A BIM model comparison and merging method based on graph algorithm

Abstract

The present invention discloses a BIM model comparison and merging method based on a graph algorithm, comprising the following steps: preprocessing the input BIM models to be compared and merged, converting them into a unified data format, and extracting geometric information, material information, component parameter information, spatial position information and logical relationship information between elements therein; respectively constructing independent initial graph data structures, wherein the nodes in each graph data structure represent specific elements in the BIM model, and the edges represent various relationships between the elements, including spatial position relationships, logical connection relationships and dependency relationships; comprehensively considering the multi-dimensional key elements of the geometric information, material information, component parameter information, spatial position information and logical relationships of the elements in the BIM model, avoiding the deviation and misjudgment caused by the traditional method focusing only on a single factor, and adopting strict threshold conditions and a multi-factor comprehensive judgment strategy to ensure the high accuracy of the comparison and merging results.

Description

Technical Field

[0001] The present invention relates to the technical field of BIM model comparison and merging methods, and in particular to a BIM model comparison and merging method based on a graph algorithm. Background Art

[0002] With the widespread application of Building Information Modeling (BIM) technology in the field of construction engineering, the effective management and utilization of BIM models have become crucial. In actual engineering projects, it is often the case that multiple teams or individuals create BIM models separately, or modify and update the models at different stages of the project. This leads to the need to compare and merge multiple BIM models to ensure the consistency and integrity of the models, improve work efficiency, and reduce errors and duplication of work.

[0003] Traditional BIM model comparison and merging methods often have many limitations. Some methods are based only on simple geometric shape comparison, ignoring important information such as the material, parameters, spatial position and logical relationship of elements in the model, resulting in inaccurate and incomplete comparison results. Other methods are inefficient when dealing with complex model structures and large amounts of data, and cannot meet the needs of actual engineering projects.

[0004] In addition, since BIM models contain rich information, how to accurately extract and analyze this information and effectively apply it to the comparison and merging of models has always been a technical challenge. Moreover, in the process of model merging, how to reasonably handle the difference elements and follow certain rules and strategies to ensure the accuracy and rationality of the merging results is also an urgent problem to be solved.

[0005] With the development of graph algorithms, their advantages in processing complex relationships and large-scale data are gradually emerging. Applying graph algorithms to the comparison and merging of BIM models is expected to overcome the shortcomings of traditional methods and achieve more accurate and efficient model management. However, the current BIM model comparison and merging methods based on graph algorithms are still immature and need further research and improvement to meet the growing needs of the construction engineering field.

[0006] In summary, in order to improve the accuracy, efficiency and rationality of BIM model comparison and merging, developing an innovative method based on graph algorithm has important practical significance and application value. Summary of the invention

[0007] The present invention proposes a BIM model comparison and merging method based on graph algorithm to solve the problems mentioned in the above-mentioned prior art.

[0008] In order to achieve the above object, the present invention adopts the following technical solution: a BIM model comparison and merging method based on graph algorithm, comprising the following steps:

[0009] S1. Preprocess the input BIM models to be compared and merged, convert them into a unified data format, and extract the geometric information, material information, component parameter information, spatial position information, and logical relationship information between the elements;

[0010] S2. constructing independent initial graph data structures respectively, where the nodes in each graph data structure represent specific elements in the BIM model, and the edges represent various relationships between the elements, including spatial position relationships, logical connection relationships, and dependency relationships;

[0011] S3. Use a depth-first search or breadth-first search graph traversal algorithm, combine the attribute information of nodes and edges, perform multi-level traversal on the graph data structure corresponding to the two BIM models, and calculate the similarity of nodes and edges. The calculation of similarity comprehensively considers the geometric shape similarity of elements, material property similarity, parameter value similarity, spatial position deviation, and consistency dimension of logical relationship;

