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张仲荣，男，汉族，工学博士，教授。中国现场统计研究会大数据统计分会第一、第二届理事；中国优选法统筹法与经济数学研究会数据科学分会第二届理事；甘肃省人工智能学会专家；教育部学位中心硕、博士学位论文抽检评审专家；甘肃省社会科普研究会数据科学与人工智能科普特聘专家。在国内外重要学术期刊上发表论文60余篇，其中SCI论文18篇，EI论文7篇。个人H指数为9，论文总被数累计316次，在Journal of Hydrology、Urban Climatei和Scientific Reports等顶级期刊累计发表8次。应用统计专业硕士点初创人，应用统计硕士点主任。研究方向：基于数学、统计的人工智能算法设计及应用研究。聚焦于孪生数字底座的时空智能（孪生智能）算法及应用研究。孪生智能是新质生产力、数字经济、低空经济的前沿热点研究，是数字孪生的智能化提升。2024年获得数转智改主题项目：“水利孪生数字底座时空预测预警”项目获甘肃省科技厅重点研发计划支持；依托甘肃省计算中心申报的“量超融合”算力服务平台关键技术研发与应用项目25年获中央引导地方科技发展资金支持。25年获得国网甘肃电力委托研发项目“基于全景监造数字底座的生产进度异常时空智能预测系统开发及响应激励机制研究”。数据科学与时空智能预测预警方向学术带头人。2023年面对核验评估重任，二度出任硕士点主任，带头撰写了应用统计专业学位研究生核验评估材料，硕士点最终赢得到教育指导委员会专家全票通过核验评估的结论。主要培养数学类学术型研究生和应用统计专业学位研究生。已经培养毕业硕士生38人，在读研究生18人，联合培养3人。在读研究生中学术型硕士3人，专业型硕士18人。经多年坚持，23级起，面对就业难题、在团队成员高频发表SCI论文榜样引领和科研成果跨越式提升就业质量效应引领下，团队研究生深刻理解“人工智能就是统计”的科学论述，走出了读研“混文凭”认知。2025年来团队成果成规模、高频产出，进一步验证了我们基于数学和统计的时空智能算法研究的巨大价值，验证了有好的研究方向和团队多次成功经验的激励和团队互助，团队在读研究生完全可以人人SCI论文在手。“站在风口，猪都能飞起来”。我们基于数学和统计AI研究，只要你是富有责任感的健康人，在大团队里你就能有超出自己认知的成就。高质量的成果给同学们带来自信和高质量就业的底气，大团队优势凸显，凝聚力加强，协作互助、传帮带成风尚。主持和参与各类科研项目20余项，代表性项目有：[1] 省级生态文明建设重点研发专项：24YFFA055，基于水利孪生数字底座的智能预测预警及典型应用研究，在研，主持。[2] 中央引导地方科技发展资金项目“量超融合”算力服务平台关键技术研发与应用项目，25ZYJA039，在研，参与。[3] 国家自然科学基金重点项目，41930101，面向全自动地图综合的空间相似关系理论，2020-01至2024-12，在研，参与。直接经费303万元，第二参与人（除主持人外是第一参与人）。代表性论文： [1] Zhongrong Zhang*, Xuan Yang, Hao Li, Weide Li, Haowen Yan, Fei Shi. Application of a novel hybrid method for spatiotemporal data imputation: a case study of the Minqin County groundwater level[J]. Journal of Hydrology, 2017, Vol.553: 384-397. [2] Xingyu Yang , Zhongrong Zhang*. An attention-based domain spatial-temporal meta-learning (ADST-ML) approach for PM2.5 concentration dynamics prediction[J]. Urban Climate 47 (2023) 101363. Available online 2 December 2022. https://doi.org/10.1016/j.uclim.2022.101363.[3] Xingyu Yang, Zhongrong Zhang*. A CNN-LSTM Model Based on a Meta-Learning Algorithm to Predict Groundwater Level in the Middle and Lower Reaches of the Heihe River, China[J]. Water, 2022, 14(15): 2377.[4] Huiqin Xu†, Zhongrong Zhang†, Yin Gao, Haizhong Liu, Feng Xie and Jun Li*. Adaptive Bilateral Texture Filter for Image Smoothing[J]. Frontiers in Neurorobotics 2022,16. Aitcle 729924.[5] Zhongrong Zhang, Yijia Liu, Haizhong Liu, Aihong Hao, Zhongwei Zhang. The impact of lockdown on nitrogen dioxide (NO2) over Central Asian countries during the COVID-19 pandemic[J]. Environmental Science and Pollution Research (2022) 29:18923–18931.[6] Xiaoya Chang, Zhongrong Zhang*, Jianguo Sun, Kang Lin, & Ping'an Song. Breast cancer image classification based on H&E staining using a causal attention graph neural network model[J]. Medical & Biological Engineering & Computing(2025). https://doi.org/10.1007/s11517-025-03303-3.[7] Zhenghao Sui, Zhongrong Zhang*, Ruiqi Wang, Kang Li, Yuyuan Pan. Quantum-transformer integration in H2STGCN: a novel approach for spatiotemporal pattern modeling[J]. Appl Intell 55, 1079 (2025). https://doi.org/10.1007/s10489-025-06976-3.[8] Zhongrong Zhang*, Junyan Sun, Yulin Shen*, et al. WD-HRNN-BiGRU: A Novel Spatiotemporal Sequence Multi-Value Prediction Model[J]. Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2026, 40(4): 1-17. https://doi.org/10.1007/s11269-026-04541-2.[9] Wen Yang, Zhongrong Zhang*, Yunyun Zhang, et al. LPT-Former: Local-Point Transformer for 3D point cloud completion[J]. Journal of Visual Communication and Image Representation, 2026, 117: 104749. DOI: 10.1016/j.jvcir.2026.104749.