A grid engineering-oriented gim and ifc standard automatic mapping method and system

Abstract

This invention discloses an automatic mapping method and system for GIM and IFC standards in power grid engineering. The method includes acquiring and parsing source model data and target standard model data of the current power grid project to obtain type information, attribute information, and relationship information of each data object; constructing a directed attribute graph based on the type, attribute, and relationship information; performing semantic matching, attribute matching, and topological matching between objects in the source and target standard model data sequentially based on a preset mapping rule base and the directed attribute graph to obtain an initial mapping relationship; performing conflict detection and correction processing on the initial mapping relationship sequentially to obtain the target mapping relationship; and converting the source model data into a data file conforming to the target standard model based on the target mapping relationship. This invention achieves automated and structured conversion from GIM model attributes and geometric information to the IFC standard.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an automatic mapping method and system for GIM and IFC standards for power grid engineering. Background Technology

[0002] With the development of digitalization in power grid engineering and BIM technology, various data standard systems have gradually formed in the engineering design, construction, and operation and maintenance processes. Among them, the GIM (Power Grid Information Model) standard is mainly aimed at power grid industry applications, emphasizing the semantic expression of equipment and the description of business attributes; while the IFC (Industry Foundation Classes) standard, as an internationally recognized BIM data standard, is mainly used for data exchange and sharing in the building and infrastructure fields.

[0003] However, the GIM standard focuses on the expression of power equipment topology, electrical attributes, and functional logic, while the IFC standard focuses on the description of building space geometry, component hierarchy, and physical attributes. Significant differences exist between the two in object classification systems, attribute semantic definitions, and relationship expression methods. This heterogeneity has led to a long-standing reliance on manual comparison or simple name matching for data conversion between GIM and IFC models in practical engineering. This is not only inefficient and prone to errors, but also fails to preserve complete topological relationships and semantic information, severely hindering the integration and reuse of power grid engineering data throughout its entire lifecycle. Summary of the Invention

[0004] This invention provides a method and system for automatic mapping between GIM and IFC standards for power grid engineering, so as to achieve the effect of automatic and structured conversion from GIM model attributes and geometric information to IFC standards.

[0005] To address the aforementioned technical problems, this invention provides, in one aspect, an automatic mapping method between GIM and IFC standards for power grid engineering, comprising: Obtain the source model data and target standard model data of the current power grid project, and parse the source model data and the target standard model data respectively to obtain the type information, attribute information and relationship information of each data object; Based on the type information, attribute information, and relationship information, a directed attribute graph representing the semantic information of the source model data and the target standard model data is constructed. Based on the preset mapping rule base and the directed attribute graph, semantic matching, attribute matching and topological matching are performed sequentially between each object in the source model data and the target standard model data to obtain the initial mapping relationship; The initial mapping relationship is sequentially subjected to conflict detection and correction processing to obtain the target mapping relationship; wherein, the conflict detection is used to identify ambiguous associations in the mapping relationship, and the correction processing is used to determine the target mapping entry from the candidate mapping entries based on a preset correction strategy, wherein the preset correction strategy is dynamically configured according to the type of ambiguous association and the semantic association of the object to be matched. Based on the target mapping relationship, the source model data is transformed to generate a data file that conforms to the target standard model.

[0006] As a preferred embodiment, the construction of a directed attribute graph representing the semantic information of the source model data and the target standard model data based on the type information, the attribute information, and the relationship information includes: The electrical equipment in the object type information is abstracted into equipment nodes and the spatial unit is abstracted into spatial nodes. Based on the attribute data, the physical attributes of the electrical equipment and the geometric attributes of the spatial unit are respectively bound to the corresponding nodes. Based on the relational data, topological directed edges representing electrical connection relationships are established between the device nodes, spatial directed edges representing hierarchical inclusion relationships are established between the spatial nodes, and associated directed edges representing device affiliation relationships are established between the device nodes and the spatial nodes. A directed attribute graph is constructed based on the topological directed edges, the spatial directed edges, the associated directed edges, the device nodes, and the spatial nodes.

[0007] As one preferred embodiment, the step of performing semantic matching, attribute matching, and topological matching sequentially between objects in the source model data and the target standard model data based on a preset mapping rule base and the directed attribute graph to obtain an initial mapping relationship includes: The device nodes and spatial nodes of the directed attribute graph are traversed as objects to be matched, and corresponding candidate mapping entries are retrieved from the preset mapping rule base based on the node type identifier of the objects to be matched. Determine the semantic matching degree, attribute matching degree, and topological matching degree between the object to be matched and the candidate mapping entries, respectively. A comprehensive similarity is obtained based on the semantic matching degree, the attribute matching degree, and the topological matching degree. Candidate mapping entries whose comprehensive similarity satisfies a preset threshold are determined as the matching results of the object to be matched, and the initial mapping relationship is generated.

[0008] As one preferred embodiment, determining the semantic matching degree, attribute matching degree, and topological matching degree between the object to be matched and the candidate mapping entries includes: The semantic matching degree is determined based on the semantic hierarchy distance between the node type identifier and the target type defined in the candidate mapping entry; The attribute matching degree is determined based on the degree of conformity between the physical or geometric attributes bound to the object to be matched and the attribute constraints defined in the candidate mapping entries; The topological matching degree is determined based on the consistency between the spatial positional relationship represented by the spatial directed edges or associated directed edges connected to the object to be matched and the spatial constraints defined in the candidate mapping entries.

[0009] As one preferred embodiment, the step of performing conflict detection and correction processing on the initial mapping relationship to obtain the target mapping relationship includes: Based on the comprehensive similarity and node type identifier of each object to be matched and its corresponding candidate mapping entry in the initial mapping relationship, it is identified that there are one-to-many conflicts where the same object to be matched corresponds to multiple candidate mapping entries, and many-to-one conflicts where multiple objects to be matched correspond to the same candidate mapping entry. The one-to-many conflict or the many-to-one conflict is corrected, and the corrected mapping relationship is determined as the target mapping relationship; wherein, the correction process includes: For the one-to-many conflict, a unique candidate mapping entry is selected based on the comprehensive similarity; for the many-to-one conflict, it is determined whether multiple objects to be matched belong to the same power grid engineering equipment. If so, the mapping is merged; otherwise, the optimal mapping is retained and the remaining objects to be matched are rearranged.

[0010] Another aspect of the present invention provides an automatic mapping device between GIM and IFC standards for power grid engineering, comprising: The parsing module is used to acquire the source model data and target standard model data of the current power grid project, and to parse the source model data and the target standard model data respectively to obtain the type information, attribute information and relationship information of each data object; The construction module is used to construct a directed attribute graph representing the semantic information of the source model data and the target standard model data based on the type information, the attribute information, and the relationship information. The matching module is used to perform semantic matching, attribute matching and topological matching between each object in the source model data and the target standard model data based on the preset mapping rule library and the directed attribute graph, so as to obtain the initial mapping relationship. The correction module is used to sequentially perform conflict detection and correction processing on the initial mapping relationship to obtain the target mapping relationship; wherein, the conflict detection is used to identify ambiguous associations in the mapping relationship, and the correction processing is used to determine the target mapping entry from the candidate mapping entries based on a preset correction strategy, wherein the preset correction strategy is dynamically configured according to the type of ambiguous association and the semantic association of the object to be matched. The generation module is used to convert the source model data into a data file that conforms to the target standard model according to the target mapping relationship.

[0011] As one preferred embodiment, the construction module is specifically used for: The electrical equipment in the object type information is abstracted into equipment nodes and the spatial unit is abstracted into spatial nodes. Based on the attribute data, the physical attributes of the electrical equipment and the geometric attributes of the spatial unit are respectively bound to the corresponding nodes. Based on the relational data, topological directed edges representing electrical connection relationships are established between the device nodes, spatial directed edges representing hierarchical inclusion relationships are established between the spatial nodes, and associated directed edges representing device affiliation relationships are established between the device nodes and the spatial nodes. A directed attribute graph is constructed based on the topological directed edges, the spatial directed edges, the associated directed edges, the device nodes, and the spatial nodes.

[0012] As one preferred embodiment, the matching module is specifically used for: The device nodes and spatial nodes of the directed attribute graph are traversed as objects to be matched, and corresponding candidate mapping entries are retrieved from the preset mapping rule base based on the node type identifier of the objects to be matched. Determine the semantic matching degree, attribute matching degree, and topological matching degree between the object to be matched and the candidate mapping entries, respectively. A comprehensive similarity is obtained based on the semantic matching degree, the attribute matching degree, and the topological matching degree. Candidate mapping entries whose comprehensive similarity satisfies a preset threshold are determined as the matching results of the object to be matched, and the initial mapping relationship is generated.

[0013] As one preferred embodiment, the matching module is further configured to: The semantic matching degree is determined based on the semantic hierarchy distance between the node type identifier and the target type defined in the candidate mapping entry; The attribute matching degree is determined based on the degree of conformity between the physical or geometric attributes bound to the object to be matched and the attribute constraints defined in the candidate mapping entries; The topological matching degree is determined based on the consistency between the spatial positional relationship represented by the spatial directed edges or associated directed edges connected to the object to be matched and the spatial constraints defined in the candidate mapping entries.

[0014] As one preferred embodiment, the correction module is specifically used for: Based on the comprehensive similarity and node type identifier of each object to be matched and its corresponding candidate mapping entry in the initial mapping relationship, it is identified that there are one-to-many conflicts where the same object to be matched corresponds to multiple candidate mapping entries, and many-to-one conflicts where multiple objects to be matched correspond to the same candidate mapping entry. The one-to-many conflict or the many-to-one conflict is corrected, and the corrected mapping relationship is determined as the target mapping relationship; wherein, the correction process includes: For the one-to-many conflict, a unique candidate mapping entry is selected based on the comprehensive similarity; for the many-to-one conflict, it is determined whether multiple objects to be matched belong to the same power grid engineering equipment. If so, the mapping is merged; otherwise, the optimal mapping is retained and the remaining objects to be matched are rearranged.

[0015] Compared with the prior art, the beneficial effects of the present invention are at least one of the following: 1) This invention effectively solves the problems of low efficiency and error-proneness in converting BIM models of power grid projects between different standards by establishing an automatic mapping mechanism between GIM and IFC standards. By combining semantic parsing and a rule engine, it achieves automated and structured conversion from GIM model attributes and geometric information to IFC standards, significantly improving the accuracy and consistency of model interoperability, reducing the technical threshold and labor costs of cross-platform data exchange, and providing a reliable data foundation for the digital management of the entire lifecycle of power grid projects.

[0016] 2) This invention has good scalability and adaptability, and can support future iterations and updates of GIM or IFC standard versions. It can quickly adapt to new standards by simply adjusting the mapping rule base, avoiding redundant development. At the same time, the automatic mapping process retains the key engineering semantic information of the original model, ensuring the application value of the converted IFC model in the subsequent design, construction and operation and maintenance stages, and effectively promoting the standardization process of BIM technology in power grid engineering and the deep integration and application of multi-source heterogeneous data. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an automatic mapping method between GIM and IFC standards for power grid engineering in one embodiment of the present invention. Figure 2This is a structural block diagram of an automatic mapping system between GIM and IFC standards for power grid engineering, according to one embodiment of the present invention. Figure label: Among them, 11 is the parsing module; 12 is the construction module; 13 is the matching module; 14 is the correction module; and 15 is the generation module. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] In the description of this invention, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] With the advancement of smart grid construction, the digital management of the entire lifecycle of power grid projects places higher demands on the interoperability of model data. GIM (Generic Modeling Model) refers to the three-dimensional design model interaction specification for power transmission and transformation projects, widely used in the design and construction phases of power grids; while IFC (Integrated Functional Design) is an internationally recognized BIM data exchange standard, dominating in general building and operation and maintenance platforms. However, due to significant differences between GIM and IFC in semantic definitions, attribute structures, and topological relationships, existing conversion methods often rely on manual configuration or simple geometric transformations, which are not only inefficient but also prone to losing electrical connection relationships and spatial hierarchy information, resulting in the converted IFC model being unable to support subsequent operation and maintenance operations. Therefore, there is an urgent need for an automated mapping method that can take into account semantic, attribute, and topological relationships to achieve high-fidelity data conversion between GIM and IFC standards.

[0022] One embodiment of the present invention provides an automatic mapping method between GIM and IFC standards for power grid engineering. For details, please refer to [link to documentation]. Figure 1 , Figure 1 The diagram shown is a flowchart illustrating an automatic mapping method between GIM and IFC standards for power grid engineering, according to one embodiment of the present invention, which includes steps S1-S5: S1: Obtain the source model data and target standard model data of the current power grid project, and parse the source model data and target standard model data respectively to obtain the type information, attribute information and relationship information of each data object.

[0023] In this embodiment, the source model data is a three-dimensional design model file of power transmission and transformation engineering conforming to the GIM standard, and its storage format may be XML, JSON or a special binary format; the target standard model data is IFC (Industry Foundation Classes). The acquisition method can be read through the local file system, received through the network interface or downloaded from cloud storage, and this invention does not limit it.

[0024] Among them, GIM (Grid Information Model) refers to the "Interactive Specification for 3D Design Model of Power Transmission and Transformation Engineering", which is a BIM data interaction standard for the entire life cycle of power grid engineering. It defines the entity types, attributes and relationships of professional models such as substations and transmission lines. IFC (Industry Foundation Classes) is an open BIM data model developed by the buildingSMART organization and incorporated into the ISO 16739 international standard. It is used to exchange building and infrastructure project information between different software platforms.

[0025] After acquiring the data, the source model data and the target standard model data are traversed and parsed according to the data structure defined in their respective standard specifications, and the type information, attribute information and relation information of each data object are extracted to provide structured input for the subsequent construction of a unified semantic expression layer.

[0026] Specifically, type information refers to the classification identifier of a data object in its respective standard system, used to characterize the semantic category of the object; attribute information refers to the descriptive data attached to the data object, including physical parameters, geometric parameters, functional parameters, etc.; relationship information refers to the logical or physical connections between data objects, including topological connections, spatial inclusion, functional affiliation, etc., which are key to maintaining the integrity of the model and the business logic.

[0027] Specifically, the parsing of the source model data involves identifying and extracting the type information of each data object based on the entity classification system defined in the GIM specification, such as object categories like "oil-immersed transformer," "SF6 circuit breaker," "substation," and "cable trench." Simultaneously, the attribute information carried by each data object is parsed, including the physical attributes of electrical equipment (such as rated voltage, rated current, capacity, and manufacturer) and the geometric attributes of spatial units (such as coordinate location, bounding box dimensions, and elevation information).

