CN104521699A - Field intelligent irrigation on-line control management method - Google Patents

Abstract

The invention discloses a field intelligent irrigation on-line control management method. According to the invention, a small automatic weather station collects meteorological data; a soil water environment monitoring device determines the soil water content of a crop root area in real time; the soil water content of the root area is imported and stored in a database; a client logs in a field real-time intelligent irrigation system on-line; and field soil information management, crop information management, field real-time monitoring, field real-time irrigation simulation and irrigation forecasting, field information inquiry, and information statistics can be carried out. With the method provided by the invention, remote on-line irrigation real-time guidance can be carried out. Based on a network, real-time data collected by a data collection system is subjected to secondary processing such as analysis, induction and processing; the crop real-time evapotranspiration is calculated; and crop real-time insufficient irrigation simulation, irrigation forecasting and irrigation management are carried out on-line. Through system internal processing such as model simulation, parameter simulation and the like, decisions that whether the crop needs irrigation, irrigation water amount and irrigation time can be precisely made.

Description

Field intelligent irrigation On-line Control management method

Technical field

The present invention relates to farmland intelligent irrigation administrative skill field, particularly relate to a kind of field intelligent irrigation On-line Control management method.

Background technology

Current China water resource critical shortage, the contradiction of supply and demand for the water resource becomes increasingly conspicuous, and agricultural water is especially nervous.Therefore, strengthen China's field irrigation management, realize the precision of field irrigation, automation, intellectuality and modernization, efficient, the water saving of practical raising agricultural water resources utilize level, that constantly improves agricultural water resources distributes system rationally, improves water resource utilization efficiency comprehensively, realizes the sustainable development of water resource and become the task of top priority; Also be support with the modernization of field irrigation and promote modernization of water resources construction, realize an urgent demand of agricultural modernization.

China's agricultural water resources is in short supply causes very large threat to grain security, at crop growth period how fully, effectively utilize local precipitation, alleviating agricultural water resources in short supply, conscientiously improve crop yield, is one of difficulties being badly in need of at present solving.

Traditional farmland irrigation water management is generally pre-establish irrigation program according to the water allocation scheme of different water year, but in production practices, owing to there is difference between actual model year and the hydrology model year of prediction, traditional water management is difficult to adapt to fast changing weather condition., irrigation management level increasingly serious in current water scarcity, agriculture water supply situation is lower, it is significant that farmland irrigation water dynamic management is carried out in the weather forecast in conjunction with future time period.Along with the fast development of computer networking technology and monitoring technology, using agricultural, the real time data such as meteorological as input, inquire into the real-time irrigation program of crop and forecasting technique, the problem in science that under solution insufficient irrigation condition, crop is irrigated in real time, is also becoming a kind of new research contents.

Summary of the invention

The present invention is directed to China and lack the remote online control technology and the High-efficient Agricultural Water Use Intelligent Decision Technology problem that can be used for practical operation, a kind of field intelligent irrigation On-line Control management method is provided.

Technical scheme: a kind of field intelligent irrigation On-Line Control Method, comprises the following steps:

Step one, miniature automatic meteorological station gathers meteorological data, the soil moisture content of soil-water environment monitoring equipment the real time measure crop root zone, and the data importing gathering and measure also is stored into the database of the server be arranged on internet;

Step 2, client logs in the farmland real-time intelligent be positioned on described server online and irrigates software systems;

Step 3, described farmland real-time intelligent is irrigated software systems and is called data in described database, carries out the management of field soil information, crop information management, farmland are monitored in real time, field irrigates simulation and Irrigation Forecast, field information inquiry, Information Statistics in real time according to client operation.

Described field irrigates simulation and Irrigation Forecast in real time, on the basis of short-term weather prediction, according to the actual soil moisture of each field in irrigated area, the actual Evapotranspiration of crop, crop growth conditions, by setting up Forecast of Soil Moisture Content model, crop water forecast model, real-time irrigation forecast model, the real-time correction model of crop coefficient, real-time estimate crop in forecast period the need of the time of pouring water and pouring water, irrigation quantity, and at the end of each calculation interval, with soil moisture content measured value, the analogue value of soil moisture content, crop coefficient are revised.

A kind of field intelligent irrigation On-line Control management method, comprise the soil moisture detection unit for monitoring soil moist layer water content W, for monitoring the rainfall detecting unit of actual rainfall P, and irrigation system, also comprise Forecast of Soil Moisture Content model, the fuzzy clustering forecast model of the real-time water requirement of crop and online real-time irrigation forecast and Controlling model;

The real-time prediction model of described soil moisture content: W _i=W _i-1+ P _0i+ W _ti-ET _i+ M _i+ K _i

Wherein: W _i-1---the soil moisture content of i-th day original plan wettable layer;

W _i---the soil moisture content of irrigation wetting depth at the end of i-th day;

P _0i---the effective precipitation of i-th day;

W _ti---within i-th day, increased by irrigation wetting depth and the water yield increased;

ET _i---the water demand of crop of i-th day;

M _i---the irrigation quantity of i-th day;

K _i---the increment of groundwater of i-th day;

The fuzzy clustering forecast model of the real-time water requirement of described crop: ET=ET ₀' K _ck _w=sET ₀k _ck _w;

Wherein: ET _ithe water demand of crop of-the i-th day;

ET _0ithe crop reference evapotranspiration of-the i-th day;

K _cithe crop coefficient of-the i-th day;

K _wi-the soil moisture coefficient of i-th day when carrying out insufficient irrigation;

The cluster centre of s-different weather type,

Obtaining take sky as the online real-time irrigation forecast model of crop of period:

M _i＝W _i-1+P _0i+W _Ti-ET _i+K _i-W _i

Described online real-time irrigation forecast and Controlling model are according to the real-time irrigation quantity M of crop _icontrol irrigation system is irrigated in real time to crop.

Also comprising the progressively predictive model day by day of crop irrigation wetting depth moisture content, if the bury of groundwater of survey region is comparatively dark, when underground water is ignored to crop supply, is then that the water balance model of the crop irrigation wetting depth of forecasting period is with sky:

W _i＝W _i-1+P _0i+W _Ti-ET _i+M _i；

Wherein, i-th day initial, at the end of irrigation wetting depth soil moisture content as follows:

W _i-1＝1000nH _i-1θ _i-1

W _i＝1000nH _iθ _i

Wherein: H _i-1-the i-th day moistening layer depth of original plan;

H _iirrigation wetting depth at the end of-the i-th day is dark;

θ _i-1-the i-th day initial soil moisture content, to account for soil volume percentage;

θ _isoil moisture content at the end of-the i-th day, to account for soil volume percentage;

N-porosity of soil;

The increase of the i-th period internal cause irrigation wetting depth and the water yield W increased _ti;

W _Ti＝1000(H _i-H _i-1)·n·θ _deep

Wherein: θ _deep-deep soil moisture content;

The progressively predictive model day by day of crop irrigation wetting depth moisture content can be drawn:

${\mathrm{\θ}}_i=\frac{H_{i-1}}{H_i}{\mathrm{\θ}}_{i-1}-({\mathrm{ET}}_{i-1}-P_{0i-1}-W_{\mathrm{Ti}}-M_{i-1})/\left(1000n{H}_i\right).$

According to the progressively predictive model day by day of crop irrigation wetting depth moisture content, from the plantation date of crop, day by day the soil moisture content of crop root zone is predicted, until crop harvesting, thus carry out the stepwise predict day by day of soil water regime in crop whole breeding time.

Also comprise water demand of crop ET ₀fuzzy clustering Forecasting Methodology: to the long serial ET of history under different weather type ₀carry out fuzzy clustering, obtain the cluster centre s of different weather type, carry out ET ₀self-correction: ET ' ₀=sET ₀, reach and predict the water demand of crop under future weather sight more accurately;

The method of described cluster centre s is determined:

If x _ijfor the ET under different weather type ₀value, wherein, i represents weather pattern, i=1,2,3,4, represent fine respectively, cloud, the moon, rain; J represents ten days, j=1,2 ..., 36; By ET ₀categorised statistical form can to obtain under annual each ten days different weather type average ET for many years ₀eigenvalue matrix:

$x_{\mathrm{ij}}=\left[\begin{array}{cccc}{x}_{11}& x_{12}& ...& x_{1j}\\ x_{21}& x_{21}& ...& x_{2j}\\ ...& ...& ...& ...\\ x_{i1}& x_{i2}& ...& x_{\mathrm{ij}}\end{array}\right]$

Each ten days ET ₀the relative defects matrix of eigen value:

Wherein: maxx _ij---j ten days ET ₀the maximum of eigen value;

Relative defects matrix then under j ten days i-th kind of weather pattern is:

$R=r_{\mathrm{ij}}=\left[\begin{array}{cccc}{r}_{11}& r_{12}& ...& r_{1j}\\ r_{21}& r_{22}& ...& r_{2j}\\ ...& ...& ...& ...\\ r_{i1}& r_{i2}& ...& r_{\mathrm{ij}}\end{array}\right]$

If ET under i kind weather pattern ₀fuzzy clustering center matrix be:

$S=\left(S_{\mathrm{ih}}\right)=\left[\begin{array}{cc}{S}_{11}& S_{12}\\ S_{21}& S_{22}\\ ...& ...\\ S_{i1}& S_{i2}\end{array}\right]$

Wherein: s _ih---be ET under i kind weather pattern under classification mode h ₀eigen value normalized number, 0≤s _ih≤ 1;

h＝1，2，…，c；

Suppose to press fine day ET ₀relative defects classify, i.e. c=2, then its fuzzy clustering matrix is:

$U=\left(u_{\mathrm{hj}}\right)=\left[\begin{array}{cccc}{u}_{\mathrm{1,1}}& u_{\mathrm{1,2}}& ...& u_{\mathrm{1,36}}\\ u_{\mathrm{2,1}}& u_{\mathrm{2,1}}& ...& u_{\mathrm{2,36}}\end{array}\right]$

And satisfy condition:

$\left.\begin{array}{c}0\≤u_{\mathrm{hj}}\≤1\\ \underset{h=1}{\overset{c}{\mathrm{\Σ}}}{u}_{\mathrm{hj}}=0\\ \underset{j=1}{\overset{n}{\mathrm{\Σ}}}{u}_{\mathrm{hj}}>0\end{array}\right\}$

Then j ten days sample and different weather type ET ₀difference between cluster centre h, available broad sense Euclidean power distance represents, namely

$\left|\right|w_i(r_j-s_h)\left|\right|={\left\{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\right[w_i{(r_{\mathrm{ij}}-s_{\mathrm{ih}})}^2\left]\right\}}^{\frac{1}{2}}$

Wherein: i---weather pattern sum;

With u _hjthe weighting broad sense Euclidean obtained between sample j and classification h for weight weighs distance:

F(u _hj)＝u _hj||w _i(r _j-s _h)||，

Calculate cluster centre s, obtain ET ₀correction value ET ₀'.

Optimal fuzzy clustering center matrix s solution procedure is specific as follows:

(1) given u _hjwith s _ihthe required computational accuracy ε met ₁, ε ₂;

(2) suppose that meets a constraints u _hjand the initial fuzzy clustering matrix that element is all inequal

(3) substitute into corresponding fuzzy clustering centers matrix

$S_{\mathrm{ih}}^0=\frac{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{u}_{\mathrm{hj}}^{0^2}{w_i}^2{r}_{\mathrm{ij}}}{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{u}_{\mathrm{hj}}^{0^2}{w_i}^2}$

(4) substitute into ask first approximation fuzzy clustering matrix

$u_{\mathrm{hj}}^1=\frac{1}{\underset{h=1}{\overset{c}{\mathrm{\Σ}}}\frac{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{[w_i-(r_{\mathrm{ij}}-s_{\mathrm{ih}}^0)]}^2}{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{[w_i-(r_{\mathrm{ij}}-s_{\mathrm{ih}})]}^2}}$

(5) substitute into ask first approximation fuzzy clustering center matrix

(6) comparator matrix one by one with and matrix corresponding element, if and then iteration terminates, for Optimal cluster centers matrix s, otherwise repeat the step of (3) to (5) until meet required precision, existing research has demonstrated the convergence of iteration; Calculate through fuzzy clustering, cloud can be obtained, the moon, rain weather condition descend ET ₀for fine day ET ₀fuzzy clustering center.

