CN112700140A - River pollution key section quantitative identification method based on fuzzy comprehensive evaluation - Google Patents

River pollution key section quantitative identification method based on fuzzy comprehensive evaluation Download PDF

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CN112700140A
CN112700140A CN202011630782.9A CN202011630782A CN112700140A CN 112700140 A CN112700140 A CN 112700140A CN 202011630782 A CN202011630782 A CN 202011630782A CN 112700140 A CN112700140 A CN 112700140A
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田禹
李运东
孟一鸣
李俐频
刘伟岩
胡智超
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Harbin Institute of Technology
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Abstract

A river pollution key section quantitative identification method based on fuzzy comprehensive evaluation belongs to the field of river pollution source tracing intersection. The invention aims to solve the problem that the river treatment work precision is low because the pollution degree of the river cannot be judged at present. Dividing the upstream of the river into a plurality of control sections, arranging 1 monitoring section at the downstream of the river, and establishing an evaluation factor set for the plurality of control sections according to the plurality of control sections and the 1 monitoring section; dividing each evaluation factor into 5 evaluation grades; weighting each evaluation factor by adopting a Delphi method subjective weighting method to obtain the final weight of each evaluation factor, and constructing a weight vector according to the final weight of each evaluation factor; calculating the membership degree of each evaluation grade; and establishing a fuzzy relation matrix based on the membership degree according to the membership degree so as to obtain a final evaluation grade of each control section, and judging the pollution degree of each control section at the upstream to the monitoring section according to the final evaluation grade. It was used to assess the degree of upstream versus downstream contamination.

Description

River pollution key section quantitative identification method based on fuzzy comprehensive evaluation
Technical Field
The invention relates to a river pollution key section quantitative identification method based on fuzzy comprehensive evaluation, which comprehensively analyzes factors such as upstream river section pollutant input amount, section water quality current situation, distance from a downstream monitoring section, type of a region where the upstream river section pollutant is located and the like, quantifies contribution degree of accounting to pollution of the downstream monitoring section, and analyzes a river pollution control key section. The application belongs to the cross fields of fuzzy mathematical analysis, water environment pollution control, river pollution source tracing and the like.
Background
At present, ecological civilization construction is highly regarded, and river pollution treatment is mainly concerned. At present, the river pollution treatment generally adopts a 'cutting' mode, and lacks effective identification on a key section, so that the river pollution treatment work has certain blindness, the accurate pollution treatment is difficult to realize, and the river pollution treatment investment is large and the effect is poor.
The traditional river section evaluation method only considers two factors of pollutant input quantity and river water pollutant concentration, and the evaluation result is one-sided. In the actual section evaluation process, section pollution types, geographic positions and administrative division factors also have important influence on river pollution, and the indexes are difficult to quantitatively analyze and cannot be incorporated into a river key section evaluation index system. The fuzzy comprehensive evaluation is an objective method for converting qualitative indexes into quantitative evaluation, is suitable for fuzzy problem analysis which is difficult to quantify, and can play an important role in river key section identification.
Disclosure of Invention
The invention aims to solve the problem that the river treatment work precision is low because the river pollution degree cannot be judged at present. A river pollution key section quantitative identification method based on fuzzy comprehensive evaluation is provided.
A river pollution key section quantitative identification method based on fuzzy comprehensive evaluation comprises the following steps:
step 1, dividing the upstream of a river into a plurality of control sections, arranging 1 monitoring section at the downstream of the river, and establishing an evaluation factor set for the plurality of control sections according to the plurality of control sections and the 1 monitoring section;
step 2, dividing each evaluation factor in the evaluation factor set into 5 evaluation grades;
step 3, weighting each evaluation factor by adopting a Delphi method subjective weighting method, carrying out normalization processing after weighting to obtain the final weight of each evaluation factor, and constructing a weight vector according to the final weight of each evaluation factor;
step 4, calculating the membership degree of each evaluation grade in the step 2;
and 5, establishing a membership-based fuzzy relation matrix according to the membership degree of each evaluation grade in the step 4, multiplying the membership-based fuzzy relation matrix by a weight vector to obtain the membership degree of each evaluation factor of the plurality of control sections under 5 evaluation grades, carrying out normalization treatment on the membership degree of each evaluation factor of the plurality of control sections under 5 evaluation grades to obtain a final evaluation grade of each control section, and judging the pollution degree of each control section at the upstream to the monitoring section according to the final evaluation grade.
