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An intelligent sprinkler irrigation method and system based on edge machine learning

A machine learning and intelligent technology, applied in the field of garden irrigation, can solve the problems of many parameters and difficult to obtain individual parameters, achieve huge economic and social benefits, wide application range, and improve water efficiency.

Active Publication Date: 2021-02-12
江苏久智环境科技服务有限公司 +1
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Problems solved by technology

However, there are many parameters in this method, and individual parameters are difficult to obtain. How to effectively improve the method of obtaining some parameters, so as to make full use of the Penman formula to design a scientific, low-complexity, and easy-to-implement scientific irrigation scheme, and perform quantitative and scientific irrigation according to the water demand of crops , is an urgent problem to be solved in today's garden industry

Method used

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  • An intelligent sprinkler irrigation method and system based on edge machine learning

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Embodiment Construction

[0032] Taking the most common lawn in urban gardens as an example, the specific implementation method of the present invention is described, so that those skilled in the art can understand the present invention. Inventions and creations are all included in the protection list.

[0033] An intelligent sprinkler irrigation method based on edge machine learning, such as figure 1shown, including the following steps:

[0034] Step 101: Collect meteorological data, soil moisture data, and battery charge of the lawn sprinkler irrigation site for one year.

[0035] Step 102: The core calculation layer calculates the daily evapotranspiration of this area for a year:

[0036] PE=[0.408Δ(R n -G)+900γu 2 (e s -e a ) / (T mean +273)] / [Δ+γ(1+0.34u 2 )]

[0037] In the formula, PE is the possible evapotranspiration, Δ is the slope of the saturated vapor pressure-temperature curve, G is the energy consumed by heating the soil, γ is the hygrometer constant, T mean is the daily average ...

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Abstract

The invention discloses an intelligent sprinkling irrigation method and system based on edge machine learning. The method first uses the gradual gradient regression tree (GBRT) training to obtain the prediction model of evapotranspiration according to the historical data of the sprinkling irrigation area, and then calculates the crop according to the water balance formula Water demand; finally, if the real-time soil humidity is lower than the set threshold, the sprinkler irrigation equipment will be driven to spray irrigation directly according to the established sprinkler irrigation command or the user's sprinkler irrigation command, otherwise, the sprinkler irrigation equipment will be driven to perform sprinkler irrigation according to the water demand of the crop. The invention can realize on-demand sprinkling irrigation and real-time monitoring, so as to grasp the growth status of crops at the sprinkling irrigation site in time and improve the utilization rate of water resources.

Description

technical field [0001] The invention relates to an intelligent sprinkler irrigation method and system based on edge machine learning, belonging to the technical field of garden irrigation. Background technique [0002] In my country, the garden industry mostly uses traditional irrigation methods such as manual irrigation and timed sprinkler irrigation for irrigation. However, there are many deficiencies in traditional irrigation methods. First of all, due to social environment, location, irrigation equipment, irrigation technology, personnel experience and other reasons, traditional irrigation methods cannot make better use of water resources, resulting in an extreme waste of fresh water and other resources. Secondly, the traditional sprinkler irrigation method based on artificial watering is mostly concentrated in hot and dry summer, requiring a large number of heatstroke prevention items and a complete logistics support system to maintain normal operation, with low operati...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A01G25/16A01G25/02G06N20/00G08B21/18H04L29/08H04W4/38
CPCA01G25/02A01G25/167G08B21/18G06N20/00H04L67/025H04L67/12H04W4/38Y02A40/22
Inventor 刘洪全陈超倪艺洋张国文王毅张正刘娅璇
Owner 江苏久智环境科技服务有限公司
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