Constructional engineering data sharing method and system based on cloud platform

Abstract

The invention relates to the technical field of building information, in particular to a building engineering data sharing method and system based on a cloud platform, and the method comprises the following steps: extracting an initial model and structural constraints based on a building engineering full-life-cycle data specification, analyzing the data efficiency stability and cross-stage connection capability under a difference scene, recognizing key fracture nodes, and carrying out the construction engineering data sharing. And optimizing a data structure and sharing response through hierarchical indexing and a prediction map, analyzing key performance indexes and adjusting configuration, and obtaining a data sharing performance optimization trend analysis conclusion, in the method, through accurate data description specifications and intelligent data organization, the efficiency and accuracy of building engineering data sharing are improved, the data organization is identified and optimized, and the data sharing performance optimization trend analysis conclusion is obtained. The problems of data redundancy and unsmooth connection are solved, the data connection capability is enhanced, the information island phenomenon is reduced, the response speed is optimized, cross-stage data analysis improves information collaboration, the flexibility and real-time response of constructional engineering management are enhanced, and the problems of information sharing lag and conflict are solved.

Description

Technical Field

[0001] This invention relates to the field of building information technology, and in particular to a method and system for sharing building engineering data based on a cloud platform. Background Technology

[0002] The field of building information technology mainly involves the digital acquisition, organization, storage, transmission, and utilization of various types of information throughout the entire lifecycle of a building project. This includes drawings and parameter data from the engineering design phase, progress and quality data from the construction phase, and inspection and record data from the supervision phase. Its core objective is to achieve standardized expression and collaborative use of building project data across different participating entities and business processes through information technology, thereby supporting project management activities. Traditional building project data sharing methods rely on information systems built by each participating unit to exchange data based on the needs of design, construction, and supervision. Data such as design documents, construction records, and supervision reports are transmitted through manual input, file copying, email transmission, or point-to-point interfaces. This data is stored in fixed-format documents or dedicated data tables, and data processing and conversion are completed manually or according to pre-defined rules. When sharing is needed, data is integrated between different systems through centralized aggregation or hierarchical reporting.

[0003] Current technologies in the field of building information systems rely on information systems independently built by each participating unit. Data exchange is achieved through manual entry, file copying, and email transmission. While these methods meet basic data sharing needs, they have significant limitations in practice. First, the use of fixed-format documents and dedicated data tables for storage lacks flexibility during information transmission, leading to frequent information delays or data conflicts during cross-departmental and cross-phase data integration. Second, relying on manual or pre-defined rules for data processing and conversion is not only inefficient but also prone to errors, affecting the accuracy and timeliness of information sharing. Traditional methods, which rely on hierarchical reporting or centralized aggregation to connect different systems, are prone to data redundancy during transmission and cannot quickly respond to real-time changes in project requirements, severely hindering the efficient operation of project management activities. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a cloud-based method for sharing construction engineering data, comprising the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a cloud platform-based method for sharing construction engineering data, comprising the following steps: S1: Based on the data description specifications for the entire life cycle of building engineering, extract the initial data model and structural constraints, identify multiple sets of candidate data organization patterns, and perform preliminary screening through the engineering logic consistency verification mechanism to obtain a data organization dataset that meets the constraints. S2: Based on the constrained data organization dataset, extract the data calling efficiency and structural stability of each data organization pattern under differentiated engineering scenarios, analyze the cross-stage data connection capability and redundancy control level, identify key data breakpoints, and obtain the key node data association feature set. S3: Based on the key node data association feature set, a hierarchical index reconstruction strategy is adopted to adjust the data storage and retrieval path, construct a data association prediction map, and optimize the data organization structure and sharing response speed by adjusting the logical hierarchy distribution to obtain the building engineering data sharing architecture scheme. S4: Based on the aforementioned building engineering data sharing architecture scheme, extract data call failure records and structural conflict logs from the original engineering cases, test the robustness of the architecture scheme, analyze its continuous service capability in the engineering environment, and obtain a data table of results after data sharing performance optimization.

[0005] As a further embodiment of the present invention, the constrained data organization dataset includes data hierarchy definition, entity relationship mapping rules, and cross-stage reference constraints; the key node data association feature set includes data break location identifier, reference failure frequency, and structural coupling strength; the construction engineering data sharing architecture scheme includes a reconstructed logical hierarchy structure, index path configuration strategy, and sharing response latency index; and the data table of results after data sharing performance optimization includes robustness assessment conclusions and environmental continuity service capability analysis data.

[0006] As a further aspect of the present invention, the step of organizing the data dataset that satisfies the constraints specifically includes: S101: Based on the data description specifications for each stage of the entire life cycle of a building project, extract the hierarchical division rules, entity attribute definitions, and reference boundary conditions of the initial data model, and construct multiple sets of candidate data organization patterns; S102: Based on the multiple sets of candidate data organization patterns, mark the structural constraints of each pattern, eliminate patterns that violate engineering logic consistency, store them in the candidate scheme database, and generate a candidate scheme sequence. S103: Based on the candidate scheme sequence, invoke the engineering logic consistency verification mechanism to perform structural compliance verification on each data organization mode, filter out the scheme combinations that meet the constraints, store them in the scheme database, update the verification status identifier, and obtain the data organization dataset that meets the constraints.

