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CN113673827A - Regional water resource vulnerability early warning system and method - Google Patents

Regional water resource vulnerability early warning system and method Download PDF

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CN113673827A
CN113673827A CN202110831404.5A CN202110831404A CN113673827A CN 113673827 A CN113673827 A CN 113673827A CN 202110831404 A CN202110831404 A CN 202110831404A CN 113673827 A CN113673827 A CN 113673827A
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刘阳
曹升乐
常乐冉
游瑞峰
李新迎
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Abstract

本发明属于信息处理领域，提供了一种区域水资源脆弱性预警系统及方法。其中，该系统包括指标数据采集模块，其用于采集区域水资源的关键影响因子指标数据；脆弱性分级模块，其用于利用模糊识别模型计算得到关键影响因子的相对隶属度矩阵作为初始聚类中心矩阵，构建出模糊聚类双重迭代模型，迭代计算得到各关键影响因子优化后的聚类中心，形成指标门限值矩阵，再基于模糊识别模型计算区域水资源脆弱性隶属度矩阵，进而得到区域水资源脆弱性等级；风险预警显示模块，其用于根据预先水资源脆弱性等级与预警级别的对应关系，将不同预警级别配置不同颜色，再结合GIS数据，将不同区域水资源脆弱性预警级别进行展示。

Figure 202110831404

The invention belongs to the field of information processing, and provides a regional water resource vulnerability early warning system and method. Among them, the system includes an index data collection module, which is used to collect the key impact factor index data of regional water resources; a vulnerability classification module, which is used to calculate the relative membership degree matrix of the key impact factors using the fuzzy identification model as the initial clustering Center matrix, construct a fuzzy clustering double iterative model, iteratively calculate to obtain the optimized cluster centers of each key influencing factor, form the index threshold value matrix, and then calculate the regional water resource vulnerability membership matrix based on the fuzzy identification model, and then obtain Regional water resource vulnerability level; risk warning display module, which is used to configure different early warning levels with different colors according to the corresponding relationship between the pre-water resources vulnerability level and the early warning level, and then combine with GIS data to provide early warning of water resource vulnerability in different regions. level to display.

Figure 202110831404

Description

Regional water resource vulnerability early warning system and method
Technical Field
The invention belongs to the field of information processing, and particularly relates to a regional water resource vulnerability early warning system and method.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The vulnerability of water resource mainly refers to the difficulty degree of water resource pollution caused by natural environment and human activities, wherein the natural environment is used as an uncontrollable factor to determine the vulnerability of the water resource, but the human activities are used as external vulnerability to play a main role in the difficulty degree of water quality deterioration of the water resource. The water resource vulnerability is a relative parameter to a certain extent, which can not be directly measured, and has a certain regionality, so that the regional water resource vulnerability early warning accuracy is poor, and the geographical region of the regional water resource vulnerability can not be visually checked.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a regional water resource vulnerability early warning system and method, which can accurately early warn regional water resource vulnerability for regional display.
In order to achieve the purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides a regional water resource vulnerability early warning system, which comprises:
the index data acquisition module is used for acquiring key influence factor index data of regional water resources;
the vulnerability classification module is used for utilizing a fuzzy recognition model to calculate to obtain a relative membership matrix of key influence factors as an initial clustering center matrix, constructing a fuzzy clustering dual iteration model, obtaining a clustering center after optimizing each key influence factor through iteration calculation to form an index threshold value matrix, and calculating a regional water resource vulnerability membership matrix based on the fuzzy recognition model so as to obtain a regional water resource vulnerability grade;
and the risk early warning display module is used for configuring different colors for different early warning levels according to the corresponding relation between the water resource vulnerability level and the early warning level in advance, and displaying the water resource vulnerability early warning levels in different areas by combining GIS data.
The second aspect of the invention provides a regional water resource vulnerability early warning method, which comprises the following steps:
collecting key influence factor index data of regional water resources;
calculating a relative membership matrix of the key influence factors by using a fuzzy recognition model to serve as an initial clustering center matrix, constructing a fuzzy clustering dual iteration model, performing iterative calculation to obtain a clustering center after optimization of each key influence factor to form an index threshold value matrix, and calculating a regional water resource vulnerability membership matrix based on the fuzzy recognition model to further obtain a regional water resource vulnerability grade;
and configuring different colors for different early warning levels according to the corresponding relation between the pre-water resource vulnerability levels and the early warning levels, and displaying the water resource vulnerability early warning levels in different areas by combining GIS data.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the regional water resource vulnerability warning method as described above.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the regional water resource vulnerability warning method as described above.
