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在全球新能源产业向智能化转型的关键阶段,传统单一专业培养模式已难以满足产业需求,产教融合成为破解人才供给与产业需求结构性矛盾的关键路径。本文结合某职业技术学院典型案例,分析产教融合下的新能源智能制造人才培养模式的具体实施路径与成效,提出深化产学研协同、拓展数字化平台等优化建议,以期为新能源智能制造人才培养提供理论参考与实践指导。
Abstract:At the critical stage of the global new energy industry' s transformation towards intelligence,the traditional single-discipline training model is no longer capable of meeting the industry' s demands.The integration of industry and education has become the key path to resolving the structural contradiction between talent supply and industry demand.This article,based on the typical case of Suzhou Polytechnic Institute of Agriculture,analyzes the specific implementation paths and achievements of the new energy intelligent manufacturing talent training model under the integration of industry and education,and puts forward optimization suggestions such as deepening industry-university-research collaboration and expanding digital platforms,with the aim of providing theoretical references and practical guidance for the training of new energy intelligent manufacturing talents.
[1]朱宏,朱燕媚.浅析农业无人机发展现状及展望[J].农机质量与监督,2025(8):28-30.
[2]贾天豪.基于数据驱动的农业无人机锂电池状态估算研究[D].新乡:河南科技学院,2025.
[3]刘金南.植保无人机在现代农业创新发展中的应用研究[J].农业开发与装备,2025(7):166-168.
[4]杨梅,马炼,林洁,等.大力推广植保无人机飞防作业应用[J].云南农业,2025(7):20-21.
[5]祝劲平.农用植保无人机关键技术创新与多元广泛应用措施的研究[J].种子世界,2025(7):138-140.
[6]高德欣,刘欣,杨清.基于卷积神经网络与双向长短时融合的锂离子电池剩余使用寿命预测[J].信息与控制,2022(3):318-329,360.
[7]刘芳,邵晨,苏卫星,等.基于全新等效电路模型的电池关键状态在线联合估计器[J].控制与决策,2023(6):1620-1628.
[8]李超然,肖飞,樊亚翔,等.基于深度学习的锂离子电池SOC和SOH联合估算[J].中国电机工程学报,2021,41(2):681-692.
[9]薛会鸽.植保无人机的电池使用与管理措施[J].河北农机,2025(12):45-47.
[10]SUN H,JIANG H,GU Z,et al.A novel multiple kernel extreme learning machine model for remaining useful life prediction of lithium-ion batteries[J].Journal of Power Sources,2024,613:234912.
基本信息:
DOI:10.19996/j.cnki.ChinBatlnd.2025.06.019
中图分类号:TK01-4;G712
引用信息:
[1]余雁,金小花,胡舒洋.“制造强国”战略下新能源智能制造产教融合育人路径探索[J].电池工业,2025,29(06):554-556.DOI:10.19996/j.cnki.ChinBatlnd.2025.06.019.
基金信息: