Distributed judicial data management platform architecture

Abstract

The invention discloses a distributed judicial data management platform architecture, which comprises a front-end application module connected with a cloud server and used for providing a user login interface, authenticating user identity information and providing interaction authority for a user according to the user identity information; the data acquisition module is used for butt-joint acquisition of historical judicial data and input acquisition of current judicial data; the data storage module adopts a distributed database and a file system and supports distributed storage and efficient access of data; the data processing and analyzing module is used for automatically generating a report by retrieving and analyzing the data; and a safety management module. The invention belongs to the technical field of data management, provides convenience for different users to query information of legal provisions, cases, case information, judgment processes and legal instruments, supports Web, mobile terminal and terminal equipment access, improves the convenience of actual operation, saves time and labor, and improves the information query efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a distributed judicial data management platform architecture. Background Art

[0002] Judicial data is the information generated by judicial organs through the collection and processing of raw data in judicial activities. It is characterized by large quantity, diversity, authenticity and openness. The basic function of judicial data is to improve the quality and efficiency of trials, its auxiliary function is to implement judicial responsibilities, its derivative function is to promote judicial openness, and its core function is to assist in social governance. Improving legislative norms in data, doing a good job in data collection and processing, improving the intelligence of judicial data, and realizing open sharing of data are the paths to realize the function of judicial big data.

[0003] Currently, when making legal judgments, it is usually done manually based on the description of the case and the relevant laws, and the judgment is made according to the legal provisions. However, manual review of judicial data is time-consuming and laborious, and there are problems such as long time consumption, low accuracy and low efficiency. Therefore, a distributed judicial data management platform architecture is urgently needed to solve the above problems. Summary of the invention

[0004] To this end, the present invention provides a distributed judicial data management platform architecture to solve the above-mentioned problems in the prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] It includes a front-end application module connected to the cloud server, a data acquisition module, a data storage module, a data processing and analysis module, and a security management module;

[0007] The front-end application module provides a user login interface, authenticates user identity information, and provides users with interaction permissions based on user identity information;

[0008] Data collection module, used for docking and collection of historical judicial data and input and collection of current judicial data;

[0009] The data storage module uses a distributed database and file system to support distributed storage and efficient access to data;

[0010] Data processing and analysis module, which automatically generates reports by retrieving and analyzing data;

[0011] The security management module is responsible for encrypting judicial data, limiting user access rights, and checking access audit compliance.

[0012] As a further optimized solution of the present invention, the front-end application module includes:

[0013] The identity authentication unit uses multi-factor authentication to ensure the authenticity of the user's identity;

[0014] The permission management unit authenticates users and controls permissions to ensure that different users can only access data within their permission range;

[0015] The user interaction unit provides information query on legal provisions, typical cases, case information, trial process and legal documents, and supports access via the Web, mobile terminals and terminal devices.

[0016] As a further optimized solution of the present invention, the identity authentication unit supports authentication methods such as username and password, OAuth2.0 or digital certificate.

[0017] As a further optimized solution of the present invention, the authority management unit performs fine-grained authority management based on role-based access control (RBAC) to ensure the security of data access.

[0018] As a further optimized solution of the present invention, the user interaction unit includes:

[0019] The intelligent legal analysis subunit conducts intelligent analysis of laws, regulations and case documents based on natural language processing and machine learning technologies. Users can obtain relevant legal provisions and typical cases by entering keywords or case descriptions.

[0020] The case management subunit is responsible for querying and statistically analyzing case information and automatically assigning judges and reviewers;

[0021] The trial process management sub-unit records each link and information flow of the trial process, tracks the progress of case handling in real time, and provides decision support and statistical analysis;

[0022] The legal document generation subunit automatically generates legal documents based on case information and legal provisions.

[0023] As a further optimized solution of the present invention, the data acquisition module includes:

[0024] The data source docking unit connects with the existing systems of courts, procuratorates, and public security agencies to collect data through file transfer;

[0025] Case entry unit, supports manual entry or automatic API interface entry of basic case information;

[0026] Document scanning and recognition unit, which automatically recognizes and extracts the content of paper documents through OCR technology;

[0027] The data cleaning and verification unit cleans and verifies the collected data, removes duplicate, erroneous, and incomplete data, and performs standardized processing.

[0028] As a further optimized solution of the present invention, the data storage module includes:

[0029] Distributed database unit, using NoSQL database, supports distributed storage and query of massive data;

[0030] Distributed file system storage unit, using distributed file system to store large files;

[0031] The data backup and recovery unit backs up data regularly to ensure data security and recoverability.

