Situational interactive teaching tool for cerebral palsy language retardation intervention

Abstract

The invention relates to the technical field of cerebral palsy language education, and discloses a cerebral palsy language slow intervention-oriented situational interactive teaching tool, which comprises a situational interaction module used for constructing a virtual scene and realizing language training through a multi-modal interaction technology; the personalized adaptation module is used for carrying out personalized adjustment on the interaction mode and the difficulty level in the situation interaction module according to the physical condition and the language ability evaluation result of the user; the reward and feedback module is used for monitoring operation and performance of the user in real time and giving corresponding rewards according to a preset rule; the user management module is used for storing personal information, training records and evaluation data of the user; and an intelligent analysis module. By means of the voice, gesture and eye movement multi-mode interaction technology, a natural interaction mode matched with the physical condition of a patient is constructed, language training is stimulated in an all-round mode in a scene contact interaction mode, the language expression and communication ability of the patient is remarkably improved, and the training enthusiasm is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of cerebral palsy language education, and in particular to a situational interactive teaching tool for intervention of language delay in cerebral palsy. Background Art

[0002] Patients with cerebral palsy often have language delays, which hinder their communication with the outside world and their integration into society. With the rapid development of science and technology and the continuous deepening of research on cerebral palsy rehabilitation, people are paying more and more attention to using advanced technical means to improve the language rehabilitation training effect of cerebral palsy patients. Situational interactive teaching can create a language learning environment close to real life for patients by constructing virtual scenes, allowing patients to naturally exercise their language skills in interaction.

[0003] However, current technology often uses a single-expression teaching model for cerebral palsy patients, which makes it difficult to combine data on the physical condition, language foundation, etc. of cerebral palsy patients, leading to a reduction in enthusiasm for training and teaching. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a situational interactive teaching tool for intervention of cerebral palsy language delay, which solves the problem that it is impossible to combine data on the physical condition, language foundation, etc. of cerebral palsy patients, resulting in reduced enthusiasm for training and teaching.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a situational interactive teaching tool for intervention of cerebral palsy language delay, comprising:

[0006] The contextual interaction module is used to construct virtual scenarios and implement language training through multimodal interaction technology;

[0007] A personalized adaptation module, used to personalize the interaction mode and difficulty level in the contextual interaction module based on the user's physical condition and language ability assessment results;

[0008] The reward and feedback module is used to monitor user operations and performance in real time and give corresponding rewards according to preset rules;

[0009] User management module, used to store user personal information, training records and evaluation data, and add corresponding timestamps to each data;

[0010] The intelligent analysis module is used to analyze the data generated by users during the teaching process, generate analysis reports, and feed back the analysis results to the personalized adaptation module in real time.

[0011] Preferably, the context interaction module includes a scene construction unit, a display module and a multimodal interaction unit;

[0012] The scene construction unit is used to create a virtual interactive scene through 3D modeling software;

[0013] The display module is used to present the constructed virtual scene and feedback information during the interaction process through a display screen and multiple display devices.

[0014] Preferably, the multimodal interaction unit includes:

[0015] Voice interaction: Using speech recognition technology, the system performs semantic analysis on text to understand the cerebral palsy patient's command intent. Based on the large language dialogue model, the AI virtual character's intention is inferred and corrected to generate dialogue tasks that meet the cerebral palsy patient's language level, and then controls the virtual scene to make corresponding responses.

[0016] Gesture interaction: By deploying cameras and using image recognition algorithms to capture and analyze user gestures, the system identifies the object the gesture is pointing to and pops up a relevant information box with a verbal description of the object's name and purpose, guiding users in language learning.

[0017] Eye interaction: Use eye tracking devices to track the direction of the user's gaze and determine the object of the user's attention based on where the gaze rests.

