Data comprehensive performance evaluation method and system for court information system

Abstract

The application discloses a kind of court information system-oriented data comprehensive performance evaluation method and system, belong to information measurement field, including: obtaining the court data set stored in court information system, and respectively calculating 12 types of information measurement indexes, realize court information system data comprehensive performance evaluation;Court data set includes: legal regulations data, judicial case data, judicial personnel data, judicial research data, judicial administrative data, external data and information management data;12 types of information measurement methods are: capacity, delay, breadth, granularity, variety, time length, sampling rate, aggregation degree, pervasion degree, distortion degree, mismatch degree and efficiency.The application can consider different types of data resources and service fields, realize the comprehensive and accurate measurement of court data, effectively solve the problem of previous evaluation being not comprehensive and inaccurate, and provide reliable quantitative basis for promoting digital court construction and judicial technology application.

Description

Technical Field

[0001] This invention belongs to the field of information measurement, and more specifically, relates to a method and system for evaluating the comprehensive effectiveness of data in court information systems. Background Technology

[0002] Court data refers to the initial collection of raw information directly generated by the courts in their business activities such as adjudication, enforcement, and judicial transparency. This includes case information (such as cause of action, trial period, and judgment documents), party information, and judicial statistical reports. At this stage, court data primarily consists of business records and archives, with the core characteristics of "recordability" and "passive storage." Court data embodies the initial accumulation of fundamental elements of judicial information and is of great significance in supporting judicial operations, tracing judicial activities, and statistical analysis; however, it currently lacks systematic management and application.

[0003] With the accelerated pace of court informatization, the concept of court data resources has gradually taken shape. Breakthroughs at this stage are reflected in court data governance, application orientation, and sharing and collaboration. First, based on unified standards, information technology is used to clean, integrate, and standardize data scattered across the trial system, enforcement system, and judicial disclosure platforms, forming a shareable and analyzable data set. This data set will be uniformly managed through the court information system. Second, data is increasingly application-oriented, with data resources beginning to serve specific scenarios such as improving trial efficiency (e.g., case recommendation), judicial transparency (e.g., online document publication), and judicial supervision (e.g., process compliance monitoring). Its characteristic has shifted from "recording history" to "supporting business decision-making." Third, data sharing and collaboration have been improved, gradually breaking down data silos and promoting data flow across departments (e.g., courts, procuratorates, public security bureaus) and levels (e.g., high courts and intermediate courts, basic courts), forming the concept of a "resource pool."

[0004] Court data is a crucial guarantee for the informatization of the people's courts and a fundamental support for the application of artificial intelligence in the judiciary. It plays a vital role in serving adjudication and enforcement, judicial management, promoting clean justice, supporting economic and social development, and serving the people. Unlike other types of data, court data is highly dependent on external resources and possesses diversity and multiple derivation potential. For example, trial trend analysis derived from basic case data can be applied to decision support, predictive analysis, and efficiency optimization. By deeply analyzing the inherent characteristics of court data and conducting scientific and reasonable measurements, the comprehensive effectiveness of court information system data resources in five service areas can be comprehensively and accurately evaluated. This evaluation result supports comprehensive analysis and research of the court information system and is a prerequisite for efficient and scientific management of court data. It not only helps administrators fully understand the application effects of relevant legal data in the judicial field but also provides data support for judicial decision-making and resource allocation, promoting the informatization of the people's courts and the application of artificial intelligence in the judiciary.

[0005] However, while existing technologies (such as CN113641825B) propose using objective information theory to measure the data text set within a target time period of the court system, the relevant evaluation index system does not consider the relevant goals of the people's court informatization construction and artificial intelligence judicial application, nor does it consider the complexity and diversity of court data. The measurement results cannot directly reflect the actual value of court data in the informatization construction of the court and the application of artificial intelligence in the judicial process, cannot accurately reflect the comprehensive effectiveness of the court information system data, and cannot provide reliable data support for related applications. Summary of the Invention

[0006] In response to the shortcomings and improvement needs of existing technologies, this invention provides a method and system for comprehensive performance evaluation of court information system data. Its purpose is to comprehensively and accurately measure court data in accordance with the relevant goals of the informatization construction of people's courts and the application of artificial intelligence in the judiciary, and to achieve comprehensive performance evaluation of court information system data.

[0007] To achieve the above objectives, on the one hand, the present invention provides a method for evaluating the comprehensive data performance of a court information system, comprising: The system acquires the court data set stored in the court information system and calculates 12 types of information metrics to achieve a comprehensive data performance evaluation of the court information system.

[0008] The court data set includes: legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data; the 12 information measurement methods are capacity, latency, breadth, granularity, type, duration, sampling rate, aggregation degree, reach, distortion degree, mismatch degree, and efficiency, and the 12 information measurement indicators include: Capacity: Total amount of data resources, total amount of case data, total amount of judicial and administrative data; Delays: Delays in updating case data and judicial statistics. Breadth: Comprehensive coverage of data resources, coverage of information management data, and geographical coverage of legal and regulatory data; Granularity: Completeness of personnel data coverage, completeness of judicial statistics data coverage, and completeness of industry coverage of legal and regulatory data; Category: Number of data resource types; Duration: The time span of the data resources; Sampling rate: Frequency of case data updates; Aggregation degree: correlation degree of data on people, cases, and objects; correlation degree of data on petitions and complaints related to litigation; Coverage: Data resource access volume, judicial open data access volume, total amount of data resource sharing and exchange with external parties, online mediation data access volume, litigation service data access volume, and number of electronic service of process; Distortion: Confidence level of case data, pass rate of electronic case files, and confidence level of judicial statistics; Mismatch: Overall user satisfaction; Efficiency: Saves a total amount of paper and reduces the number of trips.

[0009] Furthermore, the expression for calculating the total amount of data resources is as follows:

[0010] The formula for calculating the total amount of case data is:

[0011] The formula for calculating the total amount of judicial and administrative data is as follows:

[0012] in, Indicates the total amount of data resources. Indicates the first Storage capacity of class data Indicates the total number of data types; This represents the total number of case data. Indicates the first The first type of case Storage capacity of class data Indicate the case type, Indicates the trial of a case. Indicates the execution of a case. This refers to petitions and complaints involving litigation. Represents data type, Represents case data, Represents electronic case file data. Represents electronic record data; This indicates the total amount of judicial and administrative data. Indicates the first Storage volume of judicial and administrative data This indicates the total number of data types related to judicial and administrative affairs.

[0013] Furthermore, the calculation expression for the case data update delay is as follows:

[0014] The formula for calculating the delay in updating judicial statistics data is as follows:

[0015] in, This indicates a delay in updating case data. Indicates the total number of cases. Indicates the first The data aggregation time for each case Indicates the first The time of case filing and registration; This indicates a delay in updating judicial statistics. This indicates the total number of types of judicial statistics. Indicates the first Update time of class statistics Indicates the first The time when the statistical data was generated.

