A distributed judicial data management platform architecture

Abstract

This invention discloses a distributed judicial data management platform architecture, including a front-end application module connected to a cloud server, providing a user login interface, authenticating user identity information, and granting users interactive permissions based on their identity information; a data acquisition module for collecting historical judicial data and inputting current judicial data; a data storage module employing a distributed database and file system to support distributed data storage and efficient access; a data processing and analysis module for automatically generating reports through data retrieval and analysis; and a security management module. This invention belongs to the field of data management technology, facilitating information retrieval for different users of legal provisions, case examples, case information, trial procedures, and legal documents. It supports access via Web, mobile devices, and terminal devices, improving operational convenience, saving time and effort, and enhancing the efficiency and accuracy of information retrieval.

Description

[0001] This application is a divisional application of the invention patent application with application number CN202411916440.1 and invention title: A Distributed Judicial Data Management Platform Architecture, with the parent application date being December 24, 2024. Technical Field

[0002] This invention relates to the field of data management technology, and more specifically to a distributed judicial data management platform architecture. Background Technology

[0003] Judicial data refers to information formed by judicial organs through the collection and processing of raw data in judicial activities. It is characterized by its large volume, diversity, authenticity, and openness. The basic function of judicial data is to improve the quality and efficiency of trials, the auxiliary function is to implement judicial responsibility, the derivative function is to promote judicial openness, and the core function is to assist in social governance. Improving legislation and norms related to data, doing a good job in data collection and processing, improving the intelligence level of judicial data, and realizing the open sharing of data are the paths to realizing the functions of judicial big data.

[0004] Currently, legal judgments are typically made manually based on the description of the case and by consulting relevant laws. However, manually reviewing judicial data is time-consuming and laborious, and suffers from problems such as long processing time, low accuracy, and low efficiency. Therefore, there is an urgent need for a distributed judicial data management platform architecture to solve these problems. Summary of the Invention

[0005] To address these issues, the present invention provides a distributed judicial data management platform architecture.

[0006] To achieve the above objectives, the present invention provides the following technical solution: This includes a front-end application module that connects to a cloud server, a data acquisition module, a data storage module, a data processing and analysis module, and a security management module; among which: The front-end application module includes a user interaction unit; The user interaction unit includes an intelligent legal analysis subunit, which uses natural language processing and machine learning technologies to intelligently analyze laws, regulations and case documents. Users can obtain relevant legal provisions and typical cases by inputting keywords or case descriptions. The data acquisition module is used for the collection and integration of historical judicial data as well as the input and collection of current judicial data. The data storage module uses a distributed database and file system to support distributed data storage and efficient access. The data processing and analysis module automatically generates reports by retrieving and analyzing data. The security management module is responsible for encrypting judicial data, restricting user access permissions, and checking the compliance of access audits.

[0007] Preferably, the front-end application module further includes: The identity authentication unit employs multi-factor authentication to ensure the authenticity of the user's identity; The access control unit authenticates users and controls their access, ensuring that different users can only access data within their authorized scope.

[0008] Preferably, the identity authentication unit supports authentication methods such as username and password, OAuth2.0, or digital certificate.

[0009] Preferably, the permission management unit performs fine-grained permission management based on role-based access control (RBAC) to ensure the security of data access.

[0010] Preferably, the user interaction unit further includes: The case management subunit is responsible for querying and statistically analyzing case information, and automatically assigning judges and approval personnel. The trial process management subunit records each stage of the trial process and the flow of information, tracks the progress of case handling in real time, and provides decision support and statistical analysis. The legal document generation sub-unit automatically generates legal documents based on case information and legal provisions.

[0011] Preferably, the data acquisition module includes: The data source interface unit connects with the existing systems of courts, procuratorates, and public security agencies, and collects data via file transfer. The case entry unit supports manual or automatic API interface entry of basic case information; The document scanning and recognition unit automatically recognizes and extracts the content of paper documents using OCR technology; The data cleaning and verification unit cleans and verifies the collected data, removes duplicate, erroneous, and incomplete data, and performs standardization processing.

[0012] Preferably, the data storage module includes: Distributed database unit, using NoSQL database, supports distributed storage and querying of massive amounts of data; Distributed file system storage unit, using a distributed file system to store large files; The data backup and recovery unit backs up data regularly to ensure data security and recoverability.

