摘要
元启发式算法作为农业工程智能化升级的核心驱动力,通过多目标参数寻优与动态策略融合,在控制器调参、监测模型构建、复杂系统预测及机械系统优化等环节展现出显著优势。然而,现有算法在处理高维非线性问题时易陷入局部最优,且通用化框架难以适配农业场景的动态异构特性,同时离线优化模式导致实时响应能力不足,制约了智能装备的精准控制与自主决策水平。未来需聚焦混合优化策略增强全局搜索能力,构建场景驱动的定制化算法以提升跨任务适应性,并依托边缘计算与轻量化设计突破实时整定瓶颈,从而推动农业工程向高效、智能、可持续方向发展。
As the core driving force for intelligent upgrading of agricultural engineering,Meta-heuristic algorithm shows significant advantages in controller tuning,monitoring model construction,complex system prediction and mechanical system optimization through multi-objective parameter optimization and dynamic strategy fusion.However,the existing algorithms are easy to fall into local optimal when dealing with high-dimensional nonlinear problems,and the generalized framework is difficult to adapt to the dynamic and heterogeneous characteristics of agricultural scenarios,while the offline optimization mode leads to insufficient real-time response capability,which restricts the precision control and autonomous decision-making of intelligent equipment.In the future,hybrid optimization strategies should be focused to enhance the global search capability,scenario-driven customized algorithms should be built to improve the cross-task adaptability,bottleneck of real-time calibration should be broken through by relying on edge computing and lightweight design,so as to promote agricultural engineering in the direction of high efficiency,intelligence and sustainability.
作者
王淞
衣淑娟
李衣菲
安磊
徐景辉
孙文胜
Wang Song;Yi Shujuan;Li Yifei;An Lei;Xu Jinghui;Sun Wensheng(College of Engineering of Heilongjiang Bayi Agricultural University,Daqing 163319;People's Government of Dongjin Township,Beilin District of Suihua City,Suihua 152042)
出处
《中国农机装备》
2025年第7期64-66,共3页
China Agricultural Machinery Equipment
基金
黑龙江省重点研发计划项目(2022ZX05B02)
黑龙江八一农垦大学研究生创新科研项目(YJSCX2024-Z02、YJSCX2024-Y12、YJSCX2024-Y13)。
关键词
农业工程
元启发式算法
智能控制
监测算法
预测模型
研究现状
Agricultural engineering
Meta-heuristic algorithms
Intelligent control
Monitoring algorithms
Predictive modeling
Status of research
