File digital intelligent management system

Abstract

The invention provides an archive digital intelligent management system, and relates to the technical field of digital intelligent management. The archive digital intelligent management system comprises a user interface layer, the user interface layer is connected with a function module layer, the function module layer is connected with a data layer, and the data layer is connected with an infrastructure layer. And the user interface layer comprises a multi-modal interaction interface and a self-adaptive interface. According to the technology, the advantages of multi-modal interaction, an adaptive interface, intelligent retrieval, automatic workflow, data analysis, block chain evidence storage, community cooperation, a virtual assistant, edge calculation and the like are integrated in archive management, convenient, efficient, safe and personalized user experience is provided, task execution and resource utilization are optimized, the data security and credibility are improved, and the user experience is improved. And large-scale data processing and high-concurrency access are supported, and high availability and expansibility of the system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of digital intelligent management, and in particular to a digital intelligent archive management system. Background Art

[0002] Archives management refers to the process of systematically and scientifically collecting, organizing, preserving, utilizing and destroying various documents, materials and records. It covers the entire life cycle from the generation to the destruction of archives, ensuring the integrity, accessibility and security of archives. The main goal of archives management is to improve the utilization efficiency of archives, reduce management costs, and ensure the authenticity and legal effect of archives.

[0003] Existing technologies mainly implement archive management through traditional paper archive management and electronic archive management systems, as well as cloud computing and big data technologies. Traditional paper archive management methods include manual classification, labeling, and archiving, storage through physical facilities such as archive cabinets and archive rooms, and retrieval and management using archive catalogs, index cards, etc. However, this method is inefficient, prone to errors, has high management costs, insufficient security, is difficult to expand, and has a poor user experience. The electronic archive management system digitizes paper archives, converts paper archives into electronic documents using document scanners, uses relational databases to store archive metadata, provides keyword- and tag-based retrieval functions, manages user permissions based on role-based access control, and regularly backs up data to ensure data security and recoverability. However, the retrieval accuracy of the electronic archive management system is low when processing complex queries and semantic retrieval, the management cost is still high, there is a risk of data leakage, insufficient scalability, and a poor user experience. Cloud computing and big data technologies store archival data in the cloud, use big data analysis technology to analyze and optimize archival usage, store archival data through cloud object storage, use data analysis tools to analyze user behavior and archival usage, and ensure high availability and fault tolerance of the system through multi-region deployment and load balancing. However, there are still challenges in terms of security, cost, and user-friendliness. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a digital intelligent archive management system, which solves the problems of low efficiency, high cost and poor security of the prior art.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A digital archives intelligent management system, comprising a user interface layer, the user interface layer is connected to a function module layer, the function module layer is connected to a data layer, and the data layer is connected to an infrastructure layer; The user interface layer includes a multimodal interactive interface and an adaptive interface; the functional module layer includes a user management module, a file management module, an intelligent retrieval module, an automated workflow module, a blockchain evidence storage module, a community collaboration module, and a virtual assistant module; the data layer includes a distributed file system, a relational database, and a graph database; the infrastructure layer includes cloud services, a containerized platform, and edge computing.

[0006] Preferably, the multimodal interactive interface supports text input, voice input and gesture recognition, wherein voice input integrates Google Speech-to-Text technology, and gesture recognition supports operations on AR devices.

[0007] Preferably, the adaptive interface can automatically adjust the interface layout and function display according to the user's device type and usage scenario, including detecting the user's device type through user agent information and identifying the user's usage scenario through sensor data.

[0008] Preferably, the user management module supports multiple user authentication methods, including username and password, fingerprint and facial recognition, and performs permission management based on role-based access control.

[0009] Preferably, the archive management module supports archive entry, automatic classification and distributed storage of multimodal input, wherein archive classification is implemented using machine learning models BERT or TF-IDF.

[0010] Preferably, the intelligent retrieval module supports semantic retrieval and intelligent recommendation, adopts BERT or Sentence-BERT of NLP technology to analyze the user's retrieval intention, and recommends relevant archives and management operations based on the user's historical behavior and preferences.

[0011] Preferably, the automated workflow module decomposes the archive management task into multiple steps to form a task chain, supporting automatic execution, intelligent scheduling and task reminders of the task.

[0012] Preferably, the data analysis module records each operation of the user, including retrieval, archiving, and borrowing, and uses big data analysis technology Pandas or Spark to generate a visual report.

[0013] Preferably, the blockchain evidence storage module stores the key metadata of the archive on the blockchain to ensure the immutability and security of the data, and records the sharing history and modification records of the archive.