[0007] As a further aspect of the present invention, the step of associating the key node data with the feature set specifically includes: S201: Based on the data organization dataset that meets the constraints, extract the data call response time, structural change frequency and cross-stage reference success rate of each data organization pattern under differentiated engineering scenarios, analyze its data continuity and structural evolution risks, identify key data break points, and obtain a data break point distribution map. S202: Based on the data fracture node distribution map, extract the location and frequency of reference failure events, combine with the structural coupling strength assessment model, analyze potential data isolation risks, record key data association features, and obtain a key node data association feature set.

[0008] As a further aspect of the present invention, the steps of the building engineering data sharing architecture scheme are as follows: S301: Based on the key node data association feature set, extract the association fracture pattern of the data fracture node, analyze the potential relationship between data, identify influencing factors and optimize the association and visualization of the map, and construct a data association prediction map. S302: Based on the data association prediction map, adjust the logical hierarchy distribution, optimize the index path configuration, analyze the response efficiency of the path configuration, reconstruct and optimize the path, balance the data call and storage paths, optimize the data flow and test the response speed after optimization, and obtain the preliminary optimized architecture. S303: Based on the preliminary optimized architecture, analyze the reconstructed logical hierarchy and index path configuration, compare the shared response latency index before and after optimization, statistically analyze the optimization effect, and obtain the building engineering data sharing architecture scheme.

[0009] As a further aspect of the present invention, the steps for creating the data table after performance optimization of data sharing are as follows: S401: Based on the aforementioned building engineering data sharing architecture scheme, extract data call failure records and structural conflict logs from the original engineering cases, including multi-professional collaboration interruption events and version misalignment cases, test the robustness of the architecture scheme, analyze its adaptability in the engineering environment, and obtain robustness evaluation conclusions. S402: Based on the robustness assessment conclusions and combined with the environmental continuous service capability analysis model, predict the service stability trend of the shared architecture throughout the project lifecycle, statistically analyze the fluctuation range of key performance indicators, and obtain a data table of results after data sharing performance optimization.

[0010] As a further aspect of the present invention, the environmental continuous service capability analysis model refers to combining robustness assessment conclusions, employing a key service stability prediction algorithm, analyzing the service stability trend of the data sharing architecture throughout the entire project lifecycle, statistically analyzing the fluctuation range of key performance indicators, and obtaining prediction results. The service stability trend refers to the data results obtained by predicting the changes in service stability of the shared architecture at different time points based on the robustness assessment conclusions and the environmental continuous service capability analysis model.

[0011] As a further aspect of the present invention, the method further includes step S5: S5: Based on the data table of the results after the performance optimization of data sharing, extract the key performance indicators of the optimized architecture, analyze its performance in differentiated engineering scenarios, screen service fluctuation scenarios, combine data correlation prediction graphs, adjust the architecture configuration, and obtain the data sharing performance optimization trend analysis conclusion. The conclusions of the data sharing performance optimization trend analysis include the changing trends of key performance indicators, scenario adaptability analysis, and assessment of architecture optimization potential.

[0012] As a further aspect of the present invention, the steps for drawing conclusions on the data sharing performance optimization trend are as follows: S501: Based on the data table of the results after the performance optimization of the data sharing, extract the key performance indicator data under the differentiated engineering scenarios, remove outliers, match the scenario number, analyze the service fluctuation range, and obtain the performance indicator set. S502: Based on the performance index set, classify and arrange them according to engineering scenario type, identify scenarios with large service fluctuations, analyze their data association breakage characteristics, combine with the architecture optimization potential assessment model, identify optimization space, and obtain the data sharing performance optimization trend analysis conclusion.

