Electric power project model examination method and system based on SG-CIM model

Abstract

The invention relates to an SG-CIM model-based electric power project model review method and system, and belongs to the technical field of electric power system data management and intelligent review, and the method comprises the following steps: extracting a logic model from an electric power project design document, building a dynamic rule engine based on a power grid public data SG-CIM model library, performing real-time or regular rule matching on the logic model by adopting a three-stage filtering algorithm to generate a logic model difference report; a physical model in the power project implementation database is collected and analyzed, and a mapping matrix of the physical model and the logic model is constructed; when the physical model is updated, capturing real-time physical model data change through a database trigger, and verifying a mapping relation between the physical model and the logic model through a mapping matrix; calculating an electric power project model compliance score according to the logic model difference report and the mapping relation verification result; and outputting an electric power project review report and a rectification suggestion based on the electric power project model compliance score, the logic model difference report and the mapping relation verification result.

Description

Technical Field

[0001] The present application relates to the technical field of power system data management and intelligent review, and more specifically, to a power project model review method and system based on the SG-CIM model. Background Art

[0002] With the development of smart grid construction, the complexity of data models in power projects has increased significantly. The degree of standardization of logical models (defined during the design phase) and physical models (built during the implementation phase) directly impacts data interoperability and system integration efficiency. While power grids use the SG-CIM (Common Information Model) as a unified semantic specification, the following issues still exist in actual projects:

[0003] Poor model consistency: The logical model in the design document and the physical model of the database often fail to map correctly due to naming differences, type conversion errors, or missing constraints. Manual checks are required, which is time-consuming and prone to omissions.

[0004] Low rule review efficiency: Existing tools (such as static model checkers) cannot dynamically adapt to SG-CIM version iterations, require manual updates of the rule base, and lack the ability to detect semantic-level conflicts.

[0005] Lack of quantitative evaluation: Model quality relies on subjective experience and cannot identify problem priorities through objective scoring, resulting in irrational allocation of rectification resources.

[0006] Existing technologies, such as the Chinese patent application with publication number "CN114021335A," disclose a shape-constrained power grid model verification method. This method embeds a standard information model (such as CIM) within a verification tool and converts model constraints into specific rules (such as SHACL shape diagrams) for verification. This method is effective when the model version is stable, but it lacks the ability to capture and review the physical model in the implementation database and its dynamic changes in real time. Furthermore, it cannot dynamically adapt to rule changes, resulting in delays in model verification and increased maintenance costs. Summary of the Invention

[0007] In order to solve the above technical problems, the present invention proposes a power project model review method and system based on the SG-CIM model.

[0008] The technical solutions of the present invention are as follows:

[0009] The present invention proposes a power project model review method based on the SG-CIM model, comprising the following steps:

[0010] Extract logical models from power project design documents, build a dynamic rule engine based on the SG-CIM model library of power grid public data, use a three-level filtering algorithm to perform real-time or periodic rule matching on the logical model, and generate a logical model difference report;

[0011] Collect and analyze the physical model in the power project implementation database and build a mapping matrix between the physical model and the logical model;

[0012] When the physical model is updated, the real-time physical model data changes are captured through database triggers, and the mapping relationship between the physical model and the logical model is verified through the mapping matrix;

[0013] Calculate the power project model compliance score based on the logical model difference report and mapping relationship verification results; output the power project review report and rectification suggestions based on the power project model compliance score, logical model difference report and mapping relationship verification results.

[0014] Preferably, the logical model includes table structure, field definition and relationship constraints; the physical model includes physical table name, field type, index and storage parameters.

[0015] Preferably, the construction of the dynamic rule engine includes:

[0016] Extract the three elements of class attribute constraints, association relationships, and business rules from the SG-CIM model library;

[0017] Convert the three elements of class attribute constraints, association relationships, and business rules into SPARQL query templates;

[0018] Configure the rule trigger threshold to generate mandatory rectification items when the difference in the logical model exceeds the threshold.

