Family education AI simulation system and device

Abstract

The invention is suitable for the field of psychological health services, and provides a family education AI simulation system and device. According to the system, voice and image information of a simulated user is acquired through the user information acquisition module, so that the real-time emotional state of the user is determined. And then a simulation scene of the user is constructed through the scene parameters, and voice and real-time emotion of the user are analyzed in the simulation scene, so that real-time feedback communication with the user in the simulation scene is realized. And finally, generating a feedback report recording simulation problems, improvement suggestions and other information through the above information, such as voice and emotion of the user and feedback information during real-time communication with the user. The user can diagnose problems according to the feedback report and determine the improvement direction, so that parents are helped to improve the education ability, and healthy growth of children is promoted.

Description

Technical Field

[0001] This application relates to the field of mental health services, and in particular to a family education AI simulation system and device. Background Art

[0002] In traditional family education, parents often lack professional educational knowledge and experience, making it difficult to adopt scientific and effective communication and education methods when faced with problems their children encounter in learning, daily life, and social situations. Existing educational assistance products or systems are mostly single-function and unable to fully simulate diverse family education scenarios. They also struggle to provide targeted analysis and guidance based on children's individual characteristics, failing to meet the actual needs of parents and children in the family education process. Summary of the Invention

[0003] In view of this, the present application provides a family education AI simulation system and device, which first constructs a simulation scene based on the user's voice, image, and real-time emotions, and then analyzes the user's voice, image, and real-time emotions to determine targeted feedback information to help parents improve their educational abilities and promote the healthy growth of their children.

[0004] A first aspect of the present application provides a family education AI simulation system and device, the system comprising a user information collection module, a scene simulation module, an AI analysis module, and a guidance feedback module; The user information collection module is used to collect basic information of each family member, and obtain voice information and image information of the user performing simulated family education through voice and video collection equipment, and determine the real-time emotional state of the user through analysis of the voice information and image information; The scenario simulation module is used to generate a corresponding simulation scenario according to the scenario parameters, and present the simulation feedback information generated by the AI ​​analysis module based on the simulation scenario and the user's voice information, image information, and real-time emotional state to the user, wherein the scenario parameters are the scenario information input by the user; The AI ​​analysis module is configured to convert the voice information into text information, perform text analysis on the user's text expression based on the simulated scenario, perform emotional attitude analysis on the user based on the real-time emotional state, perform problem-solving strategy analysis on the user based on the solution in the text information, and organize the text analysis, emotional attitude analysis, and problem-solving strategy analysis into simulated feedback information of the user; The guidance feedback module is used to generate a corresponding feedback report based on the simulated feedback information and the user's basic information, voice information, image information and real-time emotional state. The feedback report includes simulated problems, improvement suggestions and maintenance information.

[0005] Optionally, the scene parameters in the scene simulation module also include voice information, image information, real-time emotional state and basic information of the user.

[0006] Optionally, analyzing the user's problem-solving strategy based on the solution in the text message includes: Extract key information from the solution, match the key information with a preset educational strategy knowledge base, and determine the problem-solving strategy analysis based on the matching result, wherein the key information includes keywords, key words, keyword combinations or keyword combinations.

[0007] Optionally, the AI ​​analysis module constructs a conversation analysis model by training the collected family education conversation data, and then processes the voice information through the conversation analysis model to obtain the simulated feedback information.

[0008] Optionally, the system further includes a learning resource recommendation and continuous optimization module; The learning resource recommendation and continuous optimization module is used to determine the keywords in the feedback report, and then determine the target educational resources from the preset learning resource library based on the matching conditions based on the keywords, and then send the target educational resources to the user, wherein the matching conditions include synonym matching, exact matching, and matching rate.

