54 results about "Training period" patented technology

The invention relates to the field of mode recognition, and discloses a traditional physical health intervention system and method combined with multi-mode data, and the traditional physical health intervention method combined with the multi-mode data comprises the steps: continuously collecting physiological monitoring signals, action execution records and training scene parameters; assigning differentiated attention weights to various monitoring segments; performing cross-channel feature comparison on the real-time monitoring segment, and extracting a comprehensive training state feature group of the body load bearing capacity and the action control state of the training participant; based on the comprehensive training state feature group, identifying an action risk fragment influencing the training safety; performing reordering according to the concentration degree of the action risk fragment triggering conditions, and constructing a training risk labeling set of the potential risk change range and degree; and through training the risk labeling set, determining a key period with a relatively high risk accumulation speed in a specific training period. The device has the advantage that the safety of traditional physical training is improved.

The invention discloses an intelligent diopter training system and method based on a multi-modal physiological feedback and reinforcement learning algorithm. The intelligent diopter training system comprises a multi-modal physiological data acquisition module, a central processing PC, a user terminal and a cloud data platform. Visual behavior data, ciliary muscle state data and autonomic nerve reaction data of a user are collected in real time through a multi-modal physiological data collection module, multi-modal physiological data fusion and feature extraction are carried out, and a refraction state evaluation model is used for calculating a comprehensive score of a refraction state grade, visual fatigue and adjustment sensitivity of the current user. A personalized training parameter set of the next training period is dynamically decided and generated based on a reinforcement learning algorithm, and the user terminal presents visual training stimulation according to the current personalized training parameter set. The cloud data platform stores the data and generates a visual training effect trend chart, a diopter change prediction report and personalized training suggestions to complete training scheme personalization, training means intellectualization and feedback evaluation comprehensiveness.

The invention relates to the technical field of federated learning in an Internet of Vehicles scene, in particular to a federated learning method suitable for a high dynamic environment of a vehicle. According to the method, adaptive vehicle federation learning is provided, so that a local training period adapts to the vehicle capability, the scheduling flexibility is improved, the influence of insufficient local period on model updating is relieved, the training efficiency is improved, and the communication competition is reduced. Meanwhile, a federal learning scheduler based on an actor-commentator structure is designed to realize a dual-time scale optimization method, so that the training time is minimized and the model performance is maximized. According to the method, the vehicle federated learning performance reduction caused by the dynamic complex vehicle edge network is effectively relieved, and the training efficiency of the vehicle federated learning is improved.

The invention discloses a return anxiety intervention method and system based on VR scene self-adaption and data feedback, and relates to the technical field of return anxiety intervention methods.The return anxiety intervention method comprises the steps that occupational attributes, disease stages and psychological state evaluation data of a user are collected, and physiological baseline information obtained by wearable equipment is combined; constructing an initial user portrait containing individual features and function states; matching a preset intervention path template according to the initial user portrait, and generating a personalized VR training scheme with a module sequence, a scene difficulty level and a training period; executing the personalized training scheme in a virtual reality environment, carrying out commuting simulation, task operation and social interaction training through an immersive office scene, and synchronously collecting multi-modal physiological response data of a user in a training process; the anxiety level of the user is dynamically evaluated based on physiological response data in the training process, and when it is detected that the anxiety state exceeds a preset tolerance range, the stimulation intensity of the current scene is automatically reduced, and a relaxation bootstrap program is started.

The invention discloses a virtual sensory integration training method based on force feedback, and the method comprises the steps: constructing a hot chamber virtual environment containing a mechanical arm model and an operation object through a scene simulation platform, and configuring a joint position according to a real mechanical arm parameter; the dynamics simulation platform builds a corresponding dynamics model and configures inverse kinematics solution and collision detection functions; force feedback equipment is integrated, and a working space mapping model is constructed to convert hand motion into the expected pose of the tail end of the mechanical arm; the double platforms synchronize data through a dynamic link library, and a force sense calculation module calculates collision force and bolt assembling and disassembling torque and transmits the collision force and the bolt assembling and disassembling torque to force feedback equipment. The method does not need to depend on a real mechanical arm, safety risks and high cost are avoided, training repeatability and pertinence are improved, immersion and operation precision are enhanced through force sense and visual sense synchronous feedback, the training period is shortened, and the method is suitable for fine operation training of the mechanical arm in a dangerous environment.