[0019] Preferably, the three-stage filtering algorithm includes the following levels:

[0020] Entity type matching layer: Based on the core entity classes in the SG-CIM model library, the entity names in the logical model are extracted and the matching degree with the core class names is calculated through cosine similarity. Entities with matching degrees greater than the preset value enter the next layer;

[0021] Attribute specification verification layer: For entities that pass the entity type matching layer, the field naming rules are verified, and regular expressions are used to match the field encoding format. Fields that fail the verification are marked as naming violations.

[0022] Semantic constraint reasoning layer: Converts the relational constraints of the logical model into RDF triples, uses the Jena inference engine to load the SG-CIM model, verifies the consistency of the inheritance relationship with the business rules, and determines a semantic conflict if it is detected that the parent class does not declare the subclass attribute.

[0023] Preferably, verifying the mapping relationship between the physical model and the logical model through the mapping matrix specifically includes:

[0024] Verify the naming consistency between the logical model table name and the physical model table name;

[0025] Verification of conversion rules from logical model field types to physical model field types;

[0026] Integrity check of the primary key and foreign key constraints defined in the logical model in the physical model.

[0027] Preferably, the calculation formula for the power project model compliance score is:

[0028] S=α·S L +β·S P +γ·VersionFactor;

[0029] Where: S is the model compliance score; S L and S P are the logical model compliance score and the physical model compliance score respectively; α and β are the logical model compliance weight factor and the physical model compliance weight factor respectively; VersionFactor is the model version impact factor; γ is the model version impact weight.

[0030] Preferably, the logical model compliance and the physical model compliance are calculated as follows:

[0031]

[0032] Where: N matched N is the number of entities matched by the three-level filtering algorithm; total is the total number of logical model entities; D semantic is the number of violations of the SG-CIM core rules in the semantic conflict detection results; k is the attenuation coefficient; N correctFK is the number of correctly implemented foreign keys in the physical model; N FK is the total number of foreign keys in the physical model; N illegalDeviation is the number of illegal deviation fields in the physical model; N totalFields is the total number of fields in the physical model; δ is the foreign key integrity weight; ε is the illegal deviation control weight.

[0033] On the other hand, the present invention also provides a power project model review system based on the SG-CIM model, comprising:

[0034] The logical model comparison module extracts logical models from power project design documents, builds a dynamic rule engine based on the SG-CIM model library of power grid public data, uses a three-level filtering algorithm to perform real-time or periodic rule matching on the logical models, and generates a logical model difference report;

[0035] The mapping matrix construction module collects and analyzes the physical model in the power project implementation database and constructs a mapping matrix between the physical model and the logical model;

[0036] The physical model verification module captures real-time physical model data changes through database triggers when the physical model is updated, and verifies the mapping relationship between the physical model and the logical model through the mapping matrix;

[0037] The review and rectification module calculates the power project model compliance score based on the logical model difference report and mapping relationship verification results; and outputs the power project review report and rectification suggestions based on the power project model compliance score, logical model difference report and mapping relationship verification results.

[0038] On the other hand, the present invention also provides an electronic device having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for reviewing a power project model based on the SG-CIM model as described in any embodiment of the present invention is implemented.

[0039] On the other hand, the present invention also provides a computer-readable medium for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement a power project model review method based on the SG-CIM model as described in any embodiment of the present invention.

[0040] The present invention has the following beneficial effects:

[0041] Dynamic Adaptation and Real-Time Review: A dynamic rules engine built on SG-CIM automatically adapts to model standard version updates and expansions, eliminating the need to manually rewrite the rule base. Combined with database triggers, this enables real-time capture and review of changes to the physical model during the implementation phase, significantly improving timeliness.

[0042] Full lifecycle coverage and consistency assurance: Simultaneously handles the logical model during the design phase and the physical model during the implementation phase. By constructing a mapping matrix and automatically verifying consistency between the two in terms of table names, field types, and constraints (such as primary and foreign keys), accurate mapping from design to implementation is ensured, resolving the disconnect between logical and physical models.

[0043] Quantitative Assessment and Precise Corrective Action: A comprehensive compliance scoring formula is developed, integrating the logical model's fit (including semantic conflict penalties), the physical model's implementation quality (such as foreign key integrity), and the impact of version compatibility to generate an objective, quantitative score. The review report includes specific discrepancies, quantitative scores, corrective action suggestions (such as renaming, adding or deleting fields), and issue priority (such as mandatory items), providing precise guidance for closed-loop corrective actions.

