Surface water pollution grade evaluation method based on multi-source data

Abstract

The invention discloses a surface water pollution grade evaluation method based on multi-source data. The method comprises the following steps: acquiring underground water data, standard data and multi-source monitoring data; obtaining a comprehensive quality grade according to the standard data and the multi-source monitoring data, obtaining a dynamic environmental pollution limit value according to the standard data and the underground water data, and obtaining a single pollution index according to the multi-source monitoring data and the environmental pollution limit value; obtaining the comprehensive pollution index according to the single pollution index, clustering the comprehensive pollution index and the comprehensive quality grade to obtain a comprehensive pollution evaluation grade, and establishing a pollution grade evaluation model by adopting the comprehensive pollution evaluation grade; and optimizing the pollution level evaluation model by adopting a bat algorithm, inputting to-be-evaluated data into the pollution level evaluation model, and outputting an evaluation result. The method not only can improve the precision of surface water pollution grade evaluation, but also has good interpretability, and can be directly applied to a surface water pollution grade evaluation system.

Description

Technical Field

[0001] The invention relates to the field of environmental science, and in particular to a surface water pollution level assessment method based on multi-source data. Background Art

[0002] With the rapid development of industrialization and urbanization, the problem of surface water pollution is becoming increasingly serious, and traditional assessment methods have limitations. On the one hand, relying solely on a single type of monitoring data is difficult to fully reflect the pollution status of water bodies, and it is easy to ignore the synergistic effects of multiple pollutants and the influence of complex environmental factors, resulting in a one-sided assessment. On the other hand, some methods have poor accuracy and cannot accurately define the pollution level, making it difficult to provide a reliable basis for pollution control. When faced with dynamically changing pollution conditions, they have poor adaptability and cannot track pollution trends in a timely and accurate manner, affecting the effective management and restoration decision-making of the water environment. New methods are urgently needed to improve the scientificity and accuracy of the assessment.

[0003] In recent years, the development of multi-source data fusion technology has provided a new approach for surface water pollution assessment. However, existing methods still have problems with insufficient accuracy and poor interpretability in data fusion, pollution index calculation, and assessment model construction. Therefore, developing a surface water pollution level assessment method based on multi-source data to improve assessment accuracy and interpretability has become an important research direction in the field of environmental science. Summary of the invention

[0004] The purpose of the present invention is to provide a surface water pollution level assessment method based on multi-source data.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] The present invention comprises the following steps:

[0007] Obtain groundwater data, standard data and multi-source monitoring data;

[0008] Obtain a comprehensive quality grade based on the standard data and the multi-source monitoring data, obtain a dynamic environmental pollution limit based on the standard data and the groundwater data, and obtain a single pollution index based on the multi-source monitoring data and the environmental pollution limit;

[0009] The comprehensive pollution index is obtained according to the single pollution index, the comprehensive pollution index and the comprehensive quality grade are clustered to obtain a comprehensive pollution assessment grade, and a pollution grade assessment model is established using the comprehensive pollution assessment grade;

[0010] The bat algorithm is used to optimize the pollution level assessment model, the data to be assessed is input into the pollution level assessment model, and the assessment result is output.

[0011] Furthermore, the groundwater data, the standard data and the multi-source monitoring data include:

[0012] The groundwater data are the toxicity indicators of groundwater, and the standard data are the quality grading standards of multi-source monitoring data of different waters. The multi-source monitoring data include physical indicators, general chemical indicators and toxicity indicators of surface water. The physical indicators include chromaticity, total dissolved solids, total hardness and turbidity. The toxicity indicators are the concentrations of harmful elements. Harmful elements are elements that are harmful to the human body and elements that are harmful to the environment. The general chemical indicators are the concentrations of non-toxic elements and compounds.

[0013] Furthermore, the method for obtaining the comprehensive quality level according to the standard data and the multi-source monitoring data includes:

[0014] According to the quality grading standard of the water area where the monitoring point is located, the quality grade data of each indicator of the multi-source monitoring data is obtained by quality grading, and the quality grade data of the indicator with the most serious pollution is used as the comprehensive quality grade of the multi-source monitoring data.

