A Digital Traceability Method and System for Multi-Level Supervision of Construction Projects Based on Distributed Collaboration

Abstract

This invention provides a digital traceability method and system for multi-level supervision of construction projects based on distributed collaboration. It relates to the field of construction project supervision technology, and includes acquiring project data objects, constructing a supervisory authority topology for hierarchical classification, generating data fingerprints to form hierarchical traceability identifiers, constructing a multi-level associated traceability structure, using a distributed storage network for verification and storage, and forming hierarchical traceability paths based on traceability query requests for distributed verification. This invention achieves multi-level classification and traceability of supervisory data, improving the transparency and reliability of project supervision.

Description

Technical Field

[0001] This invention relates to the field of construction project supervision technology, and in particular to a digital traceability method and system for multi-level supervision of construction projects based on distributed collaboration. Background Technology

[0002] In the field of construction engineering, with the rapid development of information technology, the digitization and networking of project management data are constantly improving. Construction projects typically involve numerous stakeholders, including construction companies, supervision companies, owners, and government regulatory departments at multiple levels, generating a large amount of project data that needs to be traced and monitored. Traditional construction project supervision mainly relies on paper documents and centralized database systems for recording and storage. However, with the increasing scale and complexity of construction projects, the traceability, transparency, and security of data face severe challenges.

[0003] Existing construction project data supervision systems generally lack effective multi-level supervision mechanisms, making it difficult to meet the tiered requirements of different regulatory bodies regarding data access permissions and supervisory responsibilities. The data boundaries between different levels of regulatory bodies are blurred, making it impossible to establish a clear hierarchical relationship, resulting in difficulty in defining regulatory responsibilities and unclear data rights and obligations. Traditional construction project data storage often adopts a centralized architecture, with data reliability relying on a single authoritative institution, posing a risk of single-point failure and data tampering. Once data is illegally modified, there is a lack of effective technical means to detect and hold those responsible accountable, failing to guarantee data integrity and traceability. Existing construction project data traceability technologies lack systematic verification mechanisms, making it difficult to reliably verify the source and flow of data. When project quality or safety accidents occur, it is impossible to quickly and accurately trace relevant responsible parties and key data, affecting the efficiency of accident investigation and handling, and increasing project risks and regulatory costs. Summary of the Invention

[0004] This invention provides a digital traceability method and system for multi-level supervision of construction projects based on distributed collaboration, which can solve the problems in the prior art.

[0005] A first aspect of this invention provides a digital traceability method for multi-level supervision of construction projects based on distributed collaboration, comprising:

[0006] Retrieve engineering data objects generated during the construction process of a building project;

[0007] By constructing a regulatory authority topology diagram, the engineering data objects are classified according to regulatory levels, resulting in multiple data subsets from different regulatory entities.

[0008] A cryptographic digest algorithm is used to generate a data fingerprint corresponding to the data subset and bind it to the identifier of the regulatory level to form a hierarchical traceability identifier;

[0009] The hierarchical traceability identifiers are constructed into a multi-level associated traceability structure according to the subordinate relationship of the regulatory levels. In the multi-level associated traceability structure, the hierarchical traceability identifiers of the lower-level regulatory levels contain references to the hierarchical traceability identifiers of the higher-level regulatory levels.

[0010] The multi-level related tracing structure is distributed and stored in a distributed storage network through multiple cooperating nodes. Each cooperating node verifies the integrity of the multi-level related tracing structure based on a consensus protocol and generates a node verification credential.

[0011] Receive the traceability query request of the engineering data object, and extract the hierarchical traceability identifier corresponding to the target regulatory level and the associated upper and lower hierarchical traceability identifiers from the multi-level associated traceability structure according to the target regulatory level in the traceability query request to form a hierarchical traceability path;

[0012] The validity of the hierarchical traceability path is verified in a distributed manner using the node verification credentials, generating multi-level regulatory digital traceability results for the construction project.

[0013] By constructing a regulatory authority topology diagram to classify the engineering data objects according to regulatory levels, multiple data subsets of different regulatory entities are obtained, including:

[0014] Extract the construction operation type attribute, quality responsibility attribution attribute, and time-space correlation attribute from the engineering data object;

[0015] Based on the construction operation type attribute, the process dependency relationship between the engineering data objects is identified, and based on the quality responsibility attribution attribute, the responsibility transfer path between the engineering data objects is identified, forming an engineering data association graph;

[0016] Based on the process dependency relationship and the responsibility transfer path, the nodes in the engineering data association graph are mapped to the corresponding regulatory entities, and the jurisdictional boundaries between the regulatory entities are determined according to the time-space association attributes to obtain the regulatory authority topology graph.

[0017] Traverse the regulatory entity nodes in the regulatory authority topology graph, extract the engineering data object nodes that are directly connected to the regulatory entity nodes in the regulatory authority topology graph, calculate the association weight between the engineering data object nodes and the regulatory entity nodes, and assign the engineering data objects whose association weight exceeds a preset attribution threshold to the data subset corresponding to the regulatory entity node.

[0018] A cryptographic digest algorithm is used to generate data fingerprints corresponding to the data subset and bind them to identifiers at the regulatory level to form a hierarchical traceability identifier, including:

[0019] Extract the content feature vectors of the engineering data objects in the data subset, perform a weighted aggregation operation based on node centrality on the content feature vectors, perform dimensionality reduction mapping and hash quantization on the aggregated content feature vectors to generate semantic fingerprints; generate topological structure fingerprints based on the topological positional relationships between the engineering data objects in the engineering data association graph.

[0020] The semantic fingerprint and the topological fingerprint are fused to generate a composite data fingerprint;

[0021] Obtain the regulatory level identifier of the regulatory entity corresponding to the data subset;

[0022] Extract the level depth value and jurisdictional scope identifier of the regulatory level identifier in the regulatory authority topology map, and construct a level feature code;

[0023] The composite data fingerprint and the hierarchical feature encoding are signed using an asymmetric encryption key pair to generate the hierarchical traceability identifier, which includes proof of data integrity and a binding relationship with the regulatory hierarchy.

[0024] The hierarchical traceability identifiers are used to construct a multi-level associated traceability structure according to the hierarchical relationship of the regulatory levels. In this multi-level associated traceability structure, the hierarchical traceability identifiers of lower-level regulatory levels include references to the hierarchical traceability identifiers of higher-level regulatory levels, including:

[0025] The regulatory authority topology graph is analyzed to extract the hierarchical relationships and authority transfer paths between regulatory entity nodes. The regulatory coverage of each regulatory entity node is calculated based on the authority transfer paths, and a hierarchical dependency weight matrix is ​​constructed based on the regulatory coverage.

[0026] Based on the hierarchical dependency weight matrix, identify all associated superior regulatory entities and their corresponding dependency weight values ​​of the lower-level regulatory entity, and perform a weighted hash operation on the hierarchical tracing identifiers corresponding to the associated superior regulatory entities according to the dependency weight values ​​to generate a parent-level reference digest.

[0027] Merkle tree construction operation is performed on the parent reference digest and the hierarchical traceability identifier of the lower-level regulatory entity to generate an extended hierarchical traceability identifier containing the hierarchical reference proof path. The extended hierarchical traceability identifier embeds the verification path information of the parent reference digest.

[0028] According to the hierarchical structure of the regulatory authority topology, the extended-level traceability identifiers and verification path information of all regulatory entities are organized into a directed acyclic graph structure to form the multi-level associated traceability structure.

[0029] The multi-level associative tracing structure is distributed and stored in a distributed storage network using multiple cooperating nodes. Each cooperating node verifies the integrity of the multi-level associative tracing structure based on a consensus protocol, generating node verification credentials including:

[0030] Based on the topological hierarchy of the directed acyclic graph structure in the multi-level associative tracing structure, the multi-level associative tracing structure is divided into multiple tracing data pieces. A data dependency graph is calculated for each tracing data piece, and the data dependency graph records the reference dependency relationships between the tracing data pieces.

[0031] Based on the storage capacity and network connectivity indicators of the cooperating nodes in the distributed storage network, the redundancy of the traceability data shards is configured, the number of replicas of each traceability data shard and the set of target cooperating nodes are determined, and the traceability data shards and their data dependency graphs are distributed to the corresponding set of target cooperating nodes for storage.

[0032] After each collaborative node receives the traceability data fragment, it extracts the verification path information of the extended-level traceability identifier in the traceability data fragment, performs Merkle tree integrity verification on the extended-level traceability identifier based on the verification path information, and generates fragment verification results.

