398 results about "Training plan" patented technology

The invention provides an intelligent rehabilitation training method and system based on artificial intelligence and virtual reality, a user wears an intelligent wearable device to collect multi-modal data such as electroencephalogram, myoelectricity, physiological features and motion signals, the multi-modal data is preprocessed and then input into an artificial intelligence training model, and key features are extracted and fused by using a graph convolutional network and an attention mechanism algorithm. And constructing a digital twinborn model by using the fusion features, performing real-time dynamic mapping and predictive simulation, and generating a customized training scheme by means of a reinforcement learning algorithm in combination with a rehabilitation target and a physical state of the user. A user is trained in the virtual reality interaction model, the system monitors actions and physiological states in real time, the digital twin model synchronously acts, and the scene is dynamically adjusted. After training, the rehabilitation effect is evaluated according to the physiological indexes, the motion data and the twinning optimization analysis result, and an optimization training scheme and a digital twinning model are fed back. Precision, individuation and intelligentization of rehabilitation training are achieved, and the training effect and quality are improved.

The invention discloses an intelligent nursing training system and method based on a large language model, and belongs to the cross technical field of artificial intelligence and nursing education. The system comprises a large language model, a dynamic knowledge graph, a virtual case generation module, a multi-modal evaluation module and a federal learning framework. A dynamic knowledge network is constructed by integrating a hospital information system, a high-simulation case containing 60% of error scenes is generated in combination with a generative adversarial network, and a personalized training scheme is optimized by utilizing reinforcement learning. The method covers multi-source data management, nurse ability grading, real-time decision support (four-level alarm system) and closed-loop effect evaluation. The innovation points comprise: (1) a professional nursing large model, wherein the medical term understanding accuracy is greater than or equal to 95%; (2) hour-level updating of the dynamic knowledge graph; (3) clinical-training two-way data linkage, wherein the critical response time is less than or equal to 6 seconds; and (4) realizing cross-department collaboration by federal learning. The system provides an intelligent and personalized solution for nursing talent cultivation, and has industrial popularization value.

The invention relates to the technical field of training assistance, in particular to a training quality prediction method and system based on training damage analysis, and the method comprises the steps: multi-modal data collection and accumulative superposition, causal graph construction, attitude risk modeling, data enhancement, dynamic prediction and training plan optimization. According to the method, daily exercise data of a user is continuously collected, a chronic load accumulative calculation model is established, and a time sequence analysis method is adopted to perform long-term tracking and accumulative calculation on core indexes such as exercise amount, heart rate variability and joint activity, so that a complete personal exercise state database is formed; the chronic load accumulation mechanism breaks through the limitation of traditional single training evaluation, comprehensive monitoring and long-term trend analysis of the motion state of the user are achieved, a potential risk mode can be found earlier, a reliable data basis is provided for personalized risk evaluation and scientific training optimization, and the method is suitable for popularization and application. The problems that a traditional motion monitoring system is single in data and one-sided in evaluation are effectively solved.

The invention discloses a hip and knee joint replacement postoperative rehabilitation method and system based on mobile medical treatment, relates to the technical field of medical rehabilitation, and discloses the hip and knee joint replacement postoperative rehabilitation method and system based on mobile medical treatment. The training plan is dynamically adjusted in combination with multi-dimensional indexes, the problems of compensation recognition deficiency, feedback lag and plan stiffness in a traditional rehabilitation scheme are effectively solved, the rehabilitation effect can be improved, and the injury risk of adjacent joints can be reduced.

The invention discloses a pump station maintenance-oriented dismounting path practical training method and system, and relates to the technical field of pump station dismounting. The method comprises the following steps: constructing a virtual-real simulation environment of pump station equipment, carrying out dynamic dismounting monitoring identification on pump station parts in the pump station equipment based on the virtual-real simulation environment, and determining a dismounting path; the method comprises the following steps: acquiring operation track data of a training person in real time through a motion capture device, obtaining a somatosensory interaction motion signal, mapping the somatosensory interaction motion signal to a virtual-real simulation environment for disassembly and assembly analysis, and generating a disassembly and assembly motion deviation parameter set; and performing dynamic optimization based on the disassembly and assembly action deviation parameter set in combination with disassembly and assembly multi-dimensional feedback data, generating multi-stage practical training difficulty, performing adaptation on practical training personnel according to a disassembly and assembly path according to the multi-stage practical training difficulty, and determining a multi-stage practical training scheme. The technical problems that in the prior art, pump station maintenance dismounting practical training lacks real scene simulation, and efficient skill training is difficult to achieve are solved, and the technical effect of improving the practical training effect is achieved.