[0028] Furthermore, it is necessary to parse the relationship information between data objects, specifically including the electrical connection relationships between electrical devices, the hierarchical inclusion relationships between spatial units, and the ownership relationships between devices and spaces. Preferably, the parsing process can employ DOM parsing, SAX parsing, or memory-based object deserialization to transform unstructured or semi-structured model files into a structured list of intermediate data objects, ensuring the completeness and accuracy of information extraction.

[0029] For parsing the target standard model data, based on the entity types, attribute sets, and relational constraints defined in the IFC Schema (such as IFC2x3 or IFC4), the following information is extracted: type information (such as IfcTransformer, IfcSwitchingDevice, IfcSpace, etc.), attribute information (such as RatedVoltage in the predefined attribute set Pset_TransformerCommon, etc.), and relational information (such as connection relationships represented by IfcRelConnectsPortToPort, aggregation relationships represented by IfcRelAggregates, etc.). It should be noted that the parsing results of the target standard model data are mainly used to construct the subsequent mapping rule base and as a reference benchmark for matching. Its parsing granularity should be consistent with the source model data to ensure the feasibility of subsequent semantic alignment. During the parsing process, inheritance relationships and enumeration value constraints in the target standard can also be preprocessed to form a standardized set of target semantic descriptions.

[0030] After obtaining the type, attribute, and relationship information of the source model data and the target standard model data, further standardization preprocessing can be performed on the above information. For example, unifying data types (such as converting string-formatted voltage values ​​to floating-point numbers), cleaning redundant fields, completing missing default attributes, and normalizing namespaces can eliminate data noise caused by differences in standard versions or different modeling habits.

[0031] Preferably, the above parsing and preprocessing process can be encapsulated as an independent parsing function or script, which supports adaptation to different versions of GIM or IFC standards through parameter configuration. When the standard is upgraded, only the parser configuration or rules need to be adjusted, without reconstructing the entire mapping process, which significantly improves the scalability and engineering practicality of the method. This invention does not limit the scope of the invention.

[0032] S2: Based on type information, attribute information, and relation information, construct a directed attribute graph that represents the semantic information of the source model data and the target standard model data.

[0033] In this embodiment, electrical equipment in the object type information is abstracted as device nodes, spatial units are abstracted as spatial nodes, and the physical attributes of electrical equipment and the geometric attributes of spatial units are bound to the corresponding nodes based on attribute data.

[0034] For example, entities such as "main transformer" and "circuit breaker" in the GIM model are mapped as equipment nodes, and physical attributes such as rated voltage and capacity are stored as key-value pairs in the node attributes; spatial entities such as "main control building" and "power distribution room" are mapped as spatial nodes, and geometric attributes such as coordinates and dimensions are bound to the corresponding nodes. It should be noted that the node abstraction process is not limited to the above examples, and other types of nodes can be extended according to actual engineering needs, which is not limited in this invention.

[0035] After the nodes are constructed, three types of directed edges are established in the directed attribute graph based on the relational data to represent multidimensional semantic relationships. Topological directed edges are established between equipment nodes to represent electrical connection relationships, such as the electrical connection path between a busbar and a switch; spatial directed edges are established between spatial nodes to represent hierarchical inclusion relationships, such as a spatial nesting structure where "substation site" includes "main control building" and "main control building" includes "relay protection room"; and associative directed edges are established between equipment nodes and spatial nodes to represent equipment affiliation relationships, such as a switch cabinet belonging to a specific distribution room.

[0036] Preferably, each directed edge can carry additional attributes, such as connection type, containment depth, or belonging status, to further enhance semantic expressiveness, thereby forming a complete directed attribute graph composed of device nodes, spatial nodes, topological directed edges, spatial directed edges, and associated directed edges.

[0037] It should be noted that the directed attribute graph involved in this embodiment refers to a graph data structure composed of nodes and directed edges, where attribute information can be bound to both nodes and edges, used to uniformly express the semantic structure under heterogeneous standards. Device nodes refer to the abstract representation of entities with electrical functions in a power grid project within the graph, while spatial nodes refer to the abstract representation of buildings or geographic spatial units that carry the equipment. Topological directed edges specifically refer to directed lines that characterize the physical or logical connection relationships between electrical devices, spatial directed edges characterize the hierarchical inclusion relationships between spatial units, and associative directed edges characterize the attribution or installation relationships between devices and spaces. These terms collectively constitute the core elements of the intermediate semantic expression layer of this invention, providing a structured foundation for subsequent multi-dimensional matching.

[0038] By constructing a directed attribute graph, a unified semantic representation of heterogeneous GIM and IFC models is achieved. This graph structure not only preserves the type and attribute information of the original model, but more importantly, it explicitly encodes key relationships such as electrical topology, spatial hierarchy, and equipment affiliation, avoiding the relationship loss problem caused by traditional conversion methods that only focus on geometry or attributes. Simultaneously, as an intermediate representation decoupled from specific file formats, the directed attribute graph eliminates the need for subsequent matching algorithms to directly handle underlying standard differences, significantly improving the versatility and maintainability of the mapping method. It should be noted that this graph structure supports incremental updates and local modifications; when the source model changes, only the corresponding subgraph needs adjustment without a full reconstruction, further enhancing its engineering practicality.

[0039] Preferably, semantic normalization can be introduced during the construction of the directed attribute graph. For example, synonym merging can be performed on the type identifiers of device nodes (e.g., unifying "oil-immersed transformer" and "Oil-immersed Transformer" into a standard semantic label), coordinate system transformation or unit standardization can be performed on the geometric attributes of spatial nodes, and consistency checks can be performed on the directions of relation edges. Such preprocessing operations help improve the accuracy of subsequent semantic matching, attribute matching, and topology matching. In addition, the storage format of the directed attribute graph can adopt an in-memory graph structure, a graph database, or a serialized file. The specific choice depends on the model size and performance requirements, and this invention does not limit it.

[0040] S3: Based on the preset mapping rule base and directed attribute graph, semantic matching, attribute matching and topological matching are performed sequentially between objects in the source model data and the target standard model data to obtain the initial mapping relationship.

[0041] In this embodiment, device nodes and spatial nodes in the directed attribute graph are traversed as objects to be matched, and corresponding candidate mapping entries are retrieved from a preset mapping rule base based on the node type identifier of the objects to be matched. The preset mapping rule base stores mapping knowledge between GIM and the IFC standard, including type correspondences, attribute constraints, and spatial location constraints.

[0042] It should be noted that the retrieval process can employ exact matching, fuzzy matching, or ontology-based semantic retrieval methods; this invention does not impose any limitations on these methods. This retrieval mechanism can quickly filter out candidate entries potentially related to the current object to be matched, providing a foundation for subsequent multi-dimensional matching calculations.

[0043] After obtaining the candidate mapping entries, the semantic matching degree, attribute matching degree, and topological matching degree between the object to be matched and the candidate mapping entries are determined respectively.

[0044] The semantic matching degree is determined based on the semantic hierarchical distance between the node type identifier and the target type defined in the candidate mapping entry. For example, if the object to be matched is "oil-immersed transformer" and the target type of the candidate entry is "IfcTransformer", then the hierarchical distance between the two in the power equipment body is relatively close, and the semantic matching degree is high. If the target type is "IfcDistributionElement", then the hierarchical distance is relatively far, and the matching degree is correspondingly reduced.

