CN105809285A - Water resource-based industrial structure diagnosis method - Google Patents

Abstract

The invention relates to a water resource-based industrial structure diagnosis method. The method includes the following steps that: step 1, a data envelopment analysis model is utilized to perform diagnosis on urban industrial structure optimization degree; step 2, an analytic hierarchy process is utilized to perform diagnosis on tertiary industry water use structure and efficiency; and step 3, linear weighting is adopted to combine the results of diagnosis which is carried out twice, so that a water resource-based urban industrial structure optimization degree diagnosis result can be obtained. With the method of the invention adopted, the important role of water resources in an industrial structure and the rationalization degree of the industrial structure can be better disclosed, and therefore, the optimization direction of the industrial structure can be determined, and theoretical basis and decision-making support can be provided for urban industrial structure upgrading.

Description

A kind of industrial structure diagnostic method based on water resource

Technical field

The present invention relates to city planning and administration and water resources management general field; specifically; particularly relate to and a kind of utilize the urban industrial structure diagnostic method that water resource is constraints; the method can be widely applied to diagnose towards the industrial structure of the different aspects such as water resource saving and the city of water environment protection, region, country, for providing theory support with water fixed output quota.

Background technology

Water resource natural talent is not enough, people Duo Shuishaoshi China need long-term faced by basic regimen, and along with the development of economy, population, water resource has become as the principal element of restriction especially megapolis, China city development, and due to China's technical merit restriction, all there is the situation that water resource utilization efficiency is low in many industries especially agricultural and manufacturing industry, this water resources problems making China severe makes the matter worse, and the rapport nowadays how solving urban development and water resource is extremely urgent.

The industrial structure refers to the contact between the composition of industry and each industry and proportionate relationship.Industry restructuring is the important topic that countries nowadays is developed the economy, and adjusts and set up the rational industrial structure, it is achieved the rationalization of the industrial structure and High Level are the core links of city and state's industrial structure planning management.When urban development to certain phase, sustainability and environmental friendliness will become the main demand of economic development, industrial structure optimization is the inevitable choice meeting this demand.According to achievement by advanced countries, industrial structure upgrading is the immediate cause that water for industrial use realizes zero growth rate, and namely industrial structure optimization is the motive force realizing high efficient utilization of water resources；Shortage of water resources then limits industry development, and some highly water intensive industries will carry out accommodation towards water resource, causes that the industrial structure carries out accommodation therewith, and namely industrial structure optimization is had important facilitation by the Appropriate application of water resource and distribution.

The existing patent of invention for the industrial structure mainly contributes to the optimization problem solving a certain specific industry at present, or combines with ecological environment, it is proposed to method for optimizing industrial structure or model.Such as by name a kind of method that several wastes comprehensive industry chain type can be combined cascade utilization that application number is 99121489.7, publication number is the patent of invention of CN1275447, it by refuse produced in papermaking industry all of, thus realizing the optimization of papermaking industry.And for example application number is sea-buckthorn industry ecological technology amusement park by name and the implementation thereof of 200610010545.6, publication number is the patent of invention of CN101145236, Fructus Hippophae gardens, Fructus Hippophae product deep processing and eco-tour are combined, achieves the optimization of sea-buckthorn industry by building Fructus Hippophae ecological technology amusement park.For another example application number is the decision method of by name a kind of ecological industry symbiotic system stability of 200710132104.8, publication number is the patent of invention of CN101145220, application nonlinear discrete difference mathematical model judges the stability of ecological industry symbiotic system in conjunction with the practical situation of ecological industry symbiotic system, thus instructs optimization and the regulation and control of ecological industry symbiotic system.Application number is the Eco-economics evaluation method of by name a kind of industrial system of 201010107141.5, publication number is the patent of invention of CN101777157, " can value " this evaluation index is devised from ecological hot amechanical angle, thus complete the economic benefit of Different Industries and the contrast of ecological pressure, thus realizing the se-quence of priority to the development of regional industrial is judged.Application number is by name a kind of method for optimizing industrial structure of 200910250306.7, publication number is the patent of invention of CN101901466, from energy efficiency and ecological efficiency, and based on the different typical environment problem that zones of different has, by improving the ratio of the sub-industry with higher ecological efficiency, reduce the ratio with the sub-industry of relatively low ecological efficiency, it is achieved the optimization of industrial system structure.Application number is 201210322399.6, publication number is the patent of invention of the technical method of by name a kind of urban industrial structure optimum organization in length and breadth diagnosis of CN102831563A, by city time series data and industry cross-sectional data being combined diagnosis, it is judged that the degree of optimization of urban industrial structure.