Further, also to the analytical method of forecast precision:

Adopt absolute accumulative deviation (ABE), root-mean-square error (RMSE), relative error (RE), determination coefficient (R ²) and the approval coefficient index such as (IA) evaluate forecast precision;

$\mathrm{ABE}=\underset{k=1}{\overset{n}{\mathrm{\Σ}}}|x_k-y_k|$

$\mathrm{RMSE}=\sqrt{\frac{{\mathrm{\Σ}}_{k=1}^m{(x_k-y_k)}^2}{n}}$

$\mathrm{RE}=\frac{\mathrm{RMSE}}{\stackrel{\‾}{x}}$

$R^2=\frac{{\left[\mathrm{\Σ}(x_k-\stackrel{\‾}{x})(y_k-\stackrel{\‾}{y})\right]}^2}{\mathrm{\Σ}{(x_k-\stackrel{\‾}{x})}^2\mathrm{\Σ}{(y_k-\stackrel{\‾}{y})}^2}$

$\mathrm{IA}=1-\frac{{\mathrm{\Σ}}_{k=1}^n{(x_k-y_k)}^2}{{\mathrm{\Σ}}_{k=1}^n{\left(\right|x_k^{\′}|+|y_k^{\′}\left|\right)}^2}$

$x_k^{\′}=x_k-\stackrel{\‾}{x}$

$y_k^{\′}=y_k-\stackrel{\‾}{y}$

Various middle x above _kfor predicted value, y _kfor real value, k=1,2,3,4,5 ..., n; be respectively the mean value of predicted value sequence and real value sequence, n is the sample number of predicted value and real value sequence.

Predict the outcome precision evaluation standard

Further, carry out water condition according to crop and carry out fully or insufficient irrigation, very large when carrying out the water yield, when being enough to satisfied irrigation demand, irrigation quantity M _ifor:

M _i＝1000·n·H _i(1-θ _i)·θ _max

Wherein: θ _ithe initial soil moisture content in-the i-th day (stage);

H _ithe crop irrigation wetting depth degree of depth in-the i-th day (stage);

When water is not enough or water resources quantity is in short supply, insufficient irrigation is carried out to crop; Irrigation quantity M _ifor:

M _i＝1000·n·H _i(θ _c1-θ _i)·θ _max

Wherein: θ _c1-irrigate the rear soil moisture content that will reach, according to crop tolerance level, insufficient irrigation determines that concrete value is further, the real-time adjusted coefficient K of soil moisture _wiamending method be:

$K_{\mathrm{wi}}=\left\{\begin{array}{cc}\ln (1+100\frac{{\mathrm{\&theta;}}_i}{{\mathrm{\&theta;}}_{\max }})/\ln 101& {\mathrm{\&theta;}}_{c2}\&le;{\mathrm{\&theta;}}_i<{\mathrm{\&theta;}}_{c1}\\ \mathrm{\&alpha;}\&CenterDot;\exp \left[({\mathrm{\&theta;}}_i-{\mathrm{\&theta;}}_{c2})\right]/{\mathrm{\&theta;}}_{c2}& {\mathrm{\&theta;}}_i<{\mathrm{\&theta;}}_{c2}\end{array}\right.$

Wherein: θ _i-the i-th day soil moisture content, to account for soil volume percentage;

θ _max-field capacity, to account for soil volume percentage;

θ _c1-insufficient irrigation Suitable Soil Moisture upper limit index, to account for field capacity θ _maxpercentage represent, determine by different experimental programs respectively in research;

θ _c2-insufficient irrigation Lower Limit of Suitable Soil Moisture index, to account for θ _maxpercentage represent; Determine by different experimental programs respectively in research;

α-empirical coefficient, same crop is fixed value.

Further, described crop coefficient K _cithe determination of initial value, when operational system first, determines crop coefficient K _ciinitial value, K _cicalculating initial value adopt with the computational methods of Crop growing stage characteristics i Day-to-day variability:

$K_{\mathrm{ci}}=\left\{\begin{array}{cc}7.346{(i/I)}^2-1.606(i/I)+0.0972& i=I\&le;0.058\\ -3.463\ln (i/I)-0.1909& i/I\&GreaterEqual;0.058\end{array}\right.$

Wherein: I-breeding time total number of days.

I-th at the end of-1 day, the actual measurement soil moisture initial value θ of i-th day _ibe known; If the soil moisture initial value θ of prediction in i-th day _i' and actual measurement soil moisture initial value θ _iclosely, the crop coefficient of the i-th-1 day just gets initial value; If θ _i' and θ _idiffer larger, by actual measurement Soil moisture θ _iinstead can push away the actual water requirement of crop of the i-th-1 day:

ET′ _i-1＝1000nH _i-1θ _i-1+P _0i-1+W′ _ri+M _i-1-1000nH _iθ _i

Wherein: ET _i-1'-revised the i-th-1 day the actual of crop needs water;

M _i-1the irrigation quantity of-the i-th-1 day;

After then revising, the actual water requirement of crop of the i-th-1 day is:

ET′ _i-1＝W _i-1+P _0i-1+W′ _ri+M _i-1-W _i'

In formula: W _i-1, W _i'---be respectively the i-th-1 day initial, at the end of soil moisture content;

And then revised the i-th-1 day crop coefficient K ' can be obtained _ci-1.

K' _c,i-1＝ET′ _i-1/(K' _w,i-1·ET _0,i-1)

Namely at the end of the i-th-1 day, have modified the crop coefficient value on the same day, and using the input value of this correction value as next calculation interval, the rest may be inferred, realize crop coefficient K in the time of infertility _cday by day the correction of value.

Further, also comprise the dark initial value Confirming model day by day of crop irrigation wetting depth:

$H_i=h_{n-1}+(h_n-h_{n-1})\&CenterDot;(i-\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}l_{j-1}^{j-1}{}_{j-1})/l_n^n$

Wherein: H _i---the irrigation wetting depth degree of depth in i-th day (stage) of crop;

H _n-1---irrigation wetting depth degree of depth when the n-th breeding time is initial;

H _n---the irrigation wetting depth degree of depth at the end of the n-th breeding time;

N---breeding time residing for crop;

I---crop growth accumulation number of days after planting;

---the growth number of days of the n-th breeding time;

---the growth number of days of jth breeding time, j=1,2 ..., n.

The layering mensuration of this soil moisture and the real-time correction of soil moisture.Above for crop root zone of action soil layer is considered as a total system by native system, when choosing the same day 2 from monitored soil moisture data, 8 time, 14 time, 20 time etc. the actual measurement soil water moisture content in four monitoring moment, be taken as the above all the sensors of the irrigation wetting depth degree of depth residing for thing not in the same time the average of surveyed soil moisture content as the soil moisture measured value on the same day.

Make θ w _i(j, h _l) represent i-th day j monitor the moment be positioned at the dark h of soil layer _lplace's soil moisture content of surveying of sensor, then i-th day 2 time, 8 time, 14 time, 20 time at the dark h of soil layer _lthe Soil moisture at place just can be designated as respectively: θ w _i(2, h _l), θ w _i(8, h _l), θ w _i(14, h _l), θ w _i(20, h _l), then arithmetic mean method can be adopted to calculate i-th day dark h of given soil layer _lthe Soil moisture at place

${\mathrm{\&theta;}}_{i,h_l}=\frac{{\mathrm{\&theta;w}}_i(2,h_l)+{\mathrm{\&theta;w}}_i(8,h_l)+{\mathrm{\&theta;w}}_i(14,h_l)+\mathrm{\&theta;}{w}_i(20,h_l)}{4}$

Adopt weighted mean rule can obtain the Soil moisture θ of i-th day irrigation wetting depth _i:

${\mathrm{\&theta;}}_i=\underset{l=1}{\overset{m}{\mathrm{\&Sigma;}}}{w}_i{\mathrm{\&theta;}}_{i,h_l}$

In formula: w _i-be soil level h _lsoil moisture to θ _iweighing factor;

M-soil layer number.

When carrying out soil moisture content prediction, take sky as calculation interval, recursive process is as follows: when breeding time, first time was run, a soil moisture content is surveyed when breeding time, first day started, as stage initial value, the soil moisture content of recurrence formula (6) recursion stage end every day day by day that utilizes soil moisture day by day, and carry out contrasting and revising with the soil moisture content of actual measurement on the same day, and using the initial value of revised soil moisture content as next stage, so day by day order recursion, carries out simulation and the correction of irrigation wetting depth moisture content every day.

Carrying out in the process calculated, if calculate that obtaining moisture content on the i-thth is less than or equal to minimum permission moisture content breeding time residing for crop, consider weather forecast situation simultaneously, when without rain or Extreme Low Precipitation, adopt the real-time irrigation forecast model of Section 5.2 to make Irrigation Forecast to this day, and carry out Web Publishing, the same day soil moisture content be adapted to and pour water after soil moisture content, the recursion of irrigation wetting depth moisture content is carried out again, until terminate breeding time as initial value.If weather forecast has rainfall to occur, then need to consider effective precipitation, adopt soil moisture recursion day by day ${\mathrm{\&theta;}}_i=\frac{H_{i-1}}{H_i}{\mathrm{\&theta;}}_{i-1}-({\mathrm{ET}}_{i-1}-P_{0i-1}-W_{\mathrm{Ti}}-M_{i-1})/\left(1000n{H}_i\right)$ Analyses and prediction are carried out to the moisture content of irrigation wetting depth, occur if any larger rainfall at Crop growing stage, cause when occurring that soil moisture content exceedes the situation of field capacity in analog computation process, then soil moisture content is treated to field capacity, the unnecessary water yield infiltrates deep soil.

Crop coefficient K _cithe determination of initial value, when operational system first, determines crop coefficient K _ciinitial value, K _cicalculating initial value adopt with the computational methods of Crop growing stage characteristics i Day-to-day variability

$K_{\mathrm{ci}}=\left\{\begin{array}{cc}7.346{(i/I)}^2-1.606(i/I)+0.0972& i/I\&le;0.058\\ -3.463\ln (i/I)-0.1909& i/I\&GreaterEqual;0.058\end{array}\right.$

Wherein: I-breeding time total number of days.

The real-time correction model of described crop coefficient is K' _{c, i-1}=ET ' _i-1/ (K' _{w, i-1}eT _{0, i-1}), in formula, ET ' _i-1for revising the actual water requirement of crop of latter the i-th-1 day, computing formula is: ET ' _i-1=W _i-1+ P _0i-1+ W ' _ri+ M _i-1-W _i', K' _{w, i-1}be the actual soil moisture coefficient of the i-th-1 day, W _i-1and W _i-1' be respectively the i-th-1 day initial, at the end of soil moisture content, M _i-1be the irrigation quantity of the i-th-1 day, P _0i-1it is the effective precipitation of the i-th-1 day.

I-th at the end of-1 day, the actual measurement soil moisture initial value θ of i-th day _ibe known; If the soil moisture initial value θ of prediction in i-th day _i' and actual measurement soil moisture initial value θ _iclosely, the crop coefficient of the i-th-1 day just gets initial value; If θ _i' and θ _idiffer larger, by actual measurement Soil moisture θ _iinstead can push away the actual water requirement of crop of the i-th-1 day:

ET′ _i-1＝1000nH _i-1θ _i-1+P _0i-1+W′ _ri+M _i-1-1000nH _iθ _i

Wherein: ET _i-1'-revised the i-th-1 day the actual of crop needs water;

M _i-1the irrigation quantity of-the i-th-1 day;

After then revising the _i- ₁it the actual water requirement of crop is:

ET′ _i-1＝W _i-1+P _0i-1+W′ _ri+M _i-1-W _i'

In formula: W _i-1, W _i'---be respectively the i-th-1 day initial, at the end of soil moisture content;

And then revised the i-th-1 day crop coefficient K ' can be obtained _ci-1.

