A method, system and medium for intelligent case processing in litigation mediation

Abstract

The present application relates to the field of judicial informatization and artificial intelligence technology, and specifically provides a method, system and medium for intelligent processing of litigation mediation cases. The method comprises: obtaining litigation data of a complaint and filling it into a complaint form, and determining the compliance status of the complaint form; if it is compliant, extracting keywords from the complaint form and performing vector conversion processing to obtain a litigation mediation semantic vector; inputting the litigation mediation semantic vector into a preset litigation mediation classification model for processing, obtaining litigation mediation category feature data, generating a mediation plan based on the litigation mediation category feature data, and conducting mediation; if the mediation is successful, generating a mediation document and delivering it to the parties involved in the case; the present application automatically generates a complaint form and performs compliance status verification, determines litigation mediation category feature data through keyword extraction and vector representation, and then generates a mediation plan, thereby realizing full-process intelligent processing of litigation mediation cases and improving the efficiency and success rate of litigation mediation.

Description

Technical Field

[0001] The present application relates to the field of judicial informatization and artificial intelligence technology, and more specifically, to a method, system, and medium for intelligent case processing in litigation mediation. Background Art

[0002] Litigation mediation helps improve the efficiency of court case settlement, reduce the number of backlogs, and thus save judicial resources. At present, traditional litigation mediation cases mainly rely on manual processing, requiring case mediators to manually collect case-related information, such as the complaints and evidence materials submitted by the parties, and read, analyze and organize them one by one. This case handling method is inefficient and tests the professional quality of case mediators. There is a strong degree of subjectivity in the identification of the focus of the dispute, the formulation of the case mediation plan and the mediation of the case. At the same time, it is difficult to achieve the optimal allocation of judicial resources. There is an urgent need for an automated and intelligent processing method for the full process management of litigation mediation cases.

[0003] In response to the above problems, effective technical solutions are urgently needed. Summary of the Invention

[0004] The purpose of this application is to provide a method, system and medium for intelligent processing of litigation mediation cases. It can automatically generate a complaint form and perform compliance status verification, determine the litigation and mediation category feature data through keyword extraction and vector representation, and then generate a mediation plan, thereby realizing full-process intelligent processing of litigation mediation cases and improving the efficiency and success rate of litigation mediation.

[0005] First, the present application provides a method for intelligently processing litigation mediation cases, comprising the following steps:

[0006] Obtaining litigation data from the complaint, entering the litigation data into the complaint form, and determining the compliance status of the complaint form;

[0007] If it is compliant, keywords are extracted based on the complaint form and vectorized to obtain a litigation and mediation semantic vector;

[0008] Inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain litigation and mediation category feature data;

[0009] A mediation plan is generated based on the characteristic data of the litigation and mediation categories, and mediation is carried out. If the mediation is successful, a mediation document is generated and delivered to the parties involved in the case.

[0010] Optionally, in the intelligent case processing method for litigation mediation described in this application, obtaining litigation data of the complaint, filling the litigation data into the complaint form, and determining the compliance status of the complaint form include:

[0011] Obtain litigation data of the complaint, including party identity data and semantic data of the complaint evidence;

[0012] Filling the party identity data and the complaint evidence semantic data into the complaint form to obtain the complaint form;

[0013] Performing integrity check on the complaint form;

[0014] If the integrity check fails, the first outbound call response is activated;

[0015] If the integrity check passes, the complaint form is logically checked using the preset complaint knowledge graph;

[0016] If the logic check fails, the first outbound call response is activated;

[0017] If the logic check passes, the compliance status of the complaint form is determined to be compliant.

[0018] Optionally, in the intelligent case processing method for litigation mediation described in this application, if the case is compliant, keyword extraction is performed based on the complaint form, and vector conversion processing is performed to obtain a litigation mediation semantic vector, including:

[0019] If it is compliant, the complaint form is matched with a preset keyword database to obtain a litigation and mediation keyword set;

[0020] The litigation and mediation keyword set is converted into a vector using a preset language model to obtain a litigation and mediation semantic vector.

[0021] Optionally, in the intelligent case processing method for litigation mediation described in the present application, the inputting of the litigation mediation semantic vector into a preset litigation mediation classification model for processing to obtain litigation mediation category feature data includes:

[0022] Inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain initial litigation and mediation category feature data and corresponding probability values;

[0023] The probability values ​​are arranged in descending order, and the initial litigation and mediation category characteristic data corresponding to the maximum probability value is determined as the litigation and mediation category characteristic data.

[0024] Optionally, in the intelligent case processing method for litigation mediation described in this application, generating a mediation plan based on the litigation and mediation category feature data and conducting mediation, and if the mediation is successful, generating a mediation document and delivering it to the parties to the case, includes:

[0025] Querying a preset mediation solution database based on the litigation and mediation category characteristic data and combining it with the complaint form to generate multiple initial mediation solutions, and extracting corresponding mediation solution characteristic data;

[0026] Obtaining the sufficiency of evidence in litigation and mediation cases;

[0027] Inputting the evidence sufficiency and mediation scheme characteristic data into a preset mediation scheme success rate prediction model for processing to obtain a corresponding mediation success rate, and arranging the mediation success rates of all initial mediation schemes in descending order;

[0028] Determine the initial mediation plan with the highest mediation success rate as the mediation plan and conduct mediation;

[0029] If the mediation is successful, a mediation document will be generated and delivered to the parties involved in the case.

[0030] Optionally, in the intelligent case handling method for litigation mediation described in this application, if the mediation is successful, generating a mediation document and delivering it to the parties involved in the case includes:

[0031] Obtain contact information of the parties involved in the case and perform semantic analysis;

[0032] If the semantic analysis is normal, the contact information is verified through the preset household registration data platform;

[0033] If the verification passes, the service risk assessment data of the parties involved in the case will be obtained and processed to obtain the service difficulty level, and the case service method will be determined based on the service difficulty level.

