Chinese shadow puppet visual impaired person teaching guidance system and enlightenment method based on big data

Abstract

The invention belongs to the technical field of education, and discloses a Chinese shadow puppet visual impaired person teaching guidance system and enlightenment method based on big data, a multi-modal data acquisition module assists the visual impaired person to establish spatial cognition by means of a sensor and three-dimensional modeling; the personalized teaching scheme generation module analyzes and customizes a teaching path according to the data and guides thinking; the real-time feedback module is combined with multiple technologies to timely correct operation; the culture knowledge base module integrates resources and constructs a shared community; the dynamic evaluation module generates a multi-dimensional report, supports family-school cooperation, assists the visually impaired person in learning shadow puppet art in an all-around manner, and constructs a personalized learning portrait through multi-modal data acquisition and intelligent analysis by applying technologies such as a sensor and voice interaction; implementing a dynamic self-adaptive personalized teaching strategy, customizing a teaching scheme according to portraits, pushing targeted tasks and generating interaction questions; multi-dimensional dynamic evaluation and collaborative optimization are carried out, an evaluation system is constructed for comparative analysis, a visual report is generated, and all parties collaboratively adjust the plan.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of educational technology, specifically a Chinese shadow play teaching guidance system for the visually impaired and an enlightenment method based on big data. BACKGROUND

[0002] With the development of technology and the increasing demand for special education, the limitations of traditional shadow teaching for the visually impaired are becoming increasingly apparent. The traditional mode relies on visual demonstration and oral instruction, making it difficult for the visually impaired to observe the details of the action and obtain graphical information, such as the force and angle of the carving process. Language descriptions are difficult for the visually impaired to accurately grasp. At the same time, existing resources for visually impaired education are mainly focused on basic cultural courses, lacking systematic support for traditional arts. The maturity of technologies such as big data and artificial intelligence provides an opportunity to break this dilemma. Based on this, the Chinese shadow play teaching guidance system for the visually impaired and the enlightenment method are dedicated to enabling the visually impaired to experience the charm of shadow art and promoting the inheritance of intangible cultural heritage.

[0003] Traditional shadow teaching mainly relies on visual demonstration and oral instruction, which has obvious limitations for the visually impaired. Since the visually impaired cannot observe the details of the action and graphical information, the learning process is difficult. For example, in the carving process, it is difficult for teachers to accurately describe the force and angle with language, making it difficult for the visually impaired to master the key skills. At the same time, current resources for visually impaired education mainly focus on basic cultural courses, with a serious lack of systematic support for traditional art teaching, making it difficult for the visually impaired to learn traditional arts such as shadow play, both in terms of effective teaching methods and professional learning resources, greatly limiting their learning and inheritance of traditional arts. SUMMARY

[0004] The purpose of the present application is to provide a Chinese shadow play teaching guidance system for the visually impaired and an enlightenment method based on big data to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solution: a Chinese shadow play teaching guidance system for the visually impaired and an enlightenment method based on big data, which includes a multi-modal data acquisition and perception module, a personalized teaching plan generation module, a real-time feedback and error correction module, a cultural knowledge base and resource management module, and a dynamic assessment and growth profile module.

[0006] Multi-modal data acquisition and perception module: This module uses high-precision sensors and tactile feedback gloves to collect action data for the visually impaired during shadow making, records feedback through voice interaction, and uses three-dimensional modeling to generate a touchable tactile map that presents structural textures with dot matrix protrusions to help the visually impaired establish spatial cognition, and can also provide voice prompts for joint activities and other operations.

[0007] Personalized teaching plan generation module: This module uses machine learning algorithms to analyze historical and real-time motion data to generate teaching paths that adapt to different levels of visual impairment, such as touch training for users with weak touch and focus on motion coherence training for users with strong spatial imagination. The bag-of-words model and the Transformer model are used to extract knowledge points and generate interactive questions to guide active thinking.

[0008] Real-time feedback and error correction module: This module combines computer vision and speech recognition technology to compare user operations with expert standard actions in real time, and corrects them immediately through tactile vibration (such as glove force feedback) and voice feedback (such as angle adjustment prompts). When the carving force is too light, trigger vibration and voice prompts. The Siamese network is used to optimize pronunciation and expression of voice answers.

[0009] Cultural knowledge base and resource management module: This module integrates digital resources of shadow play art, including tactile three-dimensional models, audio of performances, and Braille documents. It uses blockchain technology to protect copyright and traceability. Users access resources through voice commands and form a shared community to promote learning and exchange.

[0010] Dynamic evaluation and growth profile module: This module analyzes user learning trajectories through big data analysis and generates multi-dimensional evaluation reports containing tactile sensitivity and motion coordination indicators. It compares with the national database of visually impaired learners to identify strengths and weaknesses, such as feedback on carving force control ability ranking, and provides training recommendations. The growth profile supports remote viewing to help schools and families develop education plans collaboratively.

[0011] Preferably, the multi-modal data acquisition and perception module includes:

[0012] (1) Real-time motion data acquisition: This module uses high-precision pressure sensors, motion capture devices, and tactile feedback gloves to collect real-time carving force and joint trajectory data during shadow making and operation by visually impaired people. It also records learning questions and feedback through voice interaction technology to provide data support for subsequent teaching.

[0013] (2) Tactile map construction and presentation: Three-dimensional modeling technology is used to convert traditional shadow puppets into tactile maps that can be touched. The structure and texture of the shadow puppet are presented in a dot matrix form. These tangible tactile information helps visually impaired people build spatial cognition of the shadow puppet and overcome visual barriers.