[0013] A cloud-based construction engineering data sharing system includes: The data organization and filtering module extracts the hierarchical structure definition, entity relationship rules and reference constraints from the initial data model according to the engineering life cycle data description specification, groups the candidate data organization patterns, checks whether each group of patterns meets the structural constraints, filters the combination of schemes that meet the requirements, and obtains the data organization dataset that meets the constraints. Based on the constrained data organization dataset, the data association analysis module extracts the data retrieval efficiency and structural stability data of each data organization mode under differentiated engineering scenarios, and analyzes the cross-stage reference success rate, structural change sensitivity and redundancy control capability under each scheme. It also compares and analyzes the data continuity of the schemes, marks the break risk nodes, and obtains the key node data association feature set. Based on the key node data association feature set, the shared architecture generation module reconstructs and optimizes the logical hierarchy, index path and reference boundary at the risk node. By analyzing the coupling strength between the data call path and the local structure, it dynamically adjusts the hierarchical distribution and optimizes the data organization structure to obtain the building engineering data sharing architecture scheme. The robustness verification module, based on the aforementioned building engineering data sharing architecture scheme, extracts call failure records and structural conflict logs from the original engineering cases. Combined with multi-professional collaboration interruption data and version misalignment events, it analyzes the stability and continuous service capability of the shared architecture in the engineering environment, identifies potential service failure points, and obtains a data table of results after data sharing performance optimization. Based on the data table of the results after the performance optimization of data sharing, and combined with the parameters of changes in engineering scenarios, the scenario adaptation module analyzes the scenario adaptation performance and service fluctuation characteristics of the shared architecture, reconstructs the index paths that exceed the response latency limit in the target scenario, optimizes the architecture configuration, improves the sharing adaptability, and obtains the conclusion of the data sharing performance optimization trend analysis.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention improves the efficiency and accuracy of construction engineering data sharing through precise data description specifications and intelligent data organization methods. By using a data model and structural constraints based on the entire lifecycle, multiple candidate data organization patterns are identified, enabling preliminary screening and optimization of data organization methods. This effectively solves the problems caused by data redundancy and poor connectivity in existing technologies. By analyzing data retrieval efficiency and structural stability, the invention enhances data connectivity in different engineering scenarios, reduces information silos caused by data fragmentation, and optimizes data sharing response speed. Furthermore, the architecture, based on precise analysis of cross-stage data, strengthens the collaborative use of information, improves the flexibility and real-time response capabilities of construction engineering management, and effectively solves the problems of information sharing lag and conflict in existing technologies. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7This is a system module diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] Please see Figure 1 This invention provides a cloud-based method for sharing construction project data, comprising the following steps: S1: Based on the data description specifications for the entire life cycle of building engineering, extract the initial data model and structural constraints, identify multiple sets of candidate data organization patterns, and perform preliminary screening through the engineering logic consistency verification mechanism to obtain a data organization dataset that meets the constraints. S2: Based on the constrained data organization dataset, extract the data calling efficiency and structural stability of each data organization pattern under differentiated engineering scenarios, analyze the cross-stage data connection capability and redundancy control level, identify key data breakpoints, and obtain the key node data association feature set; S3: Based on the key node data association feature set, a hierarchical index reconstruction strategy is adopted to adjust the data storage and retrieval path, construct a data association prediction map, and optimize the data organization structure and sharing response speed by adjusting the logical hierarchy distribution to obtain the building engineering data sharing architecture scheme; S4: Based on the data sharing architecture scheme for building engineering, extract data call failure records and structural conflict logs from the original engineering cases, test the robustness of the architecture scheme, analyze its continuous service capability in the engineering environment, and obtain the data table of results after data sharing performance optimization. S5: Based on the data table of results after data sharing performance optimization, extract the key performance indicators of the optimized architecture, analyze its performance in differentiated engineering scenarios, screen service fluctuation scenarios, combine data correlation prediction graphs, adjust the architecture configuration, and obtain the conclusion of data sharing performance optimization trend analysis.

[0023] The constrained data organization dataset includes data hierarchy definition, entity relationship mapping rules, and cross-stage reference constraints. The key node data association feature set includes data break location identifiers, reference failure frequency, and structural coupling strength. The construction engineering data sharing architecture scheme includes the reconstructed logical hierarchy structure, index path configuration strategy, and sharing response latency index. The data table of results after data sharing performance optimization includes robustness assessment conclusions and environmental continuous service capability analysis data. The data sharing performance optimization trend analysis conclusions include the changing trends of key performance indicators, scenario adaptability analysis, and architecture optimization potential assessment.

[0024] Please see Figure 2 The specific steps for organizing a dataset to satisfy the constraints are as follows: S101: Based on the data description specifications for each stage of the entire life cycle of a building project, extract the hierarchical division rules, entity attribute definitions, and reference boundary conditions of the initial data model, and construct multiple sets of candidate data organization patterns; Based on the data description specifications for each stage of the entire lifecycle of a building project, the data parsing unit extracts geometric parameters, material parameters, progress management data, and material consumption data for the planning and design, construction, and operation and maintenance management stages, respectively. The hierarchical partitioning rule extraction module parses the root, child, and leaf node relationships of the initial data model, while the entity attribute definition unit clarifies the physical and functional attributes of beams, columns, and slabs. Reference boundary conditions are established by scanning the reference relationships between data, clarifying the scope of cross-level calls. The multi-group candidate data organization pattern construction module uses permutation and combination algorithms to generate different data architecture topologies based on the extracted hierarchical rules and attribute definitions. Taking a commercial complex project containing 50,000 building component entities as an example, five hierarchical division rules were extracted, and 12 entity attribute definitions were included, including structural strength and fire resistance rating. The cross-professional call depth was set to no more than three levels based on the reference boundary conditions. Based on the basic parameters, four candidate data organization patterns were constructed through combination generation logic. Pattern 1 focuses on the nesting relationship of physical space, and Pattern 2 focuses on the logical connection of electromechanical system. The number of hierarchical division rules was obtained by parsing the inheritance relationship of IFC standard file, and the number of entity attribute definition items was obtained by scanning the metadata fields of the model database.

[0025] S102: Based on multiple candidate data organization patterns, mark the structural constraints of each pattern, eliminate patterns that violate engineering logic consistency, store them in the candidate solution database, and generate a candidate solution sequence. Based on the four generated candidate data organization patterns, the structural scanning component analyzes the internal topology of each pattern. For each pattern, the constraint marking unit sets constraints on the uniqueness of parent and child nodes, the data type of attribute fields, and the integrity of reference relationships. The logical consistency check module compares the topology of each pattern with the engineering logic rule base to identify violations of engineering logic structure. If a pattern contains a suspended load-bearing column or a pipe passing through electrical equipment, it is determined to be a violation of consistency. The filtering module removes patterns with logical conflicts and retains patterns that conform to basic logic. The retained patterns are sorted according to the generation timestamp order and structural complexity score and stored in the candidate scheme database to generate a candidate scheme sequence. For the four candidate patterns of the aforementioned commercial complex project, the structural constraint condition sets that the structural strength field must be numerical data. During the inspection process, it was found that Pattern 3 had 150 logical errors in beam-column node connections, and Pattern 4 had 200 errors in pipe connection direction. Both were determined to violate engineering logic consistency and were removed. Patterns 1 and 2 were retained. Pattern 1 had a structural complexity score of 85 points, and Pattern 2 had a structural complexity score of 90 points. Patterns 1 and 2 were stored in the candidate scheme database to form a candidate scheme sequence containing two valid schemes.

