CN113762637B - Dynamic intelligent prediction method for watering amount of greenhouse-planted cucumbers - Google Patents

Dynamic intelligent prediction method for watering amount of greenhouse-planted cucumbers Download PDF

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CN113762637B
CN113762637B CN202111084678.9A CN202111084678A CN113762637B CN 113762637 B CN113762637 B CN 113762637B CN 202111084678 A CN202111084678 A CN 202111084678A CN 113762637 B CN113762637 B CN 113762637B
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cucumber
water
greenhouse
permeability
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CN113762637A (en
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汪志强
刘芳
谭峰
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Heilongjiang Bayi Agricultural University
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention belongs to the technical field of cucumber cultivation, and particularly relates to a dynamic and intelligent prediction method for watering amount of cucumbers planted in a greenhouse. Mainly comprises the following steps of 1, determining the watering quantity Q of cucumbers planted in a greenhousetWith cucumber Water absorption XStThe relationship of (1); 2. determining the water demand N for cucumber planting in greenhousetAnd determining the water demand N for planting cucumbers in the greenhousetAnd cucumber Water absorption XStThe relationship of (1); 3. permeability measuring sensor array for measuring permeability xi arranged in greenhouse for planting cucumbertAnd to xitIdentifying and correcting the outliers; 4. determining cucumber watering quantity Q for greenhouse plantingt. The method accurately gives the value of the watering amount of the greenhouse cucumber and provides the accurate watering amount for the greenhouse cucumber.

Description

Dynamic intelligent prediction method for watering amount of greenhouse-planted cucumbers
The technical field is as follows:
the invention belongs to the technical field of cucumber cultivation, and particularly relates to a dynamic and intelligent prediction method for watering amount of cucumbers planted in a greenhouse.
Background art:
cucumber contains rich nutrients including vitamin C, carotene and potassium, and also contains components capable of inhibiting the proliferation of cancer cells. Therefore, it is one of the vegetables that people eat for a long time from the nutritional point of view. At present, most cucumbers are planted in a greenhouse especially in the north, in order to improve the modernization level of cucumber planting, scientific researchers develop automatic control systems for greenhouse temperature, humidity, watering and the like, but when the automatic watering control system is adopted for watering, the needed watering amount is often difficult to accurately determine. Because the water poured into the ground is not completely absorbed by the cucumber during the planting process, and the cucumber is a water-loving plant, the water absorbed by the cucumber is not the water required by the cucumber, and may be completely more than the water required by the cucumber.
At present, cucumbers planted in greenhouses are watered by adopting a method of manually controlling watering equipment to determine the watering amount according to human experience, and the method has the following problems: (1) the watering amount is determined by depending on manual experience, the requirement on watering personnel is high, and the realization of agricultural automation and intellectualization is not facilitated; (2) the watering amount is controlled without considering factors such as the growth period of the cucumbers, the humidity of the greenhouse, the temperature of the greenhouse, the absorption coefficient of the cucumbers and the like, so that the watering amount is difficult to accurately and dynamically determine, dead seedlings and the like occur if the watering amount is too large, and the phenomenon that the growth of the cucumbers is influenced by insufficient water absorption of the cucumber seedlings occurs if the watering amount is too small.
Aiming at the problems, the invention provides a dynamic and intelligent prediction method for the watering amount of the cucumbers planted in the greenhouse, which solves the problem of inaccurate watering amount caused by the fact that the watering amount of the cucumbers depends on artificial experience and the consideration factors of the watering amount are insufficient, and provides the prediction method for the watering amount of the cucumbers planted in the greenhouse.
The invention content is as follows:
the invention aims to overcome the defect that the water injection amount of the greenhouse-planted cucumbers is inaccurate, and provides a dynamic and intelligent prediction method for the water injection amount of the greenhouse-planted cucumbers, so that the yield of the greenhouse-planted cucumbers is indirectly improved.
