CN109858180A - A kind of area crops water consumption spatial framework optimum design method - Google Patents
A kind of area crops water consumption spatial framework optimum design method Download PDFInfo
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Abstract
The invention discloses a kind of area crops water consumption spatial framework optimum design methods for belonging to crop planting technical field.Survey region is chosen, determines research crop species；Evaluation index relevant to plant growth is chosen, using principles of fuzzy mathematics quantitatively evaluating index, the dimension difference between index is eliminated, forms evaluation index subordinated-degree matrix；Parameter weight is up to objective function with folk prescription water economic benefit, optimizes by water quantity restraint, neighbours' constraint, conversion priority, transformation rule and establishes area crops water consumption spatial framework Optimized model；Most suitable prioritization scheme is selected according to the actual situation, obtains the spatial distribution of optimization ensuing crop pattern of farming and its water consumption.The present invention is other than it can adjust crop water consumption spatial framework to the status year of research to obtain higher economic benefit, it is often more important that the spatial framework under Future Climate scene by adjusting future plantings crop can be applicable to carry out space optimization to crop water consumption.
Description
Technical field
The invention belongs to crop planting technical field, in particular to a kind of area crops water consumption spatial framework optimizationDesign
Method.The design method of specifically a kind of region water consumption spatial framework optimization based on cellular Automation Model.
Background technique
Water is a valuable source indispensable in people's production and living, and China belongs to the country of serious water shortage, water money
The spatial distribution in source is very uneven.With the continuous expansion of economic development and population size, the pole of water resources development and utilization is not
Rationally, so that the contradiction of supply and demand for the water resource becomes increasingly conspicuous, a series of problems is also resulted in.
China is the large agricultural country for possessing 1,300,000,000 populations, and agricultural is related to each of us vital interests.Closely
Nian Lai, " water resources in china bulletin " show that agricultural water accounting is up to 60% or so.This for by agricultural as pillar industry
For northern area, water resource becomes an important factor for influencing its development.It is printed and distributed from State Council in 2012 " most stringent about carrying out
The opinion of water resources management system " start after carrying out strict control to water resources quantity, agricultural water conservation becomes very urgent.In agricultural
In the restricted situation of Available water resources total amount, comprehensively consider growing environment of crop, including weather, soil, landform etc. because
Limited irrigation water is carried out space optimization and distribution, adjusts agricultural planting structure by element, and to promote, regional agriculture is water-saving, improves
Economic benefit provides new method.
It is mostly both at home and abroad at present quantity optimization about the optimization of crop water consumption, i.e., macroscopical adjusts pattern of farming ratio
Whole, the research specific to spatial position adjustment is seldom.Then more consideration is given to Crop Planting Structures for the optimization of crop water consumption spatial framework
Spatial distribution and quantity configuration, common mathematical model is the function in this space of being beyond expression.Cellular Automation Model
Different from general kinetic model, without specific equation, but a series of rule of model constructions is contained, in practical work
It can flexibly be optimized according to demand in the optimization of material consumption water.It is the space-time based on microscopic individual interaction again simultaneously
Dynamic simulation model, it is automatic by the cellular space of Research on partition object and research original state and state transition rules, cellular
Machine can be achieved with automatic Iterative operation.Since cellular automata can handle complicated optimization problem, and it can be carried out space optimization, often
In terms of being used in landscape pattern optimizing.Based on this, the present invention is directed to the realistic problem of shortage of water resources, fully considers and makees material consumption
The Spatial-Temporal Variability and its growth suitability of water impact factor, are based on cellular automata (Cellular by establishing
Automata area crops water consumption spatial framework Optimized model (Water Consumption Spatial)
Optimization, abbreviation GCA-WCSO), target is up to folk prescription water economic benefit, realizes area crops pattern of farming
The spatial framework of adjustment and water consumption optimizes, to improve local economic benefit.
