Abstract: The present invention discloses a control method and equipment for a livestock and poultry health inspection robot for multi-index collection. The method comprises the following steps: S1: performing initial detection on the robot; S2: according to a set inspection time and a target inspection point, generating a target position instruction and sending the target position instruction to the robot; S3: controlling the robot to reach a target inspection point according to the target position instruction in the step S2; S4: controlling the robot to collect multi-index data at the target inspection point reached in the step S3; and S5: judging whether the inspection of the set target inspection point is completed; if yes, returning to a charging pile; otherwise, jumping to the step S2. The present invention can control the robot to automatically complete data collection tasks at a fixed time and a fixed point.

Abstract: The present invention discloses a control method and equipment for a livestock and poultry health inspection robot for multi-index collection. The method comprises the following steps: S1: performing initial detection on the robot; S2: according to a set inspection time and a target inspection point, generating a target position instruction and sending the target position instruction to the robot; S3: controlling the robot to reach a target inspection point according to the target position instruction in the step S2; S4: controlling the robot to collect multi-index data at the target inspection point reached in the step S3; and S5: judging whether the inspection of the set target inspection point is completed; if yes, returning to a charging pile; otherwise, jumping to the step S2. The present invention can control the robot to automatically complete data collection tasks at a fixed time and a fixed point.

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.

Abstract: The technology of this application relates to an account binding method, a device, and a system, and relates to the field of electronic technologies. An electronic device logs in to a user account of a first application. After detecting a first preset operation of a user, the electronic device obtains, by using the first application, an OpenID corresponding to the user account of the first application, where the first preset operation is used to indicate to associate a second application with the first application. The electronic device sends the OpenID of identity information of the second application to a server of the first application, and obtains account binding success information from the server of the first application.

Abstract: An electronic device receives an operation of a user, sends a first authentication obtaining request to a first server, where the first authentication obtaining request includes a first identity authentication token allocated by the first server, the first authentication obtaining request requests a second identity authentication token to access a second server, and the first identity authentication token is associated with the second identity authentication token, sends a first content obtaining request including the second identity authentication token to the second server, where the first content obtaining request requests content information to be displayed in the widget, receives content information from the second server, and displays the second user interface including the widget.