A simulation scene element automatic modeling method based on generative AI
By using generative AI technology for automatic modeling, the problem of low efficiency in scene construction on mechanical simulation platforms has been solved, enabling rapid and batch scene generation, reducing technical barriers and labor costs, and improving scene consistency and reusability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING JUNHE CHUANGXIANG TECH DEV CO LTD
- Filing Date
- 2026-02-10
- Publication Date
- 2026-05-29
AI Technical Summary
The scene construction of existing mechanical simulation platforms relies on manual operation, resulting in long project production cycles and high costs, making it difficult to achieve rapid and batch scene generation, especially when dealing with complex mechanical equipment or changing training needs, where efficiency is low.
By employing generative AI technology, natural language processing is used to identify scene elements and automatically generate 3D models, materials, exploded views, interaction logic, and UI interfaces, thus achieving automatic modeling of simulation scenes, including model layout, material configuration, exploded view generation, interaction logic construction, and automatic UI interface generation.
It significantly improves the efficiency and consistency of scenario building, reduces manual intervention, supports rapid customization and batch generation, and is suitable for application scenarios that require high-frequency scenario updates, such as training, assessment and collaborative drills.
Smart Images
Figure CN122115786A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of virtual simulation technology, and more specifically to an automatic modeling method for simulation scene elements based on generative AI. Background Technology
[0002] Currently, the construction of mechanical simulation platforms mainly relies on manual operation, such as manually importing 3D models, setting materials, editing exploded views, configuring interactive steps, and designing UI interfaces. The process is cumbersome and highly dependent on the experience and time investment of professionals.
[0003] While existing simulation platforms offer rich editing functions, such as blueprint-based assembly / disassembly sequences, dynamic structural explosion editing, story animation editing, and step-by-step interactive configuration, each step requires user intervention, resulting in long project production cycles, high costs, and difficulty in ensuring consistency between scene logic and interactive behavior. Especially when dealing with complex mechanical equipment or changing training needs, traditional modeling methods are inefficient and struggle to achieve rapid, batch scene generation. Summary of the Invention
[0004] To address this, the present invention provides an automatic modeling method for simulation scene elements based on generative AI, in order to solve the problem that traditional modeling methods are inefficient and difficult to achieve rapid and batch scene generation when facing complex mechanical equipment or changing training needs.
[0005] To achieve the above objectives, the present invention provides the following technical solution:
[0006] An automatic modeling method for simulation scene elements based on generative AI includes the following steps;
[0007] S1, Input parsing and scene element recognition:
[0008] It receives user input of structural description text, working principle description or maintenance operation process text, parses the text content through natural language processing technology, and identifies the model components, material properties, assembly relationships, operation steps and interaction logic elements required in the scene;
[0009] S2, Automatic Generation and Layout of 3D Models:
[0010] Based on the identified component information, a pre-trained generative 3D model generation network is invoked to automatically generate or match the corresponding 3D model from the model library, and the position, rotation and scaling parameters of the model are automatically calculated according to the assembly relationship.
[0011] S3, intelligent configuration of materials and textures:
[0012] Based on the component functions and material descriptions, the system automatically assigns appropriate material properties to the 3D model, including color, transparency, and reflectivity, and automatically generates or matches the corresponding texture map based on the texture generation algorithm.
[0013] S4, Exploded view sequence is automatically generated:
[0014] Based on the identified assembly hierarchy and structural relationships, the system automatically calculates the explosion direction, explosion distance, and explosion sequence, generates a dynamic sequence of exploded views, and supports previewing and parameter adjustment.
[0015] S5, automatic construction of interaction logic and step sequence:
[0016] Based on the maintenance or operation process text, it is automatically broken down into multiple operation steps, and each step is configured with trigger nodes, tools to be used, action instructions and preconditions and prerequisites to generate an interactive step sequence, and supports the automatic arrangement of linear disassembly and assembly sequences.
