CN107274297A - A kind of soil crop-planting suitability assessment method - Google Patents
A kind of soil crop-planting suitability assessment method Download PDFInfo
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Abstract
The invention belongs to soil crop-planting technical field, a kind of soil crop-planting suitability assessment method is disclosed, including：According to user's querying condition display data storehouse data, statistical graph is generated further according to the graph style that user specifies, selective graph style includes line chart, block diagram, pie chart；Newly-built VectorLayer and image layer；Roaming, amplification, diminution, full figure and the data display for managing figure layer display properties；The modification of data；Using multiple linear regression analysis and two kinds of analysis methods of stepwise regression analysis；Data gridization is sufficient by assessment area rasterizing；According to specific cultivar, according to its biological characteristics, evaluation criteria is determined, with scientific method, data are analyzed, estimated and professional comment is delivered；Result of calculation export by Treatment Analysis.The present invention has the ability of efficiently management mass data, has positive meaning for guiding agricultural production structural adjustment and layout.
Description
Technical field
The invention belongs to soil crop-planting technical field, more particularly to a kind of soil crop-planting suitability assessment side
Method.
Background technology
Refer to the various plants agriculturally cultivated.Biological one kind.Zuo Wu ﹑ industrial crops (oil crops, vegetables are eaten including Liang
Crop, flower, grass, trees) two major classes.Edible crops are one of sources of the basic food of the mankind." people with food for the day ", table
The relation of people and food is reached, rational meals collocation could bring health to the mankind.Food it is self-sufficient, be only a state
The basis of family's sustainable development.The growth of crops, be unable to do without the scientific and technological production technology of science, and infant industry is produced
That comes can aid in the plant equipment of agricultural production.
In summary, the problem of prior art is present be：There is intelligence in current soil crop-planting suitability assessment method
Degree can be changed relatively low, data processing speed is slow, assess and the result of zoning is inaccurate.
The content of the invention
The problem of existing for prior art, the invention provides a kind of soil crop-planting suitability assessment method.
The present invention is achieved in that a kind of soil crop-planting suitability assessment method, and the soil crop-planting is fitted
Suitable property appraisal procedure comprises the following steps：
Step one, according to user's querying condition display data storehouse data, generate and unite further according to the graph style that user specifies
Chart is counted, selective graph style includes line chart, block diagram, pie chart；
Step 2, newly-built VectorLayer and image layer；Simultaneously import vector data, raster data, discrete data and
View data, Various types of data is respectively as respective type | j } | layer is added in Mapoobject working space；It is described new
VectorLayer and image layer are built, while transmission function when importing is：
Wherein, ω0For the centre frequency of wave filter, for different ω0, k makes k/ ω0Keep constant；
In frequency domain construction wave filter, corresponding polar coordinates expression way is：
G (r, θ)=G (r, r)-G (θ, θ)；
In formula, Gr(r) it is the radial component of control filter bandwidht, Gθ(θ) is the angle component of control filter direction；
R represents radial coordinate, and θ represents angle coordinate, f0Centered on frequency, θ0For filter direction, σfFor determining band
It is wide；
σθDetermine angular bandwidth,
Step 3, roaming, amplification, diminution, full figure and the data display for managing figure layer display properties；
The operations such as step 4, the modification of data, including object addition, deletion, cutting；
Step 5, using multiple linear regression analysis and two kinds of analysis methods of stepwise regression analysis, user can preserve
The Regression Analysis Result completed through analysis and the regression equation generated；
Step 6, Data gridization foot carries out grid one by one by assessment area rasterizing, and according to known discrete points data
The process of assignment；
Step 7, according to specific cultivar, according to its biological characteristics, determines evaluation criteria, right with scientific method
Data are analyzed, estimated and delivered professional comment；
Step 8, the result of calculation by Treatment Analysis can export as vector data, raster data and view data 3
The form of kind.The method of the process Treatment Analysis includes：
For the biological characteristics signal of each cultivar, according to the following equation to each cultivar
Each frame biological characteristics signal in biological characteristics signal enters Row noise tracking, obtains each frame biological characteristics signal
Noise spectrum N (w, n)：
Wherein, X (w, n) represents the Short Time Fourier Transform of the biological characteristics signal；α u, α d are predetermined coefficient and 0<
αd<αu<1；W represents the frequency sequence number on frequency domain；N represents the frame number in time domain；
The Short Time Fourier Transform progress binary conversion treatment to each frame biological characteristics signal is obtained according to the following equation
Two-value spectrum Xb (w, n)：
Tb is preset first threshold value；
Will wherein all the way the corresponding Ka two-value of biological characteristics signal compose it is corresponding with another road biological characteristics signal
Coherence's matching between Kb two-value spectrum is carried out two-by-two obtains first matching result, and first matching result includes matching
Spend one group of two-value of highest and compose corresponding matched position and matching degree, Ka, Kb are positive integer；
For per biological characteristics signal all the way, each frame in the biological characteristics signal is calculated according to the following equation
The power spectrum P (w, n) of biological characteristics signal：
P (w, n)=αpP (w, n-1)+(1- αp) | X (w, n) |2
Wherein, X (w, n) represents the Short Time Fourier Transform of the biological characteristics signal；
α p are predetermined coefficient and 0 ＜ α p ＜ 1；W represents the frequency sequence number on frequency domain；N represents the frame number in time domain；
The Spectral correlation DP (w, n) of the power spectrum of each frame biological characteristics signal is calculated according to the following equation：
DP (w, n)=| P (w+1, n)-P (w, n) |
Enter Row noise tracking to the Spectral correlation DP (w, n) according to the following equation, obtain each frame biological characteristics
The Spectral correlation NDP (w, n) of the power noise spectrum of signal：
Wherein, β u, β d are predetermined coefficient and 0 ＜ β d ＜ β u ＜ 1.
Further, the addition of object is the profile class according to layer type and figure layer on current layer in the step 4
Different data are not added；After user-selected area, according to object within region is searched, then the deletion of object is
Data in deleting layer one by one；Object reduce is divided into vector data reduce and raster data reduce, vector data reduce be
After user-selected area, secondly inversion region first is searched object in inversion region, is deleted one by one；Grid number
It is that a width mask data picture is generated according to selection region according to reduction, former grating image is given birth to again with mask image data
Into, using mask data change raster data, make the region not shown data be all no data completes reduce.
Further, the discrete data that multiple linear regression analysis is chosen from discrete file in the step 5 is as recurrence
Wither and analyzed with the analytic function of multiple regression after the variable data of analysis, one dependent variable of selection and two independents variable,
Then result analysis drawn is shown in list box.
Further, the assignment of grid is realized in the step 6 using the method for interpolation, from some given of certain function
Given value or derivative value on discrete point set out interpolation go out the function numerical method general name；Using by function limited
Value situation at point, estimates approximation of the function at other points；By interpolation calculation, by the prison of limited meteorological site
Survey data science and be reasonably extrapolated to whole assessment area, realize the proportion of crop planting suitability assessment in regional extent.
Further, agroclimatological resources includes heat, intensity of solar radiation, sunshine time, precipitation in the step 7；
By the comprehensive identification to agricultural land natural quality, agricultural land is classified by mass discrepancy；According to weather, soil, landform
Each influence factor degree determines the principle of weight, with GIS overlay analysis, and final determination proportion of crop planting is to determine first
The essential elements of evaluation of the kind, and weight is assigned, and reclassification is carried out for each essential elements of evaluation, according to influence proportion of crop planting suitability
Degree be classified.
Further, the essential elements of evaluation for determining the kind, is specifically included：
Set up set of factors：
The various parameters compositing factor set for influenceing the kind to plant, U={ u1,u2,u3,u4}={ temperature, water, illumination
Time, intensity of illumination }；
Set up evaluate collection：
In order to carry out quantitative analysis it needs to be determined that the evaluate collection of each index to each evaluation index, using 5 grades of hundred-mark system evaluations
Evaluate collection V is divided 5 opinion ratings, i.e. V={ v1, v2, v3, v4, v5}={ is minimum, very little, small, larger, big }, wherein v1For
Kind plantation is dangerous minimum, and interval scoring is 90~100, and intermediate value is 95；v2For dangerous very little, scoring it is interval for 80~
89, intermediate value is 84.5；The rest may be inferred；Each interval intermediate value is selected as the parameter of grade, then the parameter corresponding to 5 grades is
{ 95,84.5,74.5,64.5,49.5 }, parameter column vector is ν={ 95,84.5,74.5,64.5,49.5 }T。
Further, in the imparting weight, weight sets is set up：Including：
(1) recursive hierarchy structure is set up：
It is assessment indicator system to plant factor of evaluation collection according to the kind of foundation, each factor that problem is included point
For：First layer is the general objective layer G evaluated, i.e. kind plantation comprehensive safety；The second layer is rule layer C, i.e. temperature, water,
Light application time, intensity of illumination；Finally it regard specific targets as third layer, i.e. indicator layer P；
(2) multilevel iudge matrix two-by-two is constructed：
Invite the kind to plant secure context expert, weight is successively carried out according to 1~9 scaling law between any two to each key element
The property wanted assigning degrees, Judgement Matricies U=(uij)n×n, wherein uijExpression factor uiAnd ujRelative to the importance value of rule layer,
Matrix U has property：uii=1, uij=1/uji, i, j=1,2 ..., n draw judgment matrix：By matrix X1~X5By row normalizing
Change, i.e.,：
Calculating matrix Y is：
Further, the weight sets of setting up also includes：
The calculating of element relative weighting under single criterion：
Y matrix by rowss are added, by formulaDraw：
W1=(2.652 0.686 0.253 0.409)T
W2=(1 1)T
W3=(1.273 0.371 0.221 2.135)T
W4=(1.9 0.319 0.781)T
W5=(2.121 0.604 0.275)T
Obtain and vector is normalized, by formulaWeight vector can be obtained：
Further, the weight sets of setting up also includes：
The consistency check of judgment matrix：
Calculate the Maximum characteristic root λ of judgment matrixmax, by formulaCalculate:
According to formulaConsistency check is carried out, is obtained：
CI1=0.019
CI2=0
CI3=0.031
CI4=0.020
CI5=0.048
By formula：
CR1=0.022
CR2=0
CR3=0.035
CR4=0.038
CR5=0.092
CR ＜ 0.1, are satisfied by coherence request, therefore the relative weighting of each factor
Further, the weight sets of setting up also includes：
Degree of membership is calculated：
Multidigit uses frequency number analysis, and the indices being evaluated are planted with the dangerous journey of index by evaluate collection to changing kind
Degree is graded, and obtains the degree of membership of set of factors：
It is determined that judging Subject Matrix：
By the relative defects matrix for obtaining k-th of set of factors：
Wherein：
In formula：RkThe relative defects matrix of-k-th set of factors；
rkijThe degree of membership for the j that i-th of factor of-k-th set of factors belongs in evaluate collection；
pkij- group membership is rated j frequency to i-th of factor index of k-th of set of factors.
Further, the weight sets of setting up also includes：
Construct fuzzy matrix for assessment：
By the weight vector of each indexFuzzy matrix for assessment B can be constructed with matrix R,
Calculate Comprehensive Evaluation result：
By fuzzy matrix for assessment B and the parameter column vector of evaluate collection, Comprehensive Evaluation result Z can be tried to achieve；
Z=BV
The result of fuzzy overall evaluation is arrived as available from the above equation, is provided further according to opinion rating, evaluates kind plantation factor
Dangerous size.
