Situation simulation method and system based on vehicle-mounted large language model

Abstract

The invention discloses a situation simulation method and system based on a vehicle-mounted large language model, and belongs to the technical field of vehicle-mounted artificial intelligence, and the method comprises the steps: carrying out simulation situation construction and role setting based on a simulation situation type and a role selected by a user, and generating a virtual dialogue scene; voice features and emotional states of the user are analyzed, and simulation situation construction and role setting are dynamically adjusted; according to the voice features and the emotional state, selecting a proper voice synthesis role from a vehicle-mounted voice database to represent the voice of the role of the opposite side; based on the language and the expression content of the user, using a large language model to generate words and logics of the role of the opposite side; and real-time situational simulation feedback of the opposite role is provided according to the synthesized role and the generated words and logics of the opposite role in combination with real-time analysis of the voice feature and the emotional state of the user. The method helps the user to improve the expression ability and emotion management ability in a specific scene. A user is allowed to select an interested simulation situation from a preset situation library.

Description

Technical Field

[0001] The present invention belongs to the technical field of in-vehicle artificial intelligence, and particularly relates to a situation simulation method and system based on an in-vehicle large language model. Background Art

[0002] With the development of society and the intensification of workplace competition, people are facing increasing pressure in work and life. Especially in complex situations such as disputes, interviews, and debates, users often fall into an unfavorable position due to insufficient expression or improper emotional control. Although existing in-vehicle systems have certain voice interaction capabilities, these systems mainly focus on basic functions such as entertainment, navigation, and voice assistants, and no specialized training tools have been developed to help users cope with complex interpersonal interactions.

[0003] At the technical level, existing sentiment analysis technologies are mainly applied in fields such as social media analysis and customer service systems, and are usually used to analyze the emotional state in text or speech. However, most of these applications rely on non-real-time batch data processing and are difficult to meet the requirements of real-time and highly interactive scenarios. Although natural language processing (NLP) technology has made remarkable progress in fields such as text generation and machine translation, in practical applications, especially in the field of simulating interpersonal interactions in combination with sentiment analysis, it still faces many challenges. The application of existing NLP models in the in-vehicle environment mainly focuses on navigation instructions, entertainment search, and basic dialogue assistant functions, lacking the ability to deeply understand and cope with complex situations.

[0004] In addition, although speech recognition and synthesis technologies are relatively mature, in existing in-vehicle systems, they are mostly used to implement the recognition of voice commands and simple dialogue interactions, and have not combined sentiment analysis and NLP technology to create a comprehensive and multi-scenario situation simulation system. Therefore, the current in-vehicle technology lacks a solution that can integrate these key technologies to help users conduct effective expression training and emotion management in the private environment of the vehicle.

[0005] In modern society, people often need to communicate effectively in complex social situations such as disputes, interviews, and debates. However, the lack of effective training and preparation may lead to communication failure and even psychological pressure.

[0006] Based on this background, there is an urgent need to develop a new technical solution that can organically combine technologies such as natural language processing, sentiment analysis, speech recognition, and synthesis to form an integrated situation simulation system dedicated to helping users improve their expression ability and emotion management ability in complex situations such as disputes, interviews, and debates.

[0007] Existing in-vehicle systems mainly focus on entertainment, navigation, and voice assistant functions, lacking a system that can provide complex scenario simulations in a private in-vehicle environment, making it difficult for users to effectively practice coping strategies and emotional management skills. Summary of the Invention

[0008] The present invention aims to solve the adverse situations that users encounter in specific scenarios such as arguments, interviews, and debates due to insufficient expression ability or improper emotional control. The present invention provides a scenario simulation method and system based on an in-vehicle large language model, which helps users improve their expression ability and emotional management ability in specific scenarios. Users are allowed to select simulation scenarios of interest from a preset scenario library.

[0009] To solve the above technical problems, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a scenario simulation method based on an in-vehicle large language model, including: Obtain the user's start simulation scenario instruction, and display the types of simulation scenarios generated based on the scenario models preset in the in-vehicle system; Based on the selected simulation scenario type and role by the user, construct the simulation scenario and set the role, and generate a virtual dialogue scene; In the virtual dialogue scene, based on the user's language and expression content, analyze the user's voice characteristics and emotional state, and dynamically adjust the construction of the simulation scenario and the role setting; according to the voice characteristics and emotional state, select an appropriate voice synthesis role from the in-vehicle voice database to represent the voice of the other party's role; Based on the user's language and expression content, use the large language model to generate the words and logic of the other party's role; According to the synthesized role and the generated words and logic of the other party's role, combined with real-time analysis of the user's voice characteristics and emotional state, provide instant scenario simulation feedback of the other party's role.

