Environmental Project's Achievements Reports Query System

Using IoT with auto-monitoring technology to improve the procedures of audit and certification for waste container recycling

Absrtact	This project plans to introduce the concept of IoT to sensor devices, mobile devices and other tools, and to strengthen the self-management and audit certification benefits of the industry. The work includes: the use of AI detection technology to promote data import, the introduction of IoT tools to perform intelligent monitoring and management, and the development of an independent inspection mechanism for waste container impurities. The goal is to build automated monitoring equipment and develop a management model to effectively refine the audit and certification operation mode and reduce audit labor costs. In the implementation of the first two tasks, we have completed the license plate recognition system, real-time connection of weighbridge information, and IoT smart meter measurement of processing equipment. After the comparison, the success rate of vehicle license plate recognition is 92%; the weight data of the real-time connection of weighbridge information matches 69%; and 226,012 data of IoT smart meter measurement are collected, which can clearly determine the actual operation time and production capacity of the equipment after the comparison. In the study of waste container impurities independent inspection mechanism, we try to establish an early warning mechanism by using the KLR signal method to replace the current human audit method. The team selected 9 reporting data related to the processing volume, and then derived 12 alert variables, and simulated two kinds of abnormal situations according to the monitoring equation and the alert variables. Then, the RRMS system intercepted the previous years' data to establish the appropriate threshold values for the early warning signals. Although the required reporting information is not yet fully established in the current RRMS, this study uses hypothetical conditions to estimate the reporting data and early warning variables, and proposes amendments to the way of determining the threshold values of abnormal signals.
Keyword	Smart meter, Internet of Things, IoT, Automatic License Plate Recognition, ALPR, Early warning system, KLR Signals Approach

Absrtact

This project plans to introduce the concept of IoT to sensor devices, mobile devices and other tools, and to strengthen the self-management and audit certification benefits of the industry. The work includes: the use of AI detection technology to promote data import, the introduction of IoT tools to perform intelligent monitoring and management, and the development of an independent inspection mechanism for waste container impurities. The goal is to build automated monitoring equipment and develop a management model to effectively refine the audit and certification operation mode and reduce audit labor costs. In the implementation of the first two tasks, we have completed the license plate recognition system, real-time connection of weighbridge information, and IoT smart meter measurement of processing equipment. After the comparison, the success rate of vehicle license plate recognition is 92%; the weight data of the real-time connection of weighbridge information matches 69%; and 226,012 data of IoT smart meter measurement are collected, which can clearly determine the actual operation time and production capacity of the equipment after the comparison. In the study of waste container impurities independent inspection mechanism, we try to establish an early warning mechanism by using the KLR signal method to replace the current human audit method. The team selected 9 reporting data related to the processing volume, and then derived 12 alert variables, and simulated two kinds of abnormal situations according to the monitoring equation and the alert variables. Then, the RRMS system intercepted the previous years' data to establish the appropriate threshold values for the early warning signals. Although the required reporting information is not yet fully established in the current RRMS, this study uses hypothetical conditions to estimate the reporting data and early warning variables, and proposes amendments to the way of determining the threshold values of abnormal signals.

Keyword

Smart meter, Internet of Things, IoT, Automatic License Plate Recognition, ALPR, Early warning system, KLR Signals Approach