The invention discloses an electric power engineering project management talent intelligent evaluation and dynamic training system, and the system comprises a multi-dimensional quality capability model construction module which constructs a four-dimensional capability framework based on the characteristics of an electric power engineering industry, generates a differentiated post capability model through a dynamic weight distribution algorithm, and carries out the dynamic training of the differentiated post capability model; constructing an industry ability dictionary library by utilizing a knowledge graph technology; the intelligent evaluation and matching module integrates a multi-modal evaluation tool to collect dominant and implicit capability data, dynamically adjusts capability index weights through a pre-trained AI model based on project characteristic parameters, and calculates man-post adaptation indexes by adopting a cosine similarity algorithm; and the dynamic training and development module is used for generating a personalized training scheme by quantifying the capability difference, tracking the training effect in a closed-loop manner based on a Korotkoff four-level evaluation model, and dynamically optimizing a training path by adopting a Bayesian network. The technical defects of single evaluation dimension, low man-post matching degree and insufficient cultivation efficiency of traditional power engineering human resource management can be solved.

The invention discloses a portable respiratory function rehabilitation training device and a method thereof, and belongs to the technical field of medical rehabilitation. The device comprises a data acquisition module which integrates a plurality of micro sensors to acquire respiratory airflow, heart rate, oxyhemoglobin saturation and environmental parameters of a user in real time; the data preprocessing and fusion module is used for cleaning, standardizing and performing time synchronization on the collected original data and fusing the collected original data into a uniform data format; the intelligent analysis module is responsible for performing real-time analysis on the fused data, identifying a breathing mode, evaluating a current health state and predicting a rehabilitation trend; the training scheme generation module is used for dynamically generating and adjusting a personalized breathing training scheme; the real-time feedback and interaction module is used for providing real-time training guidance and progress feedback for the user; the effect evaluation and optimization module is used for evaluating the rehabilitation effect and continuously optimizing a training strategy by using a reinforcement learning algorithm; according to the portable hardware integration module, all modules are integrated into small-sized and low-power-consumption portable equipment.

The invention relates to the technical field of vocal music training evaluation, and discloses a vocal music training intelligent evaluation method and system based on voice analysis. The method comprises the following steps: firstly, acquiring vocal music training original data, and processing and aligning multi-modal data by using a transfer learning small sample completion method when the data is insufficient; constructing a pronunciation stability evaluation model and a comprehensive pronunciation performance evaluation model; constructing a pronunciation scene simulator for different vocal music styles; the evaluation parameters are analyzed through a Shapley value decomposition algorithm, and a lightweight index system is constructed; and establishing a training scheme optimization agent model by using Gaussian process regression and a Bayesian optimization algorithm. The system comprises a data acquisition and completion module, an evaluation module, a simulation module, an analysis module, an optimization module and the like. The vocal music training system can comprehensively and accurately evaluate vocal music training, adapts to various styles, optimizes a training scheme, and assists a singer to improve the singing level through real-time feedback and visual display.

The invention relates to the technical field of medical rehabilitation monitoring, in particular to a rehabilitation cloud platform system for cardiopulmonary function monitoring. According to the system, an intelligent monitoring terminal collects physiological data; the cloud computing platform adopts wavelet threshold denoising and an index moving average method to remove noise, combines dynamic time warping to achieve multi-physiological signal time sequence alignment, constructs a double-flow model, generates a health state index and a comprehensive health risk index, introduces a multi-scale branch and differential attention mechanism, and achieves the multi-scale health state index and the comprehensive health risk index. High-precision anomaly detection is realized through prediction deviation analysis, multi-target reinforcement learning is adopted to balance health improvement and risk control, and a personalized training scheme is generated; the remote medical support module supports a doctor to monitor patient data in real time and intervene in an adjustment scheme; the user interaction end provides a real-time monitoring data visualization and individuation rehabilitation plan. Through multi-modal data fusion, time sequence synchronous optimization and adaptive reinforcement learning, accurate monitoring, dynamic rehabilitation optimization and remote medical collaboration are realized.

