Engineering project multi-source heterogeneous data global consistency verification and version tracing method

Abstract

The invention discloses an engineering project multi-source heterogeneous data global consistency verification and version tracing method, which comprises the following steps: acquiring a multi-source heterogeneous data file, and carrying out data analysis and information extraction; constructing a unified data model based on a knowledge graph by using the extracted information; creating a new-version snapshot for the generated knowledge graph, and judging whether a corresponding old-version snapshot exists or not; if the old-version snapshot does not exist, performing compliance verification on the new-version snapshot based on a static verification rule; and if the old-version snapshots exist, calculating a change set between the new-version snapshots and the old-version snapshots, and performing compliance verification on the change set based on a dynamic process verification rule. According to the method, multi-source heterogeneous data (such as computer-aided design CAD drawings, building information models BIM, project documents and the like) can be automatically integrated, and semantic-based global consistency verification, refined version control and traceable engineering change process management are carried out on the multi-source heterogeneous data.

Description

Technical Field

[0001] This invention relates to the field of engineering data management technology, specifically to a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects. Background Technology

[0002] Large-scale engineering projects generate massive amounts of data in various formats throughout their long lifecycles. This data is scattered across different media: for example, CAD drawings (such as .dwg format) define the two-dimensional geometric information and topological connections of equipment; BIM models (such as .rvt, .ifc format) provide three-dimensional spatial parameters, material information, and the basis for collision detection; while a large number of project documents (such as .doc, .pdf, .xls format) carry key non-geometric constraint information such as equipment lists, technical specifications, and functional requirements.

[0003] Currently, ensuring consistency among these scattered data points relies primarily on time-consuming, expensive, and error-prone manual reviews. Engineers need to manually compare the equipment quantities in CAD drawings with the Excel list, or verify whether the equipment parameters in the BIM model meet the constraints defined in the PDF technical specifications. Any data inconsistencies can lead to design rework and material procurement errors, or even construction delays, cost overruns, and safety hazards. Therefore, intelligent drawing review technologies, such as Automated Compliance Checking, have emerged.

[0004] The core technological challenge of intelligent drawing review lies in solving two fundamental problems: first, the computability of the design information itself, i.e., how to enable computers to recognize and understand design schemes; and second, the computability of regulatory knowledge, i.e., how to enable computers to understand regulatory clauses and perform logical reasoning. However, existing technological solutions have significant limitations in addressing these challenges:

[0005] 1) Insufficient computability of design information: While Product Lifecycle Management (PLM) or Product Data Management (PDM) systems attempt to integrate with CAD / BIM tools, they often face issues such as data format incompatibility and synchronization delays. They typically manage information in files, making it difficult to delve into the content and semantic levels of these files for correlation and verification. For example, they cannot automatically verify whether a cable connection in a CAD drawing conforms to the safety specifications outlined in the project documentation. This keeps design information at a basic "digital" stage, lacking deep semantic understanding and model-based representation, making cross-source and cross-disciplinary consistency verification difficult.

[0006] 2) Limited computability of specification knowledge: Existing BIM model verification tools (such as Navisworks and Solibri) perform well in geometric conflict detection and IFC standard compliance checks. However, they typically embed specification knowledge in the system in a hard-coded form. This approach only achieves basic "perceptual intelligence" and lacks the ability to flexibly express and reason about complex and abstract specification knowledge, especially in automatically parsing and applying constraints implied in unstructured text.

[0007] 3) Lack of effective integration mechanisms and time-dimensional control: Existing technologies generally lack effective management of the evolution of project data status. When inconsistencies are found, it is difficult to trace which design change caused them; when conducting Engineering Change Management (ECM), it is also unable to automatically enforce rules regarding the compliance of the "change process" itself (e.g., the movement of critical equipment cannot exceed a specific threshold). Most importantly, there is a huge "semantic gap" between design information and specification knowledge. Existing technologies fail to provide an effective mechanism to bridge this gap and achieve automatic alignment and integration between the two. Furthermore, they generally lack effective management of the evolution of project data status and cannot perform dynamic rule verification at the "cognitive intelligence" level for the "change process" itself.

