Construction design data management method and system

Abstract

The invention discloses a construction design data management method and system, and relates to the technical field of data management, and the method comprises the steps: obtaining construction design data, employing a standardization conversion engine to unify the format and verify the integrity, and outputting the standardization design data; performing access control on the standardized design data by adopting multi-level authority management, and outputting an operation auditing record in combination with a digital signature method of Hash calculation and public key infrastructure; based on the change data in the operation auditing record, utilizing a dynamic data flow updating mechanism to adjust an entity relationship in the construction knowledge graph in real time, and generating a digital twinborn synchronization instruction; and performing security evidence storage on the operation audit record and the digital twinborn synchronization instruction through a quantum encryption protocol, performing data consistency verification in combination with multi-dimensional distributed AI cooperative computing, and outputting an audit closed-loop management report. According to the method, dual verification of dominant rule check and implicit semantic analysis is realized through combination of the dynamic mapping table and deterministic logic.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, and particularly relates to a construction design data management method and system. BACKGROUND

[0002] At present, construction design data management mostly adopts a centralized architecture based on a relational database, and combines static rules to perform data verification. At present, multi-source data format conversion is usually realized through an ETL tool, and structured processing is performed using pre-defined industry standard codes. In terms of compliance verification, the mainstream scheme adopts hard rule matching, supplemented by a manual review mechanism to handle un-covered specification clauses. Permission management mostly uses a role-based access control model, and data synchronization generally adopts a full backup or timed incremental synchronization strategy.

[0003] However, the current construction design data management can only process explicit rules in structured data, and cannot effectively analyze implicit constraints in unstructured specification texts, so it is an important problem to realize double verification of explicit rule checking and implicit semantic analysis. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a construction design data management method to solve the problems of low dynamic compliance verification coverage and poor multi-source data synchronization efficiency in construction design data management.

[0006] To solve the above technical problems, the present application provides the following technical solutions. In a first aspect, the present application provides a construction design data management method, which comprises: acquiring construction design data, and uniformly formatting and verifying integrity by using a standardization conversion engine to output standardized design data; inputting the standardized design data into a construction knowledge graph for dynamic compliance verification, performing feature extraction by principal component analysis (PCA), and performing semantic analysis by combining a deep learning model to output an executable correction scheme; performing access control on the standardized design data by using multi-level permission management, combining a hash calculation and a digital signature method of a public key infrastructure to output an operation audit record; based on change data in the operation audit record, using a dynamic data flow update mechanism to real-time adjust entity relationships in the construction knowledge graph to generate a digital twin synchronization instruction; performing secure evidence storage on the operation audit record and the digital twin synchronization instruction by using a quantum encryption protocol, performing data consistency verification by combining multi-dimensional distributed AI collaborative computing to output an audit closed-loop management report.

[0007] As a preferred embodiment of the construction design data management method of the present invention, the steps of acquiring construction design data, using a standardized conversion engine to unify the format and verify its integrity, and outputting standardized design data are as follows: Data is collected through an application programming interface, and data extraction is used to perform preliminary format conversion and add basic metadata to output a structured raw dataset. Data is encoded using a rule engine and construction industry standard classifications, and the execution units are uniformly converted. Required fields are validated through predefined rules, and a standard dataset is output. By combining database constraints and validation scripts, check data correlation, verify basic business rules, and output standardized design data.

[0008] As a preferred embodiment of the construction design data management method described in this invention, the steps of inputting standardized design data into a construction knowledge graph for dynamic compliance verification, extracting features through principal component analysis (PCA), and performing semantic analysis using a deep learning model to output an executable correction scheme are as follows. Standardized design data is converted into structured triples through semantic parsing, and features are extracted using principal component analysis (PCA). The data is then matched with specification nodes in the construction knowledge graph through graph query, and rules are added to supplement unmatched items to generate a dynamic mapping table. Based on dynamic mapping tables and combined with deep learning models, we conduct in-depth analysis of design data, identify potential compliance issues, and build compliance diagnostic reports. A parameterized adjustment algorithm based on rule-based automatic correction is used to generate standard correction schemes by combining structured templates, and an executable correction scheme is output.

[0009] As a preferred embodiment of the construction design data management method described in this invention, the step of employing multi-level access control to manage standardized design data, and combining hash calculation and public key infrastructure digital signature methods to output operation audit records, includes the following specific steps: Based on the executable modification scheme, operation requests for standardized design data are filtered through a multi-level permission verification interface. The distributed log collection framework is used to collect the passed operation behavior data in real time and output the raw operation event stream. Using a public key infrastructure-based digital signature method, hash calculations and digital signatures are performed on each operation record to construct a structured digital signature operation audit record; Combined with a stream processing engine, perform real-time compliance checks on the structured operation audit logs of digital signatures and output the operation audit logs.

[0010] As a preferred embodiment of the construction design data management method described in this invention, the step of adjusting entity relationships in the construction knowledge graph in real time based on change data in operation audit records and generating digital twin synchronization instructions, using a dynamic data flow update mechanism, includes the following specific steps. Digital signatures are used to verify the integrity of operation audit records, and structured parsing is used to extract operation types, entity IDs, and change content, outputting a standardized change dataset. Based on the standardized change dataset, the affected subgraph is located by graph traversal query, and the entity relationship in the construction knowledge graph is adjusted in real time by combining the dynamic data flow update mechanism. The construction knowledge graph is recorded with a directed acyclic graph version management system to record the change history, and the updated construction knowledge graph is constructed. The comparison results of the old and new states of the construction knowledge graph are output. The comparison results of the old and new states of the knowledge graph are compressed using binary differential encoding, and then pushed to the digital twin in a hierarchical manner using the MQTT protocol to generate synchronization instructions for the digital twin.