The technical scheme adopted by the invention is as follows: a dynamic intelligent prediction method for watering amount of greenhouse cucumber comprises the following steps:
the method comprises the following steps: determining cucumber watering quantity Q for greenhouse plantingtWith cucumber Water absorption XStThe relationship of (1);
step two: determining the water demand N for cucumber planting in greenhousetAnd determining the water demand N for planting cucumbers in the greenhousetAnd cucumber Water absorption XStThe relationship of (1);
step three: method for measuring soil permeability xi by arranging permeability measuring sensor array in greenhouse for planting cucumbertAnd to xitIdentifying and correcting the outliers;
step four: determining cucumber watering quantity Q for greenhouse plantingt。
Furthermore, the watering quantity Q of the cucumbers planted in the greenhouse in the step onetWith cucumber absorbed water amount XStThe relationship establishing method (2) is as follows:
because the water poured into the ground is not completely absorbed by the cucumber during the planting process, and the cucumber is a water-loving plant, the water absorbed by the cucumber is not the water required by the cucumber, and may be completely more than the water required by the cucumber. Part of the water poured into the greenhouse permeates the underground, part of the water is evaporated into the air, and the rest part of the water is the absorbed quantity. For accurately predicting required watering quantity QtThe amount of watering water Q must be obtainedtWith cucumber Water absorption XStRelation between cucumber Water absorption XStLength l of cucumber root systemtThe water absorption part of the root system of the cucumber can be regarded as the radius of ltHemispheroid of (1), cucumber uptake XStThe expression can be expressed as:
Figure GDA0003550910160000031
wherein t is the growth time of the cucumber, and the timing of seedling emergence is started from the cucumber; xitThe soil permeability is adopted, omega is the cucumber water absorption coefficient, and the value can be obtained by calibration, and the calibration method comprises the following steps: fix and keep xi in the experimental greenhousetConstant, changing a number of times ltBy measuring XSt、QtAnd (4) obtaining.
Root length of cucumber ltThe change along with the time is continuous, only partial data is known at present, and the length l of the root system of the cucumber must be known to realize automatic wateringtAll real-time values of (1), cucumber root length ltConforming to homogeneous exponential law, for this purpose, carrying out l by using DGM (1,1) model (discrete grey model)tPrediction of lt+1The expression of the predicted value is as follows:
Figure GDA0003550910160000032
in the formula (I), the compound is shown in the specification,
Figure GDA0003550910160000033
the root length of cucumber at the t +1 th moment, betal1For the root system of cucumber to be longDegree first ash coefficient, betal2The second grey coefficient is the root length of cucumber.
βl1And betal2The method comprises the following steps:
Figure GDA0003550910160000034
in the formula, BgIs gray matrix of cucumber root length, YgIs an original matrix of the root length of the cucumber.
Figure GDA0003550910160000035
Wherein m is the number of data of known cucumber root system length.
Further, the water demand N of the cucumbers planted in the greenhouse at different time is determined in the second steptWater demand N for planting cucumbers in greenhousetAnd cucumber Water absorption XStThe relationship of (a) to (b) is as follows:
according to the growth cycle analysis of cucumbers, the water quantity required by the cucumbers mainly comprises two parts, wherein the first part is the water required by the seedlings and the roots of the cucumbers, and the second part is the water required by the fruits of the cucumbers. The planting of cucumbers in the modern agricultural greenhouse adopts a uniform management mode, adopts a uniform seedling raising and uniform picking mode, and the cucumbers with smaller fruits are picked off uniformly, so that the growth cycles of all the cucumbers can be regarded as the same. At present, only a small amount of water required by seedlings and root systems and water required by cucumber fruits are known in cucumber planting, and the water required at all moments in the growth cycle of the cucumber needs to be known to meet the requirement of the optimal planting process of modern agriculture, so that the required water needs to be predicted.
The moisture required by the cucumber seedlings and the root systems tends to be stable after rapidly increasing in the growth period, the single model is difficult to accurately describe, the gray prediction model has good predictability on stable slightly-fluctuating data, and polynomial fitting has high accuracy on rapidly-changing data prediction, so that the invention combines the two fitting methods to predict the moisture required by the cucumber seedlings and the root systems, and the expression is as follows:
Figure GDA0003550910160000041
in the formula (I), the compound is shown in the specification,
Figure GDA0003550910160000042
predicted value of water needed by cucumber seedlings and root systems, zeta1The ash coefficient of the predicted value of the water needed by the cucumber seedling and the root system,
Figure GDA0003550910160000043
predicted value of water ash needed by melon seedlings and root systems, zeta2Is a polynomial coefficient of a predicted value of water needed by cucumber seedlings and root systems,
Figure GDA0003550910160000044
a polynomial forecast value of water needed by melon seedlings and root systems.
Figure GDA0003550910160000045
The expression is as follows:
Figure GDA0003550910160000046
in the formula, GRNtIs known water data, alpha, required by cucumber seedlings and rootshCoefficient of water development required for cucumber seedlings and root systems, muhThe water ash content required by the cucumber seedlings and the root systems. Alpha is alphahAnd muhThe acquisition method comprises the following steps:
Figure GDA0003550910160000051
in the formula, BhA water data gray matrix, Y, required by cucumber seedlings and root systemshThe water data original matrix is needed by the cucumber seedling and the root system.
