CN113269396A - Water environment risk evaluation method and system based on multilevel grey correlation - Google Patents
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Abstract
The invention relates to the technical field of water environment risk evaluation, in particular to a water environment risk evaluation method and system based on multilevel grey correlation, wherein the method comprises the following steps: acquiring water quality data obtained by monitoring regional water environment, and extracting indexes contained in the water quality data; constructing an index system for regional water environment risk evaluation, and screening key indexes used in the index system from the water quality data, wherein the index system comprises key indexes, an element layer and a target layer; determining key factors for the index system by a factor analysis method, determining element layers of the index system based on the key factors, and determining the weight of each key factor in the element layers; the risk level of the regional water environment is determined by adopting a multi-level grey correlation risk evaluation method, the result of the water environment risk evaluation has good accuracy, and the method is small in calculated amount and more convenient and quicker.
Description
Technical Field
The invention relates to the technical field of water environment risk evaluation, in particular to a water environment risk evaluation method and system based on multilevel grey correlation.
Background
At present, a series of water quality standards and water pollution prevention laws and regulations are provided in China, but the development trend of water pollution is not effectively controlled. Serious organic pollution exists in most urban rivers, which causes the quality reduction of urban water sources and the increase of treatment cost, and seriously threatens the drinking water safety of urban residents and the health of people.
In order to prevent water pollution in advance, the water environment risk assessment is required, and the common water environment risk assessment methods comprise a graph superposition method, an information diffusion method, an index system method, fuzzy mathematics synthesis and the like,
however, when the traditional mathematical statistics method is adopted to evaluate the water environment risk, a large number of samples need to be collected, a characteristic index system can be established according to the pollution characteristics of water quality indexes of different water bodies, and the method is only suitable for typical distribution rules; when the water environment risk evaluation is carried out, the calculation amount is large and the efficiency is not high.
Disclosure of Invention
The invention aims to provide a water environment risk evaluation method and system based on multi-level ash association, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
In order to achieve the purpose, the invention provides the following technical scheme:
a water environment risk evaluation method based on multilevel grey correlation comprises the following steps:
s100, acquiring water quality data obtained by monitoring regional water environment, and extracting indexes contained in the water quality data;
s200, constructing an index system for regional water environment risk evaluation, and screening key indexes used in the index system from the water quality data, wherein the index system comprises key indexes, an element layer and a target layer;
step S300, determining key factors for the index system through a factor analysis method, determining an element layer of the index system based on the key factors, and determining the weight of each key factor in the element layer; the element layer comprises a plurality of key factors, and each key factor is determined according to a plurality of key indexes;
and S400, determining the risk level of the regional water environment by adopting a multilevel grey correlation risk evaluation method.
Further, the key indexes screened from the water quality data for the index system include:
and respectively carrying out single-factor evaluation on the water quality on the indexes contained in the water quality data, determining the weight of each index to the regional water environment risk according to the hazard degree of each index, and screening key indexes for regional water environment risk evaluation based on the weights.
Further, the step S400 includes:
s410, determining the weight of each key index by using an analytic hierarchy process, and constructing a hierarchical structure model of the index system according to the weight and the membership of each key index;
step S420, carrying out dimensionless processing on the key indexes of the index system to obtain dimensionless sequences;
step S430, a target sequence is obtained, the target sequence comprises standard indexes corresponding to each key index of the dimensionless sequence, and a correlation coefficient of each key index of the dimensionless sequence relative to the corresponding standard index in the target sequence is determined;
step S440, carrying out weighted layer-by-layer association degree calculation on the obtained association coefficients in sequence to obtain association vectors;
and S450, determining grey membership of each level of key indexes and standard indexes under a grading standard, determining a grey comprehensive index of environment risk evaluation based on the grey membership, and determining the risk level of the regional water environment according to the grey comprehensive index.
Further, the calculation formula of the dimensionless sequence is:
Yj(k)＝(xj(k)-Tmax)/(Tmax-Tmin)；
wherein: x is the number ofj(k) Is the sequence value of the key index,
j is a key index, k is a key index, and the dimensionless sequence is Yj(k)。
Further, the correlation coefficient calculation formula of the jth key index relative to the standard index is as follows:
wherein: zetaj(k) I.e. the correlation coefficient of the jth key index relative to the standard index, wherein rho is a resolution coefficient and delta ist(j) To represent the absolute difference between the target sequence and the dimensionless sequence,
represents the minimum value of absolute difference values obtained by each standard index in the target sequence and the corresponding key index in the dimensionless sequence,
and expressing the maximum value of absolute differences obtained by each standard index in the target sequence and the corresponding key index in the dimensionless sequence.
