660 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 a multi-modal rehabilitation data intelligent evaluation method and system based on a large model, and relates to the field related to data processing, and the method comprises the steps: carrying out the data collection based on a plurality of data sources, obtaining and integrating multi-modal data collection results, obtaining a multi-modal data set, synchronizing the multi-modal data set to an information extraction model, and carrying out the retrieval, a rehabilitation training scheme is executed to track the rehabilitation progress of a target user in real time; rehabilitation effect data is obtained and synchronized to the rehabilitation evaluation large model; a multi-dimensional evaluation result is obtained to perform feedback optimization on the rehabilitation training scheme; and a rehabilitation optimization training scheme is determined. The technical problem that traditional rehabilitation evaluation depends on professionals and lacks enough professional knowledge and experience, so that the accuracy and efficiency of evaluation are affected is solved, and the technical effects of performing intelligent personalized evaluation on rehabilitation training and improving the precision and efficiency of rehabilitation evaluation are achieved.

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 belongs to the field of remote virtual rehabilitation training, and particularly relates to a remote rehabilitation management method and system based on virtual reality. The method comprises the steps that firstly, the current health condition, the expected rehabilitation target and the needed period of a rehabilitation patient are predicted through a comprehensive evaluation algorithm and machine learning, secondly, a personalized training plan is intelligently recommended from a virtual rehabilitation training strategy library according to an evaluation result, and in the training implementation process, a sensor continuously monitors physiological indexes and micro-expression response of the rehabilitation patient; the rehabilitation progress is analyzed and evaluated through real-time data and fed back to a training strategy library in time, the training intensity and period are dynamically adjusted, and it is ensured that training is closely matched with the actual health state of a rehabilitative person; by fusing electronic medical record processing, virtual reality technology, machine learning prediction and visual emotion recognition, precise and intelligent management of rehabilitation training is realized, and rehabilitation efficiency and experience are remarkably improved.

The invention relates to a muscle strength and muscle fatigue prediction algorithm based on surface electromyogram signals, which comprises the following steps of: (1) acquiring the surface electromyogram signals of a measured muscle area by using a non-invasive sensor, and acquiring muscle mechanics parameters at the same time; (2) carrying out filtering, amplification and noise reduction preprocessing on the collected surface electromyogram signals to eliminate interference and enhance signal quality; and (3) extracting multi-dimensional features from the preprocessed surface electromyogram signals and muscle mechanics parameters, establishing a frequency domain data set by using the feature values, and calibrating the muscle mechanics parameters. The method has the advantage that the prediction accuracy and reliability are enhanced. Muscle strength and muscle fatigue states of different individuals can be reflected more accurately, and accurate prediction of individual differences is realized. The adopted surface electromyogram signal acquisition mode is non-invasive, and a plug-in electrode is not needed, so that the test discomfort is reduced, and the acceptability and participation degree of a testee are increased; by establishing an individual model and combining with real-time signal acquisition, real-time monitoring and prediction of muscle strength and muscle fatigue can be realized, and a user can obtain feedback of a muscle state at any time, so that the exercise intensity, posture and training plan are adjusted, and the training effect is optimized.

The invention discloses a rehabilitation assisting method and device suitable for cognitive disorder treatment and a medium, and relates to medical rehabilitation. A tablet personal computer, a millimeter wave radar and the like are included, artificial intelligence technologies such as signal processing and deep learning are utilized, and rehabilitation training of patients with mild cognitive impairment caused by brain injury and cerebral apoplexy is assisted. Basic information, medical history and current cognitive conditions of the patient are recorded and collected through the tablet computer, and a rehabilitation file is generated; based on the cognitive disorder type and severity of the patient, a rehabilitation training plan is intelligently made; a patient performs rehabilitation training through a gamified training interface, and the physiological state in the training process is monitored in real time in a non-contact mode based on millimeter wave radar; a deep learning model is used for analyzing training data of the patient, and rehabilitation progress is evaluated; training and evaluation data are summarized, and a subsequent rehabilitation scheme is optimized. Full-process automatic management from patient information collection, training plan making, training implementation, rehabilitation evaluation to data analysis report generation can be rapidly and accurately achieved, and the cognitive rehabilitation efficiency and effect are improved.

