Electronic file quality evaluation and correction method, device and system based on case element cognitive network

Abstract

The invention discloses an electronic file quality evaluation and correction method, device and system based on a case element cognitive network, relates to the technical field of electronic file quality evaluation, and aims to realize comprehensive and automatic evaluation and correction of electronic file quality. The method comprises the following steps: constructing an electronic file knowledge graph reflecting a case element cognitive network according to an electronic file; evaluating the electronic file according to the electronic file knowledge graph and an electronic file quality evaluation index system, wherein the electronic file quality evaluation index system comprises a plurality of evaluation indexes and evaluation methods of the evaluation indexes; and correcting the electronic file according to the evaluation result. The system comprises a knowledge graph construction module, an evaluation index system module, an evaluation and correction algorithm module, a report generation module, a quality monitoring module and an interaction module. According to the invention, the transformation of the quality of the electronic file from traditional manual auditing to machine automatic auditing is realized, and the efficiency and accuracy of electronic file management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic case file quality assessment, and particularly to a method and device for electronic case file quality assessment and correction based on a case element cognitive network. Background Art

[0002] With the continuous advancement of judicial informatization, electronic case files have been widely used in judicial trials, executions and other links. However, there are many problems in the existing electronic case file quality assessment technology. On the one hand, there is a lack of a systematic assessment index system, making it difficult to comprehensively and accurately assess the quality of electronic case files. On the other hand, the existing assessment algorithms and correction algorithms have low intelligence, cannot effectively handle complex case element relationships, and it is difficult to cover all error types. In addition, most of the existing electronic case file quality assessment systems rely on manual intervention, with low automation and low processing efficiency, making it difficult to meet the processing requirements of large-scale electronic case files. Summary of the Invention

[0003] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is to achieve comprehensive and automated assessment and correction of the quality of electronic case files.

[0004] To achieve the above object, the present invention provides a method for electronic case file quality assessment and correction based on a case element cognitive network, including: constructing an electronic case file knowledge graph reflecting the case element cognitive network according to the electronic case file; based on the electronic case file knowledge graph, assessing the electronic case file according to an electronic case file quality assessment index system, the electronic case file quality assessment index system including a number of assessment indicators and assessment methods for each assessment indicator; and correcting the electronic case file according to the assessment result.

[0005] In a preferred embodiment of the present invention, the constructing an electronic case file knowledge graph reflecting the case element cognitive network according to the electronic case file includes: performing text parsing on the electronic case file and extracting case elements; performing image recognition on the electronic case file and extracting key information; associating the key information with the case elements to form a complete case information representation; and establishing relationships between case elements according to the case information to form an electronic case file knowledge graph capable of reflecting the case element cognitive network.

[0006] In a preferred embodiment of the present invention, the electronic case file quality assessment index system includes assessment indicators and assessment methods for six dimensions: coverage rate, connectivity rate, normativity, timeliness rate, accuracy, and integrity.

[0007] In a preferred embodiment of the present invention, the method further includes: correcting the electronic case file according to the assessment result.

[0008] In a preferred embodiment of the present invention, the method further includes: based on the electronic case file knowledge graph, deeply evaluating the quality of the electronic case file according to the evaluation index system, and generating a quality evaluation report.

[0009] In a preferred embodiment of the present invention, the quality evaluation report includes the quality score, error list, correction suggestions, and improvement suggestions of the electronic case file.

[0010] On the other hand, the present invention also provides an electronic case file quality evaluation and correction device based on a case element cognitive network, including: a knowledge graph construction unit adapted to construct an electronic case file knowledge graph reflecting the case element cognitive network according to the electronic case file; an evaluation unit adapted to evaluate the electronic case file based on the electronic case file knowledge graph according to the electronic case file quality evaluation index system, where the electronic case file quality evaluation index system includes a number of evaluation indicators and evaluation methods for each evaluation indicator; and a correction unit adapted to correct the electronic case file according to the evaluation result.

[0011] The third aspect embodiment of the present application also provides an electronic case file quality evaluation and correction system based on a case element cognitive network, including: a knowledge graph construction module configured to construct an electronic case file knowledge graph reflecting the case element cognitive network according to the electronic case file; an evaluation index system module configured to create an evaluation index system according to the user's input; an evaluation and correction algorithm module configured to develop an evaluation and correction algorithm according to the user's input; a report generation module configured to generate a quality evaluation report; a quality monitoring module configured to perform dynamic quality monitoring and early warning on the electronic case file; and an interaction module configured to implement the interaction between the user and the electronic case file quality evaluation system.

