Intelligent measurement and control apparatus, and industrial design application method

Abstract

Disclosed in the present invention are an intelligent measurement and control apparatus and an industrial design application method. The key to the technical solution comprises: construction and application of an AIGC agile industrial design model, a human-computer collaborative design process, construction of the application paradigm of industrial product generation scenarios, model optimization decision based on cognitive analysis, and application case research and verification. The AIGC model, by means of quickly generating and optimizing the design solution, significantly improves the design efficiency and innovativeness. By means of accurate demand analysis and decision-making optimization, the design solution is ensured to be consistent with the expected level of the user, and the user satisfaction is improved. The agile design method and multi-iterative optimization ensure that the design process is efficient and accurate, and the design quality and reliability are improved. The design solution is quickly generated and optimized, such that the product development period is effectively shortened, and the market reaction speed and competitiveness of an enterprise are improved.

Description

An intelligent measurement and control device and industrial design application method Technical Field

[0001] The present invention relates to the technical field of intelligent measurement and control devices, and in particular to an intelligent measurement and control device and an industrial design application method. Background Art

[0002] The development trend of the AIGC agile industrial design model refers to the application and development direction of agile methodologies in the design process within the industrial design field. This model focuses on rapid feedback, flexible adaptation, and team collaboration to improve design efficiency and quality, and is currently a research hotspot in the industrial design field. AIGC's image generation function is based on a generative adversarial network (GAN). The human-machine collaborative AIGC agile industrial design model plays a key decision-making role in intelligent human-machine interactive collaborative systems. It integrates the comprehensive decision-making of humans and intelligent systems, can quickly respond to interactive cognition, and achieve interactive effects efficiently and accurately. Industrial design can drive the integrated optimization of the entire life cycle of Hanzhong's equipment manufacturing industry. The AIGC model is deeply integrated into the human-machine collaborative system, optimizing human-machine task allocation. Based on its own efficient recognition and cognition capabilities, it quickly generates reference solutions, shortening the design and manufacturing cycle and improving overall manufacturing capabilities. To this end, we propose an intelligent measurement and control device and industrial design application method.

[0003] Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent measurement and control device and an industrial design application method to solve the problems raised in the background technology.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions:

[0006] An industrial design application method for an intelligent measurement and control device includes the construction and application of an AIGC agile industrial design model, a human-machine collaborative design process, the construction of an application paradigm for industrial product generation scenarios, model optimization decision-making based on cognitive analysis, and application case research and verification; the construction and application of the AIGC agile industrial design model includes model construction and application scenario modeling; the model construction includes the following steps: S1: introducing AIGC (generative adversarial network GAN) to assist in industrial product design; S1.1: constructing a behavior-scenario-product interaction design paradigm; S1.2: establishing system modeling based on functional elements, using IDEF0 for functional models and IDEF8 for user interface modeling.

[0007] Preferably, the application scenario modeling includes the following steps: S2: using the IDEF modeling method to describe the functions and user interface of the torque tester manufacturing system; S2.1: analyzing the multimodal form of information flow and constructing a mapping model that runs through the product layer, interaction layer and user layer.

[0008] Preferably, the human-machine collaborative design process includes input and prompt word design, generation and optimization;

[0009] The input and prompt word design includes the following steps: S3: determining the design intention input prompt words, creative sketches, and simple models; S3.1: performing logical analysis and semantic decomposition on the prompt words, encoding and matching the user's implicit cognitive elements and emotional images.

[0010] Preferably, the generation and optimization include the following steps: S4: using generative AI models such as Midjourney to generate a modeling result case library; S4.1: iteratively optimizing the generated graphic information through multiple machine learning and model training; S4.2: building a multi-channel perception system to perform decision optimization on the generated results.

[0011] Preferably, the application paradigm construction of the industrial product generation scenario includes the practical application of the industrial design method; the practical application of the industrial design method includes the following steps: S5: defining the function and structural system of the design product and generating a design prototype; S5.1: combining the preferred AI model with design thinking to quickly generate design drawings; S5.2: building an agile industrial design model and obtaining an optimized collaborative design method.

