CN-122021640-A - Transformer substation SPD file compliance detection method and system based on semantic verification

CN122021640ACN 122021640 ACN122021640 ACN 122021640ACN-122021640-A

Abstract

The invention relates to the technical field of semantic analysis, and discloses a transformer substation SPD file compliance detection method and system based on semantic verification, wherein the method comprises the steps of converting an SPD file data set into an SPD semantic knowledge graph; the method comprises the steps of constructing a substation configuration semantic number twin through entity linking, attribute fusion and context injection, starting a semantic reasoning machine to materialize a direct electrical connection relation and a direct communication subscription relation between conductive devices, utilizing SWRL rules to infer and generate high-order service semantics such as a protection logic chain and a signal flow direction to form an enhanced semantic model, constructing an enhanced SPARQL query engine, executing multidimensional constraint verification on the model based on a predefined SHACL shape graph, detecting semantic defects and generating a report. The invention overcomes the defect that the prior art only stays in surface syntax verification, can go deep into document semantic connotation, effectively verifies complex business logic and cross-document semantic dependence, and improves the validity of SPD document compliance detection.

Inventors

WU CHUANGUO
SUN MINGZE
ZHOU HAORAN
HAN ZHEN
ZHANG CHENGFEI
WANG CHI
LI QIMING
CAO ZHAN
SONG ZIJIAN
LIU ZHIHAN

Assignees

国网江苏省电力有限公司建设分公司

Dates

Publication Date: 20260512
Application Date: 20251231

Claims (10)