[0016] Furthermore, the formula for calculating the comprehensive coverage rate of data resources is as follows:

[0017] The formula for calculating the data coverage rate of information management is:

[0018] The formula for calculating the geographical coverage of legal and regulatory data is as follows:

[0019] in, Indicates the overall coverage rate of data resources. Indicates the first Whether the class data is aggregated, This indicates that the court information system has gathered the first... Class data, This indicates that the court information system has not aggregated the first Class data, Indicates the total number of data types; Indicates the coverage rate of information management data. This indicates the number of dimensions covered by the information management data. This indicates the total number of dimensions that information management should cover; This indicates the geographical coverage of legal and regulatory data. Indicates the number of covered areas. This indicates the total number of regions.

[0020] Furthermore, the expression for calculating the completeness of personnel data coverage is as follows:

[0021] The formula for calculating the completeness of judicial statistics coverage is as follows:

[0022] The formula for calculating the completeness of industry coverage of legal and regulatory data is as follows:

[0023] in, This indicates the completeness of personnel data coverage. This indicates the number of personnel data items that have been collected. Indicates the total number of personnel data items; This indicates the completeness of judicial statistics coverage. This indicates the number of cases included in judicial statistics. Indicates the total number of cases; This indicates the completeness of industry coverage of legal and regulatory data. Indicates the first Whether the industry is covered, and if so, then... ,otherwise This indicates the total number of industries.

[0024] Furthermore, the expression for calculating the number of data resource types is:

[0025] The expression for calculating the time span of data resources is:

[0026] The formula for calculating the frequency of case data updates is:

[0027] in, Indicates the number of data resource types. Indicates the first Whether the class data is aggregated, This indicates that the court information system has gathered the first... Class data, This indicates that the court information system has not aggregated the first Class data, Indicates the total number of data types; Indicates the time span of data resources. Indicates the latest year of the data. Indicates the earliest year of the data; Indicates the frequency of case data updates. Indicates a specific time period Total number of newly received cases updated Indicates a specific time period Number of newly received cases Indicates the length of the time period.

[0028] Furthermore, the expression for calculating the correlation degree of person, case, and object data is as follows:

[0029] The formula for calculating the correlation of petition-related data is as follows:

[0030] in, Indicates the correlation between data on people, cases, and objects. Indicates the degree of correlation between individual data. Indicates the correlation between case data. Indicates the correlation between case data and person-to-person data. Indicates the correlation degree of case data (unit: %). , , , They represent , , and The weighting coefficients, and ; Indicates the correlation between data related to petitions and complaints. This indicates the number of petitions and complaints related to trial and enforcement cases. This indicates the total number of petitions and complaints involving litigation.

[0031] Furthermore, the expression for calculating data resource access volume is as follows:

[0032] The formula for calculating the number of visits to judicial open data is:

[0033] The formula for calculating the total amount of data resources shared and exchanged externally is:

[0034] The expression for calculating the number of online mediation data accesses is:

[0035] The formula for calculating the number of accesses to litigation service data is:

[0036] The formula for calculating the number of electronic deliveries is:

[0037] in, This represents the total number of accesses to the data resource. This represents the total number of accesses to all data resources in the court information system; This indicates the number of times judicial data has been accessed. Indicates the first Access volume of judicial public data This indicates the total number of data types disclosed by the judiciary. This indicates the total amount of data resources shared and exchanged externally. Indicates the first Number of times class data is shared and exchanged externally. Indicates the total number of data types; This indicates the number of online mediation data accesses. This indicates the total number of visits to the People's Court's online mediation platform; This indicates the number of times litigation service data has been accessed. This indicates the number of visits to the litigation service website; Indicates the number of electronic deliveries. This indicates the total number of electronically served documents, notices, copies of complaints, copies of answers, evidence materials, summonses, rulings, judgments, and mediation agreements served through the service platform.

[0038] Furthermore, the expression for calculating the confidence level of case data is as follows:

[0039] The formula for calculating the electronic case file pass rate is:

[0040] The expression for calculating the confidence level of judicial statistics is as follows:

[0041] in, Indicates the confidence level of the case data. Indicates the exact number of case data entries. This indicates the total number of case data entries; Indicates the pass rate of electronic case files. This indicates the number of qualified electronic case files as determined by the electronic case file quality inspection rules. Indicates the total number of electronic case files; Indicates the confidence level of judicial statistics. This indicates the exact number of judicial statistics entries. This indicates the total number of judicial statistics entries.

[0042] Furthermore, the formula for calculating overall user satisfaction is:

[0043] The formula for calculating the total amount of paper saved is:

[0044] The formula for calculating the reduction in the number of trips is:

[0045] in, Indicates overall user satisfaction. Indicates the first Weight coefficients for class services Indicates the first Satisfaction with similar services Indicates the total number of service types; This indicates the total amount of paper saved. This indicates that the number of papers saved in processing newly received cases is [number missing]. This indicates that electronic delivery saves paper. This indicates the total number of newly received cases. This indicates the average amount of paper used per case. This indicates the digitization rate of case files. Indicates the number of electronic deliveries. This indicates the average amount of paper used per delivery; This indicates a reduction in the number of travelers. This indicates the number of cases filed online. This indicates the average number of people traveling per case.

[0046] On the other hand, the present invention provides a data comprehensive performance evaluation system for court information systems, including: a data interaction module and an evaluation module.

[0047] The data interaction module is used to obtain the set of court data stored in the court information system; The evaluation module includes 12 information measurement units, which are used to compute 12 types of information measurement indicators for the court dataset in parallel.

[0048] The court data set includes: legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data; the 12 information measurement methods are: capacity, latency, breadth, granularity, type, duration, sampling rate, aggregation degree, reach, distortion degree, mismatch degree, and efficiency. Furthermore, the 12 information measurement indicators include: Capacity: Total amount of data resources, total amount of case data, total amount of judicial and administrative data; Delays: Delays in updating case data and judicial statistics. Breadth: Comprehensive coverage of data resources, coverage of information management data, and geographical coverage of legal and regulatory data; Granularity: Completeness of personnel data coverage, completeness of judicial statistics data coverage, and completeness of industry coverage of legal and regulatory data; Category: Number of data resource types; Duration: The time span of the data resources; Sampling rate: Frequency of case data updates; Aggregation degree: correlation degree of data on people, cases, and objects; correlation degree of data on petitions and complaints related to litigation; Coverage: Data resource access volume, judicial open data access volume, total amount of data resource sharing and exchange with external parties, online mediation data access volume, litigation service data access volume, and number of electronic service of process; Distortion: Confidence level of case data, pass rate of electronic case files, and confidence level of judicial statistics; Mismatch: Overall user satisfaction; Efficiency: Saves a total amount of paper and reduces the number of trips.

