CN113128893A - Regional drought prevention and disaster reduction assessment method and device - Google Patents

Regional drought prevention and disaster reduction assessment method and device Download PDF

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CN113128893A
CN113128893A CN202110467129.3A CN202110467129A CN113128893A CN 113128893 A CN113128893 A CN 113128893A CN 202110467129 A CN202110467129 A CN 202110467129A CN 113128893 A CN113128893 A CN 113128893A
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屈艳萍
雷添杰
吕娟
李翔宇
马苗苗
苏志诚
高辉
杨晓静
张学君
张保山
罗嘉铭
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Abstract

The invention discloses a regional drought prevention and disaster reduction assessment method and a device, wherein the method comprises the following steps: acquiring drought and disaster prevention evaluation indexes of a preset area, and establishing a drought and disaster prevention index evaluation system; determining subjective weights of indexes of all levels in the index evaluation system by adopting an analytic hierarchy process; determining objective weights of indexes at all levels in the index evaluation system by adopting a CRITIC method; respectively assigning a corresponding first coefficient and a second coefficient to each subjective weight and each objective weight by adopting a golden section method, and calculating to obtain comprehensive weights corresponding to indexes at all levels; respectively establishing a single-index uncertain measure matrix and a multi-index uncertain measure matrix according to the comprehensive weight and the uncertain measure theory; calculating according to the multi-index uncertain measurement matrix to obtain a comprehensive measurement evaluation result corresponding to each index, and performing confidence recognition on the evaluation results; and determining the evaluation result of the drought and disaster prevention capability of each region in the preset region according to the confidence coefficient identification result.

Description

Regional drought prevention and disaster reduction assessment method and device
Technical Field
The invention relates to the technical field of disaster assessment, in particular to a regional drought prevention and reduction assessment method and device.
Background
With the increasing global warming phenomenon, the increasing extreme disaster events caused by climate change has become one of the serious challenges facing mankind in the 21 st century. The current ecological environment on earth has been seriously damaged due to the excessive exploitation of natural resources by human beings. China is one of countries where natural disasters occur frequently around the world, and annual loss caused by natural disasters reaches billions of yuan. The economic loss caused by drought disasters is more serious, and the drought damage is recorded to be gradually increased after 21 st century, the drought occurrence duration is prolonged, the influence range is large, the drought damage is increased gradually, the drought damage area in China accounts for more than 40% of the natural disaster damage area, the agricultural loss caused by drought accounts for more than 60% of the total loss of various natural disasters, and the disaster-stricken population caused by drought accounts for more than 50% of the natural disaster population. Drought disasters become important influence factors influencing the grain yield of China and are important stress factors inhibiting the rapid development of agriculture of China, so that the drought resistance and disaster reduction capability of drought regions is evaluated reasonably, scientifically and quantitatively, and the drought resistance and disaster reduction method has very important significance on drought resistance and disaster reduction projects of China.
Economic losses, disaster bearing capacity, disaster degree, disaster population and the like in drought regions are large at home and abroad, and the method has very important significance for the research on risk assessment, drought resistance and disaster reduction, drought control and the like in drought regions. Most scholars often evaluate the whole drought region aiming at the researches of disaster population, drought bearing capacity, disaster degree and economic loss of the drought region. However, the evaluation research on drought prevention and reduction capability in drought regions is less, so the research on drought should focus on the evaluation on drought prevention and reduction capability, and further actively cope with the current drought situation, so as to change the drought resistance and reduction from passive to active, and turn from a single relative means to the drought resistance work.
Disclosure of Invention
In view of the above problems, the present invention provides a method and a corresponding device for evaluating drought protection and disaster reduction in an area, so as to scientifically and reasonably evaluate the drought protection and disaster reduction capability of each area in an arid area.