Advantages of the present invention and good effect are：The present invention has the efficient ability for managing mass data, and there is provided convenient
Database data display, inquiry and management function；Realized using overlay analysis and function of statistic analysis to proportion of crop planting
Suitability carries out qualitative assessment and agricultral climatic regionalization, assess and zoning achievement can in the form of thematic map output display, make
Proportion of crop planting Suitable Area distribution situation is very clear, for crops climate regionalization provide science analysis and decision put down
Platform, has positive meaning for guiding agricultural production structural adjustment and layout.
The newly-built VectorLayer of the present invention and image layer, transmission function during importing greatly ensure that data transmission procedure
The accuracy of middle data；The need for further increasing intelligent control.Result of calculation of the present invention Jing Guo Treatment Analysis can be led
Go out in vector data, 3 kinds of forms of raster data and view data, the method for Treatment Analysis is tracked by noise, can be real-time
Accurate vector data, raster data and view data are obtained, the practicality assessed has been fully ensured that, this is the one of the present invention
Key point.
The safe evaluation method that the present invention is provided, overcomes the difficulty for being unable to dynamic detection disaster trend, can be more preferable, more accurate
Timely discovery disaster, accomplish to prevent in advance；Using comprehensive evaluation system, quantification is combined with qualitative analysis, with reference to
Evaluate collection is actually set up, overall merit judgment matrix is set up, damage ratio and safety in being planted according to each Failure Factors to kind
The total damage ratio of the weight calculation of influence, abandon using the evaluation of single angle, it is undue rely on or field data by the way of, it is comprehensive
Consider influence all principal elements, and it is clear and definite respectively influence connect each other, comprehensive safety evaluation is made on this basis；No
Be only capable of correctly draw whether can safety conclusion, moreover it is possible to the problem of solving safe coefficient；Simplify evaluation procedure, eliminate the master evaluated
Randomness is seen, is easy to those skilled in the art to be applied to actual.The reliability of the present invention is high, operability is good, makes assessment result
Reality can be reflected more objective reality.
Brief description of the drawings
Fig. 1 is crop-planting suitability assessment method flow diagram in soil provided in an embodiment of the present invention.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
The application principle of the present invention is explained in detail below in conjunction with the accompanying drawings.
As shown in figure 1, crop-planting suitability assessment method in soil provided in an embodiment of the present invention comprises the following steps：
S101：According to user's querying condition display data storehouse data, generate and count further according to the graph style that user specifies
Chart, selective graph style includes line chart, block diagram, pie chart；
S102：Newly-built VectorLayer and image layer；Vector data, raster data, discrete data and figure are imported simultaneously
As data, Various types of data is respectively as respective type | j } | layer is added in Mapoobject working space；
S103：Roaming, amplification, diminution, full figure and the data display for managing figure layer display properties；
S104：The modification of data, including object addition, delete, cut etc. operation；
S105：Using multiple linear regression analysis and two kinds of analysis methods of stepwise regression analysis, user can preserve
Analyze the Regression Analysis Result completed and the regression equation generated；
S106：Data gridization foot carries out grid tax one by one by assessment area rasterizing, and according to known discrete points data
The process of value；
S107：According to specific cultivar, according to its biological characteristics, evaluation criteria is determined, with scientific method, logarithm
According to being analyzed, estimated and delivered professional comment；
S108：Result of calculation by Treatment Analysis can export as 3 kinds of vector data, raster data and view data
Form.
The newly-built VectorLayer and image layer, while transmission function when importing is：
Wherein, ω0For the centre frequency of wave filter, for different ω0, k makes k/ ω0Keep constant；
In frequency domain construction wave filter, corresponding polar coordinates expression way is：
G (r, θ)=G (r, r)-G (θ, θ)；
In formula, Gr(r) it is the radial component of control filter bandwidht, Gθ(θ) is the angle component of control filter direction；
R represents radial coordinate, and θ represents angle coordinate, f0Centered on frequency, θ0For filter direction, σfFor determining band
It is wide；
σθDetermine angular bandwidth,
In step S104：The addition of object is according to the addition of the profile classification of layer type and figure layer on current layer
Different data.When figure layer is in vector point figure layer, it may only select to add point, system record adds when clicking on left mouse button
The position added some points, is added to point in the data of figure layer.Other are as similar such as line type, face type procedure.The deletion of object be
After user-selected area, according to the object searched within region, the data in then deleting layer one by one.Object, which is reduced, to be divided
It is that vector data is reduced and raster data is reduced, it is the inversion region first after user-selected area that vector data, which is reduced, its
Object of the secondary lookup in inversion region, is deleted one by one.To avoid some objects without completely within inversion region
Without the situation completely within former region, remaining data are judged whether within former region one by one, if Bu Yuan areas
Then deleted within domain.It is to generate a width mask data picture according to selection region first that raster data, which is reduced, then to former grid
Image is regenerated with mask image data, then changes raster data using mask data, the region for making not show
Data are all no data and complete reduction.
In step S105：Multiple linear regression analysis discusses that multiple independents variable and the linear dependence of multiple dependent variables are closed enough
The theory and method of system.The discrete data chosen first from discrete file as regression analysis variable data, when choosing
Can carry out square, cube, the data conversion such as square root.At least withered after one dependent variable of selection and two independents variable with polynary
The analytic function of recurrence is analyzed, and the result that then analysis is drawn is shown in list box.
Stepwise regression analysis is constantly to introduce variable into equation and reject the process of variable, can set up optimal reach the same goal
Model, during its basic thought is never selected whole variables, selects the maximum variable of its sum of squares of partial regression contribution, uses variance
Method than carrying out significance test discriminates whether to choose；From selected whole variables, sum of squares of partial regression contribution is found
Minimum variable, the method for carrying out significance test with variance ratio discriminates whether to reject from regression equation.Choose and rejecting is followed
Ring is repeated, until without qualified item of choosing, also without qualified rejecting item untill.Reached the same goal analysis with multiple linear
Implementation method principle is the same, selects significance, conventional for 0.01 and 0.05, the result that analysis is drawn is shown to list
In frame.
