[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-76":3,"related-76":67},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":29,"classification":30,"search_phrases":62,"slug":65,"view_count":19,"doi":8,"paper":8,"created_at":66},76,"农业农村部党组召开扩大会议 研究部署加快农业人工智能发展、农业防灾减灾救灾等工作","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260731\u002F20260731_1\u002Fnmrb_20260731_13365_1_2082942060109860874.html","7月30日,农业农村部党组书记、部长张柱主持召开党组扩大会议,传达学习习近平总书记在2026世界人工智能大会开幕式上的重要讲话精神,部署加快农业人工智能发展。会议提出:强化前瞻性穿透式研究,完善农业人工智能顶层设计,健全农情调度系统,研发农业农村专业模型,加快突破农业传感器、专用芯片等关键核心技术;加力推动农业人工智能转化应用,支持有条件地区先行先试;健全农业人工智能标准体系,逐步建立风险监测、行业审查、安全管理等制度机制。同时会议学习贯彻习近平总书记对重庆彭水县山体崩塌的重要指示,要求毫不放松抓好'七下八上'防汛关键期农业防灾减灾救灾。",null,"农民日报 2026-07-31 头版","2026-07-31T00:00:00Z","政策",10,true,94,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},28,23,18,15,1,"全国性政策部署，含具体技术研发、标准体系等新条款，来源权威，时效性强。",[23],{"name":9,"url":6},[25,26,27,28],"智慧农业","农业农村部","人工智能","防灾减灾",[],{"status":31,"locked":32,"version":33,"method":34,"evidence":35},"needs_review",false,"2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":13,"reason":61},[37,43,49,56],{"code":38,"score":39,"supported":13,"title":40,"abstract":41,"tags":42},"H7",12,[28],[28],[28],{"code":44,"score":39,"supported":13,"title":45,"abstract":47,"tags":48},"I2",[46],"农业人工智能",[46],[27],{"code":50,"score":51,"supported":13,"title":52,"abstract":53,"tags":55},"I5",3,[],[54],"农业传感",[],{"code":57,"score":20,"supported":32,"title":58,"abstract":59,"tags":60},"I1",[],[],[25],"多个产业证据相当，需要确认主归属",[63,64],"农业农村部 人工智能 智慧农业 防灾减灾","农业农村部 人工智能","农业农村部人工智能智慧农业防灾减灾-76","2026-07-31T00:00:53.279158Z",{"total":68,"page":20,"page_size":68,"items":69},6,[70,167,221,278,350,412],{"id":71,"title":72,"url":73,"summary":74,"summary_zh":75,"content":8,"source_name":76,"source_url":73,"published_at":77,"category":78,"cover_url":8,"hotness":12,"is_selected":32,"score":79,"score_detail":80,"sources":86,"tags":88,"primary_topic":91,"secondary_topics":96,"classification":97,"search_phrases":138,"slug":141,"view_count":142,"doi":143,"paper":144,"created_at":166},4985,"Hybrid deep ensemble learning for crop disease prediction and climate-resilient disaster management in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10791-026-10579-y","Abstract Accurate crop disease prediction and climate-resilient disaster management are essential for improving agricultural productivity and ensuring food security under increasingly dynamic environmental conditions. Existing deep learning approaches typically perform crop disease detection and climate forecasting independently and often rely on deterministic CNN–LSTM architectures without effective multimodal feature fusion, predictive uncertainty estimation, or comprehensive statistical validation. Consequently, their ability to provide reliable decision support under changing climatic conditions remains limited. To address these challenges, this paper proposes a novel hybrid deep ensemble learning framework for integrated crop disease prediction and climate-resilient disaster management in smart agriculture. The proposed framework combines convolutional neural networks (CNNs) for spatial crop image feature extraction with long short-term memory (LSTM) networks for temporal climate forecasting. An attention-based multimodal feature fusion mechanism is employed to integrate heterogeneous crop and climate information, while Bayesian uncertainty estimation and deep ensemble learning are incorporated to improve prediction reliability, robustness, and confidence-aware decision support. Experimental evaluation is performed using multimodal datasets comprising crop images and climate observations collected from the PlantVillage, NOAA, and NASA EOS datasets. The dataset is divided into training, validation, and testing subsets using a 70:15:15 ratio. The proposed framework is evaluated using classification and regression metrics, including Accuracy, Precision, Recall, F1-score, RMSE, MAE, MAPE, and AUC-ROC. To ensure reproducibility, all experiments are repeated five times using different random seeds, and the results are reported with mean $$\\pm$$ standard deviation, 95% confidence intervals, and paired Student’s $$t$$ -tests. Experimental results demonstrate that the proposed framework consistently outperforms conventional CNN, LSTM, and CNN–LSTM hybrid models by achieving higher classification accuracy, lower climate forecasting error, improved robustness under varying climate conditions, and statistically significant performance improvements. The proposed framework provides a reliable, scalable, and uncertainty-aware solution for intelligent climate-resilient smart agriculture and early agricultural disaster management.","摘要：准确的作物病害预测和气候适应性灾害管理对于在日益动态的环境条件下提高农业生产力和保障粮食安全至关重要。