According to a first aspect of the embodiments of the present invention, there is provided a method for evaluating regional drought prevention and disaster reduction, including:
acquiring drought and disaster prevention evaluation indexes of a preset area, and establishing a drought and disaster prevention index evaluation system;
determining subjective weights of indexes of all levels in the index evaluation system by adopting an analytic hierarchy process;
determining objective weights of indexes at all levels in the index evaluation system by using a CRITIC (criterion impact high Interfrieria correlation) method;
respectively assigning a corresponding first coefficient and a second coefficient to each subjective weight and each objective weight by adopting a golden section method, and calculating to obtain comprehensive weights corresponding to indexes at all levels;
respectively establishing a single-index uncertain measure matrix and a multi-index uncertain measure matrix according to the comprehensive weight and the uncertain measure theory;
calculating according to the multi-index uncertain measurement matrix to obtain a comprehensive measurement evaluation result corresponding to each index, and performing confidence recognition on the evaluation results;
and determining the evaluation result of the drought and disaster prevention capability of each region in the preset region according to the confidence coefficient identification result.
In one embodiment, preferably, the index evaluation system comprises a target layer, a criterion layer and an index layer, wherein the target layer comprises comprehensive evaluation of agricultural drought resistance, the criterion layer comprises engineering defense capacity, production technology capacity, resource guarantee capacity and emergency management capacity, and the index layer comprises water storage engineering regulation and storage rate, effective farmland irrigation rate, drought and flood conservation rate, grain water consumption per kilogram, water-saving irrigation rate, unit farmland irrigation water amount, land-based financial budget income, farmer-based income, unit farmland agriculturin, unit farmland electromechanical wells and unit farmland agricultural power.
In one embodiment, preferably, the determining the subjective weight of each level of the index in the index evaluation system by using an analytic hierarchy process includes:
constructing all judgment matrixes of each level by adopting a consistent matrix method to obtain a hierarchical structure analysis model; all judgment matrixes of all levels are constructed by adopting a consistent matrix method, namely all factors are not put together for comparison, but all factors are compared with each other pairwise, the difficulty of comparing different factors with each other is reduced as much as possible, and the accuracy of the judgment matrixes is improved. When the judgment matrixes of all the levels are established, the expert scores corresponding to all the factors are scaled by adopting a judgment matrix Aij scaling method. The decision equation for the decision matrix is as follows:
A＝|aij|n*n (1)
and the judgment matrix has the following properties (1), aij>0(2)，
Figure BDA0003043593000000031
aii＝1
Performing hierarchical single sequencing and consistency check on the hierarchical structure analysis model;
the specific method comprises the following steps:
calculating and judging the maximum eigenvalue lambda of the matrix eigenvectormax。
Figure BDA0003043593000000032
Where CW is C w, and C is a determination matrix.
Calculating a consistency index CI, introducing a random consistency index RI for measuring the size of the CI, wherein the consistency index CI has the following specific formula:
Figure BDA0003043593000000033
Figure BDA0003043593000000034
Figure BDA0003043593000000035
Figure BDA0003043593000000036
wherein i denotes the matrix order, WiThe weight value of a factor under the condition of a single criterion is referred; CR is consistency ratio, CI is consistency index, RI is random consistency index, when CR is consistency ratio<And when the judgment matrix is 0.1, the judgment matrix passes the consistency check, otherwise, the judgment matrix A is reconstructed, and aij is adjusted until the constructed judgment matrix passes the consistency check.
Performing total hierarchical ranking on the hierarchical structure analysis model to complete the calculation of the subjective weight, wherein the subjective weight is calculated by adopting the following first calculation formula:
Figure BDA0003043593000000041
wherein, WAHPRepresents the subjective weight, ajRepresenting the order of the hierarchy drawn by the criterion layer A to the overall target layer C, bijRepresenting that i elements in the scheme layer B are A relative to the factors in the standard layer AjThe resulting hierarchical list is ordered.
And calculating indexes of relative importance of all factors of a certain level to the highest level in sequence from the highest level to the lowest level. The rule layer A has M factors A1，A2，A3.......AmThe hierarchy list obtained by the criterion layer A to the total target layer C is ordered as a1，a2，a3，...，am. N elements in the scheme layer B are ranked as B relative to the hierarchical list obtained by the factor Aj in the criterion layer A1j，b2j，b3j，...，bnj(j ═ 1,2,3.. m), so the weight of the ith factor in the solution layer B to the total target layer C is:
Figure BDA0003043593000000042
and checking the consistency of the total sequence of the layers from high to low layer by layer.
