CN111582620A - Water environment bearing capacity data processing method - Google Patents

Water environment bearing capacity data processing method Download PDF

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CN111582620A
CN111582620A CN202010091477.0A CN202010091477A CN111582620A CN 111582620 A CN111582620 A CN 111582620A CN 202010091477 A CN202010091477 A CN 202010091477A CN 111582620 A CN111582620 A CN 111582620A
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bearing capacity
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刘桂中
凌文翠
孙长虹
陈大地
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Beijing Municipal Research Institute of Environmental Protection
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Abstract

The embodiment of the invention provides a water environment bearing capacity data processing method, which comprises the following steps: step 1, carrying out normalization processing on various indexes of bearing capacity of a water environment; step 2, determining the weight of each index of the bearing capacity of the water environment; step 3, generating an evaluation model according to each index of the determined water environment bearing capacity and the corresponding weight value of the index; and 4, checking the bearing capacity of the water environment according to the standard exceeding index of the concentration of the water pollutants. The technical scheme of the invention provides a water environment bearing capacity data processing method, which can more accurately evaluate the water environment bearing capacity and prevent the human society from causing irreversible damage to the natural environment.

Description

Water environment bearing capacity data processing method
Technical Field
The invention relates to the technical field of data processing, in particular to a water environment bearing capacity data processing method.
Background
Due to the high population aggregation, the rapid economic development, the limited resource environment bearing capacity and the special geographical conditions, the severe situations of resource constraint trend and ecosystem degradation are faced. Particularly in the aspect of water environment, water resources are deficient, although the discharge amount of water pollutants is in a descending trend year by year in recent years, compared with the water environment capacity, the discharge amount of pollutants in partial areas exceeds the maximum allowable discharge amount of the water environment, and the water quality of many rivers exceeds the standard because the discharge amount of pollutants far exceeds the environment capacity. In order to enable the social environment to be continuously developed, the water environment needs to be controlled and managed finely, and the current situation of extensive management is broken away; therefore, a technology for accurately analyzing and processing various data of the bearing capacity of the water environment is urgently needed.
Disclosure of Invention
The invention provides a water environment bearing capacity data processing method, which is used for carrying out fine analysis and processing on data of various indexes of water environment bearing capacity.
In order to solve the technical problems, the invention provides a water environment bearing capacity data processing method, and solves the problem that the water environment bearing capacity judgment result is inaccurate due to extensive data processing.
In order to solve the above problems, the technical problem to be solved by the present invention is to provide a water environment bearing capacity data processing method, comprising:
step 1, carrying out normalization processing on various indexes of bearing capacity of a water environment;
step 2, determining the weight of each index of the bearing capacity of the water environment;
step 3, generating an evaluation model according to each index of the determined water environment bearing capacity and the corresponding weight value of the index;
and 4, checking the bearing capacity of the water environment according to the standard exceeding index of the concentration of the water pollutants.
In some embodiments, the step 1 comprises:
processing a positive index processing formula, wherein the larger the value of the positive index is, the larger the bearing capacity of the water environment is; the reverse index processing formula is used for processing, and the larger the value of the reverse index is, the smaller the bearing capacity of the water environment is;
the processing formula for the forward index is as follows:
Figure RE-GDA0002507408060000021
the processing formula for the reverse index is as follows:
Figure RE-GDA0002507408060000022
in the formula, VjFor normalized index value, Vj≤1；bjThe actual value of the j index; b isjmaxThe upper limit value of the standard value of the interval corresponding to the j-th index actual value; b isjminThe lower limit value of the standard value of the interval corresponding to the j-th index actual value; qjmaxThe upper limit value of the loading degree corresponding to the j index actual value is set; qjminThe lower limit value of the corresponding load degree of the j index actual value is set.
