Simulated court dialectical care scene prediction method and system

Abstract

The invention relates to the technical field of simulation scenes, in particular to a simulation court dialectical scene prediction method and system, and aims to analyze rationality of prepared evidences, ensure that the prepared evidences have sufficient rationality and persuasion in law and logic, and effectively predict the dialectical scene of a simulation court by analyzing possible dialectical viewpoints of an opposite side. The effectiveness and relevance of evidences are evaluated, vulnerabilities and deficiencies in the evidences are found in time, so that targeted supplement and improvement are carried out, the evidences are supplemented and screened according to the evidence rationality analysis result, the evidences which have little case support strength or possibly have flaws can be removed, and the most powerful evidences are concentrated to use. The court hearing efficiency is improved, time and energy are prevented from being wasted on irrelevant evidences, and the overall proving force of the evidences is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of simulation scene, and in particular to a simulation court defense scene prediction method and system. BACKGROUND

[0002] In judicial practice, court trial is an important link to solve various legal disputes and maintain social fairness and justice. The traditional trial preparation mainly relies on the review of paper files, the research of legal provisions and the experience judgment of lawyers. In terms of case analysis and defense prediction, the current main method is based on subjective judgment of the case and limited understanding of the other party and legal personnel, lacking scientific and systematic analysis method and technical means, and it is difficult to accurately predict the defense strategy and possible evidence of the other party. This leads to the fact that it is often impossible to make sufficient preparation in advance during the trial preparation process, and it may be in a passive situation during the trial process. In terms of evidence preparation and processing, the traditional method mainly relies on manual collection, arrangement and review of evidence, which is low in efficiency and easy to miss or misanalyze evidence. Moreover, the display and explanation of evidence in the trial process are often not clear and intuitive, making it difficult for judges and juries to fully understand the importance and relevance of the evidence. In addition, the analysis and selection of evidence mainly rely on the subjective judgment of legal personnel, lacking objective and scientific evaluation standards and methods.

[0003] By constructing a highly realistic virtual court scene, participants can experience the trial atmosphere and become familiar with the trial process and rules in advance. At the same time, the virtual court scene can be used to more intuitively display case evidence and related information, conduct case review and defense prediction, improve the efficiency and quality of trial preparation, and enhance the fairness and transparency of trial. Therefore, it is of great practical significance and application value to develop a simulation court defense scene prediction method.

[0004] In order to solve the technical problem of the prior art that the analysis and selection of evidence mainly rely on the subjective judgment of legal personnel, lacking objective and scientific evaluation standards and methods, the present application designs a simulation court defense scene prediction method and system. SUMMARY

[0005] In order to overcome the technical problem of the prior art that the analysis and selection of evidence mainly rely on the subjective judgment of legal personnel, lacking objective and scientific evaluation standards and methods, the present application provides a simulation court defense scene prediction method and system.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: In a first aspect, the present application provides a simulation court defense scene prediction method, comprising the following steps: S1, acquire court personnel information and file information, build a corresponding virtual court on virtual reality software, simulate the court trial process on the virtual court; S2, combine the judge personnel information and the file information in the court trial process to review the case, and acquire the prepared evidence; S3, based on the case situation, the acquisition situation of the prepared evidence, and the historical evidence attention situation of the legal personnel, defense prediction of the opposite party is carried out; S4, based on the defense prediction mode of the corresponding evidence, the rationality of the prepared evidence is analyzed; S5, based on the rationality analysis result of the prepared evidence, the prepared evidence is supplemented and screened.

[0007] In an implementation manner of the present application, the virtual court construction manner is: the duties of judges, lawyers, witnesses and other roles are acquired through a judicial system interface or manual input, historical evidence attention information of legal agents is acquired, past case databases of legal personnel are searched, habitual strategies are extracted, a virtual court is constructed by using virtual reality software to simulate the court trial process, the participating personnel can be familiar with the court trial environment and process in advance, and the court trial atmosphere can be intuitively felt, which helps to reduce the nervousness in the real court trial and improve the court trial performance. For example, lawyers can simulate the process of interrogating witnesses and debating in the virtual court, and better organize language and adjust strategies.

