89 results about "Training period" patented technology

The invention discloses a reinforcement learning model training method and device based on dynamic data selection and electronic equipment, and the method comprises the steps: carrying out the screening from an original mathematical problem data pool according to preset data quality, diversity and difficulty evaluation indexes, and obtaining a candidate training set; calculating a data validity score of each training sample in the candidate training set; difficulty filtering is carried out on the candidate training set, and an adaptive training course is constructed; further filtering the candidate training set according to the solution reasoning process and the reward score of the candidate training set; dynamically selecting a data subset for the current training period by setting a temperature parameter attenuated along with the training process and a probability threshold based on the further filtered candidate training set and the data validity score; the selected data subsets are put into a reinforcement learning model for training, and the reinforcement learning model is trained in an optimized mode according to the correctness reward and the format reward. The model training period can be remarkably shortened, and the performance equivalent to or even better than that of full data training is achieved.

The invention belongs to the technical field of rehabilitation training, and particularly relates to an active stroke rehabilitation auxiliary training system which generates a personalized training base line through clinical evaluation data of a patient and guides the patient to cooperatively execute training actions through rehabilitation equipment according to the training base line. Training execution efficiency scores, patient active motion features and equipment auxiliary performance features are analyzed in real time, and rehabilitation equipment auxiliary strength and training task difficulty are dynamically adjusted in the training duration according to the double-feature real-time interaction state and the synchronous training execution efficiency scores; finally, the rehabilitation ability state of the patient is updated according to the multi-dimensional time sequence characteristics of the whole single training process, and a baseline correction instruction of the next training period is output based on the updated rehabilitation ability state, so that the limitation of singly paying attention to patient actions in the past is broken, and the boundary of patient independent effort and equipment assistance in the rehabilitation process is clearly distinguished; and the dynamic adaptability and accuracy of stroke rehabilitation training are effectively improved.

The invention provides a concentration training method and device based on visual tracking, and the method comprises the steps: collecting the real-time eye movement data of a user, including the staring point coordinate and the eye movement speed, and initializing the target trajectory of a training task; calculating a concentration deviation between the current gazing point and the target trajectory, and dynamically adjusting the target trajectory through nonlinear mapping based on the deviation to generate a dynamic trajectory; constructing a multi-feature fusion distraction scoring model based on the concentration deviation and the change trend of the concentration deviation in combination with the geometric features of the trajectory, and detecting and marking distraction events; when a distraction event is detected, self-adaptive visual correction stimulation is executed at the event position immediately, and a user is guided to return to a task; and after the training period is finished, dynamically adjusting the task difficulty coefficient of the next period according to the frequency and the average deviation of the distraction events in the period.

The invention relates to the technical field of model training sample processing, in particular to a sample distribution adaptive adjustment method and system based on model training feedback driving, and the method comprises the following steps: S1, collecting feedback indexes in a training process in real time in a model training period, and obtaining a feedback index set; s2, weak item categories are analyzed and recognized based on the feedback index set, and an optimization instruction is generated; s3, dynamically adjusting the sampling weight to form an updated sampling strategy; s4, samples are collected according to a sampling strategy, and weak item category samples are supplemented; s5, putting the optimized sample set into the next round of training, and circulating the steps S1 to S4 until the training indexes of the learning effects of all key categories meet the termination condition; and acquiring a sampling strategy and a sample set. According to the method, real-time, refined and automatic sampling adjustment can be realized by taking the actual performance of model training as a core feedback index, so that the training effect and generalization ability of the model in key areas such as weak-term categories and long-tail categories are improved.

The invention relates to the technical field of medical training, and discloses a sputum suction operation training method and system based on a virtual reality technology. The method comprises the steps that historical operation records of a trainee are collected, key operation nodes are extracted, and an airway state model of a virtual patient is constructed based on the nodes; secondly, setting a virtual sputum suction equipment model, performing a multi-working-condition simulation test on the virtual sputum suction equipment model to obtain operation response characteristics, and mapping the equipment model into the virtual airway state model; then calculating a secretion distribution parameter of a virtual airway in a training period and a theoretical clearance load of virtual equipment, and combining operation response characteristics to obtain an operation intensity parameter; generating an operation triggering instruction sequence based on the operation intensity parameter, and performing staged operation control on the virtual equipment according to the sequence; and finally, carrying out quantitative evaluation on the operation effect of the virtual equipment in the virtual airway. According to the method, a targeted virtual training scene can be constructed, a real operation response is restored, and accurate tracking and effect quantitative evaluation of the operation process are realized.

