A simulation method and system for predicting a courtroom defense scenario

Abstract

The present application relates to the technical field of simulation scene, especially to a simulation court defense scene prediction method and system, the present application carries out rationality analysis to the prepared evidence, can ensure that the prepared evidence has sufficient rationality and persuasiveness in law and logic, through analyzing the possible defense point of view of the other party, the effectiveness and relevance of the evidence are evaluated, the loopholes and deficiencies in the evidence are found in time, so that targeted supplement and improvement are carried out, according to the evidence rationality analysis result, the supplement screening of the evidence can remove those evidence which has little support or may have defects, and concentrate on using the most powerful evidence. This helps to improve the efficiency of court trial, avoid wasting time and energy on irrelevant evidence, and enhance the overall proof of evidence.

Description

Technical Field

[0001] This invention relates to the field of simulation scenario technology, and in particular to a method and system for predicting simulated courtroom defense scenarios. Background Technology

[0002] In judicial practice, court hearings are a crucial link in resolving various legal disputes and upholding social fairness and justice. Traditional trial preparation mainly relies on reviewing paper files, studying legal provisions, and the experience and judgment of lawyers. In terms of case analysis and defense prediction, current methods primarily depend on subjective judgment of the case and limited understanding of the opposing party and legal personnel, lacking scientific and systematic analytical methods and technical means. This makes it difficult to accurately predict the opposing party's defense strategies and potential evidence. Consequently, adequate preparation for the trial is often insufficient, potentially leading to a passive situation during the trial. Regarding evidence preparation and processing, traditional methods mainly involve manual collection, organization, and examination of evidence, which is inefficient and prone to omissions or misanalysis. Furthermore, the presentation and explanation of evidence during the trial are often unclear and unintuitive, making it difficult for judges and juries to fully understand the importance and relevance of the evidence. In addition, the analysis and selection of the reasonableness of evidence mainly relies on the subjective judgment of legal personnel, lacking objective and scientific evaluation standards and methods.

[0003] By constructing highly realistic virtual courtroom scenarios, participants can experience the courtroom atmosphere firsthand, familiarize themselves with the trial process and rules in advance, and use the virtual courtroom scenarios 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 the trial. Therefore, developing a method for predicting simulated courtroom defense scenarios has important practical significance and application value.

[0004] In order to address the technical problem that "the reasonableness analysis and screening of evidence mainly relies on the subjective judgment of legal personnel, and lacks objective and scientific evaluation standards and methods," this application designs a method and system for predicting simulated courtroom defense scenarios. Summary of the Invention

[0005] In order to overcome the technical problem that the existing technology relies mainly on the subjective judgment of legal personnel for the rationality analysis and screening of evidence, and lacks objective and scientific evaluation standards and methods, this invention provides a method and system for predicting simulated court defense scenarios.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for predicting simulated courtroom defense scenarios, comprising the following steps:

[0008] 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.

[0009] S2. Review the case by combining information about the judges and personnel during the trial with information from the case file, and prepare evidence for acquisition.

[0010] S3. Based on the case details, the acquisition of evidence, and the opposing legal personnel's historical evidence focus, predict the opposing party's defense strategy.

[0011] S4. Analyze the rationality of preparing evidence based on the defense prediction method corresponding to the evidence;

[0012] S5. Based on the results of the rationality analysis of the prepared evidence, conduct supplementary screening of the prepared evidence.

[0013] In one implementation of this invention, the virtual court is constructed as follows: The roles of judges, lawyers, and witnesses are obtained through a judicial system interface or manual input. Simultaneously, historical evidence focus information of the opposing legal representative is acquired. The opposing legal representative's past case database is searched to extract their habitual strategies. Virtual reality software is used to construct a virtual court to simulate the trial process. This allows participants to familiarize themselves with the trial environment and procedures in advance, and to 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, allowing them to better organize their language and adjust their strategies.

[0014] In one implementation of the present invention, step S2 involves reviewing the case by combining information about the judges and personnel during the trial with information from the case file, and simultaneously preparing evidence acquisition, including the following specific steps:

[0015] S21. Obtain personnel information and case file information during the trial process. By obtaining historical case file information of the same case type and the types of evidence required by the corresponding judge in court, and at the same time obtaining the types of evidence that have been prepared, ensure that the case analysis is based on the complete case file structure and the judge's requirements, improve the accuracy of subsequent analysis, automatically collect historical data, reduce manual search time, compare the evidence preparation status of the current case, and find possible omissions or deficiencies.

