Product shape design system

Abstract

The invention discloses a product appearance design system, and belongs to the field of household product design, and the system comprises a personalized creativity generation module which comprises a multivariate data fusion analysis sub-module, a creativity inspiration intelligent recommendation sub-module, and a creativity visualization and interactive design sub-module. The creative inspiration recommendation module is used for collecting multi-source data, performing fusion analysis, generating and recommending creative inspiration and realizing creative visualization and interactive design; the sustainable material selection module is used for establishing a sustainable material database and providing material selection support and life cycle evaluation for product design; the personalized creative generation module provides rich and personalized creative inspiration for designers through multivariate data fusion and an innovation algorithm, and effectively solves the problem of homogenization of appearance design of household articles; the attention mechanism-based fusion method of the multivariate data fusion analysis sub-module can dynamically allocate data source weights and accurately mine design inspiration.

Description

Technical Field

[0001] The present invention relates to the technical field of home product design, and in particular to a product appearance design system. Background Art

[0002] The field of home furnishing product design currently faces numerous challenges. First, product homogeneity is severe. Designers rely on traditional approaches and ignore the individual needs of consumers. As a result, products on the market are similar in appearance, color, and functional layout, limiting consumer choice and hindering competitiveness. Second, sustainable design is insufficient. Design focuses primarily on cost and performance, consuming extensive amounts of non-renewable resources, and making it difficult to recycle discarded products, which aligns with the concept of green development. Furthermore, design data is underutilized. Traditional design relies on small amounts of data and subjective judgment, lacking the ability to integrate and analyze multi-source data from the Internet and the Internet of Things. This leads to a disconnect between design and market demand. Furthermore, collaborative design is inefficient. Product design involves multiple parties, but communication is poor, resulting in delayed and biased information transmission. This makes it difficult to integrate consumer feedback into design, impacting the market performance of home furnishing products. Summary of the Invention

[0003] In view of the deficiencies of the prior art, the present invention provides a product appearance design system, which solves the problems raised in the above background technology.

[0004] Technical Solution: To solve the above technical problems, according to one aspect of the present invention, more specifically, a product appearance design system includes a personalized creative generation module, a sustainable material selection module, a life cycle design module, and a collaborative design and feedback module;

[0005] The personalized creative generation module includes a multi-data fusion analysis submodule, a creative inspiration intelligent recommendation submodule, and a creative visualization and interactive design submodule;

[0006] Multivariate data fusion analysis submodule: collects various types of data, including conventional data such as global home design trends, consumer aesthetic preferences, and cultural and artistic elements, and expands data collection channels with the help of IoT technology. By deploying sensors in smart home devices and home display venues, interactive data such as the frequency of users using sofas, sitting habits, lamp brightness, and color temperature adjustment habits are collected to accurately grasp users' actual needs and behavior patterns. Deep learning algorithms are used to fuse and analyze multi-source data, and convolutional neural networks (CNNs) are used to extract features such as color, shape, and material texture in design images; recurrent neural networks (RNNs) are used to process text data to understand the semantics and emotional tendencies of design concepts and consumer evaluations; reinforcement learning algorithms are used to model IoT behavior data to explore the potential correlation between user behavior and home design. In the data fusion process, a fusion weight calculation method based on the attention mechanism is introduced to more accurately mine design inspiration information. Design image data feature vector I, text data feature vector T, and IoT behavior data feature vector B; the fused feature vector F, the attention weight calculation function is defined as:

[0007]

[0008] where ω Ι 、ω T 、ω B is a learnable weight vector, and the fused feature vector F is calculated by the following formula: F = α Ι I+α T T+α B B. Dynamically assign weights to different data sources through the attention mechanism, enabling the system to adaptively integrate data based on its importance and more accurately mine design inspiration information.

