A device-edge-cloud collaborative data transmission method for smart factory AGV scene movement

Abstract

The invention discloses a device-edge-cloud collaborative data transmission method for intelligent factory AGV scene movement, and relates to the technical field of intelligent factory AGV. For the delay-tolerant mobile sensor network DTMSN composed of wireless sensors, AGV, and AGV dispatching system AGVS in smart factories, establish a mobile terminal-edge-cloud collaboration architecture; integrate AGV, terminal-edge-cloud collaboration, and DTMSN technology; consider terminal (wireless sensor) and edge ( The impact of AGV) movement on the network structure of the device-edge-cloud architecture and the effect of data transmission; improve the existing DTMSN algorithm, and use the idea of ​​analog quenching to accept inferior solutions by probability to replace the staged method for the selection of intermediate nodes; the data transmission task Factors such as its own characteristics, node density, node speed, and node type are combined into the utility function; thereby improving data transmission quality, reducing data transmission delay and energy consumption, ensuring the feasibility and effectiveness of mobile terminal edge-cloud collaboration, and making full use of AGV's computing resources reduce task response time and cloud computing and storage pressure.

Description

technical field [0001] The present invention relates to the field of smart factory AGV technology, in particular to a device-edge-cloud collaborative data transmission method for smart factory AGV scene movement. Background technique [0002] In the context of Industry 4.0, most factories have realized the automation of production and logistics. By being equipped with AGVs with material delivery and information transmission functions, manpower can be greatly saved, and the last piece of the puzzle of the smart factory can be completed. However, due to the generally large area of ​​the factory and the existence of a large number of moving metal objects that block the signal, the wireless sensors and AGV are relatively sparse and have limited power and computing power. The AGV has high mobility and a large and unfixed range of activities. Quality, latency, and power consumption pose huge challenges. Therefore, according to the characteristics of the smart factory AGV, it is ...

AI Technical Summary

Problems solved by technology

The research on the device-edge cloud is carried out under the assumption that all devices are fixed, the topology of the network remains unchanged, and the network is in good condition, without considering the impact on the network structure and transmission effect when the device and the edge move

The staged design of the existing DTMSN algorithm is not smooth enough, the harshness of the first stage and the second stage of the propagation strategy is very different, but in reality there is no staged difference in the movement and encounter probability of the nodes, which will lead to The blindness of the spray stage, on the other hand, causes the transmission effect to be very sensitive to the number of tokens

The current DTMSN data transmission scheme does not consider the characteristics of the data transmission task itself, that is, the number of tokens is individually set according to the data delay and the distance from the target node, which leads to the current dissemination strategy ignoring the data itself. The same treatment either fails to meet the delay requirements of high-latency data transmission tasks, or exceeds the standard to meet the delay requirements of low-latency data transmission tasks, resulting in serious waste of resources

The current DTMSN data transmission scheme does not comprehensively consider the node density, node speed, especially the impact of distinguishing nodes by attribute and type on node selection

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