Intelligent measurement and control device and industrial design application method

Abstract

The application discloses an intelligent measurement and control device and an industrial design application method, and technical scheme points are as follows: construction and application of an AIGC agile industrial design model, man-machine collaborative design process, application paradigm construction of an industrial product generation scene, model optimization decision based on cognitive analysis, application case study and verification; the AIGC model generates and optimizes a design scheme quickly, significantly improves design efficiency and innovation; through accurate requirement analysis and optimization decision, it is ensured that a design scheme is highly consistent with user expectation, and user satisfaction is improved; the agile design method and multiple iteration optimization ensure that a design process is efficient and accurate, and improve design quality and reliability; a design scheme is quickly generated and optimized, a product development cycle is effectively shortened, and market reaction speed and competitiveness of an enterprise are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent measurement and control device technology, specifically to an intelligent measurement and control device and its industrial design application method. Background Technology

[0002] The development trend of the AIGC agile industrial design model refers to the application and development direction of agile methodologies in the industrial design process. This model emphasizes rapid feedback, flexible adaptation, and teamwork to improve design efficiency and quality, and is currently a research hotspot in the field of industrial design. AIGC's image generation function is based on Generative Adversarial Networks (GANs) to achieve its main function. The AIGC agile industrial design model, based on human-machine collaboration, plays a crucial decision-making role in intelligent human-machine interaction collaborative systems. It integrates comprehensive decision-making between humans and intelligent systems, enabling rapid response to interactive cognition and achieving efficient and accurate interactive effects. Industrial design can drive the integration and optimization of the entire lifecycle of Hanzhong's equipment manufacturing industry. The AIGC model, deeply integrated into human-machine collaboration systems, optimizes human-machine task allocation and rapidly generates reference solutions based on its efficient recognition and cognitive capabilities, shortening the design and manufacturing cycle and improving overall manufacturing capabilities. Therefore, we propose an intelligent measurement and control device and its industrial design application method. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an intelligent measurement and control device and an industrial design application method, solving the problems mentioned in the background technology.

[0004] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0005] 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 studies 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 an interaction design paradigm of behavior-scenario-product; S1.2: Establishing system modeling based on functional elements, with IDEF0 used for functional modeling and IDEF8 used for user interface modeling.

[0006] Preferably, the application scenario modeling includes the following steps: S2: Using the IDEF modeling method, describe the functions and user interface of the torque detector manufacturing system; S2.1: Analyze the multimodal form of information flow, construct a mapping model, and run through the product layer, interaction layer and user layer.

[0007] Preferably, the human-computer collaborative design process includes the design, generation, and optimization of input and prompt words;

[0008] The input and prompt word design includes the following steps: S3: Determine the design intention 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 imagery.

[0009] Preferably, the generation and optimization includes 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: constructing a multi-channel perception system to optimize the generated results through decision-making.

[0010] Preferably, the application paradigm construction of the industrial product generation scenario includes the practical application of industrial design methods; the practical application of industrial design methods includes the following steps: S5: Define the function and structural system of the designed product and generate a design prototype; S5.1: Combine the preferred AI model with design thinking to quickly generate design drawings; S5.2: Build an agile industrial design model and obtain optimized collaborative design methods.

[0011] Preferably, the model optimization decision based on cognitive analysis includes the construction of a multi-channel perception system and sensory evaluation and decision-making; the construction of the multi-channel perception system includes the following steps: S6: analyzing the process of converting perceptual representations into behavioral representations; S6.1: constructing synesthetic channels color + shape and intention + shape; S6.2: performing machine learning and optimization analysis through the Midjourney model.

[0012] Preferably, the emotional evaluation and decision-making includes the following steps: S7: setting a set of emotional attributes and defining quantitative emotional preferences; S7.1: constructing a hesitant and fuzzy emotional evaluation matrix and conducting expert evaluation; S7.2: applying hierarchical cluster analysis to make optimization decisions on color configuration.

[0013] Preferably, the application case study and verification includes the following steps: S8: Select typical cases for application study; S8.1: Demonstrate the usability of the model and optimize the design method.

