Detection model optimization method and system based on BIM technology

Abstract

The invention discloses a BIM (Building Information Modeling) technology-based detection model optimization method and system. A collision detection matrix is generated by setting collision importance and subsystem type and grade information, a collision type and a reference value are automatically set, and a report is exported. And the system final assembly BIM model automatically reads the matrix, screens the collision detection group, operates the detection process, and sends the result to the user. And the user corrects the model according to the result and circularly detects until the standard is reached. And the system records detection and correction data, generates a report and counts correction times in real time. And when the correction frequency reaches the expectation, the system analyzes the collision characteristics before and after the correction period, forms a characteristic matrix, evaluates the correction effect and generates early warning information. According to the method, the collision detection efficiency and accuracy are effectively improved, the correction efficiency of professionals is improved, and the overall quality of a construction project is further improved.

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) technology is a design, construction and operation management process based on three-dimensional digital technology. It integrates the geometric, physical, functional information of the construction project and related project life cycle information into a single, complete, logically related building information model. The application of BIM technology covers multiple stages such as architectural design, construction, and operation, aiming to improve project efficiency, reduce costs, and improve quality.

[0003] As a core application in the BIM technology system, collision detection plays a vital role in the design and construction of engineering projects, and is a key link in achieving efficient and accurate management of engineering projects. In the engineering design stage, collision detection through BIM models can comprehensively and carefully analyze potential conflict points in various structures. These conflicts may arise from the overlap of spatial positions between different components, or they may be caused by the mismatch of design parameters. With the three-dimensional visualization capabilities and model numerical accuracy of BIM technology, software tools can automatically identify and mark potential collision points, making it easier for the design team to quickly locate and solve problems and improve the overall quality of the project.

[0004] However, in the existing collision detection technology, the operation is somewhat complex. The installation and use interface and functions of mainstream collision detection software such as Navisworks are relatively complex. For beginners or designers who are not familiar with the software, it may take a certain amount of time and effort to familiarize themselves with and master its operation methods. This increases the learning cost and difficulty of use to a certain extent. The key issues in collision detection and model optimization are how to define "collision" by numerical values ​​and how to input "defined collision detection parameters" in the collision detection software. In fact, the definition of collision detection spacing is largely positively correlated with international or domestic standards and has a strong regional universality. In other words, the collision detection parameters are relatively fixed and can be reused by multiple projects. In the current technical route or software, this part of the work needs to rely on manual input, which has a large amount of repetitive work and the accuracy varies from person to person. In addition, although the collision detection report generated by the current general software contains information such as the specific location of the collision point, related images, and axis grid location, its report format and content cannot fully meet the actual needs of designers. The output interface of these reports lacks the function of customizing parameters or formats, which often requires designers to invest a lot of time in secondary processing and collision 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 detection model optimization method based on BIM technology, comprising:

[0007] S1: Based on the target building planning, 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 initial detection grouping;

[0008] S2: Through the initial inspection grouping defined in S1 and combined with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision inspection matrix, and exports the collision inspection 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 the collision detection results, collect statistics on collision information between professional models, sort the collision records, and predict the time required for correction based on the collision type to generate collision decision information;

[0012] S6: sending the collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model, and repeating S3 to S6 after the correction is completed, 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 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, and 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, and 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 building 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 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 of relevant professional models, counting the proportion of collision problems in each profession and the frequency of occurrence 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 the 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, the collision type ranking table, and the prediction schedule.

[0020] In this solution, S8 is specifically:

[0021] In S7, the number of revisions of 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 cycle is set, which is the time period before and after the revision when the total number of revisions reaches the expected value.

[0022] In the detection platform, the collision detection results obtained from each detection analysis are used to obtain the collision position, collision type, collision level, and collision correction times of each collision record before the correction cycle;

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

[0024] The collision position, collision type, collision level and the associated building component position, component quantity 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 between the collision feature matrix after the correction period and the preset feature matrix 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] The second aspect of the present invention further provides a detection model optimization system based on BIM technology, the system comprising: a memory, a processor, the memory comprising a detection model optimization program based on BIM technology, and the detection model optimization program based on BIM technology, when executed by the processor, implements the following steps:

[0029] S1: Based on the target building planning, 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 initial detection grouping;

[0030] S2: Through the initial inspection grouping defined in S1 and combined with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision inspection matrix, and exports the collision inspection 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 the collision detection results, collect statistics on collision information between professional models, sort the collision records, and predict the time required for correction based on the collision type to generate collision decision information;

[0034] S6: sending the collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model, and repeating S3 to S6 after the correction is completed, 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 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, and 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, and 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] Through the present invention, the following beneficial effects can be achieved:

[0039] Simplified operation process: The present invention has greatly reduced the learning cost and difficulty of use for beginners and unfamiliar users by developing an intuitive and easy-to-use interface and integrated functional modules. This not only reduces the time and effort required to familiarize and master the software operation, but also improves the overall work efficiency.

