A detection model optimization method and system based on BIM technology

Abstract

The present invention discloses a detection model optimization method and system based on BIM technology. By setting the collision importance, subsystem type and level information, a collision detection matrix is generated, the collision type and reference value are automatically set, and a report is exported. The system assembles the BIM model, automatically reads the matrix, screens the collision detection group, runs the detection process, and sends the results to the user. The user modifies the model based on the results and performs a cyclic detection until the standard is met. The system records the detection and correction data, generates a report, and counts the number of corrections in real time. When the number of corrections reaches the expected number, the system analyzes the collision characteristics before and after the correction cycle, forms a characteristic matrix, evaluates the correction effect and generates early warning information. The present invention effectively improves the efficiency and accuracy of collision detection, improves the correction efficiency of professionals, and further improves the overall quality of the construction project.

Description

Technical Field

[0001] The present invention relates to the field of BIM building detection and analysis, and more specifically, to a detection model optimization method and system based on BIM technology. Background Art

[0002] BIM (Building Information Modeling) is a design, construction, and operations management process based on three-dimensional digital technology. It integrates geometric, physical, and functional information from a building project, as well as relevant project lifecycle information, into a single, complete, and logically structured Building Information Model (BIM). BIM technology is applied across multiple phases, including building design, construction, and operations, aiming to improve project efficiency, reduce costs, and enhance quality.

[0003] As a core application within the BIM technology system, collision detection plays a vital role in the design and construction of engineering projects and is a key step in achieving efficient and accurate project management. During the engineering design phase, collision detection using BIM models enables a comprehensive and detailed analysis of potential conflict points within various structures. These conflicts may arise from spatial overlaps between different components or from mismatches in design parameters. Leveraging BIM technology's 3D visualization capabilities and model numerical accuracy, software tools can automatically identify and mark potential collision points, enabling design teams to quickly locate and resolve issues, thereby improving the overall quality of the project.

[0004] However, existing clash detection technology is inherently complex. The installation and user interface and functionality of mainstream clash detection software, such as Navisworks, are relatively complex. For beginners or designers unfamiliar with the software, mastering its operation can require considerable time and effort. This significantly increases the learning curve and user experience. Key issues in clash detection and model optimization are how to numerically define "clash" and how to input "defined clash detection parameters" into the clash detection software. In reality, clash detection spacing definitions are largely positively correlated with international and domestic standards and are highly regionally applicable. In other words, clash detection parameters are relatively fixed and reusable across multiple projects. Current technology and software rely on manual input, resulting in significant repetitive workload and varying accuracy. Furthermore, while the clash detection reports generated by current general-purpose software include information such as the specific location of collision points, related images, and grid locations, their format and content do not fully meet the actual needs of designers. The output interfaces of these reports lack the ability to customize parameters or formats, often requiring designers to invest significant time in secondary processing and clash statistics. Summary of the Invention

[0005] The present invention overcomes the defects of the prior art and proposes a detection model optimization method and system based on BIM technology.

[0006] A first aspect of the present invention provides a method for optimizing a detection model based on BIM technology, comprising:

[0007] S1: Based on the target building plan, set the collision importance, subsystem type, and collision level information of each discipline. After the setting is completed, generate the initial detection grouping between different professional subsystems, and generate the collision detection matrix through the detection of the initial grouping;

[0008] S2: Based on the initial inspection group defined in S1 and in combination with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision detection matrix, and exports the collision detection matrix to form a report file;

[0009] S3: Through the detection platform, various professional BIM models are assembled. The detection platform automatically reads the collision detection matrix and generates multiple collision detection groups. The collision detection groups are screened for valid groups and the collision detection process is run to obtain the collision detection results.

[0010] S4: Determine whether to store the operation result according to the collision detection result, and send the collision detection result to the user terminal;

[0011] S5: Extract and store relevant professional model information based on collision detection results, collect statistics on collision information between professional models, sort collision records, and predict the time required for correction based on collision type to generate collision decision information;

[0012] S6: Sending collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model. After the correction is completed, steps S3 to S6 are repeated until the collision result meets the preset standard;

[0013] S7: Record the statistical data and model modification records of each collision detection, summarize the work completion status, and generate a detection and modification report;

[0014] S8: In S7, the number of corrections for each profession is counted in real time. When the number of corrections reaches the expected value, a correction cycle is set. In the detection platform, the collision position, collision type, and collision level information before and after the correction cycle are obtained. According to the collision position, collision type, and collision level information, the associated component analysis and collision feature analysis of the collision point are performed. The collision features are stored in a matrix to form two collision feature matrices before and after the correction cycle. The similarity between the collision feature matrices is analyzed. Combined with the preset feature matrix, the correction cycle is evaluated for overall correction, and corresponding correction warning information is generated.

