Three-dimensional model generation method and device based on parameter adaptive conversion, electronic equipment and storage medium

Abstract

The application discloses a three-dimensional model generation method and device based on parameter adaptive conversion, electronic equipment and storage medium, the method comprises the following steps: in response to the model generation demand of the intelligent production platform, the device type of the target device to be generated is determined; according to the device type of the target device, the parameter set to be converted corresponding to the target device is determined; according to the parameter to be converted, the parameter adaptive conversion processing is carried out, the parameter to be converted is converted into the model parameter form of the three-dimensional model, and then the input data of the converted to-be-generated model is obtained; according to the input data, the parameter adjustment processing of the to-be-generated model is carried out, and the target three-dimensional model is generated. The application can improve the standardization degree of the model, avoid the low efficiency and the risk of reproduction failure caused by manual positioning and updating of multiple characteristics, quickly adapt to new requirements through parameter adjustment, greatly reduce the modification workload, promote the reuse of model data, and can be widely applied to the technical field of model design.

Description

Technical Field

[0001] This application relates to the field of model design technology, and in particular to a method, apparatus, electronic device and storage medium for generating three-dimensional models based on parameter adaptive transformation. Background Technology

[0002] In intelligent manufacturing platforms, efficiently generating 3D models of equipment is crucial for improving design efficiency. However, the current reliance on traditional 3D software for manual design has significant drawbacks: low model standardization, inconsistent modeling rules for similar parts leading to chaotic model structures and inconsistent quality, severely hindering collaborative design and data management; cumbersome and error-prone design modification processes, requiring designers to manually locate and update multiple related features for adjustments to model dimensions or parameters, a method that is inefficient and prone to model regeneration failure due to oversight; and difficulty in reusing historical models, as differences in parameter systems and modeling logic mean that calling and modifying historical models requires almost the same amount of work as redesigning, resulting in a significant waste of model data. Therefore, existing technologies are insufficient to meet the urgent needs of intelligent manufacturing platforms for automated and standardized model generation. Summary of the Invention

[0003] The main objective of this application is to propose a method, apparatus, electronic device, and storage medium for generating three-dimensional models based on parameter adaptive transformation, in order to solve at least one problem in the prior art. This application can efficiently realize the generation of three-dimensional models based on parameter adaptive transformation.

[0004] To achieve the above objectives, one aspect of this application proposes a method for generating 3D models based on parameter adaptive transformation, the method comprising:

[0005] In response to the model generation requirements of the intelligent manufacturing platform, the equipment type of the target equipment to be generated is determined;

[0006] Based on the equipment type of the target equipment, determine the set of parameters to be converted for the target equipment; wherein, the set of parameters to be converted includes any one or more of the following: equipment operating parameters, equipment selection parameters, equipment structural parameters, equipment material parameters, or equipment design parameters;

[0007] Based on the parameters to be transformed, adaptive parameter transformation processing is performed to convert the parameters to be transformed into the model parameter form of the 3D model, thereby obtaining the input data of the transformed model to be generated.

[0008] The parameters of the model to be generated are adjusted based on the input data to generate the target 3D model; the target 3D model is used to generate the design scheme of the target equipment of the intelligent production platform.

[0009] In some embodiments, in response to the model generation requirements of the intelligent manufacturing platform, the device type of the target device for which the model to be generated is determined, including at least one of the following:

[0010] In response to the parameter adjustment command issued by the user, the parameter adjustment command is identified to determine the model generation requirements of the intelligent production platform, and then the equipment type of the target equipment to be generated is determined according to the model generation requirements.

[0011] Alternatively, in response to the user's template selection instruction, the model generation requirements of the intelligent production platform are determined from the selected target parameter template, and then the equipment type of the target device to be generated is determined based on the model generation requirements.

[0012] Alternatively, in response to the operation trigger command of the intelligent production platform, based on the scenario status corresponding to the operation trigger command, switch to the corresponding target parameter template to determine the model generation requirements of the intelligent production platform, and then determine the equipment type of the target device to be generated based on the model generation requirements.

[0013] In some embodiments, in response to a parameter adjustment instruction issued by a user, the parameter adjustment instruction is identified, and the model generation requirements of the intelligent production platform are determined, including at least one of the following:

[0014] In response to the parameter adjustment command issued by the user, the type of the target device is identified from the parameter adjustment command, and the historical demand set corresponding to the type of the target device is obtained from the historical model data of the intelligent production platform. Multiple frequently used model generation requirements are obtained from the historical demand set as candidates to determine the model generation requirements of the intelligent production platform.

[0015] Alternatively, in response to a user's parameter adjustment command, the parameter adjustment command is input into a large language model for preliminary screening to obtain a preliminary adjustment plan for the target device corresponding to the current parameter adjustment command. Based on the preliminary adjustment plan, and combined with a secondary screening command issued by the user, the model generation requirements of the intelligent production platform are then determined.

[0016] Alternatively, in response to a user's parameter adjustment command, a parameter design interface can be displayed, the adjustment information input by the user into the parameter design interface can be received, and the model generation requirements of the intelligent production platform can be determined based on the personalized adjustment information input by the user.

[0017] In some embodiments, the target equipment includes, but is not limited to, a turntable, a vibratory feeder, a double-speed chain, a chassis frame, a drag chain, a roller, or a conveyor.

[0018] In some embodiments, when the target device is a turntable or a vibratory feeder, the set of parameters to be converted corresponding to the target device is determined according to the device type of the target device, including:

[0019] Based on the type of equipment where the target device is a turntable, determine the turntable parameter set, the turntable drive selection parameter set, and the turntable motor selection parameter set; based on the turntable parameter set, the turntable drive selection parameter set, and the turntable motor selection parameter set, construct the turntable's set of parameters to be converted;

[0020] Alternatively, based on the type of equipment where the target device is a vibratory feeder, determine the vibratory feeder parameter set, the vibratory feeder discharge channel parameters, the vibratory feeder direct vibration selection parameter set, the vibratory feeder screw selection parameter set, and the vibratory feeder through-beam photoelectric selection parameter set; based on the vibratory feeder parameter set, discharge channel parameters, direct vibration selection parameter set, screw selection parameter set, and through-beam photoelectric selection parameter set, construct the vibratory feeder's parameter set to be converted.

[0021] In some embodiments, when the target device is a speed-up chain, the set of parameters to be converted corresponding to the target device is determined according to the device type of the target device, including:

[0022] Based on the type of equipment where the target equipment is a double-speed chain, determine the double-speed chain parameter set, double-speed chain selection parameter set, bearing selection parameter set, and double-speed chain transmission selection set;

[0023] Calculate the set of chain parameters for the speed-multiplying chain based on the set of parameters for the speed-multiplying chain.

[0024] Based on the set of parameters for the double-speed chain, the set of parameters for selecting double-speed chains, the set of parameters for selecting bearings, the set of parameters for selecting transmissions of double-speed chains, and the set of parameters for chain assembly, a set of parameters to be converted for the double-speed chain is constructed.

[0025] In some embodiments, when the target device is a chassis rack, cable chain, or conveyor rack, the set of parameters to be converted corresponding to the target device is determined according to the device type of the target device, including:

[0026] Based on the equipment type where the target equipment is a chassis or rack, determine the chassis / rack parameter set; based on the chassis / rack parameter set, determine the square tube design parameters of the chassis / rack; based on the square tube design parameters, determine the selection parameter set for purchased components; based on the chassis / rack parameter set, square tube design parameters, and purchased component selection parameter set, construct the set of parameters to be converted for the chassis / rack.