BhAnd YhAre respectively:
Figure GDA0003550910160000052
wherein n is the known water data number required by the seedlings and the roots.
Figure GDA0003550910160000053
The expression of (a) is as follows:
Figure GDA0003550910160000054
in the formula, z0Polynomial constant term of water content required for melon seedlings and root systems, z1Polynomial primary term of water content needed by melon seedling and root system2Polynomial quadratic term of water content needed by melon seedling and root system, z3Polynomial cubic term of water needed by melon seedling and root system0、z1、z2And z3The value of (c) can be obtained by solving an over-determined equation.
ζ1And ζ2Can be obtained by taking the following derivative:
Figure GDA0003550910160000055
for the fact that the water needed by the cucumber fruits accords with the rapid growth rule, the water can be predicted by adopting a grey system theory, and the expression is as follows:
Figure GDA0003550910160000061
in the formula (I), the compound is shown in the specification,
Figure GDA0003550910160000062
GGN is the predicted value of water required by cucumber fruitstIs known cucumber fruitThe moisture data required, αβCoefficient of development of moisture data required for cucumber fruits, muβAmount of water graying required for cucumber fruits, alphaβAnd muβThe acquisition method comprises the following steps:
Figure GDA0003550910160000063
in the formula, BβGray matrix of water fraction data, Y, required for cucumber fruitsβThe data of the water fraction required by the cucumber fruits is an original matrix.
BβAnd YβAre respectively:
Figure GDA0003550910160000064
wherein m is the known water data number of the cucumber fruits.
Water demand N for cucumber planted in greenhousetCan be expressed as follows:
Figure GDA0003550910160000065
in the formula, thIs the time when the cucumber fruit begins to grow.
Water demand N for cucumber planted in greenhousetAnd cucumber Water absorption XStCan be represented by the following formula:
Nt＝ψt×XSt
in the formula, #tFor transforming the coefficients, #tIn a laboratory, the method is only partial discrete values, shows an irregular state and shows convexity of data, so that a grey system theoretical model cannot be used for fitting, and in order to ensure the accuracy of fitting data, the method adopts a cubic B-spline method to carry out psitThe fitting is specifically as follows:
Figure GDA0003550910160000071
in formula (II), psi'tThe value of the conversion coefficient at the present time t, F the number of the conversion coefficient, h the temporary variable of the conversion, Fh,3(t) is a transfer basis function, which is expressed as follows:
Figure GDA0003550910160000072
further, a permeability measuring sensor array is arranged in the greenhouse for planting the cucumbers in the third step to measure the soil permeability xitAnd to xitThe method for identifying and correcting the outliers comprises the following steps:
because the soil is always kept loose and the temperature and humidity in the greenhouse are not changed greatly in the existing agriculture, the invention changes xitConsidered as a constant. To obtain an accurate permeability xitTaking the average value of the array sensor as the permeability xitHowever, due to differences in terrain and the like in the greenhouse, the measured permeability value may have a wild value, and therefore, the wild value needs to be identified and corrected.
P permeability measuring sensors are arranged along the diagonal line of the greenhouse at the depth of 10 cm of greenhouse soil, and the actual measured permeability xi f is { xi f1，ξf2，ξf3，…，ξfpAnd predicting the actually measured permeability xi f by adopting a GM (1,1) model, wherein the kth actually measured permeability xi fkPredicted value of (2)
Figure GDA0003550910160000073
Comprises the following steps:
Figure GDA0003550910160000081
wherein k is the actual measured permeability number, alphashFor practical measurement of permeability coefficient of development, mushFor the actual measurement of the amount of permeability ash, alphashAnd mushThe acquisition method comprises the following steps:
Figure GDA0003550910160000082
in the formula, BshFor practical measurement of permeability gray matrix, YshFor the actual measurement of the permeability original matrix, BshAnd YshThe expression is as follows:
Figure GDA0003550910160000083
because the measured value of the penetration measuring sensor does not conform to the normal rule due to the interference of the terrain, vibration, impact and the like of the greenhouse, and the distribution rule of the measured value of the penetration measuring sensor cannot be accurately obtained in practice, the abnormal value is difficult to identify and judge, the invention provides a method for identifying the wild value in the measured value of the penetration measuring sensor by adopting gray prediction, and the method comprises the following specific steps:
if the k actual measured permeability ξ fkPredicted value of (2)
Figure GDA0003550910160000084
Satisfies the following conditions:
Figure GDA0003550910160000085
then consider the actual measured permeability ξ fkAs outliers, the values here are
Figure GDA0003550910160000086
And (6) correcting.