[0049] In summary, the above-described technical solutions conceived in this invention can achieve the following beneficial effects: This invention uses legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data stored in the court information system as the core data set. In addition to 11 general information measurement methods—capacity, latency, breadth, granularity, type, duration, sampling rate, aggregation degree, coverage, distortion degree, and mismatch degree—it extends a dedicated information measurement method, efficiency, specifically tailored to the characteristics of court data resources. Based on these measurement methods, an evaluation index system comprising 28 evaluation indicators is established. This evaluation index system covers five service areas: serving trial and enforcement, serving judicial management, serving clean justice, serving economic and social development, and serving the people. Measuring the data set in the court information system based on this evaluation index system enables a comprehensive, accurate, and objective assessment of the overall effectiveness of court data resources. It provides data support for the analysis and optimization of the court information system, as well as judicial decision-making and resource allocation, promoting the informatization of the people's courts and the application of artificial intelligence in the judiciary. Attached Figure Description

[0050] Figure 1 A schematic diagram illustrating the construction of the comprehensive data efficiency evaluation index system for the court information system provided by this invention.

[0051] Figure 2 A flowchart of the method for evaluating the comprehensive effectiveness of court information system data provided by the present invention.

[0052] Figure 3 A schematic diagram of the court information system data comprehensive efficiency evaluation system provided by the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0054] In this invention, the terms "first," "second," etc. (if applicable) in the invention and the accompanying drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0055] Before explaining the counting scheme of the present invention in detail, the evaluation index system constructed by the present invention for comprehensively and accurately measuring court data will be introduced as follows.

[0056] To meet the needs of digital court construction, this invention standardizes, regulates, and systematizes massive, diverse, and complex judicial data resources, forming a judicial data resource catalog (hereinafter referred to as the "People's Court Data Resource Catalog") centered on seven major categories: legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data. This lays a solid data foundation for deepening the application of judicial big data and improving the modernization level of the trial system and trial capabilities. Based on the People's Court Data Resource Catalog, this invention determines the set of court data upon which the indicator system is based, including legal and regulatory data, judicial case data (including trial and enforcement case data, litigation service data, judicial assistance data, judicial disclosure data, case-related property data, trial management data, litigation-related petition data, and litigation party data), judicial personnel data, judicial research data, judicial administration data, external data, and information management data.

[0057] Optionally, in this invention, the People's Court Data Resource Catalog is specifically shown in Table 1.

[0058] Table 1. People's Court Data Resource Catalog

[0059] It should be noted that after standardizing, normalizing, and systematizing the seven categories of data—legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data—the specific content of the resulting People's Court Data Resource Catalog may differ from Table 1 above, but it is equally applicable to this invention.

[0060] Court data is a crucial guarantee and core achievement of the informatization construction of the people's courts, and also a fundamental support for the application of artificial intelligence in the judiciary. It plays a vital role in serving trial and enforcement, judicial management, judicial connectivity, economic and social development, and the general public. To comprehensively and accurately measure court data in accordance with the relevant goals of the informatization construction and the application of artificial intelligence in the judiciary, this invention designs an evaluation index system based on the following five service areas.

[0061] (1) Serving Adjudication and Enforcement: Adjudication and enforcement are the core functions of the court, and the comprehensive effectiveness of the court's data resources directly affects the quality and efficiency of adjudication. For example, the total amount of data resources reflects the scale of the court's informatization construction. The larger the total amount of data, the more helpful it is to fully understand the adjudication and enforcement situation.

[0062] (2) Serving judicial management: Scientific, standardized, and refined judicial management is the key to improving the quality and efficiency of court work. For example, the completeness of personnel data coverage reflects the completeness of judicial personnel data. The higher the completeness, the more accurately and comprehensively the situation of judicial personnel can be reflected.

[0063] (3) Serving the integrity of the judiciary: The integrity of the judiciary is the foundation for enhancing judicial credibility. For example, the correlation of petition data reflects the degree of correlation between petition data and case data.

[0064] (4) Serving economic and social development: The work of the courts not only includes core business such as case handling, but also undertakes the important responsibility of serving economic and social development. For example, the geographical coverage rate of legal and regulatory data reflects the breadth of legal and regulatory data, and at the same time reflects the comprehensiveness of data resources in providing legal protection for economic and social development.

[0065] (5) Serving the people: Adhering to the people-centered development philosophy, the work of the courts ultimately serves the people. For example, the number of accesses to litigation service data reflects the frequency and value of the use of litigation service data, and at the same time reflects the degree of service to the people.

[0066] Research on information measurement methods and systems, both domestically and internationally, covers various fields. However, comprehensive and systematic research on information measurement based on general fundamental information theory is rare. Researchers such as Xu Jianfeng have systematically studied and proposed a basic theoretical system and methods for information measurement in relevant literature, specifically referring to "Objective informationtheory: A sextuple model and 9 kinds of metrics[C]", "Research on the Model and Measurement of Objective Information[J]", "Objective information theory exemplified in air traffic control system[J]", "Model Framework and Innovative Practice of my country's Smart Court System Engineering", "Foundations and Applications of Information Systems Dynamics[J]", and "Research and Application of General Information Measures Based on a Unified Model[J]". This invention follows a relevant basic information measurement framework when establishing the relevant evaluation index system. This framework includes the following 11 general basic information measurement methods: Volume: Represents the size of information, usually in bytes or bits; Delay: Represents the time delay between the generation of information and its perception; Scope: Represents the range of information covered; Granularity: Represents the level of detail in information. Intuitively, this level of detail can be understood as the coarseness of the particles into which the ontology can be broken down. Variety: Indicates the category of information; Duration: Indicates the length of time the information lasts; Sampling rate: Represents the frequency at which information is sampled; Aggregation: Represents the distance between elements in a state set, reflecting the relevance of information; Coverage: Indicates the degree to which information is disseminated; Distortion: Indicates the deviation between the information and the original state; Mismatch: Indicates the degree of mismatch between information and specific user needs.

[0067] In addition, based on the general information measurement methods mentioned above and considering the actual needs of the courts, a specific information measurement method has been developed to address the specific circumstances of court data resources: Efficiency: refers to the amount of work or information completed per unit of time during information processing or transmission.

[0068] This invention fully considers the balanced distribution of indicators across different data resource catalogs to more comprehensively present the value of different court data resources. Simultaneously, considering their balanced distribution across measurement methods and service areas, and combining prior information from the judicial field, an evaluation indicator system covering 12 information measurement methods and a total of 28 evaluation indicators has been established. In this system, the number of indicators is distributed across five service areas: 11 for trial execution, 7 for judicial management, 2 for clean and honest justice, 3 for economic and social development, and 5 for the general public. By information measurement method, the indicators are distributed as follows: 3 for capacity, 2 for delay, 3 for breadth, 3 for granularity, 1 for type, 1 for duration, 1 for sampling rate, 2 for aggregation, 6 for coverage, 3 for distortion, 1 for mismatch, and 2 for efficiency. By data resource catalog, the indicators are distributed as follows: 8 comprehensive data resource indicators and 20 other types of data resource indicators. "Comprehensive data resource" represents the overall situation involving seven major categories of data: legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data. The process of establishing this evaluation index system is as follows: Figure 1 As shown in Tables 2 to 6, the various evaluation indicators are as follows.