[0026] S103: Based on the candidate scheme sequence, call the engineering logic consistency verification mechanism to verify the structural compliance of each data organization mode, filter the scheme combination that meets the constraints, store it in the scheme database, update the verification status flag, and obtain the data organization dataset that meets the constraints. For Mode 1 and Mode 2 in the candidate solution sequence, the engineering logic consistency verification module performs deep structural compliance verification, covering the legality of attribute values ​​and the matching degree of business rules. The verification unit checks whether the concrete strength grade is within the reasonable range of C15 to C80 and whether the fire compartment area meets the specification requirements. The screening module confirms that only the combination of all verification items passes is the final solution, which is stored in the solution database and the verification status is updated to compliance. For Mode 1, 500 detailed rule verifications are performed. Regarding the concrete strength grade verification, the component attribute values ​​are read, and the numerical comparison logic is used to determine whether it is within the range of 15 MPa to 80 MPa. If a component strength is marked as 10 MPa, the verification fails. In this verification, all key component attributes of Mode 1 are within the reasonable range, and the verification pass rate is 100%. In the fire compartment area verification of Mode 2, it was found that the area of ​​a certain compartment is 3000 square meters, which exceeds the benchmark value of 2500 square meters and requires local adjustment. Mode 1 is confirmed as fully satisfying the constraint data organization mode, stored in the solution database, and the verification status is updated to 1, resulting in a dataset containing Mode 1 that satisfies the constraint data organization.

[0027] Please see Figure 3 The specific steps for associating key node data with feature sets are as follows: S201: Based on the data organization dataset that meets the constraints, extract the data call response time, structural change frequency and cross-stage reference success rate of each data organization pattern in differentiated engineering scenarios, analyze its data continuity and structural evolution risks, identify key data breakpoints, and obtain a data breakpoint distribution map. Based on a constrained data organization dataset, the key node identification module performs stress tests on Mode 1 under simulated differentiated engineering scenarios, covering high-concurrency data reading and frequent command change scenarios. The monitoring unit records the data node call response time in real time, counts the frequency of structural changes, and calculates the cross-stage reference success rate. The analysis module judges data continuity and structural evolution risk based on indicators. If the response time exceeds the set threshold or the reference success rate is lower than the standard, the data continuity is judged to be poor; if the change frequency is too high, the risk is judged to be high. The identification unit defines the abnormal node as a data break node and maps it to the topology map to generate a data break node distribution map. As shown in Table 1, three typical data nodes in the project are selected for monitoring. For node ID_Wall_05, the monitoring module initiates a data request every 5 minutes for 24 consecutive hours, records the time difference from sending the request to receiving the complete data packet, and calculates the arithmetic mean to obtain the call response time. The frequency of structural changes is obtained by analyzing the database log file and counting the number of update or modification commands executed for this node. The cross-stage reference success rate is obtained by simulating 1000 cross-stage data mapping operations and counting the percentage of times the operation is completed without errors. For the shear wall node of the main tower, the call response time is 450 milliseconds, the structural change frequency is 12 times per hour, the fracture judgment benchmark value is set to a response time of 300 milliseconds and a change frequency of 10 times per hour. 450 milliseconds is greater than 300 milliseconds, and 12 times per hour is greater than 10 times per hour. Therefore, the node is judged to have a data fracture risk. The curtain wall embedded parts are similarly identified as fracture nodes. Table 1: Key Data Node Monitoring Table Node Name Node number Call response time (milliseconds) Structural change frequency (times / hour) Cross-stage citation success rate (%) Main tower shear wall ID_Wall_05 450 12 88.5 Basement ventilation ducts ID_Duct_22 120 2 99.0 Curtain wall embedded parts ID_Part_89 680 25 75.2 Table 1 lists the monitoring data for key data nodes.

[0028] S202: Based on the data fracture node distribution map, extract the location and frequency of reference failure events, combine with the structural coupling strength assessment model, analyze potential data isolation risks, record key data association characteristics, and obtain the key node data association feature set; Based on the data fracture node distribution map, the failure location module accurately locates the reference failure events and counts the frequency of failure events. The structural coupling strength assessment module uses a graph neural network architecture for deep analysis. The network includes an input layer, two hidden layers, and an output layer. The input layer receives the node feature matrix and adjacency matrix. The node features cover node degree and betweenness centrality indicators. The first hidden layer contains 64 neurons and uses graph convolution operators to aggregate neighborhood features. The activation function uses a linear rectified function to introduce nonlinear features. The second hidden layer contains 32 neurons and uses graph convolution operators and linear rectified functions to extract high-order structural features. The output layer contains 1 neuron and uses an S-shaped growth curve function to map the output value to the interval between 0 and 1, representing the probability of data isolation risk. Before data input, the preprocessing module exports a node relationship table from the engineering database to construct an adjacency matrix and performs standard score standardization on the node feature data. Taking the embedded parts node of the curtain wall as an example, the feature vector is input into the model. The first layer aggregates the information of neighboring nodes and truncates negative values ​​through matrix multiplication and addition. The second layer deepens the feature extraction. The output layer calculates the risk probability value of 0.85. The high risk threshold is set to 0.7. 0.85 is greater than 0.7, so the node is determined to have an extremely high risk of data isolation. The associated feature recording module compiles the fracture location, failure frequency and isolation risk value calculated by the model to obtain the key node data association feature set.

