Court case trial simulation method and system based on complex adaptive system

Abstract

The invention discloses a court case trial simulation method and system based on a complex adaptive system, and belongs to the crossing field of judicial management and computer simulation. According to the method, in order to solve the problems that traditional court management lacks quantitative predictive analysis and influences of various factors on the judgment efficiency are difficult to evaluate, a court case judgment full-process simulation scheme based on Multi-Agent Dynamics is provided. The method specifically comprises the following steps: 1) establishing a three-layer nested model with case characteristics, judge characteristics and judicial program parameters, and realizing full-cycle simulation of a case from case filing, delivery, identification, mediation to case settlement; 2) introducing multi-element random influence factors, such as case complexity, common litigation, lawyer participation rate, crime penalty and the like, and accurately depicting judgment difficulty differences of different types of cases; 3) constructing a judge interaction model, fusing attributes such as qualification and experience, and realizing dynamic balance of working efficiency and case allocation; 4, a Monte Carlo simulation method is adopted, a large amount of simulation data is generated through parameter perturbation, and a sensitivity curve of key indexes is drafted. The system is applied to court trial situation analysis, the case trial efficiency under different parameter changes is accurately predicted, and a scientific basis is provided for court resource allocation and policy making.

Description

Technical Field

[0001] This invention relates to the field of judicial informatization and intelligent decision support technology, specifically to a method and system for simulating court case trial processes based on complex adaptive system theory. This technology utilizes computer simulation techniques such as multi-agent modeling, Monte Carlo simulation, and sensitivity analysis to achieve quantitative modeling and predictive analysis of court trial operations, providing a scientific basis for optimizing judicial resource allocation and trial management decisions. Background Technology

[0002] With the deepening of judicial reforms, the efficiency and quality of court trials have become important indicators for measuring the level of justice. However, existing court management methods suffer from the following technical problems:

[0003] Existing statistical analysis methods mainly rely on regression analysis of historical data, which can only reveal the statistical correlation between variables and cannot simulate the dynamic interaction of multiple factors in a complex trial system. For example, traditional methods struggle to provide quantitative answers to how factors such as case complexity, judge's experience, and procedural settings synergistically affect trial efficiency.

[0004] While traditional queuing theory models can analyze service efficiency, they treat cases as homogeneous entities, ignoring the significant differences between different types of cases in terms of trial processes, workload requirements, and procedural complexity, resulting in insufficient prediction accuracy.

[0005] Existing court information management systems are mainly used for case transfer and data recording, lacking predictive analysis functions for decision support, and unable to answer key questions such as "how will trial efficiency change if the number of judges is increased or the case allocation strategy is adjusted".

[0006] Lacking comprehensive evaluation tools, existing methods typically focus only on a single indicator (such as trial time) and fail to establish a comprehensive evaluation system that includes multiple dimensions such as trial efficiency, case quality, and resource utilization.

[0007] Complex adaptive systems theory has been successfully applied in fields such as finance and transportation, but its application in the judicial field is still in its infancy, lacking mature modeling methods and practical tools. Summary of the Invention

[0008] This invention aims to address the problem of insufficient accuracy in existing court case trial efficiency analysis tools. It proposes a court case trial simulation method and system based on a complex adaptive system. By constructing a multi-level, multi-dimensional case-judge-court model, it achieves accurate simulation and prediction of the judicial operation status.

[0009] The technical solution of the present invention is as follows:

[0010] A court case trial simulation method based on a complex adaptive system, characterized by the following steps:

[0011] S1. Construct a case interaction subject model, representing the case as a multi-dimensional entity with difficulty, type identification, procedural status, and party characteristics;

[0012] S2. Construct a judge interaction subject model, including judge qualification level, trial ability and case allocation mechanism;

[0013] S3. Establish a comprehensive court simulation environment, including a case generation module, a case allocation module, and a trial mechanism module;

[0014] S4. Using the Monte Carlo simulation method, calculate key indicators for case trials under different parameter conditions;

[0015] S5. Based on the simulation results, conduct multi-indicator sensitivity analysis to generate a multi-dimensional evaluation report including average case closure time, appeal rate, and enforcement completion rate.

