CN111340666A - Urban garden tree species planning model based on analytic hierarchy process - Google Patents

Urban garden tree species planning model based on analytic hierarchy process Download PDF

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CN111340666A
CN111340666A CN201911282263.5A CN201911282263A CN111340666A CN 111340666 A CN111340666 A CN 111340666A CN 201911282263 A CN201911282263 A CN 201911282263A CN 111340666 A CN111340666 A CN 111340666A
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郑伟
朱奕婕
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Kunming University of Science and Technology
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Abstract

The invention discloses an urban garden tree species planning model based on an analytic hierarchy process, and belongs to the field of tree species planning. The invention effectively utilizes the comprehensive evaluation analysis capability of the analytic hierarchy process to process multi-criteria complex problems, calculates the weight of each layer of the constructed hierarchical structure in a pairwise comparison mode, reflects the influence degree of each element of each layer on the result in quantification, applies the principle and the method of the analytic hierarchy process to the tree species planning of the urban garden, combines the quantitative analysis and the qualitative analysis, gives more details to the qualitative analysis and judgment than the common quantitative method, can fully utilize the qualitative and quantitative data, and can realize the effective combination of scientificity and artistry in the tree species planning. The grade ranking of the urban garden tree species can be determined through the established urban garden tree species planning comprehensive grade function, and scientific and objective reference basis is provided for selection of the basic tree species and the backbone tree species.

Description

Urban garden tree species planning model based on analytic hierarchy process
Technical Field
The invention relates to an urban garden tree species planning model based on an analytic hierarchy process, and belongs to the field of tree species planning.
Background
The tree species planning is one of important components of urban green land system planning, and the selection of the tree species is directly related to the quality of the urban green land. The method is characterized in that the tree species is properly selected, the trees can grow healthily and meet the requirements of greening functions, and if the tree species is improperly selected and the trees grow badly, the trees need to be maintained and replaced by continuously investing manpower and financial resources, so that economic waste is caused, and the urban environment quality and landscape effect are greatly lost.
In the current urban garden tree species planning in China, most of the urban garden tree species planning is carried out by using a qualitative analysis method, the use of a quantitative analysis method is lacked, and the qualitative analysis method has the defects that the method has certain subjective components and is easily influenced by the emotion and the situational atmosphere of an analysis judge, so that the result of the tree species planning is lacked in certain scientificity and objectivity, and the urban landscaping construction work cannot be guided more effectively.
Disclosure of Invention
The invention provides an urban garden tree species planning model based on an analytic hierarchy process, which is characterized in that various types of data of relevant indexes of tree species planning are fully considered, the calculation process is simple and convenient, the result is clear, and the tree species planning can be quickly and effectively evaluated.
The technical scheme of the invention is as follows: an urban garden tree species planning model based on an analytic hierarchy process comprises the following steps:
s1, decomposing the complex problem of the urban garden tree species planning into various elements;
s2, establishing a hierarchical structure with the elements ordered from top to bottom; wherein the hierarchical structure is divided into three layers: a target layer A, a criterion layer B and an index layer C, wherein the target layer A is composed of 1 element, and the criterion layer B is composed of an element B1,B2,B3,B4The index layer C is composed of an element C1,C2,....CnComposition is carried out; one element in the target layer A dominates the element B in the criterion layer1,B2,B3,B4；B1Dominating element C in the index layer1,...,Cn1，B2Dominating element C in the index layern1+1,...,Cn2，B3Dominating element C in the index layern2+1,...,Cn3，B4Dominating element C in the index layern3+1,...,Cn(ii) a N1 is not less than 1, n2, n3 is less than n, and n represents the total number of the elements of the index layer, namely the total number of the evaluation indexes;