[0027] Alternatively, based on the type of equipment for which the target device is a cable chain, determine the cable chain design parameter set, cable design parameter set, cable chain selection parameter set, fixed end fixing plate parameter set, and moving end fixing plate design set; based on the cable chain design parameter set, cable design parameter set, cable chain selection parameter set, fixed end fixing plate parameter set, and moving end fixing plate design set, construct the cable chain's parameter set to be converted;

[0028] Alternatively, based on the equipment type where the target equipment is a conveyor frame, determine the set of conveyor frame parameters; calculate the square tube specifications of the conveyor frame based on the set of conveyor frame parameters; and construct the set of parameters to be converted for the conveyor frame based on the set of conveyor frame parameters and the square tube specifications.

[0029] In this process, after the set of parameters to be converted for the conveyor frame is used to generate the target 3D model, the design drawings are modified based on the parameterization information of the target 3D model, thereby refreshing the design drawings of the conveyor frame and obtaining the latest 3D design drawings of the conveyor frame.

[0030] In some embodiments, when the target device is a roller, the set of parameters to be converted corresponding to the target device is determined according to the device type of the target device, including:

[0031] Based on the target equipment type of roller, determine the roller type parameter set, roller material parameter set, and roller default parameter set;

[0032] Based on the set of parameters for roller type, roller material, and default parameters for roller, a set of parameters to be converted for roller is constructed.

[0033] To achieve the above objectives, another aspect of this application proposes a three-dimensional model generation apparatus based on parameter adaptive transformation, the apparatus comprising:

[0034] The first module is used to respond to the model generation requirements of the intelligent manufacturing platform and determine the equipment type of the target equipment to be generated in the model.

[0035] The second module is used to determine the set of parameters to be converted for the target equipment based on the equipment type of the target equipment; wherein the set of parameters to be converted includes any one or more of the following: equipment operating parameters, equipment selection parameters, equipment structural parameters, equipment material parameters, or equipment design parameters.

[0036] The third module is used to perform adaptive parameter transformation processing based on the parameters to be transformed, converting the parameters to be transformed into the model parameter form of the three-dimensional model, and thus obtaining the input data of the transformed model to be generated.

[0037] The fourth module is used to adjust the parameters of the model to be generated based on the input data, and generate the target 3D model; the target 3D model is used to generate the design scheme of the target equipment of the intelligent production platform.

[0038] To achieve the above objectives, another aspect of the embodiments of this application proposes an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0039] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0040] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0041] The embodiments of this application include at least the following beneficial effects: This application provides a method, apparatus, electronic device, and storage medium for generating three-dimensional models based on parameter adaptive transformation. This solution, in response to the model generation requirements of an intelligent production platform, determines the equipment type of the target equipment to be generated; based on the equipment type of the target equipment, it determines the set of parameters to be transformed corresponding to the target equipment; wherein the set of parameters to be transformed includes any one or more of equipment operating parameters, equipment selection parameters, equipment structural parameters, equipment material parameters, or equipment design parameters; based on the parameters to be transformed, it performs parameter adaptive transformation processing to convert the parameters to be transformed into the model parameter form of a three-dimensional model, thereby obtaining the input data of the transformed model to be generated; based on the input data, it performs parameter tuning processing on the model to be generated to generate the target three-dimensional model; the target three-dimensional model is used to generate the design scheme of the target equipment of the intelligent production platform. This invention determines the set of parameters to be converted based on the device type and performs adaptive parameter conversion processing, ensuring unified modeling rules for similar devices and generating highly consistent models. This improves model standardization and facilitates collaborative design and data management. Furthermore, by automatically converting the parameters to be converted into model parameters and performing parameter tuning based on input data, this invention automates model generation. Users only need to adjust the parameters, and the system automatically updates related features, avoiding the inefficiency and risk of regeneration failure caused by manually locating and updating multiple features. At the same time, since the parameter system and modeling logic are unified through adaptive conversion, historical models can quickly adapt to new requirements through parameter adjustments, greatly reducing the workload of modifications, promoting the reuse of model data, and avoiding waste. Attached Figure Description

[0042] Figure 1 This is a flowchart of the 3D model generation method based on parameter adaptive transformation provided in the embodiments of this application;

[0043] Figure 2 This is a schematic diagram illustrating a flow example of the turntable design provided in an embodiment of this application;

[0044] Figure 3 This is a schematic diagram illustrating a process example of the vibratory feeder design provided in an embodiment of this application;

[0045] Figure 4This is a schematic diagram illustrating a process example of the speed-multiplying chain design provided in an embodiment of this application;

[0046] Figure 5 This is a schematic diagram illustrating a process example of chassis and rack design provided in an embodiment of this application;

[0047] Figure 6 This is a schematic diagram illustrating a process example of the cable chain design provided in an embodiment of this application;

[0048] Figure 7 This is a schematic diagram illustrating a process example of the roller design provided in an embodiment of this application;

[0049] Figure 8 This is a schematic diagram illustrating a process example of the conveyor frame design provided in an embodiment of this application;

[0050] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0052] It is understood that the terms "first," "second," etc., used in this application may be used to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words "if" or "when" as used herein may be interpreted as "when," "in response to a determination," or "in the event of a determination."

[0053] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0054] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this application is for the purpose of describing embodiments of this application only and is not intended to be limiting of this application.

[0055] In related technologies, the low standardization of models and the lack of uniform modeling rules for similar parts lead to chaotic model structures and inconsistent quality, severely hindering collaborative design and data management. Design modification processes are cumbersome and error-prone; adjustments to model dimensions or parameters require designers to manually locate and update multiple related features, a method that is inefficient and prone to model regeneration failure due to oversight. Historical models are difficult to reuse; due to differences in parameter systems and modeling logic, the workload required to call and modify historical models is almost equivalent to redesign, resulting in a significant waste of model data. Therefore, existing technologies struggle to meet the urgent needs of intelligent manufacturing platforms for automated and standardized model generation.

[0056] In view of this, this invention provides a method, apparatus, electronic device, and storage medium for generating a 3D model based on adaptive parameter transformation. This solution, in response to the model generation requirements of an intelligent manufacturing platform, determines the equipment type of the target device to be generated; based on the equipment type, it determines the set of parameters to be transformed corresponding to the target device; wherein the set of parameters to be transformed includes any one or more of equipment operating parameters, equipment selection parameters, equipment structural parameters, equipment material parameters, or equipment design parameters; it performs adaptive parameter transformation processing based on the parameters to be transformed, converting them into model parameter form of a 3D model, thereby obtaining the input data of the transformed model to be generated; it performs parameter tuning processing on the model to be generated based on the input data, generating the target 3D model; the target 3D model is used to generate the design scheme of the target equipment of the intelligent manufacturing platform. This invention determines the set of parameters to be converted based on the device type and performs adaptive parameter conversion processing, ensuring unified modeling rules for similar devices and generating highly consistent models. This improves model standardization and facilitates collaborative design and data management. Furthermore, by automatically converting the parameters to be converted into model parameters and performing parameter tuning based on input data, this invention automates model generation. Users only need to adjust the parameters, and the system automatically updates related features, avoiding the inefficiency and risk of regeneration failure caused by manually locating and updating multiple features. At the same time, since the parameter system and modeling logic are unified through adaptive conversion, historical models can quickly adapt to new requirements through parameter adjustments, greatly reducing the workload of modifications, promoting the reuse of model data, and avoiding waste.

[0057] The 3D model generation method based on parameter adaptive transformation provided in this application relates to the field of model design technology. This method can be applied to terminals, servers, or software running on either a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the 3D model generation method based on parameter adaptive transformation, but is not limited to the above forms.

[0058] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0059] like Figure 1 As shown, Figure 1 This is an optional flowchart of the 3D model generation method based on parameter adaptive transformation provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S100 to S400.