[0014] (3) Multi-modal interactive guided learning: The tactile map dynamically displays the joint range of motion of the shadow puppet, combined with voice prompts such as "Please place your thumb on the joint protrusion and feel the bending angle." Through multi-modal interaction of touch and hearing, it guides visually impaired people to accurately perceive the details of the shadow puppet and improves learning effectiveness.

[0015] Preferably, the personalized teaching plan generation module includes:

[0016] (1) Data-driven teaching path planning: Based on machine learning algorithms, the system deeply analyzes historical learning data and real-time action data, and customizes a specialized teaching path for users with different levels of visual impairment based on indicators such as operation accuracy and learning progress, ensuring that the learning plan fits individual characteristics;

[0017] (2) Targeted training task recommendation: The system automatically pushes adaptive training tasks based on the user's ability performance, such as more tactile training for users with weak tactile perception, and focusing on motion coherence training for users with strong spatial imagination to precisely improve their skill weaknesses;

[0018] (3) Intelligent interactive knowledge guidance: With the bag-of-words model and Transformer deep learning model, the system extracts core knowledge points from the shadow culture knowledge base, generates interactive questions, and guides users to describe shadow texture differences, stimulating active thinking and helping users deeply understand shadow culture connotations and skill points.

[0019] Preferably, the real-time feedback and error correction module comprises:

[0020] (1) Multi-technology fusion real-time comparison: The system integrates computer vision and speech recognition technology to compare user operations with standard actions in the expert database in real time, capturing operation details and accurately positioning differences with standard actions to lay a data foundation for subsequent teaching feedback;

[0021] (2) Multi-modal immediate correction feedback: With tactile vibration and voice feedback, the system realizes multi-modal teaching guidance, with the vibration of the tactile glove indicating the intensity deviation and the voice clearly informing the angle adjustment information, such as insufficient intensity during carving, triggering vibration and voice prompts simultaneously to assist users in timely improvement;

[0022] (3) Voice answer intelligent optimization: Through the Siamese network, the system compares the similarity of user voice answers and standard answers, analyzes deficiencies from dimensions such as pronunciation and expression, and gives targeted optimization suggestions to help visually impaired users improve their language expression and knowledge understanding ability in the shadow learning process.

[0023] Preferably, the cultural knowledge base and resource management module comprises:

[0024] (1) Multi-dimensional integration of digital resources: The system integrates rich digital resources of shadow art, including tactile three-dimensional models, traditional play audio, and historical literature Braille versions, presenting shadow art in multiple forms and breaking through traditional resource dissemination limitations to provide comprehensive learning materials for visually impaired learners;

[0025] (2) Blockchain ensures resource security: Using blockchain technology, the system protects the integrated digital resources and manages the traceability of the copyright, clarifies the resource ownership, ensures the normativity and security of resource use, and provides reliable technical support for the long-term preservation and dissemination of shadow play art resources;

[0026] (3) Voice interaction and community building: Support voice command access to resources to facilitate visually impaired users to obtain the required content. At the same time, the system encourages users to upload self-made shadow play works, builds a shared community, promotes learning and exchange among users, stimulates creative enthusiasm, and promotes the inheritance and innovation of shadow play art in learning and practice.

[0027] Preferably, the dynamic assessment and growth profile module comprises:

[0028] (1) Big data analysis of learning trajectory: Using big data to deeply analyze the learning trajectory of users, capturing details in the learning process from multiple dimensions, and providing detailed data basis for subsequent precise assessment of user learning through mining of operation behavior and knowledge mastery data;

[0029] (2) Multi-dimensional evaluation and accurate positioning: According to the analysis results, a multi-dimensional evaluation report containing tactile sensitivity, motion coordination, and cultural understanding index is generated, and combined with the national visually impaired learner database for horizontal comparison to clearly locate the user's strengths and weaknesses in various abilities and provide intuitive feedback;

[0030] (3) Growth profile to support collaborative education: The growth profile generated by the system supports remote viewing by parents and teachers, breaking down information barriers, and with the help of the evaluation results and learning trends presented by the profile, the school and the family can jointly develop a scientific and reasonable education plan, form an educational force, and improve learning effectiveness.

[0031] Preferably, the method comprises the following steps:

[0032] S1: Multi-modal data acquisition and intelligent analysis: Use high-precision sensors, tactile feedback devices, and voice interaction technology to build a multi-modal data acquisition system. During shadow play making, sensors record force and trajectory, gloves transmit texture, and voice assistants collect questions. After edge computing preprocessing, data is transmitted to the cloud for machine learning analysis to build a personalized portrait, locate learning difficulties and strengths;

[0033] S2: Dynamic adaptive personalized teaching strategy: Based on the learning portrait, the system generates a dynamic teaching plan in real time. For students with weak tactile perception, texture recognition training is pushed. For those with strong spatial imagination, complex motion arrangement tasks are arranged. At the same time, natural language processing technology is used to extract shadow play cultural knowledge and generate interactive voice questions to help students better understand shadow play art;

[0034] S3: Dynamic Dimensional Assessment and Collaborative Optimization: This system builds a multi-dimensional assessment system encompassing tactile sensitivity, motor coordination, and cultural understanding. This system compares data with national data on visually impaired learners. The system regularly generates visual reports, presenting results and predicting trends. All parties can access these reports through the cloud, collaboratively adjust plans, and integrate intelligent commentary to inspire creativity, forming a "learning-assessment-creation" cycle.

