File full-life-cycle management method and system based on automatic paper composition and archiving

Abstract

The invention provides an archive full-life-cycle management method and system based on automatic paper composition and archiving, and relates to the technical field of computers. The method comprises the steps of carrying out multi-source collection and standardized import on archives, carrying out intelligent classification and volume formation based on NLP and machine learning, carrying out dynamic metadata extraction and management, optimizing storage and migration according to life cycles, and carrying out multi-dimensional retrieval and analysis. According to the system, the functions are modularly integrated, and intelligent, efficient and extensible archive management is realized through security authority control and a bottom data access layer.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and system for managing the entire life cycle of archives based on automatic filing. Background Art

[0002] In the modern information society, with the continuous business expansion and in-depth digital transformation of enterprises, institutions and government departments, a large number of archives are constantly generated in electronic and digital forms. These archives cover text, images, audio and video, and composite document formats. They are huge in number and diverse in type. Not only are storage costs increasing day by day, but management difficulties are also significantly increased. Traditional archive management methods usually rely on manual classification, numbering, archiving and retrieval of archives, which is not only time-consuming and labor-intensive, but also prone to problems such as improper classification, inaccurate retrieval, data redundancy and insufficient security.

[0003] With the rapid development of information technology, especially artificial intelligence, big data analysis, natural language processing and distributed storage technology, archive management is transforming from the traditional manual, static and decentralized mode to intelligent, dynamic and full life cycle management. Traditional archive management systems often face the following typical challenges: Data multi-source and heterogeneity: Archival data comes from a wide range of sources and in various forms, and lacks a unified format standard, which makes data integration and processing difficult.

[0004] High complexity of classification and organization: Manual classification and filing of massive archives is prone to errors and inefficient. There is a lack of intelligent means to automatically identify the subject associations of archives, which cannot meet the needs of rapid classification and dynamic allocation.

[0005] Insufficient metadata management: The lack of in-depth mining of the semantic level of archival content and automatic metadata extraction methods leads to inefficient subsequent retrieval, analysis and migration, and cannot effectively achieve in-depth mining of archival content and knowledge association.

[0006] Poor storage resource utilization: As the number of archives surges, there is a lack of dynamic storage and migration strategies for archive lifecycles and access frequencies, resulting in waste of storage resources or performance bottlenecks.

[0007] Weak intelligent retrieval and analysis capabilities: Traditional retrieval methods are mostly keyword matching, which makes it difficult to understand user query intent and unable to perform semantic-level association analysis and deep insights, reducing the efficiency of users in obtaining effective information.

[0008] Increasing security and compliance requirements: With the tightening of data protection laws and regulations, the requirements for file security, refined permission control, and auditability and non-tamperability are constantly increasing. Traditional solutions lack traceable, secure and reliable technical means. Summary of the invention

[0009] A method for managing the entire life cycle of archives based on automatic filing and archiving, the method comprising: Step S1: Archive collection and import, collect archive data through multiple channels, including electronic documents, paper scans and multimedia files, perform preliminary format conversion on the collected archive data, convert archives of different formats into a standard format supported by the system, perform preliminary data cleaning, detect and remove duplicate archives, repair or mark damaged files, and ensure the basic quality of imported archives; Step S2: Automatic classification and grouping. Use natural language processing (NLP) technology to parse the imported archive content and extract key topics, keywords and entities. Use machine learning algorithms to automatically classify archives into a predefined classification system based on content analysis results. Based on content similarity and relevance, related archives are automatically grouped into the same file. Provide a manual review and adjustment interface to allow administrators to confirm and modify the automatic classification and grouping results, and optimize the classification and grouping algorithms through a feedback mechanism. Step S3: Dynamic metadata extraction and management, automatically extracting advanced metadata from the archive content, including keywords, subjects, authors, creation dates, and associated documents, and storing the extracted metadata in a structured manner in the database, supporting multi-dimensional retrieval and version control, real-time monitoring of changes in archive content, and dynamic updates of related metadata to ensure the accuracy and timeliness of metadata; Step S4: Intelligent storage and migration management, formulate corresponding storage strategies (such as hot storage, cold storage) according to the life cycle stage, access frequency and importance of the archives. Automatically distribute files to suitable storage media (local storage, cloud storage, distributed storage) through intelligent allocation algorithms. Regularly evaluate the use of archives, automatically migrate low-frequency archives to cold storage, release hot storage resources, ensure data consistency and integrity, monitor the performance and capacity of the storage system in real time, dynamically optimize the allocation of storage resources, and improve resource utilization; Step S5: Intelligent retrieval and analysis, providing multi-dimensional retrieval functions, including keyword search, semantic search and metadata search, using NLP technology to understand natural language queries; supporting advanced search functions, such as Boolean logic, fuzzy search and range search, to improve the flexibility and accuracy of retrieval; conducting big data analysis, counting the use of archives, conducting trend forecasting and correlation analysis, and tapping into potential business value; providing visual display tools, including dashboards, charts and custom reports, to help users intuitively understand and analyze data.

