16results about How to "Learn accurately" patented technology

This application discloses a data learning method, data learning device, and medium for elevator shafts, applicable to the field of elevator technology. The method includes: based on the obtained information about the length of the magnetic shielding plate within the elevator shaft, stopping the elevator at a preset position in the leveling zone; controlling the elevator to move to another floor corresponding to the target floor; during the elevator's operation, detecting the first signal conversion of the elevator's leveling switch signal to determine the departure position from the target floor; controlling the elevator to continue moving from the departure position; based on the signal conversion of the elevator's up / down forced switch signal, determining the trigger position of the elevator's up / down forced switch; detecting the second signal conversion of the elevator's leveling switch signal to determine the arrival position at the other floor; and determining the floor distance between the two floors based on the departure position of the target floor and the arrival position of the other floor. In the case of two floors, there is no need to shorten the length of the magnetic shielding plate, accurately learning the elevator shaft data.

The application discloses a bucket tooth health assessment method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring state data of multiple bucket teeth on a bucket; mapping the multiple bucket teeth as nodes in a graph structure respectively, constructing a physical adjacent edge according to the spatial adjacency relationship among the multiple bucket teeth, and constructing a symmetric coupling edge according to the symmetry relationship among the multiple bucket teeth about the center of the bucket, so as to establish a bucket tooth group compound graph structure containing the physical adjacent edge and the symmetric coupling edge; initializing the node features of the corresponding nodes in the bucket tooth group compound graph structure according to the state data of the multiple bucket teeth, and initializing the edge features of the edges in the bucket tooth group compound graph structure according to the spatial geometric relationship and the load transmission relationship among the multiple bucket teeth; inputting the bucket tooth group compound graph structure, the node features of each node and the edge features of each edge into a graph neural network model, and outputting the failure probability of the multiple bucket teeth and the overall health degree of the bucket. The application improves the accuracy and comprehensiveness of the bucket tooth health assessment.

The method belongs to the technical field of panoramic monitoring of ultra-high voltage converter station, and aims to solve the problem that panoramic monitoring image data is directly uploaded to the cloud, occupying a large amount of cloud resources. By adopting multi-scale convolution blocks in the deep multi-scale residual network model to construct low-order and high-order features of images of various scales, the incomplete phenomenon of image detail extraction is avoided, and the residual learning mechanism is adopted to retain low-order rough features, thereby improving the reconstruction ability of the image. Topology optimization constructs the topology structure of the heterogeneous network, and the framework combining deep reinforcement learning and Monte Carlo tree search is used to construct the network according to the pre-defined topology rules. The search result of the Monte Carlo tree strengthens the learning of the deep convolutional neural network, so as to obtain more accurate prediction in the next iteration. After the data is processed in the edge side, it is transmitted to the cloud storage, saving the cloud storage space and transmission bandwidth.

The invention relates to a language learning system, which comprises a learning system, a video player and a foreign language video material, and is characterized in that the output end of the learning system is electrically connected with the video player, and the foreign language video material is in data connection with the learning system and the video player through a data memory. By adopting the translation algorithm module and the subtitle display module, an English learning program is highly simplified, English learning becomes closer to a native language learning process, information of a foreign language video material is decomposed, extracted, translated and combined, a learner can comprehensively and accurately learn the foreign language video material, and meanwhile, the learning efficiency is improved. The translated subtitle information is transmitted to the video player in real time to be displayed, so that a learner can view the translated subtitle information while watching the video, and the learning efficiency and effect are improved.

The invention relates to an intelligent leakage detection method based on a water supply pipeline sound signal, and relates to the technical field of water supply pipe network leakage monitoring. The method comprises the following steps: acquiring a sound signal of a water supply pipeline, and extracting an acoustic feature vector of the sound signal; the sound signals are manually labeled, a sound sample set with labels is obtained, and the labels comprise a first label representing leakage and a second label representing normal; and constructing a leakage probability model, and training the leakage probability model through the sound sample set. And inputting pipeline sound signals collected in real time into the leakage probability model, and judging whether the water supply pipeline leaks or is normal according to the predicted leakage probability output by the leakage probability model. The method has the effect of improving the recognition efficiency and accuracy of water supply pipeline leakage state detection.

