A method for constructing a local soil moisture characteristic model for gardens

Abstract

The present invention relates to the field of data processing technology, and in particular to a method for constructing a local garden soil moisture characteristic model, comprising: dividing the soil into three layers, and dividing the time into several time periods; obtaining the soil water holding degree of each time period through the change of soil moisture of different layers at adjacent moments in historical soil moisture data, clustering each time period to obtain several clusters, and obtaining the root index of the soil surface layer of the next time period; obtaining the root index of the soil surface layer according to the soil moisture of different layers in the current time period, thereby obtaining the soil moisture loss coefficient of the next time period; obtaining the soil moisture characteristic smoothing factor and constructing a local garden soil moisture characteristic model. The present invention accurately constructs a local garden soil moisture characteristic model by analyzing the change of soil state in historical data and the difference of soil moisture of different layers of soil.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a method for constructing a local garden soil moisture characteristic model. Background Art

[0002] Under different landforms and topographic features, the surface water resources and groundwater resources are also different. The moisture status in the soil greatly affects the growth and development of the green vegetation in the garden. Too high or too low soil moisture will have an adverse effect on the state of the green plants in the garden. Therefore, it is necessary to dynamically monitor the local soil moisture content in the garden and build a digital model of the local soil moisture model in the garden to provide scientific theoretical support for the study of dynamic changes in garden moisture.

[0003] In the existing soil moisture model construction process, there are two technical means: instrument monitoring and model simulation. Instrument monitoring requires long-term real-time measurement and monitoring of soil status, which is time-consuming and labor-intensive and has a high monitoring cost. In the model simulation process, the characteristics of various data are not obvious, resulting in inappropriate parameter accuracy of the soil moisture model and large errors, which cannot accurately reflect the characteristics of soil moisture changes. Summary of the invention

[0004] The invention provides a method for constructing a local garden soil moisture characteristic model to solve the existing problems.

[0005] The method for constructing a local garden soil moisture characteristic model of the present invention adopts the following technical scheme:

[0006] An embodiment of the present invention provides a method for constructing a local soil moisture characteristic model of a garden, the method comprising the following steps:

[0007] Obtain historical soil moisture data, historical weather data and weather forecast data; divide the soil into three layers and divide the time into several time periods; the historical soil moisture data includes soil moisture of different layers at different times, the historical weather data includes precipitation and temperature at several times, and the weather forecast data includes precipitation and temperature in the next week;

[0008] The soil moisture retention degree in each time period is obtained by analyzing the changes in soil moisture in different layers at adjacent moments in the historical soil moisture data;

[0009] Based on historical weather data and soil water holding capacity, cluster each time period to obtain several clusters, and combine the weather forecast for the next time period to obtain the soil water holding capacity for the next time period;

[0010] According to the soil moisture of different layers in the current time period, the root index of the soil surface layer in the current time period is obtained;

[0011] According to the root index of the soil surface layer and the soil water holding degree in the current time period and the soil water holding degree in the next time period, the soil water loss coefficient in the next time period is obtained;

[0012] Obtain the mean of the soil moisture loss coefficients of all time periods before the next time period, and obtain the soil moisture characteristic smoothing factor according to the soil moisture loss coefficient of the next time period and the mean of the soil moisture loss coefficient;

[0013] The soil moisture characteristic smoothing factor is used to construct a local soil moisture characteristic model for gardens.

[0014] Furthermore, the soil is divided into three layers, and the specific steps include the following:

[0015] The topmost layer of soil among the three layers where the sensors are installed is recorded as the first layer, the middle layer of soil is recorded as the second layer, and the deepest layer of soil is recorded as the third layer.

[0016] Furthermore, the specific steps of obtaining the soil water holding capacity in each time period are as follows:

[0017]

[0018] In the formula, represents the soil water holding capacity in the tth time period, represents the number of moments in each time period, represents the water holding factor at the ith moment in the tth time period, represents the second layer soil moisture at the i-th moment in the t-th time period, represents the second layer soil moisture at the i+1th moment in the tth time period, is a linear normalization function.

[0019] Furthermore, the specific method of obtaining the water holding factor is as follows:

[0020]

[0021] In the formula, represents the water holding factor at the i-th moment in the t-th time period, represents the soil moisture of the ath layer at the i+1th moment in the tth time period, represents the soil moisture of the a+1th layer at the i+1th moment in the tth time period, represents the soil moisture of the a-th layer at the i-th moment in the t-th time period, It represents the soil moisture of the a+1th layer at the i-th moment in the t-th time period.