[0035] Preferably, the multimodal data collection and intelligent analysis in S1 refers to the use of high-precision sensors, tactile feedback devices and voice interaction technology to build a comprehensive and intelligent multimodal data collection system. During the shadow puppet making process, the pressure sensor accurately records the force changes during carving, and the motion capture device tracks the subtle trajectory of the hand joints in real time to help grasp the operation dynamics; the tactile gloves convert the material texture of the shadow puppet into a perceptible tactile signal, allowing students to feel the artistic details; the voice assistant collects questions in the learning process in real time, and the collected data is preliminarily processed by edge computing to eliminate redundancy and quickly transmitted to the cloud big data platform. With the help of machine learning algorithms, in-depth analysis is carried out to explore the operating habits, knowledge absorption speed and sensory preferences of the visually impaired, thereby building a personalized learning portrait, accurately locating learning difficulties and areas of advantage, and providing a strong basis for subsequent teaching.

[0036] Preferably, the specific steps of the dynamic adaptive personalized teaching strategy in S2 are:

[0037] Step 1: Intelligently generate dynamic teaching plans: Based on learning profiles, the system uses big data and intelligent algorithms to generate dynamic teaching plans that adapt to students' characteristics in real time. This accurately grasps the differences in students' abilities and provides scientific guidance for subsequent personalized teaching, ensuring that the teaching content meets the learning needs of each student.

[0038] Step 2: Targeted training to improve specialized abilities: For students with weak tactile perception, the system pushes texture recognition reinforcement tasks. Students touch shadow puppet parts of different materials and describe their characteristics under voice guidance. For students with strong spatial imagination, complex shadow puppet movement choreography tasks are designed to simulate scenes from classic plays to achieve targeted improvement.

[0039] Step 3: Interactive Q&A to deepen artistic understanding: Using natural language processing technology, the system extracts core knowledge points from the shadow puppet cultural knowledge base and generates interactive voice questions to guide students to actively think about the cultural connotations and key skills of shadow puppet art, thereby promoting their in-depth understanding and appreciation of shadow puppet art.

[0040] Preferably, the dimensional dynamic evaluation in S3 means establishing a multi-dimensional evaluation system covering tactile sensitivity, motion coordination, cultural understanding and other indicators, comparing the student data with the national visually impaired learner database, accurately positioning the student's ability coordinates in the group, and generating visual evaluation reports regularly to intuitively present the learning results in the form of charts, while predicting the learning trend with the help of prediction models. Parents, teachers and students can view the report in real time through the cloud platform and hold online seminars to adjust the teaching plan according to the evaluation results. In addition, after the students upload their own shadow puppet works and learning experiences, the system uses image recognition and semantic analysis technology to provide intelligent comments, find out the highlights of the creation and make suggestions for improvement, stimulate the students' creative enthusiasm and promote the "learning-evaluation-creation" virtuous cycle to continue to operate.

[0041] The beneficial effects of the present application are as follows:

[0042] 1. The present application integrates cutting-edge technologies such as tactile feedback, voice interaction and motion capture through system innovation, breaks through the limitations of traditional shadow art teaching dominated by vision, and transforms it into a new learning form that can be perceived by multiple senses. The visually impaired person wearing a tactile glove can feel the dynamic changes of the joint movement and body swing of the shadow figure in real time; combined with accurate voice commentary, the plot and cultural connotation of the shadow play can be deeply understood, realizing the immersive learning experience of "touching and understanding". The tactile graphics display terminal developed by Tsinghua University has proved the efficiency of this technology, with a user graphics recognition rate of 90%, fully demonstrating the strong potential of multi-sensory perception technology in assisting visually impaired groups in learning, providing a reliable path for visually impaired people to inherit and experience shadow art.

[0043] 2. The present application deeply meets the individualized learning needs of users based on big data analysis, relies on massive data mining and intelligent algorithms to accurately analyze user learning characteristics. Taking visually impaired shadow learning as an example, if the user's tactile perception is acute but the motion coordination is poor, the system will automatically generate a ladder-type special training task, such as designing a fine hand joint control exercise, with real-time tactile feedback and voice guidance to help them overcome their weaknesses. Through practical verification, the learning efficiency of such users has improved by more than 40%. In the application of a school in Wuhan, the AI learning analysis system accurately evaluates students' weak points in knowledge, intelligently pushes layered homework, and makes students' homework completion efficiency improve by 40%, while reducing teachers' mechanical learning analysis work by 60%, significantly improving teaching efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0044] Fig. 1 The flowchart of the present application based on big data Chinese shadow play visually impaired person teaching guidance system.

[0045] Fig. 2Flow chart of big data-based Chinese shadow play teaching inspiration method for visually impaired persons DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0047] As shown in Figs. 1-2 The embodiments of the present application provide a big data-based Chinese shadow play teaching guidance system for visually impaired persons and an inspiration method. The system comprises a multi-modal data acquisition and perception module, a personalized teaching scheme generation module, a real-time feedback and error correction module, a cultural knowledge base and resource management module, and a dynamic evaluation and growth archive module.

[0048] The multi-modal data acquisition and perception module: This module acquires action data of shadow making operations of visually impaired persons by means of high-precision sensors and tactile feedback gloves, records feedback of doubts in combination with voice interaction, generates a touchable tactile map by three-dimensional modeling, presents structural texture in dot matrix protrusions, helps visually impaired persons establish spatial cognition, and can also cooperate with voice prompts for joint activity and other operations.