[0011] As a preferred embodiment of the construction design data management method described in this invention, the steps include: encrypting and storing operation audit records and digital twin synchronization instructions using blockchain evidence storage, verifying data consistency by combining edge cloud collaborative computing, and outputting an audit closed-loop management report. The specific steps are as follows: The operation audit records and digital twin instructions are encrypted using a quantum encryption protocol, and fragmented storage is used to split the data into segments and put them on the blockchain, outputting the blockchain evidence hash value and timestamp proof. Deploy verifiable random functions to assign verification tasks, use multi-dimensional distributed AI collaborative algorithms for cross-node verification, use differential hash comparison to detect whether the original data corresponding to the blockchain evidence hash value has been tampered with and whether the timestamp proof matches, and output consistency verification results. The backpropagation tracing algorithm is used to locate problem nodes, zero-knowledge proof is used to generate verification evidence, and combined with smart contracts, an audit closed-loop management report is output.

[0012] As a preferred embodiment of the construction design data management method described in this invention, the operation audit records and digital twin instructions are encrypted using a quantum encryption protocol, and fragmented storage is used to complete the data uploading to the blockchain, outputting the blockchain evidence hash value and timestamp proof. The specific steps are as follows. The operation audit logs and digital twin instructions are encrypted using a quantum encryption protocol to generate an encrypted data stream, which is then fragmented to generate multiple data slices. Data is sharded and distributed to blockchain nodes, and then uploaded to the blockchain through a consensus mechanism to generate on-chain transaction records. Based on on-chain transaction records, an irreversible blockchain notarization hash value is generated through secure hashing, and timestamp proof is obtained using the inherent distributed timestamps of the blockchain network.

[0013] Secondly, the present invention provides a management system for construction design data, including a data processing module, a compliance verification module, an access control module, a map update module, and an evidence storage and auditing module; The data processing module is used to acquire construction design data, use a standardized conversion engine to unify the format and verify the integrity, and output standardized design data. The compliance verification module is used to input standardized design data into the construction knowledge graph for dynamic compliance verification, extract features through principal component analysis (PCA), and perform semantic analysis in conjunction with a deep learning model to output an executable correction scheme. The permission management module is used to perform access control on standardized design data using multi-level permission management, and outputs operation audit records by combining hash calculation and digital signature methods of public key infrastructure. The graph update module is used to adjust the entity relationships in the construction knowledge graph in real time based on the change data in the operation audit record and using a dynamic data flow update mechanism to generate digital twin synchronization instructions. The evidence storage and auditing module is used to securely store operation audit records and digital twin synchronization instructions through quantum encryption protocol, perform data consistency verification by combining multi-dimensional distributed AI collaborative computing, and output an audit closed-loop management report.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the construction design data management method as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the construction design data management method as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: by combining a dynamic mapping table with deterministic logic, dual verification of explicit rule checks and implicit semantic analysis is achieved, thereby improving the compliance verification coverage. Dynamic role allocation and multi-level access control are adopted to adjust permissions in real time based on operation behavior, thereby reducing permission conflicts. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Fig. 1 A flowchart for the management method of construction design data.

[0019] Fig. 2 A schematic diagram of a management system for construction design data.

[0020] Fig. 3 A flowchart for data standardization.

[0021] Fig. 4 A flowchart of the core verification process for blockchain-based evidence storage. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figs. 1-4 As one embodiment of the present invention, this embodiment provides a method for managing construction design data, including the following steps: S1. Obtain construction design data, use a standardized conversion engine to unify the format and verify the integrity, and output standardized design data.

[0026] Furthermore, construction design data is collected through an application programming interface (API), and data extraction is used for preliminary format conversion and basic metadata is added to output a structured raw dataset.

[0027] Specifically, the application interface request parameters, including project number and time range, are used in the presentation layer state transition. A paginated data processing mechanism is employed, and the complete dataset is obtained by repeatedly identifying the next page identifier in the returned data. An exception handling mechanism is established, implementing an exponential backoff retry strategy for interface rate limiting and other situations. The original project data file and interface call logs are output. The Nafi data flow management tool is used to clean and standardize the original project data file, standardizing the mapping of execution field names, implementing unified data type conversion, and using the UDUNITS-2 unit conversion library to convert imperial units to the International System of Units (SI). Structured intermediate data files and data conversion process logs are output. Based on W3C standards... Based on the standard definition of metadata fields in the catalog glossary, standard metadata is dynamically generated, including data source identifiers, data extraction timestamps, and data integrity verification values. Data tracking information is also attached, and the binary encoding of the metadata header information is stored using a protocol buffer. The output is a structured dataset and a metadata dictionary document. The GE data validation tool is used to perform quality checks on the structured dataset. Based on the data types and field constraints defined in the Avro format, data quality rule verification and abnormal data handling are performed on the structured dataset. This includes field missing rate threshold control (e.g., field missing rate not exceeding 5%), numerical range validity verification, automatic filling of predefined default values, and marking abnormal data records for manual review. The output is the original structured dataset.

[0028] It should be noted that the exception handling mechanism includes exception classification: interface call exceptions such as API rate limiting, network interruption, and data format errors; data conversion exceptions such as missing fields, unit mismatches, and encoding errors; business rule validation exceptions such as constraint violations and numerical range exceeding limits; exception capture and handling process: interface call exception handling such as exponential backoff retry strategy and data format validation; data conversion exception handling such as unit unification fault tolerance and encoding mapping failure handling (calling fuzzy matching algorithm, automatically querying the related table, setting it to NULL and marking data integrity exception if there is no default value); business rule validation exception handling such as constraint violation and numerical range validation; exception classification and response: low risk such as missing fields and slight unit differences, automatic repair and logging; medium risk such as encoding mismatch and numerical exceeding limits ±10%, suspending the process and waiting for confirmation; high risk such as foreign key conflicts and suspected data tampering, freezing data and triggering security audit; exception logs and source tracing: log recording and source tracing analysis.