[0022] Furthermore, the clustering of each time period to obtain a number of clusters based on historical weather data and soil water retention includes the following specific steps:

[0023] The sum of the precipitation at all times in the historical weather data in each time period is taken as the total precipitation in each time period; the mean temperature at all times in the historical weather data in each time period is taken as the average temperature in each time period;

[0024] According to the total precipitation, average temperature and soil water holding capacity in each time period, each time period is clustered to obtain several clusters.

[0025] Furthermore, according to the total precipitation, average temperature and soil water holding capacity of each time period, each time period is clustered to obtain a number of clusters, including the following specific steps:

[0026] The sample space is constructed with the total precipitation, average temperature and soil water retention in each historical time period as the coordinate axes, and the total precipitation, average temperature and soil water retention in each historical time period are mapped to the sample space to obtain a number of data points; DBSCAN clustering is performed on all data points, and the distance measurement uses the Euclidean distance between data points in the sample space to obtain a number of clusters.

[0027] Furthermore, the step of obtaining the soil water holding capacity in the next time period includes the following specific steps:

[0028] The precipitation and temperature of the next time period in the weather forecast data are input into the sample space, and the mean of the soil water retention of all data points in the closest cluster is taken as the soil water retention of the next time period.

[0029] Furthermore, the step of obtaining the soil surface root index of the current time period includes the following specific steps:

[0030] The time period of the current moment is recorded as the kth time period, and the calculation method of the root index of the soil surface layer in the kth time period is:

[0031]

[0032] In the formula, represents the root index of the soil surface layer in the kth time period, represents the soil moisture of the first layer in the kth time period, represents the second layer soil moisture in the kth time period, represents the third layer of soil moisture in the kth time period, is an exponential function with a natural constant as its base.

[0033] Furthermore, the step of obtaining the soil moisture loss coefficient for the next time period includes the following specific steps:

[0034]

[0035] In the formula, represents the soil moisture loss coefficient in the k+1th time period, represents the soil water holding capacity in the kth time period, represents the soil water holding capacity in the k+1th time period, Represents the root index of the soil surface layer in the kth time period.

[0036] Furthermore, the step of obtaining the soil moisture characteristic smoothing factor includes the following specific steps:

[0037]

[0038] In the formula, It represents the soil moisture loss coefficient in the k+1th time period, It represents the mean value of soil moisture loss coefficient at all different times before the k+1th time period. represents the soil moisture characteristic smoothing factor.

[0039] The beneficial effects of the technical solution of the present invention are as follows: the soil is divided into three layers, and the time is divided into several time periods, which is helpful for analyzing the specific changes of the soil; the soil water retention degree of each time period is obtained by the changes of soil moisture of different layers at adjacent moments in the historical soil moisture data, and each time period is clustered to obtain several clusters, and the soil water retention degree of the next time period is obtained in combination with the weather forecast, showing the ability of the soil to retain water in each time period; the root system index of the soil surface layer is obtained according to the soil moisture of different layers in the current time period, thereby calculating the root system index of the soil surface layer, and further calculating the soil moisture characteristic smoothing factor, thereby avoiding the defect of low soil model accuracy caused by improper setting of the smoothing factor when the soil moisture characteristic model is constructed using the cubic exponential smoothing prediction algorithm, thereby accurately reflecting the local soil moisture change characteristics of the garden. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1The present invention is a flowchart of the steps of a method for constructing a local garden soil moisture characteristic model. DETAILED DESCRIPTION

[0042] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the construction method of a local soil moisture characteristic model of a garden proposed by the present invention, its specific implementation method, structure, characteristics and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0044] The specific scheme of the method for constructing a local garden soil moisture characteristic model provided by the present invention is described in detail below with reference to the accompanying drawings.

[0045] See also Figure 1 , which shows a flowchart of a method for constructing a local garden soil moisture characteristic model provided by an embodiment of the present invention, the method comprising the following steps:

[0046] Step S001, obtain historical soil moisture data, historical weather data and weather forecast data, and divide the time period into several time periods.

[0047] The purpose of this embodiment is to analyze the changes in soil status in historical data. Most of the local green plants in the garden are lawns and shrubs, and the roots of these plants are distributed within 20-30 cm underground. Therefore, in the soil of the local garden lawn, soil moisture sensors are installed in three layers at 20 cm, 40 cm, and 60 cm below the soil layer to collect historical soil moisture data, and at the same time obtain historical weather data composed of precipitation and temperature and weather forecast data for the next week; in this embodiment, the collection frequency of historical soil moisture data and historical weather data is once every hour, and the collection time is one month. This example is used for description. Other collection frequencies and collection times can be set in other implementations, and this embodiment does not specifically limit them.

