机械工程学报 ›› 2026, Vol. 62 ›› Issue (11): 1-13.doi: 10.3901/JME.260586
• 特邀专栏:制造互联与工业智能 • 上一篇
江沛1, 李嘉濠1, 李孝斌1, 任磊2
收稿日期:2025-06-08
修回日期:2025-10-28
发布日期:2026-07-29
作者简介:江沛,男,1985年出生,博士,副教授,博士研究生导师。主要研究方向为机器人技术、绿色制造、智能制造等。E-mail:peijiang@cqu.edu.cn;李嘉濠,男,2001年出生,硕士研究生,主要研究方向为工业机器人能耗优化。E-mail:1403974981@qq.com;李孝斌(通信作者),男,1987年出生,博士,副教授,博士研究生导师,主要研究方向为智能制造、网络协同制造,以及大数据、物联网、人工智能等新一代信息技术在离散制造行业中的应用。E-mail:xiaobin_lee@cqu.edu.cn;任磊,男,1979年出生,博士,教授,博士研究生导师,主要研究方向为工业互联网、工业大数据、工业智能、智能制造系统等。E-mail:renlei@buaa.edu.cn
基金资助:JIANG Pei1, LI Jiahao1, LI Xiaobin1, REN Lei2
Received:2025-06-08
Revised:2025-10-28
Published:2026-07-29
摘要: 制造业生产设备量大面广,能耗巨大,节能减排潜力突出。然而生产设备呈现出广域分散、海量异构、能耗特征动态演化等特征,导致设备能耗的精准预测和制造过程的高效节能管控困难。工业互联网强大的全维度数据感知能力和数据分析处理能力是实现生产设备过程节能优化运行的重要技术手段。对工业互联环境下生产设备运行能耗建模与优化技术研究进行了综述。首先从设备、工艺、系统三个维度梳理了生产设备的能耗特性及现有能耗感知、建模与优化方法研究进展。在此基础上,从工业互联网视角提出了生产设备运行能耗云边端协同管控框架,并给出了工业互联环境下生产设备运行能耗协同管控关键技术,为广大制造企业生产节能优化运行及绿色转型发展提供理论支撑。
中图分类号:
江沛, 李嘉濠, 李孝斌, 任磊. 工业互联环境下生产设备运行能耗研究现状及关键技术[J]. 机械工程学报, 2026, 62(11): 1-13.
JIANG Pei, LI Jiahao, LI Xiaobin, REN Lei. Research Status and Key Technologies for Operational Energy Consumption of Production Equipment in the Industrial Internet Environment[J]. Journal of Mechanical Engineering, 2026, 62(11): 1-13.
| [1] 国家统计局. 中华人民共和国2024年国民经济和社会发展统计公报[EB/OL]. [2025-05-31]. https://www.stats. gov.cn/sj/zxfb/202502/t20250228_1958817.html. National Bureau of Statistics. Communiqué on National economic and social development of the People's Republic of China in 2024[EB/OL]. [2025-05-31]. https://www.stats.gov.cn/sj/zxfb/202502/t20250228_1958817.html. [2] 国务院. 国务院关于印发《中国制造2025》的通知[EB/OL]. [2025-05-31]. https://www.gov.cn/zhengce/content/ 2015-05/19/content_9784.htm. China State Council. Notice of the State Council on Issuing “Made in China 2025” [EB/OL]. [2025-05-31]. https://www.gov.cn/zhengce/content/2015-05/19/content_9784.htm. [3] GUTOWSKI T,BRANHAM M,DAHMUS J,et al. Thermodynamic analysis of resources used in manufacturing processes[J]. Environmental Science & Technology,2009,43(5):1584-1590. [4] 李伯虎,张霖,王时龙,等. 云制造——面向服务的网络化制造新模式[J]. 计算机集成制造系统,2010,16(1):1-7,16. LI Bohu,ZHANG Lin,WANG Shilong,et al. Cloud manufacturing—service-oriented manufacturing paradigm for networked manufacturing[J]. Computer Integrated Manufacturing Systems,2010,16(1):1-7,16. [5] 丁凯,陈东燊,王岩,等. 基于云—边协同的智能工厂工业物联网架构与自治生产管控技术[J]. 计算机集成制造系统,2019,25(12):3127-3138. DING Kai,CHEN Dongshan,WANG Yan,et al. Intelligent factory industrial internet of things architecture and autonomous production management and control technology based on cloud-edge collaboration[J]. Computer Integrated Manufacturing Systems,2019,25(12):3127-3138. [6] 侯庆隆,杨冬,郭士杰. 工业机器人能耗优化方法研究综述[J]. 计算机工程与应用,2018,54(22):1-9. HOU Qinglong,YANG Dong,GUO Shijie. Review of energy consumption optimization methods for industrial robots[J]. Computer Engineering and Applications,2018,54(22):1-9. [7] SIHAG N,SANGWAN K S. A systematic literature review on machine tool energy consumption[J]. Journal of Cleaner Production,2020,275:123125. [8] ZHOU L,LI J,LI F,et al. Energy consumption model and energy efficiency of machine tools:A comprehensive literature review[J]. Journal of Cleaner Production,2016,112:3721-3734. [9] 杨扬,蔡旺. 数控铣削加工工艺参数优化方法综述[J]. 机械制造,2019,57(1):57-63,73. YANG Yang,CAI Wang. Review of optimization methods for CNC milling process parameters[J]. Machinery Manufacturing,2019,57(1):57-63,73. [10] 中华人民共和国应急管理部. GB 5083-2023生产设备安全卫生设计总则[S]. 