The invention provides a method for estimating large language model training and reasoning time consumption, a hyper-parameter configuration reference is provided for large model training fine tuning, and a calculator can help a user to simplify the large model training process through automatic analysis and giving of appropriate parallel configuration requirements; a user does not need to manually research and try different configurations, but can select proper configurations according to suggestions of the calculator; according to the method, the computing power and time cost required by large model training are estimated, and a user can better understand and evaluate the cost and benefit of large model training by estimating the core index of large model training, so that the user can more easily try to use, and large model vertical application landing is promoted; gPU models and large models can be flexibly added, and the calculator not only can simulate training indexes under given configuration, but also can provide reference for a user to customize a model training plan. The user can adjust the configuration parameters in the training plan according to the estimation result of the calculator so as to achieve a better training effect and a better resource utilization rate.

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 provides an intelligent fitness guidance system and method based on AI posture analysis and MR man-machine interaction. The system comprises a 3D depth global camera module used for capturing RGBD image data of a user; the AI-based fitness plan recommendation module is used for planning a fitness plan for the user; the dynamic adjusting module is used for analyzing the physical fitness change of the user and dynamically adjusting the training plan according to the actual physical fitness condition of the user; the instant feedback module is used for collecting feedback of the user on the training effect; the AI-based body-building action analysis module is used for identifying and analyzing body-building actions of the user based on the multi-modal body-building data of the user; the AI-based virtual coach interaction module is used for performing real-time communication with the user based on the fitness knowledge large model; and the fitness action demonstration module based on 3DMR imaging is used for projecting a virtual 3D fitness coach and a virtual character similar to the posture of the user. According to the invention, intelligent and personalized fitness guidance can be provided for the user.

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 field of cerebral apoplexy rehabilitation management, in particular to a cerebral apoplexy patient rehabilitation training management system and method based on an internet platform. The stroke patient rehabilitation training management system based on the internet platform comprises a training plan generation module, a training time dynamic adjustment module and a training intensity dynamic adjustment module. According to the method, personalized exercise priority indexes and cognitive priority indexes are calculated according to the specific exercise function recovery degree and cognitive function recovery degree of each cerebral apoplexy patient, the fields in which the patient needs more attention and training are determined, a targeted exercise function training plan and a targeted cognitive function training plan are generated according to the fields, and the training efficiency of the cerebral apoplexy patient is improved. Therefore, the allocation of rehabilitation resources is optimized; the personalized rehabilitation training plan can better meet individual differences of patients, the rehabilitation efficiency is improved, and the recovery progress is accelerated.

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 relates to the technical field of rehabilitation medicine, in particular to a body carving slimming method for fat metabolism based on bioelectricity feedback and acupoint stimulation. The invention provides a body carving slimming method based on bioelectricity feedback and acupoint stimulation. The body carving slimming method comprises the four steps of obtaining, processing, acupoint stimulation and outputting. Bioelectricity signals and weight data of body parts of a user are collected through a bioelectricity and weight detection device. The muscle activity state and fat distribution are analyzed, a posture improvement value is calculated, and a personalized body carving training scheme is generated. In an acupoint stimulation link, electrical stimulation is performed on a specific acupoint according to the scheme, bioelectricity feedback is monitored in real time, and stimulation parameters are dynamically adjusted. And generating a personalized report containing the posture change and the training effect evaluation, and providing suggestions to the user. According to the method, the effect, safety and individuation are remarkably improved, and a new direction is opened up for development of the body carving and slimming field.