[0015] In this solution, the preset specifications are specifically regional specifications or construction project specifications corresponding to the target building plan. The preset specifications include data standards, delivery standards, and collision standard specifications.

[0016] In this solution, the detection platform includes a web-based online detection system and a PC-based detection system.

[0017] In this solution, in S2, the collision detection matrix is stored through two-dimensional data, each dimension of data includes information of multiple professional subsystems, and the two professional subsystems are mapped and associated through the collision detection matrix.

[0018] In this solution, in S3, the collision detection groups are screened for effective detection, including eliminating duplicate groups from multiple collision detection groups, and deleting invalid detection groups in accordance with preset specifications.

[0019] In this solution, S5 specifically includes: based on the collision detection results, extracting and storing collision problems, collision types, and collision information from relevant professional models, counting the proportion of collision problems in each profession and the frequency of collision types, obtaining a collision problem proportion table, sorting the importance of different collision types, generating a collision type ranking table, predicting the correction time of collision records in the collision detection results based on historical collision correction records, generating a prediction schedule, and generating collision decision information based on the collision problem proportion table, collision type ranking table, and prediction schedule.

[0020] In this solution, the S8 is specifically:

[0021] In S7, the number of revisions for each specialty is counted in real time to calculate the total number of revisions. If the total number of revisions reaches the expected value, a revision period is set. The revision period is the time period before and after the revision when the total number of revisions reaches the expected value.

[0022] In the detection platform, through the collision detection results obtained from each detection analysis, the collision location, collision type, collision level, and collision correction times of each collision record before the correction period are obtained;

[0023] Based on the collision location, collision type, and collision level, the collision location of the professional model where the collision point is located is determined in the detection platform. The collision type and collision level information are analyzed, and the associated building components at the collision location are determined. The associated building components are marked and counted to obtain the location, number, and collision level of the associated building components.

[0024] The collision location, collision type, collision level and the associated building component location, component number and component collision level are used as the first dimension information of the matrix, and the corresponding professional subsystem is used as the second dimension information to matrix the collision feature data to form a collision feature matrix;

[0025] Perform collision feature analysis and corresponding feature matrix generation on the collision detection results after the correction period, and finally form two collision feature matrices before and after the correction period;

[0026] Based on the Jordan standard form method, the similarity of the two collision feature matrices is determined to obtain a first similarity, and the similarity of the collision feature matrix after the correction period is determined to obtain a second similarity;

[0027] An overall correction evaluation of the correction period is performed based on the first similarity and the second similarity, and corresponding correction warning information is generated.

[0028] A second aspect of the present invention further provides a detection model optimization system based on BIM technology, the system comprising: a memory and a processor, wherein the memory comprises a detection model optimization program based on BIM technology, and when the detection model optimization program based on BIM technology is executed by the processor, the following steps are implemented:

[0029] S1: Based on the target building plan, set the collision importance, subsystem type, and collision level information of each discipline. After the setting is completed, generate the initial detection grouping between different professional subsystems, and generate the collision detection matrix through the detection of the initial grouping;

[0030] S2: Based on the initial inspection group defined in S1 and in combination with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision detection matrix, and exports the collision detection matrix to form a report file;

[0031] S3: Through the detection platform, various professional BIM models are assembled. The detection platform automatically reads the collision detection matrix and generates multiple collision detection groups. The collision detection groups are screened for valid groups and the collision detection process is run to obtain the collision detection results.

[0032] S4: Determine whether to store the operation result according to the collision detection result, and send the collision detection result to the user terminal;

[0033] S5: Extract and store relevant professional model information based on collision detection results, collect statistics on collision information between professional models, sort collision records, and predict the time required for correction based on collision type to generate collision decision information;

[0034] S6: Sending collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model. After the correction is completed, steps S3 to S6 are repeated until the collision result meets the preset standard;

[0035] S7: Record the statistical data and model modification records of each collision detection, summarize the work completion status, and generate a detection and modification report;

[0036] S8: In S7, the number of corrections for each profession is counted in real time. When the number of corrections reaches the expected value, a correction cycle is set. In the detection platform, the collision position, collision type, and collision level information before and after the correction cycle are obtained. According to the collision position, collision type, and collision level information, the associated component analysis and collision feature analysis of the collision point are performed. The collision features are stored in a matrix to form two collision feature matrices before and after the correction cycle. The similarity between the collision feature matrices is analyzed. Combined with the preset feature matrix, the correction cycle is evaluated for overall correction, and corresponding correction warning information is generated.

[0037] The third aspect of the present invention also provides a computer-readable storage medium, which includes a detection model optimization program based on BIM technology. When the detection model optimization program based on BIM technology is executed by a processor, the steps of the detection model optimization method based on BIM technology as described in any one of the above items are implemented.

