Cargo Load Sensing Estimator for Stable Time-of-Flight Detection
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Solution Overview
Problem
Current cargo detection systems using time-of-flight technology face challenges due to variations in distance measurements caused by temperature, condensation, dirt, and crosstalk, leading to false empty or loaded status determinations.
Innovation Solution
A cargo detection system utilizing an estimator that stabilizes readings by integrating a reference point into time-of-flight measurements, compensating for noise factors such as temperature, condensation, and crosstalk, and employing a computing device with various sensors and communication capabilities to calculate accurate load status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If time-of-flight sensors are used to detect cargo load status, then automated detection is achieved, but measurement precision deteriorates due to temperature, condensation, dirt, and crosstalk
Solution Approach 1:
The system uses multiple sensors to continuously monitor environmental factors (temperature, condensation, dirt) and feeds this information back to the processing system. The processing system then adjusts the time-of-flight measurements based on this feedback, compensating for environmental effects and maintaining measurement precision while preserving automated detection capability
Solution Approach 2:
The patent introduces environmental sensors as intermediary devices that detect conditions (temperature, condensation, dirt) and convert them into signal adjustments. These intermediaries mediate between the physical environment and the measurement system, allowing the time-of-flight sensor to operate automatically while the intermediary components compensate for environmental degradation of measurement precision
2Measurement precision
If manual yard check is performed to determine trailer loading, then measurement precision is maintained, but productivity deteriorates due to significant time and human resources required
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated optical time-of-flight sensing system. The mechanical action of manually opening and checking containers is substituted by optical sensors that non-contactly measure distance to cargo surfaces, achieving both high productivity through automation and maintained precision through multiple compensated measurements
3Device complexity
If environmental factors are not compensated for in time-of-flight measurements, then device complexity is reduced, but reliability deteriorates due to false empty or loaded determinations
Solution Approach 1:
The patent merges multiple sensor types (time-of-flight sensor, temperature sensor, condensation sensor, dirt sensor) into an integrated system. By combining these sensors and their data processing functions into a unified cargo detection system, the patent achieves high reliability through comprehensive environmental compensation while managing device complexity through integration rather than separate independent systems
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system significantly reduces false detection rates, providing stable and accurate cargo load measurements by minimizing errors from environmental and manufacturing variations.
Implementation Method 1
Trailer cargo detection devices in the market today typically use ultrasonic or light sensors to measure the distance from the load to the measurement device using time of flight (ToF) technology
Implementation Method 2
Trailer cargo detection devices in the market today typically use ultrasonic or light sensors to measure the distance from the load to the measurement device using time of flight (ToF) technology
Data Source
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AI summary
A method at a computing device, the method including obtaining a cargo load signal measurement; and applying an estimator to the cargo load signal measurement, the estimator being in the form of x̂ = ay + bZ, where x̂is an estimate of a true signal vector, y is the cargo load signal measurement, Z is a calculated or pre-determined reference vector, and a and b are weighting factors.