[0015] Furthermore, a method for obtaining a dynamic environmental pollution limit value according to the standard data and the groundwater data comprises:

[0016] Perform stability analysis on groundwater data:

[0017]

[0018] Where S t is the groundwater data at time point t, ΔS t YesS t The first-order difference of t-1 is the groundwater data at time point t-1, S t-i is the groundwater data at time point ti, α is the constant term, β is the coefficient of the time trend term, γ is the stationary coefficient of the autoregressive term, is the coefficient of the i-th order lag term, p is the order of the autoregressive term, ∈ t is the error term, δ K is the coefficient of the seasonal effect of the Kth season, J t,K is the seasonal effect of the Kth season at time point t,

[0019] If γ=0, the groundwater data is non-stationary; if γ<0, the groundwater data is stationary;

[0020] The average value of toxicological indicators with stable data in groundwater is calculated to obtain the background value of harmful elements, and the background value is used as the environmental pollution limit of harmful elements. The third-level standard limit of the quality grading standard of the water area where the monitoring point is located is used as the environmental pollution limit of harmful elements without background value.

[0021] Furthermore, a method for obtaining a single pollution index according to the multi-source monitoring data and the environmental pollution limit value includes:

[0022] Single pollution index:

[0023]

[0024] Where P i (j) is the single pollution index of pollutant j in sample i, C i (j) is the concentration of pollutant j in surface water sample i, C0(j) is the environmental pollution limit of pollutant j in surface water sample i, and a single pollution index greater than 1 indicates that the pollutant exceeds the environmental pollution limit.

[0025] Furthermore, the method for obtaining the comprehensive pollution index according to the single pollution index includes:

[0026] Comprehensive pollution index:

[0027]

[0028] Where P Z (i) is the comprehensive pollution index of sample i, maxP i is the maximum value of the single pollution index of sample i, is the average value of the single pollution index of sample i.

[0029] Furthermore, the method of clustering the comprehensive pollution index and the comprehensive quality grade to obtain a comprehensive pollution assessment grade includes:

[0030] The comprehensive pollution index and comprehensive quality level are used to establish data points. The number of clusters is preset to 5, and 5 objects are randomly selected from the data points as the initial cluster centers.

[0031] Attribute importance formula:

[0032]

[0033] Among them, σ a (C, D) represents the importance of attribute a relative to attribute set C and decision attribute D, where represents the dependency between C and D, C-{a} represents the removal of attribute a from the attribute set C, X is a subset of U, U / D represents the partition of U with respect to D, U is the domain, representing a finite non-empty data set, |U| is the total number of objects in the domain, C (X) represents the lower approximation of X with respect to C, | C (X)| C The total number of objects in (X), σ ′a (C, D) is the normalized importance of attribute a relative to attribute set C and decision attribute D.

[0034] Rough entropy formula:

[0035]

[0036] Where E A (X) is the rough entropy of attribute set A with respect to set X, B nR (X) = R (X) - R (X), respectively represent the upper approximation and lower approximation of set X with respect to the equivalence relation R, X i is the i-th equivalence class in U / IND(R), the i-th subset in the partition of U with respect to the indiscernibility relation IND(R), |·| represents the total number of objects in the set, n is the number of equivalence classes in U / IND(R),

[0037] Attribute importance formula:

[0038] Sig(a,A)=E A-{a} (X)-E A (X),

[0039]

[0040] Where Sig(a,A) is the importance of attribute a in attribute set A, E A-{a} (X) represents the rough entropy of the set after removing attribute a from attribute set A. Indicates removing the jth attribute a from attribute set A j Then about the rough entropy of the set, W j for a j The weight vector, Sig(a j ,A) is attribute a j The importance of attribute set A, m is the total number of attributes in attribute set A,

[0041] The similarity measurement formula between data points is:

[0042]

