8results about How to "Enhanced feature information" patented technology

The invention provides a gesture interaction method and system, a readable storage medium and a computer. The method comprises the following steps: acquiring a sight focusing area of a user for a virtual marker; inputting the hand image into a convolutional neural network model for convolution operation to obtain a convolutional feature image; performing pixel processing based on the hand coding image of the hand image to obtain a corresponding pixel feature image; importing the multi-scale fusion unit into a multi-resolution network model for model optimization to obtain a network optimization model; and inputting the convolutional feature image and the pixel feature image into a network optimization model to output a hand posture estimation result of the user, and performing collision judgment according to the hand posture estimation result and the sight focusing area to realize gesture interaction of the user. According to the method, attitude estimation is carried out on the convolution feature image and the pixel feature image by using the network optimization model, and the influence of network parameter quantity on hand attitude estimation is reduced, so that the detection error of hand joint points is reduced.

The invention provides a government purchase commodity transaction price prediction method and device based on a CMPSOLSTMTLM model, and belongs to the technical field of data analysis. The method comprises the following steps: acquiring purchase price historical data of the same commodity in an administration and acquisition contract, preprocessing and normalizing the purchase price historical data, and dividing the purchase price historical data into a training set and a test set; constructing a government purchase commodity transaction price prediction model; adopting an improved algorithm CMPSO based on particle swarm optimization to carry out parameter optimization on the government purchase commodity transaction price prediction model; and segmenting data of the test set, inputting the segmented data into the trained government purchase commodity transaction price prediction model for prediction, and carrying out reverse normalization on an output result to obtain a commodity price prediction result. According to the method, the advantages of the LSTM and the Transform model are combined, and the particle swarm optimization algorithm is used for parameter adjustment and optimization, so that the price prediction precision and efficiency are improved.

The invention relates to an abnormal sound detection method, which comprises the following steps of: installing an abnormal sound detection device at a to-be-detected position of an automobile part, and detecting a vibration signal at an abnormal sound occurrence moment by using the abnormal sound detection device; and analyzing the vibration signal, determining an abnormal sound frequency band, performing noise reduction on the abnormal sound frequency band, and analyzing the to-be-detected position according to the vibration signal after noise reduction. The abnormal sound detection device is installed at the to-be-detected position of the automobile part, the abnormal sound detection device is used for detecting the vibration signal at the abnormal sound occurrence moment, then the vibration signal is analyzed, the abnormal sound frequency band is determined, the detection range of the vibration signal is reduced, noise reduction is conducted on the abnormal sound frequency band, the signal-to-noise ratio of the signal of the abnormal sound frequency band is increased, and the abnormal sound detection accuracy is improved. The interference of external noise is reduced, the feature information of the vibration signal is improved, and the technical problem that the fault position cannot be accurately judged due to the fact that the abnormal sound signal detected by the abnormal sound stethoscope in the vehicle driving process is interfered by other irrelevant signals in the related technology is solved.

The invention discloses a multi-modal data fusion crop yield prediction method and device and a medium, and relates to the field of crop yield assessment, and the method comprises the steps: collecting an original image of a target farmland; preprocessing the original image of the target farmland to obtain preprocessed images of a plurality of quadrat areas; based on the preprocessed image of each quadrat region, extracting crop parameters of each quadrat region; determining feature information of each quadrat region based on the preprocessed image and crop parameters of each quadrat region; respectively inputting the feature information of each quadrat region into a multi-modal data fusion prediction model to obtain a crop yield prediction value of each quadrat region; and summing the crop yield predicted values of all the quadrat areas to obtain a crop yield predicted value of the target farmland. According to the invention, manual intervention is reduced, and the crop yield prediction efficiency and accuracy are improved.

The invention provides a target detection method and device combining a multi-branch decoupling head and feature enhancement. According to the method, a YOLOv5 model is selected as a reference framework to design a target detection network for target detection; the target detection network includes a backbone network, a neck network, and a head module. The method comprises the following steps: firstly, performing feature extraction on an input image by using a convolutional neural network through a backbone network to obtain three feature maps of different levels; the neck network performs feature enhancement on the three feature maps of different levels and then performs feature fusion so as to further extract features; the feature enhancement is added into cavity convolution processing to enlarge a receptive field; the head module adopts a three-branch decoupling head, independently divides category prediction, prediction frame positioning and confidence calculation in target detection into three network branches, performs convolution operation on extracted features, and outputs category prediction, prediction frame positioning and confidence calculation results. By using the method, the small-scale target detection precision of the remote sensing image can be improved.

The invention discloses an industrial control system anomaly detection method based on differential switching value coding, and relates to the technical field of industrial control system safety, and the method comprises the steps: carrying out the probability conversion of a switching value numerical value (0 or 1) in a training data set, obtaining a switching change probability value, and capturing the probability information of the state change of a switching device; therefore, the feature information of the numerical vector of the device is expanded, and a differential switching value coding technology is designed. The industrial control system anomaly detection method based on differential switching value coding solves the problem that an existing industrial control anomaly detection method based on deep learning is not sensitive to numerical value changes of a switching device in an industrial control system, and consequently the detection accuracy is not high, and the industrial control system anomaly detection method based on differential switching value coding is provided.

The application discloses a marine ship detection method based on feature extraction and feature weighting selection fusion. First, remote sensing ship images are collected to construct a data set. Then, a ship detection model is constructed, the remote sensing ship images are input into the model to obtain fusion features, and then the fusion features are input into a detection head of the model for detection to obtain a detection result. Then, the model is trained, the remote sensing ship images are input into the model, and after multiple rounds of training, a final model is obtained. Finally, the remote sensing ship images are input into the trained model to output ship types and positioning information. The application utilizes dynamic learning sampling point offset and modulation factors to enable convolution kernels to change according to ship shapes and enhance feature information. In the feature fusion stage, feature pyramid networks based on hierarchical scales are developed to fully utilize feature maps from different scales and enhance the feature expression capability of the model.

The present disclosure provides a data classification method, device, storage medium and electronic equipment. The method comprises: obtaining first generated content data to be classified, and performing feature extraction on the first generated content data based on a first neural network model to obtain first features; obtaining a generated content reference feature set, the generated content reference feature set comprising a plurality of generated content feature pairs, each generated content feature pair comprising a key feature and a value feature, the key feature being extracted based on the first neural network model, and the value feature being extracted based on a second neural network model; determining a plurality of target generated content feature pairs in the generated content reference feature set according to a first similarity between the first features and the key features; performing feature fusion on the first features and the value features in the plurality of target generated content feature pairs to obtain second features; and determining a classification label of the first generated content data according to the second features. The method can improve the accuracy of data classification.