[0018] Preferably, the personalized adaptation module includes an evaluation data receiving unit, a strategy formulation unit and an adjustment execution unit;

[0019] The evaluation data receiving unit is used to receive the user's learning situation, which includes the learning process, language receiving function and language comprehension ability;

[0020] The strategy formulation unit is used to formulate a language learning strategy of corresponding difficulty based on the received data and in combination with the language teaching curriculum;

[0021] The adjustment execution unit is used to feed back the formulated learning strategy to the context interaction module for adjustment, and optimize the teaching strategy by modifying the scene element parameters and the interactive response mode.

[0022] Preferably, the reward and feedback module includes a reward determination unit and a feedback presentation unit;

[0023] The reward determination unit is used to determine the user's behavior data monitored, including conversations with virtual characters, language understanding, and object name recognition;

[0024] The feedback presentation unit is used to present rewards through sound, image and vibration, and gradually increase the teaching difficulty and reward value.

[0025] Preferably, the user management module includes an information entry unit and a data transmission unit;

[0026] The information entry unit is used to provide a user interface to record the user's personal information, including age, gender and medical history;

[0027] The data storage unit is used to observe the user's training process in real time through the movement of the parent terminal.

[0028] Preferably, the intelligent analysis module includes a data acquisition unit, a data analysis algorithm unit and a report generation unit;

[0029] The data acquisition unit is used to obtain user teaching data in real time through the context interaction module, the reward and feedback module and the user management module, including user operation data, behavior data and interaction scene data;

[0030] The data analysis algorithm unit is used to analyze the voice quality and pronunciation accuracy of the patient's voice characteristics based on the deep learning neural network of the voice data, and output the development trend of the user's teaching language ability, learning difficulties and training effect evaluation;

[0031] The report generation unit is used to generate a visual analysis report based on the analysis results, and the report format includes charts and text summaries.

[0032] The situational interactive teaching method for intervention of language delay in cerebral palsy includes the following steps:

[0033] S1. User information entry: through the information entry unit of the user management module, and customized initial language teaching strategy based on the user's physical condition;

[0034] S2. Interactive scene development: using 3D modeling software to create virtual scenes for language teaching needs, and using models, textures, and sound effects resources to enrich the scene details;

[0035] S3, multimodal real-time interactive training, starts a virtual scene, triggers language training through multimodal interaction, and monitors performance in real time through the reward and feedback module, triggering virtual rewards after completing the goal;

[0036] S4, dynamic difficulty adjustment and real-time feedback. If the pronunciation accuracy rate remains above 80%, the task will be upgraded to complex sentence training. If the error rate increases sharply, the task will automatically switch to a low-pressure reward task.

[0037] S5. Data collection and intelligent analysis: Through deep learning neural networks, the changing trends of patients' voice characteristics, voice quality, and pronunciation accuracy are analyzed to output the development trend of users' teaching language ability, learning difficulties, and training effect evaluation.

[0038] The present invention provides a situational interactive teaching tool for intervention of cerebral palsy language delay.

[0039] Beneficial effects:

[0040] 1. The present invention uses multimodal interaction technologies such as voice, gestures, and eye movements to construct an interaction mode that is natural and adapted to the patient's physical condition, and stimulates language training in all directions through contact scenario interaction, significantly improving the patient's language expression and communication ability and enhancing training enthusiasm.

[0041] 2. The present invention uses a personalized adaptation module to deeply analyze data based on the patient's physical condition and language ability assessment results, customizes exclusive teaching strategies, and dynamically adjusts the interaction mode and difficulty level of the situational interaction module based on the individual differences of the patient. The difficulty is gradually updated to accurately match the patient's rehabilitation process, greatly improving the efficiency and effectiveness of language rehabilitation training.