Above patent of invention is had to can be seen that, launch in specific industry or ecological environment among the current method for industrial structure optimization is main, application has certain limitation, it is difficult to be widely used in zones of different or city, and does not emphasize the water resource material impact for industry restructuring.Develop a kind of new industrial structure diagnostic method, and the combination of water resource utilization efficiency and industrial structure efficiency is realized towards water resource, so as to extensive use in the industrial structure optimization system of the different aspects such as city, region, country, the rationalization High Level of the efficient and rational utilization and the industrial structure that realize water resource is had important practical significance.

Summary of the invention

The present invention devises a kind of industrial structure diagnostic method based on water resource, it solves the technical problem that it is the existing diagnostic method rationalization degree that do not disclose water resource important function in the industrial structure and industrial water structure, provides theoretical foundation and decision support also without developing in harmony for city water resource and the industrial structure.

In order to solve the technical problem of above-mentioned existence, present invention employs below scheme:

The present invention comprises the following steps:

(1) building the input and output index system of DEA Model, input-occupancy-output analysis includes primary industry investment in fixed assets, secondary industry investment in fixed assets, tertiary industry investment in fixed assets, primary industry water consumption, secondary industry water consumption, tertiary industry water consumption, primary industry quantity of employment, secondary industry quantity of employment, tertiary industry quantity of employment, water conservancy always put into, farmland under irrigation, expenditures in the local budget；Output index includes value-added of the primary industry, the value of secondary industry, value-added of the tertiary industry, total financial income of local government.

(2) the calculating sample being data envelopment model with each input in research level year, output desired value, application related software calculates relative efficiency, returns to scale value, input redundancy value and the output deficiency value that urban industrial structure is run then, and computing formula is:

$\min \[\mathrm{\θ}-\mathrm{\ϵ}({\hat{e}}^T{S}^{-}+e^T{S}^{+})\]$

$s.t.\left\{\begin{array}{c}{\mathrm{\Σ}}_{j=1}^n{X}_j{\mathrm{\λ}}_j+S^{-}={\mathrm{\θX}}_0\\ \underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Y}_j{\mathrm{\λ}}_j-S^{+}=Y_0\\ {\mathrm{\λ}}_j\≥0,S^{-}\≥0,S^{+}\≥0\\ K=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_j\end{array}\right.$

X in formula_j、Y_jRespectively the input of urban industries, output vector, wherein X₀And Y₀The respectively urban industries input and output vector of standard year.S^-For putting into redundancy value, S⁺For output deficiency value.N is number of samples, and j represents which sample, λ_jFor jth sample weight in a certain index, θ is the relative efficiency that each year urban industrial structure is run, and its value is between 0～1, and K is the returns to scale value that each year urban industrial structure is run, and ε is that non-Archimedes is infinitely small,e^T=(1,1 ..., 1) ∈ E_s.Relative efficiency reflects the actual gap put between best output of different year urban industries, and the more big expression industry input and output of its numerical value are more big, and industrial structure optimization degree is more high；Otherwise then illustrate that industrial structure optimization degree is more low.