Summary of the invention
The object of the present invention is to provide a kind of design methods of area crops water consumption spatial framework optimization, which is characterized in that
Include the following steps:
Step 1: choosing survey region, determine research crop species；
Step 2: choosing evaluation index relevant to plant growth, using principles of fuzzy mathematics quantitatively evaluating index, eliminate
Dimension difference between index forms evaluation index subordinated-degree matrix；Application factor analytic approach and improved H respectively
Parameter weight passes through evaluation model meter using Polynomial combination index weights model Calculation Estimation indicator combination weight
Calculate the crop Appropriate on each grid point；Utilize nature step-wise process, equidistant method, standard deviation and statistical approach, quantile method
Subregion is carried out to Appropriate, the growth for dividing crop is suitable for section；
Step 3: the area crops water consumption spatial framework Optimized model (GCA-WCSO) based on cellular automata is established, with list
Square water economic benefit is up to objective function, is optimized by water quantity restraint, neighbours' constraint, conversion priority, transformation rule；
Optimum results are compared by the way that different searching routes and direction are arranged, most suitable prioritization scheme is selected according to the actual situation, obtains
The pattern of farming distribution of the planting area and size of Different Crop and the spatial distribution of Different Crop water consumption after optimization.
The step 1 includes choosing survey region and determining status year, is extracted according to remote sensing images by ArcGIS software
Distribution of ploughing in area is studied, passes through NDVI image recognition Different Crop type in conjunction with the breeding time of local chief crop, is somebody's turn to do
Crop spatial distribution situation in region；Even local main long-term cropping is corn, considers the breeding time of local corn in April
Between 15 days to September 25th, all NDVI images in breeding time are analyzed, the grid that extraction NDVI value is all larger than 0 is corn,
Otherwise consider next agrotype.
It is to comprehensively consider weather, landform, soil resource and work that the step 2, which chooses evaluation index relevant to plant growth,
The matching of object growth will not only consider influence of the various indexs to plant growth, also to take into account data when selecting evaluation index
Accessibility, while meeting the requirement of quantification, spatialization.
It is described to select climatic factor for Crop growing stage accumulated temperature；Orographic factor is elevation, the gradient；Edphic factor is soil appearance
Weight, the soil texture, full nitrogen, full phosphorus, full potassium, organic matter or pH value totally 10 indexs to the plant growth suitability on each grid point
It is evaluated；Its evaluation index contains quantitative target and qualitative index, for qualitative index, needs according to the actual situation to it
Assignment；It is the quantization of evaluation index to be carried out using the principle of fuzzy mathematics, and eliminate between each evaluation index to evaluation quantification
Dimension difference, establish the subordinated-degree matrix F of evaluation indexj×k；Wherein j: grid cell, k: influence factor.
The evaluation index is divided into again: discrete type, forward direction S type and reverse S type index；For discrete type index, according to point
Grade standard determination is subordinate to angle value, and calculation formula is
In formula: f (x) is to be subordinate to angle value, a1、a2For index bound.
It is described to use Polynomial combination index weights model Calculation Estimation indicator combination weight, parameter combined weights
Value, W=λ1ω1+λ2ω2
In formula: W is combined weights weight values, λ1For the coefficient for the index weights that factor analysis calculates, λ2For improved level point
The coefficient for the index weights that analysis method calculates, ω1For the index weights that factor analysis calculates, ω2For improved H meter
The index weights of calculation；From there through on each grid point evaluation index data carry out frequency analysis be classified: for accumulated temperature,
This kind of continuous data of bulk density, elevation, the gradient determines the maximum value and minimum value of each grade；For other discrete datas,
The frequency for analyzing each Index grading takes single grade frequency to be greater than 10%, frequency summation 80% or more be classified as it is each suitable
The optimum range of suitable property grade；By on obtained each grid point Appropriate and suitable grade comparison Different Crop in same grid
Appropriate size on point selects Appropriate maximum for relative advantage crop, division space relative advantage area.
The crop water consumption spatial framework Optimized model, the model are constructed by MATLAB software platform, and GIS is provided
The input data of model and the rasterizing of output data, Crop Planting Structure after being optimized accordingly and make material consumption accordingly
Hydrospace pattern.
The beneficial effects of the invention are as follows higher to obtain in addition to that can adjust crop water consumption spatial framework to the status year of research
Economic benefit outside, it is often more important that can be applicable under Future Climate scene by adjusting the spatial framework of future plantings crop come
Space optimization is carried out to crop water consumption.Crop water consumption space is calculated using the meteorological data under Future Climate scene in a model
It is distributed, the Appropriate of the following crop-planting, calculates net irrigation requirement and net irrigation benifit, will plan the agriculture water volume that can be utilized in year
And cultivated area can be used as constraint condition, the transfer function of model is modified, Different Optimization path and method are set, so that it may
Crop Planting Structure scheme and water consumption spatial framework after to optimization.In addition to this, since the present invention is based on cellular automata moulds
The flexibility of type, user have other more constraint conditions in model optimization use process, can also be voluntarily in conversion letter
It is added and modifies in number.To obtain the maximum pattern of farming allocation plan of area crops folk prescription water net benefits value, reach
Optimize the purpose of area crops water consumption spatial framework.