[0009] A second aspect of the present application provides a family education AI simulation device, which is applied to an intelligent electronic device, and the device includes: A user information collection unit is used to collect basic information of each family member, and obtain voice information and image information of the user performing simulated family education through a voice and video collection device, and determine the real-time emotional state of the user through analysis of the voice information and image information; a scenario simulation unit, configured to generate a corresponding simulated scenario according to scenario parameters, and present simulated feedback information generated by the AI ​​analysis module based on the simulated scenario and the user's voice information, image information, and real-time emotional state to the user, wherein the scenario parameters are scenario information input by the user; An AI analysis unit is configured to convert the voice information into text information, perform text analysis on the user's text expression based on the simulated scenario, perform an emotional attitude analysis on the user based on the real-time emotional state, perform a problem-solving strategy analysis on the user based on the solution in the text information, and organize the text analysis, emotional attitude analysis, and problem-solving strategy analysis into simulated feedback information of the user; The guidance feedback unit is used to generate a corresponding feedback report based on the simulated feedback information and the user's basic information, voice information, image information and real-time emotional state. The feedback report includes simulated problems, improvement suggestions and maintenance information.

[0010] Optionally, the scene parameters in the scene simulation unit further include voice information, image information, real-time emotional state and basic information of the user.

[0011] Optionally, the AI ​​analysis unit analyzing the user's problem-solving strategy based on the solution in the text message includes: Extract key information from the solution, match the key information with a preset educational strategy knowledge base, and determine the problem-solving strategy analysis based on the matching result, wherein the key information includes keywords, key words, keyword combinations or keyword combinations.

[0012] Optionally, the AI ​​analysis unit constructs a conversation analysis model by training the collected family education conversation data, and then processes the voice information through the conversation analysis model to obtain the simulated feedback information.

[0013] Optionally, the device further includes: The learning resource recommendation and continuous optimization unit is used to determine the keywords in the feedback report, and then determine the target educational resources from the preset learning resource library according to the matching conditions based on the keywords, and then send the target educational resources to the user, wherein the matching conditions include synonym matching, exact matching, and matching rate.

[0014] In the embodiment provided by the present application, the system first collects the voice and image information of the simulated user through the user information collection module to determine the real-time emotional state of the user. Then, a simulation scene of the user is constructed through scene parameters, and the user's voice and real-time emotions are analyzed in the simulation scene, so as to achieve real-time feedback communication with the user in the simulation scene. Finally, a feedback report is generated that records information such as simulation problems and improvement suggestions based on the above-mentioned information, such as the user's voice, emotions, and feedback information during real-time communication with the user. This allows the user to diagnose problems based on the feedback report and determine the direction of improvement, thereby helping parents improve their educational abilities and promote the healthy growth of their children. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A system module diagram provided for an embodiment of the present application; Figure 2 A diagram of the device structure provided in an embodiment of the present application; Figure 3A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0017] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0018] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0019] This application provides a family education AI simulation system to help parents improve their educational abilities and promote the healthy growth of their children.

[0020] The following specific embodiments are used to describe the technical solution of the present application in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0021] like Figure 1 The figure shows a module diagram of a family education AI simulation system provided by this application, which can be applied to intelligent electronic devices in a room used for family simulation education. The functions and effects of each module are described below.

[0022] Module 1, the user information collection module is used to collect basic information of each family member, and obtain voice information and image information of users performing simulated family education through voice and video collection equipment, and determine the real-time emotional state of the user through analysis of the voice information and image information.

[0023] In this embodiment, the user information to be collected can be divided into two parts. One part is the basic information collected in advance, such as gender, age, and personality, such as introversion, extroversion, liveliness, calmness, etc. The other part is the real-time information during the simulation, including the voice, image, and real-time emotional state during the simulation. The basic information can be determined by user input, and the voice and image in the real-time information can be collected and determined by audio and video collection equipment. The real-time emotional state can be determined by analyzing the collected voice and image, as follows: Speech feature extraction is performed to obtain features such as fundamental frequency, speaking rate, energy, spectrum, and pause patterns. The user's real-time emotional state is then determined from these features. For example, if the user's speech features include a high fundamental frequency and large energy fluctuations, their real-time emotion can be determined to be anger. If the user's speaking rate is slow, their real-time emotion can be determined to be sadness.

[0024] Feature extraction is performed on the image to obtain features such as facial action units (such as raised eyebrows, tightened mouth corners), micro-expressions, gaze direction, and head posture. The user's real-time emotional state is then determined from these features. For example, if the user's mouth corners are raised and the area around their eyes is wrinkled, their real-time emotion can be determined to be happiness. If the user's eyebrows are raised and their pupils are dilated, their real-time emotion can be determined to be surprise.

