Standardized full-chain AI auxiliary management platform

Abstract

The invention relates to the technical field of AI generation, in particular to a standardized full-chain AI auxiliary management platform, which comprises a demand analysis module used for identifying a standardized demand direction; the project establishment evaluation module is used for generating project establishment priorities; the draft auxiliary module is used for generating a standard draft based on the standardized template and the standardized demand direction; the expert review module is used for reviewing the standard draft based on review experts matched in the standardized demand direction to obtain a release standard; and the release management module is used for generating interpretation information according to the release standard and pushing the release standard and the interpretation information to the target user. According to the method, standardization requirements are recognized through the AI model, project establishment evaluation is quantified, a draft is generated in a templated mode, expert review is intelligently matched, and published content is accurately pushed, so that scientificity, efficiency and consistency of standardization work are effectively improved, and an intelligent management system of a standard full life cycle is constructed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of AI generation technology, and particularly relates to a standardized full-chain AI auxiliary management platform. BACKGROUND

[0002] With the in-depth development of industrial digitization and intelligentization, standardization work plays an increasingly important role in promoting technological innovation, ensuring product quality and promoting industrial collaborative development. However, the current standardization work system is still mainly based on manual processing, and there are problems such as long process, low efficiency, and information asymmetry.

[0003] Firstly, in the standard demand analysis stage, the industry information sources are scattered and updated quickly, and the traditional method of relying on manual collection and experience judgment is difficult to identify the potential standardization demand direction in time, which is easy to cause demand lag or omission, and is difficult to fully reflect the historical standard system, industrial trend and technological innovation degree; in the standard drafting link, the text preparation relies on manual editing and experience inheritance, which leads to low efficiency of draft generation, much repetitive work, and inconsistent format and terminology; in addition, in the expert review stage, there are problems such as long review period, scattered feedback and poor consistency; with the maturity of artificial intelligence (AI), natural language processing (NLP) and knowledge graph, introducing AI technology into the whole life cycle management of standardization has become an effective way to improve the efficiency and intelligent level of work. SUMMARY

[0004] (I) Invention purpose The purpose of the present application is to provide a standardized full-chain AI auxiliary management platform, which identifies standardization demand through AI model, quantifies project evaluation, generates draft through template, intelligently matches expert review and accurately pushes and publishes content, effectively improves the scientificity, efficiency and consistency of standardization work, and builds an intelligent management system for the whole life cycle of standards.

[0005] (II) Technical solution To solve the above problems, the present application provides a standardized full-chain AI auxiliary management platform, comprising: a demand analysis module, a project evaluation module, a drafting assistance module, an expert review module and a release management module; The demand analysis module is used to collect industry information and identify standardization demand direction based on LDA topic model; The project evaluation module is used to evaluate the identified standardization demand direction based on historical standard library information, and generate project priority according to the evaluation result; The drafting assistance module is used to generate a standard draft based on standardization templates and standardization demand direction; The expert review module is used for reviewing the standard draft based on the matched review experts according to the standardized demand direction, and obtaining a published standard; The publishing management module is used for generating interpretation information according to the published standard, and pushing the published standard and the interpretation information to target users.

[0006] In another aspect of the present application, preferably, the demand analysis module comprises an information collection unit, a text preprocessing unit and a topic identification unit; The information collection unit is used for collecting industry policies, enterprise feedback reports and related news data through network crawling to form an original text data set; The text preprocessing unit is used for denoising, word segmentation and word frequency statistics on the original text data set to establish a text feature matrix; The topic identification unit is used for topic extraction and clustering analysis on the text feature matrix based on an LDA topic model to obtain a plurality of potential standardized demand topics, and identifying a standardized demand direction according to a topic heat value.

[0007] In another aspect of the present application, preferably, the input of the LDA topic model comprises the text feature matrix, and the LDA topic model represents each document as a probability distribution of a plurality of potential topics and represents each potential topic as a probability distribution of a word set; The output of the LDA topic model comprises a keyword weight vector corresponding to each potential topic and a document topic weight vector.

[0008] In another aspect of the present application, preferably, the LDA topic model is used for topic extraction and clustering analysis on the text feature matrix to obtain a plurality of potential standardized demand topics, and identifying a standardized demand direction according to a topic heat value, which comprises: Based on the keyword weight vector, a number of keywords with high weight values are determined as a feature word set of the corresponding potential topic through sorting; The feature word set is subjected to semantic clustering and artificial label mapping to generate a topic semantic label to obtain a plurality of potential standardized demand topics; According to the document topic weight vector, the frequency of each potential standardized demand topic in the text set is calculated, According to the frequency, a topic heat value is calculated; According to the topic heat value, all potential standardized demand topics are sorted to identify a standardized demand direction.

[0009] In another aspect of the present application, preferably, the project evaluation module comprises a historical standard information analysis unit, a repeatability evaluation unit, an industrial value evaluation unit and a priority generation unit; The historical standard information analysis unit is configured to extract a keyword set corresponding to the standardized demand direction based on historical standard library information; The repeatability evaluation unit is configured to calculate semantic similarity with historical standards based on the keyword set, and determine the repeatability of the demand direction according to the similarity score to obtain a repeatability evaluation index; The industrial value evaluation unit is configured to calculate an industrial value index according to the number of intellectual property applications and the number of academic paper citations; The priority generation unit is configured to calculate a project evaluation score according to the repeatability evaluation index and the industrial value index, and generate a project priority list according to the evaluation score.

[0010] In another aspect of the present application, the drafting assistance module preferably comprises a standard template analysis unit and a draft generation unit; The standard template analysis unit is configured to read and analyze a preset standardized template, convert the template content into a structured data representation, and obtain an analyzed template; The draft generation unit is configured to generate a standard draft based on the standardized demand direction and the analyzed template, including: mapping the standardized demand direction to a corresponding chapter in the analyzed template, automatically filling in the term definition, technical parameters, and citation specifications to generate a first draft; verifying the first draft based on a preset verification mechanism to generate a standard draft.

[0011] In another aspect of the present application, the verification of the first draft based on the preset verification mechanism to generate a standard draft preferably comprises: comparing the chapter hierarchy structure of the first draft with the structure of the preset standard template to generate a structure verification report; matching and comparing the terms in the first draft based on a standard term library to generate a term verification report; detecting whether there are missing external standards, literature, and appendices, incorrect citation formats, or inconsistent citation versions in the first draft to generate a citation verification report; verifying the logical relationships in the first draft based on a rule-based reasoning algorithm, detecting whether there are conflicts or contradictions between definitions, requirements, and test methods, and generating a logical verification report; when the structure verification report, the term verification report, the citation verification report, and the logical verification report all meet the corresponding preset qualified threshold, confirming that the first draft passes the verification, and generating a standard draft.

[0012] In another aspect of the present application, the expert review module preferably comprises an expert matching unit, a review scheduling unit, an opinion analysis unit, and a result generation unit; The expert matching unit is configured to extract corresponding technical field tags based on the standardized demand direction, retrieve experts with corresponding professional backgrounds, research directions and review qualifications from an expert knowledge base, calculate expert matching degree scores, and generate an expert candidate list according to the matching degree scores; The review scheduling unit is configured to distribute a standard draft to selected experts according to the expert candidate list, expert idle time and task priority; The opinion analysis unit is configured to perform semantic analysis on review opinions submitted by each expert, identify consistency, conflict points and modification suggestions in the review opinions, classify and aggregate the review opinions based on a natural language processing algorithm, and generate a structured review opinion set; The result generation unit is configured to integrate and update the standard draft based on the structured review opinion set, and confirm review passing and generate a published standard when the updated draft passes the expert consistency review threshold.