[0010] As a further improvement of the present invention, the obtaining the user's start simulation scenario instruction and displaying the types of simulation scenarios generated based on the scenario models preset in the in-vehicle system includes: The user selects the simulation scenario type or describes a specific scenario by voice or touch; Analyze the user's selected simulation scenario type or described specific scenario through NLP, and generate an appropriate simulation scenario based on the preset scenario model, and display it to the user for selection.

[0011] As a further improvement of the present invention, the constructing the simulation scenario and setting the role based on the selected simulation scenario type and role by the user, and generating a virtual dialogue scene includes: Obtain the type of simulated scenario and the role selected by the user, construct the simulated scenario and set the role. The simulated scenario includes the scenario type and difficulty; the role includes the personality traits, behavior patterns, and speech styles of the other party's role.

[0012] As a further improvement of the present invention, in the virtual dialogue scenario, based on the user's language and expression content, analyze the user's voice characteristics and emotional state, and dynamically adjust the construction of the simulated scenario and the role setting, including: Obtain the user's language and expression content in real time, analyze the user's voice and expression content through NLP, identify the user's performance characteristics, and then obtain the user's emotional state; Based on the analysis of the emotional state, dynamically adjust the construction of the simulated scenario and the role setting; According to the voice characteristics and emotional state, select an appropriate voice synthesis role from the in-vehicle voice database to represent the voice of the other party's role, including: Select an appropriate tone, emotional state, and the corresponding voice synthesis role from the in-vehicle voice database to represent the voice of the other party's role; adjust the parameters of the voice synthesis according to the speech and emotional state of the other party's role to make the voice match the dialogue content.

[0013] As a further improvement of the present invention, generate the speech and logic of the other party's role based on the user's language and expression content using a large language model, including: Use a pre-trained large language model to generate the speech and logic of the other party's role, which can generate coherent and reasonable dialogue content according to the context information, and fine-tune the generated dialogue according to the personality traits and speech styles of the role set by the user to achieve the simulated effect that meets the user's expectations.

[0014] As a further improvement of the present invention, according to the synthesized role and the generated speech and logic of the other party's role, combined with the real-time analysis of the user's voice characteristics and emotional state, provide instant situational simulation feedback of the other party's role, including: Real-time analyze the user's expression content and emotional state, including logical clarity, tone intensity, and emotional fluctuations; Using natural language processing technology, according to the synthesized role and the generated speech and logic of the other party's role, combined with the real-time analysis of the user's voice characteristics and emotional state, use voice synthesis technology to generate virtual role feedback with natural and fluent speech; Through the interaction process, provide instant situational simulation feedback of the other party's role to achieve real-time interaction between the user and the virtual role; And based on the feedback generation algorithm, generate targeted and practical feedback suggestions according to the user's expression content and emotional state, and provide instant feedback and suggestions, including logical suggestions and tone adjustment; record the user's practice process, including dialogue content, emotional state, and feedback suggestions.

[0015] In a second aspect, a scenario simulation system based on an in-vehicle large language model according to the present invention includes: An acquisition module, configured to acquire a start simulation scenario instruction of a user and display simulation scenario types generated based on a scenario model preset in the vehicle system; A scene generation module, configured to construct a simulation scenario and set roles based on the selected simulation scenario type and role of the user, and generate a virtual dialogue scene; A voice synthesis module, configured to, in the virtual dialogue scene, analyze the voice characteristics and emotional state of the user based on the language and expression content of the user, and dynamically adjust the construction of the simulation scenario and role setting; according to the voice characteristics and emotional state, select an appropriate voice synthesis role from the in-vehicle voice database to represent the voice of the other party's role; A speech and logic generation module, configured to generate the speech and logic of the other party's role based on the language and expression content of the user by using a large language model; A scenario simulation module, configured to provide instant scenario simulation feedback of the other party's role according to the synthesized role and the generated speech and logic of the other party's role, in combination with real-time analysis of the voice characteristics and emotional state of the user.

[0016] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the scenario simulation method based on the in-vehicle large language model is implemented.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the scenario simulation method based on the in-vehicle large language model is implemented.

[0018] In a fifth aspect, the present invention provides a computer program product, wherein the computer program product includes computer instructions, and is characterized in that the computer instructions instruct a computer to execute the scenario simulation method based on the in-vehicle large language model.