This invention relates to the field of satellite communication technology, and particularly to an inter-satellite routing method and apparatus for low-Earth orbit (LEO) constellation networks. The method includes: autonomously learning and calculating routes within the constellation network based on the OSPF and / or BGP protocols running in the constellation network, and storing a routing table; the routing table includes at least the north-south vertical hop count and the east-west horizontal hop count from the current satellite node to every other node in the constellation network; in response to a route forwarding service to the destination satellite node, determining the hop count based on the routing table; determining whether a forwarding rectangle can be formed based on the hop count; wherein the current satellite node and the destination satellite node are the two opposite vertices of the forwarding rectangle, and the north-south vertical hop count and the east-west horizontal hop count are the two side lengths of the forwarding rectangle; if a forwarding rectangle can be formed, determining the output interface for route forwarding based on the area of ​​the forwarding rectangle and the routing table. This solution can efficiently and accurately achieve inter-satellite routing in constellation networks.

The invention provides a multi-dimensional force decoupling method and system, a storage medium and a computer program product, and the method comprises the steps: obtaining at least one sensing feature which is used for representing the deformation of a contact surface; at least one sensing feature is input into a decoupling model to output each force component of the multi-dimensional force, a total loss function of the decoupling model is configured as a combination of a plurality of sub-losses, and each sub-loss corresponds to a prediction error of one force component. The invention provides a scheme for improving the accuracy of multi-dimensional force decoupling.

The invention discloses a graph structure perception channel parameter frequency domain extrapolation method, which comprises the following steps of: constructing a graph structure perception attention mechanism, constructing a channel parameter extrapolation network comprising the graph structure perception attention mechanism, and utilizing a channel parameter sequence data set comprising known frequency points and corresponding target frequency points to carry out frequency domain extrapolation on the known frequency points and the corresponding target frequency points. A channel parameter extrapolation network is trained, a channel parameter sequence of a known frequency point is input into the trained network, the network firstly extracts local features through a multi-scale convolution module, then feature coding is performed through an encoder integrated with a graph structure perception attention mechanism, and the feature coding is performed through a multi-scale convolution module. And finally, the decoder outputs a channel parameter prediction sequence of the target frequency point, and the prediction precision of the channel parameters in the frequency extrapolation process is effectively improved through joint modeling of the multipath structure information. Experimental results show that under the condition of the same training data scale and network complexity, the method is superior to an existing method in a channel parameter extrapolation task.

The application discloses a kind of based on deep learning second-hand car price evaluation model training method and device, by obtaining second-hand car historical transaction data, and data preprocessing is obtained training sample;Sample is selected from training sample and constructs the sample pair of real sample and sample to be evaluated;Real sample and sample to be evaluated are input into the first subnetwork and the second subnetwork of double-tower model respectively, and first sample vector and second sample vector are obtained;Wherein, first subnetwork and second subnetwork all adopt deep cross network architecture;First sample vector and second sample vector are carried out element level product, and interactive feature vector is obtained, and interactive feature vector is mapped as scalar by output layer;According to the loss function of scalar and sample price difference, all parameters of double-tower model are updated by back propagation, and the second-hand car price evaluation model of training completion is obtained, solves the problem that second-hand car price prediction method feature cross is weak and sample correlation is insufficient.

The invention discloses a multi-source data fusion-based sintered ore FeO content prediction method and system, and belongs to the technical field of sintering process control, and the method comprises the following steps: S1, multi-source data collection and feature extraction; s2, image feature prediction; s3, temperature and process data fusion prediction; and S4, carrying out adaptive weighted fusion. According to the method, the characteristics of the tail section, the temperature distribution of the tail section and the production process data are processed in parallel, the strong time sequence characteristic extraction capability of the TCN-BiLSTM network is utilized, and the two-way prediction result is fused through adaptive weighted average, so that the FeO content of the sintered ore is predicted in real time with high precision.

The invention relates to the technical field of smart homes, and discloses a defrosting detection method for an air conditioner, which comprises the following steps: acquiring real-time operation condition data of the air conditioner in a heating mode; different types of feature parameters in the real-time operation condition data are subjected to differential preprocessing, feature data after differential preprocessing are obtained, and differential preprocessing comprises feature explicit and feature implicit; inputting the preprocessed feature data into a neural network model to obtain a frosting degree prediction result; and defrosting control is conducted according to the frosting degree prediction result. Thus, the dynamic mode of the frosting state can be learned more accurately and efficiently, the detection precision, robustness and generalization ability under complex and changeable actual working conditions are remarkably improved, and real self-adaptive and high-reliability intelligent defrosting control is achieved. The invention further discloses a defrosting detection device for the air conditioner, the air conditioner and a computer readable storage medium.