[0013] In another aspect of the present application, the publishing management module preferably includes a standard analysis unit and an interpretation generation unit; The standard analysis unit is configured to perform structured analysis on the published standard, extract core provisions, technical parameters, applicable scope and key change information in the standard, and generate a standard knowledge graph; The interpretation generation unit is configured to generate corresponding standard interpretation information, including standard revision background, key provision changes and application suggestions, using a pre-set large language model based on the standard knowledge graph.

[0014] In another aspect of the present application, the publishing management module preferably further includes a user portrait matching unit and a content pushing unit; The user portrait matching unit is configured to generate a target user list based on the industry attributes of target users; The content pushing unit is configured to distribute the published standard and corresponding interpretation information to target users through multi-channel information publishing interfaces according to the target user list.

[0015] (Three) beneficial effects The above technical solutions of the present application have the following beneficial technical effects: The application introduces artificial intelligence technology, collects and performs semantic analysis on multi-source industry data, automatically identifies potential standardization demand directions, and effectively solves the problem that traditional standard demand identification relies on manual experience and incomplete information coverage. In the project approval stage, the system uses the historical standard library to generate a quantitative priority result, improving the scientificity and transparency of project approval decisions. In the drafting link, the platform automatically generates a draft and performs structure, terminology and logical consistency checking through standard template analysis and intelligent filling mechanism, greatly improving the efficiency and accuracy of preparation. In the expert review stage, the system intelligently matches expert resources based on the standardization demand direction, integrates review comments and automatically generates optimization suggestions, reducing the burden of manual coordination. In the release link, the platform performs structured analysis and intelligent interpretation of the standard content, improving the dissemination efficiency and application effect of the standard. The platform realizes the automation, collaboration and traceable management of the whole process of standardization, significantly improving the efficiency and quality of standard development work. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a schematic diagram of the overall structure of an embodiment of the present application. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of the present application clearer, further detailed descriptions will be given below in conjunction with specific embodiments and with reference to the drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present application. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present application.

[0018] Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0019] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0020] The present application will be described in more detail below with reference to the accompanying drawings. In each of the drawings, the same elements are denoted by similar reference numerals. Each part in the drawings is not drawn to scale for the sake of clarity.

[0021] Embodiment one A standardization whole-chain AI auxiliary management platform, Figure 1 shows a schematic diagram of the overall structure of an embodiment of the present application, as Figure 1 shown, comprising a demand analysis module, a project evaluation module, a drafting assistance module, an expert review module, and a release management module; The demand analysis module is used for collecting industry information and identifying a standardized demand direction based on an LDA topic model; can form a standardized demand map through comprehensive analysis of data from different sources including policy documents, industry reports, technical papers, patent documents, market research data, etc., to provide a scientific basis for subsequent project approval.

[0022] In this embodiment, the demand analysis module includes an information collection unit, a text preprocessing unit and a topic identification unit. The information collection unit is used for collecting industry policies, enterprise feedback reports and related news data through network crawling to form an original text data set; can automatically collect data from government department websites, industry association portals, scientific research institution databases, enterprise feedback systems and mainstream news media through a preset network crawling engine and API interface, and the data types include but are not limited to policy documents, industrial development plans, technical standard texts, enterprise reports, patent abstracts and related news reports, etc. The data can be collected in a specific industry or a specific technical field through a keyword search strategy and a topic filtering rule; at the same time, the system can set a data update period to collect and dynamically update the newly added data to form a structured and unstructured mixed original text data set.

[0023] The text preprocessing unit is used for denoising, word segmentation and word frequency statistics on the original text data set to establish a text feature matrix; performs a denoising operation on the text content, including removing webpage tags, special symbols, repeated content and irrelevant information; then uses a natural language processing algorithm to perform word segmentation processing on the text, constructs a vocabulary, and filters stop words such as virtual words, auxiliary words and common punctuation symbols to retain key words with semantic meaning. Next, the text preprocessing unit generates a word vector representation through word frequency statistics, TF-IDF (Term Frequency-Inverse Document Frequency) weighting and context window modeling, and finally establishes a text feature matrix for reflecting the distribution relationship of different words in the semantic space. This module can also support stem extraction, part-of-speech tagging and synonym normalization operations to enhance semantic consistency.