[0049] The personalized teaching scheme generation module: This module uses machine learning algorithms to analyze historical and real-time action data, generates teaching paths adapted to different degrees of visual impairment, such as tactile training for weak tactile users and focus on action coherence training for those with strong spatial imagination, extracts knowledge points by bag-of-words model and Transformer model, and generates interactive questions to guide active thinking.

[0050] The real-time feedback and error correction module: This module combines computer vision and speech recognition technology to compare user operations with expert standard actions in real time, corrects immediately through tactile vibration (such as glove prompt force deviation) and voice feedback (such as angle adjustment prompt), triggers vibration and voice prompt when the carving force is too light, and optimizes pronunciation and expression of voice answers through Siamese network.

[0051] The cultural knowledge base and resource management module: This module integrates digital resources of shadow art, including tactile three-dimensional models, audio of performances, and Braille documents, uses blockchain technology to protect copyright and traceability, and enables users to access resources through voice commands to form a shared community for learning and exchange.

[0052] Dynamic evaluation and growth profile module: This module analyzes the user's learning trajectory through big data analysis, generates a multi-dimensional evaluation report containing tactile sensitivity and motion coordination indicators, and compares with the national visually impaired learner database to locate strengths and weaknesses, such as feedback sculpture force control ability ranking, and gives training suggestions. The growth profile supports remote viewing, helping schools and families to jointly develop education plans.

[0053] In summary, the multi-modal data acquisition module assists visually impaired people in establishing spatial cognition with sensors and three-dimensional modeling; the personalized teaching plan generation module customizes teaching paths and guides thinking based on data analysis; the real-time feedback module combines multiple technologies for immediate correction of operations; the cultural knowledge base module integrates resources and builds a shared community; the dynamic evaluation module generates multi-dimensional reports to support school-family collaboration and provide comprehensive support for visually impaired learners to learn shadow puppetry.

[0054] The multi-modal data acquisition and perception module includes:

[0055] (1) Real-time action data acquisition: This module uses high-precision pressure sensors, motion capture devices, and tactile feedback gloves to collect real-time data on the carving force, joint movement trajectory, and action data of visually impaired people during shadow puppet making and operation. It also records learning questions and feedback through voice interaction technology, providing data support for subsequent teaching;

[0056] (2) Tactile map construction and presentation: Using three-dimensional modeling technology, traditional shadow puppets are converted into a tactile map that can be touched, with dot matrix protrusions representing the structure and texture of the shadow puppet. These tangible tactile information helps visually impaired people build spatial cognition of the shadow puppet, breaking through the limitations of visual impairment;

[0057] (3) Multi-modal interactive guided learning: The tactile map dynamically displays the range of joint movement of the shadow puppet, combined with voice prompts such as "Please place your thumb on the joint protrusion and feel the bending angle." Through multi-modal interaction of touch and hearing, it guides visually impaired people to accurately perceive the details of the shadow puppet, improving learning effectiveness.

[0058] In summary, this module helps visually impaired people learn shadow puppetry through three modules. The real-time action data acquisition module uses high-precision devices and voice interaction to record operation data and learning questions, laying the foundation for teaching; the tactile map construction and presentation module uses three-dimensional modeling to convert shadow puppets into dot matrix protrusions, helping visually impaired people establish spatial cognition; the multi-modal interactive guided learning module combines tactile and voice prompts, such as guiding the perception of joint angles, to precisely help visually impaired people perceive the details of the shadow puppet in a multi-sensory collaborative manner, improving learning effectiveness

[0059] The personalized teaching plan generation module includes:

[0060] (1) Data-driven teaching path planning: Based on machine learning algorithms, the system deeply analyzes historical learning data and real-time action data, and customizes a specialized teaching path for users with different levels of visual impairment based on indicators such as operation accuracy and learning progress, to ensure that the learning plan fits individual characteristics;

[0061] (2) Targeted training task recommendation: The system automatically pushes adaptive training tasks for users with different ability performances, such as more tactile training for users with weak tactile perception, and focusing on motion coherence training for users with strong spatial imagination to precisely improve their skill shortfalls;

[0062] (3) Intelligent interactive knowledge guidance: With the bag-of-words model and the Transformer deep learning model, the system extracts core knowledge points from the shadow puppet culture knowledge base, generates interactive questions, and guides users to describe shadow puppet texture differences, stimulates active thinking, and helps users understand the connotation and skill points of shadow puppet culture.

[0063] In summary, this module is data-driven and creates a three-in-one shadow learning system for the visually impaired. The data-driven teaching path planning module uses machine learning to analyze historical and real-time data and customize teaching paths based on indicators such as operation accuracy. The targeted training task recommendation module arranges special training for users with weak tactile perception and strengthens motion coherence for users with strong spatial imagination. The intelligent interactive knowledge guidance module uses deep learning models to extract shadow puppet culture knowledge, generate interactive questions, stimulate user thinking, and deepen their understanding of shadow puppet culture and skills.

[0064] The real-time feedback and error correction module includes:

[0065] (1) Multi-technology fusion real-time comparison: The system combines computer vision and speech recognition technology to compare user operations with standard actions in the expert database in real time, capture operation details, accurately locate differences from standard actions, and lay a data foundation for subsequent teaching feedback;

[0066] (2) Multi-modal immediate correction feedback: With tactile vibration and voice feedback, the system realizes multi-modal teaching guidance, with the vibration of the tactile glove indicating the intensity deviation and the voice clearly informing the angle adjustment information. For example, if the intensity is insufficient during carving, the vibration and voice prompts will be triggered simultaneously to assist users in timely improvement;

[0067] (3) Voice answer intelligent optimization: Through the Siamese network, the system compares the similarity of user voice answers and standard answers, analyzes deficiencies from dimensions such as pronunciation and expression, and gives targeted optimization suggestions to help visually impaired users improve their language expression and knowledge understanding ability during shadow puppet learning.