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps of utilizing a fuzzy recognition model to calculate to obtain a relative membership matrix of key influence factors as an initial clustering center matrix, constructing a fuzzy clustering dual iteration model, carrying out iteration calculation to obtain a clustering center after optimization of each key influence factor, forming an index threshold value matrix, calculating a regional water resource vulnerability membership matrix based on the fuzzy recognition model, further obtaining regional water resource vulnerability grades, configuring different colors for different early warning grades according to the corresponding relation between the pre-set water resource vulnerability grades and the early warning grades, combining GIS data, displaying the early warning grades of the different regional water resource vulnerabilities, finally realizing accurate early warning of the regional water resource vulnerability to carry out regional display, and simultaneously providing technical support for reasonable development of regional water resources.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic structural diagram of a regional water resource vulnerability warning system according to an embodiment of the present invention;
FIG. 2 illustrates the frequency of use of the water vulnerability index in accordance with an embodiment of the present invention;
FIG. 3 is a lithograph of principal component characteristic values of water resource vulnerability impact factors in accordance with an embodiment of the present invention;
FIG. 4 is a comparison of two model vulnerability assessment results for embodiments of the present invention;
fig. 5 is a flowchart of a regional water resource vulnerability warning method according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
As shown in fig. 1, the embodiment provides a regional water resource vulnerability early warning system, which includes an index data acquisition module, a vulnerability classification module, and a risk early warning display module.
Wherein:
(1) and the index data acquisition module is used for acquiring key influence factor index data of regional water resources.
In a specific implementation, the key impact factor index data includes water resource intrinsic index data, climate index data, underlying surface index data, human activity index data, and water quality index data. These indices are screened out by statistically analyzing the use of each index by a frequency statistical method, as shown in fig. 2.
And screening the key influence factor index data from a set influence factor index data set by using a principal component analysis method.
TABLE 1 set of impact factor indicator data sets
Figure BDA0003175579180000041
Figure BDA0003175579180000051
The SPSS is used for specific operation, the characteristic value of the principal component of the water resource vulnerability influencing factor is shown in a figure 3, and the variance contribution rate and the variance accumulated contribution rate of the selected principal component are shown in a table 2.
TABLE 2 Water resource vulnerability influencing factor principal component eigenvalue and variance contribution rate
Figure BDA0003175579180000052
As can be seen from fig. 3, the eigenvalues of the first four components are greater than 1, and from the fifth start, the eigenvalues of the components are less than 1. As can be seen from table 2, the variance cumulative contribution rate of the first four principal components was 87.604%, which was greater than 85%, so that the components 1,2, 3, and 4 were extracted as principal components. And (3) performing variance maximization rotation on the influence factor load matrix of the 4 main components to obtain a rotation factor load matrix shown in a table 3, and obtaining a component diagram in a rotation space shown in a table 4.
TABLE 3 twiddle factor load matrix
Figure BDA0003175579180000053
Figure BDA0003175579180000061
In this embodiment, the key influence factors obtained by the principal component analysis method are appropriately adjusted, and a water resource vulnerability evaluation factor system is established by using a "driving force-pressure-state-influence-response" conceptual model (DPSIR model), which is shown in table 4.
TABLE 4 Water resource vulnerability assessment factor System in research district
Figure BDA0003175579180000062
For the factor with higher numerical value and better water resource condition, the corresponding vulnerability grade is higher and is set as a forward type factor; the factor with the larger numerical value and the worse water resource condition is, the corresponding vulnerability grade is smaller, and the factor is set as a reverse type factor. Meanwhile, each influence factor is divided into five vulnerability grades, and the grades 1-5 correspond to the conditions of poor, medium, good and excellent water resources respectively, which are shown in Table 5.
TABLE 5 classification of water resource vulnerability
Figure BDA0003175579180000071
The total reserve amount of water resources occupied by everyone and mu is a comprehensive reaction of parameters such as surface water resource amount, underground water resource amount, passenger water amount, total variation of stored water at the end of the year and the like, and can be said to be the water resource condition under the influence of human activities; the climate index shows a more natural water resource endowment condition by less considering the influence of human activities on the water resource state in the natural environment for years; the underlying surface condition is inevitably influenced by human activities in the development process of the human society, and the three cover surface and underground water resource influence factors in a natural state and a human intervention state, so that the factor selection is more comprehensive. The human activity index relates to the water usage level and the water usage efficiency of the first industry, the second industry and the third industry, and the selection of the multiple factors can eliminate the influence of abnormal water usage in a certain aspect of a city on vulnerability evaluation due to different main industries.