[0024] The topic identification unit is used for topic extraction and clustering analysis on the text feature matrix based on an LDA topic model to obtain a plurality of potential standardized demand topics, and identifies a standardized demand direction according to a topic heat value.

[0025] The input of the LDA topic model includes the text feature matrix, the LDA topic model represents each document as a probability distribution of a plurality of potential topics, and represents each potential topic as a probability distribution of a set of words; the text feature matrix is a document-word matrix, each row represents a document, and each column represents a word; through the text feature matrix, the LDA topic model can perform semantic modeling on the document set to realize statistical analysis of potential topics.

[0026] The output of the LDA topic model includes a keyword weight vector corresponding to each potential topic and a document topic weight vector. Within the LDA topic model, it is assumed that each document is generated by mixing a number of potential topics, and each potential topic is generated by a certain set of words. Specifically, the LDA topic model first assigns a topic probability distribution to each document, indicating the contribution proportion of each topic in the document; at the same time, a word probability distribution is assigned to each potential topic, indicating the possibility of the occurrence of each word under the topic. The keyword weight vector corresponding to each potential topic lists the probability or weight value of each word under the topic, which is used to describe the semantic core content of the topic; the document topic weight vector reflects the proportion of each potential topic in the document, which is used to characterize the topic distribution characteristics of the document.

[0027] Further, in the embodiment, the LDA topic model is used to perform topic extraction and clustering analysis on the text feature matrix to obtain a plurality of potential standardized demand topics, and a standardized demand direction is identified according to a topic heat value, which includes: The keyword weight vector is sorted to determine a number of keywords with high weight values as a feature word set of the corresponding potential topic; and the top-ranked keywords are selected as the feature word set of the potential topic. The feature word set can reflect the core semantic information of the topic, and provides basic data for subsequent semantic clustering and topic understanding.

[0028] The feature word set is subjected to semantic clustering and artificial label mapping to generate a topic semantic label, thereby obtaining a plurality of potential standardized demand topics; the semantic clustering can use a word vector-based similarity calculation method to classify keywords with similar semantics or functionally related keywords into the same subcategory, thereby reducing the influence of noise words and non-core words. Subsequently, the clustering results are subjected to artificial label mapping in combination with an expert knowledge base or a standard term library, that is, a standardized and easily understood topic semantic label is assigned to each clustering result.

[0029] The occurrence frequency of each potential standardized demand topic in the text set is calculated according to the document topic weight vector, and a topic heat value is calculated according to the occurrence frequency; the document topic weight vector indicates the proportion of each potential topic in each document, and the total proportion of each topic in the text set can be obtained by accumulating and counting all documents.

[0030] All potential standardized demand topics are sorted according to the topic heat value, and a standardized demand direction is identified. The standardized demand direction represents the key field that is urgently needed to be standardized in the industry, and provides a guidance basis for subsequent project evaluation, standard draft drafting and expert review.

[0031] The project evaluation module is configured to evaluate the identified standardization demand direction based on historical standard library information, and generate a project priority according to an evaluation result. In this embodiment, the project evaluation module includes a historical standard information analysis unit, a repetitiveness evaluation unit, an industrial value evaluation unit, and a priority generation unit. The historical standard information analysis unit is configured to extract a keyword set corresponding to the standardization demand direction based on historical standard library information. The historical standard library information can be classified according to industry categories or technical fields to form a field-specific keyword library. The keyword set includes information such as standard titles, chapter titles, technical terms, definition clauses, and key technical indicators.

[0032] The repetitiveness evaluation unit is configured to calculate a semantic similarity with historical standards based on the keyword set, and determine the repetitiveness of the demand direction according to the similarity score to obtain a repetitiveness evaluation index. By analyzing the similarity of each standardization demand direction with historical standards, the system can determine the repetitiveness of the demand direction in the historical standard system, and further generate a repetitiveness evaluation index. In this embodiment, for convenience of statistics, a higher value of the repetitiveness evaluation index indicates a higher standardization potential value of the direction; a lower value indicates that the demand direction is highly consistent with existing standards.