If the comparison judgment matrix related to Aj in the scheme layer B is subjected to consistency check in the single sequence, and the value of a single sequence consistency index ci (j) is calculated, (j is 1,2,3.. m), and an average random consistency index ri (j) can be obtained through a formula four, then the total sequence random consistency ratio of the layer B is:
Figure BDA0003043593000000043
if CR <0.1, the B layer total ordering passes through the random consistency ratio, otherwise, the value of the judgment matrix needs to be adjusted again.
Assuming that n samples to be evaluated are provided, p evaluation indexes form an original data matrix as shown in (9):
Figure BDA0003043593000000051
wherein XijThe j-th evaluation index value of the ith sample is obtained.
In one embodiment, preferably, the determining the objective weight of each level of the index in the index evaluation system by using the CRITIC method includes:
calculating the objective weight using the following calculation formula:
Figure BDA0003043593000000052
Figure BDA0003043593000000053
Figure BDA0003043593000000054
wherein, wcriticRepresenting said objective weight, CjIndicates the total information amount contained in the j-th evaluation index,
Figure BDA0003043593000000055
a quantitative formula representing the conflict between the jth evaluation index and other evaluation indexes, rijDenotes a correlation coefficient, δ, between evaluation indexes i and jjStandard deviation, x, representing the jth evaluation indexiIs the ith value of the jth index, mu is xiN is xiThe total number of (c).
In one embodiment, preferably, assigning a first coefficient and a second coefficient to each of the subjective weight and the objective weight by a golden section method, and calculating to obtain a comprehensive weight corresponding to each level of index, includes:
determining a first coefficient corresponding to the subjective weight to be 0.382 and a second coefficient corresponding to the objective weight to be 0.618 by adopting a golden section method;
the integrated weight is calculated using the following calculation formula,
W＝0.382wAHP+0.618wCRITIC (13)
wherein W ═ { W ═ W1,W2,W3...Wj}。
Let sample be n, n ═ x1,x2,x3...xnWith m evaluation indices, x, per sampleijA value representing the jth index in sample i, where i is 1,2,3.. times.n; j 1,2,3. Each evaluation index has P evaluation grades, which are respectively: c. C1,c2,c3....,cpThereby forming an evaluation space U ═ c1,c2,c3...cp}. If P evaluation grades satisfy c1Is superior to c2，c2Is superior to c3，.......，cp-1Is superior to cpThen c is1>c2>c3>......>cp-1>cp，{c1,c2,c3...cpAnd f, an ordered segmentation class in an evaluation space.
In one embodiment, preferably, the single-index undetermined measure matrix is:
Figure BDA0003043593000000061
wherein, mujikThe degree of the kth evaluation level of the jth index in the sample i is shown, m represents the number of evaluation indexes per sample, and p represents the number of evaluation levels per evaluation index.
In one embodiment, preferably, the multi-index undetermined measurement matrix is:
Figure BDA0003043593000000062
Figure BDA0003043593000000063
wherein, muikIndicates the degree to which sample xi belongs to the Kth evaluation class ck,. mu.jikThe degree of the k-th evaluation level of the j-th index in the sample i is shown, and wj represents the comprehensive weight of the j-th index.
According to a second aspect of the embodiments of the present invention, there is provided an evaluation apparatus for regional drought prevention and disaster reduction, including:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring drought and disaster prevention evaluation indexes of a preset area and establishing a drought and disaster prevention index evaluation system;
the first determination module is used for determining the subjective weight of each level of index in the index evaluation system by adopting an analytic hierarchy process;
the second determination module is used for determining the objective weight of each level of index in the index evaluation system by adopting a CRITIC method;
the calculation module is used for respectively endowing each subjective weight and each objective weight with a corresponding first coefficient and a corresponding second coefficient by adopting a golden section method, and calculating to obtain comprehensive weights corresponding to indexes of all levels;
the matrix establishing module is used for respectively establishing a single-index undetermined measurement matrix and a multi-index undetermined measurement matrix according to the comprehensive weight and the undetermined measurement theory;
the identification module is used for calculating to obtain a comprehensive measurement evaluation result corresponding to each index according to the multi-index uncertain measurement matrix and carrying out confidence identification on the evaluation results;
and the evaluation module is used for determining the evaluation result of the drought and disaster prevention capability of each region in the preset region according to the confidence coefficient identification result.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored therein instructions which, when run on an apparatus, perform the method according to any one of the embodiments of the first aspect.