[0034] In a second aspect, the present application provides an intelligent case processing system for litigation mediation, the system comprising:

[0035] The litigation and mediation case information preprocessing module is used to obtain the litigation data of the complaint and perform integrity and logic verification preprocessing. If the verification fails, the first contact outbound call response is triggered;

[0036] The intelligent analysis module for litigation and mediation cases is used to extract litigation and mediation keywords, convert litigation and mediation semantic vectors, and obtain litigation and mediation category feature data after passing integrity and logic verification;

[0037] The intelligent mediation module for litigation and mediation cases generates multiple initial mediation plans based on litigation and mediation category feature data combined with the complaint form, and evaluates the mediation success rate. If the mediation is successful, it performs semantic analysis and verification on the contact information of the parties involved in the case. If all passes, the case delivery method is determined based on the delivery difficulty level. If not, a manual confirmation response is activated;

[0038] A mediation document generation module for litigation and mediation cases, used to generate mediation documents based on preset mediation document templates;

[0039] The data transmission module is used to encrypt the data of the litigation and mediation case intelligent analysis module, the litigation and mediation case intelligent auxiliary mediation module and the litigation and mediation case mediation document generation module and transmit them to the storage space.

[0040] Optionally, in the intelligent case processing system for litigation mediation described in the present application, the system further includes: a memory and a processor, wherein the memory includes a program for an intelligent case processing method for litigation mediation, and when the program for the intelligent case processing method for litigation mediation is executed by the processor, the following steps are implemented:

[0041] Obtaining litigation data from the complaint, entering the litigation data into the complaint form, and determining the compliance status of the complaint form;

[0042] If it is compliant, keywords are extracted based on the complaint form and vectorized to obtain a litigation and mediation semantic vector;

[0043] Inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain litigation and mediation category feature data;

[0044] A mediation plan is generated based on the characteristic data of the litigation and mediation categories, and mediation is carried out. If the mediation is successful, a mediation document is generated and delivered to the parties involved in the case.

[0045] Optionally, in the case intelligent processing system for litigation mediation described in the present application, obtaining litigation data of the complaint, filling the litigation data into the complaint form, and determining the compliance status of the complaint form include:

[0046] Obtain litigation data of the complaint, including party identity data and semantic data of the complaint evidence;

[0047] Filling the party identity data and the complaint evidence semantic data into the complaint form to obtain the complaint form;

[0048] Performing integrity check on the complaint form;

[0049] If the integrity check fails, the first outbound call response is activated;

[0050] If the integrity check passes, the complaint form is logically checked using the preset complaint knowledge graph;

[0051] If the logic check fails, the first outbound call response is activated;

[0052] If the logic check passes, the compliance status of the complaint form is determined to be compliant.

[0053] On the third aspect, the present application also provides a computer-readable storage medium, which stores a program for an intelligent case processing method for litigation mediation. When the program for an intelligent case processing method for litigation mediation is executed by a processor, the steps of the intelligent case processing method for litigation mediation as described in any one of the above items are implemented.

[0054] From the above, it can be seen that the present application provides an intelligent case processing method, system and medium for litigation mediation. By automatically generating a complaint form and performing compliance status verification, it determines the litigation and mediation category feature data through keyword extraction and vector representation, and then generates a mediation plan, thereby realizing full-process intelligent processing of litigation mediation cases and improving the efficiency and success rate of litigation mediation.

[0055] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 A flowchart of a method for intelligently handling litigation mediation cases provided in an embodiment of the present application;

[0058] Figure 2 A flowchart of obtaining the compliance status of a complaint form in an intelligent case processing method for litigation mediation provided in an embodiment of the present application;

[0059] Figure 3 A flowchart of obtaining a litigation mediation semantic vector for an intelligent case processing method for litigation mediation provided in an embodiment of the present application;

[0060] Figure 4 A high-level flow chart of various embodiments of the present application, which can be used for intelligent case processing methods for litigation mediation;

[0061] Figure 5 A system diagram of an intelligent case processing system for litigation mediation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0063] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0064] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for intelligently handling litigation mediation cases in some embodiments of the present application. This method is used in a terminal device, such as a computer or mobile phone terminal. This method includes the following steps:

[0065] S11. Obtaining litigation data of the complaint, filling the litigation data into the complaint form, and determining the compliance status of the complaint form;

[0066] S12. If the application is compliant, extract keywords based on the complaint form and perform vector conversion to obtain a litigation and mediation semantic vector;

[0067] S13, inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain litigation and mediation category feature data;

[0068] S14. Generate a mediation plan based on the litigation and mediation category characteristic data and conduct mediation. If the mediation is successful, generate a mediation document and deliver it to the parties involved in the case.

[0069] It should be noted that after the parties to the case submit the complaint and evidence materials, the system will perform intelligent identification based on the complaint form requirement information, such as automatic identification using OCR technology, extract the corresponding litigation data and fill in the complaint form, and perform integrity and logic verification to reduce the workload of data entry and improve data accuracy. In order to further understand the needs of litigation mediation, the system uses a preset keyword vocabulary to match and extract keywords, identify the litigation and mediation categories, and obtain litigation and mediation category feature data, such as the right to terminate the contract and the determination of breach of contract liability. Based on the determined litigation and mediation category feature data, the system generates multiple initial mediation plans based on historical litigation mediation cases, and predicts the success rate based on the preset model. The initial mediation plan with the highest success rate is determined as the mediation plan to improve the success rate of litigation mediation. Finally, litigation mediation is carried out according to the determined mediation plan. If the mediation is successful, the mediation document is automatically generated according to the preset template and delivered to the parties to the case to complete the litigation mediation. If unsuccessful, the case will be adjudicated.