[0069] Table 2 Court Data Evaluation Indicators-1

[0070] Table 3 Court Data Evaluation Indicators - 2

[0071] Table 4 Court Data Evaluation Indicators - 3

[0072] Table 5 Court Data Evaluation Indicators - 4

[0073] Table 6 Court Data Evaluation Indicators - 5

[0074] Overall, this invention, focusing on the goals of information technology development in people's courts and the application of artificial intelligence in the judiciary, divides the measurement of court data into five service areas, fully reflecting the multidimensional value of court work. In constructing the indicator system, 11 general basic information measurement methods were introduced, and one "efficiency"-specific information measurement method was added, thus building a comprehensive information measurement system. This system quantitatively evaluates the comprehensive effectiveness of data resources from multiple perspectives, including basic data characteristics, processing efficiency, and coverage, demonstrating significant scientific rigor. The resulting indicator system covers different types of data resources and service areas, enabling comprehensive and accurate measurement of court data. This refined indicator design effectively solves the problems of incomplete and inaccurate previous assessments, providing a reliable quantitative basis for improving court data quality, supporting the analysis and research of court information systems, and promoting the digital construction of courts and the application of artificial intelligence in the judiciary.

[0075] The following is an explanation of each indicator.

[0076] (1) Total Data Resources: The total storage capacity of all data resources aggregated by the court information system reflects the scale of the court's data resources. The larger the total data storage capacity, the larger the scale of data resources that can be provided, which helps to grasp the overall situation of the court's data resources. The formula for calculating the total data resources is:

[0077] in, This indicates the total amount of data resources (unit: GB). Indicates the first Storage size of class data (unit: GB) This indicates the total number of data types, i.e., the number of data types in the People's Court Data Resource Catalog (Level 1) (unit: number).

[0078] (2) Total Case Data: The total storage capacity of all case data in the court information system, reflecting the scale of trial, enforcement, and petition-related case data. The larger the scale, the richer the case data provided. The formula for calculating the total case data is:

[0079] in, This represents the total amount of case data (unit: GB). Indicates the first The first type of case Storage size of class data (unit: GB) Indicate the case type (1-trial case, 2-enforcement case, 3-litigation-related petition case). Indicate the data type (1-Case data, 2-Electronic case file, 3-Electronic archive).

[0080] (3) Total Amount of Judicial and Administrative Data: The total storage capacity of all administrative data in the court information system. This helps to grasp the scale of court administrative data and provides data support for judicial administration and decision-making. The formula for calculating the total amount of judicial and administrative data is:

[0081] in, This represents the total amount of judicial and administrative data (unit: GB). Indicates the first Storage volume of judicial and administrative data (unit: GB). This indicates the total number of judicial and administrative data types (unit: number).

[0082] (4) Case data update delay: The average delay time from the generation of court case data to its update (processing or transmission), reflecting the court's response speed to changes in case data. The calculation expression for case data update delay is:

[0083] in, This indicates a delay in case data updates (unit: hours). This indicates the total number of cases (unit: cases). Indicates the first Data aggregation time for each case (the exact time is accurate to the second). Indicates the first The time of case filing and registration (the specific time is accurate to the second).

[0084] (5) Delay in updating judicial statistics: The average delay between the generation and updating of court trial, enforcement, and petition statistics reflects the timeliness of court judicial statistics updates. The shorter the delay, the more timely the court data updates. The formula for calculating the delay in updating judicial statistics is:

[0085] in, This indicates a delay in updating judicial statistics data (unit: hours). This indicates the total number of types of judicial statistics (1-trial, 2-enforcement, 3-petitions). Indicates the first Update time of statistical data (the exact time is accurate to the second). Indicates the first The time when the statistical data was generated (the specific time is accurate to the second).

[0086] (6) Comprehensive Coverage Rate of Data Resources: The coverage ratio of data resources for each data type in the court's data resource catalog reflects the completeness of data aggregation in the court's information system. A higher coverage rate means a higher degree of completion of the aggregated data, a higher degree of completeness of the court's data resources, and thus higher value. The formula for calculating the comprehensive coverage rate of data resources is:

[0087] in, This indicates the overall coverage rate of data resources (unit: %). Indicates the first Whether the class data is aggregated, This indicates that the court information system has gathered the first... Class data, This indicates that the court information system has not aggregated the first Class data, This indicates the total number of data types, specifically the total number of data types in the People's Court Data Resource Catalog (Level 3) (unit: number).

[0088] (7) Information Management Data Coverage Rate: The degree of coverage of court information management data across different dimensions. A higher coverage rate means that the information management data can broadly support the court's information management work in various dimensions, and that the information management data has higher value. The formula for calculating the information management data coverage rate is:

[0089] in, This indicates the coverage rate of information management data (unit: %). This indicates the number of dimensions (unit: number) that have been covered by information management data. This indicates the total number of dimensions that information management should cover (unit: number).

[0090] (8) Geographical Coverage Rate of Legal and Regulatory Data: The ratio of the geographical scope covered by the legal and regulatory data aggregated by the court information system. The higher the geographical coverage rate, the more complete the legal and regulatory data from various regions aggregated by the court information system. The formula for calculating the geographical coverage rate of legal and regulatory data is:

[0091] in, This indicates the geographical coverage rate of legal and regulatory data (unit: %). Indicates the number of covered regions (unit: regions). This indicates the total number of regions (unit: regions).

[0092] (9) Personnel Data Coverage Completeness: The data coverage ratio of the judicial personnel data field reflects the completeness of judicial personnel data collection. The higher the completeness, the more complete the judicial personnel data, and the more accurately and comprehensively it reflects the true situation of judicial personnel. The formula for calculating personnel data coverage completeness is:

[0093] in, Indicates the completeness of personnel data coverage (unit: %). This indicates the number of information items that have been collected from personnel data (unit: item), which is the total number of non-empty information items in all personnel data that have been collected. This represents the total number of personnel data information items (unit: item), which is the sum of the number of all information items in all personnel data, including non-empty information items with collected data and empty information items with uncollected data.

[0094] (10) Completeness of Judicial Statistical Data Coverage: This refers to the degree to which judicial statistical data covers all types of case-related business. Higher completeness indicates a broader reflection of various judicial business activities by the statistical data. The expression for calculating the completeness of judicial statistical data coverage is:

[0095] in, Indicates the completeness of judicial statistics coverage (unit: %). This indicates the number of cases included in judicial statistics (unit: cases). This indicates the total number of cases (unit: cases).