[0012] Preferably, the model optimization decision based on cognitive analysis includes the construction of a multi-channel perceptual system and sensory evaluation and decision-making; the multi-channel perceptual system construction includes the following steps: S6: analyzing the process of converting perceptual representation into behavioral representation; S6.1: constructing synesthesia channels color + shape, intention + shape; S6.2: performing machine learning and optimization analysis through the Midjourney model.

[0013] Preferably, the perceptual evaluation and decision-making includes the following steps: S7: setting a perceptual attribute set and defining quantitative perceptual preferences; S7.1: constructing a hesitant fuzzy perceptual evaluation matrix and performing expert evaluation; S7.2: applying a hierarchical cluster analysis method to make an optimization decision on color configuration.

[0014] Preferably, the application case study and verification includes the following steps: S8: selecting typical cases for application research; S8.1: demonstrating the usability of the model and optimizing the design method.

[0015] An intelligent measurement and control device includes a perception module, an appearance module, a machine learning module, a user interaction module, a multimodal model, an AIGC model, a decision module, and an evaluation module; the perception module is used to perceive the appearance of the device and user behavior information, and the module preliminarily collects user needs and device status through information processing and perceptual conversion; the appearance module is used to divide the appearance of the device into different module groups based on the information collected by the perception module, and analyze and design through perceptual intention extraction; the machine learning module is used to use the collected data for training, analyze the logical mapping relationship between the input form and the output result, and optimize the design; the user interaction module is used to analyze the user's intention through human-computer collaboration. The system designs prompt words to ensure that the design process meets user needs; the multimodal model is used to deal with the problem of prompt word design, perform semantic logic decomposition, and match it with the user's cognition to generate a preliminary design plan; the AIGC model is used to generate modeling renderings of equipment components, and conduct machine learning through creative sketches and simple models to generate detailed product shapes; the decision module is used to make preliminary and secondary decisions based on multi-channel factors and perceptual intentions, optimize the design goals, and ensure that the final design meets user needs; the evaluation module uses the Kansei-TOPSIS evaluation model to evaluate the color emotional quality deviation and similarity of the product color configuration plan to ensure that the product appearance design meets the user's perceptual needs.

[0016] In summary, the present invention mainly has the following beneficial effects.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] The AIGC model significantly improves design efficiency and innovation by rapidly generating and optimizing design solutions. It ensures that design solutions are highly consistent with user expectations and enhances user satisfaction through precise demand analysis and optimized decision-making. Agile design methods and multiple iterative optimizations ensure an efficient and accurate design process, improving design quality and reliability. It rapidly generates and optimizes design solutions, effectively shortening the product development cycle and enhancing the company's market response speed and competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] FIG1 is a schematic diagram of a device module of the present invention;

[0020] FIG2 is a schematic flow chart of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] The following examples are intended to illustrate the present invention but are not intended to limit the scope of protection of the present invention. The conditions in the examples may be further adjusted according to specific conditions. Simple improvements to the method of the present invention within the scope of the present invention are also within the scope of protection claimed in the present invention.

[0023] The technical solution of this patent is further described in detail below in conjunction with specific implementation methods.

[0024] Example 1

[0025] An industrial design application method for intelligent measurement and control devices, including the construction and application of the AIGC agile industrial design model, the human-machine collaborative design process, the construction of an application paradigm for industrial product generation scenarios, model optimization decision-making based on cognitive analysis, and application case research and verification;

[0026] The construction and application of the AIGC agile industrial design model includes model construction and application scenario modeling;

[0027] The model construction includes the following steps:

[0028] S1: Introducing AIGC (Generative Adversarial Network GAN) to assist industrial product design;

[0029] S1.1: Construct a behavior--scenario--product interaction design paradigm;

[0030] S1.2: Establish system modeling based on functional elements, using IDEF0 for functional models and IDEF8 for user interface modeling;