[0033] Based on the consensus protocol, the cooperating nodes in the distributed storage network perform multiple rounds of voting and consensus calculations on their respective generated shard verification results. When the number of shard verification results that have reached consensus exceeds a preset consensus threshold, each cooperating node generates a node verification credential containing consensus signature information.

[0034] Based on the target regulatory level in the traceability query request, the hierarchical traceability identifier corresponding to the target regulatory level and the associated upper and lower level traceability identifiers are extracted from the multi-level associated traceability structure to form a hierarchical traceability path, including:

[0035] Based on the source tracing query request, locate the corresponding regulatory entity node in the regulatory authority topology graph and retrieve the corresponding extended-level source tracing identifier from the directed acyclic graph structure, and extract the parent reference digest embedded in the extended-level source tracing identifier;

[0036] Based on the parent reference digest, in the multi-level associated tracing structure, all associated upper-level regulatory entity nodes corresponding to the regulatory entity node are recursively traced to the extended-level tracing identifiers. The reference propagation distance from the regulatory entity node to each associated upper-level regulatory entity node is calculated. The traced extended-level tracing identifiers are sorted according to the reference propagation distance to generate a sequence of upper-level tracing identifiers.

[0037] Based on the subordinate relationships in the regulatory authority topology diagram, in the multi-level association tracing structure, all associated subordinate regulatory entity nodes of the regulatory entity node are retrieved for their extended-level tracing identifiers, and the corresponding parent reference digest is verified to contain the extended-level tracing identifiers of the regulatory entity node. The verified extended-level tracing identifiers are then selected to form a sequence of subordinate tracing identifiers.

[0038] The extended-level traceability identifiers corresponding to the regulatory entity nodes, the upper-level traceability identifier sequences, and the lower-level traceability identifier sequences are organized into an ordered chain structure according to the regulatory hierarchy, forming the hierarchical traceability path.

[0039] The validity of the hierarchical traceability path is distributedly verified using the node verification credentials, generating multi-level regulatory digital traceability results for construction projects, including:

[0040] For each extended-level tracing identifier in the hierarchical tracing path, the signature validity of the corresponding node verification credential is verified according to the consensus signature information. The number of node verification credentials with valid signatures is counted. When the number of node verification credentials with valid signatures meets the preset verification threshold, it is determined that the storage integrity verification of the extended-level tracing identifier has passed, and an identifier verification status record is generated.

[0041] Based on the identifier verification status record corresponding to each extended level traceability identifier in the hierarchical traceability path, the overall verification pass rate of the hierarchical traceability path is calculated. When the overall verification pass rate meets the preset path validity threshold, the hierarchical traceability path is marked as a valid traceability path.

[0042] Extract the regulatory entity information and regulatory operation timestamp information corresponding to the traceability identifiers of each extended level in the effective traceability path, organize them into structured traceability records according to the order of regulatory levels, and encapsulate the structured traceability records and the overall verification pass rate of the effective traceability path into the multi-level regulatory digital traceability results of the construction project.

[0043] A second aspect of this invention provides a digital traceability system for multi-level supervision of construction projects based on distributed collaboration, comprising:

[0044] The data acquisition unit is used to acquire engineering data objects generated during the construction process of building engineering.

[0045] The hierarchical classification unit is used to classify the engineering data objects according to the regulatory hierarchy by constructing a regulatory authority topology diagram, thereby obtaining multiple data subsets of different regulatory subjects;

[0046] The fingerprint generation unit is used to generate data fingerprints corresponding to the data subset using a cryptographic digest algorithm and bind them to the identifiers of the regulatory level to form hierarchical traceability identifiers;

[0047] The structural building unit is used to construct a multi-level associated traceability structure by constructing the hierarchical traceability identifiers according to the subordinate relationship of the regulatory levels. In the multi-level associated traceability structure, the hierarchical traceability identifiers of the lower-level regulatory levels contain reference relationships to the hierarchical traceability identifiers of the higher-level regulatory levels.

[0048] The storage verification unit is used to perform distributed storage of the multi-level associated tracing structure through multiple cooperative nodes in the distributed storage network, wherein each cooperative node verifies the integrity of the multi-level associated tracing structure based on a consensus protocol and generates a node verification credential.

[0049] The path extraction unit is used to receive the traceability query request of the engineering data object, and extract the hierarchical traceability identifier corresponding to the target regulatory level and the associated upper and lower level hierarchical traceability identifiers from the multi-level associated traceability structure according to the target regulatory level in the traceability query request, so as to form a hierarchical traceability path.

[0050] The result generation unit is used to perform distributed verification of the validity of the hierarchical traceability path using the node verification credentials, and generate multi-level supervision digital traceability results for the construction project.

[0051] A third aspect of the present invention provides an electronic device, comprising:

[0052] processor;

[0053] Memory used to store processor-executable instructions;

[0054] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0055] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0056] The beneficial effects of this application are as follows:

[0057] By hierarchically classifying engineering data through a regulatory authority topology diagram and establishing data subsets for different regulatory bodies, the problems of unclear data responsibilities and difficulty in traceability in traditional construction engineering supervision are solved. Cryptographic digest algorithms are used to generate data fingerprints and bind them to regulatory level identifiers, ensuring that engineering data is not tampered with during transmission and improving data credibility. By constructing a multi-level associative traceability structure according to the hierarchical relationship of regulatory levels, a lower-level to higher-level reference relationship is established, making the transmission process of engineering data between different regulatory levels traceable and enhancing regulatory transparency. Multiple collaborative nodes in a distributed storage network are used for verification based on a consensus protocol to generate node verification credentials, avoiding the data tampering risks and single points of failure problems associated with centralized storage. Based on the target regulatory level in the query request, the relevant hierarchical traceability identifiers and associated superior and subordinate identifiers can be accurately extracted to form a complete traceability path, significantly improving the traceability efficiency in engineering supervision. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating the multi-level digital traceability method for construction engineering supervision based on distributed collaboration, as described in an embodiment of the present invention.

[0059] Figure 2 A schematic diagram illustrating the process of constructing a hierarchical tracing path. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0062] Figure 1 This is a flowchart illustrating the multi-level digital traceability method for construction engineering supervision based on distributed collaboration, as described in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0063] Retrieve engineering data objects generated during the construction process of a building project;

[0064] By constructing a regulatory authority topology diagram, the engineering data objects are classified according to regulatory levels, resulting in multiple data subsets from different regulatory entities.

[0065] A cryptographic digest algorithm is used to generate a data fingerprint corresponding to the data subset and bind it to the identifier of the regulatory level to form a hierarchical traceability identifier;

[0066] The hierarchical traceability identifiers are constructed into a multi-level associated traceability structure according to the subordinate relationship of the regulatory levels. In the multi-level associated traceability structure, the hierarchical traceability identifiers of the lower-level regulatory levels contain references to the hierarchical traceability identifiers of the higher-level regulatory levels.

[0067] The multi-level related tracing structure is distributed and stored in a distributed storage network through multiple cooperating nodes. Each cooperating node verifies the integrity of the multi-level related tracing structure based on a consensus protocol and generates a node verification credential.

[0068] Receive the traceability query request of the engineering data object, and extract the hierarchical traceability identifier corresponding to the target regulatory level and the associated upper and lower hierarchical traceability identifiers from the multi-level associated traceability structure according to the target regulatory level in the traceability query request to form a hierarchical traceability path;

[0069] The validity of the hierarchical traceability path is verified in a distributed manner using the node verification credentials, generating multi-level regulatory digital traceability results for the construction project.

[0070] In one optional implementation, the engineering data objects are classified into regulatory hierarchies by constructing a regulatory authority topology graph, resulting in multiple data subsets from different regulatory bodies, including:

[0071] Extract the construction operation type attribute, quality responsibility attribution attribute, and time-space correlation attribute from the engineering data object;

[0072] Based on the construction operation type attribute, the process dependency relationship between the engineering data objects is identified, and based on the quality responsibility attribution attribute, the responsibility transfer path between the engineering data objects is identified, forming an engineering data association graph;

[0073] Based on the process dependency relationship and the responsibility transfer path, the nodes in the engineering data association graph are mapped to the corresponding regulatory entities, and the jurisdictional boundaries between the regulatory entities are determined according to the time-space association attributes to obtain the regulatory authority topology graph.