现有深度学习方法通常独立执行作物病害检测和气候预测，且往往依赖确定性CNN–LSTM架构，缺乏有效的多模态特征融合、预测不确定性估计或全面的统计验证。因此，其在气候变化条件下提供可靠决策支持的能力仍然有限。为应对这些挑战，本文提出了一种新颖的混合深度集成学习框架，用于智慧农业中的作物病害预测与气候适应性灾害管理一体化。该框架将用于空间作物图像特征提取的卷积神经网络（CNN）与用于时间气候预测的长短期记忆（LSTM）网络相结合。采用基于注意力的多模态特征融合机制整合异构的作物与气候信息，同时引入贝叶斯不确定性估计和深度集成学习，以提高预测可靠性、鲁棒性和置信感知决策支持能力。实验评估使用包含作物图像和气候观测数据的多模态数据集，数据来源于PlantVillage、NOAA和NASA EOS数据集。数据集按70：15：15的比例划分为训练集、验证集和测试集。采用分类和回归指标对所提框架进行评估，包括准确率、精确率、召回率、F1分数、RMSE、MAE、MAPE和AUC-ROC。为确保可复现性，所有实验使用不同随机种子重复五次，结果以均值$$\\pm$$标准差、95%置信区间和配对Student's $$t$$检验报告。实验结果表明，所提框架在分类准确率、气候预测误差、不同气候条件下的鲁棒性以及统计显著性性能提升方面均持续优于传统CNN、LSTM和CNN–LSTM混合模型。该框架为智能气候适应性智慧农业和早期农业灾害管理提供了一种可靠、可扩展且具有不确定性感知能力的解决方案。","Discover Computing","2026-10-14T00:00:00Z","论文",71,{"impact":39,"substance":81,"depth":82,"authority":83,"freshness":84,"relevant":20,"comment":85},20,17,13,9,"提出CNN-LSTM注意力融合与贝叶斯不确定性估计的混合深度集成框架，多源数据与统计验证扎实，但属方法类论文，产业落地影响有限。",[87],{"name":76,"url":73},[25,46,89,28,90],"农业遥感","作物病害预测",{"code":44,"slug":92,"name":93,"path":94,"parent_code":95},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":98,"locked":32,"version":33,"method":34,"evidence":99},"classified",{"candidates":100,"agricultural_evidence":13},[101,106,113,120,127,133],{"code":57,"score":19,"supported":13,"title":102,"abstract":104,"tags":105},[103],"smart agriculture",[25,103],[25],{"code":44,"score":12,"supported":13,"title":107,"abstract":108,"tags":112},[],[109,110,111],"深度学习","deep learning","decision support",[46],{"code":38,"score":114,"supported":13,"title":115,"abstract":116,"tags":119},7,[],[117,118],"气候变化","气候适应",[28],{"code":121,"score":68,"supported":13,"title":122,"abstract":123,"tags":126},"A8",[],[124,125],"粮食安全","food security",[],{"code":128,"score":51,"supported":13,"title":129,"abstract":130,"tags":132},"A7",[],[131],"间作",[],{"code":134,"score":20,"supported":32,"title":135,"abstract":136,"tags":137},"I3",[],[],[89],[139,140],"PlantVillage NOAA NASA 作物病害预测","CNN LSTM 气候韧性 智慧农业","PlantVillageNOAANASA作物病害预测-4985",0,"10.1007\u002Fs10791-026-10579-y",{"doi":143,"openalex_id":145,"authors":146,"venue":76,"cited_by_count":142,"oa_url":73,"card":158,"direction":164,"ingested_from":165},"W7221778141",[147,150,153,155],{"name":148,"orcid":149},"Deepak Motwani","https:\u002F\u002Forcid.org\u002F0000-0002-0217-7155",{"name":151,"orcid":152},"Sellappan Palaniappan","https:\u002F\u002Forcid.org\u002F0009-0009-1168-2864",{"name":154,"orcid":8},"A. Daniel",{"name":156,"orcid":157},"Azween B. Abdullah","https:\u002F\u002Forcid.org\u002F0000-0003-4425-8604",{"tldr":159,"method":160,"finding":161,"direction":162,"opportunity":163},"提出混合深度集成框架，融合CNN与LSTM并引入注意力机制，实现作物病害预测与气候韧性灾害管理。","CNN提取图像特征、LSTM预测气候，注意力多模态融合，贝叶斯不确定性估计与深度","框架在分类精度与气候预测误差上均优于CNN、LSTM及CNN-LSTM基线，且性能提升具统计显著性。","农业人工智能与决策模型","可探索轻量化边缘部署与多作物跨区域迁移，结合实时物联网数据提升不确定性感知的灾害预警时效。","智慧农业 \u002F 农业物联网","openalex","2026-10-15T23:30:08.408875Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":8,"content":172,"source_name":173,"source_url":8,"published_at":174,"category":175,"cover_url":8,"hotness":12,"is_selected":32,"score":176,"score_detail":177,"sources":181,"tags":183,"primary_topic":187,"secondary_topics":193,"classification":194,"search_phrases":216,"slug":219,"view_count":203,"doi":8,"paper":8,"created_at":220},3711,"黄淮海气候韧性农业关键技术在山东泰安集成示范","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F572040.shtm","9月19日中国农业科学院重大科技任务黄淮海气候韧性农业关键技术研发与示范现场观摩会在山东泰安召开。围绕精准预警、韧性品种、韧性农田、防控减损、快速重建五维联动技术体系，开展气象灾害监测预警决策平台与无人机、农业机器人、智能灌排协同集成应用。","9月19日，中国农业科学院重大科技任务“黄淮海气候韧性农业关键技术研发与示范”现场观摩及任务推进研讨会在山东省泰安市召开。会上集中展示了夏玉米抗逆品种、韧性农田、抗逆产品、智能装备及技术集成应用，交流研究进展，为黄淮海粮食主产区防灾减损、稳产增效提供科技支撑。会议由中国农业科学院农业环境与可持续发展研究所组织。\n\n科技赋能抗逆增产——黄淮海气候韧性农业抗逆产品制剂与品种展示。中国农科院供图\n\n黄淮海是我国小麦、玉米重要产区，高温热害、旱涝急转、连阴雨渍涝等灾害给粮食稳产带来挑战。任务团队围绕“精准预警—韧性品种—韧性农田—防控减损—快速重建”五维联动技术体系，将灾害预警与品种选用、农田改良、栽培管理和应急措施相结合，着力提升农业生产灾前预防、灾中减损和灾后恢复能力。