s3, constructing a pairwise comparison judgment matrix;
for a single domination relationship, giving a proportional scale to the importance degree of elements under domination for constructing a judgment matrix; wherein in a criterion layer under the control of one element in the target layer AElement B of1,B2,B3,B4Pairwise comparison is carried out to construct a 4-order A-B judgment matrix aiming at B1Element C in the dominating index layer1,...,Cn1Pairwise comparison to construct n1 order B1-C decision matrix for B2Element C in the dominating index layern1+1,...,Cn2Pairwise comparison to construct n2-n1 th order B2-C decision matrix for B3Element C in the dominating index layern2+1,....,Cn3Pairwise comparison to construct n3-n2 th order B3-C decision matrix for B4Element C in the dominating index layern3+1,...,CnPairwise comparison to construct n-n3 order B4-C a decision matrix; the scale of the ratio is 1-9;
s4, calculating the relative weight of each layer element;
the maximum characteristic root of a judgment matrix T obtained by pairwise comparison is lambdamaxAnd the corresponding feature vector is w, solving the feature root problem:
Tw＝λmaxw
after calculating to obtain w and lambdamaxAfter that, a consistency check is performed:
1) calculating a consistency index CI:
Figure BDA0002317086090000021
in the formula, p is the order of the judgment matrix;
2) calculating the consistency ratio CR:
Figure BDA0002317086090000022
wherein T is A-B, B1-C、B2-C、B3-C or B4-C；
When CR is less than 0.1, the consistency of the judgment matrix is considered to be acceptable, otherwise, the judgment matrix needs to be adjusted until CR is less than 0.1;
by step S4, there is obtained: criterion layer element B1,B2,B3,B4The relative weight under the control of one element in the target layer A is w1,w2,w3,w4(ii) a Index layer element C1,...,Cn1In B1Dominant relative weight v1,…,vn1Index layer element Cn1+1,...,Cn2In B2Dominant relative weight vn1+1,...vn2Index layer element Cn2+1,...,Cn3In B3Dominant relative weight vn2+1,...vn3Index layer element Cn3+1,...,CnIn B4Dominant relative weight vn3+1,...vn；
S5, checking the general consistency of hierarchical structure, when CR is3When the value is less than 0.1, the whole judgment of the hierarchical structure on the level of the layer 3 is considered to have satisfactory consistency, and the obtained index layer element combination weight
Figure BDA0002317086090000028
Reliable, otherwise, the decision matrix needs to be adjusted until CR3Less than 0.1;
Figure BDA0002317086090000023
Figure BDA0002317086090000024
Figure BDA0002317086090000025
wherein, CR3For the CR values at the level 3 index level of the hierarchical hierarchy,
Figure BDA0002317086090000026
and
Figure BDA0002317086090000027
respectively as third layer at criterion layer element B1CI and RI values, CR, of the dominating decision matrix2Judging the CR value of a matrix, namely an A-B judgment matrix, for the layer 2 criterion layer under the control of one element of the layer 1 target layer;
s6, calculating the combination weight of each element of the index layer
Figure BDA0002317086090000032
With respect to B1The combined weight of the evaluation indexes corresponding to the index layer elements under control is
Figure BDA0002317086090000033
Wherein the content of the first and second substances,
Figure BDA0002317086090000034
with respect to B2The combined weight of the evaluation indexes corresponding to the index layer elements under control is
Figure BDA0002317086090000035
Wherein the content of the first and second substances,
Figure BDA0002317086090000036
with respect to B3The combined weight of the evaluation indexes corresponding to the index layer elements under control is
Figure BDA0002317086090000037
Wherein the content of the first and second substances,
Figure BDA0002317086090000038
with respect to B4The combined weight of the evaluation indexes corresponding to the index layer elements under control is
Figure BDA0002317086090000039
Wherein the content of the first and second substances,
Figure BDA00023170860900000310
wherein the content of the first and second substances,
Figure BDA00023170860900000311
a combination weight indicating the nth evaluation index;
s7, establishing a comprehensive grading function for planning the urban garden tree species:
Figure BDA0002317086090000031
wherein, ainAnd the score of the ith garden tree species in the nth evaluation index is shown.
The importance of the elements to each other is obtained as follows: b is obtained by setting the sequencing questions of the questionnaire1,B2,B3,B4Average the composite score under a single criterion to obtain C1,C2,....CnAveraging the comprehensive scores under a single criterion, and taking the ratio of the average comprehensive scores as the importance degree; approximate values are given on a scale of 1-9 according to the degree of importance.