[0031] Quickly generate product shapes from creative ideas to improve design efficiency; optimize human-machine task allocation and shorten the design and manufacturing cycle;

[0032] The application scenario modeling comprises the following steps:

[0033] S2: Use the IDEF modeling method to describe the functions and user interface of the torque tester manufacturing system;

[0034] S2.1: Analyze the multimodal forms of information flow and build a mapping model across the product layer, interaction layer, and user layer;

[0035] Reduce cognitive load, improve the efficiency and accuracy of product-human interaction; enhance the smoothness of information flow and optimize the interactive experience during the design process;

[0036] The human-computer collaborative design process includes input and prompt word design, generation and optimization;

[0037] The input and prompt word design comprises the following steps:

[0038] S3: Determine the design intention and input prompt words, creative sketches, and simple models;

[0039] S3.1: Perform logical analysis and semantic decomposition of prompt words to encode and match the user's implicit cognitive elements and emotional images;

[0040] Described generation and optimization comprises the following steps:

[0041] S4: Use generative AI models such as Midjourney to generate a case library of modeling results;

[0042] S4.1: Iteratively optimize the generated graphical information through multiple machine learning and model training;

[0043] S4.2: Build a multi-channel perception system and optimize the decision-making of the generated results;

[0044] The construction of the application paradigm of the industrial product generation scenario includes the practical application of industrial design methods;

[0045] The practical application of the industrial design method comprises the following steps:

[0046] S5: Define the function and structural system of the design product and generate a design prototype;

[0047] S5.1: Optimize the combination of AI models and design thinking to quickly generate design drawings;

[0048] S5.2: Build an agile industrial design model and obtain optimized collaborative design methods;

[0049] The model optimization decision based on cognitive analysis includes the construction of a multi-channel perception system and perceptual evaluation and decision-making;

[0050] The multi-channel perception system construction includes the following steps:

[0051] S6: Analyze the process of converting perceptual representations into behavioral representations;

[0052] S6.1: Construct synesthetic channels of color + shape, intention + shape;

[0053] S6.2: Machine learning and optimization analysis using midjourney models;

[0054] The perceptual evaluation and decision-making process includes the following steps:

[0055] S7: Set the perceptual attribute set and define the quantitative perceptual preference;

[0056] S7.1: Construct a hesitant fuzzy perceptual evaluation matrix for expert evaluation;

[0057] S7.2: Apply hierarchical cluster analysis to optimize color configuration decisions;

[0058] The application case study and verification includes the following steps:

[0059] S8: Select typical cases for applied research;

[0060] S8.1: Demonstrate the usability of the model and optimize the design method;

[0061] Two key concepts in cognitive theory are that information processing relies on the transformation of internal and mental representations—that is, the transformation of perceptual representations into behavioral representations. Information processing is not a simple sequence from sensation to perception to memory. Perception is the key to understanding the transition from information to form. Multi-channel perception allows for in-depth analysis of the internal drivers of perception, thereby capturing the hidden elements of implicit perception. In product design, a synesthesia channel is constructed: the color + shape channel; and a perceptual reorganization channel: the intention + shape channel, forming a multi-channel perceptual system for perceptual intention in product appearance. Within the shape channel elements, the shape representation is divided into different module groups using structural modules for perceptual intention extraction. The grouped modules are then imported into the Midjourney model for machine learning, analyzing the logical mapping between input form and output information processing. From the perspective of the evolution of handwheel design, cognition of the handwheel begins with behavior, first serving as a guide for behavioral operation. This involves seeing the shape, processing the visual information into perceptual information, and then, through memory, transforming it into behavioral guidance. Based on this principle, a decision is made on the AI-generated product module design. Secondary decision-making involves styling decisions based on design objectives. Within this process, classification decisions are made based on the constructed multi-channel elements. The extraction method and results of perceptual intentions are obtained. Hierarchical cluster analysis is used for decision analysis. The steps are as follows: ① Establish a multimodal hierarchical original value matrix for product styling; ② Aggregate the data to determine the distance matrix for multimodal cognitive channels; ③ Use Xsort to create a dendrogram and classification results.