[0013] Preferably, the data processing and analysis module includes: Indexes and search units enable the construction of efficient indexes, supporting fast searches and queries; The data analysis unit utilizes big data analytics tools to perform data mining and statistical analysis, providing support for decision-making. The report generation unit automatically generates reports based on the analysis results.

[0014] The present invention has the following advantages: (i) By implementing front-end identity authentication and corresponding permission management, different users can query the corresponding information, including login access for different roles such as judges, prosecutors, lawyers, and the public. Through user interaction units, users can query information on legal provisions, case examples, case information, trial procedures, and legal documents. This supports access via Web, mobile devices, and terminal devices, improving the convenience of actual operation, saving time and effort, and improving the efficiency and accuracy of information query. (ii) By connecting to data sources, entering cases, scanning and recognizing documents, and cleaning and verifying data, the accuracy of collected data information can be achieved. Then, through distributed databases, distributed file systems, and data backup and recovery, distributed storage and management of data can be achieved, improving the scalability and availability of data. This will improve the convenience and accuracy of users in obtaining data, reduce the time spent on information retrieval, and help improve the efficiency of actual operations. Attached Figure Description

[0015] Figure 1 A schematic diagram illustrating the principle of a distributed judicial data management platform architecture provided by this invention; Figure 2 A schematic diagram of the front-end application module of a distributed judicial data management platform architecture provided by the present invention; Figure 3 A schematic diagram of the data acquisition module of a distributed judicial data management platform architecture provided by this invention; Figure 4 A schematic diagram of the data storage module of a distributed judicial data management platform architecture provided by the present invention; Figure 5 A schematic diagram of the data processing and analysis module of a distributed judicial data management platform architecture provided by this invention; Figure 6 This invention provides a block diagram illustrating the security management module principle of a distributed judicial data management platform architecture. Detailed Implementation

[0016] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] like Figure 1 As shown, it includes a front-end application module connected to a cloud server, a data acquisition module, a data storage module, a data processing and analysis module, and a security management module; The front-end application module provides a user login interface, authenticates user identity information, and grants users interaction permissions based on their identity information. The data acquisition module is used for the collection and integration of historical judicial data as well as the input and collection of current judicial data. The data storage module uses a distributed database and file system to support distributed data storage and efficient access. The data processing and analysis module automatically generates reports by retrieving and analyzing data. The security management module is responsible for encrypting judicial data, restricting user access permissions, and checking the compliance of access audits.

[0018] In this embodiment, as Figure 2 As shown, the front-end application module includes: The identity authentication unit employs multi-factor authentication to ensure the authenticity of the user's identity; The access control unit authenticates and controls user permissions, ensuring that different users can only access data within their authorized scope. The user interaction unit provides access to information such as legal provisions, case examples, case information, trial procedures, and legal documents, and supports access via Web, mobile devices, and terminal devices.

[0019] Specifically, the identity authentication unit supports authentication methods such as username and password, OAuth2.0, or digital certificate.

[0020] Specifically, the permission management unit performs fine-grained permission management based on role-based access control (RBAC) to ensure the security of data access.