(3) step analysis index system such as following table is built:

(4) with each desired value in research level year for sample, being standardized sample processing sample architecture judgment matrix, and carry out consistency check, utilize analytic hierarchy process (AHP) agriculture products weight, method is as follows:

1. Judgement Matricies: each has the element (being referred to as criterion) first element (being positioned at the upper left corner) as judgment matrix of downward membership, and each element being under the jurisdiction of it is arranged in order the first row behind and first row.

2. introduce and judge scale:

Judgment matrix is A=(a_ij)_n×n, structure is as follows:

Judgment matrix

	C1	C2	C3
				C1	1	A12	A13
C2	A21	1	A23
				C3	A31	A32	1

3. judge to put to the proof concordance: calculate matrix Maximum characteristic root λ, define coincident indicator:

$CI=\frac{\mathrm{\λ}-n}{n-1}$

CI value is more big, and the inconsistent degree of matrix is more serious；

Random index RI:

n	1	2	3	4	5	6	7	8	9	10	11
												RI	0	0	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49	1.51

Definition Consistency Ratio:

$CR=\frac{CI}{RI}$

As CR < 0.1, the inconsistent degree of matrix is in tolerance.

Compose and temporary first different rule layers are composed power, namely first different rule layers are carried out scale judgement, Judgement Matricies (matrix exponent number is identical with the criterion number of plies), judge concordance, each index under each rule layer is being carried out scale judgement, Judgement Matricies (matrix exponent number is identical with the index number that each rule layer comprises), it is judged that concordance.

4. Mode of Level Simple Sequence:

Mode of Level Simple Sequence refers to each judgment matrix each factor relative weighting for its criterion, so being substantially calculate weight vector.The present invention adopts and method calculates weight vector.

It is, for concordance judgment matrix, be exactly corresponding weight after every string normalization with ratio juris.For nonuniformity judgment matrix, after every string normalization, its corresponding weight approximate, is asking for arithmetic mean of instantaneous value as last weight to this n column vector.Concrete formula is:

$W_i=\frac{1}{n}\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}\frac{a_{ij}}{\underset{k=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_{kl}}$

5. total hierarchial sorting and inspection

Total sequence refers to each judgment matrix each factor relative weighting for destination layer (the superiors).The calculating of this weight adopts method from top to down, successively synthesizes.

Assuming that calculated-1 layer of m the element of the kth weight w relative to general objective^(k-1)=(w₁ ^(k-1),w₂ ^(k-1),…,w_m ^(k-1))^T, n element of kth layer is p for single weight order of last layer (kth layer) jth element_j(k)=(p_1j(k),p_2j(k),…,p_nj(k))^T, wherein the weight of element by j domination is not zero.Make P (k)=(p₁(k),p₂(k),…,p_n(k)), represent the sequence to kth-1 layer element of the kth layer element, then kth layer element being always ordered as general objective:

W (k)=(w₁(k),w₂(k),…,w_n(k))^T=p (k) w (k-1)

Or

Then y=Σ w_iφ_i

Wherein φ is each index result of calculation of indicator layer.

(5) adopting the method for linear weighted function by combined to step 1, evaluation result in 2, its computing formula is

A=α y+ (1-α) θ

Wherein, y is water resources efficiency evaluation result, and α is weight coefficient.

(6) according to A, urban industrial structure optimization being combined diagnosis, standard is:

If 1. A >=0.9, then urban industrial structure degree of optimization is outstanding；

If 2. 0.7≤A < 0.9, then urban industrial structure degree of optimization is good；

If 3. 0.6≤A < 0.7, then urban industrial structure degree of optimization is qualified；

If 4. A < 0.6, then urban industrial structure degree of optimization is defective.

Should have the advantages that based on the industrial structure rationality diagnostic method of water resource

(1) the inventive method can better disclose the rationalization degree of the water resource middle important function in the industrial structure and the industrial structure and then the optimization direction of the clear and definite industrial structure, provides theoretical foundation and decision support for urban industrial structure upgrading.