[0012] S4. Based on the similarity results calculated above, by setting strict threshold conditions and multi-factor comprehensive judgment strategies, the different elements and the same elements in the two BIM models are accurately determined. Elements with similarity higher than a specific threshold and consistent in multiple key attributes are determined as the same elements, otherwise they are determined as different elements;

[0013] S5. For the elements that are judged to be different, we further use the analytic hierarchy process and fuzzy comprehensive evaluation method to deeply analyze their specific differences in geometric shape, material properties, parameter values, spatial position and logical relationship, and generate a detailed and well-organized difference report, which not only includes the clear identification of the difference elements, but also covers the precise numerical description of the difference attributes, the quantitative evaluation of the degree of difference, and the specific location of the difference elements in the BIM model and detailed information on the level to which they belong;

[0014] S6. When merging BIM models, fully rely on the similarity calculated in the early stage and the carefully generated difference report. For the elements that are confirmed to be the same, directly perform error-free merging. For the difference elements, intelligent processing and merging are carried out according to the pre-set complex rule set. The rule set covers priority sorting rules based on element type, conflict resolution rules based on project requirements and standards, default processing rules based on historical data and experience, etc. At the same time, a manual intervention interface is provided when necessary to ensure the accuracy and rationality of the merged results.

[0015] Furthermore, in step S1, the preprocessing not only includes data cleaning and error correction of the extracted information to remove invalid or erroneous data, but also standardization of the data to make it conform to specific data format specifications, including: uniformly converting geometric dimension data of different precisions and units into standard precisions and units, standardizing the description of material names and parameters to eliminate ambiguities in understanding caused by differences in descriptions, and at the same time, identifying and marking possible data missing situations to provide clear prompts for subsequent processing.

[0016] Furthermore, in step S2, the attributes of the node include not only the time attribute and version information of the element, but also the creator information, modification history, and comments and annotations related to the element; the time attribute is accurate to the specific creation time, modification time and last access time, and the version information records the version number of each major modification and the corresponding modification content overview; the creator information includes the creator's name, department and contact information to facilitate traceability and communication when necessary, the modification history records the specific operations and reasons for each modification in detail, and the comments and annotations are used to record special instructions and precautions related to the element.

[0017] Furthermore, in step S3, during the execution of the depth-first search or breadth-first search graph traversal algorithm, the search strategy will be dynamically adjusted according to the structural characteristics of the graph and the distribution of nodes. Specifically, when the graph is deep and has fewer branches, depth-first search is preferred to quickly explore deep nodes; when the graph is wide and the associations between nodes are more complex, breadth-first search is switched to more comprehensively cover adjacent nodes; during the traversal process, the search path is optimized in real time according to factors such as node and edge weights, access frequencies, and similarities with other graph structures to avoid falling into local optimal solutions, thereby ensuring that the entire graph structure can be traversed comprehensively and efficiently.

[0018] Furthermore, in step S4, the threshold conditions and the multi-factor comprehensive judgment strategy can be flexibly adjusted according to the specific application scenarios and accuracy requirements of the BIM model. Specifically, in the comparison of BIM models of large-scale bridge projects with extremely high accuracy requirements, the similarity threshold can be set higher and the consideration factors of key attributes can be increased; for general comparison of BIM models of building interior decoration, the threshold can be appropriately lowered and the focus can be placed on attributes such as appearance and layout. According to the early design stage or the late construction stage of the project, the judgment strategy can be optimized in a targeted manner to adapt to the key needs for model comparison and merging at different stages.

[0019] Furthermore, in step S5, in the analytic hierarchy process and fuzzy comprehensive evaluation analysis methods, sensitivity analysis is used to evaluate the influence of different factors on the difference analysis results, specifically: the uncertainty factors are quantitatively analyzed using the Monte Carlo simulation method to evaluate their uncertainty contribution to the quantitative assessment of the difference degree, so as to further improve the reliability and scientificity of the difference report.

[0020] Furthermore, in step S6, in the priority sorting rule based on element type, the priority of structural components is higher than that of decorative components. Within the structural components, the priority of load-bearing structures such as frame columns and load-bearing walls is higher than that of non-load-bearing structures such as partition walls and stair railings. Among the decorative components, the priority of functional decorations such as fire retardant coatings and sound insulation materials is higher than that of purely aesthetic decorations such as murals and shaped ceilings. For composite components involving both structural and decorative functions, the priority is determined according to their main functions and the degree of influence on the overall structural stability.

[0021] Furthermore, among the structural components, the priority of the load-bearing wall is not only higher than that of the non-load-bearing wall, but it will also be further subdivided and ranked according to the material, thickness, and bearing capacity factors of the load-bearing wall. Specifically, the priority of the load-bearing wall made of high-strength reinforced concrete is higher than that of the load-bearing wall of ordinary brick-concrete structure; the load-bearing wall with thicker thickness will be given priority to retain its original design when merging; the load-bearing wall with stronger bearing capacity has a higher decision-making weight when it conflicts with other difference elements.

[0022] Furthermore, in step S6, the default processing rules based on historical data and experience are obtained through learning and summarizing a large number of similar BIM model comparison and merging cases, specifically: using machine learning algorithms to analyze and model the data features, processing methods and final effects in historical cases, extracting common processing patterns and rules, continuously incorporating new cases into the learning sample set, continuously optimizing and updating the model to adapt to the ever-changing development trends of BIM model design and construction technology, and regularly manually reviewing and adjusting the default processing rules to ensure that they meet actual business needs and professional standards.

[0023] Furthermore, the default processing rules will be regularly updated and optimized based on new cases and experiences. The update frequency will be dynamically adjusted according to the number and complexity of the projects. When a sufficient number of representative new cases have been accumulated within a certain period of time, the update process will be initiated. During the update process, the latest data analysis techniques and algorithms will be used to deeply explore and analyze new cases, extract valuable information and patterns, supplement, revise or replace the original default processing rules, and strictly test and verify the updated rules to ensure their accuracy and reliability in practical applications.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] It comprehensively considers the key factors of multiple dimensions such as geometric information, material information, component parameter information, spatial position information and logical relationships of elements in the BIM model, avoiding the deviation and misjudgment caused by traditional methods that only focus on a single factor.

[0026] The use of strict threshold conditions and multi-factor comprehensive judgment strategies can accurately identify identical elements and different elements, reduce misjudgments and omissions, and ensure the high accuracy of comparison and merging results.

[0027] The use of efficient graph algorithms to traverse and analyze models greatly shortens the time required for comparison and merging, which can significantly improve work efficiency, especially when dealing with large-scale and complex BIM models.

[0028] The automated processing flow reduces the need for manual intervention and reduces labor costs, while avoiding human errors and further speeding up project progress.

[0029] For the difference elements, we conduct in-depth analysis through advanced methods such as hierarchical analysis method and fuzzy comprehensive evaluation method, and intelligently process and merge them according to pre-set complex rule sets. We fully consider various factors such as project requirements, industry standards and historical experience, so that the merged results are more in line with actual engineering requirements and improve the quality and usability of the model.

[0030] Detailed difference reports provide project managers with a clear basis for decision-making, helping to promptly identify problems and resolve conflicts, effectively coordinate resources from all parties, and improve the scientificity and refinement of project management.

[0031] It can better integrate BIM models from different stages and sources, promote information sharing and collaborative work, and improve the communication efficiency and collaboration effect of the project team.

[0032] The flexibly adjustable threshold conditions, judgment strategies and rule sets make this method widely applicable to construction projects of various types and sizes, and can achieve effective model comparison and merging, whether it is large-scale infrastructure or small civil buildings.

[0033] It provides new ideas and methods for the application of BIM technology in the field of construction engineering, helps to promote technological innovation and progress in the entire industry, and improves the level of informatization and intelligence in the construction industry.

[0034] Accurate model comparison and reasonable merging can reduce construction errors and rework caused by model inconsistencies, reduce project costs and risks, and ensure smooth project implementation and delivery.

[0035] By optimizing the model merger, we can better achieve energy conservation, environmental protection and rational use of resources in buildings, which is in line with the concept and requirements of sustainable development.

[0036] In summary, the BIM model comparison and merging method based on graph algorithm of the present invention has significant beneficial effects in improving accuracy, efficiency, rationality, optimizing project management, adapting to diversified scenarios, promoting technological development, reducing cost risks and promoting sustainable development, and has important application value and positive impact on the field of construction engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic block diagram of a modeling method for a BIM model comparison and merging method based on a graph algorithm proposed in the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0040] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0041] Reference Figure 1 : A BIM model comparison and merging method based on graph algorithm, comprising the following steps:

[0042] S1. Preprocess the input BIM models to be compared and merged, convert them into a unified data format, and extract the geometric information, material information, component parameter information, spatial position information, and logical relationship information between the elements;

[0043] S2. constructing independent initial graph data structures respectively, where the nodes in each graph data structure represent specific elements in the BIM model, and the edges represent various relationships between the elements, including spatial position relationships, logical connection relationships, and dependency relationships;

[0044] S3. Use a depth-first search or breadth-first search graph traversal algorithm, combine the attribute information of nodes and edges, perform multi-level traversal on the graph data structure corresponding to the two BIM models, and calculate the similarity of nodes and edges. The calculation of similarity comprehensively considers the geometric shape similarity of elements, material property similarity, parameter value similarity, spatial position deviation, and consistency dimension of logical relationship;

[0045] S4. Based on the similarity results calculated above, by setting strict threshold conditions and multi-factor comprehensive judgment strategies, the different elements and the same elements in the two BIM models are accurately determined. Elements with similarity higher than a specific threshold and consistent in multiple key attributes are determined as the same elements, otherwise they are determined as different elements;

[0046] S5. For the elements that are judged to be different, we further use the analytic hierarchy process and fuzzy comprehensive evaluation method to deeply analyze their specific differences in geometric shape, material properties, parameter values, spatial position and logical relationship, and generate a detailed and well-organized difference report, which not only includes the clear identification of the difference elements, but also covers the precise numerical description of the difference attributes, the quantitative evaluation of the degree of difference, and the specific location of the difference elements in the BIM model and detailed information on the level to which they belong;

[0047] S6. When merging BIM models, fully rely on the similarity calculated in the early stage and the carefully generated difference report. For the elements that are confirmed to be the same, directly perform error-free merging. For the difference elements, intelligent processing and merging are carried out according to the pre-set complex rule set. The rule set covers priority sorting rules based on element type, conflict resolution rules based on project requirements and standards, default processing rules based on historical data and experience, etc. At the same time, a manual intervention interface is provided when necessary to ensure the accuracy and rationality of the merged results.

[0048] In the present invention, in step S1, the preprocessing not only includes data cleaning and error correction of the extracted information to remove invalid or erroneous data, but also standardization of the data to make it conform to a specific data format specification, specifically including: uniformly converting geometric dimension data of different precisions and units into standard precisions and units, standardizing the description of material names and parameters to eliminate ambiguities in understanding caused by differences in descriptions, and at the same time, identifying and marking possible data missing situations to provide clear prompts for subsequent processing. The preprocessing comprehensive quality score (PQ) = (α×cleaning accuracy (A)+β×error correction accuracy (B)+γ×standardization accuracy (C))×(1-δ×data missing identification omission rate (D))×(1-ε×expression ambiguity elimination failure rate (E))×(1-ζ×subsequent processing prompt error rate (F))

[0049] in:

[0050] Cleaning accuracy (A) = effective data volume after cleaning / total data volume before cleaning;

[0051] Error correction accuracy (B) = amount of correct data after error correction / amount of erroneous data before error correction;