[0043] Among them, dist(x i ,x j ) is the data point x i and x j The distance between them is the similarity between data points, d i and d j x i and x j The decision attribute value of is the normalized sth attribute a s Relative to the importance of attribute set C and decision attribute D, w s For attribute a s The weight vector, e is the number of conditional attributes, c is and c js They are x i and x j The sth conditional attribute value of

[0044] Calculate the distance between the data point and the cluster center, assign each data point to the nearest cluster, and in each attribute of each cluster, select the mode of the attribute value as the new cluster center, and iterate until the cluster center no longer changes;

[0045] The five clusters correspond to the five comprehensive pollution assessment levels of safety, warning line, light pollution, moderate pollution and heavy pollution. The pollution levels of the five comprehensive pollution assessment levels are clean, fairly clean, light pollution, moderate pollution and severe pollution.

[0046] Furthermore, a method for establishing a pollution level assessment model using a comprehensive pollution assessment level includes:

[0047] Random sampling was performed on groundwater data, standard data, multi-source monitoring data and comprehensive pollution assessment levels to obtain training sets and validation sets. The kernel ridge regression algorithm was used to train the pollution level assessment model based on the training set to predict the comprehensive pollution assessment level. The intelligent optimization algorithm was used to optimize the pollution level assessment model based on the validation set.

[0048] Furthermore, the method of optimizing the pollution level assessment model using a bat algorithm includes:

[0049] Position update formula:

[0050]

[0051] in, and are the k-th velocity components of the ith bat at the t-th and t+1-th iterations, respectively. and are the k-th coordinates of the ith bat at the t-th and t+1-th iterations, rand is a random number uniformly distributed between 0 and 1, Y(·) is the speed threshold function,

[0052] Speed ​​update formula:

[0053]

[0054] Among them, ω max is the maximum weight, ω min is the minimum weight, is the weight of the i-th bat at the t-th iteration, is the k-th velocity component of the i-th bat at the t-1th iteration, T max is the maximum number of iterations, t is the current number of iterations, f i is the frequency of the i-th bat, is the k-th dimension coordinate of the current best position,

[0055] Local search formula:

[0056]

[0057] in, is the new value of the k-th dimension coordinate of the i-th bat at the t-th iteration after local search, p m is the mutation probability, flip bit value is the value of the mutation operation on the current position, and the bit flip mutation operator is used.

[0058] The optimization goal is to minimize the objective function value of the pollution level assessment model, and the fitness is the inverse of the objective function value of the hyperparameter combination corresponding to the bat.

[0059] The beneficial effects of the present invention are:

[0060] The present invention is a surface water pollution level assessment method based on multi-source data. Compared with the prior art, the present invention has the following technical effects:

[0061] The present invention is based on multi-source data, comprehensively considers multiple indicators, and improves the accuracy of assessment. It dynamically calculates environmental pollution limits to enhance the timeliness and scientificity of the method. Through clustering and model optimization, it improves the accuracy and interpretability of assessment, effectively assists in the precise management and scientific control of surface water pollution, and provides key support for water resource protection and ecological balance maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a flowchart of the steps of the surface water pollution level assessment method based on multi-source data. DETAILED DESCRIPTION

[0063] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0064] The surface water pollution level assessment method based on multi-source data of the present invention comprises the following steps:

[0065] like Figure 1 As shown, in this embodiment, the following steps are included:

[0066] Obtain groundwater data, standard data and multi-source monitoring data;

[0067] Obtain a comprehensive quality grade based on the standard data and the multi-source monitoring data, obtain a dynamic environmental pollution limit based on the standard data and the groundwater data, and obtain a single pollution index based on the multi-source monitoring data and the environmental pollution limit;

[0068] The comprehensive pollution index is obtained according to the single pollution index, the comprehensive pollution index and the comprehensive quality grade are clustered to obtain a comprehensive pollution assessment grade, and a pollution grade assessment model is established using the comprehensive pollution assessment grade;

[0069] The pollution level assessment model is optimized by using a bat algorithm, the data to be assessed is input into the pollution level assessment model, and an assessment result is output;

[0070] In the actual evaluation, the data to be evaluated are 15 samples in the mining area, and the comprehensive quality grade and quality grade data of the samples are:

[0071]

[0072] The background value of Sb in the sample is 0.015mg / L, the background value of As is 0.05mg / L, and the environmental pollution limit of other harmful elements is the third-level standard limit of the quality classification standard of surface water in the mining area.

