摘要： 微型仿生机器人作为尺寸在厘米级及以下的微型机电系统，具有体积小、质量轻、便于携带等特点，被广泛应用于环境探测、目标搜索、侦查打击等复杂环境中。为使广大研究人员了解微型仿生机器人的研究进展，基于全球最大的文摘和引文数据库Scopus对近15年的相关文献进行总结和分析，直观描绘了微型仿生机器人领域的发展趋势。从微型仿生机器人的仿生运动形式、制造技术、驱动技术三个关键点入手，辅以生物机电混合微型机器人特殊研究方向的介绍，总结了微型仿生机器人的总体特征和研究现状。分析目前微型仿生机器人发展的技术瓶颈，提出能源-驱动-感知-控制全柔性一体化的发展思路，同时促进一体化制造技术的创新发展。基于军事和反恐防暴应用背景，充分分析微型仿生机器人的特征优势，进行以微型仿生机器人为核心的作战应用构想，并拓展讨论了微型仿生机器人在民用方面的应用。最后，对现有微型仿生机器人的不足与未来发展进行讨论与总结，为微型仿生机器人技术领域的发展及其军事应用前景提供有价值的参考。
In recent years,various deep learning-based health management models for mechanical equipment have made significant progress.However,existing models tend to be smaller in scale and are typically designed to handle data from specific frequencies,speeds,or modes,focusing on particular components such as gears and bearings to perform tasks like monitoring,diagnosis,and prediction.These models struggle to adapt to new scenarios and lack the capability for continuous evolution.With the increasing precision and complexity of high-end equipment,there is a growing demand for highly general,scalable,and evolvable"one-stop"health management services.Inspired by the trend of generalization in large language models like ChatGPT,which excel in handling diverse data,tasks,and scenarios,a large model for general prognostics and health management of machinery is proposed.First,multimodal data is resampled in the angular domain and segmented to token sequence.Then,the data is input into a Transformer-based information integration foundational model to extract health and degradation information into specific tokens.Finally,these specific tokens are used to perform downstream tasks such as monitoring,diagnosis,and prediction.The proposed large model's baseline performance,multitask synergy,and scalability were verified using fault and long-term degradation datasets.The results show that the proposed large model can simultaneously perform condition monitoring,fault diagnosis,and remaining useful life prediction for multiple objects like bearings and gears.Additionally,the diagnostic and predictive multitasks can effectively collaborate,mutually enhancing performance,and achieving better results compared to single-task models.In few-shot learning and continual learning scenarios,the large model can be rapidly deployed and continuously evolved.Therefore,the proposed large model features high generality,scalability,and sustainability,and is expected to provide universal"one-stop"health management services for mechanical equipment.
摘要： 在工业4.0向工业5.0的发展过程中，以人为本逐渐成为智能制造领域关注的焦点之一。当前的人机协作不仅强调要聚焦于技术的进步与效率的提升，更强调将人类的高阶认知思维与机器的计算能力相结合，实现认知赋能。基于此，梳理人机协作中认知赋能在交互感知、任务规划与执行、技能学习等关键领域的现有研究，揭示了多模态信息整合、任务推理、动态决策与技能知识表征的挑战。进一步，提出通过应用知识图谱构建的相关技术来支持人与其机器认知对齐的方法，以及通过应用知识图谱推理的相关技术来支持复杂环境下人机协作的任务优化和动态决策。在分析现有人机协作认知赋能研究局限性的基础上，展望未来智能制造环境下的深度认知协同的发展方向。
摘要： 双级蒸发有机朗肯循环(Organic rankine cycle，ORC)能有效提高热源利用效率，回热-ORC能有效利用膨胀机排出余热，结合两者优点，提出双级蒸发双回热有机朗肯循环系统(Double-stage evaporation and double recuperation Organic rankine cycle，DEDR-ORC)。常规㶲法侧重于独立组件的㶲损失，无法识别系统组件之间的相互作用对系统性能的实际影响，提出高级㶲和高级㶲经济法对DEDR-ORC进行分析。探讨DEDR-ORC的先进性、组件㶲损失和组件之间相互作用关系和组件的实际改进潜力。结果表明：DEDR-ORC的㶲效率和净输出功相对双级蒸发-ORC和回热-ORC最大提高了27.24%和4 436.97 kW，平准化电能成本降低了0.006 。常规㶲法得到膨胀机和冷凝器的㶲损失最大为755.15 kW和567.20 kW，高级㶲法得到膨胀机、冷凝器和蒸发器1总可避免㶲损失为553.28 kW、223 kW、103.20 kW，其组件改进潜力依据总可避免㶲损失大小排序，因此，膨胀机、冷凝器和蒸发器1改进潜力最大。高级㶲经济法得到膨胀机、蒸发器1、混合回热器的总可避免成本为71.16 $/h、1.14 $/h、0.94 $/h，均为正值，其余组件均为负值，其降低成本的改进潜力仅有膨胀机、蒸发器1和混合回热器，其余组件无改进潜力。研究结果可为ORC系统设计及优化提供理论支撑与技术参考。
Defects in metal welding structures can cause serious harm to key equipment during service.Traditional defect identification is mainly based on manual inspection.However,the amount of actual inspection data is huge,and defects vary in shape and size,making the inspection results susceptible to various influencing factors.A multi-layer feature fusion target detection network is proposed to intelligently identify the ultrasonic total-focusing image of steel plate welds.Since the proportion of defective pixels in ultrasound images is low and there are many noises and artifacts,the original VGG backbone network is replaced with a dense convolutional network with feature reuse and fewer parameters to optimize the computing resources and speed of the model.At the same time,a multi-layer network fusion module is embedded to fuse the upper and lower adjacent network feature maps,fully combining large-size features and small-size features to improve the detection ability of the model.In addition,a spatial attention mechanism is embedded to enhance defect information and suppress background noise.Finally,the intersection over union ratio non-maximum suppression algorithm in the original SSD is replaced with a distance intersection over union ratio to better detect closely spaced defects in using simulation and experiments to obtain the ultrasonic total-focusing data set of typical weld defects for model testing,the results show that the average accuracy of the model is 10.45％higher than the original SSD model,and the detection frame rate is increased by 17％.The proposed multi-layer feature fusion target detection network can be used for fast,accurate and automatic identification of large-volume ultrasonic total-focusing inspection results of welded steel structures.