[0045] The attribute matching degree is determined based on the degree of conformity between the physical or geometric attributes bound to the object to be matched and the attribute constraints defined in the candidate mapping entry; for example, if the candidate entry requires a rated voltage range of 10kV–35kV, and the rated voltage of the object to be matched is 22kV, then the attribute matching degree is high.

[0046] The topology matching degree is determined based on the consistency between the spatial positional relationship represented by the spatial directed edges or associated directed edges connected to the object to be matched and the spatial constraints defined in the candidate mapping entry; for example, if the candidate entry requires that the device must be located under an "indoor" spatial node, and the object to be matched is connected to an indoor type spatial node through an associated directed edge, then the topology matching degree is high.

[0047] In a preferred embodiment of the present invention, the topology matching degree is used to determine the degree of consistency between the GIM object and the IFC candidate object in terms of spatial location and spatial relationship.

[0048] Specifically, first extract the coordinates of the spatial center point of the GIM object. Simultaneously extract the spatial center point coordinates of the IFC candidate objects. Then calculate the spatial distance between the two: Where d is the spatial distance between the two objects, this embodiment is based on the spatial distance d and a preset spatial threshold. Calculate spatial distance similarity: When d is greater than or equal to hour, Set to 0. In addition to spatial distance, the system also verifies spatial container relationships and relative positional relationships. Spatial container relationships are used to determine whether objects are located in the same or similar spatial areas; relative positional relationships are used to determine whether the spatial relationships between objects and surrounding devices are consistent.

[0049] Spatial matching degree can be calculated as follows: in, Spatial distance similarity; For spatial container matching degree; The degree of matching in relative positional relationships; , , For spatial matching weights, and By using the spatial matching degree calculation method described above, the system can avoid incorrectly mapping devices of the same type but located in different regions.

[0050] After obtaining the matching scores in three dimensions, a comprehensive similarity score is calculated based on semantic matching score, attribute matching score, and topological matching score. Preferably, the comprehensive similarity score can be obtained through weighted summation, product fusion, or machine learning models, and the weight coefficients can be dynamically configured according to the actual engineering scenario. For example, in scenarios that emphasize electrical accuracy, the weights of semantic and topological matching can be increased.

[0051] Subsequently, candidate mapping entries with a comprehensive similarity score meeting a preset threshold (e.g., 0.8) are identified as matching results for the objects to be matched, generating an initial mapping relationship. It should be noted that the preset threshold can be adaptively adjusted based on the accuracy feedback of historical conversion data to balance mapping coverage and accuracy; this invention does not impose limitations on this. Candidate entries with a comprehensive similarity score below the threshold are excluded to avoid introducing erroneous mappings with low confidence.

[0052] Semantic hierarchical distance refers to the shortest path length or inheritance hierarchy difference between two concept nodes in a domain ontology or classification system, used to quantify semantic similarity; attribute constraints refer to the restrictions on the range, type, or existence of attribute values ​​of the target object in candidate mapping entries; spatial constraints refer to the limitations on the spatial type, hierarchical position, or relative relationship of the device in candidate mapping entries. These terms collectively constitute the semantic foundation of multidimensional matching calculation, ensuring that the matching process conforms to both standard specifications and the realities of power grid engineering.

[0053] This embodiment utilizes a multi-dimensional matching mechanism to achieve a refined evaluation of the mapping relationships between heterogeneous model objects. Compared to traditional methods that rely solely on names or single attributes, this invention comprehensively considers semantics, attributes, and topology, significantly improving the accuracy and robustness of the initial mapping relationships. Especially for the numerous homonymous or heteronymous objects present in power grid engineering, the introduction of topology matching can effectively distinguish their true business affiliation, avoiding mismatches caused by non-standard naming.

[0054] Preferably, before performing multidimensional matching, candidate mapping entries can be pre-filtered or prioritized to reduce unnecessary matching computation overhead. For example, obviously irrelevant entries can be quickly eliminated based on semantic matching degree, and then the remaining entries can be finely calculated based on attribute and topological matching degree. In addition, for certain special devices or complex spatial relationships, exception rules or custom matching functions can be configured in the preset mapping rule base to deal with edge scenarios not covered by the standard. It should be noted that the above optimization strategies do not affect the core matching logic, but are only optional implementation methods to improve efficiency and adaptability, and the present invention does not limit them.

[0055] S4: Perform conflict detection and correction processing on the initial mapping relationship in sequence to obtain the target mapping relationship; wherein, conflict detection is used to identify ambiguous associations in the mapping relationship, and correction processing is used to determine the target mapping entry from the candidate mapping entries based on a preset correction strategy. The preset correction strategy is dynamically configured according to the type of ambiguous association and the semantic association of the object to be matched.

[0056] In this embodiment, ambiguous associations in the mapping relationship are identified based on the comprehensive similarity between each object to be matched and its corresponding candidate mapping entry, as well as the node type identifier. Ambiguous associations mainly include two types: one-to-many conflicts, where the same object to be matched corresponds to multiple candidate mapping entries in the initial mapping; and many-to-one conflicts, where multiple different objects to be matched are mapped to the same candidate mapping entry. It should be noted that the conflict detection process relies not only on numerical comparison of the comprehensive similarity but also on joint judgment based on the semantic consistency of the node type identifier to avoid misjudging the conflict type due to mere numerical similarity. This invention does not impose limitations on this aspect.

[0057] For identified one-to-many conflicts, the correction process employs a strategy of selecting the optimal match based on comprehensive similarity. Specifically, from multiple candidate mapping entries corresponding to the object to be matched, the entry with the highest comprehensive similarity is selected as the unique target mapping entry, and the remaining entries are discarded. Preferably, when the difference between the highest and second-highest comprehensive similarity values ​​is less than a preset tolerance threshold, attribute matching degree or topological matching degree can be further introduced as a secondary ranking criterion to ensure the stability of the selection result. This strategy effectively solves the mapping ambiguity problem caused by semantic overlap between standards or redundancy in the rule base, ensuring that each source model object has a unique and optimal correspondence in the target standard.

[0058] To address many-to-one conflicts, the correction process first determines whether multiple objects to be matched belong to the same power grid engineering equipment. If so, a merge mapping is performed, mapping multiple source model nodes belonging to the same physical equipment to the same target IFC entity. This aligns with the reality that equipment in a GIM model may be modeled as multiple logical components, but in an IFC, they need to be aggregated into a single entity. If not, the mapping relationship of the object with the highest overall similarity is retained, and a rearrangement operation is performed on the remaining objects to be matched. This involves re-searching and matching suboptimal candidate entries in a preset mapping rule base until the many-to-one conflict is eliminated or no usable mapping is confirmed. It should be noted that determining whether objects belong to the same power grid engineering equipment can be based on semantic clues such as associated directed edges in the directed attribute graph, equipment number prefixes, or spatial affiliation relationships; this invention does not impose such limitations.

[0059] Ambiguous associations refer to uncertain mapping states caused by differences in standards, non-standard naming, or incomplete rule coverage during the initial mapping stage, manifesting as unconventional one-to-many or many-to-one correspondences; one-to-many conflicts indicate that a single source object has multiple potential matches on the target side, usually stemming from finer-grained target standard classification or the existence of synonymous entries in the rule library; many-to-one conflicts indicate that multiple source objects compete for the same target entity, commonly seen in scenarios where the source model performs component-level decomposition of the device while the target standard requires a holistic representation; merged mapping refers to the processing method of aggregating and mapping multiple source nodes that logically belong to the same physical entity to a single target entity; rearrangement refers to an iterative correction mechanism in many-to-one conflicts, where the matching process is re-executed for objects that do not have priority mapping rights to find alternative mapping entries.