[0019] The beneficial effects of the present invention compared with the prior art are as follows: The scenario simulation method of the present invention can provide a private and safe training environment in the vehicle, helping users improve their expression ability and emotional control level when dealing with complex scenarios such as disputes, interviews, and debates. Through repeated practice and system feedback, users can effectively improve their performance and reduce the psychological pressure brought by corresponding scenarios. In addition, this system also has functions of intelligent review and personalized optimization suggestions, enabling users to continuously improve their coping strategies and psychological qualities, and ultimately obtain better performance in actual scenarios. Description of the Drawings

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following introduces the accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings in the following introduction are only for conveniently and clearly presenting some embodiments of the technical solutions in the present invention. For those skilled in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a flowchart of a situation simulation method based on an in-vehicle large language model in an embodiment of the present application; Figure 2 It is a schematic diagram of the specific process of situation simulation based on an in-vehicle large language model in an embodiment of the present application; Figure 3 It is a situation simulation system based on an in-vehicle large language model provided by the present invention; Figure 4 It is a schematic diagram of an electronic device provided by the present invention. Specific Embodiments

[0022] The following details the embodiments of the present invention. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as a limitation of the present invention. For the step numbers in the following embodiments, they are only set for the convenience of elaboration and explanation, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0023] In the description of the present invention, unless otherwise clearly defined, words such as "set", "installed", "connected", etc. should be understood in a broad sense. Those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0024] This system is used to simulate and analyze emotional processing and expression training in complex interpersonal scenarios such as disputes, interviews, and debates. The system combines technologies such as natural language processing (NLP), sentiment analysis, and speech recognition, aiming to help users improve their expression ability and emotion management ability in actual interpersonal conflicts, interviews, debates, etc. through in-vehicle situation simulation.

[0025] The present invention provides a situation simulation method based on an in-vehicle large language model, as Figure 1 shown, including: S101, obtaining a start simulation situation instruction from the user, and presenting the types of simulation situations generated based on the situation models preset in the in-vehicle system; S102, construct a simulation scenario and set the roles based on the type of simulation scenario and the role selected by the user, and generate a virtual dialogue scenario; S103, in the virtual dialogue scenario, analyze the user's speech features and emotional state based on the user's language and expression content, and dynamically adjust the construction of the simulation scenario and the role setting; according to the speech features and emotional state, select an appropriate speech synthesis role from the in-vehicle speech database to represent the voice of the other role; S104, generate the words and logic of the other role based on the user's language and expression content using a large language model; S105, according to the synthesized role and the generated words and logic of the other role, combined with the real-time analysis of the user's speech features and emotional state, provide an instant simulation feedback of the other role's situation.

[0026] The situation simulation method based on the in-vehicle large language model of the present invention is based on a series of steps, aiming to help users improve their expression ability and emotion management ability in specific scenarios by simulating real or fictional scenarios. Users are allowed to select simulation scenarios of interest from a preset scenario library.

[0027] As an example, according to the user's selection, the system further constructs a specific virtual dialogue scenario and sets the corresponding roles. During the virtual dialogue, the system analyzes the user's speech features and emotional state in real time to identify their emotional reactions. According to the identification results, the system selects a speech synthesis role from the speech database that matches the current situation and emotion to represent the voice of the other role. Using the large language model, the system generates logical words and dialogue content based on the user's identified emotional reactions and the setting of the other role. This helps to make the simulation scenario more realistic and challenges the user's coping ability. During the dialogue, the system continuously analyzes the user's expressions and emotions and adjusts the simulation scenario and the feedback of the other role as needed. This instant feedback mechanism helps users better understand and adapt to the communication needs in different scenarios.

[0028] By simulating different dialogue scenarios and role settings, users can exercise their expression ability in different scenarios, including aspects such as language expression, body language, and emotion management. By continuously simulating and coping with various scenarios, users can gradually enhance their adaptability to different scenarios and improve their decision-making ability in emergency or complex situations. Users are allowed to select simulation scenarios and role settings according to their own interests and needs, thus providing a personalized training experience. The real-time feedback mechanism helps users promptly understand their performance and problems and make adjustments and improvements as needed.

[0029] The scenario simulation system of the present invention can provide a private and secure training environment in the vehicle to help users improve their expression ability and emotional control level. Through repeated practice and system feedback, users can effectively improve their performance and reduce the psychological pressure brought by corresponding scenarios. In addition, this system also has functions of intelligent review and personalized optimization suggestions, enabling users to continuously improve their coping strategies and psychological qualities, and ultimately achieve better performance in actual situations.

[0030] The following will further elaborate on the present invention in conjunction with the accompanying drawings and specific embodiments.

