtain the structured data includes:

[0178] Based on a pre-defined attribute rule set, an attribute matching degree is calculated according to the attribute characteristic information to obtain an attribute matching degree;

[0179] When the attribute matching degree is greater than or equal to a pre-set attribute matching degree threshold, a relationship matching degree is calculated according to the associated characteristic information based on a pre-defined relationship rule set;

[0180] When the relationship matching degree is greater than or equal to a pre-set relationship matching degree threshold, the database data corresponding to the relationship matching degree is determined as the structured data;

[0181] When the relationship matching degree is less than the pre-set relationship matching degree threshold, a relationship splitting operation is performed to obtain a sub-relationship set matching degree; and a split relationship matching degree is calculated according to the sub-relationship matching degree;

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

[0183] 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.

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

[0185]

[0186] In the formula, A is the attribute matching degree; M attr is a predefined attribute rule set; S attr is an attribute feature information set; |S attr ∩M attr is a Jaccard similarity coefficient of an intersection of the predefined attribute rule set and the attribute feature information set; |S attr ∪M attr is a Jaccard similarity coefficient of a 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] In the formula, A rel is the relationship matching degree; M rel is a predefined association rule set; S rel is an association feature information set; |S rel ∩M rel is a Jaccard similarity coefficient of an intersection of the predefined association rule set and the association feature information set; |S rel ∪M rel is a Jaccard similarity coefficient of a 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] In the formula, A' rel is the split relationship matching degree; A rel,i is a sub-relationship set matching degree; and n is a number of the sub-relationship set matching degrees.

[0193] The attribute optimization operation performed according to the attribute matching degree and the relationship matching degree when the attribute matching degree is less than a preset attribute matching degree threshold comprises:

[0194] performing calculation according to the attribute matching degree and the relationship matching degree to obtain an adjusted matching score;

[0195] When the adjustment matching score is greater than or equal to a preset adjustment matching score threshold, the step of calculating the relationship matching degree based on the set of predefined relationship rules according to the associated feature information when the attribute matching degree is greater than or equal to a preset attribute matching degree threshold is executed again.

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

[0197] A splitting attribute matching degree is obtained by performing splitting calculation according to the sub-attribute matching degree.

[0198] When the splitting attribute matching degree is greater than or equal to the preset attribute matching degree threshold, the step of calculating the relationship matching degree based on the set of predefined relationship rules according to the associated feature information when the attribute matching degree is greater than or equal to the preset attribute matching degree threshold is executed again.

[0199] The calculation formula of the adjustment matching score is:

[0200] C = a x A + b x A rel

[0201] In the formula, C is the adjustment matching score; A is the attribute matching degree; A rel is the relationship matching degree; a and b are weight coefficients.

[0202] The calculation formula of the splitting attribute matching degree is:

[0203]

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

[0205] Preferably, the query establishing module is configured to extract attribute conditions and relationship conditions related to a current business demand type of the structured data, and determine the attribute conditions and the relationship conditions as query conditions.

[0206] Preferably, the data screening module is configured to perform an index query classification operation on the structured data according to the query conditions to obtain a final main data set, including:

[0207] The index query classification operation on the structured data according to the query conditions to obtain the final main data set includes:

[0208] The index query classification operation on the structured data according to the query conditions to obtain the final main data set includes:

[0209] According to the database data set and the query condition, further identification operations of attribute characteristics, hierarchical characteristics and correlation characteristics of data are performed to obtain a characteristic database data set;

[0210] Based on a current business demand type, a main data range determination operation is performed on the characteristic database data set to obtain a main data set conforming to the current business demand type;

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

[0212] Preferably, the data loading module is configured to perform initialization and data loading operations according to the final main data set to obtain a basic data structure and a complete data set supporting visual display of power grid engineering business, including:

[0213] The initialization and data loading operations according to the final main data set to obtain the basic data structure and the complete data set supporting the visual display of the power grid engineering business include:

[0214] Power grid characteristic information of the final main data set is extracted, and data definition operations are performed according to the power grid characteristic information to obtain an initial main data table, an initial attribute data table and an initial relationship data table;

[0215] According to the final main data set, shortest path calculation is performed based on a Floyd-Warshall algorithm to obtain a shortest path, and the shortest path is stored in the initial relationship data table to obtain an initial topological relationship data table;

[0216] According to the initial main data table, the initial attribute data table and the initial topological relationship data table, an index table establishment operation is performed to obtain a power grid main data table, a power grid attribute data table and a power grid topological relationship data table;