Preferably, in step 1, the evaluation factor set includes control of section water quality, pollutant input amount, section control and section monitoring distance, section pollution discharge amount, section water amount control, section area type control and section pollution type control.
Preferably, the evaluation factor set U is represented as:
U＝{X1,X2,X3,X4,X5,X6,X7the formula 1 is described in the specification,
in the formula, X1～X7Respectively representing the water quality of a control section, the input quantity of pollutants, the distance between the control section and a monitoring section, the sewage discharge quantity of the control section, the water quantity of the control section, the type of a control section area and the type of pollution of the control section;
controlling cross-section water quality X1Expressed as:
X1＝{X11,X12,X13,…X1nequation 2
In the formula, X1nIs equal to X1The water quality index in the river corresponds to n main pollution index input quantities in the river;
input of contaminants X2Expressed as:
X2＝{X21,X22,X23,…X2nthe formula (3) is described in the following formula,
in the formula, X2nIs equal to X2The water quality index in the river corresponds to n main pollution index input quantities in the river.
Preferably, in step 2, 5 evaluation levels are 5 equal points of the value between the minimum value and the maximum value of each evaluation factor.
Preferably, the number of control sections is 4.
Preferably, in step 2, the evaluation set V of 5 evaluation levels is represented as:
V＝{v1,v2,v3,v4,v5formula 4 (i, ii, iii, iv, v),
in the formula, v1,v2,v3,v4,v5Respectively representing the pollution contribution degree of the upstream control section to the downstream monitoring section, wherein the pollution contribution degree is respectively no contribution, almost no contribution, higher contribution and huge contribution.
Preferably, in step 3, the weight vector a is represented as:
A＝{a1,a2,a3,a4,a5,a6,a7the formula 4 is described in the following formula,
in the formula, a1,a2,a3,a4,a5,a6,a7The final weight of each evaluation factor is represented.
Preferably, in step 4, the membership degree of each evaluation level in step 2 is calculated, specifically:
calculating the membership degree of the water quality of the control section and the membership degree of the input quantity of pollutants by adopting a semi-trapezoid distribution diagram reducing method:
Figure BDA0002876368740000031
in the formula, ciActual detection value s representing evaluation index iij，si(j+1)Respectively represent the j, j + 1-th evaluation grade index standard values, r, corresponding to the ith indexijRepresenting the degree of membership of the ith evaluation factor to the jth evaluation level;
Figure BDA0002876368740000032
Figure BDA0002876368740000033
in the formula, si(j-1)The j-1 level evaluation grade index standard value corresponding to the ith index is shown,
calculating the distance from the control section to the monitoring section, the pollution discharge capacity of the control section and the membership degree of the water quantity of the control section by adopting a half-trapezoid distribution diagram method:
Figure BDA0002876368740000034
Figure BDA0002876368740000035
Figure BDA0002876368740000036
preferably, in step 5, the membership-based fuzzy relation matrix R is represented as:
Figure BDA0002876368740000041
multiplying the fuzzy relation matrix based on the membership by the weight vector to obtain the membership B of each evaluation factor of the plurality of control sections under 5 evaluation levels, wherein the membership B is expressed as:
Figure BDA0002876368740000042
wherein, B is Bj＝max(b1,b2,b3,b4,b5) And the final evaluation result of the pollution contribution level of the control section to the downstream monitoring section is j.