[0029] Please see Figure 4 The specific steps of the building engineering data sharing architecture solution are as follows: S301: Based on the key node data association feature set, extract the association fracture pattern of data fracture nodes, analyze the potential relationship between data, identify influencing factors and optimize the association and visualization of the map, and construct a data association prediction map. Based on the key node data association feature set, the data association prediction graph construction module extracts the fracture patterns of fracture nodes, distinguishing between attribute fractures and version fractures. The relationship analysis unit identifies the core factors affecting the association, covering data indexing methods and storage paths. The graph optimization module uses knowledge graph technology to connect discrete fracture nodes and construct a visualized data association prediction graph. For the curtain wall embedded parts node, the analysis determines that the fracture pattern is version fracture, and the influencing factor is excessive storage path depth. The calculation unit measures the current storage path depth to 6 layers and uses the graph construction algorithm to predict the data call association strength when the path depth is optimized to 3 layers. The path weight calculation logic stipulates that the association strength is equal to the basic weight divided by the square of the path length. Assuming the basic weight is 100 and the current path length is 6, the existing strength is calculated by dividing 100 by 36, resulting in approximately 2.78. After the prediction optimization, the path length is 3, and the predicted strength is calculated by dividing 100 by 9, resulting in approximately 11.11. The predicted changes are drawn in the form of a directed graph, forming the data association prediction graph.

[0030] S302: Based on the data association prediction graph, adjust the logical hierarchy distribution, optimize the index path configuration, analyze the response efficiency of the path configuration, reconstruct and optimize the path, balance the data call and storage paths, optimize the data flow and test the response speed after optimization, and obtain the preliminary optimized architecture. Based on the data association prediction map, the architecture adjustment module implemented logical hierarchical distribution adjustments, moving frequently accessed deep nodes to shallow directories. The index configuration optimization module introduced hash indexes or B-tree index mechanisms to replace linear scanning. The path reconstruction unit balanced the storage locations of hot data to avoid single-point overload. The testing module measured the data flow response speed of the optimized architecture. Taking the shear wall node of the main tower as an example, the original linear scan index scanned an average of 25,000 times to find the target among 50,000 entities. After optimization, using a B-tree index, the search depth was reduced to the logarithm of the total number of entities with the order as the base. Assuming the order is 100, the number of searches was reduced. In actual testing, 1,000 random query requests were sent to the optimized architecture. The test results showed that the average response time decreased from 450 milliseconds before optimization to 80 milliseconds. The comparison unit compared this result with the preset performance target of 100 milliseconds, confirming that the target was met. Based on the test results, the new hierarchical structure and index configuration were determined, forming the preliminary optimized architecture.

[0031] S303: Based on the preliminary optimized architecture, analyze the reconstructed logical hierarchy and index path configuration, compare the shared response latency indicators before and after optimization, statistically analyze the optimization effect, and obtain the building engineering data sharing architecture scheme. For the initial optimized architecture, the evaluation module analyzes the reconstructed logical hierarchy, calculates the average and variance of the hierarchy depth, assesses the degree of structural flattening, and compares the shared response latency index and performance improvement percentage before and after optimization, as shown in Table 2. Comparing the key indicators before and after optimization, the average response latency decreased from 450 milliseconds to 80 milliseconds. The improvement was calculated by subtracting the value after optimization from the value before optimization, dividing by the value before optimization, and then multiplying by 100, which is approximately 82.2%. The cross-stage data mapping time decreased from 15.5 seconds to 3.2 seconds, and the average depth of the data storage path decreased from 6 layers to 3 layers. The statistical module summarizes the results and confirms that the optimization effect is significant. The configuration is then solidified to generate the final building engineering data sharing architecture scheme. Table 2: Performance Comparison Before and After Architecture Optimization Performance indicators Values ​​before optimization Optimized values Increase (%) Average response latency (milliseconds) 450 80 82.2 Cross-stage data mapping time (seconds) 15.5 3.2 79.4 Average depth (layers) of data storage path 6 3 50.0 Table 2 shows the performance comparison data before and after the architecture optimization.

