[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3908":3,"related-3908":97},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":96},3908,"A Situational Awareness-Driven Human-Machine Interface Framework for Enhancing Operators’ Performance in Agricultural Robotic Teleoperation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10846-026-02469-8","Abstract Despite the increasing adoption of automated\u002Fautonomous guided vehicles (AGVs) in precision agriculture, existing human-machine interfaces (HMIs) remain function-centric, frequently neglecting the cognitive and user experience (UX) needs of non-technical operators. Consequently, current dashboards suffer from limited usability, high cognitive load, and reduced system transparency, increasing the risk of operator error. To address this gap within agricultural teleoperation interface design, this study presents a theory-driven HMI framework that systematically translates Endsley’s Situational Awareness (SA) theory (Perception, Comprehension, Projection) and ISO 9241 ergonomic principles into a structured three-panel AGV teleoperation interface. Unlike conventional function-oriented dashboards, the proposed framework organises interface components according to operator cognitive requirements to support information perception, comprehension, and projection during remote operation. A theory-driven agricultural teleoperation HMI framework was developed by translating cognitive and ergonomic principles into practical interface organization strategies. To formatively evaluate this SA-driven design architecture, we conducted a user-perception study with non-expert digitally literate participants (N = 20), evaluating three iterative HMI prototypes. The comparative results indicate that the SA-aligned layout received higher user ratings in perceived decision-support capability (SA3), cognitive workload reduction, and overall usability compared with baseline designs during formative evaluation. The study provides preliminary methodological guidance for agricultural teleoperation HMI design, while recognizing that objective operational validation with target agricultural users remains necessary.","摘要 尽管自动\u002F自主导引车（AGV）在精准农业中的应用日益广泛，现有的人机界面（HMI）仍以功能为中心，常常忽视非技术操作人员的认知与用户体验（UX）需求。因此，当前仪表盘存在可用性有限、认知负荷高、系统透明度低等问题，增加了操作人员出错的风险。为弥补农业远程操作界面设计中的这一空白，本研究提出了一种理论驱动的HMI框架，将Endsley的情境意识（SA）理论（感知、理解、预测）与ISO 9241工效学原则系统性地转化为结构化的三面板AGV远程操作界面。与传统以功能为导向的仪表盘不同，所提框架依据操作人员的认知需求组织界面组件，以支持远程操作过程中的信息感知、理解与预测。通过将认知与工效学原则转化为实际的界面组织策略，开发了理论驱动的农业远程操作HMI框架。为对该SA驱动设计架构进行形成性评估，我们开展了一项用户感知研究，招募具有数字素养的非专家参与者（N = 20），对三个迭代HMI原型进行评价。比较结果表明，在形成性评估中，与基线设计相比，符合SA的布局在感知决策支持能力（SA3）、认知负荷降低和整体可用性方面获得了更高的用户评分。本研究为农业远程操作HMI设计提供了初步的方法学指导，同时认识到仍需以目标农业用户开展客观的操作验证。",null,"Journal of Intelligent & Robotic Systems","2026-09-30T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,13,9,1,"该论文提出基于态势感知理论的农机遥操作人机界面框架，方法新颖且有用户实验支撑，对智慧农业交互设计有参考价值，但属初步形成性评估，实际应用影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","农业机器人","人机交互","远程操控",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,58],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":52},7,true,[],[50,51],"精准农业","precision agriculture",[27],{"code":54,"score":22,"supported":14,"title":55,"abstract":56,"tags":57},"F6",[],[],[29],{"code":59,"score":22,"supported":14,"title":60,"abstract":61,"tags":62},"I2",[],[],[28],[64,65],"农业机器人 远程操控 人机界面","态势感知 农机 界面设计","农业机器人远程操控人机界面-3908",4,"10.1007\u002Fs10846-026-02469-8",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":87,"oa_url":6,"card":88,"direction":94,"ingested_from":95},"W7214925819",[72,75,78,81,84],{"name":73,"orcid":74},"Khang Duong","https:\u002F\u002Forcid.org\u002F0009-0005-6817-9646",{"name":76,"orcid":77},"Thien Anh Tran","https:\u002F\u002Forcid.org\u002F0000-0001-6780-6664",{"name":79,"orcid":80},"Minh Quang Tran","https:\u002F\u002Forcid.org\u002F0000-0002-1215-3587",{"name":82,"orcid":83},"Dinh-Son Vu","https:\u002F\u002Forcid.org\u002F0000-0002-0567-6419",{"name":85,"orcid":86},"Byron Mason","https:\u002F\u002Forcid.org\u002F0000-0002-9530-5020",0,{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"提出基于态势感知理论的农机遥操作人机界面框架，提升非专业操作者表现。","将Endsley态势感知理论与ISO 