[0010] An archive full life cycle management system based on automatic filing and archiving, comprising: a user interface (UI); An API gateway connected to the user interface and receiving a request from the user interface; The archive collection and import module is connected to the API gateway to receive and pre-process multiple types of archive data; An intelligent classification and test paper assembly module is connected to the API gateway for performing content analysis and classification structure generation on the data output by the file collection and import module; A metadata extraction and management module is connected to the API gateway for extracting corresponding metadata from the data processed by the intelligent classification and volume assembly module and performing structured storage updates; A storage and migration management module is connected with the output data of the archive collection and import module, the intelligent classification and volume assembly module, and the metadata extraction and management module, and distributes and migrates the archive data according to a preset strategy; An intelligent retrieval and analysis module, connected to the storage and migration management module, performs query processing and correlation analysis on the data provided by the module; A security and authority management module, connected in parallel with the storage and migration management module and the intelligent retrieval and analysis module, performs access control and records on access requests; The data access layer includes at least one index database and at least one relational library or non-relational library, which is connected with the intelligent retrieval and analysis module and the security and authority management module to provide underlying data support for the query and access of each module.

[0011] Furthermore, the archive collection and import module includes an interface for digitizing input paper archives, a processing unit for converting electronic documents into a unified format, and a data cleaning unit for cleaning redundant data.

[0012] Furthermore, the intelligent classification and paper organization module includes a natural language processing unit for parsing text content and a classification and paper organization unit for generating a classification structure and associated files based on the parsing results.

[0013] Furthermore, the metadata extraction and management module includes an extraction unit for identifying archive keywords, themes and author information, and a metadata management unit for performing version control on the metadata output by the extraction unit.

[0014] Furthermore, the storage and migration management module includes a storage policy unit for allocating storage media according to archive life cycle and access frequency, and a migration control unit for performing archive data migration across local and cloud platforms.

[0015] Furthermore, the intelligent retrieval and analysis module includes a retrieval unit that performs keyword search and semantic analysis query, and an analysis unit that performs association analysis and statistical processing on the query results.

[0016] Furthermore, the security and authority management module includes an authority control unit for authenticating requests based on access rules, and a log recording unit for recording access operation behaviors.

[0017] Furthermore, the data access layer includes an index database supporting full-text retrieval and a relational database or non-relational database storing structured data, the index database is connected to the intelligent retrieval and analysis module data, and the relational database or non-relational database is connected to the metadata extraction and management module data.

[0018] Compared with the prior art, the present invention has the following advantages: Difficulty in processing multi-source heterogeneous data: Through "Step S1: Archives Collection and Import" and the "Archives Collection and Import Module" in the system, the present invention supports multi-channel archive input (electronic documents, scanned copies, multimedia files), and automatically performs format conversion and data cleaning. In this way, archives of different formats and sources are processed in a unified manner, reducing manual participation and data inconsistency problems, so that archives have a usable standardized format after entering the system.

[0019] Inefficient manual classification and filing: By using "Step S2: Automatic Classification and Filing" and "Intelligent Classification and Filing Module", the present invention uses natural language processing (NLP) and machine learning technology to achieve automatic analysis and intelligent classification of archive content, and automatically completes filing based on relevance. This process does not require manual processing of each file, greatly improving filing efficiency and accuracy, and reducing manual errors and time consumption.

[0020] Insufficient metadata extraction and difficult management: Through "Step S3: Dynamic metadata extraction and management" and the corresponding "metadata extraction and management module", the present invention automatically extracts high-level metadata (keywords, topics, authors, dates, related documents, etc.) from the archive content. These metadata are structured and dynamically updated, which can support multi-dimensional retrieval and version control, thereby converting the originally more mixed archive information into organized and searchable resources. This significantly enhances the accuracy and flexibility of archive retrieval and management.