[0070] Please refer to Figure 2 , Figure 2 This is a flow chart of obtaining the compliance status of a complaint form in a method for intelligently processing litigation mediation cases in some embodiments of the present application. According to an embodiment of the present invention, obtaining litigation data of the complaint, filling the litigation data into the complaint form, and determining the compliance status of the complaint form include:

[0071] S21. Obtain litigation data of the complaint, including party identity data and complaint evidence semantic data;

[0072] S22. Fill the party identity data and the complaint evidence semantic data into the complaint form to obtain the complaint form;

[0073] S23, performing a completeness check on the complaint form;

[0074] S24. If the integrity check fails, the first outbound call response is activated;

[0075] S25. If the integrity check passes, the complaint form is logically checked using a preset complaint knowledge graph;

[0076] S26. If the logic check fails, the first outbound call response is activated;

[0077] S27. If the logic check passes, the compliance status of the complaint form is determined to be compliant.

[0078] It should be noted that in order to improve the efficiency of the early processing of litigation mediation cases and reduce the workload of case handlers, after receiving the complaint from the parties to the case, the complaint data including the party identity data and the complaint evidence semantic data is automatically identified through OCR technology, and the complaint form is automatically filled in. Among them, the party identity data includes name, contact information, plaintiff or defendant, and the complaint evidence semantic data includes time, amount, interest and evidence type. The integrity is checked according to the preset elements of the complaint form. If the elements are incomplete, such as missing transfer record evidence materials, the first outbound call response is activated, that is, the first call, WeChat, etc. is made to the parties to the case, requiring them to complete the missing elements. If the check passes, further logical verification is performed based on the preset complaint knowledge graph, such as whether the loan interest rate exceeds the upper limit. If any logical check fails, the first outbound call response is activated. If all pass, it means that the complaint expression meets the requirements. Among them, the preset complaint knowledge graph is constructed by technical personnel in this field by obtaining entities and relationship edges of a large number of historical case samples.

[0079] Please refer to Figure 3 , Figure 3 This is a flowchart of a method for intelligently processing litigation mediation cases in some embodiments of the present application for obtaining a litigation mediation semantic vector. According to an embodiment of the present invention, if the case is compliant, keyword extraction is performed based on the complaint form, and vector conversion processing is performed to obtain a litigation mediation semantic vector, including:

[0080] S31. If the case is compliant, matching is performed using a preset keyword database based on the complaint form to obtain a litigation and mediation keyword set;

[0081] S32. Convert the litigation and mediation keyword set into a vector using a preset language model to obtain a litigation and mediation semantic vector.

[0082] It should be noted that litigation mediation cases involve different types of mediation, such as the determination of liability for breach of contract and the right to terminate a contract. In order to accurately identify the category of litigation mediation, keyword matching and extraction are performed based on the generated complaint form, and the keywords are matched based on a preset keyword vocabulary. The preset keyword vocabulary is pre-set by technical personnel in this field based on historical cases. The matched litigation mediation keyword set is converted into a vector through a preset language model to obtain a litigation mediation semantic vector, and the keywords are structured. Among them, the preset language model is obtained by technical personnel in this field through training a large number of historical case samples of litigation mediation keyword sets and corresponding litigation mediation semantic vectors using a neural network.

[0083] According to an embodiment of the present invention, inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain litigation and mediation category feature data includes:

[0084] Inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain initial litigation and mediation category feature data and corresponding probability values;

[0085] The probability values ​​are arranged in descending order, and the initial litigation and mediation category characteristic data corresponding to the maximum probability value is determined as the litigation and mediation category characteristic data.

[0086] It should be noted that in order to accurately identify the litigation mediation category, the structured representation of the litigation mediation semantic vector is input into the preset litigation mediation classification model for processing to obtain the initial litigation mediation category feature data and the corresponding probability value. The preset litigation mediation classification model is obtained by technical personnel in this field based on the litigation mediation semantic vectors of a large number of historical case samples and the corresponding initial litigation mediation category feature data and calibrated probability values ​​using a neural network with Softmax regression as the output layer for training. Then, by arranging the probability values ​​in descending order, the initial litigation mediation category feature data with the largest probability value is determined as the litigation mediation category feature data. For example, the probability value of the recognition of breach of contract liability is 0.92, and the probability value of the right to terminate the contract is 0.81. In this case, the recognition of breach of contract liability is determined as the litigation mediation category feature data.

[0087] According to an embodiment of the present invention, generating a mediation plan based on the litigation and mediation category characteristic data and conducting mediation, and if the mediation is successful, generating a mediation document and delivering it to the parties involved in the case, includes:

[0088] Querying a preset mediation solution database based on the litigation and mediation category characteristic data and combining it with the complaint form to generate multiple initial mediation solutions, and extracting corresponding mediation solution characteristic data;

[0089] Obtaining the sufficiency of evidence in litigation and mediation cases;

[0090] Inputting the evidence sufficiency and mediation scheme characteristic data into a preset mediation scheme success rate prediction model for processing to obtain a corresponding mediation success rate, and arranging the mediation success rates of all initial mediation schemes in descending order;

[0091] Determine the initial mediation plan with the highest mediation success rate as the mediation plan and conduct mediation;

[0092] If the mediation is successful, a mediation document will be generated and delivered to the parties involved in the case.