In the formula (I), the compound is shown in the specification,
Figure GDA0003550910160000087
is a predicted value
Figure GDA0003550910160000088
The two-norm of the right scale factor,
Figure GDA0003550910160000089
is a predicted value
Figure GDA00035509101600000810
The two norms of the left scale factor.
The corrected actual measured permeability is recorded as ξ fs, which can be expressed as:
ξfs＝{ξfs1，ξfs2，ξfs3，…，ξfsp}
therefore, it is
Figure GDA0003550910160000091
Further, the watering quantity Q of the cucumbers planted in the greenhouse is determined in the fourth steptThe method comprises the following steps:
the watering quantity Q of the cucumbers planted in the greenhouse is determined in the step onetWith cucumber Water absorption XStIn the second step, the water demand N for cucumber planted in the greenhouse is determinedtAnd cucumber Water absorption XStSo that the watering quantity Q of the cucumbers planted in the greenhouse can be determinedtThe expression is as follows:
Figure GDA0003550910160000092
the invention has the beneficial effects that:
(1) the relation among the watering amount of the greenhouse planted cucumbers, the water absorption amount of the cucumbers and the cucumber water demand is established, and a foundation is provided for accurate prediction of the watering amount of the greenhouse planted cucumbers;
(2) the wild value in the permeability measurement is identified and corrected by adopting a grey prediction theory, so that a foundation is provided for accurate prediction of watering quantity of the greenhouse-planted cucumbers;
(3) the watering quantity of the cucumbers planted in the greenhouse is accurately determined, and accurate watering service is provided for modern cucumber planting.
Description of the drawings:
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a comparison of water for the present invention versus a prior art process;
FIG. 3 is a comparison of permeability measurements of the present invention and without a method of identifying and correcting outliers;
FIG. 4 shows the cucumber yield of the present invention compared to the prior art.
The specific implementation mode is as follows:
example one
A dynamic intelligent prediction method for watering amount of greenhouse cucumber comprises the following steps:
the method comprises the following steps: determining cucumber watering quantity Q for greenhouse plantingtWith cucumber Water absorption XStThe relationship of (1);
step two: determining the water demand N for cucumber planting in greenhousetAnd determining the water demand N for planting cucumbers in the greenhousetAnd cucumber Water absorption XStThe relationship of (1);
step three: permeability measuring sensor array for measuring permeability xi arranged in greenhouse for planting cucumbertAnd to xitIdentifying and correcting the outliers;
step four: determining cucumber watering quantity Q for greenhouse plantingt。
Example two
Furthermore, the watering quantity Q of the cucumbers planted in the greenhouse in the step onetWith cucumber absorbed water amount XStThe relationship establishing method (2) is as follows:
because the water poured into the ground is not completely absorbed by the cucumber during the planting process, and the cucumber is a water-loving plant, the water absorbed by the cucumber is not the water required by the cucumber, and may be completely more than the water required by the cucumber. Part of the water poured into the greenhouse permeates the underground, part of the water is evaporated into the air, and the rest part of the water is the absorbed quantity. For accurately predicting required watering quantity QtThe amount of watering water Q must be obtainedtWith cucumber Water absorption XStRelation between cucumber Water absorption XStLength l of cucumber root systemtThe water absorption part of the root system of the cucumber can be regarded as the radius of ltHemispheroid of (1), cucumber uptake XStThe expression can be expressed as:
Figure GDA0003550910160000101
wherein t is the growth time of the cucumber, and the timing of seedling emergence is started from the cucumber; xitAnd omega is the water absorption coefficient of the cucumber, and the value can be obtained by calibration.
Root length of cucumber ltThe change along with the time is continuous, only partial data is known at present, and the length l of the root system of the cucumber must be known to realize automatic wateringtAll real-time values of (1), cucumber root length ltConforming to homogeneous exponential law, for this purpose, carrying out l by using DGM (1,1) model (discrete grey model)tPrediction of lt+1The expression of the predicted value is as follows:
Figure GDA0003550910160000111
in the formula (I), the compound is shown in the specification,
Figure GDA0003550910160000112
the root length of cucumber at the t +1 th moment, betal1Is the first grey coefficient, beta, of root system length of cucumberl2The second grey coefficient is the root length of cucumber.