[0008] In summary, existing technologies have significant gaps in achieving deep semantic fusion of heterogeneous data and establishing dynamic process verification mechanisms in the time dimension based on this. There is an urgent need for a data consistency guarantee solution that can achieve automation, globalization, and traceability. Summary of the Invention

[0009] This application provides a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects, in order to solve the problems of lack of global consistency verification, difficulty in version tracing, and insufficient dynamic process control capabilities in existing technologies.

[0010] According to the first aspect, one embodiment provides a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects, the method comprising:

[0011] Acquire multi-source heterogeneous data files and perform data parsing and information extraction;

[0012] The extracted information will be used to construct a unified data model based on knowledge graphs.

[0013] Create a new version snapshot for the generated knowledge graph and determine if a corresponding old version snapshot exists;

[0014] If no old version snapshot exists, the new version snapshot will be validated for compliance based on static validation rules.

[0015] If an old version snapshot exists, the change set between the old and new version snapshots is calculated, and the change set is verified for compliance based on dynamic process verification rules.

[0016] Furthermore, acquire multi-source heterogeneous data files and perform data parsing and information extraction, specifically including:

[0017] Parse CAD / BIM files to extract geometric, parametric, and topological relationships;

[0018] Using an NLP-based semantic constraint extraction algorithm, constraints are automatically extracted from unstructured text and transformed into structured semantic triples: T =<E, P, C> Where E is the entity, P is the attribute, and C is the constraint expression.

[0019] Furthermore, the extracted information is used to construct a unified data model based on knowledge graphs, specifically including:

[0020] Predefine core entities, attributes, and relationships within the engineering domain, manage the knowledge graph schema, and provide formal interpretation and constraints for semantic fusion across data sources;

[0021] The extracted information is loaded into the tag attribute graph database to generate a knowledge graph.

[0022] Furthermore, a new version snapshot is created for the generated knowledge graph, specifically including:

[0023] Use the version control engine to create a new version snapshot of the current knowledge graph state;

[0024] Content-addressable storage is used to calculate the hash value for each node and edge in the knowledge graph and construct a Merkle tree. Finally, a root hash representing the data state of the entire knowledge graph is generated, which serves as the unique identifier for the new version snapshot.

[0025] Furthermore, the changeset between the old and new version snapshots is calculated, specifically including:

[0026] The version snapshot KG_V of the knowledge graph with version V is represented as a set of nodes and edges (N_V, E_V), where N_V represents nodes and E_V represents edges;

[0027] The change set Δ(V_1, V_2) between the old version V_1 and the new version V_2 is defined as follows:

[0028]

[0029] in:

[0030] Δ added To represent a newly added object, the format is:

[0031]

[0032] Δ deleted To delete an object, the format is:

[0033]

[0034] Δ modified This indicates a modified object: a node or edge x that exists in both versions, but whose attribute set A(x) has changed, in the form of:

[0035]

[0036] in:

[0037] N1 represents the set of all nodes contained in the knowledge graph snapshot of the old version V_1;

[0038] N2 represents the set of all nodes included in the knowledge graph snapshot of the new version V_2;

[0039] E1 represents the set of all edges or relations contained in the knowledge graph snapshot of the old version V_1;

[0040] E2 represents the set of all edges or relations contained in the knowledge graph snapshot of the new version V_2;

[0041] A(x) represents the set of attributes of node or edge object x;

[0042] This represents the set of attributes of a node or edge object x in the older version V_1.

[0043] This represents the set of attributes of a node or edge object x in the new version V_2.

[0044] Furthermore, the change set is validated based on dynamic process validation rules, specifically including:

[0045] Change range verification: Used to verify whether the change range of the numerical attribute P of a specified entity E between versions exceeds the preset maximum allowable deviation M;

[0046] Trend constraint verification: Used to verify whether the evolution trend of the project's core indicator I in a continuous set of versions meets the preset trend requirement R.

[0047] Furthermore, the method also includes:

[0048] Generates visual verification reports and supports interactive problem location and tracing.