Detailed description of the invention
Fig. 1 is area crops water consumption spatial framework optimized flow chart.
Specific embodiment
The present invention provides a kind of design method of area crops water consumption spatial framework optimization, with reference to the accompanying drawing, to preferred
Embodiment elaborates.
It is as shown in Figure 1 area crops water consumption spatial framework optimized flow chart.Step includes: as shown in the figure
Step 1: choosing survey region, determine the crop species of survey region.
It chooses survey region and determines status year, arable land point in research area is extracted by ArcGIS software according to remote sensing images
Cloth passes through NDVI image recognition Different Crop type in conjunction with the breeding time of local chief crop, and the crop obtained in the region is empty
Between distribution situation.Even local main long-term cropping is corn, and the breeding time of the local corn of consideration is April 15 to September 25th
Between, all NDVI images in breeding time are analyzed, the grid that extraction NDVI value is all larger than 0 is corn, is otherwise considered next
Agrotype.
Step 2: choosing evaluation index relevant to plant growth, using principles of fuzzy mathematics quantitatively evaluating index, eliminate
Dimension difference between index forms evaluation index subordinated-degree matrix.Application factor analytic approach and improved H respectively
Parameter weight, using Polynomial combination index weights model Calculation Estimation indicator combination weight.Pass through evaluation model meter
Calculate the crop Appropriate on each grid point.Using natural step-wise process, equidistant method, standard deviation and statistical approach, quantile
The methods of method carries out subregion to Appropriate, and the growth for dividing crop is suitable for section.
Step 201: Calculation Estimation index is subordinate to angle value.
Comprehensively consider weather, landform, soil resource and the matching of plant growth, when selecting evaluation index, not only to examine
Consider influence of the various indexs to plant growth, also to take into account the accessibility of data, at the same meet quantification, spatialization is wanted
It asks.The present invention selects climatic factor (Crop growing stage accumulated temperature), orographic factor (elevation, the gradient), edphic factor (soil weight,
The soil texture, full nitrogen, full phosphorus, full potassium, organic matter, pH) totally 10 indexs on each grid point plant growth suitability carry out
Evaluation.Evaluation index contains quantitative target and qualitative index, for the qualitative index such as soil texture, needs according to the actual situation
To its assignment.In order to make to evaluate quantification, the present invention carries out the quantization of evaluation index using the principle of fuzzy mathematics, and eliminates each
Dimension difference between a evaluation index establishes the subordinated-degree matrix F of evaluation indexj×k(j: grid cell, k: influence factor).
Evaluation index is divided into again: discrete type, forward direction S type and reverse S type index.For discrete type index, marked according to classification
Accurately it is subordinate to angle value surely, is mainly built by ready-made quantitative criteria and policy regulation or using empirical data and historical data
Vertical grade scale；For positive S type, index is the bigger the better in restriction range, is subordinate to angle value and is calculated by formula (1)；For reversed S
Type, index is the smaller the better in restriction range, is subordinate to angle value and is calculated by formula (2)；
In formula: f (x) is to be subordinate to angle value, a1、a2For index bound.
Step 202: application factor analytic approach and improved H parameter weight respectively, using multinomial group
Close index weights model Calculation Estimation indicator combination weight.
In order to more rationally, accurately determine index weights, select Subjective-objective Combination weight method calculate combining weights with
Balance the pros and cons between subjective evaluation, the index weights meter being calculated in conjunction with improved H and factor analysis
Calculate combining weights.
(1) factor analysis parameter weights omega is utilized1。
Parametric test is carried out first.Factor Analysis mainly passes through SPSS software and executes, first progress KMO and
Bartlett's sphericity test, correlation of the KMO statistic between 1, variable is stronger, and partial correlation is weaker, Factor minute
The effect of analysis is better, in real process, is suitble to when the unsuitable factorial analysis in KMO≤0.5, KMO >=0.7, KMO is in 0.5-0.7
Between relatively be suitble to.Judged according to Bartlett's sphericity test, if Correlation Matrix is unit matrix, each variable independent factor analysis
Method is invalid, and when Sig. < 0.05 shows between correlation matrix there is stronger correlation, the factor between variable with the presence of common factor
Analysis is effective.Calculate the KMO value of Different Crop, it is determined whether factorial analysis can be carried out.