The application provides a rule cultivation quality full life cycle monitoring method and system, which comprises the following steps: in a training period, collecting operation logs of rule cultivation personnel in multiple clinical information systems, and reconstructing the operation logs into behavior trajectory sequences reflecting clinical core capabilities; evaluating the quality deviation degree of the rule cultivation personnel in the clinical case processing process based on the behavior trajectory sequences; obtaining the skill mastering vector of the rule cultivation personnel in each clinical core capability based on the quality deviation degrees of multiple clinical operations in a historical period and a preset capability dimension mapping rule; generating the capability evolution characteristics of the rule cultivation personnel in the training period according to the change law of the skill mastering vector over time; and generating a multi-dimensional evaluation result including skill maturity, risk deviation degree, diagnosis and treatment logic consistency and clinical efficiency factors based on the capability evolution characteristics and the quality deviation degree. The application improves the timeliness and accuracy of rule cultivation quality evaluation.

The application belongs to the field of police dog training system, and provides a police dog search smell ability automatic training system, which comprises a base and a support frame, a function frame fixedly arranged on the base, an odor generating mechanism arranged on the function frame, a driving mechanism arranged in the function frame, a connecting mechanism arranged between the function frame and the support frame, and a shaping mechanism slidingly arranged on the support frame. Through conditional automatic reward to the police dog, the key elements in the police dog search smell training are solved, the purpose of police dog search behavior training under the condition of no monitoring is realized, the positive reinforcement method is adopted to make the police dog operation more active and lively, meanwhile, unmanned autonomous training can be realized, passive reaction of the police dog is prevented, the training and the police dog life scene are integrated, and the training efficiency can be greatly improved, the training period can be shortened, and the labor cost and expense can be saved.

The invention discloses a fitness intensity self-adaptive adjusting method and system based on physiological data feedback, and relates to the technical field of medical rehabilitation. The method comprises the following steps: acquiring historical physiological limit data of a target rehabilitation user and an initial physiological state baseline of a current training period; calculating a dynamic safety intensity interval of the user individual; collecting physiological feedback data in real time in training, inputting the physiological feedback data into the stimulation efficiency evaluation network, and outputting an actual physiological load value and an effective stimulation coefficient of current training; calculating the recommendation intensity of the next training unit in the safety interval according to the effective stimulation coefficient and the rehabilitation progress function; and executing the recommendation intensity, updating the evaluation value based on a new round of physiological feedback data, and carrying out loop iteration until a training cycle target is reached. According to the invention, through dynamic safety boundary and intelligent closed-loop regulation and control, personalized and adaptive optimization of medical rehabilitation training intensity under safety guarantee is realized, and the safety and efficiency of training are improved.

The application provides a trigger type wearable ultrasonic device and method, and relates to the technical field of medical imaging and rehabilitation engineering. The device comprises: an ultrasonic imaging module for generating an ultrasonic image of a target part; a training intensity monitoring module for monitoring rehabilitation training actions of a user in real time and generating a training intensity signal; a central processing and control module for generating an action type and a training intensity parameter according to the training intensity signal, generating an imaging trigger instruction according to a trigger condition, and synchronously and associatively storing the collected ultrasonic image and the training intensity parameter and the action type at the trigger time. Thus, the ultrasonic imaging is intelligently triggered through the training intensity signal, the system only works in an effective training period, thereby significantly reducing the power consumption of the system and prolonging the endurance time; and the multimodal synchronization and association of the ultrasonic image data and the training intensity parameter are realized, thereby providing an objective and fused data basis for the quantitative evaluation of rehabilitation effects and the accurate formulation of rehabilitation plans.

