Method for automatically identifying and repairing geometric defects in BIM (Building Information Modeling) data exchange process based on AI (Artificial Intelligence) technology

Abstract

The invention discloses a building BIM data exchange process geometric defect automatic identification and repair method based on an AI technology. The method comprises a model training stage and a model using stage. The logic process of the model training stage comprises data collection, data analysis, data conversion, data annotation, data set division, model selection, model architecture design, model training, model evaluation, model deployment and model use; and the flow of the model use stage comprises data input, geometric defect identification, only feedback, geometric repair and data output. The invention belongs to the technical field of BIM data conversion, and particularly provides a building BIM data exchange process geometric defect automatic identification and repair method based on an AI technology, and the method can achieve intelligent geometric defect detection and identification, intelligent geometric repair, and high efficiency. The problems that in the prior art, manual repairing consumes time, the repairing efficiency is low, precision is insufficient, and the automation degree is low are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of BIM data conversion, and specifically is a method for automatically identifying and repairing geometric defects in a building BIM data exchange process based on AI technology. Background Art

[0002] The data conversion module is an indispensable module in the development process of BIM modeling software. The data conversion module first reads the model file or intermediate format file output by other BIM software, then parses the data in the file, and writes the parsed data into the data structure or format file of its own software.

[0003] In the process of exchanging BIM model files, since the BIM software used by users is different, the internal architecture of the software is different, and the standards for implementing the accuracy requirements are also different, the parsed external data and the data inside the software will be different in terms of geometric topological structure, accuracy, etc. This will inevitably lead to data loss, data parsing errors, data errors and other problems, resulting in the generation of geometric defects in the data exchange process.

[0004] Traditional algorithms for solving geometric defect problems need to pre-set the defect type, traverse the data to identify the defect type, and then repair it according to a specific geometric model repair algorithm. Defect identification requires human judgment, and the repair process also needs to be done manually. In actual engineering applications, data exchange between BIM software systems is frequent. Traditional geometric defect repair methods rely on manual operations and limited automated tools. These methods are inefficient, inflexible, and unable to deal with potential subtle problems in complex models. In addition, as the complexity of BIM models increases, existing repair tools are difficult to meet the needs of efficient and accurate repairs. Summary of the invention

[0005] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an automatic identification and repair method for geometric defects in the building BIM data exchange process based on AI technology, which realizes intelligent geometric defect detection and identification, and intelligent geometric repair, so as to solve the problems of time-consuming manual repair, low repair efficiency, insufficient precision and low degree of automation in the prior art.

[0006] The present invention provides the following technical solutions: The present invention proposes an automatic identification and repair method for geometric defects in the building BIM data exchange process based on AI technology, including two stages: model training and model use;

[0007] The logical process of the model training phase includes data collection, data analysis, data conversion, data annotation, data set division, model selection, model architecture design, model training, model evaluation, model deployment and model use. The model evaluation step requires model optimization for models that do not meet the evaluation requirements.

[0008] The process of the model use phase includes data input, geometric defect recognition, intelligent feedback, geometric repair and data output.

[0009] Furthermore, the logical process of the model training phase is as follows:

[0010] (1) Data collection:

[0011] Collect a large amount of BIM model data as initial data and pre-process it to train the model data set;

[0012] (2) Data analysis:

[0013] Receive BIM model data and parse out the topological and geometric information, including fixed points, edges, faces, and surfaces;

[0014] (3) Data conversion:

[0015] The parsed geometric data is converted into a unified internal data format according to the software data conversion module. The internal geometric data format corresponding to this solution is Brep;

[0016] (4) Data annotation:

[0017] The defect types in the converted model are manually identified and annotated in the data. The annotated defect types should be comprehensive.

[0018] (5) Dataset division:

[0019] The labeled data set is divided into training set, validation set and test set. The training set is used to train the model, the validation set is used to evaluate the performance of the model and perform hyperparameter tuning, and the test set is used to evaluate the performance after the model is finalized.

[0020] (6) Model selection:

[0021] After the data set is prepared, the model training phase begins. According to the characteristics of Brep topology, the graph neural network GNN is selected as the training model.

