
Computer Engineering and Applications ›› 2025, Vol. 61 ›› Issue (15): 54-71.DOI: 10.3778/j.issn.1002-8331.2411-0453
• Research Hotspots and Reviews • Previous Articles Next Articles
PENG Hailong, ZHU Yuhua, ZHEN Tong, LI Zhihui, ZHANG Qinghui, PAN Quan
Online:2025-08-01
Published:2025-07-31
彭海龙,祝玉华,甄彤,李智慧,张庆辉,潘泉
PENG Hailong, ZHU Yuhua, ZHEN Tong, LI Zhihui, ZHANG Qinghui, PAN Quan. Review of Digital Twin Technology and Its Application in Grain Gepot[J]. Computer Engineering and Applications, 2025, 61(15): 54-71.
彭海龙, 祝玉华, 甄彤, 李智慧, 张庆辉, 潘泉. 数字孪生技术及其在粮库中的应用综述[J]. 计算机工程与应用, 2025, 61(15): 54-71.
Add to citation manager EndNote|Ris|BibTeX
URL: http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2411-0453
| [1] 2024年全国粮食和物资储备工作会议[J]. 中国粮食经济, 2024(1): 6-9. National conference on grain and material reserve in 2024[J]. China Grain Economy, 2024(1): 6-9. [2] 乔炎, 甄彤, 李智慧. 基于边缘计算的粮食信息化探究[J]. 中国粮油学报, 2022, 37(8): 36-43. QIAO Y, ZHEN T, LI Z H. Research on grain informationization based on edge computation[J]. Journal of the Chinese Cereals and Oils Association, 2022, 37(8): 36-43. [3] POTTER B. The problem with stored grain—now and this fall[EB/OL]. (2020-07-08). https: //www.farmprogress.com/marketing/the-problem-with-stored-grain-now-and-this-fall. [4] 周颖, 项海玲, 刘悦, 等. 我国粮食存储现状分析[J]. 产业与科技论坛, 2021, 20(7): 66-67. ZHOU Y, XIANG H L, LIU Y, et al. Analysis on the current situation of grain storage in China[J]. Industrial & Science Tribune, 2021, 20(7): 66-67. [5] 郑秉照, 卓先锋, 许明锋, 等. 气调结合低剂量熏蒸投药储藏技术对平房仓储粮的影响[J]. 粮油仓储科技通讯, 2024, 40(2): 52-56. ZHENG B Z, ZHUO X F, XU M F, et al. Controlled atmosphere combined with low-dose fumigation dosing the influence of storage technology on grain storage in bungalows [J]. Technology Communication of Grain and Oil Storage, 2024, 40(2): 52-56. [6] 周士杨. 粮食出入仓作业风险隐患及对策[J]. 黑龙江粮食, 2024(3): 39-41. ZHOU S Y. Hidden dangers and countermeasures of grain storage operation[J]. Heilongjiang Grain, 2024(3): 39-41. [7] 吕宗旺, 吴建军, 孙福艳, 等. 现代智慧粮库系统的设计与研究[J]. 河南工业大学学报(自然科学版), 2013, 34(5): 79-82. Lǚ Z W, WU J J, SUN F Y, et al. Research and design of modern smart grain depot system[J]. Journal of Henan Univers-ity of Technology(Natural Science Edition), 2013, 34(5): 79-82. [8] 屈登辉, 刘笑睿, 齐颖, 等. 粮食企业储藏技术应用现状、问题及对策探讨[J]. 粮食储藏, 2023, 52(1): 54-56. QU D H, LIU X R, QI Y, et al. Discussion on the application status, problems and countermeasures of storage technology in grain enterprises[J]. Grain Storage, 2023, 52(1): 54-56. [9] LAMONT J. Overcoming data silos: a range of options[J]. KM World, 2024, 33(1): 6-8. [10] TAO F, QI Q L. Make more digital twins[J]. Nature, 2019, 573(7775): 490-491. [11] 陶飞, 马昕, 胡天亮, 等. 数字孪生标准体系[J]. 计算机集成制造系统, 2019, 25(10): 2405-2418. TAO F, MA X, HU T L, et al. Research on digital twin standard system[J]. Computer Integrated Manufacturing Systems, 2019, 25(10): 2405-2418. [12] LIU Y, FENG J, LU J M, et al. A review of digital twin capabilities, technologies, and applications based on the mat-urity model[J]. Advanced Engineering Informatics, 2024, 62: 102592. [13] 郑焱诚, 屈登辉, 赵金辉. 粮食仓储智能化对人与粮食空间关系升级研究[J]. 粮食问题研究, 2024(1): 40-44. ZHENG Y C, QU D H, ZHAO J H. Research on the upgrading of spatial relationship between people and grain by intelligent grain storage[J]. Grain Issues Research, 2024(1): 40-44. [14] 伍朝辉, 徐建达, 符志强, 等. 交通强国建设视域下公路交通数字孪生体系架构、关键技术与实践案例[J]. 