[0008] In an implementation manner of the present application, the step S2 combines the judge personnel information and the file information in the court trial process to review the case, and acquire the prepared evidence, including the following specific steps: S21, acquire personnel information and file information in the court trial process, acquire historical file information of the same case type and evidence type information required by the corresponding judge in the court through acquisition, acquire the prepared evidence type situation, ensure that the case analysis is based on complete file structure and judge requirements, improve the accuracy of subsequent analysis, automatically collect historical data, reduce manual search time, compare the evidence preparation situation of the current case, and find possible omissions or deficiencies; S22, based on historical file information and this time file information, file similarity analysis is carried out, wherein the file similarity analysis includes case content similarity analysis and case appeal similarity analysis, then the case content similarity analysis result and the case appeal similarity analysis result obtained by analysis are weighted and summed to obtain the file similarity, wherein the proportion weight of the case content similarity is greater than the proportion weight of the appeal similarity, the case content similarity and the case appeal similarity are calculated by the text similarity, the text similarity is obtained by the cosine similarity of the obtained continuous characters or words, by weighting calculation, more attention is paid to the core content of the case (such as fact description) rather than only appeal, the NLP conventional method (such as cosine similarity) is adopted, the calculation is stable and reliable, and different similarity algorithms (such as Google plagiarism detection and CNKI plagiarism detection) can be adapted; S23, the file similarity of the obtained historical similar file information in the period and the file information of this time and the evidence type involved are obtained, the historical similar file information is the historical file with the file similarity greater than or equal to the similarity threshold value with the file of the case, and the corresponding case is set as a similar case, so as to avoid citing outdated cases, ensure the legal applicability, only keep high similarity cases, improve the reference value of subsequent analysis, limit the time range, reduce the data amount, and improve the calculation speed; S24, based on the file similarity of all historical similar cases in the period and the evidence type involved, the submission coefficient of the evidence type involved is obtained, the necessity of evidence submission is calculated through similarity and frequency, the evidence with high submission coefficient is more likely to be adopted by the judge, and the evidence preparation strategy can be dynamically adjusted according to the case similarity; S25, the evidence type with the submission coefficient greater than or equal to the set submission coefficient threshold value is obtained to prepare the evidence, only the evidence needed with high probability is prepared, redundant work is reduced, key evidence is recommended by the system, and the influence of the case result caused by omission is avoided, the submission coefficient threshold value can be adjusted to adapt to the needs of different courts or case types; in the case of case review, the prepared evidence can be collected in a targeted manner, resource waste caused by blind collection of evidence is avoided, and meanwhile, in combination with the trial simulation and review, it can be more accurately judged which evidence has greater support for the case.

[0009] In an implementation manner of the present application, the defense prediction of the opponent in step S3 includes the following specific steps: S31, a database of past cases of the opponent lawyer is obtained, the file information of the same case type of the opponent lawyer is obtained, the defense words of the historical evidence types of the opponent lawyer are analyzed by using NLP keyword extraction, and the frequency of the defense mode of each evidence type is calculated; S32, based on the analysis of the similarity of the files of the same case type of the opposite party in the same period of the legal personnel, the file information of the same case type of the opposite party in the same period of the legal personnel is obtained, which is set as the corresponding similar file, and the frequency of the defense mode of each evidence type of the opposite similar file is obtained; the similarity threshold can be adjusted in combination with the case complexity to balance the recall rate and the accuracy; S33, the frequency of the defense mode of the evidence type is calculated based on the case of the opposite similar file, wherein the calculation method of the probability of the vth defense mode of the corresponding evidence is as follows: Wherein pv is the number of times that the vth defense mode of the corresponding evidence of the opposite similar file is adopted, xc is the file similarity of the corresponding cth opposite similar file adopting the vth defense mode and the file information of this time, f is the number of the opposite similar files, Xp is the file similarity of the pth opposite similar file and the file information of this time, the similarity weight is introduced in the formula to ensure that the more similar files to the current case have greater influence on the prediction result, and deviation caused by simple counting is avoided; S34, the probability of all defense modes of the corresponding evidence is obtained, and the defense mode with a probability greater than or equal to the set probability threshold is set as the defense prediction mode of the corresponding evidence of the opposite party, and the probability threshold is set to automatically filter low-probability strategies and concentrate resources to defend high-threat items.

