Boe Technology Group Co., Ltd.

Ordos Yuansheng Optoelectronics Co., Ltd.

China Institute of Water Resources and Hydropower Research

Xin Han

Feng Li

Jingyi Xu

Jianyun Xie

Peirong Huo

Zhenhong Xiao

Zheng Wei

He Chen

Yi Fan

Baozhong Zhang

Yinong Li

Zhigong Peng

Jiabing Cai

Yaqi Wang

Congying Han

Taisheng Du

A display panel includes a display area, a peripheral area and an opening area. The display panel includes: first data lines including first sub-data lines and second sub-data lines; data selectors including first data selectors and second data selectors, where the first data selectors are electrically connected with the first sub-data lines, and the second data selectors are electrically connected with the second sub-data lines; a plurality of data signal input terminals; a plurality of first data selection data lines including first sub-data selection data lines and second sub-data selection data lines, where the first sub-data selection data lines are electrically connected with the first data selectors, and the second sub-data selection data lines are electrically connected with the second data selectors and the data signal input terminal; and a plurality of first connection leads located outside the opening area and extending from the display area to the peripheral area.

Display panel and display apparatus

A farmland reference crop evapotranspiration prediction method based on uncertainty of meteorological factors, includes: S. acquiring a set number of groups of weather forecast data of a prediction region within a preset time period; S. inputting each group of weather forecast data into a Bayesian probability forecast system to obtain corrected weather forecast data; and S. inputting each group of the corrected weather forecast data into a trained RBF neural network, and predicting to obtain a farmland reference crop evapotranspiration. In the present invention, the Bayesian probability forecast system is configured to correct the weather forecast data and eliminate uncertainty of weather forecast data to obtain the accurate reference crop evapotranspiration forecasted by the RBF neural network using these data.

Farmland reference crop evapotranspiration prediction method considering uncertainty of meteorological factors

Growing community of inventors

Beijing, China

Xin Han