\n\n在洼里村示范区，与会代表实地观摩生态基础设施、技术验证试验和集成示范田，听取示范区建设及关键技术应用情况介绍。示范区依托已有高标准农田，将生态沟渠、灌排一体、水肥一体化等设施与抗逆品种、农艺调控、应急防控等技术配套应用，探索工程设施与农艺措施相结合的韧性农田建设路径。在技术验证和集成示范田，任务团队通过单项技术与集成技术的对比试验，评价不同技术组合的抗逆减损效果，为优化夏玉米全生育期防灾措施提供依据。\n\n围绕打造“天空地”一体化农业防灾减灾智慧体系，任务团队将气象灾害监测预警决策平台与田间监测、无人机、农业机器人、智能灌排等技术装备协同集成，探索形成“灾害感知—智能研判—精准决策—快速响应”的智慧防灾链条。平台可开展高温、干旱等气象灾害风险识别和防控措施推送，无人机及农业机器人可开展灾情勘查、精准施肥、抗逆喷施和应急作业，智能灌排装备根据田间水分状况开展快速调控，推动农业防灾减灾由经验判断、被动应对向智能感知、主动防控转变。\n\n智慧引领精准防控——“天空地”一体化农业智能机器人装备演示。中国农科院供图\n\n此次观摩集中呈现了重大任务4个子任务协同攻关的阶段性进展。任务团队将灾害智能监测评估、韧性新种质与耕作栽培、韧性农田与抗逆产品等研究成果集成应用于田间，推动“天上看、地上测、平台算、装备干”相互衔接，逐步形成从灾前预警、灾中防控到灾后恢复的全链条技术体系，促进单项技术向系统化、智能化综合防控方案转变。\n\n会议期间，任务团队汇报了总体实施进展和各子任务研究情况，与会专家围绕技术集成效果、区域适配和示范推广等进行交流，并就后续任务实施提出意见建议。下一步，任务团队将继续开展黄淮海不同区域的技术验证与模式优化，推进标准化技术规程编制，推动成熟适用技术推广应用。\n\n有关管理部门、任务咨询专家、涉农高校和科研院所，以及当地农业农村部门、气象部门、科技部门、示范基地、新型农业经营主体和任务团队代表共50余人参加会议。\n\n版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。","科学网","2026-09-24T01:29:00Z","报道",75,{"impact":178,"substance":18,"depth":19,"authority":179,"freshness":68,"relevant":20,"comment":180},22,14,"国家级科研机构在黄淮海主产区集成示范气候韧性农业技术，含五维技术体系与天空地一体化智慧防灾链条，信息增量与专业深度较好，但属阶段性进展通报，时效性一般。",[182],{"name":173,"url":170},[25,184,28,185,186],"智能装备","黄淮海","气候韧性农业",{"code":188,"slug":189,"name":190,"path":191,"parent_code":192},"F6","agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots","F",[],{"status":98,"locked":32,"version":33,"method":34,"evidence":195},{"candidates":196,"agricultural_evidence":13},[197,202,207,212],{"code":188,"score":51,"supported":13,"title":198,"abstract":199,"tags":201},[],[200],"农业机器人",[],{"code":38,"score":203,"supported":32,"title":204,"abstract":205,"tags":206},2,[],[],[186,28],{"code":208,"score":20,"supported":32,"title":209,"abstract":210,"tags":211},"F5",[],[],[184],{"code":57,"score":20,"supported":32,"title":213,"abstract":214,"tags":215},[],[],[25],[217,218],"中国农科院 黄淮海 气候韧性农业","泰安 夏玉米 抗逆品种","中国农科院黄淮海气候韧性农业-3711","2026-09-29T00:08:22.375819Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":8,"content":226,"source_name":227,"source_url":8,"published_at":228,"category":175,"cover_url":8,"hotness":12,"is_selected":32,"score":229,"score_detail":230,"sources":234,"tags":236,"primary_topic":240,"secondary_topics":245,"classification":246,"search_phrases":273,"slug":276,"view_count":203,"doi":8,"paper":8,"created_at":277},1839,"秋粮主产区打好田间管理组合拳 分类施策全力以赴确保粮食丰产归仓","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7681217168805052943\u002F","山东德州天衢新区赵虎镇1300亩玉米田铺设毛细血管般密集滴灌带，应用水肥一体化节水节肥均30%，预计亩均增产300斤；河南中部农田在9190万元救灾资金支撑下运用多光谱无人机进行智能体检和飞防；财政部和农业农村部已下达农业生产防灾救灾资金20.37亿元支持一喷多促。","## 秋粮主产区打好田间管理“组合拳” 分类施策全力以赴确保粮食丰产归仓\n\n2026-09-03 16:05·[北青网](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAkBInTefEXGPcS0avMIcyzcyDIb8T8hL6gUlrrWDJxIw\u002F?source=tuwen_detail)\n\n央视网消息：眼下，全国大部分秋粮正值灌浆期，主产区分类施策，加强田间管理，全力夺取丰收。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F65a56d2a6a6b4e34b08e7aea1684de39~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789430857&x-signature=zP8NhPM%2B5KRq0Fsi%2BUZYNgzfw6A%3D)\n农业农村部最新农情调度显示，2026年，全国秋粮播种面积预计稳中略增。目前，除局部地区受灾外，大部分秋粮长势良好。 在山东德州天衢新区赵虎镇的高标准农田里，这片1300亩的玉米田里铺设着如毛细血管般密集的黑色滴灌带，在手机App上点开开关，滴灌带可以精准地将水肥送到玉米根系。 2026年，山东大力推广水肥一体化技术，面积达到930万亩，增产趋势明显。 山东省德州市天衢新区赵虎镇种植户刘杰超介绍，他们应用水肥一体化，节水节肥都在30%，预计产量每亩会增加300斤，整体收益大概会多收入39万元。 作为粮油作物大面积单产提升的关键举措，全国水肥一体化应用面积，在2025年达到8800万亩的基础上，2026年持续增加。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fbed4b5aa6d903868be667ba1c533bd7e~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789430857&x-signature=AkUhh%2Ffb4JUI87EPkj7z2nxvgns%3D)\n在黑龙江宝清县，65厘米的小垄拓宽成了110厘米的大垄，不仅种得更多，分布也更均匀，光、热、水、肥利用率同步提升，作物长势明显优于周边地块。 黑龙江省双鸭山市宝清县五九七农场有限公司农业发展部副部长李龙介绍，这块地每亩播量是6000株，保苗率在97%以上。 受前期降雨影响，河南中部部分农田受灾。当地在用好9190万元中央农业生产救灾资金的基础上，派出6个专家组指导农田恢复生产。在漯河，搭载高清成像的多光谱无 人机，对受灾地块进行了智能“体检”，随后开展无 人机飞防预防病虫害滋生扩散。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fed7be700bf580f06660201a6cec4ae8c~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1789430857&x-signature=2Z5OzHZB9y%2FbccgnAd0pFkL6ufU%3D)\n目前，距离全国秋粮大面积收获还有不到一个月的时间。