[0029] Ideally, standardized data collection through application programming interfaces improves data real-time performance and compatibility, data cleaning using Nafi enhances data consistency and machine readability, and quality verification based on GE data verification tools improves data reliability and risk controllability.

[0030] Data is encoded using a rule engine and standard classifications for the construction industry, with unified conversion of execution units. Required fields are validated using predefined rules, and a standard dataset is output.

[0031] Specifically, the system uses the Drus rule engine to call the building industry standard classification library, inputs a structured raw dataset, and uses the Drus rule engine to parse features such as component type and cross-sectional attributes. It matches the standard coding library to generate specification codes, generates a list of unmatched items to be reviewed, and outputs a structured raw dataset with standard codes. Utilizing the UDUNITS-2 unit library, it automatically identifies field unit types, calls pre-replacement rules, performs unit standardization conversion, and marks data exceeding ±1% tolerance as anomalies, outputting a unit-based dataset. Based on shape constraint language, it verifies field integrity, triggers compensation mechanisms for missing fields (automatically filling in default values ​​if they exist in the related tables, and setting null values ​​and recording them if no default values ​​exist), outputting a complete dataset. Combining Git version control and Jenkins automated operation and maintenance tools, it detects new specification releases, incrementally updates the rule library, tests and hot-loads it into the production environment, builds rule execution audit reports and dynamic rule library snapshots, and outputs a standard dataset.

[0032] It should be noted that the Drus rule engine stores the building industry standard classification in the form of rules in the knowledge base, supports dynamic loading, and automatically triggers rules that meet the conditions through pattern matching, such as verifying whether the component type conforms to the national standard classification. The best approach is to automatically match industry standard codes through the Drus rules engine, which improves the standardization and interoperability of the data; to achieve intelligent conversion using the UDUNITS-2 unit library, which enhances the consistency of the data and engineering compliance; and to use shape constraint language to ensure that required fields are complete, which strengthens the reliability and integrity of the data.

[0033] By combining database constraints and validation scripts, check data correlation, verify basic business rules, and output standardized design data.

[0034] Specifically, foreign key constraints and check constraints are defined in the database constraints. These constraints are enforced by modifying table statements. Foreign key constraints and check constraints are automatically triggered when a standard dataset is written. Error codes are generated for records that violate foreign key constraints and check constraints. A list of valid data that passes the foreign key constraints and check constraints and a list of records that violate the constraints are output. Verification scripts and a pre-set verification rule library are used to perform correlation checks (such as verifying the temporal relationship between components and construction procedures, and detecting circular dependencies through graph queries) and business rule verification (such as verifying beam reinforcement ratios and steel structure height-to-thickness ratio limits), and a verification report is output. Pre-set corrections are performed on quantifiable errors in the verification report. Logical errors are tagged, and an automatic correction pipeline is triggered for numerical errors. Logical errors are pushed to the collaborative platform for confirmation, and the corrected design data and a list of data awaiting manual review are output. A snapshot of each verification data is stored in Git large files, and a secure hash algorithm (-256 bits) ensures the data is immutable. After verification, a design data version is submitted, and standardized design data is output.

[0035] It should be noted that foreign key constraints are used to maintain referential integrity between tables, ensuring that a field value in one referencing table must match the primary key or unique key value in another referenced table; check constraints are used to limit the business rule range of field values, ensuring that data conforms to predefined logical conditions; a pre-built validation rule base is constructed by obtaining publicly available specification documents through web crawling technology, connecting with the enterprise to extract approval opinions data from historical design drawings, establishing rule priorities (e.g., national standards are higher than industry standards, which are higher than enterprise standards), using the World Wide Web ontology language to describe the inheritance and overriding relationships between rules, and combining a dynamic loading mechanism.

[0036] Ideally, database constraint validation ensures data relevance and integrity, improving data structure standardization and reference consistency; validation scripts enhance data business compliance and engineering reliability; and correction mechanisms handle quantifiable errors, improving data accuracy and usability.

[0037] S2. Input standardized design data into the construction knowledge graph for dynamic compliance verification, extract features through principal component analysis (PCA), and perform semantic analysis using a deep learning model to output an executable correction scheme.

[0038] Furthermore, standardized design data is converted into structured triples through semantic parsing, and principal component analysis (PCA) is used for feature extraction. Then, the specification nodes in the construction knowledge graph are matched through graph query, and rules are added to supplement unmatched items to generate a dynamic mapping table.

[0039] Specifically, standardized design data is converted into structured triples (entity data, relation data, and attribute data) through semantic parsing, transforming textual information into a standard expression of entity, relation, and attribute information. Principal Component Analysis (PCA) is used to extract features from the structured triples, reducing their dimensionality through linear transformation while retaining key feature vectors. The extracted key feature vectors are then matched against specification nodes in the construction knowledge graph using graph queries. A graph traversal algorithm is used to find the corresponding specification clauses and their node relationships within the knowledge graph. For parts where no specification node is matched, rules are added based on the classification of building industry standards, transforming missing constraints into new graph nodes or edge relationships. Finally, a dynamic mapping table records the correspondence between standardized design data and construction knowledge graph nodes, along with the supplementary rules, forming an updatable dynamic mapping table.

[0040] It should be noted that textual information refers to the natural language descriptions and unstructured content contained in construction design data. Specifically, this includes non-numerical expressions such as textual descriptions in design drawings, textual descriptions of specification clauses, and written requirements for material specifications.

[0041] Graph traversal algorithms explore the node and edge structure in the construction knowledge graph using depth-first search or breadth-first search strategies. This is used to identify specification nodes that match the main feature vectors and their relationships, ultimately achieving accurate location and mapping of specification clauses.