Then the common factor number of each crop is determined.To avoid common factor number is less from leading to loss of learning, accumulative contribution is extracted
N number of common factor number when rate > 85%, obtains the variance contribution ratio X of each common factorn=[x1, x2... xn,] and each influence factor
Eigenvectors matrix Pk×n:
Last parameter weight:
W1n=Xn×Pkn (4)
It obtains the weight of each index and then weighted value is normalized to obtain ω1。
(2) weights omega is calculated using improved AHP method2。
Weight is determined first.General analytic hierarchy process (AHP) requires to determine weight by expert estimation, improves step analysis
The sample standard deviation S of each evaluation index of method(i)(i=1~n) reflects the influence degree of each evaluation index, therefore present invention selection changes
Into analytic hierarchy process (AHP) parameter weight.Judgement Matricies Dn×n, D in matrixijFormula (6) and (7) calculate.
dm=min { 9, int [smax/smin+0.5]} (7)
In formula: SmaxAnd SminFor the maximum value and minimum value of each crop different evaluation index S, dmFor relative importance
Parameter.
Matrix Dn×nMaximum characteristic root corresponding to feature vector be each factor of evaluation importance ranking, therefore feature
Vector (i.e. weight) is calculated by following formula:
Weight is normalized, ω is obtained2:
Then consistency check is carried out.Matrix consistency inspection formula are as follows:
In formula: CR is coincident indicator, then thinks that judgment matrix meets consistency check as CR < 0.10.RI is average
Random index, value depend on order of matrix number.λmaxFor Maximum characteristic root, it is calculate by the following formula:
In formula: (B ω)iFor i-th of element of vector B ω, vector B ω is judgment matrix Dn×nWith the product of weight matrix.
(3) combining weights ω is calculated.
Using Polynomial combination index weights model (13) parameter Combining weights.
W=λ1ω1+λ2ω2 (13)
In formula: W is combined weights weight values, λ1For the coefficient for the index weights that factor analysis calculates, λ2For improved level point
The coefficient for the index weights that analysis method calculates, ω1For the index weights that factor analysis calculates, ω2For improved H meter
The index weights of calculation, in which:
In formula: ωaFor the coefficient of variation of each weight of factorial analysis, n is evaluation index number；p1, p2, p3…,pnFor ω1In it is each
The rearrangement of weight from small to large, can similarly calculate λ2.Then normalized is made to W, obtain each crop Different Effects because
The Combining weights ω of son.
Step 203: the crop Appropriate on each grid point being calculated by Crop suitability evaluation model, using being interrupted naturally
The methods of method, equidistant method, standard deviation and statistical approach, quantile method carry out subregion to Appropriate, divide the growth of crop
It is suitable for section.
(1) Appropriate calculates
Suitable sex index is to be subordinate to the product of angle value and comprehensive weight, i.e., the subordinated-degree matrix and evaluation index of every kind crop
The product of combining weights matrix is calculated according to evaluation model (formula 16):
In formula: f (x)iIt is subordinate to angle value, ω for indexiFor index weights.
After obtaining the Appropriate on each grid point, classified using nature step-wise process, M grades can be divided into.
(2) each indication range under each suitable grade is determined.
It is classified by carrying out frequency analysis to the evaluation index data on each grid point: for accumulated temperature, bulk density, height
This kind of continuous data of journey, the gradient determines the maximum value and minimum value of each grade；For other discrete datas, analysis is every
The frequency of a Index grading, such as can use single grade frequency greater than 10%, frequency summation 80% or more be classified as each be suitable for
The optimum range of property grade.
Step 204: dividing space relative advantage area.
The Appropriate on each grid point obtained according to step 203 and suitable grade comparison Different Crop are in same grid point
On Appropriate size, select Appropriate maximum for relative advantage crop.
Step 3: the area crops water consumption spatial framework Optimized model (GCA-WCSO) based on cellular automata is established, with list
Square water economic benefit is up to objective function, is optimized by water quantity restraint, neighbours' constraint, conversion priority, transformation rule.
Optimum results are compared by the way that different searching routes and direction are arranged, most suitable prioritization scheme is selected according to the actual situation, obtains
Optimize the spatial distribution of ensuing crop pattern of farming and its water consumption.
Step 301: establishing the area crops water consumption spatial framework Optimized model (GCA-WCSO) based on cellular automata.