The invention relates to the field of exercise training, in particular to a respiratory training guidance system and method.Body part motion data of a target object in a first scene under a plurality of respiratory modes are obtained, so that body surface motion of the target object is determined, and multi-mode respiratory training guidance is provided for the target object; generating a training result record corresponding to breathing training completed by the target object in the first scene; according to the training result record, providing multi-modal respiratory training guidance for the target object in the second scene; whether the target object meets a preset requirement or not in the breathing training period of the second scene is checked; and when the preset requirement is not met, respiratory gating training is provided for the target object. Multi-mode prompts such as audio / video prompts used in the breathing training process are provided for the target object, the target object is guided to achieve the periodic and synchronized breathing rhythm, and it is ensured that the target object executes repeatable and stable breathing.

The invention discloses a development system suitable for a 9H gas turbine simulation training system, which belongs to the technical field of heavy gas turbines in electric power systems and comprises a hardware support module, a simulation modeling module, a grouping training management module, a data communication module, an environment simulation module, an operation interaction module, an examination and evaluation module and a system maintenance module. The simulation technology, the graphic image technology, the database technology and the like are integrated, design is carried out according to power plant power equipment real objects, such as a main control room, a control screen and equipment connection conditions, corresponding operation can be carried out on simulation equipment, a mouse click operation mode is adopted, and the method is simple, visual and easy to learn; the training means of operators is greatly updated, the training efficiency is improved, and the training period is shortened; and the capability of correctly judging and handling accidents of operators is further improved, the accidents are prevented from being expanded, and the accident handling time is shortened.

The application relates to the technical field of rehabilitation training, in particular to a home environment cognitive rehabilitation training action guiding system based on visual interaction. The system can realize the following steps through mutual cooperation among multiple modules: acquiring a rehabilitation training image of a target patient in each unit training period within each preset rehabilitation training period, and acquiring a cognitive recovery score corresponding to each preset rehabilitation training period; determining a target motion vector sequence and a target state value sequence; performing interval division and clustering on the training action period; determining an action proficiency index corresponding to each training action period; and determining a target recommended guiding value corresponding to a training action represented by each target cluster. The application quantifies the target recommended guiding value corresponding to different training actions by analyzing the self condition of the target patient in the rehabilitation training process, thereby improving the rationality of cognitive rehabilitation training action recommendation and the guiding effect of cognitive rehabilitation training action.

The invention relates to the technical field of image recognition, and particularly discloses a face recognition model training method and a noise detection method and device in training. According to the method, the to-be-trained face image data set is divided into two parts, the cosine similarity is utilized to screen samples with high similarity with the target category in one part of the data set, the maximum similarity and the minimum similarity are calculated, the discrimination threshold is dynamically generated, and the samples with the cosine similarity lower than the discrimination threshold in the other part of the data set are accurately screened out, so that the recognition accuracy is improved. Noise data can be effectively removed; moreover, the discrimination threshold is dynamically adjusted along with the training period, so that the model adapts to data feature changes in different training stages, proper samples are dynamically screened to participate in training, and the adaptability and generalization ability of the model are enhanced.

This invention provides a power trading training system, belonging to the technical field of training systems. The system includes: a login module: upon detecting a user's login, it verifies the user's legitimacy based on the power trading training platform, grants the user login privileges, and stores the corresponding login information in a database; a course assistance module: after successful login, based on user privileges and a decision support mechanism, it performs a time-limited key verification and matching to decrypt the encrypted user-specific training courses; a learning module: based on the user's learning hours for decrypted courses during the training period, it obtains a first learning pass status and a second learning pass status based on the final exam results for all decrypted courses, and obtains a learning pass score for each decrypted course; a ranking module: based on an indicator evaluation model, it obtains the weight of each decrypted course for the user and, combined with the learning pass score, generates a corresponding ranking. This provides a convenient training method and improves training efficiency.