K' _c,i-1＝ET′ _i-1/(K' _w,i-1·ET _0,i-1)

Namely at the end of the i-th-1 day, have modified the crop coefficient value on the same day, and using the input value of this correction value as next calculation interval, the rest may be inferred, realize crop coefficient K in the time of infertility _cday by day the correction of value.

The systemic-function of invention comprises:

1, carry out remote online and irrigate real-time instruction.This management system is Network Based, according to the real-time moisture content result of short term forecast of weather result and soil, according to the data analysis of model to data collecting system Real-time Collection, conclude, the secondary operations such as process, calculate the real-time Evapotranspiration of crop and carry out the real-time insufficient irrigation simulation of crop online, Irrigation Forecast and irrigation management, pass through modeling, the internal system process such as parameters simulation, precisely determine that crop is the need of irrigation, the decision-makings such as the water yield of irrigating and the time of irrigation, for user provides the Instant-Counseling of irrigation decision, and the service function of assisting water user to determine Different Irrigation scheme is provided, real-time instruction is made to agricultural irrigation.The Long-distance Control of crop irrigation, information management can be realized and monitor in real time, can also back up data, inquire about and add up, generating the statistical report form of data.

2, long-range displaying data in real-time information curve map.The long-range farmland microenvironment system that realizes irrigates the collection of information, inquiry and storage management in real time, the situation of change of the real time datas such as Dynamic Announce soil moisture content, the situation of valid data to monitored object is utilized to analyze, manage, the real-time change of soil moisture content can be provided continuously, and figure and Data pre-and post-processing.

3, the growth simulation of crop growth period is carried out in real time.Utilize the model in model library to carry out real-time Simulation to crop root zone soil moisture content, deeply dynamic real-time simulator is carried out to the root of root district moisture, crop, shows real-time monitored results.

4, real-time monitoring and the on-line normalization of crop soil moisture is carried out.Layering measurement and Real-Time Monitoring are carried out to crop root zone soil moisture content, collection, the remote transmission of completion system data and import in real time.

Accompanying drawing explanation

Fig. 1 is main function of system module map;

Fig. 2 is that system hardware is measured and data export overall construction drawing;

Fig. 3 is that farmland real-time irrigation system database ER schemes;

Fig. 4 gathers real-time soil moisture content schematic diagram data;

Fig. 5 is that crop irrigates model program Planning procedure figure in real time;

Fig. 6 is crop coefficient correction flow chart;

Fig. 7 is crop coefficient K in irrigating in real time _cconcrete correction figure.

Embodiment

Embodiment 1: see Fig. 1, field intelligent irrigation On-line Control management method comprises field trial station monitoring and management unit and central station administrative unit, and wherein, field trial station monitoring and management unit comprises meteorological data collection module and soil moisture content monitoring modular; Central station administrative unit comprises user management module, data upload module, field management module, crop information management, crop irrigation simulation, farmland monitors in real time, the display of real-time irrigation forecast, chart, field information inquiry and Information Statistics.

1 user management module

User management module mainly manages system user, can inquire about, add, revise and deletion action to system user.User can login system by username and password, and user management module stores the information such as Real Name, contact method of user, and user can check, changes and add oneself information.

System carries out remote access by network, and in order to ensure the safety and stability of system, except user profile and contact method, module is also provided with two rights management patterns of user, and namely user is divided into domestic consumer's identity and keeper's identity according to authority.Equity stock limits the use of family only to intrasystem display data and image browsing access, can cannot be added system data, revise and the operation such as deletion; And super-ordinate right user, i.e. administrator, not only can browse access system data and chart, and have the right to manage the various information of system and data and operate.

2 data upload modules

Data upload module mainly imports or data in derived data storehouse, safeguards system.

3 field management modules

The information mainly comprised in field management module is soil parameters value.In field management module, the inquiry to soil information, soil water appropriated moisture upper limit value and lower limit value, interpolation, amendment and delete function can be realized, facilitate user carry out managing to multiple field soil regime and monitor.

4 crop information management

Crop information module can present monitored crop information, comprises the information such as the kind of crops, field, soil moisture content upper and lower limit, precipitation station, crop-planting date, crop harvesting date, field initial soil water moisture content.User can inquire about above information, add, revise and delete according to demand, and by Ajax technology advanced in Java language synchronized update carried out to information on soil moisture, rainfall data and corresponding Monitoring Data etc. and asynchronously to transfer, for plant imitation and Irrigation Forecast are made sufficient preparation.

5 crop irrigation simulations

Crop irrigation analog module function can according to the data in database, the model in model library is utilized to realize recursion day by day to parameters such as plant growth and root region soil moisture content, crop coefficients, formulate the real-time irrigation program of crop, and adopt measured result to revise in real time analog result, thus School Affairs correction is carried out to the parameter in simulation model, number according to change relation when determining the parameters such as crop coefficient, root be dark and plant growth.

Log in successfully and can realize following a few partial function:

Calculate crop root zone soil moisture content: calculate the soil day moisture content average in certain a period of time as required from the plantation date;

Simulation irrigation date and irrigation volume: according to the information such as wind speed, air pressure of rainfall data, meteorological station, and system configuration parameter, analog computation faces the stage noly to be needed to irrigate, when irrigate, fills with the problem such as how many;

Carry out soil moisture coefficient k _wcorrection: according to actual measurement soil moisture content, analog computation result is revised.

6 farmlands are monitored in real time

System carries out remote monitoring to monitored object, utilizes valid data analyze monitored object and manage, and utilizes the intuitive of curve map to carry out dynamic real-time simulator to monitored object, show real-time monitored results.

7 real-time irrigation forecasts

Based on insufficient irrigation theory, the non-abundant real-time irrigation forecast of real-time online and simulation model, utilize weather forecast and the field soil moisture Real-time Monitoring Data of short-term, take into full account the utilization of precipitation, carry out the forecast of 1 day of irrigation program, 3 days and Different periods on the 7th.Operated by system model, long-rangely can determine irrigation time, the irrigation norm of crop in real time, realize crop coefficient K simultaneously _creal-time correction.

This module mainly realizes the prediction to irrigating the date.This module is by keeper from the current generation, and order calculates 1,3,7 backward, by deficit irrigation schedule simulation model, the soil moisture content of stage end can be obtained, compare with the soil moisture content lower limit of setting, thus judge to face the stage the need of irrigation, and irrigation date.

8 chart displays

Can realize rainfall, evaporation, soil moist layer, soil period moisture content, soil average moisture content and irrigate the inquiry of data, interpolation, amendment and delete function, crop coefficient empirical value and the crop coefficient analogue value are compared, and graphically can show intuitively the data of statistics.

9 field information inquiries

Can realize the single of field information or query composition.User carries out single or query composition according to soil name, information time, data value, and for user provides multiple inquiry mode, convenient user inquires about field information.

10 Information Statistics

Realize the Information Statistics that soil moisture content is relevant.After have selected a certain field, the total irrigation frequency of inquiry, total irrigation quantity, secondary irrigation volume and irrigation time etc. can be shown intuitively.

The software and hardware composition of system

Measure see Fig. 2 system hardware and data output overall construction drawing, adopt temperature sensor, humidity sensor, precipitation detecting instrument, wind speed and direction detecting instrument and intensity of illumination sensor monitoring atmospheric environment, soil moisture collector (as the EnviroScan) soil moisture to root system different depth is utilized to detect, using database as intermediate layer, use interface to the transmission of data, complete the Real-time Collection to soil regime, treatment and monitoring.

The present invention is to realize the automation of field irrigation, intelligent management for target, with soil-water environment real-time watch device EnviroScan and farmland miniature automatic meteorological station AWS for hardware relies on, based on meteorological data and basic information of field, for the deficiency of failing to make full use of rainfall in existing crop real-time irrigation forecast, to conclude the mathematical model of formation for foundation, Predicting and analysis is carried out to soil moisture, aid decision person carries out decision-making, in order to realize the intelligent management system of Farmland Water irrigation automation.For the master-plan of system, total management system database ER schemes as shown in Figure 3.Accurate, efficient quantitative decision-making that this systematic difference will realize crop water with development and manages, for Developing Water-saving Agriculture provides a new solution and thinking.

With the difference of other similar systems domestic and superiority as follows:

The water saving real-time online integrated management that the information gathering-process-decision-making-information feed back-monitoring that 1, can realize irrigation management is integrated.The problem of online irrigation management system is still lacked for current China, with crop non-fully online irrigation model and forecasting model in real time for core theory, use Target-oriented thought, adopt JAVA language, develop the online real time comprehensive management system of visual, interactively field irrigation, achieve the water saving integrated management that information gathering-process-decision-making-information feed back-monitoring is integrated.This system is to make full use of rainfall, and real-time, appropriate water operation is starting point, achieves the networking and long-range management of irrigating in real time farmland.System fast reaction can go out the change of parameter, the data syn-chronization surveyed with automatic weather station and soil-water environment real-time monitoring system EnviroScan; Irrigation decision and forecast suggestion can be made according to Changes in weather and crop growth conditions, system is to raising agricultural water efficiency, save agricultural water resources, alleviate agricultural water contradiction, solve semi-dryland farming grain security production problem, the modernization realizing field irrigation has important practical significance and huge social and economic benefits.

2, in irrigation forecast model, real-time key parameter is adopted first.For in existing domestic and international irrigation management, irrigating model key parameter adopts stage average to cause the present situation of model distortion, propose determination and the real-time correcting method of key parameter in irrigation and forecasting model, what field intelligent irrigation On-line Control management method adopted is short-term real-time parameter, belong to and initiating both at home and abroad, achieve non-fully online the irrigation in real time of crop and simulate and forecast.

3, Irrigation Forecast: the deficiency failing to make full use of rainfall in forecasting for existing crop irrigation, existing system lacks crop real-time root district data and the consideration facing stage weather condition, particularly lacks the problems such as operability when application implementation.This system is in conjunction with the measurement hardware in field, to obtain in plant growing process surrounding environment to the monitoring result of its upgrowth situation influence factor, the model library of calling system, based on short term forecast of weather, the soil moisture content of future time period (1,3,7 etc.) can be predicted, to the dynamic change of water demand in real-time Simulation process of crop growth, the upgrowth situation of timely grasp crops, and the soil moisture content according to crop root zone carries out Irrigation Forecast, thus reach and make full use of precipitation, effectively improve the effect of agricultural water resource utilization rate.The irrigation program formulated both had met the growth needs of crop, can meet its requirement to soil-water environment, improve again the effective rate of utilization of resource to greatest extent, can reduce production cost while ensuring the quality of products, improving crop yield.

4, realize the non-of water saving to irrigate fully in real time: for the deficiency underusing precipitation in real-time online deficit irrigation scheduling and Irrigation Forecast technology, model key parameter adopts stage average, number according to change relation when system model storehouse establishes the dark and plant growth of crop coefficient, root, establish and irrigate simulation and irrigation forecast model in real time fully online so that 1,3 and the 7 days crop that is the period is non-, achieve the object making full use of precipitation, precisely irrigate in real time, save agricultural water, improve irrigation water using efficiency.Can be applicable to limited irrigation and sufficient irrigation two kinds of situations, user is helped to utilize various irrigation strategy to carry out limitation and adequate water supply decision-making, lack of water is once reach a certain restriction degree, and system according to actual conditions, will carry out irrigation arrangement under the prerequisite considering irrigation system basis instrument.

5, precisely, intelligent irrigation: system by the arrangement of a large amount of information and related data, analyze and carry out soil moisture content forecast, formulate the real-time online irrigation program of corresponding optimum, technical support is provided to science, Precision Irrigation decision-making, long-range, real-time, the automation realizing irrigating Farmland Water, the intelligent management of networking, facilitate administrative staff and water user to the understanding of system, grasp and use.

6, realize storage, the inquiry of bulk information rapidly and accurately, process and upgrade in time, avoid the loss of available information; Meanwhile, can timely, intuitively, image and the omnibearing information representing complication system feature, real-time Irrigation Forecast is carried out to crop, and represent result mainly with chart, data mode occur, be display platform with Internet, be easy to user grasp and understand.

