Intelligent quality detection method for parametric modeling and design of hyperbolic curtain walls

Abstract

The present application provides an intelligent quality detection method for the parametric modeling and design process of a hyperbolic curtain wall. The method comprises: when modeling a hyperbolic curtain wall model in a preset modeling environment, obtaining modeling parameter data corresponding to the hyperbolic curtain wall model and preprocessing the modeling parameter data; the preprocessing at least includes data cleaning, formatting, and standardization; obtaining historical error data corresponding to the hyperbolic curtain wall model and preset expert knowledge to construct an error pattern library; the error pattern library includes error features, and the error features include at least structural errors, dimensional errors, material matching errors, and node closing errors; during the modeling process of the hyperbolic curtain wall model, real-time monitoring of the modeling process is performed according to the error pattern library; if it is determined according to the error pattern library that the modeling process has at least one error feature, an error warning message is generated, and a visual report of the error location is generated, thereby completing the intelligent quality detection during the parametric modeling and design process of the hyperbolic curtain wall.

Description

Technical Field

[0001] The present application relates to the technical field of architectural modeling, and in particular to an intelligent quality detection method for use in the parametric modeling design process of hyperbolic curtain walls. Background Art

[0002] With the continuous innovation of architectural design, hyperbolic curtain walls, as a building facade material with high visual impact and unique shape, are increasingly widely used in modern buildings. Compared with traditional flat curtain walls, hyperbolic curtain walls have greater structural complexity, more irregular component shapes, and are more difficult to design and require higher craftsmanship.

[0003] During the curtain wall modeling process, faced with complex hyperbolic models, modelers may not have a thorough understanding of curtain wall design. When dealing with details such as curtain wall nodes and closings, they may overlook some key factors, affecting the overall effect of the curtain wall. This is especially true for newcomers. Due to lack of experience, they often make some common modeling errors. These errors may affect the design quality of the curtain wall and increase costs.

[0004] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0005] The present application provides an intelligent quality detection method for the parametric modeling and design process of hyperbolic curtain walls, which aims to solve the problem that in the curtain wall modeling process, when faced with complex hyperbolic surface models, modelers may not have a thorough understanding of curtain wall design and may ignore some key factors when dealing with details such as curtain wall nodes and closings, thereby affecting the overall effect of the curtain wall. In particular, newcomers often make some common modeling errors due to lack of experience. These errors may affect the design quality of the curtain wall and increase costs.

[0006] In a first aspect, the present application provides an intelligent quality detection method for the parametric modeling design process of a hyperbolic curtain wall, comprising:

[0007] When modeling a hyperbolic curtain wall model in a preset modeling environment, obtaining modeling parameter data corresponding to the hyperbolic curtain wall model and preprocessing the modeling parameter data; the preprocessing at least includes cleaning, formatting and standardizing the data;

[0008] Acquiring historical error data corresponding to the hyperbolic curtain wall model and preset expert knowledge to construct an error pattern library; the error pattern library includes error features, and the error features include at least structural errors, dimensional errors, material matching errors, and node closing errors;

[0009] During the modeling process of the hyperbolic curtain wall model, the modeling process is monitored in real time according to the error pattern library;

[0010] If it is determined according to the error pattern library that there is at least one error feature in the modeling process, an error warning message is generated, the error warning message is displayed on the interface corresponding to the preset modeling environment, and an error location visualization report is generated to complete the intelligent quality detection in the parametric modeling design process of the hyperbolic curtain wall.

[0011] In some embodiments, the error warning information is displayed on an interface corresponding to the preset modeling environment, including: highlighting the erroneous component outline corresponding to the hyperbolic curtain wall model in the preset modeling environment; generating an error propagation link diagram with topological relationship annotations in the preset modeling environment; and displaying error correction suggestions according to a preset voice interaction form.

[0012] In some embodiments, generating the error location visualization report includes: generating a hierarchical error index directory, an interactive error impact domain stress cloud map, and a clause reference comparison table of associated design specifications corresponding to the hyperbolic curtain wall model based on the error warning information; generating the error location visualization report based on the error index directory, the interactive error impact domain stress cloud map, and the clause reference comparison table of associated design specifications.

[0013] In some embodiments, before the error warning information is generated, it also includes: performing stress compliance verification on the hyperbolic curtain wall model based on finite element analysis; generating a predicted error propagation probability corresponding to the hyperbolic curtain wall model and the error feature according to Monte Carlo; and performing a virtual installation rehearsal according to a preset digital twin model based on the predicted error propagation probability and the compliance verification result corresponding to the stress compliance verification to complete a secondary verification of the error feature. If the secondary verification passes, the error warning information is generated.

[0014] In some embodiments, after completing the intelligent quality inspection in the parametric modeling and design process of the hyperbolic curtain wall, it also includes: obtaining the error correction record corresponding to the error location visualization report; performing feature extraction based on the error correction record to generate an error feature vector; and updating the error pattern library based on the error feature vector.

[0015] Exemplarily, extracting features from the error correction record to generate an error feature vector includes: extracting features from the error correction record using a preset convolutional neural network and outputting the error feature vector; and an expression for the error feature vector includes:

[0016] ;in, Indicates that the convolutional neural network is layer The error feature vector of the position output, is the convolution kernel size corresponding to the convolutional neural network, The convolutional neural network is described in The paranoid term of the layer, is the ReLU activation function, It is The relative position of the convolution kernel of the layer ( , ) is a weight parameter used to capture the spatial correlation of local error features, For the Layer at offset position The input feature value reflects the error features transmitted by the previous network layer.

[0017] Exemplarily, the updating of the error pattern library according to the error feature vector includes: dynamically optimizing a weight coefficient of the error pattern library to complete the updating of the error pattern library.

[0018] It should be noted that, in some embodiments, the dynamic optimization equation corresponding to the weight coefficient of the error pattern library includes:

[0019] ;in, Represents the time step The weight coefficient corresponding to the error pattern library when , the range of the weight coefficient is (0,1), is an attenuation adjustment factor, the attenuation adjustment factor is greater than 0, is the current time step counter, The base time offset is used to set the starting reference point of the pattern library update cycle is the loss function In the parameters The gradient vector at reflects the optimization direction of the current model training state. It is a 2-norm operator that calculates the Euclidean length of the gradient vector and quantifies the magnitude of the update of the model parameters.

[0020] In some embodiments, before the modeling process is monitored in real time according to the error pattern library, it also includes: obtaining version information corresponding to the preset modeling environment; obtaining compatibility information corresponding to the error pattern library and the version information; if it is confirmed that the error pattern library is compatible with the preset modeling environment according to the compatibility information, the modeling process is monitored in real time according to the error pattern library.