[0016] S22. Analyze the similarity between historical case file information and current case file information. This analysis includes case content similarity analysis and case claim similarity analysis. Then, the results of the case content similarity analysis and the case claim similarity analysis are weighted and summed to obtain the case file similarity score. The weight of case content similarity is greater than that of claim similarity. Both case content similarity and case claim similarity are calculated using text similarity. The text similarity is obtained by using cosine similarity to obtain multiple consecutive characters or words. Through weighted calculation, more attention is paid to the core content of the case (such as factual description) rather than just the claims. Conventional NLP methods (such as cosine similarity) are used, which are stable and reliable and can be adapted to different similarity algorithms (such as Google plagiarism detection and CNKI plagiarism detection).

[0017] S23. Obtain the similarity between the historical similar case file information and the current case file information within the obtained period, as well as the types of evidence involved. At the same time, the historical similar case file information refers to historical case files with a similarity to the current case file that is greater than or equal to the similarity threshold. The corresponding cases are set as similar cases to avoid citing outdated cases, ensure the applicability of the law, retain only high similarity cases, improve the reference value of subsequent analysis, limit the time range, reduce the amount of data, and improve the calculation speed.

[0018] S24. Based on the similarity of case files of all historical similar cases within the period and the types of evidence involved, the submission coefficient of the types of evidence involved is obtained. The necessity of evidence submission is calculated by similarity and frequency. Evidence with a high submission coefficient is more likely to be accepted by the judge. The evidence preparation strategy can be dynamically adjusted according to the case similarity.

[0019] S25. The system acquires evidence with a submission coefficient greater than or equal to the set submission coefficient threshold. It prepares only evidence with a high probability of being needed, reducing redundant work. The system recommends key evidence to avoid affecting the case outcome due to oversight. The submission coefficient threshold is adjustable to adapt to the needs of different courts or case types. Acquiring and preparing evidence during the case review process allows for targeted collection of evidence related to the case, avoiding the waste of resources caused by blindly collecting evidence. At the same time, by combining court simulation and review, it can more accurately determine which evidence has a stronger supporting force for the case.

[0020] In one implementation of the present invention, the defense prediction of the opposing party in step S3 includes the following specific steps:

[0021] S31. Obtain the database of past cases of the opposing legal personnel, obtain the case file information of the same type of cases handled by the opposing legal personnel within the search period, use NLP keywords to extract and analyze the defense statements of the opposing legal personnel for the historical evidence types, and calculate the frequency of the defense methods for each type of evidence.

[0022] S32. Analyze the similarity of case files based on the case file information of the same type of cases handled by the opposing legal personnel within the same period and the current case file information. Obtain case file information of the same type of cases handled by the opposing legal personnel within the same period with a similarity greater than or equal to the similarity threshold, and set them as corresponding similar case files. Obtain the frequency of defense methods for each type of evidence in the opposing similar case files. The similarity threshold can be adjusted according to the complexity of the case to balance the recall rate and the accuracy.

[0023] S33. Calculate the frequency of defense methods for different types of evidence based on similar case files of the opposing party. The probability of the v-th defense method for the corresponding evidence is calculated as follows: , where pv is the number of times the corresponding evidence of the opposing party's similar case file adopts the v-th defense method, xc is the case file similarity between the c-th opposing party's similar case file that adopts the v-th defense method and the current case file, f is the number of opposing party's similar case files, and Xp is the case file similarity between the p-th opposing party's similar case file and the current case file. The formula introduces similarity weights to ensure that the more similar the case file is to the current case, the greater its impact on the prediction results, thus avoiding the bias caused by simple counting;

[0024] S34. The probability of obtaining all defense methods corresponding to the evidence. Defense methods with a probability greater than or equal to the set probability threshold are set as the defense prediction methods for the opposing party's corresponding evidence. The probability threshold is set to automatically filter low-probability strategies and concentrate resources to defend against high-threat items.

[0025] In one implementation of the present invention, the rationality analysis in step S4 includes the following specific contents:

[0026] 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 the problems involved in the predicted defense methods. If the acquisition of the prepared evidence has 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 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.

[0027] In one implementation of the present invention, step S5 involves supplementing and screening the prepared evidence based on the results of the rationality analysis, including the following specific aspects:

[0028] 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 complete evidence will be output.

[0029] Secondly, the present invention also provides a simulated courtroom defense scenario prediction system, comprising:

[0030] The data acquisition module 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.

