云环境下基于改进SEIRS模型的VOCs最优减排计算

doi:10.16182/j.issn1004731x.joss.21-1177

摘要/Abstract

摘要：

不同区域的挥发性有机物(volatile organic compounds， VOCs)排放是相互关联与影响的，为了将VOCs对大气环境的影响降到最低，实现VOCs区域协同联动治理，建立了以VOCs减排总量最大为首要目标最优减排模型，提出一种考虑环境污染因素的改进SEIRS传染病动力学优化算法(SEIRS infectious disease dynamics optimization algorithm considering environmental pollution，SEIRS-CE)在云环境下求解模型。应用案例以西安市为例，在阿里云服务器中使用SEIRS-CE算法对西安市13个气象监测站所对应VOCs关联区域减排问题进行计算并与传统智能优化算法进行对比，结果表明建立的VOCs最优减排模型具有科学性和可行性，在云环境下使用SEIRS-CE算法相较于对比算法求解VOCs最优减排问题的收敛速度更快，求解精度更高，陷入局部陷阱概率更低，具有高并发性、高安全性、高共享性，对府际间VOCs联防联控治理提供借鉴。

关键词: VOCs, 云计算, SEIRS传染病模型, 联防联控, 最优减排方案

Abstract:

Volatile organic compounds (VOCs) emissions in different regions are correlated and influenced. In order to minimize the impact of VOCs on the atmospheric environment and achieve synergistic governance of VOCs regions, an optimal emission reduction model is established with the maximum VOCs emission reduction as the primary goal. An improved SEIRS infectious disease dynamics optimization algorithm considering environmental pollution(SEIRS-CE) is proposed and the model is solved in cloud environment. Taking Xi'an city as an example, the SEIRS-CE algorithm is used in Ali cloud server to calculate the emission reduction of VOCs associated with 13 meteorological monitoring stations in Xi 'an city and compared with the traditional intelligent optimization algorithm. The results show that the established VOCs optimal emission reduction model is scientific and feasible. Compared with the contrast algorithm, the SEIRS-CE algorithm in cloud environment can solve the VOCs optimal emission reduction problem with faster convergence speed, higher solution accuracy, lower probability of falling into local trap, with high concurrency, high security, high sharing, and provide reference for the joint prevention and control of VOCs among the government.

Key words: VOCs(volatile organic compounds), cloud computing, SEIRS infectious disease model, joint prevention and control, optimal emission reduction scheme

中图分类号:

TP301.6

黄光球, 赵羲轩, 陆秋琴. 云环境下基于改进SEIRS模型的VOCs最优减排计算[J]. 系统仿真学报, 2023, 35(3): 632-645.

Guangqiu Huang, Xixuan Zhao, Qiuqin Lu. Calculation of Optimal VOCs Emission Reduction Based on Improved SEIRS Model in Cloud Environment[J]. Journal of System Simulation, 2023, 35(3): 632-645.

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特征集合类型	变量名	产生方法
强壮居民集合	SR_s	L个居民的强壮度比当前居民i的强壮度高
普通居民集合	CR_s	L个居民的强壮度与当前居民i的强壮度无关
虚弱居民集合	WR_s	L个居民的强壮度是所有居民中最小的

操作	时间复杂度	最多循环次数
初始化	O(6n+5(n+1)N+2n²N)	1
计算S_i (t)， E_i (t)，I_i (t)， R_i (t)	O(7)	(G+N+3)N
S-S、 S-E、 E-E、 E-I、 E-S、 I-I、 I-R、 I-S、 R-R、 R-E、 R-S	O((N+5+4)nE₀/18)	(G+N+7)N
状态保持	O((1-5E₀/12)n)	(G+N+7)N
目标函数计算	O(n)~O(n²)	(G+N+7)N
生长算子	O(3n)	(G+N+7)N
结果输出	O(n)	1

名称	版本	要求
阿里云服务器	轻量应用服务器，系统镜像Windows Server 2008 R2	无
Python	Python 3.8.3， 64位	安装在云服务器上
Apache 服务器	Apache-2.4.46	安装在云服务器上
Python numpy包	numpy-1.19.3+mkl-cp38-cp38-win_amd64.whl	安装在Python中
Python CGI	无	在Apache服务器中配置

参数名称	取值依据
演化时期数G	算法迭代次数，由实际问题决定，G=800
居民数量N	试探解数量，设置过少容易陷入局部最优，设置过多会影响收敛效果，N=100
每个居民的特征数n	每个试探解的维数，在本文中n=13，对应13个气象监测点
患病概率E₀	算子调度概率或居民状态变化概率，E₀=0.01
施加影响的居民数L	决定了特征集合的维度与算法时间复杂度的大小，L=3
全局最优解误差ε	ε为趋于0的数，取值越小代表精确度越高，ε=0.001

日期	PM_2.5(μg/m³) u_1,1(0)	PM₁₀(μg/m³) u_1,2(0)	SO₂(μg/m³) u_1,3(0)	NO₂(μg/m³) u_1,4(0)	O₃(μg/m³) u_1,5(0)	CO(mg/m³) u_1,6(0)
11.01	27.0	108.0	11.0	51.0	100.0	0.7
11.02	35.0	97.0	13.0	57.0	87.0	1.0
…	…	…	…	…	…	…
11.29	80.0	99.0	9.0	49.0	64.0	1.0
11.30	92.0	110.0	7.0	51.0	86.0	1.4