[0074] Traverse the regulatory entity nodes in the regulatory authority topology graph, extract the engineering data object nodes that are directly connected to the regulatory entity nodes in the regulatory authority topology graph, calculate the association weight between the engineering data object nodes and the regulatory entity nodes, and assign the engineering data objects whose association weight exceeds a preset attribution threshold to the data subset corresponding to the regulatory entity node.

[0075] During the construction process, engineering data objects contain rich technical attributes and management information. For each engineering data object, construction operation type attributes, quality responsibility attribution attributes, and temporal and spatial correlation attributes are extracted. Construction operation type attributes include, but are not limited to, specific process categories such as concrete pouring, rebar tying, formwork installation, and waterproofing construction. These attributes are identified through a predefined construction operation coding system. The quality responsibility attribution attribute records the information of the responsible parties involved in the engineering data object, such as the construction team number, project manager identifier, and supervision unit code, forming the basic data for the responsibility chain. The temporal and spatial correlation attributes include the timestamp of the data object's generation, the spatial coordinates of its construction area or floor number, and temporal correlation markers with other data objects.

[0076] In construction, strict sequential constraints exist between work processes. For example, rebar tying must be completed before concrete pouring, and formwork removal must be carried out after the concrete reaches its design strength. By analyzing the construction operation type attributes of engineering data objects, a process dependency matrix is ​​established. This matrix records whether there are preconditions between any two data objects. When the construction operation type of data object A must be executed before data object B in the process flow, the dependency edge from A to B is marked in the matrix. Simultaneously, the responsibility attribution attribute is used to identify the responsibility transfer path.

[0077] In construction engineering, quality responsibility is passed down level by level along the construction chain. For example, the construction quality completed by a work team needs to be inspected and confirmed by the project manager, and the project manager's inspection results need to be reviewed by the supervision unit. By tracing the changing trajectory of the attribution of quality responsibility, a responsibility transmission path can be constructed from the grassroots construction unit to the high-level supervision unit.

[0078] By integrating process dependencies and responsibility transfer paths, an engineering data association graph is formed. The graph uses engineering data objects as nodes and dependencies and responsibility transfer as directed edges, which fully describes the data flow and responsibility transfer during the construction process.

[0079] Based on the process dependencies and responsibility transfer paths in the engineering data association diagram, a regulatory entity mapping operation is performed. For each engineering data object node in the association diagram, the corresponding regulatory entity is determined according to its quality responsibility attribution attribute. For example, data objects marked as completed by a construction team are mapped to the project department regulatory entity, data objects marked as signed and confirmed by the project manager are mapped to the construction unit regulatory entity, and data objects marked as reviewed by the supervising engineer are mapped to the supervision unit regulatory entity. During the mapping process, the jurisdictional boundaries between regulatory entities are determined by combining time and space association attributes. In the time dimension, construction stages are divided according to the timestamps of the data objects, and different stages can correspond to different regulatory entity authority scopes. In the spatial dimension, the construction site is divided into several regulatory zones according to the spatial coordinates or floor numbers of the data objects, and each zone corresponds to the jurisdiction of a specific regulatory entity. Through the dual constraints of time and space, the jurisdictional boundaries of each regulatory entity are clearly defined, avoiding overlapping or gaps in regulatory responsibilities. After completing the mapping and boundary division, a regulatory authority topology diagram is obtained. This topology diagram uses regulatory entities as nodes and jurisdictional boundaries and hierarchical relationships as edges, clearly showing the multi-level regulatory structure in construction projects.

[0080] The process iterates through all regulatory entity nodes in the regulatory authority topology, performing a data subset extraction operation for each node to identify all engineering data object nodes directly connected to the current regulatory entity node from the engineering data association graph. This direct connection is determined by the responsibility transmission path, meaning that the quality responsibility for the engineering data object is ultimately borne or reviewed by the regulatory entity. For each identified engineering data object node, the association weight between it and the current regulatory entity node is calculated. The association weight comprehensively considers multiple factors, including the number of hops in the responsibility transmission path, the strength coefficient of the process dependency, and the matching degree of the temporal and spatial association attributes. For example, the shorter the responsibility transmission path and the fewer the hops, the higher the association weight; if there are mandatory preconditions in the process dependency, the dependency strength coefficient is larger, increasing the association weight; when the temporal and spatial association attributes completely match the jurisdiction of the regulatory entity, the matching degree is the highest, further increasing the association weight. The calculated association weight ranges from 0 to 1 as a real number.

[0081] The calculated association weights are compared with a preset attribution threshold. This threshold is set based on the specific regulatory requirements of the construction project and typically ranges from 0.6 to 0.8. When the association weight between a project data object node and a regulatory body node exceeds the preset attribution threshold, the project data object is determined to belong to the jurisdiction of that regulatory body and is assigned to the corresponding data subset. During the assignment process, the original identifier and association weight value of the project data object are retained for subsequent traceability analysis. Since the same project data object can be associated with multiple regulatory bodies, it is allowed to be assigned to multiple data subsets simultaneously. However, the association weight recorded in each data subset reflects the difference in the strength of the association between the object and the corresponding regulatory body.

[0082] After traversal, multiple data subsets from different regulatory bodies are obtained. Each subset contains a set of engineering data objects directly under the jurisdiction of that regulatory body, laying the data foundation for subsequent generation of hierarchical traceability identifiers and construction of a multi-level associated traceability structure. This classification method based on association weights enables precise hierarchical division of construction engineering data, ensuring that each regulatory body can focus on engineering data objects within its scope of responsibility, thereby improving regulatory efficiency and traceability accuracy.

[0083] In one optional implementation, a cryptographic digest algorithm is used to generate a data fingerprint corresponding to the data subset and bind it to an identifier at the regulatory level to form a hierarchical traceability identifier, including:

[0084] Extract the content feature vectors of the engineering data objects in the data subset, perform a weighted aggregation operation based on node centrality on the content feature vectors, and perform dimensionality reduction mapping and hash quantization on the aggregated content feature vectors to generate semantic fingerprints;

[0085] A topological fingerprint is generated based on the topological positional relationship between the engineering data objects in the engineering data association diagram;

[0086] The semantic fingerprint and the topological fingerprint are fused to generate a composite data fingerprint;

[0087] Obtain the regulatory level identifier of the regulatory entity corresponding to the data subset;

[0088] Extract the level depth value and jurisdictional scope identifier of the regulatory level identifier in the regulatory authority topology map, and construct a level feature code;

[0089] The composite data fingerprint and the hierarchical feature encoding are signed using an asymmetric encryption key pair to generate the hierarchical traceability identifier, which includes proof of data integrity and a binding relationship with the regulatory hierarchy.

[0090] In practical applications of multi-level supervision in construction projects, the data subsets under the jurisdiction of different regulatory bodies need to generate unique identifiers that simultaneously reflect the integrity of the data content and the level of supervision. For a given data subset, content feature vectors are extracted from each engineering data object. These content feature vectors can be obtained by numerically processing the key attribute fields of the data objects. For example, for concrete pouring records, attributes such as construction time, slump test value, temperature conditions, and curing measures can be extracted to form feature vectors.

[0091] After obtaining the content feature vectors of all data objects, a weighted aggregation operation needs to be performed based on the centrality of each object node in the engineering data association graph. Node centrality reflects the importance of a data object in the entire association network. During calculation, the number of direct connections between the node and other nodes in the engineering data association graph is counted, and this number is used as the base value for degree centrality. All connections of the node are traversed, and the association type label and association strength attribute carried by each connection edge are extracted. The association weight coefficient is determined based on the association type label; for example, the weight coefficient for strong dependency associations is set to a high value, and the weight coefficient for weak reference associations is set to a low value. The association strength attribute values ​​of all connections of the node are multiplied by the corresponding association weight coefficients, and all product results are summed to obtain the weighted degree centrality value of the node. Simultaneously, the sum of the shortest path lengths from the node to all other nodes in the engineering data association graph is calculated, and the reciprocal of this sum is used as the proximity centrality value. The weighted degree centrality value and the proximity centrality value are linearly combined to obtain the comprehensive centrality value of the node. This comprehensive centrality value is used as the weight coefficient of the node's content feature vector in subsequent weighted aggregation operations. For data objects with a high overall centrality, their content feature vectors are given a greater weight coefficient during the aggregation process to ensure that the features of the core data objects dominate the aggregation results.