[0060] S100, In response to the model generation requirements of the intelligent production platform, determine the equipment type of the target equipment to be generated;

[0061] It should be noted that in some embodiments, step S100 may include at least one of the following:

[0062] In response to the parameter adjustment command issued by the user, the parameter adjustment command is identified to determine the model generation requirements of the intelligent production platform, and then the equipment type of the target equipment to be generated is determined according to the model generation requirements.

[0063] For example, in some specific implementations, taking a speed-up chain device as an example, the above operation logic can be implemented as follows: A user views an existing speed-up chain 3D model and feels that the running speed is too slow, so they change the "running speed" from 0.5m / s to 0.8m / s in the model parameter table and click "Apply". The system responds to this parameter adjustment command issued by the user, identifies that the command is for the "speed-up chain" device, and then determines that the platform's current requirement is "update the speed-up chain model", and determines that the target device type is "speed-up chain".

[0064] Alternatively, in response to the user's template selection instruction, the model generation requirements of the intelligent production platform are determined from the selected target parameter template, and then the equipment type of the target device to be generated is determined based on the model generation requirements.

[0065] For example, in some specific implementations, taking a turntable device as an example, the above operation logic can be implemented as follows: The user needs to design a new turntable device. First, the user directly selects a target parameter template named "800mm diameter rotary detection platform" in the system's template library; then, the system responds to the user's template selection instruction, directly reads the preset model generation requirements (including size, motor model, etc.) from the template, and determines the target device type as "turntable".

[0066] Alternatively, in response to the operation trigger command of the intelligent production platform, based on the scenario status corresponding to the operation trigger command, switch to the corresponding target parameter template to determine the model generation requirements of the intelligent production platform, and then determine the equipment type of the target device to be generated based on the model generation requirements.

[0067] For example, in some specific implementations, taking a vibratory feeder as an example, the above operation logic can be specifically implemented as follows: The MES (Manufacturing Execution System) of the intelligent production platform triggers an instruction indicating that a vibratory feeder needs to be added to workstation A03 on the production line for material loading. The platform responds to the operation trigger instruction, and based on the scenario of "workstation A03 - material loading", automatically switches to the target parameter template of "workstation A03 vibratory feeder" bound to it, thereby determining the model generation requirements and the equipment type "vibratory feeder", and automatically starts the model generation process.

[0068] Specifically, embodiments of the present invention can determine model generation requirements through multiple methods (parameter adjustment commands, template selection commands, and run trigger commands), adapting to different user scenarios, reducing manual intervention, and thus improving response speed. Furthermore, embodiments of the present invention, by switching to the target parameter template through template selection commands or run trigger commands, can ensure the predefined standards of model generation, further improving the standardization of the model and reducing model confusion caused by human factors. Simultaneously, users can quickly trigger model modifications through parameter adjustment commands, enabling the system to automatically identify requirements, reducing the tedium of modifications and improving operational efficiency.

[0069] It should be noted that, in some embodiments, in response to a parameter adjustment command issued by the user, the parameter adjustment command is identified to determine the model generation requirements of the intelligent production platform, including at least one of the following:

[0070] In response to the parameter adjustment command issued by the user, the type of the target device is identified from the parameter adjustment command, and the historical demand set corresponding to the type of the target device is obtained from the historical model data of the intelligent production platform. Multiple frequently used model generation requirements are obtained from the historical demand set as candidates to determine the model generation requirements of the intelligent production platform.

[0071] For example, in some specific implementations, the above-mentioned operational logic can be implemented as follows: For instance, a user inputs a vague command: "Change the discharge port of the vibratory feeder." First, the target device is identified as a "vibratory feeder" from the command; then, all modification records related to "vibratory feeder discharge port" are matched from the platform's historical model data to form a historical requirement set. From the historical requirement set, it is found that, for example, "lengthening the discharge channel" and "adding a material full sensor" are the two most frequently used requirements in history. Therefore, the system recommends these two options as candidates to the user. After the user selects "lengthen the discharge channel," the system determines the precise model generation requirement for this time.

[0072] Alternatively, in response to a user's parameter adjustment command, the parameter adjustment command is input into a large language model for preliminary screening to obtain a preliminary adjustment plan for the target device corresponding to the current parameter adjustment command. Based on the preliminary adjustment plan, and combined with a secondary screening command issued by the user, the model generation requirements of the intelligent production platform are then determined.

[0073] For example, in some specific implementations, the above-mentioned operational logic can be implemented as follows: For instance, a user inputs a natural language description: "I need a vibratory feeder that can handle M3 screws, with stable output and a low speed to save costs," and inputs this parameter adjustment instruction into the large language model; then, after preliminary screening, the large language model outputs a preliminary adjustment scheme: "It is recommended to use a vortex vibratory feeder, made of stainless steel, with a buffer design added to the output track, and a motor power of 0.1kW"; finally, this scheme is displayed to the user, who can then determine the specific model generation requirements by using a secondary screening instruction (such as checking "confirm vortex type" but changing the material to "engineering plastic").

[0074] Alternatively, in response to a user's parameter adjustment command, a parameter design interface can be displayed, the adjustment information input by the user into the parameter design interface can be received, and the model generation requirements of the intelligent production platform can be determined based on the personalized adjustment information input by the user.

[0075] For example, in some specific implementations, the above-mentioned operational logic can be implemented as follows: For instance, when a user issues an adjustment command to a chassis / rack model, in response to the adjustment command, a pop-up window displays a structured parameter design interface, which lists parameters such as length, width, height, and plate thickness. Then, the user can input adjustment information into this interface, for example, changing the height from 1800mm to 2000mm, and additionally inputting "Add an access door to the side" in the "Personalized Adjustment Information" field. Based on this information, the system determines the complete model generation requirements, including standard size modifications and the addition of special structures.

[0076] Specifically, embodiments of the present invention, through historical data matching or large language model filtering, can intelligently parse user commands and quickly determine requirements from historical experience or AI assistance, effectively reducing user workload and lowering error rates. Furthermore, embodiments of the present invention can receive user input through a parameter design interface, meeting customized needs while maintaining a unified parameter system, thus achieving a balance between standardization and flexibility. Moreover, embodiments of the present invention, by combining secondary filtering or personalized adjustments, enable users to more precisely control the model generation process, thereby improving the method's practicality and user satisfaction.

[0077] S200. Determine the set of parameters to be converted for the target device based on the device type of the target device;

[0078] The set of parameters to be converted includes any one or more of the following: equipment operating parameters, equipment selection parameters, equipment structural parameters, equipment material parameters, or equipment design parameters.

[0079] It should be noted that the target equipment includes, but is not limited to, turntables, vibratory feeders, double-speed chains, chassis frames, drag chains, rollers, or conveyors.

[0080] Specifically, the embodiments of the present invention list common equipment types (such as turntables, vibratory feeders, etc.) to ensure the widespread application of the method in actual production, thereby improving its practicality and coverage. Furthermore, the embodiments of the present invention can define parameter sets for different equipment types, making the modeling rules more equipment-specific, further enhancing the standardization of the model, and avoiding model mismatches caused by general rules.

[0081] It should be noted that in some embodiments, when the target device is a turntable or a vibratory feeder, determining the set of parameters to be converted corresponding to the target device based on the device type may include the following steps: determining the turntable parameter set, turntable drive selection parameter set, and turntable motor selection parameter set based on the device type of the target device being a turntable; constructing the set of parameters to be converted for the turntable based on the turntable parameter set, turntable drive selection parameter set, and turntable motor selection parameter set; or, determining the vibratory feeder parameter set, vibratory feeder discharge channel parameters, vibratory feeder direct vibration selection parameter set, vibratory feeder screw selection parameter set, and vibratory feeder through-beam photoelectric selection parameter set based on the device type of the target device being a vibratory feeder; constructing the set of parameters to be converted for the vibratory feeder based on the vibratory feeder parameter set, discharge channel parameters, direct vibration selection parameter set, screw selection parameter set, and through-beam photoelectric selection parameter set.