[0031] The present invention provides a scenario simulation method based on an in-vehicle large language model, which can mainly be used for the expression training of users in complex social scenarios, such as disputes, interviews, debates, etc.; including the following means: Natural Language Processing (NLP): The system uses advanced NLP technology to generate dialogue content that matches the scenario selected by the user. The NLP model can not only understand the user's speech input, but also automatically generate realistic dialogues according to the scenario to simulate complex scenarios such as disputes, interviews, and debates in reality.

[0032] Speech Recognition and Synthesis: The system adopts high-precision speech recognition technology to capture the user's speech input in real time, and generates natural and fluent dialogue feedback through speech synthesis technology. This process ensures that the interaction between the user and the system has high real-time performance and naturalness.

[0033] Emotion Analysis: The system combines emotion analysis technology to real-time evaluate the emotional state expressed by the user during the simulation, and provides scenario feedback according to the analysis results. This function helps users better understand their emotional reactions during practice and learn how to control emotions in complex situations.

[0034] Scenario Review: After the simulation ends, the system will generate a detailed review report, including the analysis of the user's expression ability, emotional control, dialogue strategy, etc. The system will also provide personalized optimization suggestions according to the user's performance to help the user perform better in future similar scenarios.

[0035] Multiple Scenario Selection: Users can select a variety of different simulation scenarios in the system, such as disputes, interviews, debates, etc. The system will generate corresponding dialogues and scenario settings according to the selected scenario to provide targeted training for users.

[0036] Personalized Settings: Users can customize the difficulty and complexity of the simulation scenario according to their personal needs, and the system will automatically adjust the intensity of dialogue generation and emotional feedback to meet the training needs of different users.

[0037] The scenario simulation system of the present invention can provide a private and secure training environment inside the vehicle, helping users improve their expression ability and emotional control level when dealing with complex scenarios such as disputes, interviews, debates, etc. Through repeated practice and system feedback, users can effectively improve their performance and reduce the psychological pressure brought by corresponding scenarios. In addition, this system also has functions of intelligent review and personalized optimization suggestions, enabling users to continuously improve their coping strategies and psychological qualities, and ultimately achieve better performance in actual scenarios.

[0038] In a specific embodiment of the present invention, as Figure 2 shown, the scenario simulation and emotion processing are realized through the following steps: Step 1: The user starts the system and selects a scenario Obtain the user's instruction to start the simulation scenario, and display the types of simulation scenarios generated based on the scenario models preset in the vehicle system; specifically, the user starts the system through voice or touch interface, selects the type of simulation scenario or describes a specific scenario. The system analyzes the user's input through NLP and generates an appropriate simulation scene based on the preset scenario model.

[0039] Starting method: The user can start the system through the voice assistant of the vehicle system (such as "XX car, start the scenario simulation system") or the icon on the touch screen.

[0040] Scenario selection: The system provides a user-friendly interface, listing the preset scenario types (such as driving conflicts, emergency rescues, daily communications, etc., or disputes, interviews, debates, etc.) or allowing the user to describe a specific scenario through natural language. The built-in NLP module in the system will parse the user's input, match the closest preset scenario or generate a custom scenario.

[0041] Scenario generation: Once the scenario is selected, the system will generate an initial simulation scene according to the preset scenario model (including scene description, role setting, dialogue template, etc.).

[0042] Step 2: Scenario construction and role setting Based on the specific scenario type and role selected by the user, conduct simulation scenario construction and role setting, and generate a virtual dialogue scenario; specifically, the user retells the actual scenario, and the system generates a virtual dialogue scenario according to the content input by the user, including the words and reactions of the other party's role. The user can customize the difficulty of the scenario and the personality characteristics of the other party's role to be closer to the actual situation.

[0043] User retelling: The user can further refine the scenario description through voice or text input, including specific location, time, weather conditions, traffic conditions, etc.

[0044] Role setting: The system generates a virtual conversation scenario based on the user's input and allows the user to customize the personality traits (such as friendly, hostile, neutral), behavior patterns (such as active, passive, provocative), and speech styles (such as formal, casual, humorous) of the other party.

[0045] Difficulty adjustment: The user can also adjust the difficulty of the scenario, such as increasing the complexity of the conversation, raising the degree of emotional conflict, or introducing additional interference factors (such as noise, distraction tasks).

[0046] Step 3: The sentiment analysis module analyzes the user's speech features and emotional state In the virtual conversation scenario, based on the user's language and expression content, analyze the user's speech features and emotional state, and dynamically adjust the construction of the simulation scenario and role setting; specifically, the system analyzes the user's speech and expression content to identify the user's performance characteristics, such as logical clarity, tone intensity, emotional fluctuations, etc.