[0017] S6, UI interface and prompts are automatically generated:
[0018] The system automatically generates the corresponding user interface based on the scenario type, including a structure tree, function buttons, prompt information bar, and operation buttons, and automatically fills in operation prompts, work timing, and preparation text based on the step content.
[0019] S7, Automatic Camera and Viewpoint Configuration:
[0020] Based on scene layout and operation focus, multiple camera nodes are automatically generated, viewpoint parameters are configured, and camera switching logic for key steps is set.
[0021] S8, Scene Integration and Automatic Script Generation:
[0022] The generated models, materials, exploded views, interaction steps, UI, and camera elements are integrated into a unified simulation scene, and an execution script is automatically generated to link the logic and event responses between the various elements.
[0023] S9, Scene Publishing and Format Output:
[0024] The integrated scene will be automatically published in the target format, supporting export as an executable file, VR-compatible format or multi-person collaborative scene, and automatically generating resource directory and configuration file.
[0025] Preferably, in step S2, the generative 3D model generation network supports generating parametric models based on text descriptions and supports automatic conversion of model formats and platform adaptation.
[0026] Preferably, in step S5, the interactive logic automatically constructs multiple step types including disassembly, assembly, inspection, and maintenance, and automatically configures subtitle prompts, tool usage judgments, and error operation detection logic for each step.
[0027] Preferably, step S5 further includes automatically assigning roles and tasks, configuring instruction sending and receiving logic, and collaborative operation nodes based on the needs of multi-person collaborative training.
[0028] Preferably, in step S6, the UI interface automatically generates interface templates for four scenario types, including structural cognition, principle learning, maintenance training, and assessment, and adaptively selects and fills in the content.
[0029] Preferably, in step S8, the script is automatically generated including animation script, event response script, and physical interaction script, and is automatically mounted to the corresponding model or UI node.
[0030] Preferably, it also includes;
[0031] S10, Scene Optimization and Verification Feedback:
[0032] After the scene is generated, it automatically performs model collision detection, animation smoothness verification, and interaction logic consistency check, and automatically adjusts parameters or prompts the user to correct the input text based on the verification results.
[0033] Preferably, the scene optimization includes automatically adding colliders, rigid body components, repair parts and tool components, and setting virtual hand grip posture and grasping logic.
[0034] Preferably, in step S9, the output format can be automatically selected according to the target platform, including PC executable files, VR all-in-one applications, or multi-user collaborative server deployment packages.
[0035] Preferably, it also includes a text understanding module, a 3D generation module, a logical reasoning module, and an interface generation module. Each module transmits information and associates scene elements through a unified knowledge graph.
[0036] This invention has the following advantages: By introducing generative AI technology, this invention can automatically generate complete simulation scene elements, including 3D model layout, material texture, exploded view sequence, interaction steps, and UI interface, based on user-input structural description, working principle text, or maintenance procedures. This significantly improves the efficiency and consistency of scene construction. Moreover, this method achieves end-to-end automatic generation from requirement description to runnable simulation cases, reducing manual intervention, lowering the technical threshold, and supporting rapid customization and batch generation. It is especially suitable for applications that require high-frequency scene updates, such as training, assessment, and collaborative drills. Attached Figure Description
[0037] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).
[0038] Figure 1 A flowchart illustrating an automatic modeling method for simulation scene elements based on generative AI, provided in an embodiment of this application. Detailed Implementation
[0039] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these embodiments are merely for further explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Technical engineers in the field can make some non-essential improvements and adjustments to the present invention based on the above-described content. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0040] Please see Figure 1 An automatic modeling method for simulation scene elements based on generative AI includes the following steps;
[0041] S1, Input parsing and scene element recognition:
[0042] It receives user input of structural description text, working principle description or maintenance operation process text, parses the text content through natural language processing technology, and identifies the model components, material properties, assembly relationships, operation steps and interaction logic elements required in the scene;
[0043] S2, Automatic Generation and Layout of 3D Models:
[0044] Based on the identified component information, a pre-trained generative 3D model generation network is invoked to automatically generate or match the corresponding 3D model from the model library, and the position, rotation and scaling parameters of the model are automatically calculated according to the assembly relationship to complete the initial layout of the model in the scene.
