6results about How to "Shorten recognition time" patented technology

The present application relates to the field of artificial intelligence and the field of financial technology, and discloses a speech recognition method, device and equipment and a storage medium, the method comprising: splicing a first embedding vector and a second embedding vector to generate a third embedding vector, reconstructing the third embedding vector to generate a fourth embedding vector; obtaining a predicted speech recognition text output by a speech recognition model based on the fourth embedding vector, obtaining a first loss value and a second loss value between the predicted speech recognition text and a preset speech recognition text; generating a total loss value of the predicted speech recognition text according to the first loss value and the second loss value, training the speech recognition model based on the total loss value; obtaining a current speech and a current field label sent by a customer service system, inputting the current speech and the current field label into the trained speech recognition model, and obtaining a current speech recognition text output by the trained speech recognition model. The present application is beneficial to improving the efficiency of speech recognition.

This invention discloses a method and system for identifying threat targets in a driving environment based on the coordination of electroencephalography (EEG) and eye movement. The method includes the following steps: S1, acquiring real-time video of the road conditions in the foreground and performing multi-target detection and tracking; S2, displaying flashing arrows of different stimuli on the target and following its movement, collecting eye movement data and EEG data; S3, processing the eye movement data to obtain eye movement results, and processing the EEG data to obtain EEG results; S4, combining the eye movement results and EEG results to output the target tracking result. This invention has advantages such as reducing the false positive rate, shortening the target recognition time, and improving the recognition accuracy.

The invention relates to the technical field of computer vision and robots, and provides a pharmacy unmanned vehicle path planning and drug matching method based on JPS and RRT algorithms, and the method comprises the steps: constructing a pharmacy environment map, and determining the real-time position of an unmanned vehicle; performing unmanned vehicle path planning by adopting a JPS-RRT * algorithm to obtain a final driving path; the method comprises the following steps: acquiring a target drug image, constructing a drug feature template library, periodically acquiring drug images in a current visual field, pre-processing the drug images, and matching the pre-processed drug images with the drug feature template library by adopting an SAD template matching algorithm to obtain a drug classification result; and controlling the unmanned vehicle to move to the target position according to the final driving path, and executing a corresponding medicine grabbing or sorting task according to a medicine classification result.

The invention provides a vehicle diagnosis method and related equipment, and the method comprises the steps: connecting a vehicle-mounted automatic diagnosis system interface of a to-be-diagnosed vehicle, and judging whether a specified interface specification exists or not; in response to the absence of the specified interface specification, sending a diagnosis service request according to a preset interface specification sequence; responding to received response information fed back by the to-be-diagnosed vehicle according to the diagnosis service request, generating inquiry information according to a first interface specification corresponding to the response information and outputting the inquiry information; and in response to a determination instruction triggered by the object according to the inquiry information, setting the first interface specification as a specified interface specification, and performing function diagnosis on the to-be-diagnosed vehicle to obtain a diagnosis result. And the diagnosis service request is preferentially sent to the to-be-diagnosed vehicle according to the specified interface specification, so that the vehicle type can be quickly and accurately identified. In addition, the interface specification with the high weight in the preset interface specification sequence is preferentially scanned, so that the overall recognition time is shortened, and the efficiency and accuracy of vehicle diagnosis are remarkably improved.

The application discloses a behavior fingerprint digital feature extraction method, system, medium and equipment, and the method comprises the following steps: acquiring a behavior fingerprint sequence of a user and extracting digital feature information of the user, constructing a digital feature vector matrix for a to-be-matched user set and a known user set respectively, extracting a feature vector set of the user for the acquired behavior fingerprint sequence of the user, calculating the similarity distance between the digital feature vector of the known user and the digital feature vector of the to-be-matched user based on a similarity distance formula, and judging the identity of the user according to a preset matching strategy. The application expands the behavior fingerprint of the user, solves the problem of the sparsity of the behavior fingerprint of the user to a certain extent, and achieves the effect of improving the recognition accuracy.

The application belongs to the technical field of crop disease and pest identification, and discloses a wheat disease and pest intelligent identification method and an intelligent identification system based on meta learning. The collected leaf and stem images of the wheat to be identified are subjected to filtering processing by using an adaptive Wiener filtering method; the filtered leaf and stem images of the wheat are subjected to gray scale processing to obtain the gray scale images of the leaf and stem of the wheat; the filtered gray scale images of the leaf and stem of the wheat are subjected to enhancement processing by using a cross line gray scale image definition improvement model; and the diseases and pests, symptoms, disease spots and pest body characteristics of the leaf and stem of the wheat are identified according to the enhanced images. The application can be used in agricultural actual production, can provide a reference for further chemical control and biological control of wheat diseases and pests, and can provide a reference for the application of automatic distinguishing and identifying plant stress.