Salt field evaporation calculation method and system based on hybrid strategy SNS, terminal and medium

Abstract

The invention discloses a salt field evaporation calculation method and system based on a hybrid strategy SNS, a terminal and a medium. The method comprises the steps that HSNS and target function parameters are initialized; initializing a user group based on improved Circle chaotic mapping; calculating the target function value of the viewpoint held by each user in the user group to obtain the target function value of each user; based on the viewpoint held by each user, randomly selecting a decision model, and updating the viewpoint of each user; generating a new user group based on the updated user group and improved lens imaging learning; on the basis of a simulated annealing mechanism, global optimal positions of users in the new user group are updated; and comparing whether the difference between the updated optimal position and the last optimal position meets the set requirement or not, or whether the number of iterations reaches the maximum value or not until the conditions are met. The algorithm search efficiency can be effectively improved, the optimization speed of the algorithm is improved, and the algorithm efficiency is improved; and increasing the probability that the algorithm jumps out of local optimum and finds global optimum.

Description

technical field [0001] The invention belongs to the field of salt field evaporation calculation, and relates to a salt field evaporation calculation method, system, terminal and medium based on a mixed strategy SNS. Background technique [0002] The evaporation model needs to calculate the freshwater evaporation with or without photovoltaic panels during this period of time by inputting the geometric parameters of photovoltaic panels, the location of the salt field, and meteorological data (solar radiation, wind speed, ambient temperature, relative humidity) for a period of time. [0003] The evaporation model can be numerically represented as a nonlinear equation system. At present, the methods for solving the nonlinear equation system can be roughly divided into two types: traditional optimization algorithm and intelligent optimization algorithm. [0004] Among them, the traditional optimization algorithm to solve nonlinear equations is usually based on gradient informatio...

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Problems solved by technology

[0006] However, ordinary intelligent optimization algorithms cannot balance the convergence speed and calculation accuracy when dealing with complex problems. When the accuracy is too high, the convergence speed will slow down, or even fail to converge. For example, the whale optimization algorithm will not be able to guarantee the solution to complex problems. Accuracy, not up to the requirements of the problem

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