[0062] Based on the theory of Kansei-TOPSIS evaluation model, the color emotional quality deviation and similarity of product color configuration schemes are evaluated, and the following four steps are set:

[0063] The first step is to set the solution to sample set A = {A i,i=1,2,…,16,…,M}. A i The corresponding sample of Pantone color.

[0064] The second step is to set the perceptual attribute set to C = {C j ,j=1,2,…,5,…,N}. C j By a pair of bipolar sensibility adjectives K Wj = <k wj- ,k wj+ > indicates that k wj－ and k wj+ Denote left and right sentiment adjectives respectively. Let the bipolar sentiment adjective set K W ={ <k wj- , kwj+ Thus, a perceptual intention scale was set up through expert review, and the color configuration was preliminarily optimized.

[0065] In the third step, combining the traditional semantic differential method and the hesitant fuzzy set proposed by Torra, the quantitative perceptual preference is defined based on expert discussion, allowing the membership of an element to be multiple different values ​​and setting any value within an interval.

[0066] The fourth step is to collect the evaluation values ​​of experts and construct the hesitant fuzzy perceptual evaluation matrix H'. i , i = 1, 2, ..., k}, the experts score and evaluate the color configuration samples, taking into account the opinions of the expert group as a whole, and give the hesitant fuzzy perceptual evaluation matrix H' = [h' ij ]M×N, where h'ij is a hesitant fuzzy element, which represents the solution A i In the perceptual attribute C j The following evaluation value.

[0067] Example 2

[0068] An intelligent measurement and control device includes a perception module, an appearance module, a machine learning module, a user interaction module, a multimodal model, an AIGC model, a decision module, and an evaluation module;

[0069] The perception module is used to perceive the appearance of the device and user behavior information. This module preliminarily collects user needs and device status through information processing and perception conversion;

[0070] The appearance module is used to divide the appearance of the device into different module groups based on the information collected by the perception module, and perform analysis and design through perceptual intention extraction;

[0071] The machine learning module is used to use the collected data for training, analyze the logical mapping relationship between the input form and the output result, and optimize the design;

[0072] The user interaction module is used to analyze the user's intention and design prompt words through human-computer collaboration to ensure that the design process meets user needs;

[0073] The multimodal model is used to handle the problem of prompt word design, perform semantic logic decomposition, match it with the user's cognition, and generate a preliminary design plan;

[0074] The AIGC model is used to generate modeling renderings of equipment components, and through machine learning from creative sketches and simple models, generates detailed product shapes;

[0075] The decision module is used to make preliminary and secondary decisions based on multi-channel factors and perceptual intentions, optimize the design goals, and ensure that the final design meets user needs;

[0076] The evaluation module uses the Kansei-TOPSIS evaluation model to evaluate the color emotional quality deviation and similarity of the product color configuration scheme to ensure that the product appearance design meets the user's emotional needs.

[0077] Although the embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that, unless otherwise defined, the technical or scientific terms used in the present invention shall have the ordinary meanings understood by persons having ordinary skills in the field to which the present invention belongs. The words "include" or "comprise" and the like used in the present invention mean that the elements or objects preceding the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. The words "connect" or "connected" and the like are not limited to physical or mechanical connections, but may also include electrical connections, whether direct or indirect. The words "upper", "lower", "left", "right", etc. are only used to indicate relative position relationships. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

[0078] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An industrial design application method for an intelligent measurement and control device, characterized in that: Including the construction and application of AIGC agile industrial design model, human-machine collaborative design process, application paradigm construction of industrial product generation scenarios, model optimization decision-making based on cognitive analysis, application case study and verification; The construction and application of the AIGC agile industrial design model includes model construction and application scenario modeling; The model construction includes the following steps: S1: Introducing AIGC (Generative Adversarial Network GAN) to assist industrial product design; S1.1: Construct the behavior-scenario-product interaction design paradigm; S1.2: Establish system modeling based on functional elements, IDEF0 is used for functional model and IDEF8 is used for user interface modeling.