[0038] The present invention can achieve the following beneficial effects:

[0039] Simplified Operational Process: This invention significantly reduces the learning curve and operational difficulty for beginners and unfamiliar users by developing an intuitive, easy-to-use interface and integrated functional modules. This not only reduces the time and effort required to familiarize and master the software, but also improves overall work efficiency.

[0040] Automated Collision Detection Parameter Setting: To address the tedious task of defining and entering collision detection parameters, this method introduces intelligent parameter configuration. The detection platform automatically adjusts collision detection spacing according to international and domestic standards, enabling fast and accurate parameter setting. This improvement reduces the repetitive labor of manual input, improves the accuracy and consistency of parameter settings, and supports reuse across multiple projects, further enhancing efficiency.

[0041] Customized report generation: To address the problem that the existing collision detection report format and content do not meet the needs of designers, the method of the present invention provides a highly customizable report generation tool that generates data in the form of a collision detection matrix, allowing users to set report parameters and format according to actual needs.

[0042] This invention integrates relevant data analysis functions, enabling in-depth mining of BIM models and project data, uncovering potential value and providing powerful support for design decisions. This not only reduces designers’ secondary processing time but also promotes the efficient use and innovative development of project data. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flow chart of a detection model optimization method based on BIM technology of the present invention is shown;

[0044] Figure 2 An example diagram of a collision detection matrix according to the present invention is shown;

[0045] Figure 3 An example diagram of parameter settings for a collision detection group according to the present invention is shown;

[0046] Figure 4 A block diagram of a detection model optimization system based on BIM technology is shown in the present invention;

[0047] Figure 5 Shown is a diagram of a Class A collision example of the present invention;

[0048] Figure 6 Shown is a diagram of a Class B collision example of the present invention;

[0049] Figure 7 An example diagram of a Class C collision of the present invention is shown. DETAILED DESCRIPTION

[0050] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0052] Figure 1 A flow chart of a detection model optimization method based on BIM technology of the present invention is shown.

[0053] like Figure 1As shown, the first aspect of the present invention provides a detection model optimization method based on BIM technology, comprising:

[0054] S1: Based on the target building plan, set the collision importance, subsystem type, and collision level information of each discipline. After the setting is completed, generate the initial detection grouping between different professional subsystems, and generate the collision detection matrix through the detection of the initial grouping;

[0055] S2: Based on the initial inspection group defined in S1 and in combination with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision detection matrix, and exports the collision detection matrix to form a report file;

[0056] S3: Through the detection platform, various professional BIM models are assembled. The detection platform automatically reads the collision detection matrix and generates multiple collision detection groups. The collision detection groups are screened for valid groups and the collision detection process is run to obtain the collision detection results.

[0057] S4: Determine whether to store the operation result according to the collision detection result, and send the collision detection result to the user terminal;

[0058] S5: Extract and store relevant professional model information based on collision detection results, collect statistics on collision information between professional models, sort collision records, and predict the time required for correction based on collision type to generate collision decision information;

[0059] S6: Sending collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model. After the correction is completed, steps S3 to S6 are repeated until the collision result meets the preset standard;

[0060] S7: Record the statistical data and model modification records of each collision detection, summarize the work completion status, and generate a detection and modification report;

[0061] S8: In S7, the number of corrections for each profession is counted in real time. When the number of corrections reaches the expected value, a correction cycle is set. In the detection platform, the collision position, collision type, and collision level information before and after the correction cycle are obtained. According to the collision position, collision type, and collision level information, the associated component analysis and collision feature analysis of the collision point are performed. The collision features are stored in a matrix to form two collision feature matrices before and after the correction cycle. The similarity between the collision feature matrices is analyzed. Combined with the preset feature matrix, the correction cycle is evaluated for overall correction, and corresponding correction warning information is generated.

[0062] It should be noted that the inspection and correction report is specifically based on the start and end time of the work, combined with multiple collision detection statistical data and intermediate optimization suggestions and other information, to summarize the corresponding BIM inspection and correction report, summarizing the time-consuming, manpower, workload and other aspects of the collision detection work, which can provide a basic reference basis for other projects.

[0063] According to an embodiment of the present invention, the preset specifications are specifically regional specifications or construction project specifications corresponding to the target building plan. The preset specifications include data standards, delivery standards, and collision standard specifications.

[0064] According to an embodiment of the present invention, the detection platform includes a web-side online detection system and a PC-side detection system.

[0065] It should be noted that the web-based online detection system and PC-based detection system correspond to the online web-based and offline PC-based collision detection platforms, respectively. Both platforms include corresponding detection modules, data storage modules, and BIM software interaction modules. The PC-based platform software imports models quickly and quickly, but requires local installation and is more complex to operate. The online web-based platform uploads BIM models and can incorporate model lightweighting capabilities. Although uploads take longer, there are no software restrictions.

[0066] According to an embodiment of the present invention, in S2, the collision detection matrix is stored via two-dimensional data, each dimension of data includes information of multiple professional subsystems, and two professional subsystems are mapped and associated via the collision detection matrix.