[0073] The single pollution index and comprehensive pollution index are:

[0074]

[0075]

[0076] In the table, P-As represents the single pollution index of As, and Pz represents the comprehensive pollution index of the sample; the comprehensive pollution assessment level is:

[0077] Comprehensive pollution assessment level Pollution degree Pollution Level Ⅰ Safety clean Ⅱ Warning Line Clean Ⅲ Light pollution Slightly polluted Ⅳ Medium pollution Moderate pollution Ⅴ Heavy pollution Severe pollution

[0078] The comprehensive pollution assessment level of the samples is:

[0079]

[0080]

[0081] In this embodiment, the groundwater data, the standard data and the multi-source monitoring data include:

[0082] The groundwater data are the toxicity indicators of groundwater, and the standard data are the quality grading standards of multi-source monitoring data of different waters. The multi-source monitoring data include physical indicators, general chemical indicators and toxicity indicators of surface water. The physical indicators include chromaticity, total dissolved solids, total hardness and turbidity. The toxicity indicators are the concentrations of harmful elements. Harmful elements are elements that are harmful to the human body and elements that are harmful to the environment. The general chemical indicators are the concentrations of non-toxic elements and compounds.

[0083] In this embodiment, the method for obtaining the comprehensive quality level according to the standard data and the multi-source monitoring data includes:

[0084] According to the quality grading standard of the water area where the monitoring point is located, the quality grade data of each indicator of the multi-source monitoring data is obtained by quality grading, and the quality grade data of the indicator with the most serious pollution is used as the comprehensive quality grade of the multi-source monitoring data.

[0085] In this embodiment, the method for obtaining a dynamic environmental pollution limit value according to the standard data and the groundwater data includes:

[0086] Perform stability analysis on groundwater data:

[0087]

[0088] Where S t is the groundwater data at time point t, ΔS t YesS t The first-order difference of t-1 is the groundwater data at time point t-1, S t-i is the groundwater data at time point ti, α is the constant term, β is the coefficient of the time trend term, γ is the stationary coefficient of the autoregressive term, is the coefficient of the i-th order lag term, p is the order of the autoregressive term, ∈ t is the error term, δ K is the coefficient of the seasonal effect of the Kth season, J t,K is the seasonal effect of the Kth season at time point t,

[0089] If γ=0, the groundwater data is non-stationary; if γ<0, the groundwater data is stationary;

[0090] The average value of toxicological indicators with stable data in groundwater is calculated to obtain the background value of harmful elements, and the background value is used as the environmental pollution limit of harmful elements. The third-level standard limit of the quality grading standard of the water area where the monitoring point is located is used as the environmental pollution limit of harmful elements without background value.

[0091] In this embodiment, the method for obtaining a single pollution index according to the multi-source monitoring data and the environmental pollution limit value includes:

[0092] Single pollution index:

[0093]

[0094] Where P i (j) is the single pollution index of pollutant j in sample i, C i (j) is the concentration of pollutant j in surface water sample i, C0(j) is the environmental pollution limit of pollutant j in surface water sample i, and a single pollution index greater than 1 indicates that the pollutant exceeds the environmental pollution limit.

[0095] In this embodiment, the method for obtaining the comprehensive pollution index according to the single pollution index includes:

[0096] Comprehensive pollution index:

[0097]

[0098] Where P Z (i) is the comprehensive pollution index of sample i, maxP i is the maximum value of the single pollution index of sample i, is the average value of the single pollution index of sample i.

[0099] In this embodiment, the method of clustering the comprehensive pollution index and the comprehensive quality level to obtain the comprehensive pollution assessment level includes:

[0100] The comprehensive pollution index and comprehensive quality level are used to establish data points. The number of clusters is preset to 5, and 5 objects are randomly selected from the data points as the initial cluster centers.