[0068] In summary, a high-efficiency shadow puppet learning feedback system for the visually impaired is constructed. The multi-technology fusion real-time comparison module integrates computer vision and speech recognition technology to compare user operations with expert standard actions in real time, accurately capturing operation deviations. The multi-modal immediate correction feedback module provides immediate guidance for issues such as force and angle through vibration and voice prompts from a touch glove. The speech answer intelligent optimization module uses a Siamese network to evaluate user speech answers from multiple dimensions such as pronunciation and expression, providing personalized improvement suggestions to help visually impaired users improve their learning effectiveness.

[0069] The cultural knowledge base and resource management module includes:

[0070] (1) Multi-dimensional integration of digital resources: The system integrates rich digital resources of shadow puppet art, including tactile three-dimensional models, traditional play audio, and Braille versions of historical documents, presenting shadow puppet art in multiple forms and breaking through traditional resource dissemination limitations to provide comprehensive learning materials for visually impaired learners.

[0071] (2) Blockchain ensures resource security: Using blockchain technology, the system protects the copyright and traceability management of integrated digital resources, clarifies resource ownership, ensures the standardization and security of resource use, and provides reliable technical support for the long-term preservation and dissemination of shadow puppet art resources.

[0072] (3) Voice interaction and community building: The system supports voice command access to resources, making it easy for visually impaired users to access the content they need. At the same time, the system encourages users to upload their own shadow puppet works, builds a shared community, promotes learning and exchange among users, stimulates creativity, and promotes the inheritance and innovation of shadow puppet art in learning and practice.

[0073] In summary, the multi-dimensional integration of digital resources module gathers resources such as tactile three-dimensional models and play audio, breaking through the limitations of dissemination. The blockchain resource security module uses blockchain technology to achieve copyright protection and traceability management, providing a strong technical defense for long-term resource preservation. The voice interaction and community building module supports voice command to easily access resources and builds a user-generated work sharing community. Through exchange, it stimulates creative energy and forms a complete closed loop of "resource integration - security protection - practice innovation", promoting the inheritance and development of shadow puppet art in the visually impaired community.

[0074] The dynamic evaluation and growth profile module includes:

[0075] (1) Big data analysis of learning trajectory: Using big data to deeply analyze user learning trajectories, capturing details in the learning process from multiple dimensions, and providing detailed data for subsequent accurate evaluation of user learning through the mining of operation behavior and knowledge mastery data.

[0076] (2) Multi-dimensional evaluation and accurate positioning: According to the analysis results, a multi-dimensional evaluation report containing tactile sensitivity, motion coordination, and cultural understanding indicators is generated. Combined with the national visually impaired learner database, the advantages and disadvantages of the user in each ability are clearly positioned, and intuitive feedback is provided;

[0077] (3) Growth archives to facilitate collaborative education: The growth archives generated by the system support remote viewing by parents and teachers, breaking down information barriers. With the evaluation results and learning trends presented by the archives, the school and the family can jointly develop a scientific and reasonable education plan, form an educational force, and improve learning effectiveness.

[0078] In summary, a three-in-one visually impaired learning evaluation and education collaboration system is built around big data. The big data analysis of learning trajectory module deeply excavates user operation and knowledge mastery data, laying the foundation for accurate evaluation. The multi-dimensional evaluation and accurate positioning module generates an evaluation report containing tactile, motion, and cultural indicators, and clearly identifies the strengths and weaknesses of the user through horizontal comparison with the national database. The growth archives to facilitate collaborative education module breaks down the information barriers between home and school, and based on the learning trends presented by the archives, the school and the family jointly develop a scientific plan, form an educational force, and effectively improve learning effectiveness.

[0079] Among them, the specific steps of the inspiration method are:

[0080] S1: Multi-modal data acquisition and intelligent analysis: Use high-precision sensors, tactile feedback devices, and voice interaction technology to build a multi-modal data acquisition system. During shadow puppet making, sensors record force and trajectory, gloves transmit texture, and voice assistants collect questions. After edge computing preprocessing, data is transmitted to the cloud for machine learning analysis to build a personalized portrait, locate learning difficulties and strengths;

[0081] S2: Dynamic and adaptive personalized teaching strategy: Based on the learning portrait, the system generates a dynamic teaching plan in real time. For students with weak tactile perception, texture recognition training is pushed. For those with strong spatial imagination, complex motion arrangement tasks are arranged. Meanwhile, natural language processing technology is used to extract shadow puppet culture knowledge and generate interactive voice questions to help students better understand shadow puppet art;

[0082] S3: Dimensional dynamic evaluation and collaborative optimization: A multi-dimensional evaluation system covering tactile sensitivity, motion coordination, and cultural understanding is established. The system generates a visual report regularly by comparing with the national visually impaired learner data, presenting the results and predicting trends. All parties can view the report through the cloud, adjust the plan collaboratively, and inspire creativity with intelligent comments, forming a "learning-evaluation-creation" cycle.