[0025] Furthermore, corresponding feature values ​​can be set for each of the aforementioned voice and image features, and the user's overall emotion can be determined based on these feature values. For example, a real-time emotion of 30% represents surprise and 70% represents happiness. Corresponding feature value ranges can also be set for each emotion. For example, if the feature values ​​of energy and speech rate in the voice feature are both within the feature value range corresponding to anger, the user's state can be determined to be angry.

[0026] Module 2, Scenario Simulation Module. This module is used to generate a corresponding simulated scenario based on scenario parameters, and present simulated feedback information generated by the AI ​​analysis module based on the simulated scenario and the user's voice information, image information, and real-time emotional state to the user, where the scenario parameters are the scenario information input by the user.

[0027] In this embodiment, the simulation scene can be a message interface. Based on the scene information input by the user, for example, corresponding selection buttons are set for each simulation scene, and the corresponding simulation scene is determined based on the user's selection. Simulation feedback information generated during communication with the user is then displayed on the message interface. The simulation scene can also be a 3D scene, such as a 3D scene constructed using 3D rendering technology, and the user is placed in the 3D scene by wearing a virtual reality display device.

[0028] Specific simulation scenarios can include parent-child communication scenarios. For example, parent-child communication scenarios can include child learning, family life, public places, and campus life. Child-child communication scenarios can include self-expression, teacher-student relationships, social behavior, and peer interactions. For example, in a child learning scenario, if a child forgets homework due to playfulness, the parent can voice-communicate a question to the system, such as, "My child is always playing and often forgets homework. What should I do?" Based on pre-collected child information, the system simulates the child's likely responses. For example, an introverted child might quietly say, "I just forgot while playing," while an extroverted child might argue, "Homework is boring. I'll just play for a while and do it later." During the conversation, the AI ​​analysis module conducts real-time analysis. If the parent uses simple reprimands, it will point out that this may cause the child to resist, and recommends gentle communication and creating a homework plan. If the parent guides the child to understand the importance of homework and works together to create a plan, this positive communication approach is affirmed and recommended to be maintained.

[0029] In another embodiment, the scene parameters in the scene simulation module further include voice information, image information, real-time emotional state and basic information of the user.

[0030] In this embodiment, the user does not need to manually select the simulation scene. Instead, the system determines the corresponding simulation scene based on the user's voice information, image information, real-time emotional state, and some of the user's basic information. For example, after the user enters the room for family simulation education, the audio and video acquisition device can collect the user's information. The simulation scene is determined from the collected information, for example: In scenario 1, the user directly says "family life scene" in the room. After the audio and video acquisition device collects the voice information, it directly generates the corresponding simulation scene based on the information.

[0031] Scenario 2: A corresponding simulation scene is set up in advance for a certain user. After the user enters the room for family simulation education, the video acquisition device obtains the user's image information, and after confirming his / her identity, the simulation scene corresponding to the user is directly generated.

[0032] Scenario 3 pre-sets simulation scenarios for certain emotional states, such as a communication and relief scenario for an angry parent or a soothing scenario for an angry child. Once the system determines the user's real-time emotional state and corresponding basic information through voice and image information, it directly generates the corresponding simulation scenario.

[0033] It should be noted that there are many specific methods for generating simulation scenarios based on various scenario parameters, which will not be described one by one here.

[0034] Module 3, AI Analysis Module. This module is used to convert the voice information into text information, and based on the premise of the simulated scenario, conduct text analysis on the user's text expression through the text information, then conduct emotional attitude analysis on the user based on the real-time emotional state, and then conduct problem-solving strategy analysis on the user based on the solution in the text information. The text analysis, emotional attitude analysis, and problem-solving strategy analysis are then compiled into simulated feedback information of the user.

[0035] In this embodiment, the speech input by the parents and children can be converted into text information through speech recognition technology, and the natural language processing technology can be used to perform semantic understanding and analysis on the conversation content.

[0036] For example, text analysis can assess whether the language and attitude of parents or children in conversations are appropriate. For example, it can determine whether parents use imperative or accusatory language when educating their children, or adopt an equal and respectful communication style; and analyze whether children express their opinions clearly and confidently.

[0037] Analyze the problem-solving strategies in text messages to determine whether the solutions proposed are scientific and effective. For example, if a child is experiencing academic stress, determine whether the parent's suggestions can truly alleviate the child's stress. For conflicts between children and classmates, assess whether the child's response is reasonable.

