CN117648873B - 地面沉降预测方法、训练方法、装置、设备 - Google Patents
地面沉降预测方法、训练方法、装置、设备 Download PDFInfo
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- G06F30/00—Computer-aided design [CAD]
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Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111563135A (zh) * | 2020-03-31 | 2020-08-21 | 武汉大学 | 结合地形因子与神经网络的gm(1,3)模型城市地面沉降预测方法 |
CN112561191A (zh) * | 2020-12-22 | 2021-03-26 | 北京百度网讯科技有限公司 | 预测模型的训练、预测方法、装置、设备、程序和介质 |
CN113095562A (zh) * | 2021-04-07 | 2021-07-09 | 安徽天能清洁能源科技有限公司 | 基于卡尔曼滤波和lstm发电超短期预测方法及装置 |
CN113393032A (zh) * | 2021-06-15 | 2021-09-14 | 西安电子科技大学 | 基于重采样下的航迹循环预测方法 |
CN114881323A (zh) * | 2022-05-07 | 2022-08-09 | 沈阳工业大学 | 基于深度神经网络的基坑降水区地下水位预测与更新方法 |
CN115077474A (zh) * | 2022-06-14 | 2022-09-20 | 天津华北地质勘查总院 | 一种基于机器学习的地面沉降趋势预测方法及*** |
CN115526435A (zh) * | 2022-11-09 | 2022-12-27 | 上海交通大学 | 基于卡尔曼滤波的时空神经网络模型下的油井产量预测方法 |
CN117074951A (zh) * | 2023-07-18 | 2023-11-17 | 国网重庆市电力公司营销服务中心 | 锂电池荷电状态的估计方法、装置、设备及可读存储介质 |
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- 2024-01-30 CN CN202410121478.3A patent/CN117648873B/zh active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111563135A (zh) * | 2020-03-31 | 2020-08-21 | 武汉大学 | 结合地形因子与神经网络的gm(1,3)模型城市地面沉降预测方法 |
CN112561191A (zh) * | 2020-12-22 | 2021-03-26 | 北京百度网讯科技有限公司 | 预测模型的训练、预测方法、装置、设备、程序和介质 |
CN113095562A (zh) * | 2021-04-07 | 2021-07-09 | 安徽天能清洁能源科技有限公司 | 基于卡尔曼滤波和lstm发电超短期预测方法及装置 |
CN113393032A (zh) * | 2021-06-15 | 2021-09-14 | 西安电子科技大学 | 基于重采样下的航迹循环预测方法 |
CN114881323A (zh) * | 2022-05-07 | 2022-08-09 | 沈阳工业大学 | 基于深度神经网络的基坑降水区地下水位预测与更新方法 |
CN115077474A (zh) * | 2022-06-14 | 2022-09-20 | 天津华北地质勘查总院 | 一种基于机器学习的地面沉降趋势预测方法及*** |
CN115526435A (zh) * | 2022-11-09 | 2022-12-27 | 上海交通大学 | 基于卡尔曼滤波的时空神经网络模型下的油井产量预测方法 |
CN117074951A (zh) * | 2023-07-18 | 2023-11-17 | 国网重庆市电力公司营销服务中心 | 锂电池荷电状态的估计方法、装置、设备及可读存储介质 |
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