18results about How to "Achieve training" patented technology

The utility model discloses a super-nuclear machine training device, which relates to the technical field of sports equipment and comprises a bed body, a first platform, a second platform, a folding structure and a sliding mechanism, a first platform and a second platform are respectively arranged at two ends of the bed body; the first platform and the second platform are connected through a bed body; a slide way is arranged on the inner side of the bed body, the slide way is matched with the sliding mechanism, and the sliding mechanism is connected with the slide way in a sliding mode; one end of the sliding mechanism is detachably connected with the second platform or detachably connected with the first platform; the bed body comprises a first cross beam and a second cross beam; the first cross beam and the second cross beam are both of split structures, the folding structure is arranged at the joint of the split structures, and the bed body is folded through the folding structure. Compared with a traditional device, the folding design is more convenient to store, meanwhile, the device is suitable for families and gymnasiums, and the average-effect ratio of gymnasium operation can be effectively increased.

The application provides a model training method, a speech conversion method and device, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: obtaining sample audio data of a sample speaker object; inputting the sample audio data into a neural network model comprising an encoding network and a decoding network; performing reconstruction processing on the sample audio data by using the encoding network to obtain initial audio data; performing speech alignment on the initial audio data to obtain a sample audio embedding vector; performing decoupling processing on the sample audio embedding vector, pre-acquired sample pitch parameters and a sample timbre feature vector by using the decoding network to obtain synthesized audio data; performing loss calculation on the synthesized audio data and sample speech data by using a loss function to obtain a model loss value; and performing parameter updating on the neural network model according to the model loss value to train the neural network model and obtain a speech conversion model. The application can improve the speech conversion effect.

The lower limb exoskeleton rehabilitation training robot comprises a support and a robot, the support comprises a supporting column, a cross beam, a light bar and a sliding rail, the left part and the right part of the support are symmetrically designed, the support is fixedly connected with the ground through the supporting column, and the cross beam is slidably connected with the light bar and the sliding rail; the robot comprises a waist fixing assembly, two hip training assemblies, two leg training assemblies and two ankle joint training assemblies, the waist fixing assembly is fixedly connected with the cross beam, and the hip training assemblies, the leg training assemblies and the ankle joint training assemblies are symmetrically designed. The hip training assembly is connected with the waist fixing assembly, the leg training assembly is connected with the hip training assembly and the ankle joint training assembly through keys, and the ankle joint training assembly is a three-degree-of-freedom association mechanism. The leg rehabilitation training and foot ankle joint three-degree-of-freedom training of a patient can be completed.

The application provides a personalized brain-controlled mechanical arm rehabilitation system based on individual characteristics, comprising the following steps: S1, evaluating the performance of each MI-BCI training cycle of an individual; S2, researching the correlation between the individual's electroencephalogram features and functional indicators; S3, predicting the motor imagery ability according to the cycle rating and electroencephalogram feature quantitative data of the individual, and then establishing a mapping from the motor imagery ability to the mechanical arm movement rate; S4, constructing a personalized feedback regulation loop and establishing a complete closed-loop MI-BCI mechanical arm system. The application has the beneficial effects that: the personalized brain-controlled mechanical arm system based on individual characteristics predicts the motor imagery ability according to the performance of the training cycle and the electroencephalogram features, thereby predicting the mechanical arm rate, and the MI-BCI ability of the individual is displayed in real time by the mechanical arm rate. The individual's initiative is increased, the degree of assistance of the MI-BCI to the individual is reduced, and the rehabilitation effect is improved.

The invention relates to a platform for realizing intelligent rule management and control of accounting, and belongs to the technical field of accounting. The platform comprises a background support class module, an application service class module, an accounting knowledge base related module, a plurality of MCP servers, an agent main control module and a client module. The method comprises the following steps: based on an mcp protocol, taking a large language model as an intelligent core, and constructing an accounting knowledge base by integrating knowledge loaded by an approval process and knowledge in an accounting rule base in an accounting system; through a natural language interaction interface, accountants are assisted in interpreting policies, enterprise accounting criteria and related management requirements and matching with accounting rules, the accountants are assisted in establishing and adjusting the accounting rules, the working efficiency is improved, and the consistency and integrity of the accounting rules are guaranteed.

The present application relates to the technical field of computer vision and image processing, and discloses a remote sensing weakly supervised fine-grained target detection and recognition method and device, based on a general text prompt corresponding to a small amount of labeled remote sensing data samples and a large amount of unlabeled remote sensing data samples, by extracting the geometric features of the samples and the cross-modal features representing the visual text differences, combining the fine-grained class labels of the labeled remote sensing data samples, prior knowledge prototypes of various fine-grained classes are constructed; then, the prior knowledge prototypes of various fine-grained classes are used to construct fine-grained soft labels of the unlabeled remote sensing data samples, which are used as supervision signals to realize the training of the model, solve the cost consumption problem of fine-grained labeling, improve the label labeling efficiency of the unlabeled remote sensing data samples, and further improve the model training efficiency and target recognition efficiency.