[0042] 3. The present invention collects data from multiple modules in real time, uses deep learning neural networks to deeply mine voice features, quality, and pronunciation accuracy change trends, generates visual reports and feeds them back to the personalized adaptation module, helps teaching staff accurately grasp the patient's learning situation, dynamically optimizes teaching strategies, and enables teaching tools to continuously adapt to changes in patient needs, thereby achieving accurate and efficient language rehabilitation training. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is an architectural diagram of the contextual interactive teaching tool for cerebral palsy language delay intervention of the present invention;

[0044] Figure 2 This is a diagram of the contextual interaction module architecture of the contextual interactive teaching tool for cerebral palsy language delay intervention of the present invention;

[0045] Figure 3 This is a diagram of the personalized adaptation module architecture of the contextual interactive teaching tool for cerebral palsy language delay intervention of the present invention;

[0046] Figure 4 This is a diagram of the reward and feedback module architecture of the contextual interactive teaching tool for cerebral palsy language delay intervention of the present invention;

[0047] Figure 5 This is a diagram illustrating the user management module architecture of the contextual interactive teaching tool for cerebral palsy language delay intervention of the present invention;

[0048] Figure 6 This is a diagram of the intelligent analysis module architecture of the contextual interactive teaching tool for cerebral palsy language delay intervention of the present invention;

[0049] Figure 7 The flowchart of the present invention is a situational interactive teaching method for intervention of cerebral palsy language delay. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] Please see the attached Figure 1 The embodiment of the present invention provides a situational interactive teaching tool for intervention of cerebral palsy language delay, including:

[0052] The contextual interaction module is used to construct virtual scenarios and implement language training through multimodal interaction technology;

[0053] A personalized adaptation module, which is used to adjust the interaction mode and difficulty level in the contextual interaction module according to the user's physical condition and language ability assessment results;

[0054] The reward and feedback module is used to monitor user operations and performance in real time and give corresponding rewards according to preset rules;

[0055] User management module, used to store user personal information, training records and evaluation data, and add corresponding timestamps to each data;

[0056] The intelligent analysis module is used to analyze the data generated by users during the teaching process, generate analysis reports, and feed back the analysis results to the personalized adaptation module in real time.

[0057] Specifically, through the contextual interaction module, a rich and diverse virtual scene that is close to life is constructed to provide a highly simulated and immersive language training environment for cerebral palsy patients. By integrating voice, gesture, and eye movement multimodal interaction technology, patients can interact with the virtual scene in the most natural and appropriate way for their physical condition, thereby achieving language training and improvement.

[0058] The personalized adaptation module allows for precise teaching adjustments based on individual differences. Since each cerebral palsy patient has different physical conditions and language abilities, this module collects and analyzes data such as the patient's physical condition and language ability assessment results to gain a deeper understanding of the patient's specific situation. Based on this, it makes personalized adjustments to the interaction methods and difficulty levels in the situational interaction module, while also gradually updating the difficulty level. This improves the effectiveness and efficiency of language rehabilitation training and accelerates the patient's recovery process.

[0059] The reward and feedback module monitors the patient's operation and performance during the training process in real time and gives corresponding rewards according to preset rules. For example, correctly speaking vocabulary, expressing complete sentences, and having effective conversations with virtual characters. Rewards can be in the form of virtual items, points, achievement badges, etc., and timely feedback is provided to patients through various forms such as sound, images, and vibrations to inform them of the correctness of their operations, progress, and areas for improvement. Through continuous rewards and feedback, patients can maintain a good learning state and continuously improve the effectiveness of language training.

[0060] The user management module creates a complete information file for each patient, properly storing the patient's basic information, detailed records of each training session, and regular language proficiency assessment data. Each piece of data is accurately timestamped. This allows teaching staff and parents to clearly understand the patient's learning process and development changes. The addition of timestamps also provides an accurate time dimension for data analysis and comparison, helping to more accurately assess the patient's rehabilitation effects and development trends.