受台风多发以及高温高湿天气的影响，南方水稻主产区、黄淮海主产区病虫害发生风险增加，农业农村部指导各地做好病虫害防治，并采取叶面喷施等防灾减灾稳产增产技术，全力以赴确保秋粮安全成熟。","央视网\u002F北青网 2026-09-03","2026-09-03T08:05:00Z",77,{"impact":231,"substance":81,"depth":19,"authority":39,"freshness":232,"relevant":20,"comment":233},25,5,"全国秋粮主产区田间管理综合报道，涵盖水肥一体化、大垄密植等增产技术，数据详实，来源权威，时效性稍差。",[235],{"name":227,"url":224},[25,28,237,238,239],"秋粮生产","水肥一体化","田间管理",{"code":241,"slug":242,"name":243,"path":244,"parent_code":192},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers",[],{"status":98,"locked":32,"version":33,"method":34,"evidence":247},{"candidates":248,"agricultural_evidence":13},[249,254,260,264,268],{"code":241,"score":250,"supported":13,"title":251,"abstract":252,"tags":253},4,[],[238],[238],{"code":255,"score":51,"supported":13,"title":256,"abstract":257,"tags":259},"A3",[],[258],"玉米",[],{"code":38,"score":20,"supported":32,"title":261,"abstract":262,"tags":263},[],[],[28],{"code":57,"score":20,"supported":32,"title":265,"abstract":266,"tags":267},[],[],[25],{"code":269,"score":20,"supported":32,"title":270,"abstract":271,"tags":272},"J4",[],[],[237],[274,275],"水肥一体化 智慧农业 田间管理 秋粮生产","水肥一体化 智慧农业","水肥一体化智慧农业田间管理秋粮生产-1839","2026-09-08T00:07:11.044134Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":8,"content":8,"source_name":283,"source_url":8,"published_at":284,"category":175,"cover_url":8,"hotness":12,"is_selected":13,"score":285,"score_detail":286,"sources":289,"tags":291,"primary_topic":293,"secondary_topics":298,"classification":299,"search_phrases":345,"slug":348,"view_count":250,"doi":8,"paper":8,"created_at":349},1542,"国务院副总理刘国中赴内蒙古调研：紧盯秋粮田管、推动智慧农业发展、抓好防灾减灾","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260904\u002F20260904_1\u002Fnmrb_20260904_13396_1_2095622475098460171.html","据新华社呼和浩特 9 月 3 日电，中共中央政治局委员、国务院副总理刘国中 9 月 1 日至 3 日赴内蒙古通辽科左中旗等地调研。他强调，秋粮占全年粮食产量 3\u002F4，要分区分类科学开展田管、抓紧落实\"一喷多促\"、推进良田良种良机良法集成增效；立足抗灾夺丰收，加强监测预警与应急农机具储备调度；发挥农民合作社等新型经营主体作用，发展社会化服务；要加快人工智能运用和智慧农业发展，积极选育突破性品种、推广密植精准调控等先进适用技术、加力研发高端智能与丘陵山区适用农机装备；要稳慎推进第二轮土地承包到期后再延长 30 年试点；强化农业节水增效，深化农业水价综合改革、推广水肥一体化、浅埋滴灌。","农民日报头版头条","2026-09-03T22:00:00Z",90,{"impact":16,"substance":178,"depth":287,"authority":19,"freshness":84,"relevant":20,"comment":288},16,"副总理调研强调智慧农业与防灾减灾，政策导向明确，信息权威且时效性强。",[290],{"name":283,"url":281},[25,46,292,28,237],"高标准农田",{"code":38,"slug":294,"name":295,"path":296,"parent_code":297},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":98,"locked":32,"version":33,"method":34,"evidence":300},{"candidates":301,"agricultural_evidence":13},[302,306,310,317,321,326,332,337,341],{"code":57,"score":39,"supported":13,"title":303,"abstract":304,"tags":305},[25],[25],[25],{"code":38,"score":84,"supported":13,"title":307,"abstract":308,"tags":309},[28],[],[28],{"code":311,"score":68,"supported":13,"title":312,"abstract":313,"tags":316},"G2",[],[314,315],"农民合作社","社会化服务",[],{"code":241,"score":51,"supported":13,"title":318,"abstract":319,"tags":320},[],[238],[],{"code":208,"score":51,"supported":13,"title":322,"abstract":323,"tags":325},[],[324],"农机",[],{"code":327,"score":51,"supported":13,"title":328,"abstract":329,"tags":331},"H3",[],[330],"农业节水",[],{"code":333,"score":20,"supported":32,"title":334,"abstract":335,"tags":336},"H1",[],[],[292],{"code":44,"score":20,"supported":32,"title":338,"abstract":339,"tags":340},[],[],[46],{"code":269,"score":20,"supported":32,"title":342,"abstract":343,"tags":344},[],[],[237],[346,347],"农业人工智能 高标准农田 智慧农业 秋粮生产","农业人工智能 高标准农田","农业人工智能高标准农田智慧农业秋粮生产-1542","2026-09-04T00:05:29.693313Z",{"id":351,"title":352,"url":353,"summary":354,"summary_zh":355,"content":8,"source_name":356,"source_url":353,"published_at":357,"category":78,"cover_url":8,"hotness":12,"is_selected":32,"score":229,"score_detail":358,"sources":360,"tags":362,"primary_topic":364,"secondary_topics":365,"classification":366,"search_phrases":388,"slug":391,"view_count":20,"doi":392,"paper":393,"created_at":411},1437,"SFNL-Former: A Novel Spatial-Frequency Non-local Network for Precipitation Forecast","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rcradv.2026.200381","ABSTRACT Accurate, high-resolution precipitation forecast is vital for urban disaster mitigation and agriculture, underpinning fine-grained social management. Furthermore, as the primary input to the terrestrial hydrological cycle, its accurate quantification is essential for sustainable water resource management and efficient circulation. However, this task remains highly challenging due to the complex spatiotemporal and non-linear dynamical evolution inherent in atmospheric processes. Deep learning has been