[0044] Deep semantic reasoning capabilities: Adopting a three-level filtering algorithm (metadata, schema mapping, and semantic constraint reasoning layer), especially by converting logical constraints into RDF triples for ontology reasoning, it can deeply detect complex semantic conflicts (such as missing inheritance attributes) and business rules (such as data update frequency restrictions), providing a more comprehensive and in-depth review. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are to be executed.

[0049] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0050] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0051] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0052] Example 1:

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be combined with the specific embodiments of the present application and refer to the attached Figure 1 , clearly and completely describe the technical solution of the present invention.

[0054] This example uses the "Smart Meter Data Management System" project implemented by a power company as an example. This project aims to collect, store, and analyze smart meter data. During the design phase, UML tools were used to define logical models (such as class diagrams and sequence diagrams), and during the implementation phase, a MySQL database was used to store the physical models. The project requires that the model strictly adhere to the SG-CIM model library (version 2.0) to ensure data consistency and interoperability. The design documentation includes a "Smart Meter System Design Manual" in Word format, and the implementation database contains physical tables such as meter_data (meter data table) and customer_info (customer information table).

[0055] To solve the problems of the prior art, the present invention provides a power project model review method based on the SG-CIM model, comprising the following steps:

[0056] Step S1 extracts the logical model from the power project design document, builds a dynamic rule engine based on the grid public data SG-CIM model library, uses a three-level filtering algorithm to perform real-time or periodic rule matching on the logical model, and generates a logical model difference report;

[0057] As a preferred implementation of this embodiment, the logical model includes table structure, field definition and relationship constraints; the physical model includes physical table name, field type, index and storage parameters.

[0058] As a preferred implementation of this embodiment, the construction of the dynamic rule engine includes:

[0059] Extract the three elements of class attribute constraints, association relationships, and business rules from the SG-CIM model library;

[0060] Convert the three elements of class attribute constraints, association relationships, and business rules into SPARQL query templates;

[0061] Configure the rule trigger threshold to generate mandatory rectification items when the difference in the logical model exceeds the threshold.

[0062] As a preferred implementation of this embodiment, the three-stage filtering algorithm includes the following levels:

[0063] Entity type matching layer: Based on the core entity classes in the SG-CIM model library, the entity names in the logical model are extracted and the matching degree with the core class names is calculated through cosine similarity. Entities with matching degrees greater than the preset value enter the next layer;

[0064] Attribute specification verification layer: For entities that pass the entity type matching layer, the field naming rules are verified, and regular expressions are used to match the field encoding format. Fields that fail the verification are marked as naming violations.

[0065] Semantic constraint reasoning layer: Converts the relational constraints of the logical model into RDF triples, uses the Jena inference engine to load the SG-CIM model, verifies the consistency of the inheritance relationship with the business rules, and determines a semantic conflict if it is detected that the parent class does not declare the subclass attribute.

[0066] Step S101 extracts the logical model from the "Smart Meter System Design Manual" (a Word document). The logical model includes table structures (e.g., the Meter entity class), field definitions (e.g., the meter_id field is a string type), and relationship constraints (e.g., a one-to-many relationship between Meter and Customer). The extraction tool uses the Apache POI library to parse Word and output an RDF representation of the logical model.

[0067] Step S102: Based on the SG-CIM model library (version 2.0 loaded from the power grid public library), a dynamic rule engine is constructed:

[0068] Three elements are extracted from SG-CIM: class attribute constraints (e.g., the voltage attribute of the Meter class must be a floating-point type), association relationships (e.g., Meter inherits from the Device class), and business rules (e.g., the meter data update frequency is ≤ 1 time / minute).

[0069] Convert the three elements into a SPARQL query template. For example, the property constraint is converted to:

[0070] PREFIX cim:<http: / / iec.ch / TC57 / CIM#>

[0071] ASK WHERE{? meter a cim:Meter; cim:voltage? vol.FILTER(datatype(?vol)!=xsd:float)}

[0072] The rule trigger threshold is configured as difference > 10% (i.e., a mandatory rectification item is generated when more than 10% of entities do not match the SG-CIM).