[0031] 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.

[0032] 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.

[0033] The rationality analysis module analyzes the rationality of prepared evidence based on the defense prediction method corresponding to the evidence.

[0034] The supplementary screening module performs supplementary screening of the prepared evidence based on the results of the rationality analysis of the prepared evidence.

[0035] Thirdly, the present invention 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 method for predicting simulated courtroom defense scenarios by calling the computer program stored in the memory.

[0036] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method for predicting simulated courtroom defense scenarios.

[0037] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0038] This invention conducts a reasonableness analysis of prepared evidence, ensuring that the evidence is legally and logically sound and persuasive. By analyzing the opposing party's possible 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 evidence can be screened, eliminating 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. Attached Figure Description

[0039] 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:

[0040] Figure 1 This is a schematic diagram of the overall process of an embodiment of the method of the present invention;

[0041] Figure 2 This is a schematic diagram of the S2 process in an embodiment of the method of the present invention;

[0042] Figure 3 This is a schematic diagram of the structure in a system embodiment of the present invention. Detailed Implementation

[0043] 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.

[0044] 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.

[0045] Example 1

[0046] like Figure 1 and Figure 2 As shown, this embodiment provides a method for predicting simulated courtroom defense scenarios, specifically including the following steps:

[0047] 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.

[0048] 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.

[0049] S2. Review the case by combining information about the judges and personnel during the trial with information from the case file, and prepare evidence for acquisition.

[0050] In this embodiment, step S2 involves reviewing the case by combining information about the judges and personnel during the trial with information from the case file, and simultaneously preparing evidence acquisition, including the following specific steps:

[0051] S21. Obtain personnel information and case file information during the trial process. By obtaining historical case file information of the same case type and the types of evidence required by the corresponding judge in court, and at the same time obtaining the types of evidence that have been prepared, ensure that the case analysis is based on the complete case file structure and the judge's requirements, improve the accuracy of subsequent analysis, automatically collect historical data, reduce manual search time, compare the evidence preparation status of the current case, and find possible omissions or deficiencies.

[0052] S22. Analyze the similarity between historical case file information and current case file information. This analysis includes case content similarity analysis and case claim similarity analysis. The results of the case content similarity analysis and the case claim similarity analysis are then weighted and summed to obtain the case file similarity score. The weight of case content similarity is greater than that of claim similarity. Both case content similarity and case claim similarity are calculated using text similarity methods, which are common techniques in this field. For example, Google similarity check or CNKI similarity check can be used. The preferred method is to remove irrelevant characters from the text, such as punctuation, HTML tags, and special symbols. Then, use software to segment the text into multiple consecutive characters or words. The text similarity score is obtained by using cosine similarity to calculate the cosine similarity of these consecutive characters or words. This weighted calculation focuses more on the core content of the case (such as factual descriptions) rather than just the claims. Using common NLP methods (such as cosine similarity) ensures stable and reliable calculation and is adaptable to different similarity algorithms (such as Google similarity check and CNKI similarity check).

[0053] S23. Obtain the similarity between the historical similar case file information within the period and the current case file information, as well as the types of evidence involved. Here, we explain why we collect historical case file information within the period, rather than all historical case file information. This is because courts have a certain time limit when hearing cases. Historical case file information from a long period of time is no longer relevant due to changes in legal provisions or court regulations. The period selected here is one month to one year, which can serve as a reference for this case. At the same time, historical similar case file information refers to historical case files with a similarity to the current case file that is greater than or equal to the similarity threshold. The corresponding cases are set as similar cases to avoid citing outdated cases, ensure the applicability of the law, retain only highly similar cases, improve the reference value of subsequent analysis, limit the time range, reduce the amount of data, and improve the calculation speed.

[0054] S24. Based on the file similarity of all historical similar cases within the period and the types of evidence involved, the submission coefficients for the relevant evidence types are obtained. The submission coefficient for the i-th type of evidence is obtained as follows: Where Ni is the number of all historically similar cases that require the i-th type of evidence, pij is the case file similarity of the j-th historically similar case that requires the i-th type of evidence, and N is the total number of all historically similar cases. The necessity of evidence submission is calculated by similarity and frequency. Evidence with a high submission coefficient is more likely to be accepted by the judge. Evidence preparation strategies can be dynamically adjusted according to case similarity.