extensively applied to weather forecast through CNN-RNN or Transformer architectures, yet these methods often struggle to capture global meteorological motions while simultaneously preserving the fine-grained details of localized strong convection. Furthermore, they are prone to producing blurred predictions or exhibiting high false alarm rates when encountering extreme precipitation events characterized by long-tailed distributions. To address these issues, we propose the Spatial-Frequency and Non-Local Former Network (SFNL-Former), which accurately predicts complex precipitation evolution via explicit spatial-frequency domain feature separation and efficient long-range spatiotemporal modeling. A Spatial-Frequency Domain Attention (SFDA) encoder is designed to separately extract low-frequency global backgrounds and high-frequency local textures of precipitation fields. Furthermore, the Locality-Sensitive Hashing (LSH) based Non-Local Sparse-Aware Transformer (NLSAT) is introduced to capture long-range spatiotemporal dependencies with linear complexity. Experiments on real-world datasets demonstrate that SFNL-Former outperforms existing baseline methods across multiple metrics and spatial scales. Specifically, under challenging high-intensity precipitation thresholds, the model improves the Critical Success Index and substantially reduces the False Alarm Rate, while maintaining optimal prediction coherence. Consequently, the framework enables critical decision support for proactive disaster response, fostering more resilient social management systems under future extreme weather scenarios.","准确、高分辨率的降水预报对于城市灾害缓解和农业至关重要，是精细化管理的基础。此外，作为陆地水文循环的主要输入，其精确量化对于可持续水资源管理和高效循环至关重要。然而，由于大气过程固有的复杂时空和非线性动力学演变，这一任务仍极具挑战性。深度学习已通过CNN-RNN或Transformer架构广泛应用于天气预报，但这些方法往往难以在捕捉全球气象运动的同时保留局部强对流的细粒度细节。此外，在面对以长尾分布为特征的极端降水事件时，它们容易产生模糊预测或表现出高误报率。为解决这些问题，我们提出了空间频率与非局部Former网络（SFNL-Former），通过显式的空间频率域特征分离和高效的长距离时空建模，准确预测复杂的降水演变。设计了一种空间频率域注意力（SFDA）编码器，分别提取降水场的低频全局背景和高频局部纹理。此外，引入了基于局部敏感哈希（LSH）的非局部稀疏感知Transformer（NLSAT），以线性复杂度捕捉长距离时空依赖性。在真实数据集上的实验表明，SFNL-Former在多个指标和空间尺度上均优于现有基线方法。具体而言，在具有挑战性的高强度降水阈值下，该模型提高了关键成功指数并大幅降低了误报率，同时保持了最优的预测一致性。因此，该框架能够为主动灾害响应提供关键决策支持，在未来极端天气情景下促进更具韧性的社会管理系统。","Resources Conservation & Recycling Advances","2026-09-01T00:00:00Z",{"impact":18,"substance":178,"depth":18,"authority":39,"freshness":114,"relevant":20,"comment":359},"论文提出SFNL-Former模型提升降水预报精度，对农业防灾有应用价值，方法新颖，数据可靠。",[361],{"name":356,"url":353},[25,46,28,109,363],"降水预报",{"code":44,"slug":92,"name":93,"path":94,"parent_code":95},[],{"status":98,"locked":32,"version":33,"method":34,"evidence":367},{"candidates":368,"agricultural_evidence":13},[369,374,380,384],{"code":44,"score":370,"supported":13,"title":371,"abstract":372,"tags":373},11,[],[109,110,111],[46,109],{"code":327,"score":68,"supported":13,"title":375,"abstract":376,"tags":379},[],[377,378],"水资源","water resource",[],{"code":38,"score":20,"supported":32,"title":381,"abstract":382,"tags":383},[],[],[28],{"code":57,"score":20,"supported":32,"title":385,"abstract":386,"tags":387},[],[],[25],[389,390],"农业人工智能 智慧农业 深度学习 防灾减灾","农业人工智能 智慧农业","农业人工智能智慧农业深度学习防灾减灾-1437","10.1016\u002Fj.rcradv.2026.200381",{"doi":392,"openalex_id":394,"authors":395,"venue":356,"cited_by_count":142,"oa_url":404,"card":405,"direction":410,"ingested_from":165},"W7204864017",[396,398,401],{"name":397,"orcid":8},"Hanjie Wang",{"name":399,"orcid":400},"Xinyue Mo","https:\u002F\u002Forcid.org\u002F0000-0002-4685-3645",{"name":402,"orcid":403},"Huan Li","https:\u002F\u002Forcid.org\u002F0000-0002-7683-2589","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2667378926000702\u002Fpdf",{"tldr":406,"method":407,"finding":408,"direction":162,"opportunity":409},"提出SFNL-Former网络，通过空间-频率分离和长程建模提升降水预报精度。","空间-频率域注意力编码器与基于LSH的非局部稀疏感知Transformer。","在高强度降水阈值下，CSI提升且误报率显著降低，预测一致性最优。","可探索将SFNL-Former应用于农业区域精细化降水预报，结合多源数据提升极端天气下的农业灾害预警能力。","农业遥感与作物表型","2026-09-02T23:30:51.395339Z",{"id":413,"title":414,"url":415,"summary":416,"summary_zh":8,"content":417,"source_name":418,"source_url":8,"published_at":419,"category":175,"cover_url":8,"hotness":12,"is_selected":32,"score":420,"score_detail":421,"sources":423,"tags":425,"primary_topic":428,"secondary_topics":429,"classification":430,"search_phrases":442,"slug":445,"view_count":250,"doi":8,"paper":8,"created_at":446},1342,"新华日报关注江苏近 20 个农业大模型落地：\"光伏慧农\"\"瑞雪\"\"豆好\"智能体深耕田间","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-09-02\u002Fdoc-iniqkkph4632699.shtml","江苏省农业科学院已研发\"光伏慧农\"（新能源设施农业）、\"瑞雪\"（地膜领域全球首个垂直大语言模型，整合 2800 余个县域数据）、\"豆好\"（江苏农科院联合浙江大学研发，绿豆智能育种）等模型。