Stage identifier tracing method and system for BIM (Building Information Modeling) data of building construction operation and maintenance

Abstract

The invention relates to the technical field of identification tracing, in particular to a stage identification tracing method and system for building construction operation and maintenance BIM data. The method comprises the steps of performing data preprocessing on acquired BIM model data; component ID distribution binding is carried out according to the preprocessed BIM model data; stage identification codes are generated based on the component IDs; recording component version information and responsibility information according to the stage identification code; establishing a multi-stage evolution linked list based on the recorded information; and generating a tracing report according to the multi-stage evolution chain table. Through unified component full life cycle ID (LCID) binding, stage identification code generation and multi-stage evolution chain table construction, the problem that BIM data in the design, construction and operation and maintenance stages are independent and repeated in modeling is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of identification traceability, in particular to a construction operation and maintenance BIM data stage identification traceability method and system. BACKGROUND

[0002] As the core tool of digital transformation of the construction industry, the building information model (BIM) technology has been widely used in the whole life cycle of design, construction and operation and maintenance. However, in actual engineering practice, the cross-stage management of BIM data still faces many bottlenecks, resulting in prominent problems such as "stage fragmentation, information disconnection and responsibility ambiguity", which are specifically manifested as follows: 1. Data fragmentation in each stage, poor information transmission The BIM application in the design, construction and operation and maintenance stages often presents the characteristics of "islandization": The BIM model in the design stage is mostly created based on ideal working conditions, without fully considering the construction feasibility and operation and maintenance requirements, resulting in the need for re-modeling or substantial modification in the construction stage, and the data inheritance rate is less than 30%; The information such as deepening design and site change in the construction stage is difficult to be fed back to the design model, and the operation and maintenance stage needs to be processed based on the completed model again, forming a repeated modeling cycle of "design → construction → operation and maintenance", which causes more than 50% of human and time waste; The software platforms used in each stage are not compatible in format, and information loss may occur in the data conversion process, and the accuracy rate of cross-stage data connection is less than 60%.

[0003] 2. Lack of tracking mechanism for component information evolution, and ambiguous responsibility subject In the whole life cycle of a building project, the component information will evolve many times due to design changes, construction optimization and operation and maintenance adjustment, but the existing management mode lacks effective tracking means: Design changes only record the final results, without keeping key information such as change reasons, approval process and influence scope, so that when problems are encountered in the construction stage, the source of the change cannot be traced back; Optimization measures such as component size adjustment and material replacement in the construction stage cannot be associated with the design model, so that in the operation and maintenance stage, it is difficult to determine the responsibility for the inconsistency between the actual site and the design; The information such as equipment failure and maintenance record in the operation and maintenance stage cannot be associated with the design parameters or construction quality, forming a "problem closed loop fracture".

[0004] 3. Lack of basis for delivery and acceptance, and data validity is questionable When a project is delivered, the data validity and reliability of the BIM model as one of the core achievements are difficult to verify: It is difficult to confirm whether the "final delivery version contains all legal changes" because the history versions of each component in the model cannot be traced back; The authenticity and compliance of the data cannot be determined during auditing or acceptance because the data formation process is not recorded; There is no uniform BIM data delivery standard in the industry, and disputes often arise between the two parties regarding "whether the model information is complete" and "whether the data is valid". On average, each project is delayed for 7-15 days due to these disputes.

[0005] 4. Lack of standardized identification system, data attributes are difficult to distinguish Currently, there is no uniform identification of the stage attributes and state characteristics of BIM data in the industry: The names of the same component in the design, construction, and operation stage models are inconsistent, making it difficult to match across stages; The "state" of the component lacks coding definition and cannot be automatically recognized by machines; Data version management is chaotic, and different versions of the same component are only distinguished by file names, which can lead to version misuse or coverage and cause engineering risks.

[0006] To address the above problems, existing research focuses on single-stage BIM data management or relies on manual recording for tracing, which has the following shortcomings: There is no uniform identification mechanism across stages, making it difficult to automatically associate design, construction, and operation data; No quantitative change impact assessment model has been established, making it difficult to determine the impact of component evolution on the associated system; The tracing process relies on manual sorting, which is inefficient and prone to errors, and cannot meet the needs of fine management in large projects.

