An AI-based CAD operation prediction system
The AI-based CAD operation prediction system solves the problems of complexity and high learning cost in CAD software operation, realizes intelligent prompts and personalized services, improves design efficiency and accuracy, and is applicable to various CAD environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- PAN-CHINA CONSTR GRP
- Filing Date
- 2026-04-02
- Publication Date
- 2026-06-30
Smart Images
Figure CN122309999A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of computer-aided design technology, specifically an artificial intelligence-based CAD operation prediction system. Background Technology
[0002] Computer-aided design (CAD) technology is an indispensable tool in the field of modern engineering design. It uses computer software to help designers perform 2D drawing, 3D modeling, engineering analysis, and the generation of production drawings. With technological advancements, CAD software has become increasingly powerful, but this has also led to increased operational complexity.
[0003] The defects and shortcomings of existing technologies include: Operational complexity: Although existing CAD software has rich functions, it has many operation steps. For novice designers, learning and mastering these operations requires a lot of time and effort.
[0004] Low design efficiency: Due to the complexity of the operation, designers often need to frequently consult help documents or watch tutorials when designing, which undoubtedly reduces design efficiency.
[0005] Unintelligent human-computer interaction: Existing CAD software lacks intelligent prompts and prediction functions during operation. When designers perform the next operation, the software cannot provide effective suggestions, which may lead to unnecessary errors and repetitive work in the design process.
[0006] Lack of personalized services: Existing CAD software fails to provide personalized operation suggestions based on the designer's personal habits and preferences, which limits the user experience and efficiency of the software.
[0007] High learning cost: For beginners, the learning curve of CAD software is steep, requiring a lot of practice and trial and error to accumulate experience, which increases the learning cost.
[0008] Lack of operational prediction: During the design process, designers often need to perform a series of operational steps according to project requirements, but existing CAD software cannot predict the designer's next operation, thus making it impossible to prepare relevant tools and functions in advance.
[0009] Poor adaptability: Different designers may have different design styles and workflows, and existing CAD software lacks sufficient adaptability, failing to adjust the user interface and functions according to the specific needs of designers. Summary of the Invention
[0010] In view of the above problems, the present invention is proposed to provide an artificial intelligence-based CAD operation prediction system that overcomes or at least partially solves the above problems.
[0011] To achieve the above objectives, the present invention adopts the following technical solution: An artificial intelligence-based CAD operation prediction system, the system comprising: The data collection module is used to collect data; The data preprocessing module is used to preprocess the collected data; The feature extraction module is used to extract operational features; The prediction model training module is used to train the prediction model; The operation prediction module is used to generate prediction results for the next operation; The results feedback and interaction module is used to provide feedback on the prediction results.
[0012] Optionally, the data collection module includes: Collect operation logs during the use of CAD software; Real-time recording of operational behavior can be achieved through software interfaces or plugins.
[0013] Optionally, the operation log includes operation type, operation parameters, operation sequence, and operation timestamp.
[0014] Optionally, the data preprocessing module includes: The collected operation logs are cleaned to remove invalid and erroneous data; Perform deduplication on the operation logs; The operational data is formatted to meet the requirements of feature extraction and model training.
[0015] Optionally, the feature extraction module includes: Extract operational features from the preprocessed data; Time series analysis techniques are used to extract the temporal features and patterns of the operation sequences.
[0016] Optionally, the operation features include operation type, operation parameters, operation frequency, and operation context.
[0017] Optionally, the prediction model training module includes: An operational prediction model is constructed using deep learning algorithms; The extracted operational features are used as input to train the model to predict the designer's next action; Model performance is optimized through cross-validation and hyperparameter tuning.
[0018] Optionally, the deep learning algorithm includes convolutional neural networks, recurrent neural networks, long short-term memory networks, and Transformers.
[0019] Optionally, the operation prediction module includes: During CAD operations, real-time data collection of current operations is performed. The current operation data is input into the trained prediction model to generate the prediction result for the next operation; Based on the forecast results, provide operational suggestions or automatically execute the predicted actions.
[0020] Optional, the results feedback and interaction module includes: The prediction results are fed back to the designer through a graphical interface, a dialog box, or an audio prompt. Designers can choose to accept, modify, or ignore the suggestions based on the prediction results. The predictive model is adjusted based on feedback from designers to enable personalized services and self-optimization.