交通运输研究, 2023, 9(4): 104-124. WU Z H, XU J D, FU Z Q, et al. System architecture, key technologies, and practical cases of highway traffic digital twin from the perspective of building a country with strong transportation network[J]. Transport Research, 2023, 9(4): 104-124. [15] GRIEVES M W. Product lifecycle management: the new paradigm for enterprises[J]. International Journal of Product Development, 2005, 2(1/2): 71. [16] JOLLIFF B L, ROBINSON M S. The scientific legacy of the Apollo program[J]. Physics Today, 2019, 72(7): 44-50. [17] CHEN H Y, SHAO H J, DENG X, et al. Comprehensive survey of the landscape of digital twin technologies and their diverse applications[J]. Computer Modeling in Engineering & Sciences, 2024, 138(1): 125-165. [18] ZHENG Y, WANG X, XU Z, et al. Research on issues related to digital twin modeling[J]. International Journal of Frontiers in Engineering Technology, 2023, 5(7): 24-29. [19] TEKINERDOGAN B. On the notion of digital twins: a mode-ling perspective[J]. Systems, 2023, 11(1): 15. [20] 张霖, 陆涵. 从建模仿真看数字孪生[J]. 系统仿真学报, 2021, 33(5): 995-1007. ZHANG L, LU H. Discussing digital twin from of modeling and simulation[J]. Journal of System Simulation, 2021, 33(5): 995-1007. [21] 孙侠生, 肖迎春. 飞机结构健康监测技术的机遇与挑战[J]. 航空学报, 2014, 35(12): 3199-3212. SUN X S, XIAO Y C. Opportunities and challenges of aircraft structural health monitoring[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(12): 3199-3212. [22] QI Q L, TAO F, HU T L, et al. Enabling technologies and tools for digital twin[J]. Journal of Manufacturing Systems, 2021, 58: 3-21. [23] SUN B, LI Y, ZHANG Y Y, et al. Multi-source heterogeneous data fusion prediction technique for the utility tunnel fire detection[J]. Reliability Engineering & System Safety, 2024, 248: 110154. [24] 李嘉楠. 基于大数据技术的公路桥梁全寿命周期管理[J]. 交通世界, 2022(12): 127-128. LI J N. Life cycle management of highway bridges based on big data technology[J]. Transpo World, 2022(12): 127-128. [25] 倪廉钦, 李树芳, 高杰, 等. 数字孪生技术在安全工程实践教学中的应用[J]. 华北科技学院学报, 2024, 21(2): 118-124. NI L Q, LI S F, GAO J, et al. The application of digital twin technology in safety engineering practice teaching[J]. Journal of North China Institute of Science and Technology, 2024, 21(2): 118-124. [26] 刘刚, 马智亮, 曾勃, 等. 数字孪生技术在建筑工程中的应用研究综述[J]. 土木建筑工程信息技术, 2023, 15(6): 1-8. LIU G, MA Z L, ZENG B, et al. A review of the application of digital twin technology in construction engineering[J]. Journal of Information Technology in Civil Engineering and Architecture, 2023, 15(6): 1-8. [27] DEL CAMPO G, SAAVEDRA E, PIOVANO L, et al. Virtual reality and Internet of Things based digital twin for smart city cross-domain interoperability[J]. Applied Sciences, 2024, 14(7): 2747. [28] 赵沁平. 虚拟现实综述[J]. 中国科学(F辑: 信息科学), 2009, 39(1): 2-46. ZHAO Q P. Overview of virtual reality[J]. Science in China (Series F (Information Sciences)), 2009, 39(1): 2-46. [29] 田阔, 孙志勇, 李增聪. 面向结构静力试验监测的高精度数字孪生方法[J]. 航空学报, 2024, 45(7): 429134. TIAN K, SUN Z Y, LI Z C. High-precision digital twin method for structural static test monitoring[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(7): 429134. [30] 孙水发, 汤永恒, 王奔, 等. 动态场景的三维重建研究综述[J]. 计算机科学与探索, 2024, 18(4): 831-860. SUN S F, TANG Y H, WANG B, et al. Review of research on 3D reconstruction of dynamic scenes[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(4): 831-860. [31] 张新长, 廖曦, 阮永俭. 智慧城市建设中的数字孪生与元宇宙探讨[J]. 测绘通报, 2023(1): 1-7. ZHANG X C, LIAO X, RUAN Y J. Discussion on digital twin and metaverse in smart city construction[J]. Bulletin of Surveying and Mapping, 2023(1): 1-7. [32] WANG Y J, KANG X, CHEN Z H. A survey of digital twin techniques in smart manufacturing and management of ene-rgy applications[J]. Green Energy and Intelligent Transportation, 2022, 1(2): 100014. [33] LEHNER C, PADOVANO A, ZEHETNER C, et al. Digital twin and digital thread within the product lifecycle management[J]. Procedia Computer Science, 2024, 232: 2875-2886. [34] JIA W J, WANG W, ZHANG Z Z. From simple digital twin to complex digital twin part II: multi-scenario applications of digital twin shop floor[J]. Advanced Engineering Informatics, 2023, 56: 101915. [35] LIU L, WANG X, WANG Z G. Recent progress and emerging strategies for carbon peak and carbon neutrality in China[J]. Greenhouse Gases: Science and Technology, 2023, 13(5): 732-759. [36] AMBARITA E E, KARLSEN A, SCIBILIA F, et al. Industrial digital twins in offshore wind farms[J]. Energy Informatics, 2024, 7(1): 5. [37] 王中贝. 