Gim data processing method and system for digital handover

Abstract

This invention discloses a GIM data processing method and system for digital handover, relating to the field of data processing technology. The method includes: acquiring heterogeneous GIM handover data from different departments, including spatial location data, equipment attribute data, status monitoring data, and semantic data; performing standardization processing and setting hierarchical relationships according to the processing objectives of power grid equipment; converting the handover data into a unified geometric algebra multivector based on a high-dimensional geometric mapping relationship and configuring geometric dimension identifiers; and projecting the unified geometric algebra multivector onto a constructed high-dimensional geometric algebra space. This invention solves the technical problems of inefficiently integrating multi-source heterogeneous GIM handover data and the lack of a unified mathematical framework for integrated computational expression of complex relationships between equipment in existing technologies. It achieves the standardization, structuring, and deep integration of cross-departmental GIM data, improving the quality, consistency, and analyzability of digital handover data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a GIM data processing method and system for digital handover. Background Technology

[0002] Digital handover of power grid infrastructure is a crucial part of power grid construction and operation and maintenance management. The Power Grid Information Model (GIM) is a digital representation of the power sector. However, the handover process of GIM data faces numerous challenges in practical applications. First, GIM data sources exhibit significant heterogeneity. Different design units, equipment suppliers, construction units, and monitoring agencies employ their own data standards and formats, resulting in differences in the structure, semantics, and geometric representation of the transferred data. Spatial location data may use different coordinate systems and precision standards, equipment attribute data often follows different classification and coding systems, and status monitoring data suffers from inconsistent sampling frequencies and formats. Semantic data often results in information silos due to differences in domain knowledge expression methods. Second, existing GIM data processing methods struggle to effectively express complex power grid equipment relationships, such as equipment attribute information, real-time status data, and rich semantic relationships. Furthermore, geometric data, attribute data, and monitoring data are often stored and processed separately, leading to complex data fusion operations in operation and maintenance analysis, fault diagnosis, and simulation, reducing data utilization efficiency and decision support capabilities.

[0003] Therefore, in the current related technologies, there are technical problems such as the difficulty in efficiently integrating multi-source heterogeneous GIM handover data and the lack of a unified mathematical framework for integrated calculation and expression of complex relationships between devices. Summary of the Invention

[0004] This application provides a GIM data processing method and system for digital handover, which solves the technical problems in the prior art of inefficiently integrating multi-source heterogeneous GIM handover data and lacking a unified mathematical framework for integrated calculation and expression of complex relationships between devices. It achieves the technical effect of standardizing, structuring and deeply integrating cross-departmental GIM data, and improving the quality, consistency and analyzability of digital handover data.

[0005] This application provides a GIM data processing method for digital handover, the method comprising: acquiring heterogeneous GIM handover data from different departments, the GIM handover data including spatial location data, equipment attribute data, status monitoring data, and semantic data; standardizing the GIM handover data and setting hierarchical relationships according to the processing objectives of power grid equipment to establish a high-dimensional geometric mapping relationship; based on the high-dimensional geometric mapping relationship, converting the standardized handover data into unified geometric algebraic multivectors and configuring a geometric dimension identifier for each multivector; constructing a high-dimensional geometric algebraic space and projecting the unified geometric algebraic multivectors into the high-dimensional geometric algebraic space, wherein a network of association relationships between equipment multivectors is established in the high-dimensional geometric algebraic space through geometric algebraic operations.

[0006] In a possible implementation, the GIM handover data is standardized and hierarchically configured according to the processing objectives of the power grid equipment to establish a high-dimensional geometric mapping relationship. This includes: parsing, cleaning, format unification, and semantic alignment of the GIM handover data to generate structured and semantically consistent standardized data; organizing the data according to the power grid physical topology and the hierarchical relationship of equipment-bay-voltage level-substation to determine a multi-layer equipment organization framework; analyzing the multi-dimensional feature expression requirements of the power grid equipment based on the multi-layer equipment organization framework and the processing objectives of the power grid equipment, and designing a geometric algebra high-dimensional analytical framework; and establishing a mapping relationship from the standardized data to the geometric algebra space based on the correlation and correspondence between the GIM handover data, the equipment organization framework, and the geometric algebra high-dimensional analytical framework to obtain the high-dimensional geometric mapping relationship.

[0007] In a possible implementation, a geometric algebraic high-dimensional analytical framework is designed, including: determining physical or business dimensions based on the multi-layered device organization framework and the processing objectives of power grid equipment, and configuring a dimensional basis, wherein the basis satisfies the orthogonality and completeness requirement; analyzing the business feature description requirements of the multi-layered device organization framework under the processing objective scenario of power grid equipment, and defining business feature variables that can characterize the device state, attributes, relationships, and behaviors as high-dimensional variables in the dimensional basis; and setting high-dimensional variable operation rules based on geometric algebra based on the high-dimensional variables and the corresponding dimensional basis to support cross-dimensional calculation and analysis.

[0008] In a possible implementation, obtaining the high-dimensional geometric mapping relationship includes: for each device data item in the standardized data, determining the target dimension subspace in the corresponding geometric algebra high-dimensional parsing framework based on its position and type in the hierarchical framework; selecting the corresponding dimension basis in the geometric algebra high-dimensional parsing framework according to the device data type and business semantics, and establishing a mapping table from data fields to geometric algebra basis; defining a coefficient transformation function based on the mapping table to convert the standard data values ​​of the device into geometric algebra basis through normalization, quantization, or encoding; and defining a synthesis function to synthesize the algebraic expressions of the device in each target dimension subspace into a unified geometric algebraic multivector according to a preset algebraic combination rule; wherein, the high-dimensional geometric mapping relationship includes the mapping table, the transformation function, and the synthesis function.

[0009] In possible implementations, determining the target dimensional subspace within the corresponding geometric algebraic high-dimensional analytical framework includes: parsing the hierarchical paths of power grid equipment within the hierarchical framework; activating the corresponding dimensional subspace set based on the type and function of each node in the path; determining the required association dimensions based on the roles and connection relationships of the power grid equipment in the hierarchy, including spatial association dimensions, electrical association dimensions, functional association dimensions, and management association dimensions; and allocating the corresponding sub-dimensional space based on the association dimensions and the dimensional subspace set. For complex equipment, the internal hierarchical relationships are further subdivided according to the internal component structure, and corresponding sub-dimensional spaces are allocated.