βl1And betal2The method comprises the following steps:
Figure GDA0003550910160000113
in the formula, BgIs gray matrix of cucumber root length, YgIs an original matrix of the root length of the cucumber.
Figure GDA0003550910160000114
Wherein m is the number of data of known cucumber root system length.
EXAMPLE III
The second step is to determine the water demand N of the cucumbers planted in the greenhouse at different timetWater demand N for planting cucumbers in greenhousetAnd cucumber Water absorption XStThe relationship of (a) to (b) is as follows:
according to the growth cycle analysis of cucumbers, the water quantity required by the cucumbers mainly comprises two parts, wherein the first part is the water required by the seedlings and the roots of the cucumbers, and the second part is the water required by the fruits of the cucumbers. The planting of cucumbers in the modern agricultural greenhouse adopts a uniform management mode, adopts a uniform seedling raising and uniform picking mode, and the cucumbers with smaller fruits are picked off uniformly, so that the growth cycles of all the cucumbers can be regarded as the same. At present, only a small amount of water required by seedlings and root systems and water required by cucumber fruits are known in cucumber planting, and the water required at all moments in the growth cycle of the cucumber needs to be known to meet the requirement of the optimal planting process of modern agriculture, so that the required water needs to be predicted.
The moisture required by the cucumber seedlings and the root systems tends to be stable after rapidly increasing in the growth period, the single model is difficult to accurately describe, the gray prediction model has good predictability on stable slightly-fluctuating data, and polynomial fitting has high accuracy on rapidly-changing data prediction, so that the invention combines the two fitting methods to predict the moisture required by the cucumber seedlings and the root systems, and the expression is as follows:
Figure GDA0003550910160000121
in the formula (I), the compound is shown in the specification,
Figure GDA0003550910160000122
predicted value of water needed by cucumber seedlings and root systems, zeta1The ash coefficient of the predicted value of the water needed by the cucumber seedling and the root system,
Figure GDA0003550910160000123
predicted value of water ash needed by melon seedlings and root systems, zeta2Is a polynomial coefficient of a predicted value of water needed by cucumber seedlings and root systems,
Figure GDA0003550910160000124
a polynomial forecast value of water needed by melon seedlings and root systems.
Figure GDA0003550910160000125
The expression is as follows:
Figure GDA0003550910160000126
in the formula, GRNtIs known water data, alpha, required by cucumber seedlings and rootshCoefficient of water development required for cucumber seedlings and root systems, muhThe water ash content required by the cucumber seedlings and the root systems. Alpha is alphahAnd muhThe acquisition method comprises the following steps:
Figure GDA0003550910160000131
in the formula, BhA water data gray matrix, Y, required by cucumber seedlings and root systemshThe water data original matrix is needed by the cucumber seedling and the root system.
BhAnd YhAre respectively:
Figure GDA0003550910160000132
wherein n is the known water data number required by the seedlings and the roots.
Figure GDA0003550910160000133
The expression of (a) is as follows:
Figure GDA0003550910160000134
in the formula, z0Polynomial constant term of water content required for melon seedlings and root systems, z1Is seedling and root of melonIs a polynomial linear term of the required water content, z2Polynomial quadratic term of water content needed by melon seedling and root system, z3Polynomial cubic term of water needed by melon seedling and root system0、z1、z2And z3The value of (c) can be obtained by solving an over-determined equation.
ζ1And ζ2Can be obtained by taking the following derivative:
Figure GDA0003550910160000135
for the fact that the water needed by the cucumber fruits accords with the rapid growth rule, the water can be predicted by adopting a grey system theory, and the expression is as follows:
Figure GDA0003550910160000141
in the formula (I), the compound is shown in the specification,
Figure GDA0003550910160000142
GGN is the predicted value of water required by cucumber fruitstIs the water data required by the known cucumber fruits, alphaβCoefficient of development of moisture data required for cucumber fruits, muβAmount of water graying required for cucumber fruits, alphaβAnd muβThe acquisition method comprises the following steps:
Figure GDA0003550910160000143
in the formula, BβGray matrix of water fraction data, Y, required for cucumber fruitsβThe data of the water fraction required by the cucumber fruits is an original matrix.
BβAnd YβAre respectively:
Figure GDA0003550910160000144
wherein m is the known water data number of the cucumber fruits.
Water demand N for cucumber planted in greenhousetCan be expressed as follows:
Figure GDA0003550910160000145
in the formula, thIs the time when the cucumber fruit begins to grow.
