System for monitoring and predicting water damage in shipping containers
AssigneeHitachi, Ltd.
InventorsLin MENG, Arnab CHAKRABARTI
Life of this patent
Estimating the term…
Abstract
The world's cargo is transported in shipping containers. Historically, there has been little visibility into cargo once it goes inside a container. Moisture-sensitive cargo is often damaged in transportation. The present disclosure involves a system for monitoring and predicting water damage in containers. Use of this system can enable pre-emptive action to lessen damage. Specific considerations of container transportation and availability of weather data give this problem unique characteristics, distinguishing it from generic monitoring. These considerations in turn motivate the solution framework presented here.
Claims13
A method, comprising:
- executing a first model configured to model an internal environment of a shipping container, the first model configured to intake internal sensor data received from sensors internal to the shipping container and external data associated with an environment external to the shipping container, the first model configured to output internal environment parameters of the shipping container;
- executing a second model configured to derive water damage incurred to the shipping container, the second model comprising a machine learning model configured to intake the internal environment parameters of the shipping container and output one or more of a probability of loss due to water damage or a predicted severity of water damage to the shipping container or one or more other metrics that depend on loss probability or severity;
- and
- prioritizing the shipping container for processing based on the one or more of the probability of loss due to water damage or the predicted severity of water damage to the shipping container.
A non-transitory computer readable medium, storing instructions for executing a process, the instructions comprising:
- executing a first model configured to model an internal environment of a shipping container, the first model configured to intake internal sensor data received from sensors internal to the shipping container and external data associated with an environment external to the shipping container, the first model configured to output internal environment parameters of the shipping container;
- executing a second model configured to derive water damage incurred to the shipping container, the second model comprising a machine learning model configured to intake the internal environment parameters of the shipping container and output one or more of a probability of loss due to water damage or a predicted severity of water damage to the shipping container or one or more other metrics that depend on loss probability or severity;
- and
- prioritizing the shipping container for processing based on the one or more of the probability of loss due to water damage or the predicted severity of water damage to the shipping container.
An apparatus, comprising:
- a processor, configured to:
- execute a first model configured to model an internal environment of a shipping container, the first model configured to intake internal sensor data received from sensors internal to the shipping container and external data associated with an environment external to the shipping container, the first model configured to output internal environment parameters of the shipping container;
- execute a second model configured to derive water damage incurred to the shipping container, the second model comprising a machine learning model configured to intake the internal environment parameters of the shipping container and output one or more of a probability of loss due to water damage or a predicted severity of water damage to the shipping container or one or more other metrics that depend on loss probability or severity;
- and
- prioritize the shipping container for processing based on the one or more of the probability of loss due to water damage or the predicted severity of water damage to the shipping container.
Dependencies are inferred from the claim text.