[0082] Specifically, this invention, through the construction of detailed parameter sets (such as the turntable parameter set and transmission selection for a turntable, and the vibratory feeder parameter set and discharge channel parameters for a vibratory feeder), covers multiple core aspects of equipment design, further improving model accuracy and reliability by avoiding parameter omissions during manual operation. Furthermore, the automated construction of parameter sets reduces manual input and calculation, accelerating model generation, while standardized parameter selection ensures model consistency and reusability.

[0083] For example, in some specific implementations, when the target device is a turntable, determining the set of parameters to be converted for the target device based on its device type can be achieved as follows: Step 1: First, after determining the device type as "turntable," the following parameter sets are determined: Turntable parameter set: Basic dimensions and functional requirements input by the user, such as: turntable diameter = 800mm, load capacity = 50kg, rotation speed = 10rpm; Turntable transmission selection parameter set: Based on the above parameters, the system determines the required parameters for the transmission components, such as: reducer reduction ratio requirement = 50:1, output torque requirement ≥ 100N·m; Turntable motor selection parameter set: Based on speed and torque, the drive motor parameters are determined, such as: motor power ≥ 0.5kW, motor type = servo motor, rated speed = 3000rpm. Step 2: The parameters of each subset in the preceding steps are summarized to construct a complete and structured set of parameters to be converted for the turntable. This set contains all information from structural dimensions to the selection of core purchased components, providing a complete data foundation for subsequent adaptive parameter conversion and model generation.

[0084] For example, in some specific implementations, when the target device is a vibratory feeder, determining the set of parameters to be converted for the target device according to its device type can be achieved as follows: Step 1: For example, if a user needs to design a vibratory feeding device for screening M3 screws, and thus determines the device type as a "vibratory feeder," the following parameter set can be determined: Vibratory feeder parameter set: disc diameter = 300mm, incoming material type = M3*10 pan head screws; discharge channel parameters: for example, discharge height = 100mm, discharge direction = clockwise; linear vibration selection parameter set: used for linear feeding, for example: linear vibration length = 500mm, feeding frequency = 50Hz; screw selection parameter set: used to adjust the feeding amount, for example: screw type = M8*1.25, screw stroke = 50mm; photoelectric photoelectric selection parameter set: used to detect material fullness, for example: detection distance = 10mm, photoelectric model = E3Z-T61. Step 2: Integrate the parameters from each dimension of the aforementioned steps to construct the set of parameters to be converted for the vibratory feeder. This set ensures that all components, from the main disc to the discharge, linear feeding, flow control, and detection feedback, can be considered uniformly and driven parametrically.

[0085] It should be noted that in some embodiments, when the target device is a double-speed chain, determining the set of parameters to be converted corresponding to the target device based on the device type may include the following steps: determining the double-speed chain parameter set, double-speed chain selection parameter set, bearing selection parameter set, and double-speed chain transmission selection set based on the double-speed chain device type; calculating the double-speed chain assembly parameter set based on the double-speed chain parameter set; and constructing the set of parameters to be converted for the double-speed chain based on the double-speed chain parameter set, double-speed chain selection parameter set, bearing selection parameter set, double-speed chain transmission selection set, and assembly parameter set.

[0086] Specifically, this invention, through the calculation of the chain parameter set, enables the parameter set to be automatically adjusted according to specific needs, improving the model's adaptability to actual working conditions while reducing manual calculation errors. By configuring parameters across multiple dimensions, such as speed chain selection and bearing selection, the integrity of the model design is ensured, preventing model failures due to improper selection and thus improving model quality. Furthermore, this invention reduces design complexity and improves generation efficiency through automated parameter calculation and integration, making it particularly suitable for the rapid generation of complex chain structures.

[0087] For example, in some specific implementations, when the target equipment is a double-speed chain, determining the set of parameters to be converted for the target equipment according to its equipment type can be achieved as follows: Step 1: For example, if a user needs to design a 10-meter-long double-speed chain for an assembly line, after determining the equipment type, the parameters are determined as follows: Double-speed chain parameter set: total length = 10000mm, chain plate width = 150mm, design load = 30kg / m; Double-speed chain selection parameter set: selected according to load and speed, for example: chain model = 084-3WT; Bearing selection parameter set: bearing parameters for supporting rollers, for example: bearing inner diameter = 12mm, bearing type = deep groove ball bearing; Double-speed chain transmission selection set: drive component parameters, for example: motor power requirement = 1.5kW, reducer model selected = XWED1.5-63. Step 2: Based on "total length = 10000mm" and "chain model = 084-3WT" (its single pitch is P = 38.1mm), the pre-integrated calculation logic can be invoked to automatically calculate the required set of chain assembly parameters, including: total number of links = 10000 / 38.1 ≈ 263 links, the number of chain segments to be connected, etc. Automated calculation avoids errors from manual calculation. Step 3: Integrate all user-input parameters, selection parameters, and automatically calculated chain assembly parameters to form a highly complete set of parameters for the speed-boosting chain to be converted. This set ensures that the generated model is accurate and manufacturable in all aspects, including length, number of links, and driving capability.

[0088] It should be noted that in some embodiments, when the target device is a chassis rack, cable chain, or conveyor rack, determining the set of parameters to be converted corresponding to the target device based on the device type may include the following steps: determining the chassis rack parameter set based on the target device type (chassis rack); determining the square tube design parameters of the chassis rack based on the chassis rack parameter set; determining the set of selected external components selection parameters based on the square tube design parameters; and constructing the set of parameters to be converted for the chassis rack based on the chassis rack parameter set, the square tube design parameters, and the selected external components selection parameter set; or, determining the cable chain design parameter set, cable design parameter set, cable chain selection parameter set, fixed end fixing plate parameter set, and moving... The design set of the end fixing plate is used; based on the cable chain design parameter set, cable design parameter set, cable chain selection parameter set, fixed end fixing plate parameter set, and mobile end fixing plate design set, the parameter set to be converted for the cable chain is constructed; or, based on the equipment type of the target equipment being a conveyor frame, the conveyor frame parameter set is determined; the square tube specification parameters of the conveyor frame are calculated based on the conveyor frame parameter set; the parameter set to be converted for the conveyor frame is constructed based on the conveyor frame parameter set and the square tube specification parameters; wherein, after the parameter set to be converted for the conveyor frame is used to generate the target 3D model, the design drawings are also modified based on the model parameterization information of the target 3D model, thereby refreshing the design drawings of the conveyor frame and obtaining the latest 3D design drawings of the conveyor frame.

[0089] Specifically, this invention reduces design errors while ensuring the rationality of the model structure and component compatibility by constructing a detailed parameter set that includes parameters such as square tube design and purchased component selection. Furthermore, for the conveyor frame, this invention ensures consistency between the 3D model and 2D drawings by associating and changing design drawings, avoiding the tediousness and errors caused by manual updates, and further improving design collaboration efficiency. In particular, this invention supports the selection of purchased components and the design of fixed ends through the standardization of the parameter set, making the model easier to modify and reuse, and reducing the difficulty of reusing historical models.

[0090] For example, in some specific implementations, when the target device is a chassis or rack, determining the set of parameters to be converted for the target device based on its type can be implemented as follows: Step 1: For example, if a user needs to design a control cabinet rack, after determining the device type, the parameters obtained include: a set of chassis / rack parameters, such as: length = 600mm, width = 800mm, height = 2000mm; Step 2: Based on the rack dimensions and default load requirements, automatically determine or match the design parameters of the main frame's square tubes from the rule base, such as: main column square tube specifications = 40x40x2.0mm; Step 3: Based on the square tube specifications, the system determines the standard parts required for connection, i.e., the set of parameters for selected external parts, such as: corner bracket model = JM-40, bolt specifications = M8x20; Step 4: Integrate the overall dimensions, main structural profile specifications, and connection standard part information to construct the set of parameters to be converted for the chassis / rack, ensuring the integrity of the model from the overall structure to the details.