[0060] The conflict detection and dynamic correction mechanism in this embodiment significantly improves the accuracy and engineering applicability of the mapping results. Compared to static rule matching methods, this solution can dynamically configure correction strategies based on the specific type of ambiguous associations and the semantic relevance of the objects to be matched. This avoids information loss caused by simply discarding conflicting data and prevents semantic distortion of the model due to incorrect mapping. Especially for complex scenarios such as equipment splitting and modeling and cross-disciplinary collaboration commonly found in power grid engineering, the merging mapping and rearrangement mechanism effectively ensures the integrity and business logic consistency of the converted IFC model.

[0061] Preferably, after performing the correction process, a consistency check can be performed on the target mapping relationship. For example, verifying whether the target entities after merging the mappings meet the cardinality constraints of the IFC Schema, checking whether the newly established mappings after rearrangement introduce new conflicts, or confirming that the affiliation of all device nodes remains connected in the target model. If an anomaly is found during the check, a local rollback can be triggered, and the correction strategy parameters can be adjusted before re-execution. It should be noted that this type of verification and iterative optimization is an enhanced implementation method, and the core correction logic is still based on a dynamic strategy driven by conflict type; this invention does not limit this aspect. The target mapping relationship obtained in this way has both high accuracy and strong robustness, providing a reliable basis for subsequent model instance generation.

[0062] S5: Based on the target mapping relationship, the source model data is transformed to generate a data file that conforms to the target standard model.

[0063] In this embodiment, each mapping entry in the target mapping relationship is traversed, and the corresponding type information, attribute information and relationship information are extracted from the parsed source model data according to the correspondence rules between the source object and the target entity indicated by the entry. The data is then instantiated and constructed according to the schema definition of the target standard (such as IFC).

[0064] For example, when the target mapping relationship indicates that a certain GIM "oil-immersed transformer" equipment node should be mapped to an "IfcTransformer" entity in IFC, a corresponding IfcTransformer instance is created, and the physical attributes such as rated voltage and capacity bound in the source node are written into the predefined attribute set (Pset) of this instance. It should be noted that the instantiation process strictly follows the syntax specifications and constraints of the target standard to ensure that the generated data file can be correctly parsed and loaded by mainstream BIM software. This invention does not impose any limitations on this.

[0065] During attribute assignment, in addition to directly mapping explicit attributes, it is also necessary to handle the derivation of implicit attributes and the filling of default values. Preferably, when the source model lacks the required attributes specified by the target standard, the default values ​​can be automatically filled in according to the preset attribute completion strategy or indirectly deduced through the association relationship.

[0066] For example, if the source model does not provide information on the number of phases of the transformer, but the busbar it is connected to is a three-phase system, then the number of phases of the transformer can be inferred to be 3 and written into the target instance. Simultaneously, for cases of inconsistent data types (such as voltage units in kV in GIM while IFC requires V), automatic unit conversion and format standardization are performed. This type of attribute enhancement mechanism effectively improves the completeness and usability of the converted model, avoiding target model validation failures due to missing source data.

[0067] For the transformation of relational information, based on the topological directed edges, spatial directed edges, and associated directed edges represented by the directed attribute graph in the target mapping relationship, the corresponding semantic connections are reconstructed in the target standard model. Specifically, the electrical connection relationships between devices in the source model are converted into connection instances of IfcRelConnectsPortToPort or IfcDistributionFlowElement; spatial hierarchy containment relationships are converted into IfcRelAggregates aggregation relationships; and device affiliation relationships are converted into IfcRelContainedInSpatialStructure containment relationships. It should be noted that the creation order of relational instances must meet the dependency constraints of the target standard. Typically, the spatial structure is created first, then the device instances, and finally the connection relationships are established to ensure reference integrity. By fully preserving the multidimensional relational semantics of the original model, the transformed IFC file not only possesses geometric expression capabilities but also carries the business logic and topological context required for power grid engineering.

[0068] In this embodiment, instantiation construction refers to the process of creating specific data objects in memory or files and assigning attribute values ​​to them according to the target standard schema definition; predefined attribute sets (Psets) are standardized mechanisms in the IFC standard used to extend entity attributes, allowing the addition of domain-specific parameters without modifying the core schema; attribute completion strategies refer to the processing methods that automatically generate target attribute values ​​based on rules, default values, or contextual reasoning when source data is missing; dependency constraints refer to the technical requirements in the target standard for entity creation order, referential integrity, and cardinality limits, and violating these constraints will result in invalid data files.

[0069] The conversion mechanism in this embodiment achieves high-fidelity, automated model generation from GIM to IFC. Compared to traditional manual export or simple field comparison methods, this solution is based on conflict-corrected target mapping relationships, significantly reducing the risk of semantic loss and structural errors. Simultaneously, the conversion process fully utilizes the structured multidimensional information in the directed attribute graph, enabling context-aware attribute assignment and relationship reconstruction, rather than processing individual objects in isolation. Especially for complex equipment-space coupling relationships and cross-disciplinary topology connections in power grid engineering, this method can accurately restore their business semantics, ensuring the applicability of the converted model in downstream stages such as design review, construction simulation, and operation and maintenance management.

[0070] Another embodiment of the present invention provides an automatic mapping system between GIM and IFC standards for power grid engineering. For details, please refer to [link to documentation]. Figure 2 , Figure 2 The diagram shown is a structural block diagram of an automatic mapping system between GIM and IFC standards for power grid engineering, according to one embodiment of the present invention, which includes: The parsing module 11 is used to obtain the source model data and target standard model data of the current power grid project, and to parse the source model data and target standard model data respectively to obtain the type information, attribute information and relationship information of each data object; Module 12 is used to construct a directed attribute graph representing the semantic information of the source model data and the target standard model data based on type information, attribute information and relation information; The matching module 13 is used to perform semantic matching, attribute matching and topological matching between objects in the source model data and the target standard model data in sequence based on the preset mapping rule base and the directed attribute graph, so as to obtain the initial mapping relationship. The correction module 14 is used to perform conflict detection and correction processing on the initial mapping relationship in sequence to obtain the target mapping relationship. Conflict detection is used to identify ambiguous associations in the mapping relationship, and correction processing is used to determine the target mapping entry from the candidate mapping entries based on a preset correction strategy. The preset correction strategy is dynamically configured according to the type of ambiguous association and the semantic association of the object to be matched. The generation module 15 is used to convert the source model data into a data file that conforms to the target standard model based on the target mapping relationship.

[0071] Preferably, in one embodiment of the present invention, the construction module is specifically used for: The electrical equipment in the object type information is abstracted into equipment nodes and the spatial unit is abstracted into spatial nodes. Based on the attribute data, the physical attributes of the electrical equipment and the geometric attributes of the spatial unit are bound to the corresponding nodes respectively. Based on the relational data, topological directed edges representing electrical connection relationships are established between device nodes, spatial directed edges representing hierarchical inclusion relationships are established between spatial nodes, and associative directed edges representing device affiliation relationships are established between device nodes and spatial nodes. A directed attribute graph is constructed based on topological directed edges, spatial directed edges, associated directed edges, device nodes, and spatial nodes.