[0047] The system judges the user's emotional state, such as anger, tension, anxiety, etc. by analyzing the user's speech features (such as speech rate, pitch, emotional intensity, etc.).

[0048] Based on the emotional state analysis, the system can dynamically adjust the reaction of the other party role in the simulation to make it more close to the real situation, or help the user improve the emotional expression and control ability through practice.

[0049] Speech feature analysis: The system captures the user's speech signal through the microphone and uses speech processing technology to extract features (such as speech rate, pitch, volume, emotional intensity).

[0050] Emotion recognition: Based on the extracted features, the system uses sentiment analysis algorithms (such as machine learning models or deep learning networks) to judge the user's emotional state (such as anger, tension, anxiety, calm, etc.).

[0051] Dynamic adjustment: According to the user's emotional state, the system can dynamically adjust the reaction of the other party role in the simulation, such as increasing or decreasing provocative words, adjusting the tone and attitude, to better meet the user's practice needs.

[0052] As an example, in specific implementation, the user's language and expression content are obtained and analyzed in real time through speech recognition technology or NLP technology.

[0053] Speech recognition technology is the process of converting speech signals into text, which is a technology that the machine transforms speech signals into text through recognition and then into instructions through understanding.

[0054] NLP technology performs semantic analysis on the text obtained through speech recognition technology or the text directly input by the user to understand the user's intentions and expressed emotions. Key information is extracted from the text for subsequent user emotion analysis.

[0055] As an example, identifying user performance characteristics and emotional states includes emotion recognition algorithms and multimodal emotion analysis methods; emotion recognition algorithms use machine learning or deep learning algorithms, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), etc., to perform sentiment analysis on the user's voice and text content and identify the user's emotional state, such as happy, sad, angry, etc. Multimodal emotion analysis combines non-verbal information such as the user's facial expressions, postures, and actions, as well as verbal information such as speech and text, to perform multimodal emotion analysis and improve the accuracy and robustness of emotion recognition.

[0056] During the scenario construction process, the construction of the simulated scenario is dynamically adjusted according to the user's emotional state. For example, when the user is in a happy state, some positive and interesting scenario elements can be added; when the user is in a sad state, some warm and comforting scenarios can be constructed. The character setting is dynamically adjusted according to the user's emotional state and behavioral characteristics. For example, when the user shows a strong desire to explore, a character full of adventurous spirit can be set; when the user shows negative emotions, a character who can give comfort and support can be set.

[0057] During the process of the simulated scenario, the user's emotional state and behavioral characteristics are continuously monitored, and real-time feedback and adjustment are made as needed. For example, when the user shows dissatisfaction or confusion, the scenario or character setting can be adjusted in a timely manner to better meet the user's needs and expectations.

[0058] Step 4: The large speech model generates the words and logic of the other party's character Based on the user's language and expression content, the large language model is used to generate the words and logic of the other party's character, specifically including: the system generates the words and logic of the other party's character through the large language model (such as GPT).

[0059] Large language model: The system uses pre-trained large language models (such as the GPT series) to generate the words and logic of the other party's character. These models can generate coherent and reasonable dialogue content based on context information.

[0060] Personalized customization: The system can also fine-tune the generated dialogue according to the character traits and speech styles set by the user to make it more in line with the simulated effect expected by the user.

[0061] Step 5: The system's voice database synthesizes a speaker representing the other party's voice Select an appropriate voice synthesis character from the in-vehicle voice database according to the voice characteristics and emotional state to represent the voice of the other character; specifically, the system will select an appropriate tone, emotional state, and the corresponding voice synthesis character from the voice database to represent the voice of the other character.

[0062] Voice database: The system has a rich voice database containing voice samples of different genders, ages, accents, and speech rates.

[0063] Voice synthesis: According to the characteristics and emotional state of the other character set by the user, the system will select the most appropriate voice sample from the voice database and use voice synthesis technology to generate a speaker representing the voice of the other party.

[0064] Emotion matching: The system will also adjust the parameters of voice synthesis (such as pitch, speech rate, volume, etc.) according to the words and emotional state of the other character to ensure that the voice matches the content of the conversation.

[0065] Step 6: Scenario simulation and real-time feedback Based on the synthesized character and the words and logic of the generated other character, combined with real-time analysis of the user's voice characteristics and emotional state, provide real-time feedback on the scenario simulation of the other character. Specifically, the system starts to simulate an argument scenario, and the user interacts with the virtual character through voice or text input. The system real-time analyzes the user's expression content and emotional state, combined with real-time analysis of the user's voice characteristics and emotional state, to provide real-time feedback on the scenario simulation of the other character, such as logical suggestions, tone adjustment, etc.