(2) urban industrial structure is diagnosed by the inventive method initially with DEA Model, by the developing state of the clear and definite urban industrial structure of this process and residing stage；Adopt analytic hierarchy process (AHP) that tertiary industries water-use efficiency and water environment are diagnosed, by the clear and definite industrial water level of this process and developing direction；Twice diagnostic result is carried out linear weighted function, and comprehensively analyzes the degree of optimization of urban industrial structure and water-using structure and efficiency according to final result, realize the integrated analysis of the industrial structure and water resource with this, for water fixed output quota, offer data supporting in city be provided with water.

Detailed description of the invention

Below in conjunction with embodiment, the present invention will be further described:

A kind of industrial structure rationality diagnostic method based on water resource, comprises the following steps:

Step 1, utilize DEA Model that urban industrial structure degree of optimization is diagnosed；

Step 2, utilize analytic hierarchy process (AHP) that tertiary industries water-using structure and efficiency are diagnosed；

Step 3, utilize twice diagnostic result of linear weighted function to be combined, obtain the city industrial structure optimization degree diagnostic result based on water resource.

Specifically, in step 1, existence is following step by step:

Step 1.1, building the input and output index system of DEA Model, input-occupancy-output analysis includes primary industry investment in fixed assets, secondary industry investment in fixed assets, tertiary industry investment in fixed assets, primary industry water consumption, secondary industry water consumption, tertiary industry water consumption, primary industry quantity of employment, secondary industry quantity of employment, tertiary industry quantity of employment, water conservancy always put into, farmland under irrigation, expenditures in the local budget；Output index includes value-added of the primary industry, the value of secondary industry, value-added of the tertiary industry, total financial income of local government；

Step 1.2, the city input of research on utilization forcasted years, output data, as the sample of DEA Model, are run related software and are calculated the relative efficiency θ of urban industrial structure, returns to scale value K, put into redundancy value S^-With output deficiency value S⁺；

Step 1.3, the standard carrying out diagnosing according to DEA Model result of calculation be:

If 1. θ=1 in a certain year and S^-=0, S⁺=0, then to operate to DEA effective for this year industrial structure；

If 2. θ=1 in a certain year and S^-≠ 0 or S⁺≠ 0, then this year industrial structure operates to weak DEA effectively, and θ value is more big, and industrial structure relative efficiency is more high；

If the 3. θ < 1 in a certain year, then to operate to non-DEA effective for this year industrial structure；

If the 4. K=1 in a certain year, then this year industrial structure operates to constant returns to scale；

If the 5. K < 1 in a certain year, then this year industrial structure operates to increasing return to scale；

If the 6. K > 1 in a certain year, then this year industrial structure operates to decreasing return to scale.

Below step 2 exists step by step:

Step 2.1, structure step analysis index system such as following table；

Water resource utilization efficiency index system

Step 2.2, with each desired value in research level year for sample, it is standardized sample processing sample architecture judgment matrix, and carry out consistency check, utilize analytic hierarchy process (AHP) agriculture products weight, compose and temporary first different rule layers are composed power, namely first different rule layers are carried out scale judgement, Judgement Matricies, matrix exponent number is identical with the criterion number of plies, judge concordance, the more each index under each rule layer is carried out scale judgement, Judgement Matricies, matrix exponent number is identical with the index number that each rule layer comprises, it is judged that concordance.