北京:中国标准出版社,2023. Ministry of Emergency Management of the People's Republic of China. GB 5083-2023 general rules for safety and health design of production equipment[S]. Beijing:China Standards Press,2023. [11] ISO 14955-1:2017. Machine tools—Environmental evaluation of machine tools—Part 1:Design methodology for energy-efficient machine tools[S]. Geneva:ISO,2017. [12] ISO 14955-2:2018. Machine tools—Environmental evaluation of machine tools—Part 2:Methods for measuring energy supplied to machine tools and machine tool components[S]. Geneva:ISO,2018. [13] ISO 14955-3:2020. Machine tools—Environmental evaluation of machine tools—Part 3:Principles for testing metal-cutting machine tools with respect to energy efficiency[S]. Geneva:ISO,2020. [14] European Union. Ecodesign for sustainable products regulation[EB/OL]. [2025-05-31]. https://eur-lex.europa. eu/eli/reg/2024/1781/oj. [15] United States Department of Energy. Industrial training and assessment centers (ITAC) program[EB/OL]. [2025-05-31]. https://www.energy.gov/mesc/industrial- assessment-centers-iacs. [16] 国家发展改革委. 重点用能产品设备能效先进水平、节能水平和准入水平(2024年版) [EB/OL]. [2025-05-31]. https://www.ndrc.gov.cn/xxgk/zcfb/ghxwj/202402/t20240207_1364001.html. National Development and Reform Commission. Advanced,energy-saving and access energy efficiency levels for key energy-using products and equipment (2024 Edition) [EB/OL]. [2025-05-31]. https://www.ndrc.gov.cn/ xxgk/zcfb/ghxwj/202402/t20240207_1364001.html. [17] 国家发展改革委. 关于统筹节能降碳和回收利用加快重点领域产品设备更新改造的指导意见[EB/OL]. [2025-05-31].https://www.ndrc.gov.cn/xxgk/zcfb/tz/202302/t20230224_1349405.html. National Development and Reform Commission. Guiding opinions on coordinating energy conservation,carbon reduction and recycling to accelerate the renewal and transformation of product equipment in key areas [EB/OL]. [2025-05-31]. https://www.ndrc.gov.cn/xxgk/ zcfb/tz/202302/t20230224_1349405.html. [18] 倪恒欣,阎春平,孙菡,等. 高速干切滚齿机床能耗分布特性及其预测模型[J]. 中国机械工程,2022,33(7):842-851. NI Hengxin,YAN Chunping,SUN Han,et al. Energy consumption distribution characteristics and prediction model of high-speed dry hobbing machine tools[J]. China Mechanical Engineering,2022,33(7):842-851. [19] YOON H,SINGH E,MIN S. Empirical power consumption model for rotational axes in machine tools[J]. Journal of Cleaner Production,2018,196:370-381. [20] LU S,LI Y,DING B. Kinematics and dynamics analysis of the 3PUS-PRU parallel mechanism module designed for a novel 6-DOF gantry hybrid machine tool[J]. Journal of Mechanical Science and Technology,2020,34(1):345-357. [21] ZHOU L,LI F,WANG Y,et al. A new empirical standby power and auxiliary power model of CNC machine tools[J]. International Journal of Advanced Manufacturing Technology,2022,120(5-6):3995-4010. [22] PAWANR S,GARG G K,ROUTROY S. A novel approach to model the energy consumption of machine tools for machining cylindrical parts[J]. Journal of Manufacturing Processes,2022,84:28-42. [23] WANG X,WU H,YANG J,et al. Modeling and prediction method for inherent energy consumption of CNC machine tool spindle systems[J]. Journal of Mechanical Science and Technology,2025,39:4129-4145. [24] ZHOU J,YI H,CAO H,et al. Structural decomposition- based energy consumption modeling of robot laser processing systems and energy-efficient analysis[J]. Robotics and Computer-Integrated Manufacturing,2022,76:102327. [25] ZHANG J,LI C,LI Y,et al. Energy consumption modeling and optimization of a hobbing machine tool considering multi-axis coupling[J]. IEEE Transactions on Automation Science and Engineering,2024,21(3):3289-3297. [26] ZHANG M,YAN J. A data-driven method for optimizing the energy consumption of industrial robots[J]. Journal of Cleaner Production,2021,285:124862. [27] HE Y,WU P,LI Y,et al. A generic energy prediction model of machine tools using deep learning algorithms[J]. Applied Energy,2020,275:115402. [28] JIANG P,WANG Z,LI X,et al. Energy consumption prediction and optimization of industrial robots based on LSTM[J]. Journal of Manufacturing Systems,2023,70:137-148. [29] JIANG P,ZHENG J,WANG Z,et al. Industrial robot energy consumption model identification:A coupling model-driven and data-driven paradigm[J]. Expert Systems with Applications,2025,262:125604. [30] LI