[0010] In an implementation manner of the present application, the rationality analysis in step S4 includes the following specific contents: The defense prediction mode of the corresponding evidence is compared with the acquisition situation of the prepared evidence of the case one by one, and it is judged whether the acquisition situation of the corresponding prepared evidence exists the problem involved in the corresponding defense prediction mode. If the acquisition situation of the corresponding prepared evidence exists the problem involved in the corresponding defense prediction mode, the acquisition situation of the corresponding prepared evidence is unreasonable. If the acquisition situation of the corresponding prepared evidence does not exist the problem involved in the corresponding defense prediction mode, the acquisition situation of the corresponding prepared evidence is reasonable. According to the evidence rationality analysis result, the prepared evidence is supplemented and screened, which can remove the evidence with little support for the case or the evidence with possible defects, concentrate on using the most powerful evidence, which is helpful to improve the efficiency of the court trial, avoid wasting time and energy on irrelevant evidence, and enhance the overall proof of the evidence.

[0011] In an implementation manner of the present application, the prepared evidence is supplemented and screened based on the rationality analysis result of the prepared evidence in step S5, which includes the following specific contents: If a reasonable judgment result is obtained for the acquisition of the corresponding prepared evidence, the corresponding prepared evidence is supplemented according to the problem involved in the corresponding defense prediction mode, so that the corresponding evidence can solve the problem involved in the corresponding defense prediction mode, and then the operations of S31-S34 are performed again to avoid new problems caused by evidence expansion.

[0012] In a second aspect, the present application also provides a simulation court defense scene prediction system, comprising: A data acquisition module acquires court personnel information and file information, constructs a corresponding virtual court on virtual reality software, and simulates a trial process on the virtual court. A prepared evidence acquisition module replays a case in combination with judge personnel information and file information in a trial process, and simultaneously acquires prepared evidence. A defense prediction module performs defense prediction of an opposite party based on a case situation, an acquisition situation of prepared evidence, and historical evidence attention of legal personnel. A rationality analysis module performs rationality analysis of prepared evidence based on a defense prediction mode of corresponding evidence. A supplementary screening module performs supplementary screening of prepared evidence based on a rationality analysis result of prepared evidence.

[0013] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a simulation court defense scene prediction method by calling the computer program stored in the memory.

[0014] In a fourth aspect, the present application provides a computer readable storage medium storing instructions, which, when executed on a computer, cause the computer to execute a simulation court defense scene prediction method.

[0015] Compared with the prior art, the present application has the following advantages and beneficial effects: The present application performs rationality analysis on prepared evidence, can ensure that the prepared evidence has sufficient rationality and persuasiveness in law and logic, analyzes possible defense viewpoints of an opposite party, evaluates effectiveness and relevance of evidence, discovers defects and deficiencies in evidence in a timely manner, and performs targeted supplementation and improvement, performs supplementary screening of evidence according to a rationality analysis result of evidence, can remove evidence that has little support for a case or may have defects, and focuses on using the most powerful evidence. This helps to improve trial efficiency, avoids wasting time and effort on irrelevant evidence, and enhances overall proof of evidence. BRIEF DESCRIPTION OF DRAWINGS