[0033] The industrial value evaluation unit is configured to calculate an industrial value index according to the number of intellectual property applications and the number of academic paper citations. By collecting indicators such as the number of intellectual property applications related to the demand direction, the number of patent citations, the number of core academic paper citations, and research project achievements, an industrial value evaluation system is constructed. The system normalizes each indicator and generates an industrial value index by weighting. A higher industrial value index of a demand direction indicates stronger technical development potential, market demand, or academic attention in the field, providing a basis for prioritizing projects.

[0034] The priority generation unit is configured to calculate a project evaluation score according to the repetitiveness evaluation index and the industrial value index, and generate a project priority list according to the evaluation score. The repetitiveness evaluation index and the industrial value index are normalized and summed by weighting to calculate the project evaluation score.

[0035] The drafting assistance module is configured to generate a standard draft based on standardization templates and standardization demand directions. In this embodiment, the drafting assistance module includes a standard template analysis unit and a draft generation unit. The standard template parsing unit is configured to read and parse a preset standardized template, convert the template content into a structured data representation, and obtain a parsed template. The preset standardized template can include a template of a national standard, an industrial standard, or an internal standard of an enterprise, and contains fixed chapters such as a standard title, a preface, terms and definitions, technical requirements, test methods, inspection rules, appendices, and references. The standard template parsing unit converts the template content into a structured data representation that can be processed by a computer through text analysis and structured processing, including chapter level information, paragraph identification, field type, fillable parameter position, and constraint rules, and generates the parsed template.

[0036] The draft generation unit is configured to generate a standard draft based on the standardized requirement direction and the parsed template, including: The standardized requirement direction is mapped to a corresponding chapter in the parsed template, and terms and definitions, technical parameters, and reference specifications are automatically filled in to generate a first draft. For example, the requirement direction of the technical requirement type is mapped to the “technical requirements” chapter, the test or verification type requirement is mapped to the “test methods” chapter, and the terms and definitions type requirement is mapped to the “terms and definitions” chapter, so as to ensure that the content of the draft is consistent with the structure of the template. Subsequently, the system automatically fills in the terms and definitions, technical parameters, and reference specifications in the template to generate a preliminary document, i.e., the first draft. This process can be combined with a standard term library, a knowledge graph, and a historical standard database to achieve term unification, technical parameter standardization, and reference specification consistency.

[0037] Based on a preset verification mechanism, the first draft is verified to generate a standard draft, including: The chapter level structure of the first draft is compared with the structure of the preset standard template to generate a structure verification report. It is detected whether the chapters are complete, the level relationship is correct, and the paragraph order is in accordance with the specification.

[0038] Based on a standard term library, the terms in the first draft are matched and compared to generate a term verification report. It is identified whether there are non-standard, repeated definition, or missing terms, and modification suggestions are provided.

[0039] It is detected whether there are missing, incorrect reference format, or inconsistent reference version problems in the external standards, literature, and appendices cited in the first draft to generate a reference verification report. Based on a rule reasoning algorithm, the logical relationship in the first draft is verified, and it is detected whether there are conflicts or contradictions between the definitions, requirements, and test methods to generate a logic verification report. It includes whether the term definition matches the technical requirement, whether the technical requirement corresponds to the test method, whether there are conflicts or contradictions between chapters, and specific modification prompts are given.

[0040] When the structure verification report, the term verification report, the reference verification report and the logic verification report all satisfy the corresponding preset qualified threshold, it is confirmed that the first draft passes the verification, and a standard draft is generated. The generated standard draft not only retains the standardized structure of the standard template, but also has verified terms, technical requirements and reference documents, and the logic relationship is self-consistent, so that the standard draft can be directly submitted as a preliminary draft to the expert review link.