[0014] An intelligent measurement and control device includes a sensing module, an appearance module, a machine learning module, a user interaction module, a multimodal model, an AIGC model, a decision-making module, and an evaluation module. The sensing module senses the device's appearance and user behavior information, and through information processing and perceptual transformation, initially collects user needs and device status. The appearance module, based on the information collected by the sensing module, groups the device's appearance into different module groups and analyzes and designs it through perceptual intention extraction. The machine learning module uses the collected data for training, analyzes the logical mapping relationship between input forms and output results, and optimizes the design. The user interaction module, through human-machine collaboration, analyzes user intentions... The system incorporates a multimodal model to design prompts, ensuring the design process meets user needs. This model addresses prompt design issues by performing semantic logic decomposition and matching it with user cognition to generate preliminary design schemes. An AIGC model generates modeling renderings of device components, using machine learning through creative sketches and simple models to produce detailed product designs. A decision-making module performs preliminary and secondary decisions based on multi-channel factors and emotional intentions, optimizing design goals to ensure the final design meets user needs. An evaluation module utilizes the Kansei-TOPSIS evaluation model to assess color emotional quality deviations and similarities in the product's color scheme, ensuring the product's appearance design meets user emotional needs.

[0015] In summary, the present invention has the following main beneficial effects.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] The AIGC model significantly improves design efficiency and innovation by rapidly generating and optimizing design solutions; through precise requirements analysis and optimization decisions, it ensures that design solutions are highly consistent with user expectations, thereby enhancing user satisfaction; agile design methods and multiple iterative optimizations ensure an efficient and accurate design process, improving design quality and reliability; and the rapid generation and optimization of design solutions effectively shortens product development cycles, enhancing the company's market responsiveness and competitiveness. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the device module of the present invention;

[0019] Figure 2 This is a flowchart illustrating the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The following embodiments are used to illustrate the present invention, but should not be used to limit the scope of protection of the present invention. The conditions in the embodiments can be further adjusted according to specific conditions, and simple improvements to the method of the present invention under the premise of the concept of the present invention are all within the scope of protection claimed by the present invention.

[0022] The technical solution of this patent will be further described in detail below with reference to specific embodiments.

[0023] Example 1

[0024] An industrial design application method for intelligent measurement and control devices includes the construction and application of the AIGC agile industrial design model, human-machine collaborative design process, construction of application paradigms for industrial product generation scenarios, model optimization decision-making based on cognitive analysis, and application case studies and verification.

[0025] The construction and application of the AIGC agile industrial design model includes model construction and application scenario modeling;

[0026] The model construction includes the following steps:

[0027] S1: Introducing AIGC (Generative Adversarial Network GAN) to assist in industrial product design;

[0028] S1.1: Constructing an interaction design paradigm for behavior-scenario-product;

[0029] S1.2: Establish system modeling based on functional elements, with IDEF0 used for functional modeling and IDEF8 used for user interface modeling;

[0030] Quickly generate product designs from ideas to improve design efficiency; optimize human-machine task allocation to shorten design and manufacturing cycles;

[0031] The application scenario modeling includes the following steps:

[0032] S2: Using the IDEF modeling method, describe the functions and user interface of the torque measuring instrument manufacturing system;

[0033] S2.1: Analyze the multimodal forms of information flow, construct a mapping model, and span the product layer, interaction layer, and user layer;

[0034] Reduce cognitive load and improve the efficiency and accuracy of product-human interaction; enhance the smoothness of information flow and optimize the interactive experience during the design process;

[0035] The human-computer collaborative design process includes the design, generation, and optimization of input and prompt words;

[0036] The input and prompt word design includes the following steps:

[0037] S3: Determine design intent by inputting prompts, creative sketches, and simple models;

[0038] S3.1: Perform logical analysis and semantic decomposition on the prompt words, encode and match the user's implicit cognitive elements and emotional imagery;

[0039] The generation and optimization process includes the following steps:

[0040] S4: Utilize generative AI models such as Midjourney to generate a library of modeling result examples;

[0041] S4.1: The generated graphical information is iteratively optimized through multiple machine learning and model training processes;

[0042] S4.2: Construct a multi-channel perception system and optimize the generated results through decision-making;

[0043] The construction of the application paradigm for the industrial product generation scenario includes the practical application of industrial design methods;

[0044] The practical application of the industrial design method includes the following steps:

[0045] S5: Define the functional and structural system of the designed product and generate a design prototype;

[0046] S5.1: Combining optimal AI models with design thinking to quickly generate design drawings;

[0047] S5.2: Build an agile industrial design model and obtain optimized collaborative design methods;

[0048] The model-based optimization decision-making based on cognitive analysis includes the construction of a multi-channel perception system and sensory evaluation and decision-making;

