基于线性模型的管路内农药混合均匀性评价方法

针对混合试验图像所得均匀性指数计算结果难以直接匹配于被广泛认可的数值仿真参考值的问题,本文基于线性模型方法,将混合试验图像处理与数值仿真结果进行映射,在黏性水溶性农药与水在长直混合管内进行在线混合的试验条件下构建对应的线性预测模型,并采用射流混药器在线混合图像及仿真结果对上述模型进行检验。研究结果表明：不同图像方法(灰度直方图二阶矩(HSM)、改进面积加权法(OAU)、主成分分析法(PCA))对应最优线性拟合阶数不同,采用单独图像方法构建模型时最优阶次为4,决定系数R2高于0.95,采用2种图像方法组合和3种图像方法组合时最优阶次可分别降至3阶和2阶,R2则接近或高于0.98;载流流量Q为800~2000mL/min、混合比P为0.01~0.10条件下,基于HSM、OAU、PCA和线性模型,可实现实际混药器均匀性预测,所有模型预测误差均小于0.05,且采用一元和二元线性模型使得平均预测误差分别降低84.1%和79.8%,不同算法间预测结果极差分别降低31.6%和78.0%;采用基于PCA或OAU算法的一元模型进行预测时误差可控制在0.03以内,其精度高于不同算法组合预测的结果;采用基于HSM-PCA等算法组合的二元模型误差虽稍高于0.03,但也可避免单一图像指标计算不准确带来的预测风险。通过构建图像处理-数值仿真之间的映射关系,可为基于图像处理进行农药在线混合均匀性评估提供更加可行和合理的方法。

Methodology to Evaluate Pesticide Inline Mixing Uniformity inside Pipelines Based on Linear Models

The non-contact evaluation of pesticide inline mixing uniformity based on image processing can promote the development and performance evaluation of the mixers in direct nozzle injection spraying systems (DNIS). In view of the phenomenon that the uniformity results obtained by image processing cannot be directly matched to the traditionally widely accepted and referenced numerical simulation results, the linear models to map the image processing results with numerical simulation results was constructed as inline injecting and mixing viscous water-soluble pesticides and water in a long transparent detection tube, and tested by a jet mixer in a DNIS. Results showed that differing image methods (HSM, OAU, PCA) corresponded to varying optimal linear fitting orders. The optimal order was 4 and the fit goodness was higher than 0.95 when each single image method of them was applied. When each combination of two image methods of them and all the three methods were applied, the order for them can be reduced to 3 and 2, respectively, and the goodness of fit can increase to about 0.98. Based on the above image processing methods and linear models, the uniformity performance of the mixer can be predicted with the error (Mean absolute error, MAE) universally less than 0.05 as the carrier flow rates (Q) were in the range of 800~2000mL/min and the mixing ratios (P) were in the range of 0.01~0.10. Also, the use of univariate and bivariate linear models reduced the average prediction error by 84.1% and 79.8%, respectively, and reduced the variations in prediction results between different algorithms by 31.6% and 78.0%, respectively. The MAE can be limited within 0.03 when univariate models based on the PCA algorithm or the OAU algorithm were applied alone for prediction, and their accuracy was higher than the prediction results of the combinations of different algorithms, indicating the rationality of uniformity prediction using the linear models based on image processing. Though the MAE for the bivariate model based on HSM-PCA algorithm combination was only slightly higher than 0.03, it may have the advantages of avoiding inaccuracy risks of prediction caused by using a single indicator. The research established the relationship between image processing and numerical simulation, thus further improving the feasibility of inline pesticide uniformity assessment inside pipelines based on experiments.