[0021] Poor storage resource utilization: With the help of "Step S4: Intelligent Storage and Migration Management" and "Storage and Migration Management Module", the present invention formulates storage strategies based on the life cycle, access frequency and importance of archives, and realizes intelligent allocation and dynamic migration of archives between hot storage, cold storage and cloud storage. Through regular evaluation and automatic migration, storage resource utilization can be optimized, storage costs can be reduced, and high availability and consistency of data can be guaranteed.

[0022] Limited retrieval and analysis capabilities: Using "Step S5: Intelligent Retrieval and Analysis" and "Intelligent Retrieval and Analysis Module", the present invention provides multi-dimensional retrieval capabilities (keywords, semantics, metadata), advanced search (Boolean, fuzzy, range query) and big data analysis and visualization display tools. This module uses NLP to understand natural language queries, improves retrieval accuracy and intelligence; through association analysis and statistical prediction, it meets users' needs for deep data insights.

[0023] Improved security and compliance requirements: In conjunction with the "Security and Rights Management Module", the present invention achieves full traceability and management of archive access and operation behaviors through blockchain records, fine-grained rights control, encryption, and audit logs. This module ensures that archive data meets security and compliance requirements throughout its life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of the method of the present invention; Figure 2 is a system structure diagram of the present invention; DETAILED DESCRIPTION

[0025] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and is not a limitation of the scope of protection, applicability or examples set forth in the claims. The functions and arrangements of the elements discussed can be changed without departing from the scope of protection of the contents of this specification. Various examples can omit, replace or add various processes or components as needed. For example, the described method can be performed in an order different from the described order, and various steps can be added, omitted or combined. In addition, the features described relative to some examples can also be combined in other examples.

[0026] As used herein, the term "including" and its variations represent open terms, meaning "including but not limited to". The term "based on" means "based at least in part on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment". The term "another embodiment" means "at least one other embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other definitions may be included below, whether explicit or implicit. Unless the context clearly indicates otherwise, the definition of a term is consistent throughout the specification.

[0027] Example according to Figure 1 An embodiment provided is as follows: A method for managing the entire life cycle of archives based on automatic filing and archiving, the method comprising: Step S1: Archive collection and import, collect archive data through multiple channels, including electronic documents, paper scans and multimedia files, perform preliminary format conversion on the collected archive data, convert archives of different formats into a standard format supported by the system, perform preliminary data cleaning, detect and remove duplicate archives, repair or mark damaged files, and ensure the basic quality of imported archives; Step S2: Automatic classification and grouping. Use natural language processing (NLP) technology to parse the imported archive content and extract key topics, keywords and entities. Use machine learning algorithms to automatically classify archives into a predefined classification system based on content analysis results. Based on content similarity and relevance, related archives are automatically grouped into the same file. Provide a manual review and adjustment interface to allow administrators to confirm and modify the automatic classification and grouping results, and optimize the classification and grouping algorithms through a feedback mechanism. Step S3: Dynamic metadata extraction and management, automatically extracting advanced metadata from the archive content, including keywords, subjects, authors, creation dates, and associated documents, and storing the extracted metadata in a structured manner in the database, supporting multi-dimensional retrieval and version control, real-time monitoring of changes in archive content, and dynamic updates of related metadata to ensure the accuracy and timeliness of metadata; Step S4: Intelligent storage and migration management, formulate corresponding storage strategies (such as hot storage, cold storage) according to the life cycle stage, access frequency and importance of the archives. Automatically distribute files to suitable storage media (local storage, cloud storage, distributed storage) through intelligent allocation algorithms. Regularly evaluate the use of archives, automatically migrate low-frequency archives to cold storage, release hot storage resources, ensure data consistency and integrity, monitor the performance and capacity of the storage system in real time, dynamically optimize the allocation of storage resources, and improve resource utilization; Step S5: Intelligent retrieval and analysis, providing multi-dimensional retrieval functions, including keyword search, semantic search and metadata search, using NLP technology to understand natural language queries; supporting advanced search functions, such as Boolean logic, fuzzy search and range search, to improve the flexibility and accuracy of retrieval; conducting big data analysis, counting the use of archives, conducting trend forecasting and correlation analysis, and tapping into potential business value; providing visual display tools, including dashboards, charts and custom reports, to help users intuitively understand and analyze data.