[0041] The expert review module is configured to review the standard draft based on the matched review experts according to the standardization requirement direction, and obtain a published standard. In this embodiment, the expert review module includes an expert matching unit, a review scheduling unit, an opinion analysis unit and a result generation unit. The units cooperate with each other to form a closed-loop review process from expert selection, task distribution, opinion analysis to standard confirmation.

[0042] The expert matching unit is configured to extract corresponding technical field tags based on the standardization requirement direction, and retrieve experts with corresponding professional background, research direction and review qualification from an expert knowledge base, calculate an expert matching degree score, and generate an expert candidate list according to the matching degree score. The corresponding technical field tags are extracted from the standardization requirement direction, including key technical words, application scenarios and industry categories and the like. Then, the experts are preliminarily screened in combination with the expert information in the expert knowledge base, including professional background, research direction, academic achievement, review qualification, historical review experience and activity level and the like. The expert matching degree score can be obtained by a cosine similarity algorithm.

[0043] The review scheduling unit is configured to distribute the standard draft to selected experts according to the expert candidate list, expert idle time and task priority. The distribution strategy can be optimized according to the number of experts, task urgency and parallel processing capability, so as to realize simultaneous review of the same draft by multiple experts or independent review of different chapters.

[0044] The opinion analysis unit is configured to perform semantic analysis on the review opinions submitted by each expert, identify consistency, conflict points and modification suggestions in the review opinions, classify and aggregate the review opinions based on a natural language processing algorithm, and generate a structured review opinion set. The opinion analysis unit performs semantic analysis on the opinion text of each expert, including word segmentation, part-of-speech tagging, entity recognition and dependency relationship analysis and the like, to identify technical problems, logical contradictions, modification suggestions and consistency evaluations involved in the opinions. Then, the natural language processing algorithm is used to classify and aggregate the opinions, merge repeated or semantically similar modification suggestions, label the priority of conflicting opinions or propose a reconciliation plan. The structured review opinion set is generated, including modification items, logic conflict points, term adjustment suggestions and chapter optimization schemes and the like, to provide executable operation guidance for the revision of the standard draft.

[0045] The result generation unit is configured to integrate and update the draft based on the set of structured review opinions, and confirm the review as passed and generate the published standard when the updated draft passes the expert consistency review threshold.

[0046] The publishing management module is configured to generate interpretation information based on the published standard, and push the published standard and the interpretation information to target users. The standard analysis unit is configured to analyze the published standard, extract core provisions, technical parameters, applicable scope, and key change information in the standard, and generate a standard knowledge graph.

[0047] The interpretation generation unit is configured to generate corresponding standard interpretation information based on the standard knowledge graph and using a pre-set large language model, including standard revision background, key provision changes, and application suggestions.

[0048] Further, the publishing management module further includes a user portrait matching unit and a content pushing unit. The user portrait matching unit is configured to generate a target user list based on the industry attributes of target users, establish a user portrait based on the industry attributes, job responsibilities, areas of interest, and historical behavior data of target users, and generate a target user list.

[0049] The content pushing unit is used for distributing the publishing standard and corresponding interpretation information to target users through a multi-channel information publishing interface according to the target user list. The pushing channel includes multiple forms such as an email, an industry portal website, an API interface, a mobile terminal application notification, a subscription message, and a social media platform, so as to meet the acquisition habits of different users. The system can support batch pushing, timing publishing, and personalized recommendation, and realizes efficient propagation of the standard information. Meanwhile, the content pushing unit can record the pushing log, user reading condition, and feedback information, and provide data basis for standard application effect analysis and subsequent optimization.