[0092] After weighted aggregation, the resulting aggregated feature vector is typically high-dimensional, requiring dimensionality reduction mapping to improve the efficiency of subsequent hash operations. Principal component analysis (PCA) can be used for dimensionality reduction, projecting the high-dimensional feature vector onto a low-dimensional space while retaining the principal components that best represent the data features. The dimensionality-reduced feature vector is then converted into a fixed-length binary code through hash quantization. This binary code is the semantic fingerprint, and its length can be set to 256 bits or 512 bits, ensuring both feature expressiveness and ease of subsequent storage and comparison.

[0093] Semantic fingerprints only reflect the characteristics of data content, while the relationships between data objects also need to be included in the fingerprint generation process. A topological fingerprint is generated based on the topological positional relationships of each data object in the engineering data association graph. Generating a topological fingerprint requires determining the adjacency relationships of each object within the data subset in the association graph and constructing an adjacency matrix to represent the connection state between objects. A graph isomorphic hash operation is performed on the adjacency matrix to convert the topological structure into a unique hash value. In practice, the rows of the adjacency matrix can be sorted to ensure that the same topological structure generates consistent hash results under different node traversal orders. The topological fingerprint captures the dependencies and flow paths between data objects. For example, a hidden works acceptance record depends on preceding material arrival inspection records and construction operation records; this dependency is reflected through the topological fingerprint.

[0094] A composite data fingerprint is generated by fusing semantic fingerprints and topological fingerprints. This fusion can be performed using concatenated hashing, where the semantic and topological fingerprints are concatenated in a specific order, and then a cryptographic hash algorithm such as SHA-256 or SHA-3 is applied to generate a unified composite data fingerprint. This composite data fingerprint contains both the semantic features of the data content and the structural features of the data associations, ensuring that any modification to a subset of data or change in associations will result in a change in the fingerprint value, thus effectively protecting data integrity.

[0095] After the composite data fingerprint is generated, it needs to be bound to the regulatory level identifier to obtain the regulatory level identifier of the regulatory entity corresponding to the current data subset. This identifier has a clear hierarchical position in the regulatory authority topology diagram. The hierarchical depth value of this regulatory level identifier in the topology diagram is extracted. The hierarchical depth value indicates the level of the regulatory entity in the entire regulatory system; for example, project department level is depth value 1, construction unit level is depth value 2, construction unit level is depth value 3, and administrative supervision department level is depth value 4. Simultaneously, the jurisdiction identifier of the regulatory entity is extracted. The jurisdiction identifier defines the scope of engineering projects or professional fields under the responsibility of the regulatory entity, including information such as project section number, professional classification code, and geographical area identifier. The hierarchical depth value and jurisdiction identifier are combined to construct a hierarchical feature code. The hierarchical feature code adopts a structured format; for example, the first 8 bits represent the hierarchical depth value, the middle 16 bits represent the hash value of the jurisdiction identifier, and the remaining bits are reserved for extended attributes.

[0096] The composite data fingerprint and hierarchical feature code are signed using an asymmetric encryption key pair. Each regulatory body holds a pair of asymmetric keys; the private key is securely kept by the regulatory body, while the public key is publicly available in a distributed storage network. During the signature operation, the composite data fingerprint and hierarchical feature code are concatenated to form the message to be signed, and the regulatory body's private key is used to digitally sign the message. The digital signature algorithm can employ standard algorithms such as RSA, ECDSA, or EdDSA. The generated signature value is appended to the message to be signed, together forming a hierarchical traceability identifier. The hierarchical traceability identifier contains three layers of information: the composite data fingerprint proves the integrity of the data content and structure; the hierarchical feature code identifies the regulatory level and jurisdiction to which the data belongs; and the digital signature ensures that the identifier was legally generated by the corresponding regulatory body and is tamper-proof. Any third party can use the regulatory body's public key to verify the signature in the hierarchical traceability identifier, confirming the authenticity and integrity of the identifier.

[0097] In practical construction project supervision scenarios, a subset of steel reinforcement acceptance data submitted by a subcontractor is first processed by extracting the content features of each acceptance record and combining them with the node centrality in the association graph for weighted aggregation and dimensionality reduction hashing to generate a semantic fingerprint. Simultaneously, a topological fingerprint is generated based on the dependencies between the acceptance records, and the two are merged to form a composite data fingerprint. The subcontractor's hierarchical depth value in the supervision authority topology graph is determined to be 2, and its jurisdiction is identified as steel reinforcement professional subcontracting section A, thus constructing a corresponding hierarchical feature code. The subcontractor's private key is used to perform a signature operation on the composite data fingerprint and the hierarchical feature code to generate a hierarchical traceability identifier for this data subset. This identifier is subsequently incorporated into a multi-level association traceability structure, forming an association reference relationship with the hierarchical traceability identifiers of the general contractor, construction unit, supervision unit, and other upper and lower-level supervision layers, realizing multi-level digital traceability of construction project data.

[0098] In one optional implementation, the hierarchical traceability identifiers are constructed into a multi-level associated traceability structure according to the subordinate relationship of the regulatory levels. In this multi-level associated traceability structure, the hierarchical traceability identifiers of lower-level regulatory levels include references to the hierarchical traceability identifiers of higher-level regulatory levels, including:

[0099] The regulatory authority topology graph is analyzed to extract the hierarchical relationships and authority transfer paths between regulatory entity nodes. The regulatory coverage of each regulatory entity node is calculated based on the authority transfer paths, and a hierarchical dependency weight matrix is ​​constructed based on the regulatory coverage.

[0100] Based on the hierarchical dependency weight matrix, identify all associated superior regulatory entities and their corresponding dependency weight values ​​of the lower-level regulatory entity, and perform a weighted hash operation on the hierarchical tracing identifiers corresponding to the associated superior regulatory entities according to the dependency weight values ​​to generate a parent-level reference digest.

[0101] Merkle tree construction operation is performed on the parent reference digest and the hierarchical traceability identifier of the lower-level regulatory entity to generate an extended hierarchical traceability identifier containing the hierarchical reference proof path. The extended hierarchical traceability identifier embeds the verification path information of the parent reference digest.

[0102] According to the hierarchical structure of the regulatory authority topology, the extended-level traceability identifiers and verification path information of all regulatory entities are organized into a directed acyclic graph structure to form the multi-level associated traceability structure.

[0103] In practical applications of construction engineering, regulatory bodies encompass multiple levels, including general contractors, specialized subcontractors, material suppliers, quality inspection departments, and government regulatory agencies. To accurately reflect the management authority transfer relationships among these regulatory bodies, a deep analysis of the established regulatory authority topology is performed. The regulatory authority topology is essentially a directed graph structure, where each node represents a specific regulatory body, and edges indicate the direction of authority transfer and hierarchical relationships. Through graph traversal algorithms, it is possible to extract whether a direct or indirect hierarchical relationship exists between any two regulatory body nodes.

[0104] In calculating the permission transfer path, a breadth-first search strategy is adopted, starting from the top-level regulatory body and exploring all reachable lower-level regulatory bodies layer by layer downwards. For a given lower-level regulatory body, it simultaneously receives management permissions from multiple higher-level regulatory bodies; for example, a professional subcontractor is directly managed by the general contractor and also subject to professional supervision from the quality inspection department. Based on these permission transfer paths, the regulatory coverage of each regulatory body node is calculated. This coverage is defined as the set of all lower-level regulatory bodies that the regulatory body can directly or indirectly manage.

[0105] The calculation results of regulatory coverage are used to construct a hierarchical dependency weight matrix. The row indices of the matrix represent lower-level regulatory entities, the column indices represent higher-level regulatory entities, and the numerical values ​​of the matrix elements represent the dependency weight of the lower level on the higher level. The determination of dependency weights comprehensively considers multiple factors, including the length of the permission transfer path, the scope of management responsibilities, and the frequency of data flow. The dependency weight of direct superiors is typically set to a higher value, while the weight of indirect superiors decays according to the path length. In cases with multiple permission transfer paths, the maximum value of the weights corresponding to all paths is taken as the final dependency weight to ensure that key management relationships are fully reflected.

[0106] For each lower-level regulatory entity, all associated higher-level regulatory entities and their dependency weights are identified by querying the non-zero elements in the corresponding row of the matrix. For example, a material supplier node is associated with both a purchasing department node and a quality supervision department node, each corresponding to a different dependency weight value. After obtaining the hierarchical traceability identifiers corresponding to these associated higher-level regulatory entities, a weighted hash operation is performed. Specifically, the hierarchical traceability identifier of each higher-level entity is concatenated with its dependency weight value to form a combined string. All combined strings are then arranged in descending order of dependency weight. The SHA-256 hash algorithm is applied to the arranged complete string sequence to generate a fixed-length parent reference digest. This weighted hash operation method ensures the complete recording of reference relationships while reflecting the relative importance of different higher-level regulatory entities through weight sorting.

