平行智能风沙防护治理决策支持系统——塔克拉玛干沙漠公路及其防沙体系

doi:10.11959/j.issn.2096-6652.202150

智能科学与技术学报 ›› 2021, Vol. 3 ›› Issue (4): 499-506.doi: 10.11959/j.issn.2096-6652.202150

平行智能风沙防护治理决策支持系统——塔克拉玛干沙漠公路及其防沙体系

常方乐¹^,², 康孟珍¹^,³, 王秀娟¹^,³, 雷加强⁴, 王飞跃¹^,³^,⁵

¹ 中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京100190
² 浙江大学宁波研究院，浙江宁波315100
³ 中国科学院自动化研究所北京智能技术工程研究中心，北京100190
⁴ 中国科学院新疆生态与地理研究所，新疆乌鲁木齐 830011
⁵ 中国科学院大学人工智能学院，北京 100049

修回日期:2021-02-04 出版日期:2021-12-15 发布日期:2021-12-01
作者简介:常方乐（1988- ），女，博士，浙江大学宁波研究院助理研究员，主要研究方向为平行智能生态环境系统智能决策、智慧农业、智慧大棚、多传感器数据融合
康孟珍（1975- ），女，中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员，主要研究方向为平行农业和计算植物
王秀娟（1982- ），女，中国科学院自动化研究所复杂系统管理与控制国家重点实验室助理研究员，主要研究方向为平行农业和植物建模
雷加强（1961- ），男，中国科学院新疆生态与地理研究所研究员、博士生导师，中国治沙暨沙业学会副会长、“一带一路”国际科学组织联盟荒漠化防治联盟主任、新疆生地所干旱区防沙治沙与沙产业国家创新联盟理事长、新疆地理学会理事长、《干旱区研究》主编，主要从事风沙环境及防沙治沙研究工作
王飞跃（1961- ），男，博士，中国科学院自动化研究所复杂系统管理与控制国家重点实验室主任，主要研究方向为复杂系统、智能控制、社会计算、平行理论
基金资助:
中国科学院战略性先导科学专项（A类）(XDA20030102);中国科学院与泰国科技发展署合作研究项目(GJHZ2076)

Windblown sand control decision-making support system based on parallel intelligence: Taklimakan desert highway and its sand-breaking system

Fangle CHANG¹^,², Mengzhen KANG¹^,³, Xiujuan WANG¹^,³, Jiaqiang LEI⁴, Fei-Yue WANG¹^,³^,⁵

¹ The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
² Ningbo Research Institute, Zhejiang University, Ningbo 315000, China
³ Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
⁴ Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
⁵ School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

Revised:2021-02-04 Online:2021-12-15 Published:2021-12-01
Supported by:
The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20030102);Chinese Academy of Sciences-Thailand National Science and Technology Development Agency Joint Research Program(GJHZ2076)

摘要/Abstract

摘要：

针对风沙防护治理系统难以建模、难以解析预测等问题，以塔克拉玛干沙漠公路及其防沙体系为研究对象，提出平行智能风沙防护治理决策支持系统。基于防沙专家常年积累的经验知识，结合实际系统风沙环境信息，模拟风沙防护治理过程，构建沙漠公路防沙体系人工系统；基于计算实验，评估防沙体系人工系统的防沙效果，通过与实际系统进行比较与学习，对系统进行评估与校准；基于平行智能理论，分析风沙防护治理决策支持系统，实现人工系统与实际系统的相互学习、相互借鉴，为风沙防护治理提供决策支持，促进风沙环境可持续化发展。

关键词: 平行智能, 计算实验, 沙漠公路防护体系, 决策支持

Abstract:

Using Taklimakan desert highway and its sand-breaking system as the research object for complex system management and control, the ACP-based parallel intelligence theory to deal with the problems of the difficulty in modeling, analyzing, and predicting of sand-breaking system was applied, to realize the intelligent decision support for sand-breaking system management and control, support the sustainable development of aeolian environment.The expert experience knowledge to construct the artificial desert highway sand-breaking system was extracted by simulating physical process.The sand-control efficiency index of the artificial system was calculated using equations, then evaluated and modified by comparing and learning with the actual system.Using parallel intelligent theory, the artificial system and the actual system can learn from each other to provide decision support for sand control.