[0010] In possible implementations, a mapping table is established between data fields and geometric algebraic bases. This includes: for spatial location data, a conformal geometric algebraic base is used to establish mapping rules from point, line, surface, and volume geometric elements to multidimensional vectors; for equipment attribute data, it is classified into electrical parameters, mechanical parameters, and management parameters, with an independent attribute base assigned to each parameter type and dimension normalization coefficients set; for condition monitoring data, a dynamic base coding scheme is designed based on the monitoring variable type and sampling characteristics to support the conversion of real-time data to time series vectors; for business semantic data, based on the power grid domain ontology, a mapping relationship between semantic tags and discrete symbol bases is established to form a semantic coding dictionary.

[0011] In a possible implementation, based on the high-dimensional geometric mapping relationship, the standardized transfer data is converted into a unified geometric algebra multivector, including: for each device instance in the standardized transfer data, applying a transformation function to convert the device data value into geometric algebra basis coefficients; based on the basis coefficients, generating an algebraic expression for the device in each target dimension subspace; synthesizing the algebraic expressions of each subspace into a unified geometric algebra multivector using a synthesis function, and configuring a geometric dimension identifier code for each unified geometric algebra multivector, wherein the identifier code includes at least the device type code, the hierarchical path to which it belongs, and data version information.

[0012] Among possible implementations, a high-dimensional geometric algebraic space is constructed, including: determining the total number of dimensions of the geometric algebraic space based on a high-dimensional analytical framework of geometric algebra; designing an orthogonal and complete basis system, including geometric basis, attribute basis, state basis, and semantic basis; defining a metric tensor for the space to compute geometric relationships between vectors in the space; configuring the coordinate system of the space and establishing a mapping relationship between spatial coordinates and device identifiers.

[0013] In a possible implementation, a network of relationships between multiple vectors of devices is established in a high-dimensional geometric algebraic space through geometric algebraic operations. This includes: calculating the geometric distance between multiple vectors of devices, determining spatial adjacency relationships based on distance thresholds, and establishing geometric algebraic edge vectors representing spatial adjacency relationships; parsing the connection relationships of the power grid topology, defining geometric algebraic operators for electrical connection relationships, calculating the electrical connectivity strength between power grid devices, and establishing electrical connection relationship edges; parsing the functional coupling relationships between power grid devices, calculating the functional coupling degree, and establishing functional relationship edges; calculating the correlation of state vectors in the geometric algebraic space based on the time series of power grid device state data, and constructing a state-dependent relationship network; and fusing the spatial adjacency relationship edges, electrical connection relationship edges, functional relationship edges, and state-dependent relationship network into a multi-attribute relationship graph network through geometric algebraic operations, and encoding different types of relationship relationships into corresponding geometric algebraic operators to obtain a computable relationship network.

[0014] This application also provides a GIM data processing system for digital handover, the system comprising: a GIM handover data acquisition module for acquiring heterogeneous GIM handover data from different departments, the GIM handover data including spatial location data, equipment attribute data, status monitoring data, and semantic data; a geometric mapping relationship establishment module for standardizing the GIM handover data and setting hierarchical relationships according to the processing objectives of power grid equipment to establish a high-dimensional geometric mapping relationship; a handover data conversion module for converting the standardized handover data into unified geometric algebraic multivectors based on the high-dimensional geometric mapping relationship, and configuring a geometric dimension identifier for each multivector; and a geometric algebraic space construction module for constructing a high-dimensional geometric algebraic space and projecting the unified geometric algebraic multivectors into the high-dimensional geometric algebraic space, wherein a network of association relationships between equipment multivectors is established in the high-dimensional geometric algebraic space through geometric algebraic operations.

[0015] This application proposes a GIM data processing method and system for digital handover, which aims to acquire heterogeneous GIM handover data from different departments, including spatial location data, equipment attribute data, condition monitoring data, and semantic data. The method will then standardize the data and establish hierarchical relationships according to the processing objectives of power grid equipment. Based on high-dimensional geometric mapping relationships, the handover data will be converted into unified geometric algebraic multivectors and configured with geometric dimension identifiers. Finally, these unified geometric algebraic multivectors will be projected into a constructed high-dimensional geometric algebraic space. This addresses the technical problems in existing technologies, such as the difficulty in efficiently integrating multi-source heterogeneous GIM handover data and the lack of a unified mathematical framework for integrated computational expression of complex relationships between equipment. It achieves the standardization, structuring, and deep integration of cross-departmental GIM data, improving the quality, consistency, and analyzability of digital handover data. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a schematic diagram of a GIM data processing method for digital handover provided in an embodiment of this application.

[0018] Figure 2 A schematic diagram of the structure of a GIM data processing system for digital handover provided in an embodiment of this application.

[0019] Figure labeling: GIM handover data acquisition module 10, geometric mapping relationship establishment module 20, handover data conversion module 30, geometric algebraic space construction module 40. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by the present invention in order to achieve the intended purpose, the following detailed description is provided in conjunction with the accompanying drawings and preferred embodiments, based on the specific implementation methods, structures, features and effects of the present invention.

[0021] This application provides a GIM data processing method for digital handover, such as... Figure 1 As shown, the method includes: Step S100: Obtain heterogeneous GIM handover data from different departments. The GIM handover data includes spatial location data, equipment attribute data, status monitoring data, and semantic data.

[0022] Preferably, heterogeneous GIM handover data is obtained from different departments, including design units, equipment suppliers, construction units, and monitoring agencies. This data includes spatial location data, equipment attribute data, condition monitoring data, and semantic data. Spatial location data refers to the coordinates, shape, and topological orientation of power grid equipment and its components in three-dimensional space. This includes equipment shape, size, and coordinate information extracted from the three-dimensional design model, such as the Gaussian plane coordinates and elevations of equipment feature points, contour points, and installation points; vector representations of equipment and its components, such as towers, transformers, circuit breakers, conductors, and insulator strings; installation positions of equipment within bays; connection relationships between conductors and insulator strings; relative positional relationships between equipment and buildings / foundations; and minimum electrical safety clearance between equipment.