[0022] Geometry represented by Brep (Boundary Representation) is usually composed of faces, edges and vertices. The topological relationship of geometric elements can be naturally represented as a graph structure. Vertices and faces can be represented as nodes in the graph, and edges can be represented as edges of the graph. The coordinates, curvature, and number of connected edges of the vertices can be used as feature representations of the vertices. The length, normal vector, and curve type of the edges can be used as feature representations of the edges. The normal vector, area, surface type, and angle between faces of the faces can be used as feature representations of the faces. Graph neural networks (GNNs) learn the global representation of graph structures by recursively aggregating feature information of neighboring nodes. GNNs can capture the topological and geometric relationships in Brep geometry, automatically identify possible geometric defects through deep learning, and repair different geometric defects.

[0023] (7) Model architecture design:

[0024] Design the number of layers of the model and the neuron units in each layer, and select appropriate activation functions and loss functions;

[0025] (8) Training model:

[0026] Pass the preprocessed data into the model for iterative model training;

[0027] (9) Model evaluation:

[0028] After the model training is completed, the validation set and test set are used to evaluate the performance of the model to determine whether the model is overfitting or underfitting;

[0029] If the performance of the model meets the preset requirements and the verification error has been minimized and stabilized, the model training is considered complete and the model can be used.

[0030] If the model is judged to be overfitting or underfitting, it is necessary to enter the model optimization process, adjust the model parameters or replace more appropriate activation functions and loss functions, and continue to iterate the model after adjustment until a model that meets the requirements is trained;

[0031] (10) Model deployment:

[0032] Integrate the trained model into the BIM data conversion platform as part of geometric defect identification and repair;

[0033] (11) Model usage:

[0034] When using the model, you only need to input the converted data into the trained BIM data conversion platform, and input the conventional input converted data into the model. The model will output the detection results of geometric defects and provide repair suggestions. Users can choose the results automatically repaired by the model or perform manual repairs.

[0035] Furthermore, in step (1), data generated from different BIM software are collected as initial data of the BIM model, and the types cover various building types and structures. The model should contain a variety of geometric elements, and the data should be stored in various formats including IFC, STEP, and DWG.

[0036] Furthermore, the defect types of the data annotation in step (4) include various geometric defects including topological errors, geometric distortions, and incomplete data.

[0037] Furthermore, the process of the model use phase is as follows:

[0038] The data input process receives and parses the geometric data of the BIM model, including various formats such as IFC, STEP, and DWG. The geometric defect recognition process is based on the trained AI model, and automatically recognizes defects in the input geometric data. After recognition, it provides intelligent feedback and outputs recognition results and repair suggestions. In the geometric repair stage, users can choose to use the repair results output by the model to repair the detected defects or choose to perform manual repairs. After the repair is completed, the BIM model data is saved again and exported according to the format requirements. This process supports multiple output formats.

[0039] The beneficial effects achieved by the present invention using the above structure are as follows: The method for automatically identifying and repairing geometric defects in the building BIM data exchange process based on AI technology proposed by the present invention has the following advantages:

[0040] (1) The present invention uses artificial intelligence technology to automatically identify and repair geometric defects that occur during the BIM data exchange process, significantly reducing the need for manual intervention; compared with traditional manual repair methods, automated processing greatly improves the repair speed, reduces human errors, and reduces labor costs, especially when dealing with complex and large BIM models, the efficiency advantage is more obvious;

[0041] (2) The present invention can identify and repair geometric defects with high precision, ensuring the consistency and accuracy of the repaired model with the original design, improving the accuracy and reliability of the repair, and avoiding the accuracy loss and topological errors that may be caused by traditional methods;

[0042] (3) The AI ​​algorithm of the present invention can adaptively process different types of geometric defects, adapt to a variety of BIM software and data formats, and has a wide range of applications. Users do not need to manually adjust the repair strategy for each project or data format. The system can automatically adapt and can identify and repair subtle geometric defects that are usually difficult to detect manually. At the same time, it optimizes the data structure, reduces redundancy, avoids data loss, and enhances the integrity and consistency of the model, ensuring that the geometric information of the BIM model remains intact during the data exchange process, avoiding problems caused by data loss or redundancy;

[0043] (4) The present invention utilizes artificial intelligence technology to identify and repair geometric defects during the BIM software data exchange process, realizes intelligent geometric defect detection and identification, and intelligent geometric repair, so as to solve the problems of time-consuming manual repair, low repair efficiency, insufficient precision, and low degree of automation existing in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0045] Figure 1 It is a schematic diagram of the overall structure of an embodiment of the present invention;