[0021] Exemplarily, if it is confirmed based on the compatibility information that the error pattern library is incompatible with the preset modeling environment, the method includes: parsing the API calling protocol of the preset modeling environment based on reverse engineering; performing version-compatible packaging on the open graphical interface of the preset modeling environment; and constructing an independent operating environment sandbox corresponding to the error pattern library based on containerization technology to run the error pattern library in the independent operating environment sandbox to ensure that the error pattern library is compatible with the preset modeling environment.

[0022] In a second aspect, the present application provides an intelligent quality detection device for use in the parametric modeling and design process of a hyperbolic curtain wall, comprising:

[0023] A data acquisition unit is used to acquire modeling parameter data corresponding to the hyperbolic curtain wall model when modeling the hyperbolic curtain wall model in a preset modeling environment, and preprocess the modeling parameter data; the preprocessing at least includes data cleaning, formatting and standardization;

[0024] An error acquisition unit is used to acquire historical error data corresponding to the hyperbolic curtain wall model and preset expert knowledge to construct an error pattern library; the error pattern library includes error features, and the error features include at least structural errors, size errors, material matching errors, and node closing errors;

[0025] A real-time monitoring unit is used to monitor the modeling process in real time according to the error pattern library during the modeling process of the hyperbolic curtain wall model;

[0026] The detection completion unit is used to generate error warning information if it is determined according to the error pattern library that there is at least one error feature in the modeling process, display the error warning information on the interface corresponding to the preset modeling environment, and generate an error location visualization report to complete the intelligent quality detection in the parametric modeling design process of the hyperbolic curtain wall.

[0027] In a third aspect, the present application provides a computer device comprising a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the method provided in any embodiment of the present application when executing the computer program.

[0028] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer-readable instructions are executed by the processor, one or more processors execute the method provided in any embodiment of the present application.

[0029] The embodiment of the present application provides an intelligent quality detection method for the parametric modeling and design process of a hyperbolic curtain wall. The specific technical process is as follows:

[0030] Modeling parameter data preprocessing: Acquiring modeling parameter data: In the pre-set modeling environment, when the user begins modeling a hyperbolic curtain wall model, all relevant parameter data for the model is automatically acquired. Invalid or redundant data is removed to ensure data accuracy and consistency. The data is converted to a unified format for subsequent processing. The data is standardized to conform to a specific standard range for easy algorithm recognition and processing.

[0031] Build an error pattern library: Collect and organize various error cases that occurred during the modeling process to form a historical error dataset. Combine the knowledge and experience of domain experts to define various possible error characteristics. Based on historical error data and expert knowledge, build an error pattern library containing various error characteristics. Common error characteristics include but are not limited to:

[0032] Structural errors: such as insufficient structural strength, unreasonable force distribution, etc.

[0033] Dimension error: such as size exceeding the allowable range, disproportion, etc.

[0034] Material matching error: such as using inappropriate materials or material properties that do not meet the requirements.

[0035] Node closing errors: such as improper node connection method, inadequate closing treatment, etc.

[0036] Real-time monitoring and error detection: During the modeling process, the system continuously monitors user operations and compares the current modeling state with a library of error patterns. If the current modeling state matches an error signature in the library, the system immediately generates a corresponding error warning message.

[0037] Generate error warnings: Display generated error warnings within the pre-set modeling environment interface to alert users to potential issues. In addition to simple text prompts, the system automatically generates detailed error location visualization reports, graphically displaying the specific location and cause of the error, helping users more intuitively understand the problem.

[0038] The method can be implemented through the following steps: Environment construction: Select professional software that supports parametric modeling, such as Rhino, Grasshopper, etc. Integrate intelligent detection module: Integrate an intelligent quality detection module in the modeling software, which is responsible for executing all the above steps.

[0039] Data Collection and Preprocessing: Automatic Collection: When the user begins modeling, the system automatically collects all relevant parameter data. Data Cleaning: Invalid data is automatically removed through scripting or using existing tools. Formatting and Standardization: Data is formatted and standardized using standard data processing libraries (such as Pandas).

[0040] Building an Error Pattern Library: Data Collection: Collect error cases from historical projects and invite experts to participate in discussions to determine the specific characteristics of each error. Pattern Library Creation: Enter the collected error characteristics into the database and write corresponding query logic to quickly match them in real-time monitoring.

[0041] Real-time monitoring and error detection: Monitoring logic: Write real-time monitoring logic to regularly check the current modeling status and compare it with the error pattern library. Error detection: Once an error is detected, the alarm mechanism is immediately triggered to generate and display error warning information.

[0042] Generate error warning information: Interface display: Pop up an error warning window in the modeling software interface, showing the specific error information. Visual report: Generate a detailed error location report, showing the specific location of the error in the form of 3D graphics, and providing solution suggestions.

[0043] The provided method has at least the following beneficial effects:

[0044] Improve modeling quality: Through real-time monitoring and immediate feedback, common modeling errors can be effectively avoided and the overall design quality can be improved.

[0045] Reduce design costs: Reduce rework and modifications caused by design errors, saving time and resources.

[0046] Assisting novice learning: For inexperienced novice designers, this method can serve as a good guide and help them master the correct modeling techniques more quickly.

[0047] Improve work efficiency: Automated inspection reduces manual review time and improves the efficiency of the entire design process.

[0048] Enhanced user experience: Friendly user interface and intuitive error reporting make it easier for users to understand and solve problems, improving user experience.

[0049] In summary, this method has important application value and practical significance in the parametric modeling and design process of hyperbolic curtain walls.

[0050] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0052] Figure 1 This is a schematic flow chart of the steps of an intelligent quality detection method for a parametric modeling design process of a hyperbolic curtain wall provided by an embodiment of the present application;

[0053] Figure 2 This is a schematic block diagram of the structure of an intelligent quality detection device for use in the parametric modeling and design process of a hyperbolic curtain wall provided in one embodiment of the present application;

[0054] Figure 3 This is a schematic block diagram of the structure of a computer device provided in one embodiment of the present application.

[0055] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0057] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0058] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items having substantially the same functions and effects. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

[0059] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0060] It will also be understood that the term "and / or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0061] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0062] With the continuous innovation of architectural design, hyperbolic curtain walls, as a building facade material with high visual impact and unique shape, are increasingly widely used in modern buildings. Compared with traditional flat curtain walls, hyperbolic curtain walls have greater structural complexity, more irregular component shapes, and are more difficult to design and require higher craftsmanship.

[0063] During the curtain wall modeling process, faced with complex hyperbolic models, modelers may not have a thorough understanding of curtain wall design. When dealing with details such as curtain wall nodes and closings, they may overlook some key factors, affecting the overall effect of the curtain wall. This is especially true for newcomers. Due to lack of experience, they often make some common modeling errors. These errors may affect the design quality of the curtain wall and increase costs.

[0064] Therefore, a method is urgently needed to solve at least one of the above problems.