Further, the step S450 includes:
step S451, weighting the association vector by using the grey membership to obtain the difference degree between a key index and a standard index;
and S452, constructing a standard vector of the risk level based on the difference degree, and obtaining a gray comprehensive index for risk evaluation of the regional water environment based on the standard vector and the gray membership degree.
A computer-readable storage medium, wherein a multi-level grey correlation-based water environment risk assessment program is stored on the computer-readable storage medium, and when being executed by a processor, the multi-level grey correlation-based water environment risk assessment program realizes the steps of the multi-level grey correlation-based water environment risk assessment method according to any one of the above items.
A water environment risk assessment system based on multi-level grey correlation, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to implement any one of the methods for evaluating the risk of the aquatic environment based on the multi-level grey scale association.
The invention has the beneficial effects that: the invention provides a water environment risk evaluation method based on multilevel gray correlation. Compared with the prior art, the method can establish a characteristic index system according to the pollution characteristics of water quality indexes of different water bodies; the method has the advantages of small requirement on the sample size, no need of a typical distribution rule, good accuracy, small calculation amount and convenience and rapidness.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described 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 without inventive exercise.
FIG. 1 is a schematic flow chart of a water environment risk evaluation method based on multi-level grey correlation in an embodiment of the invention;
fig. 2 is a schematic overall structure diagram of a water environment risk evaluation step in the embodiment of the invention.
Detailed Description
The conception, specific structure and technical effects of the present application will be described clearly and completely with reference to the following embodiments and the accompanying drawings, so that the purpose, scheme and effects of the present application can be fully understood. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic flow chart of a water environment risk evaluation method based on multi-level grey correlation, which is provided by the present application, and the method includes the following steps:
s100, acquiring water quality data obtained by monitoring regional water environment, and extracting indexes contained in the water quality data;
s200, constructing an index system for regional water environment risk evaluation, and screening key indexes used in the index system from the water quality data, wherein the index system comprises key indexes, an element layer and a target layer;
step S300, determining key factors for the index system through a factor analysis method, determining an element layer of the index system based on the key factors, and determining the weight of each key factor in the element layer;
the element layer comprises a plurality of key factors, and each key factor is determined according to a plurality of key indexes; the key factors include: dissolved oxygen, ammonia nitrogen, total phosphorus and cadmium.
And S400, determining the risk level of the regional water environment by adopting a multilevel grey correlation risk evaluation method.
In the embodiment provided by the invention, an index system is constructed by combining a big data method, and a risk evaluation technical method for the water environment water ecology is carried out by adopting a multi-level grey correlation risk evaluation method. Because the correlation analysis is used for analyzing the development trend, the correlation analysis has no high requirement on the sample size, does not need a typical distribution rule, can establish a characteristic index system according to the pollution characteristics of water quality indexes of different water bodies, has small requirement on the sample size, and does not need the typical distribution rule; the result obtained by adopting the embodiment of the invention is matched with qualitative analysis, the accuracy is good, the calculated amount is small, the method is more convenient and faster, and the method is also very suitable for dynamic process analysis. Meanwhile, by processing the random sequence data, the degree of association of each internal factor to the system is determined, and main factors influencing the system are found out from incomplete information, so that the related factors are developed or limited.
The invention applies the multilevel gray correlation to risk evaluation, and overcomes the defects caused by adopting a mathematical statistical method for system analysis. As a further improvement of the above embodiment, the screening of the water quality data for the key indexes of the index system includes:
respectively carrying out water quality single-factor evaluation on indexes contained in the water quality data, determining the weight (influence degree) of each index on the regional water environment risk according to the hazard degree of each index, and screening key indexes for regional water environment risk evaluation based on the weight;
wherein the key indicators include: permanganate index, ammonia nitrogen, dissolved oxygen, total phosphorus, conductivity, PH, water temperature, lead, cadmium, zinc, nickel and copper.