[0023] Preferably, equipment attribute data refers to structured data describing the inherent characteristics, technical parameters, and management information of power grid equipment. This includes electrical parameters such as rated voltage, rated current, short-circuit capacity, insulation level, number of phases, and frequency; mechanical and structural parameters such as equipment model, manufacturer, serial number, dimensions, weight, material, mechanical strength, and equipment capacity; and management attributes such as asset number, name of the substation / line to which it belongs, commissioning date, last maintenance date, design life, supplier information, and technical document index number. Condition monitoring data refers to dynamic data collected by sensors or monitoring systems that reflects the real-time or historical operating conditions of power grid equipment. This includes timestamped real-time operating parameters, historical status records, and health status information, such as three-phase current, voltage, active power, reactive power, power factor, harmonic content, zero-sequence current / voltage, as well as temperature of key equipment components, partial discharge signals, dissolved gas content in oil, mechanical vibration amplitude and frequency, and image / video monitoring data.

[0024] Preferably, semantic data refers to descriptive information that gives power grid data a clear business meaning and logical relationship. This includes equipment coding, functional role definition and business rule description based on the power grid domain ontology, such as equipment type coding, functional location coding, asset category coding, equipment classes, attributes and interrelationships defined based on the domain ontology, rules describing equipment operation and maintenance logic, and data attribute information such as data source, data accuracy, confidence level, generation time, version number, and conforming standards.

[0025] Step S200: Standardize the GIM handover data and set hierarchical relationships according to the processing objectives of the power grid equipment to establish a high-dimensional geometric mapping relationship.

[0026] Step S200 further includes parsing, cleaning, format unification, and semantic alignment standardization of the GIM handover data to generate structured and semantically consistent standardized data; organizing the data according to the hierarchical relationship of equipment-bay-voltage level-substation based on the power grid physical topology to determine a multi-layer equipment organization framework; performing a multi-dimensional feature expression requirement analysis of the power grid equipment based on the multi-layer equipment organization framework and the processing objectives of the power grid equipment, and designing a geometric algebra high-dimensional analytical framework; and establishing a mapping relationship from the standardized data to the geometric algebra space based on the correlation and correspondence between the GIM handover data, the equipment organization framework, and the geometric algebra high-dimensional analytical framework to obtain the high-dimensional geometric mapping relationship.

[0027] Preferably, the GIM handover data is parsed to identify and extract valid information from the original data files, such as parsing the geometric coordinates and attribute sets of equipment components from the IFC file; the GIM handover data is cleaned to correct or remove erroneous, incomplete, or unreasonable data, including data whose spatial coordinates are significantly outside the reasonable range, data with empty equipment model or rated parameter fields, and data with abnormally small rated capacity values ​​for transformers; the GIM handover data is formatted to be unified, converting data from different sources into the same target format, for example, unifying all coordinate systems to the same projected coordinate system and unifying all timestamps to the ISO 8601 format; the GIM handover data is semantically aligned and standardized to ensure that data items with the same meaning use unified terminology and coding, for example, mapping circuit breakers in system A and switches in system B to the standard term "circuit breaker" and the standard code "B", and establishing an attribute mapping dictionary based on industry standards to ensure that the rated voltage field represents the same physical quantity in all data; finally, structured and semantically consistent standardized data is output, where each row represents a piece of equipment or component, and the column fields are clearly defined and the values ​​are standardized.

[0028] Preferably, the power grid physical topology refers to the actual electrical connections and functional affiliations between devices in the power grid. Devices are the most basic units, and bays are the core units organizing equipment within a substation—that is, a set of electrically related devices that work together to perform a specific function. Voltage levels are sets of devices and bays with the same nominal operating voltage, and a substation is a site containing multiple voltage levels and all their bays and devices. The system is organized according to the hierarchical relationship of device-bay-voltage level-substation, establishing a tree-like or graph-like index structure as a multi-layered device organization framework, assigning clear context and location information to each standardized device data. The processing objectives of power grid equipment may be fault analysis, condition assessment, or electromagnetic field calculation. Then, based on the multi-layered device organization framework and the processing objectives of power grid equipment, a multi-dimensional feature expression requirement analysis of the power grid equipment is performed. This involves analyzing the characteristics of the power grid equipment that need to be expressed. If the processing objective is fault analysis, the spatial location, electrical connection relationships, real-time current and voltage, and equipment health status of the equipment need to be expressed. If the processing objective is electromagnetic field calculation, then a more refined expression of the equipment's geometry, material properties, and operating current is required.

[0029] Preferably, the dimensions describing these features are determined, and basis vectors are defined for each dimension, satisfying the orthogonality and completeness requirement, i.e., they are independent of each other and can span the entire feature space. Then, in a high-dimensional space composed of multiple basis vectors, operations such as addition, subtraction, inner product, outer product, and geometric product are performed on the vectors representing different features to generate a geometric algebraic high-dimensional analytical framework. Based on the correlation and correspondence between GIM handover data and equipment organization framework and the geometric algebraic high-dimensional analytical framework, data fields are matched with the dimensions of the analytical framework, and equipment attribute fields correspond to spatial geometric basis vectors. A mapping relationship from standardized data to the geometric algebraic space is established, the transformation function is determined, and finally, a high-dimensional geometric mapping relationship is obtained.

[0030] Furthermore, step S200 also includes: determining physical or business dimensions based on the multi-layered device organization framework and the processing objectives of the power grid equipment, configuring a dimensional basis, wherein the basis satisfies the orthogonality and completeness requirement; analyzing the business feature description requirements for the multi-layered device organization framework under the processing objective scenario of the power grid equipment, defining business feature variables that can characterize the device status, attributes, relationships, and behaviors as high-dimensional variables in the dimensional basis; and setting high-dimensional variable operation rules based on geometric algebra based on the high-dimensional variables to support cross-dimensional calculation and analysis.

[0031] Preferably, based on the hierarchical relationships of equipment in the multi-layered equipment organizational framework and the processing objectives of power grid equipment, physical or business dimensions are determined. For example, if the processing objective is fault impact range analysis, the physical dimension is a spatial dimension, and the business dimensions are topology connection dimension, equipment health dimension, and protection action logic dimension. Then, basis vectors are configured as the dimensional basis for each determined dimension. The basis satisfies the orthogonality and completeness requirement, meaning that the basis vectors of different dimensions are independent of each other, their inner product is zero, and the space spanned by all basis vectors is sufficient to completely express all equipment characteristics; any equipment state can be represented by a linear combination of basis vectors. For the processing objectives of power grid equipment, the business characteristic description requirements for the multi-layered equipment organizational framework are analyzed. Business characteristic variables that can characterize equipment states, attributes, relationships, and behaviors are defined as high-dimensional variables in the dimensional basis. For example, for electromagnetic compatibility analysis, state variables, attribute variables, relationship variables, and behavioral variables are determined, and each specific business characteristic is defined as a linear combination or geometric algebra object of the basis vectors. Then, by utilizing operations unique to geometric algebra, such as inner and outer products and geometric products, the interactions between variables of different dimensions are defined. High-dimensional variable operation rules based on geometric algebra are set and cross-dimensional calculation and analysis are supported. That is, it is possible to directly perform operations on a mixture containing spatial, attribute, and state information. For example, the current element is defined as a vector located at a point in space, and the magnetic field strength is defined as a two-vector. Based on the geometric algebraic form of the Biot-Savart law, a geometric algebraic operator is directly defined. Applying this geometric algebraic operator to the multiple vectors of the current element can directly output the resulting two-vector of magnetic field strength.