In the embodiment of the invention, in order to reasonably, scientifically and quantitatively estimate drought-prevention and disaster-reduction capacity of arid regions, the regional drought-prevention and disaster-reduction evaluation method based on an unknown measure model of AHP-CRITIC is provided. And establishing a regional drought and disaster prevention capability evaluation model through an uncertain measurement theory, respectively establishing a single-index uncertain measurement matrix and a multi-index uncertain measurement matrix, and performing confidence recognition on the evaluation result to respectively obtain the evaluation result of the drought and disaster prevention capability of each region of the region. Has very important significance for the projects of drought area risk assessment, drought resistance and disaster relief, disaster relief and the like in China.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of an evaluation method for regional drought control and disaster reduction according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an index evaluation system according to an embodiment of the present invention.
Fig. 3 is a block diagram of an evaluation apparatus for regional drought control and disaster reduction according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.
In some of the flows described in the present specification and claims and in the above figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being indicated as 101, 102, etc. merely to distinguish between the various operations, and the order of the operations by themselves does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of an evaluation method for regional drought protection and reduction according to an embodiment of the present invention, and as shown in fig. 1, the evaluation method for regional drought protection and reduction includes:
and S101, acquiring drought and disaster prevention evaluation indexes of a preset area, and establishing a drought and disaster prevention index evaluation system.
In one embodiment, preferably, as shown in fig. 2, the index evaluation system comprises a target layer, a criterion layer and an index layer, wherein the target layer comprises comprehensive evaluation of agricultural drought resistance, the criterion layer comprises engineering defense capacity, production technology capacity, resource guarantee capacity and emergency management capacity, and the index layer comprises water storage engineering regulation rate, farmland effective irrigation rate, drought and flood conservation rate, grain water consumption per kilogram, water-saving irrigation rate, unit farmland irrigation water quantity, land-based financial budget income, farmer-based income, unit farmland agriculturists, unit farmland electromechanical wells and unit farmland agricultural mechanical power.
And S102, determining the subjective weight of each level of index in the index evaluation system by adopting an analytic hierarchy process. All judgment matrixes of all levels are constructed by adopting a consistent matrix method, namely all factors are not put together for comparison, but all factors are compared with each other pairwise, the difficulty of comparing different factors with each other is reduced as much as possible, and the accuracy of the judgment matrixes is improved. When establishing judgment matrixes of all the levels, the expert scores corresponding to each factor adopt a judgment matrix AijThe calibration method performs calibration. The decision equation for the decision matrix is as follows:
A＝|aij|n*n (1)
and the decision matrix has the following properties, (1), aij>0(2)，
Figure BDA0003043593000000091
aii＝1
After all judgment matrixes of each hierarchy of a hierarchical structure analysis model (AHP) are established, the model is subjected to hierarchical single sequencing and consistency check, and whether the judgment matrixes meet the consistency check is checked. The specific method comprises the following steps:
calculating and judging the maximum eigenvalue lambda of the matrix eigenvectormax。
Figure BDA0003043593000000092
Where CW is C w, and C is a determination matrix.
Calculating a consistency index CI, introducing a random consistency index RI for measuring the size of the CI, wherein the consistency index CI has the following specific formula:
Figure BDA0003043593000000093
Figure BDA0003043593000000094
Figure BDA0003043593000000095
Figure BDA0003043593000000096
wherein i denotes the matrix order, WiThe weight value of a factor under the condition of a single criterion is referred; CR is consistency ratio, CI is consistency index, RI is random consistency index, when CR is consistency ratio<When 0.1, the judgment matrix passes the consistency check, otherwise, the judgment matrix A is reconstructed, and the judgment matrix A is compared with the judgment matrix AijAnd adjusting until the constructed judgment matrix passes the consistency check.