数字孪生技术道德化研究[D]. 合肥: 中国科学技术大学, 2023. WANG Z B. Research on the moralization of digital twin technology[D]. Hefei: University of Science and Technology of China, 2023. [38] 张绿原, 胡露骞, 沈启航, 等. 水利工程数字孪生技术研究与探索[J]. 中国农村水利水电, 2021(11): 58-62. ZHANG L Y, HU L Q, SHEN Q H, et al. Research and exploration of digital twin technology in water conservancy engineering[J]. China Rural Water and Hydropower, 2021(11): 58-62. [39] 蔡阳, 成建国, 曾焱, 等. 加快构建具有“四预” 功能的智慧水利体系[J]. 中国水利, 2021(20): 2-5. CAI Y, CHENG J G, ZENG Y, et al. Accelerate to build smart water system with the function of “four pres”[J]. China Water Resources, 2021(20): 2-5. [40] 刘占省, 张安山, 王文思, 等. 数字孪生驱动的冬奥场馆消防安全动态疏散方法[J]. 同济大学学报(自然科学版), 2020, 48(7): 962-971. LIU Z S, ZHANG A S, WANG W S, et al. Dynamic fire evacuation guidance method for winter Olympic venues based on digital twin-driven model[J]. Journal of Tongji University (Natural Science), 2020, 48(7): 962-971. [41] TAO F, ZHANG H, LIU A, et al. Digital twin in industry: state-of-the-art[J]. IEEE Transactions on Industrial Informatics, 2019, 15(4): 2405-2415. [42] 张滨团. 基于数字孪生的轮式机器人三维仿真监测系统研究[D]. 太原: 太原科技大学, 2023. ZHANG B T. Research on 3D simulation and monitoring system of wheeled robot based on digital twinning[D]. Taiyuan: Taiyuan University of Science and Technology, 2023. [43] 李鹏, 潘凯, 刘小川. 美国空军机体数字孪生计划的回顾与启示[J]. 航空科学技术, 2020, 31(9): 1-10. LI P, PAN K, LIU X C. Retrospect and enlightenment of the AFRL airframe digital twin program[J]. Aeronautical Science & Technology, 2020, 31(9): 1-10. [44] 孟松鹤, 叶雨玫, 杨强, 等. 数字孪生及其在航空航天中的应用[J]. 航空学报, 2020, 41(9): 023615. MENG S H, YE Y M, YANG Q, et al. Digital twin and its aerospace applications[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(9): 023615. [45] 邹鹏, 肖泽文, 李珺, 等. 数字孪生城市及其关键技术[J]. 智能建筑与智慧城市, 2024(3): 42-44. ZOU P, XIAO Z W, LI J, et al. Digital twin city and key technologies[J]. Intelligent Building & Smart City, 2024(3): 42-44. [46] 李伟. 新加坡基于虚拟模型发展智慧城市[J]. 检察风云, 2021(15): 34-35. LI W. Developing smart city in Singapore based on virtual model[J]. Prosecutorial View, 2021(15): 34-35. [47] 李超, 刘君武, 王理, 等. 数字孪生在智慧城市中的应用[J]. 中国检验检测, 2022, 30(4): 42-46. LI C, LIU J W, WANG L, et al. Application of digital twin in smart city[J]. China Inspection Body & Laboratory, 2022, 30(4): 42-46. [48] PASION C. Siemens partners with CEA-List to advance digital twin technology[J]. Modern Machine Shop, 2023, 96(6): 50-51. [49] 李轩, 郑练, 李济龙, 等. 黑灯工厂发展路径探究[J]. 新技术新工艺, 2022(12): 7-17. LI X, ZHENG L, LI J L, et al. Research on development path of dark factory[J]. New Technology & New Process, 2022(12): 7-17. [50] ATTARAN S, ATTARAN M, CELIK B G. Digital twins and industrial Internet of things: uncovering operational intelligence in industry 4.0[J]. Decision Analytics Journal, 2024, 10: 100398. [51] VENKATESH K P, BRITO G, KAMEL BOULOS M N. Health digital twins in life science and health care innovation[J]. Annual Review of Pharmacology and Toxicology, 2023, 64: 159-170. [52] LI L, CAMPS J, WANG Z J, et al. Toward enabling cardiac digital twins of myocardial infarction using deep computational models for inverse inference[J]. IEEE Transactions on Medical Imaging, 2024, 43(7): 2466-2478. [53] CORRAL-ACERO J, MARGARA F, MARCINIAK M, et al. The ‘Digital Twin’ to enable the vision of precision cardiology[J]. European Heart Journal, 2020, 41(48): 4556-4564. [54] LIU Y, ZHANG L, YANG Y, et al. A novel cloud-based framework for the elderly healthcare services using digital twin[J]. IEEE Access, 2019, 7: 49088-49101. [55] 赵霞, 曹晓均, 李小华. 