A method, device, device and storage medium for determining a target location point

Abstract

The present application provides a method, device, device, and storage medium for determining target location points, which relate to high-precision map technology in the field of artificial intelligence. The method includes: acquiring multiple object location points corresponding to multiple objects; determining multiple objects The target object area where the position point is located; divide the target object area into multiple grids; select any grid including the object position point from the multiple grids as the target grid, and determine the neighborhood network corresponding to the target grid grid; starting from the object location point in the target grid, clustering the object location points in the target grid and the object location points in the neighborhood grid to obtain multiple candidate location points belonging to the same category; determine center points of multiple candidate position points to obtain target position points corresponding to the multiple candidate objects. The application can improve the clustering speed of the target location points, control the aggregation category volume and update the movement trend of the target location points, so as to facilitate the spatial management of massive devices.

Description

technical field [0001] The present application belongs to the field of computer technology, and specifically relates to a method, device, equipment and storage medium for determining a target location point. Background technique [0002] In the prior art, when spatially managing a large number of smart devices, the number of smart devices to be displayed is first reduced by classification, grading or clustering, and the center point is used to represent the position of the category smart devices, and then displayed. When the smart device is about to be in the task collection area, the operation or the background will automatically deliver tasks such as batch updates collected by crowdsourcing, road conditions, etc. [0003] However, the existing mass smart device management methods cannot support the display of all device locations, so it is necessary to aggregate and update the smart device locations and movement trends. However, the existing clustering method for calculat...

AI Technical Summary

Problems solved by technology

[0003] However, the existing massive smart device management methods cannot support the display of all device locations, so it is necessary to aggregate and update the location and movement trends of smart devices

However, the existing clustering method for calculating the center point representing the location of a large number of smart devices has a slow aggregation speed and cannot control the aggregation category volume

And when the position of the smart device is changing in real time, the category and center point need to be recalculated each time, which cannot respond quickly, and cannot obtain the movement trend of a large number of smart devices at the same time, resulting in a lag in updating the position and movement trend of the smart device, which has a great impact Intelligent device control, collection task delivery, trend warning, etc.

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