[0028] An archive full life cycle management system based on automatic filing and archiving, comprising: a user interface (UI); An API gateway connected to the user interface and receiving a request from the user interface; The archive collection and import module is connected to the API gateway to receive and pre-process multiple types of archive data; An intelligent classification and test paper assembly module is connected to the API gateway for performing content analysis and classification structure generation on the data output by the file collection and import module; A metadata extraction and management module is connected to the API gateway for extracting corresponding metadata from the data processed by the intelligent classification and volume assembly module and performing structured storage updates; A storage and migration management module is connected with the output data of the archive collection and import module, the intelligent classification and volume assembly module, and the metadata extraction and management module, and distributes and migrates the archive data according to a preset strategy; An intelligent retrieval and analysis module, connected to the storage and migration management module, performs query processing and correlation analysis on the data provided by the module; A security and authority management module, connected in parallel with the storage and migration management module and the intelligent retrieval and analysis module, performs access control and records on access requests; The data access layer includes at least one index database and at least one relational library or non-relational library, which is connected with the intelligent retrieval and analysis module and the security and authority management module to provide underlying data support for the query and access of each module.

[0029] Furthermore, the archive collection and import module includes an interface for digitizing input paper archives, a processing unit for converting electronic documents into a unified format, and a data cleaning unit for cleaning redundant data.

[0030] Furthermore, the intelligent classification and paper organization module includes a natural language processing unit for parsing text content and a classification and paper organization unit for generating a classification structure and associated files based on the parsing results.

[0031] Furthermore, the metadata extraction and management module includes an extraction unit for identifying archive keywords, themes and author information, and a metadata management unit for performing version control on the metadata output by the extraction unit.

[0032] Furthermore, the storage and migration management module includes a storage policy unit for allocating storage media according to archive life cycle and access frequency, and a migration control unit for performing archive data migration across local and cloud platforms.

[0033] Furthermore, the intelligent retrieval and analysis module includes a retrieval unit that performs keyword search and semantic analysis query, and an analysis unit that performs association analysis and statistical processing on the query results.

[0034] Furthermore, the security and authority management module includes an authority control unit for authenticating requests based on access rules, and a log recording unit for recording access operation behaviors.

[0035] Furthermore, the data access layer includes an index database supporting full-text retrieval and a relational database or non-relational database storing structured data, the index database is connected to the intelligent retrieval and analysis module data, and the relational database or non-relational database is connected to the metadata extraction and management module data.

[0036] This method solves the problems of data heterogeneity, multi-source collection, time-consuming manual classification, and inaccurate retrieval in traditional archive management by automating and intelligently processing the entire process from archive collection to retrieval and analysis.

[0037] Use NLP (natural language processing) and machine learning to achieve automatic classification and test paper composition, reduce manual participation, and improve classification accuracy and processing speed.

[0038] Dynamic metadata management ensures the timeliness and accuracy of archival information, supports multi-dimensional retrieval, and provides a basis for subsequent intelligent retrieval and analysis.

[0039] Intelligent storage and migration management reduces storage costs and improves storage resource utilization, ensuring high availability of data throughout its life cycle.

[0040] Intelligent retrieval and analysis uses big data analysis and semantic retrieval technology to provide users with deeper data insights and auxiliary decision support.

[0041] Archives collection and import (S1): A unified and standardized processing process is used for multi-source data (electronic, paper, audio and video), including format conversion (such as converting Word to PDF / A format), OCR (optical character recognition) technology to convert scanned copies into searchable text, and data cleaning (deduplication, verification). These technologies are mature document processing algorithms and data preprocessing methods. For example, duplicate detection can use hash algorithms or similarity calculations based on feature extraction (Jaccard similarity, cosine similarity, etc.) to judge the duplication of documents.

[0042] Automatic classification and test paper generation (S2): Use NLP algorithms to perform word segmentation, part-of-speech tagging, topic modeling (such as LDA: Latent Dirichlet Allocation) or text vectorization (Word2Vec, BERT and other deep learning models) to extract keywords and entities. Machine learning classification algorithms can use supervised learning models (such as SVM, Random Forest or deep learning text classification models) to predict and classify archive topics. When automatic test paper generation is required, document similarity calculation can be used:

[0043] A and B are text vector representations obtained through word embedding or TF-IDF vectorization. Similar documents are grouped into the same file, thus achieving automatic file grouping. The feedback mechanism provides additional training data for the model through manual adjustments of the classification results by the user, and iteratively optimizes the classification model.

[0044] Dynamic metadata extraction and management (S3): Use NLP to extract advanced metadata from text (such as temporal entity recognition, author information extraction, etc.). Entity recognition can use CRF (conditional random field) or BERT-CRF model; metadata structured storage uses relational database or NoSQL database. The metadata version control mechanism uses the version field or log table of the database to record metadata changes. There is no specific complex formula, and it is mostly implemented by data structuring and indexing. Dynamic updates rely on real-time monitoring: when the archive content is updated, the NLP parsing module is triggered to re-extract metadata and update the storage.

[0045] Intelligent storage and migration management (S4): Allocate storage media based on the frequency of archive use and life cycle. Strategies can calculate average access intervals or access thresholds based on access counts and timestamps.

[0046] The migration policy can set trigger conditions: Use distributed storage (such as HDFS, object storage) and API to automatically migrate data, and use load balancing and resource scheduling algorithms (such as greedy strategies or resource optimization algorithms based on dynamic programming) to dynamically optimize storage allocation.

[0047] Intelligent retrieval and analysis (S5): Multi-dimensional retrieval includes keyword search (building an inverted index through an index database such as Elasticsearch), semantic search (using word vectors or pre-trained language models to semantically understand queries and retrieve similar documents), and metadata search (based on SQL or NoSQL queries in structured databases). Fuzzy search can calculate the similarity between keywords and texts through the Levenshtein Distance algorithm. Range search is based on database indexes (B-tree, R-tree, etc.). Big data analysis can use statistical methods (such as frequency statistics, time series prediction model ARIMA) or machine learning clustering (such as K-Means) and association rule mining algorithms (Apriori) to analyze archive relevance. Visualization is achieved using standard front-end chart libraries and BI tools.

[0048] according to Figure 2 An embodiment provided is as follows: An archive life cycle management system based on automatic filing and archiving, which modularizes the functions required by the above method steps to form a comprehensive system that can be implemented in an actual deployment environment. The communication connection between modules ensures the efficient flow and processing of data between modules, and builds a complete archive management ecosystem. The user interface (UI) and API gateway act as the system entrance, passing user requests to the back-end modules in the form of REST API or RPC. Each module is scheduled by the API gateway through a loosely coupled service-oriented architecture (microservice technology can be used). The data access layer provides underlying support, the index database (such as Elasticsearch, Solr) is responsible for full-text retrieval, and the relational database (MySQL, PostgreSQL) or NoSQL database (MongoDB, Cassandra) is responsible for metadata and archive information storage. The principle of the entire system is based on a layered architecture, UI layer-API gateway-business logic module-data access layer.

[0049] The detailed features of the archive collection and import module process paper archives (OCR scanning, image processing) through digital interfaces, unified format conversion (converting various document formats to standard formats), and data cleaning (de-duplication, recovery of missing files), to achieve data unification and high-quality input, providing a reliable data foundation for subsequent processing. OCR algorithm: OCR engines such as Tesseract convert images into text. Document format unification based on open source libraries (such as LibreOffice conversion components). Compare document content based on hash or feature vectors. If the hash value or similarity exceeds a certain threshold, it is considered a duplicate document and is removed.

[0050] The refined features of the intelligent classification and paper grouping module automatically classify and group documents through NLP analysis of text content (word segmentation, keyword extraction, entity recognition) and the classification and paper grouping unit to improve management efficiency and organization. The NLP unit tokenizes (word segmentation) and POS Tagging (part-of-speech tagging) the text, and uses algorithms such as BiLSTM+CRF or Transformer models to identify entities. The classification and paper grouping unit inputs the extracted feature vector into a machine learning classifier (such as SVM) or a deep learning classification model (such as BERT classification head) to classify the documents by subject, and creates files for related documents by calculating similarity.

[0051] The detailed features of the metadata extraction and management module can be used to upgrade the archival data from unstructured text to a searchable structured metadata system by extracting keywords, topics, author information, etc. Version control ensures that metadata history records are traceable. Metadata extraction extracts specific entities from text based on the Named Entity Recognition algorithm of NLP (such as the BERT-CRF model). The metadata management unit saves the extraction results to the database and uses the version control fields of the database (such as the timestamp and history table for storing metadata changes) to achieve metadata version backtracking.