In some embodiments, the step 2 comprises:
step 21, establishing a hierarchical structure model: decomposing elements influencing the water environment into a plurality of layers from top to bottom according to attributes, wherein the elements in each layer belong to or influence the elements in the upper layer; the top layer of the hierarchical mechanism model is a target layer, and the bottom layer of the hierarchical mechanism model is an index layer;
step 22, constructing a pairwise comparison judgment matrix: determining the membership between elements of different layers in the hierarchical structure model, and comparing the elements pairwise according to the membership; comparing every two elements in the same layer; determining the relative importance among the elements and generating a judgment matrix B, which is marked as (B)ij)n×n：
Figure RE-GDA0002507408060000023
Step 23, for the constructed judgment matrix B, finding the eigenvector corresponding to the maximum eigenvalue includes:
computing per-column normalization
Figure RE-GDA0002507408060000031
Averaging canonical columns
Figure RE-GDA0002507408060000032
Vector W ═ W1,w2,…,wn) Where W is the feature vector sought;
calculating the maximum eigenvalue of the judgment matrix B
Figure RE-GDA0002507408060000033
In the formula (BW)iIs the ith element in the vector BW;
determining the maximum characteristic root lambda of the judgment matrixmaxAnd its corresponding eigenvector W; carrying out consistency check on the judgment matrix according to the consistency ratio CR, and if the judgment matrix has satisfactory consistency, normalizing the eigenvector W to be used as a single-ordering weight vector; otherwise, correcting the scale of the judgment matrix;
judging whether the matrix has satisfactory consistency by using a random consistency ratio CR, and if CR is generally considered to be less than 0.1, judging that the matrix has satisfactory consistency; the formula for CR is as follows:
Figure RE-GDA0002507408060000034
Figure RE-GDA0002507408060000035
in the formula, CR is a random consistency ratio; CI is a consistency index; RI is the average random consistency index, which can be found in Table 3; lambda [ alpha ]maxJudging the maximum eigenvalue of the matrix; n is the order of the decision matrix.
In some embodiments, the step 3 comprises:
obtaining a comprehensive index evaluation model S representing the relative size of the water environment bearing capacity of the region by adopting a weighted summation methodWECC：
Figure RE-GDA0002507408060000036
In the formula, SWECCThe comprehensive evaluation index of the bearing capacity of the water environment is obtained; siThe fraction value of the ith index in the index layer is obtained; omegaiThe weight of the ith index in the index layer; m is the number of indexes.
In some embodiments, the method step 4 comprises: determining the over-standard index of the water pollutant concentration:
when i ═ 1, i.e. the water contaminant is DO:
Rwater ijk＝1/(Cijk/Sik)-1
When i is 2, …,6, i.e. the water pollutant is COD respectivelyMn、BOD5、CODCr、NH3N, TP time:
Rwater ijk＝Cijk/Sik-1
Figure RE-GDA0002507408060000041
RWater jk＝max(RWater ijk)，i＝1,2,…,6
Figure RE-GDA0002507408060000042
Wherein R isWater ijkI term water pollutant concentration standard exceeding index R of k section of area jWater ijIs the i-th water pollutant concentration standard exceeding index, R, of the area jWater jkIs the water pollutant concentration standard exceeding index, R, of the kth section of the area jWater jIs the region j water pollutant concentration standard exceeding index, CijkThe annual average concentration monitoring value S of the ith water pollutant of the kth section of the area jikIs the water quality standard limit of the ith water pollutant of the kth section. i is 1,2, …,6, corresponding to DO and COD respectivelyMn、BOD5、CODCr、NH3-N, TP; k is a control section, k is 1,2, …, Nj，NjThe number of control sections in the region j is shown.
When the pollutant concentration standard exceeding index is larger than 0, the water pollutant concentration is in a standard exceeding state; when the pollutant concentration standard exceeding index is between-0.2 and 0, the pollutant concentration is in a state close to standard exceeding; when the pollutant concentration overproof index is less than-0.2, the water pollutant concentration is in an overproof state.
In some embodiments, the method further comprises:
according to the bearing capacity S of the water environmentWECCDetermining the bearing capacity of the regional water environment in the value range;
wherein the bearing capacity S of the water environmentWECCWhen the value range of (1) is 0-0.25, the bearing capacity is low; bearing capacity S of water environmentWECCWhen the value range of (1) is 0.25-0.5, the bearing capacity is middle; bearing capacity S of water environmentWECCWhen the value range of (1) is 0.5-0.75, the bearing capacity is high; bearing capacity S of water environmentWECCWhen the value range of (A) is 0.75-1, the bearing capacity is extremely high.