江苏目前已有近 20 个农业大模型及智能体投入研发或应用，数量和密度居全国前列。","*   [新浪首页](http:\u002F\u002Fwww.sina.com.cn\u002F)\n*   [新闻](http:\u002F\u002Fnews.sina.com.cn\u002F)\n*   [体育](http:\u002F\u002Fsports.sina.com.cn\u002F)\n*   [财经](http:\u002F\u002Ffinance.sina.com.cn\u002F)\n*   [娱乐](http:\u002F\u002Fent.sina.com.cn\u002F)\n*   [科技](http:\u002F\u002Ftech.sina.com.cn\u002F)\n*   [博客](http:\u002F\u002Fblog.sina.com.cn\u002F)\n*   [图片](http:\u002F\u002Fphoto.sina.com.cn\u002F)\n*   [专栏](http:\u002F\u002Fzhuanlan.sina.com.cn\u002F)\n*   [更多](https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-09-02\u002Fdoc-iniqkkph4632699.shtml#)\n\n*   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王徐霖\n\n过去，育种专家寻找一个优良基因，要在海量资料和多年试验中反复比对；农户面对田里的异常状况，更多依靠经验判断。如今，人工智能正在成为农业科研和生产的新助手。\n\n近日，记者走访我省科研院所和农业科技企业发现，一批深耕细分领域的农业垂直大模型正快速落地，覆盖种植、养殖、育种、全链管理等环节，为传统农业装上数字化“大脑”。\n\n据不完全统计，江苏目前已有近20个农业大模型及智能体投入研发或应用，数量和密度居全国前列。然而，要真正服务万千农户，仍需跨越数据、成本、门槛、装备等多道坎。\n\n大模型走进田间棚舍\n\n在大模型操作界面，选择地区盐城市射阳县，选定农作物小麦，短短几秒，一套完整的光伏高效发电方案跃然屏幕：光伏板倾斜多少角度、板与板之间留出多大距离，同时算出小麦大概产量、光伏发电收益以及整体经济效益。8月31日，在江苏省农业科学院农业设施与装备研究所实验室内，新能源设施农业创新团队负责人鲍恩财副研究员向记者演示“光伏慧农”模型。“过去在田地里安装光伏发电板之前，工程师和农业技术人员得反复开会讨论，全凭经验估摸着调整，不仅费时，还很难把种地和发电两方面兼顾好。”鲍恩财说，这套模型不会一味偏向发电，也不是只顾庄稼，而是帮两者找一个最合适的平衡点，项目建设方在动工之前就能看得明明白白：多发一点电，粮食产量会少多少；想要保障收成，发电收益又会损失多少。\n\n在江苏省农科院农用新材料创新团队，“瑞雪”大模型把智能方案延伸到了地膜领域。团队负责人徐磊研究员介绍，长期以来，普通塑料地膜带来的白色污染是农业绿色发展的痛点，降解地膜又因各地气候、土壤差异大，基层很难精准选型。“瑞雪”作为全球地膜领域首个垂直大语言模型，整合全国2800余个县域的长时序气象与土壤数据，输入县域和作物就能输出适配的材料配方、最佳厚度和全周期性能预测，实现“一区一膜、一作一策”，不用再靠经验凭感觉选地膜。\n\n育种赛道上，由江苏省农科院联合浙江大学共同研发的“豆好”智能体于近日发布。江苏省农科院经济作物研究所豆类作物创新团队首席助理、研究员袁星星介绍，1500份绿豆种质数据收集历时六年，可靠性很强。国家食用豆产业技术体系首席科学家、省农科院副院长陈新表示，借助这套模型可以少走育种弯路，将育种时间缩短到两三年。\n\n养殖场里，大模型正在替人巡棚舍。8月30日，在邳州市燕子埠镇一处规模化笼养肉鸭场，一阵警报声响起，几个标着红色预警的笼位号出现在显示器上，提示雏鸭是否死亡。江苏省农业科学院农业设施与装备研究所副所长柏宗春研究员给记者算了一笔账：传统模式下，一栋饲养3万只肉鸭的标准鸭舍，工作人员每天早中晚都要巡棚，每次用时45到60分钟，一天下来光巡查就要三个小时。应用这套模型后，一轮巡查可以缩短一个小时，还能精准定位异常位置，有四栋鸭舍的养殖场一天能省出一个人的全天工作量。\n\n田埂上，智慧工具正在服务万千农户。苏州吴江的种田大户老夏举着手机，对着自家田块拍了两张照片，上传到“作物云”小程序，没过几分钟，系统就完成了作物识别和面积测算，原本要半个月才能办完的农业补贴申报，在家门口就搞定了。这个小程序背后，是中科禾信遥感科技（苏州）有限公司自主研发的金垄大模型。该公司总经理陆洲告诉记者，平台利用人工智能技术与卫星遥感影像协同分析，除了补贴申报，还能精准给出灌溉、施肥、病虫害防治的建议，目前已在苏州全市推广。\n\n百模竞发的底气在哪\n\n把视野放到全国，农业大模型的赛道上百舸争流。中国农业大学“神农大模型4.0”覆盖全国31个省区市，服务百万农户，落地应用超500万亩农田。上海推出国内首个生物育种大模型“丰登”。安徽农业大学发布大豆垂直大模型“丰菽”2.0和梨产业“棃想”大模型。\n\n与这些省份相比，江苏的差异化优势十分鲜明。“‘神农’走的是通用底座路线，‘龙江农知’打的是区域特色牌，江苏选择的则是‘垂直深耕加产业落地’的路径。”江苏省农学会专家表示，我省不追求一个大模型包打天下，而是针对地膜、绿豆、肉鸭、光伏农业、澳龙养殖等极其细分的场景，一个个啃硬骨头。比如全球地膜领域首个垂直大语言模型“瑞雪”、业界首个绿豆智能体“豆好”、江苏省首款通过国家备案的农业垂直大模型“谷语”、国内首个落地农业生产决策的大模型“新犁”，这些“首个”的背后，是江苏科研力量和产业生态的厚积薄发。\n\n江苏农业垂直大模型“百模竞发”的底气还来自更多方面。首先是科研力量雄厚。南京农业大学今年1月发布国内首个面向通用农业领域的开源垂直大语言模型“司农”，构建起总量超40亿token的农业基础数据集。随后又发布世界首个开放式梨行业大语言模型“问梨”和面向农机装备的“司农智机”；江苏省农科院在光伏农业、地膜、绿豆育种、水禽养殖等方向多点突破，形成了完整的垂直模型矩阵。其次是产业基础扎实，苏州已形成人工智能全产业链体系，江苏数字经济核心产业增加值占GDP比重超11%，居全国第二。最后是农业底子好。江苏农业科技进步贡献率超72%，农作物耕种收综合机械化率约90%，两项指标均高出全国平均水平10个百分点，为大模型落地提供了难得的规模化、标准化场景。\n\n要“接地气”还得迈过几道坎\n\n然而热闹的表象之下，农业大模型“不接地气”的问题依然突出。记者在采访中发现，当前不少农业大模型还停留在实验室和示范基地里，真正被普通农户日常使用的并不多。用业内人士的话说，“只是盆景，还不是风景”。\n\n“第一道坎是数据。”专家解释说，大模型要有海量优质数据支持，但农业面对的是分散的主体、细碎的地块、琐碎的农事，土壤、气象、种业等数据分属不同平台，没有统一标准，“数据孤岛”现象普遍。小农户田间真实样本短缺，有些算法只适配平整大田，山区、零散地块精准度大打折扣。\n\n第二道坎是成本。“大型智能设备投入成本高，农业周期长、比较效益低，短期难形成完整商业模式，适合散户的低价简易产品供给不足。”专家表示。\n\n第三道坎是“使用门槛”，农村老人数字操作能力弱，基层缺少既懂农业又懂技术的复合型人才。“一开始说大模型，农户们听不懂，我们就说这是个智能助手，有啥不懂的就问，手把手教农民用一次两次，他们就学会了。”中国农业大学王耀君说，这种基于人际信任的技术扩散，远比任何广告都有效。\n\n最后一道坎是装备。农业很多领域连机械化都没完全实现，除农业无人机领跑全球外，其他智慧农机仍处于起步阶段。人工智能不是万能灵药，不能脱离物联网、大数据、智能装备单独作用于农业。\n\n如何让“最强大脑”真正接上“地气”？江苏正在探索答案。苏州今年8月发布三项“人工智能加农业”团体标准，覆盖水稻苗情遥感、养殖水质监测、设施园艺数据采集，从标准层面破解数据采集不统一的难题。苏州还全面推行“揭榜挂帅”机制，让企业和科研团队围绕真实农业场景攻关，瞄准低成本、易操作，降低使用门槛。\n\n海安发布[智慧农业](https:\u002F\u002Ffinance.sina.com.cn\u002Frealstock\u002Fcompany\u002Fsz000816\u002Fnc.shtml)(3.050, 0.11, 3.74%)三年行动计划，明确到2028年农业生产信息化率提升至65%，打造千亩级无人农场。扬州瑞丰信息的做法是把知识库做深做透，“这个知识库不是从网上扒的，是我们跟农技专家按照品种和区域磨出来的。”企业负责人丁永华说，每一条知识都经过专家验证，这样农户用起来才放心。\n\n受访专家表示，农业人工智能的生命力在于扎根实际场景，持续积累本地化数据，不断优化迭代。要以真实场景为目标，加速技术装备熟化，培育领军企业，引导科研与制造合作，多方协作、长期投入。同时也要清醒认识到，农业是慢变量，说人工智能重塑农业也好，变革农业也罢，都不能浮躁，更不能一哄而起、一哄而散。\n\n体验完实验室里的科研模型和田埂上的手机应用，记者直观感受到，江苏农业大模型的应用范围越来越广，这些大模型不是飘在天上的概念，而是实实在在解决种地、养殖具体麻烦的生产工具。