[0040] Automatic collision detection parameter setting: To solve the cumbersome problem of collision detection parameter definition and input, the method of the present invention introduces intelligent parameter configuration. The detection platform can automatically adjust the collision detection spacing according to international or domestic standards to achieve fast and accurate parameter setting. This improvement reduces the repetitive labor of manual input, improves the accuracy and consistency of parameter setting, and supports multiple project reuse, further improving efficiency.

[0041] Customized report generation: In view of 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, which generates data in the form of a collision detection matrix, allowing users to set report parameters and formats according to actual needs.

[0042] The present invention integrates the corresponding data analysis function, which can deeply mine BIM models and project data, discover potential value, and provide strong auxiliary support for design decisions. This not only reduces the secondary processing time of designers, 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 of the present invention is shown;

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

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

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

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

[0049] Figure 7 A diagram showing an example of a Class C collision according to the present invention is shown. DETAILED DESCRIPTION

[0050] In order to more clearly understand the above-mentioned purpose, 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 the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[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 protection scope 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 planning, 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 initial detection grouping;

[0055] S2: Through the initial inspection grouping defined in S1 and combined with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision inspection matrix, and exports the collision inspection 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 the collision detection results, collect statistics on collision information between professional models, sort the collision records, and predict the time required for correction based on the collision type to generate collision decision information;

[0059] S6: sending the collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model, and repeating S3 to S6 after the correction is completed, 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 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, and 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, and 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 detection and correction report is specifically summarized according to the start and end time of the work, combined with multiple collision detection statistical data and intermediate optimization suggestions and other information, to correspond to the BIM detection and correction report, summarize the time-consuming, manpower, workload and other aspects of the collision detection work, and provide a basic reference for other projects.

[0063] According to an embodiment of the present invention, the preset specifications are specifically regional specifications or building project specifications corresponding to the target building plan, and 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-side online detection system and the PC-side detection system correspond to the collision detection platforms of the online web side and the offline PC side, respectively. Both platforms include corresponding detection modules, data storage modules, and BIM software interaction modules. The PC-side platform software imports models quickly and takes a short time, but needs to be installed locally, which is more complicated to operate. The online Web side can upload BIM models and can be combined with the model lightweight function. The upload takes a long time, but 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 dimensional data includes information of a plurality of 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 result, the relevant professional models are subjected to collision problem, collision type, and collision information extraction and storage, the proportion of each professional collision problem and the frequency of occurrence of the collision type are counted, and a collision problem proportion table is obtained, and the importance of different collision types is sorted to generate a collision type sorting table, and based on the 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 according to the collision problem proportion table, the collision type sorting 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, BIM model location of the collision, collision detection time, etc. The collision decision information is a kind of auxiliary decision information, which is used to assist the responsible personnel of each professional model to conduct precise collision analysis and auxiliary correction to improve the correction efficiency.

[0072] The collision problem ratio table specifically counts the collision problem ratios by profession, so as to better identify and resolve design conflicts. The collision type ranking table specifically sorts by importance of architectural design, such as gravity flow collision before sorting and pressure flow collision after sorting, to provide decision-making direction for resolving collision conflicts. The correction time prediction specifically predicts the final modification completion time of the 0 collision model according to the number of collision modifications.

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

[0074] In S7, the number of revisions of 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 cycle is set, which is the time period before and after the revision when the total number of revisions reaches the expected value.