[0072] Preferably, in one embodiment of the present invention, the matching module is specifically used for: The device nodes and spatial nodes of the directed attribute graph are traversed as objects to be matched, and the corresponding candidate mapping entries are retrieved from the preset mapping rule library based on the node type identifier of the objects to be matched. Determine the semantic matching degree, attribute matching degree, and topological matching degree between the object to be matched and the candidate mapping entries, respectively; A comprehensive similarity is obtained based on semantic matching degree, attribute matching degree, and topological matching degree. Candidate mapping entries whose comprehensive similarity meets a preset threshold are determined as the matching results of the objects to be matched, and an initial mapping relationship is generated.

[0073] Preferably, in one embodiment of the present invention, the matching module is further configured to: The semantic matching degree is determined based on the semantic hierarchy distance between the node type identifier and the target type defined in the candidate mapping entry; The attribute matching degree is determined based on the degree of conformity between the physical or geometric attributes bound to the object to be matched and the attribute constraints defined in the candidate mapping entries; The topological matching degree is determined based on the consistency between the spatial positional relationship represented by the spatial directed edges or associated directed edges connected to the object to be matched and the spatial constraints defined in the candidate mapping entries.

[0074] Preferably, in one embodiment of the present invention, the correction module is specifically used for: Based on the comprehensive similarity between each object to be matched and its corresponding candidate mapping entry in the initial mapping relationship and the node type identifier, we can identify one-to-many conflicts where the same object to be matched corresponds to multiple candidate mapping entries, and many-to-one conflicts where multiple objects to be matched correspond to the same candidate mapping entry. Corrective processing is performed on one-to-many or many-to-one conflicts, and the corrected mapping relationship is determined as the target mapping relationship; the correction process includes: For one-to-many conflicts, a unique candidate mapping entry is selected based on comprehensive similarity; for many-to-one conflicts, it is determined whether multiple objects to be matched belong to the same power grid engineering equipment. If so, the mapping is merged; otherwise, the optimal mapping is retained and the remaining objects to be matched are rearranged.

[0075] This invention also provides two embodiments of an automatic mapping method between GIM and IFC standards for power grid engineering, to illustrate the beneficial effects of the technical solution of this invention.

[0076] Example 1 Specifically, this embodiment uses the No. 1 main transformer in a 110kV substation project as an example to illustrate the automatic mapping method between GIM and IFC standards proposed in this invention for power grid engineering. This embodiment is used to explain how power grid professional equipment objects in the GIM model are automatically mapped to corresponding equipment entities in the IFC model through the system corresponding to the method of this invention.

[0077] Step 1: Model data parsing.

[0078] First, the system reads the GIM model file and parses the device objects, hierarchical structure, attribute data, and spatial location information in the GIM model.

[0079] In this embodiment, the system identifies a main transformer object from the GIM model and extracts the following information: object type is main transformer; equipment name is No. 1 main transformer; equipment number is #1 main transformer; voltage level is 110kV; rated capacity is 50MVA; area is the main transformer area of ​​a 110kV substation; installation location is the main transformer foundation platform; spatial coordinates include the coordinates of the equipment center point, installation elevation, and external enclosure dimensions; electrical topology relationships include the connection relationships with 110kV busbars, 35kV busbars, 10kV busbars, disconnect switches, circuit breakers, and other equipment; professional attributes include wiring group, cooling method, number of phases, oil weight, equipment manufacturer, commissioning time, etc.

[0080] Simultaneously, the system parses the data structure of the target IFC model and identifies candidate entity types within the IFC model that can be used to carry the main transformer object, including IfcTransformer, IfcEnergyConversionDevice, and IfcBuildingElementProxy. Specifically, when the target IFC version supports the IfcTransformer entity, IfcTransformer is preferentially used as the target mapping entity; when the target IFC version does not support this entity, IfcEnergyConversionDevice can be used as an alternative entity; when the target system only supports general component representation, IfcBuildingElementProxy can be used as a fallback representation entity, and power grid semantics are preserved through a custom attribute set.

[0081] Step 2: Standardize the model structure.

[0082] In this embodiment, the system uses a directed attribute graph as a unified intermediate data model, converts the No. 1 main transformer in the GIM into a device node, converts its connection relationship with the 110kV bus, 35kV bus, and 10kV bus into an electrical topology edge, converts its main transformer area and main transformer base platform into a spatial node and a spatial container edge, and stores attributes such as voltage level, rated capacity, and cooling method as an attribute set.

[0083] For example, the device node of the main transformer in the intermediate model may include: EntityID=E_TR_001; SourceStandard=GIM; SourceObjectID=GIM_TR_001; EntityType=device node; GIMClass=main transformer; IFCClass=candidate IfcTransformer; ElectricalAttributeSet={VoltageLevel=110kV, RatedCapacity=50MVA, CoolingMode=oil-immersed air-cooled}; SpatialAttributeSet={installation area=main transformer area, installation location=main transformer foundation platform}; TopologyPointer points to the electrical topology edges corresponding to the 110kV bus, 35kV bus, and 10kV bus; SpatialContainerPointer points to the spatial node of the main transformer area.

[0084] This directed attribute graph allows for the unified representation of electrical topology information in the GIM model and spatial container information in the IFC model, providing a foundation for subsequent object type mapping, attribute mapping, and semantic matching.

[0085] Step 3: Object type mapping.

[0086] The system automatically matches GIM master transformer objects with IFC candidate entities based on the object type mapping rule base. In this embodiment, the rule base pre-determines "master transformer → IfcTransformer" as the preferred mapping relationship, "master transformer → IfcEnergyConversionDevice" as the alternative mapping relationship, and "master transformer → IfcBuildingElementProxy" as the fallback mapping relationship.

[0087] The system first performs rule matching based on the GIM object type "main transformer" to obtain the preferred candidate entity IfcTransformer. Then, the system performs a secondary judgment based on the object's attributes. Since this object possesses typical transformer attributes such as voltage level, rated capacity, and wiring group, it is determined to have the highest match degree with IfcTransformer.

[0088] If the target IFC version does not support IfcTransformer, the system maps it to IfcEnergyConversionDevice according to the rules and adds the EquipmentCategory=Transformer field to its attribute set. Ultimately, the system generates the type mapping result: GIM: Main Transformer → IFC: IfcTransformer; or, if IfcTransformer is not supported, it generates: GIM: Main Transformer → IFC: IfcEnergyConversionDevice.

[0089] Furthermore, after completing the object type mapping, the system converts the attributes of the GIM master transformer into IFC object attributes based on the attribute mapping rule base. In this embodiment, some attribute mapping relationships are shown in Table 1.

[0090] Table 1 For fields that already exist in the IFC standard, the system directly writes the corresponding fields; for power grid professional attributes that do not have directly corresponding fields in the IFC standard, the system writes the custom attribute set Pset_PowerGridEquipment.

[0091] For example, the attributes "Equipment No.: #1 Main Transformer, Voltage Level: 110kV, Rated Capacity: 50MVA, Cooling Method: Oil Immersion Air Cooling" in GIM are converted to IFC attribute expressions as: Name=#1 Main Transformer; Pset_PowerGridEquipment.VoltageLevel=110kV; Pset_PowerGridEquipment.RatedCapacity=50MVA; Pset_PowerGridEquipment.CoolingMode=Oil Immersion Air Cooling.

[0092] Step 4: Semantic matching and mapping optimization.