[0101] Attribute importance formula:

[0102]

[0103] Among them, σ a (C, D) represents the importance of attribute a relative to attribute set C and decision attribute D, where represents the dependency between C and D, C-{a} represents the removal of attribute a from the attribute set C, X is a subset of U, U / D represents the partition of U with respect to D, U is the domain, representing a finite non-empty data set, |U| is the total number of objects in the domain, C (X) represents the lower approximation of X with respect to C, | C (X)| C The total number of objects in (X), σ ′ a(C, D) is the normalized importance of attribute a relative to attribute set C and decision attribute D.

[0104] Rough entropy formula:

[0105]

[0106] Where E A (X) is the rough entropy of attribute set A with respect to set X, B nR (X) = R (X) - R (X), respectively represent the upper approximation and lower approximation of set X with respect to the equivalence relation R, X i is the i-th equivalence class in U / IND(R), the i-th subset in the partition of U with respect to the indiscernibility relation IND(R), |·| represents the total number of objects in the set, n is the number of equivalence classes in U / IND(R),

[0107] Attribute importance formula:

[0108] Sig(a,A)=E A-{a} (X)-E A (X),

[0109]

[0110] Where Sig(a,A) is the importance of attribute a in attribute set A, E A-{a} (X) represents the rough entropy of the set after removing attribute a from attribute set A. Indicates removing the jth attribute a from attribute set A j Then about the rough entropy of the set, W j for a j The weight vector, Sig(a j ,A) is attribute a j The importance of attribute set A, m is the total number of attributes in attribute set A,

[0111] The similarity measurement formula between data points is:

[0112]

[0113] Among them, dist(x i ,x j ) is the data point x i and x j The distance between them is the similarity between data points, d i and d j x i and x j The decision attribute value of is the normalized sth attribute as Relative to the importance of attribute set C and decision attribute D, w s For attribute a s The weight vector, e is the number of conditional attributes, c is and c js They are x i and x j The sth conditional attribute value of

[0114] Calculate the distance between the data point and the cluster center, assign each data point to the nearest cluster, and in each attribute of each cluster, select the mode of the attribute value as the new cluster center, and iterate until the cluster center no longer changes;

[0115] The five clusters correspond to the five comprehensive pollution assessment levels of safety, warning line, light pollution, moderate pollution and heavy pollution. The pollution levels of the five comprehensive pollution assessment levels are clean, fairly clean, light pollution, moderate pollution and severe pollution.

[0116] In this embodiment, the method of establishing a pollution level assessment model using comprehensive pollution assessment levels includes:

[0117] Random sampling was performed on groundwater data, standard data, multi-source monitoring data and comprehensive pollution assessment levels to obtain training sets and validation sets. The kernel ridge regression algorithm was used to train the pollution level assessment model based on the training set to predict the comprehensive pollution assessment level. The intelligent optimization algorithm was used to optimize the pollution level assessment model based on the validation set.

[0118] In this embodiment, the method of optimizing the pollution level assessment model using the bat algorithm includes:

[0119] Position update formula:

[0120]

[0121] in, and are the k-th velocity components of the ith bat at the t-th and t+1-th iterations, respectively. and are the k-th coordinates of the ith bat at the t-th and t+1-th iterations, rand is a random number uniformly distributed between 0 and 1, Y(·) is the speed threshold function,

[0122] Speed ​​update formula:

[0123]

[0124] Among them, ω max is the maximum weight, ω min is the minimum weight, is the weight of the i-th bat at the t-th iteration, is the k-th velocity component of the i-th bat at the t-1th iteration, T max is the maximum number of iterations, t is the current number of iterations, f i is the frequency of the i-th bat, is the k-th dimension coordinate of the current best position,

[0125] Local search formula:

[0126]

[0127] in, is the new value of the k-th dimension coordinate of the i-th bat at the t-th iteration after local search, p m is the mutation probability, flip bit value is the value of the mutation operation on the current position, and the bit flip mutation operator is used.

[0128] The optimization goal is to minimize the objective function value of the pollution level assessment model, and the fitness is the inverse of the objective function value of the hyperparameter combination corresponding to the bat.