Step 2.2 particularly as follows:

2.2.1, Judgement Matricies: each has the element (being referred to as criterion) first element (being positioned at the upper left corner) as judgment matrix of downward degree of membership relation, and each element being under the jurisdiction of it is arranged in order the first row behind and first row；

2.2.2, introducing judges scale:

Need rule layer first carries out scale and matrix interpretation:

ab

$A=\left(\begin{array}{cc}1& 3\\ 1/3& 1\end{array}\right)\begin{array}{c}a\\ b\end{array}$

Again to 6 indexs judgment matrix about 2 criterions:

$B_1^{\left(3\right)}=\left(\begin{array}{ccc}1& 1& 3\\ 1& 1& 3\\ 1/3& 1/3& 1\end{array}\right)$

$B_2^{\left(3\right)}=\left(\begin{array}{ccc}1& 3& 5\\ 1/3& 1& 2\\ 1/5& 1/2& 1\end{array}\right)$

For three rank determinants, being constructed as follows judgment matrix is A=(a_ij)_n×n, structure is as follows:

Judgment matrix

	C1	C2	C3
				C1	1	A12	A13
C2	A21	1	A23 7 -->
				C3	A31	A32	1

2.2.3, judgment matrix approach: calculate matrix Maximum characteristic root λ, define coincident indicator:

Calculate matrix Maximum characteristic root λ, define coincident indicator:

$CI=\frac{\mathrm{\&lambda;}-n}{n-1}$

CI value is more big, and the inconsistent degree of matrix is more serious；

Random index RI:

n	1	2	3	4	5	6	7	8	9	10	11
												RI	0	0	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49	1.51

Definition Consistency Ratio:

$CR=\frac{CI}{RI}$

As CR < 0.1, the inconsistent degree of matrix is in tolerance；

2.2.4, Mode of Level Simple Sequence；

Mode of Level Simple Sequence refers to each judgment matrix each factor relative weighting for its criterion, so being substantially calculate weight vector；The weight vector computational methods adopted are and method；

It is for concordance judgment matrix with ratio juris, is exactly corresponding weight after every string normalization；For nonuniformity judgment matrix, after every string normalization, its corresponding weight approximate, is asking for arithmetic mean of instantaneous value as last weight to this n column vector；Concrete formula is:

$W_i=\frac{1}{n}\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}\frac{a_{ij}}{\underset{k=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_{kl}}$

2.2.5, total hierarchial sorting and inspection；

Total sequence refers to each judgment matrix each factor relative weighting for destination layer (the superiors), and the calculating of this weight adopts method from top to down, successively synthesizes；

Assuming that calculated-1 layer of m the element of the kth weight w relative to general objective^(k-1)=(w₁ ^(k-1),w₂ ^(k-1),…,w_m ^(k-1))^T, n element of kth layer is p for single weight order of last layer (kth layer) jth element_j(k)=(p_1j(k),p_2j(k),…,p_nj(k))^T, wherein the weight of element by j domination is not zero；Make P (k)=(p₁(k),p₂(k),…,p_n(k)), represent the sequence to kth-1 layer element of the kth layer element, then kth layer element being always ordered as general objective:

W (k)=(w₁(k),w₂(k),…,w_n(k))^T=p (k) w (k-1)

Or

Then

Wherein φ is each index result of calculation of indicator layer, and y is water resources efficiency evaluation result.

Below step 3 exists step by step:

Step 3.1, adopt linear weighted function method by combined for the evaluation result in step 1 and step 2, its computing formula is:

A=α y+ (1-α) θ

Wherein, y is the water resources efficiency evaluation result of step 2 gained, and α is weight coefficient, and θ is the relative efficiency of gained urban industrial structure in step 1；

Step 3.2, according to step 3.1 gained A, urban industrial structure optimization is combined diagnosis, standard is:

If 1. A >=0.9, then urban industrial structure degree of optimization is outstanding；

If 2. 0.7≤A < 0.9, then urban industrial structure degree of optimization is good；

If 3. 0.6≤A < 0.7, then urban industrial structure degree of optimization is qualified；

If 4. A < 0.6, then urban industrial structure degree of optimization is defective.

Above in conjunction with embodiment, the present invention is carried out exemplary description; the realization of the obvious present invention is not subject to the restrictions described above; as long as have employed the various improvement that the design of the method for the present invention carries out with technical scheme; or the not improved design by the present invention and technical scheme directly apply to other occasion, all in protection scope of the present invention.