[0093] It should be noted that after determining the characteristic data of the litigation and mediation category, the mediation procedure is entered. In order to provide mediation guidance to the mediator and improve the success rate of mediation, the preset mediation solution database is queried based on the characteristic data of the litigation and mediation category, and then multiple initial mediation solutions are generated based on the specific case facts extracted from the complaint form. Among them, the preset mediation solution database is extracted by technical personnel in this field from the complaint form of historical cases based on the intelligent analysis module of litigation and mediation cases, including specific elements such as the amount involved in the case, the time of execution, and the details of the focus of the dispute. It is constructed by combining historical litigation mediation cases of the same category and the corresponding mediation solutions. In order to determine the most effective solution, the initial mediation solution is subjected to the extraction of mediation solution characteristic data, such as the amount of compensation and the method of execution, and then combined with the sufficiency of the evidence obtained for this case. The sufficiency of evidence is rated by technical personnel in this field based on the complaint form and is divided into levels 1, 2, 3, 4, and 5, with level 5 evidence being the most sufficient. Finally, it is processed according to the preset mediation solution success rate prediction model to obtain the corresponding mediation success rate. The preset mediation solution success rate prediction model is obtained by training a neural network using the evidence sufficiency and mediation solution characteristic data of a large number of historical samples and the corresponding mediation success rates, and the mediation success rates are arranged in descending order. The initial mediation solution with the highest mediation success rate is determined as the mediation solution, and then the mediator mediates the case according to the mediation solution. If the mediation is successful, the system automatically generates a mediation document according to the mediation solution and delivers it to the parties to the case. If the mediation fails, the arbitration procedure is carried out.

[0094] According to an embodiment of the present invention, if the mediation is successful, generating a mediation document and delivering it to the parties involved in the case includes:

[0095] Obtain contact information of the parties involved in the case and perform semantic analysis;

[0096] If the semantic analysis is normal, the contact information is verified through the preset household registration data platform;

[0097] If the verification passes, the service risk assessment data of the parties involved in the case will be obtained and processed to obtain the service difficulty level, and the case service method will be determined based on the service difficulty level.

[0098] It should be noted that in order to deliver the mediation documents to the parties in a timely and successful manner, the contact information of the parties, such as contact number, contact address, and contact email, must be obtained first, and semantic analysis must be performed. Semantic analysis refers to determining whether the contact information of the parties meets the format requirements, such as whether the number of digits in the contact number is accurate and whether the email format is correct. If the analysis is normal, further verification is performed through the preset household registration data platform to check whether they are consistent. If not, manual confirmation is required. If the verification passes, the delivery risk assessment and delivery difficulty level are analyzed, and different delivery methods are determined according to different delivery difficulty levels, such as door-to-door delivery, postal delivery, or email delivery.

[0099] Please refer to Figure 4 , Figure 4 This is a high-level flowchart of various embodiments of the present application, which can be used for intelligent case processing in litigation mediation. According to embodiments of the present invention, for example, if mediation is conducted according to a mediation plan, a mediation document is automatically generated and served if the mediation is successful. If the mediation fails, the adjudication process is executed.

[0100] It is worth mentioning that according to an embodiment of the present invention, if the verification is passed, the service risk assessment data of the case parties is obtained and processed to obtain the service difficulty level, and the case service method is determined based on the service difficulty level, including:

[0101] Obtain service risk assessment data for the parties involved in the case, including historical rejection rates, service address location data, and pre-set service method characteristics;

[0102] Inputting the historical rejection rate, delivery address location data, and preset delivery method feature data into a preset case delivery risk assessment model to obtain case delivery risk parameters;

[0103] The case delivery risk parameters are compared with the preset case delivery risk control thresholds to obtain the case delivery difficulty level, including low risk, medium risk or high risk.

[0104] It should be noted that the specific method for assessing the difficulty level of case delivery is as follows: First, obtain the historical rejection rate, address matching data, delivery address location data and preset delivery method feature data of the parties to the case, wherein the delivery method refers to the ordinary delivery method, electronic delivery method or retention delivery method. The preset delivery method feature data is represented by a numerical value by technical personnel in this field according to the specific situation and can be adjusted dynamically, such as the ordinary delivery method uses a numerical value of 1, and the electronic delivery method uses a numerical value of 2. The preset case delivery risk assessment model is used for processing to obtain the case delivery risk parameters, wherein the preset case delivery risk assessment model obtains the historical rejection rate, delivery address location data and preset delivery method feature data of a large number of example case samples and the corresponding cases. The delivery risk parameters are obtained by training the neural network, and then the case delivery risk parameters are compared with the preset case delivery risk control thresholds. The preset case delivery risk control thresholds include the first preset case delivery risk control threshold and the second preset case delivery risk control threshold, and the first preset case delivery risk control threshold is less than the second preset case delivery risk control threshold. If the case delivery risk parameter is less than or equal to the first preset case delivery risk control threshold, it is low risk; if it is greater than the first preset case delivery risk control threshold and less than or equal to the second preset case delivery risk control threshold, it is medium risk; if it is greater than the second preset case delivery risk control threshold, it is high risk. For example, if the risk is high, door-to-door delivery is selected; if the risk is medium, mail delivery is selected; if the risk is low, mailbox delivery is selected.

[0105] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes:

[0106] Obtaining service orders for mediation documents;

[0107] Obtain confirmation service instructions from the parties involved in the case at a preset time based on the service instructions;

[0108] If service is confirmed, the time of service of the case shall be recorded;

[0109] If delivery is not confirmed, activate the outbound call confirmation response.

[0110] It should be noted that after the case is served, timely confirmation of delivery feedback from the parties to the case is obtained to realize the closed loop of litigation mediation. First, the delivery instructions of the mediation documents are obtained, including logistics signature, email sent or door-to-door delivery. Then, at the predetermined time point, such as 24 hours after the delivery instruction, the confirmation delivery instructions of the parties to the case are obtained. If the delivery is confirmed, the case delivery time is recorded, the closed loop of the litigation mediation case is completed, and the case file is sealed. If the delivery is not confirmed, the outbound confirmation response is activated to urge the parties to the case to confirm in a timely manner.

[0111] Please refer to Figure 5 , Figure 5This is a system diagram of an intelligent case processing system for litigation mediation in some embodiments of the present application.