[0067] It should be noted that the collision detection matrix can be visualized and the content can be marked with different colors. The collision detection matrix list will be read by the platform software to implement parameter setting, eliminating the manual input process and improving the efficiency of the detection model.

[0068] According to an embodiment of the present invention, in S3, effective detection screening is performed on the collision detection groups, including eliminating duplicate groups from multiple collision detection groups and deleting invalid detection groups in accordance with preset specifications.

[0069] It should be noted that the invalid detection group includes some unnecessary detection items and detection items that are meaningless to the construction process.

[0070] According to an embodiment of the present invention, in S5, specifically: based on the collision detection results, collision problems, collision types, and collision information are extracted and stored for relevant professional models, the proportion of collision problems in each profession and the frequency of occurrence of collision types are counted to obtain a collision problem proportion table, different collision types are sorted according to their importance, and a collision type ranking table is generated; based on historical collision correction records, the correction time of the collision records in the collision detection results is predicted to generate a prediction schedule; and collision decision information is generated based on the collision problem proportion table, the collision type ranking table, and the prediction schedule.

[0071] It should be noted that the collision detection results include information such as the collision type, collision level, collision problem, collision location in the BIM model, and collision detection time. Collision decision information is used to assist personnel responsible for each professional model in conducting precise collision analysis and auxiliary corrections, thereby improving correction efficiency.

[0072] The collision problem ratio table specifically summarizes the collision problem ratio by discipline to better identify and resolve design conflicts; the collision type ranking table specifically ranks by architectural design importance, such as gravity flow collision before sorting and pressure flow after sorting, to provide decision-making direction for resolving collision conflicts; the correction time prediction specifically predicts the final modification completion time of the zero-collision model based on the number of collision modifications, etc.

[0073] According to an embodiment of the present invention, the S8 is specifically:

[0074] In S7, the number of revisions for each specialty is counted in real time to calculate the total number of revisions. If the total number of revisions reaches the expected value, a revision period is set. The revision period is the time period before and after the revision when the total number of revisions reaches the expected value.

[0075] In the detection platform, through the collision detection results obtained from each detection analysis, the collision location, collision type, collision level, and collision correction times of each collision record before the correction period are obtained;

[0076] Based on the collision location, collision type, and collision level, the collision location of the professional model where the collision point is located is determined in the detection platform. The collision type and collision level information are analyzed, and the associated building components at the collision location are determined. The associated building components are marked and counted to obtain the location, number, and collision level of the associated building components.

[0077] The collision location, collision type, collision level and the associated building component location, component number and component collision level are used as the first dimension information of the matrix, and the corresponding professional subsystem is used as the second dimension information to matrix the collision feature data to form a collision feature matrix;

[0078] Perform collision feature analysis and corresponding feature matrix generation on the collision detection results after the correction period, and finally form two collision feature matrices before and after the correction period;

[0079] Based on the Jordan standard form method, the similarity of the two collision feature matrices is determined to obtain a first similarity, and the similarity of the collision feature matrix after the correction period is determined to obtain a second similarity;

[0080] An overall correction evaluation of the correction period is performed based on the first similarity and the second similarity, and corresponding correction warning information is generated.

[0081] It should be noted that the start and end times of the correction cycle are the end of the previous correction cycle and the time point when the total number of corrections reaches the expected value. The component collision level refers to the search for the corresponding collision points existing in the associated building components, and the highest collision level among the existing collision points is used as the component collision level (i.e., the most severe level). This is then used as feature information to generate a feature matrix. The collision feature matrix includes two-dimensional data. The first dimension is the collision feature data (collision location, collision type, collision level and associated building component location, component number, component collision level), and the second dimension is the corresponding professional subsystem category. The preset feature matrix is a feature matrix under ideal collision conditions, which is used for data comparison and can be set by the user. The first similarity represents the quality of the correction effect before and after the correction cycle. The larger the value, the better the correction effect. The second similarity represents the degree of consistency with the expected collision state after the correction cycle. The larger the value, the more consistent the overall correction process is with expectations and the overall collision situation is consistent with the expected trend.

[0082] It is worth mentioning here that the occurrence and correction of collisions are often not necessarily in line with expectations. After correcting certain models involved in collisions, new collisions may occur, or the number and level of collisions may remain unchanged, but the overall collision situation may not meet expectations, resulting in an increase in invalid or repetitive correction work. Based on this, the present invention generates corresponding feature data by analyzing the corresponding collision features and the building components associated with the collision points, and performs feature similarity comparison in matrix form, effectively improving the overall collision feature comparison effect, realizing an overall deviation analysis of the collision situation, generating corresponding early warning information, and then realizing an overall analysis of the collision situation, and making effective decision-making suggestions and secondary corrections for the correction of each professional model, so that the overall collision situation meets expectations.