2. The industrial design application method of an intelligent measurement and control device according to claim 1, characterized in that: The application scenario modeling comprises the following steps: S2: Use IDEF modeling method to describe the functions and user interface of the torque tester manufacturing system; S2.1: Analyze the multimodal forms of information flow and build a mapping model across the product layer, interaction layer, and user layer.

3. The industrial design application method of an intelligent measurement and control device according to claim 1, characterized in that: The human-computer collaborative design process includes input and prompt word design, generation and optimization; The input and prompt word design comprises the following steps: S3: Determine the design intention and input prompt words, creative sketches, and simple models; S3.1: Perform logical analysis and semantic decomposition on the prompt words, encode and match the user's implicit cognitive elements and emotional images.

4. The industrial design application method of an intelligent measurement and control device according to claim 3 is characterized in that: Described generation and optimization comprises the following steps: S4: Use generative AI models such as Midjourney to generate a case library of modeling results; S4.1: Iteratively optimize the generated graphical information through multiple machine learning and model training; S4.2: Build a multi-channel perception system and optimize the decision-making of the generated results.

5. The industrial design application method of an intelligent measurement and control device according to claim 1, characterized in that: The construction of the application paradigm of the industrial product generation scenario includes the practical application of industrial design methods; The practical application of the industrial design method comprises the following steps: S5: Define the function and structural system of the design product and generate a design prototype; S5.1: Optimize the combination of AI model and design thinking to quickly generate design drawings; S5.2: Build an agile industrial design model and obtain optimized collaborative design methods.

6. The method for preparing the industrial design application method of an intelligent measurement and control device according to claim 1, characterized in that: The model optimization decision based on cognitive analysis includes the construction of multi-channel perception system and perceptual evaluation and decision-making; The multi-channel perception system construction includes the following steps: S6: Analyze the process of converting perceptual representation into behavioral representation; S6.1: Construct synesthesia channels color + shape, intention + shape; S6.2: Machine learning and optimization analysis through the Midjourney model.

7. The method for preparing the industrial design application method of the intelligent measurement and control device according to claim 6, characterized in that: The perceptual evaluation and decision-making includes the following steps: S7: Set the perceptual attribute set and define the quantitative perceptual preference; S7.1: Construct hesitant fuzzy perceptual evaluation matrix for expert evaluation; S7.2: Apply hierarchical cluster analysis to optimize color configuration decisions.

8. The method for preparing the industrial design application method of the intelligent measurement and control device according to claim 1, characterized in that: The application case study and verification includes the following steps: S8: Select typical cases for applied research; S8.1: Demonstrate the usability of the model and optimize the design approach.

9. An intelligent measurement and control device, applicable to the preparation method of the industrial design application method of an intelligent measurement and control device according to any one of claims 1 to 8, characterized in that: Including perception module, appearance module, machine learning module, user interaction module, multimodal model, AIGC model, decision module, and evaluation module; The perception module is used to perceive the appearance of the device and user behavior information. The module preliminarily collects user needs and device status through information processing and perception conversion; The appearance module is used to divide the appearance of the device into different module groups according to the information collected by the perception module, and perform analysis and design through perceptual intention extraction; The machine learning module is used to use the collected data for training, analyze the logical mapping relationship between the input form and the output result, and optimize the design; The user interaction module is used to analyze the user's intention and design prompt words through human-computer collaboration to ensure that the design process meets the user's needs; The multimodal model is used to handle the problem of prompt word design, perform semantic logic decomposition, and match it with the user's cognition to generate a preliminary design plan; The AIGC model is used to generate modeling renderings of equipment components, and to generate detailed product shapes through machine learning through creative sketches and simple models; The decision module is used to make preliminary and secondary decisions based on multi-channel elements and perceptual intentions, optimize the design goals, and ensure that the final design meets user needs; The evaluation module uses the Kansei-TOPSIS evaluation model to evaluate the color emotional quality deviation and similarity of the product color configuration scheme to ensure that the product appearance design meets the user's emotional needs.

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