[0091] For example, in some specific implementations, when the target device is a cable chain, determining the set of parameters to be converted for the target device according to its type can be implemented as follows: Step 1: For example, if a user needs to design an energy supply cable chain for a gantry crane, after determining the type, multiple subsets are determined in parallel, such as: Cable chain design parameter set: stroke = 5m, bending radius = 75mm; Cable design parameter set: number of cables = 5, total cable diameter = 30mm; Cable chain selection parameter set: based on the bending radius and cable diameter, select cable chain model = TRV-75; Fixed end fixing plate parameter set: mounting hole spacing = 100mm; Moving end fixing plate design set: connecting plate thickness = 10mm. Step 2: Summarize all parameters related to the cable chain body, internal cables, and fixing methods at both ends to construct the set of parameters to be converted for the cable chain. This parameter set enables subsequent model generation to generate a complete assembly model containing the cable chain, cables, and installation structure in one go.

[0092] For example, in some specific implementations, when the target device is a conveyor frame, determining the set of parameters to be converted for the target device according to its type can be implemented as follows: Step 1: Obtain the user-input conveyor frame parameter set, for example: length L=6000mm, width W=800mm, height H=1000mm. Step 2: Based on the length, width, and preset load conditions, automatically calculate the square tube specifications required to ensure structural strength using built-in mechanical calculation rules, for example: main beam square tube specification = 60x60x3.0mm, support leg square tube specification = 40x40x2.5mm. Step 3: Combine the user-input overall dimensions with the automatically calculated profile specifications to form the set of parameters to be converted for the conveyor frame.

[0093] In some optional implementations, when the user modifies the conveyor frame length and the model is updated, the system can also automatically trigger an update of the engineering drawing. Specifically, the dimensions in the model (e.g., L=6000mm) and the length of the square tubes in the bill of materials will be automatically updated accordingly. The user only needs to "refresh" with one click to obtain a latest 2D design drawing of the conveyor frame that is completely consistent with the 3D model. By synchronously updating the model and the drawing, errors caused by inconsistencies between the two can be effectively prevented.

[0094] It should be noted that in some embodiments, when the target device is a roller, determining the set of parameters to be converted corresponding to the target device according to the device type of the target device may include the following steps: determining the roller type parameter set, roller material parameter set, and roller default parameter set according to the device type of the target device (roller); and constructing the set of parameters to be converted for the roller based on the roller type parameter set, roller material parameter set, and roller default parameter set.

[0095] Specifically, this embodiment of the invention constructs a parameter set by using roller type parameters, material parameters, and default parameters, covering the basic elements of roller design, enabling rapid and accurate model generation. Furthermore, the use of default parameters reduces user input requirements, while adapting to different scenarios through type and material parameters improves the model's applicability and facilitates rapid adjustment and reuse of historical models. In particular, the comprehensiveness of the parameter set ensures the rationality of the roller model in terms of function and structure, avoiding model defects caused by missing parameters.

[0096] For example, in some specific implementations, when the target device is a guide roller, determining the set of parameters to be converted for the target device according to its device type can be implemented as follows: Step 1: For example, if a user needs to add a guide roller to the conveyor line, after determining the device type as "guide roller", the following parameters are determined: Guide roller type parameter set: The user selects the roller type, for example: Roller type = flat roller, roller length = 1000mm, roller diameter = 50mm; Roller material parameter set: The user selects or the system recommends based on the application scenario, for example: Surface material = galvanized steel; Default guide roller parameter set: System-built-in general parameters that do not require frequent user modification, for example: Bearing housing standard model = UCP204, shaft end chamfer = C1. Step 2: The user-defined type and material are integrated with the system's default standard parameters to quickly construct a complete and standardized set of parameters to be converted for the guide roller. The aforementioned steps can greatly simplify the design process of standard parts, thereby enabling rapid calling and modification.

[0097] S300. Perform adaptive parameter transformation processing based on the parameters to be transformed, converting the parameters to be transformed into the model parameter form of the three-dimensional model, thereby obtaining the input data of the transformed model to be generated.

[0098] For example, in some specific embodiments, step S300 can be implemented as follows: For instance, the system receives the user input of "conveyor length = 5000mm" and "maximum load = 200kg". The following operations are performed through parameter adaptive conversion processing: Based on "maximum load = 200kg", and combined with built-in mechanical calculation rules, the system automatically calculates the required drive motor power and roller shaft diameter, etc., which were originally required to be calculated manually by engineers; then, all parameters (including user-input parameters and system-derived parameters, and may also include default parameters that may be involved in the default base model [e.g., component parameters of certain default parts, such as dimensions, weight, and efficiency values]) are mapped and converted into model parameter forms that can be recognized by 3D modeling software (such as SolidWorks). For example, "conveyor length" is mapped to a drive dimension D1@Sketch1 in the sketch, and its value is set to 5000mm; finally, a structured input data file that can be read by the modeling software is generated.

[0099] Specifically, the embodiments of the present invention can realize the transformation and translation from "business language" to "modeling language", which lowers the technical threshold; and by automatically calculating derived parameters, it ensures the accurate embedding and consistency of engineering logic; at the same time, by building standardized parameter interfaces, it lays the foundation for data unification and management.

[0100] It should be noted that the core technology of this invention lies in the process logic of parameter transformation for model generation. The specific calculation rules can be adaptively adjusted according to actual needs, and this invention does not impose any specific limitations.

[0101] S400: Based on the input data, perform parameter tuning on the model to be generated to generate the target 3D model;

[0102] Among them, the target 3D model is used to generate the design scheme of the target equipment of the intelligent production platform;

[0103] For example, in some specific implementations, step S400 can be implemented as follows: First, the system imports the input data file into a pre-set, parameterized basic 3D model of a "conveyor"; then, the modeling software automatically drives the size update and configuration adjustment of all related features (i.e., parameter tuning) based on the input data, and finally generates a 3D model that conforms to all specified parameters. This model can be used for subsequent possible scheme review, interference checking, and manufacturing.

[0104] Specifically, the embodiments of the present invention can achieve "one-click" full-association update of the model, which greatly improves design efficiency; in addition, the embodiments of the present invention can avoid model regeneration failure caused by manual intervention, ensuring the robustness of the generation process; furthermore, through the data flow involved in model design, the traceability and repeatability of model versions can be ensured, which facilitates design iteration and optimization; finally, by adaptively transforming and generating a structured target model, a high-quality data source can be provided for downstream applications.

[0105] To explain in detail the principles of the technical solution of this application, the overall process of this application will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principles of this application and should not be regarded as a limitation of this application.

[0106] First, it should be noted that existing technologies have the following drawbacks: 1. Low standardization: Multiple independent designs for similar parts result in chaotic modeling rules; 2. In traditional design processes, each dimensional modification requires manual model updates, which is time-consuming and prone to errors; 3. Low reusability: Historical model drawings cannot be reused, and the process of calling them up is no less than redesigning, resulting in low usability.

