SAAS material coding method based on artificial intelligence

Abstract

The invention provides an SAAS material coding method based on artificial intelligence, and relates to the technical field of material management in digital factory construction, and the method comprises the steps: S1, building an SAAS material coding platform which is composed of a management center, a word segmentation subsystem and a matching and filing subsystem; s2, constructing an industry knowledge base on the platform, wherein the industry knowledge base comprises a standard coding specification base and a description matching base; s3, receiving material description information submitted by a user, and performing word segmentation and semantic analysis on the material description information through a word segmentation subsystem; and S4, matching the material description information after word segmentation with an industry knowledge base by using an AI fuzzy matching technology to generate a material code. According to the method, an efficient and universal material coding engine can be established, a material coding library and a description knowledge library can be constructed according to the industry, and rapid and accurate material coding can be realized by utilizing an AI fuzzy matching technology.

Description

Technical Field

[0001] The present invention relates to the technical field of material management in the construction of digital factories. Specifically, it relates to a SAAS material coding method based on artificial intelligence. Background Art

[0002] Due to the non-standard description of materials based on natural language in existing material coding methods, there are many difficulties in the coding process. Although the manufacturing industry adopts a coding method according to category attributes in the material code specification, it faces a large number of challenges in actual applications. Therefore, there is an urgent need for a material coding method that can self-learn, be efficient and accurate. Summary of the Invention

[0003] The problem solved by the present invention is how to improve the efficiency and accuracy of material management.

[0004] To solve the above problems, the present invention provides a SAAS material coding method based on artificial intelligence, including the steps of:

[0005] S1: Build a SAAS material coding platform, which consists of a management center, a word segmentation subsystem and a matching and filing subsystem;

[0006] S2: Build an industry knowledge base on the platform, including a standard coding specification library and a description matching library;

[0007] S3: Receive the material description information submitted by the user, and perform word segmentation and semantic analysis on the material description information through the word segmentation subsystem;

[0008] S4: Use AI fuzzy matching technology to match the word-segmented material description information with the industry knowledge base to generate a material code;

[0009] S5: Store the generated material code in the matching and filing subsystem and establish a corresponding material file.

[0010] Further, the AI fuzzy matching technology in step S4 includes at least one of a matching method based on part-of-speech tagging, a matching method based on vocabulary, and a matching method based on machine learning.

[0011] Further, the matching method based on part-of-speech tagging introduces part-of-speech information into the matching process by analyzing the part-of-speech of words in a sentence.

[0012] Further, the matching method based on vocabulary constructs a vocabulary list containing common words and technical terms, and processes the vocabulary to match the words in the sentence.

[0013] Further, the machine learning-based matching method uses support vector machines and neural network machine learning models for feature extraction and classification to automatically match fuzzy strings.

[0014] Further, the management center includes user management, permission control, external interface management, and system log recording.

[0015] Further, the matching and filing subsystem further includes coding review and correction.

[0016] Further, it further includes the steps of:

[0017] S6: Dynamically update and optimize the industry knowledge base.

[0018] The present invention adopts the above technical solutions and includes the following beneficial effects:

[0019] The present invention can build an efficient and general material coding engine, construct a material coding library and a description knowledge base according to the industry, and use AI fuzzy matching technology to achieve fast and accurate material coding. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 For the process of the AI-based SAAS material coding method provided by the embodiment of the present invention Figure 1 ;

[0021] Figure 2 For the process of the AI-based SAAS material coding method provided by the embodiment of the present invention Figure 2 。 DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the accompanying drawings.

[0023] The following are the specific embodiments of the present invention and, in combination with the accompanying drawings, further describe the technical solutions of the present invention, but the present invention is not limited to these embodiments.