[0065] To resolve the above issues, please refer to Figure 1 ,like Figure 1 As shown, the provided intelligent quality detection method for the parametric modeling design process of a hyperbolic curtain wall includes steps S101 to S104. The intelligent quality detection method for the parametric modeling design process of a hyperbolic curtain wall is executed by a computer device, which can be a single server or a server cluster, or can be a handheld terminal, a laptop computer, a wearable device, or a robot.

[0066] like Figure 1 As shown, steps S101-S104 are described in detail as follows:

[0067] Step S101. When modeling a hyperbolic curtain wall model in a preset modeling environment, obtain modeling parameter data corresponding to the hyperbolic curtain wall model and preprocess the modeling parameter data; the preprocessing at least includes data cleaning, formatting and standardization.

[0068] Specifically, at the beginning of the design of the hyperbolic curtain wall model, designers usually use professional building information modeling (BIM) software such as Revit, AutoCAD or Rhino for preliminary design. These software can generate detailed three-dimensional models and export data files containing all key parameters. Data extraction: Extract the required parameters from these design files, including but not limited to dimensions (length, width, height), material type, location and shape of structural components, detailed information of connection nodes, etc. In addition, indirect information needs to be extracted from project requirement documents, such as specifications and standards, construction requirements, etc. Data storage: The extracted data is stored in a unified database for subsequent processing and analysis.

[0069] Data cleaning includes: Invalid value processing: Checking for invalid values ​​in the data, such as negative numbers, values ​​outside the acceptable range, and null values, and marking and processing them accordingly. For example, if the width of a panel is negative, it will be marked as invalid and removed. Duplicate record processing: Identifying and removing duplicate data records to ensure that each data entry is unique. Outlier detection: Using statistical methods (such as Z-score and IQR) to detect outliers and decide whether to retain or remove them based on the specific situation.

[0070] Formatting includes: Unit conversion: unify all length units into meters and angle units into radians to ensure data consistency. Timestamp standardization: unify all dates and timestamps into a standard format (such as ISO 8601) for subsequent processing. Text encoding: For descriptive features (such as material name, node type, etc.), encoding technology is used to convert them into numerical form for computer processing. Standardization includes: Normalization: Use methods such as Z-score normalization or minimum and maximum scaling to adjust the data distribution to a standard range to improve prediction accuracy. Feature engineering: Further process the data as needed, such as creating new features, deleting redundant features, etc., to improve model performance.

[0071] This step has at least the following beneficial effects:

[0072] Improve data quality: Through data cleaning and formatting, the risk of misjudgment caused by dirty, messy, and poor data is reduced, and data quality is improved. Facilitate data exchange: Unified data formats and standardized processing facilitate data exchange and integration across systems. Improve processing efficiency: Standardized data enables subsequent analysis steps to run more efficiently, speeding up the entire testing process. Enhance readability: Clear data formats and standardized processing enhance data readability and usability, facilitating communication and collaboration among team members.

[0073] Step S102: Acquire historical error data corresponding to the hyperbolic curtain wall model and preset expert knowledge to construct an error pattern library; the error pattern library includes error features, which include at least structural errors, size errors, material matching errors, and node closing errors.

[0074] Specifically, the collection of historical error data includes internal database access: reviewing historical projects in the company's internal database and collecting typical problem cases that have occurred, including error types, manifestations, solutions, etc. Literature review includes reviewing relevant academic papers, industry reports and technical documents to understand common error types and how to deal with them. Expert interviews include inviting professionals with rich practical experience to participate. They can point out some potential risk points based on their own experience and provide corresponding solution suggestions. The introduction of expert knowledge includes expert interview records: recording the opinions and suggestions of experts to form detailed documents. AI-assisted learning: using natural language processing (NLP) technology to automatically learn the key knowledge points in a large number of literature reports, extract key information and organize it into structured data.

[0075] Pattern summarization includes error type definition: Based on the collected data, various common error types are defined, such as structural errors, dimensional errors, material mismatch errors, and joint and joint failures. Example demonstrations: Specific example images or videos are provided for each error type to help users intuitively understand. Preventative measures: Detailed preventative measures and solutions are provided for each error type to help users avoid similar errors.

[0076] The continuous update mechanism includes regular review: the error pattern library is regularly reviewed and updated to ensure its timeliness and practicality. User feedback: users are encouraged to provide feedback on new error types and solutions during use to continuously enrich and improve the pattern library.

[0077] This step has at least the following beneficial effects:

[0078] Theoretical Foundation: This provides a solid theoretical foundation for quality inspection, ensuring the accuracy and reliability of testing. Reduce Human Errors: This reduces errors caused by human factors, enhancing the reliability and stability of the system. Cultivate New Talent: This helps novice designers quickly master complex tasks, shortening the learning curve. Continuous Improvement: This continuous update mechanism maintains the timeliness and practicality of the pattern library, continuously improving system performance.

[0079] Step S103: During the modeling process of the hyperbolic curtain wall model, the modeling process is monitored in real time according to the error pattern library.

[0080] Specifically, automated tracking includes scripting: using scripting languages ​​like Python and JavaScript to write programs that automatically capture key indicators of the current state as users operate. Event monitoring: setting up event listeners to monitor user operations in real time and capture data changes at each step.

[0081] Anomaly detection algorithms include: Isolation Forest: This algorithm uses the Isolation Forest algorithm to detect outliers in data. This algorithm is suitable for high-dimensional datasets and can effectively identify outliers. Local Outlier Factor: This algorithm uses the Local Outlier Factor (LOF) algorithm to detect outliers based on the local density of data points. Threshold Setting: This algorithm sets a reasonable threshold based on historical data and expert experience. When the data exceeds the threshold, anomaly detection is triggered.

[0082] The feedback loop design includes: Immediate warnings: Once a potential problem is detected, the system immediately pauses the current action and issues a warning to the user, alerting them. Confirmation mechanisms: Users are asked to confirm whether to proceed to the next step, ensuring that the problem is resolved before continuing. Model optimization: The model training set is adjusted based on actual conditions to optimize future performance and gradually improve the accuracy and robustness of the system.

[0083] This step has at least the following beneficial effects:

[0084] Instant error correction: The instant error correction function is realized, which greatly shortens the time cycle from problem discovery to problem resolution.

[0085] Reduce additional costs: Reduce additional costs caused by later modifications and improve the economic benefits of the project.

[0086] Enhance user experience: Allow users to understand their mistakes immediately, thus improving user experience.

[0087] Improve work efficiency: Through real-time monitoring and instant feedback, designers' work efficiency is improved and unnecessary rework is reduced.

[0088] Step S104. If it is determined according to the error pattern library that there is at least one error feature in the modeling process, an error warning message is generated, the error warning message is displayed on the interface corresponding to the preset modeling environment, and an error location visualization report is generated to complete the intelligent quality detection in the parametric modeling design process of the hyperbolic curtain wall.