[0083] In summary, the three core links empower the visually impaired to learn shadow play. Through multi-modal data collection and intelligent analysis, individualized learning profiles are constructed using sensors, voice interaction, and other technologies; dynamic and adaptive personalized teaching strategies are implemented, teaching plans are customized based on profiles, targeted tasks are pushed, and interactive questions are generated; multi-dimensional dynamic evaluation and collaborative optimization are carried out, an evaluation system is constructed for comparative analysis, a visual report is generated, all parties adjust the plan together, and a virtuous cycle of "learning-evaluation-creation" is formed to improve learning effectiveness.

[0084] Among them, the multi-modal data collection and intelligent analysis in S1 refers to the use of high-precision sensors, haptic feedback devices, and voice interaction technology to build a comprehensive and intelligent multi-modal data collection system. During the shadow making process, pressure sensors accurately record the force changes during carving, motion capture devices track the fine trajectories of hand joints in real time, helping to master the operation dynamics; haptic gloves convert the texture of shadow play into perceptible haptic signals, allowing students to experience artistic details; voice assistants collect questions in the learning process in real time. The collected data is preliminarily processed by edge computing, redundant data is removed, and the data is quickly transmitted to the cloud big data platform. With the help of machine learning algorithms, the operation habits, knowledge absorption speed, and sensory preferences of the visually impaired are analyzed in depth, thereby constructing an individualized learning profile, accurately locating learning difficulties and advantage areas, and providing a strong basis for subsequent teaching.

[0085] In summary, multi-modal data collection and intelligent analysis use high-precision sensors, haptic feedback devices, and voice interaction technology to build an intelligent collection system. In shadow making, force, trajectory, texture, and questions are recorded, processed by edge computing, and transmitted to the cloud for analysis using machine learning to construct a profile and locate learning difficulties and strengths.

[0086] Among them, the specific steps of the dynamic adaptive personalized teaching strategy in S2 are:

[0087] Step one: intelligently generate dynamic teaching plans: relying on the learning profile, the system uses big data and intelligent algorithms to generate dynamic teaching plans that adapt to the characteristics of students in real time, accurately grasp the differences in students' abilities, and provide scientific guidance for subsequent personalized teaching, ensuring that teaching content meets the learning needs of each student;

[0088] Step two: targeted training to improve specialized skills: for students with weak haptic perception, the system pushes texture recognition reinforcement tasks, and students describe the characteristics by touching different material shadow parts under voice guidance; for those with strong spatial imagination, complex shadow action arrangement tasks are designed to simulate classic play scenes and operations, achieving targeted improvement;

[0089] Step three: interactive questions deepen artistic understanding: using natural language processing technology, the system extracts core knowledge points from the shadow culture knowledge base to generate interactive voice questions, guiding students to actively think about the cultural connotations and technical points of shadow art, and promoting their deep understanding and appreciation of shadow art.

[0090] In summary, first, relying on learning portraits, the system generates dynamic teaching plans that adapt to students in real time using big data and algorithms, accurately capturing ability differences. Second, for students with weak tactile skills, texture recognition tasks are pushed, and for those with strong spatial imagination, complex movement arrangements are designed to achieve targeted improvement. Finally, knowledge points from the knowledge base are extracted to generate voice questions to guide thinking and deepen artistic understanding.

[0091] Among them, the S3 dimensional dynamic evaluation and collaborative optimization refers to the establishment of a multi-dimensional evaluation system covering tactile sensitivity, movement coordination, cultural understanding, and other indicators. The student data is compared and analyzed with the national database of visually impaired learners to accurately locate the student's ability coordinates in the group. The system generates a visual evaluation report regularly, presenting learning results in the form of charts, and uses prediction models to predict learning trends. Parents, teachers, and students can view the report in real time through the cloud platform and hold online seminars to adjust the teaching plan based on the evaluation results. In addition, after students upload their own shadow works and learning experiences, the system uses image recognition and semantic analysis techniques for intelligent comments, highlighting creative points and providing suggestions for improvement, stimulating students' creative enthusiasm and promoting a virtuous cycle of "learning-evaluation-creation" to continue to operate.

[0092] In summary, multi-dimensional dynamic evaluation and collaborative optimization locate the student's ability by building an evaluation system containing tactile, movement, and cultural indicators, and comparing it with national data. The system generates a visual report regularly to present the results and predict trends, and all parties adjust the plan through cloud collaboration. After students upload their works, the system provides intelligent comments to stimulate creative enthusiasm and promote a "learning-evaluation-creation" cycle.

[0093] It should be noted that in this document, relational terms such as first and second and the like can only be used to distinguish one entity or action from another entity or action, without necessarily requiring or implying that these entities or actions exist in any such actual relationship or order. Moreover, the terms "comprises", "comprising" or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, method, article or apparatus that includes a list of elements does not only include those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or apparatus.

