Case filing risk assessment method and system based on multi-dimensional features

Abstract

The invention discloses a multi-dimensional feature-based case filing risk assessment method and system, and belongs to the field of law science and technology. According to the algorithm, a multi-dimensional risk feature library including party features, case behavior features, social influence features, special subject features and production breaking risk features is constructed, and a machine learning technology is adopted to realize automatic assessment of case filing risks. According to the invention, the efficiency and accuracy of case filing risk assessment can be improved, assessment standards are unified, scientific decision support is provided for case filing examination, and the method has important judicial practice value.

Description

Technical Field

[0001] This invention relates to the fields of legal technology and judicial informatization, and in particular to a method and system for assessing case filing risks based on multi-dimensional characteristics. Background Technology

[0002] With the rapid increase in the number and diversification of judicial cases, the court system faces problems such as low efficiency in case filing review and insufficient risk identification capabilities. Traditional case filing risk assessment mainly relies on the personal experience of judges, which has shortcomings such as strong subjectivity, inconsistent standards, and low efficiency. Existing technologies lack systematic, multi-dimensional risk assessment methods, making it difficult to comprehensively identify various risks that may exist in the case filing process, such as fraudulent litigation, duplicate filing, and professional lending. Summary of the Invention

[0003] To address the problems of existing technologies, such as the single dimension of risk assessment which fails to fully reflect case risks, low efficiency of manual assessment which makes it difficult to cope with the increasing number of cases, inconsistent risk identification standards, strong subjectivity of assessment results, and lack of risk assessment mechanisms for special entities (such as key enterprises, people's representatives, etc.), this invention provides a case filing risk assessment method and system based on multi-dimensional characteristics.

[0004] The objective of this invention is achieved through the following technical solutions.

[0005] A method for assessing case filing risk based on multi-dimensional characteristics, comprising the following steps: 1) Risk Feature Database Construction: Establish a risk feature system, the dimensions of which include: party characteristics, case behavior characteristics, social impact characteristics, special subject characteristics, and bankruptcy risk characteristics; 2) Risk weight allocation: The weight of each risk characteristic is determined by the analytic hierarchy process (AHP), and the weight parameters are dynamically adjusted according to judicial practice; 3) Construct a machine learning-based risk assessment model: input case feature data, output comprehensive risk score and risk level; 4) Risk warning and visualization: Generate risk warning reports based on the assessment results and provide a risk visualization display interface.

[0006] The machine learning-based risk assessment model includes the following sub-modules: a false litigation identification module, a duplicate case filing detection module, a special subject risk assessment module, and a petition risk prediction module.

[0007] The characteristics of the parties involved include debtors who refuse to pay, professional counterfeiters, and retired court personnel; the characteristics of the case behavior include high-frequency representation, repeated filing of cases, and suspected abuse of litigation; the characteristics of social impact include group visits, persistent and disruptive visits, and long-term petitioners; the characteristics of special entities include national high-tech enterprises and people's congress representatives; and the characteristics of bankruptcy risk include different color-coded risk levels.

[0008] The weighting of the party characteristics is 35%; the weighting of the case behavior characteristics is 30%; the weighting of the social impact characteristics is 15%; the weighting of the special subject characteristics is 12%; and the weighting of the bankruptcy risk characteristics is 8%.

[0009] Step 3) specifically includes: 3.1) Initial input stage: First, the case information input operation is performed to enter the data preprocessing stage; 3.2.) Data preprocessing stage: Processing is based on data type: feature extraction is performed on structured data, and natural language processing is performed on unstructured data; after processing, both types of data are uniformly entered into multi-dimensional feature classification to complete data standardization and feature sorting; 3.3) Parallel computing analysis stage: After feature classification, the parallel computing of sub-modules is started, and four core analysis modules are run simultaneously: false litigation identification, duplicate case filing detection, special subject assessment, and petition risk prediction; the calculation results of each module are summarized into risk weighted fusion to complete the integration of multi-dimensional risk data; 3.4) Risk Level Determination Stage: The merged risk data enters the risk level determination stage, and is divided into three categories according to the degree of risk: High risk is indicated by a red alert; medium risk by a yellow warning; low risk by a green sign indicating passage. 3.5) Results Output Stage: The processing results of different risk levels are ultimately unified and the process ends with the generation of a report.