[0089] Specifically, information presentation includes: Visual prompts: Once an error is identified, the system will clearly display the specific error location and related information to the user (e.g., red highlighting, pop-up notifications, etc.). Text descriptions: Provide a detailed description of the error, including the error type, specific location, possible causes, and recommended solutions. Multi-channel notifications: In addition to interface prompts, relevant responsible parties can also be notified via email, SMS, and other means to ensure timely and accurate information delivery.

[0090] Positioning Visualization: 3D View: Generates detailed 3D views to help users pinpoint the exact location of the deviation. 2D Plan View: Combined with the 2D plan view, it provides more perspectives and details, making it easier for users to understand and process the error. Annotation: Marks the specific error area in the view and adds detailed annotations to guide how to correct it.

[0091] Report Preparation: Basic Information: The report should include basic project information, such as project name, designer, and test date. Test Results: List all issues found, including error type, severity assessment, and specific location. Improvement Suggestions: Provide detailed improvement suggestions and solutions for each issue to help users quickly resolve the problem. Summary and Recommendations: Summarize the overall test results and propose further improvement measures and suggestions for management's reference. Archive Management: Archive the report for future reference and accumulation of experience and lessons learned.

[0092] This step has at least the following beneficial effects:

[0093] Comprehensive and accurate basis: Provides managers with comprehensive and accurate quality control basis to facilitate decision-making and management.

[0094] Communication and collaboration: Facilitate communication and collaboration among team members and avoid ambiguity in the process of information transmission.

[0095] Experience accumulation: It is conducive to the long-term accumulation of experience and lessons, and promotes the overall improvement of the organization.

[0096] Transparency: It improves the transparency of the project, facilitates supervision and management by all parties, and ensures the smooth progress of the project.

[0097] Continuous improvement: Through regular reporting and summarization, we continuously improve the design and construction process to enhance the overall quality of the project.

[0098] The method can be implemented through the following steps: Environment construction: Select professional software that supports parametric modeling, such as Rhino, Grasshopper, etc. Integrate intelligent detection module: Integrate an intelligent quality detection module in the modeling software, which is responsible for executing all the above steps.

[0099] Data Collection and Preprocessing: Automatic Collection: When the user begins modeling, the system automatically collects all relevant parameter data. Data Cleaning: Invalid data is automatically removed through scripting or using existing tools. Formatting and Standardization: Data is formatted and standardized using standard data processing libraries (such as Pandas).

[0100] Building an Error Pattern Library: Data Collection: Collect error cases from historical projects and invite experts to participate in discussions to determine the specific characteristics of each error. Pattern Library Creation: Enter the collected error characteristics into the database and write corresponding query logic to quickly match them in real-time monitoring.

[0101] Real-time monitoring and error detection: Monitoring logic: Write real-time monitoring logic to regularly check the current modeling status and compare it with the error pattern library. Error detection: Once an error is detected, the alarm mechanism is immediately triggered to generate and display error warning information.

[0102] Generate error warning information: Interface display: Pop up an error warning window in the modeling software interface, showing the specific error information. Visual report: Generate a detailed error location report, showing the specific location of the error in the form of 3D graphics, and providing solution suggestions.

[0103] The provided method has at least the following beneficial effects:

[0104] Improve modeling quality: Through real-time monitoring and immediate feedback, common modeling errors can be effectively avoided and the overall design quality can be improved.

[0105] Reduce design costs: Reduce rework and modifications caused by design errors, saving time and resources.

[0106] Assisting novice learning: For inexperienced novice designers, this method can serve as a good guide and help them master the correct modeling techniques more quickly.

[0107] Improve work efficiency: Automated inspection reduces manual review time and improves the efficiency of the entire design process.

[0108] Enhanced user experience: Friendly user interface and intuitive error reporting make it easier for users to understand and solve problems, improving user experience.

[0109] In summary, this method has important application value and practical significance in the parametric modeling and design process of hyperbolic curtain walls.

[0110] In some embodiments, the error warning information is displayed on an interface corresponding to the preset modeling environment, including: highlighting the erroneous component outline corresponding to the hyperbolic curtain wall model in the preset modeling environment; generating an error propagation link diagram with topological relationship annotations in the preset modeling environment; and displaying error correction suggestions according to a preset voice interaction form.

[0111] Specifically, incorrect component outlines are highlighted: Within the pre-set modeling environment, when an error is detected, the system automatically highlights the corresponding incorrect component outlines in the hyperbolic curtain wall model. This can be achieved by changing the component's color, adding a border, or using other visual effects. If a node is positioned incorrectly, the node and the surrounding area will be marked in red and may flash to attract the user's attention.

[0112] Generate an error propagation link diagram with topological annotations: This system automatically generates an error propagation link diagram with topological annotations, showing how errors propagate from one component to another. This diagram can be two-dimensional or three-dimensional, depending on the specific situation. If a dimensional error causes problems at multiple connected nodes, the system generates a link diagram showing the topological relationships between these nodes, using arrows and color coding to indicate the error propagation path.

[0113] Voice interaction displays error correction suggestions: Using speech synthesis technology, error correction suggestions are conveyed to the user in the form of voice. This is achieved through an integrated TTS (Text-to-Speech) engine. If a material mismatch is detected, the user will be prompted with a voice prompt: "Please note that the panel material on layer 5 does not meet the design specifications. Please check and change to the specified material."

[0114] This embodiment has at least the following beneficial effects:

[0115] Intuitive: Highlighting the outline of the error component allows users to quickly locate the problem and improve work efficiency.

[0116] Visualization: The error propagation link diagram with topological relationship annotations helps users understand the error propagation path and grasp the overall impact of the problem.

[0117] Convenience: Error correction suggestions in the form of voice interaction provide a more natural and convenient user experience, especially when multitasking.

[0118] In some embodiments, generating the error location visualization report includes: generating a hierarchical error index directory, an interactive error impact domain stress cloud map, and a clause reference comparison table of associated design specifications corresponding to the hyperbolic curtain wall model based on the error warning information; generating the error location visualization report based on the error index directory, the interactive error impact domain stress cloud map, and the clause reference comparison table of associated design specifications.

[0119] Hierarchical error index directory: Based on the error warning information, the system generates a hierarchical error index directory, listing all detected errors and their categories. Each error entry contains a detailed description and location information.

[0120] Example: The directory structure is as follows: structural error; incorrect node position; missing connectors; incorrect dimensions; inconsistent panel dimensions; incorrect support rod length; incorrect material matching; inconsistent panel material; incorrect sealant type; incorrect node joint; joint not sealed; poor sealing of joint.

[0121] Interactive Error Impact Area Stress Contour: Generates an interactive stress contour map to demonstrate the impact of errors on the structural stress distribution. Users can click on different areas to view detailed information. For example, users can see different colors representing different stress levels on the stress contour map. Clicking on a high-stress area reveals the specific stress value and related component information.