And after finishing the hierarchical single sequencing and consistency check of the judgment matrix, carrying out the hierarchical total sequencing and consistency check on the model, and finishing the calculation of the relative weight of each scheme in the lowest layer to the target layer. And calculating indexes of relative importance of all factors of a certain level to the highest level in sequence from the highest level to the lowest level. The rule layer A has M factors A1，A2，A3.......AmThe hierarchy list obtained by the criterion layer A to the total target layer C is ordered as a1，a2，a3，...，am. The N elements in the scheme layer B are A relative to the factor in the criteria layer AjThe obtained hierarchical single ordering is b1j，b2j，b3j，...，bnj(j ═ 1,2,3.. m), so the weight of the ith factor in the solution layer B to the total target layer C is:
Figure BDA0003043593000000101
and carrying out consistency check on the total sequence of the layers from high to low layer by layer. If the scheme layer B is identical to the scheme layer AjThe consistency of the related comparison judgment matrix in the single-rank order is checked, the value of a single-rank order consistency index CI (j) is calculated, (j is 1,2,3.. m), the average random consistency index RI (j) can be obtained through a formula (4), and then the B-layer total rank ordering is carried outRandom consistency ratio:
Figure BDA0003043593000000102
if CR <0.1, the B layer total ordering passes through the random consistency ratio, otherwise, the value of the judgment matrix needs to be adjusted again.
And S103, determining the objective weight of each level of index in the index evaluation system by adopting a CRITIC method.
Assuming that n samples to be evaluated are provided, p evaluation indexes form an original data matrix as shown in (9):
Figure BDA0003043593000000103
wherein XijThe j-th evaluation index value of the ith sample is obtained.
The standard deviation of the jth index is calculated, as shown in (10):
Figure BDA0003043593000000111
wherein deltajFor the standard deviation found, xiIs the ith value of the jth index, mu is xiN is xiThe total number of (c).
The quantization formula of the conflict between the jth index and other indexes is
Figure BDA0003043593000000112
Wherein r isijThe correlation coefficient between the evaluation indexes i and j is represented.
The objective weight of each index is comprehensively measured by the information quantity and the conflict. Is provided with CjC represents the total information content contained in the jth evaluation indexjThe expression is shown as (11):
Figure BDA0003043593000000113
objective weight wcriticThe calculation formula is shown as (12):
Figure BDA0003043593000000114
step S104, a golden section method is adopted to respectively endow a first coefficient and a second coefficient corresponding to each subjective weight and each objective weight, and comprehensive weights corresponding to indexes of all levels are obtained through calculation;
coefficients having different subjective and objective weights are given by the golden section method, and since the overall weight is mainly based on the objective weight and is auxiliary to the subjective weight, the subjective weight coefficient is set to 0.382 and the objective weight coefficient is set to 0.618. Therefore, the calculation process of the combining weight is as shown in (13):
W＝0.382wAHP+0.618wCRITIC (13)
wherein W ═ { W ═ W1,W2,W3...Wj}
Step S105, respectively establishing a single-index undetermined measure matrix and a multi-index undetermined measure matrix according to the comprehensive weight and the undetermined measure theory;
let sample be n, n ═ x1,x2,x3...xnWith m evaluation indices, x, per sampleijA value representing the jth index in sample i, where i is 1,2,3.. times.n; j 1,2,3. Each evaluation index has P evaluation grades, which are respectively: c. C1,c2,c3....,cpThereby forming an evaluation space U ═ c1,c2,c3...cp}. If P evaluation grades satisfy c1Is superior to c2，c2Is superior to c3，.......，cp-1Is superior to cpThen c is1>c2>c3>......>cp-1>cp，{c1,c2,c3...cpAnd f, an ordered segmentation class in an evaluation space.
Constructing a single index uncertainty function μ (x)ij∈ck) Let mu stand forjik＝μ(xij∈ck)，μjikThe degree of the k-th evaluation level of the j-th index in the sample i is expressed, and the single index unknown function needs to satisfy the condition of equation (14):
Figure BDA0003043593000000121
thereby establishing a single-index undetermined measurement matrix as shown in formula 15.