[0045] S3, intelligent configuration of materials and textures:
[0046] Based on the component functions and material descriptions, the system automatically assigns appropriate material properties to the 3D model, including color, transparency, and reflectivity, and automatically generates or matches the corresponding texture map based on the texture generation algorithm to achieve automated rendering of the model's appearance.
[0047] S4, Exploded view sequence is automatically generated:
[0048] Based on the identified assembly hierarchy and structural relationships, the system automatically calculates the explosion direction, explosion distance, and explosion sequence, generates a dynamic sequence of exploded views, and supports previewing and parameter adjustment.
[0049] S5, automatic construction of interaction logic and step sequence:
[0050] Based on the maintenance or operation process text, it is automatically broken down into multiple operation steps, and each step is configured with trigger nodes, tools to be used, action instructions and preconditions and prerequisites to generate an interactive step sequence, and supports the automatic arrangement of linear disassembly and assembly sequences.
[0051] S6, UI interface and prompts are automatically generated:
[0052] The system automatically generates the corresponding user interface based on the scenario type, including a structure tree, function buttons, prompt information bar, and operation buttons, and automatically fills in operation prompts, work timing, and preparation text based on the step content.
[0053] S7, Automatic Camera and Viewpoint Configuration:
[0054] Based on scene layout and operation focus, multiple camera nodes are automatically generated, viewpoint parameters are configured, and camera switching logic for key steps is set.
[0055] S8, Scene Integration and Automatic Script Generation:
[0056] The generated models, materials, exploded views, interaction steps, UI, and camera elements are integrated into a unified simulation scene, and an execution script is automatically generated to link the logic and event responses between the various elements.
[0057] S9, Scene Publishing and Format Output:
[0058] The integrated scene will be automatically published in the target format, supporting export as an executable file, VR-compatible format or multi-person collaborative scene, and automatically generating resource directory and configuration file.
[0059] In implementing this solution, intelligent parsing and element recognition of user-input natural language text (such as structural descriptions and maintenance procedures) replaces the traditional method of manually reading drawings and manuals and subjectively extracting requirements. This achieves automated understanding of requirements from the source, ensuring the accuracy of subsequent generation. Based on the parsing results, the method automatically generates or matches and lays out the 3D model, solving the problems of time-consuming and error-prone manual model import and position adjustment. Subsequently, the method intelligently configures materials and textures and generates a dynamic exploded view sequence. These two steps automate the visual presentation work that originally relied on the experience of artists and engineers, greatly improving the standardization of visual effects and generation efficiency. By automatically constructing interaction logic and step sequences, as well as generating corresponding UI interfaces and prompts, this directly corresponds to the most complex interaction logic programming and interface design stage in simulation training, achieving a key leap from "static scene" to "interactive application".
[0060] Furthermore, automatic camera view configuration and integrated script generation ensure a consistent user experience and accurate scene execution. Finally, automatic publishing and formatted output allow the generated results to be directly applied to different platforms (PC / VR).
[0061] The above solution changes the simulation scene construction mode, transforming the project development process, which is highly dependent on professional manual labor and has a lengthy process, into an efficient, standardized, and low-threshold intelligent generation process. This significantly shortens the development cycle, reduces labor costs, and improves the consistency and reusability of the scene.
[0062] In step S2, the generative 3D model generation network supports generating parametric models based on text descriptions and supports automatic conversion of model formats and platform adaptation.
[0063] Traditional methods typically require importing 3D models from external modeling software, often encountering issues such as format incompatibility, inconsistent detail levels, and inconsistent coordinate systems, necessitating extensive manual repairs. This solution, however, generates or matches models based on text, embedding parametric information for easier subsequent adjustments. Automatic format conversion eliminates the tedious manual export, import, and repair steps, ensuring perfect compatibility between the model and the simulation platform engine, further enhancing the smoothness and reliability of the entire automated process.