7, simplification and extensibility: system is simple and convenient, user only need by network, can enter system without the need to installing special client, easy to operate; System can be improved and adjustment by the continuous of practical function, can expand, control the decision-making of pouring water of zones of different, different research object flexibly according to the demand of user is convenient.

The Real-time Collection of data, obtain and process:

The content of Real-time Collection comprises meteorological data collection module and soil moisture content monitoring modular.

1 meteorological data observation

There is miniature automatic meteorological station (AWS) at deployment, in order to measure the meteorological information of every day, measure content and comprise 10 altogether, 2m wind speed, wind direction, solar radiation, soil temperature, rainfall, air humidity, air themperature, atmospheric pressure etc.Every meteorological data can adopt mode manually to obtain, and downloads to the notebook computer of user, and the mode of wireless transmission also can be adopted directly to obtain data.

2 determining soil moistures and acquisition

Soil moisture is the important ecological factors of plant growth, and the Accurate Determining of soil moisture carries out an important evidence of irrigating in real time.Native system adopts current state-of-the-art soil-water environment monitoring equipment EnviroScan to carry out the soil moisture content of the real time measure crop root zone.

During use during system, require to bury EnviroScan detector in field underground by predeterminated position, often organize EnviroScan detector and 16 sensors are installed altogether, measure the soil moisture numerical value in 0-2m soil layer.Each group detector comprises 2 EnviroScan detectors, and monitor the crop root zone soil moisture of 0-1.2m, 1.2m-2.0m respectively, the burial place between two detectors is spaced apart 2m.Consider the root growth feature of northern representative crop-winter wheat, in 0-1.2m crop root zone, detector installs a sensor every 10cm; In 1.2-2.0m crop root zone, detector installs a sensor every 20cm.Detector is connected with RT6 data acquisition unit by cable, the corresponding RT6 data acquisition unit of each EnviroScan detector.Data can with sdb form or excel formatted output, and between two forms, data can freely be changed, and acquisition time can need any setting according to user, and the soil moisture content data of sensor collection are to account for soil volume percentage (see Fig. 4).Data directly can download use with excel form, or are stored into system real-time database for model and call.Monitoring Data initial analysis.For understanding the soil water dynamics situation between crop whole breeding time, initial analysis is carried out to field crops soil water dynamics breeding time.

(domestic irrigation model is when carrying out real-time irrigation forecast with Irrigation Forecast in field intelligent irrigation simulation, mostly be after the rain soil moisture initial value is revised, lack the impact of considering to fill with front rainfall forecast, particularly lack the real-time irrigation model and the real-time irrigation forecast technology that can be used for practical operation):

1 real-time irrigation forecast

Online real-time irrigation forecast comprises based on the meteorological data of soil basic parameter and observation by farmland " in real time " data, dynamic according to up-to-date plant growth, realize to crop water situation accurate, estimate in real time, and in conjunction with the prediction of field soil moisture content and weather forecast situation, make irrigation decision in time, comprise irrigation date and irrigating water quota etc.Real-time irrigation forecast is the basis working out dynamic water plan.In traditional crop irrigation forecasting process, the meteorology change usually do not looked to the future in a short time, cause Irrigation Forecast result and actual production demand inconsistent, irrational irrigation forecasts such as irrigation are needed before may being made at precipitation, thus cause precipitation to make full use of, irrigating water quality can not play its higher production efficiency, and this is for water shortage zone, is a kind of significant wastage to water resource.

For utilizing rainfall resource as much as possible, alleviate the water scarcity situation of water-deficient area, when carrying out real-time irrigation forecast, not only to carry out the water balanced calculation of crop day by day sequentially, and when crop root region soil moisture content is down near the sufferable soil moisture content lower limit of crop, the weather condition that may occur in following short-term will be further considered when doing Irrigation Forecast, estimate the actual of crop according to water situation and need aqueous condition, and provide corresponding solution for different situation, and then formulate plan of pouring water accordingly.

When real-time irrigation forecast, first need, according to conditions such as orographic condition, the soil texture, crop varieties, crop growth stage, field microclimates, to carry out the correction of original state, determine the initial value of stage parameter; Then affect field water balance key element to all and affect the factor such as irrigation date, irrigating water quota and carry out day by day recursion and analysis, in conjunction with following field information prediction value and meteorological predicting condition, making irrigation decision.Utilize real measured data to carry out revising and self-correction by the period to correlation model parameters, the parameter impelling crop to irrigate in real time in model tends towards stability gradually, improves constantly accuracy and the real-time of Irrigation Forecast result simultaneously.

Therefore, during farmland is irrigated online in real time, how utilizing crop water model to simulate accurately and need hydrodynamic(al) state in process of crop growth, is the core of online real-time irrigation forecast, the Forecasting Soil Moisture how setting up Forecast of Soil Moisture Content model science is dynamic, is the basis of Irrigation Forecast.In crop irrigation forecasting model, often relate to the degree of depth, crop coefficient, soil moisture coefficient etc. of multiple parameter as irrigation wetting depth, the levels of precision of parameter is also the key factor affecting analog result.And make full use of Real-Time Monitoring information and feedback information and correction is day by day carried out to the parameter in crop water model, soil moisture content model and self-correction becomes the effective ways improving model accuracy, be also emphasis and the key of real-time irrigation forecast research.

2 crops are non-fully irrigates model online in real time

It is on the basis of short-term weather prediction that crop irrigates model online in real time, according to the actual soil moisture of each field in irrigated area, the actual Evapotranspiration of crop, crop growth conditions etc., by setting up Forecast of Soil Moisture Content model, crop water forecast model, real-time irrigation forecast model, the real-time correction model of crop coefficient etc., realize real-time estimate crop in the stage that faces the need of pouring water, irrigation quantity and irrigation period etc.Research is on the basis to soil moisture Real-Time Monitoring, fully model is irrigated in real time online by crop is non-, take into full account the rain fall in the stage of facing, predict the soil moisture dynamic content of following Different periods as far as possible accurately and real-time, and then formulate rational irrigation scheme, for agricultural production provides online direction and decision references, for saving agricultural water, raising irrigation water using efficiency provides scientific basis.

(1) Forecast of Soil Moisture Content model

Soil moisture content refers to the situation of contained humidity in certain volume soil, soil moisture content prediction namely according to surveying moisture in the soil result early stage, in conjunction with meteorological condition, by certain means predict soil moisture content in following a certain period number.Forecast of Soil Moisture Content is the basis of Irrigation Forecast, significant to the Reasonable Regulation And Control of the Farmland Water carried out under shortage of water resources condition.

Native system mainly uses principle of water balance analysis to formulate the irrigation program of Dry crop, sets up soil water balance model and carries out soil moisture content forecast.The water balance model taking sky as the crop irrigation wetting depth of forecasting period is as follows:

W _i=W _i-1+ P _0i+ W _ti-ET _i+ M _i+ K _i(1) in formula: W _i-1---the soil moisture content of i-th day original plan wettable layer, mm;

W _i---the soil moisture content of irrigation wetting depth at the end of i-th day, mm;

P _0i---the effective precipitation of i-th day, mm;

W _ti---within i-th day, increased by irrigation wetting depth and the water yield increased, mm;

ET _i---the water demand of crop of i-th day, mm;

M _i---the irrigation quantity of i-th day, mm;

K _i---the increment of groundwater of i-th day, mm.

If the bury of groundwater of survey region is comparatively dark, the supply of underground water to crop is negligible, therefore formula (1) can be changed into:

W _i＝W _i-1+P _0i+W _Ti-ET _i+M _i(2)

Wherein, i-th day initial, at the end of irrigation wetting depth soil moisture content can variously to be obtained by following respectively:

W _i-1＝1000nH _i-1θ _i-1(3)

W _i＝1000nH _iθ _i(4)

In formula: H _i-1-the i-th day moistening layer depth of original plan, mm;

H _iirrigation wetting depth at the end of-the i-th day is dark, mm;

θ _i-1-the i-th day initial soil moisture content, to account for soil volume percentage, %;

θ _isoil moisture content at the end of-the i-th day, to account for soil volume percentage, %;

N-porosity of soil, %.

The increase of the i-th period internal cause irrigation wetting depth can be obtained and the water yield W increased by formula (5) _ti.

W _Ti＝1000(H _i-H _i-1)·n·θ _deep(5)

In formula: θ _deep-deep soil moisture content, %;

The progressively predictive model day by day of crop irrigation wetting depth moisture content can be drawn by formula (2), (3), (4):

${\mathrm{\&theta;}}_i=\frac{H_{i-1}}{H_i}{\mathrm{\&theta;}}_{i-1}-({\mathrm{ET}}_{i-1}-P_{0i-1}-W_{\mathrm{Ti}}-M_{i-1})/\left(1000n{H}_i\right)---\left(6\right)$

According to the progressively predictive model day by day of crop irrigation wetting depth moisture content, from the plantation date of crop, day by day the soil moisture content of crop root zone is predicted, until crop harvesting, thus carry out the stepwise predict day by day of soil water regime in crop whole breeding time.

(2) the fuzzy clustering forecast model of the real-time water requirement of crop

Crop is irrigated in model online in real time, the real-time water requirement ET of crop _ithe irrigation of calculating to crop have the greatest impact, be the key of carrying out Irrigation Forecast.

The actual water requirement ET of crop is subject to the impact of the factors such as crop varieties, meteorological condition, soil types, planting conditions, and Changing Pattern is very complicated.When calculating the water demand of crop under insufficient irrigation condition, the factor of removing crop self, also must consider that soil moisture is lower than the inhibitory action in limited time to evapotranspiration under appropriate aqueous amount.On the basis analyzing crop water demand calculation method, consider the impact of crop and soil moisture, construct with the real-time water requirement estimation model of 1,3 and the 7 days crop that is calculation interval in native system respectively:

ET _i＝K _ci·K _wi·ET _0i(7)

In formula: ET _ithe water demand of crop in-the i-th day (stage), mm;

ET _0ithe crop reference evapotranspiration in-the i-th day (stage), mm;

K _cithe crop coefficient in-the i-th day (stage);

K _wi-carry out insufficient irrigation time i-th day (stage) soil moisture coefficient;

1. crop reference evapotranspiration ET ₀fuzzy clustering prediction

Reference crop evaporation transpiration quantity ET ₀reflect atmospheric environment and need water mitigation degree to plant growth, the short-term accuracy that reference crop evaporation transpiration quantity is predicted even day by day directly affects the forecast precision of irrigating in real time, is one of Focal point and difficult point of real-time irrigation forecast.The computational methods of crop reference evapotranspiration have a lot, and conventional has: method of pan evaporation, correction Penman equation, Penman-Monteith method, Hargreaves method, Priestley-Taylor method etc.When obtaining actual meteorological data, calculate ET with the Penman formula revised ₀optimal.But when predicting Methods of Reference Crop Evapotranspiration, because meteorological data can not entirely accurate be predicted, so will carry out forecast day ET according to different weather forecasting types in real-time prediction ₀estimate.

According to the needs of real-time irrigation forecast, study the ET will calculated according to historical summary ₀data carry out the statistic of classification by ten days according to fine, cloud, the moon, rain 4 kinds of weather patterns respectively, make the ET under different weather type ₀categorised statistical form.

Theoretical to the ET under different weather type based on variable fuzzy set ₀carry out fuzzy clustering, obtain the cluster centre s of different weather type, and passing type (8) determines reference crop evaporation transpiration quantity ET under forecast day weather pattern ₀' value, thus carry out ET ₀self-correction:

ET′ ₀＝s·ET ₀(8)

Wherein, cluster centre s can utilize weather pattern to send out ET to potential the rising of crop ₀the method of fuzzy cluster analysis is determined.Below that weather pattern sends out ET to potential the rising of crop ₀relative Fuzzy cluster centre s algorithm.