[0093] After object type mapping and attribute mapping are completed, the system further performs semantic consistency verification and mapping optimization to avoid incorrect or duplicate mappings. In this embodiment, the system uses a comprehensive similarity calculation method to determine the degree of matching between the GIM main transformer and the IFC candidate object.

[0094] in, For comprehensive similarity; Type matching degree; For attribute matching degree; Spatial matching degree; , , These are type matching weight, attribute matching weight, and spatial matching weight, respectively. .

[0095] In this embodiment, the GIM object type is a master transformer, and the candidate IFC type is IfcTransformer, therefore the type matching degree S_type = 1.0. If the candidate type is IfcEnergyConversionDevice, then S_type = 0.8; if the candidate type is IfcBuildingElementProxy, then S_type = 0.5.

[0096] The attribute matching degree is determined by the ratio of the number of successfully matched attributes to the total number of attributes to be matched. If the GIM main transformer needs to map 6 core attributes, including equipment number, equipment name, voltage level, rated capacity, cooling method, and wiring group, and 5 of these attributes can be mapped, then S_attr=5 / 6=0.83.

[0097] Spatial matching degree is calculated based on the distance between the GIM device coordinates and the IFC candidate object coordinates, the spatial container relationship, and the relative positional relationship. If the main transformer is located in the main transformer zone in the GIM, and the IFC candidate object is also located in the main transformer zone or its subordinate spatial container, and the spatial distance is less than a preset threshold, then the candidate object has a high spatial matching degree.

[0098] In this embodiment, the settings are as follows: =0.4、 =0.4、 =0.2, and the matching degree of a certain IFC candidate object is S_type=1.0, S_attr=0.83, S_space=0.9, then the comprehensive similarity is: S=0.4×1.0+0.4×0.83+0.2×0.9 = 0.912 If the preset threshold is 0.8, then since S=0.912>0.8, the system determines that the GIM main transformer and the IFC candidate object are successfully mapped.

[0099] If the system finds that a GIM main transformer object matches multiple IFC candidate objects simultaneously, it will process them according to the following rules: the object with the highest overall similarity will be selected first; if the difference in overall similarity is less than the preset difference threshold, the object with the highest type matching degree will be selected first; if they still cannot be distinguished, the object with the lowest attribute missing rate will be selected; if they still cannot determine a unique object, the object with the smallest spatial distance will be selected; if they still cannot determine a unique object, they will be marked as pending manual review, and the object type, attributes and spatial characteristics will be recorded for subsequent improvement of the mapping rule base.

[0100] Step 5: Generating mapping results and model conversion.

[0101] After completing the mapping optimization, the system generates a mapping table between GIM and IFC, and generates the target IFC model based on this mapping table. In this embodiment, the generated mapping relationship is shown in Table 2.

[0102] Table 2 Subsequently, the system generates an IFC model file based on the aforementioned mapping relationship. During the generation process, the system writes the geometric information, spatial location information, electrical attribute information, and professional semantic information of the main transformer from the GIM into the IFC target model. Finally, the No. 1 main transformer in the GIM is converted into a device entity in the IFC model, which can be viewed, managed, and exchanged for data on platforms that support the IFC format.

[0103] As can be seen from this embodiment, the present invention can realize the automatic mapping of GIM power grid professional model to IFC general BIM model, reduce manual configuration work, and improve model conversion efficiency and data consistency.

[0104] Example 2 The method of this invention is also applicable to the reverse mapping from the IFC model to the GIM model. The following explanation uses the mapping of a transformer object in the IFC model back to the GIM model as an example.

[0105] Step 1: Model data analysis.

[0106] The system reads the IFC model file, parses the device objects within it, and identifies an IfcTransformer object. The system extracts the following information from this IFC object: the IFC object type is IfcTransformer; the object name is #1 main transformer; the Tag field is #1 main transformer; the spatial container is the main transformer area; the spatial location is the ObjectPlacement coordinates; the attribute set is Pset_PowerGridEquipment; and the professional attributes include VoltageLevel, RatedCapacity, and CoolingMode.

[0107] Step 2: Standardize the model structure.

[0108] The system converts IfcTransformer objects in the IFC into device nodes in a unified intermediate data model based on directed attribute graphs. During the conversion process, the system converts spatial container relationships in the IFC into spatial container edges, converts electrical attributes in the attribute set into key-value pair attribute sets, and retains the object's spatial coordinates, name, identifier, and attribute information.

[0109] Step 3: Object type mapping.

[0110] The system maps IfcTransformer in IFC to the main transformer object in GIM according to the object type mapping rules. The mapping relationship is: IFC: IfcTransformer → GIM: Main Transformer. If the IFC object type is IfcEnergyConversionDevice, but its EquipmentCategory field in the attribute set is Transformer, the system can also map it to the main transformer in GIM.

[0111] Furthermore, the system converts IFC attributes into GIM attributes according to attribute mapping rules. For example, Name / Tag maps to equipment number, VoltageLevel maps to voltage level, RatedCapacity maps to rated capacity, CoolingMode maps to cooling method, ObjectPlacement maps to equipment spatial location, and IfcSpatialStructureElement maps to its region. Through this step, the system completes the reverse conversion from IFC model attributes to GIM professional attributes.

[0112] Step 4: Semantic matching and mapping optimization.

[0113] The system calculates the overall similarity between the IFC object and the GIM candidate device types based on the IFC object's type, attribute information, and spatial location. If the IfcTransformer object and the GIM main transformer object all meet the matching thresholds in terms of type, attributes, and spatial location, the system determines that the mapping is successful.

[0114] If multiple GIM candidate objects meet the criteria, the system prioritizes the object with the same device number. If the device number is missing, the system selects the object with the highest overall matching degree in voltage level, capacity, and spatial location. If the matching degree of all candidate objects is below the threshold, the system marks them as awaiting manual review and records the object's attributes and spatial characteristics for subsequent rule base optimization.

[0115] Step 4: Generating mapping results and model conversion.

[0116] The system generates a reverse mapping table from IFC to GIM based on the final mapping result, and generates the target model data for GIM. Finally, the IfcTransformer object in the IFC model is converted into the main transformer object in the GIM model, retaining its core information such as voltage level, rated capacity, spatial location, and equipment number.

[0117] As can be seen from this embodiment, the present invention can not only realize the model mapping from GIM to IFC, but also the reverse mapping from IFC to GIM, thereby improving the data interoperability and model reuse capability between the two standards. The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. An automatic mapping method between GIM and IFC standards for power grid engineering, characterized in that, include: Obtain the source model data and target standard model data of the current power grid project, and parse the source model data and the target standard model data respectively to obtain the type information, attribute information and relationship information of each data object; Based on the type information, attribute information, and relationship information, a directed attribute graph representing the semantic information of the source model data and the target standard model data is constructed. Based on the preset mapping rule base and the directed attribute graph, semantic matching, attribute matching and topological matching are performed sequentially between each object in the source model data and the target standard model data to obtain the initial mapping relationship; The initial mapping relationship is sequentially subjected to conflict detection and correction processing to obtain the target mapping relationship; wherein, the conflict detection is used to identify ambiguous associations in the mapping relationship, and the correction processing is used to determine the target mapping entry from the candidate mapping entries based on a preset correction strategy, wherein the preset correction strategy is dynamically configured according to the type of ambiguous association and the semantic association of the object to be matched. Based on the target mapping relationship, the source model data is transformed to generate a data file that conforms to the target standard model.