The invention has the beneficial effects that:
the method comprehensively considers the hydrological water quality information such as the water quality, the water quantity, the sewage discharge quantity and the pollution type of the upstream river reach and the humanistic geographic information such as the geographic position and the region type, measures the contribution degree of the upstream section to the downstream monitoring section, and guides the subsequent pollution source investigation and river subsection control. The method comprises the steps of taking a plurality of hydrological water quality and geographic information data as evaluation factors of the critical section of the river flow, combining expert professional knowledge, practical experience and practical situation, dividing the contribution of the upstream river control section to the pollution of the downstream monitoring section into five grades, weighting each factor by adopting a Delphi method, taking a semi-trapezoidal distribution function as a membership function of each factor, and realizing the positioning of the critical section of the upstream monitoring section of the monitoring section according to a large and small operator and a maximum membership principle. Effectively judge the pollution degree of the control section and effectively and quickly treat the river according to the pollution degree.
The application develops a river pollution key section quantitative identification method based on fuzzy comprehensive evaluation, which takes 7 major factors such as section pollutant input total amount, sewage input amount, section pollutant concentration, section river flow, section pollution type, section geographic position, administrative division and the like as a section pollution influence comprehensive evaluation standard, applies the fuzzy comprehensive evaluation method to carry out quantitative processing on each index, and adopts a Delphi (Delphi) weighting method to analyze the weight value of each influence factor to generate a river section pollution influence comprehensive evaluation value, thereby realizing the quantitative identification of the river pollution key section. The method systematically solves the difficulty of river treatment necessity evaluation, can guide rivers to treat pollution accurately and efficiently, greatly improves the environmental benefit of river treatment investment, effectively saves the treatment cost, and has important significance for guaranteeing the ecological safety of urban rivers.
The application develops a river pollution key section quantitative identification method based on fuzzy comprehensive evaluation, realizes systematic quantitative positioning of a river pollution control key section, solves the problem of current situation difficulty of effective evaluation means missing in river management necessity, and has the following characteristics:
(1) a systematic evaluation system is provided for the river pollution control key section, and quantitative evaluation of river reach treatment necessity is realized;
(2) the river pollution key section is identified and positioned based on the method, river pollution treatment work can be scientifically guided, pollution treatment priority of each river reach is determined, source control and pollution interception measures are implemented on key river reach by concentrated force, and pollution treatment investment cost is saved;
(3) the river pollution key section quantitative evaluation index system constructed by the method can be used as a quantitative analysis means for pollution control effects of all river reach, is brought into a national 'river growth system' performance assessment system, and promotes 'river growth system' perfection and popularization.
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FIG. 1 is a flow chart of a river pollution key section quantitative identification method based on fuzzy comprehensive evaluation;
FIG. 2 is a graph of membership function calculation using a decreasing half trapezoidal distribution plot;
FIG. 3 is a graph of membership function calculation by a half-trapezoidal distribution method.
Detailed Description
The first embodiment is as follows: the present embodiment is described with reference to fig. 1, and the method for quantitatively identifying a river pollution critical section based on fuzzy comprehensive evaluation according to the present embodiment includes the following steps:
step 1, dividing the upstream of a river into a plurality of control sections, arranging 1 monitoring section at the downstream of the river, and establishing an evaluation factor set for the plurality of control sections according to the plurality of control sections and the 1 monitoring section;
step 2, dividing each evaluation factor in the evaluation factor set into 5 evaluation grades;
step 3, weighting each evaluation factor by adopting a Delphi method subjective weighting method, carrying out normalization processing after weighting to obtain the final weight of each evaluation factor, and constructing a weight vector according to the final weight of each evaluation factor;
step 4, calculating the membership degree of each evaluation grade in the step 2;
and 5, establishing a membership-based fuzzy relation matrix according to the membership degree of each evaluation grade in the step 4, multiplying the membership-based fuzzy relation matrix by a weight vector to obtain the membership degree of each evaluation factor of the plurality of control sections under 5 evaluation grades, carrying out normalization treatment on the membership degree of each evaluation factor of the plurality of control sections under 5 evaluation grades to obtain a final evaluation grade of each control section, and judging the pollution degree of each control section at the upstream to the monitoring section according to the final evaluation grade.