[0042] Based on dynamic mapping tables and combined with deep learning models, we conduct in-depth analysis of design data, identify potential compliance issues, and build compliance diagnostic reports.

[0043] Specifically, based on a dynamic mapping table and combined with a deep learning model, standardized design data and the dynamic mapping table are used as input features. A multi-layer neural network structure in the deep learning model performs nonlinear feature transformation to extract complex patterns implicit in the standardized design data. The deep learning model analyzes the parameter correlations recorded in the dynamic mapping table, detects the degree of deviation between design parameters and specification clauses, and identifies potential compliance issues such as exceeding component size limits and material strength mismatches. The deep learning model outputs diagnostic indicators including problem type, violation level, and impact scope, ultimately generating a structured compliance diagnostic report. The report content covers problem location, specification basis, and risk level assessment.

[0044] Furthermore, the deep learning model construction process involves collecting historical construction design data and their corresponding compliance labels as a training set. Then, convolutional neural network or recurrent neural network architecture is used to initialize inter-layer parameters. The backpropagation algorithm is used to iteratively optimize the loss function to minimize the error between the prediction result and the true label, ultimately generating a deep learning model that can automatically identify potential compliance issues in the design data.

[0045] A parameterized adjustment algorithm based on rule-based automatic correction is used to generate standard correction schemes by combining structured templates, and an executable correction scheme is output.

[0046] Specifically, the rule-based automatic correction parameterized adjustment algorithm automatically calls the corresponding correction rules according to the executable correction scheme, queries the building materials market database to obtain optional specifications and combines them with the construction process library to screen feasible implementation schemes, outputs three parameter combinations that meet the specifications, and constructs a parameter adjustment scheme table; establishes an economic (e.g., calculating material price difference × project quantity), safety (compliance score against specifications), and feasibility score (feasibility assessment of construction teams) system, uses a linear weighted method to calculate the correction optimization score, and outputs an optimization scheme report; uses a structured template to pre-set a construction instruction template library and executes the construction instructions, outputting an executable correction scheme.

[0047] It should be noted that the formula for calculating the corrected optimal score is as follows: ; in, To correct the overall score of the best option, For economic efficiency, For safety rating, This is used to score feasibility.

[0048] Ideally, an expert rule base can be used to improve the accuracy of corrections, enhance the binding force of regulations, and improve the compliance and adaptability of design parameters; parametric templates can be used to improve the feasibility of solutions, enhance the matching degree of construction processes, and improve the rationality of building material selection; weighted scoring methods can be used to improve the scientific nature of decision-making; structured instructions can be used to improve execution efficiency and enhance the accuracy of construction briefings.

[0049] S3. Multi-level access control is used to manage access to standardized design data. Combined with hash calculation and digital signature methods of public key infrastructure, operation audit records are output.

[0050] Furthermore, based on the executable modification scheme, operation requests for standardized design data are filtered through a multi-level permission verification interface, and the distributed log collection framework is used to collect the passed operation behavior data in real time and output the raw operation event stream.

[0051] Specifically, dedicated lightweight agents are deployed on each data source node to continuously monitor operation log files, transaction logs of standardized design data, and API call records of the collaborative design platform, outputting raw log data. The raw log data undergoes preliminary structuring processing. For unstructured logs, regular expressions are used to match key information and convert them to JSON format. Source identifiers and collection time metadata are automatically added to each processed structured log record. The structured raw log data is sent to a distributed message queue middleware. The message queue is divided into different topic channels based on operation type, with multiple partitions set for each topic channel. Data storage locations are allocated according to the hash value of the design document ID, and a message persistence mechanism is used to ensure data is not lost during network interruptions. The message queue producer is configured with message retry mechanisms, message ordering guarantees, and message compression transmission. The stream processing engine consumes raw log data from the message queue and performs time standardization, data association, geographic tagging, and operation classification, outputting an initial event stream. Basic validation, business rule validation, and integrity validation are performed on the initial data stream, outputting the raw operation event stream.

[0052] It should be noted that the basic validation includes: field non-empty checks, verifying whether required log fields such as operation time and user ID are missing; data type matching, verifying whether numeric fields such as coordinate values ​​conform to floating-point format, and whether text fields such as document ID match regular expressions; time reasonableness, determining whether the operation timestamp is within the system's valid range; business rule validation: design specification association, calling the building specification clause library to verify whether the operation parameters are compliant; workflow status check, confirming that the operation conforms to the drawings; permission matrix verification, verifying whether the user role has the execution permission; integrity validation: operation chain tracing, verifying whether the associated operations are complete through transaction ID; and time sequence consistency, checking whether the time sequence of dependent operations is reasonable.

[0053] Ideally, a distributed message queue should be used to improve data reliability, enhance data integrity and fault tolerance; a streaming processing architecture should be used to reduce latency and improve throughput; and a standardized log format should be used to improve fault location speed and increase resource utilization.

[0054] Using a public key infrastructure-based digital signature method, each operation record is hashed and digitally signed to construct a structured digital signature operation audit record.