If x _ijfor the ET under different weather type ₀value, wherein, i represents weather pattern, i=1,2,3,4, represent fine respectively, cloud, the moon, rain; J represents ten days, j=1,2 ..., 36.By ET ₀categorised statistical form can to obtain under annual each ten days different weather type average ET for many years ₀eigenvalue matrix:

$x_{\mathrm{ij}}=\left[\begin{array}{cccc}{x}_{11}& x_{12}& ...& x_{1j}\\ x_{21}& x_{21}& ...& x_{2j}\\ ...& ...& ...& ...\\ x_{i1}& x_{i2}& ...& x_{\mathrm{ij}}\end{array}\right]---\left(9\right)$

Each ten days ET ₀the relative defects matrix of eigen value:

In formula: maxx _ij---j ten days ET ₀the maximum of eigen value.

Relative defects matrix then under j ten days i-th kind of weather pattern is:

$R=r_{\mathrm{ij}}=\left[\begin{array}{cccc}{r}_{11}& r_{12}& ...& r_{1j}\\ r_{21}& r_{22}& ...& r_{2j}\\ ...& ...& ...& ...\\ r_{i1}& r_{i2}& ...& r_{\mathrm{ij}}\end{array}\right]---\left(11\right)$

If ET under i kind weather pattern ₀fuzzy clustering center matrix be:

$S=\left(S_{\mathrm{ih}}\right)=\left[\begin{array}{cc}{S}_{11}& S_{12}\\ S_{21}& S_{22}\\ ...& ...\\ S_{i1}& S_{i2}\end{array}\right]---\left(12\right)$

In formula: s _ih---be ET under i kind weather pattern under classification mode h ₀eigen value normalized number, 0≤s _ih≤ 1;

h＝1，2，…，c。

Suppose to press fine day ET ₀relative defects classify, i.e. c=2, then its fuzzy clustering matrix is:

$U=\left(u_{\mathrm{hj}}\right)=\left[\begin{array}{cccc}{u}_{\mathrm{1,1}}& u_{\mathrm{1,2}}& ...& u_{\mathrm{1,36}}\\ u_{\mathrm{2,1}}& u_{\mathrm{2,1}}& ...& u_{\mathrm{2,36}}\end{array}\right]---\left(13\right)$

And satisfy condition:

$\left.\begin{array}{c}0\&le;u_{\mathrm{hj}}\&le;1\\ \underset{h=1}{\overset{c}{\mathrm{\&Sigma;}}}{u}_{\mathrm{hj}}=0\\ \underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{u}_{\mathrm{hj}}>0\end{array}\right\}---\left(14\right)$

Then j ten days sample and different weather type ET ₀difference between cluster centre h, available broad sense Euclidean power distance represents, namely

$\left|\right|w_i(r_j-s_h)\left|\right|={\left\{\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}\right[w_i{(r_{\mathrm{ij}}-s_{\mathrm{ih}})}^2\left]\right\}}^{\frac{1}{2}}---\left(15\right)$

In formula: i---weather pattern sum.

With u _hjthe weighting broad sense Euclidean obtained between sample j and classification h for weight weighs distance:

F(u _hj)＝u _hj||w _i(r _j-s _h)|| (16)

Optimal fuzzy clustering center matrix s solution procedure is specific as follows:

1. given u _hjwith s _ihthe required computational accuracy ε met ₁, ε ₂;

2. suppose that meets constraints (14) and all inequal initial fuzzy clustering matrix of element

3. substitute into corresponding fuzzy clustering centers matrix

$S_{\mathrm{ih}}^0=\frac{\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{u}_{\mathrm{hj}}^{0^2}{w_i}^2{r}_{\mathrm{ij}}}{\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{u}_{\mathrm{hj}}^{0^2}{w_i}^2}---\left(17\right)$

4. substitute into ask first approximation fuzzy clustering matrix

$u_{\mathrm{hj}}^1=\frac{1}{\underset{h=1}{\overset{c}{\mathrm{\&Sigma;}}}\frac{\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{[w_i-(r_{\mathrm{ij}}-s_{\mathrm{ih}}^0)]}^2}{\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{[w_i-(r_{\mathrm{ij}}-s_{\mathrm{ih}})]}^2}}---\left(18\right)$

5. substitute into ask first approximation fuzzy clustering center matrix

6. comparator matrix one by one with and matrix corresponding element, if and then iteration terminates, for Optimal cluster centers matrix s, otherwise repeat the step of (3) to (5) until meet required precision, existing research has demonstrated the convergence of iteration.

Calculate through fuzzy clustering, cloud can be obtained, the moon, rain weather condition descend ET ₀for fine day ET ₀fuzzy clustering center.Two forecast precision analyses

Adopt absolute accumulative deviation (ABE), root-mean-square error (RMSE), relative error (RE), determination coefficient (R ²) and the approval coefficient index such as (IA) evaluate forecast precision, the evaluation criterion of each statistical parameter is in table 1, and statistical variable expression is such as formula (19) ~ (25).

$\mathrm{ABE}=\underset{k=1}{\overset{n}{\mathrm{\&Sigma;}}}|x_k-y_k|---\left(19\right)$

$\mathrm{RMSE}=\sqrt{\frac{{\mathrm{\&Sigma;}}_{k=1}^m{(x_k-y_k)}^2}{n}}---\left(20\right)$

$\mathrm{RE}=\frac{\mathrm{RMSE}}{\stackrel{\&OverBar;}{x}}---\left(21\right)$

$R^2=\frac{{\left[\mathrm{\&Sigma;}(x_k-\stackrel{\&OverBar;}{x})(y_k-\stackrel{\&OverBar;}{y})\right]}^2}{\mathrm{\&Sigma;}{(x_k-\stackrel{\&OverBar;}{x})}^2\mathrm{\&Sigma;}{(y_k-\stackrel{\&OverBar;}{y})}^2}---\left(22\right)$

$\mathrm{IA}=1-\frac{{\mathrm{\&Sigma;}}_{k=1}^n{(x_k-y_k)}^2}{{\mathrm{\&Sigma;}}_{k=1}^n{\left(\right|x_k^{\&prime;}|+|y_k^{\&prime;}\left|\right)}^2}---\left(23\right)$

$x_k^{\&prime;}=x_k-\stackrel{\&OverBar;}{x}---\left(24\right)$

$y_k^{\&prime;}=y_k-\stackrel{\&OverBar;}{y}---\left(25\right)$

Various middle x above _kfor predicted value, y _kfor real value, k=1,2,3,4,5 ..., n; be respectively the mean value of predicted value sequence and real value sequence, n is the sample number of predicted value and real value sequence.

Table 1 predicts the outcome precision evaluation standard

The prediction of water demand of crop ET

Calculated by above fuzzy clustering, can determine that different weather type sends out ET to potential the rising of crop ₀fuzzy clustering center matrix s.Complete ET according to formula (8) ₀self-correction after, semiempirical formula (26) can be adopted to carry out the calculating of water demand of crop ET, namely

ET=ET ₀' K _ck _w=sET ₀k _ck _w(26) (3) effective precipitation P ₀computation model

After precipitation is stored in crop root zone, can effectively by Crop transpirstion evaporate utilize, thus reduce the irrigation requirement of crop, therefore for water-deficient area, make full use of precipitation, effectively can alleviate the present situation in short supply of water resource.Occur precipitation time, when precipitation intensity is greater than the infiltration capacity of soil, or when precipitation exceedes soil water storage capacity, understand some in precipitation and flow away with rainwash form, or formed deep percolation flow out crop root zone, thus can not utilize by crop.Therefore, that only has effective precipitation can supplement crop needs water requirement.To insufficient irrigation, effective precipitation P ₀it is the important factor in order formulating deficit irrigation schedule, irrigation water management.

The factor affecting effective precipitation is a lot, different because calculating object, determines that the evaluation method of effective precipitation is also not quite similar.The principal element affecting effective precipitation has Rainfall Characteristics, soil characteristics etc., adopts the rainfall effective utilization coefficients method of experience to calculate effective precipitation P in general production _0i, see formula (25).

P _0i=α P _i(3-25) in formula: P _i-actual rainfall (mm);

α-rainfall effective utilization coefficients, the size of α value size and rainfall, rainfall intensity, rainfall perdurabgility, soil property, covered ground and orographic factor are relevant.α value is in table 2.

Table 2 rainfall effective utilization coefficients α value

P(mm)	<5mm	5～50mm	>50mm
				α	0	0.8～1.0	0.7～0.8

(4) online real-time irrigation forecast model

The online real-time irrigation forecast model of crop is based on principle of water balance, in conjunction with the change of soil moisture soil moisture content and crop water dynamic, determine irrigating water quota and the irrigation period of reasonable, science.Good crop real-time irrigation forecast needs the up-to-date soil water to grade field data and short-term weather prediction data, face predicting the outcome of stage to each revise in real time and carry out the relevant parameter of next stage and the prediction of decision-making, also need to consider different field soil property simultaneously, according to the feature of different soils savings moisture, carry out Irrigation Forecast by dynamic numeric simulation.

According to principle of water balance, setting up with sky is the online real-time irrigation forecast model of crop of period:

M _i＝W _i-1+P _0i+W _Ti-ET _i+K _i-W _i(27)

I mono-aspect represents i-th stage of recursion, represents i-th day after crop-planting simultaneously, model parameter and plant growth is associated by i, to seek mutual according to change relation.

In formula, each symbolic significance is with various above.

To grow normally needs for meeting crop, in crop arbitrary period, the root system moisture storage capacity of irrigation wetting depth constantly remains in certain optimum range.

During system cloud gray model, the progressively predictive model day by day of crop irrigation wetting depth moisture content is first utilized to predict soil moisture content, as the planned moist layer in soil moisture content θ of prediction _ibe not more than the soil hydro-physical properties θ of setting _c2time, the corresponding date is the irrigation date of prediction, needs to carry out Irrigation Forecast and irrigation quantity calculating.Because online model of irrigating in real time is by the dark H of irrigation wetting depth in the progressively predictive model day by day of crop irrigation wetting depth moisture content _i, the isoparametric impact of crop coefficient, irrigate model simulation results and have certain error, therefore at the end of each calculation interval, all need to carry out error analysis and judgement with soil moisture content measured value to the analogue value of soil moisture content, necessary correction is carried out to model parameter simultaneously.

If the planned moist layer in soil moisture content θ of prediction period _ibe not more than the soil hydro-physical properties θ of setting _c2, but have rainfall to occur according in this period of weather forecast, then do not pour water or postpone and pour water.After pouring water, the initial soil moisture content using the soil moisture content of surveying as next stage, carries out irrigation date and the irrigation quantity forecast of next stage.So carry out by stage recursion, until crop growth period terminates from plantation day.

The computer program flow process of model is shown in Fig. 5.

(5) real-time irrigation program

Irrigation program whether rationally or efficiently depends primarily on watering period and soil water regime changes and the degree of agreement of Yield Forming Regularity.

(1) irrigating water quota

Irrigating water quota is an irrigation quantity in unit are, relevant with crop species, water capacity, irrigated area and available irrigation period.Irrigating water quota is one of main contents of irrigation program, determines rational irrigating water quota, not only can accurate instruction field irrigation saving irrigation model, and is the Main Basis of engineering design, water resources rational use.

The irrigating water quota usually upper and lower limit of soil moisture content and irrigation wetting depth THICKNESS CALCULATION in wettable layer according to schedule.Being calculated as follows of irrigation norm:

Crop irrigation quantity when water is abundant:

Very large when carrying out the water yield, when being enough to satisfied irrigation demand, irrigation quantity M _ifor:

M _i＝1000·n·H _i(1-θ _i)·θ _max(28)

In formula: θ _ithe initial soil moisture content in-the i-th day (stage);

H _ithe crop irrigation wetting depth degree of depth in-the i-th day (stage), m;

Crop irrigation quantity when water is insufficient:

When water is not enough or water resources quantity is in short supply, insufficient irrigation is carried out to crop.Irrigation quantity M _ifor:

M _i＝1000·n·H _i(θ _c1-θ _i)·θ _max(29)

In formula: θ _c1-irrigate the rear soil moisture content that will reach, θ during insufficient irrigation _c1generally get 90% θ _max.

