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线应用提供坚实的理论基础和技术支撑。","西安工业大学","2026-10-09T19:27:16.128Z",[588,589],{"code":400,"name":401},{"code":590,"name":591},"GB-39","计算机、通信和其他电子设备制造业",[401,591],{"resultType":386,"id":594,"title":595,"abstract":596,"orgName":597,"province":495,"date":598,"trl":599,"transferMode":600,"keywords":392,"industry":601,"patentType":10,"legalStatus":10,"isOpenLicense":394,"isRevitalized":394,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":10,"ipc":10,"inventors":10,"tags":602,"tagNames":625},"cmv1bi41v00edbdd86zoz60af","宏星遥感大模型","现有的遥感影像人工标注样本效率低，而且数据数量、质量、空间分辨率参差不齐。且往往在局部区域采样，缺乏多样性和变异性，使得训练得到的模型难以扩展到全球尺度。因此，遥感的专业大模型要发展，应该让专业的大模型做专业的事，“不能有了ChatGPT，所有的需求都要泛化”。同时，大模型的泛化一定存在一个临界点，不能持续泛化。宏星遥感图像解译人工智能大模型（简称宏星大模型）具有卫星或无人机航拍遥感图像自动解译，遥感数据变化分析、特定目标检测与跟踪等智能化功能。首先，它通过对遥感图像的像素级精准分割，准确识别并标注出道路、建筑、绿地、河流、农田、森林等区域，输出的智能化数据对各种下游工作，如地图制作、国土资源测绘、城市与行业规划设计等，具有极高的应用价值。其次，宏星大模型能够实时解译出同一个地区在不同时间节点的遥感数据图斑变化情况，可以为土地违规使用检测与执法、环境破坏检测与执法、自然灾害范围检测与损失估计等工作提供客观的证据。另外，宏星大模型还具有自动检测、识别与跟踪特定目标的功能，如飞机、军舰、坦克、大炮、林火等，可广泛应用于军事情报分析、交通流量分析、灾害检测与预警等各种检测与跟踪任务。","西安交通大学","2026-10-09T19:27:13.592Z",4,"面议","人工智能与大数据",[603,606,609,612,613,614,615,618,621,624],{"code":604,"name":605},"CH-09","航空航天",{"code":607,"name":608},"CH-27","建筑",{"code":610,"name":611},"CH-28","交通",{"code":397,"name":398},{"code":463,"name":117},{"code":400,"name":401},{"code":616,"name":617},"GB-02","林业",{"code":619,"name":620},"GB-48","土木工程建筑业",{"code":622,"name":623},"GB-74","专业技术服务业",{"code":530,"name":531},[605,608,611,398,117,401,617,620,623,531],{"resultType":386,"id":627,"title":628,"abstract":629,"orgName":630,"province":495,"date":631,"trl":10,"transferMode":10,"keywords":392,"industry":10,"patentType":10,"legalStatus":10,"isOpenLicense":394,"isRevitalized":394,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":10,"ipc":10,"inventors":10,"tags":632,"tagNames":640},"cmv1bi3zm00dobdd8alylbwh3","非接触式生理信号感知技术","项目成果是基于非接触式多模态生理信号感知监测系列产品的研发，利用关联成像 与机器视觉技术实现心率、血压、呼吸率等生理信号的远程实时监测。这一创新技术突破了传统接 触式监测的局限，无需直接接触被测试者，减少了不适感和限制，为远程医疗、健康管理、公共安 全和国防安全等领域提供了全新的解决方案。技术的先进性体现在对多种生理指标的准确高效检测， 通过分析人脸皮肤颜色和运动的细微变化，即使在低光照、抗扰动和非视域环境下也能进行监测。 项目团队拥有深厚的技术积累和丰富经验，确保了技术的可靠性。","西北大学","2026-10-09T19:27:13.508Z",[633,634,635,636,637],{"code":511,"name":512},{"code":400,"name":401},{"code":521,"name":522},{"code":622,"name":623},{"code":638,"name":639},"GB-84","卫生",[512,401,522,623,639],{"resultType":642,"id":643,"title":644,"abstract":645,"orgName":646,"province":647,"date":648,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":10,"legalStatus":10,"isOpenLicense":394,"isRevitalized":394,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":649,"ipc":650,"inventors":651,"tags":652,"tagNames":660},"patent","cmv1bh73908disn4muudw4ygd","基于记忆合金的机翼多模态变体驱动机构及无人机","本申请公开了基于记忆合金的机翼多模态变体驱动机构及无人机，涉及无人机领域。