[0055] Specifically, the original operation event stream is encoded and converted to UTF-8. ASCII control characters are removed through special character filtering. The newline characters of different operations are identified using a character encoding detection library to determine the encoding format of the original operation event stream, removing all control characters within the ASCII range. Newline characters are normalized to standard newline characters, and a standardized text data file and data preprocessing log are output. The SM3 hash algorithm engine is initialized, reading the standardized text data file in 1MB blocks. A 256-bit binary hash value is generated through block-by-block calculation and converted to a 64-bit hexadecimal string format. The hash calculation result and hash calculation metadata are output. The pre-stored signature private key is called through the encryption machine's standard interface to configure SM3. As a hash algorithm, it performs a signature operation containing the user's identity identifier, generates signature data, performs Base64 encoding conversion, and outputs digital signature data, signature certificate serial number, and signature timestamp record. It constructs a request message containing digital signature data, signature certificate serial number, and signature timestamp record, establishes a transport layer security protocol, submits a request to the time synchronization center and obtains a response, verifies the validity of the timestamp signature, and outputs an authoritative timestamp credential, the certificate trust chain of the timestamp authority, and the timestamp serial number. According to a predefined JSON template structure, it standardizes and assembles elements such as hash calculation results, digital signature data, and timestamp credential record, embeds the signer's timestamp authority certificate trust chain information, generates a distributed unique identifier, and constructs a structured audit record for the digital signature.

[0056] A better approach is to enhance tamper resistance and improve the integrity of verification efficiency by using the SM3 hash algorithm and block processing technology.

[0057] Combined with a stream processing engine, perform real-time compliance checks on the structured operation audit logs of digital signatures and output the operation audit logs.

[0058] Specifically, the system subscribes to audit event topics through Kafka consumer groups, retrieves data in partition order, validates the data format using a predefined JSONSchema, extracts fields such as event ID, operation type, and operation object to construct event objects, and appends receiving timestamps and message offset information, outputting structured event data and data format anomaly reports. It loads predefined compliance rules from the rules database, constructs a rule matching state machine including basic validation rules, business rules, and session rules, and performs multi-level rule matching on each event in the structured event data, outputting rule trigger records and rule matching statistical reports. It configures a basic risk score for each rule in the rule trigger records and rule matching statistical reports, calculates the dynamic weight of the basic risk score using a weighted scoring algorithm, aggregates based on time windows, and outputs risk scoring results and risk level classification reports. It predefines the mapping relationship between risk level classification reports and handling actions, selects handling strategies based on the risk level classification reports, executes handling actions through different channels, and outputs handling execution instructions and handling result feedback. Finally, it merges the original operation event stream, rule matching, and risk scoring results, adds audit metadata such as processing nodes and processing times, generates standardized audit record documents, synchronously writes them to the audit database and search index, and outputs operation audit records.

[0059] It should be noted that the handling strategies are categorized as high, medium, and low risk. The high-risk strategy involves blocking, alerting, and issuing work orders; the medium-risk strategy involves secondary verification and marking; and the low-risk strategy involves logging only. The predefined compliance rules are derived from industry norms, enterprise standards, and historical audit cases. The rule logic definition method uses graph query language to check the relationships in the construction knowledge graph, combined with the syntax of the Druss rule engine to define field-level validation. The predefined mapping relationship between risk level classification reports and handling actions is as follows: risk levels are divided into four levels: emergency, medium, and low. The handling action types are defined as blocking (blocking IPs and terminating sessions), restricting (such as demoting privileges and disabling accounts), notification (sending alerts), and logging (writing to audit logs or databases). The rule mapping can be defined as follows: in emergency situations, blocking and notification; in high-risk situations, restricting and notification; in medium-risk situations, notification and logging; and in low-risk situations, logging.

[0060] S4. Based on the change data in the operation audit record, the entity relationships in the construction knowledge graph are adjusted in real time using the dynamic data flow update mechanism to generate digital twin synchronization instructions.

[0061] Furthermore, digital signatures are used to verify the integrity of operation audit records, and structured parsing is used to extract operation types, entity IDs, and change content, outputting a standardized change dataset.

[0062] Specifically, the elliptic curve digital signature algorithm is used to verify the operation audit records. Public key infrastructure is applied to verify the legitimate identity of the signer through CA certificates. A unique hash value is generated for the original operation audit record using hash calculation. The hash value is compared with the hash in the signature to verify the integrity of the hash value data. If the verification is successful, a valid audit record is output. If the verification fails, an alarm is triggered and an abnormal operation log is output. The operation type, entity ID and change content of the valid audit record are parsed through the protocol buffer, and the unit is uniformly converted and the enumerated value is mapped to output a standardized change dataset.

[0063] The superior approach enhances data tamper resistance, non-repudiation, and compliance through signature and hash verification; improves processability and automation efficiency; and optimizes resource consumption and real-time performance by using parsing algorithms.

[0064] Based on a standardized change dataset, graph traversal queries are used to locate affected subgraphs. Combined with a dynamic data flow update mechanism, entity relationships in the construction knowledge graph are adjusted in real time. A directed acyclic graph version management system is used to record the change history of the construction knowledge graph, construct an updated construction knowledge graph, and output a comparison of the old and new states of the construction knowledge graph.

[0065] Specifically, the standardized change dataset contains structured information such as operation type, entity identifier, and change content. A graph traversal query algorithm starts from the root node of the construction knowledge graph and performs a breadth-first search along entity relationship edges to quickly locate the subgraph affected by the change. A dynamic data flow update mechanism continuously monitors the change data flow, using an event-driven approach to trigger real-time addition, deletion, and modification operations on entity relationships in the construction knowledge graph, ensuring that the construction knowledge graph state remains synchronized with the latest changes. Directed acyclic graph version management creates an independent version node for each change, recording the evolution path of the construction knowledge graph through parent-child links, and completely preserving the historical state sequence. The updated construction knowledge graph compares the differences in entity relationships between the current version and the previous version, generating a comparison result of the old and new states, including the change type, scope of impact, and topological changes.

[0066] It should be noted that the dynamic data stream update mechanism establishes a continuously monitored data pipeline, adopts an event-driven architecture to capture operational events in the standardized change dataset in real time, and automatically triggers relationship adjustments, attribute updates, or topology reconstructions of corresponding entity nodes in the construction knowledge graph through a rule engine, thereby ensuring that the knowledge graph state and the dynamic change data are synchronized at the millisecond level.