[0038] The user's emotional attitude is analyzed based on the real-time emotional state. That is, through semantic and voice intonation analysis, the emotional state of both parties in the conversation, such as anger, anxiety, happiness, etc., is identified. For example, if a child's voice tone is low and the words contain negative words, the child is judged to be depressed, and targeted emotional comfort and guidance suggestions can be given.

[0039] In another embodiment, analyzing the user's problem-solving strategy based on the solution in the text message includes: Extract key information from the solution, match the key information with a preset educational strategy knowledge base, and determine the problem-solving strategy analysis based on the matching result, wherein the key information includes keywords, key words, keyword combinations or keyword combinations.

[0040] This embodiment uses key information to match the content of the conversation with a preset educational strategy knowledge base to determine whether the communication method used by parents or children complies with the preset rules. For example, in the scenario of "children are under great academic pressure", whether the parents have adopted the correct process of "listening-empathy-planning" is compared. If the listening link is missing, the lack of listening is pointed out to the parent and marked as a lack of communication strategy when generating the feedback report. In another scenario, such as a scenario where a user is bullied by classmates, if the child's solution is to endure it silently, this problem can be pointed out in the problem-solving strategy analysis, and when the feedback report is subsequently generated, it can be predicted that the user may be bullied again, and it is recommended to actively report it to the teacher.

[0041] In another embodiment, the AI ​​analysis module can also construct a dialogue analysis model by training the collected family education dialogue data. During the model training stage, a large number of real family education dialogue cases and expert guidance suggestions are collected to construct a training data set. The model is trained through supervised learning to identify problem types and evaluate communication methods; through reinforcement learning, the model learns to generate more reasonable improvement suggestions based on different scenarios and user characteristics. For example, when the model finds that parents have repeatedly used ineffective communication methods in the "child is picky about eating" scenario, new guidance strategies are recommended through reinforcement learning, and the user's subsequent feedback is observed to adjust the recommendation priority. After the training is completed, the voice information is processed by the dialogue analysis model to obtain the simulated feedback information.

[0042] Module 4: Guidance Feedback Module: This module is used to generate a corresponding feedback report based on the simulated feedback information and the user's basic information, voice information, image information, and real-time emotional state. The feedback report includes simulated problems, improvement suggestions, and maintenance information.

[0043] In this embodiment, the feedback report can display the above information in the form of a combination of pictures and text. The report can also include functions such as voice broadcast and sharing on social platforms to facilitate communication and discussion between parents and education experts and other parents.

[0044] So far, completed Figure 1 Description of the functions and implementation methods of each module.

[0045] In an embodiment of the present application, the system first collects the voice and image information of the simulated user through the user information collection module to determine the real-time emotional state of the user. Then, a simulation scene of the user is constructed through scene parameters, and the user's voice and real-time emotions are analyzed in the simulation scene, so as to achieve real-time feedback communication with the user in the simulation scene. Finally, a feedback report is generated that records information such as simulation problems and improvement suggestions based on the above-mentioned information, such as the user's voice, emotions, and feedback information during real-time communication with the user. This allows the user to diagnose problems based on the feedback report and determine the direction of improvement, thereby helping parents improve their educational abilities and promote the healthy growth of their children.

[0046] In another embodiment, the system further includes a learning resource recommendation and continuous optimization module. This module is configured to identify keywords in the feedback report, determine target educational resources from a preset learning resource library based on matching conditions based on the keywords, and then send the target educational resources to the user. The matching conditions include synonym matching, exact matching, and matching rate.

[0047] In this embodiment, the feedback report may include several different keywords. The following describes the matching conditions for each keyword: 1. Synonym matching. Compare the keywords with the various educational resources in the learning resource library to determine the number of matches between the keyword and its synonyms and each educational resource. For example, if the feedback report contains 20 keywords, determine the synonyms corresponding to each keyword, then search each educational resource using the keywords and synonyms, and determine the keywords corresponding to the retrieved synonyms as a successful match. Finally, determine the number of keywords that successfully match each educational resource, and then determine the educational resource with the highest number as the target educational resource.