[0083] The following table shows the collision level settings, expressed as ABC levels. Based on numerical analysis requirements, it can also be converted into levels one, two, and three:

[0084]

[0085]

[0086] The classification of collision levels can be detailed based on the complexity of the project (for example, more levels of classification). The above is an exemplary classification.

[0087] Figure 2 An example diagram of a collision detection matrix according to the present invention is shown;

[0088] like Figure 2 As shown, it includes examples of multiple professions (water supply and drainage, electrical, etc.) and multiple professional subsystems (gravity rainwater pipes, domestic sewage pipes, etc.), and the collision levels between professional subsystems form a corresponding matrix diagram.

[0089] Figure 3 An example diagram of parameter settings for a collision detection group according to the present invention is shown;

[0090] This table sets the types and clearances between different collision groups. Reference values can be provided by relevant regulations and regions, or they can be entered or modified manually. They can also be exported through the collision detection matrix.

[0091] Figures 5-7 The collision example diagrams of the present invention at the A, B, and C collision levels are shown respectively;

[0092] Example of Class A collision (severe collision): collision of outdoor pipeline structural components, where post-construction modifications have a significant impact and require adjustments before construction;

[0093] Example of Class B collision (more serious collision): No holes are reserved between the faucet and the countertop. This does not affect the overall construction progress, and adjustments before construction can save work.

[0094] Example of a Class C collision (minor conflict): A table and chair collide, which can be resolved by adjusting the position of the table or chair during actual installation.

[0095] According to an embodiment of the present invention, the further embodiment includes:

[0096] By detecting the correction report, the corresponding eigenvalues of the collision characteristic matrix of N consecutive correction cycles are calculated to obtain N eigenvalues;

[0097] Calculate the numerical difference of N eigenvalues, mark and count the eigenvalues that meet the preset difference range as similar values, and obtain M similar eigenvalues;

[0098] Marking the correction periods corresponding to M similar eigenvalues to obtain multiple similar correction periods;

[0099] Through the detection and correction report, the BIM models of the collision points with similar correction cycles are marked to form cached model data;

[0100] Record cache model data of all similar revision cycles and generate a cache data table;

[0101] The cache data table is stored in the database of the detection platform.

[0102] It should be noted that the eigenvalues that meet the preset difference range are marked and counted as similar values to obtain M similar eigenvalues. The preset difference range is specifically a predetermined value. If the absolute difference between two eigenvalues is less than or equal to the predetermined value, the two eigenvalues are determined to be within the preset difference range. Further, it can be determined whether a group of data (i.e., multiple eigenvalues) is within the preset difference range. If so, the maximum and minimum eigenvalues in the group of data must be within the preset difference range. Through N eigenvalues, a group of similar data with the most eigenvalues within the preset difference range is calculated, and the eigenvalues in the group of similar data are marked as similar eigenvalues. Each eigenvalue corresponds to a correction cycle.

[0103] Through the above embodiment, it is possible to effectively and quickly analyze the cycles of similar collision situations and correction situations from multiple continuous correction cycles, and the similarity is analyzed through the difference in the eigenvalues of the collision feature matrix. The professional model statistics of the collision points of the screened similar correction cycles are performed, and the corresponding model cache data table is set to store the collision point models with similar characteristics in a cache form, which effectively improves the query efficiency and correction efficiency of each professional user for the corresponding collision model.

[0104] Figure 4 A block diagram of a detection model optimization system based on BIM technology of the present invention is shown.

[0105] A second aspect of the present invention further provides a detection model optimization system 4 based on BIM technology, the system comprising: a memory 41 and a processor 42, wherein the memory 41 includes a detection model optimization program based on BIM technology, and when the detection model optimization program based on BIM technology is executed by the processor 42, the following steps are implemented:

[0106] S1: Based on the target building plan, set the collision importance, subsystem type, and collision level information of each discipline. After the setting is completed, generate the initial detection grouping between different professional subsystems, and generate the collision detection matrix through the detection of the initial grouping;

[0107] S2: Based on the initial inspection group defined in S1 and in combination with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision detection matrix, and exports the collision detection matrix to form a report file;

[0108] S3: Through the detection platform, various professional BIM models are assembled. The detection platform automatically reads the collision detection matrix and generates multiple collision detection groups. The collision detection groups are screened for valid groups and the collision detection process is run to obtain the collision detection results.