In some embodiments, the method further comprises:
according to the determined water pollutant concentration standard exceeding index calculation result, the bearing capacity S of the water environmentWECCCorrecting; the method specifically comprises the following steps:
when the pollutant concentration standard exceeding index is larger than 0, the water pollutant concentration is in a standard exceeding state, and the bearing capacity S of the water environmentWECCShould be less than 0.5; when the pollutant concentration standard exceeding index is less than-0.2, the water pollutant concentration is in a state of not standard exceeding, and the bearing capacity S of the water environmentWECCShould be greater than 0.75.
The scheme of the invention at least comprises the following beneficial effects:
the technical scheme of the invention provides a water environment bearing capacity data processing method, which can more accurately evaluate the water environment bearing capacity and prevent the human society from causing irreversible damage to the natural environment.
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Fig. 1 is a schematic flow chart of a water environment bearing capacity data processing method in an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention provides a method for processing bearing capacity data of a water environment, including:
index normalization processing: in order to overcome the influence of different dimensions and magnitude of the evaluation indexes on the evaluation result, the evaluation indexes need to be normalized. Processing the forward index by using the formula (1), wherein the larger the value of the forward index is, the larger the bearing capacity of the water environment is; the reverse index is processed by the formula (2), and the larger the value of the reverse index is, the smaller the bearing capacity of the water environment is.
For the forward indicator:
Figure RE-GDA0002507408060000051
for the reverse indicator:
Figure RE-GDA0002507408060000052
in the formula, VjFor normalized index value, Vj≤1；bjThe actual value of the j index; b isjmaxThe upper limit value of the standard value of the interval corresponding to the j-th index actual value; b isjminThe lower limit value of the standard value of the interval corresponding to the j-th index actual value; qjmaxThe upper limit value of the loading degree corresponding to the j index actual value is set; qjminThe lower limit value of the corresponding load degree of the j index actual value is set.
Weight determination: because various influence factors of the bearing capacity of the water environment are mutually connected and restricted, the ambiguity and the uncertainty are very large, and therefore an analytic hierarchy process is required to be selected to determine the weight of each index; the method specifically comprises the following steps:
(1) building a hierarchical model
On the basis of in-depth analysis of actual problems, relevant factors are decomposed into a plurality of layers from top to bottom according to different attributes, and the factors of the same layer depend on or have influence on the factors of the previous layer. The top layer is the target layer, usually only 1 factor, and the second is the index layer.
(2) Construct pairwise comparison and judgment matrix
An important characteristic of the analytic hierarchy process is that the corresponding importance degree grades of two indexes are expressed by the ratio of two importance degrees. The hierarchical structure model determines the membership between the upper and lower elements, and for each element in the same layer, the elements connected with the adjacent upper layer are compared pairwise, and the relative importance or the relative quality degree is determined by using a 1-9 scale scoring method (see table 1). If the matrix B is judged to be (B)ij) n × n, then:
Figure RE-GDA0002507408060000061
TABLE 1 Scale of relative importance and significance
Scale Definition of
1 Of equal importance when compared to two elements
3 Two elements being compared, one being slightly more important than the other
5 Two elements being compared, one being more important than the other
7 One being more important than the other when comparing two elements
9 Two elements being compared, one being absolutely more important than the other
2，4，6，8 Median value of the above two adjacent judgments
1/bi，j The inverse ratio of two elements
(3) Determining relative weight of each element from the decision matrix
For the constructed judgment matrix, the eigenvector corresponding to the maximum eigenvalue can be obtained, and the calculation can be performed by adopting a standard column average method (sum-product method).
The calculation steps are as follows:
computing per-column normalization
Figure RE-GDA0002507408060000071
Averaging canonical columns
Figure RE-GDA0002507408060000072
Vector W ═ W1,w2,…,wn) Where T is the feature vector sought.
Calculating the maximum eigenvalue of the judgment matrix B
Figure RE-GDA0002507408060000073
In the formula (BW)iIs the ith in the vector BWAnd (4) elements.