The second embodiment is as follows: in step 1, the evaluation factor set includes control section water quality, pollutant input amount, control section and monitoring section distance, control section sewage discharge amount, control section water amount, control section area type and control section pollution type.
In this embodiment, the section water quality and the controlled section water amount represent the water quality and water amount of the river inputted to the section from the previous section, and the pollutant input amount and the controlled section sewage discharge amount represent the total pollutant amount and the total sewage discharge amount inputted to the section from the section to the next section.
The third concrete implementation mode: in the embodiment, the method for quantitatively identifying the river pollution critical section based on the fuzzy comprehensive evaluation is described in the second embodiment, and the evaluation factor set U is expressed as:
U＝{X1,X2,X3,X4,X5,X6,X7the formula 1 is described in the specification,
in the formula, X1～X7Respectively representing the water quality of a control section, the input quantity of pollutants, the distance between the control section and a monitoring section, the sewage discharge quantity of the control section, the water quantity of the control section, the type of a control section area and the type of pollution of the control section;
controlling cross-section water quality X1Expressed as:
X1＝{X11,X12,X13,…X1nequation 2
In the formula, X1nIs equal to X1The water quality index in the river corresponds to n main pollution index input quantities in the river;
input of contaminants X2Expressed as:
X2＝{X21,X22,X23,…X2nthe formula (3) is described in the following formula,
in the formula, X2nIs equal to X2The water quality index in the river corresponds to n main pollution index input quantities in the river. In the implementation mode, the conventional water quality indexes such as COD, ammonia nitrogen, total nitrogen and total phosphorus concentration, input quantity, cross section flow, discharge capacity and pollution type are used as evaluation factors, the diffusion, dilution and natural sedimentation effects of pollutants in the process of flowing along with a water body are considered, the influence of the geographical position of a river reach on the water body pollution treatment urgency is controlled, and two humanistic geographical factors of the distance from the control cross section to the monitoring cross section and the type of the area where the control cross section is located are added to comprehensively evaluate the pollution contribution degree of the upstream cross section to the downstream monitoring cross section.
The fourth concrete implementation mode: in the embodiment, 5 evaluation grades are obtained by 5 equal divisions of the value between the minimum value and the maximum value of each evaluation factor in the step 2.
The fifth concrete implementation mode: in the embodiment, the number of the cross sections is controlled to be 4 according to the river pollution key cross section quantitative identification method based on the fuzzy comprehensive evaluation.
The sixth specific implementation mode: in the embodiment, as for the method for quantitatively identifying the river pollution critical section based on the fuzzy comprehensive evaluation in the first embodiment, in the step 2, an evaluation set V of 5 evaluation grades is represented as:
V＝{v1,v2,v3,v4,v5formula 4 (i, ii, iii, iv, v),
in the formula, v1,v2,v3,v4,v5Respectively representing the pollution contribution degree of the upstream control section to the downstream monitoring section, wherein the pollution contribution degree is respectively no contribution, almost no contribution, higher contribution and huge contribution.
In the present embodiment, the evaluation set is a set of degrees of contribution to the upstream control section to the downstream monitoring section, and the result of the comprehensive evaluation of the upstream control section is used as a criterion for identifying the critical section. In the present application, the fuzzy concept with uncertainty of "contribution degree" is divided into five evaluation grades.