[0016] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process of an embodiment of the method of the present invention; Figure 2 This is a schematic diagram of the S2 process in an embodiment of the method of the present invention; Figure 3 This is a schematic diagram of the structure in a system embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Example 1

[0020] like Figure 1 and Figure 2 As shown, this embodiment provides a method for predicting simulated courtroom defense scenarios, specifically including the following steps: S1. Obtain information on court personnel and case files, construct a corresponding virtual court on virtual reality software, and simulate the trial process in the virtual court. In this embodiment, the virtual court is constructed as follows: The roles of judges, lawyers, and witnesses are obtained through the judicial system interface or manual input. Simultaneously, historical evidence focus information of the opposing legal representative is acquired. The opposing legal personnel's past case database is searched to extract their habitual strategies, such as frequently questioning the legality of testimony evidence. A 3D virtual image is generated, and OCR technology scans paper case files, storing case information in a structured manner, including parties, statements, and timelines. This information is automatically linked to interactive objects in the virtual scene, such as evidence bags and case files. A 3D court template is automatically matched according to the case type, supporting custom layouts. Case types include civil and criminal cases. The 3D court template is automatically generated through an image synthesis terminal. Virtual reality software is used to construct a virtual court to simulate the trial process, allowing participants to familiarize themselves with the trial environment and procedures in advance and intuitively experience the trial atmosphere. This helps reduce tension during real trials and improves trial performance. For example, lawyers can simulate questioning witnesses and conducting debates in the virtual court, better organizing their language and adjusting their strategies. S2, combine the judge personnel information and the file information in the court trial process to review the case, and prepare to obtain evidence; In the embodiment, the step S2 of combining the judge personnel information and the file information in the court trial process to review the case and preparing to obtain evidence includes the following specific steps: S21, obtain personnel information and file information in the court trial process, obtain historical file information of the same case type and evidence type information required by the corresponding judge in the court, and obtain the prepared evidence type situation, to ensure that the case analysis is based on complete file structure and judge requirements, improve the accuracy of subsequent analysis, automatically collect historical data, reduce manual search time, compare the evidence preparation situation of the current case, and find possible omissions or deficiencies; S22, analyze the similarity of the file based on the historical file information and the file information of this time, wherein the file similarity analysis includes case content similarity analysis and case appeal similarity analysis, and then the case content similarity analysis result and the case appeal similarity analysis result obtained by analysis are weighted and summed to obtain the file similarity, wherein the proportion weight of the case content similarity is greater than that of the appeal similarity, and the case content similarity and the case appeal similarity are calculated by the text similarity method, which is a conventional technical means in the art. For example, Google similarity check or CNKI similarity check can be realized, and the preferred acquisition method is: removing irrelevant characters from the text: punctuation, HTML tags, special symbols, etc., then using software to split the text into continuous multiple characters or words, and obtaining the continuous multiple characters or words. The cosine similarity method is used to obtain the text similarity, and the weighted calculation pays more attention to the core content of the case (such as fact description) rather than only appeal, and the NLP conventional method (such as cosine similarity) is adopted, which is stable and reliable, and can adapt to different similarity algorithms (such as Google check and CNKI check); S23, obtain the file similarity of the historical similar file information in the period and the file information of this time and the evidence type situation involved, which explains why the historical file information in the period is collected instead of all historical file information. Because the court has a certain timeliness when conducting case trials, the historical file information with long time changes in legal provisions or court regulations is not referenceable. The period is selected as one month to one year, which can be used as a reference for the case, and the historical similar file information is the historical file with a file similarity greater than or equal to the similarity threshold value, and the corresponding case is set as a similar case to avoid citing outdated cases and ensure legal applicability. Only high-similarity cases are retained to improve the reference value of subsequent