[0012] The fourth aspect embodiment of the present application also provides a computing device, including: at least one processor and a memory storing a computer program; when the computer program is read and executed by the processor, the electronic device is caused to execute the above method.

[0013] The fifth aspect embodiment of the present application also provides a readable storage medium storing a computer program, when the computer program is read and executed by an electronic device, the electronic device is caused to execute the above method.

[0014] The electronic case file quality assessment and correction method, device and system based on the case element cognitive network provided by the present invention have the following technical effects: By using knowledge graph and artificial intelligence technologies, through constructing an electronic case file knowledge graph, designing a multi-dimensional assessment index system, and developing intelligent assessment algorithms and correction algorithms, the comprehensive and automated assessment and correction of the quality of electronic case files are realized, solving the transformation of the quality of electronic case files from traditional manual review to machine automatic review, improving the efficiency and accuracy of electronic case file management, and providing strong support for judicial trials, executions and other links.

[0015] The following will further illustrate the concept, specific structure and technical effects of the present invention with reference to the accompanying drawings, so as to fully understand the purpose, features and effects of the present invention. Brief Description of the Drawings

[0016] Figure 1 is a schematic flowchart of a preferred embodiment of the electronic case file quality assessment and correction method based on the case element cognitive network in an embodiment of the present invention;

[0017] Figure 2 is a functional block diagram of an electronic case file quality assessment system based on an embodiment of the present invention;

[0018] Figure 3 is a schematic structural diagram of a preferred embodiment of the electronic case file quality assessment and correction device based on the case element cognitive network in an embodiment of the present invention;

[0019] Figure 4 is a schematic structural diagram of a computing device in an embodiment of the present invention. Detailed Embodiments

[0020] The following illustrates the embodiments of the present invention through specific specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0021] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape and size of the components in actual implementation. The type, quantity and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0022] For the purpose of illustration, some exemplary embodiments of the present invention are described. It should be understood that the present invention can be implemented in other ways not specifically shown in the drawings.

[0023] Figure 1 It is a schematic flowchart of a preferred embodiment of the method for quality assessment and correction of electronic case files based on the case element cognitive network in the embodiments of the present invention. As Figure 1 shown, the method for quality assessment and correction of electronic case files based on the case element cognitive network in the embodiments of the present invention includes: constructing an electronic case file knowledge graph reflecting the case element cognitive network according to the electronic case file; based on the electronic case file knowledge graph, assessing the electronic case file according to the electronic case file quality assessment index system, and the electronic case file quality assessment index system includes several assessment indexes and assessment methods for each assessment index; and giving correction suggestions according to the assessment results.

[0024] In step S110, an electronic case file knowledge graph reflecting the case element cognitive network is constructed according to the electronic case file.

[0025] In the embodiments of the present application, information is extracted from the electronic case file, and an electronic case file knowledge graph is constructed based on this information.

[0026] In one implementation, the electronic case file knowledge graph is constructed through the following steps.

[0027] First, perform text parsing on the electronic case file and extract case elements.

[0028] Use optical character recognition technology to recognize the text in the electronic case file, and use natural language processing technology to deeply analyze the text information in the electronic case file, specifically including steps such as word segmentation, part-of-speech tagging, named entity recognition, and syntactic analysis. Through named entity recognition technology, accurately extract case elements, such as the names of the parties, descriptions of the case facts, and legal basis clauses, etc.

[0029] The extracted case elements are text information, and this text information is structured to form a standardized data format, laying a foundation for subsequent relationship establishment.

[0030] Second, perform image recognition on the electronic case file and extract key information.

[0031] Use image recognition technology to analyze the image information in the electronic case file, and the image information includes scanned documents, photos, charts, etc. Image recognition specifically includes image preprocessing, feature extraction, target detection, etc. Through the above steps, accurately identify key information such as seals, signatures, and key evidence images in the image.

[0032] Third, associate the key information with the case elements to form a complete representation of the case information.

[0033] Finally, based on the case information, establish the relationships between case elements. These relationships reflect the cognitive network of case elements and form the knowledge graph of the electronic case file.

[0034] The relationships between elements include entity relationships (the relationships between parties and case facts) and attribute relationships (the attribute values of case facts), etc.