[0032] Please see Figure 5 The specific steps for creating the resulting data table after data sharing performance optimization are as follows: S401: Based on the data sharing architecture scheme for building engineering, extract data call failure records and structural conflict logs from the original engineering cases, including multi-professional collaboration interruption events and version mismatch cases, test the robustness of the architecture scheme, analyze its adaptability in the engineering environment, and obtain robustness evaluation conclusions. Based on the data sharing architecture scheme for building engineering, the robustness assessment module extracts historical fault data from original engineering cases, covering multi-professional collaborative interruption events and version mismatch cases. The simulation playback unit executes the fault scenarios in the new architecture scheme to test the architecture's automatic error correction and stable operation capabilities. 50 historical collaborative interruption event records are extracted, and interruption triggering conditions are injected into the new architecture. The test results show that the new architecture successfully handled 48 conflicts through the optimistic locking mechanism, and 2 conflicts required manual intervention. The robustness score is calculated by dividing the number of successful handlings by the total number of tests, i.e., 48 divided by 50, with a result of 0.96. The robustness qualification threshold is set at 0.90. 0.96 is greater than 0.90, indicating that the architecture has high robustness, and the robustness assessment conclusion is excellent.

[0033] S402: Based on the robustness assessment results and combined with the environmental continuous service capability analysis model, predict the service stability trend of the shared architecture throughout the project lifecycle, statistically analyze the fluctuation range of key performance indicators, and obtain a data table of results after data sharing performance optimization. The Environmental Continuity Service Capability Analysis Model refers to the analysis of the service stability trend of the data sharing architecture throughout the entire project lifecycle by combining the robustness assessment conclusions and using the key service stability prediction algorithm, statistically analyzing the fluctuation range of key performance indicators, and obtaining prediction results. Service stability trend refers to the data results that predict the changes in service stability of a shared architecture at different time points based on robustness assessment conclusions and environmental continuous service capability analysis models, and derive the service stability trend. Based on the robustness assessment results, the environmental sustainability service capability analysis module utilizes a long short-term memory (LSTM) network architecture to predict stability trends. The model includes an input layer, a memory network layer, a dropout layer, and a fully connected output layer. The input layer receives the system stability index sequence from the past 24 months. The memory network layer contains 128 neurons to capture long-term dependencies, with a hyperbolic tangent activation function. The dropout layer has a dropout rate of 0.2 to prevent overfitting. The fully connected output layer contains one neuron that outputs the predicted stability values ​​for future time points. The loss function is mean squared error, and the iterative parameters of the optimizer are estimated using adaptive moments with a learning rate of 0. 001. Before data input, the normalization module maps historical stability indices to the range of 0 to 1. The calculated robustness score of 0.96 is used as the current state input model to predict the service stability trend over the entire project cycle in the next 12 months. The model output shows that the stability index will remain between 0.95 and 0.97 in the next 12 months. The statistical unit calculates the fluctuation range of key performance indicators, i.e., the maximum value of 0.97 minus the minimum value of 0.95, with a fluctuation range of 0.02. Low fluctuation range indicates service stability. The predicted data and statistical results are summarized to generate a data table of results after data sharing performance optimization.

[0034] Please see Figure 6 The specific steps for analyzing data sharing performance optimization trends are as follows: S501: Based on the data table of the results after performance optimization of data sharing, extract key performance indicator data under differentiated engineering scenarios, remove outliers, match scenario numbers, analyze service fluctuation range, and obtain a set of performance indicators; Based on the data table after performance optimization of data sharing, the data extraction module obtains key performance indicator data for differentiated engineering scenarios. These scenarios cover high-frequency interactive building information model collaboration, large-volume point cloud processing, and low-latency on-site monitoring. The data cleaning unit uses statistical methods to remove outliers, with the criterion being data points whose values ​​exceed the average plus or minus three times the standard deviation. After cleaning, the data is matched with the scenario number, and the service fluctuation range is analyzed. In the building information model collaboration scenario, 1000 latency data samples are collected, with an average value of 85 milliseconds and a standard deviation of 5 milliseconds. Based on the three-standard-deviation criterion, the upper threshold is calculated as 100 milliseconds by adding 15 to 85, and the lower threshold is calculated as 70 milliseconds by subtracting 15 from 85. Data points with latency of 150 milliseconds are removed. After cleaning, the service fluctuation range for this scenario is calculated. The maximum value of the remaining data is 98 milliseconds, and the minimum value is 72 milliseconds. The fluctuation range is calculated as 26 milliseconds by subtracting the minimum value from the maximum value. This process is repeated for all scenarios to obtain the performance indicator set.

[0035] S502: Based on the performance index set, it is classified and arranged according to the engineering scenario type to identify scenarios with large service fluctuations, analyze the data correlation breakage characteristics, and combine the architecture optimization potential assessment model to identify the optimization space and obtain the data sharing performance optimization trend analysis conclusion. Based on the performance index set, the classification and arrangement module organizes data according to scenario type. The identification unit identifies scenarios with large service fluctuations as key analysis objects. The architecture optimization potential assessment module uses a multilayer perceptron model to identify the optimization space. The model includes an input layer, two fully connected hidden layers, and an output layer. The input layer receives parameters such as fluctuation amplitude, data volume, and concurrency. The two hidden layers contain 64 and 32 neurons respectively, using a linear rectified activation function. The output layer outputs an optimization potential score. Taking the point cloud processing scenario with the largest fluctuation amplitude as an example, with a fluctuation amplitude of 120 milliseconds, a data volume of 500 gigabytes, and a concurrency of 10, the parameters are input into the multilayer perceptron model. Through weighted summation and nonlinear transformation, the optimization potential score is calculated to be 0.88. The optimization potential threshold is set to 0.6. Since 0.88 is greater than 0.6, the scenario is determined to have optimization space. Comprehensive analysis indicates that the main break characteristic is large data transmission congestion, and it is recommended to introduce edge computing nodes for preprocessing. The final conclusion determines that the point cloud processing scenario is the key optimization direction for the next stage. It is expected that the fluctuation amplitude will be reduced by 50% after optimization, resulting in the data sharing performance optimization trend analysis conclusion.