The score of the evaluation index is divided into five grades by the step length of 2 and the scores of 0-10.
The invention has the beneficial effects that: the invention effectively utilizes the comprehensive evaluation analysis capability of the analytic hierarchy process to process multi-criteria complex problems, calculates the weight of each layer of the constructed hierarchical structure in a pairwise comparison mode, reflects the influence degree of each element of each layer on the result in quantification, applies the principle and the method of the analytic hierarchy process to the tree species planning of the urban garden, combines the quantitative analysis and the qualitative analysis, gives more details to the qualitative analysis and judgment than the common quantitative method, can fully utilize the qualitative and quantitative data, and can realize the effective combination of scientificity and artistry in the tree species planning. The grade ranking of the urban garden tree species can be determined through the established urban garden tree species planning comprehensive grade function, and scientific and objective reference basis is provided for selection of the basic tree species and the backbone tree species.
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FIG. 1 is a hierarchical structure diagram of the tree species planning model for urban garden of the present invention;
fig. 2 is an exemplary diagram of a questionnaire.
Detailed Description
Example 1: a city garden tree species planning model based on an analytic hierarchy process is characterized in that 10 garden tree species in Kunming city are scored in the embodiment, scoring ranking of the garden tree species can be obtained, and the 10 garden tree species are respectively camellia japonica, yunnan magnolia, yunnan machilus, yunnan camphor, cinnamomum camphora, jacaranda cophylla, winter cherry blossom, yunnan mimosa-unisexual magnolia and ginkgo.
S1, decomposing the complex problem of the urban garden tree species planning into various elements; the complex problem of the urban garden tree species planning is decomposed into various elements, wherein the elements comprise tree species planning, growth adaptation, ornamental effect, ecological benefit, economic factors, temperature adaptation, water adaptation, soil adaptation, insect and disease resistance, pollution resistance, evergreen/fallen leaves, body and tree forms, leaves, flowers, fruits, trunks, fragrance, harmful gas absorption, dust blocking, carbon fixation and oxygen release, cooling and humidifying, sterilization, noise reduction, wind resistance, local tree species, nursery resources, growth speed, service life and other values.
S2, establishing a hierarchical structure with the elements ordered from top to bottom; wherein the hierarchical structure is divided into three layers: a target layer A, a criterion layer B and an index layer C, wherein the target layer A is composed of 1 element, and the criterion layer B is composed of an element B1,B2,B3,B4The index layer C is composed of an element C1,C2,....CnComposition is carried out; one element in the target layer A dominates the element B in the criterion layer1,B2,B3,B4；B1Dominating element C in the index layer1,...,Cn1，B2Dominating element C in the index layern1+1,...,Cn2，B3Dominating element C in the index layern2+1,...,Cn3，B4Dominating element C in the index layern3+1,...,Cn(ii) a N1 is not less than 1, n2, n3 is less than n, and n represents the total number of the elements of the index layer, namely the total number of the evaluation indexes;
specifically, the method comprises the following steps: as shown in fig. 1, the hierarchical structure is divided into 3 layers, the complex problem of tree species planning in urban garden is decomposed into 29 elements in S1, all the elements are grouped according to domination relationship, growth adaptation, ornamental effect, ecological benefit and economic factor are dominated by tree species planning, temperature adaptation, moisture adaptation, soil adaptation, disease and pest resistance and pollution resistance are dominated by growth adaptation, evergreen/fallen leaves, body size and tree form, leaves, flowers, fruits, trunks and aroma are dominated by ornamental effect, harmful gas absorption, dust retardation, carbon fixation and oxygen release, temperature reduction and humidification, sterilization, noise reduction and wind resistance are dominated by ecological benefit, rural tree species, nursery resources, growth speed, life and other values are dominated by economic factor, thereby obtaining a hierarchical structure which is composed of a target layer A (1 element), a criterion layer B (4 elements) and an index layer C (24 elements) and is ordered from top to bottom. That is, n1 is 5, n2 is 12, and n3 is 19.