[0024] Embodiment

[0025] This embodiment provides an AI-based SAAS material coding method. As Figure 1 and Figure 2 shown, this method includes the steps of:

[0026] S1: Build a SAAS material coding platform, which consists of a management center, a word segmentation subsystem, and a matching and filing subsystem;

[0027] S2: Build an industry knowledge base on the platform, including a standard coding specification library and a description matching library;

[0028] S3: Receive the material description information submitted by the user, and perform word segmentation and semantic analysis on the material description information through the word segmentation subsystem;

[0029] S4: Use the AI fuzzy matching technology to match the word-segmented material description information with the industry knowledge base to generate a material code;

[0030] S5: Store the generated material code in the matching and filing subsystem, and establish a corresponding material file.

[0031] Among them, the AI fuzzy matching technology in step S4 includes at least one of a matching method based on part-of-speech tagging, a matching method based on vocabulary, and a matching method based on machine learning.

[0032] Among them, the matching method based on part-of-speech tagging introduces part-of-speech information into the matching process by analyzing the part-of-speech of words in a sentence.

[0033] Among them, the matching method based on vocabulary constructs a vocabulary list containing common words and technical terms, and processes the vocabulary to match the words in the sentence.

[0034] Among them, the matching method based on machine learning uses support vector machines and neural network machine learning models for feature extraction and classification to automatically match fuzzy strings.

[0035] Among them, the management center includes user management, permission control, external interface management, and system log recording.

[0036] Among them, the matching and filing subsystem also includes code review and correction.

[0037] Refer to Figure 2 , and also includes the steps:

[0038] S6: Dynamically update and optimize the industry knowledge base.

[0039] Specifically, the platform architecture: The SAAS material coding platform of the present invention is composed of a management center and three subsystems:

[0040] Management center: Responsible for overall management work such as user management, external interface management, and industry classification management.

[0041] Industry knowledge subsystem: Used for word maintenance, classification setting, weight setting, etc., to build and maintain an industry-specific material coding library and description knowledge base.

[0042] Matching and filing subsystem: Includes modules such as task management, preview management, conversion management, and coding input management, and is responsible for processing the specific process of material coding.

[0043] Word segmentation subsystem: It performs semantic analysis, word frequency and tag management, etc., and provides accurate text feature representations for AI fuzzy matching.

[0044] Specifically, for the AI fuzzy matching technology: The present invention adopts the following three AI fuzzy matching technologies to improve the accuracy and efficiency of material coding:

[0045] Part-of-speech tagging-based matching method: By analyzing the part of speech of words in a sentence, part-of-speech information is introduced into the matching process to ensure that words with the same part of speech are matched, thereby improving the accuracy of matching.

[0046] Lexical-based matching method: A vocabulary list containing common words and professional terms is constructed, and the vocabulary is normalized through techniques such as stemming and lemmatization, and then matched with the words in the sentence to effectively handle the problems of polysemy and out-of-vocabulary words.

[0047] Machine learning-based matching method: Using machine learning models such as support vector machines (SVM) and neural networks, feature extraction and classification are performed on material description information to achieve automatic matching of fuzzy strings. These methods can automatically learn the feature representations of texts and improve the accuracy and generalization ability of matching.

[0048] Specifically, for the construction of the industry knowledge base: An industry knowledge base is established according to the manufacturing industry standards, including a standard coding specification library and a description matching library. Starting from the industries with high urgency of coding requirements, the industry knowledge base is gradually expanded and improved to provide rich data support and standard basis for material coding.

[0049] Specifically, the part-of-speech tagging-based matching method introduces part-of-speech information into the matching process by analyzing the part of speech of words in a sentence to improve the accuracy of matching.

[0050] Specifically, the lexical-based matching method constructs a vocabulary list containing common words and professional terms, normalizes the vocabulary, and then matches it with the words in the sentence.

[0051] Specifically, the machine learning-based matching method uses machine learning models such as support vector machines (SVM) and neural networks for feature extraction and classification to achieve automatic matching of fuzzy strings.

[0052] Specifically, it also includes the steps of dynamically updating and optimizing the industry knowledge base to adapt to the material description characteristics of different industries.

[0053] Specifically, the management center is also responsible for functions such as user management, permission control, external interface management, and system log recording.