[0035] In this step, using knowledge graph construction technology, store the case elements and the relationships between case elements in the form of a graph to form the knowledge graph of the electronic case file. This knowledge graph provides the basic data support and logical framework for subsequent quality assessment and correction.

[0036] Next, in step S120, based on the knowledge graph of the electronic case file, conduct an assessment of the electronic case file according to the quality assessment index system of the electronic case file. The quality assessment index system of the sub-case file includes several assessment indicators and the assessment methods for each assessment indicator.

[0037] Before implementing the method for quality assessment and correction of the electronic case file based on the cognitive network of case elements in this application embodiment, an electronic case file quality assessment index system and an assessment algorithm should be designed.

[0038] The quality assessment index system of the electronic case file should cover multiple dimensions such as normativity, authenticity, timeliness rate, coverage rate, connectivity rate, accuracy, and integrity. Each dimension contains specific assessment indicators and the assessment methods for each assessment indicator, so as to comprehensively and objectively assess the quality of the electronic case file.

[0039] Taking the dimension of integrity as an example, the assessment indicators can include whether the case materials are complete, whether the case information is complete, whether the full-chain case information is continuous without omission, etc.; the assessment indicators for the dimension of accuracy can include whether the case information is accurate and error-free, whether the electronic case file is consistent with the original paper case file, etc.

[0040] On this basis, each assessment indicator can also be refined and quantified, and specific assessment criteria and scoring rules can be formulated. For example, for the integrity assessment indicator, the ratio of the number of case elements in the electronic case file to the preset number of complete case elements can be statistically calculated as the quantified indicator for assessing integrity. Specifically, first sort out and determine how many materials need to be submitted for each type of case, the fields in the corresponding standard material template, and the order of the materials, and make pre-configurations according to these elements. During the integrity assessment, compare the submitted materials with the preset configuration to achieve the integrity assessment; the connectivity rate assessment indicator is evaluated by analyzing the degree of association between case elements. This assessment index system ensures the comprehensiveness and objectivity of the assessment results.

[0041] In addition, corresponding evaluation algorithms (i.e., evaluation methods) need to be developed for each evaluation index. The evaluation algorithms can automatically identify the types of errors in the electronic case files, and the types of errors include but are not limited to unqualified scanning quality, incorrect indexing, missing information, formatting errors, naming errors, etc.

[0042] For example, for the integrity evaluation index, an automatic integrity check algorithm is developed, which can automatically identify the missing case materials in the electronic case files; for the accuracy evaluation index, text comparison algorithms and image recognition algorithms are developed, and the text comparison algorithms and image recognition algorithms can automatically compare the consistency between the electronic case files and the original paper case files.

[0043] After the evaluation algorithms are designed, artificial intelligence technologies such as machine learning and deep learning are used to train and optimize the evaluation algorithms to improve the accuracy and efficiency of the evaluation algorithms.

[0044] Each case type has its particularity and complexity, so different evaluation indexes, evaluation methods and evaluation algorithms can be designed for different case types.

[0045] On the premise of establishing the quality evaluation index system of electronic case files, this step extracts corresponding information from the knowledge graph of electronic case files, and evaluates the electronic case files according to the evaluation methods of each evaluation index specified in the quality evaluation index system of electronic case files.

[0046] Next, in step S130, the electronic case files are corrected according to the evaluation results.

[0047] Before implementing the electronic case file quality evaluation and correction method based on the case element cognitive network of this application embodiment, corresponding correction algorithms should be designed for each evaluation index. The correction algorithms can give correction suggestions for the unqualified evaluation indexes of the electronic case files and automatically correct the electronic case files. The correction suggestions can be, for example, re-scanning, adjusting indexing, supplementing information, adjusting formatting, re-naming, etc.

[0048] The evaluation algorithms and correction algorithms are characterized by intelligence and high efficiency, and the combination of the two can greatly improve the accuracy and efficiency of the electronic case file quality evaluation and correction.

[0049] This application embodiment has also optimized the correction algorithms to enable them to automatically correct the electronic case files. For example, for the electronic case files with unqualified scanning quality, an image enhancement algorithm is developed, which can automatically improve the quality of the scanned images; for the electronic case files with incorrect indexing, an automatic indexing adjustment algorithm is developed, which can automatically adjust the indexing, etc.