[0009] Creative Inspiration Intelligent Recommendation Submodule: Based on the results of multivariate data fusion analysis, a creative inspiration intelligent recommendation model using generative adversarial network (GAN) technology is constructed. The model consists of a generator and a discriminator. The generator generates home furnishing design concepts based on user preferences and design requirements, including unique shapes, innovative functional layouts, or novel color combinations. The discriminator evaluates the generated concepts to determine whether they conform to design trends, user aesthetics, and practicality requirements. Through adversarial training between the two, design concepts are optimized to be more creative and feasible. During the recommendation process, a personalized recommendation algorithm is combined to provide customized recommendations based on the designer's historical design preferences and the characteristics of the current project. A visual recommendation interface is also provided to support designers' interactive operations, such as filtering and sorting recommended inspirations and adjusting recommendation parameters to meet personalized needs.

[0010] Creative Visualization and Interactive Design Sub-Module: Leveraging virtual reality (VR) and augmented reality (AR) technologies, this module creates an immersive creative visualization and interactive design environment for designers. In a VR environment, designers can present recommended creative inspirations as 3D models, freely observe, touch, and manipulate the models, comprehensively examine the design effects, and experience the spatial layout and proportional relationships. For example, they can evaluate the matching effect of a sofa with surrounding furniture in a virtual living room scene. Utilizing natural interaction technologies such as gesture recognition and voice interaction, designers can modify and adjust design models in real time in a VR / AR environment, and the system will provide real-time feedback on the modification effects. In addition, the sub-module supports the combination and splicing of multiple creative inspirations, helping designers quickly generate new design solutions and improve design efficiency and flexibility.

[0011] Furthermore, the Sustainable Materials Selection module builds a comprehensive sustainable materials database, covering information on renewable materials, biodegradable materials, and environmentally friendly composite materials. During the design process, designers can select suitable materials from the database based on product functionality and design requirements. Using the material life cycle assessment function, designers can fully understand the environmental impact of materials and make appropriate choices.

[0012] Furthermore, the life cycle design module is used to consider the entire life cycle of the product, adopt a modular design concept, focus on the disassembly and maintainability of the product, design a structure that is easy to disassemble and classify for recycling, and improve resource utilization.

[0013] Furthermore, the collaborative design and feedback module is used to build an online collaborative design platform to support the collaborative work of designers, manufacturers, suppliers, and consumers. All parties can communicate, evaluate, and provide feedback on design solutions on the platform. Designers can then optimize designs based on the feedback to ensure that products meet market demand.

[0014] The beneficial effects of a product appearance design system of the present invention are:

[0015] The personalized creative generation module of this invention uses multivariate data fusion and innovative algorithms to provide designers with rich and personalized creative inspiration, effectively addressing the homogeneity of home furnishing design and meeting consumers' demand for personalized home furnishings. The attention-based fusion method of the multivariate data fusion analysis submodule dynamically assigns data source weights to accurately mine design inspiration.

[0016] The sustainable material selection module and life cycle design module promote the implementation of sustainable design concepts and reduce resource waste and environmental pollution based on material selection and product life cycle management.

[0017] The collaborative design and feedback module strengthens communication and cooperation among all parties, making the design plan more in line with actual market needs and improving the market competitiveness of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0019] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0020] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0021] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] Reference Figure 1 , a product appearance design system, including the following implementation methods:

[0023] Implementation of the personalized creative generation module: In the multivariate data fusion analysis submodule, web crawler technology is used to collect various design data on the Internet, and user interaction data is collected through IoT sensors. After preprocessing the collected data, it is input into the corresponding deep learning algorithm model for analysis. For the fusion weight calculation based on the attention mechanism, the learnable weight vector ω is first randomly initialized. Ι 、ω T 、ω B During the training process, these weights are continuously adjusted using a backpropagation algorithm, ensuring that the fused feature vector F better reflects the comprehensive characteristics of the data, thereby unearthing more valuable design inspiration. In the Creative Inspiration Intelligent Recommendation submodule, a generative adversarial network model is trained to continuously optimize the generator and discriminator. Based on the designer's individual needs, design inspiration is pushed through a personalized recommendation algorithm, and interactive operations are performed within a visual interface. In the Creative Visualization and Interactive Design submodule, designers enter a VR / AR environment to observe, adjust, and combine recommended creative models, obtaining real-time design feedback.

