Abstract: A first electronic device determines, in response to a data synchronization request, a first time required for sending first data requested by the data synchronization request to a second electronic device. The first electronic device adjusts the first data to second data in response to the first time being greater than a preset time threshold, where a data amount of the second data is less than a data amount of the first data. The first electronic device sends the second data to the second electronic device, where a second time required by the first electronic device to send the second data to the second electronic device is less than the preset time threshold.

Abstract: The present invention discloses a method and a device for non-destructive sorting of fertilization information of hatching eggs before incubation. The method comprises the following steps: obtaining a first time sequence feature of hatching eggs and collecting spectrum data of the hatching eggs according to the first time sequence feature; preprocessing the spectrum data; selecting the preprocessed spectrum data to obtain a feature band; constructing a hatching egg classification neural network model and training the hatching egg classification neural network model; classifying the hatching egg information according to the feature band by using the trained hatching egg classification neural network model to obtain fertilization information; and obtaining a second time sequence feature of the hatching eggs, and sorting the hatching eggs. The present invention improves the precision of detecting the fertilization information of hatching eggs.

Abstract: The present disclosure provides a method for estimating a body size and weight of a pig based on deep learning, and relates to the technical field of deep learning. The present disclosure predicts the weight of the pig by using a convolutional neural network. Relevant features are learned by the convolutional neural network, and feature engineering extraction is not needed to be established, so that extracted features are more comprehensive, and the convolutional neural network is superior to a linear model in processing of noisy data and nonlinear problems of data. Images of the pig are shot by an ordinary two-dimensional (2d) color camera.