In step s 106：The assignment of grid is realized using the method for interpolation.Interpolation is the important side that discrete function is approached
Method, is the numerical method for going out the function from the given value or derivative value interpolation on some given discrete points of certain function
General name.Using it can be by function at limited point value situation, estimate approximation of the function at other points.Pass through
Interpolation calculation, can by the Monitoring Data of limited meteorological site it is scientific and reasonable be extrapolated to whole assessment area, so as to realize
Proportion of crop planting suitability assessment in regional extent.The module is using distance weighting counting backward technique interpolation with entering using regression equation
Two methods of row calculating in small grid method realize the grid of each factor data.
In step s 107：Agroclimatological resources mainly includes heat, intensity of solar radiation, sunshine time, precipitation etc.,
Its quantity, distribution feature and its configuring condition decides the composition of agricultural production type, yield to a certain extent each other
Height and kind quality etc..The adaptability of different soil is different, according to a Soils In The Region attribute the characteristics of assess
The process of optimum Land use systems and long-term cropping is significant, by being reflected to the comprehensive of agricultural land natural quality
It is fixed, agricultural land is classified by mass discrepancy, to be illustrated under certain scientific and technological level, agricultural land is in various Land use systems
In quality and to the relatively suitable degree of crops, be an important foundation sex work n " ¨ of Agricultural land use decision-making].
Landform is the general designation of atural object and landforms, and [5 aspects such as 12-133 are to some extent for height above sea level, longitude, latitude, slope aspect, the gradient
The reallocation of earth's surface hydrothermal condition and water erosion intensity are influenceed, therefore the important influence of generation is distributed to proportion of crop planting.The mould
Block determines the principle of weight according to weather, soil, each influence factor degree of landform, with GIS overlay analysis, finally determines farming
What species were planted is to determine the essential elements of evaluation of the kind first, and assigns weight, and carries out reclassification for each essential elements of evaluation, according to
Influence proportion of crop planting suitability degree be classified, can such as be divided into it is suitable, secondary suitably, be not suitable for standard.Different farmings
The requirement that thing grows according to it, has different environmental requirements, therefore carry out the power that proportion of crop planting suitability assessment is used
Weight grade scale is different because of crops.
In S108, the method for the process Treatment Analysis includes：
For the biological characteristics signal of each cultivar, according to the following equation to each cultivar
Each frame biological characteristics signal in biological characteristics signal enters Row noise tracking, obtains each frame biological characteristics signal
Noise spectrum N (w, n)：
Wherein, X (w, n) represents the Short Time Fourier Transform of the biological characteristics signal；α u, α d are predetermined coefficient and 0<
αd<αu<1；W represents the frequency sequence number on frequency domain；N represents the frame number in time domain；
The Short Time Fourier Transform progress binary conversion treatment to each frame biological characteristics signal is obtained according to the following equation
Two-value spectrum Xb (w, n)：
Tb is preset first threshold value；
Will wherein all the way the corresponding Ka two-value of biological characteristics signal compose it is corresponding with another road biological characteristics signal
Coherence's matching between Kb two-value spectrum is carried out two-by-two obtains first matching result, and first matching result includes matching
Spend one group of two-value of highest and compose corresponding matched position and matching degree, Ka, Kb are positive integer；
For per biological characteristics signal all the way, each frame in the biological characteristics signal is calculated according to the following equation
The power spectrum P (w, n) of biological characteristics signal：
P (w, n)=αpP (w, n-1)+(1- αp) | X (w, n) |2
Wherein, X (w, n) represents the Short Time Fourier Transform of the biological characteristics signal；
α p are predetermined coefficient and 0 ＜ α p ＜ 1；W represents the frequency sequence number on frequency domain；N represents the frame number in time domain；
The Spectral correlation DP (w, n) of the power spectrum of each frame biological characteristics signal is calculated according to the following equation：
DP (w, n)=| P (w+1, n)-P (w, n) |
Enter Row noise tracking to the Spectral correlation DP (w, n) according to the following equation, obtain each frame biological characteristics
The Spectral correlation NDP (w, n) of the power noise spectrum of signal：
Wherein, β u, β d are predetermined coefficient and 0 ＜ β d ＜ β u ＜ 1.
