US 12,271,963 B2
Method for power consumption analysis and troubleshooting of production line based on industrial internet of things, system and storage medium thereof
Zehua Shao, Chengdu (CN); Yong Li, Chengdu (CN); Lei Zhang, Chengdu (CN); Bin Liu, Chengdu (CN); and Yongzeng Liang, Chengdu (CN)
Assigned to CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Chengdu (CN)
Filed by CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Sichuan (CN)
Filed on Oct. 15, 2023, as Appl. No. 18/487,112.
Application 18/487,112 is a continuation of application No. 18/172,274, filed on Feb. 21, 2023, granted, now 11,830,091.
Claims priority of application No. 202210965417.6 (CN), filed on Aug. 12, 2022.
Prior Publication US 2024/0037677 A1, Feb. 1, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 50/06 (2024.01); G06N 5/022 (2023.01); G06Q 10/0631 (2023.01); G16Y 10/35 (2020.01); G16Y 40/20 (2020.01)
CPC G06Q 50/06 (2013.01) [G06N 5/022 (2013.01); G06Q 10/06315 (2013.01); G16Y 10/35 (2020.01); G16Y 40/20 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A system for power consumption analysis and troubleshooting of a production line based on an Industrial Internet of Things, comprising:
a non-transitory computer-readable storage medium storing computer instruction; and
at least one processor in communication with the non-transitory computer-readable storage medium, when executing the computer instruction, the at least one processor is directed to cause the system to:
reset electric energy metering equipment based on an initialization instruction;
in response to a successful reset of the electric energy metering equipment, complete a parameter configuration of the electric energy metering equipment based on a parameter configuration instruction;
in response to a correct parameter configuration of the electric energy metering equipment, obtain power consumption data, the power consumption data including historical internal power consumption data of the production line;
determine an internal power consumption distribution of the production line through processing the historical internal power consumption data based on a distribution prediction model, wherein the distribution prediction model is a machine learning model; and
determine whether a power consumption of the production line is abnormal based on the internal power consumption distribution.