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CN-121999944-A - Material data management method and system based on artificial intelligence

CN121999944ACN 121999944 ACN121999944 ACN 121999944ACN-121999944-A

Abstract

The invention relates to the technical field of material informatics and discloses a material data management method and system based on artificial intelligence, wherein the method comprises the following steps of S1, carrying out first-level classification according to the application corresponding to the collected material data; the method comprises the steps of S2, carrying out secondary classification on material data according to the main performance of the material on the basis of primary classification, S3, carrying out tertiary classification on the material data according to the preparation process of the material on the basis of secondary classification, and S4, carrying out quaternary classification on the material data according to the auxiliary performance of the material on the basis of tertiary classification. Through four-level hierarchical association, a complete link of 'usage-main performance-preparation process-auxiliary performance' is established, data of ten major high-molecular fine processing products are effectively planned and managed, efficient support is provided for research and development links such as formula screening, process matching and performance prediction, and therefore the speed of new material research and development and iterative optimization is increased.

Inventors

  • ZHANG XIN
  • JIANG ZHIYING
  • FANG ZHAOHUA

Assignees

  • 房兆华
  • 张鑫

Dates

Publication Date
20260508
Application Date
20260206

Claims (10)

  1. 1. An artificial intelligence-based material data management method is characterized by comprising the following steps: S1, carrying out primary classification on the material data according to the application corresponding to the collected material data so as to form a plurality of material databases; s2, in each material database formed in the step S1, carrying out secondary classification on the material data according to the main performance of the material, so that the material data is divided into a plurality of main performance categories; S3, on the basis of the step S2, classifying the material data into a plurality of preparation process categories according to the preparation process of the material; s4, on the basis of the step S3, carrying out four-level classification on the material data according to the auxiliary performance of the material, dividing the material data into a plurality of auxiliary performance categories, and then storing and sharing the classified material data; Steps S1 to S4 form a four-level hierarchical classification data structure of usage-main property-preparation process-auxiliary property for supporting retrieval and screening of material data by artificial intelligence.
  2. 2. The method according to claim 1, wherein in step S1, the material data is divided into at least a structural molding class, a surface function class, a functional agent class, and an industrial auxiliary class according to the application corresponding to the material data.
  3. 3. The method for managing material data based on artificial intelligence according to claim 2, wherein the structural molding class comprises plastic and rubber products; The surface functional classes include coatings, inks and surface treatment products; The functional preparation comprises an adhesive and a textile chemical product; The industrial auxiliary agent comprises oil products, metal processing liquid and cleaning agent products.
  4. 4. The method for artificial intelligence based material data management according to claim 3, wherein the main performance parameters of the different materials according to step S2 are: the plastic comprises mechanical property, thermal property and environmental protection safety property; The rubber comprises mechanical and elastic properties, ageing resistance and medium resistance; The coating comprises the mechanical property, weather resistance and medium resistance of a coating film and application performance; The printing ink comprises printing and adhesion performance, drying performance and physical and chemical stability; the surface treating agent comprises the treatment effect performance, corrosion resistance and adhesiveness; the adhesive comprises adhesive strength performance, curing performance and environmental adaptability; the textile chemicals comprise dyeing and color fastness properties and functional auxiliary agent properties; The oil comprises physicochemical basic performance, lubrication and protection performance; The metal working fluid comprises lubricating and cooling performance and rust resistance; The cleaning agent comprises decontamination performance and corrosiveness.
  5. 5. The artificial intelligence based material data management method according to claim 3, wherein the preparation process parameters of the different materials according to step S3 are as follows: the method comprises injection molding, extrusion molding and foaming molding for plastics; the method comprises thermoplastic molding, hot press vulcanization molding, extrusion vulcanization molding and injection vulcanization molding for rubber; The coating comprises brushing, spraying, knife coating, curtain coating and dip coating; the printing ink comprises silk screen printing and offset printing; the surface treating agent comprises soaking, spraying, rolling, brushing and electrophoresis; the method comprises coating and hot melting; The method comprises dipping and pad dyeing for textile chemicals; the method comprises blending and filling for oil products; the metal processing liquid comprises blending and circulating; the cleaning agent comprises spraying and ultrasonic treatment.
  6. 6. The artificial intelligence based material data management method according to claim 3, wherein the auxiliary performance parameters of the different materials according to step S4 are: The plastic comprises weather resistance, processing fluidity, corrosion resistance, ageing resistance, flame retardance, sound absorption, dimensional stability, surface flatness, wall thickness uniformity and compressive strength; the rubber comprises weather resistance, environmental protection, flexibility, air tightness, oil resistance and ozone aging resistance; The paint comprises scrub resistance, temperature resistance, stain resistance, VOC content, leveling property and chemical resistance; The ink comprises drying speed, solvent resistance, color consistency, folding resistance, printability, light resistance and chemical resistance; The surface treating agent comprises treatment efficiency, environmental protection, abrasion resistance, construction property, film forming uniformity, film adhesion and compatibility; Aiming at the adhesive, the adhesive comprises water resistance, flexibility, low-temperature fluidity, aging resistance, VOC content and moist heat resistance; the anti-static, antibacterial, sweat stain resistant, air permeability, color migration and wash fastness are aimed at textile chemicals; the oil product comprises anti-emulsifying property, low-temperature fluidity, oxidation resistance, compatibility, rust resistance and sealing property; The metal processing liquid comprises antibacterial property, hard water resistance, cleaning property, environment protection property, defoaming property, cooling stability and cleaning temperature suitability; the detergent comprises low foaming property, biodegradability, rinsing property, stability and compatibility with nonmetal.
  7. 7. The artificial intelligence based material data management method of claim 1, wherein each level in the usage-main performance-preparation process-auxiliary performance four-level hierarchical classification data structure employs a unique encoding rule of a major class encoding-main performance encoding-process encoding-auxiliary performance encoding to form an expandable tree index structure.
  8. 8. The artificial intelligence based material data management method according to claim 1, wherein the collected material data in step S1 is data that has been subjected to normalization processing.
  9. 9. The method according to claim 1, wherein the material data storage in step S4 is a data storage method conforming to the international material standard.
  10. 10. An artificial intelligence based material data management system for use in the artificial intelligence based material data management method of any one of claims 1 to 9, comprising: The data acquisition module is used for acquiring multi-source basic data and carrying out standardized processing on the data; The data classification module classifies the collected material data and forms a corresponding material database; the data storage module is used for storing the classified material data; And the data sharing module is used for transmitting the classified material data to realize material data sharing.