[0007] Therefore, it is urgent to build a "BIM data full life cycle tracing system based on stage identification", which can connect the design, construction, and operation data chain by standardizing data collection, uniquely binding identification, dynamically recording evolution, and visualizing tracing methods, and clearly define responsibilities at each stage to provide technical support for the digital transformation of the construction industry SUMMARY To solve the above-mentioned problems, the present application provides a stage identification tracing method and system for BIM data in building construction and operation.

[0008] In a first aspect, the present application provides a stage identification tracing method for BIM data in building construction and operation, which adopts the following technical solution: A stage identification tracing method for BIM data in building construction and operation, comprising: Obtaining BIM model data in the design stage, construction stage, and operation stage; Data preprocessing is performed on the obtained BIM model data; Component ID allocation binding is performed according to the preprocessed BIM model data; A stage identification code is generated based on the component ID; Component version information and responsibility information are recorded according to the stage identification code; A multi-stage evolution chain table is established based on the recorded information; A traceability report is generated according to the multi-stage evolution chain table.

[0009] Further, the BIM model data of the design stage, construction stage and operation and maintenance stage is obtained, including connecting design software, construction management platform and operation and maintenance system through a distributed interface adaptation layer, collecting IFC, rvt, nwd, e57 point cloud, JSON sensor data format data, and ensuring data authenticity through data source credibility verification during the collection process; meanwhile, an incremental synchronization algorithm based on hash fingerprints is adopted to avoid full data repeated transmission, wherein a global hash fingerprint Global_hash is generated for historical collected data; an incremental hash fingerprint Increment_hash is generated for newly collected data; when Increment_hash≠Global_hash, only the difference is synchronized.

[0010] Further, the data preprocessing of the obtained BIM model data includes converting geometric models of different software into a unified IFC standard format through a non-uniform rational B-spline surface fitting algorithm, controlling fitting error, making Fit_error=max(|actual surface-fitted surface|)≤0.001m; realizing cross-stage attribute mapping based on an ontology library in the field of architecture, calculating attribute name similarity using a weighted Levenshtein edit distance; combining three-dimensional bounding box collision detection and attribute cosine similarity for repeated component identification; finally, using an improved Zscore algorithm to identify and mark attribute values that exceed a reasonable range.

[0011] Further, the component ID allocation binding according to the preprocessed BIM model data includes generating a tamper-proof unique identifier using a multi-level encryption hash algorithm to ensure cross-stage uniqueness, represented as: LCID=Base64(H3(H2(H1(ProjectID+TypeCode+OriginalID+CreateTime)+SpatialCode)+CheckCode)); realizing precise binding of construction and operation and maintenance components and design components based on an improved ICP iterative closest point algorithm combined with attribute feature fusion, wherein for point cloud registration, an initial transformation matrix is obtained through sampling consistency initial registration SACIA, and then ICP iteration optimization is used to make the average distance of point clouds; binding confidence calculation is used to determine whether manual intervention is needed.

[0012] Further, the stage identification code generation based on component ID includes first stage code generation, wherein a ternary bit vector is used to represent the multi-stage superposition state, design = 001, construction = 010, and operation and maintenance = 100, and when there is a design change in the construction stage, S = 011 (design + construction); then state code generation, wherein a four-dimensional state matrix is constructed, including intra-stage state, data validity, responsibility state, and change type; finally, a globally unique identification is generated by combining LCID, stage code, state code, and timestamp, and encrypted by SM4 algorithm, represented as FullID = SM4_Encrypt(LCID + "" + S + "" + T + "" + TS + "" + Nonce), wherein TS is a 64-bit nanosecond-level timestamp, and Nonce is a random number. After encryption, it is encoded into a transmissible string by Base64.

[0013] Further, the component version information and responsibility information recording according to the stage identification code includes automatically generating a version number based on a change impact quantification model, and the change impact calculation is represented as Impact = 0.5 × Geom_impact + 0.3 × Attr_impact + 0.2 × Rel_impact, wherein Geom_impact is the geometric change impact, Attr_impact is the attribute change impact, and Rel_impact is the correlation impact; and a weighted responsibility allocation algorithm is used to record the full-process responsibility subject, represented as Duty_weight = Σ(Operation_type × Time_factor × Role_weight), wherein Operation_type is the operation type weight, specifically, creation = 0.4, modification = 0.3, audit = 0.2, and confirmation = 0.1; Time_factor is the time decay factor, specifically (e^(-λ × Δt), λ = 0.01; Δt is the number of days from the current time; Role_weight is the role weight; the responsibility subject is sorted according to Duty_weight, and the one with the highest weight is the main responsible person.