The invention discloses a heterogeneous computing system and a training time consumption prediction method and device thereof, a medium and a product, and is applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring user training scheme data corresponding to a training task to be deployed, and acquiring maximum resource supply data provided by a heterogeneous computing system to meet the user training scheme data; and predicting the calculation amount and the memory reading amount of each neural layer based on the working process of each neural layer of the to-be-trained network model in the single iterative training process, and determining node time consumption information of each neural layer in combination with the maximum resource supply data. And according to the node time consumption information of each neural layer of the to-be-trained network model, determining single iteration time consumption of the heterogeneous computing system for training the to-be-trained network model. According to the method, the problems that time consumption prediction can only be carried out on actually deployed training tasks and the accuracy is not high in the prior art can be solved, and the time consumption of single iteration training of a single calculation unit can be predicted with high precision on the basis of not actually deploying the training tasks.

The invention belongs to the technical field of virtual reality, and discloses a virtual rich real environment training system based on benign stress, comprising: a database module for storing cognitive tasks and virtual scenes; the acceptance testing module is used for testing a preset cognitive task of the user and generating a scene acceptance index; and the initial training task generation module is used for generating a training task comprising two groups of scene parameters and one cognitive task according to the index. And the training execution module is used for constructing a dynamically changing virtual training scene according to the two groups of parameters and executing a cognitive task in the virtual training scene. And the training task updating module is used for generating a new training task based on the execution result and the acceptability index. And the training planning module formulates an overall training plan and controls an execution process. Personalized training content is provided by evaluating individual differences of the users, it is ensured that the training difficulty is matched with the capabilities of the users, and the training effect is optimized while the user participation degree and the possibility of insisting in training for a long time are improved.

The embodiment of the invention relates to the field of data processing, and provides a training method based on a large model and a related device, and the method comprises the steps: carrying out the interaction of an evaluation stage with a user through the large model, obtaining evaluation interaction data, and carrying out the real-time evaluation analysis based on the evaluation interaction data, so as to obtain a personalized portrait of the user; generating a training scheme according to the personalized portrait; and carrying out interaction in a training stage with the user through the large model based on the training scheme, so that the user completes the training scheme. According to the embodiment of the invention, interactive intelligent guidance can be carried out through a large model, so that more real multi-dimensional assessment interaction data for assessing the psychology of the user is obtained, more accurate psychological characteristics are obtained, a targeted training scheme is conveniently designed for the user, and the user experience is improved. The psychological ability such as psychological toughness, emotion management and cognitive level of the user can be improved, and better experience is brought to the user.

The invention discloses an old people cognitive health monitoring system based on virtual reality, and relates to the related technical field of virtual health monitoring technologies. Comprising a multi-modal sensing acquisition module, a virtual reality environment generation module, a cognitive nerve feedback module, a real-time feedback module, a deep learning analysis module, a personalized training plan generation module, a data storage and management module, a remote medical platform and a virtual assistant and coach module. The system can help the elderly to improve the work memory ability through a special work memory training task and a cognitive nerve feedback module, and the elderly can be more efficient during daily life activities by improving the work memory and the attention basic cognitive function aiming at the common attention distraction problem of the elderly, so that the life quality is improved, and the elderly can be helped to take care of the elderly. The cognitive neural feedback module not only pays attention to a cognitive function, but also helps the elderly to better manage emotions by adjusting an emotional network, promotes social interaction, and reduces aloneness and anxiety.

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 relates to the technical field of course arrangement systems, and particularly discloses an automatic course arrangement system based on an optimization algorithm, and the system comprises a training plan management module which is used for inputting training plan information according to teams, creating a training plan based on the training plan information, and modifying and deleting the training plan information; the course management module is used for providing course information input and maintenance functions and establishing course version management according to different years; the automatic course arrangement module is used for arranging courses by using an automatic course arrangement algorithm based on the course information, and the automatic course arrangement algorithm comprises the steps of obtaining the course information from the course management module, constructing a demand set and constructing a resource set, and the resource set comprises a class time set and a teaching field set; and constructing a mapping constraint matrix: M '* N' matrix, M 'being the number of elements in the demand set, N' being the number of elements in the resource set, and M 'being less than or equal to N', and based on the demand set and the resource set, performing course arrangement by using the mapping constraint matrix.