[0109] S4: Determine whether to store the operation result according to the collision detection result, and send the collision detection result to the user terminal;

[0110] S5: Extract and store relevant professional model information based on collision detection results, collect statistics on collision information between professional models, sort collision records, and predict the time required for correction based on collision type to generate collision decision information;

[0111] S6: Sending collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model. After the correction is completed, steps S3 to S6 are repeated until the collision result meets the preset standard;

[0112] S7: Record the statistical data and model modification records of each collision detection, summarize the work completion status, and generate a detection and modification report;

[0113] S8: In S7, the number of corrections for each profession is counted in real time. When the number of corrections reaches the expected value, a correction cycle is set. In the detection platform, the collision position, collision type, and collision level information before and after the correction cycle are obtained. According to the collision position, collision type, and collision level information, the associated component analysis and collision feature analysis of the collision point are performed. The collision features are stored in a matrix to form two collision feature matrices before and after the correction cycle. The similarity between the collision feature matrices is analyzed. Combined with the preset feature matrix, the correction cycle is evaluated for overall correction, and corresponding correction warning information is generated.

[0114] It should be noted that the inspection and correction report is specifically based on the start and end time of the work, combined with multiple collision detection statistical data and intermediate optimization suggestions and other information, to summarize the corresponding BIM inspection and correction report, summarizing the time-consuming, manpower, workload and other aspects of the collision detection work, which can provide a basic reference basis for other projects.

[0115] According to an embodiment of the present invention, the preset specifications are specifically regional specifications or construction project specifications corresponding to the target building plan. The preset specifications include data standards, delivery standards, and collision standard specifications.

[0116] According to an embodiment of the present invention, the detection platform includes a web-side online detection system and a PC-side detection system.

[0117] It should be noted that the web-based online detection system and PC-based detection system correspond to the online web-based and offline PC-based collision detection platforms, respectively. Both platforms include corresponding detection modules, data storage modules, and BIM software interaction modules. The PC-based platform software imports models quickly and quickly, but requires local installation and is more complex to operate. The online web-based platform uploads BIM models and can incorporate model lightweighting capabilities. Although uploads take longer, there are no software restrictions.

[0118] According to an embodiment of the present invention, in S2, the collision detection matrix is stored via two-dimensional data, each dimension of data includes information of multiple professional subsystems, and two professional subsystems are mapped and associated via the collision detection matrix.

[0119] It should be noted that the collision detection matrix can be visualized and the content can be marked with different colors. The collision detection matrix list will be read by the platform software to implement parameter setting, eliminating the manual input process and improving the efficiency of the detection model.

[0120] According to an embodiment of the present invention, in S3, effective detection screening is performed on the collision detection groups, including eliminating duplicate groups from multiple collision detection groups and deleting invalid detection groups in accordance with preset specifications.

[0121] It should be noted that the invalid detection group includes some unnecessary detection items and detection items that are meaningless to the construction process.

[0122] According to an embodiment of the present invention, in S5, specifically: based on the collision detection results, collision problems, collision types, and collision information are extracted and stored for relevant professional models, the proportion of collision problems in each profession and the frequency of occurrence of collision types are counted to obtain a collision problem proportion table, different collision types are sorted according to their importance, and a collision type ranking table is generated; based on historical collision correction records, the correction time of the collision records in the collision detection results is predicted to generate a prediction schedule; and collision decision information is generated based on the collision problem proportion table, the collision type ranking table, and the prediction schedule.

[0123] It should be noted that the collision detection results include information such as the collision type, collision level, collision problem, collision location in the BIM model, and collision detection time. Collision decision information is used to assist personnel responsible for each professional model in conducting precise collision analysis and auxiliary corrections, thereby improving correction efficiency.

[0124] The collision problem ratio table specifically summarizes the collision problem ratio by discipline to better identify and resolve design conflicts; the collision type ranking table specifically ranks by architectural design importance, such as gravity flow collision before sorting and pressure flow after sorting, to provide decision-making direction for resolving collision conflicts; the correction time prediction specifically predicts the final modification completion time of the zero-collision model based on the number of collision modifications, etc.

[0125] According to an embodiment of the present invention, the S8 is specifically:

[0126] In S7, the number of revisions for each specialty is counted in real time to calculate the total number of revisions. If the total number of revisions reaches the expected value, a revision period is set. The revision period is the time period before and after the revision when the total number of revisions reaches the expected value.

[0127] In the detection platform, through the collision detection results obtained from each detection analysis, the collision location, collision type, collision level, and collision correction times of each collision record before the correction period are obtained;

[0128] Based on the collision location, collision type, and collision level, the collision location of the professional model where the collision point is located is determined in the detection platform. The collision type and collision level information are analyzed, and the associated building components at the collision location are determined. The associated building components are marked and counted to obtain the location, number, and collision level of the associated building components.

[0129] The collision location, collision type, collision level and the associated building component location, component number and component collision level are used as the first dimension information of the matrix, and the corresponding professional subsystem is used as the second dimension information to matrix the collision feature data to form a collision feature matrix;

[0130] Perform collision feature analysis and corresponding feature matrix generation on the collision detection results after the correction period, and finally form two collision feature matrices before and after the correction period;

[0131] Based on the Jordan standard form method, the similarity of the two collision feature matrices is determined to obtain a first similarity, and the similarity of the collision feature matrix after the correction period is determined to obtain a second similarity;

[0132] An overall correction evaluation of the correction period is performed based on the first similarity and the second similarity, and corresponding correction warning information is generated.