Obtaining the maximum characteristic root lambda of each judgment matrix by using a sum-product methodmaxAnd its corresponding feature vector W. And (4) carrying out consistency check on the judgment matrix according to the consistency ratio CR, and if the judgment matrix has satisfactory consistency, normalizing the eigenvector W to be used as a single-ordering weight vector. Otherwise, the scale of the judgment matrix needs to be properly corrected.
And judging whether the matrix has satisfactory consistency by using a random consistency ratio CR, wherein the matrix is judged to have satisfactory consistency if CR is generally considered to be less than 0.1.
The formula for CR is as follows:
Figure RE-GDA0002507408060000074
Figure RE-GDA0002507408060000075
in the formula, CR is a random consistency ratio; CI is a consistency index; RI is the average random consistency index, which can be found in Table 2; λ max is the maximum eigenvalue of the judgment matrix; n is the order of the decision matrix.
TABLE 2 average random consistency index RI standard value
n 1 2 3 4 5 6 7 8 9 10 11 12
RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.48
An evaluation model determination step: obtaining a comprehensive index evaluation model S representing the relative size of the water environment bearing capacity of the region by adopting a weighted summation methodWECCNamely:
Figure RE-GDA0002507408060000081
in the formula, SWECCBearing capacity for water environmentComprehensive evaluation indexes; siThe fraction value of the ith index in the index layer is obtained; omegaiThe weight of the ith index in the index layer; m is the number of indexes.
Checking the bearing capacity of the water environment according to the standard exceeding index of the concentration of the water pollutants
And calculating the over-standard index of the water pollutant concentration of the region, wherein the over-standard index of the water pollutant concentration is reflected by the comparison value of the annual average concentration monitoring value of the main pollutants and the national current environmental quality standard.
According to the result of the calculation of the standard exceeding index of the concentration of the water pollutants, the bearing capacity S of the water environmentWECCThe necessary checks are performed. When the pollutant concentration standard exceeding index is larger than 0, the water pollutant concentration is in a standard exceeding state, and the bearing capacity S of the water environmentWECCIn principle, it should be less than 0.5; when the pollutant concentration standard exceeding index is less than-0.2, the water pollutant concentration is in a state of not standard exceeding, and the bearing capacity S of the water environmentWECCIn principle it should be greater than 0.75.
The calculation method of the water pollutant concentration standard exceeding index comprises the following steps:
when i ═ 1, i.e. the water contaminant is DO:
Rwater ijk＝1/(Cijk/Sik)-1
When i is 2, …,6, i.e. the water pollutant is COD respectivelyMn、BOD5、CODCr、NH3N, TP time:
Rwater ijk＝Cijk/Sik-1
Figure RE-GDA0002507408060000082
RWater jk＝max(RWater ijk)，i＝1,2,…,6
Figure RE-GDA0002507408060000083
Wherein R isWater ijkI term water pollutant concentration standard exceeding index R of k section of area jWater ijIs the i-th water pollutant concentration standard exceeding index, R, of the area jWater jkIs the region jthk cross-section water pollutant concentration standard exceeding index, RWater jIs the region j water pollutant concentration standard exceeding index, CijkThe annual average concentration monitoring value S of the ith water pollutant of the kth section of the area jikIs the water quality standard limit of the ith water pollutant of the kth section. i is 1,2, …,6, corresponding to DO and COD respectivelyMn、BOD5、CODCr、NH3-N, TP; k is a control section, k is 1,2, …, Nj，NjThe number of control sections in the region j is shown.
When the pollutant concentration standard exceeding index is larger than 0, the water pollutant concentration is in a standard exceeding state; when the pollutant concentration standard exceeding index is between-0.2 and 0, the pollutant concentration is in a state close to standard exceeding; when the pollutant concentration overproof index is less than-0.2, the water pollutant concentration is in an overproof state.
TABLE 3 Standard basic project standard limit value (unit: mg/L) of surface water environment quality standard
Figure RE-GDA0002507408060000091
Rating scale
Bearing capacity (S) of water environmentWECC) The value range of (1) is between 0 and 1, the magnitude of the value reflects the degree of the bearing capacity of the regional water environment, and the larger the value is, the larger the bearing capacity of the regional water environment is; the smaller value indicates that the water environment in the area has smaller bearing capacity and can not bear larger pressure, and the water environment is very fragile and even at the collapsed edge.