(2) irrigation period

Fluctuate because agricultural land soil dry and wet condition can be subject to the strong impact of precipitation, therefore the critical period of the same crop water of the same area also can change at different hydrology model years.The uncertain of Rainfall result in the uncertain of crop root zone dry and wet condition, especially in the semiarid zone of water scarcity, more need the water consumption process in conjunction with crop and Yield formation process further investigation, set up the non-fully online irrigation program in real time of crop, the limited water yield is carried out reasonable distribution in the different growing of crop.

Real-time irrigation forecast is on the basis of actual measurement meteorological data, according to the simulation model of recursion day by day setting up field soil moisture, accurate forecast is made to crop short-term and even soil moisture content transformation situation day by day, when soil moisture content permission moisture content minimum close to breeding time residing for crop, carry out irrigation decision; Occur if having precipitation between forecast period or irrigate moisturizing, then postpone irrigation, and according to the concrete condition of reality, forecast result is adjusted in time, revise soil moisture content transformation curve simultaneously.

3 crops irrigate determination and the correction of model key parameter in real time

Crop is non-fully irrigates model online in real time, on the basis realizing monitoring day by day to soil moisture, predict the soil moisture dynamic content of following Different periods accurately and real-time, and make full use of rainfall forecast information, formulate rational irrigation scheme, for Developing Water-saving Agriculture, realize high efficiency field irrigation technical support is provided.

Real-time irrigation forecast not only needs up-to-date field data and predict data, irrigate prediction to every one-phase to carry out revising the prediction carrying out next stage in real time, but also the feature of different field need be considered, by dynamic numeric simulation, Irrigation Forecast is carried out to each field soil water content.Irrigating in model in real time, relate to the various parameters such as soil, crop, meteorology, therefore determination and the correction of model parameter are related to levels of precision and the practical level of Irrigation Forecast result.

(1) research of Lower Limit of Suitable Soil Moisture index

Soil moisture is suitable for lower limit, refers to the minimum soil water-containing figureofmerit being suitable for plant growth.The size of soil moisture content and the growth of crop have close relationship.When weather and soil types timing, soil moisture content drops to certain scope, can to the restricted effect of plant growth.When carrying out insufficient irrigation, formulating rational soil hydro-physical properties and the irrigation instructing crop in good time, appropriate and saving water resource are had great importance.The lower limit of soil moisture determines also to affect the irrigation period of crop the determination of crop frequency of irrigation and irrigation quantity, can regulate and control soil moisture by formulating suitable moisture lower limit, reduces irrigation quantity and frequency of irrigation, and then improves Crop Water Use Efficiency.

Crop species is different, also different to the requirement of soil water-physical properties.Soil moisture changes with the change in crop growth stage the effect of plant growth.Crop growth early stage, soil moisture can promote to nourish and grow, to seedling number number and power play a decisive role; Crop growth mid-term, sufficient moisture can promote growing of crop, determine crop spike number number; Late growth stage, for ensureing that crop is normally in the milk substantial, also must have moisture to ensure.The different bearing stage of same crop is also different to the susceptibility of water deficit, and susceptibility is larger, and the loss of its lack of water underproduction is larger.In addition, the soil texture is different, and its water content and moisture holding capacity are also different, also different to the requirement of soil moisture content lower limit.

Because the factor affecting soil hydro-physical properties is a lot, computational methods and the standard of formulation of the soil hydro-physical properties of Different Crop, different regions are also different.Generally, mostly by crop growth divided stages, to optimize in a short time or daily to calculate the model of each parameter considerably less, combine not closely with irrigating in real time, still also need the research of this respect.

In native system, to utilize in SWAT model winter wheat yields situation of change under crop module simulation different soils moisture bound condition in advance, by project plan comparison, have chosen the soil moisture bound index that winter wheat is suitable.

(2) determination of irrigation wetting depth degree of depth initial value

The irrigation wetting depth degree of depth refers to the soil depth needed by irrigating supplementary soil moisture.Concerning Dry crop, the main root water uptake layer of the planned moist layer in soil degree of depth normally crop, it depends primarily on the degree of depth of crop growth conditions and crop root mobile layer, also has relation with crop varieties, growing stage, field soil character and the factor such as bury of groundwater and soil microbial activity.

The irrigation wetting depth degree of depth directly affects the determination of irrigating water quota, its defining method has two kinds usually, one is dynamic type, namely thinks that the irrigation wetting depth degree of depth should change along with the growth breeding time of crop, the crop root mobile layer degree of depth and bury of groundwater etc.If winter wheat is 0.3 ~ 0.4m at Seedling Stage irrigation wetting depth deeply, tillering stage is 0.4 ~ 0.5m, and the shooting stage is 0.5 ~ 0.6m, and heading stage is 0.6 ~ 0.8m, and the pustulation period is 0.8 ~ 1.0m; Corn is at the dark 0.3 ~ 0.4m of Seedling Stage irrigation wetting depth, and the shooting stage is 0.4 ~ 0.5m, and booting stage is 0.5 ~ 0.6m, and heading stage is 0.6 ~ 0.8m, and the pustulation period is 0.8; Cotton is at the dark 0.3 ~ 0.4m of Seedling Stage irrigation wetting depth, and squaring period is 0.4 ~ 0.6m, and the knot bell phase of blooming is 0.6 ~ 0.8m, and the term of opening bolls is 0.6 ~ 0.8m.Spring wheat irrigation wetting depth before heading calculates by 80cm, and in breeding time, the irrigation wetting depth of pouring water of other times is considered by 40 ~ 60cm.

Another kind of then think that the irrigation wetting depth degree of depth of same crop should adopt the same degree of depth all the time in whole breeding time, but practical plans layer depth is different in the application.The main Root Distribution layer of crops determines that crop is suitable for the basic foundation of the wettable layer degree of depth.

In native system, think that the irrigation wetting depth degree of depth should increase along with the continuous intensification of the growing of crop, root system.In Irrigation Forecast process, need the planned moist layer in soil degree of depth initial value determining i-th day whole breeding time (stage), for this reason, suppose in crop each vegetative stage in the time of infertility, irrigation wetting depth linearly evenly increases, then the crop irrigation wetting depth degree of depth of arbitrary day can adopt linear recurrence model day by day to simulate, and for this reason, sets up the dark computation model of crop irrigation wetting depth:

$H_i=h_{n-1}+(h_n-h_{n-1})\&CenterDot;(i-\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}l_{j-1}^{j-1}{}_{j-1})/l_n^n---\left(30\right)$

In formula: H _i---the irrigation wetting depth degree of depth in i-th day (stage) of crop, m;

H _n-1---irrigation wetting depth degree of depth when the n-th breeding time is initial, m;

H _n---the irrigation wetting depth degree of depth at the end of the n-th breeding time, m;

N---breeding time residing for crop;

I---crop growth accumulation number of days after planting, d;

---the growth number of days of the n-th breeding time, d;

---the growth number of days of jth breeding time, d, j=1,2 ..., n.

After determining the dark initial value of the irrigation wetting depth of i-th day, by Irrimax software, soil moisture content monitor value is analyzed, obtain real-time root of the crop dark, and then the initial value that irrigation wetting depth is dark is revised, utilize revised irrigation wetting depth dark, according to the water yield W increased because irrigation wetting depth increases in formula (5) the CALCULATING PREDICTION period _ti.

(3) the real-time Soil Moisture Monitoring of soil and data are transmitted

Soil moisture content is an important indicator of field irrigation, and farmland moisture condition observes and predicts accurately is the basis realizing the irrigation of crop timely and appropriate discovery, is the foundation that Precision Irrigation technology and irrigation manage.By the supervision to Soil Water change tread, analysis and calculation is carried out to irrigated crop water requirement and relevant parameters, the forecast to following soil moisture content and damage caused by a drought trend can be realized, thus formulate irrigation project accurately.

Above for crop root zone of action soil layer is considered as a total system by native system, when choosing the same day 2 from monitored soil moisture data, 8 time, 14 time, 20 time etc. the actual measurement soil water moisture content in four monitoring moment, be taken as the above all the sensors of the irrigation wetting depth degree of depth residing for thing not in the same time the average of surveyed soil moisture content as the soil moisture measured value on the same day.

If when residing for i-th day crop, irrigation wetting depth is 100cm deeply, read more than 100cm (containing 100cm) different soil place sensor survey Soil moisture, be designated as θ w _i(j, l), wherein j is the observation moment, and l soil layer residing for sensor is dark, then i-th day different observation moment different soil Soil moisture that dark sensor is surveyed can represent as Fig. 6.

Make θ w _i(j, h _l) represent i-th day j monitor the moment be positioned at the dark h of soil layer _lplace's soil moisture content of surveying of sensor, then i-th day 2 time, 8 time, 14 time, 20 time at the dark h of soil layer _lthe Soil moisture at place just can be designated as respectively: θ w _i(2, h _l), θ w _i(8, h _l), θ w _i(14, h _l), θ w _i(20, h _l), then arithmetic mean method can be adopted to calculate i-th day dark h of given soil layer _lthe Soil moisture at place

${\mathrm{\&theta;}}_{i,h_l}=\frac{{\mathrm{\&theta;w}}_i(2,h_l)+{\mathrm{\&theta;w}}_i(8,h_l)+{\mathrm{\&theta;w}}_i(14,h_l)+\mathrm{\&theta;}{w}_i(20,h_l)}{4}---\left(31\right)$

Adopt weighted mean rule can obtain the Soil moisture θ of i-th day irrigation wetting depth _i:

${\mathrm{\&theta;}}_i=\underset{l=1}{\overset{m}{\mathrm{\&Sigma;}}}{w}_i{\mathrm{\&theta;}}_{i,h_l}---\left(32\right)$

In formula: w _i-be soil level h _lsoil moisture to θ _iweighing factor;

M-soil layer number.

When carrying out soil moisture content prediction, take sky as calculation interval, recursive process is as follows: when breeding time, first time was run, a soil moisture content is surveyed when breeding time, first day started, as stage initial value, the soil moisture content of recurrence formula (6) recursion stage end every day day by day that utilizes soil moisture day by day, and carry out contrasting and revising with the soil moisture content of actual measurement on the same day, and using the initial value of revised soil moisture content as next stage, so day by day order recursion, carries out simulation and the correction of irrigation wetting depth moisture content every day.

Carrying out in the process calculated, if calculate that obtaining moisture content on the i-thth is less than or equal to minimum permission moisture content breeding time residing for crop, consider weather forecast situation simultaneously, when without rain or Extreme Low Precipitation, adopt the real-time irrigation forecast model of Section 5.2 to make Irrigation Forecast to this day, and carry out Web Publishing, the same day soil moisture content be adapted to and pour water after soil moisture content, the recursion of irrigation wetting depth moisture content is carried out again, until terminate breeding time as initial value.If weather forecast has rainfall to occur, then need to consider effective precipitation, employing soil moisture day by day recurrence formula (6) carries out analyses and prediction to the moisture content of irrigation wetting depth, occur if any larger rainfall at Crop growing stage, cause when occurring that soil moisture content exceedes the situation of field capacity in analog computation process, then soil moisture content is treated to field capacity, the unnecessary water yield infiltrates deep soil.

Based on Forecast of Soil Moisture Content, adopt that crop is non-fully irrigates real-time irrigation and the forecast that model, real-time irrigation forecast model then can carry out crop in real time.All the other are as 3,7 and to the Irrigation Forecast of 2 weeks, and the stage step-length of calculating is corresponding is respectively 3,7 and 2 weeks, and principle was with the Irrigation Forecast of 1 day.

(4) soil moisture coefficient K _widetermination

In insufficient irrigation, because water consumpation is subject to regulation and control, the root growth of each growing stage of crop, plant development, group structure and ecological index all can be affected.

For Dry crop, the moisture in soil from wilting point to field capacity, can remain in root layer, and can be absorbed and used by plants.When soil moisture content is between critical moisture content and field capacity, soil moisture fully supplies crop evaporation, rising needs by capillarity, and the height of soil moisture content does not affect the evapotranspiration of crop; When soil moisture content is less than critical moisture content, Soil Moisture Movement is owing to being subject to the effect of resistance, and speed required when the actual speed rate of migration will be less than abundant evapotranspiration, now the height of soil moisture content just directly has influence on the speed of evapotranspiration.