[0049] Furthermore, the interactive problem localization and tracing specifically includes:

[0050] Interactive problem localization: When an inconsistency is detected, the problem object is automatically highlighted in the original data file by using the stored source file reference metadata, enabling rapid localization;

[0051] Version Comparison View: Provides a version comparison view interface that clearly displays detailed changes in project data between any two versions.

[0052] According to the second aspect, one embodiment provides a global consistency verification and version traceability system for multi-source heterogeneous data in engineering projects, the system comprising:

[0053] The data acquisition and extraction module is used to acquire multi-source heterogeneous data files and perform data parsing and information extraction.

[0054] The semantic fusion and modeling module is used to construct a unified data model based on knowledge graphs from the extracted information;

[0055] The version snapshot generation module is used to create new version snapshots for the generated knowledge graph and to determine whether a corresponding old version snapshot exists.

[0056] The rule verification execution module is used to perform compliance verification on the new version snapshot based on static verification rules if no old version snapshot exists.

[0057] If an old version snapshot exists, the change set between the old and new version snapshots is calculated, and the change set is verified for compliance based on dynamic process verification rules.

[0058] According to a third aspect, one embodiment provides an electronic device, the device comprising: a processor and a memory;

[0059] The memory is used to store one or more program instructions;

[0060] The processor is configured to run one or more program instructions to perform the steps of a method for global consistency verification and version tracing of multi-source heterogeneous data in an engineering project as described in any of the preceding claims.

[0061] According to a fourth aspect, one embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for global consistency verification and version tracing of multi-source heterogeneous data in an engineering project as described in any of the preceding claims.

[0062] This application provides a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects, which has the following beneficial effects:

[0063] 1. Achieved deep semantic fusion of global data: Through the knowledge graph model of the Unified Data Center (UDC), for the first time, previously isolated CAD geometric topology, BIM parameter information, and constraints extracted from unstructured documents using NLP technology were unified and associated at the semantic level. This provides a solid data foundation for achieving truly cross-domain, global, and automated consistency checks.

[0064] 2. It endows engineering data with a time dimension and high-fidelity traceability: Through Git-like version control management implemented via a Version Control Engine (VCE), the system not only saves every historical state of project data but also efficiently calculates the precise differences between any two versions. Combined with DLT technology, it provides a forensic-grade, tamper-proof audit chain, greatly enhancing the transparency and accountability of project management.

[0065] 3. Pioneering an automated execution paradigm for dynamic process constraints: The core innovation of this invention lies in executing dynamic rules (such as CHECK_CHANGE, CHECK_TREND) on the version increment (Δ) calculated by VCE through a rule engine (RE). This elevates quality assurance from the traditional, passive "checking whether the current state is compliant" to a proactive, continuous "controlling whether the change process is compliant," thereby effectively enforcing engineering change management (ECM) strategies and fundamentally preventing errors from occurring. Attached Figure Description

[0066] Figure 1 A flowchart illustrating a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects, provided as an embodiment of the present invention;

[0067] Figure 2 A schematic diagram of the overall implementation architecture of a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects, provided as an embodiment of the present invention;

[0068] Figure 3 A flowchart illustrating the implementation of a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects, as provided in one embodiment of the present invention;

[0069] Figure 4 This is a schematic diagram of the structure of the knowledge graph unified data model (UDM) in the unified data center (UDC) of an engineering project global consistency verification and version traceability method provided in an embodiment of the present invention;

[0070] Figure 5 This is a schematic diagram of the workflow of the Version Control Engine (VCE) in a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects, provided as an embodiment of the present invention.

[0071] Figure 6 This is a schematic diagram illustrating the logic of the rule engine (RE) executing different types of rules in a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects, provided as an embodiment of the present invention. Detailed Implementation

[0072] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of this application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to this application are not shown or described in the specification. This is to avoid obscuring the core parts of this application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0073] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0074] like Figure 1 As shown, the first embodiment of the present invention provides a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects. The present invention is committed to solving how to unify scattered geometric, parameter and unstructured text constraints into a machine-readable model, and to establishing a mechanism that can efficiently track and compare data state changes, thereby realizing automated rule verification of the "change process" itself.