Description

Material data management method and system based on artificial intelligence Technical Field The invention relates to the technical field of material informatics, in particular to a material data management method and system based on artificial intelligence. Background Along with development of scientific technology, the development speed of new materials is required to be higher and higher, the material informatics is a product of combining modern informatics technology with material science research, the development of the material informatics is crucial to the discovery of new materials, the material informatics is taken as a new research field of material science, experimental methods and thinking modes in the material research are changed, and more changes are brought. For materials informatics, material data is extremely important. The material informatics mainly aims at finding a material rule and realizing the prediction function of a new material by analyzing the collected related data of the material, and is the basis of information penetrating through the material development period whether experimental data or calculation data. However, the current material informatics does not have a complete theoretical framework, the research direction is quite scattered, and the effective data management is lacking when the material informatics faces to massive material data. Disclosure of Invention In order to solve the problem of lack of effective management of mass material data in the prior art, the invention discloses a material data management method based on artificial intelligence, which establishes a complete link of 'usage-main performance-preparation process-auxiliary performance' through four-level hierarchical association, effectively plans and manages data of ten major high polymer fine processing products, remarkably improves the retrievability and reusability of the material data, and provides high-efficiency support for research and development links such as formula screening, process matching and performance prediction, thereby accelerating the speed of new material research and development and iterative optimization. In order to achieve the above purpose, the invention adopts the following technical scheme: an artificial intelligence-based material data management method comprises the following steps: S1, carrying out primary classification on the material data according to the application corresponding to the collected material data so as to form a plurality of material databases; s2, in each material database formed in the step S1, carrying out secondary classification on the material data according to the main performance of the material, so that the material data is divided into a plurality of main performance categories; S3, on the basis of the step S2, classifying the material data into a plurality of preparation process categories according to the preparation process of the material; s4, on the basis of the step S3, carrying out four-level classification on the material data according to the auxiliary performance of the material, dividing the material data into a plurality of auxiliary performance categories, and then storing and sharing the classified material data; Steps S1 to S4 form a four-level hierarchical classification data structure of usage-main property-preparation process-auxiliary property for supporting retrieval and screening of material data by artificial intelligence. Optionally, in step S1, the material data is at least divided into a structural molding class, a surface function class, a functional preparation class, and an industrial auxiliary class according to the application corresponding to the material data. Optionally, the structural molding class comprises plastic and rubber products; The surface functional classes include coatings, inks and surface treatment products; The functional preparation comprises an adhesive and a textile chemical product; The industrial auxiliary agent comprises oil products, metal processing liquid and cleaning agent products. Optionally, the main performance parameters of the different materials according to the step S2 are: the plastic comprises mechanical property, thermal property and environmental protection safety property; The rubber comprises mechanical and elastic properties, ageing resistance and medium resistance; The coating comprises the mechanical property, weather resistance and medium resistance of a coating film and application performance; The printing ink comprises printing and adhesion performance, drying performance and physical and chemical stability; the surface treating agent comprises the treatment effect performance, corrosion resistance and adhesiveness; the adhesive comprises adhesive strength performance, curing performance and environmental adaptability; the textile chemicals comprise dyeing and color fastness properties and functional auxiliary agent properties; Aiming at oil products, physical and c