The present application relates to the technical field of automatic driving end-to-end perception, in particular to an end-to-end automatic driving long-tail recognition method based on contrast learning pre-training, first, a synthetic image data with long-tail distribution characteristics is generated through a conditional diffusion model, then a fine-grained scene classifier is used to systematically organize and semantically label the generated samples, and a structured multi-modal graph-text alignment dataset is constructed; finally, the enhanced dataset and the original training set are fused, the visual-linguistic joint embedding space is optimized through a multi-task contrast loss function, and the parameter update of the pre-training model is realized. The method innovatively establishes a closed-loop optimization mechanism of generative data enhancement and contrast learning framework, effectively alleviates the data scarcity problem under the long-tail distribution scene, and significantly improves the cross-modal representation ability and downstream task generalization performance of the model on low-resource classes.

The application provides a kind of sheep nutrition dynamic monitoring method based on multi-source data fusion, it is related to livestock breeding intelligent monitoring technical field, the method comprises: at multiple moments of current feeding cycle, the environmental index of pasture is collected, at the end of current feeding cycle, the physiological monitoring index of multiple sheep is collected, the dosage of feed in current feeding cycle is obtained, and the proportioning of multiple nutrients in feed, through the nutrition monitoring model after training, environmental index, physiological monitoring index, the dosage of feed and the proportioning of multiple nutrients are handled, the addition suggestion information of various nutrients is obtained, in the next feeding cycle, according to the addition suggestion information, feed is made. Can make nutrition monitoring model consider the multi-source state information of current feeding cycle and historical feeding state change simultaneously, to improve the accuracy and dynamic adaptability of nutrient addition suggestion.

The application provides a bird repelling audio synthesis method and device based on few-shot concept learning, which comprises the following steps: obtaining bird repelling audio samples corresponding to a target bird song concept, generating a bird repelling audio few-shot dataset, assigning a concept placeholder and constructing a text template to form a few-shot concept training set; inputting the few-shot concept training set into a pre-trained audio diffusion generation model to generate training latent variables and a conditional vector; updating the embedding vector corresponding to the concept placeholder under the condition that the model parameters of the frozen model except the embedding vector corresponding to the concept placeholder are updated to obtain a target concept embedding; and performing conditional guided inverse diffusion denoising based on the target concept embedding to generate bird repelling audio corresponding to the target bird song concept. The technical scheme of the application realizes few-shot personalized synthesis of bird repelling audio, improves the target voiceprint consistency and resource expansion efficiency.

The application discloses an optimization control method for a ubiquitous heterogeneous network topology of panoramic monitoring of an extra-high voltage converter station, belongs to the technical field of panoramic monitoring of the extra-high voltage converter station, and solves the problem of congestion of panoramic monitoring data transmission caused by poor real-time performance and low reliability of the communication network reconstruction of the extra-high voltage converter station when a network fault occurs; a topology control algorithm based on deep reinforcement learning sequentially constructs a topology structure of the heterogeneous network; a framework combining deep reinforcement learning and Monte Carlo tree search is adopted to sequentially construct the network according to pre-defined topology rules; a deep convolutional neural network is trained to predict transmission flow of a partially established topology and guide the Monte Carlo tree to expand search in a more promising area in a search space; and the search result of the Monte Carlo tree strengthens the learning of the deep convolutional neural network so as to obtain more accurate prediction in the next iteration.

The invention discloses a dense pedestrian detection method, device and equipment, a medium and a product, and relates to the technical field of computer vision, and the method comprises the steps: obtaining a to-be-detected dense pedestrian image; inputting the dense pedestrian image into a preset dense pedestrian detection model, and outputting a dense pedestrian detection result; wherein the dense pedestrian detection model is constructed based on an improved YOLOv12 network model, and comprises the following steps: introducing an adaptive frequency weight mechanism on the basis of a large receptive field wavelet convolution WTConv, forming an adaptive wavelet transform convolution AWTConv, and embedding the adaptive wavelet transform convolution AWTConv into a C3k2 module of a Backbone network of the YOLOv12 network model to form a C3k2AWTConv module; a density adaptive mechanism is introduced on the basis of a WIOUv3 loss function, and an adaptive loss function WIOU-DC is formed. According to the method, the accuracy and recall rate of dense pedestrian target detection can be effectively improved.