[0061] Through the intelligent analysis module, the large amount of data generated by patients during the teaching process is deeply analyzed, and the analysis results are fed back to the personalized adaptation module in real time. Therefore, teaching staff can fully understand the patient's learning situation based on the analysis report, formulate more personalized and targeted teaching strategies, and realize dynamic optimization of the teaching process. At the same time, through intelligent analysis, the teaching tool can continuously adapt to the patient's learning needs and development changes, thereby improving the quality and effectiveness of speech rehabilitation training.

[0062] See attached Figure 2 ,The situational interaction module includes a scenario construction unit, a ,display module and a multimodal interaction unit;

[0063] The scene construction unit is used to create a virtual interactive scene through 3D modeling software;

[0064] The display module is used to present the constructed virtual scene and feedback information during the interaction process through a variety of display devices.

[0065] Specifically, through the scenario construction unit and the use of 3D modeling software, various realistic virtual interactive scenarios are created based on the characteristics and training needs of cerebral palsy patients, such as daily home and school scenes, to provide patients with a language learning environment that fits their lives. The rich elements in the scenarios can enhance the sense of immersion, greatly increase the enthusiasm for participating in training, strengthen language application skills in simulated situations, and provide diverse training content based on the needs of patients.

[0066] The constructed virtual scene and interactive feedback information will be presented through the display module on various display devices such as computers, tablets, smart TVs, etc. It can automatically adapt the picture according to the device screen parameters, clearly display the content, and intuitively present voice recognition results, rewards and other feedback.

[0067] Multimodal interaction units include:

[0068] Voice interaction: Using speech recognition technology, the system performs semantic analysis on text to understand the cerebral palsy patient's command intent. Based on the large language dialogue model, the AI virtual character's intention is inferred and corrected to generate dialogue tasks that meet the cerebral palsy patient's language level, and then controls the virtual scene to make corresponding responses.

[0069] Gesture interaction: By deploying cameras and using image recognition algorithms to capture and analyze user gestures, the system identifies the object the gesture is pointing to and pops up a relevant information box with a verbal description of the object's name and purpose, guiding users in language learning.

[0070] Eye interaction: Use eye tracking devices to track the direction of the user's gaze and determine the object of the user's attention based on where the gaze rests.

[0071] Specifically, through voice interaction based on speech recognition technology, the speech of cerebral palsy patients is converted into text and then deeply analyzed for semantics. Combined with the large language dialogue model, the command intentions of cerebral palsy patients are accurately understood, and adaptive dialogue tasks are generated horizontally. This significantly improves the language expression and communication ability of cerebral palsy patients, exercises language application in simulated dialogues, enhances language thinking, and also increases the enthusiasm of cerebral palsy patients to participate in training;

[0072] Through gesture interaction, image recognition algorithms are used to capture and analyze user gestures. When a gesture is identified as pointing at an object, an information box will pop up immediately, displaying the object's name and description of its use, guiding language learning. This provides an intuitive non-verbal interaction channel through physical manipulation, making up for the shortcomings of voice expression, establishing a connection between objects and language descriptions, strengthening vocabulary memory, improving language comprehension, and allowing language learning to occur naturally through hands-on operation.

[0073] Through eye movement interaction, the user's gaze direction is tracked in real time, and the object of attention is determined based on the position of the gaze. This allows the user to interact with the virtual scene through eye contact, expanding the possibility of participating in training.

[0074] See attached Figure 3 ,The personalized adaptation module includes an evaluation data receiving unit, a ,strategy formulation unit and an adjustment execution unit;

[0075] The evaluation data receiving unit is used to receive the user's learning situation, which includes the learning process, language receiving function and language comprehension ability;

[0076] The strategy formulation unit is used to formulate language learning strategies of corresponding difficulty based on the received data and in combination with the language teaching curriculum;

[0077] The adjustment execution unit is used to feed back the formulated learning strategy to the situational interaction module for adjustment, and optimize the teaching strategy by modifying the scene element parameters and interactive response methods.