[0096] (11) Industry Coverage Completeness of Legal and Regulatory Data: The ratio of the number of industry categories covered by the legal and regulatory data aggregated by the court information system. The higher the industry coverage completeness, the more complete the legal and regulatory data for each industry aggregated by the court information system. The formula for calculating the industry coverage completeness of legal and regulatory data is:

[0097] in, This indicates the completeness of industry coverage of legal and regulatory data (unit: %). Indicates the first Whether the industry is covered, and if so, then... ,otherwise , This represents the total number of industries (unit: number).

[0098] (12) Number of Data Resource Types: The total number of data types aggregated by the court information system. A larger number of types indicates a wider range of aggregated data, which is conducive to providing broader data resource services and giving the data resources higher value. The expression for calculating the number of data resource types is:

[0099] in, Indicates the number of data resource types (unit: number). Indicates the first Whether the class data is aggregated, This indicates that the court information system has gathered the first... Class data, This indicates that the court information system has not aggregated the first Class data, This indicates the total number of data types, specifically the total number of data types in the People's Court Data Resource Catalog (Level 3) (unit: number).

[0100] (13) Data Resource Time Span: The length of time covered by the data resources aggregated by the court information system. The larger the time span, the longer the time period covered by the data resources, which is more helpful for trend analysis and historical data support, and the greater the value of the data resources. The expression for calculating the data resource time span is:

[0101] in, Indicates the time span of data resources (unit: years). Indicates the latest year of the data. Indicates the earliest year of the data.

[0102] (14) Case data update frequency: The frequency with which court case data is updated within a certain period of time, reflecting the speed at which court case data is updated. The formula for calculating the case data update frequency is:

[0103] in, Indicates the frequency of case data updates (unit: times / case). Indicates a specific time period Total number of newly received cases updated (unit: times). Indicates a specific time period Number of newly received cases (unit: cases) Indicates the length of the time period (unit: months), take .

[0104] (15) Correlation between Person, Case, and Object Data: The degree of correlation between person, case, and object data. The higher the correlation between person, case, and object data, the stronger the correlation between the data, the higher the quality of the data, and the greater its value in supporting case handling, management, and decision-making. The formula for calculating the correlation between person, case, and object data is:

[0105] in, Indicates the correlation degree of data on people, cases, and objects (unit: %). This indicates the degree of correlation between the data of each individual (unit: %). Indicates the correlation degree of case data (unit: %). Indicates the correlation degree of case data (unit: %). Indicates the correlation degree of case data (unit: %). , , , These represent the weight coefficients for the correlation of each data point, and For example, each weight coefficient can be set to 0.25. , , and The calculation expressions are as follows: Renren Data Relevance: ,in, This indicates the number of case data entries (unit: entries) that the presiding judge has linked to personnel data. This indicates the total number of case data entries (unit: entries) that the presiding judge needs to link to personnel data.

[0106] Case-by-case data correlation: ,in, This indicates the number of execution case data entries that are associated with trial case data (unit: entries). This indicates the total number of execution case data entries that need to be linked to trial case data (unit: entries). This indicates the number of appeal case data entries (unit: entries) that are associated with first-instance case data. This indicates the total number of appeal case data entries (unit: entries) that need to be linked to first-instance case data. This indicates the number of petition cases linked to trial and enforcement case data (unit: cases). This indicates the total number of petition cases that need to be linked to trial and enforcement case data (unit: records). , , These represent the weighting coefficients for the correlation between execution data, appeal data, and litigation-related petition data, respectively. For example, the weighting coefficients can be taken as follows: .

[0107] Correlation between case data and person data: ,in, This indicates the number of case data entries (unit: entries) that have been linked to the data of the presiding judge. This indicates the total number of case data entries that need to be linked to the data of the presiding judge (unit: entries).

[0108] Case data correlation: ,in, This indicates the number of case data entries that have been associated with other data (unit: entries). This indicates the total number of case data entries that require related data (unit: entries).

[0109] (16) Correlation of Petition-Related Data: The degree of correlation between petition-related cases and adjudication and enforcement cases. The higher the correlation of petition-related data, the higher the correlation between petition-related cases and adjudication and enforcement cases, and the more valuable the relevant data is for effectively handling petition-related cases and improving the quality of adjudication and enforcement case handling. The formula for calculating the correlation of petition-related data is:

[0110] in, The data represents the correlation between petitions and complaints related to litigation (unit: %). This indicates the number of petitions and complaints related to trial and enforcement cases (unit: cases). This indicates the total number of petitions and complaints involving litigation (unit: cases).

[0111] (17) Data Resource Access Volume: The total access volume of all data resources in the court information system, reflecting the frequency and value of data usage. The formula for calculating data resource access volume is:

[0112] in, This represents the total number of data resource accesses (unit: times). This represents the total number of accesses to all data resources in the court information system (unit: times).

[0113] (18) Access to Judicial Open Data: Access to judicial open data reflects the public's level of attention to judicial data and the degree of impact generated by judicial open data, providing data support for optimizing the content of judicial open data and improving judicial credibility. The formula for calculating access to judicial open data is:

[0114] in, This indicates the number of times judicial data is accessed. Indicates the first Access volume of judicial data (unit: times). This indicates the total number of data types disclosed by the judiciary (unit: times).

[0115] (19) Total amount of data resources shared and exchanged externally: The total number of times the court's information system shares and exchanges data with external parties reflects the openness and sharing status of the court's data resources. A higher total amount of sharing and exchange indicates that the more times the court shares and exchanges data with external parties, the more role and influence it plays, which helps to promote external collaboration and information sharing. The formula for calculating the total amount of data resources shared and exchanged externally is:

[0116] in, This indicates the total amount of data resources shared and exchanged externally (unit: times). Indicates the first Number of times data is shared and exchanged externally (unit: times). This indicates the total number of data types, i.e., the number of data types in the People's Court Data Resource Catalog (Level 1) (unit: number).

[0117] (20) Online Mediation Data Access Volume: The total amount of online mediation data aggregated by the court information system that has been accessed reflects the frequency and value of the online mediation data. The formula for calculating the online mediation data access volume is:

[0118] in, This indicates the number of online mediation data accesses (unit: times). This indicates the total number of visits to the People's Court Online Mediation Platform (unit: visits).

[0119] (21) Litigation service data access volume: The total amount of litigation service data accessed in the court information system, reflecting the frequency and value of the use of litigation service data. The formula for calculating the litigation service data access volume is:

[0120] in, This indicates the number of times litigation service data is accessed (unit: times). This indicates the number of visits to the litigation service website (unit: times).

[0121] (22) Number of electronic services served: The number of judicial documents and notices served by the court electronically, reflecting the scale and role of the court's electronic service services based on data resources. The formula for calculating the number of electronic services served is:

[0122] in, Indicates the number of electronic deliveries (unit: times). This indicates the total number of electronically served documents, notices, copies of complaints, copies of answers, evidence materials, summonses, rulings, judgments, and mediation agreements (unit: times) delivered by the service platform.