[0049] The construction of the multi-channel perception system includes the following steps:

[0050] S6: Analyze the process of converting perceptual representations into behavioral representations;

[0051] S6.1: Constructing synesthetic channels: color + shape, intention + shape;

[0052] S6.2: Machine learning and optimization analysis using the Midjourney model;

[0053] The aforementioned emotional evaluation and decision-making includes the following steps:

[0054] S7: Set up a set of emotional attributes to define quantitative emotional preferences;

[0055] S7.1: Construct a hesitant, ambiguous, and perceptual evaluation matrix and conduct expert evaluation;

[0056] S7.2: Apply hierarchical cluster analysis to optimize color configuration decisions;

[0057] The application case study and verification includes the following steps:

[0058] S8: Select typical cases for applied research;

[0059] S8.1: Demonstrate the usability of the model and optimize the design method;

[0060] Two key concepts in cognitive theory are that information processing relies on the transformation between internal and mental representations, i.e., 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 transformation from information to form. Multi-channel perception can deeply analyze the internal causes of perception, thereby obtaining the hidden elements of implicit perception. In product design, a synesthetic channel is constructed: color + shape channel; a perceptual reorganization channel: intention + shape channel, forming a multi-channel perceptual system of product appearance perception intention. In the shape channel element, shape representation is divided into different module groups through structural modules for the extraction of perceptual intentions. The grouped modules are then imported into the Midjourney model for machine learning to analyze the information processing logic mapping relationship between the input form and the output result. From the evolution of handwheel design, the cognition of the handwheel begins with behavior, first serving as a guide for behavioral operation, i.e., seeing the shape, processing the seen information into perceptual information, and then transforming it into behavioral guidance information through memory. Based on this principle, a decision is made on the product module design generated by AI. Secondary decision-making involves making styling decisions based on design goals… This secondary decision-making process categorizes decisions based on constructed multi-channel elements. It obtains the methods and results for extracting perceptual intentions. Hierarchical clustering analysis is applied for decision analysis, with the following steps: ① Establishing a multimodal hierarchical original value matrix for product styling; ② Data aggregation to determine the distance matrix of multimodal cognitive channels; ③ Applying Xsort to draw a tree diagram and classify the results.

[0061] Based on the Kansei-TOPSIS evaluation model, the color emotional quality deviation and similarity of product color schemes are evaluated, and the following four steps are set up:

[0062] The first step is to set the sample set as A = {A}. i,i=1,2,…,16,…,M}. A i These are samples corresponding to Pantone colors.

[0063] The second step is to set the set of sensory attributes as C = {C} j ,j=1,2,…,5,…,N}. C j Composed of a pair of bipolar sensual adjectives K Wj = <k wj -,k wj+ > indicates that k wj－ and k wj+ Let K represent the left and right subjective adjectives, respectively. Let K be the set of bipolar subjective adjectives. W ={ <k wj -, kwj+ >│j=1, 2, …, 5, …, N}. Therefore, a perceptual intention vector table was created through expert review to initially optimize the color scheme.

[0064] The third step involves combining the traditional semantic difference method with the hesitant fuzzy set proposed by Torra, defining quantitative emotional preferences based on expert discussion, allowing the membership degree of an element to be multiple different values, and setting any value within a range.

[0065] The fourth step is to collect expert evaluations and construct a hesitant, ambiguous, and subjective evaluation matrix H'. The expert panel E = {E...} i The experts scored and evaluated the color configuration samples, taking into full account the overall opinions of the expert group, and gave a hesitant and fuzzy subjective evaluation matrix H' = [h' ij ]M×N, where h'ij is a hesitant fuzzy element, representing scheme A i In the emotional attribute C j The evaluation value below.

[0066] Example 2

[0067] An intelligent measurement and control device includes a sensing module, an appearance module, a machine learning module, a user interaction module, a multimodal model, an AIGC model, a decision-making module, and an evaluation module;

[0068] The sensing module is used to sense the shape of the device and user behavior information. This module initially collects user needs and device status through information processing and perception conversion.

[0069] The shape module is used to divide the shape of the device into different module groups based on the information collected by the sensing module, and to analyze and design it through sensory intention extraction.

[0070] The machine learning module is used to train the system using the collected data, analyze the logical mapping relationship between the input form and the output result, and optimize the design.