[0217] According to the power grid main data table, the power grid attribute data table and the power grid topological relationship data table, the final main data set is loaded into the database to obtain the basic data structure and the complete data set supporting the visual display of the power grid engineering business;

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

[0219]

[0220] In the formula, is the shortest path of node i to node j in the final main data set considering k intermediate nodes; d ij is an existing path distance of node i and node j in the final main data set; d ikis the path distance between node i and the kth intermediate node in the final main data set; d kj is the path distance between node j and the kth intermediate node in the final main data set; d ik + d kj is the path distance between node j and the kth intermediate node in the final main data set; d

[0221] It should be noted that the data management system based on the GIM model provided by the embodiments of the present application is used to execute all process steps of the data management method based on the GIM model of the above embodiments, and the working principles and beneficial effects of the two are one-to-one correspondence, thus not being repeated.

[0222] The embodiments of the present application further provide an electronic device. The electronic device comprises a processor, a memory, and a computer program, for example, a shortest path calculation program, stored in the memory and executable on the processor. The processor implements the steps in the above various data management methods based on the GIM model when executing the computer program, for example Figure 1 the step S11 shown. Alternatively, the processor implements the functions of each module / unit in the above various apparatus embodiments when executing the computer program, for example, a data identification module.

[0223] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device.

[0224] The electronic device can be a desktop computer, a notebook, a palm computer, and a smart tablet, etc. The electronic device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the above components are only examples of the electronic device, and do not constitute a limitation on the electronic device, and can include more or fewer components than the above, or combine certain components, or different components, for example, the electronic device can also include an input / output device, a network access device, a bus, etc.

[0225] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like, and is a control center of the electronic device, which connects various parts of the electronic device through 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 program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0227] The modules / units integrated in the electronic device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included 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, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0228] It should be noted that the above-described device embodiments are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. In addition, the connection relationship between the modules in the device embodiment provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0229] The above-described specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only for the specific embodiments of the present application and do not limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A data management method based on a GIM model, characterized in that... include: Obtain GIM model data; Import the GIM model data into the database to obtain database data; The database data is subjected to data recognition and judgment operations to obtain recognition results, and attribute relationship matching operations are performed based on the recognition results to obtain structured data; Extract the attribute conditions and relationship conditions related to the current business requirement type from the structured data, and determine the attribute conditions and relationship conditions as query conditions; Based on the query conditions, the structured data is indexed and categorized to obtain the final master data set; Based on the final master data set, initialization and data loading operations are performed to obtain the basic data structure and complete data set that support the visualization of power grid engineering business. The initialization and data loading operations based on the final master data set to obtain the basic data structure and complete data set supporting the visualization of power grid engineering business include: Extract the power grid characteristic information from the final master data set, and perform data definition operations based on the power grid characteristic information to obtain the initial master data table, the initial attribute data table, and the initial relation data table; Based on the final master data set, the shortest path is calculated using the Floyd-Warshall algorithm to obtain the shortest path, and the shortest path is stored in the initial relation data table to obtain the initial topology relation data table; Based on the initial master data table, the initial attribute data table, and the initial topology data table, an index table creation operation is performed to obtain the power grid master data table, the power grid attribute data table, and the power grid topology data table; Based on 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 the basic data structure and complete data set that support the visualization of power grid engineering business. The shortest path is calculated using the following formula: In the formula, The shortest path from node i to node j in the final master dataset, considering k intermediate nodes; The distance between nodes i and j in the final master dataset is the existing path distance. This represents the path distance between node i and the kth intermediate node in the final master dataset. This represents the path distance between node j and the kth intermediate node in the final master dataset. This represents the path distance through the kth intermediate node in the final master dataset.

2. The data management method based on the GIM model according to claim 1, characterized in that, The process of importing GIM model data into the database to obtain database data includes: Establish a data interface for the GIM model and connect it to the database; Perform data parsing operations on the GIM model data to obtain parsed data; The parsed data is converted into structural feature data, and the structural feature data is stored in a buffer. Determine whether the data table exists in the database. If the data table exists, retrieve the structural feature data from the buffer and input the structural feature data into the database through the data interface to obtain the data table data. If the data table does not exist in the database, a data table creation operation is performed, and the structural feature data is obtained from the buffer and input into the database through the data interface to obtain the data table data. A data indexing operation is performed on the data table data to obtain the database data.