The invention discloses a physical informed machine learning high-entropy alloy phase prediction system and method based on semi-empirical parameters, and belongs to the technical field of crossing of high-entropy alloy material design and machine learning. The system takes semi-empirical parameter physical constraint as a core, integrates the physical interpretability of an empirical parameter method and the data driving advantages of machine learning through a three-stage cooperation mechanism of'feature multiplexing-independent prediction-Bayesian fusion ', and solves the problems of narrow phase coverage, low multi-phase prediction precision, 'black box' defect and the like of a traditional method. The system can predict more than 10 high-entropy alloy phase types and multi-phase coexistence systems, the single-phase prediction accuracy rate in 856 groups of multi-component high-entropy alloy test sets reaches 100%, the multi-phase coexistence system prediction accuracy rate reaches 77%, phase formation physical mechanism explanation can be output, B2 phase exclusive accurate criteria are provided, and high-entropy alloy design is promoted to be transformed from a trial and error method to an accurate prediction method.

The application provides a kind of flight landing gear actuator fatigue estimation method and system, comprising: obtaining the service history sample set of flight landing gear actuator;The service history sample set is pretreated, and the service state is marked;Respectively using GRNN, RBF and DNN are trained, obtain multiple actuator fatigue estimation models, and the model output is actuator failure flag;The prediction accuracy value of each model in the preset service time cycle is counted;The prediction accuracy value of each model in the preset service time cycle is weighted and summed to determine the weighted prediction accuracy;The model with the highest weighted prediction accuracy is obtained as the final fatigue estimation prediction model;The feature vector of the aircraft is used as input and input into the fatigue estimation prediction model, and the predicted actuator failure flag is output.The application can be used for the failure warning of the actuator of the aircraft landing gear in the service state, to reduce the defects of artificial judgment and subjective judgment.

The application provides a comprehensive energy system regulation method and system considering traffic logistics facilities, and belongs to the technical field of comprehensive energy system regulation. The method comprises the following steps: encoding the driving and charging states of an electric truck, setting minimum duration constraints and state mutual exclusion constraints of the driving state and the charging state based on the encoding result to determine the working state of the electric truck, determining the actual power consumption of the electric truck to construct an average field game theory model, converting the theoretical model into a neural network parameterized representation of a federal learning algorithm, determining global model parameters through local training and parameter aggregation, training an electric truck distributed collaborative scheduling model based on federal average field game through a deep average field Actor-Critic algorithm; inputting real-time state observation data into the model to generate optimal scheduling decisions, including optimal route selection and optimal charging power. The application improves the real-time performance, accuracy and efficiency of comprehensive energy system scheduling.

The skeleton rehabilitation robot comprises a support and a robot body, the support comprises supporting columns, a cross beam, a polished rod and a sliding rail, the support is of a left-right symmetrical design and is fixedly connected with the ground through the supporting columns, and the cross beam is slidably connected with the polished rod and the sliding rail; the robot comprises a waist fixing assembly, two hip training assemblies, two leg training assemblies and two ankle joint training assemblies, the waist fixing assembly is fixedly connected with the cross beam, and the hip training assemblies, the leg training assemblies and the ankle joint training assemblies are symmetrically designed. The hip training assembly is connected with the waist fixing assembly, the leg training assembly is connected with the hip training assembly and the ankle joint training assembly through keys, and the ankle joint training assembly is a three-degree-of-freedom association mechanism. The leg rehabilitation training and foot ankle joint three-degree-of-freedom training of a patient can be completed.

The application discloses a kind of privacy protection systems for medical data based on federal learning, it is related to medical data management technical field, including medical terminal equipment, medical edge server and medical cloud center server;Medical terminal equipment can be preprocessed to medical data by variational modeling to realize privacy enhancement and obtain model training data;Medical edge server is used to pass into multimodal model, screening is obtained vital sign area feature, training is carried out to global medical model, and local model is obtained, local differential perturbation noise is added in local model gradient;Initialization and update global medical model.The application is based on the cloud edge of federal learning, intelligent, safe, trusted architecture, can realize medical model training under cloud edge intelligent cooperation;Realize the fine-grained classification of medical data multimodal fusion, improve the accuracy of model;Variational modeling and differential privacy are integrated into system architecture, ensure the high confidentiality of medical data.