(2) And the vulnerability classification module is used for utilizing the fuzzy recognition model to calculate to obtain a relative membership matrix of the key influence factors as an initial clustering center matrix, constructing a fuzzy clustering dual iteration model, obtaining a clustering center after optimizing each key influence factor through iteration calculation to form an index threshold value matrix, and calculating the vulnerability membership matrix of the regional water resources based on the fuzzy recognition model so as to obtain the vulnerability grade of the regional water resources.
In this embodiment, in the vulnerability classification module, the construction process of the fuzzy recognition model is as follows:
expressing the difference between any sample and the h-level vulnerability characteristic value by using a weighted generalized Euclidean weight distance, and constructing a target function by combining a least square criterion;
and constructing a Lagrange function according to the target function, changing the equation constraint extremum solving into an unconditional extremum solving problem, and solving partial derivatives of relative membership vectors of each sample to the vulnerability grade h to obtain a fuzzy mode identification model.
And a new variable is generated by utilizing principal component analysis according to a variance maximization method, so that the contribution degree of the new variable to the sample variance is highlighted. The method mainly comprises the following steps:
(1) carrying out standardization processing on the original data;
(2) calculating a correlation coefficient matrix;
Figure BDA0003175579180000081
rij(i, j ═ 1,2, …, p) as original variable xiAnd xjIs calculated by the formula
Figure BDA0003175579180000082
(3) Computing eigenvalues and eigenvectors
(4) Calculating principal component contribution rate and accumulated contribution rate
Principal component ziContribution rate:
Figure BDA0003175579180000083
cumulative contribution rate τ:
Figure BDA0003175579180000084
in general, when the cumulative contribution τ of the principal component reaches 85% to 95%, it is considered that the cumulative contribution τ represents information contained in the original data. Generally, the characteristic value lambda with the accumulated contribution rate of 85-95 percent is taken1，λ2，…，λmCorresponding first, second, …, mth principal component.
When the normalized moment of the key influence factors is calculated by using a fuzzy recognition model, m influence factors are arranged in a research area, n samples are arranged in each influence factor, and the samples are recorded as Xn×m。Xn×mThe normalization matrix (c) can be calculated from equation (3):
Figure BDA0003175579180000091
in the formula, Ri-a vector representation of m normalization factor values for sample i, i ═ 1, 2.. multidot.n;
ri(j) -the j-th normalization factor value of sample i, 0 ≦ ri(j)≤1，j＝1，2，...，m。
For forward form factor, ri(j) The calculation formula is as follows:
Figure BDA0003175579180000092
for negative form factor, ri(j) The calculation formula is as follows:
Figure BDA0003175579180000093
in the formula, xi(max) -the maximum eigenvalue of the ith factor in the sample;
xi(min) -the minimum eigenvalue of the ith factor in the sample.