S3, constructing a pairwise comparison judgment matrix;
for a single domination relationship, giving a proportional scale to the importance degree of elements under domination for constructing a judgment matrix; wherein element B in the criterion layer is under control of one element in the target layer A1,B2,B3,B4Pairwise comparison is carried out to construct a 4-order A-B judgment matrix aiming at B1Element C in the dominating index layer1,...,Cn1Pairwise comparison to construct n1 order B1-C decision matrix for B2Element C in the dominating index layern1+1,...,Cn2Pairwise comparison to construct n2-n1 th order B2-C decision matrix for B3Element C in the dominating index layern2+1,....,Cn3Pairwise comparison to construct n3-n2 th order B3-C decision matrix for B4Element C in the dominating index layern3+1,...,CnPairwise comparison to construct n-n3 order B4-C a decision matrix; the scale of the ratio is 1-9;
the degree of importance is characterized using a scale of 1-9: 1 indicates that the two elements are of equal importance compared; 3 indicates that one element is slightly more important than another; 5 indicates that one element is significantly more important than another; 7 indicates that one element is more important than the other; 9 indicates that one element is extremely important over the other; 2. 4, 6 and 8 are the median values of the adjacent judgments; for example, in the A-B judgment matrix, the comparison value of the element on the diagonal representing the element is 1, for example, the first row and the second listShow B1And B2And if the comparison result is 2, the value of the first column in the second row is 1/2, and the rest is the same.
The importance of the elements to each other is obtained as follows: b is obtained by setting the sequencing questions of the questionnaire1,B2,B3,B4Average the composite score under a single criterion to obtain C1,C2,....CnAveraging the comprehensive scores under a single criterion, and taking the ratio of the average comprehensive scores as the importance degree; approximate values are given on a scale of 1-9 according to the degree of importance.
Let 50 scholars with professional academic background fill in by setting up the order questions of the questionnaire (shown in FIG. 2), let b1,b2,b3,b4Respectively represent average comprehensive scores of growth adaptation, ornamental effect, ecological benefit and economic factor obtained from questionnaire survey, c1,c2,c3,c4,c5Respectively representing the average comprehensive scores of temperature adaptability, water adaptability, soil adaptability, pest and disease resistance and pollution resistance under growth adaptation, c6,c7,c8,c9,c10,c11,c12Respectively representing the average comprehensive scores of evergreen/fallen leaves, body weight, tree form, leaves, flowers, fruits, trunks and fragrance under the ornamental effect c13,c14,c15,c16,c17,c18,c19Respectively showing the average comprehensive scores of harmful gas absorption, dust retardation, carbon fixation and oxygen release, temperature reduction and humidification, sterilization, noise reduction and wind resistance under the ecological benefit c20,c21,c22,c23,c24Respectively representing average comprehensive scores of rural soil tree species, nursery resources, growth speed, service life and other values under economic factors, and taking the ratio of the average comprehensive scores as an approximate value of a proportion scale of 1-9 of a pairwise comparison judgment matrix; such as with b1/b2Approximate value of scale 1-9 as growth adaptation B in A-B judgment matrix1And ornamental effect B2Comparative value of judgment, using c1/c2On a scale of 1-9Approximation as B1-C determination of temperature adaptability C in matrix1Adaptability to moisture C2The judged comparison value is obtained by analogy with the comparison of other elements pairwise;
