Irrigation decision making method and device based on big data, server and medium
A big data and decision-making technology, applied in the field of irrigation decision-making based on big data, can solve the problems of expensive instruments, time-consuming, labor-intensive, and variable, and achieve the effect of improving utilization and reducing costs
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Embodiment 1
[0039] Example 1, such as figure 1 As shown, an irrigation decision-making method based on big data includes the following steps:
[0040] S100. Obtaining environmental monitoring data of farmland;
[0041] S200. Construct an irrigation forecast model according to the environmental monitoring data of the farmland, and use the irrigation forecast model to determine the predicted irrigation water demand of the farmland;
[0042] S300. According to the predicted irrigation water demand of the farmland and the attribute information of each field block in the farmland, use the pre-trained irrigation decision model to determine the target irrigation water amount of each field block in the farmland.
[0043] Typically, farmland environmental monitoring data include at least: meteorological data, underlying surface conditions, forecast rainfall and satellite remote sensing data.
[0044] However, in S200, the irrigation forecast model is constructed according to the environmental mo...
Embodiment 2
[0081] Example 2, such as figure 2 As shown, the difference between this embodiment and embodiment 1 is:
[0082] Before building an irrigation forecast model based on the environmental monitoring data of the farmland, the method also includes:
[0083] Preprocess the environmental monitoring data according to the data attributes included in the environmental monitoring data of the farmland, so as to build an irrigation forecast model based on the preprocessed environmental monitoring data, wherein the preprocessing includes: removing data that does not meet the data specification and noise data , clean up duplicate data and / or perform interpolation for missing data.
[0084] Exemplarily, a distributed Hadoop platform with high data throughput and high fault tolerance can be built to preprocess massive environmental monitoring data.
[0085] Specifically, use the Map function to complete the feature extraction of environmental monitoring data, realize data standardization i...
example 1
[0087] Example 1: The maximum value, minimum value, mean value, and standard deviation of each type of environmental monitoring data that do not meet the requirements are treated as bad values for elimination and filtering.
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