[0122] Reference table of associated design code clauses: The system generates a table listing each error and its corresponding design code clause. Users can click a clause link to jump directly to the corresponding code document. For example, for a node position error, the method will list "GB 50018-2002 Section 3.2.1" in the table. Users can click this entry to view the specific code requirement.

[0123] Generate a visual error location report: This report combines the three components listed above into a complete report, which is available for download or online viewing. For example, the report includes a table of contents, stress contours, and a reference table for easy access and reference.

[0124] This embodiment has at least the following beneficial effects:

[0125] Clear hierarchy: The hierarchical error index directory enables users to quickly find specific error information, improving search efficiency.

[0126] Intuitive visualization: Interactive stress cloud diagrams visually demonstrate the impact of errors on structural stress, helping users better understand the severity of the problem.

[0127] Specification guidance: The reference table of clauses in the associated design specifications provides users with a clear specification basis to ensure that the design complies with the standards.

[0128] In some embodiments, before the error warning information is generated, it also includes: performing stress compliance verification on the hyperbolic curtain wall model based on finite element analysis; generating a predicted error propagation probability corresponding to the hyperbolic curtain wall model and the error feature according to Monte Carlo; and performing a virtual installation rehearsal according to a preset digital twin model based on the predicted error propagation probability and the compliance verification result corresponding to the stress compliance verification to complete a secondary verification of the error feature. If the secondary verification passes, the error warning information is generated.

[0129] Finite element analysis uses finite element analysis to verify stress compliance on the hyperbolic curtain wall model before generating an error warning. By calculating the stress distribution of the model under various operating conditions, the system determines whether any stress exceeds the specified value. For example, if the calculated stress value at a node under wind load is 250 MPa, exceeding the maximum value of 200 MPa allowed by the design code, the node is considered to have exceeded the specified stress.

[0130] Monte Carlo simulation: Using the Monte Carlo method, a hyperbolic curtain wall model is generated and the predicted error propagation probability corresponding to the error signature is calculated. Random sampling and multiple simulations are used to estimate the probability of error propagation within the model. For example, a Monte Carlo simulation revealed that a specific dimensional error has an 80% probability of causing the displacement of adjacent nodes to exceed the specified value, triggering a chain reaction.

[0131] Digital Twin Virtual Installation Rehearsal: Based on a pre-set digital twin model, the system conducts a virtual installation rehearsal based on predicted error propagation probabilities and stress compliance verification results. The actual installation process is simulated in a simulation environment to further verify the impact of errors. For example, during the virtual installation simulation, it was found that a dimensional error indeed caused the displacement of adjacent nodes to exceed the specified limit, confirming the previous analysis results.

[0132] If the second verification passes, an error warning message is generated; otherwise, the model is adjusted and re-verified. After the virtual installation preview, it is confirmed that a certain dimensional error does exist and a corresponding error warning message is generated.

[0133] This embodiment has at least the following beneficial effects:

[0134] Accuracy: Finite element analysis and Monte Carlo simulation provide highly accurate analysis results, improving the accuracy of error detection.

[0135] Comprehensiveness: The digital twin virtual installation preview takes into account various factors in the actual installation process, ensuring the comprehensiveness and reliability of the test results.

[0136] Reliability: Through secondary verification, the possibility of false positives and missed positives is reduced, and the reliability and stability of the system are improved.

[0137] In some embodiments, after completing the intelligent quality inspection in the parametric modeling and design process of the hyperbolic curtain wall, it also includes: obtaining the error correction record corresponding to the error location visualization report; performing feature extraction based on the error correction record to generate an error feature vector; and updating the error pattern library based on the error feature vector.

[0138] After completing intelligent quality inspection, the system obtains error correction records corresponding to the error location visualization report. These records include the user's error correction operations and results. The system records the user's correction operations for a node position error, including the specific coordinate changes of the moved node. The error correction records are then feature extracted using a preset convolutional neural network (CNN) to output an error feature vector. For example, the CNN extracts the spatial correlation of local error features by performing a convolution operation on the image data of the correction record. The error pattern library is updated based on the generated error feature vector, and the weight coefficients of the error pattern library are dynamically optimized. For example, the weight coefficients in the error pattern library are adjusted based on the new error feature vector to better suit the current design situation.

[0139] This step has at least the following beneficial effects:

[0140] Continuous improvement: By continuously updating the error pattern library, the system can gradually learn new error types and solutions, improving the accuracy and comprehensiveness of detection.

[0141] Adaptability: Dynamic optimization of weight coefficients enables the error pattern library to adapt to different design projects and environments, improving the flexibility and robustness of the system.

[0142] Exemplarily, extracting features from the error correction record to generate an error feature vector includes: extracting features from the error correction record using a preset convolutional neural network and outputting the error feature vector; and an expression for the error feature vector includes:

[0143] ;in, Indicates that the convolutional neural network is layer The error feature vector of the position output, is the convolution kernel size corresponding to the convolutional neural network, The convolutional neural network is described in The paranoid term of the layer, is the ReLU activation function, It is The relative position of the convolution kernel of the layer ( , ) is a weight parameter used to capture the spatial correlation of local error features, For the Layer at offset position The input feature value reflects the error features transmitted by the previous network layer.

[0144] For example, a pre-set convolutional neural network is used to extract features from error correction records. Through multi-layer convolution operations, the convolutional neural network captures the spatial correlation of local error features. By processing the image data of the correction records, the convolutional neural network extracts features such as node position errors and size errors. Convolutional neural networks can efficiently extract complex local features, improving the efficiency and accuracy of feature extraction. Through multi-layer convolution operations, the system can capture multiple types of error features, enhancing its robustness and adaptability.

[0145] Exemplarily, the updating of the error pattern library according to the error feature vector includes: dynamically optimizing a weight coefficient of the error pattern library to complete the updating of the error pattern library.

[0146] By dynamically adjusting weight coefficients based on the current training state and gradient information, the error pattern library can better adapt to the current design situation. For example, based on the current loss function gradient information, the weight coefficients can be adjusted to bring it closer to the optimal solution. Dynamic optimization allows the error pattern library to adapt to the current design situation and training status, improving the system's adaptability and robustness. By optimizing the weight coefficients, various error types can be more accurately identified and handled, improving detection accuracy.

[0147] It should be noted that, in some embodiments, the dynamic optimization equation corresponding to the weight coefficient of the error pattern library includes:

[0148] ;in, Represents the time step The weight coefficient corresponding to the error pattern library when , the range of the weight coefficient is (0,1), is an attenuation adjustment factor, the attenuation adjustment factor is greater than 0, is the current time step counter, The base time offset is used to set the starting reference point of the pattern library update cycle is the loss function In the parameters The gradient vector at reflects the optimization direction of the current model training state. It is a 2-norm operator that calculates the Euclidean length of the gradient vector and quantifies the magnitude of the update of the model parameters.