analysis, limit the time range, reduce the data volume, and improve the calculation speed; S24, obtaining the submission coefficient of the evidence type based on the similarity of the files of all historical similar cases in the period and the types of evidence involved, wherein the obtaining method of the submission coefficient of the i-th type of evidence is: wherein Ni is the number of all historical similar cases requiring the i-th type of evidence, pij is the file similarity of the j-th historical similar case requiring the i-th type of evidence, and N is the total number of historical similar cases. The necessity of evidence submission is calculated by similarity and frequency. Evidence with high submission coefficient is more likely to be adopted by the judge. The evidence preparation strategy can be dynamically adjusted according to the similarity of the case; S25, obtaining the evidence type with a submission coefficient greater than or equal to a set submission coefficient threshold for preparing evidence, only preparing evidence that is highly likely to be needed, reducing redundant work, and the system recommending key evidence to avoid missing the case results, wherein the submission coefficient threshold can be adjusted, and the obtaining method of the submission coefficient threshold is: collecting the evidence submission and adoption of a large number of past cases of the target court, analyzing the distribution of the submission coefficient of the evidence finally adopted by the judge in different types of cases, for example, 1000 civil contract dispute cases of a certain court are counted, it is found that the submission coefficient of the adopted evidence is mostly concentrated between 0.21-0.32, so the submission coefficient threshold can be initially set in this interval, such as 0.25, which can adapt to the needs of different courts or case types; obtaining the prepared evidence during the case review process can collect evidence related to the case in a targeted manner, avoiding resource waste caused by blind evidence collection. At the same time, combined with the trial simulation and review, it can more accurately judge which evidence has greater support for the case; S3, making a defense prediction of the opposite party based on the case situation, the prepared evidence, and the historical evidence attention of the method lawyer; In this embodiment, the defense prediction of the opposite party in step S3 includes the following specific steps: S31, obtaining the past case database of the method lawyer, retrieving the file information of the same case type that the method lawyer has represented in the period, using NLP keyword extraction to analyze the defense words of the historical evidence types of the method lawyer, calculating the frequency of each evidence type appearing in the defense method, such as "questioning the credibility of the witness" and "emphasizing procedural violations"; through NLP extraction of high-frequency defense words (such as "procedural violations" and "evidence chain breakage"), the practice style of the opposite lawyer (such as "procedure justice preference type" or "substantive law offensive type") is accurately portrayed, avoiding being passive in court due to unfamiliarity with the opponent; For example, if the opposite party uses "questioning the legality of the evidence" in 70% of the cases, the proponent needs to review the evidence collection process in advance to ensure its compliance; S32, based on the analysis of the similarity of the files of the same case type of the opposite party in the same period of the legal personnel, the file information of the same case type of the opposite party in the same period of the legal personnel is obtained, which is set as the corresponding similar file, and the frequency of the defense mode of each evidence type of the opposite similar file is obtained; irrelevant cases are filtered through similarity algorithm (such as text cosine similarity), and only the files highly related to the current case (such as the same case and the same dispute focus) are reserved to avoid noise interference; exemplary: the current case is "stock right holding dispute", only the same case of the opposite party is reserved, and the labor dispute case data is excluded; dynamic threshold optimization: the similarity threshold can be adjusted combined with the complexity of the case (70% for simple cases and 50% for complex cases), and the complexity and simplicity of the case are distinguished by the court personnel, for example, for simple cases with clear facts and small disputes, the similarity threshold can be relatively high, for example, in some simple civil lending cases, the parties basically have no dispute on the fact of borrowing, and the main dispute point is the amount and time of repayment, at this time, the similarity threshold can be set to 0.7, only the evidence with high submission coefficient and significant influence on the case result is prepared, and time and effort is avoided on some insignificant evidence; for cases involving multiple parties, complex legal relationships and numerous evidence, the similarity threshold can be appropriately reduced, for example, in a large commercial dispute case, the case involves multiple contracts, multiple interest subjects and complex transaction processes, and the relevance and proof of the evidence are difficult to accurately judge, at this time, the