(1) CA modular concept
The concept of cellular automata (Cellular Automata, CA), it is that time, space, state are all discrete, space
Interaction and time upper causality be all local grid kinetic model, it using " from bottom to top " modeling side
Formula.Cellular Automation Model is different from general kinetic model, without specific equation form, but contains a series of moulds
The rule of type construction, including cellular, cellular space, neighbours, rule and time.Cellular is also known as unit, is the base of cellular automata
This component part；The space networks point set that cellular space, that is, cellular is distributed, the present invention are arranged using quad mesh；With cellular
Adjacent all cellulars are referred to as the neighbours of the cellular, and Neighbor Types have von Neumann type, mole type, mole type of extension etc.,
The present invention is using for mole type neighbours, i.e., the field of cellular is made of the cellular in eight directions around it；Rule be cellular from
The core of motivation contains the logical relation of simulation process, determines the result of spatial variations.According to cellular current state and neighbours
Situation determines the kinetic function i.e. state transition function of the subsequent time cellular state.Cellular automata is dynamic model, it
Variation in time be it is discrete, the state at t+1 moment is determined by the cellular state and neighbours' situation of t moment.It is based on
Time and spatial discretization can be able to achieve the space-time dynamic of microscopic individual by These characteristics, cellular automata in optimization process
Simulation, therefore be easier to be integrated with space-time datas such as remote sensing and not by the constraint of spatial dimension and time range.Except this with
Outside, the flexible modeling principle of cellular helps to analyze increasingly complex optimization situation in model construction.
(2) Optimized model modeling approach
Establish crop water consumption spatial framework Optimized model (the Water Consumption Spatial based on GIS and CA
Optimitation, abbreviation GCA-WCSO), model is constructed by MATLAB software platform, and GIS provides the input number of model
According to the rasterizing with output data.Therefore model is divided into three big modules: input module, output module and optimization module.
Input data turns ASCII module by grid in ArcGIS and raster data is converted to ascii data, then with square
The input of formation formula；Output data turns grid module by ASCII and ascii data is switched to raster data.Input data is mainly wrapped
Include pattern of farming distribution map, Crop suitability distribution map, crop water consumption spatial distribution map, reasonable neighbor scope and Appropriate
The data such as lower limit.Control the conversion of crop during model optimization by transformation rule based on existing Crop Planting Structure
Direction come realize Crop Planting Structure optimize, thus realize crop water consumption spatial framework optimize.
(3) model construction
1. objective function:
Objective function is that region folk prescription water net benefits i.e. folk prescription water net benefits is economy caused by the net irrigation water of unit
Benefit is maximum:
In formula: vijxFor the unit price (member/kg) of the i-th row jth column xth kind crop；yijxFor the i-th row jth column xth kind crop
Per mu yield (kg/ha)；cijxFor the production cost (member/ha) of the i-th row jth column xth kind crop, including planting cost and labour cost；
IijxFor the i-th row jth column xth kind Crop growing stage net irrigation requirement (mm)；I is the research total line number of area's grid, and J is research area's grid
The total columns of lattice, X are crop species number；MijxFor the i-th row jth column grid cell crop-planting type,
2. constraint condition:
Neighbours' constraint:
When neighbours' number around cellular is more than or equal to k, which can be converted, that is, be converted to other crops；
When less than k, which does not plant crop.
ljij≥k (18)
In formula: ljijFor neighbours' number of the i-th row jth column grid, k value should be in conjunction with depending on actual conditions.
Cultivated area constraint:
The total cultivated area of crop should be no more than a certain particular value.
In formula: NxFor the grid number of crop x；S: grid cell size (m2)；A is available total area under cultivation (m2)。
Water volume that can be utilized constraint:
Total duty after optimization should be less than the available agricultural irrigation water in In The Middle Reaches.
In formula: IijxFor the net irrigation requirement of the i-th row jth column grid xth kind crop.
Integer constrained characteristic:
A kind of crop can only be planted in one grid cell, it may be assumed that
Mijx=1 or 0 (21)
(4) transformation rule:
Transformation rule is the kinetic function that subsequent time cellular state is determined according to current state, conversion rule of the invention
There are two then main:
1. Crop suitability is regular.
Crop suitability screening parameter is selected, i.e., could pass through Appropriate when member Appropriate intracellular is more than a certain value
Screening, that is to say, that can just there be arable crop, it is proposed that the M-1 grade Suitable Area for taking M grades of suitabilities to be classified
Lower limit value is the parameter of suitability screening.Such as: subregion is carried out according to five grades in Suitability division, and level V is least to be suitable for
The grade of plant growth, so that it may the ginseng screened using the lower limit value of Crop suitability in fourth stage section as model suitability
Number.