Claims (5)

1., based on an industrial structure rationality diagnostic method for water resource, comprise the following steps:

Step 1, utilize DEA Model that urban industrial structure degree of optimization is diagnosed；

Step 2, utilize analytic hierarchy process (AHP) that tertiary industries water-using structure and efficiency are diagnosed；

Step 3, utilize twice diagnostic result of linear weighted function to be combined, obtain the city industrial structure optimization degree diagnostic result based on water resource.

2. according to claim 1 based on the industrial structure rationality diagnostic method of water resource, it is characterised in that: below step 1 exists step by step:

Step 1.1, building the input and output index system of DEA Model, input-occupancy-output analysis includes primary industry investment in fixed assets, secondary industry investment in fixed assets, tertiary industry investment in fixed assets, primary industry water consumption, secondary industry water consumption, tertiary industry water consumption, primary industry quantity of employment, secondary industry quantity of employment, tertiary industry quantity of employment, water conservancy always put into, farmland under irrigation, expenditures in the local budget；Output index includes value-added of the primary industry, the value of secondary industry, value-added of the tertiary industry, total financial income of local government；

Step 1.2, the city input of research on utilization forcasted years, output data, as the sample of DEA Model, are run related software and are calculated the relative efficiency θ of urban industrial structure, returns to scale value K, put into redundancy value S^-With output deficiency value S⁺；

Step 1.3, the standard carrying out diagnosing according to DEA Model result of calculation be:

If 1. θ=1 in a certain year and S^-=0, S⁺=0, then to operate to DEA effective for this year industrial structure；

If 2. θ=1 in a certain year and S^-≠ 0 or S⁺≠ 0, then this year industrial structure operates to weak DEA effectively, and θ value is more big, and industrial structure relative efficiency is more high；

If the 3. θ < 1 in a certain year, then to operate to non-DEA effective for this year industrial structure；

If the 4. K=1 in a certain year, then this year industrial structure operates to constant returns to scale；

If the 5. K < 1 in a certain year, then this year industrial structure operates to increasing return to scale；

If the 6. K > 1 in a certain year, then this year industrial structure operates to decreasing return to scale.

3. according to claim 1 based on the industrial structure rationality diagnostic method of water resource, it is characterised in that: below step 2 exists step by step:

Step 2.1, structure step analysis index system such as following table；

Water resource utilization efficiency index system

Step 2.2, with each desired value in research level year for sample, it is standardized sample processing sample architecture judgment matrix, and carry out consistency check, utilize analytic hierarchy process (AHP) agriculture products weight, compose and temporary first different rule layers are composed power, namely first different rule layers are carried out scale judgement, Judgement Matricies, matrix exponent number is identical with the criterion number of plies, judge concordance, the more each index under each rule layer is carried out scale judgement, Judgement Matricies, matrix exponent number is identical with the index number that each rule layer comprises, it is judged that concordance.

4. according to claim 3 based on the industrial structure rationality diagnostic method of water resource, it is characterised in that: step 2.2 particularly as follows:

2.2.1, Judgement Matricies: each has the element (being referred to as criterion) first element (being positioned at the upper left corner) as judgment matrix of downward degree of membership relation, and each element being under the jurisdiction of it is arranged in order the first row behind and first row；

2.2.2, introducing judges scale:

Need rule layer first carries out scale and matrix interpretation:

ab

$A=\left(\begin{array}{cc}1& 3\\ 1/3& 1\end{array}\right)\begin{array}{c}a\\ b\end{array}$

Again to 6 indexs judgment matrix about 2 criterions:

$B_1^{\left(3\right)}=\left(\begin{array}{ccc}1& 1& 3\\ 1& 1& 3\\ 1/3& 1/3& 1\end{array}\right)$