Key words: parallel intelligence, computational experiment, desert highway sand-breaking system, decision support

中图分类号:

TP18

常方乐, 康孟珍, 王秀娟, 等. 平行智能风沙防护治理决策支持系统——塔克拉玛干沙漠公路及其防沙体系[J]. 智能科学与技术学报, 2021, 3(4): 499-506.

Fangle CHANG, Mengzhen KANG, Xiujuan WANG, et al. Windblown sand control decision-making support system based on parallel intelligence: Taklimakan desert highway and its sand-breaking system[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(4): 499-506.

图/表 5

参考文献 35

[1]	RAFFAELE L , BRUNO L , FRANSOS D ,et al. Incoming windblown sand drift to civil infrastructures:a probabilistic evaluation[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2017,166: 37-47.
[2]	DONG Z B , CHEN G T , HE X D ,et al. Controlling blown sand along the highway crossing the Taklimakan desert[J]. Journal of Arid Environments, 2004,57(3): 329-344.
[3]	LI C J , LEI J Q , ZHAO Y ,et al. Effect of saline water irrigation on soil development and plant growth in the Taklimakan desert highway shelterbelt[J]. Soil and Tillage Research, 2015,146: 99-107.
[4]	BRUNO L , HORVAT M , RAFFAELE L . Windblown sand along railway infrastructures:a review of challenges and mitigation measures[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2018,177: 340-365.
[5]	BOULGHOBRA N , MERDAS S , LAKHDARI F . Sand encroachment in the Saharan Algeria; the not declared disaster-case study:in-Salah region in the Tidikelt[J]. Planet@ Risk, 2015,3(1): 72-76.
[6]	GEIST H . The causes and progression of desertification[J]. Pramana-Journal of Physics, 2005,15(15): 515-523.
[7]	SULTAN H , . Dust and erosion control in semi-arid climates[C]// Proceedings of the Symposium on Geotechnical Problems in Saudi Arabia.[S.l.:s.n.], 1981: 289-328.
[8]	IKAZAKI K , SHINJO H , TANAKA U ,et al. “Fallow band system,” a land management practice for controlling desertification and improving crop production in the Sahel,West Africa.1.effectiveness in desertification control and soil fertility improvement[J]. Soil Science and Plant Nutrition, 2011,57(4): 573-586.
[9]	屈建军, 刘贤万, 雷加强 ,等. 尼龙网栅栏防沙效应的风洞模拟实验[J]. 中国沙漠, 2001,21(3): 276-280.
	QU J J , LIU X W , LEI J Q . Simulation experiments on sand arresting effect of nylon net fence in wind tunnel[J]. Journal of Desert Research, 2001,21(3): 276-280.
[10]	徐先英, 孙保平, 唐进年 ,等. 干旱荒漠风沙区沙化土地综合整治技术研究——以古浪县黄花滩治沙示范区为例[J]. 中国沙漠, 2007,27(4): 539-545.
	XU X Y , SUN B P , TANG J N ,et al. Techniques of integrated controlling desertified sandy land in arid area:an example in huang-hua-tan desert control demonstration region in Gulang county,Gansu province[J]. Journal of Desert Research, 2007,27(4): 539-545.
[11]	DUAN J T , LITWILLER E , CHOI S H ,et al. Evaluation of sodium lignin sulfonate as draw solute in forward osmosis for desert restoration[J]. Journal of Membrane Science, 2014,453: 463-470.
[12]	COOKE N M , MCDONALD J E . A formal methodology for acquiring and representing expert knowledge[J]. Proceedings of the IEEE, 1986,74(10): 1422-1430.
[13]	OYAMA S . The ontogeny of information:developmental systems and evolution[J]. Quarterly Review of Biology, 2000,8(4): 597-597.
[14]	HOFFMANR R . The cognitive psychology of expertise and the domain of interpreting[J]. Interpreting, 1997,2(1-2): 189-230.
[15]	GRUBER T R . Nature,nurture,and knowledge acquisition[J]. International Journal of Human-Computer Studies, 2013,71(2): 191-194.
[16]	GAINES B R . Knowledge acquisition:past,present and future[J]. International Journal of Human-Computer Studies, 2013,71(2): 135-156.
