Monitoring the internal temperature and humidity of shipping containers is essential to preventing quality degradation during cargo transportation. Sensorless monitoring -- machine learning models that predict the internal conditions of the containers using exogenous factors -- shows promise as an alternative to monitoring using sensors. However, it does not incorporate telemetry information and correct for systematic errors, causing the predictions to differ significantly from the live data and confusing the users. In this paper, we introduce the residual correction method, a general framework for correcting for systematic biases in sensorless models after observing live telemetry data. We call this class of models ``adaptive-sensorless'' monitoring. We train and evaluate adaptive-sensorless models on the 3.48 million data points -- the largest dataset of container sensor readings ever used in academic research -- and show that they produce consistent improvements over the baseline sensorless models. When evaluated on the holdout set of the simulated data, they achieve average mean absolute errors (MAEs) of 2.24 $\sim$ 2.31$^\circ$C (vs 2.43$^\circ$C by sensorless) for temperature and 5.72 $\sim$ 7.09% for relative humidity (vs 7.99% by sensorless) and average root mean-squared errors (RMSEs) of 3.19 $\sim$ 3.26$^\circ$C for temperature (vs 3.38$^\circ$C by sensorless) and 7.70 $\sim$ 9.12% for relative humidity (vs 10.0% by sensorless). Adaptive-sensorless models enable more accurate cargo monitoring, early risk detection, and less dependence on full connectivity in global shipping.

翻译：监测海运集装箱内部的温度和湿度对于防止货物运输过程中的质量劣化至关重要。无传感器监测——即利用外部因素预测集装箱内部条件的机器学习模型——作为传感器监测的替代方案展现出潜力。然而，该方法未纳入遥测信息且未校正系统误差，导致预测结果与实时数据存在显著偏差，给用户带来困惑。本文提出残差校正方法，这是一种在观测到实时遥测数据后校正无传感器模型中系统偏差的通用框架。我们将此类模型称为“自适应无传感器”监测。我们在348万个数据点——学术研究中迄今使用的最大规模集装箱传感器读数数据集——上训练并评估了自适应无传感器模型，结果表明其相较于基线无传感器模型实现了持续改进。在模拟数据保留集上的评估显示：温度的平均绝对误差（MAE）为2.24∼2.31°C（无传感器模型为2.43°C），相对湿度为5.72∼7.09%（无传感器模型为7.99%）；温度的平均均方根误差（RMSE）为3.19∼3.26°C（无传感器模型为3.38°C），相对湿度为7.70∼9.12%（无传感器模型为10.0%）。自适应无传感器模型能够实现更精确的货物监测、早期风险预警，并降低全球海运中对全程网络连接的依赖。