Water demand N for cucumber planted in greenhousetAnd cucumber Water absorption XStCan be represented by the following formula:
Nt＝ψt×XSt
in the formula, #tFor transforming the coefficients, #tIn a laboratory, the method is only partial discrete values, shows an irregular state and shows convexity of data, so that a grey system theoretical model cannot be used for fitting, and in order to ensure the accuracy of fitting data, the method adopts a cubic B-spline method to carry out psitThe fitting is specifically as follows:
Figure GDA0003550910160000151
in formula (II), psi'tThe value of the conversion coefficient at the present time t, F the number of the conversion coefficient, h the temporary variable of the conversion, Fh,3(t) is a transfer basis function, which is expressed as follows:
Figure GDA0003550910160000152
example four
In the third step, a permeability measuring sensor array is arranged in the greenhouse for planting the cucumbers to measure the permeability xitAnd to xitThe method for identifying and correcting the outliers comprises the following steps:
because the soil is always kept loose and the temperature and the humidity in the greenhouse are not greatly changed in the prior agriculture, the invention can solve the problems of low cost and low costξtConsidered as a constant. To obtain an accurate permeability xitTaking the average value of the array sensor as the permeability xitHowever, due to differences in terrain and the like in the greenhouse, the measured permeability value may have a wild value, and therefore, the wild value needs to be identified and corrected.
P permeability measuring sensors are arranged along the diagonal line of the greenhouse at the depth of 10 cm of greenhouse soil, and the actual measured permeability xi f is { xi f1，ξf2，ξf3，…，ξfpAnd predicting the actually measured permeability xi f by adopting a GM (1,1) model, wherein the kth actually measured permeability xi fkPredicted value of (2)
Figure GDA0003550910160000153
Comprises the following steps:
Figure GDA0003550910160000161
wherein k is the actual measured permeability number, alphashFor practical measurement of permeability coefficient of development, mushFor the actual measurement of the amount of permeability ash, alphashAnd mushThe acquisition method comprises the following steps:
Figure GDA0003550910160000162
in the formula, BshFor practical measurement of permeability gray matrix, YshFor the actual measurement of the permeability original matrix, BshAnd YshThe expression is as follows:
Figure GDA0003550910160000163
because the measured value of the penetration measuring sensor does not conform to the normal rule due to the interference of the terrain, vibration, impact and the like of the greenhouse, and the distribution rule of the measured value of the penetration measuring sensor cannot be accurately obtained in practice, the abnormal value is difficult to identify and judge, the invention provides a method for identifying the wild value in the measured value of the penetration measuring sensor by adopting gray prediction, and the method comprises the following specific steps:
if the k actual measured permeability ξ fkPredicted value of (2)
Figure GDA0003550910160000164
Satisfies the following conditions:
Figure GDA0003550910160000165
then consider the actual measured permeability ξ fkAs outliers, the values here are
Figure GDA0003550910160000166
And (6) correcting.
In the formula (I), the compound is shown in the specification,
Figure GDA0003550910160000167
is a predicted value
Figure GDA0003550910160000168
The two-norm of the right scale factor,
Figure GDA0003550910160000169
is a predicted value
Figure GDA00035509101600001610
The two norms of the left scale factor.
The corrected actual measured permeability is recorded as ξ fs, which can be expressed as:
ξfs＝{ξfs1，ξfs2，ξfs3，…，ξfsp}
therefore, it is
Figure GDA0003550910160000171
EXAMPLE five
The embodiment is further explained for the first embodiment, and the watering quantity Q of the cucumbers planted in the greenhouse is determined in the fourth step of the schemetThe method comprises the following steps:
the watering quantity Q of the cucumbers planted in the greenhouse is determined in the step onetWith cucumber Water absorption XStIn the second step, the water demand N for cucumber planted in the greenhouse is determinedtAnd cucumber Water absorption XStSo that the watering quantity Q of the cucumbers planted in the greenhouse can be determinedtThe expression is as follows:
Figure GDA0003550910160000172
EXAMPLE six
This embodiment is further illustrated by the first embodiment, and the invention is implemented according to the implementation scheme of fig. 1, and after the permeability field value is treated by the invention (as shown in fig. 2), it can be seen that the watering amount is less by the invention and the highest relative water saving rate reaches 55.3% (as shown in fig. 3), and the cucumber yield is also higher by the invention and the highest yield is 545kg (as shown in fig. 4), thereby indicating that the invention is feasible.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, which are not to be construed as limiting the invention to the specific embodiments described above. Numerous other simplifications or substitutions may be made without departing from the spirit of the invention as defined in the claims and the general concept thereof, which shall be construed to be within the scope of the invention.