9241原则转化为三面板界面，20人用户感","态势感知对齐布局在决策支持、降低认知负荷和可用性上优于基线设计。","智慧农业 \u002F 农业物联网","可开展面向真实农户的客观操作绩效验证，并探索多机协同遥操作的认知适配界面。","数字乡村与农业信息化","openalex","2026-10-01T23:30:39.385589Z",{"total":98,"page":22,"page_size":98,"items":99},6,[100,155,194,240,291,346],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":9,"content":9,"source_name":105,"source_url":103,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":114,"tags":116,"primary_topic":119,"secondary_topics":120,"classification":121,"search_phrases":134,"slug":137,"view_count":87,"doi":138,"paper":139,"created_at":154},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":109,"substance":110,"depth":111,"authority":112,"freshness":13,"relevant":22,"comment":113},16,21,18,14,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[115],{"name":105,"url":103},[27,28,50,117,118],"机器视觉","作物行检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":122},{"candidates":123,"agricultural_evidence":47},[124,129],{"code":33,"score":125,"supported":47,"title":126,"abstract":127,"tags":128},5,[],[51],[27,50],{"code":59,"score":130,"supported":14,"title":131,"abstract":132,"tags":133},2,[],[],[28,117],[135,136],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539","10.1007\u002Fs11119-026-10473-z",{"doi":138,"openalex_id":140,"authors":141,"venue":105,"cited_by_count":87,"oa_url":9,"card":9,"direction":9,"ingested_from":95},"W7223117828",[142,145,148,151],{"name":143,"orcid":144},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":146,"orcid":147},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":149,"orcid":150},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":152,"orcid":153},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":9,"content":9,"source_name":160,"source_url":158,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":162,"score_detail":163,"sources":166,"tags":168,"primary_topic":170,"secondary_topics":171,"classification":172,"search_phrases":183,"slug":186,"view_count":87,"doi":187,"paper":188,"created_at":193},5380,"Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.17987.98089","Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems。OpenAlex","OpenAlex","2026-10-19T00:00:00Z",56,{"impact":111,"substance":17,"depth":13,"authority":164,"freshness":17,"relevant":22,"comment":165},12,"主题契合智慧农业，但摘要仅为标题重复、缺乏方法与数据细节，信息增量有限，暂不建议进入每日精选。",[167],{"name":160,"url":158},[27,29,50,169],"智能采收",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":173},{"candidates":174,"agricultural_evidence":47},[175,179],{"code":33,"score":20,"supported":47,"title":176,"abstract":177,"tags":178},[51],[51],[27,50],{"code":54,"score":22,"supported":14,"title":180,"abstract":181,"tags":182},[],[],[29],[184,185],"农业机器人 智慧农业 智能采收 精准农业","农业机器人 智慧农业","农业机器人智慧农业智能采收精准农业-5380","10.13140\u002Frg.2.2.17987.98089",{"doi":187,"openalex_id":189,"authors":190,"venue":9,"cited_by_count":87,"oa_url":158,"card":9,"direction":92,"ingested_from":95},"W7222971426",[191],{"name":192,"orcid":9},"Joydeep Dey","2026-10-20T23:30:08.600540Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":9,"source_name":200,"source_url":197,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":201,"sources":204,"tags":206,"primary_topic":209,"secondary_topics":210,"classification":211,"search_phrases":223,"slug":226,"view_count":87,"doi":227,"paper":228,"created_at":239},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。","Zenodo (CERN European Organization for Nuclear Research)",{"impact":111,"substance":202,"depth":111,"authority":164,"freshness":21,"relevant":22,"comment":203},22,"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[205],{"name":200,"url":197},[27,28,207,50,208],"数字孪生","植物表型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":212},{"candidates":213,"agricultural_evidence":47},[214,219],{"code":33,"score":18,"supported":47,"title":215,"abstract":217,"tags":218},[216],"digital