[0014] Further, the multi-stage evolution chain table is established based on the recorded information, including using a bidirectional circular linked list plus a blockchain storage structure, each node containing a component unique identification LCID, a previous node hash value PrevHash, a next node hash value NextHash, a version VersionInfo, and a responsibility chain information DutyChain. After determining the node association based on the space-time similarity, the influence range of the component change on the associated nodes is calculated based on the complex network node influence mechanism. When the influence factor Influence ≥ 0.5, it is marked as a key influence node, and an influence propagation path marker is added in the chain table.

[0015] Further, the generation of the traceability report according to the multi-stage evolution chain table includes mapping the evolution chain table nodes to a time axis according to timestamps, connecting the associated nodes using a cubic Bezier curve, and reflecting the change influence degree through the curve curvature; generating a responsibility heat map based on Duty_weight, with the color depth positively correlated with the weight, and the RGB value = (255 x (1-Duty_weight), 0, 0); taking the target component as a root node, generating a hierarchical tree diagram according to the Influence value, and making the node size positively proportional to the influence degree; and performing report integrity verification after generating the traceability report, wherein a report hash value is generated and compared with the node hash stored in the blockchain to ensure that the report has not been tampered with.

[0016] Further, the generation of the traceability report according to the multi-stage evolution chain table further includes performing query matching degree calculation according to the multi-dimensional combination query mode of the received stage, time, and responsible person change type, respectively, and representing it as Match_score = Σ (Weight_d x Sim_d), wherein d is the query dimension, ΣWeight_d = 1, and Sim_d is the dimension similarity, wherein the time dimension uses a Gaussian function to calculate the distance similarity, and the results with Match_score ≥ 0.6 are included in the report.

[0017] The second aspect is a stage identification traceability system for building construction operation and maintenance BIM data, comprising: A data acquisition module configured to acquire BIM model data of design stages, construction stages, and operation and maintenance stages; A data preprocessing module configured to perform data preprocessing on the acquired BIM model data; A binding module configured to perform component ID allocation binding according to the preprocessed BIM model data; An identification code module configured to generate stage identification codes based on the component IDs; A recording module configured to record component version information and responsibility information according to the stage identification codes; A chain table module configured to establish a multi-stage evolution chain table based on the recorded information; A traceability module configured to generate a traceability report according to the multi-stage evolution chain table.

[0018] The third aspect provides a computer readable storage medium, wherein a plurality of instructions are stored, the instructions being adapted to be loaded and executed by a processor of a terminal device to perform the stage identification traceability method for building construction operation and maintenance BIM data.

[0019] In a fourth aspect, the present application provides a terminal device, comprising a processor and a computer readable storage medium, the processor is used to implement instructions; the computer readable storage medium is used to store a plurality of instructions, the instructions are suitable for being loaded and executed by the processor to implement the building construction operation and maintenance BIM data stage identification tracing method.

[0020] In summary, the present application has the following beneficial technical effects: 1. By unified component life cycle ID (LCID) binding, stage identification code generation and multi-stage evolution list construction, the problem of design, construction and operation and maintenance stage BIM data "each doing its own thing, repeated modeling" is solved. In practical application, the cross-stage data inheritance rate is improved from less than 30% in the prior art to more than 95%, and the data connection accuracy is improved to more than 90%; through multi-dimensional query and visual tracing function, the historical version, change track and responsible subject of component information can be completely retrieved within 10 seconds, which is more than 80 times more efficient than traditional manual tracing, greatly reducing the time cost of data query and responsibility definition.

[0021] 2. Based on the change impact quantification model Impact and the weighted responsibility allocation algorithm Duty_weight, the influence range of component change on the related system can be accurately evaluated, the key influence node identification accuracy is greater than or equal to 90%, and the weight order of each stage responsibility subject is clear, and the main responsible person determination error is less than or equal to 5%. This mechanism shortens the influence evaluation time of design change from 3-5 days to 2 hours, and the responsibility tracing rate of construction optimization is improved from 60% to 100%, effectively avoiding disputes caused by fuzzy responsibility, and reducing project management risk.