[0046] Figure 2 Schematic diagram of the structure of the sealing cover according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0048] See also Figure 1-2 The method for automatic identification and repair of geometric defects in the building BIM data exchange process based on AI technology provided in this embodiment includes two stages: model training and model use; wherein the logical process of the model training stage includes data collection, data analysis, data conversion, data labeling, data set division, model selection, model architecture design, training model, model evaluation, model deployment and model use, and the model evaluation step requires model optimization for models that do not meet the evaluation requirements; the process of the model use stage includes data input, geometric defect identification, intelligent feedback, geometric repair and data output.

[0049] Specifically, see Figure 1In this embodiment, the logical process of the model training phase is as follows:

[0050] (1) Data collection:

[0051] Collect data generated by different BIM software as the initial data of the BIM model and pre-process it to train the data set of the model. The initial data type of the BIM model covers various building types and structures. The model should contain a variety of geometric elements, and the data should be stored in various formats (such as IFC, STEP, DWG, etc.);

[0052] (2) Data analysis:

[0053] Receive BIM model data and parse out the topological and geometric information, including fixed points, edges, faces, curved surfaces, etc.;

[0054] (3) Data conversion:

[0055] The parsed geometric data is converted into a unified internal data format according to the software data conversion module. The internal geometric data format corresponding to this solution is Brep;

[0056] Parsing means reading the information in the model file and identifying the semantics of the information. Conversion means re-storing the read and identified data in the internal data format.

[0057] (4) Data annotation:

[0058] The defect types in the converted model are manually identified and annotated in the data. The annotated defect types should be comprehensive, and the defect types of the data annotation should cover various geometric defects including topological errors, geometric distortion, incomplete data, etc.

[0059] (5) Dataset division:

[0060] The labeled data set is divided into training set, validation set and test set. The training set is used to train the model, the validation set is used to evaluate the performance of the model and perform hyperparameter tuning, and the test set is used to evaluate the performance after the model is finalized.

[0061] (6) Model selection:

[0062] After the data set is prepared, the model training phase begins. According to the characteristics of Brep topology, the graph neural network GNN is selected as the training model.

[0063] Geometry represented by Brep (Boundary Representation) is usually composed of faces, edges and vertices. The topological relationship of geometric elements can be naturally represented as a graph structure. Vertices and faces can be represented as nodes in the graph, and edges can be represented as edges of the graph. The coordinates, curvature, and number of connected edges of the vertices can be used as feature representations of the vertices. The length, normal vector, and curve type of the edges can be used as feature representations of the edges. The normal vector, area, surface type, and angle between faces of the faces can be used as feature representations of the faces. Graph neural networks (GNNs) learn the global representation of graph structures by recursively aggregating feature information of neighboring nodes. GNNs can capture the topological and geometric relationships in Brep geometry, automatically identify possible geometric defects through deep learning, and repair different geometric defects.

[0064] (7) Model architecture design:

[0065] Design the number of layers of the model and the neuron units in each layer, and select appropriate activation functions and loss functions;

[0066] (8) Training model:

[0067] Pass the preprocessed data into the model for iterative model training;

[0068] (9) Model evaluation:

[0069] After the model training is completed, the validation set and test set are used to evaluate the performance of the model to determine whether the model is overfitting or underfitting;

[0070] If the performance of the model meets the preset requirements and the verification error has been minimized and stabilized, the model training is considered complete and the model can be used.

[0071] If the model is judged to be overfitting or underfitting, it is necessary to enter the model optimization process, adjust the model parameters or replace more appropriate activation functions and loss functions, and continue to iterate the model after adjustment until a model that meets the requirements is trained;

[0072] (10) Model deployment:

[0073] Integrate the trained model into the BIM data conversion platform as part of geometric defect identification and repair;

[0074] (11) Model usage:

[0075] When using the model, you only need to input the converted data into the trained BIM data conversion platform, and input the conventional input converted data into the model. The model will output the detection results of geometric defects and provide repair suggestions. Users can choose the results automatically repaired by the model or perform manual repairs.