[0010] A case filing risk assessment system based on multi-dimensional features. The hierarchical architecture, from top to bottom, includes: Access layer: System terminal access entry point format: Access Layer: Access is achieved through SLB (Soft Load Balancer), including authentication, authorization, routing, load balancing, and rate limiting access management gateway; Application layer: includes case matching service application, risk rule base management application, dynamic weight allocation application, legal provision association module, and multi-model fusion scheduler; Basic Capability Layer: The basic capability dimensions include: characteristics of the parties involved, characteristics of case behavior, characteristics of social impact, characteristics of special entities, and characteristics of bankruptcy risk; Data storage layer: serves as the underlying data support; The supporting modules include: External data analysis, case data analysis, machine learning models, risk assessment models, and manual review and verification—these analytical and modeling capabilities provide technical support for the foundational capability layer.

[0011] Compared with existing technologies, the advantages of this invention are: it enables a comprehensive and objective assessment of case filing risks; it improves the efficiency and accuracy of risk assessment; it unifies risk assessment standards; it provides scientific decision support for case filing review; and it reduces the waste of judicial resources and the risk of erroneous case filing. Attached Figure Description

[0012] Figure 1 This is a diagram of the overall system architecture of the present invention.

[0013] Figure 2 This is a flowchart of the risk assessment algorithm of the present invention.

[0014] Figure 3 This is a schematic diagram of the risk feature weighting dimensions of the present invention.

[0015] Figure 4 This is an example diagram of the risk warning visualization interface of the present invention. Detailed Implementation

[0016] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0017] A method for assessing case filing risk based on multi-dimensional characteristics, comprising the following steps: 1) Risk Feature Database Construction: Establish a risk feature system, the dimensions of which include: party characteristics, case behavior characteristics, social impact characteristics, special subject characteristics, and bankruptcy risk characteristics; 2) Risk weight allocation: The weight of each risk characteristic is determined by the analytic hierarchy process (AHP), and the weight parameters are dynamically adjusted according to judicial practice; 3) Construct a machine learning-based risk assessment model: input case feature data, output comprehensive risk score and risk level; 4) Risk warning and visualization: Generate risk warning reports based on the assessment results and provide a risk visualization display interface.

[0018] The machine learning-based risk assessment model includes the following sub-modules: a false litigation identification module, a duplicate case filing detection module, a special subject risk assessment module, and a petition risk prediction module.

[0019] The characteristics of the parties involved include debtors who refuse to pay, professional counterfeiters, and retired court personnel; the characteristics of the case behavior include high-frequency representation, repeated filing of cases, and suspected abuse of litigation; the characteristics of social impact include group visits, persistent and disruptive visits, and long-term petitioners; the characteristics of special entities include national high-tech enterprises and people's congress representatives; and the characteristics of bankruptcy risk include different color-coded risk levels.

[0020] like Figure 3 As shown, the weighting of the party characteristics is 35%; the weighting of the case behavior characteristics is 30%; the weighting of the social impact characteristics is 15%; the weighting of the special subject characteristics is 12%; and the weighting of the bankruptcy risk characteristics is 8%.

[0021] like Figure 2 As shown, step 3) specifically includes: 3.1) Initial input stage: First, the case information input operation is performed to enter the data preprocessing stage; 3.2.) Data preprocessing stage: Processing is based on data type: feature extraction is performed on structured data, and natural language processing is performed on unstructured data; after processing, both types of data are uniformly entered into multi-dimensional feature classification to complete data standardization and feature sorting; 3.3) Parallel computing analysis stage: After feature classification, the parallel computing of sub-modules is started, and four core analysis modules are run simultaneously: false litigation identification, duplicate case filing detection, special subject assessment, and petition risk prediction; the calculation results of each module are summarized into risk weighted fusion to complete the integration of multi-dimensional risk data; 3.4) Risk Level Determination Stage: The merged risk data enters the risk level determination stage, and is divided into three categories according to the degree of risk: High risk is indicated by a red alert; medium risk by a yellow warning; low risk by a green sign indicating passage. 3.5) Results Output Stage: The processing results of different risk levels are ultimately unified and the process ends with the generation of a report.

[0022] like Figure 1 As shown, a case filing risk assessment system based on multi-dimensional features is presented. The hierarchical architecture, from top to bottom, includes: Access layer: System terminal access entry point format: Access Layer: Access is achieved through SLB (Soft Load Balancer), including authentication, authorization, routing, load balancing, and rate limiting access management gateway; Application layer: includes case matching service application, risk rule base management application, dynamic weight allocation application, legal provision association module, and multi-model fusion scheduler; Basic Capability Layer: The basic capability dimensions include: characteristics of the parties involved, characteristics of case behavior, characteristics of social impact, characteristics of special entities, and characteristics of bankruptcy risk; Data storage layer: serves as the underlying data support; The supporting modules include: External data analysis, case data analysis, machine learning models, risk assessment models, and manual review and verification—these analytical and modeling capabilities provide technical support for the foundational capability layer.