X,LAN Y,JIANG P,et al. An efficient computation for energy optimization of robot trajectory[J]. IEEE Transactions on Industrial Electronics,2022,69(11):11436-11346. [31] TORAYEV A,MARTINEZ-ARELLANO G,CHAPLIN J C,et al. Online and modular energy consumption optimization of industrial robots[J]. IEEE Transactions on Industrial Informatics,2024,20(2):1198-1207. [32] HUANG Z,LI H,CAO H,et al. Energy-saving control strategy for multisleep states of CNC machine tool considering components priority[J]. IEEE Transactions on Industrial Electronics,2024,71(2):1885-1895. [33] YI Q,LI C,JI Q,et al. Design optimization of lathe spindle system for optimum energy efficiency[J]. Journal of Cleaner Production,2020,250:119536. [34] JI Q,LI C,ZHU D,et al. Structural design optimization of moving component in CNC machine tool for energy saving[J]. Journal of Cleaner Production,2020,246:118976. [35] HE J,LI C,LV Y,et al. Optimized design of energy-saving spindle unit structure of CNC lathe[J]. Chin. Mech. Eng,2021,32:1330-1340. [36] LV Y,LI C,HE J,et al. Energy saving design of the machining unit of hobbing machine tool with integrated optimization[J]. Frontiers of Mechanical Engineering,2022,17(3):38. [37] MEIER H,SHI X. A systematic approach to resource- efficient process planning for low-carbon manufacturing[C]// Proceedings of 44th CIRP Conference on Manufacturing Systems,Madison. 2011. [38] LIU Y,JI K,ZHANG Y,et al. Energy consumption analysis for the fine blanking process[J]. The International Journal of Advanced Manufacturing Technology,2024,130(11-12):5743-5761. [39] LI L,HUANG H,ZHAO F,et al. Understanding energy consumption of hydraulic press during drawing process[J]. International Journal of Advanced Manufacturing Technology,2021,115(5-6):1497-516. [40] MENG Y,WANG L,LEE C H,et al. Plastic deformation-based energy consumption modelling for machining[J]. The International Journal of Advanced Manufacturing Technology,2018,96(1):631-641. [41] VISHNU V,VARGHESE K G,GURUMOORTHY B. Energy prediction in process planning of five-axis machining by data-driven modelling[J]. Procedia CIRP,2020,93:862-867. [42] LIU Z,SEALY M,LI W,et al. Energy consumption characteristics in finish hard milling[J]. Journal of Manufacturing Processes,2018,35:500-507. [43] YANG M,ZHANG D,WU B,et al. Energy consumption modeling for EDM based on material removal rate[J]. IEEE Access,2020,8:173267-173275. [44] SEALY M,LIU Z,ZHANG D,et al. Energy consumption and modeling in precision hard milling[J]. Journal of Cleaner Production,2016,135:1591-1601. [45] XIE J,LIU F,QIU H. An integrated model for predicting the specific energy consumption of manufacturing processes[J]. International Journal of Advanced Manufacturing Technology,2016,85(5-8):1339-1346. [46] ZHENG J,REN Y,QI T,et al. Modeling and optimization of energy consumption in wire cut electrical discharge machining[J]. Computers & Industrial Engineering,2024,191:110167. [47] MENG Y,DONG S,SUN X,et al. Cutting energy consumption modeling by considering tool wear and workpiece material properties for multi-objective optimization of machine tools[J]. Coatings,2024,14(6):691. [48] XU K,LUO M,TANG K. Machine based energy-saving tool path generation for five-axis end milling of freeform surfaces[J]. Journal of Cleaner Production,2016,139:1207-1223. [49] LI L,LI C,TANG Y,et al. Integration of process planning and cutting parameter optimization for energy-aware CNC machining [C]// Proceedings of the 13th IEEE Conference on Automation Science and Engineering (IEEE CASE),Xi’an,China,Aug 20-23,2017. [50] DENG Z,ZHANG H,FU Y,et al. Optimization of process parameters for minimum energy consumption based on cutting specific energy consumption[J]. Journal of Cleaner Production,2017,166:1407-1414. [51] XIAO Y,ZHANG H,JIANG Z,et al. Multiobjective optimization