[0021] Specifically, the user interaction unit includes: The intelligent legal analysis subunit, based on natural language processing and machine learning technologies, performs intelligent analysis of laws, regulations and case documents. Users can obtain relevant legal provisions and typical cases by inputting keywords or case descriptions. The case management subunit is responsible for querying and statistically analyzing case information, and automatically assigning judges and approval personnel. The trial process management subunit records each stage of the trial process and the flow of information, tracks the progress of case handling in real time, and provides decision support and statistical analysis. The legal document generation sub-unit automatically generates legal documents based on case information and legal provisions. It should be noted that intelligent analysis of laws, regulations, and case documents based on Natural Language Processing (NLP) and machine learning technologies can greatly improve the efficiency and accuracy of legal research and application. The following are the main steps and methods to achieve this goal: S1 Data Preprocessing Text cleaning: a. It is necessary to clean up laws, regulations, and case documents, removing irrelevant information such as format tags, duplicate content, and noise; b. Word segmentation and part-of-speech tagging: Use NLP word segmentation tools to break the text into meaningful units (such as words and phrases) and perform part-of-speech tagging, which helps with subsequent analysis; c. Named Entity Recognition (NER): Identifying legal entities in text, such as parties to a case, legal terms, dates, and locations, which are key to understanding and analyzing legal texts; S2 Feature Extraction and Representation a. Word vector representation: Using word embedding techniques (such as Word2Vec, GloVe, or BERT) to convert words in the text into high-dimensional vectors in order to capture the semantic relationships between words; b. Text representation: For an entire document or paragraph, it can be represented using sentence embeddings (such as the output of [CLS] tags generated by BERT) or document-level embeddings (such as Doc2Vec); S3 Machine Learning Model Construction a. Text classification: Based on the text content, laws, regulations and judgment documents can be divided into different categories, such as case type, legal field, etc., and classification algorithms (such as logistic regression, decision tree, random forest, support vector machine or deep learning model) are used for training; b. Relationship Extraction: Through entity recognition and relationship extraction technologies, the relationships between entities in the text are determined, such as the relationship between the plaintiff and the defendant, and the correspondence between case facts and legal clauses. c. Sentiment Analysis: Analyzing the emotional tendencies expressed in the text to understand the public's views on the case or legal provisions can be done using a sentiment analysis model based on supervised learning; d. Judgment outcome prediction: Using historical case data, train machine learning models to predict the possible judgment outcomes of similar cases; S4 Deep Learning Applications a. Pre-trained models: Fine-tuning pre-trained NLP models (such as BERT, RoBERTa, GPT, etc.) to adapt to the specific tasks of legal text analysis. These models are trained on a large number of texts and can capture the complexity and diversity of language well. b. Attention Mechanism: Introducing an attention mechanism into a deep learning model helps the model focus more on important parts of the text, improving the accuracy of the analysis; S5 Legal Knowledge Graph Construction a. Knowledge extraction: Extract key information from laws, regulations and case documents to construct a legal knowledge graph. The nodes in the graph can be legal entities, legal provisions, cases, etc., and the edges represent the relationships between them. b. Knowledge Reasoning: Using relationships within a graph to perform logical reasoning to aid legal decision-making and case analysis; S6 Privacy and Security a. Data encryption: Encrypt sensitive data to ensure security during data transmission and storage; b. Access control: Set strict access permissions to ensure that only authorized personnel can access and analyze relevant data; S7 Continuous Optimization and Iteration a. Model Evaluation: Regularly evaluate the model's performance, including metrics such as accuracy, recall, and F1 score, in order to identify problems and make optimizations in a timely manner; b. Data updates: As laws, regulations, and case documents are constantly updated, the training data needs to be updated regularly and the model needs to be retrained to maintain its accuracy and timeliness. Through the above steps, intelligent analysis of laws, regulations, and case documents can be performed based on NLP and machine learning technologies, providing strong support for the research and application of judicial data.

[0022] In this embodiment, as Figure 3 As shown, the data acquisition module includes: The data source interface unit connects with the existing systems of courts, procuratorates, and public security agencies, and collects data via file transfer. The case entry unit supports manual or automatic API interface entry of basic case information; The document scanning and recognition unit automatically recognizes and extracts the content of paper documents using OCR technology; The data cleaning and verification unit cleans and verifies the collected data, removes duplicate, erroneous, and incomplete data, and performs standardization processing.

[0023] In this embodiment, as Figure 4 As shown, the data storage module includes: Distributed database units employ NoSQL databases (such as Cassandra, HBase, MongoDB, etc.) to achieve distributed storage and management of data. They support distributed storage and querying of massive amounts of data, and support technologies such as data sharding and data replication to improve data scalability and availability. Distributed file system storage unit, using distributed file systems (such as HDFS, S3, etc.) to store large files, such as evidence materials, legal documents, video files, etc. The data backup and recovery unit regularly backs up data to ensure data security and recoverability, and can quickly recover data in the event of loss or damage. In this embodiment, as Figure 5 As shown, the data processing and analysis module includes: Indexes and search units enable the construction of efficient indexes, supporting fast searches and queries; The data analysis unit utilizes big data analytics tools (such as Spark and Hadoop) to perform data mining and statistical analysis, providing support for decision-making. The report generation unit automatically generates reports based on the analysis results, such as case statistics reports and trend analysis reports.