a questionnaire written by 50 scholars with professional academic backgrounds is input to a professional questionnaire survey platform (such as questionnaire star), and a result report, namely an average composite score of 28 elements, can be obtained: b1,b2,...b4,c1,c2,...c24E.g. b2/b3When the value is 0.842, the approximate value 1 of the scale of the value and 1-9 is used as the ornamental effect B in the A-B judgment matrix2And ecological benefits B3And judging the comparison value, and the rest of the same principles. Pairing element B under a single criterion A by a dominant relationship1、B2、B3、B4Two-by-two comparison, single criterion B1Lower pair of element C1、C2、C3、C4、C5Two-by-two comparison, single criterion B2Lower pair of element C6、C7、C8、C9、C10、C11、C12Two-by-two comparison, single criterion B3Lower pair of element C13、C14、C15、C16、C17、C18、C19Two-by-two comparison, single criterion B4Lower pair of element C20、C21、C22、C23、C24Two-by-two comparison of (1) to obtain 5 judgment matrixes:
1)A-B：
Figure BDA0002317086090000061
2)B1-C：
Figure BDA0002317086090000062
3)B2-C：
Figure BDA0002317086090000063
4)B3-C：
Figure BDA0002317086090000064
5)B4-C：
Figure BDA0002317086090000065
s4, calculating the relative weight of each layer element;
the maximum characteristic root of a judgment matrix T obtained by pairwise comparison is lambdamaxAnd the corresponding feature vector is w, solving the feature root problem:
Tw＝λmaxw
after calculating to obtain w and lambdamaxAfter that, a consistency check is performed:
1) calculating a consistency index CI:
Figure BDA0002317086090000071
in the formula, p is the order of the judgment matrix;
2) calculating the consistency ratio CR:
Figure BDA0002317086090000072
wherein T is A-B, B1-C、B2-C、B3-C or B4-C；
Wherein, RI represents average random consistency index; the following table 1 is specifically provided:
TABLE 1
Figure BDA0002317086090000073
When CR is less than 0.1, the consistency of the judgment matrix is considered to be acceptable, otherwise, the judgment matrix needs to be adjusted until CR is less than 0.1;
specifically, the method comprises the following steps: calculate A-B, B, respectively1-C、B2-C、B3-C、B4Eigenvectors w and maximum eigenroots λ of the 5 decision matrices CmaxThe eigenvector w is the relative weight of each layer element and is represented by the maximum eigen root λmaxThe consistency test was performed on each decision matrix according to the following formula, and the results are shown in table 2 below:
TABLE 2
Figure BDA0002317086090000074
By step S4, there is obtained: criterion layer element B1,B2,B3,B4The relative weight under the control of one element in the target layer A is w1,w2,w3,w4(ii) a Index layer element C1,...,Cn1In B1Dominant relative weight v1,…,vn1Index layer element Cn1+1,...,Cn2In B2Dominant relative weight vn1+1,...vn2Index layer element Cn2+1,...,Cn3In B3Dominant relative weight vn2+1,...vn3Index layer element Cn3+1,...,CnIn B4Dominant relative weight vn3+1,...vn；
It can be seen that CR of 5 judgment matrixes is less than 0.1, the consistency of the judgment matrixes is acceptable, and the next step can be carried out
S5, checking the general consistency of hierarchical structure, when CR is3When the value is less than 0.1, the whole judgment of the hierarchical structure on the level of the layer 3 is considered to have satisfactory consistency, and the obtained index layer element combination weight
Figure BDA0002317086090000076
Reliable, otherwise, the decision matrix needs to be adjusted until CR3Less than 0.1;
Figure BDA0002317086090000075
Figure BDA0002317086090000081
Figure BDA0002317086090000082
wherein, CR3For the CR values at the level 3 index level of the hierarchical hierarchy,
Figure BDA0002317086090000083
and
Figure BDA0002317086090000084
respectively as third layer at criterion layer element B1CI and RI values, CR, of the dominating decision matrix2Judging the CR value of a matrix, namely an A-B judgment matrix, for the layer 2 criterion layer under the control of one element of the layer 1 target layer;
specifically, the method comprises the following steps: calculating the overall CI of the hierarchical structure according to the formula3Is 0.015, RI3Is 1.222, CR3Is 0.030, CR3Less than 0.1, the whole judgment of the hierarchical structure on the level of the 3 rd layer has satisfactory consistency, and the obtained combination weight of each element of the index layer
Figure BDA0002317086090000088
The data is reliable.