The invention relates to the field of swimming physical training analysis, and particularly discloses a swimming underwater physical training effect analysis system based on training data, which is characterized in that video data, motion data and physiological data of a swimmer are acquired by integrating an underwater camera, an inertial measurement unit, a myoelectricity sensor, a heart rate sensor and a blood oxygen sensor; space-time synchronous analysis of multi-modal data is realized; on the basis of joint angle change consistency and muscle activation synchronism, whether the action of the swimmer is standard or not is judged, AR real-time feedback is conducted, and real-time correction of the swimmer in training is facilitated; the training load of the swimmer is evaluated based on the underwater speed, the stroke, the stroke frequency, the swimming distance and the swimming duration, and overrun early warning is carried out, so that making of a personalized training plan is facilitated, and meanwhile, overtraining is avoided; by monitoring the heart rate, the oxyhemoglobin saturation, the muscle activation duration and the joint activity condition of the swimmer, whether the swimmer has the injury risk or not is judged, early warning is triggered, and the injury occurrence rate is reduced.

The invention provides a staged training making method and system based on artificial intelligence. The method comprises the steps of obtaining a training target and data set characteristics; according to the training target and the data set characteristics, a training plan draft is generated, and the training plan draft comprises stage division, a learning rate strategy of each stage, a data enhancement strategy and a model evaluation standard; based on the training plan draft, initializing one or more neural network models, and allocating corresponding training resources for each stage; before each stage starts, training parameters of the current stage are adjusted according to the training result of the previous stage, and the adjustment at least comprises learning rate adjustment, regularization parameter adjustment or data enhancement strategy adjustment; at the end of each stage, the performance of the model is evaluated, and whether to enter the next stage or repeat the training of the current stage is determined according to the evaluation result, so that the efficiency and quality of model training can be effectively improved, and effective utilization of resources is ensured. Due to the flexibility and the self-adaptability of the method, the method can well adapt to various complex training tasks and data set characteristics.

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 discloses an action rehabilitation method based on training, and relates to the field of medical rehabilitation, and the main scheme is as follows: making a joint training plan, a strength training plan and a balance training plan according to physical state data and sensor data of a patient; the difficulty level of the virtual scene is selected according to the joint training plan, the strength training plan and the balance training plan, and the problem of lack of objective evaluation standards is solved; according to the action accuracy Qn, the qualified threshold value QA and the good threshold value QB, the difficulty level of the virtual scene is adjusted, and the problem that the training intensity and progress are unreasonable is solved; the rehabilitation degree of the patient is evaluated according to the rehabilitation index mu n of the patient and the rehabilitation index threshold set, the rehabilitation stage of the patient can be determined, the patient and a rehabilitator can know the rehabilitation effect, and the problem that the rehabilitation progress of the patient is difficult to quantify accurately is solved.

The invention relates to the field of postoperative rehabilitation management, in particular to a postoperative rehabilitation management system for hand and foot surgical patients. The postoperative rehabilitation management system for the patient in the hand and foot surgery department comprises a data monitoring module, a rehabilitation plan prediction module and a rehabilitation plan dynamic adjustment module. By comprehensively analyzing the postoperative data of the patient, including the wound healing condition and the function recovery progress, the recovery trend of the patient in the postoperative training period can be predicted, a scientific initial function recovery training plan is generated, and clear guidance is provided for rehabilitation training of the patient; in the actual training process, the system further performs real-time dynamic adjustment on the rehabilitation training plan by comparing the difference between the actual recovery condition and the prediction trend; the method ensures that the rehabilitation training plan is always consistent with the actual recovery state of the patient, so that the rehabilitation effect is maximized, the rehabilitation time is shortened, and the risk of complications is reduced.

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.