[0048] 2. Exact Match: Compare the keyword with each educational resource in the learning resource library to determine the number of matches between the keyword and each educational resource. This means searching for each educational resource using the keyword and determining the educational resource that matches the keyword as a successful match. Finally, determine the number of successful keyword matches for each educational resource, and then identify the educational resource with the highest number as the target educational resource.

[0049] 3. Matching rate. After determining the keywords in the feedback report under this matching condition, it is also necessary to determine the keywords in each educational resource, and then match the keywords of the two, and then determine the one with the highest matching rate as the target educational resource. For example, there are 20 keywords in the feedback report, and educational resource A contains 50 keywords, of which 19 are matched. The matching rate of educational resource A is determined to be 19 / 50=39.5%. Educational resource B contains 30 keywords, and 15 of the keywords match the feedback report. The matching rate of educational resource B is determined to be 15 / 30=50%. At this time, the target educational resource can be determined to be educational resource B, rather than educational resource A, which has a higher number of keyword matches. This matching condition can make the matched educational resources have less information that is irrelevant to the feedback report, thereby saving users time to view the corresponding educational resources.

[0050] Furthermore, this module continuously collects user usage data and feedback, regularly updating and optimizing the AI ​​analysis model and scenario library. For example, it can develop simulation scenarios based on emerging educational hot topics, such as adolescent internet addiction. Based on user feedback, it can adjust and improve the accuracy and practicality of suggestions, continuously enhancing the system's educational service quality.

[0051] like Figure 2 As shown, the present application also provides a family education AI simulation device, which is applied to an intelligent electronic device, and the device includes: The user information collection unit 201 is used to collect basic information of each family member, and obtain voice information and image information of the user performing simulated family education through voice and video collection equipment, and determine the real-time emotional state of the user through analysis of the voice information and image information; A scenario simulation unit 202 is configured to generate a corresponding simulated scenario based on scenario parameters, and present simulated feedback information generated by the AI ​​analysis module based on the simulated scenario and the user's voice information, image information, and real-time emotional state to the user, wherein the scenario parameters are scenario information input by the user; The AI ​​analysis unit 203 is configured to convert the voice information into text information, perform text analysis on the user's text expression based on the simulated scenario, perform emotional attitude analysis on the user based on the real-time emotional state, perform problem-solving strategy analysis on the user based on the solution in the text information, and organize the text analysis, emotional attitude analysis, and problem-solving strategy analysis into simulated feedback information of the user; The guidance feedback unit 204 is used to generate a corresponding feedback report based on the simulated feedback information and the user's basic information, voice information, image information and real-time emotional state. The feedback report includes simulated problems, improvement suggestions and maintenance information.

[0052] In another embodiment, the scene parameters in the scene simulation unit further include voice information, image information, real-time emotional state and basic information of the user.

[0053] In another embodiment, the AI ​​analysis unit analyzing the user's problem-solving strategy based on the solution in the text message includes: Extract key information from the solution, match the key information with a preset educational strategy knowledge base, and determine the problem-solving strategy analysis based on the matching result, wherein the key information includes keywords, key words, keyword combinations or keyword combinations.

[0054] In another embodiment, the AI ​​analysis unit constructs a conversation analysis model by training the collected family education conversation data, and then processes the voice information through the conversation analysis model to obtain the simulated feedback information.

[0055] In another embodiment, the apparatus further comprises: The learning resource recommendation and continuous optimization unit 205 is used to determine the keywords in the feedback report, and then determine the target educational resources from the preset learning resource library according to the matching conditions based on the keywords, and then send the target educational resources to the user, wherein the matching conditions include synonym matching, exact matching, and matching rate.

[0056] The above embodiments of the present invention provide a family education AI simulation system, and based on this system, provide a family education AI simulation device. Through the above system and device, parents can improve their educational abilities and promote the healthy growth of their children.

[0057] This embodiment also discloses a computer device, such as Figure 3 As shown, the computer device includes a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement any of the above-mentioned methods on the family education AI simulation system.

[0058] In addition, in the implementation of the above-mentioned example of the family education AI simulation device, the logical division of each program module is only an example. In actual application, the above-mentioned functions can be assigned to different program modules as needed, for example, for the configuration requirements of the corresponding hardware or the convenience of software implementation. That is, the internal structure of the family education AI simulation device is divided into different program modules to complete all or part of the functions described above.

