6results about How to "Improve the determination accuracy" patented technology

The application discloses a precoding matrix determination method, device and equipment and a storage medium. The method comprises the following steps: sending a first instruction to a terminal; the first instruction is used for instructing the terminal to report at least a first matrix; the first matrix is used for determining a precoding matrix; receiving a first uplink reference signal sent by the terminal; obtaining a prediction result by using the first uplink reference signal and a pre-trained neural network model; the prediction result comprises a first downlink channel matrix between a network device and the terminal; and determining the precoding matrix based on the first downlink channel matrix.

The application discloses a parameter budget range determination method and device, a storage medium and computer equipment, relates to the high-resolution imaging system budget decomposition technical field, and is applied to a photoetching system. The method comprises the following steps: obtaining a test photoetching parameter, wherein the test photoetching parameter comprises a line width parameter and a period parameter; inputting the line width parameter and the period parameter into a pre-trained classification model; determining a target negative result farthest from a classification boundary of the classification model in negative results output by the classification model, and determining a line width parameter and the period parameter corresponding to the target negative result; and determining a parameter budget range of the photoetching system according to the line width parameter, the period parameter corresponding to the target negative result and the classification model. The above method can significantly improve the determination precision of the parameter budget range of the photoetching system.

This application provides a method, apparatus, device, and storage medium for determining inspection paths for devices. The method includes: determining alarm information and location information of alarm devices; wherein the alarm information characterizes the alarm status of the alarm devices; determining the inspection level of each alarm device based on its alarm information; wherein the inspection level characterizes the urgency of inspecting the alarm device; and determining an inspection path based on the location information and inspection level of each alarm device; wherein the inspection path characterizes the path taken from a preset starting point, passing through the alarm devices. This method, by combining the alarm information of each device, differentiates the urgency of inspecting these devices, thereby improving the accuracy of inspection path determination.

The application relates to an aorta CT image center line determination method and system, electronic equipment and a computer readable storage medium, and relates to the field of CT image processing.The method comprises the following steps: inputting a three-dimensional aorta CT image to be detected into an aorta CT image key point detection model, and outputting a key point extraction result; the aorta CT image key point detection model is determined according to training of a U-Net network; the extracted key points are sequentially connected by using a progressive optimal fast random search tree algorithm; in the connecting process, a correction process is performed on sampling candidate points generated by random sampling; if the distance between the sampling candidate points located between two key points and any point outside the aorta is less than a set threshold value, an aorta inscribed sphere containing the sampling candidate points is formed, and the sphere center is taken as a connecting point between the two key points. The application improves the determination accuracy and efficiency of the aorta CT image center line.

The application provides a two-dimensional icon labeling method, system and device based on three-dimensional model feature recognition, which obtains model parameter information; extracts three-dimensional features of a target three-dimensional model in the model parameter information, determines the correlation of the three-dimensional features under multiple views based on a multiple view search method; selects an optimal projection direction according to the correlation to project the three-dimensional features to a virtual space plane, and determines shape information and size information of two-dimensional features; determines feature naming according to the matching relationship between the two-dimensional features and the counterpart information, and matches the two-dimensional features according to a preset industry rule to determine tolerance information; encapsulates the view position information, feature space position, tolerance information and feature naming corresponding to the two-dimensional features to generate a labeling data package; and optimizes the layout formed by the labeling data package to generate a two-dimensional icon. The application does not require manual intervention throughout, greatly reduces the labor cost and human error, and improves the labeling efficiency and quality.