Wherein, the evaluation classification of the water quality of the cross section is consistent with the classification standard of the related water quality index in the environmental quality standard of surface water (GB 3838-2002); the evaluation of the pollutant input quantity is graded by five equal divisions of intervals between the minimum value and the maximum value of the pollutant input quantity in all investigated control sections; the evaluation of the distance between the control section and the monitoring section is realized by five equal divisions of the interval between the minimum distance and the maximum distance from the minimum distance to the monitoring section in all the investigated control sections; the evaluation of the discharge capacity of the control section is realized by five equal divisions of the interval between the minimum discharge capacity and the maximum discharge capacity in all the investigated control sections; the evaluation of the water quantity of the control section is realized by five equal divisions of the interval between the minimum water quantity and the maximum water quantity in all the investigated control sections; for the evaluation classification of the region types, identifying I-V grades of the influence degrees corresponding to the field, the village and the town, the suburb, the peripheral urban area and the central urban area respectively; the influence degree of the bottom mud pollution is set to be I grade, the influence degree of the non-point source pollution is set to be III grade, and the influence degree of the point source pollution is set to be V grade.
The seventh embodiment: in this embodiment, the method for quantitatively identifying a river pollution critical section based on fuzzy comprehensive evaluation according to the first embodiment is that in step 3, a weight vector a is expressed as:
A＝{a1,a2,a3,a4,a5,a6,a7the formula 4 is described in the following formula,
in the formula, a1,a2,a3,a4,a5,a6,a7The final weight of each evaluation factor is represented.
In the present embodiment, all the evaluation factors are given a certain weight to measure the magnitude of the influence weight of the factors on the overall comprehensive evaluation. The commonly used factor weighting methods include subjective weight, objective weight and combined weight. The weighting of the single-level water quality indexes can be calculated by adopting a superscript method in an objective method, the objective method lacks of subjective control and is greatly influenced by abnormal values, partial evaluation factors related to the invention do not have uniform measurement standards, the applicability of the objective method is not high, after the feasibility of various methods and the influence on the operation speed of the method are fully considered, the invention adopts a Delphi subjective weighting method, analyzes the concentration, dispersion and harmony of expert opinions, weights each evaluation factor by combining the specific opinions of the experts, and performs normalization processing to serve as the final weight of each evaluation factor.
The specific implementation mode is eight: in step 4, the membership degree of each evaluation grade in step 2 is calculated, specifically:
calculating the membership degree of the water quality of the control section and the membership degree of the input quantity of pollutants by adopting a semi-trapezoid distribution diagram reducing method:
Figure BDA0002876368740000081
in the formula, ciActual detection value s representing evaluation index iij，si(j+1)Respectively represent the j, j + 1-th evaluation grade index standard values, r, corresponding to the ith indexijRepresenting the degree of membership of the ith evaluation factor to the jth evaluation level;
Figure BDA0002876368740000082
Figure BDA0002876368740000083
in the formula, si(j-1)The j-1 level evaluation grade index standard value corresponding to the ith index is shown,
calculating the distance from the control section to the monitoring section, the pollution discharge capacity of the control section and the membership degree of the water quantity of the control section by adopting a half-trapezoid distribution diagram method:
Figure BDA0002876368740000084
Figure BDA0002876368740000085
Figure BDA0002876368740000086
in this embodiment, after the evaluation set is constructed, the membership degree of all the evaluation factors to each evaluation level can be measured by means of the quantized interval. Wherein, the cross section water quality and the pollution input amount are small distribution indexes, and the higher the value is, the higher the membership degree of the high influence evaluation grade is; the distance from the control section to the monitoring section, the discharge capacity of the control section and the water quantity of the control section are large distribution indexes, and the lower the value of the control section is, the higher the membership degree of the control section on the high-influence evaluation grade is. The five factors of the section water quality, the pollution input amount, the distance from the control section to the monitoring section, the section discharge amount control and the section water amount control have uncertainty on the division of five influence degrees, and after the deblurring treatment, the set argument domain is a real number, so that a fuzzy distribution method is adopted for description. Wherein, the section water quality and the pollution input quantity adopt a 'half-reduced trapezoid distribution diagram method' to calculate the membership function, the distance from the control section to the monitoring section, the section sewage discharge quantity and the section water quantity are controlled by adopting a 'half-increased trapezoid distribution diagram method' to calculate the membership function, and the two functions are respectively shown in figures 2 and 3. For two factors of the area type and the pollution type, the type division has strong certainty, the value of the membership degree is only two options of (0, 1), and the membership degree can be assigned according to the reality. The degree of membership is abbreviated as:
Figure BDA0002876368740000091
Figure BDA0002876368740000092
wherein, mu (x) represents the membership degree of a certain evaluation grade of the research index, x represents the actual value of the index, a represents the lower limit of the evaluation grade of the research index, and b represents the upper limit of the evaluation grade of the research index.