[0048] Furthermore, the top layer of soil among the three layers where the sensor is installed is recorded as the first layer, the middle layer of soil is recorded as the second layer, and the deepest layer of soil is recorded as the third layer; the collection time is divided into several time periods, each time period is six hours in this embodiment, and other values ​​can be set in other implementation modes, which are not specifically limited in this embodiment.

[0049] Step S002: Obtain the soil water holding capacity in each time period through the changes in soil moisture of different layers in the historical soil moisture data at adjacent moments.

[0050] It should be noted that the soil conditions of different lawns are different. Coarse sandy soil has strong permeability but poor water retention. After irrigation, water penetrates quickly and results in high water loss. Fine soil has small intergranular pores, poor permeability, and strong water retention. For different soil layers, the top layer of soil is directly exposed to the air and exposed to the sun, so the soil moisture decreases quickly and its ability to retain water is weaker. The deeper soil is covered by the upper soil layer, so the moisture evaporates slowly and is mostly absorbed by the roots of lawn plants. Therefore, the water retention capacity of each layer is different, and it is necessary to analyze the overall water retention capacity of the soil through the soil moisture of different layers in the historical soil moisture data.

[0051] It should be further explained that the different changes of each layer at each moment and the changes of soil moisture between adjacent soil layers can reflect the soil's ability to retain water; the faster the soil moisture decreases, the faster the water loss in the soil, and the weaker the soil's ability to retain water. Since the second layer of soil is in the middle of the monitoring, the soil moisture of the second layer can reflect the relative balance level in different layers of soil; the difference between adjacent soil layers can show the mutual influence of different layers on soil moisture. The faster the soil moisture changes between adjacent soil layers at different moments, the greater the difference, indicating that the soil's water retention capacity is weaker. Therefore, the soil's ability to retain water in each time period is analyzed by the change of soil moisture within the time period. The greater the difference in soil moisture between adjacent soil layers, the weaker the soil's ability to retain water. The less the soil moisture changes at adjacent moments, the relatively less water loss, and the higher the soil's ability to retain water.

[0052] Specifically, the water holding factor at the i-th moment in the t-th time period is:

[0053]

[0054] The calculation method of soil water holding capacity in the tth time period is:

[0055]

[0056] In the formula, represents the soil water holding capacity in the tth time period, represents the number of moments in each time period, represents the water holding factor at the i-th moment in the t-th time period, represents the second layer soil moisture at the i-th moment in the t-th time period, represents the second layer soil moisture at the i+1th moment in the tth time period, represents the soil moisture of the ath layer at the i+1th moment in the tth time period, represents the soil moisture of the a+1th layer at the i+1th moment in the tth time period, represents the soil moisture of the a-th layer at the i-th moment in the t-th time period, represents the soil moisture of the a+1th layer at the i-th moment in the t-th time period, is a linear normalization function, and the normalization object is all time periods .

[0057] It should be noted that It represents the ratio of the difference in soil moisture between adjacent soil layers at the i-th moment and the i+1-th moment in the t-th time period. The larger the ratio, the slower the change in soil moisture between different layers at the next moment and the previous moment, and the stronger the soil's ability to retain moisture. It is the difference in soil moisture change of the second layer of soil between the i-th moment and the i+1-th moment. The smaller the difference, the smaller the degree of soil moisture change, and the soil can effectively retain moisture.

[0058] At this point, the soil water holding capacity for each time period in the historical data is obtained.

[0059] Step S003, clustering historical weather data and soil water retention to obtain several clusters, combining the weather forecast for the next time period to obtain the soil water retention for the next time period; obtaining the root index of the soil surface layer in the current time period according to the soil moisture in different layers of the current time period; obtaining the soil moisture loss coefficient for the next time period according to the root index of the soil surface layer and the soil water retention in the current time period and the soil water retention in the next time period.

[0060] It should be noted that the daily temperature has a significant impact on the evaporation of water in the soil. As the temperature rises, the evaporation rate of water accelerates, resulting in a rapid decrease in the water content in the soil. Therefore, it is necessary to consider the water loss under different temperature conditions. At the same time, precipitation also has an important impact on soil moisture. The amount and frequency of precipitation will affect the water content in the soil and the water holding capacity of the soil. Therefore, it is necessary to comprehensively consider the impact of different weather conditions on the degree of soil water holding, correspond different weather conditions to the degree of soil water holding, and predict the degree of soil water holding under the weather in the next time period based on the weather forecast data.

