20240037677. METHOD FOR POWER CONSUMPTION ANALYSIS AND TROUBLESHOOTING OF PRODUCTION LINE BASED ON INDUSTRIAL INTERNET OF THINGS, SYSTEM AND STORAGE MEDIUM THEREOF simplified abstract (CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.)
METHOD FOR POWER CONSUMPTION ANALYSIS AND TROUBLESHOOTING OF PRODUCTION LINE BASED ON INDUSTRIAL INTERNET OF THINGS, SYSTEM AND STORAGE MEDIUM THEREOF
Organization Name
CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD.
Inventor(s)
Yongzeng Liang of Chengdu (CN)
METHOD FOR POWER CONSUMPTION ANALYSIS AND TROUBLESHOOTING OF PRODUCTION LINE BASED ON INDUSTRIAL INTERNET OF THINGS, SYSTEM AND STORAGE MEDIUM THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240037677 titled 'METHOD FOR POWER CONSUMPTION ANALYSIS AND TROUBLESHOOTING OF PRODUCTION LINE BASED ON INDUSTRIAL INTERNET OF THINGS, SYSTEM AND STORAGE MEDIUM THEREOF
Simplified Explanation
The present disclosure describes a method for analyzing power consumption and troubleshooting of a production line using an industrial internet of things (IoT) system. The method involves resetting electric energy metering equipment, configuring its parameters, obtaining power consumption data (including historical internal power consumption data of the production line), determining the internal power consumption distribution of the production line based on a distribution prediction model, and identifying abnormal power consumption.
- Method for power consumption analysis and troubleshooting of a production line using an industrial IoT system
- Resetting electric energy metering equipment based on an initialization instruction
- Completing parameter configuration of the electric energy metering equipment based on a parameter configuration instruction
- Obtaining power consumption data, including historical internal power consumption data of the production line
- Determining the internal power consumption distribution of the production line through processing the historical internal power consumption data using a distribution prediction model
- Identifying abnormal power consumption of the production line based on the internal power consumption distribution
Potential applications of this technology:
- Power consumption analysis and troubleshooting in industrial production lines
- Optimization of energy usage in manufacturing processes
- Real-time monitoring and control of power consumption in production facilities
- Predictive maintenance based on abnormal power consumption patterns
Problems solved by this technology:
- Difficulty in analyzing and troubleshooting power consumption in complex production lines
- Lack of real-time visibility into power consumption patterns and abnormalities
- Inefficient energy usage in manufacturing processes
- Inability to predict and prevent power-related issues in production facilities
Benefits of this technology:
- Improved efficiency and cost savings through optimized energy usage
- Enhanced visibility and control over power consumption in production lines
- Early detection and prevention of power-related issues
- Increased productivity and reduced downtime through predictive maintenance
Original Abstract Submitted
the present disclosure provides a method for power consumption analysis and troubleshooting of a production line based on an industrial internet of things, system, and storage medium thereof. the method comprises: resetting electric energy metering equipment based on an initialization instruction; in response to a successful reset of the electric energy metering equipment, completing 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, obtaining power consumption data, the power consumption data including historical internal power consumption data of the production line; determining an internal power consumption distribution of the production line through processing the historical internal power consumption data based on a distribution prediction model; and determining whether a power consumption of the production line is abnormal based on the internal power consumption distribution.