[0075] First, the overall architecture supporting the implementation of the method of this invention will be described, such as... Figure 2 As shown, the main components are as follows:

[0076] 1. Unified Data Center (UDC): As the core data foundation of the system, it is used to extract, transform, and load multi-source heterogeneous data into a unified semantic model. This module adopts a Knowledge Graph (KG) architecture to establish a Unified Data Model (UDM). Internally, it includes:

[0077] Heterogeneous data extractor: It can deeply parse CAD / BIM files to extract geometric, parametric and topological relationships; at the same time, it uses natural language processing (NLP) technology (such as named entity recognition (NER) and relation extraction) to automatically extract constraints from unstructured text such as project documents and transform them into structured semantic triples.

[0078] Ontology Layer: Defines the core entities, attributes, and relationships within the engineering domain, manages the schema of the knowledge graph, and provides formal interpretation and constraints for semantic fusion across data sources.

[0079] 2. Version Control Engine (VCE): Used for efficient and granular version control of the overall data state within the Unified Data Center (UDC). This module introduces a time dimension to the evolution of design information and includes:

[0080] Project snapshot mechanism: It adopts a content-addressable storage model to create atomic, immutable snapshots for each state of the knowledge graph in UDC, and minimizes storage redundancy by sharing unchanged data blocks.

[0081] The Diff engine can efficiently calculate the exact change set (Delta, Δ) between any two version snapshots, clearly representing the changes of all semantic objects and their attributes in the form of "added", "deleted", and "modified".

[0082] Integrity enhancement module: Optionally, the cryptographic hash (such as Merkle Root) of each successfully verified snapshot is submitted to a Distributed Ledger Technology (DLT) network to form an immutable audit chain that can be used for judicial evidence collection.

[0083] 3. Rule Engine (RE): As the core of the system's verification process, it is used to define and execute multi-level consistency verification rules. This module supports two core rule types:

[0084] Static consistency rules: Verify the data status of a single version snapshot, such as "the protection level of all signal controllers must be no less than IP65".

[0085] Dynamic process rules: A key innovation of this invention, these rules take the change set Δ output by the Version Control Engine (VCE) as input to verify the compliance of the change process itself. For example, a CHECK_CHANGE rule can be defined to limit the magnitude of changes to key parameters between adjacent versions, or a CHECK_TREND rule can be defined to monitor the evolution trend of core project metrics (such as the total number of devices) across consecutive versions.

[0086] 4. Verification Report and Analysis Platform: Serving as the user interface, this platform visualizes verification results and provides in-depth problem localization and traceability capabilities. Its functions include:

[0087] Interactive problem localization and bidirectional highlighting: When an inconsistency is detected, the system uses the source file reference metadata (such as block handles, component GUIDs, and cell coordinates) stored in the UDC to automatically highlight the problem object synchronously in the original CAD, BIM, and document views, enabling rapid localization.

[0088] Version Comparison View: Provides an interface similar to code comparison, clearly showing the detailed changes in project data between any two versions.

[0089] 5. Core Algorithm and Model Definition

[0090] The core technology of this invention is supported by the following key algorithms and mathematical models:

[0091] 1) Semantic Constraint Extraction Algorithm (CEA) Based on NLP

[0092] The UDC module utilizes NLP technology to transform unstructured text constraints into machine-readable structured triples. This process is implemented by the Constraint Extraction Algorithm (CEA): Let S be the input text constraint statement. CEA maps it to a structured constraint triple T = through Named Entity Recognition (NER) and dependency parsing.<E, P, C> :

[0093]

[0094] in:

[0095] E: Entity, which is the feature of interest referred to by the rule, corresponding to a concept or instance in the UDM ontology O.

[0096] P: Property, which is the key attribute of the entity.

[0097] C: Constraint Expression, which consists of the extracted predicate and quantization value.

[0098] 2) Knowledge Graph Difference Calculation Model (KG Diff)

[0099] The core of the VCE module lies in efficiently and granularly calculating the knowledge graph difference Δ between any two versions V_1 and V_2.