[0032] Furthermore, step S200 also includes, for each device data item in the standardized data, determining the target dimension subspace in the corresponding geometric algebra high-dimensional parsing framework based on its position and type in the hierarchical framework; selecting the corresponding dimension basis in the geometric algebra high-dimensional parsing framework according to the device data type and business semantics, and establishing a mapping table from data fields to geometric algebra basis; defining a coefficient transformation function based on the mapping table to convert the standard data values ​​of the device into geometric algebra basis through normalization, quantization, or encoding; and defining a synthesis function to synthesize the algebraic expressions of the device in each target dimension subspace into a unified geometric algebraic multivector according to a preset algebraic combination rule; wherein, the high-dimensional geometric mapping relationship includes the mapping table, the transformation function, and the synthesis function.

[0033] Preferably, for each device data item in the standardized data, based on its position and type within the hierarchical framework—for example, "condition monitoring - temperature" data belonging to the path "Substation A → 220kV voltage level → #1 main transformer bay → main transformer → A-phase winding"—the corresponding target dimension subspace reserved for it is found by querying the geometric algebra high-dimensional parsing framework. For instance, the geometric algebra high-dimensional parsing framework may stipulate that all transformer winding data should be mapped to the thermodynamic state subspace, bay-level connection relationship data should be mapped to the topological association subspace, and the spatial coordinate data of the device body should be mapped to the three-dimensional geometric subspace. Then, according to the device data type and business semantics, the corresponding dimensional basis is selected in the geometric algebra high-dimensional parsing framework, and a mapping table from data fields to the geometric algebra basis is established, as shown in Table 1. Table 1. Examples of Mapping Table Entries Preferably, based on the mapping table, a coefficient transformation function is defined to convert the standard data values ​​of the device into a geometric algebraic basis through normalization, quantization, or encoding. Specifically, a specific mathematical function is defined for each type of mapping in the mapping table. Normalization is used for continuous physical quantities, quantization is used for discretization (e.g., quantizing good / attentive / abnormal / severe health status into coefficients 1.0 / 0.5 / -0.5 / -1.0), and encoding is used for classifying or identifying data. When the original data value is input, the corresponding transformation function outputs an algebraic expression of coefficients × basis. Next, a synthesis function is defined to synthesize all algebraic expressions of the device scattered in different target dimension subspaces according to a preset algebraic combination rule into a unified geometric algebraic multi-vector. The preset algebraic combination rule is usually the geometric algebraic addition rule. The high-dimensional geometric mapping relationship includes the mapping table, transformation function, and synthesis function. The mapping table is a static catalog of power grid devices, the transformation function is a dynamic pricing rule, and the synthesis function is used to assemble and generate the complete mathematical identity of the device, including spatial, attribute, and status information.

[0034] Furthermore, step S200 also includes parsing the hierarchical path of the power grid equipment in the hierarchical framework, activating the corresponding set of dimensional subspaces according to the type and function of each node in the path; determining the required association dimensions based on the role and connection relationship of the power grid equipment in the hierarchy, including spatial association dimensions, electrical association dimensions, functional association dimensions, and management association dimensions; allocating the corresponding sub-dimensional spaces according to the association dimensions and the set of dimensional subspaces; wherein, for complex equipment, the internal hierarchical relationship is subdivided according to the internal component structure, and the corresponding sub-dimensional spaces are allocated.

[0035] Preferably, the hierarchical path of the power grid equipment in the hierarchical framework is analyzed, that is, the specific position of the equipment in the equipment-bay-voltage level-substation organizational framework. Then, according to the type and function of each node in the path, the path is traversed from top to bottom, and the typical dimensional subspaces of all nodes are merged. The corresponding dimensional subspace sets are activated. That is, each node on the path predefines its typical and necessary dimensional subspace set. For example, the substation node activates the macroscopic space subspace and management identification subspace, the 220kV voltage level node activates the high-voltage electrical parameter subspace, the main transformer incoming line bay node activates the primary main wiring topology subspace and protection association subspace, and the circuit breaker node activates the switchgear status subspace and operating mechanism subspace.

[0036] Preferably, the roles and connections of power grid equipment in the hierarchy are obtained. Roles include "power source," "load source," "interconnection switch," and "protection and control device." Connections refer to physical wiring relationships, such as connections with busbars, lines, and transformers. This allows for the determination of the required spatial, electrical, functional, and management association dimensions. The spatial association dimension is used to calculate the physical distance and orientation relationships with other equipment, such as parallel installation on the same tower or crossing. The electrical association dimension is used to express electrical coupling relationships, such as impedance, admittance, and power flow. The functional association dimension is used to describe logical dependencies, such as the correspondence between protection devices and protected equipment, and the interlocking logic between circuit breakers and related disconnect switches. The management association dimension is used to express organizational and operational relationships, such as belonging to the same operational team, sharing the same work order, and having the same maintenance cycle.

[0037] Preferably, various related dimension requirements are specifically mapped and allocated to the corresponding sub-dimensional spaces in the geometric algebraic high-dimensional analytical framework. For example, the requirement for the orientation relationship with neighboring towers is allocated to the spatial relationship subspace, the requirement for the impedance of the branch is allocated to the network topology parameter subspace, and the requirement for protection action logic is allocated to the protection function logic subspace. Then, the relatedness subspace is merged with the activated device's own state subspace to form a complete target dimension subspace allocation scheme for the device. Among them, for complex devices such as transformers, combined electrical appliances, and relay protection panels that contain multiple functional components, the internal hierarchical relationship is subdivided according to the internal component structure, and then its hierarchical path inside the transformer is analyzed to activate the component-level subspace. Based on its role and internal connection relationship, its internal related dimensions are determined, and finally, a more refined sub-dimensional space is allocated to it. In the end, a device is described by a principal multi-vector associated with multiple sub-multi-vectors, improving its expression accuracy and computability.

