Edge-Assisted Positioning for Low-Latency Precise Navigation
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Solution Overview
Problem
The 'cloud+end' positioning mode in high-precision vehicle navigation suffers from significant network delays due to communication between the cloud server and satellite positioning terminal, which hinders its application in fields requiring ultra-low time delay, such as automatic driving.
Innovation Solution
A positioning system utilizing a 'cloud+edge+end' architecture, incorporating Multi-access Mobile Edge Computing (MEC) nodes and a centralized second server, synchronizes differential data across a star-based network structure to reduce network delays and enhance computing power, enabling high-precision positioning with ultra-low time delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the cloud+end positioning mode is used, then high-precision positioning can be achieved, but network delay increases significantly
Solution Approach 1:
The positioning system is segmented into three parts: cloud server (second server), edge server (first server), and terminal. The cloud server provides differential data, the edge server performs real-time calculation, and the terminal receives positioning results. This segmentation allows computation to be distributed closer to the terminal, reducing network communication delay while maintaining high positioning precision.
Solution Approach 2:
The edge server acts as an intermediary between the cloud server and terminal. It receives differential data from the cloud server, performs real-time positioning calculations, and returns results to the terminal. This intermediary role eliminates the need for the terminal to directly communicate with the cloud server for each positioning calculation, significantly reducing network delay.
2Device complexity
If computing resources are centralized in the cloud server, then system complexity is reduced, but network delay increases
Solution Approach 1:
Computing resources are segmented and distributed between the cloud server and edge server. The cloud server maintains the differential data, while the edge server handles real-time positioning calculations. This segmentation of computing functions reduces network delay without significantly increasing overall system complexity, as the edge server follows straightforward calculation protocols.
3Loss of time
If differential data is calculated in real-time at the terminal, then network delay is reduced, but terminal computing power requirements increase
Solution Approach 1:
The edge server serves as an intermediary that performs the computationally intensive differential calculation operations. The terminal only needs to send raw positioning data to the edge server and receive processed results, avoiding the need for high terminal computing power while still achieving real-time processing and reduced network delay.
Data Source
AI summary
The application discloses a positioning method, device, and system, and a storage medium. The method includes: receiving first location information sent from a terminal, the first location information comprising an identity of the terminal; sending a differential data request to a second server, the differential data request comprising the first location information, and the differential data request being used by the second server to determine differential data of the terminal, receiving the differential data sent from the second server; performing calculation on the first location information according to the differential data to obtain second location information; sending the second location information to the terminal, so that a positioning result can be quickly returned to the terminal, and the network delay is greatly reduced when the terminal determines a precise location.