[0064] In step S5, the interactive logic is automatically constructed, including multiple step types such as disassembly, assembly, inspection, and maintenance. Each step is automatically configured with subtitle prompts, tool usage judgments, and error detection logic. This transforms the generated interactive logic from a simple sequence of actions into an intelligent process with complete teaching and guidance functions. The system can automatically identify operation types and match standardized prompts. The built-in tool judgment and error detection logic provide the training scenario with real-time feedback and error correction capabilities, greatly enhancing the teaching effectiveness and practicality of the generated scenario, making it directly applicable to serious training and assessment.
[0065] Step S5 also includes automatically assigning roles and tasks, configuring instruction sending and receiving logic and collaborative operation nodes based on the needs of multi-person collaborative training, which greatly reduces the threshold for generating multi-person collaborative training scenarios and enables the rapid generation of complex training scenarios for cultivating team collaboration capabilities.
[0066] In step S6, the UI interface automatically generates interface templates for four scenario types: structural cognition, principle learning, maintenance training, and assessment. These templates are adaptively selected and content is filled in, enabling the solution to not only generate general UI components during implementation but also to understand the current scenario goals, call the most suitable preset template (e.g., structural cognition emphasizes the structure tree, and assessment emphasizes timing and scoring), and automatically fill in relevant data (e.g., component names, step descriptions, and scoring criteria). This ensures that the generated application achieves a professional level in terms of user experience and functional relevance.
[0067] In step S8, the script automatically generates animation scripts, event response scripts, and physical interaction scripts, and automatically mounts them to the corresponding models or UI nodes. This solves the obstacle for non-programmer users to create complex interactive scenes and ensures that the automatically generated scenes are usable and responsive.
[0068] Also includes;
[0069] S10, Scene Optimization and Verification Feedback:
[0070] After scene generation, automatic model collision detection, animation smoothness verification, and interaction logic consistency checks are performed. Based on the verification results, parameters are automatically adjusted or users are prompted to correct their input text. This adds a "quality assurance" step to the automated process. Through automated physical detection, logic analysis, and performance verification, problems such as model interleaving, animation stuttering, and step deadlock can be discovered and attempted to be fixed, thereby improving the robustness, reliability, and user experience of the generated scene.
[0071] The scene optimization includes automatically adding colliders, rigid body components, repair parts and tool components, and setting virtual hand gripping posture and grasping logic to conform to the grasping, collision and placement rules in VR, and preset hand postures that conform to ergonomics.
[0072] In step S9, it is possible to automatically select the output format according to the target platform, including PC executable files, VR all-in-one applications, or multi-user collaborative server deployment packages.
[0073] The system can identify the release target, automatically complete platform-specific adaptation, optimization and packaging, and output a distributable application package. This avoids the need for manual repetitive conversion and testing for different platforms, greatly simplifies the delivery process, and enables the same content to quickly cover a wider range of hardware and user scenarios.
[0074] It also includes a text understanding module, a 3D generation module, a logical reasoning module, and an interface generation module. Each module transmits information and associates scene elements through a unified knowledge graph to ensure the systematicity and coordination of the entire complex generation process.