[0078] Specifically, the evaluation data receiving unit collects data on the user's learning progress, language reception function, language comprehension ability, and other learning status, providing a basis for subsequent decision-making, ensuring a comprehensive understanding of the user's language learning status, laying the foundation for the formulation of personalized strategies, and ensuring targeted strategy formulation;

[0079] The strategy formulation unit uses the data collected by the assessment data receiving unit and combines it with the language teaching curriculum to tailor a language learning strategy that suits the user's current level and determines the appropriate learning difficulty. This ensures that users with cerebral palsy can steadily improve their language skills at an appropriate level of difficulty, avoiding any impact on learning outcomes due to inappropriate difficulty levels.

[0080] By adjusting the execution unit, the learning strategy is fed back to the situational interaction module. By adjusting the scene element parameters and interactive response methods, the teaching strategy is optimized and effectively implemented. It ensures that the situational interaction module can change in real time according to user needs, and ensures that users can feel that the teaching content and methods are constantly in line with their own progress during the training process, and continue to maintain a good learning experience and improve learning efficiency.

[0081] See attached Figure 4 ,The reward and feedback module includes a reward determination unit and a feedback ,presentation unit;

[0082] The reward determination unit is used to judge the user's behavior data, including conversations with virtual characters, language understanding, and object name recognition;

[0083] The feedback presentation unit is used to present rewards through sound, images and vibration, and gradually increase the teaching difficulty and reward value.

[0084] The reward determination unit identifies the user's language learning achievements by judging behavioral data such as conversations with virtual characters, language comprehension, and object name recognition during training. Its role is to accurately capture the user's efforts and progress, provide a basis for providing appropriate rewards, and let users clearly know their learning achievements, thereby enhancing learning motivation and improving training focus and engagement.

[0085] Rewards are displayed through the feedback presentation unit with the help of sound, images, vibration, etc., while gradually increasing the teaching difficulty and reward value, thereby continuously attracting users' attention, strengthening learning behavior through positive feedback, guiding users to advance their language skills through challenges, and steadily improving their language level in the process of gradually increasing difficulty, thus achieving a virtuous cycle of learning and growth.

[0086] See attached Figure 5 ,The user management module includes an information entry unit and a data transmission unit;

[0087] The information entry unit is used to provide a user interface to record the user's personal information, including age, gender and medical history;

[0088] The data storage unit is used to observe the user's training process in real time through the parent's mobile terminal.

[0089] Specifically, through the information entry unit, the user's basic information is introduced into the system, laying the foundation for the subsequent work of each module. Based on this information, teaching staff and parents can have a deeper understanding of the user, carry out targeted teaching and guidance, and improve the accuracy of teaching;

[0090] Through the data storage unit, the user's training process can be observed through the parent's mobile device. The real-time information window breaks the limitations of time and space, allowing parents to grasp the dynamics of their children's training at any time. They can then assist in training at home based on the observation situation, form a synergy with teaching, and ensure that users can receive continuous and effective rehabilitation support at home and in teaching institutions.

[0091] See attached Figure 6 ,The intelligent analysis module includes a data acquisition unit, a data ,analysis algorithm unit and a report generation unit;

[0092] The data acquisition unit is used to obtain user teaching data in real time through the context interaction module, reward and feedback module and user management module, including user operation data, behavior data and interaction scene data;

[0093] The data analysis algorithm unit is used to analyze the voice quality and pronunciation accuracy of the patient's voice characteristics based on the deep learning neural network of voice data, and output the development trend of the user's teaching language ability, learning difficulties and training effect evaluation;

[0094] The report generation unit is used to generate a visual analysis report based on the analysis results. The report format includes charts and text summaries.

[0095] Specifically, the data collection unit collects user operations, behaviors, and interactive scenario data in real time from modules such as contextual interaction, rewards and feedback, and user management. This integrates key information scattered across various modules, providing a comprehensive and timely data foundation for subsequent analysis. This allows the analysis to be more tailored to actual conditions, ensuring the accuracy and effectiveness of data analysis and enabling teaching staff to grasp the latest user learning dynamics.

