Method and system for setting cooling parameters from a peer transport vehicle
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Solution Overview
Problem
Cargo transport vehicles often consume more energy than necessary due to non-optimized cooling parameters, leading to increased costs and environmental impact, as existing systems fail to account for specific goods and transport conditions.
Innovation Solution
A method and system that utilize a server to receive data from transport vehicles, identify peer vehicles with minimal energy and fuel consumption, and adjust cooling parameters such as temperature, humidity, and ventilation based on the most efficient settings of these peers, using Bluetooth, Wi-Fi, LoRa, or Zigbee for communication, and AI or ML models for historical data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cooling parameters are set using traditional integrated systems, then cargo quality and safety are maintained, but energy consumption increases
Solution Approach 1:
The system copies optimized cooling parameters from peer transport vehicles that have demonstrated lower energy consumption for similar cargo types and routes. By replicating proven parameter sets (temperature, humidity, ventilation rates) from efficient peer vehicles, the system maintains cargo quality while reducing energy consumption without requiring complex real-time optimization algorithms
Solution Approach 2:
The system changes cooling parameters (temperature, humidity, ventilation) based on peer vehicle performance data. By adjusting these parameters to match or exceed peer vehicle efficiency standards, the system optimizes energy consumption while maintaining cargo safety and quality requirements
2Use of energy by moving object
If cooling parameters are optimized for specific goods and conditions, then energy consumption decreases, but system complexity increases
Solution Approach 1:
The system uses a universal peer comparison framework that works across different cargo types, vehicle models, and route conditions. By establishing a general methodology for parameter optimization through peer comparison rather than custom solutions for each scenario, the system reduces complexity while maintaining energy efficiency across diverse operating conditions
Solution Approach 2:
The system automatically retrieves and applies peer vehicle parameter data without requiring manual intervention or complex configuration. The automated process of comparing with peer vehicles and implementing optimized parameters reduces system complexity while achieving energy consumption goals
Data Source
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AI summary
A method for setting cooling parameters for a refrigerated transport vehicle (101) includes receiving, from the refrigerated transport vehicle (101), first information including energy consumption and performance parameters associated with the refrigerated transport vehicle (101), and second information including vehicle type information and transit information associated with the refrigerated transport vehicle. The method further includes retrieving historical data associated with peer transport vehicles (103) similar to the refrigerated transport vehicle (101) based on the second information and identifying a peer transport vehicle (103) for which energy and fuel consumption is minimum based on the second information and the retrieved historical data. Upon determining that the energy consumption and the performance parameters of the refrigerated transport vehicle (101) are greater than the energy consumption and the performance parameters of the identified peer transport vehicle (103), the method further includes controlling setting of the cooling parameters of the refrigerated transport vehicle (101) based on the cooling parameters of the identified peer transport vehicle (103).