The invention relates to the field of basketball teaching, and discloses a basketball teaching feedback system and method based on action recognition, and the method comprises the steps: employing a multi-sensor cooperative collection mechanism to carry out the partition sampling of body key points and a basketball moving track in a fixed-point shooting training process of a student, and obtaining an original signal set for depicting detailed actions; the original signal set is mapped into a multi-stage time sequence model, and modeling is conducted on the nonlinear coupling relation between the wrist rotation amplitude and the elbow joint stretching angle; based on the multi-dimensional feature tensor, constructing a multi-scale sliding window to dynamically slice the angle track at the moment of hand release; generating a grading heat map according to the influence degree of the key abnormal segment on the shooting precision, and respectively endowing different priority weights to the hand-out angle stability, the elbow joint control force and the follow-up coherence; and based on the feedback priority queue, carrying out dynamic evolution deduction on the chain abnormity formed in the training period. The method has the advantage of improving the pertinence of teaching.

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 provides a knowledge distillation-based model compression method and system, and the method comprises the steps: inheriting a soft label of a maturely trained teacher model, defining a distillation loss function of a student model through the soft label, enabling the student model to be compressed, generating a new sample extension training set through random sample extraction and mixing, and achieving the compression of the student model. The problem of insufficient data is relieved, meanwhile, the robustness of the model to noise and distribution offset is improved, through dynamic weight adjustment, a student model is made to quickly fit teacher model knowledge, autonomous optimization is conducted in the later training period, and over-fitting of soft labels is avoided. On a test set in which the proportion of unlabeled data is 30%, on the basis that the accuracy of using a real label is 78.3%, the accuracy is improved by 11.2% by using a pseudo target, deployment is performed by using a maturely trained student model, middle layer feature alignment is omitted, the occupation of a GPU video memory is reduced from 3.2 GB to 1.8 GB, and the deployment requirement of edge equipment is met.

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 mechanical arm teleoperation 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 Unity, and configuring a joint position according to a real mechanical arm DH parameter; the dynamics simulation platform AppeliaSim builds a corresponding dynamics model, and an inverse kinematics solution and collision detection function is configured; integrating a force feedback device, and constructing a working space mapping model to convert hand motion into an expected pose of the tail end of the mechanical arm; the double platforms synchronize data through a dynamic link library, and the object control and 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 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 relates to the technical field of aviation personnel training, in particular to an aviation field large language model training method and a theoretical training auxiliary method.The method comprises the steps that multiple data sources and historical question data related to aviation field employee training are obtained; inputting the historical question data into the untrained large language model, and outputting coarse-grained answers of the historical question data by the untrained large language model; and training a large language model according to the data set, adjusting the trained large language model by using an optimization strategy model to obtain an aviation field large language model, enhancing the aviation field large language model based on combination of historical question data and coarse-grained answers, and assisting aviation field employees in theoretical training by using the aviation field large language model. Therefore, the problems that the training period is long and the rapid development requirement is difficult to meet due to the fact that a deep-qualification teacher completes theoretical training through classroom teaching or teaching material explanation in the related technology are solved.

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.

The invention relates to wireless power transfer in communications. A method is disclosed that includes instructing, by an apparatus, a set of environmental radiation power devices to suspend transmission during a training period in addition to a harvested energy report. When at least a predefined number of ambient radiation power supply devices in the set agrees to send a harvested energy report during a training period with a predefined report periodicity configured by the apparatus, the apparatus is caused to receive the harvested energy report from at least one ambient radiation power supply device in the set, wherein the predefined report is periodically configured by the device.

The invention discloses a block chain-based public data authorization operation management system and method, and relates to the technical field of block chain data analysis, and the method comprises the steps: carrying out the statistics of the successful call number of each block chain, calculating the successful call rate, obtaining the levels of a call department and a data department, carrying out the assignment, and calculating the absolute difference value of the levels, establishing a training cycle updating model in combination with the successful call rate, the absolute difference value and the training factor; the method comprises the following steps: monitoring a calling condition, obtaining real-time request characteristics, judging a calling record state, calculating a difference value of a to-be-audited record, comparing the difference value with a threshold value to determine whether to authorize, calculating a next training period of each block chain, calculating a next training period of a competent department according to the next training period of each block chain, and organizing training. The risk of data leakage and abuse is reduced, the operation efficiency and accuracy are improved, and the occurrence rate of human errors is reduced.

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.

The invention discloses a fine exercise training method and system under visual guidance for improving a visual function, and relates to the technical field of visual function improvement or recovery, and the method comprises the steps: determining a minimum threshold value according to a visual test index page, and generating a training scheme of a current training period based on a visual function impaired eye visual minimum recognition threshold value; when the training scheme is executed, to-be-copied characters with the character size being the real-time training character size are called from a paper copybook or a to-be-copied electronic character library to generate a current day copybook; the trainee is in a standard sitting posture, has a preset distance from the desktop and shields the eye-care copybook to copy the current day copybook; performing visual function testing after the training scheme is executed, and adjusting the training scheme of the next training period based on the evaluation training effect. The trainee can quickly improve the vision of the patient with the impaired optic nervous system function in a short time through fine movement under visual guidance, the compliance of the patient is improved, and the system has the advantages of being low in cost, high in feasibility and capable of being extended at home.

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.