[0100] The version snapshot KG_V of the knowledge graph with version V is represented as a set of nodes and edges (N_V, E_V), where N_V represents nodes and E_V represents edges;

[0101] The change set Δ(V_1, V_2) between the old version V_1 and the new version V_2 is defined as follows:

[0102]

[0103] in:

[0104] Δ added To represent a newly added object, the format is:

[0105]

[0106] Δ deleted To delete an object, the format is:

[0107]

[0108] Δ modified This indicates a modified object: a node or edge x that exists in both versions, but whose attribute set A(x) has changed, in the form of:

[0109]

[0110] in:

[0111] Meaning of N1: N1 is a set containing all nodes from the knowledge graph snapshot in the older version V_1. For example: nodes representing devices: { "Signal-01", "Signal-02", "Cable-C-101", ...}; nodes representing attribute values: { "Model TH-DS2", "Location (120.5, 30.2)", ...}, etc.

[0112] Meaning of N2: N2 is a set that contains all nodes in the knowledge graph snapshot of the new version V_2.

[0113] Meaning of E1: E1 is a set containing all edges or relations from the knowledge graph snapshot in the older version V_1. An edge is typically represented by a triple (head node, relation type, tail node).

[0114] Meaning of E2: E2 is also a set that contains the set of all edges in the knowledge graph snapshot of the new version V_2.

[0115] Δ_modified is a set in which every element x must satisfy all the conditions following '|'. The conditions following '|' are split into two parts, which are connected by the logical AND symbol ∧ (AND), meaning that both conditions must be true simultaneously.

[0116] A(x): Represents the set of attributes of object x.

[0117] If x is a node (e.g., “signal machine-01”), then A(x) is all the attributes carried by this node itself, such as {model: "TH-DS2", supplier: "CorpA"}.

[0118] If x is an edge (e.g., "connected to" a relation), then A(x) is the attribute carried by the edge itself (e.g., relation weight, the time when the relation occurred, if it exists).

[0119] This represents the set of attributes of a node or edge object x in the older version V_1.

[0120] This represents the set of attributes of a node or edge object x in the new version V_2.

[0121] 3) Temporal Check Model for Dynamic Process Constraints

[0122] The RE module utilizes the Δ provided by VCE to execute dynamic process rules.

[0123] Change Amplitude Check (CAC): The CHECK_CHANGE rule is implemented using the CAC model to verify whether the change in the numerical attribute P of a specified entity E between versions exceeds the preset maximum allowable deviation M. Details are as follows:

[0124] CAC(Δ(V_1, V_2), E, ​​P, M) = PASS if and only if for all n ∈ Δ_modified and n.type = E, |P(n)_V2 - P(n)_V1| ≤ M.

[0125] Trend Constraint Check (TCC): That is, the CHECK_TREND rule is implemented through the TCC model, which is used to verify whether the evolution trend of the core index I of the project in the continuous version set {V_{k - n},..., V_k} meets the preset trend requirement R (such as non-decreasing). Specifically as follows:

[0126] TCC({V_i}, I, R) = PASS if and only if for all V_j, (I(V_j) - I(V_{j - 1})) conforms to the trend R.

[0127] The following combines Figure 1 and Figure 3 to elaborate on a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects provided by an embodiment of the present invention.

[0128] As Figure 1 shown, in step S100, multi-source heterogeneous data files are obtained, and data parsing and information extraction are performed.

[0129] The above steps specifically include:

[0130] After the system starts, the heterogeneous data extractor of the unified data center (UDC) starts to work:

[0131] S110, CAD / BIM parsing: Use a professional parsing library (such as IfcOpenShell for IFC) to read the BIM model file, and extract parameters such as the unique identifier (GUID), three-dimensional coordinates, model number, and supplier of each "signal machine" component. At the same time, parse the CAD drawings, and extract the block handle, two-dimensional coordinates of the corresponding signal machine, and the cable topology relationship connected to it.

[0132] S120, unstructured document parsing: Start the NLP module to process the project "Technical Specification.pdf". The system applies the CEA model defined above to extract the structured constraint triple from the sentence "The protection level of all outdoor signal machines should not be lower than IP65": <ex: signal machine, ex: protection level, ">=IP65">.

[0133] As Figure 1 shown, in step S200, the extracted information is used to construct a unified data model based on a knowledge graph.