[0014] The present invention provides a digital intelligent archive management system, which has the following beneficial effects: The present invention provides a digitalized intelligent archive management system. The management system of the present invention supports multiple input modes such as text, voice, and gesture through a multimodal interactive function, thereby improving the convenience and naturalness of user operations. The adaptive interface dynamically adjusts the interface layout and function display according to the user device type and usage scenario to achieve personalized user experience. Intelligent retrieval utilizes natural language processing technology, BERT and Sentence-BERT, to analyze user retrieval intent, support keyword, tag, and semantic retrieval, and improve retrieval accuracy and efficiency. The automated workflow decomposes archive management tasks into multiple steps to form a task chain. The intelligent scheduling algorithm is used to dynamically adjust the execution order and priority, thereby improving management efficiency and reducing manual intervention. In terms of data analysis, Use big data analysis technology and graph database to analyze archive usage, generate visual reports, and help managers optimize management strategies. Blockchain evidence technology is used to record key metadata and sharing history of archives, ensure data immutability and transparency, and improve archive security and credibility. The community collaboration function supports archive sharing and crowdsourcing collaboration, breaks down complex tasks into simple subtasks, and publishes them to crowdsourcing platforms to improve resource utilization and task completion efficiency. The virtual assistant provides intelligent customer service based on natural language processing, parses user query intent, provides accurate retrieval results and proactive suggestions, and enhances user experience. Edge computing performs partial data processing on edge devices, reduces data transmission volume, shortens user response time, and improves system response speed and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the system framework flow of the present invention. DETAILED DESCRIPTION

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

[0017] like Figure 1 As shown, an embodiment of the present invention provides a digitalized intelligent archive management system, including a user interface layer, the user interface layer is connected to a function module layer, the function module layer is connected to a data layer, and the data layer is connected to an infrastructure layer; The user interface layer includes multimodal interactive interface and adaptive interface. The functional module layer includes user management module, archive management module, intelligent retrieval module, automated workflow module, blockchain evidence module, community collaboration module, and virtual assistant module. The data layer includes distributed file system, relational database, and graph database, and includes infrastructure layer cloud service, containerized platform, and edge computing.

[0018] The multimodal interactive interface supports text input, voice input and gesture recognition, among which voice input integrates Google Speech-to-Text technology, and gesture recognition supports operations on AR devices. The adaptive interface can automatically adjust the interface layout and function display according to the user's device type and usage scenario, including detecting the user's device type through user agent information and identifying the user's usage scenario through sensor data. The user management module supports multiple user authentication methods, including username and password, fingerprint and facial recognition, and performs permission management based on role-based access control. The archive management module supports multimodal input archive entry, automatic classification and distributed storage, among which archive classification is implemented using machine learning models BERT or TF-IDF. The intelligent retrieval module supports semantic retrieval and intelligent recommendation, using NLP technology BERT or Sentence-BERT to analyze the user's retrieval intention, and recommends relevant archives and management operations based on the user's historical behavior and preferences. The automated workflow module decomposes the archive management task into multiple steps to form a task chain, supporting automatic execution of tasks, intelligent scheduling and task reminders. The data analysis module records every user operation, including retrieval, archiving, and borrowing, and uses big data analysis technology Pandas or Spark to generate visual reports. The blockchain evidence storage module stores the key metadata of the archives (such as creation time, modification records, and access records) on the blockchain to ensure the immutability and security of the data, and records the sharing history and modification records of the archives.

[0019] Specifically: User Interface Layer Multimodal interface The front-end technology framework is React or Vue and the speech recognition library Google Speech-to-Text. The interface design supports multiple interaction methods such as text, voice, and gesture. And the speech recognition and gesture recognition functions are integrated into the front-end interface.

[0020] Adaptive interface Detect the user's device type through the User Agent. Utilize the sensor data (such as GPS, accelerometer) integrated on the user's mobile terminal, and use the machine learning model decision tree or random forest to identify the user's usage scenario. Then dynamically adjust the interface layout and function display according to the device type and usage scenario.

[0021] Functional module layer User Management Module Integrate identity authentication services such as OAuth or LDAP to implement multiple authentication methods. Use the role-based access control (RBAC) model to design permission management functions. Record user operations through logs and use data analysis tools such as Pandas or Spark for behavior analysis. Use the user management interface and backend services to implement user authentication and permission management.

[0022] File management module Through the multimodal input function, it supports multiple methods such as text, voice, and image. It uses machine learning models such as BERT or TF-IDF to automatically classify archives, and users only need to confirm or adjust. It stores archive data in the distributed file system HadoopHDFS or Ceph. It can search based on keyword, tag, and semantic search functions.