[0149] Based on the current training state and gradient information, the weight coefficients are dynamically adjusted, allowing the error pattern library to better adapt to the current design situation. For example, based on the current loss function gradient information, the weight coefficients are adjusted to bring it closer to the optimal solution. Dynamic optimization allows the error pattern library to adapt to the current design situation and training state, improving the system's adaptability and robustness. By optimizing the weight coefficients, the method can more accurately identify and handle various error types, improving detection accuracy.

[0150] In some embodiments, before the modeling process is monitored in real time according to the error pattern library, it also includes: obtaining version information corresponding to the preset modeling environment; obtaining compatibility information corresponding to the error pattern library and the version information; if it is confirmed that the error pattern library is compatible with the preset modeling environment according to the compatibility information, the modeling process is monitored in real time according to the error pattern library.

[0151] By obtaining the version information of the preset modeling environment, including software version number, API version, etc.

[0152] If it is detected that the modeling software version currently used is 2025.1.

[0153] Obtaining Compatibility Information: This system determines compatibility between the error pattern library and the pre-set modeling environment. For example, if a database query reveals that the error pattern library supports version 2025.1, the system continues to monitor the modeling process in real time based on the error pattern library. Once compatibility is confirmed, real-time monitoring of the modeling process begins. This version compatibility check ensures that the error pattern library can function properly in the current modeling environment, preventing errors caused by version incompatibility. This improves the stability and reliability of the method and ensures a smooth testing process.

[0154] Exemplarily, if it is confirmed based on the compatibility information that the error pattern library is incompatible with the preset modeling environment, the method includes: parsing the API calling protocol of the preset modeling environment based on reverse engineering; performing version-compatible packaging on the open graphical interface of the preset modeling environment; and constructing an independent operating environment sandbox corresponding to the error pattern library based on containerization technology to run the error pattern library in the independent operating environment sandbox to ensure that the error pattern library is compatible with the preset modeling environment.

[0155] If the error pattern library is found to be incompatible with the preset modeling environment, the system will analyze the API call protocol of the modeling environment through reverse engineering to understand its interface details. The API call protocol of 2025.1 is analyzed through reverse engineering to understand its interface parameters and return values. The open graphical interface of the modeling environment is packaged for version compatibility to make it compatible with the error pattern library. For example, adaptation code is written to convert the API calls of 2025.1 into a format supported by the error pattern library. Containerization technology (such as Docker) can be used to build an independent runtime environment sandbox for the error pattern library to ensure that it can run in an isolated environment. For example, a Docker container is created, the runtime environment required by the error pattern library is configured, and the error pattern library is run in it.

[0156] Through reverse engineering and version-compatible packaging, we ensure that the error pattern library can operate normally in different modeling environments. By using containerization technology to build an independent operating environment sandbox, we ensure the independence and stability of the error pattern library. This improves the flexibility and adaptability of the method, enabling deployment and operation in a variety of environments.

[0157] See also Figure 2 As shown, Figure 2 1 is a schematic diagram of the structure of an intelligent quality inspection device 200 for use in the parametric modeling and design process of a hyperbolic curtain wall, provided in an embodiment of the present application. The intelligent quality inspection device 200 for use in the parametric modeling and design process of a hyperbolic curtain wall is used to execute the steps of the intelligent quality inspection method for use in the parametric modeling and design process of a hyperbolic curtain wall, as described in the above-mentioned embodiments. The intelligent quality inspection device 200 for use in the parametric modeling and design process of a hyperbolic curtain wall can be a single server or a server cluster, or the intelligent quality inspection device 200 for use in the parametric modeling and design process of a hyperbolic curtain wall can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0158] like Figure 2 As shown, the intelligent quality detection device 200 used in the parametric modeling design process of hyperbolic curtain wall includes:

[0159] The data acquisition unit 201 is used to acquire modeling parameter data corresponding to the hyperbolic curtain wall model when modeling the hyperbolic curtain wall model in a preset modeling environment, and preprocess the modeling parameter data; the preprocessing at least includes data cleaning, formatting and standardization;

[0160] An error acquisition unit 202 is configured to acquire historical error data corresponding to the hyperbolic curtain wall model and preset expert knowledge to construct an error pattern library; the error pattern library includes error features, which include at least structural errors, dimensional errors, material matching errors, and node closing errors;

[0161] A real-time monitoring unit 203 is configured to monitor the modeling process in real time according to the error pattern library during the modeling process of the hyperbolic curtain wall model;

[0162] The detection completion unit 204 is used to generate error warning information if it is determined according to the error pattern library that there is at least one error feature in the modeling process, display the error warning information on the interface corresponding to the preset modeling environment, and generate an error location visualization report to complete the intelligent quality detection in the parametric modeling design process of the hyperbolic curtain wall.

[0163] In some embodiments, the error warning information is displayed on an interface corresponding to the preset modeling environment, including: highlighting the erroneous component outline corresponding to the hyperbolic curtain wall model in the preset modeling environment; generating an error propagation link diagram with topological relationship annotations in the preset modeling environment; and displaying error correction suggestions according to a preset voice interaction form.

[0164] In some embodiments, generating the error location visualization report includes: generating a hierarchical error index directory, an interactive error impact domain stress cloud map, and a clause reference comparison table of associated design specifications corresponding to the hyperbolic curtain wall model based on the error warning information; generating the error location visualization report based on the error index directory, the interactive error impact domain stress cloud map, and the clause reference comparison table of associated design specifications.

[0165] In some embodiments, before the error warning information is generated, it also includes: performing stress compliance verification on the hyperbolic curtain wall model based on finite element analysis; generating a predicted error propagation probability corresponding to the hyperbolic curtain wall model and the error feature according to Monte Carlo; and performing a virtual installation rehearsal according to a preset digital twin model based on the predicted error propagation probability and the compliance verification result corresponding to the stress compliance verification to complete a secondary verification of the error feature. If the secondary verification passes, the error warning information is generated.

[0166] In some embodiments, after completing the intelligent quality inspection in the parametric modeling and design process of the hyperbolic curtain wall, it also includes: obtaining the error correction record corresponding to the error location visualization report; performing feature extraction based on the error correction record to generate an error feature vector; and updating the error pattern library based on the error feature vector.

[0167] Exemplarily, extracting features from the error correction record to generate an error feature vector includes: extracting features from the error correction record using a preset convolutional neural network and outputting the error feature vector; and an expression for the error feature vector includes:

[0168] ;in, Indicates that the convolutional neural network is layer The error feature vector of the position output, is the convolution kernel size corresponding to the convolutional neural network, The convolutional neural network is described in The paranoid term of the layer, is the ReLU activation function, It is The relative position of the convolution kernel of the layer ( , ) is a weight parameter used to capture the spatial correlation of local error features, For the Layer at offset position The input feature value reflects the error features transmitted by the previous network layer.