[0016] Furthermore, the case interaction subject model in step S1 specifically includes:

[0017] S1.1 The basic case attributes module is used to set the case difficulty coefficient, case type identifier (administrative, criminal, civil) and procedural status;

[0018] S1.2 The party characteristics module is used to record joint litigation details, lawyer participation, and the completeness of evidence presented;

[0019] S1.3 Special Case Characteristics Module, used to identify joint crimes and guilty pleas in criminal cases, as well as the response of administrative agencies in administrative cases;

[0020] The S1.4 program node module is used to manage delivery time, identification time, mediation procedures, and corresponding duration settings.

[0021] Furthermore, the judge interaction subject model in step S2 specifically includes:

[0022] S2.1 Judge Attributes Module, used to set judge qualification levels and daily trial capacity values; S2.2 Case Processing Module, used to implement case allocation algorithms and manage daily trial progress; S2.3 Dynamic Adjustment Module, used to adjust trial priorities based on case backlog.

[0023] Furthermore, the overall court simulation environment in step S3 specifically includes:

[0024] S3.1 Case Generation Module: Based on the preset annual total number of cases and the proportion of case types, it generates new cases daily according to a Poisson distribution. S3.2 Case Allocation Module: Employs an allocation strategy combining random allocation and load balancing. S3.3 Time Progression Module: Advances the simulation process in calendar days as the smallest unit.

[0025] Furthermore, the Monte Carlo simulation in step S4 specifically includes:

[0026] The S4.1 parameter setting module allows users to set the parameters to be analyzed and their range of variation; the S4.2 simulation execution module performs a preset number of independent simulations for each parameter combination; and the S4.3 data collection module records the key performance indicators for each simulation.

[0027] Furthermore, the sensitivity analysis in step S5 specifically includes:

[0028] S5.1 Data Cleaning Module: Detects and handles outliers in the simulation results; S5.2 Statistical Analysis Module: Calculates the average value and confidence interval of the indicators under various parameter combinations; S5.3 Visualization Module: Generates correlation curves between parameter changes and indicator changes.

[0029] Second, the present invention also provides a court case trial simulation system for implementing the above-mentioned method, characterized in that it includes:

[0030] The server-side simulation engine is used to perform case generation, judge simulation, and trial process control.

[0031] The visualization and analysis module is used to process simulation data and generate analysis reports.

[0032] The parameter tuning control module is used for parameter configuration and result display.

[0033] Furthermore, the server-side simulation engine specifically includes: a case generator, which generates simulated cases according to preset rules; a judge simulator, which simulates the judge's trial behavior and decision-making process; and an environment controller, which manages the simulation process and time progression.

[0034] Furthermore, the visualization analysis module includes:

[0035] The data processor cleans and transforms the raw simulation data; the statistical analyzer calculates various performance indicators; and the report generator automatically generates reports containing charts and analytical conclusions.

[0036] Furthermore, it also includes: a data storage module for storing simulation parameters and results data; a permission management module for controlling access permissions for different users; and a system log module for recording system operating status and user operations.

[0037] Compared with the prior art, the present invention has the following technical effects:

[0038] It achieves high-fidelity digital modeling of the judicial trial process, which can accurately reflect the complexity and diversity of the actual trial process.

[0039] It supports multi-parameter parallel sensitivity analysis, which can simultaneously assess the impact of multiple factors such as the number of judges, the distribution of case types, and procedural settings, providing a quantitative basis for resource allocation optimization.

[0040] A multi-dimensional evaluation system encompassing efficiency, quality, and resource utilization has been established, avoiding the limitations of single-indicator evaluation and achieving comprehensive optimization of trial management.

[0041] It adopts a modular design architecture, which has good scalability and can flexibly adjust model parameters and business rules according to the actual situation of different courts. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the structure of the court case trial simulation system based on a complex adaptive system according to the present invention. Detailed Implementation

[0043] The specific embodiments of the present invention will be described in detail below with reference to specific examples.