similarity threshold can be set to 0.5, so as to prepare as much as possible the evidence that may be needed, and balance the recall rate and precision; S33, based on the case of the opposite similar file, the frequency of the defense mode of the evidence type is calculated, wherein the calculation method of the probability of the vth defense mode of the corresponding evidence is: wherein pv is the number of times that the vth defense mode of the corresponding evidence of the opposite similar file is used, xc is the file similarity of the corresponding vth defense mode of the opposite similar file and the file information of the current file, f is the number of the opposite similar files, Xp is the file similarity of the pth opposite similar file and the file information of the current file, and the similarity weight is introduced in the formula to ensure that the file more similar to the current case has greater influence on the prediction result, and avoid deviation caused by simple counting; S34. The probability of obtaining all defense strategies corresponding to the evidence is used to predict the defense strategy for the opposing party based on the probability of obtaining the evidence with a probability greater than or equal to the set probability threshold. The probability threshold is set to automatically filter low-probability strategies and concentrate resources to defend against high-threat items. The probability threshold here is set based on experience. A large amount of data from similar past cases is collected to statistically analyze the matching between the predicted defense strategy and the actual defense strategy adopted by the opposing party under different probability thresholds. Experiments show that when the probability threshold is set to 0.2, the prediction accuracy is 70%; when it is set to 0.3, the accuracy is 75%; and when it is set to 0.4, the accuracy is 80%. By analyzing this data, the probability threshold that can achieve a high level of prediction accuracy is selected. After comparing multiple sets of data, it is found that the overall prediction effect is best when the probability threshold is 0.35, so it can be used as the probability threshold. S4. Analyze the rationality of preparing evidence based on the defense prediction method corresponding to the evidence; In this embodiment, the rationality analysis in step S4 includes the following specific contents: By comparing the predicted defense methods for the corresponding evidence with the acquisition of the prepared evidence in this case, it can be determined whether the acquisition of the prepared evidence has any of the problems involved in the predicted defense methods. If the acquisition of the prepared evidence has any of the problems involved in the predicted defense methods, then the acquisition of the prepared evidence is unreasonable. If the acquisition of the prepared evidence does not have any of the problems involved in the predicted defense methods, then the acquisition of the prepared evidence is reasonable. Based on the results of the evidence reasonableness analysis, supplementary screening of evidence can be carried out to remove evidence that is not very supportive of the case or may have flaws, and focus efforts on using the most powerful evidence. This helps to improve trial efficiency, avoid wasting time and energy on irrelevant evidence, and enhance the overall probative value of the evidence. For example, if electronic data is not fully sealed (predicted probability 70%) and requires WeChat chat history electronic data, but only screenshots are provided without the original carrier data, it is deemed unreasonable; in this case, the original carrier data needs to be provided. S5. Based on the results of the rationality analysis of the prepared evidence, conduct supplementary screening of the prepared evidence; In this embodiment, step S5 involves supplementing and screening the prepared evidence based on the results of the rationality analysis, including the following specific steps: If the judgment result is that the acquisition of the corresponding prepared evidence is unreasonable, the corresponding prepared evidence will be supplemented according to the issues involved in the corresponding defense prediction method so that the corresponding evidence can solve the issues involved in the corresponding defense prediction method. Then, the S31-S34 operations will be performed again to avoid new problems arising during evidence expansion. If the judgment result is that the acquisition of the corresponding prepared evidence is reasonable, the evidence will be output completely. In this embodiment, it is important to note that it offers the following advantages: Conducting a reasonableness analysis of the prepared evidence ensures that the evidence is legally and logically sound and persuasive. By analyzing the opposing party's potential defense arguments, assessing the validity and relevance of the evidence, and promptly identifying loopholes and deficiencies, targeted supplementation and improvement can be made. Based on the results of the reasonableness analysis, supplementary screening of evidence can eliminate evidence that is not strongly supporting the case or may be flawed, allowing for focused use of the most compelling evidence. This helps improve trial efficiency, avoids wasting time and energy on irrelevant evidence, and enhances the overall probative value of the evidence.