[0067] A better approach is to improve efficiency through localized updates, maintain consistency through semantic embedding, and enhance traceability and decision-making by comparing the results of the old and new states of the knowledge graph.

[0068] The comparison results of the old and new states of the construction knowledge graph are compressed using binary differential encoding, and then pushed to the digital twin in a hierarchical manner using the MQTT protocol to generate synchronization instructions for the digital twin.

[0069] Specifically, based on the comparison results of the old and new states of the knowledge graph, a semantic parsing tool is used to convert the old and new construction drawings into binary streams and compare them. A moving window technique is used to detect duplicate patterns, generating difference patches. LZMA secondary compression is applied to reduce the patch size, and CRC32 checksums and version hash values ​​are added. A binary difference algorithm is used to perform differential calculations on the RDF triplet data, outputting a difference patch file. The difference patch file is synchronized to the digital twin, and JWT is used for device authentication, outputting a transmission status report. Jinja2 templates are used to define the synchronization logic for different entity types, automatically adding tags to changes and extracting the change parameter list to generate digital twin synchronization instructions.

[0070] A better approach is to improve transmission economy and enhance implementation feasibility through binary difference algorithms, and to enhance compatibility by using Jinja2 templates.

[0071] S5. Securely store operation audit records and digital twin synchronization instructions through quantum encryption protocol, verify data consistency by combining multi-dimensional distributed AI collaborative computing, and output audit closed-loop management report.

[0072] It should be noted that the operation audit records and digital twin instructions are encrypted using a quantum encryption protocol, and fragmented storage is used to split the data into segments for on-chain processing, outputting blockchain evidence hash values ​​and timestamp proofs.

[0073] Furthermore, quantum encryption protocols are used to encrypt operation audit logs and digital twin instructions, generating encrypted data streams, which are then fragmented to create multiple data slices.

[0074] The specific process includes generating a one-time key sequence using a quantum key distribution protocol. This key sequence ensures unconditional security of the key distribution based on the principle of quantum state transmission. Operation audit logs and digital twin instructions are used as input data and encrypted using a quantum-resistant encryption algorithm to generate a ciphertext data stream with quantum security characteristics. The encrypted data stream is divided into fixed-size data blocks using a sharding algorithm, with each block appended with erasure coding redundancy to enhance fault tolerance. During sharding, a secret sharing scheme is used to further divide the data blocks into multiple data fragments. Each data fragment contains partial ciphertext information, and a specific number of fragments are required to completely reconstruct the original data. The quantum encryption protocol ensures that each data fragment is protected by an independent key, and fragment metadata records the fragment sequence number and verification information, ultimately outputting multiple data fragments that can be distributed and stored.

[0075] It should be noted that quantum-resistant encryption algorithms are a class of cryptographic schemes that can resist quantum computing attacks. They construct encryption mechanisms through mathematical problems (such as lattice theory, multivariable equations, or hash functions) to ensure that data confidentiality and integrity can be maintained even in a quantum computer environment.

[0076] Data is sharded and distributed across blockchain nodes, and then uploaded to the blockchain through a consensus mechanism to generate on-chain transaction records.

[0077] The specific process includes distributing data shards to blockchain nodes, locating available blockchain nodes in the network through a node discovery protocol, and mapping data shards to specific node groups using a content-addressable method using distributed hash table technology. Each data shard is encapsulated in a blockchain transaction format, containing shard content, hash value, and storage metadata, and broadcast to all nodes in the network via a peer-to-peer network. The consensus mechanism employs a practical Byzantine fault-tolerant algorithm, with ledger nodes performing multiple rounds of verification and voting on transaction data to ensure the authenticity and consistency of data shards. When consensus reaches a statutory threshold, the transaction is packaged to generate a new block, which is then connected to the preceding blockchain through a hash chain to form an immutable on-chain transaction record. Blockchain nodes update the confirmed transaction record to their local ledger and synchronize it to a replica across the entire network, completing the data on-chain storage process.

[0078] It should be noted that the Practical Byzantine Fault Tolerance Algorithm is a distributed consensus protocol that uses a three-phase commit process (pre-preparation, preparation, and commit) to ensure that the distributed network can still achieve state consistency even when there are malicious nodes (not exceeding 1 / 3 of the total number of nodes). The core mechanism includes master node rotation, message signature verification, and view change protocol to ensure that the verification and voting process of data sharding has fault tolerance and finality.

[0079] The statutory threshold is set based on the scale of the blockchain network, security level requirements, and the node fault tolerance limit in Byzantine fault tolerance theory (usually set as 1 / 3 of the total number of nodes). The preset process is implemented through the initialization parameter configuration of the smart contract, and dynamically adjusted in combination with historical network performance data to ensure a balance between consensus efficiency and security.

[0080] Based on on-chain transaction records, an irreversible blockchain notarization hash value is generated through secure hashing, and timestamp proof is obtained using the inherent distributed timestamps of the blockchain network.

[0081] The specific process includes: on-chain transaction records containing structured data such as transaction content, block height, and node signatures; secure hashing using the SHA-256 algorithm to perform one-way hashing on the on-chain transaction records, generating a fixed-length unique hash value. This hash value is collision-resistant and irreversible, and serves as the output blockchain evidence hash value. The distributed timestamp service extracts a timestamp from the consensus-confirmed block header according to the blockchain consensus mechanism's time synchronization protocol. This timestamp is jointly verified by multiple nodes and recorded in the distributed ledger, forming a legally valid timestamp proof. The blockchain evidence hash value and the timestamp proof together constitute a complete evidence credential, providing a verifiable data foundation for audit closed-loop management reports.