[0066] Scenario simulation: The system starts to simulate an argument or other types of conversation scenarios, and the user interacts with the virtual character through voice or text input.

[0067] Real-time analysis: The system real-time analyzes the user's expression content and emotional state, including logical clarity, tone intensity, emotional fluctuations, etc.

[0068] Immediate feedback: Based on the analysis results, the system provides immediate feedback and suggestions, such as pointing out logical loopholes, suggesting tone or emotional expression adjustments, etc. These feedbacks can be presented to the user through voice prompts, screen displays, or vibrations.

[0069] Practice record: The system will also record the user's practice process, including conversation content, emotional state, feedback suggestions, etc., for the user to review and evaluate their progress later.

[0070] The above solution, when specifically implemented, includes the following content: Dispute scenario construction; The system constructs specific dispute scenarios based on the scenarios selected by the user or custom descriptions. The scenarios should include key elements such as location, time, relationship between people, reasons for disputes, etc. The dispute scenarios should have a certain degree of complexity and diversity to simulate various situations in real life.

[0071] Virtual character setting; According to the dispute scenario, the system sets the personality traits, speech styles, and behavior patterns of virtual characters. Virtual characters should have distinct personalities and characteristics so that users can feel a sense of realism when interacting with them.

[0072] Interaction method; Users can interact with virtual characters through voice or text input. The system should support multiple input methods to meet the needs of different users. The system should respond to the user's input in real time and generate corresponding dialogue content and feedback.

[0073] Expression content analysis; The system uses natural language processing technology (NLP) to analyze the user's expression content, identify key information and logical structures in it. The system should be able to judge whether the user's expression is clear and accurate, and point out logical loopholes or unreasonable points in it.

[0074] Emotional state analysis; The system judges the user's emotional state by analyzing the user's voice characteristics (such as speech rate, pitch, volume, etc.) and text content. The emotional state analysis should be accurate and timely so that the system can adjust the feedback and speech style of virtual characters in a timely manner.

[0075] Logical suggestions; When there are logical loopholes or unreasonable points in the user's expression, the system should provide specific logical suggestions to help the user improve the expression. The logical suggestions should be concise and clear, and easy for users to understand and accept.

[0076] Tone adjustment; The system should adjust the tone and speech style of the feedback according to the user's emotional state and the setting of virtual characters. When the user shows negative emotions such as anger and nervousness, the system should appropriately soothe and guide to help the user relieve emotions.

[0077] Role-playing guidance; The system can provide role-playing guidance for users to help them better understand and imitate the personality traits and speech styles of virtual characters. The role-playing guidance can include suggestions on language skills, emotional expression, body movements, etc.

[0078] Real-time feedback presentation; The system should present real-time feedback in multiple ways, such as voice prompts, screen displays, vibrations, etc. The presentation method of the feedback should be intuitive and clear so that users can obtain and understand the feedback content in a timely manner.

[0079] Utilize advanced natural language processing technologies, such as semantic understanding, sentiment analysis, etc., to achieve accurate analysis of the content and emotional state expressed by users. Speech synthesis technology, using high-quality speech synthesis technology, generates virtual character feedback with natural and fluent speech.

[0080] Combine multiple interaction methods such as speech recognition and text input to achieve real-time interaction between users and virtual characters. Feedback generation algorithm, adopt a developed intelligent feedback generation algorithm, and generate targeted and practical feedback suggestions according to the content and emotional state expressed by users.

[0081] Step 7: Review and optimization After the simulation ends, the system generates a review report, which details the user's performance and provides optimization suggestions. Users can enter the simulation again according to the report, try different ways of expression until they achieve satisfactory results.

[0082] Step 8: Data privacy and management The system ensures that all user data is processed locally. Users can choose to delete or modify historical scenario data, and the system can also dynamically adjust the settings of future simulation scenarios according to the user's choices.

[0083] Through the above detailed description, the specific implementation of the present invention can achieve scenario simulation in a vehicle environment, helping users effectively manage and improve their expression ability and emotional control level in interpersonal disputes.

[0084] The scenario simulation method based on the in-vehicle large language model of the present invention can simulate complex social scenarios, and help users improve their expression ability and emotional control through real-time feedback and review reports. Utilize advanced NLP technologies to generate dialogue content that matches the scenarios selected by users, understand the user's speech input, and automatically generate realistic dialogues. Adopt high-precision speech recognition technology to capture the user's speech input, and generate natural and fluent dialogue feedback through speech synthesis technology. Combine sentiment analysis technology to evaluate the user's emotional state in real time during the simulation and provide scenario feedback.