[0021] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention can significantly reduce the number of steps and time designers spend operating in CAD software by predicting their next operation, thereby improving overall design efficiency. For novice designers, the intelligent operation prediction provided by this invention can help them become familiar with and master CAD software more quickly, reducing the learning difficulty.
[0022] 2. This invention can provide customized operation suggestions based on the designer's personal operating habits and design style, enhancing the user experience; by predicting and recommending the correct operation steps, it helps reduce errors made by designers during the operation process, improving the accuracy and reliability of the design. 3. This invention can automatically adjust the prediction model based on designer feedback and behavior, achieving self-optimization. Over time, the prediction accuracy will continuously improve. By reducing unnecessary operations and errors, this invention helps save designers' time and company resources, improving the cost-effectiveness of design projects. This invention achieves a more intelligent human-computer interaction method, enabling CAD software to better understand the needs of designers, thereby providing more accurate assistance.
[0023] 4. This invention is not only applicable to a specific CAD software, but can also be extended to various CAD environments, and has wide applicability; this invention provides real-time operation suggestions and feedback, enabling designers to adjust their design ideas and operation steps in real time, thereby improving the flexibility and dynamism of the design. Attached Figure Description
[0024] Figure 1 This is a schematic diagram of a CAD operation prediction system based on artificial intelligence, provided as an embodiment of this application. Detailed Implementation
[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0026] Please see Figure 1 This embodiment provides an artificial intelligence-based CAD operation prediction system, the system comprising: The data collection module is used to collect data; The data collection module includes: Collect operation logs during the use of CAD software; the operation logs include operation type, operation parameters, operation sequence and operation timestamp.
[0027] Real-time recording of operational behavior can be achieved through software interfaces or plugins.
[0028] The data preprocessing module is used to preprocess the collected data; The data preprocessing module includes: Clean the collected operation logs to remove invalid and erroneous data.
[0029] The operation logs are deduplicated to avoid duplicate data affecting the model training effect; The operational data is formatted to meet the requirements of feature extraction and model training.
[0030] The feature extraction module is used to extract operational features; The feature extraction module includes: Operational features are extracted from the preprocessed data, including operation type, operation parameters, operation frequency, and operation context.
[0031] Time series analysis techniques are used to extract the temporal features and patterns of the operation sequences.
[0032] Launch the CAD software and monitor the designer's actions, including mouse clicks, keyboard input, tool selection, and command execution.
[0033] Record operation logs, including the timestamp of the operation, operation type (such as drawing, modification, annotation, etc.), operation parameters (such as line type, color, size, etc.), and operation objects (such as lines, circles, text, etc.).
[0034] Preprocess the operation logs to filter out invalid operations, such as duplicate clicks and undo operations.
[0035] Data mining techniques are used to extract operational features from preprocessed operation logs to form feature vectors.
[0036] The prediction model training module is used to train the prediction model; The prediction model training module includes: An operation prediction model is constructed using deep learning algorithms, including convolutional neural networks, recurrent neural networks, long short-term memory networks, and Transformers.
[0037] The extracted operational features are used as input to train the model to predict the designer's next action.
[0038] Model performance is optimized through cross-validation and hyperparameter tuning.
[0039] Choose an appropriate machine learning algorithm, such as a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory network (LSTM).
[0040] The extracted operational feature vectors are used as input data to design the corresponding network structure.
[0041] The network is trained using a large amount of historical operational data, and the network parameters are optimized until the prediction model achieves a satisfactory accuracy.
[0042] Save the trained model for subsequent operation predictions.
[0043] The operation prediction module is used to generate prediction results for the next operation; The operation prediction module includes: Designers collect real-time operation data while performing CAD operations.
[0044] The current operation data is input into the trained prediction model to generate the prediction result for the next operation.
[0045] Based on the forecast results, provide operational suggestions or automatically execute the predicted actions.
[0046] The results feedback and interaction module is used to provide feedback on the prediction results.
[0047] The results feedback and interaction module includes: The prediction results are fed back to the designer through graphical interfaces, dialog boxes, or sound prompts.
[0048] Designers can choose to accept, modify, or ignore the suggestions based on the prediction results.