twin",[50,207,51],[207,27,50],{"code":59,"score":22,"supported":14,"title":220,"abstract":221,"tags":222},[],[],[28],[224,225],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":227,"openalex_id":229,"authors":230,"venue":200,"cited_by_count":87,"oa_url":197,"card":233,"direction":237,"ingested_from":95},"W7222885652",[231],{"name":232,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":234,"method":235,"finding":236,"direction":237,"opportunity":238},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","农业遥感与作物表型","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":9,"content":9,"source_name":245,"source_url":243,"published_at":246,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":247,"sources":249,"tags":251,"primary_topic":253,"secondary_topics":254,"classification":255,"search_phrases":271,"slug":274,"view_count":130,"doi":275,"paper":276,"created_at":290},5082,"Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jip.2026.108765","Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives。Journal of Invertebrate Pathology","Journal of Invertebrate Pathology","2026-10-14T00:00:00Z",{"impact":164,"substance":109,"depth":19,"authority":112,"freshness":17,"relevant":22,"comment":248},"核心期刊综述，聚焦昆虫病原线虫在精准农业中的应用方法与挑战，专业性强但属细分领域，产业影响有限。",[250],{"name":245,"url":243},[27,28,50,252],"生物防治",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":256},{"candidates":257,"agricultural_evidence":47},[258,262,267],{"code":33,"score":20,"supported":47,"title":259,"abstract":260,"tags":261},[51],[51],[27,50],{"code":263,"score":22,"supported":14,"title":264,"abstract":265,"tags":266},"F4",[],[],[252],{"code":59,"score":22,"supported":14,"title":268,"abstract":269,"tags":270},[],[],[28],[272,273],"昆虫病原线虫 精准农业","Journal of Invertebrate Pathology 线虫","昆虫病原线虫精准农业-5082","10.1016\u002Fj.jip.2026.108765",{"doi":275,"openalex_id":277,"authors":278,"venue":245,"cited_by_count":87,"oa_url":9,"card":9,"direction":92,"ingested_from":95},"W7221746877",[279,282,285,287],{"name":280,"orcid":281},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":283,"orcid":284},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":286,"orcid":9},"İsmail Alper Susurluk",{"name":288,"orcid":289},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","2026-10-16T23:30:09.556034Z",{"id":292,"title":293,"url":294,"summary":295,"summary_zh":9,"content":9,"source_name":105,"source_url":294,"published_at":246,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":296,"score_detail":297,"sources":299,"tags":301,"primary_topic":305,"secondary_topics":306,"classification":307,"search_phrases":328,"slug":331,"view_count":22,"doi":332,"paper":333,"created_at":345},4973,"Barley head detection using UAV imagery and YOLOv10","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10468-w","Barley head detection using UAV imagery and YOLOv10。Precision Agriculture",75,{"impact":109,"substance":18,"depth":19,"authority":112,"freshness":17,"relevant":22,"comment":298},"将YOLOv10用于无人机大麦穗检测，方法新颖、数据可靠，属细分领域技术进展，值得精选。",[300],{"name":105,"url":294},[27,302,28,303,304],"无人机","遥感","大麦",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":308},{"candidates":309,"agricultural_evidence":47},[310,314,319,323],{"code":33,"score":67,"supported":47,"title":311,"abstract":312,"tags":313},[],[51],[27],{"code":315,"score":22,"supported":14,"title":316,"abstract":317,"tags":318},"A5",[],[],[304],{"code":59,"score":22,"supported":14,"title":320,"abstract":321,"tags":322},[],[],[28],{"code":324,"score":22,"supported":14,"title":325,"abstract":326,"tags":327},"I3",[],[],[303],[329,330],"无人机 大麦 穗检测","YOLOv10 大麦 遥感","无人机大麦穗检测-4973","10.1007\u002Fs11119-026-10468-w",{"doi":332,"openalex_id":334,"authors":335,"venue":105,"cited_by_count":87,"oa_url":9,"card":9,"direction":9,"ingested_from":95},"W7221758985",[336,339,342],{"name":337,"orcid":338},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":340,"orcid":341},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":343,"orcid":344},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":347,"title":348,"url":349,"summary":350,"summary_zh":351,"content":9,"source_name":352,"source_url":349,"published_at":353,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":354,"score_detail":355,"sources":357,"tags":359,"primary_topic":362,"secondary_topics":363,"classification":364,"search_phrases":375,"slug":378,"view_count":22,"doi":379,"paper":380,"created_at":394},4758,"A