As a further improvement of the above embodiment, the step S400 includes:
s410, determining the weight of each key index by using an analytic hierarchy process, and constructing a hierarchical structure model of the index system according to the weight and the membership of each key index;
in this embodiment, the hierarchical structure of the target layer, the element layer, and the index layer in the index system is determined by constructing a hierarchical structure model of the index system; the analytic hierarchy process is that the indexes of the same level are compared pairwise according to the hierarchical structure relationship of an index system, a comparison matrix is established and the ordering index of each element is calculated after each element is compared pairwise by adopting a three-scale method; then, converting the comparison matrix into a judgment matrix through transformation in a second stage; and finally, carrying out consistency check on the calculation result, ensuring that the critical ratio is less than 0.1, and judging that the matrix has better transitivity and consistency.
In order to ensure the accuracy of grey correlation calculation, the monitored water quality data is subjected to dimensionless treatment.
Step S420, carrying out dimensionless processing on the key indexes of the index system to obtain dimensionless sequences;
specifically, the following formula is adopted to perform non-dimensionalization processing on the key indexes of the index system:
Yj(k)＝(xj(k)-Tmax)/(Tmax-Tmin)；
wherein: x is the number ofj(k) Is the sequence value of the key index,
xj(k) the value of the kth key index in the jth risk standard is shown, and the dimensionless sequence is Yj(k)。
Step S430, a target sequence is obtained, the target sequence comprises standard indexes corresponding to each key index of the dimensionless sequence, and a correlation coefficient of each key index of the dimensionless sequence relative to the corresponding standard index in the target sequence is determined;
wherein, the correlation coefficient represents the relative difference between the dimensionless sequence and the target sequence, and the difference can be used as a measure of the degree of correlation. The calculation formula of the correlation coefficient of the jth key index relative to the standard index is as follows:
wherein: zetaj(k) I.e. the correlation coefficient of the jth key index relative to the standard index, wherein rho is a resolution coefficient and delta ist(j) To represent the absolute difference between the target sequence and the dimensionless sequence,
represents the minimum value of absolute difference values obtained by each standard index in the target sequence and the corresponding key index in the dimensionless sequence,
and expressing the maximum value of absolute differences obtained by each standard index in the target sequence and the corresponding key index in the dimensionless sequence.
Step S440, carrying out weighted layer-by-layer association degree calculation on the obtained association coefficients in sequence to obtain association vectors;
on the basis of considering the importance of each layer to the previous layer of corresponding indexes in the index system, carrying out weighted layer-by-layer association degree calculation on the association coefficient to obtain rjThe calculation formula is as follows:
wherein, w(l)(k) For weight vectors determined by the square root method, rjAnd (3) representing the standard relevance of the j-th risk, wherein the relevance vector is represented as: r ═ R (R)1,r2,...r4,rk)。
And S450, determining a gray comprehensive index of each layer of key indexes and standard indexes in the index system, and determining the risk level of the regional water environment according to the gray comprehensive index.
In the embodiment, the risk level of the regional water environment is evaluated by evaluating the gray comprehensive index.
In one embodiment, the grading standard of the key indexes of the regional water environment risk is determined through the 'surface water environment quality standard'; and classifying the classification standard into a high risk area, a medium risk area, a low risk area and a no risk area according to the risk from high to low, and determining the upper and lower limit values of the water quality indexes of the high risk area, the medium risk area, the low risk area and the no risk area.
As a further improvement of the above embodiment, the step S450 includes:
step S451, weighting the association vector by using the grey membership to obtain the difference degree between a key index and a standard index;
in this embodiment, the gray degree of membership (u) between the monitored data and the standards of each stage is usedjt) Weighting the correlation difference degree as weight, and representing the difference degree (d (r) between the key index and the standard index by the obtained resultj))，
The calculation formula of the difference degree is as follows:
d(rj)＝ujt(1-rj)；
step S452, constructing a standard vector of a risk level based on the difference degree, and obtaining a grey comprehensive index for risk evaluation of the regional water environment based on the standard vector and the grey membership degree;
the calculation formula of the standard vector is as follows:
ST＝(1,2,3,..c)；
wherein c represents the number of standard levels;
the calculation formula of the gray comprehensive index is as follows:
G(j)＝ujt×St(ii) a Wherein G (j) is the gray comprehensive index.