[0123] (23) Case Data Confidence: The accuracy and reliability of case data, measuring its quality. A higher confidence level indicates more accurate case data, and data resources with higher confidence levels can provide more reliable support for judicial business processing and management decisions. The formula for calculating case data confidence is:

[0124] in, Indicates the confidence level of the case data (unit: %). Indicates the exact number of case data entries (unit: entries). This indicates the total number of case data entries (unit: entries).

[0125] In practical applications, accurate case data can be defined according to the case quality inspection rules of the court's big data platform. If all quality inspection rules are met, the case data is considered accurate; if any quality inspection rule is not met, the case data is considered inaccurate.

[0126] (24) Electronic case file compliance rate: The percentage of electronic case files accepted by the court that meet the regulatory requirements; the percentage of electronic case files accepted by this court that meet the regulatory requirements. The formula for calculating the electronic case file compliance rate is:

[0127] in, This indicates the pass rate of electronic case files (unit: %). This indicates the number of qualified electronic case files as determined by the electronic case file quality inspection rules (unit: sets). This indicates the total number of electronic case files (unit: sets).

[0128] (25) Confidence level of judicial statistics: The accuracy and reliability of judicial statistics, used to measure the quality of judicial statistics. The expression for calculating the confidence level of judicial statistics is:

[0129] in, Indicates the confidence level of judicial statistics (unit: %). This indicates the number of accurate judicial statistics entries (unit: entries) (based on data that meets the judicial statistics quality inspection rules). This indicates the total number of judicial statistics entries (unit: entries).

[0130] (26) Overall User Satisfaction: Public satisfaction with court data services. A higher satisfaction rate indicates that the court's data resources and services are of high quality and effectively meet user needs. The formula for calculating overall user satisfaction is:

[0131] in, The overall user satisfaction score is expressed in points (rating system, maximum score 5 points). Indicates the first Weight coefficients for class services Indicates the first Satisfaction with this type of service (unit: points) (rating system, maximum score 5 points) This indicates the total number of service types.

[0132] (27) Total Paper Savings: The amount of paper saved by utilizing court data resources for various case handling and electronic service of process reflects the court's ability to reduce resource consumption and pollution, and lower office costs. The formula for calculating total paper savings is:

[0133] in, This indicates the total amount of paper saved (unit: sheets). This indicates the number of sheets of paper saved in processing newly received cases (unit: sheets). Indicates the number of sheets saved by electronic delivery (unit: sheets); This indicates the total number of newly received cases (unit: cases). This indicates the average amount of paper used per case (unit: sheets / case). Indicates the digitization rate of case files (unit: %). Indicates the number of electronic deliveries (unit: times). This indicates the average amount of paper used per delivery (unit: sheets / delivery).

[0134] (28) Reduction in the number of trips: The reduction in the number of trips resulting from utilizing court data resources for various case handling, online trials, and electronic service of process reflects the court's ability to reduce travel resource consumption and pollution, and lower travel costs. The formula for calculating the reduction in the number of trips is:

[0135] in, This indicates a reduction in the number of trips (unit: person-trips). This indicates the number of cases filed online (unit: cases). This represents the average number of trips per case (unit: trips / case). It assumes an average number of parties involved per case. The number of people traveling at different stages of the case handling process was as follows: 1 person for online case filing, and 1 person for online mediation or internet court hearing. Number of people, electronic delivery Number of people:

[0136] The following is an example.

[0137] Example 1: A method for evaluating the comprehensive effectiveness of data in court information systems, such as Figure 1 As shown, it includes: The system acquires the court data set stored in the court information system and calculates 12 types of information metrics to achieve a comprehensive data performance evaluation of the court information system.

[0138] The court data set includes: legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data; the 12 information measurement methods are: capacity, latency, breadth, granularity, type, duration, sampling rate, aggregation degree, reach, distortion degree, mismatch degree, and efficiency. Furthermore, the 12 information measurement indicators include: Capacity: Total amount of data resources, total amount of case data, total amount of judicial and administrative data; Delays: Delays in updating case data and judicial statistics. Breadth: Comprehensive coverage of data resources, coverage of information management data, and geographical coverage of legal and regulatory data; Granularity: Completeness of personnel data coverage, completeness of judicial statistics data coverage, and completeness of industry coverage of legal and regulatory data; Category: Number of data resource types; Duration: The time span of the data resources; Sampling rate: Frequency of case data updates; Aggregation degree: correlation degree of data on people, cases, and objects; correlation degree of data on petitions and complaints related to litigation; Coverage: Data resource access volume, judicial open data access volume, total amount of data resource sharing and exchange with external parties, online mediation data access volume, litigation service data access volume, and number of electronic service of process; Distortion: Confidence level of case data, pass rate of electronic case files, and confidence level of judicial statistics; Mismatch: Overall user satisfaction; Efficiency: Saves a total amount of paper and reduces the number of trips.

[0139] In this embodiment, the specific calculation expressions for each indicator can be found in the previous description and will not be repeated here.

[0140] This embodiment uses legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data stored in the court information system as the core data set. The evaluation index system is built upon 11 general information measurement methods and one specialized information measurement method extended to reflect the characteristics of court data. The relevant evaluation indicators cover five service areas: serving trial and enforcement, serving judicial management, serving clean justice, serving economic and social development, and serving the people. The evaluation results can accurately reflect the comprehensive effectiveness of court data in these five service areas and provide a basis for improving the overall effectiveness of court data. For example, if the total amount of data resources is low, it indicates that the scale of data collected and aggregated by the court information system is insufficient, making it difficult to effectively support business operations and management decisions. This can be addressed by strengthening the automated data collection and aggregation mechanism and expanding the interaction and connection of multi-source heterogeneous data to increase the overall scale of data resources. As another example, if the case data update delay is high, it indicates that the case data is not updated in a timely manner, which will affect the timeliness of trial management. This can be addressed by improving the interface standards and specifications for case data updates and increasing the data update frequency to further improve the timeliness of case data updates. For example, if the correlation between case and person data is low, it indicates a lack of effective connections between various data types, making it difficult to form a complete information chain. This will adversely affect business processing and management decisions. Strengthening data collection and aggregation is crucial to ensure the completeness and accuracy of information fields related to data correlation. Furthermore, information extraction techniques can be used to refine correlated information items and improve data correlation. Similarly, if the confidence level of case data is low, it means the data is incomplete or inaccurate. Automated data quality checks can be implemented at each stage of data collection, processing, and storage to promptly identify and correct data quality issues, thereby improving the confidence level of case data. Finally, if overall user satisfaction is low, it indicates that the system fails to fully meet user needs in terms of functionality and user experience. A more comprehensive user evaluation and feedback mechanism can be implemented in the court information system, and in-depth analysis of user behavior can be conducted. Based on the relevant evaluation, feedback, and analysis results, system functions and data resources can be optimized to improve user satisfaction.