[0134] The above steps specifically include:

[0135] S210, knowledge graph construction: All the extracted information is loaded into a labeled property graph (LPG) database (such as Neo4j). As Figure 4As shown, a unique “Signal Machine-01” node is created and associated with all the information in CAD, BIM and PDF through different types of edges, and stores the reference metadata of the original file.

[0136] like Figure 1 As shown, in step S300, a new version snapshot is created for the generated knowledge graph, and it is determined whether a corresponding old version snapshot exists.

[0137] The above steps specifically include:

[0138] S310, Create Initial Version Snapshot: After all initial project data has been loaded and validated, the project manager executes the "Commit" operation. For example... Figure 5 As shown, the Version Control Engine (VCE) creates a snapshot V1.0 of the entire knowledge graph state (KG_0) in the current UDC. VCE uses content-addressed storage, calculates a hash value for each node and relation in the graph, and constructs a Merkle tree, ultimately generating a root hash representing the entire data state as the unique identifier of snapshot V1.0. At this point, since it is the first commit, the process directly enters rule validation.

[0139] S320, Implement Design Changes and Create a New Version: Based on new requirements, the design engineer moved the location of "Signal Machine-01" from (120.5, 30.2) to (121.0, 30.3) in the BIM software. After the modification, the engineer submitted the new BIM model file to the system. The system then ran the S100 and S200 processes again, updating the location attributes of the "Signal Machine-01" node in the knowledge graph. Subsequently, based on the S310 process, VCE created a new snapshot V1.1.

[0140] The next step is to perform a global consistency check, such as... Figure 6 As shown.

[0141] like Figure 1 As shown, in step S400, if there is no old version snapshot, the new version snapshot is validated for compliance based on static validation rules.

[0142] The above steps specifically include:

[0143] If the system does not detect the existence of the older version V1.0:

[0144] S410, trigger static rule validation: The RE uses snapshot V1.1 as input to execute rules in the static rule base. For example, it checks whether the protection level attribute of the "Signal Machine-01" node meets the constraint of >= IP65. Assuming this is met, the validation passes.

[0145] like Figure 1 As shown, in step S500, if an old version snapshot exists, the change set between the old and new version snapshots is calculated, and the change set is verified for compliance based on the dynamic process verification rules.

[0146] The above steps specifically include:

[0147] If the system detects the existence of an older version, V1.0:

[0148] S510, Change Increment Calculation: VCE's Diff engine applies the KG Diff model defined above, compares KG_1 and KG_0 through set operations, efficiently locates the unique change, and outputs the change set Δ, such as... Figure 5 As shown.

[0149] S520, Dynamic Process Rule Validation: The RE uses the change set Δ as input to execute the rules in the dynamic rule base.

[0150] Execute the CHECK_CHANGE rule: "CHECK_CHANGE: entity_type='signal', property='location', MAX_DELTA='5m'". RE applies the CAC model defined above to calculate the Euclidean distance d between the old and new locations:

[0151]

[0152] Since d is less than the threshold of 5 meters, this verification passes.

[0153] Execute the CHECK_TREND rule: "CHECK_TREND: entity_type='Cable', aggregate='count', TREND='non-decreasing'". The RE applies the TCC model defined above to verify the trend of the total number of cables. Since this change did not involve the number of cables, the total number remains unchanged, and this verification passes.

[0154] like Figure 1 As shown, in step S600, a visual verification report is generated, which supports interactive problem location and tracing.

[0155] The above steps specifically include:

[0156] S610, Result Generation and Presentation: Once all rules pass verification, the system generates a "Verification Successful" report.

[0157] S620, Problem Identification:

[0158] Suppose that in another change, the signal model in the Excel list was mistakenly changed to "TH-DS3", while it remains "TH-DS2" in the BIM model.

[0159] The static rule "CHECK_CONSISTENCY: entity_type='signal machine', property='model', sources=['BIM', 'Excel']" in RE will detect inconsistencies.

[0160] The verification report and analysis platform will generate an error report: "The model of signal 01 is inconsistent (BIM: 'TH-DS2', Excel: 'TH-DS3')".