The specific implementation method nine: in the embodiment, the method for quantitatively identifying the river pollution critical section based on the fuzzy comprehensive evaluation is described in the specific embodiment eight, and in the step 5, a fuzzy relation matrix R based on the membership degree is represented as:
Figure BDA0002876368740000093
multiplying the fuzzy relation matrix based on the membership by the weight vector to obtain the membership B of each evaluation factor of the plurality of control sections under 5 evaluation levels, wherein the membership B is expressed as:
Figure BDA0002876368740000094
wherein, B is Bj＝max(b1,b2,b3,b4,b5) And the final evaluation result of the pollution contribution level of the control section to the downstream monitoring section is j.
In the embodiment, fuzzy operation is performed on the weight vector and the fuzzy relation matrix, operation is performed according to a method of multiplying and taking a maximum value, and according to a maximum membership rule, an evaluation grade with a maximum value in the fuzzy operation result vector is selected as a low level of influence degree corresponding to an evaluation object. If B ═ Bj＝max(b1,b2,b3,b4,b5) The contribution level of the evaluated control section to the contamination of the downstream monitoring section is j level. And performing the same fuzzy operation and comprehensive evaluation on all the control sections, wherein the key section which is required to be controlled emphatically is the section with the highest contribution level grade.
And (3) experimental verification:
the application is successfully applied to section-by-section analysis of pollution control of S rivers in Jinxi county of Jiangxi province, thirteen river sections and fourteen control sections are divided into an S river mainstream river section by using an edge village, a sewage drainage building and a bridge as references, a source is used as an initial section, a flush port is crossed as a downstream monitoring section, other sections are used as control sections, water quality and water quantity data of each section are collected, and pollution drainage data between the section and the next section and information such as a flush port crossing distance between the section and the next section, main pollution types, region planning types and the like are identified by applying the method, and the specific process is as follows:
(1) establishment of evaluation factor set
Seven factors such as section water quality of each control section of the S river, pollutant input amount, distance between the control section and the flush crossing monitoring section, sewage discharge amount between the control section and the next section, section water amount, area utilization type, main pollution type between the control section and the next section are used as evaluation factors to establish an evaluation factor set, and U is { X ═ X {1,X2,X3,X4,X5,X6,X7Expressing that the COD content, the ammonia nitrogen content, the total nitrogen content and the total phosphorus content are selected as main pollutant indexes, namely
X1＝{X11,X12,X13,X14{ COD content, ammonia nitrogen content, total phosphorus content };
X2＝{X21,X22,X23,X24and { COD input, ammonia nitrogen input, total nitrogen input and total phosphorus input }.
(2) Establishing an evaluation set
And dividing the contribution degree of the upstream control section to the pollution of the downstream monitoring section into five grades to form an evaluation set. V ═ V for evaluation set1,v2,v3,v4,v5The classification of the water quality indexes is consistent with classification standards of related water quality indexes in the environmental quality standard of surface water (GB 3838-.
TABLE 1 contribution ranking of region types and pollution types
Figure BDA0002876368740000111
(3) Constructing weight vectors
The method adopts a Delphi subjective weighting method, analyzes the concentration, dispersion and harmony of expert opinions, and combines the specific opinions of experts to score and weight each evaluation factor, wherein 0 score represents no contribution, and 10 score represents significant contribution. And performing normalization processing on the weighting result to be used as the final weight of each evaluation factor to form a weight vector. The results of the expert's weighting of the evaluation factors are shown in Table 2.
