基于用电数据的自然村空心化程度识别模型

Identification model of hollowing degree of natural village based on electricity data

受到村落分布特征差异化影响,自然村空心化程度识别难度较大。为此,提出设计基于用电数据分析的自然村空心化程度识别模型。根据特征提取层、特征映射层和特征池化层的特征分类,标记自然村空心化程度的分类结果,计算出自然村空心化的理论面积,选取主要影响指标修正自然村空心化理论值。通过计算自然村空心化程度的现实修正系数,预测自然村空心化趋势,计算自然村空心化程度的权重关系,构建自然村空心化程度识别模型。实验结果表明,所设计模型具有较高的识别效率和识别准确率。

It is difficult to identify the degree of hollowing out of natural villages since the difference existed in the village distribution characteristics. Therefore, a recognition model of the hollowing degree of natural village based on the analysis of electricity data is proposed. According to the feature classification of feature extraction layer, feature mapping layer and feature pooling layer, the classification results of hollowing degree of natural villages are marked, and the theoretical area of hollowing out of natural village is calculated. Main influence indexes are selected to revise the theoretical value of hollowing out of natural villages. By calculating the realistic correction coefficient of the degree of hollowing out of the natural village, the trend of the hollowing out of the natural village is predicted, and the weight relationship of the degree of hollowing out of the natural village is calculated, all of which leads to the construction of the recognition model of the degree of hollowing out of the natural village. Experiment results show that the proposed model has high recognition efficiency and accuracy.