The invention discloses an intelligent training method for preventing lower limb thrombus and a related device, and the method comprises the steps: inputting the physiological parameters of a patient, the initial exercise ability evaluation information and the personalized rehabilitation target information into a DQN multi-mode fusion model, and generating an ankle pump exercise training scheme meeting the rehabilitation demands of the patient; monitoring ankle pump motion execution information of the patient in real time through wearable equipment; according to the ankle pump motion training scheme and the ankle pump motion execution information of the patient, a lower limb thrombus training effect evaluation result of the patient is calculated, and the lower limb thrombus training effect evaluation result comprises the blood circulation improvement degree, the joint motion range and the gait characteristics; according to a lower limb thrombus training effect evaluation result of the patient, predicting lower limb thrombus potential recovery disorder and a falling risk level of the patient, and starting a grading alarm program according to the potential recovery disorder and the falling risk level. The training effect is comprehensively evaluated through the multi-modal data fusion training method, and the lower limb thrombus rehabilitation efficiency is improved.

The invention provides a rescue worker skill evaluation and improvement system and electronic equipment, and the system comprises a model construction module which is used for constructing a personalized skill model, and the personalized skill model is obtained through training based on the basic information of each rescue worker; the skill evaluation module is used for outputting the ability levels of the rescue workers in different skill dimensions according to the current information of the to-be-evaluated rescue workers; the training plan generation module is used for generating a recommended training plan according to the ability levels on the different skill dimensions based on a knowledge graph; and the training plan optimization module is used for optimizing the current training plan of each rescue worker based on the reinforcement learning model. According to the method, the individual ability level can be comprehensively and accurately evaluated, the training content and the personnel short board are accurately matched, and the training efficiency and effect are improved; a training plan is automatically adjusted based on a reinforcement learning model, the scientificity and effectiveness of the plan are ensured, and continuous skill advancement of rescue workers is promoted.

The invention discloses a community elderly weakness screening and intervention training method based on artificial intelligence, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining dynamic physiological parameters and medical data, and generating a weakness evaluation parameter set; determining a target weakness level and generating a personalized training scheme in combination with the user tolerance parameter set; generating a user action quality evaluation result based on real-time action monitoring, and adjusting a training intensity parameter according to a baseline error threshold; and returning an evaluation result, updating the weakness evaluation parameter set, and generating a community-level weakness early warning signal according to a group risk threshold. The time sequence physiological data fusion and decision tree-transfer learning hybrid model of the wearable device is adopted, real-time optimization of a personalized training scheme and dynamic early warning of group weakness risks can be achieved, and screening accuracy and intervention effectiveness are remarkably improved.

The invention provides a personalized training plan generation method and system in meta-universe smart sports, and relates to the technical field of smart sports, and the method comprises the steps: obtaining user meta-universe scene sports data, calculating an actual sports load based on a human body biomechanical model, carrying out the time sequence analysis through a neural network, and predicting a physiological limit threshold value; and constructing a training knowledge graph, generating a stage training plan adaptive to the user through a graph neural network, and finally presenting the stage training plan in a meta-universe virtual scene form. According to the method, personalized, safe and scientific training guidance can be provided, and the training effect and the user experience are improved.

The invention relates to a self-adaptive visual training method based on eye features, and belongs to the technical field of visual health and artificial intelligence. In order to solve the problems that traditional visual training equipment is heavy in structure, single in training mode and lack of personalized regulation and control and concentration state monitoring, the visual training equipment obtains eye images of a user in real time through an image acquisition module, adopts visual intelligent analysis, extracts double features of eye contours and pupil positions, calculates the visual concentration degree, and improves the visual training efficiency. And a concentration state is judged by combining a dynamic self-adaptive threshold value. When the concentration degree is insufficient, the control module triggers intervention mechanisms such as prompt or training pause and the like, and the remote interaction module uploads data to the cloud platform to generate a personalized training scheme. Intelligent and personalized regulation and control of the training process are achieved, the training effect and compliance are improved, the system is compact in structure and suitable for various scenes, and an efficient solution is provided for vision health.

The invention relates to the technical field of intelligent fitness equipment and exercise training control, and provides a training plan dynamic generation and action quality scoring method and system for an intelligent dumbbell. The method comprises the following steps: acquiring action data of a user during training and personalized parameters input by the user in real time through an acceleration sensor, an angular velocity sensor and a pressure sensor which are arranged in the intelligent dumbbell; inputting the action data and the personalized parameters into a preset training plan generation algorithm module so as to generate a stage training plan comprising the action type, each group of training duration, intermittent time and a weight adjustment strategy; an action quality scoring model is constructed, a standard action template is pre-stored in the scoring model, and the standard action template comprises standard trajectory curves, speed threshold ranges, amplitude reference values and force exerting symmetry parameters corresponding to different training actions; and performing multi-dimensional matching analysis on the action data and a standard action template, and dynamically correcting the score in combination with the current fatigue parameter of the user.