[0073] Step S103: Use a three-level filtering algorithm to perform real-time or periodic rule matching on the logical model:

[0074] Entity type matching layer:

[0075] Extract entity names from the logical model (e.g., Meter, Customer) and calculate cosine similarity based on the SG-CIM core entity classes (e.g., cim:Meter). For example, the similarity between Meter and cim:Meter is 0.95 (preset value 0.8), passing the screening. The similarity between User (a custom class) and cim:User is 0.6, below the threshold, and marked as a mismatch.

[0076] Attribute specification verification layer:

[0077] For entities that pass the entity layer filter (such as Meter), field naming rules are verified. Use the regular expression ^[a-zA-Z_][a-zA-Z0-9_]{3,}$ to match the field encoding format. For example, meter_id meets the rules, but 123voltage (which begins with a number) fails verification and is marked as a naming violation.

[0078] Semantic Constraint Reasoning Layer:

[0079] Convert the relational constraints of the logical model (e.g., Meter inherits Device) into RDF triples. Use the Jena inference engine to load the SG-CIM model and verify consistency. It was detected that the logical model added a location attribute to Meter, but the SG-CIM parent class Device did not declare this attribute, resulting in a semantic conflict (a violation of business rules).

[0080] Step S104: Generate a logical model discrepancy report. The output report includes: 85% matching entity ratio (N_matched = 85, N_total = 100), 3 naming violations, and 2 semantic conflicts (D_semantic = 2). The report recommends remediation: rename the 123voltage field and remove the undeclared attribute location.

[0081] Step S2: collecting and parsing the physical model in the power project implementation database, and constructing a mapping matrix between the physical model and the logical model;

[0082] Step S201: Parse the physical model from the MySQL database. The physical model includes the physical table name (e.g., meter_data), field types (e.g., DECIMAL(10,2) for the voltage field), indexes (e.g., the meter_id primary key), and storage parameters (e.g., the InnoDB engine). Parsing uses JDBC to connect to the database and extract metadata.

[0083] Step S202: Create a mapping matrix between the logical model and the physical model. The matrix structure is a two-dimensional table, as shown in Figure 1:

[0084] Table 1 Schematic diagram of the mapping relationship between logical model and physical model

[0085]

[0086]

[0087] Step S3: When the physical model is updated, the real-time physical model data changes are captured through database triggers, and the mapping relationship between the physical model and the logical model is verified through the mapping matrix;

[0088] As a preferred implementation of this embodiment, verifying the mapping relationship between the physical model and the logical model through the mapping matrix specifically includes:

[0089] Verify the naming consistency between the logical model table name and the physical model table name;

[0090] Verification of conversion rules from logical model field types to physical model field types;

[0091] Integrity check of the primary key and foreign key constraints defined in the logical model in the physical model.

[0092] Step S301: Create a trigger in MySQL. When the physical model is updated (e.g., adding a new field location), the trigger captures the change event. Verify through the mapping matrix:

[0093] Naming consistency: The new field location is not defined in the logical model and is marked as inconsistent.

[0094] Type conversion: The logical model has no corresponding field and cannot be verified.

[0095] Sanity check: Add a new foreign key, cust_id, and verify that it is correctly implemented in the physical model (referencing the customer_info table).

[0096] Output verification result: 1 mapping relationship invalid item (location field illegal deviation).

[0097] Step S4, calculating the power project model compliance score based on the logical model difference report and the mapping relationship verification result; outputting the power project review report and rectification suggestions based on the power project model compliance score, the logical model difference report and the mapping relationship verification result.