[0085] After the simulation ends, the system generates a detailed review report, including analysis of the user's expression ability, emotional control, dialogue strategies, etc., and provides personalized optimization suggestions. Users can select a variety of different simulation scenarios in the system, and the system will generate corresponding dialogues and scenario settings according to the selected scenarios. Users can customize the difficulty and complexity of the simulation scenarios according to their personal needs, and the system will automatically adjust the intensity of dialogue generation and emotional feedback.

[0086] The second object of the present invention provides a scenario simulation system based on an in-vehicle large language model, Figure 3 For the flowchart of the system, including: An acquisition module 100 for obtaining the user's instruction to start a simulated scenario and presenting the types of simulated scenarios generated based on the scenario models preset in the vehicle system; A scenario generation module 200 for constructing a simulated scenario and setting roles based on the selected type of simulated scenario and roles of the user, and generating a virtual dialogue scenario; A voice synthesis module 300 for, in the virtual dialogue scenario, analyzing the user's voice characteristics and emotional state based on the user's language and expression content, and dynamically adjusting the construction of the simulated scenario and role setting; selecting an appropriate voice synthesis role from the vehicle-mounted voice database to represent the voice of the other role according to the voice characteristics and emotional state; A speech and logic generation module 400 for generating the speech and logic of the other role based on the user's language and expression content using a large language model; A scenario simulation module 500 for providing instant scenario simulation feedback of the other role according to the synthesized role and the generated speech and logic of the other role, combined with real-time analysis of the user's voice characteristics and emotional state.

[0087] A scenario simulation system based on a vehicle-mounted large language model, including a natural language processing module, a speech recognition and synthesis module, an emotion analysis module, a scenario review module, a multi-scenario selection module, and a personalized setting module.

[0088] The natural language processing module can understand the user's voice input and automatically generate realistic conversations. The speech recognition and synthesis module can capture the user's voice input in real time and generate natural and fluent conversation feedback through speech synthesis technology. The emotion analysis module can evaluate the user's emotional state during the simulation in real time and provide scenario feedback. The scenario review module generates a detailed review report after the simulation and provides personalized optimization suggestions. The multi-scenario selection module allows the user to select multiple different simulated scenarios. The personalized setting module allows the user to customize the difficulty and complexity of the simulated scenario.

[0089] The system further includes a data privacy and management module to ensure that all user data is processed locally and allows the user to manage historical data and adjust future simulation settings.

[0090] The system simulates complex social scenarios through key technologies such as natural language processing, speech recognition and synthesis, emotion analysis, scenario review, multi-scenario selection, and personalized setting, to help users improve their expression ability and emotion control. The system can provide a private and safe training environment in the vehicle, and through repeated practice and system feedback, effectively improve the user's performance in complex scenarios, reduce psychological pressure, and enhance coping strategies and psychological quality.

[0091] The system is based on the above-mentioned scenario simulation method based on a vehicle-mounted large language model.

[0092] As Figure 4 shown, the third object of the embodiment of the present invention is to provide an electronic device, including a memory 701, a processor 702, and a computer program stored in the memory 701 and operable on the processor. When the processor executes the computer program, the method for scenario simulation based on an in-vehicle large language model is implemented. It also includes a communication interface 703 and a bus 704.

[0093] The fourth object of the embodiment of the present invention is to provide a computer-readable storage medium storing a computer program, which when executed by a processor, implements the method for scenario simulation based on an in-vehicle large language model.

[0094] The fifth object of the embodiment of the present invention is to provide a computer program product, including computer instructions, characterized in that the computer instructions direct a computer to execute the method for scenario simulation based on an in-vehicle large language model.

[0095] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0097] The present invention can be in the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, readable storage media, optical memories, etc.) containing computer-usable program codes.

[0098] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0099] Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still can modify the specific implementation manners of the present invention or make equivalent replacements, and any modification or equivalent replacement without departing from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A situation simulation method based on an in-vehicle large language model, characterized in that, including: Obtain the user's instruction to start the simulation scenario, and display the types of simulation scenarios generated based on the scenario models preset in the vehicle system; Construct the simulation scenario and set the roles based on the selected simulation scenario type and role by the user, and generate a virtual dialogue scenario; In the virtual dialogue scenario, analyze the user's speech features and emotional state based on the user's language and expression content, and dynamically adjust the construction of the simulation scenario and the role setting; according to the speech features and emotional state, select an appropriate voice synthesis role from the vehicle-mounted voice database to represent the voice of the other party's role; Generate the words and logic of the other party's role based on the user's language and expression content using a large language model; Provide instant simulation feedback of the other party's role according to the synthesized role and the generated words and logic of the other party's role, combined with real-time analysis of the user's speech features and emotional state.