The invention relates to the technical field of pelvic floor muscle training, in particular to a pelvic floor muscle recovery training system. The system comprises a physiological data acquisition module, a feature extraction and analysis module, a model training and optimization module, a personalized training plan module, a dynamic training adjustment module, a three-dimensional mapping and modeling module, a biomechanical simulation module and an intelligent state recognition module. According to the invention, through fusion of a convolutional neural network and a time sequence analysis method, key physiological features are extracted and analyzed, a personalized training plan module is combined with an intelligent algorithm, a training scheme is customized for a user, specific requirements are matched, and a dynamic training adjustment module monitors and adjusts training parameters in real time. The three-dimensional mapping and modeling module provides a muscle model by using an imaging technology and a three-dimensional reconstruction algorithm, the biomechanical simulation module simulates the mechanical response of muscles through a finite element analysis and computer simulation technology, and the intelligent state recognition module analyzes the muscle state by using a support vector machine technology.

Provided in the present application are a training system and method for the field of survey design, and a storage medium and a device. The training system comprises: a data collection module, which is configured to acquire student data and training data in the field of survey design; a knowledge graph construction module, which is configured to construct a training knowledge graph on the basis of the training data; a profile generation module, which is configured to generate a student profile on the basis of the student data, and generate a course profile on the basis of a training course in the field of survey design; and a training plan generation module, which is configured to use an artificial intelligence model to generate a personalized training plan on the basis of the student profile, the course profile and the training knowledge graph. The training system can generate a personalized training plan and has good training effects.

The invention discloses a visual training system and a visual training method, and relates to the technical field of visual training, the visual training system comprises a data acquisition module, an intelligent control module, a visual training module and a data management module; wherein the data acquisition module is used for acquiring initial visual data of a user; the intelligent control module is used for generating a training scheme according to the initial visual data, dynamically adjusting the training scheme according to the real-time training data, and generating and sending a control signal to the visual training module according to the training scheme; the visual training module is used for performing visual training according to the control signal and outputting training data; the data management module is used for storing training data and generating and outputting a visual training report according to all the training data of the user; the visual training report comprises a training progress curve and a vision change curve of the user. According to the invention, an intelligent visual training method can be provided to improve the visual training effect.

The invention relates to the technical field of data analysis, in particular to a physical exercise trajectory data analysis system based on AI recognition. Comprising a data acquisition module for acquiring multi-dimensional data of a table tennis player in a movement process through a multi-modal sensor; according to the method, a multi-dimensional complete data set is formed by accurately capturing the biomechanical data of the acceleration, the angular velocity and the force output of the ping-pong athlete in the actions of running, jumping and swinging, a basis is provided for accurate analysis of the force exerting skill of the ping-pong athlete, and the force exerting skill of the ping-pong athlete is accurately analyzed through the AI model. The exercise efficiency, physical fitness condition and action precision of the ping-pong athlete are predicted, so that the power generation skill and promotion space of the ping-pong athlete are better analyzed, a personalized training plan is generated according to the defects of the athlete, the ping-pong athlete is assisted in identifying and improving the defects, and the training efficiency is improved. Powerful support is provided for improving the power generation efficiency and the ball hitting stability of the ping-pong athletes, and the training effect of the power generation skills of the ping-pong athletes can be improved.

The invention relates to the technical field of child cognitive function evaluation, and particularly discloses an intelligent child cognitive function evaluation method and device and a medium, and the method comprises the steps: collecting real-time behavior physiological coupling response data of a tested child under the stimulation of color and digital dual tasks through a multi-modal stimulation collection device, and obtaining a cognitive response chain dynamic model; according to the cognitive reaction chain dynamic model, performing performance quantification on the joint distribution of the reaction accuracy and the reaction time of the child under the stimulation of color and digital dual tasks to obtain a cognitive performance quantitative index; meanwhile, self-adaptive calibration of the individual cognitive baseline is carried out to obtain individual cognitive baseline data; and finally obtaining a personalized course recommendation matrix. The method not only is helpful for evaluating the current cognitive ability level of children, but also can provide reference for self-adaptive calibration of individual cognitive baselines. The method is crucial for recognizing cognitive deviation and formulating a personalized training plan. Adjustment can be carried out according to the specific cognitive requirements of children, and the effectiveness and pertinence of training can be improved.