[0112] In a second aspect, the present invention further discloses an intelligent case processing system 5 for litigation mediation, comprising:

[0113] The litigation case information pre-processing module 51 is used to obtain the litigation data of the complaint and perform integrity and logic verification pre-processing. If the verification fails, the first contact outbound call response is triggered;

[0114] The intelligent analysis module 52 for litigation and mediation cases is used to extract litigation and mediation keywords, convert litigation and mediation semantic vectors, and obtain litigation and mediation category feature data after the integrity and logic verification are passed;

[0115] The intelligent mediation module 53 for litigation and mediation cases is used to generate multiple initial mediation plans based on the litigation and mediation category feature data combined with the complaint form, and evaluate the mediation success rate. If the mediation is successful, the module performs semantic analysis and verification on the contact information of the parties to the case. If both pass, the module determines the case service method based on the service difficulty level. If not, the module activates a manual confirmation response.

[0116] A mediation document generation module 54 for litigation and mediation cases is used to generate a mediation document based on a preset mediation document template;

[0117] The data transmission module 55 is used to encrypt the data of the litigation and mediation case intelligent analysis module, the litigation and mediation case intelligent auxiliary mediation module and the litigation and mediation case mediation document generation module and transmit them to the storage space.

[0118] It should be noted that the litigation and mediation case information preprocessing module corresponds to the steps of "obtaining litigation data of the complaint and determining compliance status", and is responsible for data acquisition and compliance verification. The litigation and mediation case intelligent analysis module corresponds to the steps of "keyword extraction, vector conversion, and obtaining litigation and mediation category feature data", and is executed based on the verification results of the preprocessing module. If the preprocessing module passes the verification, it enters the analysis process. If the verification fails (integrity or logic verification fails), the first outbound call response is triggered, and the execution of subsequent analysis modules is suspended. It will be restarted after supplementation and improvement. The litigation and mediation case intelligent auxiliary mediation module corresponds to the steps of "generating mediation plans and conducting mediation", and the litigation and mediation case mediation document generation module corresponds to the step of "generating mediation documents". The data transmission module runs through the entire process and is responsible for encrypting and transmitting data from each module, such as encrypting litigation data including the identity data of the parties and the semantic data of the complaint evidence and transmitting it to the storage space.

[0119] According to an example of the present invention, the intelligent case processing system for litigation mediation further includes a memory and a processor. The memory includes a program for an intelligent case processing method for litigation mediation. When the program is executed by the processor, the following steps are implemented:

[0120] Obtaining litigation data from the complaint, entering the litigation data into the complaint form, and determining the compliance status of the complaint form;

[0121] If it is compliant, keywords are extracted based on the complaint form and vectorized to obtain a litigation and mediation semantic vector;

[0122] Inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain litigation and mediation category feature data;

[0123] A mediation plan is generated based on the characteristic data of the litigation and mediation categories, and mediation is carried out. If the mediation is successful, a mediation document is generated and delivered to the parties involved in the case.

[0124] It should be noted that after the parties to the case submit the complaint and evidence materials, the system will perform intelligent identification based on the complaint form requirement information, such as automatic identification using OCR technology, extract the corresponding litigation data and fill in the complaint form, and perform integrity and logic verification to reduce the workload of data entry and improve data accuracy. In order to further understand the needs of litigation mediation, the system uses a preset keyword vocabulary to match and extract keywords, identify the litigation and mediation categories, and obtain litigation and mediation category feature data, such as the right to terminate the contract and the determination of breach of contract liability. Based on the determined litigation and mediation category feature data, the system generates multiple initial mediation plans based on historical litigation mediation cases, and predicts the success rate based on the preset model. The initial mediation plan with the highest success rate is determined as the mediation plan to improve the success rate of litigation mediation. Finally, litigation mediation is carried out according to the determined mediation plan. If the mediation is successful, the mediation document is automatically generated according to the preset template and delivered to the parties to the case to complete the litigation mediation. If unsuccessful, the case will be adjudicated.

[0125] According to an embodiment of the present invention, obtaining litigation data of a complaint, filling the litigation data into a complaint form, and determining the compliance status of the complaint form includes:

[0126] Obtain litigation data of the complaint, including party identity data and semantic data of the complaint evidence;

[0127] Filling the party identity data and the complaint evidence semantic data into the complaint form to obtain the complaint form;

[0128] Performing integrity check on the complaint form;

[0129] If the integrity check fails, the first outbound call response is activated;

[0130] If the integrity check passes, the complaint form is logically checked using the preset complaint knowledge graph;

[0131] If the logic check fails, the first outbound call response is activated;

[0132] If the logic check passes, the compliance status of the complaint form is determined to be compliant.

[0133] It should be noted that in order to improve the efficiency of the early processing of litigation mediation cases and reduce the workload of case handlers, after receiving the complaint from the parties to the case, the complaint data including the party identity data and the complaint evidence semantic data is automatically identified through OCR technology, and the complaint form is automatically filled in. Among them, the party identity data includes name, contact information, plaintiff or defendant, and the complaint evidence semantic data includes time, amount, interest and evidence type. The integrity is checked according to the preset elements of the complaint form. If the elements are incomplete, such as missing transfer record evidence materials, the first outbound call response is activated, that is, the first call, WeChat, etc. is made to the parties to the case, requiring them to complete the missing elements. If the check passes, further logical verification is performed based on the preset complaint knowledge graph, such as whether the loan interest rate exceeds the upper limit. If any logical check fails, the first outbound call response is activated. If all pass, it means that the complaint expression meets the requirements. Among them, the preset complaint knowledge graph is constructed by technical personnel in this field by obtaining entities and relationship edges of a large number of historical case samples.

[0134] According to an embodiment of the present invention, if the case is compliant, keyword extraction is performed based on the complaint form, and vector conversion processing is performed to obtain a litigation and mediation semantic vector, including:

[0135] If it is compliant, the complaint form is matched with a preset keyword database to obtain a litigation and mediation keyword set;

[0136] The litigation and mediation keyword set is converted into a vector using a preset language model to obtain a litigation and mediation semantic vector.