[0050] The correction algorithms of this application embodiment can not only provide specific correction suggestions, but also automatically execute some correction operations, improving the correction efficiency and accuracy.

[0051] Different correction tasks have different particularities and complexities, and correction algorithms and operation processes can be designed for each correction task.

[0052] Based on the above steps S110 to S130, the electronic file quality assessment and correction method based on the case element cognitive network in the embodiments of the present application can also, based on the electronic file knowledge graph, conduct in-depth quality assessment of the electronic file according to the assessment index system, and generate a quality assessment report.

[0053] The knowledge graph has a query function, and the query function of the knowledge graph is used to conduct in-depth quality assessment of the electronic file. According to the assessment index system, the case elements and their relationships in the electronic file are queried and analyzed to obtain the quality assessment result. For example, for the consistency of field information in the electronic file materials, when there are contradictions in the basic information (such as enterprise name, age, etc.) of the same subject in different materials, these contradictions can be automatically identified and marked.

[0054] Using the reasoning function of the knowledge graph, the query results are further analyzed and reasoned to obtain a more comprehensive quality assessment result, that is, the in-depth quality assessment result. For example, potential errors or inconsistencies are discovered through reasoning techniques.

[0055] A detailed quality assessment report is automatically generated according to the in-depth quality assessment result. The report includes information such as the quality score of the electronic file, error list, correction suggestions, and improvement suggestions.

[0056] The weights of the assessment indicators in each dimension of the assessment index system can be set for different types of cases, and the quality score can be calculated by weighted calculation according to the dimensions of the assessment index system to reflect the overall quality level of the electronic file.

[0057] The error list can list all the errors and their types in the electronic file, which is convenient for users to understand the specific problems of the electronic file.

[0058] The correction suggestions can provide corresponding correction methods or suggestions for each error to help users quickly solve problems.

[0059] The improvement suggestions can put forward improvement directions for the overall quality of the electronic file, providing guidance for the quality improvement of the electronic file.

[0060] The electronic file quality assessment and correction method based on the case element cognitive network in the embodiments of the present application can also conduct dynamic quality monitoring and early warning of the electronic file, specifically including real-time monitoring and data analysis, as well as early warning and emergency response.

[0061] In terms of real-time monitoring and data analysis, this method can monitor the quality changes of electronic dossiers in real time according to steps S110 to S130. By regularly or irregularly evaluating the quality of electronic dossiers, potential quality problems can be discovered and processed in a timely manner. The big data technology is used to mine and analyze the quality data of electronic dossiers to discover the trends and patterns of quality changes. For example, through data analysis, it is found that certain types of errors frequently occur within a specific time period.

[0062] In terms of early warning and emergency response, when it is found that the quality of the electronic dossier is lower than the preset threshold, an early warning signal will be automatically sent out. The early warning signal can be notified to relevant personnel in various ways such as emails, text messages, and system messages. The early warning signal contains information such as the quality problems of the electronic dossier, the reasons for the problems, the impacts of the problems, and the recommended solutions, which is convenient for users to respond and process quickly. In addition, an emergency response mechanism can be established to quickly respond to and process the early warning signal to ensure that the quality of the electronic dossier always meets the specification requirements. For example, when an early warning item is found during the quality inspection of the electronic dossier, the warning will be triggered and the subsequent process will be restricted. The user needs to handle the early warning signal, and the warning can be lifted by supplementing materials and resubmitting, or the warning item can be evaluated and ignored by the handling personnel.

[0063] The method for quality inspection and correction of electronic dossiers based on the case element cognitive network in the embodiments of the present application provides a technology for quality inspection and correction of electronic dossiers based on the case element cognitive network. By constructing an electronic dossier knowledge graph that can reflect the case element cognitive network, designing a multi-dimensional inspection index system, developing intelligent inspection algorithms and correction algorithms, and using the query and reasoning functions of the knowledge graph, the comprehensive and automated inspection and correction of the quality of electronic dossiers are realized, providing strong support for judicial trials, executions and other links.

[0064] The embodiments of the present application also provide an electronic dossier quality inspection system 200 based on the above-mentioned electronic dossier quality inspection method. As Figure 2 shown, the system 200 mainly includes a knowledge graph construction module 210, an inspection index system module 220, an inspection and correction algorithm module 230, a report generation module 240, a quality monitoring module 250, and an interaction module 260.

