基于大数据方法的垃圾焚烧发电厂环境执法监管数据预测模型探究

在垃圾焚烧发电厂运行系统负荷特性统计指标和污染源监督性监测数据积累的大数据背景下，有效提取数据之间的关联特征对于垃圾焚烧系统规划运行和执法监管具有重大意义。首先，通过Pearson关联分析获得运行负荷特性指标和排放特征指标任意2因素之间的相关性特征；然后利用SPSS及Python软件，构建用以预测烟气污染因子排放量的多元线性回归模型及BP神经网络模型。对模型预测结果进行了比较，结果表明，多元线性回归模型和BP神经网络模型都能应用于烟气污染因子排放量的预测，进一步得出的BP神经网络模型的预测效果优于多元线性回归模型。本研究对于探究工业污染源环境执法建模和定量分析污染源排放水平具有参考价值。

Prediction model analysis of environmental law enforcement supervision of waste incineration power plant based on big data method

In the context of the statistical characteristics of the load characteristics statistical indexes of the operating system of the waste incineration power plant and the monitoring data of the pollution source monitoring, the effective extraction of the correlation characteristics between the data is of great significance for the planning and operation of the waste incineration system and law enforcement supervision. This study presented a mining method which could extract the operating load characteristic index (influencing factors) influence on the monitoring index was proposed. First, the qualitative analyses of potential physical relations among the indexes as well as quantitative calculation via Pearson correlation coefficient of historical data were coordinated to draw the correlation features between two factors. Then combined with SPSS and Python software, multivariate linear regression model and BP neural network model for the prediction of Flue gas pollution factor emissions were built. The prediction results of the two models showed that multivariate linear regression model and BP neural network model can be applied to flood volume prediction, and the BP neural network model was superior to multivariate linear regression mode. This study can provide decision-making basis for the environmental law enforcement modeling of industrial pollution source and quantitative analysis of pollution source emission levels.