1. The transformer substation SPD file compliance detection method based on semantic verification is characterized by comprising the following steps of: converting the structural configuration data of the SPD file data set into an SPD semantic knowledge graph through an RDF triplet generator; The coupling entity link and attribute fusion engine utilizes a global unique identifier to disambiguate and merge cross-file entities in the SPD semantic knowledge graph, fuses attributes derived from different SCL file types, and performs semantic enhancement on the SPD semantic knowledge graph by injecting a context relation implicit in a hierarchical nested form in a standard SCL into a triplet to generate a substation configuration semantic digital twin containing the disambiguated entities and the fused attributes; Starting a semantic reasoning machine in the substation configuration semantic digital twin to execute the following operations of linkage connection node topology analyzer, materializing direct electrical connection relation among conductive devices by traversing Terminal examples sharing the same connection node, and acting a GOOSE subscription relation materialization engine, materializing direct communication subscription relation between a subscriber and a publisher control block by analyzing character string references of ExtRef elements; constructing an enhanced SPARQL query engine, performing multidimensional constraint verification on the enhanced semantic model based on a predefined SHACL shape graph, thereby detecting semantic defects, including subscription relation conflict, electrical connection broken link, protection logic link circulation and reverse signal flow, and generating a semantic defect positioning report.
2. The substation SPD file compliance detection method based on semantic verification according to claim 1 is characterized in that the SPD semantic knowledge graph generation process comprises the steps of performing structural analysis on SCL files in SPD file data sets through an XML analyzer, extracting file header information to verify file type and version consistency, analyzing IED elements, LD elements and LN elements layer by layer, recording a hierarchical structure and attributes, applying RDF mapping rules to map IED elements into subject identifications, attribute into predicate relationships and value into objects, performing complementation on nonstandard labels through a predefined rule set and a mode matching mechanism, performing marking on missing attributes, generating RDF triplet sets, and forming an SPD semantic knowledge graph to serve as input of a coupling entity link and attribute fusion engine.
3. The substation SPD file compliance detection method based on semantic verification according to claim 1, wherein the substation configuration semantic digital twin generation process comprises the following steps: the method comprises the steps of inputting SPD semantic knowledge graphs into a coupling entity link and attribute fusion engine, calculating cross-file entity similarity based on a global unique identifier and a Levenshtein distance, executing disambiguation and merging operation, merging SCD file attribute values, CID file attribute values and IID file attribute values, enabling the context relation defined by IEC 61850 standard to comprise Bay and VoltageLevel containing relations and ConductingEquipment and Terminal association relations, adding triplets to achieve semantic enhancement, checking that injection relations are not conflicted through a graph consistency verification algorithm, and generating substation configuration semantic digital twin containing disambiguated entities and fused attributes to serve as input of a semantic reasoning machine.
4. The substation SPD file compliance detection method based on semantic verification according to claim 1 is characterized in that the generation process of the enhanced semantic model comprises the steps of integrating an Apache Jena framework through a semantic reasoning machine, performing OWL ontology reasoning on substation configuration semantic numbers in a twinning mode, traversing Terminal instances of shared connection nodes through a connection node topology analyzer by adopting a depth-first search algorithm, materializing direct electrical connection relations among conductive devices, analyzing ExtRef element character string references through a GOOSE subscription relation explicit engine, and performing controlled recursion reasoning through the Drools engine based on SWRL rule groups to generate high-order business semantics, wherein the high-order business semantics comprise protection logic chains and signal flow directions, and injecting the protection logic chains and the signal flow directions to form the enhanced semantic model to serve as input of an enhanced SPAL query engine.
5. The method for detecting the compliance of the SPD file of the transformer substation based on the semantic verification, which is disclosed in claim 1, is characterized in that the direct electrical connection relation comprises the steps of generating ConductingEquipment connection edges between entities based on a traversing result of a Terminal instance of a shared connection node, injecting topological association between a breaker and an isolation knife switch, and the direct communication subscription relation comprises the steps of processing a many-to-many subscription scene through unique identifier association based on a directional edge between a subscriber IED analyzed by ExtRef elements and a publisher control block, and generating a redundancy-free subscription relation.
6. The substation SPD file compliance detection method based on semantic verification according to claim 1, wherein the construction process of the enhanced SPARQL query engine comprises the steps of expanding SPARQL grammar to support SHACL verification path query, defining a shape graph template for CID files and IID files, including node shapes for verifying IED entity attributes and attribute shapes for checking LN attribute ranges, integrating SPARQL function expansion modules to support custom constraint logic, constructing a parallel query executor through a multithread allocation algorithm, executing entity integrity check and relationship consistency check and attribute constraint check on the enhanced semantic model, generating a query engine, and outputting a check result as input of a semantic defect positioning report.
7. The substation SPD file compliance detection method based on semantic verification according to claim 1 is characterized in that the detection flow of the semantic defects comprises the steps of inputting an enhanced semantic model into an enhanced SPARQL query engine, loading SHACL shape graphs to execute node and attribute verification, detecting missing attributes, executing connectivity and directionality verification on materialized high-order business semantics through embedding SPARQL path queries to detect subscription relation conflicts and electrical connection broken links, applying custom constraint logic to identify protection logic chain loops and reverse signal flow directions, summarizing subscription relation conflicts, electrical connection broken links, protection logic chain loops and reverse signal flow directions, and generating reports containing defect types, entity identification, violation constraint description and repair paths, and mapping topological positions of defects in the enhanced semantic model through a PlantUML chart.
8. The substation SPD file compliance detection method based on semantic verification according to claim 1 is characterized in that the generation process of the SWRL rule set comprises defining rule sets based on IEC 61850 standards and substation business logic, including protection logic chain generation rules and signal flow direction inference rules, adjusting rule priorities through historical verification data analysis, verifying that the rule sets have no conflict through a Drools engine, generating the SWRL rule set capable of being executed recursively, and injecting an enhanced semantic model to serve as a high-order business semantic reasoning basis.
9. The substation SPD file compliance detection method based on semantic verification according to claim 1, wherein the generation process of the semantic defect location report comprises the steps of extracting defect types, affected entity identification and violation constraint description based on a verification result of an enhanced SPARQL query engine, generating a repair path suggestion through backtracking an inference path and/or matching a predefined defect mode, mapping defect positions into a topological graph through a PlantUML generation tool, embedding a JSON format report, merging redundant defect entries through a report optimization algorithm, and generating a final report containing defect location and repair suggestions.
10. A system for applying to a method for detecting compliance of SPD files of a substation based on semantic verification according to claim 1, comprising: The map construction module is used for converting the structural configuration data of the SPD file data set into an SPD semantic knowledge map through the RDF triplet generator; The semantic digital twin construction module is used for coupling entity links and an attribute fusion engine, performing disambiguation and merging on cross-file entities in the SPD semantic knowledge graph by utilizing a global unique identifier, fusing attributes derived from different SCL file types, performing semantic enhancement on the SPD semantic knowledge graph by explicit converting a context relationship implicit in a hierarchical nested form in a standard SCL into a triplet, and generating a substation configuration semantic digital twin comprising disambiguated entities and fused attributes; The enhanced semantic model building module starts a semantic reasoning machine to execute the following operations in the substation configuration semantic digital twin, wherein the operations comprise a linkage connection node topology analyzer, a linkage GOOSE subscription relation explicit engine, a direct communication subscription relation between a subscriber and a publisher control block, a SWRL rule, a high-order business semantic, a high-order semantic relation protection logic chain and a signal flow direction, and further an enhanced semantic model; The semantic defect positioning module is used for constructing an enhanced SPARQL query engine, the query engine is used for executing multi-dimensional constraint verification on the enhanced semantic model based on a predefined SHACL shape graph so as to detect semantic defects, the semantic defects comprise subscription relation conflict, electrical connection broken links, protection logic link circulation and reverse signal flow, and a semantic defect positioning report is generated.