[0044] 1. Case Interaction Subject Model Setup

[0045] Basic case attributes are set: cases are represented as multi-dimensional entities with difficulty, type, and status. Case difficulty is generated by random probability, which affects the workload of subsequent trials. Case types are divided into administrative, criminal, and civil, each with specific trial characteristics. Case status includes new, service of process, expert evaluation, mediation, trial in progress, and completion.

[0046] Diverse case characteristics: Simulates the special attributes of different types of cases, such as joint litigation, lawyer participation, and completeness of evidence, and introduces the agency response rate in administrative cases and the joint crime and plea bargaining in criminal cases.

[0047] Workload calculation mechanism: Through a multi-factor weighted model, various characteristics of a case are mapped to workload correction coefficients, thereby achieving precise quantification of the difficulty of adjudicating different cases.

[0048] 2. Setting up the judge's interactive subject model

[0049] Judges are classified into two categories based on random probability: senior judges and ordinary judges. Senior judges have a higher daily trial capacity.

[0050] Case trial mechanism: Judges have a fixed amount of trial capacity each day, and allocate their energy according to the case trial status and priority order to achieve the step-by-step trial of cases.

[0051] Dynamic case assignment strategy: Supports random case assignment and targeted case assignment, simulating the impact of different case assignment mechanisms on overall efficiency.

[0052] 3. Overall Simulation Environment Setup for the Court

[0053] Case generation mechanism: Based on the set annual case volume, combined with random fluctuations, the number of new cases added daily is generated.

[0054] Time iteration control: Simulations are conducted on a daily basis, with cases generated and tried every day, and the cumulative simulation cycle is usually set to one year.

[0055] Multi-indicator simulation: After the simulation, key performance indicators are calculated, including average trial time, completion rate, appeal rate, enforcement completion rate, and case ratio.

[0056] 4. Parameter sensitivity analysis

[0057] Parameter variation generation: Gradient variation points are generated for the research parameters near the baseline value, usually taking 11 uniformly distributed values ​​within ±20%.

[0058] Monte Carlo simulation: Performs multiple independent simulations for each parameter value to eliminate the influence of random factors.

[0059] Results visualization: Generate sensitivity curves to visually demonstrate the trend and magnitude of the impact of parameter changes on each indicator.

[0060] Taking the assessment of the impact of "number of judges" on "average case completion time" as an example, the system execution process is as follows:

[0061] Parameter settings:

[0062] ```

[0063] Benchmark number of judges: 20

[0064] Cases per year: 8,000

[0065] Percentage of senior judges: 0.2

[0066] Proportion of complex cases: 0.2

[0067] Administrative cases: 0.3

[0068] Criminal case rate: 0.3

[0069] Simulated days: 360 days

[0070] Number of simulations: 1000

[0071] ```

[0072] Parameter variation range generation:

[0073] ```

[0074] The range of participation rates in mediation varies as follows: [0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6]

[0075] ```

[0076] Perform the simulation:

[0077] For each number of judges, 1000 Monte Carlo simulations were performed to calculate the average trial time, appeal rate, execution completion rate, and case ratio.

[0078] Results analysis:

[0079] Simulation results show that as the proportion of participants in mediation increases from 0.2 to 0.6, the average case resolution time decreases from 31.70 days to 29.09 days, exhibiting a clear downward trend. Analysis indicates that, all other things being equal, increasing the proportion of participants in mediation significantly improves the speed of case trials; however, once the proportion exceeds 0.5, the improvement in speed plateaus, indicating a diminishing marginal benefit.

[0080] In terms of technical implementation, this system uses the Python programming language to build the core algorithm and the Streamlit framework to develop the web application interface. The front end implements parameter setting, result display, and chart generation functions; the back end uses multi-process parallel computing technology to improve the efficiency of Monte Carlo simulation. The system can be deployed as a standalone service, allowing court administrators to access it via a browser for real-time parameter adjustment and result analysis.

[0081] In practical applications, this system can also adjust model parameters and evaluation indicators according to the specific circumstances of each court to provide more accurate decision support. By analyzing simulation results under different parameter combinations, administrators can formulate more scientific resource allocation strategies and process optimization plans, effectively improving judicial efficiency and quality.