[0161] When a user clicks on the error, the platform will call the BIM browser and Excel program, and use the stored GUID and cell coordinates to highlight the inconsistent cells in the BIM model and the Excel spreadsheet, helping engineers to instantly locate and fix the problem.

[0162] S630, Audit and Traceability

[0163] Later in the project, if it is necessary to audit the entire location change history of "Signal Machine-01", users can select the device as the focus in the platform's version comparison view. The system will then clearly list the changes to the device's location attributes in each version by querying VCE historical snapshots, along with the submitter, time, and verification results for each change. If the system integrates DLT, the root hashes of all historical versions are recorded on the distributed ledger, providing a mathematically immutable chain of evidence.

[0164] Corresponding to the above-disclosed method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects, this invention also discloses a system for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects, which specifically includes:

[0165] The data acquisition and extraction module is used to acquire multi-source heterogeneous data files and perform data parsing and information extraction.

[0166] The semantic fusion and modeling module is used to construct a unified data model based on knowledge graphs from the extracted information;

[0167] The version snapshot generation module is used to create new version snapshots for the generated knowledge graph and to determine whether a corresponding old version snapshot exists.

[0168] The rule verification execution module is used to perform compliance verification on the new version snapshot based on static verification rules if no old version snapshot exists.

[0169] If an old version snapshot exists, the change set between the old and new version snapshots is calculated, and the change set is verified for compliance based on dynamic process verification rules.

[0170] Furthermore, the system also includes:

[0171] The results generation and presentation module is used to generate visual verification reports and supports interactive problem location and tracing.

[0172] Furthermore, the interactive problem localization and tracing specifically includes:

[0173] Interactive problem localization: When an inconsistency is detected, the problem object is automatically highlighted in the original data file by using the stored source file reference metadata, enabling rapid localization;

[0174] Version Comparison View: Provides a version comparison view interface that clearly displays detailed changes in project data between any two versions.

[0175] It should be noted that for a detailed description of the global consistency verification and version traceability system for multi-source heterogeneous data in engineering projects provided by the embodiments of the present invention, please refer to the relevant description of the global consistency verification and version traceability method for multi-source heterogeneous data in engineering projects provided by the embodiments of this application, which will not be repeated here.

[0176] In addition, embodiments of the present invention also provide an electronic device, the device comprising: a processor and a memory; the memory being used to store one or more program instructions; the processor being used to execute one or more program instructions to perform the steps of the method for global consistency verification and version traceability of multi-source heterogeneous data in engineering projects as described in any of the preceding embodiments.

[0177] It should be noted that for a detailed description of an electronic device provided in the embodiments of the present invention, please refer to the relevant description of a method for global consistency verification and version traceability of multi-source heterogeneous data in engineering projects provided in the embodiments of this application, which will not be repeated here.

[0178] In addition, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for global consistency verification and version tracing of multi-source heterogeneous data in an engineering project as described in any of the preceding embodiments.

[0179] It should be noted that for a detailed description of a computer-readable storage medium provided in the embodiments of the present invention, please refer to the relevant description of a method for global consistency verification and version traceability of multi-source heterogeneous data in engineering projects provided in the embodiments of this application, which will not be repeated here.

[0180] Those skilled in the art will understand that all or part of the functions of the various methods in the above embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, which may include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to achieve the above functions. For example, the program can be stored in the memory of a device, and when the program in the memory is executed by the processor, all or part of the above functions can be achieved. In addition, when all or part of the functions in the above embodiments are implemented by computer programs, the program can also be stored in a server, another computer, disk, optical disk, flash drive, or external hard drive, etc., and can be downloaded or copied to the memory of a local device, or the system of the local device can be updated. When the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be achieved.

[0181] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects, characterized in that, The method includes: Acquire multi-source heterogeneous data files and perform data parsing and information extraction; The extracted information will be used to construct a unified data model based on knowledge graphs. Create a new version snapshot for the generated knowledge graph and determine if a corresponding old version snapshot exists; If no old version snapshot exists, the new version snapshot will be validated for compliance based on static validation rules. If an old version snapshot exists, the change set between the old and new version snapshots is calculated, and the change set is verified for compliance based on dynamic process verification rules.