[0107] After generating the parent reference digest, a Merkle tree construction operation is used to cryptographically associate it with the hierarchical tracing identifier of the lower-level regulatory entity. The hierarchical tracing identifier of the lower-level regulatory entity is used as a leaf node of the Merkle tree, and the parent reference digest as another leaf node. A root hash value of the Merkle tree is generated through layer-by-layer hash calculation; this root hash value is the extended hierarchical tracing identifier. The Merkle tree construction process simultaneously generates hierarchical reference proof paths, which record the sequence of hash values ​​of all sibling nodes required to reach the root node from the parent reference digest leaf node. This verification path information is embedded in the metadata of the extended hierarchical tracing identifier, enabling rapid reconstruction of the Merkle tree and verification of the authenticity of the parent reference digest during subsequent verification.

[0108] The extended-level tracing identifier not only includes the data fingerprint of the lower-level regulatory body, but also establishes a verifiable cryptographic link between it and the tracing identifiers of all associated superiors through a Merkle tree structure. Verification path information is stored using a compact encoding method, saving only the necessary hash values ​​and location indexes to avoid data redundancy. For complex dependencies with multiple superior regulatory bodies, the verification path information can clearly distinguish the reference paths of different superiors, ensuring the accuracy of tracing verification.

[0109] Once all regulatory bodies have generated their respective extended-level traceability identifiers, these identifiers and their verification path information are organized into a directed acyclic graph (DAG) structure according to the hierarchical structure defined in the regulatory authority topology graph. In the DAG, each node stores an extended-level traceability identifier for a regulatory body, and directed edges represent references between regulatory levels, with the edges pointing from lower-level to higher-level regulatory bodies. The construction process of the DAG strictly adheres to the constraints of the regulatory authority topology graph, ensuring no circular references and guaranteeing the uniqueness and traceability of the traceability path.

[0110] To improve the query efficiency of the directed acyclic graph (DAG), a multi-level index structure is established for the nodes in the graph. The index is hierarchically categorized according to the depth of the regulatory hierarchy, with the top-level regulatory body at level zero, its direct subordinates at level one, and so on. A categorized index is created for the business type of each regulatory body to facilitate quick location of regulatory nodes of specific types. A range index is created for the regulatory coverage area to support interval queries. These index structures are stored together with the DAG ontology, forming a complete multi-level associative tracing structure.

[0111] The multi-level associative tracing structure is stored using an adjacency list representation in graph databases. Each node records its extended-level tracing identifier, verification path information, and reference pointers to all parent nodes. These reference pointers include not only the target node's identifier but also dependency weights and reference timestamps, providing time-based traceability for subsequent tracing audits. The entire directed acyclic graph structure, after serialization, is distributed to various collaborating nodes in the distributed storage network, ensuring high availability and fault tolerance of the tracing data.

[0112] In one optional implementation, the multi-level associative tracing structure is distributed and stored through multiple cooperating nodes in a distributed storage network. Each cooperating node verifies the integrity of the multi-level associative tracing structure based on a consensus protocol, and generates node verification credentials including:

[0113] Based on the topological hierarchy of the directed acyclic graph structure in the multi-level associative tracing structure, the multi-level associative tracing structure is divided into multiple tracing data pieces. A data dependency graph is calculated for each tracing data piece, and the data dependency graph records the reference dependency relationships between the tracing data pieces.

[0114] Based on the storage capacity and network connectivity indicators of the cooperating nodes in the distributed storage network, the redundancy of the traceability data shards is configured, the number of replicas of each traceability data shard and the set of target cooperating nodes are determined, and the traceability data shards and their data dependency graphs are distributed to the corresponding set of target cooperating nodes for storage.

[0115] After each collaborative node receives the traceability data fragment, it extracts the verification path information of the extended-level traceability identifier in the traceability data fragment, performs Merkle tree integrity verification on the extended-level traceability identifier based on the verification path information, and generates fragment verification results.

[0116] Based on the consensus protocol, the cooperating nodes in the distributed storage network perform multiple rounds of voting and consensus calculations on their respective generated shard verification results. When the number of shard verification results that have reached consensus exceeds a preset consensus threshold, each cooperating node generates a node verification credential containing consensus signature information.

[0117] The multi-level associative tracing structure essentially constitutes a directed acyclic graph (DAG), where each level of tracing identifier acts as a node, and the references between levels form directed edges. Based on the topological properties of the DAG, the entire structure can be divided according to the depth of the regulatory levels. The depth of the top-level regulatory level is set to 0, and the depth increases by 1 for each subsequent level. Trace identifiers of similar or identical depths and their references are grouped into the same tracing data shard. This division method ensures strong correlation among data within the same shard, reducing the frequency of cross-shard queries.

[0118] For each source data shard, it is necessary to calculate its data dependency graph with other shards. Traverse all extended-level source identifiers in the current source data shard, extracting the parent reference identifier list and child referenced identifier list recorded in each extended-level source identifier. For each reference identifier in the parent reference identifier list, query the source data shard identifier to which it belongs, and establish a directed dependency edge between the current source data shard identifier and the referenced source data shard identifier. The direction of this directed edge points to the referenced source data shard, and the weight attribute of the edge records the number of reference relationships. For each referenced identifier in the child referenced identifier list, query other source data shard identifiers that reference this identifier, and establish directed dependency edges from other source data shards to the current source data shard in the data dependency graph. Summarize all directed dependency edges involved in the current source data shard, constructing a graph structure with source data shard identifiers as nodes and directed dependency edges as connections. This graph structure is the data dependency graph of the current source data shard. The data dependency graph records which hierarchical source identifiers in other shards are referenced by the current shard's hierarchical source identifier, and which other shard's hierarchical source identifiers reference it. For example, a shard containing project-level regulatory data will have its data dependency graph record that this shard references the shard identifier containing enterprise-level regulatory data, and is also referenced by the shard identifier containing construction unit-level regulatory data. The construction of the data dependency graph provides necessary path tracing information for subsequent distributed verification.

[0119] Taking into account the storage capacity and network connectivity metrics of each collaborating node in a distributed storage network, the storage capacity metric reflects the available storage space of the collaborating node, while the network connectivity metric reflects the network latency and bandwidth between the collaborating node and other nodes. For traceability data shards containing critical regulatory-level data, such as shards containing data from government regulatory departments, a higher number of replicas is configured, typically 5 to 7 replicas, to ensure high data availability. For lower-level regulatory data, such as data at the construction team level, a lower number of replicas can be configured, typically 3 replicas, to balance storage costs.

[0120] Nodes with sufficient storage capacity and excellent network connectivity are prioritized. A scoring mechanism can be used to calculate a comprehensive score for each collaborating node: 0.6 × Normalized storage capacity score + 0.4 × Network connectivity, where 0.6 is the storage capacity weight and 0.4 is the network connectivity weight. Based on the scoring results, the highest-scoring collaborating nodes for each source data shard are selected as the target collaborating node set. The source data shards and their data dependency graphs are then distributed to these target collaborating nodes for storage.

[0121] Upon receiving the traceability data fragment, the collaborating node immediately initiates the local integrity verification process. Since the traceability data fragment contains multiple extended-level traceability identifiers, each extended-level traceability identifier has already constructed a Merkle tree structure during its generation. The verification path information includes all hash values ​​from the leaf node to the root node, as well as the hash values ​​of sibling nodes. After extracting the verification path information, the collaborating node calculates the hash value layer by layer upwards from the leaf node, comparing the calculated root hash value with the root hash value recorded in the extended-level traceability identifier. If the two are completely identical, it indicates that the extended-level traceability identifier has not been tampered with, and a successful fragment verification result is generated; if the two are inconsistent, a failed fragment verification result is generated, and the specific location of the inconsistency is recorded.

[0122] In a distributed storage network, all collaborating nodes need to reach a consensus on their respective shard verification results to prevent malicious nodes from forging verification results. The consensus protocol used is typically a Byzantine fault-tolerant protocol, which can guarantee the correctness of the consensus even if some nodes fail or act maliciously. The consensus process consists of multiple rounds of voting. In the first round, each collaborating node broadcasts its shard verification result to other nodes in the network. In the second round, each collaborating node counts the verification results received from other nodes. If the number of votes for a verification result (pass or fail) exceeds two-thirds of the total number of nodes, the verification result is considered to have reached a preliminary consensus. In the third round, each collaborating node confirms the preliminary consensus result with a vote, ensuring that all honest nodes have a consistent understanding of the consensus result.