[0096] The data analysis algorithm unit uses a deep learning neural network based on voice data to analyze the patient's voice characteristics and study the changing trends of voice quality and pronunciation accuracy. It can also output the user's language ability development trends, learning difficulties, and training effect evaluation. It can also mine the information behind the data to provide a scientific basis for teaching adjustments, helping teaching staff to accurately grasp the user's learning situation and develop more targeted teaching strategies.

[0097] The report generation unit generates visual analysis reports containing charts and text summaries, converting complex data into intuitive and easy-to-understand content, allowing teaching staff, parents and other relevant personnel to quickly understand the user's learning status, promote communication and collaboration among all parties, and facilitate the joint development of more reasonable rehabilitation plans for users.

[0098] See attached Figure 7 , a situational interactive teaching method for intervention of cerebral palsy language delay, including the following steps:

[0099] S1. User information entry: through the information entry unit of the user management module, and customized initial language teaching strategy based on the user's physical condition;

[0100] S2. Interactive scene development: using 3D modeling software to create virtual scenes for language teaching needs, and using models, textures, and sound effects resources to enrich the scene details;

[0101] S3, multimodal real-time interactive training, starts a virtual scene, triggers language training through multimodal interaction, and monitors performance in real time through the reward and feedback module, triggering virtual rewards after completing the goal;

[0102] S4, dynamic difficulty adjustment and real-time feedback. If the pronunciation accuracy rate remains above 80%, the task will be upgraded to complex sentence training. If the error rate increases sharply, the task will automatically switch to a low-pressure reward task.

[0103] S5. Data collection and intelligent analysis: Through deep learning neural networks, the changing trends of patients' voice characteristics, voice quality, and pronunciation accuracy are analyzed to output the development trend of users' teaching language ability, learning difficulties, and training effect evaluation.

[0104] Specifically, the situational interactive teaching method for intervention of cerebral palsy and language delay can assist patients in their rehabilitation in all aspects. First, information is entered based on the patient's physical condition, and the initial teaching strategy is customized to ensure that the teaching is tailored to the individual's situation from the beginning. Then, by creating virtual scenes and enriching the details, an immersive learning environment is created for patients. Multimodal interaction is used to stimulate language training, and performance is monitored in real time and rewards are given to enhance patient participation. The difficulty is then dynamically adjusted based on pronunciation accuracy to ensure that the training is challenging but not overly stressful. Finally, deep learning neural networks are used to analyze data and output evaluation results such as language ability development to provide a scientific basis for teaching adjustments, thereby achieving accurate and efficient language rehabilitation training as a whole and helping cerebral palsy patients improve their language ability.

[0105] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A situational interactive teaching tool for intervention of language delay in cerebral palsy, characterized by: It includes a situational interaction module, which is used to build virtual scenes and implement language training through multimodal interaction technology; A personalized adaptation module, used to personalize the interaction mode and difficulty level in the contextual interaction module based on the user's physical condition and language ability assessment results; The reward and feedback module is used to monitor user operations and performance in real time and give corresponding rewards according to preset rules; User management module, used to store user personal information, training records and evaluation data, and add corresponding timestamps to each data; The intelligent analysis module is used to analyze the data generated by users during the teaching process, generate analysis reports, and feed back the analysis results to the personalized adaptation module in real time.

2. The contextual interactive teaching tool for intervention of cerebral palsy language delay according to claim 1, characterized in that: The context interaction module includes a scene construction unit, a display module and a multimodal interaction unit; The scene construction unit is used to create a virtual interactive scene through 3D modeling software; The display module is used to present the constructed virtual scene and feedback information during the interaction process through a display screen and multiple display devices.