医学数字孪生应用研究与关键技术探析[J]. 医学信息学杂志, 2023, 44(4): 12-16. ZHAO X, CAO X J, LI X H. Analysis of the application research and key technology of medical digital twin[J]. Journal of Medical Informatics, 2023, 44(4): 12-16. [56] 曾焱, 程益联, 江志琴, 等. “十四五” 智慧水利建设规划关键问题思考[J]. 水利信息化, 2022(1): 1-5. ZENG Y, CHENG Y L, JIANG Z Q, et al. Thoughts on key issues of the 14th Five-Year Plan for smart water conservancy construction plan[J]. Water Resources Informatization, 2022(1): 1-5. [57] GROS A, GUILLEM A, DE LUCA L, et al. Faceting the post-disaster built heritage reconstruction process within the digital twin framework for Notre-Dame de Paris[J]. Scientific Reports, 2023, 13: 5981. [58] 刁生富, 李思琦. 基于模型的数字孪生的认知突破与方法论创新[J]. 佛山科学技术学院学报(自然科学版), 2023, 41(1): 1-10. DIAO S F, LI S Q. Cognitive breakthrough and methodological innovation of model-based digital twin[J]. Journal of Foshan University (Natural Science Edition), 2023, 41(1): 1-10. [59] 张欣. 数字孪生技术在开关电源设计中应用研究[D]. 西安: 西安石油大学, 2023. ZHANG X. Application research on digital twin technology in design of switching power supply[D]. Xi’an: Xi’an Shi-you University, 2023. [60] 马辰, 李富合, 赵宗海, 等. 舰船信息基础设施数字孪生装备研究[J]. 舰船科学技术, 2024, 46(7): 132-140. MA C, LI F H, ZHAO Z H, et al. Research on ship information infrastructure digital twin equipment[J]. Ship Science and Technology, 2024, 46(7): 132-140. [61] 杨轸, 汪名选, 刘帅. 道路环境与驾驶行为数据同步采集系统研发[J]. 计算机测量与控制, 2024, 32(4): 210-218. YANG Z, WANG M X, LIU S. Development of synchronous collection system for road environment and driving behavior data[J]. Computer Measurement & Control, 2024, 32(4): 210-218. [62] 廖伟智, 张彬, 徐国栋, 等. 基于数字孪生的粮库可视化监控与管理系统研发[J]. 制造业自动化, 2023, 45(11): 202-207. LIAO W Z, ZHANG B, XU G D, et al. Research and development of a visual monitoring and management system for grain depot based on digital twins[J]. Manufacturing Automation, 2023, 45(11): 202-207. [63] 孙晓民, 陈鹏, 王继武, 等. 基于多技术融合的粮库数字孪生系统设计与实现[J]. 粮食与食品工业, 2024, 31(5): 51-53. SUN X M, CHEN P, WANG J W, et al. Design and implementation of a granary digital twin system based on multi-technology integration[J]. Cereal & Food Industry, 2024, 31(5): 51-53. [64] 宿颖, 周亚伟, 王飞, 等. 基于数字孪生技术的粮库仓储智能化服务的分析和建议[J]. 粮食与食品工业, 2022, 29(6): 45-47. SU Y, ZHOU Y W, WANG F, et al. Analysis and suggestions on intelligent service of grain depot storage based on digital twin technology[J]. Cereal & Food Industry, 2022, 29(6): 45-47. [65] 宿颖, 张刚, 陈怡暄. 基于数字孪生技术的智慧粮库应用现状及发展趋势[J]. 食品安全导刊, 2021(27): 167-168. SU Y, ZHANG G, CHEN Y X. Application status and development trend of smart grain depot based on digital twinning technology[J]. China Food Safety Magazine, 2021(27): 167-168. [66] 刘帅, 尹强, 黄强, 等. 利用卡尔曼滤波算法的粮情预测与分析[J]. 武汉轻工大学学报, 2024, 43(2): 78-84. LIU S, YIN Q, HUANG Q, et al. Research on grain situation prediction based on Kalman filter algorithm[J]. Journal of Wuhan Polytechnic University, 2024, 43(2): 78-84. [67] 刘凯飞. 储粮粮情分析预测方法及可视化研究[D]. 郑州: 河南工业大学, 2023. LIU K F. Research on analysis and prediction method and visualization of stored grain condition[D]. Zhengzhou: Henan University of Technology, 2023. [68] 侯嘉. 基于GWO-SVR的储粮温度预测及安全预警系统设计[D]. 郑州: 河南工业大学, 2023. HOU J. Design of storage grain temperature prediction and safety warning system based on GWO-SVR[D]. Zhengzhou: Henan University of Technology, 2023. [69] 黄琦兰, 刘童. 基于PSO-LSSVM模型的粮情安全性预测研究[J]. 粮食与油脂, 2022, 35(1): 153-157. HUANG Q L, LIU T. Prediction of grain situation security based on PSO-LSSVM model[J]. Cereals & Oils, 2022, 35(1): 153-157. [70] 赵立山. 基于孤立森林与LSTM的粮情异常分析与预测方法[D]. 南京: 南京财经大学, 2021. ZHAO L S. Anomaly analysis and prediction of grain situation based on isolated forest and LSTM[D]. Nanjing: Nanjing University of Finance & Economics, 2021. [71] 程嘉蔚. 基于BP神经网络的储粮温度预测模型及应用[D]. 