[0065] The knowledge graph construction module 210 is configured to construct an electronic dossier knowledge graph that reflects the case element cognitive network according to the electronic dossier. This module has functions such as text parsing, image recognition, and relationship establishment. Using technologies such as natural language processing and image recognition, this module parses and extracts the text and image information in the electronic dossier to form structured case element data.

[0066] The evaluation index system module 220 is configured to create an evaluation index system according to the user's input. The evaluation index system includes evaluation indicators and evaluation methods in multiple dimensions such as standardization, authenticity, timeliness rate, coverage rate, and connection rate. This module can also refine and quantify each evaluation indicator according to the user's input, and formulate specific evaluation criteria and scoring rules.

[0067] The evaluation and correction algorithm module 230 is configured to develop an evaluation and correction algorithm according to the user's input, specifically including the development of evaluation algorithms and correction algorithms, as well as the training and optimization of evaluation algorithms and correction algorithms using artificial intelligence technology to improve the accuracy and efficiency of the algorithms.

[0068] The report generation module 240 is configured to generate a quality evaluation report. This module specifically includes functions such as knowledge graph query, analysis, and report generation. Among them, the knowledge graph query function can be used to conduct in-depth quality evaluation of electronic dossiers and generate detailed quality evaluation reports.

[0069] The quality monitoring module 250 is configured to perform dynamic quality monitoring and early warning on electronic dossiers, including functions such as real-time monitoring, data analysis, and early warning mechanisms. This module can monitor the quality changes of electronic dossiers in real time, and promptly discover and handle potential quality problems.

[0070] The interaction module 260 is configured to realize the interaction between the user and the electronic dossier quality evaluation system. This module specifically includes three functional units: report display and interaction operation, correction suggestion input and feedback, and operation guide and help document.

[0071] The report display and interaction operation unit is used to display the quality evaluation report. Users can conveniently view information such as the quality score, error list, and correction suggestions of the electronic dossier through the interaction interface. The interaction interface provides clear navigation and interaction operation functions, facilitating users to quickly find the information they need. For example, users can click on the error items in the error list to view detailed error information and correction suggestions. The usability of the interaction interface can enhance the user experience.

[0072] The correction suggestion input and feedback unit can provide the function for users to input correction suggestions. Users can manually input more specific correction measures or methods according to the system's correction suggestions. The system conducts intelligent analysis and processing on the correction suggestions input by users, and integrates and optimizes them with the system's correction suggestions. For example, the system can automatically adjust the correction algorithm and operation process according to the correction suggestions input by users.

[0073] The operation guide and help document unit can provide users with operation guides and help documents to assist users in better using the system for electronic case file quality assessment and correction. The operation guide includes content such as system usage instructions, common question answers, and operation video tutorials. The help document provides detailed system introductions, function descriptions, operation steps, and other information to facilitate users to quickly get started and efficiently use the system.

[0074] The embodiment of the present application also provides an electronic case file quality assessment and correction device 300 based on a case element cognitive network. As Figure 3 shown, the electronic case file quality assessment and correction device 300 based on a case element cognitive network includes a knowledge graph construction unit 310, an assessment unit 320, and a correction unit 330.

[0075] The knowledge graph construction unit 310 is adapted to construct an electronic case file knowledge graph reflecting the case element cognitive network according to the electronic case file.

[0076] The assessment unit 320 is adapted to assess the electronic case file based on the electronic case file knowledge graph according to the electronic case file quality assessment index system, and the electronic case file quality assessment index system includes a number of assessment indicators and assessment methods for each assessment indicator.

[0077] The correction unit 330 is adapted to correct the electronic case file according to the assessment result.

[0078] As a preferred embodiment of the present application, the knowledge graph construction unit 310 includes:

[0079] A parsing subunit, adapted to perform text parsing on the electronic case file and extract case elements;

[0080] A key information extraction subunit, adapted to perform image recognition on the electronic case file and extract key information;

[0081] An association subunit, adapted to associate the key information with the case elements to form a complete case information representation; and

[0082] A knowledge graph creation subunit, adapted to establish relationships between case elements according to the case information to form an electronic case file knowledge graph capable of reflecting the case element cognitive network.

[0083] As a preferred embodiment of the present application, the electronic case file quality assessment index system includes assessment indicators and assessment methods for six dimensions, namely coverage rate, connectivity rate, normativity, timeliness rate, accuracy, and integrity.