[0055] S25. Prepare evidence by identifying evidence types with submission coefficients greater than or equal to the set submission coefficient threshold. Only prepare evidence with a high probability of being needed to reduce redundant work. The system recommends key evidence to avoid oversights affecting the case outcome. The submission coefficient threshold is adjustable. It is obtained by collecting evidence submission and adoption data from a large number of past cases in the target court, analyzing the distribution of submission coefficients of evidence ultimately adopted by judges in different types of cases. For example, statistical analysis of 1000 past civil contract dispute cases in a certain court reveals that the submission coefficients of adopted evidence are mostly concentrated between 0.21 and 0.32. Therefore, the submission coefficient threshold can be initially set within this range, such as 0.25, to adapt to the needs of different courts or case types. Preparing evidence during case review allows for targeted collection of case-related evidence, avoiding the waste of resources caused by blindly collecting evidence. Furthermore, combining court simulations and reviews allows for a more accurate assessment of which evidence provides stronger support for the case.

[0056] S3. Based on the case details, the acquisition of evidence, and the opposing legal personnel's historical evidence focus, predict the opposing party's defense strategy.

[0057] In this embodiment, the defense prediction of the opposing party in step S3 includes the following specific steps:

[0058] S31. Obtain the opposing lawyer's past case database, retrieve case files of the same type of cases handled by the opposing lawyer within the search period, use NLP keywords to extract and analyze the opposing lawyer's historical evidence types and defense statements, calculate the frequency of each type of evidence and defense methods such as "questioning the credibility of witnesses" and "emphasizing procedural violations"; extract high-frequency defense terms (such as "procedural violations" and "broken evidence chain") through NLP to accurately characterize the opposing lawyer's professional style (such as "procedural justice preference" or "substantive law aggressive approach"), and avoid being passive in court due to unfamiliarity with the opponent.

[0059] For example: If the opposing party uses "questioning the legality of evidence collection" in 70% of their cases, then we need to review the compliance of the evidence collection process in advance;

[0060] S32. Analyze the similarity of case files based on the case file information of the opposing legal personnel in the same type of case within the same period and the current case file information. Identify case files of the same type of case handled by the opposing legal personnel within the same period with a similarity score greater than or equal to the similarity threshold, and designate them as corresponding similar case files. Obtain the frequency of defense methods for each type of evidence in the opposing similar case files. Filter irrelevant cases using a similarity algorithm (such as text cosine similarity), retaining only case files highly relevant to the current case (such as those with the same cause of action and the same points of contention) to avoid noise interference. For example: if the current case is a "shareholding dispute," only similar cases handled by the opposing party will be retained, excluding their labor dispute case data. Dynamic threshold optimization: The similarity threshold can be adjusted based on the case complexity (70% for simple cases, 50% for complex cases). Complex and simple cases are determined by the court. The similarity threshold can be differentiated based on the specific circumstances. For simple cases with clear facts and minimal disputes, a relatively high similarity threshold can be used. For instance, in some simple private lending cases, the parties have little dispute about the loan facts, with the main point of contention being the repayment amount and timing. In this case, the similarity threshold can be set to 0.7, allowing only evidence with a high submission coefficient and significant impact on the case outcome to be prepared, avoiding wasting time and energy on irrelevant evidence. For cases involving multiple parties, complex legal relationships, and a large amount of evidence, the similarity threshold can be appropriately lowered. Taking large-scale commercial disputes as an example, these cases involve multiple contracts, multiple stakeholders, and complex transaction processes, making it difficult to accurately determine the relevance and probative value of the evidence. In this case, the similarity threshold can be set to 0.5 to prepare as much evidence as possible, balancing recall and precision.

[0061] S33. Calculate the frequency of defense methods for different types of evidence based on similar case files of the opposing party. The probability of the v-th defense method corresponding to the evidence is calculated as follows: , where pv is the number of times the corresponding evidence of the opposing party's similar case file adopts the v-th defense method, xc is the case file similarity between the c-th opposing party's similar case file that adopts the v-th defense method and the current case file, f is the number of opposing party's similar case files, and Xp is the case file similarity between the p-th opposing party's similar case file and the current case file. The formula introduces similarity weights to ensure that the more similar the case file is to the current case, the greater its impact on the prediction results, thus avoiding the bias caused by simple counting;

[0062] 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.

[0063] S4. Analyze the rationality of preparing evidence based on the defense prediction method corresponding to the evidence;

[0064] In this embodiment, the rationality analysis in step S4 includes the following specific contents:

[0065] 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.