[0038] Furthermore, step S200 also includes: for spatial location data, using a conformal geometric algebra basis to establish mapping rules from point, line, surface, and volume geometric elements to multidimensional vectors; for equipment attribute data, classifying them according to electrical parameters, mechanical parameters, and management parameters, assigning an independent attribute basis to each parameter type, and setting a dimension normalization coefficient; for status monitoring data, designing a dynamic basis coding scheme based on the monitoring variable type and sampling characteristics to support the conversion of real-time data to time series vectors; and for business semantic data, establishing a mapping relationship from semantic tags to discrete symbol basis based on the power grid domain ontology to form a semantic coding dictionary.

[0039] Preferably, for spatial location data, based on the 3D conformal geometric algebra basis, an infinity point is introduced to construct the plane and the center of the sphere, and an origin is introduced to construct direction and translation. Mapping rules are established from point, line, surface, and volume geometric elements to multidimensional vectors. All basic geometric elements are represented by the same mathematical vector. Geometric relationships such as distance, intersection, and containment are directly obtained through simple inner and outer product operations, without the need for complex analytical geometric formulas. Equipment attribute data is classified into electrical parameters, mechanical parameters, and management parameters, and an independent attribute basis is assigned to each parameter type. Electrical parameters are assigned voltage, current, and capacity bases; mechanical parameters are assigned weight, dimensions, height, and mechanical strength bases; and management parameters are assigned service life and manufacturer code bases. Simultaneously, a reference value is set for each base as a dimensionless normalization coefficient, converting actual values ​​into dimensionless coefficient values ​​to improve the numerical stability of the calculation.

[0040] Preferably, the continuously generated status monitoring data is encoded into vectors in a subspace spanned by a time base, based on the monitoring variable type and sampling characteristics. For example, time sampling defines a set of time bases, variable bases define variable type bases, and the temperature value at a certain moment is encoded. This supports the conversion of real-time data to time series vectors, that is, the encodings of N moments are added together to form a composite vector representing the recent temperature history of the equipment. The health / fault status of different equipment corresponds to different cloud clusters of time series vectors in space. For business semantic data, a structured semantic encoding dictionary of concepts, attributes, and relationships is constructed based on the power grid domain ontology standard. Each semantic label is assigned a unique, discrete symbol base, and hierarchical relationships are reflected through the combination or projection operations of the bases, enabling the computer to understand the semantic and logical relationships of equipment types and supporting rule-based reasoning.

[0041] Step S300: Based on the high-dimensional geometric mapping relationship, the standardized transfer data is converted into a unified geometric algebra multivector, and a geometric dimension identifier is configured for each multivector.

[0042] Step S300 further includes, for each device instance in the standardized transfer data, applying a transformation function to convert the device data value into geometric algebraic basis coefficients; generating an algebraic expression for the device in each target dimension subspace based on the basis coefficients; synthesizing the algebraic expressions of each subspace into a unified geometric algebraic multivector using a synthesis function; and configuring a geometric dimension identifier code for each unified geometric algebraic multivector, wherein the identifier code includes at least the device type code, the hierarchical path to which it belongs, and the data version information.

[0043] Preferably, for all standardized data values ​​of each device instance in the standardized transfer data, a transformation function in the high-dimensional geometric mapping relationship is applied to process each data value, converting it into geometric algebraic basis coefficients to obtain a set of coefficient-basis pairs. According to the allocation scheme of the target dimension subspace, the basis coefficients belonging to the same subspace are added to generate an algebraic expression of the device in each target dimension subspace, outputting multiple sub-expressions representing different aspects of the device's characteristics. Then, a synthesis function is applied to sum and synthesize the algebraic expressions of each subspace into a unified geometric algebraic multivector, which internally encodes all information of the device in a hierarchical and structured manner. At the same time, each unified geometric algebraic multivector is configured with key descriptive information for system management and efficient retrieval, i.e., a geometric dimension identifier code, which includes at least the device type code, the hierarchical path, and the data version information. The device type code is used for rapid classification and filtering, the hierarchical path is used to accurately locate its position in the power grid topology, and the data version information is used to identify the data snapshot time or version number corresponding to the multivector.

[0044] Step S400: Construct a high-dimensional geometric algebra space and project the unified geometric algebra multivector into the high-dimensional geometric algebra space. In the high-dimensional geometric algebra space, a network of relationships between device multivectors is established through geometric algebra operations.

[0045] Step S400 further includes: determining the total number of dimensions of the geometric algebraic space based on the high-dimensional analytical framework of geometric algebra; designing an orthogonal and complete basis system, including geometric basis, attribute basis, state basis and semantic basis; defining the metric tensor of the space for calculating the geometric relationships between vectors in the space; configuring the coordinate system of the space and establishing the mapping relationship between spatial coordinates and device identifiers.

[0046] Preferably, a high-dimensional analytical framework based on geometric algebra is used to analyze all device features that need to be expressed, including space, attributes, states, semantics, and their relationships. The total number of independent, orthogonal basis vectors in the high-dimensional analytical framework is counted and used as the total dimension of the geometric algebraic space. An orthogonal and complete basis system is designed. Orthogonality and completeness require that the inner product of any two distinct basis vectors is zero, and that N basis vectors can be linearly combined to form any vector in the entire N-dimensional space. The basis system includes geometric basis, attribute basis, state basis, and semantic basis, corresponding to the physical three-dimensional space, static parameters, dynamic variables, and discrete symbols, respectively. A metric tensor for the space is defined, which is the rule for calculating the inner product of vectors in the space. This tensor is used to calculate the geometric relationships between vectors in the space, including length and angle. For example, different weights are assigned to electrical distance and geographical distance, so that electrical similarity contributes more than spatial proximity when calculating the overall distance between devices. Larger metric coefficients are defined for the key state dimension, giving it a more important role in multi-vector comparisons. Within the base system, a coordinate system for the configuration space is established, meaning any multivector can be represented by a set of coordinates on this base. This ultimately establishes a mapping relationship between spatial coordinates and device identifiers. When a coordinate point is calculated in mathematical space, the corresponding device in the physical world can be quickly retrieved through this mapping relationship. This mapping relationship corresponds to the device's geometric dimension identifier and the list of coordinate coefficients of that device's multivector within the current spatial base. This projects the unified geometric algebra multivector into a higher-dimensional geometric algebra space, enabling it to be managed and operated according to the rules of that higher-dimensional geometric algebra space.