合肥: 安徽大学, 2021. CHENG J W. Prediction model and application of stored grain temperature based on BP neural network[D]. Hefei: Anhui University, 2021. [72] 张潇. 基于数据驱动的储粮粮情系统建模与预测技术研究[D]. 郑州: 河南工业大学, 2021. ZHANG X. Research on data-driven grain storage system modeling and forecasting technology based on grain situation[D]. Zhengzhou: Henan University of Technology, 2021. [73] 郭平飞, 甄彤. 基于GANPSO-BP神经网络的粮情预测模型研究[J]. 现代电子技术, 2019, 42(20): 21-25. GUO P F, ZHEN T. Research on grain situation prediction model based on GANPSO-BP neural network[J]. Modern Electronics Technique, 2019, 42(20): 21-25. [74] 张银花. 基于云遗传RBF神经网络的粮情预测模型研究[D]. 郑州: 河南工业大学, 2016. ZHANG Y H. Application of the cloud genetic RBF neural network in the grain situation predication model[D]. Zhengzhou: Henan University of Technology, 2016. [75] 李杰. 浅圆仓不同通风方式的热湿传递及管网优化研究[D]. 无锡: 江南大学, 2023. LI J. Study on heat and moisture transfer and pipe network optimization of different aeration modes in squat silo[D]. Wuxi: Jiangnan University, 2023. [76] 郑明辉. 不同处理模式对控制储粮霉菌效果的研究[D]. 郑州: 河南工业大学, 2021. ZHENG M H. Study on the controlling effectiveness of mold in stored grain by different treatment modes[D]. Zhengzhou: Henan University of Technology, 2021. [77] 邢鑫, 王涛. 中国储粮机械通风技术的应用进展[J]. 粮食科技与经济, 2023, 48(5): 84-90. XING X, WANG T. Application progress of mechanical ventilation technology for grain storage in China[J]. Food Science and Technology and Economy, 2023, 48(5): 84-90. [78] 袁铭. 基于粮情挖掘数据的粮仓储粮作业管理专家系统研究[D]. 长春: 吉林大学, 2022. YUAN M. Research on expert management of grain storage operation based on grain situation mining data[D]. Chang-chun: Jilin University, 2022. [79] 胡荣辉. 基于大数据技术的粮仓智能通风策略研究[D]. 郑州: 河南工业大学, 2018. HU R H. Study on the intelligent ventilation strategy based on big data technology[D]. Zhengzhou: Henan University of Technology, 2018. [80] 孙彪瑞, 廉飞宇, 王珂, 等. 基于GA-BP神经网络的粮仓通风控制研究[J]. 河南工业大学学报(自然科学版), 2014, 35(4): 81-85. SUN B R, LIAN F Y, WANG K, et al. Study on granary ventilation control based on GA-BP neural network[J]. Journal of Henan University of Technology (Natural Science Edition), 2014, 35(4): 81-85. [81] 徐朝辉, 廉飞宇, 金广锋. 一种基于信息博弈的粮仓智能通风决策模型与算法[J]. 河南工业大学学报(自然科学版), 2014, 35(4): 91-96. XU Z H, LIAN F Y, JIN G F. Intelligent ventilation decision model and algorithm based on information game theory[J]. Journal of Henan University of Technology(Natural Science Edition), 2014, 35(4): 91-96. [82] 孙彪瑞, 廉飞宇. 封闭式粮仓通风控制中的智能算法研究[J]. 河南工业大学学报(自然科学版), 2014, 35(2): 93-97. SUN B R, LIAN F Y. Study on smart algorithm in ventil-ation control of enclosed barn[J]. Journal of Henan University of Technology (Natural Science Edition), 2014, 35(2): 93-97. [83] 马雪迪. 基于数字孪生技术的粮仓智能通风系统设计与实现[D]. 合肥: 安徽大学, 2022. MA X D. Design and implementation of intelligent ventilation system for granary based on digital twin technology[D]. Hefei: Anhui University, 2022. [84] 孔翎超. 融合机器学习和数字孪生的风电机组故障检测及可视化研究[D]. 青岛: 青岛科技大学, 2023. KONG L C. Research on fault detection and visualization of wind turbine generators integrating machine learning and digital twin[D]. Qingdao: Qingdao University of Science & Technology, 2023. [85] 王磊, 刘国龙, 杨磊, 等. 基于CEEMDAN-VMD融合特征和SO-SVM的风机轴承故障诊断[J]. 微电机, 2024, 57(2): 56-62. WANG L, LIU G L, YANG L, et al. Fault diagnosis of fan bearing based on CEEMDAN-VMD fusion feature and SO-SVM[J]. Micromotors, 2024, 57(2): 56-62. [86] 杜浩飞, 张超, 李建军. 基于SENet-ResNext-LSTM的风机轴承故障诊断[J]. 机械强度, 2023, 45(6): 1271-1279. DU H F, ZHANG C, LI J J. Fault diagnosis of wind turbine bearing based on SENet-ResNext-LSTM[J]. Journal of Mechanical Strength, 2023, 45(6): 1271-1279. [87] 陈奇. 基于多尺度学习的风机齿轮箱故障诊断研究[D]. 沈阳: 沈阳工业大学, 2023. CHEN Q. Research on fault diagnosis of wind turbine gearbox based on multi-scale learning[D]. Shenyang: Shenyang University of Technology, 2023. [88] 刘彬豪, 孙敬伟, 邓志华. 基于小波分析的PSO-MBCNN的风电齿轮箱故障诊断[J]. 