[0141] Furthermore, this embodiment calculates the measurement results of the data set in the court information system based on the evaluation index system, which can comprehensively, accurately and objectively measure the data resources in the court information system, provide data support for the analysis and optimization of the court information system, as well as judicial decision-making and resource allocation, and promote the informatization construction of the people's courts and the application of artificial intelligence in the judiciary.

[0142] In practical applications, in-depth analysis and quantitative evaluation of key indicators of the comprehensive effectiveness of court data resources can clarify the strengths and weaknesses of the current court information system in information management. This helps identify areas for further optimization in areas such as data collection, updating, quality management, and sharing. Identifying these issues provides precise guidance for developing subsequent optimization strategies and facilitates a comprehensive analysis of the integrity, usability, and support capabilities of the court information system. This provides strong support for improving the digital management of court data and optimizing resource allocation. For example, if the evaluation results identify deficiencies in the sharing and opening of court data, optimizing the system's interactive interfaces can promote this process. Similarly, if the evaluation results identify deficiencies in the storage and updating of court data, optimizing the system's configuration in terms of computing and storage resources can improve the storage and updating performance of court data resources.

[0143] The evaluation results obtained from the comprehensive performance evaluation method of court information system data provided in this embodiment also provide support for clarifying the key directions of technology application, and further explore the potential application scenarios of cutting-edge technologies such as large language models, knowledge graphs, and natural language processing in court data resource management, thereby promoting the intelligent and efficient development of the judicial field.

[0144] Based on the evaluation results obtained from the court information system data comprehensive effectiveness evaluation method provided in this embodiment, the advantages and disadvantages of current data resources in supporting intelligent applications can also be identified, providing data basis and technical path for promoting the implementation of AI-assisted case handling, intelligent retrieval, automatic association, quality verification, and other scenarios. For example, to implement an artificial intelligence model for improving the quality of court judgment documents, the adaptability between the data resources of each court and the relevant model training tasks can be estimated based on the comprehensive effectiveness evaluation results of court data resources, thereby selecting suitable court data resources as training data for the artificial intelligence model.

[0145] Example 2: A comprehensive data performance evaluation system for court information systems includes a data interaction module and an evaluation module.

[0146] The data interaction module is used to obtain the set of court data stored in the court information system; The evaluation module includes 12 information measurement units, which are used to compute 12 types of information measurement indicators for the court dataset in parallel.

[0147] The court data set includes: legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data; the 12 information measurement methods are: capacity, latency, breadth, granularity, type, duration, sampling rate, aggregation degree, reach, distortion degree, mismatch degree, and efficiency. Furthermore, the 12 information measurement indicators include: Capacity: Total amount of data resources, total amount of case data, total amount of judicial and administrative data; Delays: Delays in updating case data and judicial statistics. Breadth: Comprehensive coverage of data resources, coverage of information management data, and geographical coverage of legal and regulatory data; Granularity: Completeness of personnel data coverage, completeness of judicial statistics data coverage, and completeness of industry coverage of legal and regulatory data; Category: Number of data resource types; Duration: The time span of the data resources; Sampling rate: Frequency of case data updates; Aggregation degree: correlation degree of data on people, cases, and objects; correlation degree of data on petitions and complaints related to litigation; Coverage: Data resource access volume, judicial open data access volume, total amount of data resource sharing and exchange with external parties, online mediation data access volume, litigation service data access volume, and number of electronic service of process; Distortion: Confidence level of case data, pass rate of electronic case files, and confidence level of judicial statistics; Mismatch: Overall user satisfaction; Efficiency: Saves a total amount of paper and reduces the number of trips.

[0148] In this embodiment, the specific calculation expressions for each indicator can be found in the previous description and will not be repeated here.

[0149] Similar to Embodiment 1 above, this embodiment uses the legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data stored in the court information system as the core data set. The evaluation index system is based on 11 general information measurement methods and one special information measurement method extended by combining the characteristics of court data. The relevant evaluation indicators cover five service areas: serving trial and execution, serving judicial management, serving clean justice, serving economic and social development, and serving the people. This embodiment calculates the measurement results of the data set in the court information system based on this evaluation index system, which can comprehensively, accurately, and objectively measure the data resources in the court information system, provide data support for the analysis and optimization of the court information system, as well as judicial decision-making and resource allocation, and promote the informatization construction of the people's courts and the application of artificial intelligence in the judiciary.

[0150] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the comprehensive effectiveness of data in court information systems, characterized by: The system acquires the court data set stored in the court information system and calculates 12 types of information metrics to achieve a comprehensive data performance evaluation of the court information system. The court data set includes: legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data; the 12 information measurement methods are: capacity, latency, breadth, granularity, type, duration, sampling rate, aggregation degree, coverage, distortion degree, mismatch degree, and efficiency; and the 12 information measurement indicators include: Capacity: Total amount of data resources, total amount of case data, total amount of judicial and administrative data; Delays: Delays in updating case data and judicial statistics. Breadth: Comprehensive coverage of data resources, coverage of information management data, and geographical coverage of legal and regulatory data; Granularity: Completeness of personnel data coverage, completeness of judicial statistics data coverage, and completeness of industry coverage of legal and regulatory data; Category: Number of data resource types; Duration: The time span of the data resources; Sampling rate: Frequency of case data updates; Aggregation degree: correlation degree of data on people, cases, and objects; correlation degree of data on petitions and complaints related to litigation; Coverage: Data resource access volume, judicial open data access volume, total amount of data resource sharing and exchange with external parties, online mediation data access volume, litigation service data access volume, and number of electronic service of process; Distortion: Confidence level of case data, pass rate of electronic case files, and confidence level of judicial statistics; Mismatch: Overall user satisfaction; Efficiency: Saves a total amount of paper and reduces the number of trips.

2. The data comprehensive performance evaluation method for court information systems as described in claim 1, characterized in that, The formula for calculating the total amount of data resources is: The formula for calculating the total amount of case data is: The formula for calculating the total amount of judicial and administrative data is as follows: in, Indicates the total amount of data resources. Indicates the first Storage capacity of class data Indicates the total number of data types; This represents the total number of case data. Indicates the first The first type of case Storage capacity of class data Indicate the case type, Indicates the trial of a case. Indicates the execution of a case. This refers to petitions and complaints involving litigation. Represents data type, Represents case data, Represents electronic case file data. Represents electronic record data; This indicates the total amount of judicial and administrative data. Indicates the first Storage capacity of judicial and administrative data This indicates the total number of data types related to judicial and administrative affairs.

3. The data comprehensive performance evaluation method for court information systems as described in claim 1, characterized in that, The formula for calculating the case data update delay is: The formula for calculating the delay in updating judicial statistics data is as follows: in, This indicates a delay in updating case data. Indicates the total number of cases. Indicates the first The data aggregation time for each case Indicates the first The time of case filing and registration; This indicates a delay in updating judicial statistics. This indicates the total number of types of judicial statistics. Indicates the first Update time of class statistics Indicates the first The time when the statistical data was generated.

