Abstract: A license plate classification method, a license plate classification apparatus and a computer-readable storage medium are provided. The method includes: performing a license plate recognition process on a first license plate image to obtain a license plate recognition result; performing an encoding process on the license plate recognition result to obtain a first license plate feature; performing a feature extraction process on the first license plate image to obtain a second license plate feature; and processing the first license plate feature and the second license plate feature through a classification network to obtain a first license plate classification result. In this way, an accuracy of license plate classification is improved.

Abstract: This application provides an object recognition method and apparatus, and a storage medium, and relates to the technical field of image recognition. The object recognition method includes: receiving an image of an object to be recognized; inputting the image of the object to be recognized into a pre-trained image feature extractor to obtain a first feature vector of the image of the object to be recognized; and determining category information of the object to be recognized according to the first feature vector of the image of the object to be recognized as well as image feature vectors in an image database, wherein the image database includes image information of a plurality of objects.

Abstract: An embodiment of the present application provides a method and a device for predicting a pair of similar questions and an electronic equipment, wherein a pair of similar questions to be predicted are input into multiple different prediction models, and a prediction result output by each of the prediction models is obtained; a random disturbance parameter is added into an embedding layer of at least one of the prediction models; and voting operation is performed on multiple prediction results to obtain a final prediction result of the pair of similar questions to be predicted. According to the present application, the a random disturbance parameter is added into the embedding layer of the prediction model, so that over-fitting caused by over-learning of sample knowledge by the prediction model can be effectively prevented, and the prediction accuracy can be effectively improved by predicting the pair of similar questions utilizing the prediction model.

Abstract: Disclosed is a routing method named lock routing for a wireless multi-hop network, which can be based on next-hop routing and source routing and named next-hop lock routing and source lock routing respectively. The routing method utilizes traffic packets to monitor the link quality and utilizes local broadcast to help a source node keep track of the varying network topology in order to update a route path to a destination node. In local broadcast, routing control messages can be forwarded by path nodes and neighbors of an old route path, which reduces the routing control overhead and enhances the network scalability. A route update process can be triggered by the link quality and the path node state and is used to maintain ongoing traffic flows and extend the network lifetime.

Abstract: Disclosed is a routing method named lock routing for a wireless multi-hop network, which can be based on next-hop routing and source routing and named next-hop lock routing and source lock routing respectively. The routing method utilizes traffic packets to monitor the link quality and utilizes local broadcast to help a source node keep track of the varying network topology in order to update a route path to a destination node. In local broadcast, routing control messages can be forwarded by path nodes and neighbors of an old route path, which reduces the routing control overhead and enhances the network scalability. A route update process can be triggered by the link quality and the path node state and is used to maintain ongoing traffic flows and extend the network lifetime.