Clothing material database sustainability analysis method based on AI algorithm

Abstract

The invention discloses a clothing material database sustainability analysis method based on an AI algorithm, and particularly relates to the technical field of clothing materials.The specific method includes the following steps that firstly, a clothing material database is established; step 2, carrying out material identification by using an image identification technology; 3, performing data fusion and multi-dimensional analysis; 4, sustainability evaluation and index innovation are carried out; step 5, application of the intelligent recommendation system; 6, the transparency and traceability of the supply chain are improved; step 7, intelligent processing of user interaction and feedback; and 8, designing a user interface and interaction. According to the invention, by integrating an advanced AI algorithm and technology, the accuracy improvement of material identification, comprehensive data analysis, optimized sustainable evaluation, personalized recommendation service, enhanced supply chain transparency and intelligent user interaction processing are realized, and the digital transformation and sustainable development of the clothing industry are jointly promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of clothing materials, and more particularly, to a sustainability analysis method for a clothing material database based on an AI algorithm. Background Art

[0002] With the rapid development of the fashion industry, the sustainability analysis of clothing materials has become increasingly important. In recent years, artificial intelligence (AI) technology has made significant progress, especially deep learning and machine learning algorithms. These algorithms have demonstrated powerful capabilities in areas such as image recognition and natural language processing (NLP). In the clothing industry, AI algorithms have been widely used in multiple links such as design, production, and sales. For example, by using deep learning models to identify clothing images, quality inspection and classification can be performed efficiently.

[0003] In practical applications, existing sustainability analysis methods for clothing material databases, while the combination of big data and AI algorithms offers new possibilities for sustainability analysis, still lack data integrity and quality. Despite the many conveniences offered by AI technology, users may question AI's decision-making process and results. Therefore, building a comprehensive and accurate material database and collecting a large amount of diverse data to support these questions has become a current challenge. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a sustainability analysis method for a clothing material database based on an AI algorithm.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a sustainability analysis method for a clothing material database based on an AI algorithm, the specific steps of which are as follows: Step 1: Establish a clothing material database, including data collection and preprocessing; Step 2: Use image recognition technology to identify materials; Step 3: Data fusion and multi-dimensional analysis; Step 4: Sustainability assessment and indicator innovation; Step 5: Application of intelligent recommendation system; Step 6: Improve supply chain transparency and traceability; Step 7: Intelligent processing of user interaction and feedback; Step 8. User interface and interaction design.

[0006] As a further improvement to the technical solution of the present invention, the data collection in step 1 is used to collect detailed information on clothing materials, including sources, production processes, physical and chemical properties, costs, and environmental impacts, to establish a material database; preprocessing is used to clean and standardize the data to ensure data quality and consistency.

[0007] As a further improvement of the technical solution of the present invention, image recognition technology is used to identify materials in step 2. By introducing high-resolution image recognition technology, the ability to recognize subtle features of materials is improved. Combined with edge computing and cloud computing, real-time image analysis and material identification are performed to reduce processing delays.

[0008] As a further improvement of the technical solution of the present invention, the data fusion in step three refers to the integration of multiple data sources, that is, the integration of supply chain data, consumer feedback, and market dynamic information to build a comprehensive material evaluation system; multidimensional analysis refers to the use of big data technology to deeply explore the multidimensional relationship between material performance and market demand.

[0009] As a further improvement to the technical solution of the present invention, the fourth step of sustainability assessment and indicator innovation is used to explore and apply new sustainability evaluation indicators, including a comprehensive environmental impact index based on the life cycle, and continuously update and optimize the evaluation indicators based on the latest environmental policies and scientific research.

[0010] As a further improvement of the technical solution of the present invention, the application of the intelligent recommendation system in step five mainly refers to context-aware technology and personalized recommendation. Among them, context-aware technology adjusts and optimizes the material recommendation strategy in combination with the user's real-time environment and needs; personalized recommendation continuously improves the accuracy of recommendation and personalized service through machine learning and user behavior data.

[0011] As a further improvement to the technical solution of the present invention, improving the transparency and traceability of the supply chain in step 6 refers to applying blockchain technology to ensure the authenticity and non-tamperability of material information, improve the transparency of the supply chain, and conduct full-chain material traceability, establish a complete material traceability system, and enable consumers to easily obtain full-chain information about the product.

[0012] As a further improvement of the technical solution of the present invention, the intelligent processing of user interaction and feedback in step seven refers to developing an intelligent voice interaction system to simplify the user operation process and provide a convenient feedback mechanism, and using natural language processing technology to automatically analyze user feedback, quickly respond to and improve services.

[0013] As a further improvement to the technical solution of the present invention, the user interface and interaction design in step eight refers to designing an intuitive and easy-to-use user interface for users to query, screen, and compare the sustainability, performance, and cost information of different materials, and establishing a user feedback mechanism to continuously optimize and improve the AI ​​algorithm and material database.