[0082] This implementation method, through the aforementioned technical solution, achieves a high-fidelity simulation of the court trial process, providing a reliable quantitative analysis tool for judicial management decisions. The system can flexibly adjust model parameters and business rules according to actual needs, exhibiting good adaptability and scalability.

Claims

1. A method for simulating court case trials based on a complex adaptive system, characterized in that, Includes the following steps: S1. Construct a case interaction subject model, representing the case as a multi-dimensional entity with difficulty, type identification, procedural status, and party characteristics; S2. Construct a judge interaction subject model, including judge qualification level, trial ability and case allocation mechanism; S3. Establish a comprehensive court simulation environment, including a case generation module, a case allocation module, and a trial mechanism module; S4. Using the Monte Carlo simulation method, calculate key indicators for case trials under different parameter conditions; S5. Based on the simulation results, conduct multi-indicator sensitivity analysis to generate a multi-dimensional evaluation report including average case closure time, appeal rate, and enforcement completion rate.

2. The court case trial simulation method based on a complex adaptive system according to claim 1, characterized in that, The case interaction subject model in step S1 specifically includes: S1.1 The basic case attributes module is used to set the case difficulty coefficient, case type identifier (administrative, criminal, civil) and procedural status; S1.2 The party characteristics module is used to record joint litigation details, lawyer participation, and the completeness of evidence presented; S1.3 Special Case Characteristics Module, used to identify joint crimes and guilty pleas in criminal cases, as well as the response of administrative agencies in administrative cases; The S1.4 program node module is used to manage delivery time, identification time, mediation procedures, and corresponding duration settings.

3. The court case trial simulation method based on a complex adaptive system according to claim 1, characterized in that, The judge interaction subject model in step S2 specifically includes: S2.1 Judge Attributes module, used to set judge qualification level and daily trial capacity value; S2.2 Case processing module, used to implement case allocation algorithm and daily trial progress management; S2.3 Dynamic Adjustment Module, used to adjust trial priorities based on case backlog.

4. The court case trial simulation method based on a complex adaptive system according to claim 1, characterized in that, The overall court simulation environment in step S3 specifically includes: S3.1 Case generation module generates new cases daily based on the preset annual total number of cases and the proportion of case types, according to a Poisson distribution; The S3.2 case allocation module adopts an allocation strategy that combines random allocation with load balancing. The S3.3 time progression module advances the simulation process in calendar days as the smallest unit.

5. The court case trial simulation method based on a complex adaptive system according to claim 1, characterized in that, The Monte Carlo simulation in step S4 specifically includes: The S4.1 parameter setting module allows users to set the parameters to be analyzed and their range of variation; S4.2 Simulation Execution Module performs a preset number of independent simulations for each parameter combination; S4.3 Data Collection Module records key performance indicators for each simulation.

6. The court case trial simulation method based on a complex adaptive system according to claim 1, characterized in that, The sensitivity analysis in step S5 specifically includes: The S5.1 data cleaning module performs outlier detection and processing on the simulation results; The S5.2 statistical analysis module calculates the average value and confidence interval of the index under various parameter combinations. The S5.3 visualization module generates correlation curves between parameter changes and indicator changes.

7. A court case trial simulation system implementing the method of any one of claims 1-6, characterized in that: The server-side simulation engine is used to perform case generation, judge simulation, and trial process control. The visualization and analysis module is used to process simulation data and generate analysis reports. The parameter tuning control module is used for parameter configuration and result display.

8. The system according to claim 7, characterized in that, The server-side simulation engine specifically includes: a case generator, which generates simulated cases according to preset rules; a judge simulator, which simulates the judge's trial behavior and decision-making process; and an environment controller, which manages the simulation process and time progression.

9. The system according to claim 7, characterized in that, The visualization analysis module includes: The data processor cleans and transforms the raw simulation data; the statistical analyzer calculates various performance indicators; and the report generator automatically generates reports containing charts and analytical conclusions.

10. The system according to claim 7, characterized in that: The data storage module is used to save simulation parameters and results data; The access control module controls the access permissions of different users; The system log module records the system's running status and user operations.

Images