长春理工大学学报(自然科学版), 2023, 46(5): 82-90. LIU B H, SUN J W, DENG Z H. Fault diagnosis of fan gearbox based on PSO-MBCNN of wavelet analysis[J]. Journal of Changchun University of Science and Technology (Natural Science Edition), 2023, 46(5): 82-90. [89] 陈萱, 杨永超, 袁博洋, 等. NGO-VMD和SSNGO-RF算法在风机齿轮箱故障诊断中的应用[J]. 湖北民族大学学报(自然科学版), 2023, 41(4): 520-529. CHEN X, YANG Y C, YUAN B Y, et al. Application of NGO-VMD and SSNGO-RF algorithms in fault diagnosis of wind turbine gearboxes[J]. Journal of Hubei Minzu University (Natural Science Edition), 2023, 41(4): 520-529. [90] 李东东, 刘宇航, 赵阳, 等. 基于改进生成对抗网络的风机行星齿轮箱故障诊断方法[J]. 中国电机工程学报, 2021, 41(21): 7496-7507. LI D D, LIU Y H, ZHAO Y, et al. Fault diagnosis method of wind turbine planetary gearbox based on improved generative adversarial network[J]. Proceedings of the CSEE, 2021, 41(21): 7496-7507. [91] 许子昂. 基于多参数融合的风机故障预警及发电性能评估[D]. 吉林: 东北电力大学, 2021. XU Z A. Multi-parameter fusion-based wind turbine fault warning and power generation performance assessment[D]. Jilin: Northeast Dianli University, 2021. [92] 李韵仪, 沈艳霞. 基于ABC-BW优化CHMM的风机齿轮箱故障诊断[J]. 噪声与振动控制, 2021, 41(4): 80-85. LI Y Y, SHEN Y X. Fault diagnosis of fan gearboxes based on ABC-BW optimized CHMM[J]. Noise and Vibration Control, 2021, 41(4): 80-85. [93] LI C, LU P, ZHU W R, et al. Intelligent monitoring platform and application for building energy using information based on digital twin[J]. Energies, 2023, 16(19): 6839. [94] 周亚男, 杜定华, 赵新宇, 等. 四种不同仓型的建仓指标对比分析[J]. 粮食加工, 2023, 48(6): 97-99. ZHOU Y N, DU D H, ZHAO X Y, et al. Comparative analysis of four different granary construction[J]. Grain Processing, 2023, 48(6): 97-99. [95] IRUELA J R S, RUIZ L G B, CRIADO-RAMóN D, et al. A GPU-accelerated adaptation of the PSO algorithm for multi-objective optimization applied to artificial neural networks to predict energy consumption[J]. Applied Soft Computing, 2024, 160: 111711. [96] WANG S Q, AHMAD ZAWAWI E M B, KWONG Q J, et al. Predication of smart building energy consumption based on deep learning algorithm[J]. Journal of Autonomous Intelligence, 2023, 6(2): 691. [97] LEI L, SHAO S L, LIANG L X. An evolutionary deep lear-ning model based on EWKM, random forest algorithm, SSA and BiLSTM for building energy consumption prediction[J]. Energy, 2024, 288: 129795. [98] ZHANG L L, ZHANG J R, REN P P, et al. Analysis of energy consumption prediction for office buildings based on GA-BP and BP algorithm[J]. Case Studies in Thermal Engineering, 2023, 50: 103445. [99] AFZAL S, ZIAPOUR B M, SHOKRI A, et al. Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms[J]. Energy, 2023, 282: 128446. [100] LI X, YU J Q, WANG Q, et al. A short-term building energy consumption prediction and diagnosis using deep learning algorithms[J]. Journal of Intelligent & Fuzzy Systems, 2022, 43(7437): 6831-6848. [101] DING Y, FAN L X, LIU X. Analysis of feature matrix in machine learning algorithms to predict energy consumption of public buildings[J]. Energy and Buildings, 2021, 249: 111208. [102] ALI JALLAL M, GONZáLEZ-VIDAL A, SKARMETA A F, et al. A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consum-ption prediction[J]. Applied Energy, 2020, 268: 114977. [103] 戴向东, 詹秀丽, 吴义强, 等. “双碳” 目标驱动下的家具工业低碳转型与智能制造模式[J]. 中南林业科技大学学报, 2024, 44(10): 1-16. DAI X D, ZHAN X L, WU Y Q, et al. Low-carbon transformation and intelligent manufacturing model of the furniture industry driven by the “dual carbon” targets[J]. Journal of Central South University of Forestry & Technology, 2024, 44(10): 1-16. [104] 刘丹丽. 高大平房仓粮食储藏过程中碳排放量计算[D]. 郑州: 河南工业大学, 2020. LIU D L. Carbon emissions calculation of grain storage process in large warehouse[D]. Zhengzhou: Henan University of Technology, 2020. [105] 张扬. 中储粮智慧粮库出入库系统的设计与实现[D]. 