[0137] It should be noted that litigation mediation cases involve different types of mediation, such as the determination of liability for breach of contract and the right to terminate a contract. In order to accurately identify the category of litigation mediation, keyword matching and extraction are performed based on the generated complaint form, and the keywords are matched based on a preset keyword vocabulary. The preset keyword vocabulary is pre-set by technical personnel in this field based on historical cases. The matched litigation mediation keyword set is converted into a vector through a preset language model to obtain a litigation mediation semantic vector, and the keywords are structured. Among them, the preset language model is obtained by technical personnel in this field through training a large number of historical case samples of litigation mediation keyword sets and corresponding litigation mediation semantic vectors using a neural network.

[0138] According to an embodiment of the present invention, inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain litigation and mediation category feature data includes:

[0139] Inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain initial litigation and mediation category feature data and corresponding probability values;

[0140] The probability values ​​are arranged in descending order, and the initial litigation and mediation category characteristic data corresponding to the maximum probability value is determined as the litigation and mediation category characteristic data.

[0141] It should be noted that in order to accurately identify the litigation mediation category, the structured representation of the litigation mediation semantic vector is input into the preset litigation mediation classification model for processing to obtain the initial litigation mediation category feature data and the corresponding probability value. The preset litigation mediation classification model is obtained by technical personnel in this field based on the litigation mediation semantic vectors of a large number of historical case samples and the corresponding initial litigation mediation category feature data and calibrated probability values ​​using a neural network with Softmax regression as the output layer for training. Then, by arranging the probability values ​​in descending order, the initial litigation mediation category feature data with the largest probability value is determined as the litigation mediation category feature data. For example, the probability value of the recognition of breach of contract liability is 0.92, and the probability value of the right to terminate the contract is 0.81. In this case, the recognition of breach of contract liability is determined as the litigation mediation category feature data.

[0142] According to an embodiment of the present invention, generating a mediation plan based on the litigation and mediation category characteristic data and conducting mediation, and if the mediation is successful, generating a mediation document and delivering it to the parties involved in the case, includes:

[0143] Querying a preset mediation solution database based on the litigation and mediation category characteristic data and combining it with the complaint form to generate multiple initial mediation solutions, and extracting corresponding mediation solution characteristic data;

[0144] Obtaining the sufficiency of evidence in litigation and mediation cases;

[0145] Inputting the evidence sufficiency and mediation scheme characteristic data into a preset mediation scheme success rate prediction model for processing to obtain a corresponding mediation success rate, and arranging the mediation success rates of all initial mediation schemes in descending order;

[0146] Determine the initial mediation plan with the highest mediation success rate as the mediation plan and conduct mediation;

[0147] If the mediation is successful, a mediation document will be generated and delivered to the parties involved in the case.

[0148] It should be noted that after determining the characteristic data of the litigation and mediation category, the mediation procedure is entered. In order to provide mediation guidance to the mediator and improve the success rate of mediation, the preset mediation solution database is queried based on the characteristic data of the litigation and mediation category, and then multiple initial mediation solutions are generated based on the specific case facts extracted from the complaint form. Among them, the preset mediation solution database is extracted by technical personnel in this field from the complaint form of historical cases based on the intelligent analysis module of litigation and mediation cases, including specific elements such as the amount involved in the case, the time of execution, and the details of the focus of the dispute. It is constructed by combining historical litigation mediation cases of the same category and the corresponding mediation solutions. In order to determine the most effective solution, the initial mediation solution is subjected to the extraction of mediation solution characteristic data, such as the amount of compensation and the method of execution, and then combined with the sufficiency of the evidence obtained for this case. The sufficiency of evidence is rated by technical personnel in this field based on the complaint form and is divided into levels 1, 2, 3, 4, and 5, with level 5 evidence being the most sufficient. Finally, it is processed according to the preset mediation solution success rate prediction model to obtain the corresponding mediation success rate. The preset mediation solution success rate prediction model is obtained by training a neural network using the evidence sufficiency and mediation solution characteristic data of a large number of historical samples and the corresponding mediation success rates, and the mediation success rates are arranged in descending order. The initial mediation solution with the highest mediation success rate is determined as the mediation solution, and then the mediator mediates the case according to the mediation solution. If the mediation is successful, the system automatically generates a mediation document according to the mediation solution and delivers it to the parties to the case. If the mediation fails, the arbitration procedure is carried out.

[0149] According to an embodiment of the present invention, if the mediation is successful, generating a mediation document and delivering it to the parties involved in the case includes:

[0150] Obtain contact information of the parties involved in the case and perform semantic analysis;

[0151] If the semantic analysis is normal, the contact information is verified through the preset household registration data platform;

[0152] If the verification passes, the service risk assessment data of the parties involved in the case will be obtained and processed to obtain the service difficulty level, and the case service method will be determined based on the service difficulty level.

[0153] It should be noted that in order to deliver the mediation documents to the parties in a timely and successful manner, the contact information of the parties, such as contact number, contact address, and contact email, must be obtained first, and semantic analysis must be performed. Semantic analysis refers to determining whether the contact information of the parties meets the format requirements, such as whether the number of digits in the contact number is accurate and whether the email format is correct. If the analysis is normal, further verification is performed through the preset household registration data platform to check whether they are consistent. If not, manual confirmation is required. If the verification passes, the delivery risk assessment and delivery difficulty level are analyzed, and different delivery methods are determined according to different delivery difficulty levels, such as door-to-door delivery, postal delivery, or email delivery.

[0154] According to an embodiment of the present invention, for example, mediation is performed according to a mediation plan. If the mediation is successful, a mediation document is automatically generated and served. If the mediation fails, an arbitration procedure is executed.

[0155] It is worth mentioning that according to an embodiment of the present invention, if the verification is passed, the service risk assessment data of the case parties is obtained and processed to obtain the service difficulty level, and the case service method is determined based on the service difficulty level, including:

[0156] Obtain service risk assessment data for the parties involved in the case, including historical rejection rates, service address location data, and pre-set service method characteristics;

[0157] Inputting the historical rejection rate, delivery address location data, and preset delivery method feature data into a preset case delivery risk assessment model to obtain case delivery risk parameters;

[0158] The case delivery risk parameters are compared with the preset case delivery risk control thresholds to obtain the case delivery difficulty level, including low risk, medium risk or high risk.

