Goods shelf inclination detection remote maintenance system and method

Abstract

The invention discloses a goods shelf inclination detection remote maintenance system and method, and the system comprises a data acquisition module which is used for enabling an inclination angle sensor to be initialized and installed at the top of a goods shelf, enabling the inclination angle sensor to be in communication connection with a microcontroller, and enabling the microcontroller to be connected with a remote terminal, the microcontroller is used for receiving the height h of the goods shelf, the inclination angle alpha 1 of the goods shelf in the first direction axis and the inclination angle alpha 2 of the goods shelf in the second direction axis, and storing the information to the database through the remote terminal; the analysis processing module is used for accessing the database and obtaining and displaying the height h, the inclination angle alpha 1 and the inclination angle alpha 2 of the goods shelf so as to obtain the inclination angle alpha of the goods shelf in the inclination direction, and a preset threshold value of the inclination angle of the goods shelf in the inclination direction is set to be alpha 0; and the judgment module is used for starting early warning when the obtained inclination angle alpha of the goods shelf in the inclination direction is greater than a preset threshold value alpha0.

Description

technical field [0001] The invention relates to the technical field of warehousing, in particular to a remote maintenance system and method for shelf inclination detection. Background technique [0002] In the existing logistics storage system, most of the detection of shelf inclination relies on human visual recognition, or regular manual on-site lifting of the hammer line to detect whether the shelf is tilted. Manual and regular maintenance and overhaul are limited by the ability to identify with the naked eye, and only obvious shelf tilt can be observed, and accurate judgment cannot be made for tiny data of shelf tilt. At the same time, the operation behavior is a regular operation, and the occurrence of risks cannot be foreseen during the maintenance operation gap. The lack of necessary data records makes it impossible to perform remote data analysis and make a reasonable pre-judgment on shelf tilt in advance. [0003] At the same time, other non-manual detection metho...

AI Technical Summary

Problems solved by technology

Manual and regular maintenance are limited by the ability to identify with the naked eye, and only obvious shelf tilt can be observed, and accurate judgment cannot be made for tiny data of shelf tilt

At the same time, the operation behavior is a regular operation, and the occurrence of risks cannot be foreseen during the maintenance operation gap

Lack of necessary data records, remote data analysis cannot be done, and a reasonable pre-judgment can be made in advance for shelf tilt

[0003] At the same time, other non-manual detection methods either use electrical principle equipment to detect single-axis data, and the data accuracy is not enough, or use three-axis data, which can only detect axial data, which is expensive and rarely used in shelf tilt detection solutions.

At the same time, the above detection methods basically belong to on-site data detection, and do not have the function of remote maintenance.

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