3. The contextual interactive teaching tool for intervention of cerebral palsy language delay according to claim 2, characterized in that: The multimodal interaction unit includes: Voice interaction: Using speech recognition technology, the system performs semantic analysis on text to understand the cerebral palsy patient's command intent. Based on the large language dialogue model, the AI virtual character's intention is inferred and corrected to generate dialogue tasks that meet the cerebral palsy patient's language level, and then controls the virtual scene to make corresponding responses. Gesture interaction: By deploying cameras and using image recognition algorithms to capture and analyze user gestures, the system identifies the object the gesture is pointing to and pops up a relevant information box with a verbal description of the object's name and purpose, guiding users in language learning. Eye interaction: Use eye tracking devices to track the direction of the user's gaze and determine the object of the user's attention based on where the gaze rests.

4. The situational interactive teaching tool for intervention of cerebral palsy language delay according to claim 1, characterized in that: The personalized adaptation module includes an evaluation data receiving unit, a strategy formulation unit and an adjustment execution unit; The evaluation data receiving unit is used to receive the user's learning situation, which includes the learning process, language receiving function and language comprehension ability; The strategy formulation unit is used to formulate a language learning strategy of corresponding difficulty based on the received data and in combination with the language teaching curriculum; The adjustment execution unit is used to feed back the formulated learning strategy to the context interaction module for adjustment, and optimize the teaching strategy by modifying the scene element parameters and the interactive response mode.

5. The situational interactive teaching tool for intervention of cerebral palsy language delay according to claim 1, characterized in that: The reward and feedback module includes a reward determination unit and a feedback presentation unit; The reward determination unit is used to determine the user's behavior data monitored, including conversations with virtual characters, language understanding, and object name recognition; The feedback presentation unit is used to present rewards through sound, image and vibration, and gradually increase the teaching difficulty and reward value.

6. The situational interactive teaching tool for intervention of cerebral palsy language delay according to claim 1, characterized in that: The user management module includes an information entry unit and a data transmission unit; The information entry unit is used to provide a user interface to record the user's personal information, including age, gender and medical history; The data storage unit is used to observe the user's training process in real time through the movement of the parent terminal.

7. The situational interactive teaching tool for intervention of cerebral palsy language delay according to claim 1, characterized in that: The intelligent analysis module includes a data acquisition unit, a data analysis algorithm unit and a report generation unit; The data acquisition unit is used to obtain user teaching data in real time through the context interaction module, the reward and feedback module and the user management module, including user operation data, behavior data and interaction scene data; The data analysis algorithm unit is used to analyze the voice quality and pronunciation accuracy of the patient's voice characteristics based on the deep learning neural network of the voice data, and output the development trend of the user's teaching language ability, learning difficulties and training effect evaluation; The report generation unit is used to generate a visual analysis report based on the analysis results, and the report format includes charts and text summaries.

8. A situational interactive teaching method for intervention of language delay in cerebral palsy, characterized by: The contextual interactive teaching tool for intervention of cerebral palsy language delay according to any one of claims 1 to 7 comprises the following steps: S1. User information entry: through the information entry unit of the user management module, and customized initial language teaching strategy based on the user's physical condition; S2. Interactive scene development: using 3D modeling software to create virtual scenes for language teaching needs, and using models, textures, and sound effects resources to enrich the scene details; S3, multimodal real-time interactive training, starts a virtual scene, triggers language training through multimodal interaction, and monitors performance in real time through the reward and feedback module, triggering virtual rewards after completing the goal; S4, dynamic difficulty adjustment and real-time feedback. If the pronunciation accuracy rate remains above 80%, the task will be upgraded to complex sentence training. If the error rate increases sharply, the task will automatically switch to a low-pressure reward task. S5. Data collection and intelligent analysis: Through deep learning neural networks, the changing trends of patients' voice characteristics, voice quality, and pronunciation accuracy are analyzed to output the development trend of users' teaching language ability, learning difficulties, and training effect evaluation.

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