[0049] The predictive model is adjusted based on feedback from designers to enable personalized services and self-optimization.
[0050] System optimization includes: Collect information and feedback from designers regarding their use of the recommended actions.
[0051] Analyze the feedback data to identify shortcomings in the predictive model.
[0052] Regularly retrain the predictive model with new operational data to optimize model performance.
[0053] The parameters of the predictive model are adjusted according to the designer's individual needs and work habits to provide more personalized operational predictions.
[0054] This embodiment can significantly reduce the number of steps and time designers spend operating in CAD software by predicting their next operation, thereby improving overall design efficiency. For novice designers, the intelligent operation prediction provided by this embodiment can help them become familiar with and master CAD software more quickly, reducing the learning difficulty.
[0055] This embodiment can provide customized operation suggestions based on the designer's personal operating habits and design style, enhancing the user experience; by predicting and recommending the correct operation steps, it helps reduce errors made by designers during the operation process, improving the accuracy and reliability of the design. This embodiment can automatically adjust the prediction model based on designer feedback and behavior, achieving self-optimization. Over time, the prediction accuracy will continuously improve. By reducing unnecessary operations and errors, this embodiment helps save designers' time and company resources, improving the cost-effectiveness of design projects. This embodiment realizes a more intelligent human-computer interaction method, enabling CAD software to better understand the needs of designers, thereby providing more accurate assistance.
[0056] This embodiment is not only applicable to a specific CAD software, but can also be extended to a variety of CAD environments, and has wide applicability. This embodiment provides real-time operation suggestions and feedback, enabling designers to adjust their design ideas and operation steps in an instant, thereby improving the flexibility and dynamism of the design.
[0057] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.
Claims
1. An artificial intelligence-based CAD operation prediction system, characterized by, The system includes: The data collection module is used to collect data; The data preprocessing module is used to preprocess the collected data; The feature extraction module is used to extract operational features; The prediction model training module is used to train the prediction model; The operation prediction module is used to generate prediction results for the next operation; The results feedback and interaction module is used to provide feedback on the prediction results.
2. The CAD operation prediction system based on artificial intelligence as described in claim 1, characterized in that, The data collection module includes: Collect operation logs during the use of CAD software; Real-time recording of operational behavior can be achieved through software interfaces or plugins.
3. The CAD operation prediction system based on artificial intelligence as described in claim 2, characterized in that, The operation log includes operation type, operation parameters, operation sequence, and operation timestamp.
4. The CAD operation prediction system based on artificial intelligence as described in claim 1, characterized in that, The data preprocessing module includes: The collected operation logs are cleaned to remove invalid and erroneous data; Perform deduplication on the operation logs; The operational data is formatted to meet the requirements of feature extraction and model training.
5. The CAD operation prediction system based on artificial intelligence as described in claim 1, characterized in that, The feature extraction module includes: Extract operational features from the preprocessed data; Time series analysis techniques are used to extract the temporal features and patterns of the operation sequences.
6. The CAD operation prediction system based on artificial intelligence as described in claim 5, characterized in that, The operational features include operational type, operational parameters, operational frequency, and operational context.
7. The CAD operation prediction system based on artificial intelligence as described in claim 1, characterized in that, The prediction model training module includes: An operational prediction model is constructed using deep learning algorithms; The extracted operational features are used as input to train the model to predict the designer's next action; Model performance is optimized through cross-validation and hyperparameter tuning.
8. The CAD operation prediction system based on artificial intelligence as described in claim 7, characterized in that, The deep learning algorithms include convolutional neural networks, recurrent neural networks, long short-term memory networks, and Transformers.
9. The CAD operation prediction system based on artificial intelligence as described in claim 1, characterized in that, The operation prediction module includes: During CAD operations, real-time data collection of current operations is performed. The current operation data is input into the trained prediction model to generate the prediction result for the next operation; Based on the forecast results, provide operational suggestions or automatically execute the predicted actions.
10. The CAD operation prediction system based on artificial intelligence as described in claim 1, characterized in that, The results feedback and interaction module includes: The prediction results are fed back to the designer through a graphical interface, a dialog box, or an audio prompt. Designers can choose to accept, modify, or ignore the suggestions based on the prediction results. The predictive model is adjusted based on feedback from designers to enable personalized services and self-optimization.