[0021] Example 2

[0022] like Figure 3 As shown, this embodiment provides a simulated courtroom defense scenario prediction system, including: a data acquisition module, which acquires information on court personnel and case files, constructs a corresponding virtual court on virtual reality software, and simulates the trial process in the virtual court; The evidence acquisition module involves reviewing the case by combining information about judges and personnel during the trial with information from the case file, while simultaneously preparing and acquiring evidence. The defense prediction module predicts the opposing party's defense based on the case situation, the acquisition of evidence, and the opposing legal personnel's historical evidence focus. The rationality analysis module analyzes the rationality of prepared evidence based on the defense prediction method corresponding to the evidence. The supplementary screening module performs supplementary screening of the prepared evidence based on the results of the rationality analysis. The specific details of each step in this embodiment have been described in the above method embodiments, so they will not be repeated in detail.

[0023] Example 3

[0024] An electronic device according to an embodiment of the present invention includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a method for predicting simulated courtroom defense scenarios by calling the computer program stored in the memory. It should be noted that all computer programs for the method for predicting simulated courtroom defense scenarios are implemented using the C programming language.

[0025] Example 4

[0026] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored. When a computer program runs on a computer device, it causes the computer device to perform the aforementioned method for predicting simulated courtroom defense scenarios.

[0027] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0028] Those skilled in the art can clearly understand that the units and algorithm steps of the examples described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0029] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device, and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0030] In several embodiments provided by the present application, it should be understood that the disclosed system, device, and method can be implemented in other ways. For example, the above-described device embodiments are merely schematic, for example, the division of units is only one, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices, or units, which can be electrical, mechanical, or other forms.

[0031] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. may be located in one place, or may be distributed to multiple network units. Part or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.

[0032] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0033] In the description of the present specification, the description referring to the terms "one embodiment", "example", "specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0034] The basic principles and main features of the present application and the advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only illustrative of the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for predicting simulated courtroom defense scenarios, characterized in that, The method comprises the following steps: S1, obtaining court staff information and case file information, constructing a corresponding virtual court on a virtual reality software, and simulating a trial process on the virtual court; S2, combining the judge's information and the case file information during the trial process to review the case and obtain the prepared evidence; S3, based on the case situation, the prepared evidence acquisition situation, and the historical evidence attention of the legal personnel, the defense prediction of the opposite party is carried out; S4, based on the defense prediction mode of the corresponding evidence, the rationality of the prepared evidence is analyzed; S5, based on the rationality analysis result of the prepared evidence, the prepared evidence is supplemented and screened.

2. The method of claim 1, wherein, The combination of the judge's information and the case file information during the trial process to review the case and obtain the prepared evidence comprises the following specific steps: Obtain the personnel information and the case file information during the trial process, obtain the historical case file information of the same case type and the evidence type information required by the corresponding judge in the court, and obtain the prepared evidence type situation; Based on the historical case file information and the current case file information, the similarity of the case files is analyzed, wherein the similarity analysis of the case files comprises case content similarity analysis and case appeal similarity analysis, and then the case content similarity analysis result and the case appeal similarity analysis result are weighted and summed to obtain the similarity of the case files, wherein the proportion weight of the case content similarity is greater than that of the appeal similarity; Obtain the similarity of the historical similar case file information and the current case file information within the period and the evidence type situation involved, the historical similar case file information is the historical case file with a similarity to the current case file greater than or equal to a similarity threshold, and the corresponding case is set as a similar case; Based on the similarity of the historical similar case files within the period and the evidence type situation involved, the submission coefficient of the involved evidence type is obtained; Obtain the evidence type with a submission coefficient greater than or equal to a set submission coefficient threshold to obtain the prepared evidence.