[0107] Therefore, embodiments of the present invention automatically create and adjust 3D models by using user-defined parameters, rules, and algorithms, achieving intelligent driving and efficient iteration of design variables, and improving the efficiency of designers in modular designs for turntables, vibratory feeders, double-speed chains, chassis frames, cable chains, rollers, conveyor frames, etc. Specifically:

[0108] For the parametric design of the turntable model, different modules are selected, and finally integrated to generate a standard turntable model, such as... Figure 2 The diagram shows an example of the turntable design process: Turntable design start → Turntable parameters → Transmission selection → Motor selection → Parameter input → Parameter calculation → Model generation → Turntable design end. For example, it can be implemented as follows:

[0109] Turntable parameter input:

[0110] Divider: Brand, Model;

[0111] DD motor: brand, model, and installation method;

[0112] Motors & Gearboxes: Brand, Model, Power, Installation Method;

[0113] Sensor: Brand, Model, Type;

[0114] Other parameters: N equal parts for positioning, cam rotation angle θ, rotation time t1 per part, positioning time t2 per part, effective radius R of the sliding support surface at the bottom of the turntable, turntable diameter D, diameter D1 from the fixture to the center of the turntable, turntable mass M, mass of a single fixture M1, mass of a single fixture M2, default parameters (Am [acceleration coefficient, determined by the cam curve type, provided by the corresponding component manufacturer], Qm [speed coefficient], fc [safety factor], π), mechanical efficiency k of the pre-selected reducer, rated speed I of the input pre-selected motor;

[0115] Parameter output: reference value n1 for the input shaft speed of the cam divider, dynamic torque Te of the cam divider, torque Tc of the input shaft of the cam divider, motor power P, speed ratio i of the motor reducer;

[0116] Output data: 3D model of the turntable.

[0117] It should be noted that the parameters of the above turntable model design are presented as examples and should not be regarded as limitations on the present invention. Specifically, based on the parameter input and other parameters, the parameter output can be obtained by calling the preset calculation rules. The relevant calculation logic and the confirmation of parameter types and details can be set and adjusted according to actual needs, so they will not be elaborated on.

[0118] For the parametric design of vibratory feeder models, different modules are selected, and finally integrated to generate a standard vibratory feeder model; such as Figure 3 The diagram shows an example of the vibratory feeder design process: Vibratory feeder design start → Vibratory feeder parameters → Discharge channel → Straight vibration selection → Screw selection → Through-beam photoelectric selection → Setting flexibility → Model generation → Vibratory feeder design end. For example, it can be implemented as follows:

[0119] Vibratory feeder parameter input:

[0120] Vibratory feeder: Brand, discharge direction;

[0121] Direct vibration: Brand, model, top plate diameter of vibratory feeder, bottom plate diameter of vibratory feeder, height of vibratory feeder;

[0122] Through-beam photoelectric: Brand, Model, Type;

[0123] Other: discharge port height, material channel length, material channel width;

[0124] Output data: 3D model of the vibratory feeder.

[0125] For the parameterized design of the speed-multiplying chain model, different modules are selected, and finally integrated to generate a standard speed-multiplying chain model; such as Figure 4The diagram shows an example of the design process for a double-speed chain: Double-speed chain design start → Double-speed chain parameters → Chain assembly calculation → Double-speed chain selection → Bearing selection → Transmission selection → Parameter calculation → Model generation → Double-speed chain design end. For example, it can be implemented as follows:

[0126] Speed-up chain parameter input:

[0127] Speed-up chain: load capacity, length, width, brand;

[0128] Motor & Gearbox: Motor brand, gearbox reduction ratio, installation method;

[0129] Bearings: Brand;

[0130] Default parameters: friction coefficient fa between the conveyed object and the chain when there is accumulation, friction coefficient fc between the chain and the slide rail when there is accumulation, friction coefficient fr between the chain and the slide rail when there is accumulation, gravitational acceleration G, and transmission efficiency η of the drive device;

[0131] Other parameters: length of the conveyor section L1, length of the stack L2, weight of the conveyed item including the pallet of the conveyor section Hw, weight of the conveyed item including the pallet of the stacked section Aw, chain weight Cw, speed coefficient K1, load coefficient of the conveyed item K2, chain speed V, motor & reducer speed n.

[0132] Parameter output: maximum tension T acting on the chain, allowable tension F of each chain, required power P, and required torque Ta of the motor and reducer;

[0133] Output data: Speed-up chain 3D model.

[0134] For parametric design of chassis and rack models, different modules are selected, and finally integrated to generate a standard chassis and rack model; such as Figure 5 The diagram shows an example of the chassis / rack design process: Chassis / Rack Design Start → Chassis / Rack Parameters → Square Tube Design → External Component Selection → Chassis Generation → Chassis / Rack Design End. For example, it can be implemented as follows:

[0135] Chassis and rack parameter input:

[0136] Chassis design: total length, total width, total height, sheet metal thickness, main board thickness, main board material, whether there is a top chassis, height of the top chassis, height of the bottom chassis, whether chamfering is required;

[0137] Square tube design: specifications of the lower frame square tube, thickness of the lower frame square tube, and insertion point of the square tube (selected through 3D model);

[0138] Selection of outsourced components: Brand and model of foot cups, brand and model of casters, brand and model of latches, brand and model of magnets, brand and model of door catch switches, brand and model of door handles;

[0139] Door design: door type, transparent panel material, single door size, door thickness;

[0140] Electrical control box design: Select surface generation (select via 3D model);

[0141] Purchased component generation: Generate components by selecting the model and placement method of the purchased components;

[0142] Output data: 3D model of chassis and rack.

[0143] For the parametric design of cable chain models, different modules are selected, and finally integrated to generate a standard cable chain model; such as Figure 6 The diagram shows an example of the cable chain design process: Cable chain design start → Cable chain design → Cable design → Cable chain selection → Fixed end fixing plate design → Moving end fixing plate design → Model generation → Cable chain design end. For example, it can be implemented as follows:

[0144] Cable chain parameter input:

[0145] Cable chain design: type, brand, stroke, dividers;

[0146] Cable design: type, category, outer diameter, quantity;

[0147] Cable chain selection: maximum width, bending radius, opening method, fixed end opening orientation, moving end opening orientation, number of fixed end sections, number of moving end sections;

[0148] Fixed end plate design: wall thickness, plate length, plate height;

[0149] Mobile terminal mounting plate design: wall thickness, plate length, plate height;

[0150] Output data: Drag chain 3D model.

[0151] For the parametric design of the roll passing model, different modules are selected, and finally integrated to generate a standard roll passing model; such as Figure 7 The diagram shows an example of the roller design process: Roller design start → Roller type → Roller material → Roller default parameters (color, model, etc.) → Model generation → Roller design end. For example, it can be implemented as follows:

[0152] Roller parameter input:

[0153] Roller design: Roller type;

[0154] Hexagonal mandrel roller: roller material, roller surface knurling, knurling type, roller surface anti-sticking treatment, dustproof, roller cover color, retaining ring, roller outer diameter, end face connection thread, roller length, distance from roller end face to connection end face;

[0155] Single-suspension flange roller: roller material, roller surface knurling, knurling type, roller surface anti-sticking treatment, dustproof, roller cover color, retaining ring, mandrel diameter, roller outer diameter, guide mechanism, roller length, distance from roller end face to connection end face;

[0156] Unsupported rollers: roller material, roller surface knurling, knurling type, roller surface anti-sticking treatment, dustproof, roller cover color, retaining ring, mandrel diameter, roller outer diameter, distance from roller end face to connecting end face, roller coating, and outer diameter of coated roller;

[0157] Output data: 3D model of the roller.

[0158] In some optional implementations, for different types of rollers in the roller design, parameters for various rollers can be selected through preset parameter templates. The application of parameter templates can be achieved as follows:

[0159] (1) For the hexagonal mandrel roller, please refer to Table 1 below for a parameter template example:

[0160] Table 1

[0161]

[0162] (2) For single-suspension flange rollers, please refer to Table 2 below for parameter template examples:

[0163] Table 2

[0164]

[0165] (3) Unsupported rollers, please refer to Table 3 below for parameter template examples:

[0166] Table 3

[0167]

[0168] (4) Support seat over roller, parameter template example is shown in Table 4 below:

[0169] Table 4

[0170]

[0171] (4) Double-support roller, the parameter template example is shown in Table 5 below:

[0172] Table 5

[0173]

[0174] It should be noted that the specific parameters corresponding to the uppercase English letters (and number labels) in each row of Tables 1 to 5 above are only examples of identifiers after data anonymization. In actual applications, the actual values / types of the same parameter identifiers in different tables (such as A1 in Table 1 and Table 2) may be different, and the actual values / types of parameter identifiers in different rows of the same column in a table (such as B1 and B4 in Table 3) may also be the same. Specifically, the parameter identifiers in the tables should not be regarded as restrictions on the values / types of specific parameters.