A bearing device includes a bearing and a vibration sensor for measuring the vibration of the bearing, the bearing having an inner ring and an outer ring capable of rotating concentrically relative to each other. The bearing device further includes: - a first determining component (10) for determining the value of at least a first statistical parameter equal to the Hjorth complexity parameters (P1, P2, P3, P4, P5, P6); - a modeling component (11) for modeling the value of at least the first statistical parameter using a normal distribution; - a second determining component (12) for determining a detection threshold (Sd) at least based on a normal distribution during a training period; - a third determining component (13) for determining a score (Sc) during normal operation of the machine (1) at least based on the first value of the Hjorth complexity parameter and the normal distribution modeled on the value of the first statistical parameter; and a comparison component (14) for determining a failure of the bearing (4) based on the detection threshold (Sd) and the score (Sc).

The present application relates to the technical field of computer, and discloses a pod scheduling model construction method, device, equipment and medium, the method comprises the following steps: obtaining joint state data and environment data in the current training period in the current cycle; inputting the joint state data and the environment data into the pod scheduling model iterative training; when generating the joint reward corresponding to the current training period, determining whether the current cycle stops the iterative training according to the joint reward and / or the training period statistical value corresponding to the current training period; when determining that the current cycle stops the iterative training, entering the next cycle, and obtaining the joint state data and the environment data from the sample data pool corresponding to the next cycle to perform the iterative training on the pod scheduling model. The method can ensure that the model better allocates resources, reduces resource waste, and reduces scheduling delay.

The invention relates to the technical field of volleyball training devices, in particular to a spiking trainer for volleyball training, which comprises a ball feeding barrel and a ball containing barrel, two ball clamping plates are symmetrically fixed at the lower end of the ball feeding barrel, the lower end of the ball containing barrel is communicated with the inner side of the ball feeding barrel through a ball feeding pipe, and the ball containing barrel is fixed on the ground through a support. Through the effect of the transposition mechanism, after a sportsman discharges volleyballs clamped between the two movable clamping plates every time the volleyballs are spiked, the two upper stop levers can be controlled to be combined, the two lower stop levers can be controlled to be separated, so that the volleyballs supported on the two lower stop levers fall between the two movable clamping plates, the effect of automatically supplementing the volleyballs is achieved, continuous spiking training can be achieved, and the training efficiency is improved. The device is simple in structure and convenient to use, all energy of an athlete can be concentrated on spiking skills instead of auxiliary links such as ball picking and preparation, the device is particularly suitable for specialized training scenes such as high-intensity intermittent training and multi-ball continuous spiking, and the training period is effectively shortened.

The invention provides an ultrasonic data verification method and system based on federated learning and a block chain, and the method comprises the steps: maintaining a block chain based on a directed acyclic graph structure locally by each participant, so as to record a model transaction generated in a training process; each participant obtains at least one existing model from a locally maintained block chain, and performs local training by using local private ultrasonic data to obtain an updated local model; every other preset training period, all participants suspend local training and enter a consensus stage; in the consensus stage, each participant performs verification and performance evaluation on all models published to the block chain in the current period based on respective private verification data, and generates a model ranking; the model with the highest ranking is selected as a reference model of the next training period, and the participants continue to conduct the next round of asynchronous local training based on the reference model; iterating until a preset training termination condition is met, and finally outputting a global model as a cooperative training result.

The invention discloses a business process data analysis system and method based on distributed access, and relates to the technical field of big data analysis, and the method comprises the steps: determining a feature business node through analyzing a historical abnormal record; setting a training period, and constructing a feature index library and an association library; abnormal records are collected, service scores are calculated, a curve graph is drawn, and abnormal time is predicted; real-time business processes are collected, real-time business scores are calculated, abnormal time is predicted, the invalid analysis range is reduced, the abnormal detection efficiency and accuracy are improved, the depth and accuracy of abnormal diagnosis are remarkably improved, the manual monitoring workload is effectively reduced, and the operation and maintenance efficiency is improved.