3. The method of claim 1, wherein, The defense prediction of the opposite party comprises the following specific steps: Obtain the past case database of the legal personnel, obtain the case file information of the same case type represented by the legal personnel within a period, use NLP keyword extraction analysis to analyze the defense words of the historical evidence type of the legal personnel, and calculate the frequency of the defense mode of each evidence type; Based on the case file information of the same case type represented by the legal personnel within a period and the current case file information, the similarity of the case files is analyzed, the case file information of the same case type represented by the legal personnel within a period with a similarity greater than or equal to a similarity threshold is obtained, and is set as the corresponding similar case file, and the frequency of the defense mode of each evidence type of the opposite similar case file is obtained; Based on the situation of the opposite similar case file, the frequency of the defense mode of the evidence type is calculated; Obtain the probability of all defense modes of the corresponding evidence, and set the defense mode with a probability greater than or equal to a set probability threshold as the defense prediction mode of the corresponding evidence of the opposite party.

4. The method of claim 1, wherein, The rationality analysis comprises the following specific contents: The defense prediction mode corresponding to the evidence is compared with the acquisition of the prepared evidence in the case one by one, and it is judged whether the acquisition of the prepared evidence corresponding to the corresponding defense prediction mode involves the problem in the corresponding defense prediction mode. If the acquisition of the prepared evidence corresponding to the corresponding defense prediction mode involves the problem in the corresponding defense prediction mode, the acquisition of the prepared evidence corresponding to the corresponding defense prediction mode is unreasonable, and if the acquisition of the prepared evidence corresponding to the corresponding defense prediction mode does not involve the problem in the corresponding defense prediction mode, the acquisition of the prepared evidence corresponding to the corresponding defense prediction mode is reasonable.

5. The method of claim 1, wherein, The prepared evidence is supplemented and screened based on the rationality analysis result of the prepared evidence, including the following specific contents: If the judgment result of the acquisition of the corresponding prepared evidence is unreasonable, the corresponding prepared evidence is supplemented according to the problem involved in the corresponding defense prediction mode, so that the corresponding evidence can solve the problem involved in the corresponding defense prediction mode, and then the defense prediction operation of the opposite party is performed again. If the judgment result of the acquisition of the corresponding prepared evidence is reasonable, the evidence is outputted.

6. The method of claim 1, wherein, The virtual court construction mode is that the duty of the role is obtained through a judicial system interface or artificial input, and the historical evidence attention information of the lawyer is obtained, the habitual strategy is extracted by searching the past case database of the lawyer.

7. The method of claim 3, wherein, The calculation method of the probability of the fifth defense mode corresponding to the evidence is as follows: Wherein, pv is the number of times that the corresponding evidence of the similar case of the opposite party adopts the fifth defense mode, xc is the case similarity between the corresponding cth similar case of the opposite party adopting the fifth defense mode and the case information, f is the number of the similar cases of the opposite party, and xp is the case similarity between the pth similar case of the opposite party and the case information.

8. A simulated courtroom defense scenario prediction system for implementing a simulated courtroom defense scenario prediction method according to any one of claims 1-7, characterized by The system comprises: A data acquisition module obtains court personnel information and file information, constructs a corresponding virtual court on a virtual reality software, and simulates a trial process on the virtual court; A prepared evidence acquisition module combines the judge personnel information and the file information in the trial process to review the case and acquire the prepared evidence; A defense prediction module predicts the defense of the opposite party based on the case, the acquisition of the prepared evidence, and the historical evidence attention of the lawyer; A rationality analysis module analyzes the rationality of the prepared evidence based on the defense prediction mode of the corresponding evidence; A supplement and screening module supplements and screens the prepared evidence based on the rationality analysis result of the prepared evidence.

9. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes a simulated courtroom defense scene prediction method according to any one of claims 1-7 by calling the computer program stored in the memory.

Images