[0169] Exemplarily, the updating of the error pattern library according to the error feature vector includes: dynamically optimizing a weight coefficient of the error pattern library to complete the updating of the error pattern library.

[0170] It should be noted that, in some embodiments, the dynamic optimization equation corresponding to the weight coefficient of the error pattern library includes:

[0171] ;in, Represents the time step The weight coefficient corresponding to the error pattern library when , the range of the weight coefficient is (0,1), is an attenuation adjustment factor, the attenuation adjustment factor is greater than 0, is the current time step counter, The base time offset is used to set the starting reference point of the pattern library update cycle is the loss function In the parameters The gradient vector at reflects the optimization direction of the current model training state. It is a 2-norm operator that calculates the Euclidean length of the gradient vector and quantifies the magnitude of the update of the model parameters.

[0172] In some embodiments, before the modeling process is monitored in real time according to the error pattern library, it also includes: obtaining version information corresponding to the preset modeling environment; obtaining compatibility information corresponding to the error pattern library and the version information; if it is confirmed that the error pattern library is compatible with the preset modeling environment according to the compatibility information, the modeling process is monitored in real time according to the error pattern library.

[0173] Exemplarily, if it is confirmed based on the compatibility information that the error pattern library is incompatible with the preset modeling environment, the method includes: parsing the API calling protocol of the preset modeling environment based on reverse engineering; performing version-compatible packaging on the open graphical interface of the preset modeling environment; and constructing an independent operating environment sandbox corresponding to the error pattern library based on containerization technology to run the error pattern library in the independent operating environment sandbox to ensure that the error pattern library is compatible with the preset modeling environment.

[0174] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the intelligent quality detection device and each module used in the parametric modeling and design process of the hyperbolic curtain wall described above can refer to the corresponding processes in the embodiments of the intelligent quality detection method used in the parametric modeling and design process of the hyperbolic curtain wall described in the above embodiments, and will not be repeated here.

[0175] The above-mentioned intelligent quality detection method for the parametric modeling design process of hyperbolic curtain wall can be realized in the form of a computer program. The computer program can be used in Figure 2 Run on the device shown.

[0176] See also Figure 3 , Figure 3 1 is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0177] The storage medium can store an operating device and a computer program. The computer program includes program instructions, which, when executed, can cause a processor to execute any intelligent quality detection method used in a parametric modeling design process of a hyperbolic curtain wall.

[0178] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0179] The internal memory provides an environment for the operation of a computer program in a non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any intelligent quality detection method used in the parametric modeling design process of a hyperbolic curtain wall.

[0180] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0181] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0182] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0183] When modeling a hyperbolic curtain wall model in a preset modeling environment, obtaining modeling parameter data corresponding to the hyperbolic curtain wall model and preprocessing the modeling parameter data; the preprocessing at least includes cleaning, formatting and standardizing the data;

[0184] Acquiring historical error data corresponding to the hyperbolic curtain wall model and preset expert knowledge to construct an error pattern library; the error pattern library includes error features, and the error features include at least structural errors, dimensional errors, material matching errors, and node closing errors;

[0185] During the modeling process of the hyperbolic curtain wall model, the modeling process is monitored in real time according to the error pattern library;

[0186] If it is determined according to the error pattern library that there is at least one error feature in the modeling process, an error warning message is generated, the error warning message is displayed on the interface corresponding to the preset modeling environment, and an error location visualization report is generated to complete the intelligent quality detection in the parametric modeling design process of the hyperbolic curtain wall.

[0187] In some embodiments, the error warning information is displayed on an interface corresponding to the preset modeling environment, including: highlighting the erroneous component outline corresponding to the hyperbolic curtain wall model in the preset modeling environment; generating an error propagation link diagram with topological relationship annotations in the preset modeling environment; and displaying error correction suggestions according to a preset voice interaction form.

[0188] In some embodiments, generating the error location visualization report includes: generating a hierarchical error index directory, an interactive error impact domain stress cloud map, and a clause reference comparison table of associated design specifications corresponding to the hyperbolic curtain wall model based on the error warning information; generating the error location visualization report based on the error index directory, the interactive error impact domain stress cloud map, and the clause reference comparison table of associated design specifications.

[0189] In some embodiments, before the error warning information is generated, it also includes: performing stress compliance verification on the hyperbolic curtain wall model based on finite element analysis; generating a predicted error propagation probability corresponding to the hyperbolic curtain wall model and the error feature according to Monte Carlo; and performing a virtual installation rehearsal according to a preset digital twin model based on the predicted error propagation probability and the compliance verification result corresponding to the stress compliance verification to complete a secondary verification of the error feature. If the secondary verification passes, the error warning information is generated.

[0190] In some embodiments, after completing the intelligent quality inspection in the parametric modeling and design process of the hyperbolic curtain wall, it also includes: obtaining the error correction record corresponding to the error location visualization report; performing feature extraction based on the error correction record to generate an error feature vector; and updating the error pattern library based on the error feature vector.

[0191] Exemplarily, extracting features from the error correction record to generate an error feature vector includes: extracting features from the error correction record using a preset convolutional neural network and outputting the error feature vector; and an expression for the error feature vector includes:

[0192] ;in, Indicates that the convolutional neural network is layer The error feature vector of the position output, is the convolution kernel size corresponding to the convolutional neural network, The convolutional neural network is described in The paranoid term of the layer, is the ReLU activation function, It is The relative position of the convolution kernel of the layer ( , ) is a weight parameter used to capture the spatial correlation of local error features, For the Layer at offset position The input feature value reflects the error features transmitted by the previous network layer.

[0193] Exemplarily, the updating of the error pattern library according to the error feature vector includes: dynamically optimizing a weight coefficient of the error pattern library to complete the updating of the error pattern library.

[0194] It should be noted that, in some embodiments, the dynamic optimization equation corresponding to the weight coefficient of the error pattern library includes:

[0195] ;in, Represents the time step The weight coefficient corresponding to the error pattern library when , the range of the weight coefficient is (0,1), is an attenuation adjustment factor, the attenuation adjustment factor is greater than 0, is the current time step counter, The base time offset is used to set the starting reference point of the pattern library update cycle is the loss function In the parameters The gradient vector at reflects the optimization direction of the current model training state. It is a 2-norm operator that calculates the Euclidean length of the gradient vector and quantifies the magnitude of the update of the model parameters.

[0196] In some embodiments, before the modeling process is monitored in real time according to the error pattern library, it also includes: obtaining version information corresponding to the preset modeling environment; obtaining compatibility information corresponding to the error pattern library and the version information; if it is confirmed that the error pattern library is compatible with the preset modeling environment according to the compatibility information, the modeling process is monitored in real time according to the error pattern library.