至少三个锁定组件沿输出件的运动路径间隔设置；锁定组件包括记忆合金解锁件和锁销机构，在锁定状态下，锁销机构与设置在输出件上的销孔配合以限制输出件运动；记忆合金解锁件能在通电加热时产生动作，驱动锁销机构脱离销孔，使锁定组件切换至解锁状态；本申请实现了驱动单元的小型化与轻量化，同时通过沿路径设置的多个锁定组件，使同一驱动单元能精准、可靠地将机翼锁定于多个预定后掠角，并可逆向收折，满足多次飞行任务需求；协同控制策略提升动态性能，通过“预蓄力‑再解锁”时序，将解锁点与最大驱动力爆发点对齐，降低了作动冲击并提升了响应速度。","西北工业大学; 中国航空工业集团公司成都飞机设计研究所","陕西省","2026-10-09T18:46:46.821Z","CN202611047251.4","B64U30\u002F12;B64C3\u002F40;B64U30\u002F16","王骏; 程岩; 年鹏; 张卫红; 王昱森; 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曹磊磊; 褚晶; 曹盛力; 严飞; 张朵",[671,672,673],{"code":397,"name":398},{"code":400,"name":401},{"code":622,"name":623},[398,401,623],{"resultType":642,"id":676,"title":677,"abstract":678,"orgName":494,"province":647,"date":679,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":10,"legalStatus":10,"isOpenLicense":394,"isRevitalized":394,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":680,"ipc":681,"inventors":682,"tags":683,"tagNames":687},"cmv1bh6vj084tsn4mz60p5ugw","一种基于受激拉曼组织学的多模态组织细胞邻域识别方法","本发明涉及计算机视觉识别技术领域，具体涉及一种基于受激拉曼组织学（SRH）的多模态组织细胞邻域（TCNs）识别方法，该方法包括获取离体组织的SRH图像；对处理后的图像进行细胞分割，并进行细胞的特征提取获取每个细胞的结构化特征数据，根据结构化特征数据，基于跨模态注意力机制，进行多模态特征融合，获取每个细胞对应的融合特征矩阵；将组织细胞根据融合特征矩阵以及邻接矩阵，划分为不同的功能群组，每个群组对应一个组织细胞邻域。本发明在提供形态和空间信息之外，额外提供了关键的代谢功能信息，能显著提高TCNs识别的准确率和生物学可解释性。","2026-10-09T18:46:46.544Z","CN202610986496.7","G06V20\u002F69;G06T7\u002F00;G06V10\u002F80;G06V10\u002F46;G06T7\u002F62;G06V10\u002F26;G06V10\u002F77;G06V10\u002F54;G06V10\u002F82;G06V10\u002F762;G01N21\u002F65;G06V10\u002F422","祁亚峰; 施雅琦; 李文静; 吕锐婵; 徐涵; 巨子悦; 李碧霄",[684,685,686],{"code":397,"name":398},{"code":400,"name":401},{"code":590,"name":591},[398,401,591],{"resultType":642,"id":689,"title":690,"abstract":691,"orgName":692,"province":647,"date":693,"trl":10,"transferMode":10,"keywords":10,"industry":10,"patentType":10,"legalStatus":10,"isOpenLicense":394,"isRevitalized":394,"techMaturity":10,"starRating":10,"licenseFeeText":10,"patentNo":694,"ipc":695,"inventors":696,"tags":697,"tagNames":701},"cmv1bh6us0840sn4m9f2jewof","一种面向模态缺失场景的情感分析方法及装置","本发明公开了一种面向模态缺失场景的情感分析方法及装置，涉及人工智能与多模态数据处理技术领域。用于解决现有技术无法量化缺失不确定性、缺少特征校准锚点、优化策略僵化，造成对齐错乱与训练失衡的问题。该方法采集三类模态缺失序列原始数据，经处理得到模态基础特征矩阵；经不确定性估计块运算获得特征均值矩阵、模态分布方差矩阵，求解时间步置信权重并聚合生成全局语义共识矩阵作为全局语义锚点，完成注意力残差校准得到校准后单模态特征矩阵；依托单模态时序步的不确定标量方差确定相关权重与难度驱动动态重构损失，结合单模态预测偏差动态更新编码器梯度；经由多头交叉注意力融合后输出模型情感预测值。","西北工业大学","2026-10-09T18:46:46.517Z","CN202611056248.9","G06F18\u002F25;G06F123\u002F02;G06N3\u002F045;G06F18\u002F213;G06F18\u002F22;G06N3\u002F084","王震; 姜慎杰; 李向华; 刘祥凤; 吴连伟; 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