济南: 山东大学, 2018. ZHANG Y. The design and implementation of intelligent grain depot out of storage system for China grain reserves corporation[D]. Jinan: Shandong University, 2018. [106] 陈虹旭, 李胜广, 周舟. 视频孪生赋能警务数字化视算一体应用[J]. 警察技术, 2024(1): 30-33. CHEN H X, LI S G, ZHOU Z. Video-digital twin, empowers visual and computational integration applications of digital policing work[J]. Police Technology, 2024(1): 30-33. [107] 陶飞, 张辰源, 刘蔚然, 等. 数字工程及十个领域应用展望[J]. 机械工程学报, 2023, 59(13): 193-215. TAO F, ZHANG C Y, LIU W R, et al. Digital engineering and its ten application outlooks[J]. Journal of Mechanical Engineering, 2023, 59(13): 193-215. [108] XIAO M, CHEN L H, FENG H X, et al. Sustainable and robust route planning scheme for smart city public transport based on multi-objective optimization: digital twin model[J]. Sustainable Energy Technologies and Assessments, 2024, 65: 103787. [109] BAO Q W, ZHENG P, DAI S. A digital twin-driven dynamic path planning approach for multiple automatic guided vehicles based on deep reinforcement learning[J]. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2024, 238(4): 488-499. [110] 王路路. 基于改进遗传算法的孪生工厂AGV路径规划方法[J]. 软件, 2023, 44(5): 76-81. WANG L L. AGV path planning method based on improved genetic algorithm for twin plants[J]. Software, 2023, 44(5): 76-81. [111] 冯彪. 基于数字孪生的智慧仓储系统设计与路径规划算法研究[D]. 武汉: 武汉纺织大学, 2023. FENG B. Research on design and path planning algorithm of intelligent warehouse system based on digital twin[D]. Wuhan: Wuhan Textile University, 2023. [112] 闫思宇. 基于数字孪生的AGV动态路径规划研究[D]. 沈阳: 沈阳工业大学, 2022. YAN S Y. Research on AGV dynamic path planning based on digital twin[D]. Shenyang: Shenyang University of Technology, 2022. [113] 尚可, 张宇琳, 张飞舟. 基于数字孪生技术的智慧停车场总体架构[J]. 北京航空航天大学学报, 2023, 49(8): 2029-2038. SHANG K, ZHANG Y L, ZHANG F Z. Architecture of smart parking lot based on digital twin technology[J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(8): 2029-2038. [114] 范天岳. 基于多视频关联的车辆追踪与识别[D]. 北京: 北京交通大学, 2019. FAN T Y. Target vehicle tracking and recognition based on multi-camera correlation[D]. Beijing: Beijing Jiaotong University, 2019. [115] 张亨. 智慧粮库在粮食购销领域专项整治的应用探索[J]. 中国信息化, 2024(2): 73-74. ZHANG H. Exploration on the application of smart grain depot in special rectification in the field of grain purchase and sale[J]. Chinese Information, 2024(2): 73-74. [116] 数字孪生开创安防行业科技发展新机遇[J]. 中国安防, 2023(11): 29. Digital twins create new opportunities for scientific and technological development in security industry[J]. China Security & Protection, 2023(11): 29. [117] 任丽辉, 林琳, 王赫, 等. 基于“4M” 理论粮库磷化氢熏蒸作业火灾事故危险源辨识[J]. 粮食加工, 2022, 47(6): 100-102. REN L H, LIN L, WANG H, et al. Hazard identification of fire accident in phosphine fumigation in grain depot based on “4M” theory[J]. Grain Processing, 2022, 47(6): 100-102. [118] 徐辉. 基于视频图像的粮库火灾检测方法研究[D]. 郑州: 河南工业大学, 2022. XU H. Research on fire detection method of grain depot based on video image[D]. Zhengzhou: Henan University of Technology, 2022. [119] 陈长坤, 张健, 焦伟冰, 等. 数字孪生消防救援技术系统架构及关键技术分析[J]. 中国安全科学学报, 2023, 33(8): 156-163. CHEN C K, ZHANG J, JIAO W B, et al. Research on architecture and key technologies of fire rescue technology system based on digital twin[J]. China Safety Science Journal, 2023, 33(8): 156-163. [120] 王树斌, 王旭, 闫世平, 等. 基于Transformer的矿井内因火灾时间序列预测方法[J]. 工矿自动化, 2024, 50(3): 65-70. WANG S B, WANG X, YAN S P, et al. Transformer based time series prediction method for mine internal caused fire[J]. Journal of Mine Automation, 2024, 50(3): 65-70. [121] 许诗卉, 徐久成, 瞿康林, 等. 基于改进邻域粗糙集和优化BPNN的火灾预测算法[J]. 南京理工大学学报, 2024, 48(2): 192-201. XU S H, XU J C, QU K L, et al. Fire prediction algorithm based on improved neighborhood rough set and optimized BPNN[J]. Journal of Nanjing University of Science and Technology, 2024, 48(2): 192-201. [122] 郭震, 贾笑岩, 李富民, 等. 基于机器学习的建筑火灾蔓延快速预测[J]. 