The embodiment of the invention provides a base model training method and device. The method comprises the steps that a plurality of different reply texts are generated in a self-gaming mode according to a text instruction input into a base model; according to the multiple reply texts and in combination with the score values corresponding to the multiple reply texts, screening the multiple reply texts and constructing a machine preference data set; and training a base model according to the constructed machine preference data set. Therefore, through the embodiment of the invention, the problems of long training period and high cost of the base model due to the fact that the training for the base model mainly depends on a large amount of manual annotation data in the prior art can be solved, and the effect of improving the training efficiency of the base model is further achieved.

The invention relates to the technical field of rehabilitation training, in particular to a family environment cognition rehabilitation training action guiding system based on visual interaction, and the system can achieve the following steps through the mutual cooperation of a plurality of modules: obtaining rehabilitation training images of a target patient in each unit training period in each preset rehabilitation training period, obtaining a cognitive recovery score corresponding to each preset rehabilitation training period; determining a target motion vector sequence and a target state value sequence; interval division is carried out, and training action time periods are clustered; determining an action proficiency index corresponding to each training action period; and determining a target recommendation guide value corresponding to the training action represented by each target cluster. According to the cognitive rehabilitation training action recommendation method and device, the target recommendation guide values corresponding to different training actions are quantified by analyzing the conditions of the target patient in the rehabilitation training process, so that the reasonability of cognitive rehabilitation training action recommendation is improved, and the cognitive rehabilitation training action guide effect is improved.

The invention provides a flight simulator flight simulation deviation correction training method and system based on a control load. The method comprises the following steps: firstly, carrying out flight simulation training deviation analysis and visualization on the basis of control load data to improve the timeliness and accuracy of flight simulation training teaching feedback and evaluation; meanwhile, training effects in the training period are subjected to redisk and statistical analysis based on deviation of flight training control of the trainee, the accumulated deviation times and the stage deviation rate of the trainee in the standard stage of each flight mission are determined, and a stage priority strategy is generated on the basis of the stage deviation rate and in combination with the safety risk weight of the flight stage; the stage deviation-based flight training task plan is generated aiming at the weak items of the trainees, and the dynamic recovery of the training effect and the dynamic adaptation of the training plan are realized, so that the root deviation correction of the flight training is realized, and the training effect is improved.

The invention discloses a dataset distillation method based on adversarial training trajectory matching, which comprises the following steps: selecting a real dataset, and randomly extracting a small part of data from the real dataset to form an initialized synthetic dataset; training the model by adopting a real data set, recording current parameters of the model at the end of each training period, forming an expert training track by the recorded model parameters, and retaining the weight at the end of each training period; training the model by adopting the synthetic data set, for each iteration, randomly selecting an expert training track, randomly selecting a weight at the end of a certain training period as an initial weight, and taking a weight after a plurality of rounds as a target weight; in the training process, the model performs multiple rounds of gradient descent on the synthetic data set based on the initial weight to obtain a new weight, and the matching loss between the new weight and the target weight is calculated; and according to the matching loss, a back propagation algorithm is adopted to optimize the synthetic data set.

The application discloses a military high-altitude psychological behavior training device with a rapid lifting function, and relates to the technical field of psychological behavior training devices.The device comprises a supporting frame, a safety backpack, a mounting frame, a safety net, a magnetic adsorption plate, a magnetic adsorption groove and a wind frame.The mounting frame can be rotated and swung at multiple angles, so that the user can rotate along the rotating disc on the supporting plate during parachute high-altitude training, and the user can rotate and swing along with the mounting frame as a whole.The device can realize rapid lifting and slow lifting, and can adjust the falling speed of the user during parachute falling, thereby enhancing the simulation effect of the parachute falling speed.The application simulates the falling state of the parachute through a mechanical device, so as to improve the psychological endurance of soldiers to the falling state, shorten the parachute training period, and protect the personal safety of the training personnel.