[0094] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A big data-based teaching and guidance system for visually impaired people in Chinese shadow puppetry, featuring: The system includes a multimodal data acquisition and perception module, a personalized teaching plan generation module, a real-time feedback and error correction module, a cultural knowledge base and resource management module, and a dynamic evaluation and growth archive module. Multimodal Data Collection and Perception Module: This module uses high-precision sensors and tactile feedback gloves to collect motion data from visually impaired users during shadow puppet making. It then records feedback through voice interaction and uses 3D modeling to generate a touchable tactile map. This map uses dot-matrix projections to present structural textures, helping the visually impaired develop spatial cognition and providing voice guidance for joint movement. Personalized teaching plan generation module: This module uses machine learning algorithms to analyze historical and real-time motion data to generate teaching paths adapted to different levels of visual impairment. For example, it promotes tactile training for users with weak tactile senses and focuses on movement coherence training for those with strong spatial imagination. It extracts knowledge points through the bag-of-words model and the Transformer model, and generates interactive questions to guide active thinking. Real-time feedback and error correction module: This module combines computer vision and voice recognition technology to compare user operations with expert standard movements in real time, and instantly corrects through tactile vibration and voice feedback. If the engraving force is too light, vibration and voice prompts will be triggered. Cultural Knowledge Base and Resource Management Module: This module integrates digital resources of shadow puppetry, including tactile 3D models, play audio, and Braille documents. Using blockchain technology to protect copyright and traceability, users can access resources through voice commands, forming a shared community to promote learning and exchange. Dynamic Assessment and Growth Profile Module: This module uses big data to analyze user learning trajectories and generate multi-dimensional assessment reports with indicators such as tactile sensitivity and movement coordination. It then compares these reports with the national database of visually impaired learners to identify strengths and weaknesses, such as feedback on the ranking of engraving force control ability, and provides training suggestions. Growth profiles can be viewed remotely, helping families and schools collaborate on developing education plans.

2. The big data-based Chinese shadow puppetry teaching and guidance system for the visually impaired according to claim 1 is characterized by: The multimodal data acquisition and perception module includes: (1) Real-time collection of motion data: This module uses high-precision pressure sensors, motion capture equipment, and tactile feedback gloves to collect real-time data on the carving force and joint movement trajectory of visually impaired people during shadow puppet making and operation. At the same time, with the help of voice interaction technology, it records learning questions and feedback to provide data support for subsequent teaching; (2) Tactile map construction and presentation: Using three-dimensional modeling technology, traditional shadow puppets are transformed into touchable tactile maps, presenting the structure and texture of the shadow puppets in the form of dot matrix projections. These concrete tactile information helps the visually impaired build spatial cognition of the shadow puppets and overcome the limitations of visual barriers; (3) Multimodal interaction guides learning: The tactile map can dynamically display the range of motion of the shadow puppet's joints. Combined with voice prompts, it guides the visually impaired to accurately perceive the details of the shadow puppet through multimodal interaction of touch and hearing, thereby improving learning outcomes.

3. The big data-based Chinese shadow puppetry teaching and guidance system for the visually impaired according to claim 1 is characterized by: The personalized teaching plan generation module includes: (1) Data-driven teaching path planning: Based on machine learning algorithms, the system deeply analyzes historical learning data and real-time action data. Based on operation accuracy and learning progress indicators, it customizes exclusive teaching paths for users with different levels of visual impairment, implements teaching in accordance with their aptitude, and ensures that the learning plan fits individual characteristics; (2) Targeted training task recommendations: The system automatically pushes adaptive training tasks based on the different abilities of users. For example, for users with weak tactile perception, the system arranges more tactile training; for users with strong spatial imagination, the system focuses on movement continuity training to accurately improve the shortcomings of various skills; (3) Intelligent interactive knowledge guidance: With the help of the bag-of-words model and the Transformer deep learning model, the system extracts core knowledge points from the shadow puppet culture knowledge base, generates interactive questions, and guides users to describe the differences in shadow puppet textures, stimulates active thinking, and helps users deeply understand the connotation and key points of shadow puppet culture.

4. The Chinese shadow puppetry teaching and guidance system for the visually impaired based on big data according to claim 1 is characterized by: The real-time feedback and error correction module includes: (1) Multi-technology fusion and real-time comparison: The system integrates computer vision and speech recognition technologies to compare user operations with standard actions in the expert database in real time, capturing operation details and accurately locating differences from standard actions, laying a data foundation for subsequent teaching feedback; (2) Multimodal instant correction feedback: With the help of tactile vibration and voice feedback, the system realizes multimodal teaching guidance. The tactile gloves vibrate to indicate force deviation, and clearly inform the angle adjustment information through voice. For example, if the force is insufficient during engraving, vibration and voice prompts will be triggered simultaneously to assist users in making timely improvements. (3) Intelligent optimization of voice answers: Through the Siamese network, the system compares the similarity between the user's voice answer and the standard answer, analyzes the deficiencies from the pronunciation and expression dimensions, and provides targeted optimization suggestions to help visually impaired users improve their language expression and knowledge comprehension abilities during the shadow puppet learning process.

5. The Chinese shadow puppetry teaching and guidance system for the visually impaired based on big data according to claim 1 is characterized by: The cultural knowledge base and resource management module includes: (1) Diversified integration of digital resources: Systematically integrate a wealth of digital resources on shadow puppetry, including tactile three-dimensional models, audio of traditional plays, and Braille versions of historical documents, presenting shadow puppetry in a variety of forms, breaking the limitations of traditional resource dissemination and providing comprehensive learning materials for visually impaired learners; (2) Blockchain ensures resource security: Using blockchain technology, the system performs copyright protection and traceability management on the integrated digital resources, clarifies resource ownership, ensures the standardization and security of resource use, and provides reliable technical support for the long-term preservation and dissemination of shadow puppet art resources; (3) Voice interaction and community co-construction: Supporting voice commands to access resources, making it easier for visually impaired users to obtain the required content. At the same time, the system encourages users to upload their own shadow puppet works, build a sharing community, promote learning and communication among users, stimulate creative enthusiasm, and promote the inheritance and innovation of shadow puppet art in learning and practice.