of machining center process route:Tradeoffs between energy and cost[J]. Journal of Cleaner Production,2021,280:124171. [52] TIAN X,HE Y,LI Y,et al. Joint optimization of feature sequences and toolpath strategies in multi-feature workpiece machining for minimizing energy consumption and processing time[J]. Journal of Manufacturing Systems,2024,74:869-886. [53] HE Y,LIU B,ZHANG X,et al. A modeling method of task-oriented energy consumption for machining manufacturing system[J]. Journal of Cleaner Production,2012,23(1):167-174. [54] LI H,YANG D,CAO H,et al. Data-driven hybrid petri-net based energy consumption behavior modelling for digital twin of energy-efficient manufacturing system[J]. Energy,2022,239:122178. [55] LI Y,HE Y,WANG Y,et al. A framework for characterizing energy consumption of machining manufacturing systems[J]. International Journal of Production Research,2014,52(2):314-325. [56] GUO J,HAN M,XU C,et al. Symmetric spatiotemporal learning network with sparse meter graph for short-term energy-consumption prediction in manufacturing systems[J]. Heliyon,2024,10(14):e34394. [57] KONG M,WU P,ZHANG Y,et al. Energy-efficient scheduling model and method for assembly blocking permutation flow-shop in industrial robotics field[J]. Artificial Intelligence Review,2024,57(3):60. [58] TIAN G,WANG W,ZHANG H,et al. Multi-objective optimization of energy-efficient remanufacturing system scheduling problem with lot-streaming production mode[J]. Expert Systems with Applications,2024,237:121309. [59] LUO Q,DENG Q,ZHUANG H,et al. Collaborative scheduling of energy-saving spare parts manufacturing and equipment operation strategy using a self-adaptive two-stage memetic algorithm[J]. Robotics and Computer-Integrated Manufacturing,2024,87:102707. [60] ZOU J,CHANG Q,ARINEZ J,et al. Data-driven modeling and real-time distributed control for energy efficient manufacturing systems[J]. Energy,2017,127:247-257. [61] PENG G,WEN Y,LIU J,et al. Energy-aware cloud manufacturing service selection and scheduling optimization[J]. International Journal of Computer Integrated Manufacturing,2025,38(3):309-334. [62] LIU W,WANG H,ZHENG P,et al. Cloud-edge-end collaborative multi-process dynamic optimization for energy-efficient aluminum casting[J]. Journal of Manufacturing Systems,2025,79:217-233. [63] DAI M,TANG D,GIRET A,et al. Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints[J]. Robotics and Computer-Integrated Manufacturing,2019,59:143-157. [64] SHROUF F,MIRAGLIOTTA G. Energy management based on Internet of Things:Practices and framework for adoption in production management[J]. Journal of Cleaner Production,2015,100:235-246. [65] ALEX B,JOHNSON M. A framework for IoT-enabled smart manufacturing for energy and resource optimization[J]. [2025-02-05]. arXiv,2025. https://doi. org/10.48550/arXiv.2502.03040 [66] ABDOUNE F,RAGAZZINI L,NOUIRI M,et al. Toward Digital twin for sustainable manufacturing:A data-driven approach for energy consumption behavior model generation[J]. Computers in Industry,2023,150:103949. [67] XIAO H,HU W,ZHOU H,et al. Prediction-based power consumption monitoring of industrial equipment using interpretable data-driven models[J]. IEEE Transactions on Automation Science and Engineering,2024,21(2):1312-1322. [68] MIRANI A,AWASTHI A,O’MAHONY N,et al. Industrial IoT-based energy monitoring system:Using data processing at edge[J]. IoT,2024,5(4):608-633. [69] 李聪波,曹宝,吴畏,等. 基于数字孪生的机械加工车间多级能效监测[J]. 