[0023] Intelligent search module Use NLP technology BERT or Sentence-BERT to analyze the user's search intention. Recommend relevant archives and management operations based on the user's operation history and preferences. Implement efficient search algorithms and fast search of large-scale data.

[0024] Automated workflow module Decompose archive management tasks into multiple steps to form a task chain. Use the intelligent scheduling algorithm genetic algorithm to dynamically adjust the execution order and priority of tasks. Remind users to complete tasks through system notifications, emails, etc. Design an automated workflow interface to facilitate user management and monitoring of tasks.

[0025] Data Analysis Module Record every user operation, including retrieval, archiving, borrowing, etc. Use big data analysis tools such as Pandas or Spark to analyze archive usage and user behavior. Use visual report generation tools to generate various types of reports (such as usage frequency charts, user behavior heat maps, etc.).

[0026] Blockchain Evidence Storage Module Store key metadata of archives (such as creation time, modification records, access records) on the blockchain. Record the shared history and modification records of archives through blockchain technology. Implemented through Ethereum or Hyperledger Fabric blockchain platform.

[0027] Community Collaboration Module Design a task publishing interface to allow administrators to publish complex tasks. Design a task assignment algorithm to automatically assign tasks based on the complexity of the task and the user's trust score. Then perform a result aggregation function to automatically aggregate and verify the results submitted by users. Use blockchain technology to record users' task completion status and trust scores.

[0028] Virtual Assistant Module Leverage NLP models such as BERT or Transformer to achieve natural language understanding. Design virtual assistant interfaces and conversation logic to support real-time Q&A and proactive suggestions. Integrate virtual assistant functionality into the user interface.

[0029] Data Layer Distributed File System Use Hadoop HDFS or Ceph distributed file system to store archive data on multiple nodes to ensure high availability and scalability. Perform regular data backup to ensure data security. Provide data recovery mechanism to support rapid data recovery.

[0030] Relational Database Use MySQL or PostgreSQL relational database to design user information table to record user name, role, authority and other information. Then establish archive metadata table to store archive title, keywords, classification label, creation time and other metadata. According to the authority management table, record user access rights and operation records.

[0031] Graph Databases The Neo4j graph database is used to design the archive association diagram to record the semantic association and reference relationship between archives. The complex query function based on the graph database is used to improve the accuracy and efficiency of retrieval.

[0032] Infrastructure Layer Cloud Services Use AWS or Azure cloud service platforms to configure elastic computing resources so that the system supports dynamic expansion. Configure cloud object storage Amazon S3 or Alibaba Cloud OSS to store archive data and metadata. Ensure high availability and fault tolerance of the system through multi-region deployment and load balancing.

[0033] Containerized Platform Through Docker containerization technology and container orchestration tool Kubernetes, applications and services are containerized to ensure the consistency and reusability of the environment. Kubernetes is used for container orchestration and management, supporting automatic expansion and fault recovery. Computing resources are dynamically scheduled according to actual load to improve resource utilization.

[0034] Edge computing The EdgeX Foundry edge computing platform performs simple data processing and preprocessing on edge devices to reduce the amount of data transmission. Through edge computing, the user's response time is shortened and the user experience is improved. The configuration and status of edge devices are managed to ensure the normal operation of the devices.