In order to qualitatively evaluate the bearing capacity of the water environment, the value of the bearing capacity of the water environment is divided into different grades, so that the degree of the bearing capacity of the water environment is evaluated. The results of the ranking are shown in table 4.
Table 4 comprehensive evaluation grade division of bearing capacity of water environment
Figure RE-GDA0002507408060000092
Water environment bearing capacity monitoring and early warning
And (4) carrying out early warning grade division on the bearing capacity of the water environment according to the comprehensive evaluation result of the bearing capacity of the water environment, and specifically referring to table 5.
TABLE 5 early warning grade division of bearing capacity of water environment
Figure RE-GDA0002507408060000101
Proposing a countermeasure proposal
According to the evaluation result of the bearing capacity of the water environment, the main factors influencing the bearing capacity of the water environment in the area are analyzed from the aspects of natural resource conditions, social and economic development, pollutant emission, water environment management and the like by combining the actual condition of the evaluation area.
From two main lines of 'capacity increasing' and 'emission reduction', an ecological water demand guarantee scheme and a water pollutant reduction scheme are provided. Providing a water environment bearing capacity monitoring and early warning mechanism construction scheme, comprising the steps of obtaining and transmitting basic data, constructing a water environment bearing capacity studying and judging system, analyzing and publishing a bearing state, applying a monitoring and early warning result and the like
In the embodiment of the present invention, when performing the water environment bearing capacity data processing, the following parameters may be further referred to: water resource index, water environment index and water ecology index. Specifically, the method comprises the following steps:
water resource index
(1) Water resource exploitation utilization (a 1): the ratio of water usage (industrial, agricultural, domestic, environmental, etc.) to the average total water resource of the drainage basin over the years.
Figure RE-GDA0002507408060000102
(2) Ten thousand GDP water consumption (a 2): the unit GDP (total value of national production) consumes water resource, i.e. the ratio of the total water consumption to the total value of national production (GDP).
Figure RE-GDA0002507408060000103
Figure RE-GDA0002507408060000111
Figure RE-GDA0002507408060000112
(3) Average human water area (a 3): in the district, the area of the water area owned by all people, namely the ratio of the total population to the total area of the water area. The index mainly reflects the natural endowment condition of human owned water resource and also responds to the characteristics of water ecology and water environment.
Figure RE-GDA0002507408060000113
Water environment index
(1) Industrial pollution intensity index (B1): reflecting the pressure of pollutants discharged in the industrial production process of the evaluation area on the ecological environment of the drainage basin.
Figure RE-GDA0002507408060000114
Figure RE-GDA0002507408060000115
Figure RE-GDA0002507408060000116
Figure RE-GDA0002507408060000117
(2) Agricultural pollution intensity index (B2): reflecting the pressure of the pollutants discharged/lost in the agricultural production process of the assessment area on the ecological environment of the drainage basin.
Figure RE-GDA0002507408060000118
Figure RE-GDA0002507408060000119
Figure RE-GDA00025074080600001110
Figure RE-GDA0002507408060000121
(3) Town life pollution intensity index (B3): reflecting the pressure of pollutants discharged from domestic sewage in the urban assessment area on the ecological environment of the drainage basin.
Figure RE-GDA0002507408060000122
Figure RE-GDA0002507408060000123
Figure RE-GDA0002507408060000124
Figure RE-GDA0002507408060000125
(4) Urban non-point source pollution intensity index (B4): reflecting the pressure of pollutants discharged by urban non-point sources in the evaluation area on the ecological environment of the drainage basin.
Figure RE-GDA0002507408060000126
Figure RE-GDA0002507408060000127
Figure RE-GDA0002507408060000128
Figure RE-GDA0002507408060000129
The formula for calculating the discharge amount of urban non-point source pollutants is as follows:
Ggeneral assembly＝∑Gi×10-3
GGeneral assemblyUrban non-point source pollutant emission amount, ton/year; giThe annual pollutant amount of each underlying surface is kilogram per year, and urban underlying surfaces are divided into roofs, traffic pavements, greenbelts and comprehensive lands.
