Target position point determination method, device and equipment, and storage medium

Abstract

The invention provides a target position point determination method and device, equipment and a storage medium. The method comprises the following steps: obtaining a plurality of object position points corresponding to a plurality of objects; determining a target object area where the plurality of object position points are located; dividing the target object area into a plurality of grids; selecting any grid including the object position points from the plurality of grids as a target grid, and determining a neighborhood grid corresponding to the target grid; taking the object position point in the target grid as a starting point, and performing position clustering on the object position point in the target grid and the object position point in the neighborhood grid to obtain a plurality of candidate position points belonging to the same category; and determining a central point of the plurality of candidate position points to obtain target position points corresponding to the plurality of candidate objects. According to the invention, the clustering speed of the target position point can be improved, the clustering category volume can be controlled, and the motion trend of the target position point can be updated, thereby facilitating the spatial management of massive intelligent equipment through the target position point.

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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