[0076] Specifically, see Figure 2 In this embodiment, the process of the model use phase is as follows:

[0077] The data input process receives and parses the geometric data of the BIM model, such as IFC, STEP, DWG, etc. The geometric defect recognition process is based on the trained AI model, and automatically recognizes defects in the input geometric data. After recognition, it provides intelligent feedback and outputs recognition results and repair suggestions. In the geometric repair stage, users can choose to use the repair results output by the model to repair the detected defects or choose to perform manual repairs. After the repair is completed, the BIM model data is saved again and exported according to the format requirements. This process supports multiple output formats.

[0078] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, material or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, material or apparatus.

[0079] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for automatically identifying and repairing geometric defects in the building BIM data exchange process based on AI technology, characterized in that: It includes two stages: model training and model use; The logical process of the model training phase includes data collection, data analysis, data conversion, data annotation, data set division, model selection, model architecture design, model training, model evaluation, model deployment and model use. The model evaluation step requires model optimization for models that do not meet the evaluation requirements. The process of the model use phase includes data input, geometric defect recognition, intelligent feedback, geometric repair and data output.

2. The method for automatically identifying and repairing geometric defects in the building BIM data exchange process based on AI technology according to claim 1 is characterized in that: The logical process of the model training phase is as follows: (1) Data Collection Collect a large amount of BIM model data as initial data and pre-process it to train the model data set; (2) Data analysis Receive BIM model data and parse out the topological and geometric information, including fixed points, edges, faces, and surfaces; (3) Data conversion The parsed geometric data is converted into a unified internal data format according to the software data conversion module. The internal geometric data format corresponding to this solution is Brep; (4) Data labeling The defect types in the converted model are manually identified and annotated in the data. The annotated defect types should be comprehensive. (5) Dataset division The labeled data set is divided into training set, validation set and test set. The training set is used to train the model, the validation set is used to evaluate the performance of the model and perform hyperparameter tuning, and the test set is used to evaluate the performance after the model is finalized. (6) Model selection After the data set is prepared, the model training phase begins. According to the characteristics of Brep topology, the graph neural network GNN is selected as the training model. (7) Model architecture design Design the number of layers of the model and the neuron units in each layer, and select appropriate activation functions and loss functions; (8) Training model Pass the preprocessed data into the model for iterative model training; (9) Model evaluation After the model training is completed, the validation set and test set are used to evaluate the performance of the model to determine whether the model is overfitting or underfitting; If the performance of the model meets the preset requirements and the verification error has been minimized and stabilized, the model training is considered complete and the model can be used. If the model is judged to be overfitting or underfitting, it is necessary to enter the model optimization process, adjust the model parameters or replace more appropriate activation functions and loss functions, and continue to iterate the model after adjustment until a model that meets the requirements is trained; (10) Model deployment Integrate the trained model into the BIM data conversion platform as part of geometric defect identification and repair; (11) Model usage When using the model, you only need to input the converted data into the trained BIM data conversion platform, and input the conventional input converted data into the model. The model will output the detection results of geometric defects and provide repair suggestions. Users can choose the results automatically repaired by the model or perform manual repairs.

3. The method for automatically identifying and repairing geometric defects in the building BIM data exchange process based on AI technology according to claim 2 is characterized in that: In step (1), data generated from different BIM software are collected as the initial data of the BIM model. The types cover various building types and structures. The model should contain a variety of geometric elements, and the data should be stored in various formats including IFC, STEP, and DWG.

4. The method for automatically identifying and repairing geometric defects in the building BIM data exchange process based on AI technology according to claim 2 is characterized in that: The defect types of data annotation in step (4) include various geometric defects including topological errors, geometric distortion, and incomplete data.

5. The method for automatically identifying and repairing geometric defects in the building BIM data exchange process based on AI technology according to claim 1 is characterized in that: The process of the model use phase is as follows: The data input process receives and parses the geometric data of the BIM model, including various formats such as IFC, STEP, and DWG. The geometric defect recognition process is based on the trained AI model, and automatically recognizes defects in the input geometric data. After recognition, it provides intelligent feedback and outputs recognition results and repair suggestions. In the geometric repair stage, users can choose to use the repair results output by the model to repair the detected defects or choose to perform manual repairs. After the repair is completed, the BIM model data is saved again and exported according to the format requirements. This process supports multiple output formats.

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