[0129] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A surface water pollution level assessment method based on multi-source data, characterized in that: The following steps are involved: Obtain groundwater data, standard data and multi-source monitoring data; Obtain a comprehensive quality grade based on the standard data and the multi-source monitoring data, obtain a dynamic environmental pollution limit based on the standard data and the groundwater data, and obtain a single pollution index based on the multi-source monitoring data and the environmental pollution limit; The comprehensive pollution index is obtained according to the single pollution index, the comprehensive pollution index and the comprehensive quality grade are clustered to obtain a comprehensive pollution assessment grade, and a pollution grade assessment model is established using the comprehensive pollution assessment grade; The bat algorithm is used to optimize the pollution level assessment model, the data to be assessed is input into the pollution level assessment model, and the assessment result is output.

2. The surface water pollution level assessment method based on multi-source data according to claim 1 is characterized in that: The groundwater data, the standard data and the multi-source monitoring data include: The groundwater data are the toxicity indicators of groundwater, and the standard data are the quality grading standards of multi-source monitoring data of different waters. The multi-source monitoring data include physical indicators, general chemical indicators and toxicity indicators of surface water. The physical indicators include chromaticity, total dissolved solids, total hardness and turbidity. The toxicity indicators are the concentrations of harmful elements. Harmful elements are elements that are harmful to the human body and elements that are harmful to the environment. The general chemical indicators are the concentrations of non-toxic elements and compounds.

3. The surface water pollution level assessment method based on multi-source data according to claim 1 is characterized in that: The method for obtaining a comprehensive quality level according to the standard data and the multi-source monitoring data comprises: According to the quality grading standard of the water area where the monitoring point is located, the quality grade data of each indicator of the multi-source monitoring data is obtained by quality grading, and the quality grade data of the indicator with the most serious pollution is used as the comprehensive quality grade of the multi-source monitoring data.

4. The surface water pollution level assessment method based on multi-source data according to claim 1 is characterized in that: The method for obtaining a dynamic environmental pollution limit value according to the standard data and the groundwater data comprises: Perform stability analysis on groundwater data: Where S t is the groundwater data at time point t, ΔS t YesS t The first-order difference of t-1 is the groundwater data at time point t-1, S t-i is the groundwater data at time point ti, α is the constant term, β is the coefficient of the time trend term, γ is the stationary coefficient of the autoregressive term, is the coefficient of the i-th order lag term, p is the order of the autoregressive term, ∈ t is the error term, δ K is the coefficient of the seasonal effect of the Kth season, J t,K is the seasonal effect of the Kth season at time point t, If γ=0, the groundwater data is non-stationary; if γ<0, the groundwater data is stationary; The average value of toxicological indicators with stable data in groundwater is calculated to obtain the background value of harmful elements, and the background value is used as the environmental pollution limit of harmful elements. The third-level standard limit of the quality grading standard of the water area where the monitoring point is located is used as the environmental pollution limit of harmful elements without background value.

5. The surface water pollution level assessment method based on multi-source data according to claim 1 is characterized in that: The method for obtaining a single pollution index according to the multi-source monitoring data and the environmental pollution limit value includes: Single pollution index: Where P i (j) is the single pollution index of pollutant j in sample i, C i (j) is the concentration of pollutant j in surface water sample i, C0(j) is the environmental pollution limit of pollutant j in surface water sample i, and a single pollution index greater than 1 indicates that the pollutant exceeds the environmental pollution limit.

6. The surface water pollution level assessment method based on multi-source data according to claim 1 is characterized in that: The method for obtaining the comprehensive pollution index according to the single pollution index includes: Comprehensive pollution index: Where P Z (i) is the comprehensive pollution index of sample i, maxP i is the maximum value of the single pollution index of sample i, P- i is the average value of the single pollution index of sample i.