In the embodiment provided by the invention, in order to make the evaluation result more accurate and more continuous, the correlation difference degree is introduced so as to reflect the difference degree between the monitoring data and the standards of each level, and the smaller the numerical value is, the more similar the sample is to the standard. And then representing the difference degree between the environment monitoring data and the standard by using the weighted correlation difference degree taking the gray degree of membership between the monitoring data and each level of standard as the weight, constructing a target function, and further constructing a water quality standard level vector after solving the gray degree of membership of the environment to water to obtain a gray comprehensive index of environment evaluation. And evaluating the regional water environment risk of the river according to the grey comprehensive index.
The following is a specific embodiment of the present invention:
firstly, collecting historical data of water quality data of 2017 years of water diversion in Xijiang river, Shimen, Zengcheng Shibeach and Baiyun river mouth monitoring sections in Guangzhou city.
And then, performing single-factor evaluation on the water quality, wherein the main factors are dissolved oxygen, ammonia nitrogen, total phosphorus and cadmium, and the main monitored water quality indexes are conductivity, PH, permanganate index, ammonia nitrogen, dissolved oxygen, total phosphorus, water temperature, lead, cadmium, zinc, nickel and copper.
The key factors of the water diversion of the Xijiang river, the Shimen, the Zengcheng rock beach and the Baiyun river mouth are determined to be shown in the following table through factor analysis, the factor scores of the element layers and the factor scores of the element layers are obtained, and the first main factor is nutrient salt, the second main factor is heavy metal and the third main factor is basic element. And determining the regional water environment risk key index grading standard of the river through the surface water environment quality standard to construct a risk key index system. And dividing the water quality index into a high risk area, a medium risk area, a low risk area and a no risk area according to the risk from high to low, and determining the water quality index limit values of the high risk area, the medium risk area, the low risk area and the no risk area.
Determining importance degree between factors by using water quality evaluation result and three-scale method, and determining weight vector w by using square root method(l)(k) The element layers for obtaining the main water environment risk evaluation are nutrient salt, basic elements and heavy metal, the higher weight is mainly factor 1 nutrient salt weight of 0.61, and the second weight is factor 3 heavy metal weight of 0.25. The following table was obtained by verifying that the critical ratio was less than 0.1.
Carrying out dimensionless processing on the original data of the monitoring sections of the watery river, the Shimen, the Zengcheng stone beach and the Baiyun estuary in 2017 of Guangzhou city. Calculating the correlation coefficient of each index of the monitoring section of the West river in 2017, namely the indexes corresponding to the high risk area, the medium risk area, the low risk area and the no risk area, performing weighted layer-by-layer correlation calculation on the correlation coefficient on the basis of considering the importance of each layer to the corresponding index of the previous layer to obtain the risk correlation of the water quality index of each monitoring station, and analyzing the main correlation indexes of each station as permanganate index, ammonia nitrogen, dissolved oxygen and total phosphorus.
After the grey degree of membership of the environment to water is obtained, a water quality standard level vector is further constructed to obtain a grey comprehensive index of environment evaluation, and the risk levels of the water diversion of the Xijiang river, the Shimen, the Zengcheng stone beach and the Baiyun river mouth can be analyzed according to the grey comprehensive index results of all stations of the water diversion of the Xijiang river, the Shimen, the Zengcheng stone beach and the Baiyun river mouth.
Corresponding to the method of fig. 1, an embodiment of the present invention further provides a computer-readable storage medium, where a water environment risk evaluation program based on multi-level grey correlation is stored on the computer-readable storage medium, and when the water environment risk evaluation program based on multi-level grey correlation is executed by a processor, the steps of the water environment risk evaluation method based on multi-level grey correlation according to any one of the above embodiments are implemented.
Corresponding to the method in fig. 1, an embodiment of the present invention further provides a water environment risk evaluation system based on multi-level grey correlation, where the system includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the method for evaluating the risk of the aquatic environment based on the multi-level grey correlation according to any one of the embodiments.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general-purpose processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the multi-level grey correlation-based water environment risk evaluation system, and various interfaces and lines are utilized to connect various parts of the whole multi-level grey correlation-based water environment risk evaluation system operational device.