[0066] 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.

[0067] S5. Based on the results of the rationality analysis of the prepared evidence, conduct supplementary screening of the prepared evidence;

[0068] 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:

[0069] 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.

[0070] 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.

[0071] Example 2

[0072] 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;

[0073] 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.

[0074] 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.

[0075] The rationality analysis module analyzes the rationality of prepared evidence based on the defense prediction method corresponding to the evidence.

[0076] 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.

[0077] Example 3

[0078] 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.

[0079] Example 4

[0080] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0081] 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.

[0082] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A 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, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0083] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0084] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0085] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0086] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0087] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0088] In the description of this specification, references to terms such as "an embodiment," "example," and "specific example" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0089] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention 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 personnel information and 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 personnel information and the file information in the trial process to review the case, and obtaining prepared evidence; S3, predicting the defense of the opposite party based on the case situation, the prepared evidence, and the historical evidence attention of the legal personnel; The defense prediction of the opposite party comprises the following specific steps: Obtain the past case database of the legal personnel, obtain the 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 types of the legal personnel, and calculate the frequency of the defense mode of each evidence type; Based on the file information of the same case type represented by the legal personnel within a period and the file information of the present case, analyze the file similarity, obtain the file information of the same case type represented by the legal personnel within a period which has a file similarity greater than or equal to a similarity threshold, and set it as a corresponding similar file, and obtain the frequency of the defense mode of each evidence type of the opposite similar file; Based on the opponent similar file, the frequency of the defense mode of the evidence type is calculated; wherein the calculation method of the probability of the defense mode of the corresponding evidence is: Wherein pv is the number of times that the corresponding evidence of the opponent similar file uses the vth defense mode, xc is the file similarity between the corresponding cth opponent similar file using the vth defense mode and the file information of this time, f is the number of the opponent similar files, and Xp is the file similarity between the pth opponent similar file and the file information of this time. 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; S4, analyzing the rationality of the prepared evidence based on the defense prediction mode of the corresponding evidence; The rationality analysis comprises the following specific contents: Compare the defense prediction mode of the corresponding evidence with the prepared evidence acquisition situation of the present case one by one, judge whether the prepared evidence acquisition situation has the problem involved in the corresponding defense prediction mode, if the prepared evidence acquisition situation has the problem involved in the corresponding defense prediction mode, the prepared evidence acquisition situation is unreasonable, if the prepared evidence acquisition situation does not have the problem involved in the corresponding defense prediction mode, the prepared evidence acquisition situation is reasonable; S5, supplementing and screening the prepared evidence based on the rationality analysis result of the prepared evidence; The specific content comprises: If the judgment result of the prepared evidence acquisition situation is obtained, 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 prepared evidence acquisition situation is obtained, the evidence is outputted.

2. The method of claim 1, wherein, The review of the case combined with the judge personnel information and the file information in the trial process, and the obtaining of the prepared evidence comprises the following specific steps: Obtain the personnel information and the file information in the trial process, obtain the historical 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. The historical file information and the current file information are analyzed based on file similarity, 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 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 file similarity of the obtained historical similar file information in the period and the current file information and the evidence type involved are obtained, the historical similar file information is the historical file with a file similarity greater than or equal to a similarity threshold value with the current file, and the corresponding case is set as a similar case; The submission coefficient of the evidence type is obtained based on the file similarity and the evidence type involved of all the historical similar cases in the period; The evidence type with a submission coefficient greater than or equal to a set submission coefficient threshold value is obtained to prepare evidence.

3. The method of claim 1, wherein, The virtual courtroom construction method is to obtain the duty of the role through a judicial system interface or manual input, obtain historical evidence attention information of the attorney, search a past case database of the attorney, and extract the habitual strategy.

4. A simulated courtroom defense scenario prediction system for implementing a simulated courtroom defense scenario prediction method according to any one of claims 1-3, characterized by The system comprises: A data acquisition module acquires courtroom personnel information and file information, constructs a corresponding virtual courtroom on virtual reality software, and simulates a trial process on the virtual courtroom; A prepared evidence acquisition module combines the judge personnel information and the file information in the trial process to review the case and acquire 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 attorney; A rationality analysis module analyzes the rationality of the prepared evidence based on the defense prediction method of the corresponding evidence; A supplementary screening module supplements the prepared evidence based on the rationality analysis result of the prepared evidence.

5. 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 1-3 by calling the computer program stored in the memory.

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