This application provides a training method, processing method, apparatus, and device based on an image processing model, comprising: acquiring a training image; inputting the training image into a preset initial model; performing convolution processing on the training image based on a first convolution kernel of a convolutional layer in the initial model to obtain a first output vector; and performing convolution processing on the training image based on a second convolution kernel of a convolutional layer in the initial model to obtain a second output vector; and determining a third convolution kernel based on the first and second output vectors to obtain an image processing model with a third convolution kernel. This application achieves improved expressive power of the convolutional layer with a third convolution kernel while maintaining the same computational speed and resource consumption, enabling the convolutional layer to analyze target data from multiple perspectives and obtain more accurate analysis results.

The application provides a content review model training method and device, equipment, a storage medium and a computer program product; the method comprises the following steps: predicting compliance of at least one piece of content to be reviewed by a content review model to obtain a predicted review result of each piece of content to be reviewed; for each piece of content to be reviewed, determining a predicted difficulty score of the content to be reviewed based on the predicted review result; from at least one piece of content to be reviewed, a target review content is selected whose predicted difficulty score meets a difficulty score condition; the content review model is trained by taking the target review content as a sample and taking the actual review result of the target review content as a sample label of the sample, so as to update the content review model; by the application, the training cost of the content review model can be reduced, and the training speed and content review accuracy of the content review model can be improved.

The application provides a data labeling method, device and system and a storage medium, and belongs to the field of data labeling.The method comprises the following steps: S1, importing original training data and labeled data; S2, constructing an original labeling model, training the original labeling model according to the original training data and the labeled data to obtain a first labeling model; S3, importing unlabeled training data, predicting the unlabeled training data according to the first labeling model to obtain predicted data; and S4, analyzing the first labeling model according to the unlabeled training data and the predicted data to obtain a second labeling model.The application can realize the training of a pre-labeling model of a target field without the need for a large amount of manual labeling of text samples, greatly reduces the workload of manual labeling, saves the cost of data labeling work, and improves the accuracy of the model.

The invention discloses a financial document intelligent decision support system based on XAI and a dynamic knowledge graph, and the system comprises the steps: carrying out the text analysis of a financial document through multi-modal analysis, carrying out the character recognition of a table and / or an image, and generating a structural representation containing cross-modal association; according to the knowledge graph processing, financial entities are extracted on the basis of structured representation, a knowledge graph is constructed and updated, node importance scores output by XAI are introduced under an attention mechanism, an association weight with interpretability information is determined, and a reasoning path weight is obtained. And retrieval generation executes at least two stages of retrieval and sorting on the structured representation and / or the knowledge graph to obtain candidate evidences, decision support information is generated based on the candidate evidences and reasoning path weights, evidence traceability information and confidence coefficient are output and displayed, and thus interpretability and verifiability of financial document decision support output are improved.

This invention provides compositions of polyvinylpyrrolidone (PVP-based) synthetic hydrogels and methods of using them. The PVP-based hydrogels comprise thiol-functionalized PVP, multi-arm polyethylene glycol sulfone, RGD peptides, and VPM peptides. The PVP-based synthetic hydrogels are suitable for three-dimensional cell culture and are readily soluble. These PVP-based synthetic hydrogels possess at least the following advantages: their mechanical properties are comparable to non-synthetic Matrigel. ® Similar, but with a more specific definition.

The invention discloses an image classification model training method and device, an image classification method and device, equipment and a medium. The method comprises the steps that an original image is acquired, an image description text corresponding to the original image, N attribute description texts and corresponding saliency areas are determined, and N is larger than 2; performing mask processing on each saliency region, and determining N region mask images; feature coding fusion is carried out on the original image, the N area mask images, the image description text and the N attribute description texts, and fusion coding features are determined; and performing model training based on the fused coding features, and determining a target classification model. According to the method, cross-modal feature code fusion is carried out on the whole image and a saliency region to obtain a fusion feature code capable of accurately representing the image, accurate training of a model is realized through the fusion feature code, and a target classification model with relatively high image category recognition accuracy is obtained. Therefore, image classification with high precision can be carried out according to the target classification model.

Embodiments of the present application provide an image storage method and device, electronic equipment and storage medium, which are applied to the technical field of images. The method of the embodiments of the present application can encode a to-be-stored image through an encoder of a deep learning model, classify the first image feature after encoding through a classifier, decode the first latent space feature after sampling through a decoder, and obtain a first decoded image. A second loss value is calculated according to the first decoded image and the to-be-stored image, and a first loss value is calculated according to the first classification result and the extraction code. The parameters of the deep learning model are adjusted through the loss values to realize the training of the deep learning model. The present application realizes the storage of the to-be-stored image through the training of the deep learning model, and stores the first encoding information after encoding the to-be-stored image in the latent space. The storage device cannot directly view the to-be-stored image, and the image can be read only by using the extraction code, thereby improving the security of image storage.