The amount of contaminants on each underlying surface was calculated as follows.
Gi＝0.01αφPSEMC
In the formula: alpha-runoff correction factor; phi represents the runoff coefficient of the drainage area; p-annual precipitation in mm
Year; s, area of each underlying surface and hectare; EMC-mean concentration of events, mg/l.
The above factors are specifically described as follows:
firstly, a runoff correction coefficient alpha is generally 0.9;
secondly, the runoff coefficient phi of the drainage area is generally adopted as a reference value of a document, and the runoff coefficients of different underlying surfaces are different.
Reference table for runoff coefficient value in Beijing City
Categories Reference value
Roof covering 0.90
Traffic road surface 0.80
Greenbelt 0.20
Comprehensive land 0.60
And the annual precipitation P can be obtained through the data of the meteorological department or the hydrological department.
And fourthly, the area S of the drainage area refers to the areas of different underlying surfaces, namely the areas of the roof, the traffic road surface, the green land and the comprehensive land, and can be obtained through remote sensing data interpretation.
Event Mean concentration emc (event Mean concentration), which means the flow weighted Mean concentration of the pollutants in the primary runoff pollution process, i.e. the ratio of the total pollutant amount to the total runoff amount. Without actual monitoring data, the Event Mean Concentration (EMC) in the table below can be used.
Event Mean Concentration (EMC) referable value table unit: mg/L
Categories COD TN TP Ammonia nitrogen
Roof covering 75.89 8.86 0.084 5.63
Traffic road surface 197.45 7.91 0.28 5.19
Greenbelt 60.71 7 0.3 2.87
Comprehensive land 176.11 5.86 0.12 5.55
Water ecological index
(1) Vegetation coverage shoreline ratio (C1): the river vegetation covers (>3 meters) the proportion of the shoreline to the total shoreline. The influence of the vegetation coverage condition on the bank of the river on the ecology of the river is reflected, the larger the vegetation coverage shoreline proportion is, the better the ecological condition of the river is, and otherwise, the worse the ecological condition of the river is.
Figure RE-GDA0002507408060000141
(2) River natural shoreline ratio (C2): the natural shoreline of the river accounts for the proportion of the total shoreline, and the more the natural shoreline is, the more suitable the growth of the living beings is, and the better the habitat condition of the river is.
Figure RE-GDA0002507408060000142
(3) River connectivity (C3): and the number of gate dams of the hydropower station built in unit length of the river. The fewer gate dams of the hydropower station are reflected, the better the longitudinal connectivity of the river is, the better the space connectivity of nutrient flow and energy flow, the space connectivity of a biological community structure and the space connectivity of information flow are, and the larger the bearing capacity of the water environment is.
Figure RE-GDA0002507408060000143
(4) Ecological-based flow assurance rate (C4): the actual flow of the reference year and month accounts for the percentage of the minimum ecological base flow.
Figure RE-GDA0002507408060000144
Minimum ecological base flux in formula:
WEbthe average flow rate of the product is × 10 percent in nearly 10 years
For those without hydrologic site data, the minimum ecological base flow of the river can be calculated with reference to the relevant parameters of the following table.
Habitat conditions of water systems at different time intervals
Computing time period division Ecological depth of water (m) Ecological flow velocity (m/s)
Time period T1 (3 months 1-5 months 31 days) 0.6 0.02
Time period T2 (6 months 1-8 months 31 days) 0.8 0.05
Time period T3 (9 month 1-10 month 31) 0.8 0.02
Period T4 (11 month 1 day to next year 2 month 28 days) - -
The technical scheme of the invention provides a water environment bearing capacity data processing method, which can more accurately evaluate the water environment bearing capacity and prevent the human society from causing irreversible damage to the natural environment.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (7)

1. A water environment bearing capacity data processing method is characterized by comprising the following steps:
step 1, carrying out normalization processing on various indexes of bearing capacity of a water environment;
step 2, determining the weight of each index of the bearing capacity of the water environment;
step 3, generating an evaluation model according to each index of the determined water environment bearing capacity and the corresponding weight value of the index;
and 4, checking the bearing capacity of the water environment according to the standard exceeding index of the concentration of the water pollutants.