[0082] Deploy verifiable random functions to assign verification tasks, use multi-dimensional distributed AI collaborative algorithms for cross-node verification, and use differential hash comparison to detect whether the original data corresponding to the blockchain evidence hash value has been tampered with and whether the timestamp proof matches, and output consistency verification results.

[0083] Specifically, a verifiable random function generates unpredictable but publicly verifiable random numbers based on the input seed and private key. Verification tasks are dynamically assigned to different nodes based on the random number results, ensuring fairness and tamper-proof task allocation. A multi-dimensional distributed AI collaborative algorithm aggregates local verification results from multiple nodes, using a gradient sharing mechanism and averaging to derive a global consistency index and identify abnormal data patterns. The differential hash comparison operation extracts the original data fragment corresponding to the blockchain evidence hash value from distributed storage, re-derives the hash value of the current data fragment, and compares it bit-by-bit with the blockchain evidence hash value stored on-chain. Simultaneously, it verifies whether the blockchain block height for timestamp proof is within a reasonable range from the current network block height. The multi-dimensional distributed AI collaborative algorithm integrates the differential hash comparison result with the timestamp matching degree to generate a Boolean consistency verification result that includes data integrity status and timestamp validity.

[0084] It should be noted that the reasonable range refers to a dynamic range determined by the average block time of the blockchain network, the historical block height fluctuation range, and the network latency tolerance.

[0085] The multidimensional distributed AI collaborative algorithm protects node data privacy through secure multi-party computation technology, while dynamically adjusting node weights by combining an attention mechanism to ensure effective detection of the consistency status of distributed data in blockchain evidence storage and verification scenarios.

[0086] The backpropagation tracing algorithm is used to locate problem nodes, zero-knowledge proof is used to generate verification evidence, and combined with smart contracts, an audit closed-loop management report is output.

[0087] Specifically, combining the tampering detection report and the knowledge graph change history, starting from the tampered field, the system queries related nodes in reverse along the edges of the knowledge graph. Combined with the digital signature structured operation audit records, suspicious operations are filtered out, and a problem node report is output. Based on the problem node report, zero-knowledge proofs are generated using zero-knowledge proofs, and the zero-knowledge proofs are generated by inputting signature and key parameters. These zero-knowledge proofs are then bound to hash parameters to generate a verifiable evidence package. Based on the Solidity smart contract language, audit rules are automatically executed. External data is fed in through the chain loop, along with the verifiable evidence package, the problem node report, and the external data. The validity of the verifiable evidence package is verified, pre-stored rules are matched, on-chain asset contracts are invoked, and an audit closed-loop management report is output.

[0088] It should be noted that the types of suspicious operations include: suspected data tampering, time-series violations, permission overreach, and abnormal associations. Suspicious operations are screened by locating tampered fields, performing reverse association queries, and comparing operation records with input data sources such as tampering detection reports, knowledge graph change history, and digital signature structured operation audit records. Audit rules are automated verification logic executed by smart contracts. The element categories include data validity rules, business process rules, permission rules, and association constraint rules. Key parameter constraints are parsed from construction specification texts and transformed into computable logic. This is then converted into executable smart contract logic using a domain-specific language and combined with the Druss rule engine for on-chain processing. Hash parameters include data hash, state root hash, timestamp hash, identity identifier hash, and random number hash. Key parameters include the master key, key derivation path, and key usage identifier.

[0089] A better approach is to improve the accuracy and timeliness of problem node location through reverse querying using knowledge graphs; enhance privacy protection by employing zero-knowledge proof verification; and improve processing speed and reduce costs through auditing closed loops.

[0090] This embodiment also provides a management system for construction design data, including: a data processing module for acquiring construction design data, using a standardized conversion engine to unify the format and verify integrity, and outputting standardized design data; a compliance verification module for inputting standardized design data into a construction knowledge graph for dynamic compliance verification, extracting features through principal component analysis (PCA), and performing semantic analysis using a deep learning model, and outputting an executable correction scheme; an access control module for using multi-level access control to manage access to standardized design data, and outputting operation audit records by combining hash calculation and public key infrastructure digital signature methods; a graph update module for adjusting entity relationships in the construction knowledge graph in real time based on the changed data in the operation audit records using a dynamic data flow update mechanism, and generating digital twin synchronization instructions; and an audit module for securely storing operation audit records and digital twin synchronization instructions using a quantum encryption protocol, verifying data consistency by combining multi-dimensional distributed AI collaborative computing, and outputting an audit closed-loop management report.

[0091] This embodiment also provides a computer device applicable to the construction design data management method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the construction design data management method proposed in the above embodiment.

[0092] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0093] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the construction design data management method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0094] In summary, this invention achieves dual verification of explicit rule checks and implicit semantic analysis through a dynamic mapping table combined with deterministic logic, thereby improving compliance verification coverage. It also adopts dynamic role allocation and multi-level access control to adjust permissions in real time based on operational behavior, reducing permission conflicts.

[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for managing construction design data, characterized in that: include, Acquire construction design data, use a standardized conversion engine to unify the format and verify the integrity, and output standardized design data; Standardized design data is input into the construction knowledge graph for dynamic compliance verification, features are extracted using principal component analysis (PCA), and semantic analysis is performed using a deep learning model to output an executable correction scheme. Multi-level access control is used to manage access to standardized design data, and operation audit logs are output by combining hash calculation and digital signature methods of public key infrastructure. Based on the change data in the operation audit record, the entity relationships in the construction knowledge graph are adjusted in real time using a dynamic data flow update mechanism to generate digital twin synchronization instructions; The operation audit records and digital twin synchronization instructions are securely stored using a quantum encryption protocol, and data consistency is verified by combining multi-dimensional distributed AI collaborative computing to output an audit closed-loop management report.