Therefore, soil moisture coefficient K has been introduced under insufficient irrigation condition _wcome reflect soil moisture inadequate time soil moisture content on the impact of the water demand of crop.When carrying out sufficient irrigation, when soil moisture does not limit crop evapotranspiration, soil moisture coefficient k _wvalue is 1.

Due to the impact of the factors such as meteorology, soil types, crop species, the root system degree of depth, k _wvery complicated with the relation of θ.Reasonably choose and determine that crop coefficient and soil moisture coefficient have a significant impact for the Methods of Reference Crop Evapotranspiration calculated exactly under insufficient irrigation condition, production reality being applied to for insufficient irrigation also there is very important directive significance.

Binding region actual conditions, adopt following formulae discovery soil moisture coefficient K _wi:

$K_{\mathrm{wi}}=\left\{\begin{array}{cc}\ln (1+100\frac{{\mathrm{\&theta;}}_i}{{\mathrm{\&theta;}}_{\max }})/\ln 101& {\mathrm{\&theta;}}_{c2}\&le;{\mathrm{\&theta;}}_i<{\mathrm{\&theta;}}_{c1}\\ \mathrm{\&alpha;}\&CenterDot;\exp \left[({\mathrm{\&theta;}}_i-{\mathrm{\&theta;}}_{c2})\right]/{\mathrm{\&theta;}}_{c2}& {\mathrm{\&theta;}}_i<{\mathrm{\&theta;}}_{c2}\end{array}\right.---\left(33\right)$

In formula: θ _i-the i-th day (stage) soil moisture content, to account for soil volume percentage, %;

θ _max-field capacity, to account for soil volume percentage, %;

θ _c1-insufficient irrigation Suitable Soil Moisture upper limit index, to account for field capacity θ _maxpercentage represent, determine by different experimental programs respectively in research;

θ _c2-insufficient irrigation Lower Limit of Suitable Soil Moisture index, to account for θ _maxpercentage represent; Determine by different experimental programs respectively in research;

α-empirical coefficient, Dry crop desirable 0.89.

4 crop coefficient K _creal-time Simulation and correction

Crop coefficient K _cbe the important parameter for estimating the water demand of crop, and the prediction of the real-time law of needing the water of crop and water requirement is the core of real-time irrigation forecast, therefore, determines accurately and simulates crop coefficient K _cfarmland development Precision Irrigation, saving water resource are significant.

(1) crop coefficient K _cithe determination of initial value

When operational system first, need to determine crop coefficient K _ciinitial value.The factor such as weather conditions, agrotype, model accuracy in Considering experimental district, K _cicalculating initial value adopt with the computational methods of Crop growing stage characteristics i Day-to-day variability:

$K_{\mathrm{ci}}=\left\{\begin{array}{cc}7.346{(i/I)}^2-1.606(i/I)+0.0972& i/I\&le;0.058\\ -3.463\ln (i/I)-0.1909& i/I\&GreaterEqual;0.058\end{array}\right.---\left(34\right)$

In formula: I-breeding time total number of days, d.

(2) K of crop coefficient _cirevise

In research in the past, utilizing crop coefficient K _cwhen calculating the water demand of crop, be mostly to adopt by growing stage division K _cmean value, lack crop coefficient value in units of sky and to crop coefficient real-time, revise day by day, weather, soil and crop growth conditions that the use of stage average is changeable with reality obviously do not conform to, thus cause the real-time water requirement estimation of crop and farmland real-time irrigation forecast not accurate enough, affect the Precision Irrigation of crop and the efficiency utilization of limited agricultural water resources, result in the distortion of crop irrigation forecast, lose the effect of due Instructing manufacture.

For the deficiency of existing Study on Crop Water Requirement Rules research aspect, particularly need crop coefficient K in water model _coften get the problem of vegetative stage average, this research makes full use of the real time information achievement of irrigating monitoring experiment and collection, and the meteorological data in field, proposes prediction day by day and the self-correction method of crop coefficient, to crop coefficient K _ccarry out calibration and Step wise approximation correction, to simulate the real-time law of needing the water of crop more accurately, for the online irrigation program research of crop provides basic science information.Concrete computational process is as follows:

First automatic weather station actual measurement meteorological data is utilized to calculate the crop reference evapotranspiration ET of every day _0i, then by the soil moisture coefficient K calculated _wi, crop coefficient K _ci, crop reference evapotranspiration ET _0i, the actual water requirement of crop is calculated according to formula (7).Irrigate model in real time online from crop, in process of crop growth, at the end of (i-1) sky, the soil moisture content of this day, effective precipitation and duty are all known, thus can according to soil moisture coefficient K _wcomputing formula (33) calculate the actual soil moisture coefficient K' of this day _{w, i-1}; And initial crop coefficient K _cvalue then adopts the empirical value of areal.So far, formula (7) just can be utilized to calculate the actual water requirement ET of crop _i.By the actual measurement soil moisture initial value θ in (i-1) sky _i-1, according to soil moisture day by day recurrence formula (6) just can extrapolate the soil moisture initial value θ of i-th day _i'.

At the end of (i-1) sky, the actual measurement soil moisture initial value θ of i-th day _ibe known.If the soil moisture initial value θ of prediction in i-th day _i' and actual measurement soil moisture initial value θ _iclosely, the crop coefficient in (i-1) sky just gets initial value; If θ _i' and θ _idiffer larger, then by actual measurement Soil moisture θ _iinstead can push away the actual water requirement of crop in (i-1) sky:

ET _i' _-1=1000nH _i-1θ _i-1+ P _0i-1+ W _r' _i+ M _i-1-1000nH _iθ _i(35) in formula: ET _i-1the actual water requirement of '-revised (i-1) sky crop, mm;

M _i-1the irrigation quantity in-the (i-1) sky, mm, can be calculated by formula (28) or (29).

After then revising, the actual water requirement of crop in (i-1) sky can be written as:

ET′ _i-1＝W _i-1+P _0i-1+W′ _ri+M _i-1-W _i' (36)

In formula: W _i-1, W _i'---be respectively (i-1) sky initial, at the end of soil moisture content, mm;

And then revised (i-1) sky crop coefficient K ' can be obtained _ci-1.

K' _c,i-1＝ET′ _i-1/(K' _w,i-1·ET _0,i-1) (37)

Namely at the end of (i-1) sky, have modified the crop coefficient value on the same day, and using the input value of this correction value as next calculation interval, the rest may be inferred, realize crop coefficient K in the time of infertility _cday by day the correction of value.

By the correction of a breeding cycle, crop coefficient day by day can be obtained.When area and crop species constant, this group crop coefficient is irrigated online in real time the crop coefficient initial value in model as Second Year, then carries out the real-time water requirement estimation of crop, simultaneously basis then monitoring result again revise day by day.The rest may be inferred, carries out self-recision year by year to crop coefficient, improves constantly the Irrigation Forecast precision that crop is real-time online, progressively realize farmland Precision Irrigation.Crop coefficient K in real-time irrigation _cconcrete makeover process is shown in Fig. 7.Through self-recision long-term for many years, crop coefficient K _cvalue will progressively tend towards stability, close to real K _c, Irrigation Forecast also can be more accurate.

Claims (10)

1. a field intelligent irrigation On-Line Control Method, is characterized in that comprising the following steps:

Step one, miniature automatic meteorological station gathers meteorological data, the soil moisture content of soil-water environment monitoring equipment the real time measure crop root zone, and the data importing gathering and measure also is stored into the database of the server be arranged on internet;

Step 2, client logs in the farmland real-time intelligent be positioned on described server online and irrigates software systems;

Step 3, described farmland real-time intelligent is irrigated software systems and is called data in described database, carries out the management of field soil information, crop information management, farmland are monitored in real time, field irrigates simulation and Irrigation Forecast, field information inquiry, Information Statistics in real time according to client operation.

2. field according to claim 1 intelligent irrigation On-Line Control Method, it is characterized in that: described field irrigates simulation and Irrigation Forecast in real time, on the basis of short-term weather prediction, according to the actual soil moisture of each field in irrigated area, the actual Evapotranspiration of crop, crop growth conditions, by setting up Forecast of Soil Moisture Content model, crop water forecast model, real-time irrigation forecast model, the real-time correction model of crop coefficient, real-time estimate crop in forecast period the need of the time of pouring water and pouring water, irrigation quantity, and at the end of each calculation interval, by the analogue value of soil moisture content measured value to soil moisture content, crop coefficient is revised.

3. a field intelligent irrigation On-line Control management method, comprise the soil moisture detection unit for monitoring soil moist layer water content W, for monitoring the rainfall detecting unit of actual rainfall P, and irrigation system, it is characterized in that, also comprise Forecast of Soil Moisture Content model, the fuzzy clustering forecast model of the real-time water requirement of crop and online real-time irrigation forecast and Controlling model;

The real-time prediction model of described soil moisture content: W _i=W _i-1+ P _0i+ W _ti-ET _i+ M _i+ K _i

Wherein: W _i-1---the soil moisture content of i-th day original plan wettable layer;

W _i---the soil moisture content of irrigation wetting depth at the end of i-th day;

P _0i---the effective precipitation of i-th day;

W _ti---within i-th day, increased by irrigation wetting depth and the water yield increased;

ET _i---the water demand of crop of i-th day;

M _i---the irrigation quantity of i-th day;

K _i---the increment of groundwater of i-th day;

The fuzzy clustering forecast model of the real-time water requirement of described crop: ET=ET ₀' K _ck _w=sET ₀k _ck _w;

Wherein: ET _ithe water demand of crop of-the i-th day;

ET _0ithe crop reference evapotranspiration of-the i-th day;

K _cithe crop coefficient of-the i-th day;

K _wi-the soil moisture coefficient of i-th day when carrying out insufficient irrigation;

The cluster centre of s-different weather type;

Obtaining take sky as the online real-time irrigation forecast model of crop of period:

M _i＝W _i-1+P _0i+W _Ti-ET _i+K _i-W _i

Described online real-time irrigation forecast and Controlling model are according to the real-time irrigation quantity M of crop _icontrol irrigation system is irrigated in real time to crop.

4. field intelligent irrigation On-line Control management method according to claim 3, it is characterized in that, also comprise the progressively predictive model day by day of crop irrigation wetting depth moisture content, if the bury of groundwater of survey region is darker, when underground water is ignored to crop supply, be then that the water balance model of the crop irrigation wetting depth of forecasting period is with sky:

W _i＝W _i-1+P _0i+W _Ti-ET _i+M _i；

Wherein, i-th day initial, at the end of irrigation wetting depth soil moisture content as follows:

W _i-1＝1000nH _i-1θ _i-1

W _i＝1000nH _iθ _i

Wherein: H _i-1-the i-th day moistening layer depth of original plan;

H _iirrigation wetting depth at the end of-the i-th day is dark;

θ _i-1-the i-th day initial soil moisture content, to account for soil volume percentage;

θ _isoil moisture content at the end of-the i-th day, to account for soil volume percentage;

N-porosity of soil;

The increase of the i-th period internal cause irrigation wetting depth and the water yield W increased _ti;

W _Ti＝1000(H _i-H _i-1)·n·θ _deep

Wherein: θ _deep-deep soil moisture content;

The progressively predictive model day by day of crop irrigation wetting depth moisture content can be drawn:

${\mathrm{\&theta;}}_i=\frac{H_{i-1}}{H_i}{\mathrm{\&theta;}}_{i-1}-({\mathrm{ET}}_{i-1}-P_{0i-1}-W_{\mathrm{Ti}}-M_{i-1})/\left(1000n{H}_i\right);$

According to the progressively predictive model day by day of crop irrigation wetting depth moisture content, from the plantation date of crop, day by day the soil moisture content of crop root zone is predicted, until crop harvesting, thus carry out the stepwise predict day by day of soil water regime in crop whole breeding time.