[0022] 3. Combined with the blockchain storage node hash chain and report integrity verification, it is ensured that the BIM data from generation to delivery is tamper-proof, and the data credibility is 100%; through standardized stage identification, status code and delivery report template, the model information verification time at project delivery is shortened from an average of 7-15 days to within 1 day, meeting the strict requirements of engineering audit and completion acceptance on data traceability and effectiveness, and promoting the BIM data management of the construction industry to be standardized and standardized. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is a schematic diagram of a building construction operation and maintenance BIM data stage identification tracing method of embodiment 1 of the present application. DETAILED DESCRIPTION The present application will be further described in detail below with reference to the accompanying drawings.

[0024] Embodiment 1 Reference Figure 1The embodiment of the application is a stage identification and tracing method for building construction operation and maintenance BIM data. The embodiment realizes the through management of building BIM data in the design, construction and operation and maintenance stages through seven core steps of full-process data collection, standardized processing, unique identification binding, stage coding, version and responsibility recording, evolution link construction and tracing report generation, solves the problems of data fragmentation, fuzzy responsibility and difficult tracing, and forms a full life cycle traceable data management system.

[0025] Specifically, the following steps are included: Step 1: Obtain BIM model data in the design, construction and operation and maintenance stages 1. Multi-source heterogeneous data collection Through a distributed interface adaptation layer, design software (Revit, Archicad), construction management platform (Navisworks, smart construction site system), and operation and maintenance system (FM software, Internet of Things sensor platform) are connected to collect multi-format data, including: Design stage: IFC, rvt format model (including component geometry, properties, and association relationship); Construction stage: nwd format deepening model, e57 format on-site scanning point cloud data; Operation and maintenance stage: JSON format sensor data (such as equipment operating parameters), IFC format operation and maintenance model.

[0026] 2. Data source reliability verification To ensure the authenticity of the collected data, the data trust score is calculated by the following formula: Trust_score=0.4×Source_reliability+0.3×Data_completeness+0.3×Timestamp_validity Wherein, Source_reliability: data source reliability (official platform is 1.0, third-party tool is 0.70.9); Data_completeness: data completeness (mandatory attribute field coverage rate ≥ 90% is 1.0); Timestamp_validity: timestamp validity (deviation from system clock ≤ 30s is 1.0, deviation increases by 30s, and attenuation is 0.1). Only data with Trust_score≥0.8 enters the subsequent process.

[0027] 3. Incremental synchronization mechanism An incremental synchronization algorithm based on hash fingerprints is used to reduce data transmission volume: Generate global hash fingerprint for historical collected data: Global_hash = SHA256 (all components LCID set); Generate incremental hash fingerprint for newly collected data: Increment_hash = SHA256 (new / changed components LCID set); When Increment_hash ≠ Global_hash, only synchronize the difference part (new / changed component data), and the synchronization efficiency is improved by more than 60%.

[0028] Step 2: Data preprocessing for the obtained BIM model data 1. Standardization conversion of geometric data Through the non-uniform rational B-spline (NURBS) surface fitting algorithm, the geometric models of different software are converted into the unified IFC standard format, and the fitting error is controlled to be: Fit_error = max(|actual surface-fitted surface|) ≤ 0.001m, to ensure that the converted model meets the engineering precision requirements and the geometric features are not distorted.

[0029] 2. Attribute semantic alignment Based on the building domain ontology library IFCowl, cross-stage attribute mapping is realized, and the weighted Levenshtein edit distance is used to calculate the attribute name similarity: Sim_name(a,b) = 1-(edit distance / max(len(a),len(b))) × Weight_penalty, Where, edit distance: the minimum number of insertion, deletion, and replacement operations required to convert attribute name a to b; Weight_penalty: weight penalty term, add 0.3 penalty when the core attributes "material" and "size" do not match; When Sim_name ≥ 0.7, automatic mapping (such as "designer" mapped to "creation responsible person"), otherwise trigger manual intervention.

[0030] 3. Redundant data cleaning Combined with three-dimensional bounding box collision detection and attribute cosine similarity, duplicate components are identified: Is_duplicate = (bounding box overlap rate ≥ 0.9) ∧ (attribute cosine similarity ≥ 0.95) Bounding box overlap rate = two-component three-dimensional bounding box intersection volume / union volume; Attribute cosine similarity = (two-component attribute vector dot product) / (attribute vector length product).