[0098] Step S401: The specific calculation method of the logical model compliance and the physical model compliance is:

[0099]

[0100] Where: N matched The number of entities matched by the three-level filtering algorithm is 85; N total is the total number of logical model entities, which is 100; D semantic is the number of times the SG-CIM core rules are violated in the semantic conflict detection results, which is 2; k is the attenuation coefficient, which is 0.1; N correctFK N is the number of correctly implemented foreign keys in the physical model, which is 8; FK is the total number of foreign keys in the physical model, which is 10; N illegalDeviation is the number of illegal deviation fields in the physical model, which is 1; N totalFields is the total number of fields in the physical model, which is 50; δ is the foreign key integrity weight, which is 0.6; ε is the illegal deviation control weight, which is 0.4; and δ + ε = 1.

[0101] Step S402: Calculate the model compliance score of the "Smart Meter Data Management System" project based on the logical model compliance and the physical model compliance:

[0102] S=α·S L +β·S P +γ·VersionFactor

[0103] =0.5 0.696+0.5 0.872+0.1 1.0=0.884;

[0104] Where: S is the model compliance score; S L and S P are the logical model compliance score and the physical model compliance score respectively; α and β are the logical model compliance weight factor and the physical model compliance weight factor respectively; VersionFactor is the model version impact factor; γ is the model version impact weight;

[0105] Among them: The version impact factor is used to quantify the compatibility between the SG-CIM model library version and the project model version, and its value range is between [0,1]. The specific calculation method is as follows:

[0106]

[0107] Where: V project SG-CIM version number used for the power project model; V SG-CIM The version number of the SG-CIM model library loaded by the current dynamic rule engine; V major The major version number of the SG-CIM model library currently loaded by the dynamic rule engine; V project_major The major version number of SG-CIM used for the power project model.

[0108] When a project model uses an older version of SG-CIM, the factor value decays by 50% (exponentially) for every increase in the major version number difference, reflecting the disruptive impact of major version changes. Even with large jumps in major version numbers (e.g., |Δ| = 5), the factor is capped at a minimum of 0.1 to prevent the score from being completely invalidated. The dynamic rule engine automatically detects version differences upon startup, eliminating the need for manual configuration.

[0109] Step S403: Output the review report and rectification suggestions:

[0110] The compliance score S=0.884 (out of 1.0), rated as "good".

[0111] Logical model difference report: 3 naming violations and 2 semantic conflicts.

[0112] Mapping relationship verification result: 1 illegal deviation in the physical model.

[0113] Corrective suggestions: Rename the offending fields, add missing foreign keys, and remove the unmapped field location.

[0114] The report is output in PDF format and integrated into project management tools (such as Jira) to guide the development team in model optimization.

[0115] Example 2:

[0116] This embodiment provides a power project model review system based on the SG-CIM model, including:

[0117] The logical model comparison module extracts logical models from power project design documents, builds a dynamic rule engine based on the SG-CIM model library of power grid public data, uses a three-level filtering algorithm to perform real-time or periodic rule matching on the logical models, and generates a logical model difference report;

[0118] The mapping matrix construction module collects and analyzes the physical model in the power project implementation database and constructs a mapping matrix between the physical model and the logical model;

[0119] The physical model verification module captures real-time physical model data changes through database triggers when the physical model is updated, and verifies the mapping relationship between the physical model and the logical model through the mapping matrix;

[0120] The review and rectification module calculates the power project model compliance score based on the logical model difference report and mapping relationship verification results; and outputs the power project review report and rectification suggestions based on the power project model compliance score, logical model difference report and mapping relationship verification results.

[0121] Example 3:

[0122] This embodiment provides an electronic device having a computer program stored thereon. When the computer program is executed by a processor, the method for reviewing a power project model based on the SG-CIM model as described in any embodiment of the present invention is implemented.

[0123] Example 4:

[0124] This embodiment provides a computer-readable medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement a power project model review method based on the SG-CIM model as described in any embodiment of the present invention.

[0125] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c or a and b and c, where a, b, c can be single or multiple.

[0126] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0127] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0128] In the several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program code.

[0129] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A power project model review method based on the SG-CIM model, characterized in that: The following steps are involved: Extract logical models from power project design documents, build a dynamic rule engine based on the SG-CIM model library of power grid public data, use a three-level filtering algorithm to perform real-time or periodic rule matching on the logical model, and generate a logical model difference report; Collect and analyze the physical model in the power project implementation database and build a mapping matrix between the physical model and the logical model; When the physical model is updated, the real-time physical model data changes are captured through database triggers, and the mapping relationship between the physical model and the logical model is verified through the mapping matrix; Calculate the power project model compliance score based on the logical model difference report and mapping relationship verification results; Based on the power project model compliance score, logical model difference report and mapping relationship verification results, the power project review report and rectification suggestions are output.

