Logistics equipment fault prediction terminal based on edge computing

Abstract

The invention discloses a logistics equipment fault prediction terminal based on edge computing. The prediction terminal comprises a power supply circuit, an ADC detection circuit, a microprocessor circuit, a fault indication circuit and a serial port circuit. The input end of the ADC detection circuit is connected with a sensor arranged on the logistics equipment; the ADC detection circuit is used for collecting operation state data of logistics equipment. The output end of the ADC detection circuit is connected with the data input end of the microprocessor circuit; the fault indication circuit is connected with the data output end of the microprocessor circuit through an I / O interface and is used for displaying fault signals of logistics equipment. The serial port circuit is connected with the corresponding end of the microprocessor circuit and used for transmitting the data processed by the microprocessor circuit to the cloud server; the power supply circuit provides working power supply for all circuits; according to the invention, part or all of computing tasks of a cloud computing center are migrated to the vicinity of a data source of logistics equipment to be executed, so that the data transmission bandwidth is reduced, private data is protected, and the risk of privacy leakage of terminal sensitive data is reduced.

Description

Technical field [0001] The invention relates to logistics equipment technology, in particular to a logistics equipment failure prediction terminal based on edge computing. Background technique [0002] With the realization of the intelligence of the operation control system, at present, gradually perfect computer control technology and integrated sensor technology are widely used in the logistics equipment industry, and the configuration of advanced electronic control and computer operating system is very common. The structure of the mechanical system, electrical system, hydraulic system and control system of logistics equipment is becoming more and more complex. Whether the equipment can operate safely, continuously and stably will directly affect the economic and environmental effects. Due to the restriction of production site production conditions and production plans, in order to accurately, efficiently and conveniently diagnose the failure of logistics equipment and equipmen...

AI Technical Summary

Problems solved by technology

However, transmitting the data to the remote cloud center for storage and calculation will lead to excessive energy consumption and premature failure of the GPRS module carried by the logistics equipment itself, and at the same time bring huge traffic charges. On the other hand, when using cloud services, it is necessary to The sending and receiving of a large amount of data consumes a large amount of network bandwidth, which may cause I / O bottlenecks between the data center and the terminals carried by large-scale construction machinery equipment, resulting in a significant drop in transmission rate and even service interruption.

[0004] On the other hand, with the development of pervasive intelligent terminals, various heterogeneous intelligent or non-intelligent network terminal devices have emerged. Although these terminal devices are becoming smaller and smaller, their computing and storage capabilities However, the resources in terms of resources are becoming more and more powerful, resulting in excess and idle terminal equipment resources. In cloud computing, terminal equipment only performs human-computer interaction tasks for input and output. Therefore, some terminal equipment have relatively strong computing and storage capabilities. Resources are often not fully utilized, and more importantly, in cloud computing, other terminal devices with insufficient resources cannot expand their limited resources by using other nearby terminal devices with idle resources, so that they can complete Tasks that require more resources

Secondly, most of the current smart terminals, especially smart mobile terminals, are equipped with one or more kinds of sensors, which can perceive their own position (GPS) or the surrounding environment. The collection of these positions or environmental information will generate a large number of transmission If these ultra-large-scale sensing data are uploaded to the large data center of cloud computing for processing, it will not only take time and effort, but even lose the meaning and value of utilization due to untimely data processing

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