[17]	MCDERMOTT D , WALDROP M M , CHANDRASEKARAN B ,et al. The dark ages of AI:a panel discussion at AAAI-84[J]. AI Magazine, 1985,6(3): 122-134.
[18]	CORNELISSEN A M G , VAN DEN BERG J , KOOPSW J ,et al. Elicitation of expert knowledge for fuzzy evaluation of agricultural production systems[J]. Agriculture,Ecosystems and Environment, 2003,95(1): 1-18.
[19]	CAMPELLOR J , AMARAL W C . Modeling and linguistic knowledge extraction from systems using fuzzy relational models[J]. Fuzzy Sets and Systems, 2001,121(1): 113-126.
[20]	BAAS A C . Complex systems in aeolian geomorphology[J]. Geomorphology, 2007,91(3-4): 311-331.
[21]	王飞跃 . 计算实验方法与复杂系统行为分析和决策评估[J]. 系统仿真学报, 2004,5: 34-38.
	WANG F Y . Computational experiments for behavior analysis and decision evaluation of complex systems[J]. Journal of System Simulation, 2004,5: 34-38.
[22]	王飞跃, 汤淑明 . 人工交通系统的基本思想与框架体系[J]. 复杂系统与复杂性科学, 2004,1(2): 52-59.
	WANG F Y , TANG S M . Concepts and frameworks of artificial transportation systems[J]. Complex Systems and Complexity Science, 2004,1(2): 52-59.
[23]	王飞跃 . 平行系统方法与复杂系统的管理和控制[J]. 控制与决策, 2004,19(5): 485-489.
	WANG F Y . Parallel system methods for management and control of complex systems[J]. Control and Decision, 2004,19(5): 485-489.
[24]	LIMA I A , ARAúJO A D , PARTELI E J ,et al. Optimal array of sand fences[J]. Scientific Reports, 2017,7:45148.
[25]	LI B L , SHERMAN D J . Aerodynamics and morphodynamics of sand fences:a review[J]. Aeolian Research, 2015,17: 33-48.
[26]	马全林, 王继和, 詹科杰 ,等. 塑料方格沙障的固沙原理及其推广应用前景[J]. 水土保持学报, 2005,19(1): 36-39,58.
	MA Q L , WANG J H , ZHAN K J ,et al. Study on fixing sand principals of plastic checkerboard sand-barrier and its potential application[J]. Journal of Soil and Water Conservation, 2005,19(1): 36-39,58.
[27]	LIU L , BO T L . Effects of checkerboard sand barrier belt on sand transport and dune advance[J]. Aeolian Research, 2020,42:100546.
[28]	BO T L , ZHENG X J . Numerical simulation of the evolution and propagation of aeolian dune fields toward a desert-oasis zone[J]. Geomorphology, 2013,180: 24-32.
[29]	BRUNO L , COSTE N , FRANSOS D ,et al. Shield for sand:an innovative barrier for windblown sand mitigation[J]. Recent Patents on Engineering, 2018,12(3): 237-246.
[30]	BRUNO L , FRANSOS D , GIUDICE A L . Solid barriers for windblown sand mitigation:aerodynamic behavior and conceptual design guidelines[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2018,173: 79-90.
[31]	WANG T , QU J J , NIU Q H . Comparative study of the shelter efficacy of straw checkerboard barriers and rocky checkerboard barriers in a wind tunnel[J]. Aeolian Research, 2020,43:100575.
[32]	常方乐, 康孟珍, 王秀娟 ,等. 平行智能风沙防治系统构架与功能——以植物措施为例[J]. 干旱区研究, 2019,36(6): 1576-1583.
	CHANG F L , KANG M Z , WANG X J ,et al. Aeolian sand parallel prevention and control:application of artificial intelligence technology in sand prevention and control[J]. Arid Zone Research, 2019,36(6): 1576-1583.
[33]	王雪芹, 雷加强 . 塔里木沙漠公路风沙危害评估指标体系[J]. 干旱区地理, 1999,22(1): 81-87.
	WANG X Q , LEI J Q . Assessment indicators of drifting sand hazard along Tarim desert highway,Xinjiang[J]. Arid Land Geography, 1999,22(1): 81-87.
[34]	阳东升, 姜军, 王飞跃 . 从平台到体系:指挥对抗活动机理的演变及其PREA环对策[J]. 指挥与控制学报, 2018,4(4): 263-271.
	YANG D S , JIANG J , WANG F Y . From platforms to systems of systems:on mechanism evolution of command confrontation and its PREA loop[J]. Journal of Command and Control, 2018,4(4): 263-271.
[35]	FAN X R , KANG M Z , HEUVELINK E ,et al. A knowledge-and-data-driven modeling approach for simulating plant growth:a case study on tomato growth[J]. Ecological Modelling, 2015,312: 363-373.