[0061] Specifically, the sum of precipitation at all times in each time period in the historical weather data is taken as the total precipitation in each time period; the mean temperature at all times in each time period in the historical weather data is taken as the average temperature in each time period; the sample space is constructed with the total precipitation, average temperature and soil water retention of each historical time period as the coordinate axes, and the precipitation, average temperature and soil water retention of each historical time period are mapped to the sample space. DBSCAN clustering is performed on all data points, and the distance measurement uses the Euclidean distance between data points in the sample space to obtain several clusters; each cluster represents the weather conditions under different weather conditions. The soil water retention degree is obtained by inputting the precipitation and temperature of the next time period in the weather forecast data into the sample space, and the mean of the soil water retention degrees of all data points in the closest cluster is used as the soil water retention degree of the next time period; it should be noted that the closest cluster is the cluster in which the mean of the total precipitation and the mean of the average temperature of all data points have the smallest difference with the precipitation and temperature of the next time period; this embodiment is described by taking the preset clustering radius as 10 and the number of clustering samples as 5 as an example, the DBSCAN density clustering algorithm is an existing well-known technology, other embodiments may use other methods, and this embodiment will not be repeated.

[0062] It should be noted that when constructing a soil moisture model, it is necessary to consider the impact of soil vegetation roots on soil moisture absorption. When garden plants are densely distributed, the more roots there are, the more significantly the soil moisture in the corresponding local area will decrease. At this time, the moisture status of different soil layers will change, and the local soil will show dry characteristics.

[0063] Furthermore, the time period of the current moment is recorded as the kth time period, and the calculation method of the root index of the soil surface layer in the kth time period is:

[0064]

[0065] In the formula, represents the root index of the soil surface layer in the kth time period, represents the soil moisture of the first layer in the kth time period, represents the second layer soil moisture in the kth time period, represents the third layer of soil moisture in the kth time period, is an exponential function with a natural constant as its base.

[0066] It should be noted that the larger the soil root distribution index is, the smaller the ratio of the first layer of soil moisture to the deep soil moisture in the kth time period is, the lower the shallow soil moisture is, and the more developed the soil root system in the local area is.

[0067] It should be noted that, under ideal conditions without external water supplementation, soil moisture will be continuously absorbed and attenuated, and the soil moisture content will be lost rapidly. The soil moisture loss rate can be obtained based on the soil water retention rate and the root index of the soil surface layer:

[0068] Specifically, the calculation method of soil moisture loss coefficient in the k+1th time period is:

[0069]

[0070] In the formula, represents the soil moisture loss coefficient in the k+1th time period, represents the soil water holding capacity in the kth time period, represents the soil water holding capacity in the k+1th time period, Represents the root index of the soil surface layer in the kth time period.

[0071] It should be noted that when the soil water retention values ​​in two different time periods differ greatly, and the more developed the root system in the soil surface layer is, the more serious the soil water loss is and the greater the soil water loss coefficient will be.

[0072] Step S004, obtaining the mean of the soil moisture loss coefficients of all time periods before the next time period, and obtaining a soil moisture characteristic smoothing factor according to the soil moisture loss coefficient of the next time period and the mean of the soil moisture loss coefficient; and constructing a local soil moisture characteristic model of the garden using the soil moisture characteristic smoothing factor.

[0073]

[0074] In the formula, It represents the soil moisture loss coefficient in the k+1th time period, It represents the mean value of soil moisture loss coefficient at all different times before the k+1th time period. represents the soil moisture characteristic smoothing factor.

[0075] Will As the input of the triple exponential smoothing prediction algorithm, the soil moisture state in the next time period is predicted and analyzed by using the changes in soil state in historical data, so as to obtain the soil moisture state in the next time period.