Claims (1)

1. A dynamic intelligent prediction method for watering amount of greenhouse cucumber comprises the following steps:
the method comprises the following steps: determining cucumber watering quantity Q for greenhouse plantingtWith cucumber Water absorption XStThe relationship of (1);
water absorption XS of cucumbertLength l of cucumber root systemtClosely related, the water absorbing part of the root system of cucumber can be regarded as the radius of ltHemispheroid of (1), cucumber uptake XStThe expression can be expressed as:
Figure FDA0003550910150000011
wherein t is the growth time of the cucumber, and the timing of seedling emergence is started from the cucumber; xitThe soil permeability is adopted, and omega is the cucumber water absorption coefficient;
root length of cucumber ltConforming to homogeneous exponential law, adopting DGM (1,1) model to carry out ltPrediction of lt+1The expression of the predicted value is as follows:
Figure FDA0003550910150000012
in the formula (I), the compound is shown in the specification,
Figure FDA0003550910150000013
the root length of cucumber at the t +1 th moment, betal1Is the first grey coefficient, beta, of root system length of cucumberl2The second grey coefficient is the root length of the cucumber;
βl1and betal2The method comprises the following steps:
Figure FDA0003550910150000014
in the formula, BgIs gray matrix of cucumber root length, YgIs an original matrix of the root length of the cucumber;
Figure FDA0003550910150000015
in the formula, m is the number of data of known cucumber root system length;
step two: determining the water demand N for cucumber planting in greenhousetAnd determining the water demand N for planting cucumbers in the greenhousetAnd cucumber Water absorption XStThe relationship of (1);
the water quantity required by the cucumber mainly comprises two parts, wherein the first part is the water required by the cucumber seedling and the root system, and the second part is the water required by the cucumber fruit;
the gray prediction model and polynomial fitting are combined to predict the moisture required by the cucumber seedlings and the root systems, and the expression is as follows:
Figure FDA0003550910150000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003550910150000022
predicted value of water needed by cucumber seedlings and root systems, zeta1The ash coefficient of the predicted value of the water needed by the cucumber seedling and the root system,
Figure FDA0003550910150000023
predicted value of water ash needed by melon seedlings and root systems, zeta2Is a polynomial coefficient of a predicted value of water needed by cucumber seedlings and root systems,
Figure FDA0003550910150000024
polynomial forecasting value of water needed by melon seedlings and root systems;
Figure FDA0003550910150000025
the expression is as follows:
Figure FDA0003550910150000026
in the formula, GRNtIs known water data, alpha, required by cucumber seedlings and rootshCoefficient of water development required for cucumber seedlings and root systems, muhThe water ash action amount required by the cucumber seedlings and the root systems; alpha is alphahAnd muhThe acquisition method comprises the following steps:
Figure FDA0003550910150000027
in the formula, BhA water data gray matrix, Y, required by cucumber seedlings and root systemshA water data original matrix required by cucumber seedlings and root systems;
Bhand YhAre respectively:
Figure FDA0003550910150000031
in the formula, n is the known water data number required by the seedlings and the roots;
Figure FDA0003550910150000032
the expression of (a) is as follows:
Figure FDA0003550910150000033
in the formula, z0Polynomial constant term of water content required for melon seedlings and root systems, z1Polynomial primary term of water content needed by melon seedling and root system2Polynomial quadratic term of water content needed by melon seedling and root system, z3Polynomial cubic term of water needed by melon seedling and root system0、z1、z2And z3The value of (d) can be obtained by solving an over-determined equation;
ζ1and ζ2Can be obtained by taking the following derivative:
Figure FDA0003550910150000034
for the fact that the water needed by the cucumber fruits accords with a rapid growth rule, a grey system theory is adopted for prediction, and the expression is as follows:
Figure FDA0003550910150000035
in the formula (I), the compound is shown in the specification,
Figure FDA0003550910150000036
GGN is the predicted value of water required by cucumber fruitstIs the water data required by the known cucumber fruits, alphaβCoefficient of development of moisture data required for cucumber fruits, muβAmount of water graying required for cucumber fruits, alphaβAnd muβThe acquisition method comprises the following steps:
Figure FDA0003550910150000041
in the formula, BβGray matrix of water fraction data, Y, required for cucumber fruitsβAn original matrix of water fraction data required by cucumber fruits;
