The DQO modeling for the NIR-CN system has not yet seen mature research results, in which comparison with data from the Waukesha CN engine was conducted to alleviate the composite matrix effect of matching error and the uncontrollable β risk at a single level. Compared to the power function, quadratic linear combination, and slope radian angles, the DR bias correction model's maximum likelihood estimate is found to be the best-fitting line. The DR modeling, which was derived from the variable iteration of two-dimensional data weighted by the CSS, was established under the premise of the i.i.d. assumption. Consequently, the correlation coefficient cannot be regarded as the criterion for judging the model, but rather through the lack of fit and the AD goodness-of-fit test. This test can effectively mitigate the cumulative impact and interaction interference of ACF, and maximize compensation for the limitations of subjective trend analysis in control charts. The research results indicate that the DR model aligns more closely with the DQO robustness and the actual situation of the NIR system. The NIR system was previously limited to a single CN level discussion; now it has been expanded to study variations at multiple levels. Different decisions lead to different uncertainties, and wrong decisions will incur additional cost losses. Under the uniform principle of risk and cost, the variation of CN over time will inevitably lead to the choice of CSS. As long as the DQO set by the NIR system is subsequently met, the risk arising from the use conditions can be controlled within the demonstrated CSS range.