2. converting priority rule.When member several crops intracellular are all constrained by Appropriate screening and neighbours, according to list
The size of the square net irrigation benifit of water determines the priority of conversion, i.e., the big crop of first net irrigation benifit of folk prescription water intracellular is as preferential
Converting objects.
(5) transfer function:
Cellular state, neighbours, transformation rule, constraint condition together constitute the transfer function of cellular, indicate are as follows:
In formula:It is state of the i-th row jth column cellular at the t+1 moment；It is the i-th row jth column cellular in t moment
State,It is Appropriate of the i-th row jth column cellular crop x in t moment,It is the i-th row jth column cellular crop x in t moment
Folk prescription water net benefits,It is the i-th row jth column cellular crop x in the net irrigation requirement of t moment, A is cultivated area constraint,
Q is water quantity restraint,It is the neighbor state number with the i-th row jth column cellular in t moment.
Step 302: different searching routes and direction comparison optimum results being set, are selected according to the actual situation most suitable excellent
Change scheme obtains the spatial framework of optimization ensuing crop pattern of farming and its water consumption.
Can be less with duty or need to reduce arable land face according to policy planning when model is used in arid area
When product, model is larger by water quantity restraint and cultivated area effect of constraint value.The primary conversion of the every generation of model is carried out primary constraint item
Part can make model just stop fortune after meeting constraint condition in operation that is, when water volume that can be utilized and Cultivated Land Area Decrease
Row.Therefore, when model brings into operation, different Optimizing Search paths and direction can all obtain different optimum results.
Based on above-mentioned model feature, the invention proposes four kinds of searching routes: search, parity rows search, slotting row are searched line by line
Rope, random search.Two directions of search are respectively arranged in the searching route of first three mode, i.e., forward lookup since first trip and
Reverse search since footline；4th kind of search needs to mark all cellulars that can be converted.Policymaker can be according to
Respective demand selects suitable mode to optimize according to above-mentioned 7 kinds of model optimization ways of search, after being optimized accordingly
Crop Planting Structure and corresponding crop water consumption spatial framework.
Claims (7)
1. a kind of design method of area crops water consumption spatial framework optimization, which comprises the steps of:
Step 1: choosing survey region, determine research crop species；
Step 2: choosing evaluation index relevant to plant growth, using principles of fuzzy mathematics quantitatively evaluating index, eliminate index
Between dimension difference, formed evaluation index subordinated-degree matrix；Application factor analytic approach and improved H calculate respectively
Index weights are calculated each using Polynomial combination index weights model Calculation Estimation indicator combination weight by evaluation model
Crop Appropriate on grid point；Using nature step-wise process, equidistant method, standard deviation and statistical approach, quantile method to suitable
Preferably it is worth and carries out subregion, the growth for dividing crop is suitable for section；
Step 3: the area crops water consumption spatial framework Optimized model (GCA-WCSO) based on cellular automata is established, with folk prescription water
Economic benefit is up to objective function, is optimized by water quantity restraint, neighbours' constraint, conversion priority, transformation rule；Pass through
Different searching routes and direction comparison optimum results are set, selects most suitable prioritization scheme according to the actual situation, is optimized
The pattern of farming distribution of the planting area and size of Different Crop and the spatial distribution of Different Crop water consumption afterwards.
2. a kind of design method of area crops water consumption spatial framework optimization according to claim 1, which is characterized in that described
Step 1 includes choosing survey region and determining status year, is extracted in research area and is ploughed by ArcGIS software according to remote sensing images
Distribution passes through NDVI image recognition Different Crop type in conjunction with the breeding time of local chief crop, obtains the crop in the region
Space distribution situation；Even local main long-term cropping is corn, considers the breeding time of local corn in April 15 to September 25
Between day, all NDVI images in breeding time are analyzed, the grid that extraction NDVI value is all larger than 0 is corn, under otherwise considering
One agrotype.
3. a kind of design method of area crops water consumption spatial framework optimization according to claim 1, which is characterized in that described
It is to comprehensively consider weather, landform, soil resource and the matching of plant growth that step 2, which chooses evaluation index relevant to plant growth,
Property, when selecting evaluation index, not only to consider influence of the various indexs to plant growth, also to take into account the accessibility of data,
Meet the requirement of quantification, spatialization simultaneously.