The essential elements of evaluation for determining the kind, is specifically included：
Set up set of factors：
The various parameters compositing factor set for influenceing the kind to plant, U={ u1,u2,u3,u4}={ temperature, water, illumination
Time, intensity of illumination }；
Set up evaluate collection：
In order to carry out quantitative analysis it needs to be determined that the evaluate collection of each index to each evaluation index, using 5 grades of hundred-mark system evaluations
Evaluate collection V is divided 5 opinion ratings, i.e. V={ v1, v2, v3, v4, v5}={ is minimum, very little, small, larger, big }, wherein v1For
Kind plantation is dangerous minimum, and interval scoring is 90~100, and intermediate value is 95；v2For dangerous very little, scoring it is interval for 80~
89, intermediate value is 84.5；The rest may be inferred；Each interval intermediate value is selected as the parameter of grade, then the parameter corresponding to 5 grades is
{ 95,84.5,74.5,64.5,49.5 }, parameter column vector is ν={ 95,84.5,74.5,64.5,49.5 }T。
In the imparting weight, weight sets is set up：Including：
(1) recursive hierarchy structure is set up：
It is assessment indicator system to plant factor of evaluation collection according to the kind of foundation, each factor that problem is included point
For：First layer is the general objective layer G evaluated, i.e. kind plantation comprehensive safety；The second layer is rule layer C, i.e. temperature, water,
Light application time, intensity of illumination；Finally it regard specific targets as third layer, i.e. indicator layer P；
(2) multilevel iudge matrix two-by-two is constructed：
Invite the kind to plant secure context expert, weight is successively carried out according to 1~9 scaling law between any two to each key element
The property wanted assigning degrees, Judgement Matricies U=(uij)n×n, wherein uijExpression factor uiAnd ujRelative to the importance value of rule layer,
Matrix U has property：uii=1, uij=1/uji, i, j=1,2 ..., n draw judgment matrix：By matrix X1~X5By row normalizing
Change, i.e.,：
Calculating matrix Y is：
The weight sets of setting up also includes：
The calculating of element relative weighting under single criterion：
Y matrix by rowss are added, by formulaDraw：
W1=(2.652 0.686 0.253 0.409)T
W2=(1 1)T
W3=(1.273 0.371 0.221 2.135)T
W4=(1.9 0.319 0.781)T
W5=(2.121 0.604 0.275)T
Obtain and vector is normalized, by formulaWeight vector can be obtained：
The weight sets of setting up also includes：
The consistency check of judgment matrix：
Calculate the Maximum characteristic root λ of judgment matrixmax, by formulaCalculate:
According to formulaConsistency check is carried out, is obtained：
CI1=0.019
CI2=0
CI3=0.031
CI4=0.020
CI5=0.048
By formula：
CR1=0.022
CR2=0
CR3=0.035
CR4=0.038
CR5=0.092
CR ＜ 0.1, are satisfied by coherence request, therefore the relative weighting of each factor
The weight sets of setting up also includes：
Degree of membership is calculated：
Multidigit uses frequency number analysis, and the indices being evaluated are planted with the dangerous journey of index by evaluate collection to changing kind
Degree is graded, and obtains the degree of membership of set of factors：
It is determined that judging Subject Matrix：
By the relative defects matrix for obtaining k-th of set of factors：
Wherein：
In formula：RkThe relative defects matrix of-k-th set of factors；
rkijThe degree of membership for the j that i-th of factor of-k-th set of factors belongs in evaluate collection；
pkij- group membership is rated j frequency to i-th of factor index of k-th of set of factors.
The weight sets of setting up also includes：
Construct fuzzy matrix for assessment：
By the weight vector of each indexFuzzy matrix for assessment B can be constructed with matrix R,
Calculate Comprehensive Evaluation result：
By fuzzy matrix for assessment B and the parameter column vector of evaluate collection, Comprehensive Evaluation result Z can be tried to achieve；
Z=BV
The result of fuzzy overall evaluation is arrived as available from the above equation, is provided further according to opinion rating, evaluates kind plantation factor
Dangerous size.
The present invention have efficiently management mass data ability there is provided the display of convenient database data, inquiry and
Management function；Realized using overlay analysis and function of statistic analysis and qualitative assessment and agriculture gas are carried out to proportion of crop planting suitability
Wait zoning, assess and zoning achievement can in the form of thematic map output display, make proportion of crop planting Suitable Area distribution situation one
Mesh is clear, and the analysis and decision platform of science is provided for the climate regionalization of crops, for guiding agricultural production structural adjustment
There is positive meaning with layout.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
Any modifications, equivalent substitutions and improvements made within refreshing and principle etc., should be included in the scope of the protection.
Claims (7)
1. a kind of soil crop-planting suitability assessment method, it is characterised in that the soil crop-planting suitability assessment side
Method comprises the following steps：
Step one, according to user's querying condition display data storehouse data, statistical chart is generated further according to the graph style that user specifies
Table, selective graph style includes line chart, block diagram, pie chart；
Step 2, newly-built VectorLayer and image layer；Vector data, raster data, discrete data and image are imported simultaneously
Data, Various types of data is respectively as respective type | j } | layer is added in Mapoobject working space；The newly-built arrow
Spirogram layer and image layer, while transmission function when importing is：
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<mi>G</mi>
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<mi>&omega;</mi>
<mo>)</mo>
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Wherein, ω0For the centre frequency of wave filter, for different ω0, k makes k/ ω0Keep constant；
In frequency domain construction wave filter, corresponding polar coordinates expression way is：
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<mi>G</mi>
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<mo>&rsqb;</mo>
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G (r, θ)=G (r, r)-G (θ, θ)；
In formula, Gr(r) it is the radial component of control filter bandwidht, Gθ(θ) is the angle component of control filter direction；