[10] Yunyun Zhang, Zhongrong Zhang*, Yulin Shen. et al. High-fidelity point cloud generation via multi-scale attentive feature fusion with graphSAGE and transformer[J]. Vis Comput 42, 187 (2026). https://doi.org/10.1007/s00371-026-04389-y.[11] Jing Bai, Wen Nie*, Zhongrong Zhang, et al. An optimized deep learning model with error correction for forecasting particulate matter 2.5 concentrations near tailings ponds[J]. Int. J. Environ. Sci. Technol. 23, 315 (2026). https://doi.org/10.1007/s13762-026-07067-7.[12] Ruiqi Wang, Zhongrong Zhang*, Yulin Shen*,et al.Hqsan: a hybrid quantum self-attention network for remote sensing image scene classification[J].Journal of Supercomputing, 2025, 81(15). DOI:10.1007/s11227-025-07922-3.[13] Xiangxiang Zheng, Zhongrong Zhang*, Xiaona Zhang, Nengzhi Jin*, Yunyun Zhang. "Ricci-GraphDTA: A graph neural network integrating discrete Ricci curvature for drug–target affinity prediction"[J]. Journal of Molecular Graphics and Modelling, 2026.[14] Zeyu Duan, Zhongrong Zhang*, Jisheng Li*, et al. Spatiotemporal time series graph modeling method research based on the fusion of time–frequency domain analysis and deep learning: A case study of groundwater depth prediction in the Heihe River Basin, China[J]. Journal of Hydrology: Regional Studies, 2026, 64: 103217.[15] Wenyu Zhang, Zhongrong Zhang*, Nengzhi Jin*. et al. A hybrid deep learning-based framework for rolling bearing fault diagnosis: Multi-resolution feature extraction and enhanced adaptive nonlinear mapping[J]. Advances in Engineering Software 218 (2026) 104184.[16] Yang Zhu, Li H, Wang Z, et al. Optimal Evacuation Route Planning of Urban Personnel at Different Risk Levels of Flood Disasters Based on the Improved 3D Dijkstra’s Algorithm[J]. Sustainability, 2022, 14(16): 10250.[17] Yang Zhu, Yijun Gao, Zhenhao Wang, Guansen Cao, Renjie Wang, Song Lu, Wei Li, Wen Nie,* and Zhongrong Zhang*. A Tailings Dam Long-Term Deformation Prediction Method Based on Empirical Mode Decomposition and LSTM Model Combined with Attention Mechanism[J]. Water 2022, 14, 1229. [18] Zhongrong Zhang, Yiliao Song, Feng Liu, Jinpeng Liu. Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-ased Fuzzy Inference System and Singular Spectrum Analysis[J]. Sustainability, 8(2), 125.[19] Feiyu Tong, Zhongrong Zhang*, Nengzhi Jin*. et al. From Destination Image to Regional Embeddings: A Multi-Scale Framework for Cross-City Tourism Recommendation[J]. Current issues in tourism, Under Review, 2026[20] Yubin Qian, Zhongrong Zhang*, Qiang Bie*. et al. Landslide Identification Method Based on Enhanced DeepLabv3+ Architecture: Joint Optimization of Bottleneck Attention and Dual-Module Graph Convolution[J]. Applied Intelligence, Under Review, 2026.[21] Zhongrong Zhang*, Yubin Qian†, Qiang Bie*. HMA-YOLO: Hypergraph-driven Multi-scale Object Detection for Complex Remote Sensing Scenarios[J]. IEEE Transactions on Geoscience and Remote Sensing, Under Review, 2026.[22] Zhongrong Zhang*, Yunyun Zhang†, Nengzhi Jin*. et al. Topology-Aware Point Cloud Generation via Simplicial-Complex Diffusion and Multi-Scale GraphSAGE Decoding[J]. Applied Intelligence‌, Under Review, 2026.[23] 张仲荣,王亚领,闫浩文. 一种时空混合插值算法及其应用[J]. 测绘科学2016, 41(12)：265-269, 306.代表性论著：[1] 张仲荣,闫浩文. 基于混合模型的地下水埋深时空预测研究---以民勤绿洲为例[M].北京，科学出版社， 2019年5月. [2] 常迎香,栗永安,李秦,刘海忠,张仲荣,刘旭. 高等数学（共上、下两册）[M].北京，科学出版社, 2009年8月.张仲荣个人邮箱：gslzzhangzhr@163.com；微信号：zzr1969