Comprehensive analysis of water carrying capacity based on wireless sensor network and image texture of feature extraction

Abstract

This article takes the complex uncertainty in the carrying process of water resources system as the research object. Firstly, the water resources carrying capacity system is divided into three sub-systems: bearing support capacity, bearing pressure and bearing regulation capacity. System analysis methods based on wireless sensor network and image texture of feature extraction for correlation analysis, gray correlation analysis, and multi-dimensional conditional cloud algorithm are introduced into the field of water resources carrying capacity evaluation research. Moreover, combine it with the subtraction set opposition, the effect partial bias connection number, and the risk matrix. Finally, three types of models for water resource carrying capacity evaluation and trend analysis were constructed. The evaluation results of water resources carrying capacity are consistent with the actual process, and the overall development trend is gradually improving. The application results of the evaluation model of water resource carrying capacity are established. The calculation results of the image texture feature of extraction model are similar to the overall change trend of the model before the improvement. The trend of change is more obvious. It can not only describe the water resource carrying level as a whole, but also characterize the reliability of the calculation results and the bearing risk caused by the uncertainty of the water resource carrying system. The water resources carrying capacity evaluation and trend analysis of calculation results are reasonable and feasible. The research results can provide a reasonable and effective decision-making basis for the systematic analysis of the bearing boundary issues between regional complex water resources systems and human water activities, and then propose adaptive risk prevention and control strategies for water resources carrying capacity.