Temperature information prediction method and device

Abstract

The invention relates to a temperature information prediction method and device. The method comprises the steps: acquiring observation data and mode prediction data of a meteorological station, wherein the observation data comprise a plurality of element values; generating an observation vector of the meteorological station based on the plurality of element values in the observation data and the corresponding observation time; generating fusion data of the meteorological station based on the mode prediction data; and inputting the observation vector and the fusion data into a temperature prediction model to generate temperature prediction information of the meteorological station, wherein the temperature prediction model is generated through a gradient boosting algorithm. According to thetemperature information prediction method and device, historical observation data and mode prediction data can be fused, the site data scale is expanded, and a more accurate temperature prediction result is obtained.

Description

technical field [0001] The present disclosure relates to the field of computer information processing, in particular, to a temperature information prediction method and device. Background technique [0002] For temperature forecasting, there are mainly two categories: traditional statistical models and physical process models. Traditional statistical models are mainly based on historical meteorological data, relying on traditional statistical methods to establish the quantitative relationship between input and output variables; physical process models usually require a high level of physical system foundation and rely on a large amount of variable information, through extremely complex mathematics Formulas are used to effectively represent the physical process of temperature changes, so as to obtain more accurate temperature forecast values. [0003] Social development and the improvement of people's living standards have put forward higher requirements for accurate tempera...

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Problems solved by technology

Although the physical process model has achieved better results than the traditional statistical model on the whole, there are still the following problems: 1. The output grid resolution of the forecasting algorithm is low, resulting in poor forecasting results in many areas. Satisfactory, that is, the so-called forecast accuracy cannot meet the specific needs of specific activities in a specific region; second, the existing model has complex algorithms, large calculations, and slow forecast speed, resulting in insufficient timeliness of forecast; third, at the same time, due to regional economic Uneven development results in too few observation sites in certain regions to fully utilize historical and observational data to correct results

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