5. field intelligent irrigation On-line Control management method according to claim 3, is characterized in that, also comprise water demand of crop ET ₀fuzzy clustering Forecasting Methodology: to the long serial ET of history under different weather type ₀carry out fuzzy clustering, obtain the cluster centre s of different weather type, carry out ET ₀self-correction: ET ' ₀=sET ₀, reach and predict the water demand of crop under future weather sight more accurately;

The method of described cluster centre s is determined:

If x _ijfor the ET under different weather type ₀value, wherein, i represents weather pattern, i=1,2,3,4, represent fine respectively, cloud, the moon, rain; J represents ten days, j=1,2 ..., 36; By ET ₀categorised statistical form can to obtain under annual each ten days different weather type average ET for many years ₀eigenvalue matrix:

$x_{\mathrm{ij}}=\left[\begin{array}{cccc}{x}_{11}& x_{12}& ...& x_{1j}\\ x_{21}& x_{21}& ...& x_{2j}\\ ...& ...& ...& ...\\ x_{i1}& x_{i2}& ...& x_{\mathrm{ij}}\end{array}\right]$

Each ten days ET ₀the relative defects matrix of eigen value:

Wherein: maxx _ij---j ten days ET ₀the maximum of eigen value;

Relative defects matrix then under j ten days i-th kind of weather pattern is:

$R=r_{\mathrm{ij}}=\left[\begin{array}{cccc}{r}_{11}& r_{12}& ...& r_{1j}\\ r_{21}& r_{22}& ...& r_{2j}\\ ...& ...& ...& ...\\ r_{i1}& r_{i2}& ...& r_{\mathrm{ij}}\end{array}\right]$

If ET under i kind weather pattern ₀fuzzy clustering center matrix be:

$S=\left(s_{\mathrm{ih}}\right)=\left[\begin{array}{cc}{s}_{11}& s_{12}\\ s_{21}& s_{22}\\ ...& ...\\ s_{i1}& s_{i2}\end{array}\right]$

Wherein: s _ih---be ET under i kind weather pattern under classification mode h ₀eigen value normalized number, 0≤s _ih≤ 1;

h＝1，2，…，c；

Suppose to press fine day ET ₀relative defects classify, i.e. c=2, then its fuzzy clustering matrix is:

$U=\left(u_{\mathrm{hj}}\right)=\left[\begin{array}{cccc}{u}_{\mathrm{1,1}}& u_{\mathrm{1,2}}& ...& u_{\mathrm{1,36}}\\ u_{\mathrm{2,1}}& u_{\mathrm{2,1}}& ...& u_{\mathrm{2,36}}\end{array}\right]$

And satisfy condition:

$\left.\begin{array}{c}0\&le;u_{\mathrm{hj}}\&le;1\\ \underset{h=1}{\overset{c}{\mathrm{\&Sigma;}}}{u}_{\mathrm{hj}}=1\\ \underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{u}_{\mathrm{hj}}>0\end{array}\right\}$

Then j ten days sample and different weather type ET ₀difference between cluster centre h, available broad sense Euclidean power distance represents, namely

$\left|\right|w_i(r_i-s_h)\left|\right|={\left\{\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}\right[w_i{(r_{\mathrm{ij}}-s_{\mathrm{ih}})}^2\left]\right\}}^{\frac{1}{2}}$

Wherein: i---weather pattern sum;

With u _hjthe weighting broad sense Euclidean obtained between sample j and classification h for weight weighs distance:

F(u _hj)＝u _hj||w _i(r _j-s _h)||，

Calculate cluster centre s, obtain ET ₀correction value ET ' ₀.

6. field intelligent irrigation On-line Control management method according to claim 5, it is characterized in that, optimal fuzzy clustering center matrix s solution procedure is specific as follows:

(1) given u _hjwith s _ihthe required computational accuracy ε met ₁, ε ₂;

(2) suppose that meets a constraints u _hjand the initial fuzzy clustering matrix that element is all inequal

(3) substitute into corresponding fuzzy clustering centers matrix

$s_{\mathrm{ih}}^0=\frac{\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{u_{\mathrm{hj}}^0}^2{w_i}^2{r}_{\mathrm{ij}}}{\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{u_{\mathrm{hj}}^0}^2{w_i}^2}$

(4) substitute into ask first approximation fuzzy clustering matrix

$u_{\mathrm{hj}}^1=\frac{\text{1}}{\underset{h=1}{\overset{c}{\mathrm{\&Sigma;}}}\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left[w_i(r_{\mathrm{ij}}-s_{\mathrm{ih}}^0)\right]}^2}{\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{\left[w_i(r_{\mathrm{ij}}-s_{\mathrm{ih}})\right]}^2}}$

(5) substitute into ask first approximation fuzzy clustering center matrix

(6) comparator matrix one by one with and matrix corresponding element, if and then iteration terminates, for Optimal cluster centers matrix s, otherwise repeat the step of (3) to (5) until meet required precision, existing research has demonstrated the convergence of iteration; Calculate through fuzzy clustering, cloud can be obtained, the moon, rain weather condition descend ET ₀for fine day ET ₀fuzzy clustering center.

7. field intelligent irrigation On-line Control management method according to claim 3, is characterized in that, carrys out water condition carry out fully or insufficient irrigation according to crop, very large when carrying out the water yield, when being enough to satisfied irrigation demand, and irrigation quantity M _ifor:

M _i＝1000·n·H _i(1-θ _i)·θ _max

Wherein: θ _ithe initial soil moisture content in-the i-th day (stage);

H _ithe crop irrigation wetting depth degree of depth in-the i-th day (stage);

When water is not enough or water resources quantity is in short supply, insufficient irrigation is carried out to crop; Irrigation quantity M _ifor:

M _i＝1000·n·H _i(θ _c1-θ _i)·θ _max

Wherein: θ _c1-irrigate the rear soil moisture content that will reach, determine concrete value according to crop tolerance level during insufficient irrigation.

8. field intelligent irrigation On-line Control management method according to claim 3, is characterized in that, the layering mensuration of soil moisture and the real-time correction of soil moisture;

Above for crop root zone of action soil layer is considered as a total system by native system, when choosing the same day 2 from monitored soil moisture data, 8 time, 14 time, 20 time etc. the actual measurement soil water moisture content in four monitoring moment, be taken as the above all the sensors of the irrigation wetting depth degree of depth residing for thing not in the same time the average of surveyed soil moisture content as the soil moisture measured value on the same day;

Make θ w _i(j, h _l) represent i-th day j monitor the moment be positioned at the dark h of soil layer _lplace's soil moisture content of surveying of sensor, then i-th day 2 time, 8 time, 14 time, 20 time at the dark h of soil layer _lthe Soil moisture at place just can be designated as respectively: θ w _i(2, h _l), θ w _i(8, h _l), θ w _i(14, h _l), θ w _i(20, h _l), then arithmetic mean method can be adopted to calculate i-th day dark h of given soil layer _lthe Soil moisture at place

${\mathrm{\&theta;}}_{i,h_l}=\frac{\mathrm{\&theta;}{w}_i(2,h_l)+\mathrm{\&theta;}{w}_i(8,h_l)+\mathrm{\&theta;}{w}_i(14,h_l)+\mathrm{\&theta;}{w}_i(20,h_l)}{4}$

Adopt weighted mean rule can obtain the Soil moisture θ of i-th day irrigation wetting depth _i:

${\mathrm{\&theta;}}_i=\underset{l=1}{\overset{m}{\mathrm{\&Sigma;}}}{w}_i{\mathrm{\&theta;}}_{i,h_l}$

In formula: w _i-be the Soil moisture of soil level hl to θ _iweighing factor;

M-soil layer number;

When carrying out soil moisture content prediction, take sky as calculation interval, recursive process is as follows: when breeding time, first time was run, a soil moisture content is surveyed when breeding time, first day started, as stage initial value, the soil moisture content of recurrence formula (6) recursion stage end every day day by day that utilizes soil moisture day by day, and carry out contrasting and revising with the soil moisture content of actual measurement on the same day, and using the initial value of revised soil moisture content as next stage, so day by day order recursion, carries out simulation and the correction of irrigation wetting depth moisture content every day;

Carrying out in the process calculated, if calculate that obtaining moisture content on the i-thth is less than or equal to minimum permission moisture content breeding time residing for crop, consider weather forecast situation simultaneously, when without rain or Extreme Low Precipitation, adopt the real-time irrigation forecast model of Section 5.2 to make Irrigation Forecast to this day, and carry out Web Publishing, the same day soil moisture content be adapted to and pour water after soil moisture content, the recursion of irrigation wetting depth moisture content is carried out again, until terminate breeding time as initial value; If weather forecast has rainfall to occur, then need to consider effective precipitation, adopt soil moisture recursion day by day ${\mathrm{\&theta;}}_i=\frac{H_{i-1}}{H_i}{\mathrm{\&theta;}}_{i-1}-({\mathrm{ET}}_{i-1}-P_{0i-1}-W_{\mathrm{Ti}}-M_{i-1})/\left(1000n{H}_i\right)$ Analyses and prediction are carried out to the moisture content of irrigation wetting depth, occur if any larger rainfall at Crop growing stage, cause when occurring that soil moisture content exceedes the situation of field capacity in analog computation process, then soil moisture content is treated to field capacity, the unnecessary water yield infiltrates deep soil.

9. field intelligent irrigation On-line Control management method according to claim 3, it is characterized in that, the real-time correction model of described crop coefficient is in formula, ET ' _i-1for revising the actual water requirement of crop of latter the i-th-1 day, computing formula is: ET ' _i-1=W _i-1+ P _0i-1+ W ' _ri+ M _i-1-W ' _i, K ' _{w, i-1}be the actual soil moisture coefficient of the i-th-1 day, W _i-1and W _i-1' be respectively the i-th-1 day initial, at the end of soil moisture content, M _i-1be the irrigation quantity of the i-th-1 day, P _0i-1it is the effective precipitation of the i-th-1 day;

I-th at the end of-1 day, the actual measurement soil moisture initial value θ of i-th day _ibe known; If the soil moisture initial value θ of prediction in i-th day _i' and actual measurement soil moisture initial value θ _iclosely, the crop coefficient of the i-th-1 day just gets initial value; If θ _i' and θ _idiffer larger, by actual measurement Soil moisture θ _iinstead can push away the actual water requirement of crop of the i-th-1 day:

ET′ _i-1＝1000nH _i-1θ _i-1+P _0i-1+W′ _ri+M _i-1-1000nH _iθ _i

Wherein: ET _i-1'-revised the i-th-1 day the actual of crop needs water;

M _i-1the irrigation quantity of-the i-th-1 day;

After then revising, the actual water requirement of crop of the i-th-1 day is:

ET′ _i-1＝W _i-1+P _0i-1+W′ _ri+M _i-1-W′ _i

In formula: W _i-1, W ' _i---be respectively the i-th-1 day initial, at the end of soil moisture content;

And then revised the i-th-1 day crop coefficient K ' can be obtained _ci-1;

$K_{{c,}_{i-1}}^{\&prime;}={\mathrm{ET}}_{i-1}^{\&prime;}/(K_{{w,}_{i-1}}^{\&prime;}\&CenterDot;{\mathrm{ET}}_{{0,}_{i-1}})$

Namely at the end of the i-th-1 day, have modified the crop coefficient value on the same day, and using the input value of this correction value as next calculation interval, the rest may be inferred, realize crop coefficient K in the time of infertility _cday by day the correction of value.

10. field intelligent irrigation On-line Control management method according to claim 3, is characterized in that,

Also comprise the Confirming model to the dark initial value of crop irrigation wetting depth:

$H_i=h_{n-1}+(h_n-h_{n-1})\&CenterDot;(i-\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}l_{j-1}^{j-1}{}_{j-1})/l_n^n$

Wherein: H _i---the irrigation wetting depth degree of depth in i-th day (stage) of crop;

H _n-1---irrigation wetting depth degree of depth when the n-th breeding time is initial;

H _n---the irrigation wetting depth degree of depth at the end of the n-th breeding time;

N---breeding time residing for crop;

I---crop growth accumulation number of days after planting;

---the growth number of days of the n-th breeding time;

---the growth number of days of jth breeding time, j=1,2 ..., n.