[0014] Beneficial effects of the present invention: 1. Image recognition technology, especially high-resolution image recognition, can more accurately identify and classify clothing materials. Even subtle features can be captured, greatly improving the accuracy of material recognition and reducing human errors. 2. Data fusion technology integrates diverse data from different sources, including supply chain information, market trends, and consumer feedback, to form a comprehensive material evaluation system. This multi-dimensional analysis provides deeper market insights and decision support; 3. By introducing new sustainability evaluation indicators and dynamic assessment models, the sustainability of materials can be assessed more accurately, helping companies make more environmentally friendly material choices, thereby enhancing their social responsibility image and meeting consumer demand for sustainable products. 4. The intelligent recommendation system can provide highly personalized material recommendations based on users' personal preferences and needs, which not only improves the user experience but also promotes product sales and market competitiveness; 5. By applying blockchain technology and a full-chain material traceability system, the authenticity and immutability of material information are ensured, the transparency of the supply chain is improved, which helps to build consumer trust and enhance brand image; 6. User feedback can be automatically analyzed and processed, allowing companies to respond to user needs more quickly and improve products and services, thereby enhancing interaction between companies and users and improving customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] As attached Figure 1 The sustainability analysis method for clothing material database based on AI algorithm is shown in the figure. The specific steps are as follows: Step 1: Establish a clothing material database, including data collection and preprocessing; Step 2: Use image recognition technology to identify materials; Step 3: Data fusion and multi-dimensional analysis; Step 4: Sustainability assessment and indicator innovation; Step 5: Application of intelligent recommendation system; Step 6: Improve supply chain transparency and traceability; Step 7: Intelligent processing of user interaction and feedback; Step 8. User interface and interaction design.

[0018] Preferably, the data collection in step 1 is used to collect detailed information on clothing materials, including sources, production processes, physical and chemical properties, costs, and environmental impacts, and to establish a material database; and preprocessing is used to clean and standardize the data to ensure data quality and consistency.

[0019] Preferably, in step 2, image recognition technology is used to identify materials. By introducing high-resolution image recognition technology, the ability to identify subtle features of materials is improved. Combined with edge computing and cloud computing, real-time image analysis and material identification are performed to reduce processing delays.

[0020] Preferably, data fusion in step three refers to the integration of multiple data sources, that is, integrating supply chain data, consumer feedback, and market dynamic information to build a comprehensive material evaluation system; multidimensional analysis refers to the use of big data technology to deeply explore the multidimensional relationship between material performance and market demand.

[0021] Preferably, step 4, sustainability assessment and indicator innovation, is used to explore and apply new sustainability evaluation indicators, including a comprehensive environmental impact index based on the life cycle, and continuously update and optimize the evaluation indicators based on the latest environmental policies and scientific research.

[0022] Preferably, the application of the intelligent recommendation system in step five mainly refers to context-aware technology and personalized recommendation, wherein context-aware technology is to adjust and optimize the material recommendation strategy in combination with the user's real-time environment and needs; personalized recommendation is to continuously improve the accuracy of recommendation and personalized service through machine learning and user behavior data.

[0023] Preferably, improving the transparency and traceability of the supply chain in step six refers to applying blockchain technology to ensure the authenticity and non-tamperability of material information, improve the transparency of the supply chain, conduct full-chain material traceability, and establish a complete material traceability system so that consumers can easily obtain full-chain information of the product.

[0024] Preferably, the intelligent processing of user interaction and feedback in step seven refers to developing an intelligent voice interaction system to simplify the user operation process and provide a convenient feedback mechanism, and using natural language processing technology to automatically analyze user feedback, quickly respond to and improve services.

[0025] Preferably, the user interface and interaction design in step eight refers to designing an intuitive and easy-to-use user interface for users to query, screen, and compare the sustainability, performance, and cost information of different materials, and establishing a user feedback mechanism to continuously optimize and improve the AI ​​algorithm and material database.

[0026] In summary, the present invention designs a sustainability analysis method for clothing material database based on AI algorithm. The specific steps are as follows: First, data collection and preprocessing are carried out. Data collection is used to collect detailed information on clothing materials, including sources, production processes, physical and chemical properties, costs, and environmental impacts, and to establish a material database. Preprocessing is used to clean and standardize the data to ensure data quality and consistency. Secondly, by introducing high-resolution image recognition technology, the ability to identify subtle material features is improved. Combined with edge computing and cloud computing, real-time image analysis and material identification can be performed to reduce processing delays. Then, we integrate multiple data sources, namely supply chain data, consumer feedback, and market dynamics, to build a comprehensive material evaluation system. We also leverage big data technology to deeply explore the multi-dimensional correlation between material performance and market demand. Then, explore and apply new sustainability evaluation indicators, including a comprehensive environmental impact index based on the life cycle, and continuously update and optimize the evaluation indicators based on the latest environmental policies and scientific research; Then, context-aware technology and personalized recommendations are introduced. Context-aware technology adjusts and optimizes material recommendation strategies based on the user's real-time environment and needs; personalized recommendations continuously improve the accuracy of recommendations and personalized services through machine learning and user behavior data. Furthermore, blockchain technology can be applied to ensure the authenticity and immutability of material information, improve the transparency of the supply chain, and conduct full-chain material traceability. A complete material traceability system can be established to enable consumers to easily obtain full-chain information on products. Furthermore, we will develop an intelligent voice interaction system to simplify user operation processes and provide a convenient feedback mechanism. We will also use natural language processing technology to automatically analyze user feedback, quickly respond to and improve services. Finally, an intuitive and easy-to-use user interface will be designed for users to query, filter, and compare the sustainability, performance, and cost information of different materials. A user feedback mechanism will also be established to continuously optimize and improve the AI ​​algorithm and material database.