R represents radial coordinate, and θ represents angle coordinate, f0Centered on frequency, θ0For filter direction, σfFor determining bandwidth；
Bf=2 (2/ln2) 1/2 | ln σf|, σθDetermine angular bandwidth, B θ=2 (2/ln2) 1/2 σθ；
Step 3, roaming, amplification, diminution, full figure and the data display for managing figure layer display properties；
Step 4, the modification of data, including object addition, the operation deleted, cut；
Step 5, using multiple linear regression analysis and two kinds of analysis methods of stepwise regression analysis, user, which can preserve, have been divided
Analyse the Regression Analysis Result completed and the regression equation generated；
Step 6, Data gridization foot carries out grid assignment one by one by assessment area rasterizing, and according to known discrete points data
Process；
Step 7, according to specific cultivar, according to its biological characteristics, determines evaluation criteria, with scientific method, to data
Analyzed, estimated and delivered professional comment；
Step 8, the result of calculation by Treatment Analysis exports as vector data, 3 kinds of forms of raster data and view data；
The method of the process Treatment Analysis includes：
For the biological characteristics signal of each cultivar, according to the following equation to the biology of each cultivar
Learn each frame biological characteristics signal in characteristic signals and enter Row noise tracking, obtain the noise of each frame biological characteristics signal
Compose N (w, n)：
<mrow>
<mi>N</mi>
<mrow>
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<mi>w</mi>
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<mtd>
<mrow>
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<mi>u</mi>
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Wherein, X (w, n) represents the Short Time Fourier Transform of the biological characteristics signal；α u, α d are predetermined coefficient and 0<αd<α
u<1；W represents the frequency sequence number on frequency domain；N represents the frame number in time domain；
The Short Time Fourier Transform progress binary conversion treatment to each frame biological characteristics signal obtains two-value according to the following equation
Compose Xb (w, n)：
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<mi>X</mi>
<mi>b</mi>
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<mi>w</mi>
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<mo>(</mo>
<mi>w</mi>
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<mi>n</mi>
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<mo>|</mo>
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<mi>T</mi>
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<mi>b</mi>
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</mfenced>
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Tb is preset first threshold value；
Will wherein all the way the corresponding Ka two-value of biological characteristics signal to compose Kb corresponding with another road biological characteristics signal individual
Coherence's matching between two-value spectrum is carried out two-by-two obtains first matching result, and first matching result includes matching degree most
One group of high two-value composes corresponding matched position and matching degree, and Ka, Kb are positive integer；
For per biological characteristics signal all the way, each frame calculated according to the following equation in the biological characteristics signal is biological
Learn the power spectrum P (w, n) of characteristic signals：
P (w, n)=αpP (w, n-1)+(1- αp) | X (w, n) |2
Wherein, X (w, n) represents the Short Time Fourier Transform of the biological characteristics signal；
α p are predetermined coefficient and 0 ＜ α p ＜ 1；W represents the frequency sequence number on frequency domain；N represents the frame number in time domain；
The Spectral correlation DP (w, n) of the power spectrum of each frame biological characteristics signal is calculated according to the following equation：
DP (w, n)=| P (w+1, n)-P (w, n) |
Enter Row noise tracking to the Spectral correlation DP (w, n) according to the following equation, obtain each frame biological characteristics signal
Power noise spectrum Spectral correlation NDP (w, n)：
<mrow>
<mi>N</mi>
<mi>D</mi>
<mi>P</mi>
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<mtd>
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<mi>P</mi>
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Wherein, β u, β d are predetermined coefficient and 0 ＜ β d ＜ β u ＜ 1；
The addition of object is different with the profile classification addition of figure layer according to layer type on current layer in the step 4
Data；The deletion of object is that after user-selected area, according to the object searched within region, figure is then deleted one by one
Data in layer；Object, which is reduced, is divided into vector data reduction and raster data reduction, and it is to select area in user that vector data, which is reduced,
After domain, secondly inversion region first is searched object in inversion region, is deleted one by one；It is root that raster data, which is reduced,
A width mask data picture is generated according to selection region, former grating image is regenerated with mask image data, using covering
Code data modification raster data, makes the data in the region not shown be all no data and completes reduction；
The discrete data that multiple linear regression analysis is chosen from discrete file in the step 5 as regression analysis variable
Wither and analyzed with the analytic function of multiple regression after data, one dependent variable of selection and two independents variable, then analysis
The result drawn is shown in list box；
The assignment of grid is realized in the step 6 using the method for interpolation, from some given discrete points of certain function
Know value or derivative value set out interpolation go out the function numerical method general name；Utilize the value shape by function at limited point
Condition, estimates approximation of the function at other points；By interpolation calculation, the Monitoring Data science of limited meteorological site is closed
The proportion of crop planting suitability assessment for being extrapolated to whole assessment area, realizing in regional extent of reason；
Agroclimatological resources includes heat, intensity of solar radiation, sunshine time, precipitation in the step 7；By to agricultural
The comprehensive identification of soil natural quality, agricultural land is classified by mass discrepancy；According to weather, soil, each influence factor of landform