TABLE 2 results of weighting each evaluation factor by Delphi method
Figure BDA0002876368740000112
(4) Establishing a fuzzy relationship matrix
The water quality and pollution input of the cross section are small distribution indexes, and membership functions of the cross section are calculated by adopting a 'semi-trapezoidal distribution diagram reducing method', and the calculation formulas are shown as formulas 5, 6 and 7;
Figure BDA0002876368740000113
Figure BDA0002876368740000121
Figure BDA0002876368740000122
and calculating membership functions by adopting a 'half-rising trapezoidal distribution diagram method' for controlling the distance from the section to the monitored section, the sewage discharge capacity of the section and the water quantity of the section, wherein the calculation formulas are shown as formulas 8, 9 and 10. And assigning a value of 0 or a value of 1 to the membership degree of the area type and the pollution type according to the actual situation of the control section corresponding to the grade division mode in the table 1.
Figure BDA0002876368740000123
Figure BDA0002876368740000124
Figure BDA0002876368740000125
Obtaining a fuzzy relation matrix according to the calculation result of the membership function of each evaluation factor to each contribution grade, namely
Figure BDA0002876368740000126
(5) Fuzzy operation and comprehensive evaluation
Fuzzy operation is carried out on the weight vector and the fuzzy relation matrix by using a matrix multiplication method to get large value, namely
Figure BDA0002876368740000131
According to the maximum membership principle, the fuzzy operation result is subjected to a maximum value taking process, namely B is Bj＝max(b1,b2,b3,b4,b5) The contribution level of the evaluated control section to the contamination of the downstream monitoring section is considered to be j. The method is applied to fourteen control sections including a downstream monitoring section to carry out fuzzy comprehensive evaluation operation, so that the longitudinal comparison of final results is facilitated, normalization processing is carried out on the membership degree of each influence factor to each level, and the final comprehensive evaluation result is shown in table 4.
TABLE 4 comprehensive evaluation results of each section of river
Figure BDA0002876368740000132
According to the comprehensive evaluation result, the contribution evaluation grades of two sections in 14 monitored control sections are V-grade, and the contribution evaluation grades are respectively the sections at the bridge positions of the sewage plant and the XC; the contribution evaluation grade of one section is IV grade, and is the section in the south of XQ village; the contribution evaluation grade of one section is grade III, namely the section at the east of the HL bridge; the contribution evaluation grades of two sections are II grades, namely the sections at GB village and M east, and the contribution evaluation grades of the other sections are I grades. Therefore, the river reach near the sewage plant and the river reach near the XC bridge have serious pollution discharge phenomena, and the pollution sources of the two river reaches are mainly checked when river regulation is carried out in the later period so as to realize source control of pollution.

Claims (9)

1. The river pollution key section quantitative identification method based on fuzzy comprehensive evaluation is characterized by comprising the following steps of:
step 1, dividing the upstream of a river into a plurality of control sections, arranging 1 monitoring section at the downstream of the river, and establishing an evaluation factor set for the plurality of control sections according to the plurality of control sections and the 1 monitoring section;
step 2, dividing each evaluation factor in the evaluation factor set into 5 evaluation grades;
step 3, weighting each evaluation factor by adopting a Delphi method subjective weighting method, carrying out normalization processing after weighting to obtain the final weight of each evaluation factor, and constructing a weight vector according to the final weight of each evaluation factor;
step 4, calculating the membership degree of each evaluation grade in the step 2;
and 5, establishing a membership-based fuzzy relation matrix according to the membership degree of each evaluation grade in the step 4, multiplying the membership-based fuzzy relation matrix by a weight vector to obtain the membership degree of each evaluation factor of the plurality of control sections under 5 evaluation grades, normalizing the membership degree of each evaluation factor of the plurality of control sections under 5 evaluation grades to obtain the final evaluation grade of each control section, and judging the pollution degree of each control section at the upstream to the monitoring section according to the final evaluation grade.