装上“最强大脑”的土地，未来还会孕育出更多产业发展的新可能。但要让盆景真正变成风景，需要脚踏实地、久久为功。\n\n![Image 10](http:\u002F\u002Fqr.weibo.cn\u002Finf\u002Fgen?api_key=bdccdb65d34320c10b65cdf6c3503a53&data=https%3A%2F%2Ffinance.sina.cn%2Fapp%2FQRtoSFA.shtml%3Fsudatrackvalue%3Dcaitou_inQRcode_PcBottom%26schema%3Dtype%253D100%26docid%3Dniqkkph4632699%26sinaglobal%3D34.34.225.131_1788307664.155499&datetime=nocheck&level=H&output_type=img&redirect=0&size=100&type=url&sign=4097b80a2212a80c67d959c672ecef3b)\n\n海量资讯、精准解读，尽在新浪财经APP\n\n[![Image 11: 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23](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F46b04612def6b8bfdff5a97990cc9b62.png)开播时间 | 9-01 16:00\n\n##### 永赢基金：基金经理任桀面对面，深聊永赢成长先锋思路\n\n![Image 24](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202609\u002F47ce17d8f8f529a2ece50bb8d656c6aa.png)开播时间 | 9-01 16:00\n\n##### 光大证券：社保基金公布业绩，累计收益超2.2万亿元\n\n![Image 25](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202609\u002F1db7efd9b8a2d47b6ce80b2afb37cad2.png)开播时间 | 9-01 15:30\n\n##### 华宝基金 【每周策略】银行起势！避风港or新主线？\n\n![Image 26](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F9456d671273a9e8b93050ea4844875ec.png)开播时间 | 9-01 15:00\n\n##### 国投瑞银：投资攻守道 | 震荡市的“攻守平衡”\n\n![Image 27](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202609\u002F6fb9399b2bfc03c8dede6fa3de9dd359.png)开播时间 | 9-01 14:00\n\n##### 银华基金 【财富风向标 第16期】 又接近4000关口，如何应对？\n\n![Image 28](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202609\u002Fac14c2402d8632096fd4c0711e6e2ace.png)开播时间 | 9-01 13:30\n\n##### 富国基金：粮价上涨！农业如何布局？\n\n![Image 29](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F6e1897a8b7c98bcdd14f1237926f62e3.png)开播时间 | 9-01 11:00\n\n##### 永赢基金：AI算力硬件解析，哪些环节值得关注？\n\n![Image 30](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F142511efc4a2094bbf6873878256487d.png)开播时间 | 9-01 11:00\n\n##### 国泰海通：9月开门红？如何蓄力？\n\n![Image 31](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002Fff99afa0ea46034ea13841949877859b.png)开播时间 | 9-01 20:00\n\n##### 博时基金：美债再起波澜！如何重构资产配置？\n\n![Image 32](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202609\u002F5a9740d426bd45760fc304afc6fc5ded.png)开播时间 | 9-01 20:00\n\n##### 浙商固收 | 低利差下的信用ETF策略思考\n\n![Image 33](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F46b04612def6b8bfdff5a97990cc9b62.png)开播时间 | 9-01 16:00\n\n##### 永赢基金：基金经理任桀面对面，深聊永赢成长先锋思路\n\n![Image 34](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202609\u002F47ce17d8f8f529a2ece50bb8d656c6aa.png)开播时间 | 9-01 16:00\n\n##### 光大证券：社保基金公布业绩，累计收益超2.2万亿元\n\n![Image 35](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202609\u002F1db7efd9b8a2d47b6ce80b2afb37cad2.png)开播时间 | 9-01 15:30\n\n##### 华宝基金 【每周策略】银行起势！避风港or新主线？\n\n![Image 36](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F9456d671273a9e8b93050ea4844875ec.png)开播时间 | 9-01 15:00\n\n##### 国投瑞银：投资攻守道 | 震荡市的“攻守平衡”\n\n![Image 37](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202609\u002F6fb9399b2bfc03c8dede6fa3de9dd359.png)开播时间 | 9-01 14:00\n\n##### 银华基金 【财富风向标 第16期】 又接近4000关口，如何应对？\n\n![Image 38](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202609\u002Fac14c2402d8632096fd4c0711e6e2ace.png)开播时间 | 9-01 13:30\n\n##### 富国基金：粮价上涨！农业如何布局？\n\n![Image 39](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F6e1897a8b7c98bcdd14f1237926f62e3.png)开播时间 | 9-01 11:00\n\n##### 永赢基金：AI算力硬件解析，哪些环节值得关注？\n\n![Image 40](https:\u002F\u002Frls3.cj.sina.com.cn\u002Fproducts\u002F202608\u002F142511efc4a2094bbf6873878256487d.png)开播时间 | 9-01 11:00\n\n##### 国泰海通：9月开门红？如何蓄力？\n\n[上一页](javascript:;)[下一页](javascript:;)\n\n1\u002F10\n\n## 热门推荐\n\n_收起_\n\n![Image 41: 新浪财经公众号](https:\u002F\u002Fn.sinaimg.cn\u002Ffinance\u002F72219a70\u002F20180103\u002F_thumb_23666.png)\n\n新浪财经公众号\n24小时滚动播报最新的财经资讯和视频，更多粉丝福利扫描二维码关注（sinafinance）\n\n[相关新闻](javascript:;)\n\n[财经头条作者库](https:\u002F\u002Ffinance.sina.com.cn\u002Fcj\u002Fauthorlist.shtml?fr=pc_finarticle)\n\n## [投资研报](https:\u002F\u002Fdaxue.sina.cn\u002Fcategory\u002F130)扫码订阅\n\n*   [全球首款AI智能体手机：豆包手机9月上市！持有努比亚78.33%股权+算力业务加速放量+AI手机与游戏手机双线布局深化+端侧AI战略持续推进](https:\u002F\u002Fdaxue.sina.cn\u002Fdetail\u002Fgraphics\u002F1a3971a466489e1796dc092794f59956\u002Ff1e48030a45f)09-01 12:38[火线电报会员专享](https:\u002F\u002Fdaxue.sina.cn\u002Fcolumn\u002F1a3971a466489e1796dc092794f59956)\n*   [英伟达+苹果+华为+光模块+CPO+先进封装+AI数据中心+商业航天，Q2增长强劲超预期，顶级投行大幅上调盈利预测！