[0071] The user interaction module is used to analyze the user's intentions and design prompts through human-computer collaboration, ensuring that the design process meets the user's needs.

[0072] The multimodal model is used to address the issue of prompt word design, performing semantic logic decomposition and matching it with the user's cognition to generate a preliminary design scheme.

[0073] The AIGC model is used to generate modeling renderings of device components. It uses machine learning to generate detailed product designs by drawing creative sketches and simple models.

[0074] The decision-making module is used to make preliminary and secondary decisions based on multi-channel factors and emotional intentions, optimize the design objectives, and ensure that the final design meets user needs.

[0075] The evaluation module uses the Kansei-TOPSIS evaluation model to evaluate the color emotional quality deviation and similarity of the product color configuration scheme, ensuring that the product appearance design meets the user's emotional needs.

[0076] Although 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 this invention should be understood in the ordinary sense by those skilled in the art to which this invention pertains. Terms such as "comprising" or "including" as used in this invention mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but may also include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships, and these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0077] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An industrial design and application method for an intelligent measurement and control device, characterized in that, This includes the construction and application of the AIGC agile industrial design model, human-machine collaborative design process, construction of application paradigms for industrial product generation scenarios, model optimization decision-making based on cognitive analysis, and application case studies 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: AIGC is introduced to assist in industrial product design. AIGC's image generation function is based on Generative Adversarial Network (GAN). S1.1: Constructing an interaction design paradigm for behavior-scenario-product; S1.2: Establish system modeling based on functional elements, with IDEF0 used for functional modeling and IDEF8 used for user interface modeling; The application scenario modeling includes the following steps: S2: Using the IDEF modeling method, describe the functions and user interface of the torque measuring instrument manufacturing system; S2.1: Analyze the multimodal forms of information flow, construct a mapping model, and span the product layer, interaction layer, and user layer; 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 method; The human-computer collaborative design process includes the design, generation, and optimization of input and prompt words; The input and prompt word design includes the following steps: S3: Determine design intent by inputting prompts, 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 imagery; The generation and optimization process includes the following steps: S4: Utilize the Midjourney generative AI model to generate a library of modeling result examples; S4.1: The generated graphical information is iteratively optimized through multiple machine learning and model training processes; S4.2: Construct a multi-channel perception system and optimize the generated results through decision-making; The construction of the application paradigm for the industrial product generation scenario includes the practical application of industrial design methods; The practical application of the industrial design method includes the following steps: S5: Define the functional and structural system of the designed product and generate a design prototype; S5.1: Combines AI models with design thinking to quickly generate design drawings; S5.2: Build an agile industrial design model and obtain optimized collaborative design methods; The model-based optimization decision-making based on cognitive analysis includes the construction of a multi-channel perception system and sensory evaluation and decision-making; The construction of the multi-channel perception system includes the following steps: S6: Analyze the process of converting perceptual representations into behavioral representations; S6.1: Constructing synesthetic channels: color + shape, intention + shape; S6.2: Machine learning and optimization analysis using the Midjourney model; The aforementioned emotional evaluation and decision-making includes the following steps: S7: Set up a set of emotional attributes to define quantitative emotional preferences; S7.1: Construct a hesitant, ambiguous, and perceptual evaluation matrix and conduct expert evaluation; S7.2: Apply hierarchical cluster analysis to optimize color configuration decisions.

2. An intelligent measurement and control device, applicable to the industrial design application method of the intelligent measurement and control device as described in claim 1, characterized in that, It includes a perception module, an appearance module, a machine learning module, a user interaction module, a multimodal model, an AIGC model, a decision-making module, and an evaluation module; The sensing module is used to sense the shape of the device and user behavior information. This module initially collects user needs and device status through information processing and perception conversion. The shape module is used to divide the shape of the device into different module groups based on the information collected by the sensing module, and to analyze and design it through sensory intention extraction. The machine learning module is used to train the system using the collected data, 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 intentions and design prompts through human-computer collaboration, ensuring that the design process meets the user's needs. The multimodal model is used to address the issue of prompt word design, performing semantic logic decomposition and matching it with the user's cognition to generate a preliminary design scheme. The AIGC model is used to generate modeling renderings of device components. It uses machine learning to generate detailed product designs by drawing creative sketches and simple models. The decision-making module is used to make preliminary and secondary decisions based on multi-channel factors and emotional intentions, optimize the design objectives, 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, ensuring that the product appearance design meets the user's emotional needs.

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