[0024] In this embodiment, as Figure 6 As shown, the security management module includes: The data encryption and desensitization unit encrypts and stores sensitive data for transmission, and desensitizes non-sensitive data. Access control unit restricts user access to data, ensuring that only authorized users can access the relevant data; The audit log unit records and monitors user actions on data for compliance checks and security analysis, ensuring data security and compliance.

[0025] In summary, this invention enables different users to query relevant information through front-end identity authentication and corresponding permission management. This includes login access for different roles such as judges, prosecutors, lawyers, and the general public. Users can query legal provisions, case examples, case information, trial procedures, and legal documents through user interaction units. Access is supported via Web, mobile devices, and terminal devices, improving operational convenience, saving time and effort, and enhancing the efficiency and accuracy of information retrieval. Through data source integration, case entry, document scanning and recognition, and data cleaning and verification, the accuracy of collected data is ensured. Furthermore, distributed databases, distributed file systems, and data backup and recovery enable distributed storage and management of data, improving data scalability and availability. This, in turn, enhances the convenience and accuracy of data access for users, reduces the time spent on information retrieval, and ultimately improves operational efficiency.

Claims

1. A distributed justice data management platform architecture, characterized by, This includes a front-end application module that connects to a cloud server, a data acquisition module, a data storage module, a data processing and analysis module, and a security management module; among which: The front-end application module includes a user interaction unit; The user interaction unit includes an intelligent legal analysis subunit, which uses natural language processing and machine learning technologies to intelligently analyze laws, regulations and case documents. Users can obtain relevant legal provisions and typical cases by inputting keywords or case descriptions. The data acquisition module is used for the collection and integration of historical judicial data as well as the input and collection of current judicial data. The data storage module uses a distributed database and file system to support distributed data storage and efficient access. The data processing and analysis module automatically generates reports by retrieving and analyzing data. The security management module is responsible for encrypting judicial data, restricting user access permissions, and checking the compliance of access audits. 2.The distributed judicial data management platform architecture of claim 1, wherein, The front-end application module also includes: The identity authentication unit employs multi-factor authentication to ensure the authenticity of the user's identity; The access control unit authenticates users and controls their access, ensuring that different users can only access data within their authorized scope.

3. The distributed justice data management platform architecture of claim 2, wherein, The identity authentication unit supports authentication methods such as username and password, OAuth2.0, or digital certificate.

4. The distributed judicial data management platform architecture of claim 2, wherein, The permission management unit performs fine-grained permission management based on role-based access control (RBAC) to ensure the security of data access.

5. The distributed justice data management platform architecture of claim 2, wherein, The user interaction unit further includes: The case management subunit is responsible for querying and statistically analyzing case information, and automatically assigning judges and approval personnel. The trial process management subunit records each stage of the trial process and the flow of information, tracks the progress of case handling in real time, and provides decision support and statistical analysis. The legal document generation sub-unit automatically generates legal documents based on case information and legal provisions.

6. The distributed justice data management platform architecture of claim 1, wherein, The data acquisition module includes: The data source interface unit connects with the existing systems of courts, procuratorates, and public security agencies, and collects data via file transfer. The case entry unit supports manual or automatic API interface entry of basic case information; The document scanning and recognition unit automatically recognizes and extracts the content of paper documents using OCR technology; The data cleaning and verification unit cleans and verifies the collected data, removing duplicate, erroneous, and incomplete data, and performs standardization processing.

7. The distributed justice data management platform architecture of claim 1, wherein, The data storage module includes: Distributed database unit, using NoSQL database, supports distributed storage and querying of massive amounts of data; Distributed file system storage unit, using a distributed file system to store large files; The data backup and recovery unit backs up data regularly to ensure data security and recoverability.

8. The distributed justice data management platform architecture of claim 1, wherein, The data processing and analysis module includes: Indexes and search units enable the construction of efficient indexes, supporting fast searches and queries; The data analysis unit utilizes big data analytics tools to perform data mining and statistical analysis, providing support for decision-making. The report generation unit automatically generates reports based on the analysis results.

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