2. The method for managing construction design data as described in claim 1, characterized in that: The specific steps for acquiring construction design data, using a standardized conversion engine to unify the format and verify its integrity, and outputting standardized design data are as follows. Data is collected through an application programming interface, and data extraction is used to perform preliminary format conversion and add basic metadata to output a structured raw dataset. Data is encoded using a rule engine and construction industry standard classifications, and the execution units are uniformly converted. Required fields are validated through predefined rules, and a standard dataset is output. By combining database constraints and validation scripts, check data correlation, verify basic business rules, and output standardized design data.

3. The method for managing construction design data as described in claim 2, characterized in that: The process involves inputting standardized design data into a construction knowledge graph for dynamic compliance verification, extracting features using principal component analysis (PCA), and performing semantic analysis using a deep learning model to output an executable correction scheme. The specific steps are as follows: Standardized design data is converted into structured triples through semantic parsing, and features are extracted using principal component analysis (PCA). The data is then matched with specification nodes in the construction knowledge graph through graph query, and rules are added to supplement unmatched items to generate a dynamic mapping table. Based on dynamic mapping tables and combined with deep learning models, we conduct in-depth analysis of design data, identify potential compliance issues, and build compliance diagnostic reports. A parameterized adjustment algorithm based on rule-based automatic correction is used to generate standard correction schemes by combining structured templates, and an executable correction scheme is output.

4. The method for managing construction design data as described in claim 3, characterized in that: The method employs multi-level access control to manage access to standardized design data, combining hash calculations and digital signature methods from public key infrastructure to output operation audit logs. The specific steps are as follows: Based on the executable modification scheme, operation requests for standardized design data are filtered through a multi-level permission verification interface. The distributed log collection framework is used to collect the passed operation behavior data in real time and output the raw operation event stream. Using a public key infrastructure-based digital signature method, hash calculations and digital signatures are performed on each operation record to construct a structured digital signature operation audit record; Combined with a stream processing engine, perform real-time compliance checks on the structured operation audit logs of digital signatures and output the operation audit logs.

5. The method for managing construction design data as described in claim 4, characterized in that: The process involves using a dynamic data stream update mechanism to adjust entity relationships in the construction knowledge graph in real time based on change data in the operation audit logs, generating digital twin synchronization instructions. The specific steps are as follows: Digital signatures are used to verify the integrity of operation audit records, and structured parsing is used to extract operation types, entity IDs, and change content, outputting a standardized change dataset. Based on the standardized change dataset, the affected subgraph is located by graph traversal query, and the entity relationship in the construction knowledge graph is adjusted in real time by combining the dynamic data flow update mechanism. The construction knowledge graph is recorded with a directed acyclic graph version management system to record the change history, and the updated construction knowledge graph is constructed. The comparison results of the old and new states of the construction knowledge graph are output. The comparison results of the old and new states of the construction knowledge graph are compressed using binary differential encoding, and then pushed to the digital twin in a hierarchical manner using the MQTT protocol to generate synchronization instructions for the digital twin.

6. The method for managing construction design data as described in claim 5, characterized in that: The process involves securely storing operation audit records and digital twin synchronization instructions using a quantum encryption protocol, verifying data consistency using multi-dimensional distributed AI collaborative computing, and outputting an audit closed-loop management report. The specific steps are as follows: The operation audit records and digital twin instructions are encrypted using a quantum encryption protocol, and fragmented storage is used to split the data into segments and put them on the blockchain, outputting the blockchain evidence hash value and timestamp proof. Deploy verifiable random functions to assign verification tasks, use multi-dimensional distributed AI collaborative algorithms for cross-node verification, use differential hash comparison to detect whether the original data corresponding to the blockchain evidence hash value has been tampered with and whether the timestamp proof matches, and output consistency verification results. The backpropagation tracing algorithm is used to locate problem nodes, zero-knowledge proof is used to generate verification evidence, and combined with smart contracts, an audit closed-loop management report is output.

7. The method for managing construction design data as described in claim 6, characterized in that: The operation audit records and digital twin instructions are encrypted using a quantum encryption protocol, and fragmented storage is used to complete the data uploading to the blockchain. The blockchain evidence hash value and timestamp proof are output. The specific steps are as follows. The operation audit logs and digital twin instructions are encrypted using a quantum encryption protocol to generate an encrypted data stream, which is then fragmented to generate multiple data slices. Data is sharded and distributed to blockchain nodes, and then uploaded to the blockchain through a consensus mechanism to generate on-chain transaction records. Based on on-chain transaction records, an irreversible blockchain notarization hash value is generated through secure hashing, and timestamp proof is obtained using the inherent distributed timestamps of the blockchain network.

8. A management system for construction design data, based on the management method for construction design data according to any one of claims 1 to 7, characterized in that: It includes a data processing module, a compliance verification module, an access control module, a graph update module, and an evidence storage and auditing module; The data processing module is used to acquire construction design data, use a standardized conversion engine to unify the format and verify the integrity, and output standardized design data. The compliance verification module is used to input standardized design data into the construction knowledge graph for dynamic compliance verification, extract features through principal component analysis (PCA), and perform semantic analysis in conjunction with a deep learning model to output an executable correction scheme. The permission management module is used to perform access control on standardized design data using multi-level permission management, and outputs operation audit records by combining hash calculation and digital signature methods of public key infrastructure. The graph update module is used to adjust the entity relationships in the construction knowledge graph in real time based on the change data in the operation audit record and using a dynamic data flow update mechanism to generate digital twin synchronization instructions. The evidence storage and auditing module is used to securely store operation audit records and digital twin synchronization instructions through quantum encryption protocol, perform data consistency verification by combining multi-dimensional distributed AI collaborative computing, and output an audit closed-loop management report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the construction design data management method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the construction design data management method according to any one of claims 1 to 7.

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