[0052] Standardization accuracy (C) = the amount of data that meets the standard after standardization / the total amount of data before standardization;

[0053] Data missing identification omission rate (D) = the amount of missing data that has not been identified / the actual amount of missing data;

[0054] Failure rate of expression ambiguity elimination (E) = the amount of data with expression ambiguity / the total amount of data with expression ambiguity before processing;

[0055] The error rate of subsequent processing prompts (F) = the number of incorrect subsequent processing prompts / the total number of subsequent processing prompts;

[0056] Moreover, α, β, γ, δ, ε, ζ are weight coefficients, and α+β+γ=1, which respectively represent the importance of cleaning accuracy, error correction accuracy, and standardization accuracy in the comprehensive quality score. For example, α=0.3, β=0.3, and γ=0.4 can be set according to actual conditions.

[0057] This formula considers the various key factors and their influence in the pretreatment process more comprehensively and complexly, thereby more accurately evaluating the overall quality of pretreatment.

[0058] In the present invention, in step S2, the attributes of the node include not only the time attribute and version information of the element, but also the creator information, modification history, and comments and annotations related to the element; the time attribute is accurate to the specific creation time, modification time, and last access time, and the version information records the version number of each major modification and the corresponding modification content overview; the creator information includes the creator's name, department, and contact information to facilitate tracing and communication when necessary, and the modification history lists in detail the specific operations and reasons for each modification, and the comments and annotations are used to record special instructions and precautions related to the element;

[0059] Node information integrity (NI) = (α×time attribute integrity rate (TA) + β×version information integrity rate (VI) + γ×creator information integrity rate (CI) + δ×modification history integrity rate (MH) + ε×annotation and comment content integrity rate (AC))

[0060] in:

[0061] Time attribute completeness rate (TA) = number of nodes with complete time attributes (creation time, modification time, last access time) / total number of nodes;

[0062] Version information completeness rate (VI) = number of nodes with complete version information (version number and corresponding modified content overview) / total number of nodes;

[0063] Creator information completeness rate (CI) = number of nodes with complete creator information (name, department, contact information) / total number of nodes;

[0064] Modification history completeness rate (MH) = number of nodes with detailed modification history (specific operations and modification reasons) / total number of nodes;

[0065] Completeness rate of annotation and comment content (AC) = number of nodes with complete annotation and comment content / total number of nodes;

[0066] Moreover, α, β, γ, δ, and ε are weight coefficients, and α+β+γ+δ+ε=1, which can be set according to the importance of each part of information. For example, α=0.2, β=0.2, γ=0.2, δ=0.2, ε=0.2;

[0067] This formula can be used to evaluate the completeness of the node attribute information in step S2.

[0068] In the present invention, in step S3, the graph traversal algorithm of depth-first search or breadth-first search will dynamically adjust the search strategy according to the structural characteristics of the graph and the distribution of nodes during execution, specifically: when the depth of the graph is deep and there are fewer branches, depth-first search is preferred to quickly explore deep nodes; when the width of the graph is large and the associations between nodes are more complex, breadth-first search is switched to more comprehensively cover adjacent nodes; during the traversal process, the search path is optimized in real time according to factors such as the weights of nodes and edges, access frequencies, and similarities with other graph structures to avoid falling into local optimal solutions, thereby ensuring that the entire graph structure can be traversed comprehensively and efficiently.