Figure BDA0003043593000000122
Let wjTo evaluate factor xiEvaluation index I ofjRelative degree of importance compared with other criteria, where wjThe following condition is satisfied, as shown in formula 16:
Figure BDA0003043593000000123
let mu letik＝μ(xi∈ck) Represents a sample xiBelong to the Kth evaluation class ckTo the extent of
Figure BDA0003043593000000124
Establishing a multi-index comprehensive measure evaluation matrix, wherein the matrix is as shown in (18):
Figure BDA0003043593000000125
thus, (μi1，μi2，μi3...μiP) Is xiThe vector is evaluated by the integrated measure of (1).
Step S106, calculating according to the multi-index uncertain measurement matrix to obtain a comprehensive measurement evaluation result corresponding to each index, and performing confidence recognition on the evaluation results;
and S107, determining the evaluation result of the drought and disaster prevention capability of each region in the preset region according to the confidence coefficient identification result.
When { c1，c2，c3...cPWhen the evaluation space U is divided in order, the identification criterion of the maximum membership degree is not applicable, namely, the identification criterion of the confidence degree is adopted, and the value of the confidence degree lambda is usually (0.5)<λ<1) The range of (c), let:
Figure BDA0003043593000000131
then sample i belongs to class k ck。
Fig. 3 is a block diagram of an evaluation apparatus for regional drought control and disaster reduction according to an embodiment of the present invention.
As shown in fig. 3, according to a second aspect of the embodiments of the present invention, there is provided an evaluation apparatus for regional drought protection and disaster reduction, including:
the acquisition module 31 is used for acquiring evaluation indexes of drought prevention and disaster reduction in a preset area and establishing an index evaluation system of drought prevention and disaster reduction;
the first determining module 32 is configured to determine subjective weights of indexes at different levels in the index evaluation system by using an analytic hierarchy process;
a second determining module 33, configured to determine objective weights of indexes at each level in the index evaluation system by using a CRITIC method;
a calculating module 34, configured to assign a first coefficient and a second coefficient to each of the subjective weights and the objective weights by using a golden section method, and calculate to obtain a comprehensive weight corresponding to each level of index;
the matrix establishing module 35 is configured to respectively establish a single-index undetermined measurement matrix and a multi-index undetermined measurement matrix according to the comprehensive weight and the undetermined measurement theory;
the identification module 36 is configured to obtain a comprehensive measurement evaluation result corresponding to each index according to the multiple-index uncertain measurement matrix, and perform confidence identification on the evaluation result;
and the evaluation module 37 is configured to determine an evaluation result of the drought and disaster prevention capability of each region in the preset region according to the confidence coefficient recognition result.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored therein instructions which, when run on an apparatus, perform the method according to any one of the embodiments of the first aspect.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by hardware that is instructed to implement by a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
While the portable multifunctional device provided by the present invention has been described in detail, those skilled in the art will appreciate that the various embodiments and applications of the invention can be modified, and that the scope of the invention is not limited by the disclosure of the present invention.

Claims (9)

1. An assessment method for regional drought prevention and disaster reduction is characterized by comprising the following steps:
acquiring drought and disaster prevention evaluation indexes of a preset area, and establishing a drought and disaster prevention index evaluation system;
determining subjective weights of indexes of all levels in the index evaluation system by adopting an analytic hierarchy process;
determining objective weights of indexes at all levels in the index evaluation system by adopting a CRITIC method;
respectively assigning a corresponding first coefficient and a second coefficient to each subjective weight and each objective weight by adopting a golden section method, and calculating to obtain comprehensive weights corresponding to indexes at all levels;
respectively establishing a single-index uncertain measure matrix and a multi-index uncertain measure matrix according to the comprehensive weight and the uncertain measure theory;
calculating according to the multi-index uncertain measurement matrix to obtain a comprehensive measurement evaluation result corresponding to each index, and performing confidence recognition on the evaluation results;
and determining the evaluation result of the drought and disaster prevention capability of each region in the preset region according to the confidence coefficient identification result.
2. The method of claim 1, wherein the index evaluation system comprises a target layer, a criterion layer and an index layer, the target layer comprises comprehensive evaluation of agricultural drought resistance, the criterion layer comprises engineering defense capacity, production technology capacity, resource guarantee capacity and emergency management capacity, and the index layer comprises water storage engineering regulation rate, farmland effective irrigation rate, drought and flood conservation rate, grain water consumption per kilogram, water-saving irrigation rate, unit farmland irrigation water quantity, land-based financial budget income, farmer-based income, unit farmland agriculturists, unit farmland electromechanical wells and unit farmland agricultural dynamics.