[0075] In the detection platform, the collision detection results obtained from each detection analysis are used to obtain the collision position, collision type, collision level, and collision correction times of each collision record before the correction cycle;

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

[0077] The collision position, collision type, collision level and the associated building component position, component quantity 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 between the collision feature matrix after the correction period and the preset feature matrix 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 time of the correction cycle is the time point at which the end of the previous correction cycle and the total number of corrections reach the expected value. The component collision level is to retrieve the corresponding collision points existing in the component as a whole in the corresponding associated building components, and take the highest collision level among the existing collision points as the component collision level (i.e. the most serious level), and subsequently generate a feature matrix as feature information. The collision feature matrix includes two-dimensional data, the first dimension is the collision feature data (collision position, collision type, collision level and associated building component position, component number, component collision level), and the second dimension is the corresponding professional subsystem category. The preset feature matrix is ​​a feature matrix under an ideal collision state, which is used to compare data and can be set by the user. The first similarity represents the degree of 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 the integrity of the correction process is in line with expectations, and the integrity of the collision situation is in line 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 the collision, new collisions may occur, or the number and level of collisions remain unchanged, but the overall collision situation does 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 in ABC levels. Based on numerical analysis requirements, it can also be converted into levels 1, 2, and 3:

[0084]

[0085]

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

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

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

[0089] Figure 3 An example diagram showing parameter settings of a collision detection group of 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 to 7 The collision example diagrams of the A, B and C collision levels of the present invention are shown respectively;

[0092] Example of Class A collision (serious collision): collision of outdoor pipeline structural components, which has a great impact on post-construction modification and needs to be adjusted before construction;

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

[0094] Example of Class C collision (minor conflict): a table and a 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, it also 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 is performed based on the correction periods corresponding to the M similar characteristic values ​​to obtain multiple similar correction periods;

[0099] Through the detection and correction report, the BIM model to which the collision points of similar correction cycles belong is marked to form cache model data;

[0100] Record cache model data of all similar modification 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 feature values ​​that meet the preset difference range are marked and counted for similarity values, and among the M similar feature values ​​obtained, the preset difference range is specifically a preset value. If the absolute difference between two feature values ​​is less than or equal to the preset value, the two feature values ​​are determined to be within the preset difference range. Further, it can be determined whether a group of data (i.e., multiple feature values) are within the preset difference range. If so, the maximum and minimum feature values ​​in the group of data must be within the preset difference range. Through N feature values, a group of similar data with the most feature values ​​within the preset difference range is calculated, and the feature values ​​in the group of similar data are marked as similar feature values. Each feature value corresponds to a correction cycle.

[0103] Through the above-mentioned embodiments, it is possible to 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, thereby effectively improving 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] The 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 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 42, the following steps are implemented:

[0106] S1: Based on the target building planning, 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 initial detection grouping;

[0107] S2: Through the initial inspection grouping defined in S1 and combined with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision inspection matrix, and exports the collision inspection 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 the collision detection results, collect statistics on collision information between professional models, sort the collision records, and predict the time required for correction based on the collision type to generate collision decision information;

[0111] S6: sending the collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model, and repeating S3 to S6 after the correction is completed, 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 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, and 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, and 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 detection and correction report is specifically summarized according to the start and end time of the work, combined with multiple collision detection statistical data and intermediate optimization suggestions and other information, to correspond to the BIM detection and correction report, summarize the time-consuming, manpower, workload and other aspects of the collision detection work, and provide a basic reference for other projects.

[0115] According to an embodiment of the present invention, the preset specifications are specifically regional specifications or building project specifications corresponding to the target building plan, and 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-side online detection system and the PC-side detection system correspond to the collision detection platforms of the online web side and the offline PC side, respectively. Both platforms include corresponding detection modules, data storage modules, and BIM software interaction modules. The PC-side platform software imports models quickly and takes a short time, but needs to be installed locally, which is more complicated to operate. The online Web side can upload BIM models and can be combined with the model lightweight function. The upload takes a long time, but 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 dimensional data includes information of a plurality of 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 result, the relevant professional models are subjected to collision problem, collision type, and collision information extraction and storage, the proportion of each professional collision problem and the frequency of occurrence of the collision type are counted, and a collision problem proportion table is obtained, and the importance of different collision types is sorted to generate a collision type sorting table, and based on the 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 according to the collision problem proportion table, the collision type sorting 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, BIM model location of the collision, collision detection time, etc. The collision decision information is a kind of auxiliary decision information, which is used to assist the responsible personnel of each professional model to conduct precise collision analysis and auxiliary correction to improve the correction efficiency.