[0197] Exemplarily, if it is confirmed based on the compatibility information that the error pattern library is incompatible with the preset modeling environment, the method includes: parsing the API calling protocol of the preset modeling environment based on reverse engineering; performing version-compatible packaging on the open graphical interface of the preset modeling environment; and constructing an independent operating environment sandbox corresponding to the error pattern library based on containerization technology to run the error pattern library in the independent operating environment sandbox to ensure that the error pattern library is compatible with the preset modeling environment.

[0198] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the computer equipment and each module described above can refer to the corresponding processes in the embodiments of the intelligent quality detection method for the parametric modeling design process of hyperbolic curtain walls described in the above embodiments, and will not be repeated here.

[0199] The present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the steps of the intelligent quality detection method for the parametric modeling design process of hyperbolic curtain walls as provided in any embodiment of the present application.

[0200] The computer-readable storage medium may be an internal storage unit of the computer device described in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash memory card, etc., equipped on the computer device.

[0201] Exemplarily, the medium is used to implement the following steps:

[0202] When modeling a hyperbolic curtain wall model in a preset modeling environment, obtaining modeling parameter data corresponding to the hyperbolic curtain wall model and preprocessing the modeling parameter data; the preprocessing at least includes cleaning, formatting and standardizing the data;

[0203] Acquiring historical error data corresponding to the hyperbolic curtain wall model and preset expert knowledge to construct an error pattern library; the error pattern library includes error features, and the error features include at least structural errors, dimensional errors, material matching errors, and node closing errors;

[0204] During the modeling process of the hyperbolic curtain wall model, the modeling process is monitored in real time according to the error pattern library;

[0205] If it is determined according to the error pattern library that there is at least one error feature in the modeling process, an error warning message is generated, the error warning message is displayed on the interface corresponding to the preset modeling environment, and an error location visualization report is generated to complete the intelligent quality detection in the parametric modeling design process of the hyperbolic curtain wall.

[0206] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the storage medium and each module described above can refer to the corresponding processes in the embodiments of the intelligent quality detection method for the parametric modeling design process of hyperbolic curtain walls described in the above embodiments, and will not be repeated here.

[0207] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. An intelligent quality detection method for the parametric modeling design process of hyperbolic curtain walls, characterized in that: include: When modeling a hyperbolic curtain wall model in a preset modeling environment, obtaining modeling parameter data corresponding to the hyperbolic curtain wall model and preprocessing the modeling parameter data; the preprocessing at least includes cleaning, formatting and standardizing the data; Acquire historical error data corresponding to the hyperbolic curtain wall model and preset expert knowledge to construct an error pattern library; The error pattern library includes error features, and the error features include at least structural errors, size errors, material matching errors, and node closing errors; During the modeling process of the hyperbolic curtain wall model, the modeling process is monitored in real time according to the error pattern library; If it is determined according to the error pattern library that at least one error feature exists in the modeling process, an error warning message is generated, the error warning message is displayed on an interface corresponding to the preset modeling environment, and an error location visualization report is generated, thereby completing intelligent quality detection in the parametric modeling design process of the hyperbolic curtain wall; After completing the intelligent quality inspection in the hyperbolic curtain wall parametric modeling design process, the method further includes: obtaining an error correction record corresponding to the error location visualization report; performing feature extraction based on the error correction record to generate an error feature vector, including: performing feature extraction on the error correction record based on a preset convolutional neural network to output the error feature vector; the expression of the error feature vector includes: ;in, Indicates that the convolutional neural network layer The error feature vector of the position output, is the convolution kernel size corresponding to the convolutional neural network, The convolutional neural network is described in The paranoid term of the layer, is the ReLU activation function, It is The relative position of the convolution kernel of the layer ( , ) is a weight parameter used to capture the spatial correlation of local error features, For the Layer at offset position The input feature value reflects the error feature transmitted by the previous network layer; the error pattern library is updated according to the error feature vector, including: dynamically optimizing the weight coefficient of the error pattern library to complete the update of the error pattern library; the dynamic optimization equation corresponding to the weight coefficient of the error pattern library includes: ;in, Represents the time step The weight coefficient corresponding to the error pattern library when , the range of the weight coefficient is (0,1), is the attenuation adjustment factor, the attenuation adjustment factor is greater than 0, is the current time step counter, It is the base time offset, which is used to set the starting reference point of the pattern library update cycle. is the loss function In the parameters The gradient vector at reflects the optimization direction of the current model training state. It is a 2-norm operator that calculates the Euclidean length of the gradient vector and quantifies the magnitude of the update of the model parameters.

2. The method according to claim 1, characterized in that The displaying of the error warning information on the interface corresponding to the preset modeling environment includes: Highlighting the erroneous component outline corresponding to the hyperbolic curtain wall model in the preset modeling environment; Generating an error propagation link graph with topological relationship annotations in the preset modeling environment; Displays error correction suggestions based on the preset voice interaction form.

3. The method according to claim 1, characterized in that The generating of the error location visualization report includes: Generating a hierarchical error index directory, an interactive error impact domain stress cloud map, and a clause reference comparison table of associated design specifications corresponding to the hyperbolic curtain wall model according to the error warning information; The error location visualization report is generated according to the error index directory, the interactive error impact domain stress cloud map and the clause reference comparison table of the associated design specifications.

4. The method according to claim 1, wherein Before generating the error warning information, the method further includes: Performing stress compliance verification on the hyperbolic curtain wall model based on finite element analysis; generating a predicted error propagation probability corresponding to the hyperbolic curtain wall model and the error characteristics according to Monte Carlo; According to the preset digital twin model, a virtual installation rehearsal is performed according to the compliance verification results corresponding to the predicted error propagation probability and the stress compliance verification to complete the secondary verification of the error characteristics. If the secondary verification passes, the error warning information is generated.

5. The method according to claim 1, wherein Before real-time monitoring of the modeling process according to the error pattern library, the method further includes: Obtaining version information corresponding to the preset modeling environment; Obtaining compatibility information corresponding to the error pattern library and the version information; If it is confirmed according to the compatibility information that the error pattern library is compatible with the preset modeling environment, the modeling process is monitored in real time according to the error pattern library.

6. The method according to claim 5, characterized in that If it is determined according to the compatibility information that the error pattern library is incompatible with the preset modeling environment, the method includes: Analyzing the API calling protocol of the preset modeling environment based on reverse engineering; Provide version-compatible packaging for the open graphical interface of the preset modeling environment; An independent operating environment sandbox corresponding to the error pattern library is constructed based on containerization technology to run the error pattern library in the independent operating environment sandbox, thereby ensuring that the error pattern library is compatible with the preset modeling environment.

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