中国安全科学学报, 2023, 33(11): 117-125. GUO Z, JIA X Y, LI F M, et al. Fast prediction for building fire spread based on machine learning[J]. China Safety Science Journal, 2023, 33(11): 117-125. [123] WANG Y D, YUAN K X, CAO X H. Research on fire prediction model based on improved Harris hawk optimization algorithm[C]//Proceedings of the 5th International Conference on Information Science, Electrical, and Automation Engineering, 2023: 76. [124] SHU L, ZHANG H G, YOU Y M, et al. Towards fire prediction accuracy enhancements by leveraging an improved Na?ve Bayes algorithm[J]. Symmetry, 2021, 13(4): 530. [125] ZHOU F R, PAN H, GAO Z Y, et al. Fire prediction based on CatBoost algorithm[J]. Mathematical Problems in Engineering, 2021, 2021(1): 1929137. [126] 袁朋伟, 宋守信, 董晓庆. 基于灰色神经网络优化组合模型的火灾预测研究[J]. 中国安全生产科学技术, 2014, 10(3): 119-124. YUAN P W, SONG S X, DONG X Q. Study on fire accident prediction based on optimized grey neural network combination model[J]. Journal of Safety Science and Technology, 2014, 10(3): 119-124. [127] 任福鹏, 高攀祥. 粒子群优化小波神经网络在火灾预测中的应用研究[J]. 西安建筑科技大学学报(自然科学版), 2014, 46(3): 348-352. REN F P, GAO P X. Application and research on particle swarm optimizing wavelet neural network in the predi-ction of fire[J]. Journal of Xi’an University of Architecture & Technology (Natural Science Edition), 2014, 46(3): 348-352. [128] 李昌夏, 加文浩, 黄政龙, 等. 基于YOLOv5的实时抽烟检测研究[J]. 电脑知识与技术, 2022, 18(8): 100-102. LI C X, JIA W H, HUANG Z L, et al. Research on real-time smoking detection based on YOLOv5[J]. Computer Knowledge and Technology, 2022, 18(8): 100-102. [129] 姚向前, 景雷, 石井峰, 等. 粮食出入库作业安全隐患及改进应用[J]. 粮油仓储科技通讯, 2023, 39(6): 56-58. YAO X Q, JING L, SHI J F, et al. Potential safety hazards of grain leaving warehousing operation and its improvement and application[J]. Technology Communication of Grain and Oil Storage, 2023, 39(6): 56-58. [130] 吴建民. 粮仓智慧安全预警系统在粮食储备库中的应用[J]. 粮油仓储科技通讯, 2023, 39(5): 56-58. WU J M. Application of granary intelligent safety early warning system in grain storage[J]. Technology Communication of Grain and Oil Storage, 2023, 39(5): 56-58. [131] 乔炎, 甄彤, 李智慧. 改进YOLOv5的安全帽佩戴检测算法[J]. 计算机工程与应用, 2023, 59(11): 203-211. QIAO Y, ZHEN T, LI Z H. Improved helmet wear detection algorithm for YOLOv5[J]. Computer Engineering and Applications, 2023, 59(11): 203-211. [132] 朱高明. 基于目标检测识别与人体姿态估计的粮库智能监控技术研究[D]. 南京: 南京财经大学, 2020. ZHU G M. Research on grain depot intelligent monitoring system based on target detection and human body attitude estimation[D]. Nanjing: Nanjing University of Finance & Economics, 2020. [133] 浙江省粮食和物资储备局: 数字赋能护航粮食安全 创新推动粮食购销领域监管变革[J]. 中国粮食经济, 2023(1): 13-15. Zhejiang grain and material reserve bureau: digital empowerment escort food safety innovation and promote regulatory reform in the field of grain purchase and sale[J]. China Grain Economy, 2023(1): 13-15. |
| [1] | MA Yuwei, DU Haitao, SU Li, AN Ningyu. 5G Network Security Deduction Based on Digital Twin [J]. Computer Engineering and Applications, 2024, 60(5): 291-298. |
| [2] | LI Conglin, WANG Qibing, LU Jiawei, ZHAO Guojun, HU Hao, XIAO Gang. Modeling and Recognition Method of Elevator Passenger Abnormal Behavior Based on Digital Twin [J]. Computer Engineering and Applications, 2023, 59(19): 274-284. |
| [3] | CHEN Xuan, SHU Liang, LENG Yuxiang, YANG Yanfang. Design of Digital Twin System for Circuit Breaker Flexible Assembly Workshop [J]. Computer Engineering and Applications, 2022, 58(14): 245-257. |
| [4] | JI Guang, HAO Jianguo, ZHANG Zhongjie, GAO Jialong. Twin Simulation of Flight Process of Quadrotor UAV [J]. Computer Engineering and Applications, 2022, 58(12): 66-73. |
| [5] | WU Dongyang, DOU Jianping, LI Jun. Design of Digital Twin System for Quadrotor [J]. Computer Engineering and Applications, 2021, 57(16): 237-244. |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||