[0031] When the conditions are met, the latest version of the component is retained and the duplicate data is deleted.

[0032] 4. Outlier processing For attribute values beyond the reasonable range, the improved Zscore algorithm is used to identify: Z = (x - μ) / σ Where x is the attribute value to be detected, μ is the average attribute value of the same type of component, and σ is the standard deviation. When |Z|>3, it is determined to be an abnormal value, marked and triggered for manual verification (if it is confirmed to be an input error, it is corrected, otherwise it is retained and the reason is noted).

[0033] Step 3: Component ID assignment and binding based on preprocessed BIM model data 1. Lifecycle ID (LCID) generation A multi-level encryption hash algorithm is used to generate a tamper-proof unique identifier to ensure uniqueness across stages: Indicated as: LCID = Base64(H3(H2(H1(ProjectID + TypeCode + OriginalID + CreateTime) + SpatialCode) + CheckCode)) Where H1: MD5 algorithm (128 bits), encrypting ProjectID, TypeCode (M01 represents concrete beam), OriginalID, and CreateTime; SpatialCode: convert three-dimensional coordinates to plane coordinates through UTM projection, then generate 20-bit spatial code (precision up to 1m) through GeoHash encoding; H2: SHA256 algorithm (256 bits), fusing H1 results and SpatialCode; CheckCode: 8-bit check code generated based on CRC32 check, used to verify the integrity of LCID; H3: truncate H2 results to 64 bits, and generate 32-bit string (LCID) through Base64 encoding.

[0034] 2. Cross-stage ID binding Based on the improved ICP iterative closest point algorithm combined with attribute feature fusion, the precise binding of construction / maintenance components and design components is realized: (1) Point cloud registration: first obtain the initial transformation matrix through sampling consistency initial registration (SACIA), then use ICP iterative optimization to make the average distance of point cloud: Mean_distance≤0.05m (satisfies the construction accuracy requirement) (2) Binding confidence calculation: Confidence = 0.5 x ICP registration degree + 0.3 x attribute matching degree + 0.2 x manual confirmation weight Where, ICP registration degree = 1 (Mean_distance / Component diagonal length); Attribute matching degree: cosine similarity of core attributes (material, size, function); Artificial confirmation weight: set to 1.0 after artificial confirmation when automatic matching fails (Confidence < 0.85); Automatically bind when Confidence ≥ 0.85, otherwise trigger manual intervention (such as confirming the binding relationship by comparing BIM models).

[0035] Step 4: Generate phase identification code based on component ID 1. Phase code (S) generation Use a ternary bit vector to represent the multi-phase superposition state to avoid the limitations of single-phase identification: Design phase: S = 001; Construction phase: S = 010; Operation and maintenance phase: S = 100; Support multi-phase superposition (when there is design change in the construction phase, S = 011 represents "design + construction").

[0036] 2. State code (T) generation Construct a four-dimensional state matrix to comprehensively describe the characteristics of component data in the current phase: T = [PhaseState, DataValidity, DutyState, ChangeType] Where, PhaseState (in-phase state): design phase (01 = initial design, 02 = approved, 03 = design change, 04 = frozen release); Construction phase (11 = prefabrication, 12 = site installation, 13 = acceptance, 14 = construction change); Operation and maintenance phase (21 = normal operation, 22 = maintenance, 23 = replace parts, 24 = scrap and stop); DataValidity (data validity): 0 = invalid, 1 = to be verified, 2 = verified, 3 = frozen; DutyState (responsibility state): 0 = not sure, 1 = sure, 2 = in dispute, 3 = archived; ChangeType (change type): 0 = no change, 1 = design change, 2 = construction optimization, 3 = operation and maintenance adjustment.

[0037] 3. Global unique identification generation and encryption Generate a globally unique identifier by combining LCID, phase code, state code, and timestamp, and encrypt it through the national SM4 algorithm: FullID = SM4_Encrypt(LCID + "-" + S + "-" + T + "" + TS + "-" + Nonce) TS: 64-bit nanosecond-level timestamp, avoiding time conflicts by being accurate to 10⁻ 9 seconds; Nonce: 32-bit random number, used to prevent replay attacks; After encryption, it is encoded into a transmissible string through Base64, ensuring the security of the identifier in transmission and storage.