Degree determines the principle of weight, with GIS overlay analysis, and final determination proportion of crop planting is to determine commenting for the kind first
Valency key element, and weight is assigned, and reclassification is carried out for each essential elements of evaluation, carried out according to the degree of influence proportion of crop planting suitability
Classification.
2. crop-planting suitability assessment method in soil as claimed in claim 1, it is characterised in that the determination kind
Essential elements of evaluation, is specifically included：
Set up set of factors：
The various parameters compositing factor set for influenceing the kind to plant, U={ u1,u2,u3,u4}={ temperature, water, light application time,
Intensity of illumination }；
Set up evaluate collection：
In order to carry out quantitative analysis to each evaluation index it needs to be determined that the evaluate collection of each index, is commented using 5 grades of hundred-mark system evaluations handles
Valency collection V divides 5 opinion ratings, i.e. V={ v1, v2, v3, v4, v5}={ is minimum, very little, small, larger, big }, wherein v1For the product
Plantation is dangerous minimum, and scoring interval is 90~100, and intermediate value is 95；v2For dangerous very little, interval scoring is 80~89,
Intermediate value is 84.5；The rest may be inferred；Each interval intermediate value is selected as the parameter of grade, then the parameter corresponding to 5 grades is
{ 95,84.5,74.5,64.5,49.5 }, parameter column vector is ν={ 95,84.5,74.5,64.5,49.5 }T。
3. crop-planting suitability assessment method in soil as claimed in claim 2, it is characterised in that in the imparting weight,
Set up weight sets：Including：
(1) recursive hierarchy structure is set up：
It is assessment indicator system to plant factor of evaluation collection according to the kind of foundation, and each factor that problem is included is divided into：The
One layer is the general objective layer G evaluated, i.e. kind plantation comprehensive safety；The second layer is rule layer C, i.e. temperature, water, during illumination
Between, intensity of illumination；Finally it regard specific targets as third layer, i.e. indicator layer P；
(2) multilevel iudge matrix two-by-two is constructed：
Invite the kind to plant secure context expert, importance is successively carried out according to 1~9 scaling law between any two to each key element
Assigning degrees, Judgement Matricies U=(uij)n×n, wherein uijExpression factor uiAnd ujRelative to the importance value of rule layer, matrix U
With property：uii=1, uij=1/uji, i, j=1,2 ..., n draw judgment matrix：By matrix X1~X5By row normalization, i.e.,：
<mrow>
<msub>
<mi>y</mi>
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<mi>i</mi>
<mi>j</mi>
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<mi>i</mi>
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<mrow>
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<mrow>
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</mrow>
<mi>n</mi>
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<msub>
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<mi>j</mi>
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<mo>,</mo>
<mn>2</mn>
<mo>...</mo>
<mi>n</mi>
<mo>)</mo>
</mrow>
</mrow>
Calculating matrix Y is：
<mrow>
<msup>
<mi>Y</mi>
<mn>1</mn>
</msup>
<mo>=</mo>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.681</mn>
</mtd>
<mtd>
<mn>0.732</mn>
</mtd>
<mtd>
<mn>0.572</mn>
</mtd>
<mtd>
<mn>0.667</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0.136</mn>
</mtd>
<mtd>
<mn>0.146</mn>
</mtd>
<mtd>
<mn>0.214</mn>
</mtd>
<mtd>
<mn>0.190</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0.085</mn>
</mtd>
<mtd>
<mn>0.049</mn>
</mtd>
<mtd>
<mn>0.071</mn>
</mtd>
<mtd>
<mn>0.048</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0.098</mn>
</mtd>
<mtd>
<mn>0.073</mn>
</mtd>
<mtd>
<mn>0.143</mn>
</mtd>
<mtd>
<mn>0.095</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<msup>
<mi>Y</mi>
<mn>2</mn>
</msup>
<mo>=</mo>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.5</mn>
</mtd>
<mtd>
<mn>0.5</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0.5</mn>
</mtd>
<mtd>
<mn>0.5</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<msup>
<mi>Y</mi>
<mn>3</mn>
</msup>
<mo>=</mo>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.293</mn>
</mtd>
<mtd>
<mn>0.348</mn>
</mtd>
<mtd>
<mn>0.353</mn>
</mtd>
<mtd>
<mn>0.279</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0.073</mn>
</mtd>
<mtd>
<mn>0.087</mn>
</mtd>
<mtd>
<mn>0.118</mn>
</mtd>
<mtd>
<mn>0.093</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0.049</mn>
</mtd>
<mtd>
<mn>0.043</mn>
</mtd>
<mtd>
<mn>0.059</mn>
</mtd>
<mtd>
<mn>0.070</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0.585</mn>
</mtd>
<mtd>
<mn>0.522</mn>
</mtd>
<mtd>
<mn>0.470</mn>
</mtd>
<mtd>
<mn>0.558</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<msup>
<mi>Y</mi>
<mn>4</mn>
</msup>
<mo>=</mo>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.652</mn>
</mtd>
<mtd>
<mn>0.556</mn>
</mtd>
<mtd>
<mn>0.692</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0.131</mn>
</mtd>
<mtd>
<mn>0.111</mn>
</mtd>
<mtd>
<mn>0.077</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0.217</mn>
</mtd>
<mtd>
<mn>0.333</mn>
</mtd>
<mtd>
<mn>0.231</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
3
<mrow>
<msup>
<mi>Y</mi>
<mn>5</mn>
</msup>
<mo>=</mo>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.732</mn>
</mtd>
<mtd>
<mn>0.789</mn>
</mtd>
<mtd>
<mn>0.600</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0.146</mn>
</mtd>
<mtd>
<mn>0.158</mn>
</mtd>
<mtd>
<mn>0.300</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0.122</mn>
</mtd>
<mtd>
<mn>0.053</mn>
</mtd>
<mtd>
<mn>0.100</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>.</mo>
</mrow>
4. crop-planting suitability assessment method in soil as claimed in claim 2, it is characterised in that described to set up weight sets also
Including：
The calculating of element relative weighting under single criterion：
Y matrix by rowss are added, by formulaDraw：
W1=(2.652 0.686 0.253 0.409)T
W2=(1 1)T
W3=(1.273 0.371 0.221 2.135)T
W4=(1.9 0.319 0.781)T
W5=(2.121 0.604 0.275)T
Obtain and vector is normalized, by formulaWeight vector can be obtained：
<mrow>
<msup>
<mover>
<mi>W</mi>
<mo>&OverBar;</mo>
</mover>
<mn>1</mn>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.663</mn>
</mtd>
<mtd>
<mn>0.172</mn>
</mtd>
<mtd>
<mn>0.063</mn>
</mtd>
<mtd>
<mn>0.102</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
<mrow>
<msup>
<mover>
<mi>W</mi>
<mo>&OverBar;</mo>