[0032] As a further optimized solution of the present invention, the data processing and analysis module includes:

[0033] Index and search unit, which builds efficient indexes and supports fast search and query;

[0034] The data analysis unit uses big data analysis tools to conduct data mining and statistical analysis to provide support for decision-making;

[0035] The report generation unit automatically generates reports based on the analysis results.

[0036] As a further optimized solution of the present invention, the security management module includes:

[0037] Data encryption and desensitization unit, which encrypts the storage and transmission of sensitive data and desensitizes non-sensitive data;

[0038] Access control unit to limit user access rights to data, ensuring that only authorized users can access relevant data;

[0039] The audit log unit records and monitors and manages user operations on data for compliance checks and security analysis to ensure data security and compliance.

[0040] The present invention has the following advantages:

[0041] (1) Through front-end identity authentication and corresponding authority management, different users can query corresponding information, including login access by judges, prosecutors, lawyers, the public and other different roles, and query information on legal provisions, typical cases, case information, trial procedures and legal documents through user interaction units, supporting access from the Web, mobile terminals and terminal devices, thus improving the convenience of actual operations, saving time and effort, and improving the efficiency and accuracy of information query;

[0042] (ii) The accuracy of collected data information is achieved through data source connection, case entry, document scanning and recognition, and data cleaning and verification. Distributed storage and management of data is achieved through distributed databases, distributed file systems, and data backup and recovery, thereby improving the scalability and availability of data, thereby improving the convenience and accuracy of users in obtaining data, reducing the time spent on information query, and helping to improve actual work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A principle block diagram of a distributed judicial data management platform architecture provided by the present invention;

[0044] Figure 2 A principle block diagram of a front-end application module of a distributed judicial data management platform architecture provided by the present invention;

[0045] Figure 3 A principle block diagram of a data acquisition module of a distributed judicial data management platform architecture provided by the present invention;

[0046] Figure 4 A principle block diagram of a data storage module of a distributed judicial data management platform architecture provided by the present invention;

[0047] Figure 5 A principle block diagram of a data processing and analysis module of a distributed judicial data management platform architecture provided by the present invention;

[0048] Figure 6 A principle block diagram of a security management module of a distributed judicial data management platform architecture provided by the present invention. DETAILED DESCRIPTION

[0049] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are 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.

[0050] 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;

[0051] The front-end application module provides a user login interface, authenticates user identity information, and provides users with interaction permissions based on user identity information;

[0052] Data collection module, used for docking and collection of historical judicial data and input and collection of current judicial data;

[0053] The data storage module uses a distributed database and file system to support distributed storage and efficient access to data;

[0054] Data processing and analysis module, which automatically generates reports by retrieving and analyzing data;

[0055] The security management module is responsible for encrypting judicial data, limiting user access rights, and checking access audit compliance.

[0056] In this embodiment, if Figure 2 As shown, the front-end application module includes:

[0057] The identity authentication unit uses multi-factor authentication to ensure the authenticity of the user's identity;

[0058] The permission management unit authenticates users and controls permissions to ensure that different users can only access data within their permission range;

[0059] The user interaction unit provides information query on legal provisions, typical cases, case information, trial process and legal documents, and supports access via the Web, mobile terminals and terminal devices.

[0060] Specifically, the identity authentication unit supports authentication methods such as username and password, OAuth2.0 or digital certificate.

[0061] Specifically, the authority management unit performs fine-grained authority management based on role-based access control (RBAC) to ensure the security of data access.

[0062] Specifically, the user interaction unit includes:

[0063] The intelligent legal analysis subunit conducts intelligent analysis of laws, regulations and case documents based on natural language processing and machine learning technologies. Users can obtain relevant legal provisions and typical cases by entering keywords or case descriptions.

[0064] The case management subunit is responsible for querying and statistically analyzing case information and automatically assigning judges and reviewers;

[0065] The trial process management sub-unit records each link and information flow of the trial process, tracks the progress of case handling in real time, and provides decision support and statistical analysis;

[0066] The legal document generation subunit automatically generates legal documents based on case information and legal provisions;

[0067] It should be noted that intelligent analysis of laws, regulations and case documents based on natural language processing (NLP) and machine learning technology can greatly improve the efficiency and accuracy of legal research and application. The following are the main steps and methods to achieve this goal:

[0068] S1 Data Preprocessing

[0069] Text cleaning:

[0070] a. It is necessary to clean up the legal regulations and case documents and remove irrelevant information such as format tags, repeated content, noise, etc.;