[0175] The parameters in the window are displayed according to the selected roller type, and 2D graphs and 3D views of the variation parameters of the selected roller type can be added above.

[0176] In some specific application scenarios, taking rollers as an example, the automatic model generation can be achieved through the following process:

[0177] First, the overall process for parameter selection includes: roller type → roller material → other parameters → workstation number to be called;

[0178] The parameter input items include:

[0179] First input item (Roller type - Mandrel connection method): Roller type: Hexagonal mandrel roller, single-suspension flange roller, unsupported roller, supported roller, double-supported roller.

[0180] Second input item (roller material): Roller material: 304 stainless steel, POM, aluminum alloy, carbon fiber, aluminum alloy with rubber coating, high-precision aluminum guide roller (only materials available in this type will be displayed in the options, otherwise they will not be displayed); Roller surface treatment: straight knurling, mesh knurling, anti-stick treatment. When the corresponding surface treatment is selected, the corresponding treatment information will be sketched on the roller surface, otherwise no sketching will be done).

[0181] Third input item (other options): Universal end cap / pressure cap (optional color), retaining ring: none, yes, the size of the model changes with the outer diameter of the roller according to the assembly relationship, and a retaining ring of the corresponding size is generated; whether dust protection is required: select whether dust protection is required in the function;

[0182] Default parameters (uneditable, automatically generated; except in a few cases, the corresponding parameter content is generated in the corresponding option box when clicking to generate the roller diagram), for example: bearing model; roller plug: none, yes; mandrel flange diameter (mm); end cap / pressure cap screw; retaining ring; hexagonal mandrel specification; flange bearing seat: this option is only displayed when double-support roller is selected; guide mechanism: this option is only available for the roller diameter and length corresponding to single-suspension flange roller and support seat roller; support seat: this option is only available for support seat roller, not for others; roller coating and coating roller outer diameter: roller coating and coating roller outer diameter selection is only available when selecting unsupported roller, not for others; end face connection screw: the corresponding connection screw will pop up when the corresponding roller is selected; variable parameters: cannot be customized, made into the form of options, and can only be selected from the provided parameters (the relevant setting rules can be adaptively adjusted later if needed); roller outer diameter Φ (mm); roller length (mm); distance from roller end face to connection end face (mm).

[0183] It should be noted that when the parameter input box of the first input item has an editable field but no editing action has been performed (i.e., the editable input box is blank), subsequent content cannot be edited. The default item's value will automatically update the parameter box content when it meets the automatic generation conditions. The same rule applies to other editable input boxes that meet the automatic generation conditions. Designers must select items sequentially from front to back, not from back to front. Furthermore, in some optional implementations, a modification function can be added. Based on the parameters modified by the designer, the backend can identify whether it is a new model. If "yes," when the designer clicks "Generate Model," a new model is generated and the old model is deleted. If "no," when the designer clicks "Generate Model," the dimensions are adjusted on the original model.

[0184] For the parametric design of the conveyor frame model, different modules are selected, and finally integrated to generate a standard conveyor frame model; such as Figure 8 The following is an example of the conveyor frame design process: Conveyor frame design start → Frame parameters → Square tube specification calculation → Conveyor module parameters → Model generation → Drawing association (based on the parameterized information of the generated model, the drawing annotation BOM table is changed for association) → Refresh drawings (after the drawings are associated, they need to be refreshed for the changes to take effect [automatic refresh]) → Conveyor frame design end. For example, it can be implemented as follows:

[0185] Conveyor frame parameter input:

[0186] Conveyor frame design: inner width of conveyor line, pallet length, pallet width, number of layers, first layer height, second layer height, third layer height, fourth layer height, fifth layer height, standard layer height;

[0187] Output data: 3D model of the conveyor frame.

[0188] In summary, the core principle of this invention lies in responding to user parameter input and calling upon the basic model to ultimately generate the relevant model. Specifically, this invention achieves standardized design by adjusting parameters, improving design consistency and standardization. Furthermore, this invention allows for rapid modification of the 3D model by adjusting its parameter information, improving the efficiency of design engineers. Simultaneously, by responding to various customer parameters and integrating a parametric design process according to module division, this invention allows for the direct reuse of existing modules such as turntables, vibratory feeders, double-speed chains, chassis frames, cable chains, rollers, and conveyor frames, avoiding redesign. Specifically, this invention defines design modules for different scenarios based on the needs of the design process, enabling the rapid generation of modular 3D models and 2D drawings, improving the standardization, accuracy, and efficiency of model generation. Moreover, this invention employs rigorous and reliable computational logic, improving the design efficiency of engineers, reducing tedious manual operations, and ensuring the software's long-term, stable, and efficient operation.

[0189] This application also provides a three-dimensional model generation device based on parameter adaptive transformation, which may include:

[0190] The first module is used to respond to the model generation requirements of the intelligent manufacturing platform and determine the equipment type of the target equipment to be generated in the model.

[0191] The second module is used to determine the set of parameters to be converted for the target equipment based on the equipment type of the target equipment; wherein the set of parameters to be converted includes any one or more of the following: equipment operating parameters, equipment selection parameters, equipment structural parameters, equipment material parameters, or equipment design parameters.

[0192] The third module is used to perform adaptive parameter transformation processing based on the parameters to be transformed, converting the parameters to be transformed into the model parameter form of the three-dimensional model, and thus obtaining the input data of the transformed model to be generated.

[0193] The fourth module is used to adjust the parameters of the model to be generated based on the input data, and generate the target 3D model; the target 3D model is used to generate the design scheme of the target equipment of the intelligent production platform.

[0194] The content of the method embodiments in this application is applicable to the device embodiments. The specific functions implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0195] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method for generating a 3D model based on parameter adaptive transformation. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0196] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0197] Please see Figure 9 , Figure 9 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes:

[0198] The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0199] The memory 1002 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1002 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 to execute the 3D model generation method based on parameter adaptive transformation of the embodiments of this application.

[0200] Input / output interface 1003 is used to implement information input and output;

[0201] The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0202] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0203] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0204] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for generating three-dimensional models based on parameter adaptive transformation.

[0205] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0206] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0207] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0208] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0209] The 3D model generation method, apparatus, electronic device, and storage medium based on parameter adaptive transformation provided in this application embodiment, respond to the model generation requirements of an intelligent production platform by determining the equipment type of the target equipment to be generated; determining the set of parameters to be transformed corresponding to the target equipment based on the equipment type; wherein the set of parameters to be transformed includes any one or more of the following: equipment operating parameters, equipment selection parameters, equipment structural parameters, equipment material parameters, or equipment design parameters; performing parameter adaptive transformation processing based on the parameters to be transformed into the model parameter form of the 3D model, thereby obtaining the input data of the transformed model to be generated; performing parameter tuning processing on the model to be generated based on the input data to generate the target 3D model; the target 3D model is used to generate the design scheme of the target equipment of the intelligent production platform. This invention determines the set of parameters to be converted based on the device type and performs adaptive parameter conversion processing, ensuring unified modeling rules for similar devices and generating highly consistent models. This improves model standardization and facilitates collaborative design and data management. Furthermore, by automatically converting the parameters to be converted into model parameters and performing parameter tuning based on input data, this invention automates model generation. Users only need to adjust the parameters, and the system automatically updates related features, avoiding the inefficiency and risk of regeneration failure caused by manually locating and updating multiple features. At the same time, since the parameter system and modeling logic are unified through adaptive conversion, historical models can quickly adapt to new requirements through parameter adjustments, greatly reducing the workload of modifications, promoting the reuse of model data, and avoiding waste.