[0075] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for automatic modeling of simulation scene elements based on generative AI, characterized in that, Includes the following steps; S1, Input parsing and scene element recognition: It receives user input of structural description text, working principle description or maintenance operation process text, parses the text content through natural language processing technology, and identifies the model components, material properties, assembly relationships, operation steps and interaction logic elements required in the scene; S2, Automatic Generation and Layout of 3D Models: Based on the identified component information, a pre-trained generative 3D model generation network is invoked to automatically generate or match the corresponding 3D model from the model library, and the position, rotation and scaling parameters of the model are automatically calculated according to the assembly relationship. S3, intelligent configuration of materials and textures: Based on the component functions and material descriptions, the system automatically assigns appropriate material properties to the 3D model, including color, transparency, and reflectivity, and automatically generates or matches the corresponding texture map based on the texture generation algorithm. S4, Exploded view sequence is automatically generated: Based on the identified assembly hierarchy and structural relationships, the system automatically calculates the explosion direction, explosion distance, and explosion sequence, generates a dynamic sequence of exploded views, and supports previewing and parameter adjustment. S5, automatic construction of interaction logic and step sequence: Based on the maintenance or operation process text, it is automatically broken down into multiple operation steps, and each step is configured with trigger nodes, tools to be used, action instructions and preconditions and prerequisites to generate an interactive step sequence, and supports the automatic arrangement of linear disassembly and assembly sequences. S6, UI interface and prompts are automatically generated: The system automatically generates the corresponding user interface based on the scenario type, including a structure tree, function buttons, prompt information bar, and operation buttons, and automatically fills in operation prompts, work timing, and preparation text based on the step content. S7, Automatic Camera and Viewpoint Configuration: Based on scene layout and operation focus, multiple camera nodes are automatically generated, viewpoint parameters are configured, and camera switching logic for key steps is set. S8, Scene Integration and Automatic Script Generation: The generated models, materials, exploded views, interaction steps, UI, and camera elements are integrated into a unified simulation scene, and an execution script is automatically generated to link the logic and event responses between the various elements. S9, Scene Publishing and Format Output: The integrated scene will be automatically published in the target format, supporting export as an executable file, VR-compatible format or multi-person collaborative scene, and automatically generating resource directory and configuration file.
2. The method for automatic modeling of simulation scene elements based on generative AI according to claim 1, characterized in that, In step S2, the generative 3D model generation network supports generating parametric models based on text descriptions and supports automatic conversion of model formats and platform adaptation.
3. The method for automatic modeling of simulation scene elements based on generative AI according to claim 1, characterized in that, In step S5, the interactive logic automatically constructs multiple step types including disassembly, assembly, inspection, and maintenance, and automatically configures subtitle prompts, tool usage judgments, and error operation detection logic for each step.
4. The method for automatic modeling of simulation scene elements based on generative AI according to claim 3, characterized in that, Step S5 also includes automatically assigning roles and tasks, configuring instruction sending and receiving logic, and collaborative operation nodes based on the needs of multi-person collaborative training.
5. The method for automatic modeling of simulation scene elements based on generative AI according to claim 1, characterized in that, In step S6, the UI interface automatically generates interface templates for four scenario types: structural cognition, principle learning, maintenance training, and assessment, and adaptively selects and fills in the content.
6. The method for automatic modeling of simulation scene elements based on generative AI according to claim 1, characterized in that, In step S8, the script automatically generates animation scripts, event response scripts, and physical interaction scripts, and automatically mounts them to the corresponding model or UI node.
7. The method for automatic modeling of simulation scene elements based on generative AI according to claim 1, characterized in that, Also includes; S10, Scene Optimization and Verification Feedback: After the scene is generated, it automatically performs model collision detection, animation smoothness verification, and interaction logic consistency check, and automatically adjusts parameters or prompts the user to correct the input text based on the verification results.
8. The method for automatic modeling of simulation scene elements based on generative AI according to claim 7, characterized in that, The scene optimization includes automatically adding colliders, rigid body components, repair parts and tool components, and setting virtual hand grip posture and grasping logic.
9. The method for automatic modeling of simulation scene elements based on generative AI according to claim 1, characterized in that, In step S9, it is possible to automatically select the output format according to the target platform, including PC executable files, VR all-in-one applications, or multi-user collaborative server deployment packages.
10. The method for automatic modeling of simulation scene elements based on generative AI according to claim 1, characterized in that, It also includes a text understanding module, a 3D generation module, a logical reasoning module, and an interface generation module. Each module transmits information and associates scene elements through a unified knowledge graph.