[0036] Please see Figure 7 A cloud-based construction engineering data sharing system includes: The data organization and filtering module extracts the hierarchical structure definition, entity relationship rules and reference constraints from the initial data model according to the engineering life cycle data description specification, groups the candidate data organization patterns, checks whether each group of patterns meets the structural constraints, filters the combination of schemes that meet the requirements, and obtains the data organization dataset that meets the constraints. The data association analysis module extracts data on data calling efficiency and structural stability of each data organization pattern under differentiated engineering scenarios based on the data organization dataset that meets the constraints. It also analyzes the cross-stage reference success rate, structural change sensitivity and redundancy control capability under each scheme, compares and analyzes the data continuity of the schemes, marks the nodes with breakage risk, and obtains the key node data association feature set. The shared architecture generation module reconstructs and optimizes the logical hierarchy, index path, and reference boundary at risk nodes based on the key node data association feature set. By analyzing the coupling strength between data call path and local structure, it dynamically adjusts the hierarchical distribution and optimizes the data organization structure to obtain the building engineering data sharing architecture scheme. The robustness verification module is based on the building engineering data sharing architecture scheme. It extracts call failure records and structural conflict logs from the original engineering cases, combines multi-professional collaboration interruption data and version misalignment events, analyzes the stability and continuous service capability of the shared architecture in the engineering environment, identifies potential service failure points, and obtains a data table of results after data sharing performance optimization. The scenario adaptation module analyzes the scenario adaptation performance and service fluctuation characteristics of the shared architecture based on the data table after the performance optimization of data sharing, combined with the parameters of changes in engineering scenarios. It reconstructs the index paths that exceed the response latency limit in the target scenario, optimizes the architecture configuration, improves the adaptability of sharing, and obtains the conclusion of data sharing performance optimization trend analysis.

[0037] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including technical improvements such as implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the protection scope defined by the technical solution of the present invention.

Claims

1. A method for sharing construction project data based on a cloud platform, characterized in that: Includes the following steps: S1: Based on the data description specifications for the entire life cycle of building engineering, extract the initial data model and structural constraints, identify multiple sets of candidate data organization patterns, and perform preliminary screening through the engineering logic consistency verification mechanism to obtain a data organization dataset that meets the constraints. S2: Based on the constrained data organization dataset, extract the data calling efficiency and structural stability of each data organization pattern under differentiated engineering scenarios, analyze the cross-stage data connection capability and redundancy control level, identify key data breakpoints, and obtain the key node data association feature set. S3: Based on the key node data association feature set, a hierarchical index reconstruction strategy is adopted to adjust the data storage and retrieval path, construct a data association prediction map, and optimize the data organization structure and sharing response speed by adjusting the logical hierarchy distribution to obtain the building engineering data sharing architecture scheme. S4: Based on the aforementioned building engineering data sharing architecture scheme, extract data call failure records and structural conflict logs from the original engineering cases, test the robustness of the architecture scheme, analyze its continuous service capability in the engineering environment, and obtain a data table of results after data sharing performance optimization.

2. The cloud-based construction project data sharing method according to claim 1, characterized in that, The constrained data organization dataset includes data hierarchy definition, entity relationship mapping rules, and cross-stage reference constraints. The key node data association feature set includes data break location identifiers, reference failure frequency, and structural coupling strength. The building engineering data sharing architecture scheme includes the reconstructed logical hierarchy structure, index path configuration strategy, and sharing response latency index. The data table after data sharing performance optimization includes robustness assessment conclusions and environmental sustainability service capability analysis data.

3. The cloud-based construction project data sharing method according to claim 1, characterized in that, The specific steps for organizing the dataset to satisfy the constraints are as follows: S101: Based on the data description specifications for each stage of the entire life cycle of a building project, extract the hierarchical division rules, entity attribute definitions, and reference boundary conditions of the initial data model, and construct multiple sets of candidate data organization patterns; S102: Based on the multiple sets of candidate data organization patterns, mark the structural constraints of each pattern, eliminate patterns that violate engineering logic consistency, store them in the candidate scheme database, and generate a candidate scheme sequence. S103: Based on the candidate scheme sequence, invoke the engineering logic consistency verification mechanism to perform structural compliance verification on each data organization mode, filter out the scheme combinations that meet the constraints, store them in the scheme database, update the verification status identifier, and obtain the data organization dataset that meets the constraints.

4. The cloud-based construction project data sharing method according to claim 3, characterized in that, The specific steps for associating the key node data with the feature set are as follows: S201: Based on the data organization dataset that meets the constraints, extract the data call response time, structural change frequency and cross-stage reference success rate of each data organization pattern under differentiated engineering scenarios, analyze its data continuity and structural evolution risks, identify key data break points, and obtain a data break point distribution map. S202: Based on the data fracture node distribution map, extract the location and frequency of reference failure events, combine with the structural coupling strength assessment model, analyze potential data isolation risks, record key data association features, and obtain a key node data association feature set.