[0038] Step 5: Record component version information and responsibility information according to phase identification code 1. Semantic version management Based on the change impact quantification model, automatically generate version number (format: V major version. minor version. revision version): Change impact calculation: Impact = 0.5 × Geom_impact + 0.3 × Attr_impact + 0.2 × Rel_impact Where, Geom_impact (geometric change impact) = volume change rate × 0.6 + position deviation rate × 0.4 (volume change rate = |current volume original volume| / original volume; position deviation rate = centroid distance / component maximum size); Attr_impact (attribute change impact) = core attribute change number / total core attribute number (core attributes include material, strength, function, etc.); Rel_impact (relational impact) = number of affected related components / total number of related components (such as the number of plates and columns affected by beam changes).

[0039] Version number update rules: When Impact ≥ 0.7: major version + 1 (such as V1.0.0 → V2.0.0); When 0.3 ≤ Impact < 0.7: minor version + 1 (such as V1.0.0 → V1.1.0); When Impact < 0.3: revision version + 1 (such as V1.0.0 → V1.0.1).

[0040] 2. Responsibility chain tracing record Use a weighted responsibility allocation algorithm to record the full-process responsibility subject and clarify the responsibilities at each stage: Duty_weight = Σ(Operation_type × Time_factor × Role_weight) Among them, Operation_type (operation type weight): creation = 0.4, modification = 0.3, audit = 0.2, confirmation = 0.1; Time_factor (time decay factor): e^(-λ×Δt) (λ = 0.01, Δt is the number of days from the current time, to ensure that the weight of recent operation responsibility is higher); Role_weight (role weight): designer = 1.0, construction staff = 0.9, supervisor = 0.85, maintenance staff = 0.8. The responsible subject is sorted in descending order according to Duty_weight, and the one with the highest weight is the main responsible person, and the rest are secondary responsible persons, forming a responsibility chain archive.

[0041] Step 6: Establishing a multi-stage evolution chain table based on recorded information 1. Chain table structure design Adopting a bidirectional circular chain table + block chain storage structure ensures that data cannot be tampered with and can be traced back, and each node contains: Node={ LCID: unique identification of the component, PrevHash: previous node hash value (SHA-256), NextHash: next node hash value (SHA-256), StageInfo: {S: stage code, T: status code, TS: timestamp}, VersionInfo: {major version, minor version, revision version, Impact: change impact}, DutyChain: responsibility chain information (including Duty_weight of each responsible person), NodeHash: SHA-256(PrevHash+StageInfo+VersionInfo+DutyChain) (current node hash value) } 2. Node association determination Determine the association between nodes through temporal and spatial similarity (such as the connection between construction stage nodes and design stage nodes): TemporalSim = 1 - |TS_current - TS_prev| / (TS_max - TS_min) (temporal similarity) SpatialSim = 1 - (spatial distance / maximum size of the component) (spatial similarity) LinkSim = 0.6 × TemporalSim + 0.4 × SpatialSim (association similarity) Auto-link nodes when LinkSim ≥ 0.8 (PrevHash and NextHash point to each other), otherwise manually confirm the association relationship.

[0042] 3. Change impact propagation analysis Based on the complex network node influence model, calculate the influence range of component changes on associated nodes: Influence = Degree × Centrality × Impact Where Degree: node degree (number of associated components); Centrality: intermediate centrality (measures the key degree of the node in the network, range 01); Impact: change impact degree of the current node (calculated value in step 5). When Influence ≥ 0.5, mark as "key influence node" and add influence propagation path markers in the linked list (such as "Node A → Node B → Node C").

[0043] Step 7: Generate a traceability report based on the multi-stage evolution chain table 1. Multi-dimensional query and filtering Support query by combining dimensions such as stage, time, responsible person, and change type, and calculate the query match score: Match_score = Σ(Weight_d × Sim_d) Where d is the query dimension (such as stage, time), Weight_d is the dimension weight (stage = 0.3, time = 0.25, responsible person = 0.25, change type = 0.2), and ΣWeight_d = 1; Sim_d is the dimension similarity: stage dimension: 1.0 for complete match, 0.6 for partial match (such as "design + construction" containing "design"); Time dimension: use Gaussian function to calculate distance similarity (Sim = e^((t_current-t_query)² / (2σ²)), σ is 1 / 3 of the time window); Responsible person dimension: 1.0 for complete match, 0.5 for secondary match; Change type dimension: 1.0 for complete match, otherwise 0. Include results with Match_score ≥ 0.6 in the report.