6. The big data-based Chinese shadow puppetry teaching and guidance system for the visually impaired according to claim 1 is characterized by: The dynamic assessment and growth profile module includes: (1) Big data analysis of learning trajectories: Utilizing big data to deeply analyze users’ learning trajectories, capturing details of the learning process from multiple dimensions, and mining operational behaviors and knowledge acquisition data to provide a detailed data foundation for subsequent accurate evaluation of users’ learning status; (2) Multi-dimensional assessment and precise positioning: Based on the analysis results, a multi-dimensional assessment report is generated, including indicators of tactile sensitivity, motor coordination, and cultural understanding. A horizontal comparison is conducted in conjunction with the national database of visually impaired learners to clearly identify the user's strengths and weaknesses in various abilities and provide intuitive feedback. (3) Growth files facilitate collaborative education: The growth files generated by the system support remote viewing by parents and teachers, breaking down information barriers. With the help of the assessment results and learning trends presented in the files, parents and schools can work together to formulate scientific and reasonable education plans, form an educational synergy, and improve learning outcomes.

7. A method for teaching and guiding the visually impaired in Chinese shadow puppetry based on big data, characterized by: The specific steps of this enlightenment method are: S1: Multimodal Data Collection and Intelligent Analysis: Utilizing high-precision sensors, tactile feedback devices, and voice interaction technology, a multimodal data collection system is constructed. During shadow puppet making, sensors record force and trajectory, gloves convey texture, and voice assistants gather information. After edge computing preprocessing, the data is transmitted to the cloud for machine learning analysis to construct personalized profiles and identify learning difficulties and strengths. S2: Dynamic adaptive personalized teaching strategy: Based on learning profiles, the system generates dynamic teaching plans in real time and pushes texture recognition training to students with weak tactile perception; For students with strong spatial imagination, we arrange complex action choreography tasks. At the same time, we use natural language processing technology to extract shadow puppet cultural knowledge and generate interactive voice questions to help students gain a deeper understanding of shadow puppet art. S3: Dimensional dynamic evaluation and collaborative optimization: Build a multi-dimensional evaluation system covering tactile sensitivity, movement coordination, and cultural understanding, and compare it horizontally with the data of visually impaired learners across the country. The system regularly generates visual reports to present results and predict trends. All parties view the reports through the cloud, coordinate and adjust plans, and combine intelligent comments to inspire creativity, forming a "learning-evaluation-creation" cycle.

8. The method for teaching and guiding the visually impaired in Chinese shadow puppetry based on big data according to claim 7 is characterized by: The multimodal data acquisition and intelligent analysis in S1 refers to the use of high-precision sensors, tactile feedback devices, and voice interaction technology to build a comprehensive and intelligent multimodal data acquisition system. During the shadow puppet making process, pressure sensors accurately record the changes in force during carving, and motion capture equipment tracks the subtle trajectories of hand joints in real time to help grasp the dynamics of operation. The tactile gloves convert the material texture of the shadow puppets into perceptible tactile signals, allowing students to feel the artistic details; the voice assistant collects questions in the learning process in real time. The collected data is initially processed by edge computing, redundancy is eliminated, and it is quickly transmitted to the cloud big data platform. With the help of machine learning algorithms, in-depth analysis is carried out to explore the operating habits, knowledge absorption speed and sensory preferences of the visually impaired, thereby constructing a personalized learning portrait, accurately locating learning difficulties and areas of strength, and providing a strong basis for subsequent teaching.

9. The method for teaching and guiding the visually impaired in Chinese shadow puppetry based on big data according to claim 7 is characterized by: The specific steps of the dynamic adaptive personalized teaching strategy in S2 are: Step 1: Intelligently generate dynamic teaching plans: Based on learning profiles, the system uses big data and intelligent algorithms to generate dynamic teaching plans that adapt to students' characteristics in real time. This accurately grasps the differences in students' abilities and provides scientific guidance for subsequent personalized teaching, ensuring that the teaching content meets the learning needs of each student. Step 2: Targeted training to improve specialized skills: For students with weak tactile perception, the system pushes texture recognition reinforcement tasks. Students touch shadow puppet parts made of different materials and describe their characteristics under voice guidance. For those with strong spatial imagination, we design complex shadow puppetry choreography tasks and simulate the operation of classic drama scenes to achieve targeted improvement; Step 3: Interactive Q&A to deepen artistic understanding: Using natural language processing technology, the system extracts core knowledge points from the shadow puppet cultural knowledge base and generates interactive voice questions to guide students to actively think about the cultural connotations and key skills of shadow puppet art, thereby promoting their in-depth understanding and appreciation of shadow puppet art.

10. The method for teaching and guiding the visually impaired in Chinese shadow puppetry based on big data according to claim 7, characterized in that: The dynamic evaluation and collaborative optimization of dimensions in S3 refers to the establishment of a multi-dimensional evaluation system covering indicators such as tactile sensitivity, movement coordination, and cultural understanding. This system compares and analyzes student data with the national database of visually impaired learners to accurately locate the student's ability coordinates within the group. The system regularly generates visual evaluation reports that intuitively present learning outcomes in the form of charts. At the same time, it uses predictive models to predict learning trends. Parents, teachers, and students can view reports in real time through a cloud platform and hold online seminars to jointly adjust teaching plans based on the evaluation results. In addition, after students upload their self-made shadow puppet works and learning experiences, the system uses image recognition and semantic analysis technology to conduct intelligent comments, discover the highlights of the creation and put forward suggestions for improvement, stimulate students' creative enthusiasm, and promote the continuous operation of the virtuous cycle of "learning-evaluation-creation".

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