[0123] When the number of consensus-reached shard verification results exceeds a preset consensus threshold, it indicates that the integrity of the entire multi-level related tracing structure has been verified. The preset consensus threshold is typically set to above 95% of the total number of tracing data shards. Each collaborating node then generates a node verification credential, which contains consensus signature information. This consensus signature information is generated by each participating collaborating node digitally signing the consensus result using its own private key, and all signatures are aggregated to form a multi-signature structure. The node verification credential also includes a list of participating collaborating nodes, a timestamp of consensus achievement, and a summary of the verification status of each tracing data shard. Through this mechanism, any subsequent tracing query request can verify the credibility of the tracing data by verifying the multi-signature in the node verification credential, without needing to re-execute the entire verification process, significantly improving the response speed and verification efficiency of tracing queries.

[0124] In one optional implementation, based on the target regulatory level in the traceability query request, the hierarchical traceability identifier corresponding to the target regulatory level and the associated upper and lower level hierarchical traceability identifiers are extracted from the multi-level associated traceability structure to form a hierarchical traceability path, including:

[0125] Based on the source tracing query request, locate the corresponding regulatory entity node in the regulatory authority topology graph and retrieve the corresponding extended-level source tracing identifier from the directed acyclic graph structure, and extract the parent reference digest embedded in the extended-level source tracing identifier;

[0126] Based on the parent reference digest, in the multi-level associated tracing structure, all associated upper-level regulatory entity nodes corresponding to the regulatory entity node are recursively traced to the extended-level tracing identifiers. The reference propagation distance from the regulatory entity node to each associated upper-level regulatory entity node is calculated. The traced extended-level tracing identifiers are sorted according to the reference propagation distance to generate a sequence of upper-level tracing identifiers.

[0127] Based on the subordinate relationships in the regulatory authority topology diagram, in the multi-level association tracing structure, all associated subordinate regulatory entity nodes of the regulatory entity node are retrieved for their extended-level tracing identifiers, and the corresponding parent reference digest is verified to contain the extended-level tracing identifiers of the regulatory entity node. The verified extended-level tracing identifiers are then selected to form a sequence of subordinate tracing identifiers.

[0128] The extended-level traceability identifiers corresponding to the regulatory entity nodes, the upper-level traceability identifier sequences, and the lower-level traceability identifier sequences are organized into an ordered chain structure according to the regulatory hierarchy, forming the hierarchical traceability path.

[0129] like Figure 2 As shown, the method includes:

[0130] Upon receiving a source tracing query request, the system parses the regulatory entity identification information contained within the request. This identification information can be a unique code for the regulatory unit, a digital certificate identifier for the regulatory personnel, or a classification code for the regulatory level. Based on this identification information, a node location operation is performed in the regulatory authority topology graph. The regulatory authority topology graph is organized using a directed acyclic graph structure, where each node represents a regulatory entity, and directed edges between nodes represent the hierarchical relationship of regulatory authority. A graph traversal algorithm can quickly locate the regulatory entity node matching the query request. After successful location, the corresponding extended-level source tracing identifier is extracted from the data area associated with that node. Compared to the basic hierarchical source tracing identifier, the extended-level source tracing identifier additionally embeds parent reference digest information. This digest information is generated by cryptographically hashing the hierarchical source tracing identifier of the superior regulatory level, typically using secure hash algorithms such as SHA-256 or SM3.

[0131] Based on the extracted parent reference digest, a recursive tracing process is initiated to obtain all associated superior regulatory entity nodes. The tracing mechanism relies on pre-established reference relationships in the multi-level association tracing structure. Specifically, the parent reference digest of the current regulatory entity node is used as the query key to perform a matching search in the index table of the multi-level association tracing structure to locate the corresponding superior regulatory entity node. After obtaining the extended-level tracing identifier of this superior node, the parent reference digest is extracted again, and the search operation is repeated until the top-level node of the regulatory hierarchy is reached. The identification mark of the top-level node is that its parent reference digest is null or a special identifier pointing to the root node.

[0132] During the recursive tracing process, the reference propagation distance is calculated for each traced superior regulatory entity node. The reference propagation distance is defined as the number of hops from the currently queried regulatory entity node to a specific superior node. For example, the reference propagation distance of the direct superior is 1, the distance of the superior's superior is 2, and so on. After calculation, the extended-level tracing identifiers of all traced superior regulatory entity nodes are sorted in ascending order of reference propagation distance, forming a superior tracing identifier sequence. This sequence accurately reflects the complete regulatory chain from the current regulatory level to the highest regulatory level.

[0133] After tracing upwards, it is necessary to continue searching downwards for all associated subordinate regulatory entities of the current regulatory entity node. This operation is achieved by querying the subordinate relationships recorded in the regulatory authority topology graph. The regulatory authority topology graph explicitly records the set of direct subordinate nodes for each regulatory entity node. Based on this subordinate relationship information, the extended-level traceability identifiers corresponding to these subordinate regulatory entity nodes are retrieved in batches within the multi-level associated traceability structure.

[0134] After the retrieval is complete, the validity of each lower-level extended-level traceability identifier needs to be verified. The core of the verification is to check whether the parent reference digest embedded in the lower-level extended-level traceability identifier correctly points to the currently queried regulatory entity node. The specific verification steps are as follows: extract the parent reference digest from the lower-level extended-level traceability identifier, perform the same cryptographic hash operation on the extended-level traceability identifier of the current regulatory entity node, and compare whether the two digest values ​​are completely identical. If they are identical, the verification passes, indicating that the lower-level regulatory entity indeed belongs to the current regulatory level; if they are inconsistent, the verification fails, and the lower-level extended-level traceability identifier is excluded. All verified lower-level extended-level traceability identifiers are collected to form a lower-level traceability identifier sequence. This sequence contains the traceability information of all lower-level regulatory entities directly under the jurisdiction of the current regulatory level.

[0135] After obtaining the superior and subordinate traceability identifier sequences, these two sequences are integrated with the extended-level traceability identifiers corresponding to the current regulatory entity node to construct a complete hierarchical traceability path. The integration operation is organized according to the subordinate order of regulatory levels, forming an ordered chain structure. The superior traceability identifier sequences are arranged in descending order of reference distance, with the top-level regulatory entity at the beginning; the extended-level traceability identifiers of the current regulatory entity node are inserted in the middle of the sequence; and the subordinate traceability identifier sequences are appended to the end in descending order of regulatory level. The resulting ordered chain structure clearly demonstrates the complete regulatory path from the highest regulatory level to the specific implementation level.

[0136] To enhance the verifiability of this path, a reference relationship marker is added to each adjacent hierarchical traceability identifier in the chain structure, clearly identifying the subordinate relationships between superiors and subordinates. The final generated hierarchical traceability path not only includes the traceability identifier of the target regulatory level, but also fully records the traceability information of all related superior and subordinate regulatory levels, providing a complete data foundation for subsequent distributed verification and traceability result generation.

[0137] In one optional implementation, the validity of the hierarchical traceability path is distributedly verified using the node verification credentials to generate multi-level regulatory digital traceability results for the construction project, including:

[0138] For each extended-level tracing identifier in the hierarchical tracing path, the signature validity of the corresponding node verification credential is verified according to the consensus signature information. The number of node verification credentials with valid signatures is counted. When the number of node verification credentials with valid signatures meets the preset verification threshold, it is determined that the storage integrity verification of the extended-level tracing identifier has passed, and an identifier verification status record is generated.

[0139] Based on the identifier verification status record corresponding to each extended level traceability identifier in the hierarchical traceability path, the overall verification pass rate of the hierarchical traceability path is calculated. When the overall verification pass rate meets the preset path validity threshold, the hierarchical traceability path is marked as a valid traceability path.

[0140] Extract the regulatory entity information and regulatory operation timestamp information corresponding to the traceability identifiers of each extended level in the effective traceability path, organize them into structured traceability records according to the order of regulatory levels, and encapsulate the structured traceability records and the overall verification pass rate of the effective traceability path into the multi-level regulatory digital traceability results of the construction project.

