Automated Carbon Emission Identification System
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Solution Overview
Problem
Current systems for tracking carbon emissions are not time-efficient and prone to human error, making it difficult for manufacturers to accurately identify and reduce excessive carbon emissions.
Innovation Solution
An apparatus and method utilizing a processor and sensor data to calculate and classify carbon emission values, identifying excessive emissions by associating activity data with action carbon emission values and categorizing them using machine-learning models and fuzzy inference systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual tracking methods are used for carbon emissions, then flexibility and adaptability are maintained, but time efficiency and accuracy deteriorate
Solution Approach 1:
The patent replaces manual tracking systems with an automated computing system that uses sensors to collect activity data and algorithms to calculate carbon emission values. This substitution eliminates human error and significantly improves both time efficiency and measurement precision by automating the entire tracking process from data collection to emission calculation.
Solution Approach 2:
The system enables self-service by automatically collecting activity data through sensors, processing the data through classification algorithms, and generating carbon emission reports without requiring manual intervention. The automated identification of excessive emission values allows the system to self-correct and alert users, improving both efficiency and accuracy while reducing human workload.
2Measurement precision
If automated processing systems are implemented, then time efficiency and accuracy improve, but system complexity increases
Solution Approach 1:
The patent divides the carbon emission tracking system into distinct functional modules: sensor data collection, activity classification, carbon emission calculation, and excessive value identification. Each module performs a specific function, making the overall complex system manageable through clear segmentation. This modular approach allows for easier maintenance and reduces the perceived complexity while maintaining high accuracy.
Solution Approach 2:
The computing system is designed to handle multiple types of activity data (manufacturing, transportation, logistics) through a unified classification framework. The system can process various sensor inputs and apply consistent algorithms across different activity types, reducing complexity by using a universal approach rather than separate specialized systems for each activity type.
3Reliability
If detailed classification of activity data is performed, then measurement precision and reliability improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary classification of activity data into predefined categories before detailed carbon emission calculation. By pre-establishing activity categories and associated emission factors, the system reduces the computational burden during actual processing while maintaining reliable and precise measurements. This preliminary organization allows for faster processing without sacrificing reliability.
Data Source
AI summary
An apparatus for identifying an excessive carbon emission value is disclosed. The apparatus may include at least a processor, and a memory communicatively connected to the at least a processor. The memory contains instructions configuring the at least a processor to receive a plurality of activity datum from at least a sensor, calculate a plurality of action carbon emission values, wherein calculating the plurality of action carbon emission values includes associating each of the plurality of activity datum to an action carbon emission value of the plurality of action carbon emission values and classifying each of the plurality of activity datum to an activity category of a plurality of activity categories. The memory further contains instructions configuring the at least a processor to identify an excessive carbon emission value from the plurality of action carbon emission values. A method for identifying an excessive carbon emission value is also disclosed.