[0084] As a preferred embodiment of the present application, the electronic case file quality assessment and correction device 300 based on a case element cognitive network further includes:

[0085] A rectification suggestion sub-unit, adapted to give rectification suggestions according to the review results.

[0086] As a preferred embodiment of the present application, the electronic file quality review and rectification device 300 based on the case element cognitive network further includes:

[0087] A report generation sub-unit, adapted to perform in-depth quality assessment on the electronic file according to the review index system based on the electronic file knowledge graph, and generate a quality review report.

[0088] As a preferred embodiment of the present application, the quality review report includes the quality score, error list, rectification suggestions, and improvement suggestions of the electronic file.

[0089] The electronic file quality review and rectification device 300 based on the case element cognitive network in the embodiment of the present application can execute the processing of each step of the above-mentioned electronic file quality review and rectification method based on the case element cognitive network, and has the same principle and effect as the above-mentioned electronic file quality review and rectification method based on the case element cognitive network, which will not be elaborated here.

[0090] The embodiment of the present application also provides a computing device. As Figure 4 shown, the computing device includes a memory 410 and one or more processors 420, which communicate through a bus. The memory 410 stores a computer program 430, and when the processor 420 executes the computer program 430, the steps of the above-mentioned electronic file quality review method are realized. At the same time, the embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor, the steps of the above-mentioned electronic file quality review and rectification method based on the case element cognitive network are also realized.

[0091] The above embodiments only illustrate the principles and effects of the present invention by way of example, and are not used to limit the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. An electronic case file quality assessment and correction method based on a case element cognitive network, characterized in that including: constructing an electronic case file knowledge graph that reflects the cognitive network of case elements based on the electronic case file; evaluating the electronic case file based on the electronic case file knowledge graph according to the electronic case file quality assessment index system, where the electronic case file quality assessment index system includes a number of assessment indicators and the assessment methods for each assessment indicator; and correcting the electronic case file according to the evaluation result.

2. The method according to claim 1, wherein The constructing an electronic case file knowledge graph that reflects the cognitive network of case elements based on the electronic case file includes: performing text parsing on the electronic case file and extracting case elements; performing image recognition on the electronic case file and extracting key information; associating the key information with the case elements to form a complete representation of case information; and establishing the relationships between case elements according to the case information to form an electronic case file knowledge graph that can reflect the cognitive network of case elements.

3. The method according to claim 1, characterized in that, The electronic case file quality assessment index system includes assessment indicators in six dimensions: coverage rate, connectivity rate, normativity, timeliness rate, accuracy, and integrity, and the assessment methods for each assessment indicator.

4. The method according to claim 1, wherein It also includes: giving correction suggestions according to the evaluation result.

5. The method according to claim 1, characterized in that, It also includes: based on the electronic case file knowledge graph, performing a deep quality assessment on the electronic case file according to the assessment index system and generating a quality assessment report.

6. The method according to claim 5, wherein The quality assessment report includes the quality score, error list, correction suggestions, and improvement suggestions of the electronic case file.

7. An electronic case file quality assessment and correction device based on a case element cognitive network, characterized in that including: a knowledge graph construction unit, adapted to construct an electronic case file knowledge graph that reflects the cognitive network of case elements based on the electronic case file; an evaluation unit, adapted to evaluate the electronic case file based on the electronic case file knowledge graph according to the electronic case file quality assessment index system, where the electronic case file quality assessment index system includes a number of assessment indicators and the assessment methods for each assessment indicator; and a correction unit, adapted to correct the electronic case file according to the evaluation result.

8. An electronic case file quality assessment and correction system based on a case element cognitive network, characterized in that, including: a knowledge graph construction module, configured to construct an electronic case file knowledge graph that reflects the cognitive network of case elements based on the electronic case file; an assessment index system module, configured to create an assessment index system according to the user's input; an assessment and correction algorithm module, configured to develop an assessment and correction algorithm according to the user's input; a report generation module, configured to generate a quality assessment report; a quality monitoring module, configured to perform dynamic quality monitoring and early warning on the electronic case file; an interaction module, configured to implement the interaction between the user and the electronic case file quality assessment system.

9. A computing device, characterized in that, including: a memory; a processor; and a computer program; wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, There is a computer program stored thereon; the computer program is executed by the processor to implement the method according to any one of claims 1 to 6.

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