The memory can be used for storing the computer programs and/or modules, and the processor can realize various functions of the water environment risk evaluation system based on the multi-level grey correlation by operating or executing the computer programs and/or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a Flash-memory Card (Flash-Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the description of the present application has been made in considerable detail and with particular reference to a few illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed that the present application effectively covers the intended scope of the application by reference to the appended claims, which are interpreted in view of the broad potential of the prior art. Further, the foregoing describes the present application in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial changes from the present application, not presently foreseen, may nonetheless represent equivalents thereto.
Claims (8)
1. A water environment risk evaluation method based on multilevel grey correlation is characterized by comprising the following steps:
s100, acquiring water quality data obtained by monitoring regional water environment, and extracting indexes contained in the water quality data;
s200, constructing an index system for regional water environment risk evaluation, and screening key indexes used in the index system from the water quality data, wherein the index system comprises key indexes, an element layer and a target layer;
step S300, determining key factors for the index system through a factor analysis method, determining an element layer of the index system based on the key factors, and determining the weight of each key factor in the element layer; the element layer comprises a plurality of key factors, and each key factor is determined according to a plurality of key indexes;
and S400, determining the risk level of the regional water environment by adopting a multilevel grey correlation risk evaluation method.
2. The method for evaluating the risk of the water environment based on the multi-level grey correlation according to claim 1, wherein the step of screening the key indexes used for the index system from the water quality data comprises the following steps:
and respectively carrying out single-factor evaluation on the water quality on the indexes contained in the water quality data, determining the weight of each index to the regional water environment risk according to the hazard degree of each index, and screening key indexes for regional water environment risk evaluation based on the weights.
3. The method for evaluating the risk of the water environment based on the multi-level gray correlation as claimed in claim 2, wherein the step S400 comprises:
s410, determining the weight of each key index by using an analytic hierarchy process, and constructing a hierarchical structure model of the index system according to the weight and the membership of each key index;
step S420, carrying out dimensionless processing on the key indexes of the index system to obtain dimensionless sequences;
step S430, a target sequence is obtained, the target sequence comprises standard indexes corresponding to each key index of the dimensionless sequence, and a correlation coefficient of each key index of the dimensionless sequence relative to the corresponding standard index in the target sequence is determined;
step S440, carrying out weighted layer-by-layer association degree calculation on the obtained association coefficients in sequence to obtain association vectors;
and S450, determining grey membership of each level of key indexes and standard indexes under a grading standard, determining a grey comprehensive index of environment risk evaluation based on the grey membership, and determining the risk level of the regional water environment according to the grey comprehensive index.
4. The method for evaluating the risk of the water environment based on the multilevel gray correlation as recited in claim 3, wherein the non-dimensional sequence has a calculation formula:
Yj(k)＝(xj(k)-Tmax)/(Tmax-Tmin)；
wherein: x is the number ofj(k) Is the sequence value of the key index,
j is a key index, k is a key index, and the dimensionless sequence is Yj(k)。
5. The method for evaluating the risk of the water environment based on the multilevel gray correlation as recited in claim 4, wherein the calculation formula of the correlation coefficient of the jth key index relative to the standard index is as follows:
wherein: zetaj(k) I.e. the correlation coefficient of the jth key index relative to the standard index, wherein rho is a resolution coefficient and delta ist(j) To represent the absolute difference between the target sequence and the dimensionless sequence,
represents the minimum value of absolute difference values obtained by each standard index in the target sequence and the corresponding key index in the dimensionless sequence,
and expressing the maximum value of absolute differences obtained by each standard index in the target sequence and the corresponding key index in the dimensionless sequence.
6. The method for evaluating the risk of the water environment according to claim 5, wherein the step S450 comprises:
step S451, weighting the association vector by using the grey membership to obtain the difference degree between a key index and a standard index;
and S452, constructing a standard vector of the risk level based on the difference degree, and obtaining a gray comprehensive index for risk evaluation of the regional water environment based on the standard vector and the gray membership degree.
7. A computer-readable storage medium, wherein the computer-readable storage medium stores thereon a multi-level grey correlation-based water environment risk assessment program, and when the multi-level grey correlation-based water environment risk assessment program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.
8. A water environment risk evaluation system based on multi-level grey correlation is characterized by comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, the at least one program causes the at least one processor to implement the method for assessing a risk of an aquatic environment based on a multi-level grey scale association according to any one of claims 1 to 6.
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