</mover>
<mn>2</mn>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.5</mn>
</mtd>
<mtd>
<mn>0.5</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
<mrow>
<msup>
<mover>
<mi>W</mi>
<mo>&OverBar;</mo>
</mover>
<mn>3</mn>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.381</mn>
</mtd>
<mtd>
<mn>0.093</mn>
</mtd>
<mtd>
<mn>0.055</mn>
</mtd>
<mtd>
<mn>0.534</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
<mrow>
<msup>
<mover>
<mi>W</mi>
<mo>&OverBar;</mo>
</mover>
<mn>4</mn>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.633</mn>
</mtd>
<mtd>
<mn>0.106</mn>
</mtd>
<mtd>
<mn>0.261</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
<mrow>
<msup>
<mover>
<mi>W</mi>
<mo>&OverBar;</mo>
</mover>
<mn>5</mn>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.707</mn>
</mtd>
<mtd>
<mn>0.201</mn>
</mtd>
<mtd>
<mn>0.092</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
<mo>.</mo>
</mrow>
5. crop-planting suitability assessment method in soil as claimed in claim 2, it is characterised in that described to set up weight sets also
Including：
The consistency check of judgment matrix：
Calculate the Maximum characteristic root λ of judgment matrixmax, by formulaCalculate:
<mrow>
<msubsup>
<mover>
<mi>&lambda;</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
<mn>1</mn>
</msubsup>
<mo>=</mo>
<mn>4.085</mn>
</mrow>
<mrow>
<msubsup>
<mover>
<mi>&lambda;</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
<mn>2</mn>
</msubsup>
<mo>=</mo>
<mn>2</mn>
</mrow>
<mrow>
<msubsup>
<mover>
<mi>&lambda;</mi>
<mo>&OverBar;</mo>
</mover>
<mi>max</mi>
<mn>3</mn>
</msubsup>
<mo>=</mo>
<mn>4.031</mn>
</mrow>
<mrow>
<msubsup>
<mover>
<mi>&lambda;</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
<mn>4</mn>
</msubsup>
<mo>=</mo>
<mn>0.304</mn>
</mrow>
<mrow>
<msubsup>
<mover>
<mi>&lambda;</mi>
<mo>&OverBar;</mo>
</mover>
<mi>max</mi>
<mn>5</mn>
</msubsup>
<mo>=</mo>
<mn>3.096</mn>
</mrow>
According to formulaConsistency check is carried out, is obtained：
CI1=0.019
CI2=0
CI3=0.031
CI4=0.020
CI5=0.048
By formula：
CR1=0.022
CR2=0
CR3=0.035
CR4=0.038
CR5=0.092
CR ＜ 0.1, are satisfied by coherence request, therefore the relative weighting of each factor
<mrow>
<msup>
<mover>
<mi>W</mi>
<mo>&OverBar;</mo>
</mover>
<mn>1</mn>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.663</mn>
</mtd>
<mtd>
<mn>0.172</mn>
</mtd>
<mtd>
<mn>0.063</mn>
</mtd>
<mtd>
<mn>0.102</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
<mo>;</mo>
<msup>
<mover>
<mi>W</mi>
<mo>&OverBar;</mo>
</mover>
<mn>2</mn>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.5</mn>
</mtd>
<mtd>
<mn>0.5</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
<mo>;</mo>
<msup>
<mover>
<mi>W</mi>
<mo>&OverBar;</mo>
</mover>
<mn>3</mn>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.381</mn>
</mtd>
<mtd>
<mn>0.093</mn>
</mtd>
<mtd>
<mn>0.055</mn>
</mtd>
<mtd>
<mn>0.534</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
<mo>;</mo>
</mrow>
<mrow>
<msup>
<mover>
<mi>W</mi>
<mo>&OverBar;</mo>
</mover>
<mn>4</mn>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.633</mn>
</mtd>
<mtd>
<mn>0.106</mn>
</mtd>
<mtd>
<mn>0.261</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
<mo>;</mo>
<msup>
<mover>
<mi>W</mi>
<mo>&OverBar;</mo>
</mover>
<mn>5</mn>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "(" close = ")">
<mtable>
<mtr>
<mtd>
<mn>0.707</mn>
</mtd>
<mtd>
<mn>0.201</mn>
</mtd>
<mtd>
<mn>0.092</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
<mo>.</mo>
</mrow>
6. crop-planting suitability assessment method in soil as claimed in claim 2, it is characterised in that described to set up weight sets also
Including：
Degree of membership is calculated：
Multidigit uses frequency number analysis, and the indices being evaluated are entered by evaluate collection to the degree of danger for changing kind plantation index
Row grading, obtains the degree of membership of set of factors：
It is determined that judging Subject Matrix：
By the relative defects matrix for obtaining k-th of set of factors：
<mrow>
<msub>
<mi>R</mi>
<mi>k</mi>
</msub>
<mo>=</mo>
<mfenced open = "|" close = "|">
<mtable>
<mtr>
<mtd>
<msub>
<mi>r</mi>
<mrow>
<mi>k</mi>
<mn>11</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>r</mi>
<mrow>
<mi>k</mi>
<mn>1</mn>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>r</mi>
<mrow>
<mi>k</mi>
<mi>m</mi>
<mn>1</mn>
</mrow>
</msub>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<msub>
<mi>r</mi>
<mrow>
<mi>k</mi>
<mi>m</mi>
<mi>n</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein：
In formula：RkThe relative defects matrix of-k-th set of factors；
rkijThe degree of membership for the j that i-th of factor of-k-th set of factors belongs in evaluate collection；
pkij- group membership is rated j frequency to i-th of factor index of k-th of set of factors.
7. crop-planting suitability assessment method in soil as claimed in claim 2, it is characterised in that described to set up weight sets also
Including：
Construct fuzzy matrix for assessment：
By the weight vector of each indexFuzzy matrix for assessment B can be constructed with matrix R,
<mrow>
<mi>B</mi>
<mo>=</mo>
<mover>
<mi>W</mi>
<mo>&OverBar;</mo>
</mover>
<mo>&CenterDot;</mo>
<mi>R</mi>
</mrow>
Calculate Comprehensive Evaluation result：
By fuzzy matrix for assessment B and the parameter column vector of evaluate collection, Comprehensive Evaluation result Z can be tried to achieve；
Z=BV
The result of fuzzy overall evaluation is arrived as available from the above equation, is provided further according to opinion rating, evaluates kind plantation factor dangerous
Property size.
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| CN108984803A (en) * | 2018-10-22 | 2018-12-11 | 北京师范大学 | A kind of method and system of crop yield spatialization |
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| CN109272201A (en) * | 2018-08-23 | 2019-01-25 | 山东省农业可持续发展研究所 | A kind of suitability evaluation methods for peanut cultivation |
| CN109741205A (en) * | 2019-01-11 | 2019-05-10 | 成都工业学院 | Planting site searches modeling method, device, planting site lookup method and device |
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| CN108596448A (en) * | 2018-04-04 | 2018-09-28 | 桐城市气象局 | A method of oil tea weather quality ratings are established based on fuzzy mathematics method |
| CN109272201A (en) * | 2018-08-23 | 2019-01-25 | 山东省农业可持续发展研究所 | A kind of suitability evaluation methods for peanut cultivation |
| CN109214459A (en) * | 2018-09-20 | 2019-01-15 | 中国电建集团昆明勘测设计研究院有限公司 | Clustering division method for geographic suitability |
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