2. The method for processing the bearing capacity data of the aquatic environment according to claim 1, wherein the step 1 comprises:
processing the forward index by using the formula (1), wherein the larger the value of the forward index is, the larger the bearing capacity of the water environment is; the reverse index is processed by the formula (2), and the larger the value of the reverse index is, the smaller the bearing capacity of the water environment is;
the processing formula for the forward index is as follows:
Figure RE-FDA0002507408050000011
the processing formula for the reverse index is as follows:
Figure RE-FDA0002507408050000012
in the formula, VjFor normalized index value, Vj≤1；bjThe actual value of the j index; b isjmaxThe upper limit value of the standard value of the interval corresponding to the j-th index actual value; b isjminThe lower limit value of the standard value of the interval corresponding to the j-th index actual value; qjmaxThe upper limit value of the loading degree corresponding to the j index actual value is set; qjminThe lower limit value of the corresponding load degree of the j index actual value is set.
3. The method for processing the bearing capacity data of the aquatic environment according to claim 1, wherein the step 2 comprises:
step 21, establishing a hierarchical structure model: decomposing elements influencing the water environment into a plurality of layers from top to bottom according to attributes, wherein the elements in each layer belong to or influence the elements in the upper layer; the top layer of the hierarchical mechanism model is a target layer, and the bottom layer of the hierarchical mechanism model is an index layer;
step 22, constructing a pairwise comparison judgment matrix: determining the membership between elements of different layers in the hierarchical structure model, and comparing the elements pairwise according to the membership; comparing every two elements in the same layer; determining the relative importance among the elements and generating a judgment matrix B, which is marked as (B)ij)n×n：
Figure RE-FDA0002507408050000021
Step 23, for the constructed judgment matrix B, finding the eigenvector corresponding to the maximum eigenvalue includes:
computing per-column normalization
Figure RE-FDA0002507408050000022
Averaging canonical columns
Figure RE-FDA0002507408050000023
Vector W ═ W1,w2,…,wn) Where W is the feature vector sought;
calculating the maximum eigenvalue of the judgment matrix B
Figure RE-FDA0002507408050000024
In the formula (BW)iIs the ith element in the vector BW;
determining the maximum characteristic root lambda of the judgment matrixmaxAnd its corresponding eigenvector W; carrying out consistency check on the judgment matrix according to the consistency ratio CR, and if the judgment matrix has satisfactory consistency, normalizing the eigenvector W to be used as a single-ordering weight vector; otherwise, correcting the scale of the judgment matrix;
judging whether the matrix has satisfactory consistency by using a random consistency ratio CR, and if CR is generally considered to be less than 0.1, judging that the matrix has satisfactory consistency; the formula for CR is as follows:
Figure RE-FDA0002507408050000025
Figure RE-FDA0002507408050000026
in the formula, CR is a random consistency ratio; CI is a consistency index; RI is the average random consistency index, which can be found in Table 3; lambda [ alpha ]maxTo determine the maximum eigenvalue of the matrix(ii) a n is the order of the decision matrix.
4. The method for processing the bearing capacity data of the aquatic environment according to claim 1, wherein the step 3 comprises:
obtaining a comprehensive index evaluation model S representing the relative size of the water environment bearing capacity of the region by adopting a weighted summation methodWECC：
Figure RE-FDA0002507408050000031
In the formula, SWECCThe comprehensive evaluation index of the bearing capacity of the water environment is obtained; siThe fraction value of the ith index in the index layer is obtained; omegaiThe weight of the ith index in the index layer; m is the number of indexes.