[0059] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A family education AI simulation system, characterized by: The system includes a user information collection module, a scenario simulation module, an AI analysis module, and a guidance feedback module; The user information collection module is used to collect basic information of each family member, and obtain voice information and image information of the user performing simulated family education through voice and video collection equipment, and determine the real-time emotional state of the user through analysis of the voice information and image information; The scenario simulation module is used to generate a corresponding simulation scenario according to the scenario parameters, and present the simulation feedback information generated by the AI ​​analysis module based on the simulation scenario and the user's voice information, image information, and real-time emotional state to the user, wherein the scenario parameters are the scenario information input by the user; The AI ​​analysis module is configured to convert the voice information into text information, perform text analysis on the user's text expression based on the simulated scenario, perform emotional attitude analysis on the user based on the real-time emotional state, perform problem-solving strategy analysis on the user based on the solution in the text information, and organize the text analysis, emotional attitude analysis, and problem-solving strategy analysis into simulated feedback information of the user; The guidance feedback module is used to generate a corresponding feedback report based on the simulated feedback information and the user's basic information, voice information, image information and real-time emotional state. The feedback report includes simulated problems, improvement suggestions and maintenance information.

2. The system according to claim 1, wherein: The scene parameters in the scene simulation module also include voice information, image information, real-time emotional state and basic information of the user.

3. The system according to claim 1, wherein: The analyzing the user's problem-solving strategy based on the solution in the text message includes: Extract key information from the solution, match the key information with a preset educational strategy knowledge base, and determine the problem-solving strategy analysis based on the matching result, wherein the key information includes keywords, key words, keyword combinations or keyword combinations.

4. The system according to claim 1, wherein: The AI ​​analysis module constructs a conversation analysis model by training the collected family education conversation data, and then processes the voice information through the conversation analysis model to obtain the simulated feedback information.

5. The system according to claim 1, wherein: The system also includes a learning resource recommendation and continuous optimization module; The learning resource recommendation and continuous optimization module is used to determine the keywords in the feedback report, and then determine the target educational resources from the preset learning resource library based on the matching conditions based on the keywords, and then send the target educational resources to the user, wherein the matching conditions include synonym matching, exact matching, and matching rate.

6. A family education AI simulation device, applied to intelligent electronic devices, characterized in that: The device comprises: A user information collection unit is used to collect basic information of each family member, and obtain voice information and image information of the user performing simulated family education through a voice and video collection device, and determine the real-time emotional state of the user through analysis of the voice information and image information; a scenario simulation unit, configured to generate a corresponding simulated scenario according to scenario parameters, and present simulated feedback information generated by the AI ​​analysis module based on the simulated scenario and the user's voice information, image information, and real-time emotional state to the user, wherein the scenario parameters are scenario information input by the user; An AI analysis unit is configured to convert the voice information into text information, perform text analysis on the user's text expression based on the simulated scenario, perform an emotional attitude analysis on the user based on the real-time emotional state, perform a problem-solving strategy analysis on the user based on the solution in the text information, and organize the text analysis, emotional attitude analysis, and problem-solving strategy analysis into simulated feedback information of the user; The guidance feedback unit is used to generate a corresponding feedback report based on the simulated feedback information and the user's basic information, voice information, image information and real-time emotional state. The feedback report includes simulated problems, improvement suggestions and maintenance information.

7. The device according to claim 6, characterized in that The scene parameters in the scene simulation unit also include voice information, image information, real-time emotional state and basic information of the user.

8. The device according to claim 6, characterized in that The AI ​​analysis unit analyzing the user's problem-solving strategy based on the solution in the text message includes: Extract key information from the solution, match the key information with a preset educational strategy knowledge base, and determine the problem-solving strategy analysis based on the matching result, wherein the key information includes keywords, key words, keyword combinations or keyword combinations.

9. The device according to claim 6, characterized in that The AI ​​analysis unit constructs a conversation analysis model by training the collected family education conversation data, and then processes the voice information through the conversation analysis model to obtain the simulated feedback information.

10. The device according to claim 6, characterized in that The device further comprises: The learning resource recommendation and continuous optimization unit is used to determine the keywords in the feedback report, and then determine the target educational resources from the preset learning resource library according to the matching conditions based on the keywords, and then send the target educational resources to the user, wherein the matching conditions include synonym matching, exact matching, and matching rate.

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