[0047] Furthermore, step S400 also includes: calculating the geometric distance between multiple vectors of devices, determining spatial adjacency relationships based on distance thresholds, and establishing geometric algebraic edge vectors representing spatial adjacency associations; performing connection relationship analysis on the power grid topology, defining geometric algebraic operators for electrical connection associations, calculating the electrical connectivity strength between power grid devices, and establishing electrical connection association edges; analyzing the functional coupling relationship between power grid devices, calculating the functional coupling degree, and establishing functional association edges; calculating the correlation of state vectors in the geometric algebraic space based on the time series of power grid device state data, and constructing a state-dependent association network; and fusing the spatial adjacency association edges, electrical connection association edges, functional association edges, and state-dependent association network into a multi-attribute association graph network through geometric algebraic operations, and encoding different types of association relationships into corresponding geometric algebraic operators to obtain a computable association relationship network.

[0048] Preferably, the spatial geometric part of the multivectors of two devices is extracted, and their geometric distance is calculated using a spatially defined metric tensor. A spatial adjacency threshold, such as 10 meters, is set. If the geometric distance is less than the spatial adjacency threshold, the two devices are considered to have a spatial adjacency relationship. Then, a geometric algebraic edge vector representing the spatial adjacency relationship is established for each pair of adjacent devices, which at least includes direction, adjacency type, and distance scalar coefficient. Next, the connection relationship of the power grid topology is analyzed to identify the direct electrical connection relationship between devices, such as circuit breaker connecting to busbars and transformer connecting two voltage level busbars. A geometric algebraic operator for electrical connection relationship is defined. When this geometric algebraic operator is applied to the device subvector representing current or potential, it can simulate the transmission law of electrical energy on the connection. The multivectors of two devices are processed through this geometric algebraic operator to obtain the scalar value of electrical connectivity strength between power grid devices, which represents the tightness of electrical coupling. Then, an electrical connection relationship edge is established, whose basis is related to the special basis describing the circuit topology.

[0049] Preferably, the functional coupling relationships between power grid equipment are analyzed, and the functional dependencies of equipment in the power grid are analyzed. These may include protection relationships, control relationships, coordination relationships, and logical interlocking. The functional coupling degree is calculated based on business rules, configuration information, or historical action logs to quantify the strength of functional dependencies, and then functional association edges are established, with the functional relationship basis as the basis and the coefficient as the calculated functional coupling degree. Then, the vector sequence formed by the change of the state basis part of the equipment over time is extracted from the multi-vector of the equipment, that is, the time series of the power grid equipment state data. Based on the cosine similarity and covariance of the inner product, the correlation measure of the state time series vectors of two equipment is calculated in the geometric algebraic space. High correlation indicates that their state change patterns are similar and there may be potential dependencies, such as the same source fault or thermal interaction. Then, all equipment pairs with correlation exceeding the threshold are connected to form a dynamic, data-driven state dependency network, with each edge associated with a correlation coefficient.

[0050] Preferably, spatial adjacency-related edges, electrical connection-related edges, functional-related edges, and state-dependent-related networks are fused through geometric algebra operations. For example, multiple edge vectors of different types describing the same pair of devices are combined into a unified hyperedge multivector through geometric product, which simultaneously includes spatial adjacency, electrical strength, functional coupling degree, and state dependence, generating a multi-attribute relational graph network. That is, the entire power grid is represented as a graph network, where nodes are device multivectors and edges are fused hyperedge multivectors. At the same time, different types of electrical influence, thermal radiation, and other correlations are encoded into corresponding geometric algebra operators. For example, a fault propagation operator is defined. When this operator is applied to the node vector representing the device with the initial fault, the probability distribution of the affected devices is automatically and in parallel calculated through continuous operations with adjacent hyperedges, simulating the fault propagation process, and finally obtaining a computable relational network. This realizes a paradigm upgrade from data transfer to computable knowledge transfer, improving the quality and consistency of digitally transferred data.

[0051] In the above text, refer to Figure 1 A GIM data processing method for digital handover according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 A GIM data processing system for digital handover according to an embodiment of the present invention is described.

[0052] The GIM data processing system for digital handover according to embodiments of the present invention addresses the technical problems in the prior art, namely, the difficulty in efficiently integrating multi-source heterogeneous GIM handover data and the lack of a unified mathematical framework for integrated calculation and expression of complex relationships between devices. It achieves the technical effect of standardizing, structuring, and deeply integrating cross-departmental GIM data, thereby improving the quality, consistency, and analyzability of digital handover data. Figure 2 As shown, the GIM data processing system for digital handover includes: a GIM handover data acquisition module 10, a geometric mapping relationship establishment module 20, a handover data transformation module 30, and a geometric algebraic space construction module 40.

[0053] The GIM handover data acquisition module 10 is used to acquire heterogeneous GIM handover data from different departments. The GIM handover data includes spatial location data, equipment attribute data, status monitoring data, and semantic data. The geometric mapping relationship establishment module 20 is used to standardize the GIM handover data and set hierarchical relationships according to the processing objectives of power grid equipment to establish a high-dimensional geometric mapping relationship. The handover data conversion module 30 is used to convert the standardized handover data into unified geometric algebra multivectors based on the high-dimensional geometric mapping relationship and configure a geometric dimension identifier for each multivector. The geometric algebra space construction module 40 is used to construct a high-dimensional geometric algebra space and project the unified geometric algebra multivectors into the high-dimensional geometric algebra space. In the high-dimensional geometric algebra space, a network of association relationships between equipment multivectors is established through geometric algebra operations.

[0054] The specific configuration of the geometric mapping relationship establishment module 20 will be described in detail below. The geometric mapping relationship establishment module 20 further includes: parsing, cleaning, format unification, and semantic alignment standardization of the GIM handover data to generate structured and semantically consistent standardized data; organizing the data according to the power grid physical topology and the hierarchical relationship of equipment-bay-voltage level-substation to determine a multi-layer equipment organization framework; analyzing the multi-dimensional feature expression requirements of power grid equipment based on the multi-layer equipment organization framework and the processing objectives of power grid equipment, and designing a geometric algebra high-dimensional analytical framework; and establishing a mapping relationship from standardized data to geometric algebra space based on the correlation and correspondence between the GIM handover data, the equipment organization framework, and the geometric algebra high-dimensional analytical framework to obtain the high-dimensional geometric mapping relationship.