2. The automatic mapping method between GIM and IFC standards for power grid engineering as described in claim 1, characterized in that, The construction of a directed attribute graph representing the semantic information of the source model data and the target standard model data based on the type information, the attribute information, and the relationship information includes: The electrical equipment in the object type information is abstracted into equipment nodes and the spatial unit is abstracted into spatial nodes. Based on the attribute data, the physical attributes of the electrical equipment and the geometric attributes of the spatial unit are respectively bound to the corresponding nodes. Based on the relational data, topological directed edges representing electrical connection relationships are established between the device nodes, spatial directed edges representing hierarchical inclusion relationships are established between the spatial nodes, and associated directed edges representing device affiliation relationships are established between the device nodes and the spatial nodes. A directed attribute graph is constructed based on the topological directed edges, the spatial directed edges, the associated directed edges, the device nodes, and the spatial nodes.

3. The automatic mapping method between GIM and IFC standards for power grid engineering as described in claim 1, characterized in that, Based on the preset mapping rule base and the directed attribute graph, semantic matching, attribute matching, and topological matching are sequentially performed between objects in the source model data and the target standard model data to obtain an initial mapping relationship, including: The device nodes and spatial nodes of the directed attribute graph are traversed as objects to be matched, and corresponding candidate mapping entries are retrieved from the preset mapping rule base based on the node type identifier of the objects to be matched. Determine the semantic matching degree, attribute matching degree, and topological matching degree between the object to be matched and the candidate mapping entries, respectively. A comprehensive similarity is obtained based on the semantic matching degree, the attribute matching degree, and the topological matching degree. Candidate mapping entries whose comprehensive similarity satisfies a preset threshold are determined as the matching results of the object to be matched, and the initial mapping relationship is generated.

4. The automatic mapping method between GIM and IFC standards for power grid engineering as described in claim 3, characterized in that, The step of determining the semantic matching degree, attribute matching degree, and topological matching degree between the object to be matched and the candidate mapping entry includes: The semantic matching degree is determined based on the semantic hierarchy distance between the node type identifier and the target type defined in the candidate mapping entry; The attribute matching degree is determined based on the degree of conformity between the physical or geometric attributes bound to the object to be matched and the attribute constraints defined in the candidate mapping entries; The topological matching degree is determined based on the consistency between the spatial positional relationship represented by the spatial directed edges or associated directed edges connected to the object to be matched and the spatial constraints defined in the candidate mapping entries.

5. The automatic mapping method between GIM and IFC standards for power grid engineering as described in claim 3, characterized in that, The process of performing conflict detection and correction on the initial mapping relationship to obtain the target mapping relationship includes: Based on the comprehensive similarity and node type identifier of each object to be matched and its corresponding candidate mapping entry in the initial mapping relationship, it is identified that there are one-to-many conflicts where the same object to be matched corresponds to multiple candidate mapping entries, and many-to-one conflicts where multiple objects to be matched correspond to the same candidate mapping entry. The one-to-many conflict or the many-to-one conflict is corrected, and the corrected mapping relationship is determined as the target mapping relationship; wherein, the correction process includes: For the one-to-many conflict, a unique candidate mapping entry is selected based on the comprehensive similarity; for the many-to-one conflict, it is determined whether multiple objects to be matched belong to the same power grid engineering equipment. If so, the mapping is merged; otherwise, the optimal mapping is retained and the remaining objects to be matched are rearranged.

6. An automatic mapping system between GIM and IFC standards for power grid engineering, characterized in that, include: The parsing module is used to acquire the source model data and target standard model data of the current power grid project, and to parse the source model data and the target standard model data respectively to obtain the type information, attribute information and relationship information of each data object; The construction module is used to construct a directed attribute graph representing the semantic information of the source model data and the target standard model data based on the type information, the attribute information, and the relationship information. The matching module is used to perform semantic matching, attribute matching and topological matching between each object in the source model data and the target standard model data based on the preset mapping rule library and the directed attribute graph, so as to obtain the initial mapping relationship; The correction module is used to sequentially perform conflict detection and correction processing on the initial mapping relationship to obtain the target mapping relationship; wherein, the conflict detection is used to identify ambiguous associations in the mapping relationship, and the correction processing is used to determine the target mapping entry from the candidate mapping entries based on a preset correction strategy, wherein the preset correction strategy is dynamically configured according to the type of ambiguous association and the semantic association of the object to be matched. The generation module is used to convert the source model data into a data file that conforms to the target standard model according to the target mapping relationship.

7. The automatic mapping system for GIM and IFC standards for power grid engineering as described in claim 6, characterized in that, The building module is specifically used for: The electrical equipment in the object type information is abstracted into equipment nodes and the spatial unit is abstracted into spatial nodes. Based on the attribute data, the physical attributes of the electrical equipment and the geometric attributes of the spatial unit are respectively bound to the corresponding nodes. Based on the relational data, topological directed edges representing electrical connection relationships are established between the device nodes, spatial directed edges representing hierarchical inclusion relationships are established between the spatial nodes, and associated directed edges representing device affiliation relationships are established between the device nodes and the spatial nodes. A directed attribute graph is constructed based on the topological directed edges, the spatial directed edges, the associated directed edges, the device nodes, and the spatial nodes.

8. The automatic mapping system between GIM and IFC standards for power grid engineering as described in claim 6, characterized in that, The matching module is specifically used for: The device nodes and spatial nodes of the directed attribute graph are traversed as objects to be matched, and corresponding candidate mapping entries are retrieved from the preset mapping rule base based on the node type identifier of the objects to be matched. Determine the semantic matching degree, attribute matching degree, and topological matching degree between the object to be matched and the candidate mapping entries, respectively. A comprehensive similarity is obtained based on the semantic matching degree, the attribute matching degree, and the topological matching degree. Candidate mapping entries whose comprehensive similarity satisfies a preset threshold are determined as the matching results of the object to be matched, and the initial mapping relationship is generated.

9. The automatic mapping system between GIM and IFC standards for power grid engineering as described in claim 8, characterized in that, The matching module is also used for: The semantic matching degree is determined based on the semantic hierarchy distance between the node type identifier and the target type defined in the candidate mapping entry; The attribute matching degree is determined based on the degree of conformity between the physical or geometric attributes bound to the object to be matched and the attribute constraints defined in the candidate mapping entries; The topological matching degree is determined based on the consistency between the spatial positional relationship represented by the spatial directed edges or associated directed edges connected to the object to be matched and the spatial constraints defined in the candidate mapping entries.

10. The automatic mapping system between GIM and IFC standards for power grid engineering as described in claim 8, characterized in that, The correction module is specifically used for: Based on the comprehensive similarity and node type identifier of each object to be matched and its corresponding candidate mapping entry in the initial mapping relationship, it is identified that there are one-to-many conflicts where the same object to be matched corresponds to multiple candidate mapping entries, and many-to-one conflicts where multiple objects to be matched correspond to the same candidate mapping entry. The one-to-many conflict or the many-to-one conflict is corrected, and the corrected mapping relationship is determined as the target mapping relationship; wherein, the correction process includes: For the one-to-many conflict, a unique candidate mapping entry is selected based on the comprehensive similarity; for the many-to-one conflict, it is determined whether multiple objects to be matched belong to the same power grid engineering equipment. If so, the mapping is merged; otherwise, the optimal mapping is retained and the remaining objects to be matched are rearranged.

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