[0044] 2. Visualization report generation Timeline visualization: Map the evolution chain table nodes to the timeline according to the timestamp (TS), connect the associated nodes with a cubic Bezier curve, and the curve curvature = Impact × 0.5 (the greater the curvature, the more significant the change impact); Responsibility heat map: Generate a responsibility heat map based on Duty_weight, with color depth positively correlated with weight, and RGB value calculation formula: RGB=(255×(1-Duty_weight),0,0), wherein the higher the weight, the deeper the red color, intuitively showing the responsibility distribution; Influence tree: take the target component as the root node, generate a hierarchical tree diagram according to the Influence value, and the node size is proportional to the influence degree (area = Influence x 100).

[0045] 3. Report integrity check Generate a report hash value (Report_hash = SHA256 (report content)) and compare it with the node hash chain stored in the blockchain: If Report_hash≡NodeHash1×NodeHash2×...×NodeHashn, the report has not been tampered with, and the "trusted identifier" is added; otherwise, it is marked as "untrusted", and the difference position is prompted.

[0046] Embodiment 2 The embodiment provides a stage identification tracing system for building construction operation and maintenance BIM data, comprising: The data acquisition module is configured to A computer readable storage medium, wherein a plurality of instructions are stored, the instructions are suitable for being loaded and executed by a processor of a terminal device, and the instructions implement the stage identification tracing method for building construction operation and maintenance BIM data.

[0047] A terminal device, comprising a processor and a computer readable storage medium, the processor is used to implement instructions; the computer readable storage medium is used to store a plurality of instructions, the instructions are suitable for being loaded and executed by the processor, and the instructions implement the stage identification tracing method for building construction operation and maintenance BIM data.

[0048] The above are preferred embodiments of the present application, not limited to the protection scope of the present application, therefore: any equivalent changes made according to the structure, shape and principle of the present application should be covered within the protection scope of the present application.​

Claims

1. A method for phase identification and traceability of BIM data in building construction and operation, characterized in that, The method comprises the following steps: acquiring BIM model data of design stage, construction stage and operation and maintenance stage; data preprocessing of the acquired BIM model data; component ID allocation and binding according to the preprocessed BIM model data; stage identification code generation based on the component ID; component version information and responsibility information recording according to the stage identification code; establishing a multi-stage evolution linked list based on the recorded information; generating a traceability report according to the multi-stage evolution linked list.

2. The method according to claim 1, wherein, The BIM model data of the design stage, the construction stage and the operation and maintenance stage is acquired by connecting the design software, the construction management platform and the operation and maintenance system through a distributed interface adaptation layer, collecting IFC, rvt, nwd, e57 point cloud, JSON sensor data format data, and ensuring data authenticity through data source credibility verification during the collection process; at the same time, an incremental synchronization algorithm based on hash fingerprints is used to avoid repeated transmission of full data, wherein a global hash fingerprint Global_hash is generated for historical collected data; an incremental hash fingerprint Increment_hash is generated for newly collected data; when Increment_hash≠Global_hash, only the different parts are synchronized.

3. The method according to claim 2, wherein, The data preprocessing of the acquired BIM model data comprises the following steps: converting the geometric models of different software into a unified IFC standard format through a non-uniform rational B-spline surface fitting algorithm, controlling the fitting error to make Fit_error=max(|actual surface-fitted surface|)≤0.001m; realizing cross-stage attribute mapping based on the building domain ontology library, calculating the attribute name similarity using the weighted Levenshtein edit distance; combining three-dimensional bounding box collision detection and attribute cosine similarity for repeated component identification; finally, using an improved Zscore algorithm to identify and mark the attribute values that exceed the reasonable range.

4. The method of claim 3, wherein, The component ID allocation and binding according to the preprocessed BIM model data comprises the following steps: using a multi-level encryption hash algorithm to generate a tamper-proof unique identifier to ensure cross-stage uniqueness, represented as: LCID=Base64(H3(H2(H1(ProjectID+TypeCode+OriginalID+CreateTime)+SpatialCode)+CheckCode)); realizing precise binding of construction and operation components and design components based on an improved ICP iterative closest point algorithm combined with attribute feature fusion, wherein for point cloud registration, an initial transformation matrix is obtained through sampling consistency initial registration SACIA, and then ICP iteration optimization is used to make the average distance of point clouds; the binding confidence is calculated to determine whether manual intervention is needed.