[0133] It should be noted that the start and end times of the correction cycle are the end of the previous correction cycle and the time point when the total number of corrections reaches the expected value. The component collision level refers to the search for the corresponding collision points existing in the associated building components, and the highest collision level among the existing collision points is used as the component collision level (i.e., the most severe level). This is then used as feature information to generate a feature matrix. The collision feature matrix includes two-dimensional data. The first dimension is the collision feature data (collision location, collision type, collision level and associated building component location, component number, component collision level), and the second dimension is the corresponding professional subsystem category. The preset feature matrix is a feature matrix under ideal collision conditions, which is used for data comparison and can be set by the user. The first similarity represents the quality of the correction effect before and after the correction cycle. The larger the value, the better the correction effect. The second similarity represents the degree of consistency with the expected collision state after the correction cycle. The larger the value, the more consistent the overall correction process is with expectations and the overall collision situation is consistent with the expected trend.

[0134] It is worth mentioning here that the occurrence and correction of collisions are often not necessarily in line with expectations. After correcting certain models involved in collisions, new collisions may occur, or the number and level of collisions may remain unchanged, but the overall collision situation may not meet expectations, resulting in an increase in invalid or repetitive correction work. Based on this, the present invention generates corresponding feature data by analyzing the corresponding collision features and the building components associated with the collision points, and performs feature similarity comparison in matrix form, effectively improving the overall collision feature comparison effect, realizing an overall deviation analysis of the collision situation, generating corresponding early warning information, and then realizing an overall analysis of the collision situation, and making effective decision-making suggestions and secondary corrections for the correction of each professional model, so that the overall collision situation meets expectations.

[0135] The third aspect of the present invention also provides a computer-readable storage medium, which includes a detection model optimization program based on BIM technology. When the detection model optimization program based on BIM technology is executed by a processor, the steps of the detection model optimization method based on BIM technology as described in any one of the above items are implemented.

[0136] The present invention discloses a detection model optimization method and system based on BIM technology. By setting the collision importance, subsystem type and level information, a collision detection matrix is generated, the collision type and reference value are automatically set, and a report is exported. The system assembles the BIM model, automatically reads the matrix, screens the collision detection group, runs the detection process, and sends the results to the user. The user modifies the model based on the results and performs a cyclic detection until the standard is met. The system records the detection and correction data, generates a report, and counts the number of corrections in real time. When the number of corrections reaches the expected number, the system analyzes the collision characteristics before and after the correction cycle, forms a characteristic matrix, evaluates the correction effect and generates early warning information. The present invention effectively improves the efficiency and accuracy of collision detection, improves the correction efficiency of professionals, and further improves the overall quality of the construction project.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0138] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0139] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0140] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0141] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0142] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A detection model optimization method based on BIM technology, characterized in that: include: S1: Based on the target building plan, set the collision importance, subsystem type, and collision level information of each discipline. After the setting is completed, generate the initial detection grouping between different professional subsystems, and generate the collision detection matrix through the detection of the initial grouping; S2: Based on the initial inspection group defined in S1 and in combination with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision detection matrix, and exports the collision detection matrix to form a report file; S3: Through the detection platform, various professional BIM models are assembled. The detection platform automatically reads the collision detection matrix and generates multiple collision detection groups. The collision detection groups are screened for valid groups and the collision detection process is run to obtain the collision detection results. S4: Determine whether to store the operation result according to the collision detection result, and send the collision detection result to the user terminal; S5: Extract and store relevant professional model information based on collision detection results, collect statistics on collision information between professional models, sort collision records, and predict the time required for correction based on collision type to generate collision decision information; S6: Sending collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model. After the correction is completed, steps S3 to S6 are repeated until the collision result meets the preset standard; S7: Record the statistical data and model modification records of each collision detection, summarize the work completion status, and generate a detection and modification report; S8: In S7, the number of corrections for each profession is counted in real time. When the number of corrections reaches the expected value, a correction cycle is set. In the detection platform, the collision position, collision type, and collision level information before and after the correction cycle are obtained. According to the collision position, collision type, and collision level information, the associated component analysis and collision feature analysis of the collision point are performed. The collision features are stored in a matrix to form two collision feature matrices before and after the correction cycle. The similarity between the collision feature matrices is analyzed. Combined with the preset feature matrix, the correction cycle is evaluated for overall correction, and corresponding correction warning information is generated.

2. The method for optimizing a detection model based on BIM technology according to claim 1, characterized in that: The preset specifications mentioned above are specifically regional specifications or construction project specifications corresponding to the target building plan. The preset specifications include data standards, delivery standards, and collision standard specifications.