2. The power project model review method based on the SG-CIM model according to claim 1, characterized in that: The logical model includes table structure, field definition and relationship constraints; the physical model includes physical table name, field type, index and storage parameters.

3. The power project model review method based on the SG-CIM model according to claim 1, characterized in that: The construction of the dynamic rule engine includes: Extract the three elements of class attribute constraints, association relationships, and business rules from the SG-CIM model library; Convert the three elements of class attribute constraints, association relationships, and business rules into SPARQL query templates; Configure the rule trigger threshold to generate mandatory rectification items when the difference in the logical model exceeds the threshold.

4. The power project model review method based on the SG-CIM model according to claim 1, characterized in that: The three-stage filtering algorithm includes the following levels: Entity type matching layer: Based on the core entity classes in the SG-CIM model library, the entity names in the logical model are extracted and the matching degree with the core class names is calculated through cosine similarity. Entities with matching degrees greater than the preset value enter the next layer; Attribute specification verification layer: For entities that pass the entity type matching layer, the field naming rules are verified, and regular expressions are used to match the field encoding format. Fields that fail the verification are marked as naming violations. Semantic constraint reasoning layer: Converts the relational constraints of the logical model into RDF triples, uses the Jena inference engine to load the SG-CIM model, verifies the consistency of inheritance relationships with business rules, and determines a semantic conflict if it is detected that the parent class does not declare the subclass attributes.

5. The power project model review method based on the SG-CIM model according to claim 1 is characterized by: Verifying the mapping relationship between the physical model and the logical model through the mapping matrix specifically includes: Verify the naming consistency between the logical model table name and the physical model table name; Verification of conversion rules from logical model field types to physical model field types; Integrity check of the primary key and foreign key constraints defined in the logical model in the physical model.

6. The power project model review method based on the SG-CIM model according to claim 1, characterized in that: The calculation formula for the power project model compliance score is: S=α·S L +β·S P +γ·VersionFactor; Where: S is the model compliance score; S L and S P are the logical model compliance score and the physical model compliance score respectively; α and β are the logical model compliance weight factor and the physical model compliance weight factor respectively; VersionFactor is the model version impact factor; γ is the model version impact weight.

7. The power project model review method based on the SG-CIM model according to claims 4 and 6 is characterized in that: The specific calculation method of the logical model compliance and the physical model compliance is: Where: N matched N is the number of entities matched by the three-level filtering algorithm; total is the total number of logical model entities; D semantic is the number of violations of the SG-CIM core rules in the semantic conflict detection results; k is the attenuation coefficient; N correctFK is the number of correctly implemented foreign keys in the physical model; N FK is the total number of foreign keys in the physical model; N illegalDeviation is the number of illegal deviation fields in the physical model; N totalFields is the total number of fields in the physical model; δ is the foreign key integrity weight; ε is the illegal deviation control weight.

8. A power project model review system based on the SG-CIM model, characterized in that: include: The logical model comparison module extracts logical models from power project design documents, builds a dynamic rule engine based on the SG-CIM model library of power grid public data, uses a three-level filtering algorithm to perform real-time or periodic rule matching on the logical models, and generates a logical model difference report; The mapping matrix construction module collects and analyzes the physical model in the power project implementation database and constructs a mapping matrix between the physical model and the logical model; The physical model verification module captures real-time physical model data changes through database triggers when the physical model is updated, and verifies the mapping relationship between the physical model and the logical model through the mapping matrix; Review and rectification module, calculate the power project model compliance score based on the logical model difference report and mapping relationship verification results; Based on the power project model compliance score, logical model difference report and mapping relationship verification results, the power project review report and rectification suggestions are output.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the power project model review method based on the SG-CIM model as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements a power project model review method based on the SG-CIM model as described in any one of claims 1 to 7.

Images