And (3) setting m influence factors to perform vulnerability evaluation according to k grades, wherein the evaluation grade division method I can use a k multiplied by m order factor grade threshold value matrix as shown in a formula (6):
Figure BDA0003175579180000094
identifying a water resource vulnerability factor system according to c grades, and standardizing a factor grade threshold value matrix, so that the clustering centers of the m influencing factors of the c grades can be represented by a c multiplied by m-grade clustering center matrix, which is shown in formula (7):
Figure BDA0003175579180000101
the m factor criterion features of level h are represented as:
Sh＝[sh(1)，sh(2)，...，sh(m)] (8)
0＜sh(j)＜1，j＝1，2，...，m；h＝1，2，...，c (9)
the weight of each influence factor is quantitatively determined by applying an entropy method:
Figure BDA0003175579180000102
considering different indexes with different functions on clustering, combining the weight omega of each factor to be (omega)1，ω2，...，ωj，...，ωm) Calculating the generalized Euclidean weight distance d between the sample i and the class hihSee formula (11):
Figure BDA0003175579180000103
substituting the formula (1), the formula (3) and the formula (7) into the fuzzy pattern recognition model to obtain a sample relative membership matrix of the n samples to each vulnerability grade:
Un×c＝(U1，U2，…，Uc)T (12)
the relative membership vector for each sample for rank h is:
Uh＝[uh(1)，uh(2)，...，uh(m)] (13)
Figure BDA0003175579180000111
and (3) expressing the difference between the ith sample and the h-level vulnerability characteristic value by using the weighted generalized Euclidean weight distance, and establishing an objective function according to the formula (15) by using a least square criterion:
Figure BDA0003175579180000112
constructing a Lagrange function according to an objective function, changing equation constraint extremum solving into unconditional extremum solving, and solving the problem of uh(i) And (3) solving a partial derivative, and finally obtaining a multistage fuzzy pattern recognition model shown in the formula (16):
Figure BDA0003175579180000113
when the fuzzy clustering dual iteration model is used for optimizing the clustering center of the key influence factor, the relative membership matrix U of the influence factor sample is not only optimizedn×cConstructing an iterative objective function, performing iterative optimization, and solving the optimized membership matrix U'n×c. Also for the cluster center matrix Sc×mEstablishing an objective function, carrying out iterative optimization, and solving an optimized cluster center matrix S'c×m。
Normalization matrix R of known impact factor valuesn×mTo solve the optimized membership matrix U'k×nEstablishing an iterative optimization objective function, see formula (17):
Figure BDA0003175579180000121
constructing a Lagrangian function according to equation (17) and fitting ui(h) And (3) solving the partial derivatives to obtain an optimal membership matrix calculation formula (18):
Figure BDA0003175579180000122
using a matrix S of eigenvalues of key impact factorsc×mTo solve the optimized cluster center matrix S'c×mEstablishing an iterative optimization objective function, see formula (19):
Figure BDA0003175579180000123
constructing a Lagrangian function according to equation (19) and applying to sh(j) And (3) solving partial derivatives to obtain an optimal clustering center matrix calculation formula (20):
Figure BDA0003175579180000124
forming a loop iteration formula by the formulas (17), (18) and (19), (20) to iteratively solve the optimized membership matrix U'k×nAnd a cluster center matrix S'c×m. The U solved by the fuzzy pattern recognition modeln×cSubstituting the initial clustering center matrix into an equation (17), and repeatedly carrying out iterative computation; simultaneously clustering a center matrix S 'according to given influence factors'c×mThe initial clustering matrix is substituted into an equation (19), and iterative calculation is repeated. Performing n times of iterative calculation through MATLAB until the difference between the calculation results of the two sets of iterations and the results of n-1 times is less than 10-6And the mean value of the two groups of calculation results is the optimal fuzzy clustering center matrix.
According to the vulnerability evaluation index system constructed in the last step, data of 11 water resource vulnerability key influence factors in 2000-2016 in a certain urban area are brought into the formulas (1) to (6), and a standard characteristic value matrix S of each factor is obtained through calculation5×9And factor relative membership matrix R17×9The following were used:
Figure BDA0003175579180000131
Figure BDA0003175579180000132
will S5×9And R17×9Substituting the multi-stage fuzzy recognition model and the fuzzy clustering dual iteration model, and obtaining an optimized influence factor clustering center S 'through iterative computation'5×9。
Figure BDA0003175579180000141
The weight vector of each key influence factor in the Jinan city is determined to be w [0.113,0.113,0.112,0.113,0.115,0.108,0.109,0.110,0.107] by an entropy method, and the obtained assessment results of the vulnerability of the water resources in the Jinan city are shown in table 6 and table 7, and the comparison of the two model results is shown in fig. 5.
TABLE 6 fuzzy recognition model evaluation results of water resource vulnerability in certain urban area
Figure BDA0003175579180000142
TABLE 7 fuzzy clustering double iteration model evaluation result of water resource vulnerability in Jinan City
Figure BDA0003175579180000143
Figure BDA0003175579180000151
(3) And the risk early warning display module is used for configuring different colors for different early warning levels according to the corresponding relation between the water resource vulnerability level and the early warning level in advance, and displaying the water resource vulnerability early warning levels in different areas by combining GIS data.
Example two
As shown in fig. 5, the embodiment provides a method for warning vulnerability of regional water resources, which includes:
step S101: acquiring key influence factor index data of regional water resources;
step S102: calculating a relative membership matrix of the key influence factors by using a fuzzy recognition model to serve as an initial clustering center matrix, constructing a fuzzy clustering dual iteration model, performing iterative calculation to obtain a clustering center after optimization of each key influence factor to form an index threshold value matrix, and calculating a regional water resource vulnerability membership matrix based on the fuzzy recognition model to further obtain a regional water resource vulnerability grade;
step S103: and configuring different colors for different early warning levels according to the corresponding relation between the pre-water resource vulnerability levels and the early warning levels, and displaying the water resource vulnerability early warning levels in different areas by combining GIS data.