Gateway-Based Approach for Integrating Digital Compass Data into Controller Area Network (CAN) in Precision Farming Systems","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2860-2869.9031","The integration of high precision orientation sensors into resilient communication infrastructures is a key requirement for next-generation autonomous systems. Digital compass sensors typically generate data in the NMEA format, whereas distributed control architectures in mobile robotics predominantly utilize the CAN 2.0B protocol. This study presents the design and implementation of a microcontroller-based hardware-software gateway that enables seamless interoperability between these heterogeneous communication standards within autonomous agricultural platforms. The proposed system operates as a protocol translation layer, acquiring orientation data (heading, pitch, and roll) from a digital compass via serial communication and converting it into CAN-compliant messages. The incoming NMEA sentences are parsed, validated, and filtered before being encapsulated into extended CAN frames. To address the payload limitations of Classical CAN and discrepancies in transmission rates, a multi-frame segmentation strategy and a synchronization mechanism based on handshaking are implemented. Additionally, buffering techniques are employed to ensure temporal consistency and prevent data loss during high-frequency data exchange. Experimental evaluations demonstrated that the proposed gateway achieved reliable real-time communication performance, with an average end to end communication latency of 19.6 ms, a 99.98% message reconstruction success rate, and 0.02% packet loss during continuous CAN Bus data transmission tests. The proposed integration framework effectively bridges the gap between legacy serial sensor protocols and modern CAN Bus-based distributed systems. Consequently, this work offers a scalable and practical solution for protocol interoperability in precision agriculture and contributes to the development of robust sensor integration strategies for autonomous field robotics.","将高精度定向传感器集成到弹性通信基础设施中，是下一代自主系统的关键需求。数字罗盘传感器通常以NMEA格式生成数据，而移动机器人中的分布式控制架构则主要采用CAN 2.0B协议。本研究介绍了一种基于微控制器的硬件-软件网关的设计与实现，该网关能够在自主农业平台中实现这些异构通信标准之间的无缝互操作。所提出的系统作为协议转换层运行，通过串行通信从数字罗盘获取定向数据（航向、俯仰和横滚），并将其转换为符合CAN标准的报文。传入的NMEA语句在封装为扩展CAN帧之前，经过解析、验证和过滤。为解决经典CAN的有效载荷限制和传输速率差异问题，实现了多帧分段策略和基于握手的同步机制。此外，采用缓冲技术以确保时间一致性，并防止高频数据交换过程中的数据丢失。实验评估表明，所提出的网关实现了可靠的实时通信性能，在连续CAN总线数据传输测试中，平均端到端通信延迟为19.6 ms，报文重构成功率为99.98%，丢包率为0.02%。所提出的集成框架有效弥合了传统串行传感器协议与现代基于CAN总线的分布式系统之间的差距。因此，这项工作为精准农业中的协议互操作性提供了一种可扩展且实用的解决方案，并为自主田间机器人稳健传感器集成策略的发展做出了贡献。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",71,{"impact":164,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":356},"该论文提出基于网关的NMEA-CAN协议转换方案，实测延迟19.6ms、重建成功率99.98%，对精准农业自主农机传感器集成有实用价值，但属细分技术进展，影响范围有限。",[358],{"name":352,"url":349},[27,29,50,360,361],"传感器集成","CAN总线",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":365},{"candidates":366,"agricultural_evidence":47},[367,371],{"code":33,"score":17,"supported":47,"title":368,"abstract":369,"tags":370},[],[50,51],[27,50],{"code":54,"score":22,"supported":14,"title":372,"abstract":373,"tags":374},[],[],[29],[376,377],"数字罗盘 CAN总线 精准农业","NMEA CAN网关 农业机器人","数字罗盘CAN总线精准农业-4758","10.24925\u002Fturjaf.v14i10.2860-2869.9031",{"doi":379,"openalex_id":381,"authors":382,"venue":352,"cited_by_count":87,"oa_url":349,"card":389,"direction":94,"ingested_from":95},"W7221447512",[383,386],{"name":384,"orcid":385},"İ̇lker Ünal","https:\u002F\u002Forcid.org\u002F0000-0002-5188-4438",{"name":387,"orcid":388},"Osman Eceoğlu","https:\u002F\u002Forcid.org\u002F0000-0001-5778-6655",{"tldr":390,"method":391,"finding":392,"direction":92,"opportunity":393},"设计微控制器网关，将数字罗盘NMEA数据转换为CAN总线消息，用于精准农业自主平台。","微控制器网关、NMEA解析、CAN 2.0B、多帧分段与握手同步。","平均延迟19.6ms，消息重构成功率99.98%，丢包率0.02%，实现可靠实时通信。","可研究多传感器异构协议统一网关及时间同步，提升农机自主系统实时性与可扩展性。","2026-10-12T23:30:30.266640Z"]