1. 格拉斯哥大学社会与政治科学学院,英国 苏格兰 G12 8QQ
2. 澳门科技大学创新工程学院工程科学系,澳门 999078
3. 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
4. 青岛智能产业技术研究院,山东 青岛 266109
5. 中国科学院大学人工智能学院,北京 100049
6. 不列颠哥伦比亚大学土地与食品系统学院,加拿大 温哥华 10027
7. 新疆博尔塔拉蒙古自治州人民政府,新疆 博州 833412
[ "皮佩定(1999- ),男,毕业于英国格拉斯哥大学,主要研究方向为国际金融" ]
[ "倪清桦(1999- ),女,澳门科技大学博士生,主要研究方向为平行智能、区块链、数字孪生、智能系统等" ]
[ "杨静(1998- ),女,中国科学院自动化研究所多模态人工智能系统全国重点实验室博士生,主要研究方向为平行制造、社会制造、人工智能和社会物理信息系统" ]
[ "康孟珍(1975- ),女,博士,中国科学院自动化研究所副研究员,主要研究方向为平行农业和计算植物" ]
[ "李宣昊(2004- ),男,不列颠哥伦比亚大学本科生,主要研究方向食物营养与健康" ]
[ "杜应昆(1979- ),男,新疆博尔塔拉蒙古自治州人民政府副州长" ]
[ "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,澳门科技大学特聘教授。主要研究方向为平行系统的方法与应用、社会计算、平行智能、知识自动化" ]
修回:2023-08-19,
网络首发:2023-09,
纸质出版:2023-09-15
移动端阅览
皮佩定, 倪清桦, 杨静, 等. 平行夏尔希里:生态资源智能管护及其可持续发展新途径[J]. 智能科学与技术学报, 2023,5(3):283-292.
Peiding PI, Qinghua NI, Jing YANG, et al. Parallel Sharhili: a new approach to sustainable development and intelligent management of ecological resources[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 283-292.
皮佩定, 倪清桦, 杨静, 等. 平行夏尔希里:生态资源智能管护及其可持续发展新途径[J]. 智能科学与技术学报, 2023,5(3):283-292. DOI: 10.11959/j.issn.2096-6652.202325.
Peiding PI, Qinghua NI, Jing YANG, et al. Parallel Sharhili: a new approach to sustainable development and intelligent management of ecological resources[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 283-292. DOI: 10.11959/j.issn.2096-6652.202325.
新疆夏尔希里自然保护区是一处珍稀生物资源与无法估量的生态价值共融的自然瑰宝,被誉为“中国最后的净土”和“珍稀的天然基因库”。为确保这一自然宝库的可持续发展,提出了“平行夏尔希里”概念,以智能化手段全方位助力其生态资源的精细化管理。核心思想是利用描述智能建立高精度模型,通过预测智能分析生态系统变化,借助引导智能科学地制定保护策略。探索了通过智能科技建立自然保护区管理与应用的新模式,以期推动保护区及其珍贵生物资源的可持续发展。
Sharhili Nature Reserve is a natural treasure which enjoys both rare biological resources and incalculable ecological value
and it is known as “China’s last pure land” and “rare natural gene pool”.To ensure the sustainable development of this natural treasure
this study proposes the concept of “Parallel Sharhili”
which comprehensively assists in the precise management of ecological resources through intelligent means.The core idea involves utilizing “artificial systems” for description to build high-precision models
“computational experiments” for prediction to analyze ecosystem changes
and “parallel execution” for prescription or control and management to scientifically formulate conservation strategies.This study explores an intelligent management model for nature reserves
aiming at promoting the sustainable development of the reserve and its precious biological resources.
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