[0024] Implementation of the Sustainable Materials Selection Module: Continuously update the sustainable materials database to ensure data accuracy and timeliness. During the design process, designers screen materials in the database based on product function and design direction, and refer to material life cycle assessment reports to select appropriate sustainable materials.

[0025] Implementation of Lifecycle Design Modules: During the initial stages of product design, a modular design approach is employed to divide functional modules and determine inter-module connections and fastening methods. During the design process, the ease of disassembly and repair, as well as the feasibility of recycling and reuse, are fully considered, and corresponding design plans are developed.

[0026] Implementation of a collaborative design and feedback module: Build a stable online collaborative design platform. After all parties register and log in, designers publish their design proposals, and other participants provide evaluation and feedback. Designers regularly collect feedback and optimize and adjust the design proposal based on it.

[0027] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A product appearance design system, comprising a personalized creative generation module, a sustainable material selection module, a lifecycle design module, and a collaborative design and feedback module, characterized by: The personalized creative generation module includes a multi-source data fusion analysis submodule, a creative inspiration intelligent recommendation submodule, and a creative visualization and interactive design submodule, which are used to collect multi-source data and perform fusion analysis, generate and recommend creative inspiration, and realize creative visualization and interactive design; The sustainable material selection module establishes a sustainable material database to provide material selection support and life cycle assessment for product design; The life cycle design module optimizes the design of household products from the perspective of the entire product life cycle, taking into account disassembly, repairability, and recycling; The collaborative design and feedback module builds an online platform to promote collaborative design and feedback optimization among all parties.

2. A product appearance design system according to claim 1, characterized in that: The workflow of the multivariate data fusion analysis submodule is as follows: S1. Data collection step: using web crawlers to collect general design data and IoT sensors to collect user interaction data with household items; S2. Data preprocessing step: Clean and convert the collected data to meet the algorithm input requirements; S3, feature extraction step: use convolutional neural network to process design image data, recurrent neural network to process text data, and reinforcement learning algorithm to process behavior data, to obtain design image data feature vector I, text data feature vector T and IoT behavior data feature vector B; S4, fusion analysis step: introduce the fusion weight calculation method based on the attention mechanism, according to the formula: Calculate the attention weight α Ι , α T , α B , where ω Ι 、ω T 、ω B Is a learnable weight vector; then through the formula F = α Ι I+α T T+α B B calculates the fused feature vector F and mines design inspiration information.

3. A product appearance design system according to claim 1, characterized in that: The workflow of the creative inspiration intelligent recommendation submodule is as follows: S1. Model construction steps: Build a creative inspiration intelligent recommendation model based on generative adversarial network technology, including a generator and a discriminator; S2, model training step: the generator generates design concepts, the discriminator evaluates them, and the two conduct adversarial training to optimize the design concepts; S3. Recommendation and interaction steps: Combine personalized recommendation algorithms to provide customized recommendations for designers and support designer interaction operations through a visual interface.

4. A product appearance design system according to claim 1, characterized in that: The workflow of the creative visualization and interaction design submodule is as follows: S1. Environment construction steps: Use VR and AR technologies to build an immersive creative design environment; S2, model presentation step: presenting the recommended creative inspiration in the form of a 3D model in a VR / AR environment; S3. Interactive design steps: Use gesture recognition and voice interaction technology to modify, adjust, and combine models in real time, and the system will provide real-time feedback on the design results.

5. A product appearance design system according to claim 1, characterized in that: In the design process of the sustainable material selection module, designers screen materials from the sustainable material database based on product functions and design requirements, and make selections based on material life cycle assessment results.

6. A product appearance design system according to claim 1, characterized in that: The life cycle design module adopts a modular design method to divide household items into functional modules and design a structure that is easy to disassemble, repair, and recycle.

7. A product appearance design system according to claim 1, characterized in that: In the collaborative design and feedback module, designers publish design plans on an online platform, manufacturers, suppliers and consumers evaluate and provide feedback, and designers optimize the design plans based on the feedback.

Images