[0050] The application introduces artificial intelligence technology, collects and performs semantic analysis on multi-source industry data, automatically identifies potential standardization demand direction, and effectively solves the problem that traditional standard demand identification relies on manual experience and information coverage is not complete. In the project establishment stage, the system uses the historical standard library to generate a quantitative priority result, and improves the scientificity and transparency of project establishment decision. In the drafting link, the platform automatically generates a draft and performs structure, terminology and logical consistency checking through standard template analysis and intelligent filling mechanism, greatly improving the preparation efficiency and accuracy. In the expert review stage, the system intelligently matches expert resources based on the standardization demand direction, integrates the review opinions and automatically generates optimization suggestions, and reduces the manual coordination burden. In the publishing link, the platform performs structured analysis and intelligent interpretation on the standard content, improves the propagation efficiency and application effect of the standard. The platform realizes the automation, collaboration and traceable management of the whole standardization process, and significantly improves the efficiency and quality of standard development work.

[0051] It should be understood that the above specific embodiments of the present application are only used for illustrative or explanatory purposes of the principles of the present application, and do not constitute a limitation on the present application. Therefore, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present application shall be included in the protection scope of the present application. In addition, the appended claims of the present application are intended to cover all variations and modifications falling within the scope and boundary of the appended claims, or the equivalent forms of such scope and boundary.

[0052] The present application has been described above with reference to embodiments. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present application. The scope of the present application is defined by the appended claims and their equivalents. Without departing from the scope of the present application, those skilled in the art can make various substitutions and modifications, which should fall within the scope of the present application.

[0053] Although the embodiments of the present application have been described in detail, it should be understood that various changes, replacements and modifications can be made to the embodiments of the present application without departing from the spirit and scope of the present application.

[0054] Obviously, the above embodiments are merely example for clearly illustrating but not limitation to the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments need not and can not be enumerated. The obvious changes or variations derived from the above description are still within the protection scope of the present application.

Claims

1. A standardized, end-to-end AI-assisted management platform, characterized in that, include: The module includes a requirements analysis module, a project evaluation module, a drafting support module, an expert review module, and a release management module. The demand analysis module is used to collect industry information and identify standardized demand directions based on the LDA topic model; The project evaluation module is used to evaluate the identified standardization needs based on historical standard library information, and generate project priorities based on the evaluation results. The drafting assistance module is used to generate standard drafts based on standardized templates and standardized requirements. The expert review module is used to review the draft standard based on the review experts matched with the standardization needs, and to obtain the published standard. The release management module is used to generate interpretation information based on the release standards, and push the release standards and interpretation information to the target users.

2. The standardized full-chain AI-assisted management platform according to claim 1, characterized in that, The requirements analysis module includes an information collection unit, a text preprocessing unit, and a topic recognition unit. The information collection unit is used to crawl industry policies, enterprise feedback reports and related news data through the network to form a raw text dataset. The text preprocessing unit is used to perform noise reduction, word segmentation, and word frequency statistics on the original text dataset to establish a text feature matrix. The topic identification unit is used to extract topics and perform cluster analysis on the text feature matrix based on the LDA topic model, obtain multiple potential standardization demand topics, and identify the standardization demand direction based on the topic popularity value.

3. The standardized full-chain AI-assisted management platform according to claim 2, characterized in that, The input to the LDA topic model includes the text feature matrix. The LDA topic model represents each document as a probability distribution of multiple potential topics, and each potential topic as a probability distribution of a set of words. The output of the LDA topic model includes keyword weight vectors and document topic weight vectors for each potential topic.

4. The standardized full-chain AI-assisted management platform according to claim 3, characterized in that, The LDA topic model is used to extract topics and perform cluster analysis on the text feature matrix to obtain multiple potential standardization demand topics, and to identify standardization demand directions based on topic popularity values, including: Based on the keyword weight vector, sort the keywords and determine the top-ranked keywords as the feature word set for the corresponding potential topic. Semantic clustering and manual label mapping are performed on the feature word set to generate topic semantic labels and obtain multiple potential standardized demand topics. Based on the document topic weight vector, calculate the frequency of occurrence of each potential standardized demand topic in the text set. Calculate the topic popularity value based on the frequency of occurrence; Based on the topic popularity value, all potential standardization demand topics are sorted to identify the direction of standardization demand.