[0124] The collision problem ratio table specifically counts the collision problem ratios by profession, so as to better identify and resolve design conflicts. The collision type ranking table specifically sorts by importance of architectural design, such as gravity flow collision before sorting and pressure flow collision after sorting, to provide decision-making direction for resolving collision conflicts. The correction time prediction specifically predicts the final modification completion time of the 0 collision model according to the number of collision modifications.

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

[0126] In S7, the number of revisions of 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 cycle is set, which is the time period before and after the revision when the total number of revisions reaches the expected value.

[0127] In the detection platform, the collision detection results obtained from each detection analysis are used to obtain the collision position, collision type, collision level, and collision correction times of each collision record before the correction cycle;

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

[0129] The collision position, collision type, collision level and the associated building component position, component quantity 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 between the collision feature matrix after the correction period and the preset feature matrix 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 time of the correction cycle is the time point at which the end of the previous correction cycle and the total number of corrections reach the expected value. The component collision level is to retrieve the corresponding collision points existing in the component as a whole in the corresponding associated building components, and take the highest collision level among the existing collision points as the component collision level (i.e. the most serious level), and subsequently generate a feature matrix as feature information. The collision feature matrix includes two-dimensional data, the first dimension is the collision feature data (collision position, collision type, collision level and associated building component position, component number, component collision level), and the second dimension is the corresponding professional subsystem category. The preset feature matrix is ​​a feature matrix under an ideal collision state, which is used to compare data and can be set by the user. The first similarity represents the degree of 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 the integrity of the correction process is in line with expectations, and the integrity of the collision situation is in line 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 the collision, new collisions may occur, or the number and level of collisions remain unchanged, but the overall collision situation does 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 importance of collision, 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 according to the results and performs 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 feature 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 the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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 on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present 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 can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0141] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function 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 can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a 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 implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope 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 planning, 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 initial detection grouping; S2: Through the initial inspection grouping defined in S1 and combined with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision inspection matrix, and exports the collision inspection 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 the collision detection results, collect statistics on collision information between professional models, sort the collision records, and predict the time required for correction based on the collision type to generate collision decision information; S6: sending the collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model, and repeating S3 to S6 after the correction is completed, 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 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, and 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, and combined with the preset feature matrix, the correction cycle is evaluated for overall correction, and corresponding correction warning information is generated.

2. The detection model optimization method based on BIM technology according to claim 1 is 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 detection model optimization method based on BIM technology according to claim 1 is characterized in that: In S2, the collision detection matrix is ​​stored through 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 detection model optimization method based on BIM technology according to claim 1 is 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 detection model optimization method based on BIM technology according to claim 1 is characterized in that: In the S5, specifically: according to the collision detection results, the collision problems, collision types, and collision information of the relevant professional models are extracted and stored, the proportion of collision problems in each profession and the frequency of collision types are counted, and a collision problem proportion table is obtained, and the importance of different collision types is sorted to generate a collision type sorting table, and based on the 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 according to the collision problem proportion table, the collision type sorting table and the prediction schedule.

7. The detection model optimization method based on BIM technology according to claim 1 is characterized in that: The S8 is specifically: In S7, the number of revisions of 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 cycle is set, which is the time period before and after the revision when the total number of revisions reaches the expected value. In the detection platform, the collision detection results obtained from each detection analysis are used to obtain the collision position, collision type, collision level, and collision correction times of each collision record before the correction cycle; Based on the collision position, collision type and collision level, in the detection platform, the collision position point of the professional model where the collision point is located is determined, the collision type and collision level information is analyzed, the associated building components at the collision position point are determined, the associated building components are marked and counted, and the position, number and collision level of the associated building components are obtained; The collision position, collision type, collision level and the associated building component position, component quantity 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 between the collision feature matrix after the correction period and the preset feature matrix 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 planning, 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 initial detection grouping; S2: Through the initial inspection grouping defined in S1 and combined with the preset specifications, the inspection platform automatically sets the collision type and reference value, determines the collision inspection matrix, and exports the collision inspection 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 the collision detection results, collect statistics on collision information between professional models, sort the collision records, and predict the time required for correction based on the collision type to generate collision decision information; S6: sending the collision decision information to the user terminal, and based on each professional user, correcting and replacing the relevant professional model, and repeating S3 to S6 after the correction is completed, 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 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, and 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, and 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 is 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 as described in any one of claims 1 to 7 are implemented.

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