[0052] The detailed features of the storage and migration management module are based on the archive life cycle and frequency of use. High-frequency archives are stored in high-performance storage media, and low-frequency archives are migrated to low-cost cold storage media to achieve storage resource optimization and cost control. The storage policy unit makes statistical judgments on the archive access frequency based on the access log count. If the access frequency is lower than a certain threshold (such as the number of accesses in a month < X times), it is migrated to cold storage. The migration control unit calls the storage API to migrate data from the local storage volume to the cloud storage (via REST or S3 protocol) or distributed file system. Distributed locks and transaction mechanisms can be used to ensure data consistency.

[0053] The refined features of the intelligent retrieval and analysis module use keywords, semantic analysis, fuzzy search, range search and other retrieval methods to achieve accurate and flexible queries, while providing users with deeper data insights through association analysis and statistical processing. The retrieval unit builds an inverted index and uses a query parser to convert user queries into index search statements. Semantic search can calculate similarity in the embedded vector space through vector search. The analysis unit uses statistics and data mining algorithms (such as Apriori algorithm for document association rule mining, K-Means clustering archive topics) to perform advanced analysis on the result set.

[0054] The detailed features of the security and permission management module ensure that only authorized users can access specific archives through access rule authentication and logging mechanisms, and all access behaviors have traceable logs, providing technical guarantees for archive security and compliance. The permission control unit can match access policies based on user roles and organizational structure information (RBAC model: role-based access control). The logging unit writes access and operation records into an unalterable log system, and can use blockchain technology (using hash chains and consensus mechanisms) to ensure that logs cannot be forged. There are no complex formulas, and it is mainly for access control policy matching and log writing operations.

[0055] The detailed features of the data access layer, the data access layer provides underlying indexing and storage support to ensure efficient full-text search operations (through index databases) and structured data operations (relational or NoSQL databases). Index databases (such as Elasticsearch) convert text into inverted index structures to quickly retrieve relevant documents with a complexity of (O(1)) or O(logN). Relational databases (such as MySQL) or NoSQL databases (such as MongoDB) are used to store and manage metadata, classification information, and access records to adapt to different data types and query patterns.

[0056] Those skilled in the art should understand that the various embodiments disclosed above can be changed and modified in various ways without departing from the essence of the invention. Therefore, the protection scope of the present invention should be defined by the appended claims.

[0057] It should be noted that not all steps and units in the above processes are necessary, and some steps or units can be ignored according to actual needs. The execution order of each step is not fixed and can be determined as needed. The device structure described in the above embodiments can be a physical structure or a logical structure, that is, some units may be implemented by the same physical entity, or some units may be implemented by multiple physical entities, or some components in multiple independent devices may be implemented together.

[0058] The specific embodiments set forth above describe exemplary embodiments, but do not represent all embodiments that may be implemented or fall within the scope of protection of the claims. The term "exemplary" used throughout this specification means "used as an example, instance, or illustration" and does not mean "preferred" or "having advantages" over other embodiments. For the purpose of providing an understanding of the described techniques, the specific embodiments include specific details. However, these techniques may be implemented without these specific details. In some instances, in order to avoid obscuring the concepts of the described embodiments, well-known structures and devices are shown in block diagram form.

[0059] The above description of the present disclosure is provided to enable any person of ordinary skill in the art to implement or use the present disclosure. Various modifications to the present disclosure will be apparent to those of ordinary skill in the art, and the general principles defined herein may be applied to other variations without departing from the scope of protection of the present disclosure. Therefore, the present disclosure is not limited to the examples and designs described herein, but is consistent with the widest range of principles and novel features disclosed herein.