[0023] Example 1: Risk Assessment of False Litigation The system captures case information and extracts characteristics of the parties involved (such as markers of suspected professional lending) and characteristics of case behavior (such as multiple similar lawsuits in a short period of time). Use the fake lawsuit detection module for evaluation Output the risk level and basis for false litigation for judges' reference. Example 2: Risk Assessment of Special Entities Identify special entities involved in the case (such as people's congress deputies). Search the entity's historical litigation records and social impact data. Assess the potential social impact and risk level of the case. Provide risk warnings for special entities to assist in case filing decisions. Example 3: Dynamic Assessment of Bankruptcy Risk The risk level is dynamically adjusted based on the characteristics of corporate bankruptcy risk (such as financial status and credit record).

[0024] Analyze bankruptcy risk trends using historical data.

[0025] It outputs dynamic risk scores to provide real-time reference for case filing and review.

Claims

1. A method for assessing case filing risk based on multi-dimensional features, characterized by the following steps: include: 1) Risk Feature Database Construction: Establish a risk feature system, the dimensions of which include: party characteristics, case behavior characteristics, social impact characteristics, special subject characteristics, and bankruptcy risk characteristics; 2) Risk weight allocation: The weight of each risk characteristic is determined by the analytic hierarchy process (AHP), and the weight parameters are dynamically adjusted according to judicial practice; 3) Construct a machine learning-based risk assessment model: input case feature data, output comprehensive risk score and risk level; 4) Risk warning and visualization: Generate risk warning reports based on the assessment results and provide a risk visualization display interface.

2. The case filing risk assessment method based on multi-dimensional features according to claim 1, characterized in that... The machine learning-based risk assessment model includes the following sub-modules: The module includes modules for identifying false lawsuits, detecting duplicate cases, assessing risks of special entities, and predicting petition risks.

3. The case filing risk assessment method based on multi-dimensional features according to claim 1, characterized in that... The characteristics of the parties involved include debtors who refuse to pay, professional counterfeiters, and retired court personnel; the characteristics of the case behavior include high-frequency representation, repeated filing of cases, and suspected abuse of litigation; the characteristics of social impact include group visits, persistent and disruptive visits, and long-term petitioners; the characteristics of special entities include national high-tech enterprises and people's congress representatives; and the characteristics of bankruptcy risk include different color-coded risk levels.

4. A case filing risk assessment method based on multi-dimensional features as described in claim 1 or 3, characterized in that... The weighting of the party characteristics is 35%; the weighting of the case behavior characteristics is 30%; the weighting of the social impact characteristics is 15%; the weighting of the special subject characteristics is 12%; and the weighting of the bankruptcy risk characteristics is 8%.

5. The case filing risk assessment method based on multi-dimensional features according to claim 2, characterized in that... Step 3) specifically includes: 3.1) Initial input stage: First, the case information input operation is performed to enter the data preprocessing stage; 3.2.) Data preprocessing stage: Processing is based on data type: feature extraction is performed on structured data, and natural language processing is performed on unstructured data; after processing, both types of data are uniformly entered into multi-dimensional feature classification to complete data standardization and feature sorting; 3.3) Parallel computing analysis stage: After feature classification, the sub-modules are started to perform parallel computing, and four core analysis modules are run simultaneously: false litigation identification, duplicate case filing detection, special subject assessment, and petition risk prediction; The calculation results of each module are aggregated into a risk-weighted fusion to complete the integration of multi-dimensional risk data; 3.4) Risk Level Determination Stage: The merged risk data enters the risk level determination stage, and is divided into three categories according to the degree of risk: High risk is indicated by a red alert; Medium risk is indicated by a yellow warning. Low risk is green pass; 3.5) Results Output Stage: The processing results of different risk levels are ultimately unified and the process ends with the generation of a report.

6. A case filing risk assessment system based on multi-dimensional features, characterized in that... : The hierarchical architecture, from top to bottom, includes: Access layer: System terminal access entry point format: Access Layer: Access is achieved through SLB (Soft Load Balancer), including authentication, authorization, routing, load balancing, and rate limiting access management gateway; Application layer: includes case matching service application, risk rule base management application, dynamic weight allocation application, legal provision association module, and multi-model fusion scheduler; Basic Capability Layer: The basic capability dimensions include: characteristics of the parties involved, characteristics of case behavior, characteristics of social impact, characteristics of special entities, and characteristics of bankruptcy risk; Data storage layer: serves as the underlying data support; The supporting modules include: External data analysis, case data analysis, machine learning models, risk assessment models, and manual review and verification—these analytical and modeling capabilities provide technical support for the foundational capability layer.

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