[0069] In the present invention, in step S4, the threshold conditions and the multi-factor comprehensive judgment strategy can be flexibly adjusted according to the specific application scenarios and accuracy requirements of the BIM model. Specifically, in the comparison of BIM models of large-scale bridge projects with extremely high accuracy requirements, the similarity threshold can be set higher and the consideration factors of key attributes can be increased; for the comparison of general building interior decoration BIM models, the threshold can be appropriately lowered and the focus can be placed on attributes such as appearance and layout. According to the initial design stage or the final construction stage of the project, the judgment strategy can be optimized in a targeted manner to adapt to the key requirements for model comparison and merging at different stages;

[0070] Determination strategy adaptability comprehensive index (DSACI) = (α×scenario adaptation accuracy (SAA)×(1+δ×key attribute consideration completeness rate (KACR))+β×precision requirement matching (PRM)×(1+ε×threshold adjustment rationality (TAR))+γ×stage optimization effect (POE)×(1+ζ×key demand satisfaction rate (KDR)))×(1-η×false positive rate (MR))

[0071] in:

[0072] Scenario Adaptation Accuracy (SAA) = Number of correctly adapted application scenarios / Total number of application scenarios;

[0073] Key attribute consideration completeness rate (KACR) = number of key attributes actually considered / number of key attributes that should be considered;

[0074] Precision Requirement Match (PRM) = the degree of closeness between the actual set similarity threshold and the ideal threshold;

[0075] Threshold adjustment rationality (TAR) = the rationality evaluation value of the actual threshold adjustment (between 0 and 1);

[0076] Phase optimization effect (POE) = the effect improvement value of the optimized judgment strategy at different project stages / the effect before optimization;

[0077] Key demand satisfaction rate (KDR) = number of determinations that meet key demands at different stages / total number of determinations;

[0078] False positive rate (MR) = number of false positives / total number of positive positives;

[0079] α, β, γ, δ, ε, ζ, and eta are weight coefficients, and α+β+γ=1, and δ, ε, ζ, and eta are set according to the actual importance. For example, α=0.3, β=0.3, γ=0.4, δ=0.2, ε=0.2, ζ=0.2, η=0.1;

[0080] This formula considers all aspects of the adaptability of the strategy determined in step S4 more comprehensively and meticulously, and evaluates its effectiveness through comprehensive calculation of multiple factors.

[0081] In the present invention, in step S5, in the analytic hierarchy process and fuzzy comprehensive evaluation analysis methods, sensitivity analysis is used to evaluate the influence of different factors on the difference analysis results, specifically: the uncertainty factors are quantitatively analyzed using the Monte Carlo simulation method to evaluate their uncertainty contribution to the quantitative evaluation of the difference degree, so as to further improve the reliability and scientificity of the difference report.

[0082] In the present invention, in step S6, in the priority sorting rules based on element types, the priority of structural components is higher than that of decorative components. Within the structural components, the priority of load-bearing structures such as frame columns and load-bearing walls is higher than that of non-load-bearing structures such as partition walls and stair railings; among decorative components, the priority of functional decorations such as fire retardant coatings and sound insulation materials is higher than that of purely aesthetic decorations such as murals and shaped ceilings. For composite components involving both structural and decorative functions, the priority is determined according to their main functions and the degree of influence on the overall structural stability.

[0083] In the present invention, among structural components, the priority of load-bearing walls is not only higher than that of non-load-bearing walls, but will also be further subdivided and ranked according to the material, thickness, and bearing capacity factors of the load-bearing walls. Specifically, load-bearing walls made of high-strength reinforced concrete have a higher priority than load-bearing walls of ordinary brick-concrete structures; thicker load-bearing walls will be given priority to retain their original designs when merged; load-bearing walls with stronger bearing capacity have a higher decision weight when they conflict with other difference elements.

[0084] In the present invention, in step S6, the default processing rules based on historical data and experience are obtained by learning and summarizing a large number of similar BIM model comparison and merging cases, specifically: using machine learning algorithms to analyze and model the data features, processing methods and final effects in historical cases, extracting common processing patterns and rules, continuously incorporating new cases into the learning sample set, continuously optimizing and updating the model to adapt to the ever-changing development trends of BIM model design and construction technology, and regularly manually reviewing and adjusting the default processing rules to ensure that they meet actual business needs and professional standards.