[0159] It should be noted that the specific method for assessing the difficulty level of case delivery is as follows: First, obtain the historical rejection rate, address matching data, delivery address location data and preset delivery method feature data of the parties to the case, wherein the delivery method refers to the ordinary delivery method, electronic delivery method or retention delivery method. The preset delivery method feature data is represented by a numerical value by technical personnel in this field according to the specific situation and can be adjusted dynamically, such as the ordinary delivery method uses a numerical value of 1, and the electronic delivery method uses a numerical value of 2. The preset case delivery risk assessment model is used for processing to obtain the case delivery risk parameters, wherein the preset case delivery risk assessment model obtains the historical rejection rate, delivery address location data and preset delivery method feature data of a large number of example case samples and the corresponding cases. The delivery risk parameters are obtained by training the neural network, and then the case delivery risk parameters are compared with the preset case delivery risk control thresholds. The preset case delivery risk control thresholds include the first preset case delivery risk control threshold and the second preset case delivery risk control threshold, and the first preset case delivery risk control threshold is less than the second preset case delivery risk control threshold. If the case delivery risk parameter is less than or equal to the first preset case delivery risk control threshold, it is low risk; if it is greater than the first preset case delivery risk control threshold and less than or equal to the second preset case delivery risk control threshold, it is medium risk; if it is greater than the second preset case delivery risk control threshold, it is high risk. For example, if the risk is high, door-to-door delivery is selected; if the risk is medium, mail delivery is selected; if the risk is low, mailbox delivery is selected.

[0160] It is worth mentioning that according to an embodiment of the present invention, the present invention further includes:

[0161] Obtaining service orders for mediation documents;

[0162] Obtain confirmation service instructions from the parties involved in the case at a preset time based on the service instructions;

[0163] If service is confirmed, the time of service of the case shall be recorded;

[0164] If delivery is not confirmed, activate the outbound call confirmation response.

[0165] It should be noted that after the case is served, timely confirmation of delivery feedback from the parties to the case is obtained to realize the closed loop of litigation mediation. First, the delivery instructions of the mediation documents are obtained, including logistics signature, email sent or door-to-door delivery. Then, at the predetermined time point, such as 24 hours after the delivery instruction, the confirmation delivery instructions of the parties to the case are obtained. If the delivery is confirmed, the case delivery time is recorded, the closed loop of the litigation mediation case is completed, and the case file is sealed. If the delivery is not confirmed, the outbound confirmation response is activated to urge the parties to the case to confirm in a timely manner.

[0166] The third aspect of the present invention provides a readable storage medium, which stores a program for an intelligent case processing method for litigation mediation. When the program for an intelligent case processing method for litigation mediation is executed by a processor, the steps of the intelligent case processing method for litigation mediation as described in any one of the above items are implemented.

[0167] The present invention discloses an intelligent case processing method, system and medium for litigation mediation. By automatically generating a complaint form and performing compliance status verification, keyword extraction and vector representation are used to determine litigation and mediation category feature data, and then a mediation plan is generated, thereby realizing full-process intelligent processing of litigation mediation cases and improving the efficiency and success rate of litigation mediation.

[0168] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0169] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0170] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0171] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware related to program instructions, and the aforementioned program may be stored in a readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0172] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as standalone products, they can also be stored on a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. An intelligent case processing method for litigation mediation, characterized in that: The following steps are involved: Obtaining litigation data from the complaint, entering the litigation data into the complaint form, and determining the compliance status of the complaint form; If it is compliant, keywords are extracted based on the complaint form and vectorized to obtain a litigation and mediation semantic vector; Inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain litigation and mediation category feature data; Generate a mediation plan based on the litigation and mediation category characteristic data and conduct mediation. If the mediation is successful, generate a mediation document and deliver it to the parties involved in the case; The generating of a mediation plan based on the litigation and mediation category characteristic data and conducting mediation; if the mediation is successful, generating a mediation document and delivering it to the parties involved in the case, including: Querying a preset mediation solution database based on the litigation and mediation category characteristic data and combining it with the complaint form to generate multiple initial mediation solutions, and extracting corresponding mediation solution characteristic data; Obtaining the sufficiency of evidence in litigation and mediation cases; Inputting the evidence sufficiency and mediation scheme characteristic data into a preset mediation scheme success rate prediction model for processing to obtain a corresponding mediation success rate, and arranging the mediation success rates of all initial mediation schemes in descending order; Determine the initial mediation plan with the highest mediation success rate as the mediation plan and conduct mediation; If the mediation is successful, a mediation document will be generated and served to the parties involved in the case; If the mediation is successful, a mediation document will be generated and delivered to the parties involved, including: Obtain contact information of the parties involved in the case and perform semantic analysis; If the semantic analysis is normal, the contact information is verified through the preset household registration data platform; If the verification passes, the service risk assessment data of the parties involved in the case will be obtained and processed to obtain the service difficulty level. The case service method will be determined based on the service difficulty level. If the verification is passed, the service risk assessment data of the parties involved in the case is obtained and processed to obtain the service difficulty level. The case service method is determined based on the service difficulty level, including: Obtain service risk assessment data for the parties involved in the case, including historical rejection rates, service address location data, and pre-set service method characteristics; Inputting the historical rejection rate, delivery address location data, and preset delivery method feature data into a preset case delivery risk assessment model to obtain case delivery risk parameters; The case delivery risk parameters are compared with the preset case delivery risk control thresholds to obtain the case delivery difficulty level, including low risk, medium risk or high risk.