4. a kind of design method of area crops water consumption spatial framework optimization according to claim 3, which is characterized in that described
Select climatic factor for Crop growing stage accumulated temperature；Orographic factor is elevation, the gradient；Edphic factor be the soil weight, the soil texture,
Totally 10 indexs evaluate the plant growth suitability on each grid point for full nitrogen, full phosphorus, full potassium, organic matter or pH value；Its
Evaluation index contains quantitative target and qualitative index, for qualitative index, needs according to the actual situation to its assignment；To evaluation
Quantification is the quantization of evaluation index to be carried out using the principle of fuzzy mathematics, and eliminate the dimension difference between each evaluation index,
Establish the subordinated-degree matrix F of evaluation indexj×k；Wherein j: grid cell, k: influence factor.
5. a kind of design method of area crops water consumption spatial framework optimization according to claim 1, which is characterized in that described
Evaluation index is divided into again: discrete type, forward direction S type and reverse S type index；For discrete type index, is determined and be subordinate to according to grade scale
Belong to angle value, calculation formula is
In formula: f (x) is to be subordinate to angle value, a1、a2For index bound.
6. a kind of design method of area crops water consumption spatial framework optimization according to claim 1, which is characterized in that described
Using Polynomial combination index weights model Calculation Estimation indicator combination weight, parameter Combining weights, W=λ1ω1+λ2
ω2
In formula: W is combined weights weight values, λ1For the coefficient for the index weights that factor analysis calculates, λ2For improved H
The coefficient of the index weights of calculating, ω1For the index weights that factor analysis calculates, ω2It is calculated for improved H
Index weights；It is classified from there through frequency analysis is carried out to the evaluation index data on each grid point: for accumulated temperature, holding
This kind of continuous data of weight, elevation, the gradient determines the maximum value and minimum value of each grade；For other discrete datas, divide
The frequency for analysing each Index grading, take single grade frequency be greater than 10%, frequency summation 80% or more be classified as it is each be suitable for
The optimum range of property grade；By on obtained each grid point Appropriate and suitable grade comparison Different Crop in same grid point
On Appropriate size, select Appropriate maximum for relative advantage crop, divide space relative advantage area.
7. a kind of design method of area crops water consumption spatial framework optimization according to claim 1, which is characterized in that described
Crop water consumption spatial framework Optimized model, the model are constructed by MATLAB software platform, and GIS provides the input number of model
Crop Planting Structure and corresponding crop water consumption spatial framework according to the rasterizing with output data, after being optimized accordingly.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110751320A (en) * | 2019-09-29 | 2020-02-04 | 北京师范大学 | Agricultural land optimization method based on random fuzzy analysis |
| CN111507646A (en) * | 2020-06-09 | 2020-08-07 | 中国水利水电科学研究院 | Agricultural water-saving planning method based on remote sensing ET |
| CN112446155A (en) * | 2020-12-09 | 2021-03-05 | 四川省农业科学院农业信息与农村经济研究所 | Method for obtaining spatial pattern simulation model of target crops |
| CN113159560A (en) * | 2021-04-15 | 2021-07-23 | 中国科学院地理科学与资源研究所 | Optimized configuration method for psammophyte industry |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1987004278A1 (en) * | 1985-12-27 | 1987-07-16 | Thinking Machines Corporation | Method and apparatus for simulating systems described by partial differential equations |
| CN102169117A (en) * | 2010-12-30 | 2011-08-31 | 南京大学 | Evaluation system suitable for micro-ecosystem of soil with transgenic plants and application thereof |
| CN102880752A (en) * | 2012-09-14 | 2013-01-16 | 中国农业大学 | Optimized design method of regional crop evapotranspiration spatial-temporal pattern |