[0071] b. Word segmentation and part-of-speech tagging: Use NLP word segmentation tools to split the text into meaningful units (such as words and phrases) and perform part-of-speech tagging, which helps with subsequent analysis;

[0072] c. Named Entity Recognition (NER): Identifying legal entities in text, such as parties to a case, legal terms, dates, locations, etc. These entities are key to understanding and analyzing legal texts;

[0073] S2 feature extraction and representation

[0074] a. Word vector representation: Use word embedding technology (such as Word2Vec, GloVe or BERT) to convert words in the text into high-dimensional vectors to capture the semantic relationship between words;

[0075] b. Text representation: For an entire document or paragraph, it can be represented using sentence embeddings (such as the output of the [CLS] tag generated by BERT) or document-level embeddings (such as Doc2Vec);

[0076] S3 Machine Learning Model Building

[0077] a. Text classification: Based on the text content, laws, regulations and judgment documents can be classified into different categories, such as case type, legal field, etc., and trained using classification algorithms (such as logistic regression, decision tree, random forest, support vector machine or deep learning model);

[0078] b. Relationship extraction: Through entity recognition and relationship extraction technology, determine the relationship between entities in the text, such as the relationship between the plaintiff and the defendant, the correspondence between case facts and legal provisions, etc.;

[0079] c. Sentiment analysis: Analyze the emotional tendencies expressed in the text to understand the public's views on the case or legal provisions. A sentiment analysis model based on supervised learning can be used;

[0080] d. Prediction of judgment results: Using historical case data, train machine learning models to predict possible judgment results of similar cases;

[0081] S4 Deep Learning Applications

[0082] a. Pre-trained models: Use pre-trained NLP models (such as BERT, RoBERTa, GPT, etc.) to fine-tune to the specific task of legal text analysis. These models are trained on a large amount of text and can well capture the complexity and diversity of language;

[0083] b. Attention mechanism: Introducing the attention mechanism into the deep learning model helps the model focus more on the important parts of the text and improves the accuracy of the analysis;

[0084] S5 Legal Knowledge Graph Construction

[0085] a. Knowledge extraction: Extract key information from laws, regulations and case documents to build a legal knowledge graph. The nodes in the graph can be legal entities, legal provisions, cases, etc., and the edges represent the relationship between them.

[0086] b. Knowledge reasoning: Use the relationships in the graph to perform logical reasoning to assist in legal decision-making and case analysis;

[0087] S6 Privacy and Security

[0088] a. Data encryption: Encrypt sensitive data to ensure the security of data transmission and storage;

[0089] b. Access control: Set strict access rights to ensure that only authorized personnel can access and analyze relevant data;

[0090] S7 continues to optimize and iterate

[0091] a. Model evaluation: Regularly evaluate the performance of the model, including accuracy, recall, F1 score and other indicators, so as to identify problems and optimize them in time;

[0092] b. Data update: With the continuous updating of laws, regulations and case documents, it is necessary to regularly update the training data and retrain the model to maintain its accuracy and timeliness;

[0093] Through the above steps, intelligent analysis of laws, regulations and case documents can be performed based on NLP and machine learning technology, which can provide strong support for the research and application of judicial data.

[0094] In this embodiment, if Figure 3 As shown, the data acquisition module includes:

[0095] The data source docking unit connects with the existing systems of courts, procuratorates, and public security agencies to collect data through file transfer;

[0096] Case entry unit, supports manual entry or automatic API interface entry of basic case information;

[0097] Document scanning and recognition unit, which automatically recognizes and extracts the content of paper documents through OCR technology;

[0098] The data cleaning and verification unit cleans and verifies the collected data, removes duplicate, erroneous, and incomplete data, and performs standardized processing.

[0099] In this embodiment, if Figure 4 As shown, the data storage module includes:

[0100] Distributed database unit uses NoSQL databases (such as Cassandra, HBase, MongoDB, etc.) to achieve distributed storage and management of data, support distributed storage and query of massive data, support data sharding, data replication and other technologies, and improve data scalability and availability;

[0101] 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.;

[0102] Data backup and recovery unit, which backs up data regularly to ensure data security and recoverability, and can quickly restore data when it is lost or damaged;

[0103] In this embodiment, if Figure 5 As shown, the data processing and analysis module includes:

[0104] Index and search unit, which builds efficient indexes and supports fast search and query;

[0105] Data analysis unit, which uses big data analysis tools (such as Spark and Hadoop) to conduct data mining and statistical analysis to provide support for decision-making;

[0106] The report generation unit automatically generates reports based on the analysis results, such as case statistics reports, trend analysis reports, etc.

