Irrigation decision learning method and device, server and storage medium
A learning method and decision-making technology, applied in instruments, data processing applications, computing, etc., can solve the problems of crop yield reduction due to drought, irrigation waste and other problems, and achieve the effect of improving accuracy and scientificity, and improving utilization rate.
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Embodiment 1
[0034] Such as figure 1 As shown, the irrigation decision-making learning method provided in the first embodiment includes the following steps:
[0035] S110. Obtain current environmental parameters of the target irrigation area, wherein the current environmental parameters include weather forecast data.
[0036] In the initial cycle of irrigation decision learning, the current environmental parameters of the target irrigation area are obtained, and the parameters are initialized. The target irrigation area can be any area to be irrigated in the dry field or paddy field, and a sensing module, such as an environmental monitoring sensor, is set in the area to collect environmental parameters. The weather forecast data in the environmental parameters refers to the public weather forecast data of the target irrigation area within the preset time in the future, including but not limited to sunshine, temperature, wind and rainfall. Weather forecast data is an indispensable referen...
Embodiment 2
[0049] The second embodiment provides an irrigation decision-making learning method, which is further optimized on the basis of the first embodiment, such as figure 2 As shown, the method includes the following steps:
[0050] S210. Obtain current environmental parameters of the target irrigation area. In this embodiment, the current environmental parameters include weather forecast data, depth of soil and water layers, and growth and development period of crops. In addition, the current environmental parameters may also include soil water content, and the soil water content and the depth of the soil water layer can be converted to each other according to the conversion relationship.
[0051] S220. Determine the forecast crop evapotranspiration in the target irrigation area according to the weather forecast data and the crop growth and development period. Forecast crop evapotranspiration represents predicted evapotranspiration based on forecast weather data.
[0052] S230. ...
Embodiment 3
[0089] This embodiment three provides an irrigation decision-making learning method, which is further optimized on the basis of embodiment two, such as image 3 As shown, the method includes the following steps:
[0090] S310. Obtain the initial environmental parameters of the target irrigation area in the t-th irrigation decision cycle.
[0091] Wherein, t=1, 2, 3, 4...n, n is the last cycle of crop growth and development.
[0092] S320. Using the initialized decision value function, determine the irrigation decision in the t-th irrigation decision cycle according to the initial environmental parameters.
[0093] S330. Execute the determined irrigation decision in the t-th irrigation decision-making cycle, and update the environmental parameters of the t+1-th irrigation decision-making cycle through the transfer function.
[0094] S340. Obtain the feedback reward for the irrigation decision of the t-th irrigation decision cycle.
[0095] S350. Update the initialized decisi...
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