[0076] So far, this embodiment is completed, and because the soil moisture characteristic model can be used to determine the soil moisture situation, this embodiment is an embodiment of constructing local soil moisture characteristics of a garden.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing a local soil moisture characteristic model for a garden, characterized in that: The method comprises the following steps: Obtain historical soil moisture data, historical weather data and weather forecast data; divide the soil into three layers and divide the time into several time periods; the historical soil moisture data includes soil moisture of different layers at different times, the historical weather data includes precipitation and temperature at several times, and the weather forecast data includes precipitation and temperature in the next week; The soil moisture retention degree in each time period is obtained by analyzing the changes in soil moisture in different layers at adjacent moments in the historical soil moisture data; Based on historical weather data and soil water holding capacity, cluster each time period to obtain several clusters, and combine the weather forecast for the next time period to obtain the soil water holding capacity for the next time period; According to the soil moisture of different layers in the current time period, the root index of the soil surface layer in the current time period is obtained; According to the root index of the soil surface layer and the soil water holding degree in the current time period and the soil water holding degree in the next time period, the soil water loss coefficient in the next time period is obtained; Obtain the mean of the soil moisture loss coefficients of all time periods before the next time period, and obtain the soil moisture characteristic smoothing factor according to the soil moisture loss coefficient of the next time period and the mean of the soil moisture loss coefficient; The soil moisture characteristic smoothing factor is used to construct the local soil moisture characteristic model of the garden; The specific steps of obtaining the soil water holding capacity in each time period are as follows: In the formula, represents the soil water holding capacity in the tth time period, represents the number of moments in each time period, represents the water holding factor at the i-th moment in the t-th time period, represents the second layer soil moisture at the i-th moment in the t-th time period, represents the second layer soil moisture at the i+1th moment in the tth time period, is a linear normalization function; The specific method of obtaining the water holding factor is as follows: In the formula, represents the water holding factor at the i-th moment in the t-th time period, represents the soil moisture of the ath layer at the i+1th moment in the tth time period, represents the soil moisture of the a+1th layer at the i+1th moment in the tth time period, represents the soil moisture of the a-th layer at the i-th moment in the t-th time period, represents the soil moisture of the a+1th layer at the i-th moment in the t-th time period; The specific steps of obtaining the soil surface root index of the current time period are as follows: The time period of the current moment is recorded as the kth time period, and the calculation method of the root index of the soil surface layer in the kth time period is: In the formula, represents the root index of the soil surface layer in the kth time period, represents the soil moisture of the first layer in the kth time period, represents the second layer soil moisture in the kth time period, represents the third layer soil moisture in the kth time period, is an exponential function with a natural constant as its base.

2. The method for constructing a local garden soil moisture characteristic model according to claim 1, characterized in that: The soil is divided into three layers, and the specific steps include the following: The topmost layer of soil among the three layers where the sensors are installed is recorded as the first layer, the middle layer of soil is recorded as the second layer, and the deepest layer of soil is recorded as the third layer.

3. The method for constructing a local soil moisture characteristic model of a garden according to claim 1, characterized in that: The method of clustering each time period based on historical weather data and soil water retention to obtain a number of clusters includes the following specific steps: The sum of the precipitation at all times in the historical weather data in each time period is taken as the total precipitation in each time period; the mean temperature at all times in the historical weather data in each time period is taken as the average temperature in each time period; According to the total precipitation, average temperature and soil water holding capacity in each time period, each time period is clustered to obtain several clusters.

4. The method for constructing a local soil moisture characteristic model of a garden according to claim 3, characterized in that: According to the total precipitation, average temperature and soil water holding capacity of each time period, each time period is clustered to obtain a number of clusters, and the specific steps include the following: The sample space is constructed with the total precipitation, average temperature and soil water retention in each historical time period as the coordinate axes, and the total precipitation, average temperature and soil water retention in each historical time period are mapped to the sample space to obtain a number of data points; DBSCAN clustering is performed on all data points, and the distance measurement uses the Euclidean distance between data points in the sample space to obtain a number of clusters.

5. The method for constructing a local garden soil moisture characteristic model according to claim 4, characterized in that: The specific steps of obtaining the soil water holding capacity for the next time period are as follows: The precipitation and temperature of the next time period in the weather forecast data are input into the sample space, and the mean of the soil water retention of all data points in the closest cluster is taken as the soil water retention of the next time period.

6. The method for constructing a local garden soil moisture characteristic model according to claim 1, characterized in that: The specific steps of obtaining the soil moisture loss coefficient for the next time period are as follows: In the formula, represents the soil moisture loss coefficient in the k+1th time period, represents the soil water holding capacity in the kth time period, represents the soil water holding capacity in the k+1th time period, Represents the root index of the soil surface layer in the kth time period.

7. The method for constructing a garden local soil moisture characteristic model according to claim 1, characterized in that: The specific steps of obtaining the soil moisture characteristic smoothing factor are as follows: In the formula, It represents the soil moisture loss coefficient in the k+1th time period, It represents the mean value of soil moisture loss coefficient at all different times before the k+1th time period. represents the soil moisture characteristic smoothing factor.

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