[0107] In this embodiment, if Figure 6 As shown, the security management module includes:

[0108] Data encryption and desensitization unit, which encrypts the storage and transmission of sensitive data and desensitizes non-sensitive data;

[0109] Access control unit to limit user access rights to data, ensuring that only authorized users can access relevant data;

[0110] The audit log unit records and monitors and manages user operations on data for compliance checks and security analysis to ensure data security and compliance.

[0111] In summary, the present invention manages front-end identity authentication and corresponding permissions so that different users can query corresponding information, including login access by different roles such as judges, prosecutors, lawyers, and the public. Information query of legal provisions, typical cases, case information, trial procedures, and legal documents is performed through a user interaction unit. Access via the Web, mobile terminals, and terminal devices is supported, thereby improving the convenience of actual operations, saving time and effort, and improving the efficiency and accuracy of information query. The accuracy of collected data information is achieved through data source docking, case entry, document scanning and recognition, and data cleaning and verification. Distributed storage and management of data is achieved through distributed databases, distributed file systems, and data backup and recovery, thereby improving the scalability and availability of data, thereby improving the convenience and accuracy of users in obtaining data, reducing the time spent on information query, and helping to improve actual work efficiency.

Claims

1. A distributed judicial data management platform architecture, characterized in that: It includes a front-end application module connected to the 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 provides users with interaction permissions based on user identity information; Data collection module, used for docking and collection of historical judicial data and input and collection of current judicial data; The data storage module uses a distributed database and file system to support distributed storage and efficient access to data; Data processing and analysis module, which automatically generates reports by retrieving and analyzing data; The security management module is responsible for encrypting judicial data, limiting user access rights, and checking access audit compliance; The security management module includes: Data encryption and desensitization unit, which encrypts the storage and transmission of sensitive data and desensitizes non-sensitive data; Access control unit to limit user access rights to data, ensuring that only authorized users can access relevant data; The audit log unit records and monitors and manages user operations on data for compliance checks and security analysis to ensure data security and compliance.

2. A distributed judicial data management platform architecture according to claim 1, characterized in that: The front-end application module includes: The identity authentication unit uses multi-factor authentication to ensure the authenticity of the user's identity; The permission management unit authenticates users and controls permissions to ensure that different users can only access data within their permission range; The user interaction unit provides information query on legal provisions, typical cases, case information, trial process and legal documents, and supports access via the Web, mobile terminals and terminal devices.

3. A distributed judicial data management platform architecture according to claim 2, characterized in that: The identity authentication unit supports authentication methods such as username and password, OAuth2.0 or digital certificate.

4. A distributed judicial data management platform architecture according to claim 2, characterized in that: The authority management unit performs fine-grained authority management based on role-based access control (RBAC) to ensure the security of data access.

5. A distributed judicial data management platform architecture according to claim 2, characterized in that: The user interaction unit comprises: The intelligent legal analysis subunit conducts intelligent analysis of laws, regulations and case documents based on natural language processing and machine learning technologies. Users can obtain relevant legal provisions and typical cases by entering keywords or case descriptions. The case management subunit is responsible for querying and statistically analyzing case information and automatically assigning judges and reviewers; The trial process management sub-unit records each link and information flow of the trial process, tracks the progress of case handling in real time, and provides decision support and statistical analysis; The legal document generation subunit automatically generates legal documents based on case information and legal provisions.

6. A distributed judicial data management platform architecture according to claim 1, characterized in that: The data acquisition module comprises: The data source docking unit connects with the existing systems of courts, procuratorates, and public security agencies to collect data through file transfer; Case entry unit, supports manual entry or automatic API interface entry of basic case information; Document scanning and recognition unit, which automatically recognizes and extracts the content of paper documents through OCR technology; The data cleaning and verification unit cleans and verifies the collected data, removes duplicate, erroneous, and incomplete data, and performs standardized processing.

7. A distributed judicial data management platform architecture according to claim 1, characterized in that: The data storage module comprises: Distributed database unit, using NoSQL database, supports distributed storage and query of massive data; Distributed file system storage unit, using distributed file system to store large files; The data backup and recovery unit backs up data regularly to ensure data security and recoverability.

8. A distributed judicial data management platform architecture according to claim 1, characterized in that: The data processing and analysis module includes: Index and search unit, which builds efficient indexes and supports fast search and query; The data analysis unit uses big data analysis tools to conduct data mining and statistical analysis to provide support for decision-making; The report generation unit automatically generates reports based on the analysis results.

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