[0085] In the present invention, the default processing rules are regularly updated and optimized based on new cases and experiences. The update frequency is dynamically adjusted according to the number and complexity of the projects. When a sufficient number of representative new cases are accumulated within a certain period of time, the update process is initiated. During the update process, the latest data analysis techniques and algorithms are used to deeply mine and analyze new cases, extract valuable information and patterns, supplement, revise or replace the original default processing rules, and strictly test and verify the updated rules to ensure their accuracy and reliability in practical applications.

[0086] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A BIM model comparison and merging method based on graph algorithm, characterized in that: The following steps are involved: S1. Preprocess the input BIM models to be compared and merged, convert them into a unified data format, and extract the geometric information, material information, component parameter information, spatial position information, and logical relationship information between the elements; S2. constructing independent initial graph data structures respectively, where the nodes in each graph data structure represent specific elements in the BIM model, and the edges represent various relationships between the elements, including spatial position relationships, logical connection relationships, and dependency relationships; S3. Use a depth-first search or breadth-first search graph traversal algorithm, combine the attribute information of nodes and edges, perform multi-level traversal of the graph data structure corresponding to the two BIM models, and calculate the similarity of nodes and edges. The calculation of similarity comprehensively considers the geometric shape similarity of elements, material property similarity, parameter value similarity, spatial position deviation, and consistency dimension of logical relationship; In step S3, during the execution of the graph traversal algorithm of the depth-first search or breadth-first search, the search strategy is dynamically adjusted according to the structural characteristics of the graph and the distribution of nodes. Specifically, when the depth of the graph is deep and there are fewer branches, the depth-first search is preferred to quickly explore deep nodes; when the width of the graph is large and the associations between nodes are more complex, the breadth-first search is switched to cover adjacent nodes more comprehensively; during the traversal process, the search path is optimized in real time according to the weights of nodes and edges, the access frequency, and the similarity factors with other graph structures to avoid falling into the local optimal solution, so as to ensure that the entire graph structure can be traversed comprehensively and efficiently; S4. Based on the similarity results calculated above, by setting strict threshold conditions and multi-factor comprehensive judgment strategies, the different elements and the same elements in the two BIM models are accurately determined. Elements with similarity higher than a specific threshold and consistent in multiple key attributes are judged as the same elements, otherwise they are judged as different elements; S5. For the elements that are judged to be different, we further use the analytic hierarchy process and fuzzy comprehensive evaluation method to deeply analyze their specific differences in geometric shape, material properties, parameter values, spatial position and logical relationship, and generate a detailed and well-organized difference report, which not only includes the clear identification of the difference elements, but also covers the precise numerical description of the difference attributes, the quantitative evaluation of the degree of difference, and the specific location of the difference elements in the BIM model and detailed information on the level to which they belong; S6. When merging BIM models, fully rely on the similarity calculated in the early stage and the carefully generated difference report. For the elements that are confirmed to be the same, directly perform error-free merging. For the difference elements, intelligent processing and merging are carried out according to the pre-set complex rule set. The rule set covers priority sorting rules based on element type, conflict resolution rules based on project requirements and standards, and default processing rules based on historical data and experience. At the same time, a manual intervention interface is provided to ensure the accuracy and rationality of the merged results.

2. A BIM model comparison and merging method based on graph algorithm according to claim 1, characterized in that: In step S1, the preprocessing includes not only data cleaning and error correction of the extracted information to remove invalid or erroneous data, but also standardization of the data to make it conform to specific data format specifications, including: uniformly converting geometric dimension data of different precisions and units into standard precisions and units, standardizing the description of material names and parameters to eliminate ambiguities in understanding caused by differences in descriptions, and at the same time, identifying and marking possible data missing situations to provide clear prompts for subsequent processing.

3. According to the BIM model comparison and merging method based on graph algorithm as claimed in claim 1, it is characterized in that: In step S2, the attributes of the node include not only the time attribute and version information of the element, but also the creator information, modification history, and comments and annotations related to the element; the time attribute is accurate to the specific creation time, modification time and last access time, and the version information records the version number of each major modification and the corresponding modification content overview; the creator information includes the creator's name, department and contact information to facilitate traceability and communication when necessary, the modification history lists in detail the specific operations and reasons for each modification, and the comments and annotations are used to record special instructions and precautions related to the element.

