A method and intelligent hardware for predicting user location

Abstract

The invention provides a method and an intelligent hardware for predicting the position of a user. The method comprises the following steps: clustering calculation is carried out according to the historical positioning data of a target user; according to the clustering calculation result and the behavior rule data, the target user activity area corresponding to the target user in each time periodis obtained; Clustering calculation is carried out on the historical positioning data of the user group, and the corresponding group user activity area of the user group at the time when the target user initiates the positioning request is obtained according to the clustering calculation result; the number of historical positioning data of target users is Counted; When the number is greater than or equal to the preset number, the location data of the target user at the time of initiating the positioning request is predicted according to the target user activity area, When the number is less than the preset number, the position data of the target user at the current time is predicted according to the group user activity area. The invention uses the historical positioning data of the user topredict the position with the minimum input under the limited resources, solves the positioning drift problem, reduces the complexity of the algorithm and improves the positioning accuracy.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a method and intelligent hardware for predicting user positions. Background technique [0002] With the popularity of mobile devices such as smart phones and tablets in recent years, and the rapid development of positioning technology, historical positioning data of various mobile devices have been collected and utilized. Currently, positioning technologies mainly include: GPS (Global Positioning System) positioning, Wi-Fi (Wireless Fidelity) positioning, and base station positioning. [0003] There are certain errors in the above positioning methods. Generally speaking, GPS positioning accuracy is high, but it is required to be able to obtain GPS signals transmitted by GPS satellites during positioning, which consumes a lot of power. Once in an indoor environment, GPS positioning will drift, resulting in positioning errors. When the GPS signal is weak (such as i...

AI Technical Summary

Problems solved by technology

If you need to use WiFi to achieve positioning, you need additional hardware costs, laying WiFi devices on the target building (evenly deployed according to the characteristics of the building), the cost is huge, and the positioning accuracy is low

If you need to use the base station to achieve positioning, since the base station positioning belongs to the ground positioning, the positioning accuracy depends on the signal tower, and it is necessary to add a signal tower near the target building, which is costly and low in positioning accuracy

[0004] In the above cases, the positioning error will become larger, resulting in positioning drift

In order to correct positioning drift and improve positioning accuracy, map merchants often use methods such as denoising, tying roads, supplementing road shapes, and thinning out. However, these methods have limited application scenarios and are applicable to positioning scenarios such as road positioning and navigation. For indoor scenes such as schools and offices, the positioning effect is not ideal

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