2. The intelligent case processing method for litigation mediation according to claim 1 is characterized in that: The obtaining of litigation data of the complaint, filling the litigation data into the complaint form, and determining the compliance status of the complaint form include: Obtain litigation data of the complaint, including party identity data and semantic data of the complaint evidence; Filling the party identity data and the complaint evidence semantic data into the complaint form to obtain the complaint form; Performing integrity check on the complaint form; If the integrity check fails, the first outbound call response is activated; If the integrity check passes, the complaint form is logically checked using the preset complaint knowledge graph; If the logic check fails, the first outbound call response is activated; If the logic check passes, the compliance status of the complaint form is determined to be compliant.

3. The intelligent case processing method for litigation mediation according to claim 2 is characterized in that: If the case is compliant, keywords are extracted based on the complaint form and vectorized to obtain a litigation and mediation semantic vector, including: If it is compliant, the complaint form is matched with a preset keyword database to obtain a litigation and mediation keyword set; The litigation and mediation keyword set is converted into a vector using a preset language model to obtain a litigation and mediation semantic vector.

4. The intelligent case processing method for litigation mediation according to claim 3 is characterized in that: The step of inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain litigation and mediation category feature data includes: Inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain initial litigation and mediation category feature data and corresponding probability values; The probability values ​​are arranged in descending order, and the initial litigation and mediation category characteristic data corresponding to the maximum probability value is determined as the litigation and mediation category characteristic data.

5. A system for intelligent processing of litigation mediation cases, wherein the system implements the method for intelligent processing of litigation mediation cases according to any one of claims 1 to 4, and is characterized in that: include: The litigation and mediation case information preprocessing module is used to obtain litigation data from the complaint, fill in the complaint form, and perform integrity and logic verification preprocessing. If the verification fails, the first contact outbound call response is triggered; The intelligent analysis module for litigation and mediation cases is used to extract litigation and mediation keywords, convert litigation and mediation semantic vectors, and obtain litigation and mediation category feature data after passing integrity and logic verification; The intelligent mediation module for litigation and mediation cases generates multiple initial mediation plans based on litigation and mediation category feature data combined with the complaint form, and evaluates the mediation success rate. If the mediation is successful, it performs semantic analysis and verification on the contact information of the parties involved in the case. If all passes, the case delivery method is determined based on the delivery difficulty level. If not, a manual confirmation response is activated; A mediation document generation module for litigation and mediation cases, used to generate mediation documents based on preset mediation document templates; The data transmission module is used to encrypt the data of the litigation and mediation case intelligent analysis module, the litigation and mediation case intelligent auxiliary mediation module and the litigation and mediation case mediation document generation module and transmit them to the storage space.

6. An intelligent case processing system for litigation mediation, characterized in that: The system further includes a memory and a processor, wherein the memory stores a program of a method for intelligently handling cases of litigation mediation, and when the program of the method for intelligently handling cases of litigation mediation is executed by the processor, the following steps are implemented: Obtaining litigation data from the complaint, entering the litigation data into the complaint form, and determining the compliance status of the complaint form; If it is compliant, keywords are extracted based on the complaint form and vectorized to obtain a litigation and mediation semantic vector; Inputting the litigation and mediation semantic vector into a preset litigation and mediation classification model for processing to obtain litigation and mediation category feature data; Generate a mediation plan based on the litigation and mediation category characteristic data and conduct mediation. If the mediation is successful, generate a mediation document and deliver it to the parties involved in the case; The generating of a mediation plan based on the litigation and mediation category characteristic data and conducting mediation; if the mediation is successful, generating a mediation document and delivering it to the parties involved in the case, including: Querying a preset mediation solution database based on the litigation and mediation category characteristic data and combining it with the complaint form to generate multiple initial mediation solutions, and extracting corresponding mediation solution characteristic data; Obtaining the sufficiency of evidence in litigation and mediation cases; Inputting the evidence sufficiency and mediation scheme characteristic data into a preset mediation scheme success rate prediction model for processing to obtain a corresponding mediation success rate, and arranging the mediation success rates of all initial mediation schemes in descending order; Determine the initial mediation plan with the highest mediation success rate as the mediation plan and conduct mediation; If the mediation is successful, a mediation document will be generated and served to the parties involved in the case; If the mediation is successful, a mediation document will be generated and delivered to the parties involved, including: Obtain contact information of the parties involved in the case and perform semantic analysis; If the semantic analysis is normal, the contact information is verified through the preset household registration data platform; If the verification passes, the service risk assessment data of the parties involved in the case will be obtained and processed to obtain the service difficulty level. The case service method will be determined based on the service difficulty level. If the verification is passed, the service risk assessment data of the parties involved in the case is obtained and processed to obtain the service difficulty level. The case service method is determined based on the service difficulty level, including: Obtain service risk assessment data for the parties involved in the case, including historical rejection rates, service address location data, and pre-set service method characteristics; Inputting the historical rejection rate, delivery address location data, and preset delivery method feature data into a preset case delivery risk assessment model to obtain case delivery risk parameters; The case delivery risk parameters are compared with the preset case delivery risk control thresholds to obtain the case delivery difficulty level, including low risk, medium risk or high risk.

7. The intelligent case processing system for litigation mediation according to claim 6 is characterized in that: The obtaining of litigation data of the complaint, filling the litigation data into the complaint form, and determining the compliance status of the complaint form include: Obtain litigation data of the complaint, including party identity data and semantic data of the complaint evidence; Filling the party identity data and the complaint evidence semantic data into the complaint form to obtain the complaint form; Performing integrity check on the complaint form; If the integrity check fails, the first outbound call response is activated; If the integrity check passes, the complaint form is logically checked using the preset complaint knowledge graph; If the logic check fails, the first outbound call response is activated; If the logic check passes, the compliance status of the complaint form is determined to be compliant.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program for an intelligent case processing method for litigation mediation. When the program for an intelligent case processing method for litigation mediation is executed by a processor, the steps of an intelligent case processing method for litigation mediation as described in any one of claims 1 to 4 are implemented.

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