The key influence factor index data comprises water resource endowment index data, climate index data, underlying surface index data, human activity index data and water quality index data.
And screening the key influence factor index data from a set influence factor index data set by using a principal component analysis method.
The construction process of the fuzzy recognition model comprises the following steps:
expressing the difference between any sample and the h-level vulnerability characteristic value by using a weighted generalized Euclidean weight distance, and constructing a target function by combining a least square criterion;
and constructing a Lagrange function according to the target function, changing the equation constraint extremum solving into an unconditional extremum solving problem, and solving partial derivatives of relative membership vectors of each sample to the vulnerability grade h to obtain a fuzzy mode identification model.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the regional water resource vulnerability warning method as described above.
Example four
The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps in the regional water resource vulnerability warning method.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The utility model provides a regional water resource vulnerability early warning system which characterized in that includes:
the index data acquisition module is used for acquiring key influence factor index data of regional water resources;
the vulnerability classification module is used for utilizing a fuzzy recognition model to calculate to obtain a relative membership matrix of key influence factors as an initial clustering center matrix, constructing a fuzzy clustering dual iteration model, obtaining a clustering center after optimizing each key influence factor through iteration calculation to form an index threshold value matrix, and calculating a regional water resource vulnerability membership matrix based on the fuzzy recognition model so as to obtain a regional water resource vulnerability grade;
and the risk early warning display module is used for configuring different colors for different early warning levels according to the corresponding relation between the water resource vulnerability level and the early warning level in advance, and displaying the water resource vulnerability early warning levels in different areas by combining GIS data.
2. The regional water resource vulnerability warning system of claim 1, wherein the key impact factor index data includes water resource endowment index data, climate index data, underlying surface index data, human activity index data and water quality index data.
3. The regional water resource vulnerability warning system of claim 1, wherein the key impact factor index data is screened from a set impact factor index data set using principal component analysis.
4. The regional water resource vulnerability pre-warning system of claim 1, wherein in the vulnerability classification module, the construction process of the fuzzy recognition model is as follows:
expressing the difference between any sample and the h-level vulnerability characteristic value by using a weighted generalized Euclidean weight distance, and constructing a target function by combining a least square criterion;
and constructing a Lagrange function according to the target function, changing the equation constraint extremum solving into an unconditional extremum solving problem, and solving partial derivatives of relative membership vectors of each sample to the vulnerability grade h to obtain a fuzzy mode identification model.
5. A regional water resource vulnerability early warning method is characterized by comprising the following steps:
acquiring key influence factor index data of regional water resources;
calculating a relative membership matrix of the key influence factors by using a fuzzy recognition model to serve as an initial clustering center matrix, constructing a fuzzy clustering dual iteration model, performing iterative calculation to obtain a clustering center after optimization of each key influence factor to form an index threshold value matrix, and calculating a regional water resource vulnerability membership matrix based on the fuzzy recognition model to further obtain a regional water resource vulnerability grade;
and configuring different colors for different early warning levels according to the corresponding relation between the pre-water resource vulnerability levels and the early warning levels, and displaying the water resource vulnerability early warning levels in different areas by combining GIS data.
6. The regional water resource vulnerability warning method of claim 5, wherein the key impact factor index data includes water resource endowment index data, climate index data, underlying surface index data, human activity index data and water quality index data.
7. The regional water resource vulnerability warning method according to claim 5, wherein the key impact factor index data is obtained by screening from a set impact factor index data set using principal component analysis.
8. The regional water resource vulnerability warning method according to claim 5, wherein the construction process of the fuzzy recognition model is as follows:
expressing the difference between any sample and the h-level vulnerability characteristic value by using a weighted generalized Euclidean weight distance, and constructing a target function by combining a least square criterion;
and constructing a Lagrange function according to the target function, changing the equation constraint extremum solving into an unconditional extremum solving problem, and solving partial derivatives of relative membership vectors of each sample to the vulnerability grade h to obtain a fuzzy mode identification model.
9. A computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps in the regional water resource vulnerability warning method according to any of claims 5-8.
10. A computer apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the regional water resource vulnerability warning method according to any one of claims 5-8.
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