4. The BIM model comparison and merging method based on graph algorithm according to claim 1 is characterized in that: In step S4, the threshold conditions and multi-factor comprehensive judgment strategy can be flexibly adjusted according to the specific application scenarios and accuracy requirements of the BIM model. Specifically, in the comparison of BIM models of large-scale bridge projects with extremely high accuracy requirements, the similarity threshold can be set higher and the consideration factors of key attributes can be increased; for general comparison of BIM models of building interior decoration, the threshold can be appropriately lowered and the focus can be placed on the attributes of appearance and layout. According to the early design stage or the late construction stage of the project, the judgment strategy can be optimized in a targeted manner to adapt to the key requirements for model comparison and merging at different stages.

5. The BIM model comparison and merging method based on graph algorithm according to claim 1 is characterized in that: In step S5, in the analytic hierarchy process and fuzzy comprehensive evaluation analysis methods, sensitivity analysis is used to evaluate the influence of different factors on the difference analysis results, specifically: the uncertainty factors are quantitatively analyzed using the Monte Carlo simulation method to evaluate their contribution to the uncertainty of the quantitative evaluation of the difference degree, so as to further improve the reliability and scientificity of the difference report.

6. The BIM model comparison and merging method based on graph algorithm according to claim 1 is characterized in that: In step S6, in the priority sorting rule based on element type, the priority of structural components is higher than that of decorative components. Within the structural components, the priority of load-bearing structures is higher than that of non-load-bearing structures, wherein load-bearing structures include frame columns and load-bearing walls, and non-load-bearing structures include partition walls and stair railings; among decorative components, the priority of functional decorations is higher than that of purely aesthetic decorations, wherein functional decorations include fire retardant coatings and sound insulation materials, and purely aesthetic decorations include murals and shaped ceilings. For composite components involving both structural and decorative functions, the priority is determined according to their main functions and the degree of influence on the overall structural stability.

7. The BIM model comparison and merging method based on graph algorithm according to claim 6 is characterized in that: Among the structural components, the priority of load-bearing walls is not only higher than that of non-load-bearing walls, but they will also be further subdivided and ranked according to the material, thickness, and bearing capacity of the load-bearing walls. Specifically, load-bearing walls made of high-strength reinforced concrete have a higher priority than load-bearing walls of ordinary brick-concrete structures; thicker load-bearing walls will be given priority to retain their original designs when merged; load-bearing walls with stronger bearing capacity have a higher decision-making weight when they conflict with other difference elements.

8. The BIM model comparison and merging method based on graph algorithm according to claim 1 is characterized in that: In step S6, the default processing rules based on historical data and experience are obtained through learning and summarizing a large number of similar BIM model comparison and merging cases. Specifically, the data features, processing methods and final effects in historical cases are analyzed and modeled using machine learning algorithms, and common processing modes and rules are extracted. New cases are continuously incorporated into the learning sample set, and the model is continuously optimized and updated to adapt to the ever-changing development trends of BIM model design and construction technology. The default processing rules are manually reviewed and adjusted regularly to ensure that they meet actual business needs and professional standards.

9. The BIM model comparison and merging method based on graph algorithm according to claim 8 is characterized in that: The default processing rules will be regularly updated and optimized based on new cases and experiences. The update frequency will be dynamically adjusted according to the number and complexity of the projects. When a sufficient number of representative new cases have been accumulated within a certain period of time, the update process will be initiated. During the update process, the latest data analysis techniques and algorithms will be used to deeply explore and analyze new cases, extract valuable information and patterns, supplement, revise or replace the original default processing rules, and strictly test and verify the updated rules to ensure their accuracy and reliability in practical applications.

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