计算机集成制造系统,2023,29(6):2102-2117. LI Congbo,CAO Bao,WU Wei,et al. Multi-level energy efficiency monitoring in machining workshop based on digital twin[J]. Computer Integrated Manufacturing Systems,2023,29(6):2102-2117. [70] MA S,ZHANG Y,REN S,et al. A case-practice- theory-based method of implementing energy management in a manufacturing factory[J]. International Journal of Computer Integrated Manufacturing,2021,34(7-8):829-843. [71] GORDIC D,BABIC M,JOVICIC N,et al. Development of energy management system - case study of serbian car manufacturer[J]. Energy Conversion and Management,2010,51(12):2783-2790. [72] LU R,JIANG Z,YANG T,et al. A novel hybrid- action-based deep reinforcement learning for industrial energy management[J]. IEEE Transactions on Industrial Informatics,2024,20(10):12461-12475. [73] CHEN X,LI C,TANG Y,et al. An Internet of Things based energy efficiency monitoring and management system for machining workshop[J]. Journal of Cleaner Production,2018,199:957-968. [74] AFRIN M,JIN J,RAHMAN A,et al. Multi-objective resource allocation for edge cloud based robotic workflow in smart factory[J]. Future Generation Computer Systems- the International Journal of Escience,2019,97:119-130. [75] LAILI Y,WANG X,ZHANG L,et al. DSAC-configured differential evolution for cloud-edge-device collaborative task scheduling[J]. IEEE Transactions on Industrial Informatics,2024,20(2):1753-1763. [76] HOU L,HAN R,LI X,et al. Research and application of key technologies of cloud edge collaboration[C]//6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022). SPIE,2022,12301:466-471. [77] FAN W,ZHAO L,LIU X,et al. Collaborative service placement,task scheduling,and resource allocation for task offloading with edge-cloud cooperation[J]. IEEE Transactions on Mobile Computing,2024,23(1):238-256. [78] WANG J,XU C,ZHANG J,et al. A collaborative architecture of the industrial internet platform for manufacturing systems[J]. Robotics and Computer- Integrated Manufacturing,2020,61:101854. [79] LIU C,SU Z,XU X,et al. Service-oriented industrial internet of things gateway for cloud manufacturing[J]. Robotics and Computer-Integrated Manufacturing,2022,73:102217. [80] LIU X,CHEN G,LI Y,et al. Sampling via the aggregation value for data-driven manufacturing[J]. National Science Review,2022,9(11):nwac201. [81] DING P,JIA M. Mechatronics equipment performance degradation assessment using limited and unlabeled data[J]. IEEE Transactions on Industrial Informatics,2022,18(4):2374-2385. [82] LIANG Y,LU X,LI W,et al. Cyber physical system and Big Data enabled energy efficient machining optimization[J]. Journal of Cleaner Production,2018,187:46-62. [83] BERMEO-AYERBE M,OCAMPO-MARTINEZ C,DIAZ-ROZO J. Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems[J]. Energy,2022,238:121691. [84] XIE J,HU P,GAO S,et al. Efficient cutting power modeling of three-axis milling based on transfer learning and neural network[J]. IEEE Access,2022,10:64451-64464. [85] WANG Q,CHEN X,CHEN M,et al. A rapid modelling method for machine tool power consumption using transfer learning[J]. International Journal of Advanced Manufacturing Technology,2024,131(3-4):1551-1566. [86] WANG Z,JIANG P,LI X,et al. A novel hybrid LSTM and masked multi-head attention-based network for energy consumption prediction of industrial robots[J]. Applied Energy,2025,383:125223. [87] LI H,CAO Y,LEI Y,et al. Energy-aware dynamic rescheduling of flexible manufacturing system using edge-cloud collaborative decision-making method[J]. International Journal of Computer Integrated Manufacturing,2025,38:434-449. [88] CHEN R,YANG B,LI S,et al. A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem[J]. Computers & Industrial Engineering,2020,149:106778. [89] QIN Z,LU Y. Knowledge graph-enhanced multi-agent reinforcement learning for adaptive scheduling in smart manufacturing[J]. Journal of Intelligent Manufacturing,2025,36:5943-596. [90] LAN S,JIANG Y,YANG C,et al. Knowledge-guided DRL for resource scheduling in customized and personalized production[C]// 2024 International Conference on Automation in Manufacturing,Transportation and Logistics (ICaMaL),2024:1-9. |
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