The invention provides a virtual reality training system and method for fireman stress disorder adjuvant therapy, the fireman training system and method based on virtual reality are provided by utilizing the development of the virtual reality technology and the heart rate variability monitoring technology, the virtual reality environment is selected and adjusted in combination with multiple dimensions, and the fireman stress disorder adjuvant therapy effect is improved. On one hand, the fire-fighting task processing capability of the firefighter can be trained, and on the other hand, the post-traumatic stress disorder of the firefighter can be treated in an auxiliary manner, so that the firefighter is helped to overcome the fear of trauma and high pressure in the fire-fighting task, and the post-traumatic stress disorder is prevented and treated in an auxiliary manner; the cure rate of the stress disorder of the group of firemen is effectively improved, and the prevalence rate of the stress disorder is reduced. Moreover, according to the scheme, through multi-level personalized adjustment, it is ensured that the adjuvant therapy training scheme better meets the unique requirements of each fireman according to individual characteristics, individual differences, psychological states and physiological states, and the training effect is effectively improved.

The invention discloses an evaluation method and device for joint rehabilitation training, and relates to the technical field of rehabilitation image processing. Receiving a monitoring request sent by a target user, and determining a to-be-evaluated part and rehabilitation duration according to the monitoring request; inputting the rehabilitation duration and the to-be-evaluated part into a preset training database for matching to obtain a first training plan; generating a first training video according to the first training plan, and obtaining a second training video corresponding to the target user; extracting a plurality of training images from the second training video, and processing a target training image to obtain a first training action; when the first training actions are in the preset action table, the first training actions are summarized into a first set, and a first number is obtained; and when the first number is not equal to a preset first number, determining that the target user is in a training abnormal state, and sending the training abnormal state to the target user. By implementing the technical scheme provided by the invention, the problem that the rehabilitation personnel are difficult to accurately evaluate the normalization of own actions in the training process is solved.

The invention provides a remote rehabilitation system for a stroke patient, relates to the technical field of stroke rehabilitation, and aims to remotely diagnose the patient, periodically generate a personalized rehabilitation scheme for the patient, and timely adjust and update a training scheme according to the exercise effect of the patient. The system comprises a data acquisition terminal, a data transmission terminal, a control terminal, a doctor monitoring terminal and a patient terminal, a personalized rehabilitation training scheme generation algorithm is applied, multi-dimensional data is fused to form a comprehensive feature vector, and rehabilitation potential and requirements are evaluated. Personalized schemes covering exercise, cognition and psychological intervention are customized for the patient, dynamic adjustment is conducted according to feedback, and precise rehabilitation is achieved. The system has the functions of real-time monitoring, intelligent reminding and abnormity early warning, the rehabilitation effect is optimized, the medical burden is relieved, the life quality of patients is improved, and the system is suitable for medical institutions at all levels.

The invention discloses a new line training demand analysis method based on data driving, and relates to the technical field of computers. The method aims at solving the problems that training demand analysis depends on subjective experience, data islands are serious, response lags and quantitative prediction models are lacked in the prior art. The method comprises the steps of collecting and integrating multi-source heterogeneous data such as physical assets, human capital and process knowledge; constructing a time sequence dynamic association map representing the preparation state; predicting a future preparation state by using the space-time diagram neural network model; quantizing a personnel capability gap based on the prediction result and the operation baseline, and generating a capability gap matrix; performing priority ranking and clustering on the training requirements in combination with the key weight of the system to form a structured training theme; and dynamically generating a training plan, and updating the atlas in a closed-loop manner through training feedback data. Accurate prediction, quantitative evaluation and adaptive optimization of training requirements are realized, and scientificity and foresight of new line preparation are improved.

The invention relates to the technical field of medical training equipment, in particular to a critical care ultrasonic image training system, which comprises a clinical data acquisition module for acquiring critical clinical case data, ultrasonic image materials and diagnosis and treatment process specifications; the training scene construction module is used for constructing a high-fidelity three-dimensional training scene according to severe clinical case data, ultrasonic image materials and diagnosis and treatment process specifications; the virtual ultrasonic practical operation module simulates the operation of an ultrasonic probe based on a high-fidelity three-dimensional training scene, and generates an ultrasonic image corresponding to an anatomical part in real time; the intelligent skill assessment center assesses operation normalization and diagnosis accuracy through practical operation data and real-time generation of an ultrasonic image corresponding to an anatomical site in combination with an improved ResNet-LSTM model, and an assessment result is obtained; and the personalized teaching unit generates a customized training scheme and knowledge strengthening content according to the evaluation result in combination with the learning track of the student. Therefore, the problems of insufficient dynamic scene simulation, poor authenticity and the like in the prior art are solved.

The invention relates to the technical field of intelligent medical rehabilitation, in particular to an abdominal respiration training method and system based on an intelligent guiding algorithm. The continuous real-time breathing data sequence contains at least one of a breathing frequency periodic change characteristic, a breathing amplitude dynamic change characteristic and a breathing phase accurate definition characteristic which change along with time; comprehensively evaluating the continuous real-time breathing data sequence according to a statistical analysis algorithm based on dynamic threshold setting and multi-dimensional feature fusion to obtain a comprehensive breathing health index for evaluating the breathing state of the user; according to the comprehensive respiratory health index, based on inference rules in a knowledge base, a customized respiratory training plan is generated through an intelligent decision engine combining a deep learning algorithm and a biological feedback mechanism. By implementing the method, potential information in the respiration data can be deeply mined, so that a more scientific and personalized respiration training plan is generated.

The invention provides a strength training action intelligent calculation evaluation system based on multi-modal fusion, which is used for executing a strength training action evaluation method, and comprises the following steps: collecting multi-modal input data in a strength training process of a trainer; the input data comprises visual information, muscle neural signals and physiological data of the trainee, the visual information comprises limb actions and posture tracks, and the physiological data comprises heart rate and blood sugar; preprocessing the input data, and inputting the preprocessed input data into a strength training expert agent, so that the strength training expert agent outputs an evaluation result of the trainer in combination with the cue word and the strength training expert knowledge base; and displaying an evaluation result, and determining a training plan of the trainer based on the evaluation result. The physical strength training auxiliary logic is comprehensively upgraded from data basis, intelligent analysis to training closed loop, more systematic technical support is provided for scientific training and efficient improvement, and the requirements of modern physical strength training for precision, individuation and long-term effect are met.

The invention discloses an upper limb rehabilitation training method and system based on a human body motion model, and relates to the technical field of rehabilitation training. Constructing a human body motion model and outputting simulation data; calculating a physiological index coefficient, and further calculating the exercise intensity of each training action; calculating a motion mode complexity index of each training action so as to obtain and calculate the training duration of each training action; and calculating a motion deviation value, and further calculating a comprehensive dynamic deviation index. Presetting a threshold value and judging whether the rehabilitation training plan is suitable for the current state of the patient; whether a rehabilitation training plan needs to be changed; according to the method, the suitability of the rehabilitation training plan can be evaluated in real time, and the training scheme is dynamically adjusted; the training is always fit with the condition of the patient, and the rehabilitation efficiency and success rate are improved.

The invention provides a sound source localization rehabilitation training method, system and device and a medium. The sound source localization rehabilitation training method comprises self-adaptive training and scene simulation training, the self-adaptive training comprises the steps of providing a training task in the period, and the training task in the period comprises the step of selecting training difficulty; according to the training difficulty, sound stimulation is presented in different spatial orientations, and the training difficulty can be adjusted according to a certain logic; the training difficulty of the next period of training is adaptively adjusted according to the correct rate p of one training group of the subject until the preset training amount is reached, and the difficulty setting includes but is not limited to scene setting, analog input information amount and signal-to-noise ratio adjustment. According to the scheme, a hearing-impaired person can perform rehabilitation training in a home environment, the need of manual intervention is reduced, and the training convenience and flexibility are improved. Through adaptive training, on the basis of ensuring scientificity, accuracy and effect of a training plan, the positioning ability of the hearing-impaired person is enhanced, and the integration of the hearing-impaired person into the society is accelerated.

The invention discloses a teacher teaching ability improvement training method and system, and the method comprises the steps: determining a plurality of evaluation dimensions for teacher teaching ability, obtaining a quantitative evaluation index of each evaluation dimension, and obtaining a plurality of high-quality training course resources according to the quantitative evaluation indexes; historical teaching state parameters input by each teacher are obtained, and teaching ability evaluation is carried out on each teacher; analyzing a teaching short board index and short board state description of each teacher according to an evaluation result, and determining capability improvement demand parameters; and screening out a target training course resource from the plurality of high-quality training course resources according to the ability improvement demand parameters, making a training plan based on the learning ability information of each teacher, and carrying out teaching ability improvement training on each teacher according to the training plan. According to the invention, accurate short board related ability training can be accurately carried out for teachers with different teaching levels, the training efficiency and stability are improved, the adaptability of training resources is ensured, and the practicability is improved.