| CN105608735A (en) * | 2015-12-24 | 2016-05-25 | 北京农业信息技术研究中心 | Crop root space division method and system |
| CN106202788A (en) * | 2016-07-20 | 2016-12-07 | 中国水利水电科学研究院 | A kind of tide flood combined probability analysis method based on Copula function and application thereof |
| CN106251020A (en) * | 2016-08-05 | 2016-12-21 | 辜寄蓉 | Land_use change layout method for optimizing based on resosurces environment loading capacity |
| CN106296023A (en) * | 2016-08-18 | 2017-01-04 | 南华大学 | Uranium tailings pond Environmental Improvement of Decommissioning effect evaluation methods based on three scales analytic hierarchy process |
| CN107767011A (en) * | 2017-08-21 | 2018-03-06 | 南京理工大学 | A kind of track station characteristic of pedestrian acquisition system and service horizontal dynamic evaluation method |
| CN108287974A (en) * | 2018-02-02 | 2018-07-17 | 华中师范大学 | Coupling evaluation method towards land use change survey Cellular Automata Simulation precision |
| CN108829993A (en) * | 2018-06-23 | 2018-11-16 | 东北石油大学 | Coal seam pulsed hydraulic fracturing amplitude and Frequency Design method |
| CN109190161A (en) * | 2018-07-27 | 2019-01-11 | 广州蓝图地理信息技术有限公司 | Analogy method is developed in the port city planned based on patch cellular automata and port city |
-
2019
- 2019-02-26 CN CN201910142416.XA patent/CN109858180B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1987004278A1 (en) * | 1985-12-27 | 1987-07-16 | Thinking Machines Corporation | Method and apparatus for simulating systems described by partial differential equations |
| CN102169117A (en) * | 2010-12-30 | 2011-08-31 | 南京大学 | Evaluation system suitable for micro-ecosystem of soil with transgenic plants and application thereof |
| CN102880752A (en) * | 2012-09-14 | 2013-01-16 | 中国农业大学 | Optimized design method of regional crop evapotranspiration spatial-temporal pattern |
| CN105608735A (en) * | 2015-12-24 | 2016-05-25 | 北京农业信息技术研究中心 | Crop root space division method and system |
| CN106202788A (en) * | 2016-07-20 | 2016-12-07 | 中国水利水电科学研究院 | A kind of tide flood combined probability analysis method based on Copula function and application thereof |
| CN106251020A (en) * | 2016-08-05 | 2016-12-21 | 辜寄蓉 | Land_use change layout method for optimizing based on resosurces environment loading capacity |
| CN106296023A (en) * | 2016-08-18 | 2017-01-04 | 南华大学 | Uranium tailings pond Environmental Improvement of Decommissioning effect evaluation methods based on three scales analytic hierarchy process |
| CN107767011A (en) * | 2017-08-21 | 2018-03-06 | 南京理工大学 | A kind of track station characteristic of pedestrian acquisition system and service horizontal dynamic evaluation method |
| CN108287974A (en) * | 2018-02-02 | 2018-07-17 | 华中师范大学 | Coupling evaluation method towards land use change survey Cellular Automata Simulation precision |
| CN108829993A (en) * | 2018-06-23 | 2018-11-16 | 东北石油大学 | Coal seam pulsed hydraulic fracturing amplitude and Frequency Design method |
| CN109190161A (en) * | 2018-07-27 | 2019-01-11 | 广州蓝图地理信息技术有限公司 | Analogy method is developed in the port city planned based on patch cellular automata and port city |
Non-Patent Citations (1)
| Title |
|---|
| 舒帮荣: "基于约束性模糊元胞自动机的城镇用地扩展模拟研究", 《中国博士学位论文全文数据库经济与管理科学辑（月刊）》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110751320A (en) * | 2019-09-29 | 2020-02-04 | 北京师范大学 | Agricultural land optimization method based on random fuzzy analysis |
| CN110751320B (en) * | 2019-09-29 | 2022-06-14 | 北京师范大学 | Agricultural land optimization method based on random fuzzy analysis |
| CN111507646A (en) * | 2020-06-09 | 2020-08-07 | 中国水利水电科学研究院 | Agricultural water-saving planning method based on remote sensing ET |
| CN111507646B (en) * | 2020-06-09 | 2022-07-29 | 中国水利水电科学研究院 | Agricultural water-saving planning method based on remote sensing ET |
| CN112446155A (en) * | 2020-12-09 | 2021-03-05 | 四川省农业科学院农业信息与农村经济研究所 | Method for obtaining spatial pattern simulation model of target crops |
| CN112446155B (en) * | 2020-12-09 | 2023-04-25 | 四川省农业科学院农业信息与农村经济研究所 | Method for acquiring space pattern simulation model of target crop |
| CN113159560A (en) * | 2021-04-15 | 2021-07-23 | 中国科学院地理科学与资源研究所 | Optimized configuration method for psammophyte industry |
| CN113159560B (en) * | 2021-04-15 | 2023-12-05 | 中国科学院地理科学与资源研究所 | Optimization configuration method for sandy plant industry |
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