[0141] For each extended-level tracing identifier in the hierarchical tracing path, the signature validity of its associated node verification credentials needs to be verified. The extended-level tracing identifier adds distributed storage-related metadata information to the basic-level tracing identifier, including storage timestamps, a set of storage node identifiers, and consensus signature information. When verifying signature validity, all node verification credentials corresponding to the extended-level tracing identifier are obtained. Each node verification credential contains the node's public key information and a digital signature of the data fingerprint. The signature is decrypted using the corresponding node's public key, and the decryption result is compared with the original data fingerprint. If they match completely, the node's signature is considered valid; otherwise, it is considered invalid. All node verification credentials associated with the extended-level tracing identifier are traversed, and the number of valid signature credentials is counted.

[0142] A preset verification threshold is set as the standard for judging storage integrity. The verification threshold can be an absolute numerical threshold, such as requiring at least 5 nodes to have valid signatures, or a relative proportion threshold, such as requiring the number of nodes with valid signatures to account for no less than 70% of the total number of participating storage nodes. When the number of node verification credentials with valid signatures meets the preset verification threshold, it indicates that the extended-level traceability identifier has received consensus confirmation from a sufficient number of nodes in the distributed storage network, ensuring its storage integrity and consistency. At this point, the storage integrity verification of the extended-level traceability identifier is considered successful. A corresponding identifier verification status record is generated, containing information such as the unique identifier of the extended-level traceability identifier, the verification timestamp, the total number of participating nodes, the number of nodes with valid signatures, the verification pass flag, and a list of node identifiers that failed verification. If the number of node verification credentials with valid signatures does not reach the preset verification threshold, the storage integrity verification of the extended-level traceability identifier is deemed unsuccessful. An identifier verification status record is also generated, but the verification pass flag is set to the unsuccessful state, and the specific reason for the verification failure is recorded.

[0143] After verifying all extended-level traceability identifiers in the hierarchical traceability path, the verification status records of each identifier are summarized, and the overall verification pass rate of the entire hierarchical traceability path is calculated. Specifically, the number of extended-level traceability identifiers with a "pass" flag in the path is counted, and this number is divided by the total number of extended-level traceability identifiers in the path to obtain the overall verification pass rate as a percentage. For example, if a hierarchical traceability path contains 8 extended-level traceability identifiers, and 7 of them pass verification, the overall verification pass rate is 87.5%. The calculated overall verification pass rate is compared with a preset path validity threshold. The preset path validity threshold is set according to the security requirements of the actual application scenario. For key construction projects with high security requirements, it can be set to above 95%, and for general engineering projects, it can be set to above 80%. When the overall verification pass rate meets or exceeds the preset path validity threshold, the hierarchical traceability path is marked as a valid traceability path, indicating that the regulatory information presented by the path has sufficient credibility and can serve as a valid basis for tracing engineering data objects. If the overall verification pass rate does not reach the preset path validity threshold, the path will be marked as an invalid or unreliable tracing path, and corresponding risk warning information will be given in the tracing results.

[0144] For cases marked as valid traceability paths, detailed information corresponding to the traceability identifiers at each extended level of the path is further extracted to construct structured traceability records. The extracted information includes regulatory entity information and regulatory operation timestamp information. Regulatory entity information records the specific organization or personnel that performed regulatory operations on the engineering data object at that regulatory level, such as the quality inspection department of the construction unit, the project supervisor of the supervision unit, the engineering management department of the construction unit, or regulatory personnel of the administrative department. Regulatory operation timestamp information records the precise time point when the regulatory entity at that level reviewed, confirmed, or filed the data object, using a standard time format to ensure the accuracy and traceability of the time information. Following the hierarchical order of the regulatory levels, the extracted information at each level is organized into structured traceability records. The structured organization adopts a hierarchical nested data structure, with the bottom layer containing information at the data generation level, and sequentially including regulatory entities and timestamp information at the team level, project level, enterprise level, and administrative supervision level, forming a complete regulatory chain view.

[0145] The structured traceability records and the overall verification pass rate of effective traceability paths are encapsulated to form multi-level regulatory digital traceability results for construction projects. The encapsulation process uses standardized data formats to ensure interoperability of the traceability results across different systems. Within the encapsulated traceability results, the structured traceability records provide clear regulatory process information, and the overall verification pass rate provides a quantifiable indicator of the traceability information's reliability. Furthermore, verification details can be appended to the encapsulated results, including the verification status of each level of identifier and the distribution of participating nodes, supporting in-depth auditing of traceability information. The generated multi-level regulatory digital traceability results can be presented in various formats, including visualized regulatory chain diagrams, structured data reports, or machine-readable data files, meeting the traceability information query and auditing needs of different application scenarios and achieving transparent and verifiable management of regulatory information throughout the entire construction project process.

[0146] This invention relates to a distributed collaborative digital traceability system for multi-level supervision of construction projects, the system comprising:

[0147] The first unit is used to obtain engineering data objects generated during the construction process of building projects;

[0148] The second unit is used to classify the engineering data objects by regulatory hierarchy by constructing a regulatory authority topology diagram, thereby obtaining multiple data subsets of different regulatory subjects;

[0149] The third unit is used to generate data fingerprints corresponding to the data subset using a cryptographic digest algorithm and bind them to the identifiers of the regulatory level to form a hierarchical traceability identifier;

[0150] The fourth unit is used to construct a multi-level associated traceability structure by constructing the hierarchical traceability identifiers according to the subordinate relationship of the regulatory levels. In the multi-level associated traceability structure, the hierarchical traceability identifiers of the lower-level regulatory levels contain reference relationships to the hierarchical traceability identifiers of the higher-level regulatory levels.

[0151] The fifth unit is used to perform distributed storage of the multi-level associated tracing structure through multiple cooperative nodes in a distributed storage network, wherein each cooperative node verifies the integrity of the multi-level associated tracing structure based on a consensus protocol and generates a node verification credential.

[0152] The sixth unit is used to receive the traceability query request of the engineering data object, and extract the hierarchical traceability identifier corresponding to the target regulatory level and the associated upper and lower hierarchical traceability identifiers from the multi-level associated traceability structure according to the target regulatory level in the traceability query request, so as to form a hierarchical traceability path.

[0153] The seventh unit is used to perform distributed verification of the validity of the hierarchical traceability path using the node verification credentials, and generate multi-level regulatory digital traceability results for the construction project.

[0154] A third aspect of the present invention provides an electronic device, comprising:

[0155] processor;

[0156] Memory used to store processor-executable instructions;

[0157] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0158] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0159] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital traceability method for multi-level supervision of construction projects based on distributed collaboration, characterized in that: include: Retrieve engineering data objects generated during the construction process of a building project; By constructing a regulatory authority topology diagram, the engineering data objects are classified according to regulatory levels, resulting in multiple data subsets from different regulatory entities. A cryptographic digest algorithm is used to generate a data fingerprint corresponding to the data subset and bind it to the identifier of the regulatory level to form a hierarchical traceability identifier; The hierarchical traceability identifiers are constructed into a multi-level associated traceability structure according to the subordinate relationship of the regulatory levels. In the multi-level associated traceability structure, the hierarchical traceability identifiers of the lower-level regulatory levels contain references to the hierarchical traceability identifiers of the higher-level regulatory levels. The multi-level related tracing structure is distributed and stored in a distributed storage network through multiple cooperating nodes. Each cooperating node verifies the integrity of the multi-level related tracing structure based on a consensus protocol and generates a node verification credential. Receive the traceability query request of the engineering data object, and extract the hierarchical traceability identifier corresponding to the target regulatory level and the associated upper and lower hierarchical traceability identifiers from the multi-level associated traceability structure according to the target regulatory level in the traceability query request to form a hierarchical traceability path; The validity of the hierarchical traceability path is verified in a distributed manner using the node verification credentials, generating multi-level regulatory digital traceability results for the construction project.

2. The method according to claim 1, characterized in that, By constructing a regulatory authority topology diagram to classify the engineering data objects according to regulatory levels, multiple data subsets of different regulatory entities are obtained, including: Extract the construction operation type attribute, quality responsibility attribution attribute, and time-space correlation attribute from the engineering data object; Based on the construction operation type attribute, the process dependency relationship between the engineering data objects is identified, and based on the quality responsibility attribution attribute, the responsibility transfer path between the engineering data objects is identified, forming an engineering data association graph; Based on the process dependency relationship and the responsibility transfer path, the nodes in the engineering data association graph are mapped to the corresponding regulatory entities, and the jurisdictional boundaries between the regulatory entities are determined according to the time-space association attributes to obtain the regulatory authority topology graph. Traverse the regulatory entity nodes in the regulatory authority topology graph, extract the engineering data object nodes that are directly connected to the regulatory entity nodes in the regulatory authority topology graph, calculate the association weight between the engineering data object nodes and the regulatory entity nodes, and assign the engineering data objects whose association weight exceeds a preset attribution threshold to the data subset corresponding to the regulatory entity node.