2. The situational simulation method based on an in-vehicle large language model according to claim 1, wherein The obtaining of the user's instruction to start the simulation scenario and the display of the types of simulation scenarios generated based on the scenario models preset in the vehicle system include: The user selects the simulation scenario type or describes a specific scenario through voice or touch; Analyze the selected simulation scenario type or the described specific scenario by the user through NLP, and generate an appropriate simulation scenario based on the preset scenario model, and display it to the user for selection.

3. A scenario simulation method based on an in-vehicle large language model according to claim 1, characterized in that The constructing of the simulation scenario and the setting of the roles based on the selected simulation scenario type and role by the user, and the generation of a virtual dialogue scenario include: Obtain the selected simulation scenario type and role by the user, construct the simulation scenario and set the roles. The simulation scenario includes the scenario type and difficulty; the role includes the personality characteristics, behavior patterns and speech styles of the other party's role.

4. A situation simulation method based on an in-vehicle large language model according to claim 1, characterized in that, In the virtual dialogue scenario, the analyzing of the user's speech features and emotional state based on the user's language and expression content, and the dynamic adjustment of the construction of the simulation scenario and the role setting include: Obtain the user's language and expression content in real time, analyze the user's speech and expression content through NLP, identify the user's performance characteristics, and then obtain the user's emotional state; Based on the analysis of the emotional state, dynamically adjust the construction of the simulation scenario and the role setting; The selecting of an appropriate voice synthesis role from the vehicle-mounted voice database to represent the voice of the other party's role according to the speech features and emotional state includes: Select an appropriate tone, emotional state, and the corresponding voice synthesis role from the vehicle-mounted voice database to represent the voice of the other party's role; adjust the parameters of the voice synthesis according to the words and emotional state of the other party's role to make the voice match the dialogue content.

5. A scenario simulation method based on an in-vehicle large language model according to claim 1, characterized in that, The generating of the words and logic of the other party's role based on the user's language and expression content using a large language model includes: Use a pre-trained large language model to generate the words and logic of the other party's role, which can generate coherent and reasonable dialogue content according to the context information, and fine-tune the generated dialogue according to the personality characteristics and speech styles of the roles set by the user to achieve the simulation effect that meets the user's expectations.

6. A scenario simulation method based on an in-vehicle large language model according to claim 1, characterized in that, The providing of instant simulation feedback of the other party's role according to the synthesized role and the generated words and logic of the other party's role, combined with real-time analysis of the user's speech features and emotional state includes: Real-time analyze the user's expression content and emotional state, including logical clarity, tone intensity, and emotional fluctuation; Using natural language processing technology, according to the synthesized character and the words and logic of the generated opponent character, combined with real-time analysis of the user's speech features and emotional state, use speech synthesis technology to generate virtual character feedback with natural and fluent speech; Through the interaction process, provide instant situational simulation feedback of the opponent character to achieve real-time interaction between the user and the virtual character; And based on the feedback generation algorithm, generate targeted and practical feedback suggestions according to the user's expression content and emotional state, and provide instant feedback and suggestions, including logical suggestions and tone adjustment; record the user's practice process, including conversation content, emotional state, and feedback suggestions.

7. A scenario simulation system based on an in-vehicle large language model, characterized in that, It includes: An acquisition module for acquiring the user's startup simulation scenario instruction and displaying the simulation scenario types generated based on the scenario models preset in the vehicle-mounted system; A scenario generation module for constructing a simulation scenario and setting roles based on the simulation scenario type and role selected by the user, and generating a virtual dialogue scenario; A voice synthesis module for analyzing the user's speech features and emotional state based on the user's language and expression content in the virtual dialogue scenario, and dynamically adjusting the construction of the simulation scenario and role setting; selecting an appropriate voice synthesis character from the vehicle-mounted voice database to represent the voice of the opponent character according to the speech features and emotional state; A words and logic generation module for generating the words and logic of the opponent character based on the user's language and expression content using a large language model; A situational simulation module for providing instant situational simulation feedback of the opponent character according to the synthesized character and the generated words and logic of the opponent character, combined with real-time analysis of the user's speech features and emotional state.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the situational simulation method based on the vehicle-mounted large language model according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the situational simulation method based on the vehicle-mounted large language model according to any one of claims 1-6.

10. A computer program product, the computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computer to execute the situational simulation method based on the vehicle-mounted large language model according to any one of claims 1-6.

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