2. The method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects as described in claim 1, characterized in that, Acquire multi-source heterogeneous data files and perform data parsing and information extraction, specifically including: Parse CAD / BIM files to extract geometric, parametric, and topological relationships; Using an NLP-based semantic constraint extraction algorithm, constraints are automatically extracted from unstructured text and transformed into structured semantic triples: T =<E, P, C> Where E is the entity, P is the attribute, and C is the constraint expression.

3. The method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects as described in claim 1, characterized in that, The extracted information will be used to construct a unified data model based on knowledge graphs, specifically including: Predefine core entities, attributes, and relationships within the engineering domain, manage the knowledge graph schema, and provide formal interpretation and constraints for semantic fusion across data sources; The extracted information is loaded into the tag attribute graph database to generate a knowledge graph.

4. The method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects as described in claim 1, characterized in that, Create a new version snapshot for the generated knowledge graph, specifically including: Use the version control engine to create a new version snapshot of the current knowledge graph state; Content-addressable storage is used to calculate hash values ​​for each node and edge in the knowledge graph and construct a Merkle tree. Finally, a root hash representing the data state of the entire knowledge graph is generated, which serves as the unique identifier for the new version snapshot.

5. The method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects as described in claim 1, characterized in that, Calculate the change set between the old and new version snapshots, specifically including: The version snapshot KG_V of the knowledge graph with version V is represented as a set of nodes and edges (N_V, E_V), where N_V represents nodes and E_V represents edges; The change set Δ(V_1, V_2) between the old version V_1 and the new version V_2 is defined as follows: in: Δ added To represent a newly added object, the format is: Δ deleted To delete an object, the format is: Δ modified This indicates that the modified object is a node or edge object x that exists in both versions, but its attribute set A(x) has changed, in the form of: in: N1 represents the set of all nodes contained in the knowledge graph snapshot of the old version V_1; N2 represents the set of all nodes included in the knowledge graph snapshot of the new version V_2; E1 represents the set of all edges or relations contained in the knowledge graph snapshot of the old version V_1; E2 represents the set of all edges or relations contained in the knowledge graph snapshot of the new version V_2; A(x) represents the set of attributes of node or edge object x; This represents the set of attributes of a node or edge object x in the older version V_1. This represents the set of attributes of a node or edge object x in the new version V_2.

6. The method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects as described in claim 1, characterized in that, The changeset is validated based on dynamic process validation rules, specifically including: Change range verification: Used to verify whether the change range of the numerical attribute P of a specified entity E between versions exceeds the preset maximum allowable deviation M; Trend constraint verification: Used to verify whether the evolution trend of the project's core indicator I in a continuous set of versions meets the preset trend requirement R.

7. The method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects as described in claim 1, characterized in that, The method further includes: Generates visual verification reports and supports interactive problem location and tracing.

8. The method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects as described in claim 7, characterized in that, The interactive problem location and tracing specifically includes: Interactive problem localization: When an inconsistency is detected, the problem object is automatically highlighted in the original data file by using the stored source file reference metadata, enabling rapid localization; Version Comparison View: Provides a version comparison view interface that clearly displays detailed changes in project data between any two versions.

9. A system for global consistency verification and version traceability of multi-source heterogeneous data in engineering projects, characterized in that, The system includes: The data acquisition and extraction module is used to acquire multi-source heterogeneous data files and perform data parsing and information extraction. The semantic fusion and modeling module is used to construct a unified data model based on knowledge graphs from the extracted information; The version snapshot generation module is used to create new version snapshots for the generated knowledge graph and to determine whether a corresponding old version snapshot exists. The rule verification execution module is used to perform compliance verification on the new version snapshot based on static verification rules if no old version snapshot exists. If an old version snapshot exists, the change set between the old and new version snapshots is calculated, and the change set is verified for compliance based on dynamic process verification rules.

10. An electronic device, characterized in that, The device includes: a processor and a memory; The memory is used to store one or more program instructions; The processor is configured to run one or more program instructions to perform the steps of a method for global consistency verification and version tracing of multi-source heterogeneous data in engineering projects as described in any one of claims 1 to 6.

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