S6, calculating the combination weight of each element of the index layer
Figure BDA0002317086090000089
With respect to B1The combined weight of the evaluation indexes corresponding to the index layer elements under control is
Figure BDA00023170860900000810
Wherein the content of the first and second substances,
Figure BDA00023170860900000811
with respect to B2Combination of evaluation indexes corresponding to index layer elements under controlThe weight is
Figure BDA00023170860900000812
Wherein the content of the first and second substances,
Figure BDA00023170860900000813
with respect to B3The combined weight of the evaluation indexes corresponding to the index layer elements under control is
Figure BDA0002317086090000087
Wherein the content of the first and second substances,
Figure BDA00023170860900000814
with respect to B4The combined weight of the evaluation indexes corresponding to the index layer elements under control is
Figure BDA00023170860900000815
Wherein the content of the first and second substances,
Figure BDA00023170860900000816
Figure BDA00023170860900000817
a combination weight indicating the nth evaluation index;
the combination weight of the 24 evaluation indexes is calculated by the formula, and the results are shown in the following table 3:
TABLE 3
Figure BDA0002317086090000085
S7, establishing a comprehensive grading function for planning the urban garden tree species:
Figure BDA0002317086090000086
wherein, ainAnd the score of the ith garden tree species in the nth evaluation index is shown.
The score of the evaluation index is divided into five grades by the step length of 2 and the scores of 0-10.
Wherein, the ten-system scoring standards of the 24 evaluation indexes are as the following table 4:
TABLE 4
Figure BDA0002317086090000091
Figure BDA0002317086090000101
The average value of the low temperature resistance and the high temperature resistance is taken for temperature adaptability; the water adaptability is the average value of the values of drought resistance and water-moisture resistance.
Thus, the scores of 24 evaluation indexes of 10 garden tree species in Kunming city are shown in Table 5:
TABLE 5
Figure BDA0002317086090000102
Figure BDA0002317086090000111
Finally, the comprehensive scores of 10 garden tree species in Kunming city are shown in Table 6:
TABLE 6
Figure BDA0002317086090000112
Figure BDA0002317086090000121
Thus, the score of 10 garden tree species in Kunming city is ranked as: semen Ginkgo, cortex Magnoliae officinalis, Cinnamomum yunnanense, Majorana yunnanense, Cinnamomum camphora, Machilus yunnanense, Camellia japonica, Prunus cerasifera, Prunus yunnanense, and Acacia falcata.
The embodiment is used for comprehensively scoring 10 garden tree species in Kunming city for illustration, and demonstrates the feasibility and the practicability of the urban garden tree species planning model based on the analytic hierarchy process, but the invention can comprehensively score all garden tree species in a certain city, sequentially determine the basic tone tree species, the backbone tree species and the general tree species in the city according to the ranking of the comprehensive scores of all garden tree species, and realize the effective combination of scientificity, artistry, quantitative analysis and qualitative analysis in the urban garden tree species planning.
While the present invention has been described in detail with reference to the specific embodiments thereof, it will be apparent to one skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope thereof.

Claims (3)

1. The utility model provides an urban garden tree species planning model based on analytic hierarchy process which characterized in that: the method comprises the following steps:
s1, decomposing the complex problem of the urban garden tree species planning into various elements;
s2, establishing a hierarchical structure with the elements ordered from top to bottom; wherein the hierarchical structure is divided into three layers: a target layer A, a criterion layer B and an index layer C, wherein the target layer A is composed of 1 element, and the criterion layer B is composed of an element B1,B2,B3,B4The index layer C is composed of an element C1,C2,....CnComposition is carried out; one element in the target layer A dominates the element B in the criterion layer1,B2,B3,B4；B1Dominating element C in the index layer1,...,Cn1，B2Dominating element C in the index layern1+1,...,Cn2，B3Dominating element C in the index layern2+1,...,Cn3，B4Dominating element C in the index layern3+1,...,Cn(ii) a N1 is not less than 1, n2, n3 is less than n, and n represents the total number of the elements of the index layer, namely the total number of the evaluation indexes;
s3, constructing a pairwise comparison judgment matrix;
