Path of AI-driven transformation for in the low-altitude economy under the “dual carbon” goals
WANG Mengyu1,2, LIU Zhenyu1, WANG Yuwei3
1.Key Laboratory of Nondestructive Test （Ministry of Education）， Nanchang Hangkong University， Nanchang 330063， China 2.Hefei Institutes of Physical Science， Chinese Academy of Sciences， Hefei 230031， China 3.College of Engineering， Anhui Agricultural University， Hefei 230036， China
摘要“双碳”背景下，以人工智能（AI）为核心的低空经济融合发展成为重要趋势.低空经济与 AI 深度融合既涉及效率安全又涉及能耗减排，因而面临三大重要挑战：AI 在低空典型任务中实现效率与安全如何影响能耗，当前深度融合的重要约束瓶颈是什么，受“双碳”目标约束的技术路径该如何设计与执行.本文基于感知—决策—执行链，从感知、智能、数字等方面，综述多源感知、边缘智能与数字孪生技术的低空物流、应急、农情等典型应用场景及任务相关研究和产业应用，提出以“技术-场景-绿色”为核心的AI?低空?双碳的整合机理模型，识别出标准、话语权缺失，低空空域治理碎片化，算法—能效—安全不协同三类瓶颈，明确任务能耗、能耗强度、排放因子等指标口径，并给出面向政策、产业与技术协同的可执行路线，即“场景适配型认证-智能数据与UTM 底座-绿色能源与能效一体”的路线建议，以期为我国低空经济智能化与低碳化提供分析框架与实施抓手.
Abstract：Under the background of the “dual-carbon” goals， the integrated development of low-altitude economy with AI has become an important trend. Nevertheless， in-depth integration of the low-altitude economy and AI involves both efficiency， safety， and energy consumption reduction， thus facing three major challenges： how the realization of efficiency and safety by AI in typical low-altitude tasks affects energy consumption； what the key constraints and bottlenecks of the current in-depth integration are； and how to design and implement the technology path restricted by the “dual carbon” goals. Based on the perception-decision-execution chain， this paper reviews the research and industrial applications related to typical application scenarios and tasks （such as low-altitude logistics， emergency response， and agricultural condition monitoring） of multi-source perception， edge intelligence， and digital twin technologies from the perspectives of perception， intelligence， and digitalization. It proposes an integrated mechanism model of “AI·Low-Altitude·Dual Carbon” with “technology-scenario-green” as the core.Three types of bottlenecks are identified： lack of standards and discourse power， fragmented low-altitude airspace governance and misalignment among algorithms， energy efficiency， and safety. Indicators such as task energy consumption， energy consumption intensity and emission factors are clarified. Available paths for the coordination of policies， industries， and technologies were put forward， namely “scenario adaptable certification-intelligent data and UTM （unmanned traffic management） infrastructure-integration of green energy and energy efficiency”. It is expected to provide an analytical framework and implementation tools for the intelligent and low-carbon development of China’s low-altitude economy.
王梦宇,刘振宇,王玉伟. “双碳”目标下低空经济的AI变革之路[J]. 华中师范大学学报(自然科学版), 2025, 59(5): 694-706.
WANG Mengyu,LIU Zhenyu,WANG Yuwei. Path of AI-driven transformation for in the low-altitude economy under the “dual carbon” goals. journal1, 2025, 59(5): 694-706.
2025, Vol. 59 