[0210] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for generating a three-dimensional model based on parameter adaptive conversion, characterized by, The method comprises the following steps: In response to a model generation requirement of the intelligent production platform, determining a device type of a target device of a model to be generated; The method comprises the following steps: In response to a model generation requirement of the intelligent production platform, determining a device type of a target device of a model to be generated; The method comprises the following steps: In response to a model generation requirement of the intelligent production platform, determining a device type of a target device of a model to be generated; The method comprises the following steps: In response to a model generation requirement of the intelligent production platform, determining a device type of a target device of a model to be generated; According to the device type of the target device, a set of parameters to be converted corresponding to the target device is determined; wherein the set of parameters to be converted comprises any one or several of device operation parameters, device selection parameters, device structure parameters, device material parameters, or device design parameters; According to the parameters to be converted, parameter self-adaptive conversion processing is performed to convert the parameters to be converted into model parameter forms of a three-dimensional model, and then input data of the generated model to be converted is obtained; 2. The method of claim 1, wherein, According to the parameters to be converted, parameter self-adaptive conversion processing is performed to convert the parameters to be converted into model parameter forms of a three-dimensional model, and then input data of the generated model to be converted is obtained; 3. The method of claim 2, wherein, According to the input data, the generated model to be converted is processed, and a target three-dimensional model is generated; the target three-dimensional model is used to generate a design scheme of the target device of the intelligent production platform. The target device comprises a turntable, a vibrating disc, a speed chain, a case rack, a drag chain, a roller, or a conveyor. When the target device is a turntable or a vibrating disc, the method comprises the following steps: According to the device type of the target device, a set of parameters to be converted corresponding to the target device is determined; wherein the set of parameters to be converted comprises any one or several of device operation parameters, device selection parameters, device structure parameters, device material parameters, or device design parameters; According to the device type of the target device, a set of parameters to be converted corresponding to the target device is determined; wherein the set of parameters to be converted comprises any one or several of device operation parameters, device selection parameters, device structure parameters, device material parameters, or device design parameters; Or, according to the device type of the target device being a vibrating disc, a set of vibrating disc parameters of the vibrating disc, a discharge channel parameter of the vibrating disc, a set of direct vibration selection parameters of the vibrating disc, a set of screw selection parameters of the vibrating disc, and a set of opposite light selection parameters of the vibrating disc are determined; and a set of to-be-converted parameters of the vibrating disc is constructed according to the set of vibrating disc parameters, the discharge channel parameter, the set of direct vibration selection parameters, the set of screw selection parameters, and the set of opposite light selection parameters.

4. The method of claim 2, wherein, When the target device is a speed chain, the set of to-be-converted parameters corresponding to the target device is determined according to the device type of the target device, including: According to the device type of the target device being a speed chain, a set of speed chain parameters of the speed chain, a set of speed chain selection parameters, a set of bearing selection parameters, and a set of transmission selection parameters of the speed chain are determined; According to the set of speed chain parameters, a set of group chain parameters of the speed chain is calculated; According to the set of speed chain parameters, the set of speed chain selection parameters, the set of bearing selection parameters, the set of transmission selection parameters of the speed chain, and the set of group chain parameters, a set of to-be-converted parameters of the speed chain is constructed.

5. The method of claim 2, wherein, When the target device is a case rack, a drag chain or a conveyor rack, the set of to-be-converted parameters corresponding to the target device is determined according to the device type of the target device, including: According to the device type of the target device being a case rack, a set of case rack parameters of the case rack is determined; according to the set of case rack parameters, a square tube design parameter of the case rack is determined; according to the square tube design parameter, a set of outsourcing part selection parameters is determined; and according to the set of case rack parameters, the square tube design parameter, and the set of outsourcing part selection parameters, a set of to-be-converted parameters of the case rack is constructed; Or, according to the device type of the target device being a drag chain, a set of drag chain design parameters, a set of cable design parameters, a set of drag chain selection parameters, a set of fixed end fixed plate parameters, and a set of mobile end fixed plate design parameters of the drag chain are determined; and a set of to-be-converted parameters of the drag chain is constructed according to the set of drag chain design parameters, the set of cable design parameters, the set of drag chain selection parameters, the set of fixed end fixed plate parameters, and the set of mobile end fixed plate design parameters; Or, according to the device type of the target device being a conveyor rack, a set of conveyor rack parameters of the conveyor rack is determined; according to the set of conveyor rack parameters, a square tube specification parameter of the conveyor rack is calculated; and according to the set of conveyor rack parameters and the square tube specification parameter, a set of to-be-converted parameters of the conveyor rack is constructed; Wherein, the set of to-be-converted parameters of the conveyor rack is used to generate the target three-dimensional model, and then the model parameterization information of the target three-dimensional model is used to make associated changes to the design drawing, and then the design drawing of the conveyor rack is refreshed to obtain the latest conveyor rack three-dimensional design drawing.

6. The method of claim 2, wherein, When the target device is a roller, the set of to-be-converted parameters corresponding to the target device is determined according to the device type of the target device, including: According to the device type of the target device being a roller, a roller type parameter set, a roller cylinder material parameter set, and a roller default parameter set of the roller are determined; According to the roller type parameter set, the roller cylinder material parameter set, and the roller default parameter set, a set of to-be-converted parameters of the roller is constructed.

7. An apparatus for generating a three-dimensional model based on parameter adaptive conversion, characterized by, The device comprises: A first module is configured to determine a device type of a target device for which a model is to be generated in response to a model generation requirement of an intelligent production platform; The determination of the device type of the target device for which the model is to be generated in response to the model generation requirement of the intelligent production platform comprises: In response to a parameter adjustment instruction issued by a user, the parameter adjustment instruction is identified to determine the model generation requirement of the intelligent production platform, and then the device type of the target device for which the model is to be generated is determined according to the model generation requirement; The identification of the parameter adjustment instruction in response to the parameter adjustment instruction issued by the user to determine the model generation requirement of the intelligent production platform comprises: In response to the parameter adjustment instruction issued by the user, the type of the target device is identified from the parameter adjustment instruction, and a historical requirement set corresponding to the type of the target device is matched from historical model data of the intelligent production platform, and a plurality of model generation requirements with high usage frequency are obtained from the historical requirement set as candidates to determine the model generation requirement of the intelligent production platform; A second module is configured to determine a set of to-be-converted parameters corresponding to the target device according to the device type of the target device; the set of to-be-converted parameters comprises any one or several of device operation parameters, device selection parameters, device structure parameters, device material parameters, or device design parameters; A third module is configured to perform parameter adaptive conversion processing on the to-be-converted parameters to convert the to-be-converted parameters into model parameter forms of a three-dimensional model, and then obtain input data of the to-be-generated model after conversion; The parameter adaptive conversion processing is performed by: automatically calculating required derivative parameters according to user input parameters and in combination with built-in mechanical calculation rules; and mapping and converting all parameters into model parameter forms recognizable by a three-dimensional modeling software to generate a structured input data file readable by the modeling software; the all parameters comprise user input parameters, derivative parameters, and default parameters of a default base model; A fourth module is configured to perform parameter adjustment processing on the to-be-generated model according to the input data to generate a target three-dimensional model; the target three-dimensional model is used to generate a design scheme of the target device of the intelligent production platform.

8. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method of any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to implement the method of any one of claims 1 to 6.

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