[0054] Specifically, the matching and filing subsystem also includes functions of coding review and correction to ensure the accuracy and compliance of the generated material codes.

[0055] The SAAS material coding platform of the present invention can be deployed on a cloud server and provided to enterprise users via the Internet. Enterprise users can access the platform simply through a browser or dedicated client software and submit material description information for coding. The platform will automatically select the corresponding industry knowledge base for matching based on the industry to which the user belongs and the submitted material description information, and generate accurate material codes. At the same time, the platform will continuously collect user feedback and coding data to optimize the AI fuzzy matching algorithm and the industry knowledge base, improving the accuracy and efficiency of coding.

[0056] Specifically, in a manufacturing enterprise, enterprise users can import various standardized material descriptions into the platform through an interface. The platform first uses a word segmentation subsystem to segment and perform semantic analysis on the material description, and then uses AI fuzzy matching technology to match with the industry knowledge base. During the matching process, the platform will comprehensively consider various methods such as part-of-speech tagging, lexical matching, and machine learning to ensure the accuracy and generalization ability of the matching. Finally, the platform will generate material codes that meet industry standards and store them in the matching and filing subsystem. Enterprise users can query and manage material coding information through the platform at any time, improving the efficiency and accuracy of material management.

[0057] Through the AI-based SAAS material coding method proposed by the present invention, enterprises can significantly improve the accuracy and efficiency of material coding, effectively reducing production costs and management costs caused by coding errors. At the same time, the system supports the material coding requirements of multiple industries and scenarios, demonstrating strong versatility and scalability. In addition, the system also has the ability to continuously learn and optimize, and can quickly adapt to the needs of industry changes and enterprise development, providing solid support for the construction of digital factories.

[0058] This method can build an efficient and general material coding engine, construct a material coding library and a description knowledge base according to the industry, and use AI fuzzy matching technology to achieve fast and accurate material coding.

[0059] Although the present disclosure is disclosed as above, the protection scope of the present disclosure is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present disclosure, and these changes and modifications will all fall within the protection scope of the present invention.

Claims

1. An AI-based SAAS material coding method, characterized in that, Including the steps: S1: Build a SAAS material coding platform, which consists of a management center, a word segmentation subsystem, and a matching and filing subsystem; S2: Build an industry knowledge base on the platform, including a standard coding specification library and a description matching library; S3: Receive the material description information submitted by the user, and perform word segmentation and semantic analysis on the material description information through the word segmentation subsystem; S4: Use AI fuzzy matching technology to match the word-segmented material description information with the industry knowledge base to generate a material code; S5: Store the generated material code in the matching and filing subsystem and establish a corresponding material file.

2. The SAAS material coding method based on artificial intelligence according to claim 1, characterized in that, The AI fuzzy matching technology in step S4 includes at least one of a matching method based on part-of-speech tagging, a matching method based on vocabulary, and a matching method based on machine learning.

3. The SAAS material coding method based on artificial intelligence according to claim 2, wherein The matching method based on part-of-speech tagging introduces part-of-speech information into the matching process by analyzing the part-of-speech of words in a sentence.

4. The SAAS material coding method based on artificial intelligence according to claim 2, wherein The matching method based on vocabulary constructs a vocabulary list containing common words and professional terms, and processes the vocabulary to match the words in the sentence.

5. The SAAS material coding method based on artificial intelligence according to claim 2, wherein The matching method based on machine learning uses support vector machines and neural network machine learning models for feature extraction and classification to automatically match fuzzy strings.

6. The SAAS material coding method based on artificial intelligence according to claim 1, characterized in that, The management center includes user management, permission control, external interface management, and system log recording.

7. The SAAS material coding method based on artificial intelligence according to claim 1, wherein The matching and filing subsystem also includes coding review and correction.

8. The SAAS material coding method based on artificial intelligence according to claim 1, wherein Also including the steps: S6: Dynamically update and optimize the industry knowledge base.

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