for a single domination relationship, giving a proportional scale to the importance degree of elements under domination for constructing a judgment matrix; wherein element B in the criterion layer is under control of one element in the target layer A1,B2,B3,B4Make pairwise comparisonA 4-order A-B judgment matrix is constructed for B1Element C in the dominating index layer1,...,Cn1Pairwise comparison to construct n1 order B1-C decision matrix for B2Element C in the dominating index layern1+1,...,Cn2Pairwise comparison to construct n2-n1 th order B2-C decision matrix for B3Element C in the dominating index layern2+1,....,Cn3Pairwise comparison to construct n3-n2 th order B3-C decision matrix for B4Element C in the dominating index layern3+1,...,CnPairwise comparison to construct n-n3 order B4-C a decision matrix; the scale of the ratio is 1-9;
s4, calculating the relative weight of each layer element;
the maximum characteristic root of a judgment matrix T obtained by pairwise comparison is lambdamaxAnd the corresponding feature vector is w, solving the feature root problem:
Tw＝λmaxw
after calculating to obtain w and lambdamaxAfter that, a consistency check is performed:
1) calculating a consistency index CI:
Figure FDA0002317086080000011
in the formula, p is the order of the judgment matrix;
2) calculating the consistency ratio CR:
Figure FDA0002317086080000012
wherein T is A-B, B1-C、B2-C、B3-C or B4-C；
When CR is less than 0.1, the consistency of the judgment matrix is considered to be acceptable, otherwise, the judgment matrix needs to be adjusted until CR is less than 0.1;
by step S4, there is obtained: criterion layer element B1，B2，B3，B4Relative weight under one element in the target layer AEach weight is w1，w2，w3，w4(ii) a Index layer element C1，...，Cn1In B1Dominant relative weight v1，...，vn1Index layer element Cn1+1，...，Cn2In B2Dominant relative weight vn1+1，...vn2Index layer element Cn2+1，...，Cn3In B3Dominant relative weight vn2+1，...vn3Index layer element Cn3+1，...，CnIn B4Dominant relative weight vn3+1，...vn；
S5, checking the general consistency of hierarchical structure, when CR is3When the value is less than 0.1, the whole judgment of the hierarchical structure on the level of the layer 3 is considered to have satisfactory consistency, and the obtained index layer element combination weight omeganReliable, otherwise, the decision matrix needs to be adjusted until CR3Less than 0.1;
Figure FDA0002317086080000021
Figure FDA0002317086080000022
Figure FDA0002317086080000023
wherein, CR3For the CR values at the level 3 index level of the hierarchical hierarchy,
Figure FDA0002317086080000025
and
Figure FDA0002317086080000026
respectively as third layer at criterion layer element B1CI and RI values, CR, of the dominating decision matrix2Judging the CR value of a matrix, namely an A-B judgment matrix, for the layer 2 criterion layer under the control of one element of the layer 1 target layer;
s6, calculating the combination weight omega of each element of the index layern：
With respect to B1The combination weight of the evaluation indexes corresponding to the index layer elements under control is omega1，...，ωn1(ii) a Wherein, ω is1＝v1·w1，ωn1＝vn1·w1；
With respect to B2The combination weight of the evaluation indexes corresponding to the index layer elements under control is omegan1+1，...，ωn2(ii) a Wherein, ω isn1+1＝vn1+1·w2，ωn2＝vn2·w2；
With respect to B3The combination weight of the evaluation indexes corresponding to the index layer elements under control is omegan2+1，...ωn3(ii) a Wherein, ω isn2+1＝vn2+1·w3，ωn3＝vn3·w3；
With respect to B4The combination weight of the evaluation indexes corresponding to the index layer elements under control is omegan3+1，...ωn(ii) a Wherein, ω isn3+1＝vn3+1·w4，ωn＝vn·w4；
Wherein, ω isnA combination weight indicating the nth evaluation index;
s7, establishing a comprehensive grading function for planning the urban garden tree species:
Figure FDA0002317086080000024
wherein, ainAnd the score of the ith garden tree species in the nth evaluation index is shown.
2. The analytic hierarchy process-based urban garden tree species planning model of claim 1, wherein: the importance of the elements to each other is obtained as follows: b is obtained by setting the sequencing questions of the questionnaire1，B2，B3，B4Average the composite score under a single criterion to obtain C1，C2，....CnAveraging the comprehensive scores under a single criterion, and taking the ratio of the average comprehensive scores as the importance degree; approximate values are given on a scale of 1-9 according to the degree of importance.
3. The analytic hierarchy process-based urban garden tree species planning model of claim 1, wherein: the score of the evaluation index is divided into five grades by the step length of 2 and the scores of 0-10.
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