[0055] The following will describe the specific configuration of the geometric mapping relationship establishment module 20 in detail. The geometric mapping relationship establishment module 20 further includes: determining physical or business dimensions based on the multi-layered equipment organization framework and the processing objectives of the power grid equipment; configuring a dimensional basis, wherein the basis satisfies the orthogonality and completeness requirement; parsing the business feature description requirements for the multi-layered equipment organization framework under the processing objective scenario of the power grid equipment; defining business feature variables that can characterize equipment status, attributes, relationships, and behaviors as high-dimensional variables in the dimensional basis; and setting high-dimensional variable operation rules based on geometric algebra based on the high-dimensional variables to support cross-dimensional calculation and analysis.

[0056] The specific configuration of the geometric mapping relationship establishment module 20 will be described in detail below. The geometric mapping relationship establishment module 20 further includes: for each device data item in the standardized data, determining the target dimension subspace in the corresponding geometric algebra high-dimensional parsing framework based on its position and type within the hierarchical framework; selecting the corresponding dimension basis in the geometric algebra high-dimensional parsing framework according to the device data type and business semantics, and establishing a mapping table from data fields to geometric algebra basis; defining a coefficient transformation function based on the mapping table to convert the device's standard data values ​​into geometric algebra basis through normalization, quantization, or encoding; and defining a synthesis function to synthesize the algebraic expressions of the device in each target dimension subspace into a unified geometric algebraic multi-vector according to preset algebraic combination rules; wherein, the high-dimensional geometric mapping relationship includes the mapping table, the transformation function, and the synthesis function.

[0057] The following will describe in detail the specific configuration of the geometric mapping relationship establishment module 20. The geometric mapping relationship establishment module 20 further includes: parsing the hierarchical paths of power grid equipment in the hierarchical framework; activating the corresponding set of dimensional subspaces based on the type and function of each node in the path; determining the required association dimensions based on the roles and connection relationships of the power grid equipment in the hierarchy, including spatial association dimensions, electrical association dimensions, functional association dimensions, and management association dimensions; allocating corresponding sub-dimensional spaces according to the association dimensions and the set of dimensional subspaces; wherein, for complex equipment, the internal hierarchical relationships are subdivided according to the internal component structure, and corresponding sub-dimensional spaces are allocated.

[0058] The following section will describe in detail the specific configuration of the geometric mapping relationship establishment module 20. The geometric mapping relationship establishment module 20 further includes: for spatial location data, a conformal geometric algebra basis is used to establish mapping rules from point, line, surface, and volume geometric elements to multi-dimensional vectors; for equipment attribute data, it is classified according to electrical parameters, mechanical parameters, and management parameters, assigning an independent attribute basis to each parameter type and setting dimensional normalization coefficients; for status monitoring data, a dynamic basis coding scheme is designed based on the monitoring variable type and sampling characteristics to support the conversion of real-time data to time series vectors; for business semantic data, based on the power grid domain ontology, a mapping relationship between semantic tags and discrete symbol basis is established to form a semantic coding dictionary.

[0059] The specific configuration of the data transfer conversion module 30 will be described in detail below. The data transfer conversion module 30 further includes: for each device instance in the standardized transfer data, applying a conversion function to convert the device data value into geometric algebraic basis coefficients; generating an algebraic expression for the device in each target dimension subspace based on the basis coefficients; synthesizing the algebraic expressions of each subspace into a unified geometric algebraic multivector using a synthesis function; and configuring a geometric dimension identifier code for each unified geometric algebraic multivector, wherein the identifier code includes at least the device type code, the hierarchical path, and data version information.

[0060] The specific configuration of the geometric algebraic space construction module 40 will be described in detail below. The geometric algebraic space construction module 40 further includes: determining the total dimension of the geometric algebraic space based on the high-dimensional analytical framework of geometric algebra; designing an orthogonal and complete basis system, including geometric basis, attribute basis, state basis, and semantic basis; defining the space's metric tensor for calculating the geometric relationships between vectors in the space; and configuring the space's coordinate system to establish a mapping relationship between spatial coordinates and device identifiers.

[0061] The specific configuration of the geometric algebraic space construction module 40 will be described in detail below. The geometric algebraic space construction module 40 further includes: calculating the geometric distance between multiple vectors of devices, determining spatial adjacency relationships based on distance thresholds, and establishing geometric algebraic edge vectors representing spatial adjacency associations; parsing the connection relationships of the power grid topology, defining geometric algebraic operators for electrical connection associations, calculating the electrical connectivity strength between power grid devices, and establishing electrical connection association edges; parsing the functional coupling relationships between power grid devices, calculating the functional coupling degree, and establishing functional association edges; calculating the correlation of state vectors in the geometric algebraic space based on the time series of power grid device state data, and constructing a state-dependent association network; and fusing the spatial adjacency association edges, electrical connection association edges, functional association edges, and state-dependent association network into a multi-attribute association graph network through geometric algebraic operations, and encoding different types of association relationships into corresponding geometric algebraic operators to obtain a computable association relationship network.

[0062] The GIM data processing system for digital handover provided in this embodiment of the invention can execute the GIM data processing method for digital handover provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0063] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for processing GIM data oriented to digitalization handover, characterized in that, include: Acquire heterogeneous GIM handover data from different departments, including spatial location data, equipment attribute data, status monitoring data, and semantic data; The GIM handover data is standardized and hierarchical relationships are set according to the processing objectives of power grid equipment to establish a high-dimensional geometric mapping relationship; Based on the high-dimensional geometric mapping relationship, the standardized transfer data is converted into a unified geometric algebra multivector, and a geometric dimension identifier is configured for each multivector. A high-dimensional geometric algebraic space is constructed, and the unified geometric algebraic multivector is projected into the high-dimensional geometric algebraic space. In the high-dimensional geometric algebraic space, a network of relationships between device multivectors is established through geometric algebraic operations.

2. The GIM data processing method for digitalization handover according to claim 1, characterized in that, The GIM handover data is standardized and hierarchically configured according to the processing objectives of the power grid equipment, establishing a high-dimensional geometric mapping relationship, including: The GIM handover data is parsed, cleaned, formatted, and semantically aligned to generate structured, semantically consistent standardized data. Based on the physical topology of the power grid, the equipment is organized according to the hierarchical relationship of equipment-bay-voltage level-substation to determine a multi-level equipment organization framework; Based on the multi-layered equipment organization framework and the processing objectives of power grid equipment, a multi-dimensional feature expression requirement analysis of power grid equipment is conducted, and a geometric algebraic high-dimensional analytical framework is designed. Based on the correlation and correspondence between the GIM handover data, the equipment organization framework, and the geometric algebra high-dimensional analytical framework, a mapping relationship from standardized data to geometric algebra space is established to obtain the high-dimensional geometric mapping relationship.