5. The method for processing the bearing capacity data of the aquatic environment according to claim 4, wherein the method comprises the following steps in step 4: determining the over-standard index of the water pollutant concentration:
when i ═ 1, i.e. the water contaminant is DO:
Rwater ijk＝1/(Cijk/Sik)-1
When i is 2, …,6, i.e. the water pollutant is COD respectivelyMn、BOD5、CODCr、NH3N, TP time:
Rwater ijk＝Cijk/Sik-1
Figure RE-FDA0002507408050000032
RWater jk＝max(RWater ijk)，i＝1,2,…,6
Figure RE-FDA0002507408050000033
Wherein R isWater ijkI term water pollutant concentration standard exceeding index R of k section of area jWater ijIs the i-th of the region jExcess index of concentration of pollutant in item water, RWater jkIs the water pollutant concentration standard exceeding index, R, of the kth section of the area jWater jIs the region j water pollutant concentration standard exceeding index, CijkThe annual average concentration monitoring value S of the ith water pollutant of the kth section of the area jikThe water quality standard limit value of the ith water pollutant of the kth section; i is 1,2, …,6, corresponding to DO and COD respectivelyMn、BOD5、CODCr、NH3-N, TP; k is a control section, k is 1,2, …, Nj，NjRepresenting the number of control sections in the area j;
when the pollutant concentration standard exceeding index is larger than 0, the water pollutant concentration is in a standard exceeding state; when the pollutant concentration standard exceeding index is between-0.2 and 0, the pollutant concentration is in a state close to standard exceeding; when the pollutant concentration overproof index is less than-0.2, the water pollutant concentration is in an overproof state.
6. The method for processing the bearing capacity data of the aquatic environment according to claim 5, wherein the method further comprises:
according to the bearing capacity S of the water environmentWECCDetermining the bearing capacity of the regional water environment in the value range;
wherein the bearing capacity S of the water environmentWECCWhen the value range of (1) is 0-0.25, the bearing capacity is low; bearing capacity S of water environmentWECCWhen the value range of (1) is 0.25-0.5, the bearing capacity is middle; bearing capacity S of water environmentWECCWhen the value range of (1) is 0.5-0.75, the bearing capacity is high; bearing capacity S of water environmentWECCWhen the value range of (A) is 0.75-1, the bearing capacity is extremely high.
7. The method for processing the bearing capacity data of the aquatic environment according to claim 5, wherein the method further comprises:
according to the determined water pollutant concentration standard exceeding index calculation result, the bearing capacity S of the water environmentWECCCorrecting; the method specifically comprises the following steps:
when the pollutant concentration standard exceeding index is larger than 0, the water pollutant concentration is in a standard exceeding state, and the bearing capacity S of the water environmentWECCShould be less than 0.5; when the pollutant concentration standard exceeding index is less than-0.2, the water pollutant concentration is in a state of not standard exceeding, and the bearing capacity S of the water environmentWECCShould be greater than 0.75.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113205245A (en) * 2021-04-21 2021-08-03 重庆图强工程技术咨询有限公司 Method and system for evaluating bearing capacity of regional ecological environment
CN113343413A (en) * 2021-04-22 2021-09-03 中国环境科学研究院 Water environment bearing capacity evaluation method, device, equipment and medium
CN114626771A (en) * 2022-05-18 2022-06-14 中山大学 Urban area water ecological state reactor construction method and device and reactor

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150088582A1 (en) * 2010-01-29 2015-03-26 Enginuity Worldwide, LLC Sustainability campus of co-located facilities

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150088582A1 (en) * 2010-01-29 2015-03-26 Enginuity Worldwide, LLC Sustainability campus of co-located facilities

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
刘年磊，卢亚灵，蒋洪强，程曦: "基于环境质量标准的环境承载力评价方法及其应用", 《地理科学进展》 *
张祥娟等: "用层次分析法建立京杭大运河苏州高新区段水环境承载力指标体系研究", 《环境保护与循环经济》 *
王希义，徐海量，杜清: "基于层次分析模型的乌伦古湖水环境承载力评价", 《安徽农业科学》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113205245A (en) * 2021-04-21 2021-08-03 重庆图强工程技术咨询有限公司 Method and system for evaluating bearing capacity of regional ecological environment
CN113343413A (en) * 2021-04-22 2021-09-03 中国环境科学研究院 Water environment bearing capacity evaluation method, device, equipment and medium
CN114626771A (en) * 2022-05-18 2022-06-14 中山大学 Urban area water ecological state reactor construction method and device and reactor
CN114626771B (en) * 2022-05-18 2022-08-02 中山大学 Urban area water ecological state reactor construction method and device and reactor

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