3. The method according to claim 1, characterized in that, A cryptographic digest algorithm is used to generate data fingerprints corresponding to the data subset and bind them to identifiers at the regulatory level to form a hierarchical traceability identifier, including: Extract the content feature vectors of the engineering data objects in the data subset, perform a weighted aggregation operation based on node centrality on the content feature vectors, perform dimensionality reduction mapping and hash quantization on the aggregated content feature vectors to generate semantic fingerprints; generate topological structure fingerprints based on the topological positional relationships between the engineering data objects in the engineering data association graph. The semantic fingerprint and the topological fingerprint are fused to generate a composite data fingerprint; Obtain the regulatory level identifier of the regulatory entity corresponding to the data subset; Extract the level depth value and jurisdictional scope identifier of the regulatory level identifier in the regulatory authority topology map, and construct a level feature code; The composite data fingerprint and the hierarchical feature encoding are signed using an asymmetric encryption key pair to generate the hierarchical traceability identifier, which includes proof of data integrity and a binding relationship with the regulatory hierarchy.

4. The method according to claim 1, characterized in that, The hierarchical traceability identifiers are used to construct a multi-level associated traceability structure according to the hierarchical relationship of the regulatory levels. In this multi-level associated traceability structure, the hierarchical traceability identifiers of lower-level regulatory levels include references to the hierarchical traceability identifiers of higher-level regulatory levels, including: The regulatory authority topology graph is analyzed to extract the hierarchical relationships and authority transfer paths between regulatory entity nodes. The regulatory coverage of each regulatory entity node is calculated based on the authority transfer paths, and a hierarchical dependency weight matrix is ​​constructed based on the regulatory coverage. Based on the hierarchical dependency weight matrix, identify all associated superior regulatory entities and their corresponding dependency weight values ​​of the lower-level regulatory entity, and perform a weighted hash operation on the hierarchical tracing identifiers corresponding to the associated superior regulatory entities according to the dependency weight values ​​to generate a parent-level reference digest. Merkle tree construction operation is performed on the parent reference digest and the hierarchical traceability identifier of the lower-level regulatory entity to generate an extended hierarchical traceability identifier containing the hierarchical reference proof path. The extended hierarchical traceability identifier embeds the verification path information of the parent reference digest. According to the hierarchical structure of the regulatory authority topology, the extended-level traceability identifiers and verification path information of all regulatory entities are organized into a directed acyclic graph structure to form the multi-level associated traceability structure.

5. The method according to claim 1, characterized in that, The multi-level associative tracing structure is distributed and stored in a distributed storage network using multiple cooperating nodes. Each cooperating node verifies the integrity of the multi-level associative tracing structure based on a consensus protocol, generating node verification credentials including: Based on the topological hierarchy of the directed acyclic graph structure in the multi-level associative tracing structure, the multi-level associative tracing structure is divided into multiple tracing data pieces. A data dependency graph is calculated for each tracing data piece, and the data dependency graph records the reference dependency relationships between the tracing data pieces. Based on the storage capacity and network connectivity indicators of the cooperating nodes in the distributed storage network, the redundancy of the traceability data shards is configured, the number of replicas of each traceability data shard and the set of target cooperating nodes are determined, and the traceability data shards and their data dependency graphs are distributed to the corresponding set of target cooperating nodes for storage. After each collaborative node receives the traceability data fragment, it extracts the verification path information of the extended-level traceability identifier in the traceability data fragment, performs Merkle tree integrity verification on the extended-level traceability identifier based on the verification path information, and generates fragment verification results. Based on the consensus protocol, the collaborative nodes in the distributed storage network perform multiple rounds of voting and consensus calculations on their respective generated shard verification results. When the number of shard verification results that have reached consensus exceeds a preset consensus threshold, each collaborative node generates a node verification credential containing consensus signature information.

6. The method according to claim 5, characterized in that, Based on the target regulatory level in the traceability query request, the hierarchical traceability identifier corresponding to the target regulatory level and the associated upper and lower level traceability identifiers are extracted from the multi-level associated traceability structure to form a hierarchical traceability path, including: Based on the source tracing query request, locate the corresponding regulatory entity node in the regulatory authority topology graph and retrieve the corresponding extended-level source tracing identifier from the directed acyclic graph structure, and extract the parent reference digest embedded in the extended-level source tracing identifier; Based on the parent reference digest, in the multi-level associated tracing structure, all associated upper-level regulatory entity nodes corresponding to the regulatory entity node are recursively traced to the extended-level tracing identifiers. The reference propagation distance from the regulatory entity node to each associated upper-level regulatory entity node is calculated. The traced extended-level tracing identifiers are sorted according to the reference propagation distance to generate a sequence of upper-level tracing identifiers. Based on the subordinate relationships in the regulatory authority topology diagram, in the multi-level association tracing structure, all associated subordinate regulatory entity nodes of the regulatory entity node are retrieved for their extended-level tracing identifiers, and the corresponding parent reference digest is verified to contain the extended-level tracing identifiers of the regulatory entity node. The verified extended-level tracing identifiers are then selected to form a sequence of subordinate tracing identifiers. The extended-level traceability identifiers corresponding to the regulatory entity nodes, the upper-level traceability identifier sequences, and the lower-level traceability identifier sequences are organized into an ordered chain structure according to the regulatory hierarchy, forming the hierarchical traceability path.

7. The method according to claim 5, characterized in that, The validity of the hierarchical traceability path is distributedly verified using the node verification credentials, generating multi-level regulatory digital traceability results for the construction project, including: For each extended-level tracing identifier in the hierarchical tracing path, the signature validity of the corresponding node verification credential is verified according to the consensus signature information. The number of node verification credentials with valid signatures is counted. When the number of node verification credentials with valid signatures meets the preset verification threshold, it is determined that the storage integrity verification of the extended-level tracing identifier has passed, and an identifier verification status record is generated. Based on the identifier verification status record corresponding to each extended level traceability identifier in the hierarchical traceability path, the overall verification pass rate of the hierarchical traceability path is calculated. When the overall verification pass rate meets the preset path validity threshold, the hierarchical traceability path is marked as a valid traceability path. Extract the regulatory entity information and regulatory operation timestamp information corresponding to the traceability identifiers of each extended level in the effective traceability path, organize them into structured traceability records according to the order of regulatory levels, and encapsulate the structured traceability records and the overall verification pass rate of the effective traceability path into the multi-level regulatory digital traceability results of the construction project.

8. A multi-level digital traceability system for construction engineering supervision based on distributed collaboration, used to implement the method as described in any one of claims 1-7, characterized in that, include: The first unit is used to obtain engineering data objects generated during the construction process of building projects; The second unit is used to classify the engineering data objects by regulatory hierarchy by constructing a regulatory authority topology diagram, thereby obtaining multiple data subsets of different regulatory subjects; The third unit is used to generate data fingerprints corresponding to the data subset using a cryptographic digest algorithm and bind them to the identifiers of the regulatory level to form a hierarchical traceability identifier; The fourth unit is used to construct a multi-level associated traceability structure by constructing the hierarchical traceability identifiers according to the subordinate relationship of the regulatory levels. In the multi-level associated traceability structure, the hierarchical traceability identifiers of the lower-level regulatory levels contain reference relationships to the hierarchical traceability identifiers of the higher-level regulatory levels. The fifth unit is used to perform distributed storage of the multi-level associated tracing structure through multiple cooperative nodes in a distributed storage network, wherein each cooperative node verifies the integrity of the multi-level associated tracing structure based on a consensus protocol and generates a node verification credential. The sixth unit is used to receive the traceability query request of the engineering data object, and extract the hierarchical traceability identifier corresponding to the target regulatory level and the associated upper and lower hierarchical traceability identifiers from the multi-level associated traceability structure according to the target regulatory level in the traceability query request, so as to form a hierarchical traceability path. The seventh unit is used to perform distributed verification of the validity of the hierarchical traceability path using the node verification credentials, and generate multi-level regulatory digital traceability results for the construction project.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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