Description

Transformer substation SPD file compliance detection method and system based on semantic verification Technical Field The invention relates to the technical field of semantic analysis, in particular to a transformer substation SPD file compliance detection method and system based on semantic verification. Background The configuration, description, protection and control logic of substations increasingly rely on SPD files. However, in the prior art, the detection method for the SPD files often stays at the surface structure level of lexical and syntactic verification, and cannot go deep into the semantic meaning of the files. The methods lack the semantic analysis capability of context awareness on file contents, so that complex business logic or semantic dependency relations across files cannot be effectively checked, and the effectiveness of SPD file compliance detection is improved. Therefore, a transformer substation SPD file compliance detection method and system based on semantic verification are provided. Disclosure of Invention The invention aims to provide a transformer substation SPD file compliance detection method and system based on semantic verification, which are used for converting an SPD file data set into an SPD semantic knowledge map, constructing transformer substation configuration semantic digital twin through entity linking, attribute fusion and context injection, starting a semantic reasoning machine, materializing a direct electrical connection relation and a direct communication subscription relation between conductive devices, utilizing SWRL rule inference to generate high-order service semantics such as a protection logic chain and a signal flow direction to form an enhanced semantic model, constructing an enhanced SPARQL query engine, executing multi-dimensional constraint verification on the model based on a predefined SHACL shape map, detecting semantic defects and generating a report. The invention overcomes the defect that the prior art only stays on surface syntax verification, can go deep into document semantic connotation, effectively verifies complex business logic and cross-document semantic dependence, and improves the validity of SPD document compliance detection. The invention adopts the following technical scheme: The transformer substation SPD file compliance detection method based on semantic verification comprises the following steps: converting the structural configuration data of the SPD file data set into an SPD semantic knowledge graph through an RDF triplet generator; The coupling entity link and attribute fusion engine utilizes a global unique identifier to disambiguate and merge cross-file entities in the SPD semantic knowledge graph, fuses attributes derived from different SCL file types, and performs semantic enhancement on the SPD semantic knowledge graph by injecting a context relation implicit in a hierarchical nested form in a standard SCL into a triplet to generate a substation configuration semantic digital twin containing the disambiguated entities and the fused attributes; Starting a semantic reasoning machine in the substation configuration semantic digital twin to execute the following operations of linkage connection node topology analyzer, materializing direct electrical connection relation among conductive devices by traversing Terminal examples sharing the same connection node, and acting a GOOSE subscription relation materialization engine, materializing direct communication subscription relation between a subscriber and a publisher control block by analyzing character string references of ExtRef elements; constructing an enhanced SPARQL query engine, performing multidimensional constraint verification on the enhanced semantic model based on a predefined SHACL shape graph, thereby detecting semantic defects, including subscription relation conflict, electrical connection broken link, protection logic link circulation and reverse signal flow, and generating a semantic defect positioning report. Preferably, the SPD semantic knowledge graph generation process comprises the steps of executing structural analysis on SCL files in an SPD file data set through an XML analyzer, extracting file header information to verify file type and version consistency, analyzing IED elements, LD elements and LN elements layer by layer, recording a hierarchical structure and attributes, applying RDF mapping rules to map the IED elements into subject identifications, mapping the attributes into predicate relationships and mapping values into objects, executing complementation on nonstandard labels through a predefined rule set and a pattern matching mechanism, executing marking on missing attributes, and generating RDF triplet sets to form the SPD semantic knowledge graph, wherein the SPD semantic knowledge graph is used as input of a coupling entity link and attribute fusion engine. Preferably, the generating process of the substation configuration semantic digit