The application discloses a kind of based on dynamic data selection's reinforcement learning model training method and device, electronic equipment, comprising: according to the data quality, diversity and difficulty evaluation index of pre-set, from original mathematical problem data pool, obtain candidate training set by screening;For each training sample in candidate training set, calculate its data validity score;The difficulty filtering of the candidate training set is carried out, and adaptive training course is constructed;According to the solution reasoning process and reward score of candidate training set, further filter candidate training set;Based on the further filtered candidate training set, data validity score, by setting temperature parameter and probability threshold value with training process attenuation, dynamically select data subset for current training period;The selected data subset is put into reinforcement learning model training, and the training reinforcement learning model is optimized according to correctness reward and format reward.It can significantly shorten model training period, and reach the performance comparable to full data training even better.

This invention belongs to the technical field of ischemic preconditioning training devices, specifically relating to a dual-airflow ischemic preconditioning training device and an intelligent training method. The training device includes modules for data acquisition, pressure setting, dual-airflow control, coordinated adjustment, and main control. It quantifies the physiological coupling degree by calculating the difference in blood pressure between the two arms and the pulse wave velocity, and simultaneously applies this coupling influence coefficient as a feedforward term and the coupling degree coefficient as a feedback parameter during the training period, thereby achieving adaptive and precise coordinated control of the two airflows. The method includes: acquiring basic physiological data and setting an initial target pressure individually; using a sliding mode control algorithm incorporating the physiological coupling term to track and stabilize the pressure in real time; and coordinating the adjustment of the target pressure in both arms based on the quantified coupling degree during the rest period. This invention effectively improves pressure control accuracy and training safety, enhances user experience, and enables personalized ischemic preconditioning training.

The application discloses a model training sample dynamic optimization method and device, equipment and a storage medium. The method comprises the following steps: obtaining pre-training process data corresponding to a target training model obtained by pre-training; obtaining first sample training data and first sample prediction data corresponding to the first sample training data based on a preset training period; determining a quality evaluation score corresponding to the first sample training data based on a preset evaluation index, the pre-training process data, the first sample training data and the first sample prediction data; optimizing the first sample training data based on a quality evaluation threshold and the quality evaluation score to obtain second sample training data, so as to continue training the target training model based on the second sample training data in the next preset training period. The application can realize automatic and efficient optimization of model training samples, improve the adaptability to a complex model training process, and improve the training efficiency and quality of the model.

We provide technology to predict training effectiveness while taking climate differences into account. [Solution] In the running ability prediction system 100, training load information indicating the exercise load during the user 10's training period, including a first period and a second period in which the climate may differ from that of the first period, is acquired, and climate information for the first and second periods is acquired. The prediction processing unit determines the predicted running time when the user 10 runs a second distance in the second period, based on at least the running log, training load information, and climate information. The running ability correction unit 80 corrects the resulting running ability by a correction amount calculated based on the initial running ability and climate information.

The utility model discloses an inclined sliding type abdomen exercising machine which comprises at least one sliding rail, the sliding rail and the horizontal plane are arranged at a linear inclination angle, and a first base and a second base are arranged at the two ends of the sliding rail respectively. The first supporting plate is arranged at the end, close to the second base, of the sliding rail; the sliding assembly is arranged on the sliding rail in a sliding manner; the second supporting plate is connected with the sliding assembly and can synchronously slide along with the sliding assembly; and the counterweight assembly is connected with the sliding assembly and can synchronously slide along with the sliding assembly. The sliding rail and the horizontal plane are arranged at the linear inclination angle, compared with a horizontal instrument, the exercise stroke is increased, the resistance of the abdomen is greatly increased, abdominal muscles are stimulated to the maximum degree, and therefore the abdomen exercising effect is achieved to the maximum degree; and the requirements of people in different training periods and different training levels on resistance resistance can be met.