4. The data comprehensive performance evaluation method for court information systems as described in claim 1, characterized in that, The formula for calculating the comprehensive coverage rate of data resources is: The formula for calculating the data coverage rate of information management is: The formula for calculating the geographical coverage rate of legal and regulatory data is as follows: in, Indicates the overall coverage rate of data resources. Indicates the first Whether the class data is aggregated, This indicates that the court information system has gathered the first... Class data, This indicates that the court information system has not aggregated the first Class data, Indicates the total number of data types; Indicates the coverage rate of information management data. This indicates the number of dimensions covered by the information management data. This indicates the total number of dimensions that information management should cover; This indicates the geographical coverage of legal and regulatory data. Indicates the number of covered areas. This indicates the total number of regions.

5. The data comprehensive performance evaluation method for court information systems as described in claim 1, characterized in that, The expression for calculating the completeness of personnel data coverage is: The formula for calculating the completeness of judicial statistics coverage is as follows: The formula for calculating the completeness of industry coverage of legal and regulatory data is as follows: in, Indicates the completeness of personnel data coverage. This indicates the number of personnel data items that have been collected. Indicates the total number of personnel data items; This indicates the completeness of judicial statistics coverage. This indicates the number of cases included in judicial statistics. Indicates the total number of cases; This indicates the completeness of industry coverage of legal and regulatory data. Indicates the first Whether the industry is covered, and if so, then... ,otherwise This indicates the total number of industries.

6. The data comprehensive performance evaluation method for court information systems as described in claim 1, characterized in that, The expression for calculating the number of data resource types is: The expression for calculating the time span of data resources is: The formula for calculating the frequency of case data updates is: in, Indicates the number of data resource types. Indicates the first Whether the class data is aggregated, This indicates that the court information system has gathered the first... Class data, This indicates that the court information system has not aggregated the first Class data, Indicates the total number of data types; Indicates the time span of data resources (unit: years). Indicates the latest year of the data. Indicates the earliest year of the data; Indicates the frequency of case data updates. Indicates a specific time period Total number of newly received cases updated Indicates a specific time period Number of newly received cases Indicates the length of the time period.

7. The data comprehensive performance evaluation method for court information systems as described in claim 1, characterized in that, The formula for calculating the correlation between data on people, cases, and objects is: The formula for calculating the correlation of petition-related data is as follows: in, Indicates the correlation between data on people, cases, and objects. Indicates the degree of correlation between individual data. Indicates the correlation between case data. Indicates the correlation between case data and person-to-person data. Indicates the correlation degree of case data (unit: %). , , , They represent , , and The weighting coefficients, and ; Indicates the correlation between data related to petitions and complaints. This indicates the number of petitions and complaints related to trial and enforcement cases. This indicates the total number of petitions and complaints involving litigation.

8. The data comprehensive performance evaluation method for court information systems as described in claim 1, characterized in that, The expression for calculating data resource access volume is: The formula for calculating the number of visits to judicial open data is: The formula for calculating the total amount of data resources shared and exchanged externally is: The expression for calculating the number of online mediation data accesses is: The formula for calculating the number of accesses to litigation service data is: The formula for calculating the number of electronic deliveries is: in, This represents the total number of accesses to the data resource. This represents the total number of accesses to all data resources in the court information system; This indicates the number of times judicial data has been accessed. Indicates the first Access volume of judicial public data This indicates the total number of data types disclosed by the judiciary. This indicates the total amount of data resources shared and exchanged externally. Indicates the first Number of times class data is shared and exchanged externally. Indicates the total number of data types; This indicates the number of online mediation data accesses. This indicates the total number of visits to the People's Court's online mediation platform; This indicates the number of times litigation service data has been accessed. This indicates the number of visits to the litigation service website; Indicates the number of electronic deliveries. This indicates the total number of electronically served documents, notices, copies of complaints, copies of answers, evidence materials, summonses, rulings, judgments, and mediation agreements served through the service platform.

9. The data comprehensive performance evaluation method for court information systems as described in claim 1, characterized in that, The expression for calculating the confidence level of case data is: The formula for calculating the electronic case file pass rate is: The expression for calculating the confidence level of judicial statistics is as follows: The formula for calculating overall user satisfaction is: The formula for calculating the total amount of paper saved is: The formula for calculating the reduction in the number of trips is: in, Indicates the confidence level of the case data. Indicates the exact number of case data entries. This indicates the total number of case data entries; Indicates the pass rate of electronic case files. This indicates the number of qualified electronic case files as determined by the electronic case file quality inspection rules. Indicates the total number of electronic case files; Indicates the confidence level of judicial statistics. This indicates the exact number of judicial statistics entries. This indicates the total number of judicial statistics entries; Indicates overall user satisfaction. Indicates the first Weight coefficients for class services Indicates the first Satisfaction with similar services Indicates the total number of service types; This indicates the total amount of paper saved. This indicates that the number of papers saved in processing newly received cases is [number missing]. This indicates that electronic delivery saves paper. This indicates the total number of newly received cases. This indicates the average amount of paper used per case. This indicates the digitization rate of case files. Indicates the number of electronic deliveries. This indicates the average amount of paper used per delivery; This indicates a reduction in the number of travelers. This indicates the number of cases filed online. This indicates the average number of people traveling per case.

10. A data comprehensive performance evaluation system for court information systems, characterized by: Data interaction module and evaluation module. The data interaction module is used to obtain the set of court data stored in the court information system; The evaluation module includes 12 information measurement units, which are used to calculate 12 types of information measurement indicators for the court data set in parallel. The court data set includes: legal and regulatory data, judicial case data, judicial personnel data, judicial research data, judicial administration data, external data, and information management data; the 12 information measurement methods are: capacity, latency, breadth, granularity, type, duration, sampling rate, aggregation degree, reach, distortion degree, mismatch degree, and efficiency. Furthermore, the 12 information measurement indicators include: Capacity: Total amount of data resources, total amount of case data, total amount of judicial and administrative data; Delays: Delays in updating case data and judicial statistics. Breadth: Comprehensive coverage of data resources, coverage of information management data, and geographical coverage of legal and regulatory data; Granularity: Completeness of personnel data coverage, completeness of judicial statistics data coverage, and completeness of industry coverage of legal and regulatory data; Category: Number of data resource types; Duration: The time span of the data resources; Sampling rate: Frequency of case data updates; Aggregation degree: correlation degree of data on people, cases, and objects; correlation degree of data on petitions and complaints related to litigation; Coverage: Data resource access volume, judicial open data access volume, total amount of data resource sharing and exchange with external parties, online mediation data access volume, litigation service data access volume, and number of electronic service of process; Distortion: Confidence level of case data, pass rate of electronic case files, and confidence level of judicial statistics; Mismatch: Overall user satisfaction; Efficiency: Saves a total amount of paper and reduces the number of trips.

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