Bβand YβAre respectively:
Figure FDA0003550910150000042
wherein m is the known water data number of the cucumber fruits;
water demand N for cucumber planted in greenhousetCan be expressed as follows:
Figure FDA0003550910150000043
in the formula, thThe time when the cucumber fruits start to grow;
water demand N for cucumber planted in greenhousetAnd cucumber Water absorption XStCan be represented by the following formula:
Nt＝ψt×XSt
in the formula, #tFor the conversion of the coefficients, psi is carried out using cubic B-splinetThe fitting is specifically as follows:
Figure FDA0003550910150000044
in formula (II), psi'tThe value of the conversion coefficient at the present time t, F the number of the conversion coefficient, h the temporary variable of the conversion, Fh,3(t) is a transfer basis function, which is expressed as follows:
Figure FDA0003550910150000051
step three: method for measuring soil permeability xi by arranging permeability measuring sensor array in greenhouse for planting cucumbertAnd to xitIdentifying and correcting the outliers;
because the soil is always kept loose and the temperature and the humidity in the greenhouse are not greatly changed in the existing agriculture, xi is changedtRegarded as constants; to obtain an accurate permeability xitTaking the average value of the array sensor as the soil permeability xitHowever, due to differences of terrain and the like in the greenhouse, the measured permeability value may have a wild value, so that the wild value needs to be identified and corrected;
p permeability measuring sensors are arranged along the diagonal line of the greenhouse at the depth of 10 cm of greenhouse soil, and the actual measured permeability xi f is { xi f1，ξf2，ξf3，…，ξfpAnd predicting the actually measured permeability xi f by adopting a GM (1,1) model, wherein the kth actually measured permeability xi fkPredicted value of (2)
Figure FDA0003550910150000052
Comprises the following steps:
Figure FDA0003550910150000053
wherein k is the actual measured permeability number, alphashFor practical measurement of permeability coefficient of development, mushFor the actual measurement of the amount of permeability ash, alphashAnd mushThe acquisition method comprises the following steps:
Figure FDA0003550910150000054
in the formula, BshFor practical measurement of permeability gray matrix, YshFor the actual measurement of the permeability original matrix, BshAnd YshThe expression is as follows:
Figure FDA0003550910150000061
the field value in the measured value of the penetration measuring sensor is identified by adopting a gray prediction method, which comprises the following steps:
if the k actual measured permeability ξ fkPredicted value of (2)
Figure FDA0003550910150000062
Satisfies the following conditions:
Figure FDA0003550910150000063
then consider the actual measured permeability ξ fkAs outliers, the values here are
Figure FDA0003550910150000064
Correcting;
in the formula (I), the compound is shown in the specification,
Figure FDA0003550910150000065
is a predicted value
Figure FDA0003550910150000066
The two-norm of the right scale factor,
Figure FDA0003550910150000067
is a predicted value
Figure FDA0003550910150000068
A two-norm of the left scaling factor;
the corrected actual measured permeability is recorded as ξ fs, which can be expressed as:
ξfs＝{ξfs1，ξfs2，ξfs3，…，ξfsp}
therefore, it is
Figure FDA0003550910150000069
Step four: determining cucumber watering quantity Q for greenhouse plantingt；
Watering quantity Q for greenhouse cucumber plantingtThe expression of (a) is as follows:
Figure FDA00035509101500000610
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110689173A (en) * 2019-09-12 2020-01-14 黄河水利委员会黄河水利科学研究院 Irrigation area agricultural irrigation water demand decision method and system
AU2021100962A4 (en) * 2021-02-21 2021-04-29 Chatterjee, Prasenjit Dr Smart irrigation enriched with fertilizer mixing and water wastage reduction using deep learning techniques

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104521699A (en) * 2014-11-18 2015-04-22 华北水利水电大学 Field intelligent irrigation on-line control management method
CN111492915A (en) * 2019-01-30 2020-08-07 王文梅 Cultivation method of organic desert rice

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110689173A (en) * 2019-09-12 2020-01-14 黄河水利委员会黄河水利科学研究院 Irrigation area agricultural irrigation water demand decision method and system
AU2021100962A4 (en) * 2021-02-21 2021-04-29 Chatterjee, Prasenjit Dr Smart irrigation enriched with fertilizer mixing and water wastage reduction using deep learning techniques

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A model predictive controller for precision irrigation using discrete lagurre networks;Emmanuel Abiodun Abioyea 等;《Computers and Electronics in Agriculture》;20210116;第1-11页 *
基于日需水量的作物非充分实时灌溉预报模型及应用;张振伟 等;《水电能源科学》;20140430;第167-170页 *

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