[0027] By integrating the technologies involved in the aforementioned method steps, this technical solution uses image recognition technology, particularly high-resolution image recognition, to more accurately identify and classify clothing materials. Even subtle features can be captured, significantly improving the accuracy of material identification and reducing human error. Data fusion technology integrates diverse data from various sources, including supply chain information, market trends, and consumer feedback, to form a comprehensive material evaluation system. This multi-dimensional analysis provides deeper market insights and decision-making support. By introducing new sustainability evaluation indicators and dynamic assessment models, the sustainability of materials can be more accurately assessed, helping companies make more environmentally friendly material choices, enhancing their social responsibility image, and meeting consumer demand for sustainable products. The intelligent recommendation system can provide highly personalized material recommendations based on users' personal preferences and needs, improving the user experience and boosting product sales and market competitiveness. The application of blockchain technology and a full-chain material traceability system ensures the authenticity and immutability of material information, enhances supply chain transparency, helps build consumer trust, and enhances brand image. User feedback can be automatically analyzed and processed, allowing companies to respond to user needs more quickly and improve products and services, enhancing interaction between companies and users and improving customer satisfaction.

[0028] In summary, the AI-based clothing material database sustainability analysis method designed in the present invention integrates advanced AI algorithms and technologies to achieve improved accuracy in material identification, comprehensive data analysis, optimized sustainability assessment, personalized recommendation services, enhanced supply chain transparency, and intelligent user interaction processing. These technical effects jointly promote the digital transformation and sustainable development of the clothing industry.

[0029] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A sustainability analysis method for clothing material database based on AI algorithm, characterized by: The specific steps are as follows: Step 1: Establish a clothing material database, including data collection and preprocessing; Step 2: Use image recognition technology to identify materials; Step 3: Data fusion and multi-dimensional analysis; Step 4: Sustainability assessment and indicator innovation; Step 5: Application of intelligent recommendation system; Step 6: Improve supply chain transparency and traceability; Step 7: Intelligent processing of user interaction and feedback; Step 8. User interface and interaction design.

2. The AI ​​algorithm-based clothing material database sustainability analysis method according to claim 1, characterized in that: The data collection in step 1 is used to collect detailed information on clothing materials, including sources, production processes, physical and chemical properties, costs, and environmental impacts, to establish a material database; Preprocessing is used to clean and standardize data.

3. The AI ​​algorithm-based clothing material database sustainability analysis method according to claim 1, characterized in that: In the step 2, image recognition technology is used to identify materials. By introducing high-resolution image recognition technology, real-time image analysis and material identification are performed in combination with edge computing and cloud computing.

4. The AI ​​algorithm-based clothing material database sustainability analysis method according to claim 1, characterized in that: The data fusion in step three refers to the integration of multiple data sources, that is, the integration of supply chain data, consumer feedback and market dynamic information to build a comprehensive material evaluation system; multi-dimensional analysis refers to the use of big data technology to deeply explore the multi-dimensional relationship between material performance and market demand.

5. The AI ​​algorithm-based clothing material database sustainability analysis method according to claim 1, characterized in that: The fourth step, sustainability assessment and indicator innovation, is used to explore and apply new sustainability evaluation indicators, including a comprehensive environmental impact index based on the life cycle, and continuously update and optimize the evaluation indicators based on the latest environmental policies and scientific research.

6. The AI ​​algorithm-based clothing material database sustainability analysis method according to claim 1, characterized in that: The application of the intelligent recommendation system in step five mainly refers to context-aware technology and personalized recommendation. Context-aware technology adjusts and optimizes material recommendation strategies based on the user's real-time environment and needs; personalized recommendation provides personalized recommendation services to users through machine learning and user behavior data.

7. The AI ​​algorithm-based clothing material database sustainability analysis method according to claim 1, characterized in that: Improving supply chain transparency and traceability in step six refers to applying blockchain technology to trace materials throughout the entire chain and establish a complete material traceability system.

8. The AI ​​algorithm-based clothing material database sustainability analysis method according to claim 1, characterized in that: The intelligent processing of user interaction and feedback in step seven refers to developing an intelligent voice interaction system to simplify the user operation process and provide a convenient feedback mechanism, and using natural language processing technology to automatically analyze user feedback and respond.

9. The AI ​​algorithm-based clothing material database sustainability analysis method according to claim 1, characterized in that: The user interface and interaction design in step eight refers to designing an intuitive and easy-to-use user interface for users to query, filter, and compare the sustainability, performance, and cost information of different materials, and establishing a user feedback mechanism.

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