$B_2^{\left(3\right)}=\left(\begin{array}{ccc}1& 3& 5\\ 1/3& 1& 2\\ 1/5& 1/2& 1\end{array}\right)$

For three rank determinants, being constructed as follows judgment matrix is A=(a_ij)_n×n, structure is as follows:

Judgment matrix

C1 C2 C3 C1 1 A₁₂ A₁₃ C2 A₂₁ 1 A₂₃ C3 A₃₁ A₃₂ 1

2.2.3, judgment matrix approach: calculate matrix Maximum characteristic root λ, define coincident indicator:

Calculate matrix Maximum characteristic root λ, define coincident indicator:

$CI=\frac{\mathrm{\&lambda;}-n}{n-1}$

CI value is more big, and the inconsistent degree of matrix is more serious；

Random index RI:

n 1 2 3 4 5 6 7 8 9 10 11 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51

Definition Consistency Ratio:

$CR=\frac{CI}{RI}$

As CR < 0.1, the inconsistent degree of matrix is in tolerance；

2.2.4, Mode of Level Simple Sequence；

Mode of Level Simple Sequence refers to each judgment matrix each factor relative weighting for its criterion, so being substantially calculate weight vector；The weight vector computational methods adopted are and method；

It is for concordance judgment matrix with ratio juris, is exactly corresponding weight after every string normalization；For nonuniformity judgment matrix, after every string normalization, its corresponding weight approximate, is asking for arithmetic mean of instantaneous value as last weight to this n column vector；Concrete formula is:

$W_i=\frac{1}{n}\underset{j=1}{\overset{n}{\&Sigma;}}\frac{a_{ij}}{\underset{k=1}{\overset{n}{\&Sigma;}}{a}_{kl}}$

a_klFor the column vector after concordance judgment matrix normalization；

2.2.5, total hierarchial sorting and inspection；

Total sequence refers to each judgment matrix each factor relative weighting for destination layer (the superiors), and the calculating of this weight adopts method from top to down, successively synthesizes；

Assuming that calculated-1 layer of m the element of the kth weight w relative to general objective^(k-1)=(w₁ ^(k-1),w₂ ^(k-1),…,w_m ^(k-1))^T, n element of kth layer is p for single weight order of last layer (kth layer) jth element_j(k)=(p_1j(k),p_2j(k),…,p_nj(k))^T, wherein the weight of element by j domination is not zero；Make P (k)=(p₁(k),p₂(k),…,p_n(k)), represent the sequence to kth-1 layer element of the kth layer element, then kth layer element being always ordered as general objective:

W (k)=(w₁(k),w₂(k),…,w_n(k))^T=p (k) w (k-1)

Or ${w_i}^{\left(k\right)}=\underset{j=1}{\overset{m}{\&Sigma;}}{p_{ij}}^{\left(k\right)}{w_j}^{(k-1)},i=1,2,...,n$

Then

Wherein φ is each index result of calculation of indicator layer, and y is water resources efficiency evaluation result.

5. based on the industrial structure rationality diagnostic method of water resource in any of the one of claim 1-4, it is characterised in that: below step 3 exists step by step:

Step 3.1, adopt linear weighted function method by combined for the evaluation result in step 1 and step 2, its computing formula is:

A=α y+ (1-α) θ

Wherein, y is the water resources efficiency evaluation result of step 2 gained, and α is weight coefficient, and θ is the relative efficiency of gained urban industrial structure in step 1；

Step 3.2, according to step 3.1 gained A, urban industrial structure optimization is combined diagnosis, standard is:

If 1. A >=0.9, then urban industrial structure degree of optimization is outstanding；

If 2. 0.7≤A < 0.9, then urban industrial structure degree of optimization is good；

If 3. 0.6≤A < 0.7, then urban industrial structure degree of optimization is qualified；

If 4. A < 0.6, then urban industrial structure degree of optimization is defective.