5. The standardized full-chain AI-assisted management platform according to claim 4, characterized in that, The project evaluation module includes a historical standard information analysis unit, a repeatability evaluation unit, an industry value evaluation unit, and a priority generation unit. The historical standard information analysis unit is used to extract the set of keywords corresponding to the standardization demand direction based on historical standard library information; The repeatability assessment unit is used to calculate the semantic similarity with historical standards based on the keyword set, and to determine the repeatability of the demand direction based on the similarity score, thereby obtaining a repeatability assessment index. The industry value assessment unit is used to calculate the industry value index based on the number of intellectual property applications and the number of academic paper citations. The priority generation unit is used to calculate the project evaluation score based on the repeatability evaluation index and the industry value index, and generate a project priority list based on the evaluation score.

6. The standardized full-chain AI-assisted management platform according to claim 5, characterized in that, The drafting assistance module includes a standard template parsing unit and a draft generation unit; The standard template parsing unit is used to read and parse a preset standardized template, convert the template content into a structured data representation, and obtain the parsed template. The draft generation unit is used to generate a standard draft based on the standardization requirement direction and the parsed template, including: The standardization requirements are mapped to the corresponding chapters in the parsed template, and terminology definitions, technical parameters, and reference specifications are automatically filled in to generate the first draft. Based on a preset verification mechanism, the first draft is verified to generate a standard draft.

7. The standardized full-chain AI-assisted management platform according to claim 6, characterized in that, The first draft is validated based on a preset validation mechanism to generate a standard draft, including: The chapter hierarchy structure of the first draft is compared with the structure of the preset standard template to generate a structure verification report; Based on the standard terminology database, the terms in the first draft are matched and compared to generate a terminology verification report; The system checks whether there are any missing, incorrect citation formats, or inconsistent versions of external standards, documents, and appendices cited in the first draft, and generates a citation verification report. Based on the rule-based reasoning algorithm, the logical relationships in the first draft are verified, and any conflicts or contradictions between the definitions, requirements and test methods are detected, generating a logical verification report. When the structure verification report, terminology verification report, reference verification report and logic verification report all meet the corresponding preset qualification thresholds, the first draft is confirmed to have passed the verification and a standard draft is generated.

8. The standardized full-chain AI-assisted management platform according to claim 7, characterized in that, The expert review module includes an expert matching unit, a review scheduling unit, an opinion analysis unit, and a result generation unit; The expert matching unit is used to extract the corresponding technical field tags based on the standardized demand direction, retrieve experts with corresponding professional backgrounds, research directions and review qualifications from the expert knowledge base, calculate the expert matching score, and generate an expert candidate list based on the matching score. The review and scheduling unit is used to distribute the draft standard to the selected experts based on the expert candidate list, the experts' available time, and the task priority. The opinion analysis unit is used to perform semantic parsing on the review opinions submitted by each expert, identify the consistency, conflict points and modification suggestions in the review opinions, classify and aggregate the review opinions based on natural language processing algorithms, and generate a structured review opinion set. The result generation unit is used to integrate modification suggestions and update the draft standard based on the structured review opinion set. When the updated draft passes the expert consensus review threshold, the review is confirmed to be passed and the standard is generated and published.

9. The standardized full-chain AI-assisted management platform according to claim 8, characterized in that, The release management module includes a standard parsing unit and an interpretation generation unit; The standard parsing unit is used to perform structured parsing on the published standard, extract the core clauses, technical parameters, scope of application and key change information in the standard, and generate a standard knowledge graph; The interpretation generation unit is used to generate corresponding standard interpretation information based on the standard knowledge graph and using a preset large language model, including the background of standard revision, changes in key clauses, and application suggestions.

10. The standardized full-chain AI-assisted management platform according to claim 9, characterized in that, The publishing management module also includes a user profile matching unit and a content push unit; The user profile matching unit is used to generate a target user list based on the industry attributes of the target users; The content push unit is used to distribute the publishing standards and corresponding interpretation information to target users through a multi-channel information publishing interface, based on the target user list.

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