Claims

1. A method for managing the entire life cycle of archives based on automatic filing, characterized in that: The method comprises: Step S1: Archive collection and import, collect archive data through multiple channels, including electronic documents, paper scans and multimedia files, perform preliminary format conversion on the collected archive data, convert archives of different formats into a standard format supported by the system, perform preliminary data cleaning, detect and remove duplicate archives, repair or mark damaged files, and ensure the basic quality of imported archives; Step S2: Automatic classification and grouping. Use natural language processing (NLP) technology to parse the imported archive content and extract key topics, keywords and entities. Use machine learning algorithms to automatically classify archives into a predefined classification system based on content analysis results. Based on content similarity and relevance, related archives are automatically grouped into the same file. Provide a manual review and adjustment interface to allow administrators to confirm and modify the automatic classification and grouping results, and optimize the classification and grouping algorithms through a feedback mechanism. Step S3: Dynamic metadata extraction and management, automatically extracting advanced metadata from the archive content, including keywords, subjects, authors, creation dates, and associated documents, and storing the extracted metadata in a structured manner in the database, supporting multi-dimensional retrieval and version control, real-time monitoring of changes in archive content, and dynamic updates of related metadata to ensure the accuracy and timeliness of metadata; Step S4: Intelligent storage and migration management, formulate corresponding storage strategies (such as hot storage, cold storage) according to the life cycle stage, access frequency and importance of the archives. Automatically distribute files to suitable storage media (local storage, cloud storage, distributed storage) through intelligent allocation algorithms. Regularly evaluate the use of archives, automatically migrate low-frequency archives to cold storage, release hot storage resources, ensure data consistency and integrity, monitor the performance and capacity of the storage system in real time, dynamically optimize the allocation of storage resources, and improve resource utilization; Step S5: Intelligent retrieval and analysis, providing multi-dimensional retrieval functions, including keyword search, semantic search and metadata search, using NLP technology to understand natural language queries; supporting advanced search functions, such as Boolean logic, fuzzy search and range search, to improve the flexibility and accuracy of retrieval; conducting big data analysis, counting the use of archives, conducting trend forecasting and correlation analysis, and tapping into potential business value; providing visual display tools, including dashboards, charts and custom reports, to help users intuitively understand and analyze data.

2. A full life cycle archive management system based on automatic filing, characterized in that: include: User interface (UI); An API gateway connected to the user interface and receiving a request from the user interface; The archive collection and import module is connected to the API gateway to receive and pre-process multiple types of archive data; An intelligent classification and test paper assembly module is connected to the API gateway for performing content analysis and classification structure generation on the data output by the file collection and import module; A metadata extraction and management module is connected to the API gateway for extracting corresponding metadata from the data processed by the intelligent classification and volume assembly module and performing structured storage updates; A storage and migration management module is connected with the output data of the archive collection and import module, the intelligent classification and volume assembly module, and the metadata extraction and management module, and distributes and migrates the archive data according to a preset strategy; An intelligent retrieval and analysis module, connected to the storage and migration management module, performs query processing and correlation analysis on the data provided by the module; A security and authority management module, connected in parallel with the storage and migration management module and the intelligent retrieval and analysis module, performs access control and records on access requests; The data access layer includes at least one index database and at least one relational library or non-relational library, which is connected with the intelligent retrieval and analysis module and the security and authority management module to provide underlying data support for the query and access of each module.

3. According to claim 2, the archives full life cycle management system based on automatic filing is characterized by: The archive collection and import module includes an interface for digitizing input paper archives, a processing unit for converting electronic documents into a unified format, and a data cleaning unit for cleaning redundant data.

4. According to claim 2, the archives full life cycle management system based on automatic filing is characterized by: The intelligent classification and paper assembly module includes a natural language processing unit for parsing text content and a classification and paper assembly unit for generating a classification structure and associated files according to the parsing results.

5. According to claim 2, the archives full life cycle management system based on automatic filing is characterized by: The metadata extraction and management module includes an extraction unit for identifying archive keywords, themes and author information, and a metadata management unit for performing version control on the metadata output by the extraction unit.

6. According to claim 2, the archives full life cycle management system based on automatic filing is characterized by: The storage and migration management module includes a storage strategy unit that allocates storage media according to archive life cycle and access frequency, and a migration control unit that performs archive data migration across local and cloud platforms.

7. According to claim 2, the archives full life cycle management system based on automatic filing is characterized by: The intelligent retrieval and analysis module includes a retrieval unit that performs keyword search and semantic analysis query, and an analysis unit that performs association analysis and statistical processing on the query results.

8. According to claim 2, the archives full life cycle management system based on automatic filing is characterized by: The security and authority management module includes an authority control unit for authenticating requests based on access rules, and a log recording unit for recording access operation behaviors.

9. According to claim 2, the archives full life cycle management system based on automatic filing is characterized by: The data access layer includes an index database supporting full-text retrieval and a relational database or non-relational database storing structured data. The index database is connected to the intelligent retrieval and analysis module, and the relational database or non-relational database is connected to the metadata extraction and management module.

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