Abstract: A data transmission method, apparatus, and device, and a computer storage medium are provided. The method includes: obtaining an application packet of a target application; transmitting the application packet to a first routing device by using a network data channel, and transmitting, by using the first routing device, the application packet to a proxy server; meanwhile, transmitting the same application packet to a second routing device by using a short-range wireless data channel different from the network data channel, and transmitting, by using the second routing device, the application packet to the proxy server. The proxy server performs, a deduplication mechanism on the application packet and transmit the application packet to an application server corresponding to the target application.

Abstract: A displaying base plate and a manufacturing method thereof, and a displaying device. The displaying base plate includes a substrate, and a first electrode layer disposed on one side of the substrate, wherein the first electrode layer includes a first electrode pattern; a first planarization layer disposed on one side of the first electrode layer that is away from the substrate, wherein the first planarization layer is provided with a through hole, and the through hole penetrates the first planarization layer, to expose the first electrode pattern; and a second electrode layer, a second planarization layer and a third electrode layer that are disposed in stack on one side of the first planarization layer that is away from the substrate, wherein the second electrode layer is disposed closer to the substrate, the second electrode layer is connected to the first electrode pattern and the third electrode layer.

Abstract: An obstacle distribution simulation method, device and terminal based on multiple models. The method can include: acquiring a point cloud, the point cloud including a plurality of obstacles labeled with real labeling data; extracting the real labeling data of the obstacles, and training a plurality of neural network models based on the real labeling data of the obstacles; extracting unlabeled data in the point cloud, inputting the unlabeled data into the neural network models, and outputting a plurality of prediction results. The plurality of prediction results can include a plurality of simulated obstacles with attribute data; selecting at least one simulated obstacle based on the plurality of prediction results; and inputting the attribute data of the selected simulated obstacle into the neural network models to obtain position coordinates of the simulated obstacle, and further obtain a position distribution of the simulated obstacle.

Abstract: An obstacle distribution simulation method, device and terminal based on multiple models. The method can include: acquiring a point cloud, the point cloud including a plurality of obstacles labeled with real labeling data; extracting the real labeling data of the obstacles, and training a plurality of neural network models based on the real labeling data of the obstacles; extracting unlabeled data in the point cloud, inputting the unlabeled data into the neural network models, and outputting a plurality of prediction results. The plurality of prediction results can include a plurality of simulated obstacles with attribute data; selecting at least one simulated obstacle based on the plurality of prediction results; and inputting the attribute data of the selected simulated obstacle into the neural network models to obtain position coordinates of the simulated obstacle, and further obtain a position distribution of the simulated obstacle.

Abstract: A method, device and a computer-readable storage medium for optimizing simulation data are provided. The method for optimizing simulation data can include: inputting the simulation data generated by a simulator to a first generative adversarial network comprising a migration model; and optimizing the simulation data generated by the simulator with the migration model to generate optimized simulation data. In an embodiment of the present application, the simulation data is optimized by the generative adversarial network to enable the simulation data closer to the real data in representation. Therefore, the quality and accuracy of the simulation data can be ensured, the validity and reliability of the simulation data can be improved at least to some extent, and the cost for constructing the simulator can also be reduced.