7. The surface water pollution level assessment method based on multi-source data according to claim 1 is characterized in that: The method of clustering the comprehensive pollution index and the comprehensive quality grade to obtain a comprehensive pollution assessment grade comprises: The comprehensive pollution index and comprehensive quality level are used to establish data points. The number of clusters is preset to 5, and 5 objects are randomly selected from the data points as the initial cluster centers. Attribute importance formula: Among them, σ a (C, D) represents the importance of attribute a relative to attribute set C and decision attribute D, where represents the dependency between C and D, C-{a} represents the removal of attribute a from the attribute set C, X is a subset of U, U / D represents the partition of U with respect to D, U is the domain, representing a finite non-empty data set, |U| is the total number of objects in the domain, C (X) represents the lower approximation of X with respect to C, | C (X)| C The total number of objects in (X), σ ′ a (C, D) is the normalized importance of attribute a relative to attribute set C and decision attribute D. Rough entropy formula: Where E A (X) is the rough entropy of attribute set A with respect to set X, They represent the upper approximation and lower approximation of set X with respect to the equivalence relation R, respectively. i is the i-th equivalence class in U / IND(R), the i-th subset in the partition of U with respect to the indiscernibility relation IND(R), |·| represents the total number of objects in the set, n is the number of equivalence classes in U / IND(R), Attribute importance formula: Mr(a,A)=E A-{a} (X)-E A (X), Where Sig(a,A) is the importance of attribute a in attribute set A, E A-{a} (X) represents the rough entropy of the set after removing attribute a from attribute set A. Indicates removing the jth attribute a from the attribute set A j Then about the rough entropy of the set, W j for a j The weight vector, Sig(a j ,A) is attribute a j The importance of attribute set A, m is the total number of attributes in attribute set A, The similarity measurement formula between data points is: Among them, dist(x i ,x j ) is the data point x i and x j The distance between them is the similarity between data points, d i and d j x i and x j The decision attribute value of is the normalized sth attribute a s Relative to the importance of attribute set C and decision attribute D, w s For attribute a s The weight vector, e is the number of conditional attributes, c is and c js They are x i and x j The sth conditional attribute value of Calculate the distance between the data point and the cluster center, assign each data point to the nearest cluster, and in each attribute of each cluster, select the mode of the attribute value as the new cluster center, and iterate until the cluster center no longer changes; The five clusters correspond to the five comprehensive pollution assessment levels of safety, warning line, light pollution, moderate pollution and heavy pollution. The pollution levels of the five comprehensive pollution assessment levels are clean, fairly clean, light pollution, moderate pollution and severe pollution.

8. The surface water pollution level assessment method based on multi-source data according to claim 1 is characterized in that: The method of establishing a pollution level assessment model using a comprehensive pollution assessment level includes: Random sampling was performed on groundwater data, standard data, multi-source monitoring data and comprehensive pollution assessment levels to obtain training sets and validation sets. The kernel ridge regression algorithm was used to train the pollution level assessment model based on the training set to predict the comprehensive pollution assessment level. The intelligent optimization algorithm was used to optimize the pollution level assessment model based on the validation set.

9. The surface water pollution level assessment method based on multi-source data according to claim 1 is characterized in that: The method of optimizing the pollution level assessment model using a bat algorithm includes: Position update formula: in, and are the k-th velocity components of the ith bat at the t-th and t+1-th iterations, respectively. and are the k-th coordinates of the ith bat at the t-th and t+1-th iterations, rand is a random number uniformly distributed between 0 and 1, Y(·) is the speed threshold function, Speed ​​update formula: Among them, ω max is the maximum weight, ω min is the minimum weight, is the weight of the i-th bat at the t-th iteration, is the k-th velocity component of the i-th bat at the t-1th iteration, T max is the maximum number of iterations, t is the current number of iterations, f i is the frequency of the i-th bat, is the k-th dimension coordinate of the current best position, Local search formula: in, is the new value of the k-th dimension coordinate of the i-th bat at the t-th iteration after local search, p m is the mutation probability, flip bit value is the value of the mutation operation on the current position, and the bit flip mutation operator is used. The optimization goal is to minimize the objective function value of the pollution level assessment model, and the fitness is the inverse of the objective function value of the hyperparameter combination corresponding to the bat.

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