The invention discloses a city health state estimation method based on a deep learning network and multi-source heterogeneous data. The method comprises the following steps: carrying out preprocessing and feature processing on air quality data; preprocessing the traffic jam data, and processing related characteristics of the traffic jam data; integrating the results of the data feature extraction process; and reasoning and evaluating the health condition of the city. In order to understand the influence of urban health on the life, behavior and selection of urban residents, the invention provides a public health (HOTP) framework. The prediction result of the HOTP method is matched with the actual operation of the city. It shows that the method skillfully integrates metropolitan commuting information and air pollution data, and the overall health level of a city can be accurately evaluated.
本发明公开了一种基于深度学习网络及多源异构数据的城市健康状态估计方法,方法包括:空气质量数据进行预处理和特征处理;交通拥堵数据进行预处理,对其相关特征进行处理;数据特征提取过程的结果进行整合;城市健康状况进行推理和评估。本发明提出了为了了解城市健康对城市居民生活、行为和选择的影响,提出了一个公众健康(HOTP)框架。HOTP方法的预测结果与城市实际运行相吻合。这表明我们的方法熟练地整合了大都市通勤信息和大气污染数据,可以准确地评估城市的整体健康水平。
Urban health state estimation method based on deep learning network and multi-source heterogeneous data
基于深度学习网络及多源异构数据的城市健康状态估计方法
2025-01-03
Patent
Electronic Resource
Chinese
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