3. The data management method based on the GIM model according to claim 1, characterized in that, The step of performing data identification and judgment operations on the database data to obtain identification results, and then performing attribute relationship matching operations based on the identification results to obtain structured data, includes: The database data is checked for existence. If the database data does not exist, the step of importing the GIM model data into the database is repeated. If the database data exists, perform a data identification status judgment operation on the database data; If the database data has been identified, the database data will be determined as structured data; If the database data is not fully identified, master data matching and feature extraction operations are performed on the database data to obtain attribute feature information and association feature information; based on the attribute feature information and the association feature information, attribute relationship matching operations are performed on the database data to obtain structured data.

4. The data management method based on the GIM model according to claim 3, characterized in that, The step of performing attribute relationship matching on the database data based on the attribute feature information and the association feature information to obtain structured data includes: Based on a predefined set of attribute rules, the attribute matching degree is calculated according to the attribute feature information to obtain the attribute matching degree. When the attribute matching degree is greater than or equal to the preset attribute matching degree threshold, the relationship matching degree is calculated based on the predefined set of relationship rules and the associated feature information. When the relationship matching degree is greater than or equal to a preset relationship matching degree threshold, the database data corresponding to the relationship matching degree is determined 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 the matching degree of the sub-relationship set; the splitting relationship matching degree is obtained by splitting based on the matching degree of the sub-relationship set. When the matching degree of the split relationship is greater than or equal to the preset matching degree threshold, the database data corresponding to the matching degree of the split relationship is determined as structured data; When the attribute matching degree is less than the preset attribute matching degree threshold, attribute optimization operation is performed based on 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 formula for calculating the attribute matching degree is: In the formula, A represents the attribute matching degree; A predefined set of attribute rules; It is a set of attribute feature information; The Jaccard similarity coefficient of the intersection of a predefined set of attribute rules and a set of attribute feature information; The Jaccard similarity coefficient is the union of a predefined set of attribute rules and a set of attribute feature information. The formula for calculating the relationship matching degree is: In the formula, Relationship matching degree; A predefined set of association rules; It is a set of associated feature information; The Jaccard similarity coefficient is the intersection of a predefined set of association rules and a set of association feature information. The Jaccard similarity coefficient is the union of a predefined set of association rules and a set of association feature information. The formula for calculating the matching degree of the splitting relationship is: In the formula, To split the relationship matching degree; represents the matching degree of the sub-relation set; n represents the number of sub-relation sets with matching degrees.

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, the attribute optimization operation is performed based on the attribute matching degree and the relationship matching degree, including: The matching score is calculated based on the attribute matching degree and the relationship matching degree. When the adjusted matching score is greater than or equal to the preset adjusted matching score threshold, return to the step of calculating the relationship matching degree based on the predefined set of relationship rules and the associated feature information when the attribute matching degree is greater than or equal to the preset attribute matching degree threshold; When the adjusted matching score is less than the preset adjusted matching score threshold, an attribute splitting operation is performed to obtain the sub-attribute matching degree; The matching degree of the sub-attribute is calculated by splitting the sub-attribute 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 the step of calculating the relationship matching degree based on the predefined set of relationship rules and the associated feature information.

7. The data management method based on the GIM model according to claim 6, characterized in that, The formula for calculating the adjusted matching score is as follows: In the formula, C represents the adjusted matching score; A represents the attribute matching degree. α represents the relationship matching degree; β represents the weighting coefficients. The formula for calculating the matching degree of the split attribute is: In the formula, For attribute relationship matching degree; is the matching degree of the sub-attribute set; 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, The step of performing an index query and classification operation on the structured data based on the query conditions to obtain the final master data set includes: Based on the query conditions, the structured data is filtered using an index to obtain a preliminary set of database data that meets the query conditions. Based on the database data set and the query conditions, further identification operations are performed on the attribute features, hierarchical features, and association features of the data to obtain a feature database data set; Based on the current business requirement type, the master data range determination operation is performed on the feature database data set to obtain a master data set that conforms to the current business requirement type; The master data set is then subjected to a data classification operation to obtain the final master data set.

9. A data management system based on a GIM model, characterized in that, The data management method based on the GIM model as described in any one of claims 1 to 8 includes: The data acquisition module is used to acquire GIM model data; The data import module is used to import the GIM model data into the database to obtain database data; The data recognition module is used to perform data recognition and judgment operations on the database data, obtain recognition results, and perform attribute relationship matching operations based on the recognition results to obtain structured data; The query creation module is used to extract the attribute conditions and relationship conditions related to the current business requirement type from the structured data, and to determine the attribute conditions and relationship conditions as query conditions; The data filtering module is used to perform index query and classification operations on the structured data according to the query conditions to obtain the 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 the basic data structure and complete data set that support the visualization of power grid engineering business.

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