106 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.

A computer implemented method of access control for a user device having at least one component for determining behaviors of the user. The method including defining a training period during which access to the device is determined based on a credential-based authentication scheme wherein each access determination is used to generate an item of training data and training a machine learning classifier based on the training data such that the classifier is operable to classify user behavior as compliant or non-compliant. The method further including, in response to a determination that a behavior subsequent to the training period is classified as non-compliant, requesting a credential-based authentication of the user and permitting access to the device in response to the credential-based authentication, wherein permitting access to the device further includes constructively training the classifier based on the subsequent behavior as a compliant behavior.

The invention discloses an intelligent data analysis system and method based on source-network-load-storage integration, and relates to the technical field of electric power analysis, the method comprises the steps: carrying out the statistics of the historical average power consumption of a certain time period, and determining a characteristic time period; a training period is set, the electricity consumption of the load end at the sampling time point is collected, and the electricity consumption fluctuation rate of the load end in the characteristic time period is calculated; drawing an electricity consumption fluctuation graph of the load end in the characteristic time period in the training period, and determining a target load end; generating an energy storage end group, collecting operation parameters of the energy storage end group, calculating an operation score of the energy storage end group, and drawing a feature operation score graph of an analysis period corresponding to the energy storage end group; and acquiring a real-time operation score of the energy storage end group, calculating a real-time analysis period of the energy storage end group, judging an abnormal analysis state of the energy storage end group, and ensuring efficient and stable operation of the source-grid-load-storage integrated power system.

The invention provides a model training method, mobile equipment, electronic equipment and a medium, and the model training method comprises the steps: executing a test task through a task execution model, and obtaining test data corresponding to a target time period in the process of executing the test task; under the condition that the test result corresponding to the test data does not meet the preset requirement, correcting the test data to obtain corrected data; and training the task execution model by using the correction data to obtain a trained task execution model. According to the technical scheme, the whole training period of the model can be shortened, and the training efficiency is improved.

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 provides a language model training method and device, electronic equipment and a storage medium. The method comprises the following steps: training an initial language model to obtain a weight matrix, and decomposing the weight matrix to obtain a basic feature matrix and an initial task adaptation matrix; updating the initial task adaptation matrix to obtain an updated task adaptation matrix; determining the current sensitivity of each singular vector direction in the update task adaptation matrix of the current training period; determining a sensitive direction from a plurality of singular vector directions according to the current sensitivity, and performing regularization processing on a scaling coefficient associated with the sensitive direction to obtain a regularization loss function; in response to determining that the change sensitivity of the singular vector direction in the continuous preset number of training periods is in an attenuation state, performing parameter resetting on the zoom coefficient to obtain an updated zoom coefficient; and carrying out merging processing on the basic feature matrix and the update task adaptation matrix, and taking the update language model as a target language model.

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 technical field of medical rehabilitation equipment, in particular to a surgical postoperative rehabilitation nursing auxiliary device and method based on the Internet of Things, and the device comprises a bearing part, a driving part, a base, a monitoring part and a remote interaction part, the driving part comprises a first driving assembly for providing relative resistance for a using object and a second driving assembly for driving the thigh and the shank to move relatively. The method comprises the steps that in a single training period, the passive muscle activation degree and the active muscle activation degree are determined to determine the rehabilitation degree of the muscle function of a using object; according to the limb movement track and the muscle strength balance condition of the feature movement part, the movement control ability of the using object is determined, and a rehabilitation training scheme of the using object is adjusted according to the rehabilitation degree or the movement control ability. According to the invention, comprehensive monitoring and remote transmission of the rehabilitation process of the use object are realized, and the rehabilitation process can be accurately controlled by medical personnel.

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 an exercise training recommendation method and system for disability risk crowds. According to the method, a multi-level portrait system of a patient is constructed, multi-dimensional data such as disease types, clinical features, demographic information and motion behavior features of the patient are fused, role portraits are generated, and static features are obtained. After the first exercise training scheme is pushed based on the static features, structural data, text type data and image type data of the patient in the training period are collected, a subsequent exercise training scheme is generated through a collaborative filtering model, and iterative optimization is conducted on the dynamic features of the patient through a recurrent neural network model. The exercise training scheme can be dynamically adjusted according to the individual features and training feedback of the patient, and the compliance and the training effect of the patient are 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 discloses a personalized life skill training method suitable for infants. The method comprises the following steps: S1, acquiring behavior data in a daily life scene of an infant; s2, establishing an infant life skill level evaluation model through the behavior data; s3, according to the grasp degree score, selecting a second parameter number of training course fragments matched with the current skill level and personalized characteristics of the infant from a preset training course library; s4, sequentially combining the training course fragments according to the numbering sequence of the training course fragments to form a personalized training course sequence; s5, making a training plan according to the daily work and rest rule of the infant; s6, in the training process, physiological data and training behavior data of the infant are obtained, and dynamic adjustment is carried out; and S7, after the training period is finished, collecting the behavior data in the daily life scene again, and if the expected target is not reached, repeating the steps. According to the invention, precision and high efficiency of infant life skill training are realized, and individual learning requirements of infants are met.

The invention discloses a cross-working-condition rotating machine fault diagnosis model training method and system and a storage medium, and the method comprises the steps: obtaining an original vibration signal of a rotating machine, and carrying out the preprocessing of the original vibration signal to obtain image data which comprises a training set and a test set; performing feature extraction based on a training set in the image data to obtain a depth fault feature; performing sample feature reweighting and fault classification iteration on the basis of the deep fault features to train an initial network model, and performing testing by using the test set to obtain a target model; and when the number of iteration training times reaches a preset training period, stopping training, and obtaining a cross-working-condition rotating machine fault diagnosis model training model based on the target model corresponding to the current iteration training. According to the method, the cross-working-condition problem in rotating machine fault diagnosis is effectively solved, and the generalization ability and diagnosis precision of the model are remarkably improved.

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.