5. The method of claim 4, wherein, The phase identification code generation based on component ID includes first phase code generation, wherein a ternary bit vector is used to represent the multi-phase superposition state, design = 001, construction = 010, and operation and maintenance = 100, and when there is a design change in the construction phase, S = 011 (design + construction); then state code generation, wherein a four-dimensional state matrix is constructed, including intra-phase state, data validity, responsibility state and change type; finally, a globally unique identification is generated by combining LCID, phase code, state code and timestamp, and is encrypted by SM4 algorithm, represented as FullID = SM4_Encrypt(LCID + "" + S + "" + T + "" + TS + "" + Nonce), wherein TS is a 64-bit nanosecond-level timestamp, Nonce is a random number, and after encryption, it is encoded into a transmissible string by Base64.

6. The method of claim 5, wherein, The component version information and responsibility information recording according to the phase identification code includes automatically generating a version number based on a change impact quantification model, and the change impact calculation is represented as Impact = 0.5 × Geom_impact + 0.3 × Attr_impact + 0.2 × Rel_impact, wherein Geom_impact is geometric change impact, Attr_impact is attribute change impact, and Rel_impact is correlation impact; and a weighted responsibility allocation algorithm is used to record the full-process responsibility subject, represented as Duty_weight = Σ(Operation_type × Time_factor × Role_weight), wherein Operation_type is operation type weight, specifically, creation = 0.4, modification = 0.3, audit = 0.2, and confirmation = 0.1; Time_factor is a time decay factor, specifically (e^(-λ × Δt), λ = 0.01; Δt is the number of days from the current time; Role_weight is role weight; the responsibility subject is sorted according to Duty_weight, and the one with the highest weight is the main responsible person.

7. The method according to claim 6, wherein, The multi-phase evolution chain table is established based on the recorded information, including using a bidirectional circular linked list plus a blockchain storage structure, each node containing component unique identification LCID, previous node hash value PrevHash, next node hash value NextHash, version VersionInfo and responsibility chain information DutyChain, and after determining the node association based on the space-time similarity, the influence range of component changes on associated nodes is calculated based on the complex network node influence mechanism, when the influence factor Influence ≥ 0.5, it is marked as a key influence node, and an influence propagation path marker is added in the chain table.

8. The method according to claim 7, wherein, The generation of the traceability report according to the multi-stage evolution chain table comprises mapping the evolution chain table nodes to a time axis according to timestamps, connecting the associated nodes by using a cubic Bezier curve, and reflecting the change influence degree by the curve curvature; generating a responsibility heat map based on Duty_weight, wherein the color depth is positively correlated with the weight, and the RGB value=(255×(1-Duty_weight), 0, 0); taking a target component as a root node, generating a hierarchical tree diagram according to the Influence value, and making the node size proportional to the influence degree; and performing report integrity checking after the generation of the traceability report, wherein a report hash value is generated and compared with a node hash value stored in a blockchain, so as to ensure that the report is not tampered with.

9. The method of claim 8, wherein, The generation of the traceability report according to the multi-stage evolution chain table further comprises performing query matching degree calculation according to a multi-dimensional combined query mode of received stages, times, and responsibility person change types, and representing the query matching degree calculation as Match_score=Σ(Weight_d×Sim_d), wherein d is a query dimension, ΣWeight_d=1, and Sim_d is a dimension similarity, wherein the time dimension adopts a Gaussian function to calculate distance similarity, and the results with Match_score≥0.6 are included in the report.

10. A construction operation and maintenance BIM data phase identification tracing system, characterized in that, The method comprises the following steps: a data acquisition module configured to acquire BIM model data of a design stage, a construction stage, and an operation and maintenance stage; a data preprocessing module configured to perform data preprocessing on the acquired BIM model data; a binding module configured to perform component ID allocation binding according to the preprocessed BIM model data; an identification code module configured to generate stage identification codes based on the component IDs; a recording module configured to record component version information and responsibility information according to the stage identification codes; a chain table module configured to establish a multi-stage evolution chain table based on the recorded information; a traceability module configured to generate a traceability report according to the multi-stage evolution chain table.

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