3. The GIM data processing method for digitalization handover according to claim 2, characterized in that, Design a high-dimensional analytical framework for geometric algebra, including: Based on the multi-layered equipment organization framework and the processing objectives of the power grid equipment, physical or business dimensions are determined, and dimensional bases are configured, wherein the bases satisfy the orthogonal completeness requirement. In the context of processing target scenarios for power grid equipment, this paper analyzes the business feature description requirements for a multi-layered equipment organizational framework and defines business feature variables that can characterize equipment status, attributes, relationships, and behaviors as high-dimensional variables in the dimensional basis. Based on the high-dimensional variables and their corresponding dimensional basis, operational rules for high-dimensional variables based on geometric algebra are set to support cross-dimensional computation and analysis.

4. The GIM data processing method for digitalization handover according to claim 3, characterized in that, Obtaining the high-dimensional geometric mapping relationship includes: For each device data item in the standardized data, the target dimension subspace in the corresponding geometric algebraic high-dimensional analytical framework is determined based on its position and type in the hierarchical framework. Based on the device data type and business semantics, select the corresponding dimensional basis in the geometric algebra high-dimensional parsing framework and establish a mapping table from data fields to geometric algebra basis; Based on the mapping table, a coefficient transformation function is defined to convert the standard data values ​​of the device into a geometric algebra basis through normalization, quantization, or encoding; Define a synthesis function that synthesizes the algebraic expressions of the device in each target dimension subspace into a unified geometric algebraic multivector according to a preset algebraic combination rule; The high-dimensional geometric mapping relationship includes the mapping table, transformation function, and composition function.

5. The GIM data processing method for digital handover according to claim 4, characterized in that, Determine the target dimension subspace in the corresponding geometric algebraic high-dimensional analytic framework, including: Analyze the hierarchical path of power grid equipment in the hierarchical framework, and activate the corresponding dimensional subspace set according to the type and function of each node in the path; Based on the roles and connections of power grid equipment in the hierarchy, the required association dimensions are determined, including spatial association dimensions, electrical association dimensions, functional association dimensions, and management association dimensions. Based on the associated dimension and the set of dimension subspaces, allocate the corresponding sub-dimensional space; For complex devices, the internal hierarchical relationships are subdivided according to the internal component structure, and corresponding sub-dimensional spaces are allocated.

6. The GIM data processing method for digital handover according to claim 4, characterized in that, Establish a mapping table from data fields to the geometric algebra basis, including: For spatial location data, a conformal geometric algebra basis is used to establish mapping rules from point, line, surface, and volume geometric elements to multidimensional vectors; For equipment attribute data, it is classified into electrical parameters, mechanical parameters, and management parameters. An independent attribute base is assigned to each parameter type, and a dimension normalization coefficient is set. For state monitoring data, a dynamic basis coding scheme is designed based on the monitoring variable type and sampling characteristics to support the conversion of real-time data into time series vectors; For business semantic data, based on the power grid domain ontology, a mapping relationship between semantic tags and discrete symbol bases is established to form a semantic encoding dictionary.

7. The GIM data processing method for digital handover according to claim 4, characterized in that, Based on the aforementioned high-dimensional geometric mapping relationship, the standardized transfer data is converted into a unified geometric algebraic multivector, including: For each device instance in the standardized handover data, a transformation function is applied to convert the device data values ​​into geometric algebraic basis coefficients. Based on the basis coefficients, an algebraic expression for the device in each target dimension subspace is generated; The algebraic expressions of each subspace are synthesized into a unified geometric algebraic multivector by a synthesis function, and a geometric dimension identifier is configured for each unified geometric algebraic multivector. The identifier includes at least the device type code, the hierarchical path, and the data version information.

8. The GIM data processing method for digital handover according to claim 2, characterized in that, Constructing a high-dimensional geometric algebraic space includes: Based on the high-dimensional analytical framework of geometric algebra, the total number of dimensions of the geometric algebraic space is determined. Design an orthogonal and complete basis system, including geometric basis, attribute basis, state basis and semantic basis; Define a metric tensor for a space, used to compute geometric relationships between vectors in that space; Configure the coordinate system of the space and establish the mapping relationship between spatial coordinates and device identifiers.

9. The GIM data processing method for digital handover according to claim 8, characterized in that, In a high-dimensional geometric algebraic space, a network of relationships between multiple vectors of devices is established through geometric algebraic operations, including: Calculate the geometric distance between multiple vectors of the calculation device, determine the spatial adjacency relationship based on the distance threshold, and establish a geometric algebraic edge vector representing the spatial adjacency association; The connection relationships of the power grid topology are analyzed, the geometric algebraic operators of electrical connection associations are defined, the electrical connectivity strength between power grid equipment is calculated, and the electrical connection association edges are established. Analyze the functional coupling relationships between power grid equipment, calculate the degree of functional coupling, and establish functional association edges; Based on the time series of power grid equipment state data, the correlation of state vectors is calculated in geometric algebra space to construct a state dependency association network; The spatial adjacency association edges, electrical connection association edges, functional association edges, and state dependency association networks are fused into a multi-attribute association graph network through geometric algebra operations, and different types of association relationships are encoded into corresponding geometric algebra operators to obtain a computable association relationship network.

10. A GIM data processing system for digital handover, characterized in that: The system is used to implement the GIM data processing method for digital handover as described in any one of claims 1 to 9, and the system comprises: The GIM handover data acquisition module is used to acquire heterogeneous GIM handover data from different departments. The GIM handover data includes spatial location data, equipment attribute data, status monitoring data, and semantic data. The geometric mapping relationship establishment module is used to standardize the GIM handover data and set hierarchical relationships according to the processing objectives of power grid equipment to establish high-dimensional geometric mapping relationships. The data transfer conversion module is used to convert the standardized data transfer data into unified geometric algebra multivectors based on the high-dimensional geometric mapping relationship, and to configure a geometric dimension identifier for each multivector. The geometric algebraic space construction module is used to construct a high-dimensional geometric algebraic space and project the unified geometric algebraic multivector into the high-dimensional geometric algebraic space. In the high-dimensional geometric algebraic space, a network of relationships between device multivectors is established through geometric algebraic operations.

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