5. The cloud-based construction project data sharing method according to claim 4, characterized in that, The specific steps of the aforementioned construction engineering data sharing architecture scheme are as follows: S301: Based on the key node data association feature set, extract the association fracture pattern of the data fracture node, analyze the potential relationship between data, identify influencing factors and optimize the association and visualization of the map, and construct a data association prediction map. S302: Based on the data association prediction map, adjust the logical hierarchy distribution, optimize the index path configuration, analyze the response efficiency of the path configuration, reconstruct and optimize the path, balance the data call and storage paths, optimize the data flow and test the response speed after optimization, and obtain the preliminary optimized architecture. S303: Based on the preliminary optimized architecture, analyze the reconstructed logical hierarchy and index path configuration, compare the shared response latency index before and after optimization, statistically analyze the optimization effect, and obtain the building engineering data sharing architecture scheme.

6. The cloud-based construction project data sharing method according to claim 5, characterized in that, The specific steps for creating the data table after performance optimization of data sharing are as follows: S401: Based on the aforementioned building engineering data sharing architecture scheme, extract data call failure records and structural conflict logs from the original engineering cases, including multi-professional collaboration interruption events and version misalignment cases, test the robustness of the architecture scheme, analyze its adaptability in the engineering environment, and obtain robustness evaluation conclusions. S402: Based on the robustness assessment conclusions and combined with the environmental continuous service capability analysis model, predict the service stability trend of the shared architecture throughout the project lifecycle, statistically analyze the fluctuation range of key performance indicators, and obtain a data table of results after data sharing performance optimization.

7. The cloud-based construction project data sharing method according to claim 6, characterized in that, The environmental continuous service capability analysis model refers to combining robustness assessment conclusions, using a key service stability prediction algorithm, analyzing the service stability trend of the data sharing architecture throughout the project lifecycle, statistically analyzing the fluctuation range of key performance indicators, and obtaining prediction results. The service stability trend refers to the data results obtained by predicting the changes in service stability of the shared architecture at different time points based on the robustness assessment conclusions and the environmental continuous service capability analysis model.

8. The cloud-based construction project data sharing method according to claim 1, characterized in that, The method also includes step S5: S5: Based on the data table of the results after the performance optimization of data sharing, extract the key performance indicators of the optimized architecture, analyze its performance in differentiated engineering scenarios, screen service fluctuation scenarios, combine data correlation prediction graphs, adjust the architecture configuration, and obtain the data sharing performance optimization trend analysis conclusion. The conclusions of the data sharing performance optimization trend analysis include the changing trends of key performance indicators, scenario adaptability analysis, and assessment of architecture optimization potential.

9. The cloud-based construction project data sharing method according to claim 8, characterized in that, The specific steps for drawing conclusions regarding the data sharing performance optimization trend analysis are as follows: S501: Based on the data table of the results after the performance optimization of the data sharing, extract the key performance indicator data under the differentiated engineering scenarios, remove outliers, match the scenario number, analyze the service fluctuation range, and obtain the performance indicator set. S502: Based on the performance index set, classify and arrange them according to engineering scenario type, identify scenarios with large service fluctuations, analyze their data association breakage characteristics, combine with the architecture optimization potential assessment model, identify optimization space, and obtain the data sharing performance optimization trend analysis conclusion.

10. A cloud-based construction engineering data sharing system, characterized in that: The system is used to implement the cloud platform-based construction project data sharing method according to any one of claims 1-9, and the system includes: The data organization and filtering module extracts the hierarchical structure definition, entity relationship rules and reference constraints from the initial data model according to the engineering life cycle data description specification, groups the candidate data organization patterns, checks whether each group of patterns meets the structural constraints, filters the combination of schemes that meet the requirements, and obtains the data organization dataset that meets the constraints. Based on the constrained data organization dataset, the data association analysis module extracts the data retrieval efficiency and structural stability data of each data organization mode under differentiated engineering scenarios, and analyzes the cross-stage reference success rate, structural change sensitivity and redundancy control capability under each scheme. It also compares and analyzes the data continuity of the schemes, marks the break risk nodes, and obtains the key node data association feature set. Based on the key node data association feature set, the shared architecture generation module reconstructs and optimizes the logical hierarchy, index path and reference boundary at the risk node. By analyzing the coupling strength between the data call path and the local structure, it dynamically adjusts the hierarchical distribution and optimizes the data organization structure to obtain the building engineering data sharing architecture scheme. The robustness verification module, based on the aforementioned building engineering data sharing architecture scheme, extracts call failure records and structural conflict logs from the original engineering cases. Combined with multi-professional collaboration interruption data and version misalignment events, it analyzes the stability and continuous service capability of the shared architecture in the engineering environment, identifies potential service failure points, and obtains a data table of results after data sharing performance optimization. Based on the data table of the results after the performance optimization of data sharing, and combined with the parameters of changes in engineering scenarios, the scenario adaptation module analyzes the scenario adaptation performance and service fluctuation characteristics of the shared architecture, reconstructs the index paths that exceed the response latency limit in the target scenario, optimizes the architecture configuration, improves the sharing adaptability, and obtains the conclusion of the data sharing performance optimization trend analysis.

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