[0035] The system of the present invention supports multiple input methods such as text, voice and gesture through a multimodal interactive interface, integrates Google Speech-to-Text technology and gesture recognition function, and improves the convenience and naturalness of users; the adaptive interface can automatically adjust the interface layout and function display according to the user's device type and usage scenario, detects the device type and sensor data (such as GPS, accelerometer) through the User Agent to identify the usage scenario, and uses machine learning models (decision trees or random forests) for intelligent adjustment, thereby improving the user experience. The user management module integrates OAuth or LDAP identity authentication services, supports multiple authentication methods, uses a role-based access control (RBAC) model for permission management, records user operations through logs, and uses Pandas or Spark for behavior analysis, thereby enhancing the security and management optimization of the system. The archive management module supports multimodal entry functions, uses machine learning models (BERT or TF-IDF) to automatically classify archives, and stores archive data in Hadoop HDFS or Ceph distributed file systems, ensuring high availability and scalability of data, while providing retrieval functions based on keywords, tags and semantics. The intelligent retrieval module uses NLP technology (BERT or Sentence-BERT) to analyze the user's search intention, recommend relevant archives and management operations based on the user's operation history and preferences, and realizes efficient retrieval algorithms and rapid retrieval of large-scale data. The automated workflow module decomposes archive management tasks into multiple steps to form a task chain, uses an intelligent scheduling algorithm (genetic algorithm) to dynamically adjust the execution order and priority of tasks, reminds users to complete tasks through system notifications, emails, etc., and supports automatic execution and intelligent scheduling of tasks. The data analysis module records every user operation, uses Pandas or Spark to analyze the use of archives and user behavior, and generates various types of visual reports (such as usage frequency charts, user behavior heat maps, etc.) to help managers optimize management strategies. The blockchain evidence storage module stores the key metadata of archives (such as creation time, modification records, and access records) on the blockchain, and records the shared history and modification records of archives through blockchain technology, ensuring the immutability and transparency of data and improving the credibility and security of archives. The community collaboration module designs the task publishing interface and task assignment algorithm, automatically assigns tasks based on the complexity of the task and the user's trust score, automatically summarizes and verifies the results submitted by users, and uses blockchain technology to record the user's task completion status and trust score, thereby improving resource utilization and task completion efficiency. The virtual assistant module uses NLP models (BERT or Transformer) to achieve natural language understanding, designs the virtual assistant interface and dialogue logic, supports real-time Q&A and proactive suggestions, integrates the virtual assistant function into the user interface, and enhances the user experience.The data layer uses a distributed file system (Hadoop HDFS or Ceph) to store archive data on multiple nodes to ensure high availability and scalability, perform regular data backup, provide a data recovery mechanism, and ensure data security; the relational database (MySQL or PostgreSQL) is used to store user information and archive metadata, establish an archive association relationship graph, record the semantic association and reference relationship between archives, and improve the accuracy and efficiency of retrieval through complex query functions based on graph databases (such as Neo4j). The infrastructure layer uses cloud service platforms (such as AWS or Azure) to configure elastic computing resources and cloud object storage (such as Amazon S3 or Alibaba Cloud OSS), supports dynamic expansion and high availability, and ensures the high availability and fault tolerance of the system through multi-region deployment and load balancing; containerization and orchestration through Docker and Kubernetes ensure the consistency and reusability of the environment, support automatic expansion and fault recovery; simple data processing and preprocessing are performed on edge devices, and edge computing (such as EdgeX Foundry) is used to shorten the user's response time and improve the system response speed and user experience. The combination of these technologies gives the system a significant advantage in the field of archive management and comprehensively improves the efficiency, accuracy and security of archive management.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digitalized intelligent archive management system, comprising a user interface layer, characterized in that: The user interface layer is connected to the function module layer, the function module layer is connected to the data layer, and the data layer is connected to the infrastructure layer; The user interface layer includes a multimodal interactive interface and an adaptive interface; the functional module layer includes a user management module, a file management module, an intelligent retrieval module, an automated workflow module, a blockchain evidence storage module, a community collaboration module, and a virtual assistant module; the data layer includes a distributed file system, a relational database, and a graph database; the infrastructure layer includes cloud services, a containerized platform, and edge computing.

2. The digital intelligent archive management system according to claim 1 is characterized by: The multimodal interactive interface supports text input, voice input and gesture recognition, wherein voice input integrates Google Speech-to-Text technology, and gesture recognition supports operations on AR devices.

3. The digital intelligent archive management system according to claim 1 is characterized by: The adaptive interface can automatically adjust the interface layout and function display according to the user's device type and usage scenario, including detecting the user's device type through user agent information and identifying the user's usage scenario through sensor data.

4. The digital intelligent archive management system according to claim 1 is characterized by: The user management module supports multiple user authentication methods, including username and password, fingerprint and face recognition, and performs permission management based on role-based access control.

5. The digital intelligent archive management system according to claim 1 is characterized by: The archive management module supports archive entry, automatic classification and distributed storage of multimodal input, wherein archive classification is implemented using machine learning models BERT or TF-IDF.

6. The digital intelligent archive management system according to claim 1 is characterized by: The intelligent retrieval module supports semantic retrieval and intelligent recommendation, adopts BERT or Sentence-BERT of NLP technology to analyze the user's retrieval intention, and recommends relevant archives and management operations based on the user's historical behavior and preferences.

7. The digital intelligent archive management system according to claim 1 is characterized by: The automated workflow module decomposes the archive management task into multiple steps to form a task chain, supporting automatic execution, intelligent scheduling and task reminders of tasks.

8. The digital intelligent archive management system according to claim 1 is characterized by: The data analysis module records every operation of the user, including retrieval, archiving, and borrowing, and uses big data analysis technology Pandas or Spark to generate visual reports.

9. The digital intelligent archive management system according to claim 1 is characterized by: The blockchain evidence storage module stores the key metadata of the archive on the blockchain to ensure the immutability and security of the data, and records the sharing history and modification records of the archive.

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