3results about How to "Solving recognition problems" patented technology

The invention belongs to the technical field of oil-gas field development, and discloses a low-salinity formation water identification method, which comprises the following steps of: obtaining data corresponding to a test oil well through drilling data, and comparing step by step according to the obtained data to judge whether water is discharged from a formation; the invention further discloses a low-salinity formation water recognition device and equipment and a storage medium. The method is suitable for identifying the low-salinity formation water, whether water flows out of the formation or not is judged in real time by draining the test oil well, an important basis is provided for identifying the low-salinity formation water, and the defects of a formation water judgment method are overcome.

This application relates to a method, system, and medium for extracting radio frequency (RF) signal features from unmanned aerial vehicles (UAVs). The method includes: decoupling the acquired RF signal of a target UAV using a time-frequency transformation algorithm to obtain a time-frequency distribution feature map; extracting spectral texture features from the time-frequency distribution feature map at different receptive field scales using a trained deep neural network model to obtain texture feature vectors; generating position feature vectors containing temporal logic information using the same deep neural network model; and performing a feature space fusion mapping between the texture feature vectors and the position feature vectors to generate the RF fingerprint features of the target UAV. This application achieves texture extraction based on dilated convolution and position perception based on self-attention. This dual-stream parallel network architecture simultaneously captures multi-scale spectral texture and long-range frequency hopping logic in the time-frequency domain, constructing a highly robust semantic fingerprint of RF signals and effectively improving the detection and recognition rate of UAV frequency hopping flight control signals.

The present application relates to a kind of complete parking space inference methods based on parking space features, comprising steps 1: obtaining the panoramic image around vehicle and inputting panoramic image into feature detector;Step 2: according to the number of proximal parking space mark points in parking space entrance line boundary box, entrance line is paired with proximal parking space mark points;Step 3: according to the distance between parking space entrance line type and paired proximal parking space mark points, determine the type of parking space;Step 4: generate two groups of distal parking space mark points respectively located on the opposite sides of vehicle;Step 5: calculate the intersection-over-union of two parking spaces and vehicle, determine the distal parking space mark point coordinates of actual parking space;Step 6: according to the proximal mark point and distal mark point of parking space, locate parking space, obtain complete parking space.The present application only needs to detect parking space entrance line and mark point, and complete parking space can be inferred through simple calculation.The method is high in accuracy and small in calculation amount.