3. The detection model optimization method based on BIM technology according to claim 1 is characterized in that: The detection platform includes a web-side online detection system and a PC-side detection system.

4. The method for optimizing a detection model based on BIM technology according to claim 1, characterized in that: In S2, the collision detection matrix is stored in two-dimensional data, each dimension of data includes information of multiple professional subsystems, and two professional subsystems are mapped and associated through the collision detection matrix.

5. The method for optimizing a detection model based on BIM technology according to claim 1, characterized in that: In S3, the collision detection groups are screened for effective detection, including eliminating duplicate groups from multiple collision detection groups, and deleting invalid detection groups in accordance with preset specifications.

6. The method for optimizing a detection model based on BIM technology according to claim 1, characterized in that: Specifically, in S5, based on the collision detection results, collision problems, collision types, and collision information are extracted and stored for relevant professional models, the proportion of collision problems in each profession and the frequency of collision types are counted to obtain a collision problem proportion table, different collision types are ranked according to their importance, and a collision type ranking table is generated; based on historical collision correction records, correction time predictions are made for collision records in the collision detection results to generate a prediction schedule; and collision decision information is generated based on the collision problem proportion table, the collision type ranking table, and the prediction schedule.

7. The method for optimizing a detection model based on BIM technology according to claim 1, characterized in that: Said S8 is specifically: In S7, the number of revisions for each specialty is counted in real time to calculate the total number of revisions. If the total number of revisions reaches the expected value, a revision period is set. The revision period is the time period before and after the revision when the total number of revisions reaches the expected value. In the detection platform, through the collision detection results obtained from each detection analysis, the collision location, collision type, collision level, and collision correction times of each collision record before the correction period are obtained; Based on the collision location, collision type, and collision level, the collision location of the professional model where the collision point is located is determined in the detection platform. The collision type and collision level information are analyzed, and the associated building components at the collision location are determined. The associated building components are marked and counted to obtain the location, number, and collision level of the associated building components. The collision location, collision type, collision level and the associated building component location, component number and component collision level are used as the first dimension information of the matrix, and the corresponding professional subsystem is used as the second dimension information to matrix the collision feature data to form a collision feature matrix; Perform collision feature analysis and corresponding feature matrix generation on the collision detection results after the correction period, and finally form two collision feature matrices before and after the correction period; Based on the Jordan standard form method, the similarity of the two collision feature matrices is determined to obtain a first similarity, and the similarity of the collision feature matrix after the correction period is determined to obtain a second similarity; An overall correction evaluation of the correction period is performed based on the first similarity and the second similarity, and corresponding correction warning information is generated.

8. A detection model optimization system based on BIM technology, characterized in that: The system includes: a memory and a processor. The memory includes a detection model optimization program based on BIM technology. When the detection model optimization program based on BIM technology is executed by the processor, the following steps are implemented: S1: Based on the target building plan, set the collision importance, subsystem type, and collision level information of each discipline. After the setting is completed, generate the initial detection grouping between different professional subsystems, and generate the collision detection matrix through the detection of the initial grouping; S2: Based on the initial inspection group defined in S1 and in combination with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision detection matrix, and exports the collision detection matrix to form a report file; S3: Through the detection platform, various professional BIM models are assembled. The detection platform automatically reads the collision detection matrix and generates multiple collision detection groups. The collision detection groups are screened for valid groups and the collision detection process is run to obtain the collision detection results. S4: Determine whether to store the operation result according to the collision detection result, and send the collision detection result to the user terminal; S5: Extract and store relevant professional model information based on collision detection results, collect statistics on collision information between professional models, sort collision records, and predict the time required for correction based on collision type to generate collision decision information; S6: Sending collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model. After the correction is completed, steps S3 to S6 are repeated until the collision result meets the preset standard; S7: Record the statistical data and model modification records of each collision detection, summarize the work completion status, and generate a detection and modification report; S8: In S7, the number of corrections for each profession is counted in real time. When the number of corrections reaches the expected value, a correction cycle is set. In the detection platform, the collision position, collision type, and collision level information before and after the correction cycle are obtained. According to the collision position, collision type, and collision level information, the associated component analysis and collision feature analysis of the collision point are performed. The collision features are stored in a matrix to form two collision feature matrices before and after the correction cycle. The similarity between the collision feature matrices is analyzed. Combined with the preset feature matrix, the correction cycle is evaluated for overall correction, and corresponding correction warning information is generated.

9. The detection model optimization system based on BIM technology according to claim 8, characterized in that: The preset specifications mentioned above are specifically regional specifications or construction project specifications corresponding to the target building plan. The preset specifications include data standards, delivery standards, and collision standard specifications.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a detection model optimization program based on BIM technology. When the detection model optimization program based on BIM technology is executed by a processor, the steps of the detection model optimization method based on BIM technology are implemented as described in any one of claims 1 to 7.

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