2. The method for quantitatively identifying the river pollution critical section based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein in the step 1, the evaluation factor set comprises the control of section water quality, the input amount of pollutants, the distance between the control section and the monitoring section, the control of section sewage discharge, the control of section water quantity, the control of section area type and the control of section pollution type.
3. The method for quantitatively identifying the river pollution critical sections based on the fuzzy comprehensive evaluation as claimed in claim 2, wherein the evaluation factor set U is expressed as:
U＝{X1,X2,X3,X4,X5,X6,X7the formula 1 is described in the specification,
in the formula, X1～X7Respectively representing the water quality of a control section, the input quantity of pollutants, the distance between the control section and a monitoring section, the sewage discharge quantity of the control section, the water quantity of the control section, the type of a control section area and the type of pollution of the control section;
controlling cross-section water quality X1Expressed as:
X1＝{X11,X12,X13,…X1nequation 2
In the formula, X1nIs equal to X1The water quality index in the river corresponds to n main pollution index input quantities in the river;
input of contaminants X2Expressed as:
X2＝{X21,X22,X23,…X2nthe formula (3) is described in the following formula,
in the formula, X2nIs equal to X2The water quality index in the river corresponds to n main pollution index input quantities in the river.
4. The method for quantitatively identifying the river pollution critical sections based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein in the step 2, 5 evaluation grades are 5 equal points of the value from the minimum value to the maximum value of each evaluation factor.
5. The method for quantitatively identifying the river pollution key sections based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein the number of the control sections is 4.
6. The method for quantitatively identifying the river pollution critical sections based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein in the step 2, an evaluation set V with 5 evaluation levels is represented as:
V＝{v1,v2,v3,v4,v5formula 4 (i, ii, iii, iv, v),
in the formula, v1,v2,v3,v4,v5Respectively representing the pollution contribution degree of the upstream control section to the downstream monitoring section, wherein the pollution contribution degree is respectively no contribution, almost no contribution, higher contribution and huge contribution.
7. The method for quantitatively identifying the river pollution critical sections based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein in the step 3, the weight vector A is expressed as:
A＝{a1,a2,a3,a4,a5,a6,a7the formula 4 is described in the following formula,
in the formula, a1,a2,a3,a4,a5,a6,a7The final weight of each evaluation factor is represented.
8. The method for quantitatively identifying the river pollution critical section based on the fuzzy comprehensive evaluation as claimed in claim 7, wherein in the step 4, the membership degree of each evaluation grade in the step 2 is calculated, and specifically:
calculating the membership degree of the water quality of the control section and the membership degree of the input quantity of pollutants by adopting a semi-trapezoid distribution diagram reducing method:
Figure FDA0002876368730000021
in the formula, ciActual detection value s representing evaluation index iij，si(j+1)Respectively represent the j, j + 1-th evaluation grade index standard values, r, corresponding to the ith indexijRepresenting the degree of membership of the ith evaluation factor to the jth evaluation level;
Figure FDA0002876368730000022
Figure FDA0002876368730000031
in the formula, si(j-1)The j-1 level evaluation grade index standard value corresponding to the ith index is shown,
calculating the distance from the control section to the monitoring section, the pollution discharge capacity of the control section and the membership degree of the water quantity of the control section by adopting a half-trapezoid distribution diagram method:
Figure FDA0002876368730000032
Figure FDA0002876368730000033
Figure FDA0002876368730000034
9. the method for quantitatively identifying the river pollution critical section based on the fuzzy comprehensive evaluation as claimed in claim 8, wherein in the step 5, the fuzzy relation matrix R based on the membership is expressed as:
Figure FDA0002876368730000035
multiplying the fuzzy relation matrix based on the membership by the weight vector to obtain the membership B of each evaluation factor of the plurality of control sections under 5 evaluation levels, wherein the membership B is expressed as:
Figure FDA0002876368730000036
wherein, B is Bj＝max(b1,b2,b3,b4,b5) And the final evaluation result of the pollution contribution level of the control section to the downstream monitoring section is j grade.
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