3. The method of claim 1, wherein determining subjective weights of each level of the index in the index evaluation system using an analytic hierarchy process comprises:
constructing all judgment matrixes of each level by adopting a consistent matrix method to obtain a hierarchical structure analysis model;
performing hierarchical single sequencing and consistency check on the hierarchical structure analysis model;
performing total hierarchical ranking on the hierarchical structure analysis model to complete the calculation of the subjective weight, wherein the subjective weight is calculated by adopting the following first calculation formula:
Figure FDA0003043592990000011
wherein, WAHPRepresents the subjective weight, ajRepresenting the order of the hierarchy drawn by the criterion layer A to the overall target layer C, bijRepresenting that i elements in the scheme layer B are A relative to the factors in the standard layer AjSorting the obtained hierarchical list;
and checking the consistency of the total sequence of the layers from high to low layer by layer.
4. The method of claim 1, wherein determining objective weights of indexes at different levels in the index evaluation system by using a CRITIC method comprises:
calculating the objective weight using the following calculation formula:
Figure FDA0003043592990000021
Figure FDA0003043592990000022
Figure FDA0003043592990000023
wherein, wcriticRepresenting said objective weight, CjIndicates the total information amount contained in the j-th evaluation index,
Figure FDA0003043592990000024
a quantitative formula representing the conflict between the jth evaluation index and other evaluation indexes, rijDenotes a correlation coefficient, δ, between evaluation indexes i and jjStandard deviation, x, representing the jth evaluation indexiIs the ith value of the jth index, mu is xiN is xiThe total number of (c).
5. The method according to claim 1, wherein a golden section method is adopted to assign a first coefficient and a second coefficient to each subjective weight and each objective weight, and a comprehensive weight corresponding to each level of index is obtained through calculation, and the method comprises the following steps:
determining a first coefficient corresponding to the subjective weight to be 0.382 and a second coefficient corresponding to the objective weight to be 0.618 by adopting a golden section method;
the integrated weight is calculated using the following calculation formula,
W＝0.382wAHP+0.618wCRITIC
wherein W ═ { W ═ W1,W2,W3...Wj}。
6. The method of claim 1, wherein the single index indeterminate measure matrix is:
Figure FDA0003043592990000031
wherein, mujikThe degree of the kth evaluation level of the jth index in the sample i is shown, m represents the number of evaluation indexes per sample, and p represents the number of evaluation levels per evaluation index.
7. The method of claim 6, wherein the matrix of multi-index uncertain measures is:
Figure FDA0003043592990000032
Figure FDA0003043592990000033
wherein, muikIndicates the degree to which sample xi belongs to the Kth evaluation class ck,. mu.jikThe degree of the k-th evaluation level of the j-th index in the sample i is shown, and wj represents the comprehensive weight of the j-th index.
8. An evaluation device for regional drought control and disaster reduction, comprising:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring drought and disaster prevention evaluation indexes of a preset area and establishing a drought and disaster prevention index evaluation system;
the first determination module is used for determining the subjective weight of each level of index in the index evaluation system by adopting an analytic hierarchy process;
the second determination module is used for determining the objective weight of each level of index in the index evaluation system by adopting a CRITIC method;
the calculation module is used for respectively endowing each subjective weight and each objective weight with a corresponding first coefficient and a corresponding second coefficient by adopting a golden section method, and calculating to obtain comprehensive weights corresponding to indexes of all levels;
the matrix establishing module is used for respectively establishing a single-index undetermined measurement matrix and a multi-index undetermined measurement matrix according to the comprehensive weight and the undetermined measurement theory;
the identification module is used for calculating to obtain a comprehensive measurement evaluation result corresponding to each index according to the multi-index uncertain measurement matrix and carrying out confidence identification on the evaluation results;
and the evaluation module is used for determining the evaluation result of the drought and disaster prevention capability of each region in the preset region according to the confidence coefficient identification result.
9. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the steps of the method of any one of claims 1 to 7.
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