机构重点关注这种战略小金属](https:\u002F\u002Fdaxue.sina.cn\u002Fdetail\u002Fgraphics\u002F54a2c39021ef4ec89125cd681601b84d\u002F2a4f03c31b6f)09-01 12:00[机构内参会员专享](https:\u002F\u002Fdaxue.sina.cn\u002Fcolumn\u002F54a2c39021ef4ec89125cd681601b84d)\n*   [工信部部署AI应用服务商培育专项行动！算力基建+AI场景+华为生态；智能交互显示龙头，AI与全球化双驱动；全面接入AI+深化与华为等合作](https:\u002F\u002Fdaxue.sina.cn\u002Fdetail\u002Fgraphics\u002Ff5443bd63a8b4e858b9225ffa008a6c6\u002F86f5e49a879b)09-01 11:31[会选股会员专享](https:\u002F\u002Fdaxue.sina.cn\u002Fcolumn\u002Ff5443bd63a8b4e858b9225ffa008a6c6)\n\n## [](http:\u002F\u002Flive.finance.sina.com.cn\u002F)[股市直播](http:\u002F\u002Flive.finance.sina.com.cn\u002F)\n\n*   图文直播间\n*   视频直播间\n\n*   [![Image 42: 数字江恩](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002Fac791ff8\u002F20201116\u002FShuZiJiangEn.png\u002Fw120h120f1q80liv.png)### 数字江恩 2026-09-01 23:21:07 大盘突破3930实现了一次向上扩展，目前是扩展末端，等待确认是回踩还是实现新的向上扩展。当下的核心压力是4258-4175轮峰线，若突破则实现第二次向上扩展，反弹的时间和空间都会延长。但只要达不到这个再次向上扩展条件，那么大盘都将要退守3950区间。今天等待市场选择](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F2342117515)\n*   [![Image 43: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-09-01 23:15:31 【周三（9月2日）重点关注财经事件和经济数据】① 04:30 美国至8月28日当周API原油库存；② 09:30 澳大利亚第二季度GDP年率；③ 10:00 新西兰联储公布利率决议；④ 20:15 美国8月ADP就业人数；⑤ 21:45 加拿大央行公布利率决议；⑥ 22:00 美国7月工厂订单月率；⑦ 22:30 美国至8月28日当周EIA原油库存；⑧ 22:30 美国至8月28日当周EIA库欣原油库存；⑨ 22:30 美国至8月28日当周EIA战略石油储备库存；⑩ 22:30 加拿大央行行长麦克勒姆召开新闻发布会； 次日02:00 美联储公布经济状况褐皮书。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 44: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-09-01 23:14:49 =加入潜伏擒牛VIP，享四大顶级服务=【1】购买VIP自动加入私密小直播间！【2】每周3-5只超短金股调入调出服务，适合实时看盘的投资者！【3】每周一份高端内部绝密文章：包含近期布局、热点版块、指数预判！【4】每月2~3只高端中线金股服务！（VIP超短、中线个股均有涉足，让上班族也能跟上VIP节奏！）现月课7.5折，1288元！季课6.9折，3558元，续费季度更划算！新朋友可先月课体验！点网址，直接买，订购地址：【更多独家重磅股市观点请点击】【更多独家重磅股市观点请点击】](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 45: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-09-01 23:14:26 一、美伊关系恶化 石油飙升1、美军正在霍尔木兹海峡周边对伊朗目标实施打击，最大岛屿格什姆岛也传出爆炸声；2、伊朗方面称已向美基地发射导弹和无人机；3、伊朗重型导弹打击约旦美军营地。受此消息刺激，昨晚国际石油期货全线大涨，布伦特原油日内涨超4.6%，突破95美元\u002F桶；WTI原油期货涨幅扩大至5.7%，收涨5.20%。对A股市场来说，今天石油化工板块有望活跃，但对科技题材股是利空。二、字节新一代豆包手机将于九月发布这个九月有点热闹了，苹果首款折叠屏手机将于9月10日亮相；华为新一代三折叠、小米首款阔折叠都将紧随其后，现在豆包手机也出来了，据了解，这款手机为中兴通讯（sz000063）与字节跳动联合研发，该机将搭载豆包手机助手，定位全球首款AI智能体手机。对A股市场主要利好的是消费电子，苹果概念、折叠屏等。三、降温了！多股提示风险12天8板万向德农（sh600371），12天7板金健米业（sh600127），4连板新赛股份（sh600540）、3连板时代出版（sh600551）、国芳集团（sh601086）等，昨晚都提示了风险，7连板海鸥住工（sz002084）：如未来公司股价进一步异常上涨 公司可能申请停牌核查。炒股还是要听话的，大家注意高位题材股的风险吧，特别是马上要到了周末了，小心关黑屋跳水。四、其他消息：1、交易所制定L2行情使用规范 严禁“绕道抢跑”。主要是限制量化资金和机构资金抢跑，对散户是利好；2、美联储9月份加息概率由55%飙升至66%，欧美日债市遭遇抛售潮，黄金、白银期货大跌，美股三大股指全线回调；3、芯原股份（sh688521）：第三大股东大基金8月31日减持49.47万股公司股份 不再是持股5%以上股东。芯原股份（sh688521）是先进封装概念的龙头之一，以后国家大基金减持不用公告了！4、大肉签燧原科技（sh688801）今天申购，中一签可以赚30万左右，预祝大家都中签！考虑到外围环境的影响，今天大盘还有盘整，但有石油、化工等板块护盘，大盘不会大跌，题材股就难说了。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 46: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-09-01 23:14:03 我们经常建议大家将自己想象成那个管理者，你已经将市场管理成了风生水起，一路繁花似锦，难道生日那天不应该放烟，花，反而让大家难堪吗？所以，4000点会被站上，4080点也会被突破，但时间没有到，蛋，糕不能提前吃。这可能就是A股最朴素的时间管理哲学：把最绚烂的烟花，留到最该放的那天。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 47: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-09-01 23:12:37 到今年9月24日，是两周年生日。生日时间没到，如果提前把生日，蛋，糕吃了，生日那天喝西北风吗？](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 48: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-09-01 23:11:52 重点是现在距离9.24重要窗口还有时间，2024年9月24日，\"一行一局一会\"联合放了大招，开启了这轮牛市。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 49: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffi","新华日报 · 新浪财经 · 2026-09-02","2026-09-01T23:00:00Z",72,{"impact":81,"substance":18,"depth":19,"authority":12,"freshness":84,"relevant":20,"comment":422},"报道江苏近20个农业大模型落地，展示AI在田间应用的具体案例，具有省级示范意义。",[424],{"name":418,"url":415},[25,426,27,427],"农业大模型","江苏",{"code":44,"slug":92,"name":93,"path":94,"parent_code":95},[],{"status":98,"locked":32,"version":33,"method":34,"evidence":431},{"candidates":432,"agricultural_evidence":13},[433,438],{"code":44,"score":83,"supported":13,"title":434,"abstract":436,"tags":437},[435],"大模型",[435],[27,426],{"code":57,"score":20,"supported":32,"title":439,"abstract":440,"tags":441},[],[],[25],[443,444],"农业大模型 人工智能 智慧农业 江苏","农业大模型 人工智能","农业大模型人工智能智慧农业江苏-1342","2026-09-02T00:05:03.508272Z"]