平行智能风沙防护治理决策支持系统——塔克拉玛干沙漠公路及其防沙体系

Windblown sand control decision-making support system based on parallel intelligence: Taklimakan desert highway and its sand-breaking system

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 35

相关文章 13

Metrics

推荐阅读 0

[1]	张向文, 王飞跃. 平行轮胎的基本架构与关键技术[J]. 智能科学与技术学报, 2022, 4(3): 445-457.
[2]	康孟珍, 王秀娟, 李冬, 王旭伟, 王浩宇, 樊梦涵, 许钰林, 王飞跃. 基于联邦学习的分布式农业组织[J]. 智能科学与技术学报, 2022, 4(2): 288-297.
[3]	李小双, 王晓, 杨林瑶, 田永林, 王雨桐, 张俊, 王飞跃. 元电网MetaGrid：基于平行电网的新一代智能电网的体系与架构[J]. 智能科学与技术学报, 2021, 3(4): 387-398.
[4]	王飞跃, 蒋怀光. 平行电池：智能生态化电池技术与服务体系的框架和流程[J]. 智能科学与技术学报, 2021, 3(4): 521-531.
[5]	王飞跃. 平行哲学与智能技术：平行产业与智慧社会的对偶方程与测试基础[J]. 智能科学与技术学报, 2021, 3(3): 245-255.
[6]	贾晓丰, 高嵩, 江茜, 齐红威, 王晓, 张俊, 秦蕊, 欧阳丽炜. 面向城市复杂系统的社会计算关键技术研究[J]. 智能科学与技术学报, 2021, 3(2): 228-233.
[7]	王飞跃. 平行医学：从医学的温度到智慧的医学[J]. 智能科学与技术学报, 2021, 3(1): 1-9.
[8]	王飞跃. 平行控制与数字孪生：经典控制理论的回顾与重铸[J]. 智能科学与技术学报, 2020, 2(3): 293-300.
[9]	王飞跃,曹东璞,魏庆来. 强化学习：迈向知行合一的智能机制与算法[J]. 智能科学与技术学报, 2020, 2(2): 101-106.
[10]	李宪港,李强. 典型智能博弈系统技术分析及指控系统智能化发展展望[J]. 智能科学与技术学报, 2020, 2(1): 36-42.
[11]	张俊,王飞跃,方舟. 社会能源：从社会中获取能源[J]. 智能科学与技术学报, 2019, 1(1): 7-20.
[12]	吕宜生,陈圆圆,金峻臣,李镇江,叶佩军,朱凤华. 平行交通：虚实互动的智能交通管理与控制[J]. 智能科学与技术学报, 2019, 1(1): 21-33.
[13]	沈大勇,王晓,刘胜. 平行装卸：迈向智慧物流的智能技术[J]. 智能科学与技术学报, 2019, 1(1): 34-39.