Design method for multi-target cooperative sampling scheme of randomly-distributed geographic elements

Abstract

The invention relates to a design method for a multi-target cooperative sampling scheme of randomly-distributed geographic elements, which includes the steps: 1 selecting multiple geographic elements with random distribution characteristics as sampling targets, and acquiring pre-sampling sample point data of the targets; 2 exploring spatial variation structures of the pre-sampling data by an ordinary Kriging method, fitting and generating a theory semi-variable function of each target, and obtaining a spatial distribution integral of all the targets by a Gaussian sequential random simulation method according to the theory semi-variable functions and the pre-sampling sample point data in the step 1; 3 computing the minimum cooperative sample size according to the precision requirements of the targets and prior variance, and setting a cooperative particle swarm optimization parameter; and 4 optimizing a current pre-sampling sample point scheme by means of cooperative particle swarm optimization, so that the multi-target cooperative sampling scheme is obtained. By the aid of the method, on-site sampling cost can be saved, self-organization and optimization during distribution of thesampling sample points are improved, and the design efficiency of the multi-target cooperative sampling scheme of the geographic elements is enhanced.

Description

technical field [0001] The invention relates to a method for designing a multi-objective cooperative sampling scheme, in particular to a method for designing a multi-objective cooperative sampling scheme for randomly distributed geographic elements. Background technique [0002] Geographic elements include topography, climate, hydrology, biology, and soil. Accurate surveys of them are the key to explaining the spatial distribution and evolution of geographic elements, and are also required for resource management, disaster management, ecological environment governance, and global change research. In the actual operation process, in order to save sampling cost and improve sampling efficiency, researchers usually only design a set of sampling plan to implement the spatial sampling survey task of multiple geographical element indicators at the same time, so how to design a sampling plan to ensure the sampling accuracy of multiple indicators becomes a focus of scholars' attentio...

AI Technical Summary

Problems solved by technology

However, the above-mentioned traditional methods have certain deficiencies. For example, it is difficult to obtain the weight of the weighted average method for complex multi-objective problems, and the set weights usually lead to extreme situations where some objectives are optimal while others are poor; the minimum The threshold method requires all target values ​​to reach the predetermined optimal value, so the optimal solution may not be searched, and it may be difficult to set the optimal value for some targets; in addition, there are more efficient search algorithms that can be used to replace the simulation Annealing algorithm

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