A method for evaluating environmental risk grade of a tailings pond

Abstract

The present application relates to tailing pond risk assessment technical field, especially to a kind of tailing pond environmental risk grade evaluation method;Including the following steps: S1, the attribute data of multiple tailing ponds are obtained, the attribute data of each group of tailing ponds includes first target layer data, second target layer data, third target layer data, includes multiple relevant indexes, obtain standard attribute data;S2, the attribute data of tailing pond to be evaluated are obtained, obtain the weight value corresponding to first target layer data, second target layer data, third target layer data in tailing pond to be evaluated attribute data, according to corresponding weight value and corresponding relevant index of tailing pond to be evaluated, the evaluation judgment value of tailing pond to be evaluated is calculated;S3, tailing pond environmental risk grade is set, according to the evaluation judgment value of tailing pond to be evaluated, obtain the environmental risk grade of tailing pond to be evaluated;The present application improves tailing pond environmental risk grade evaluation accuracy.

Description

Technical Field

[0001] This invention relates to the field of tailings dam risk assessment technology, and in particular to a method for assessing the environmental risk level of tailings dams. Background Technology

[0002] Tailings dams are an essential component of mineral processing plants, but also a significant hazard source. Their tailings typically contain heavy metals, chemicals, and other harmful substances; a dam failure would cause substantial losses, severely impacting the local environment and endangering human life and property. In recent years, environmental pollution, casualties, and property damage caused by tailings dam accidents have occurred frequently. Given the complexity of the environmental risks posed by tailings dams and their significant susceptibility to natural disasters in recent years, highlighting the prominent risks and hidden dangers, establishing a tiered environmental monitoring indicator system for tailings dams is crucial.

[0003] Existing technology CN106600153A discloses a method for assessing the risk of tailings dam failure, including: constructing a set of evaluation factors F; constructing a set of rating levels U corresponding to each evaluation factor in the set of evaluation factors F, and determining the weight S of each evaluation factor; obtaining the corresponding rating level of each evaluation factor in the rating level set U, and obtaining a fuzzy judgment matrix Ri; obtaining the evaluation matrix Zi of each factor for tailings dam risk, and establishing a tailings dam risk assessment matrix C for the evaluation matrix Zi of each factor for tailings dam risk, and taking the level of the evaluation object according to the principle of maximum membership. However, the above-mentioned method for assessing the risk of tailings dams does not comprehensively consider the indicators affecting the risk of tailings dams, resulting in low accuracy in assessing the risk of tailings dams.

[0004] Therefore, there is an urgent need to provide a method for assessing the environmental risk level of tailings ponds, which can improve the accuracy of tailings pond environmental risk level assessment compared with existing technologies. Summary of the Invention

[0005] This invention addresses the technical problems existing in the prior art and provides a method for assessing the environmental risk level of tailings ponds.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for assessing the environmental risk level of a tailings dam includes the following steps:

[0008] S1. Obtain attribute data for multiple tailings ponds. The attribute data for each tailings pond includes first target layer data, second target layer data, and third target layer data. The first target layer data, second target layer data, and third target layer data all include multiple relevant indicators. Based on the multiple sets of attribute data, obtain standard attribute data.

[0009] S2. Obtain the attribute data of the tailings dam to be evaluated. Based on the standard attribute data, obtain the weight values ​​corresponding to the first target layer data, the second target layer data, and the third target layer data in the attribute data of the tailings dam to be evaluated. Based on the weight values ​​corresponding to the first target layer data, the second target layer data, and the third target layer data, as well as the relevant indicators corresponding to the tailings dam to be evaluated, calculate the evaluation judgment value of the tailings dam to be evaluated.

[0010] S3. Set the environmental risk level of the tailings dam, including low risk, medium risk, high risk and ultra-high risk. Set a first risk value, a second risk value and a third risk value. The low risk level is less than or equal to the first risk value, the medium risk level is greater than the first risk value and less than or equal to the second risk value, the high risk level is greater than the second risk value and less than or equal to the third risk value, and the ultra-high risk level is greater than the third risk value. Based on the assessment judgment value of the tailings dam to be assessed obtained in step S2, the environmental risk level of the tailings dam to be assessed is obtained.

[0011] Furthermore, the assessment judgment value of the tailings dam to be assessed in step S2 is calculated according to the following formula;

[0012]

[0013] In the above formula, P represents the evaluation judgment value of the tailings dam to be evaluated, and ω1 represents the weight value of the first target layer in the attribute data of the tailings dam to be evaluated. This represents the weight value of the m1-th relevant indicator in the first target layer of the attribute data of the tailings dam to be evaluated. ω1 represents the score of the m1-th relevant indicator in the first target layer of the attribute data of the tailings dam to be evaluated; ω2 represents the weight value of the second target layer in the attribute data of the tailings dam to be evaluated. This represents the weight value of the m2-th relevant indicator in the second target layer of the attribute data of the tailings dam to be evaluated. ω represents the score of the m2-th relevant indicator in the second target layer of the attribute data of the tailings dam to be evaluated; ω3 represents the weight value of the third target layer in the attribute data of the tailings dam to be evaluated. This represents the weight value of the m3-th relevant indicator in the third target layer of the attribute data of the tailings dam to be evaluated. This represents the score of the m3rd relevant indicator in the third target layer of the attribute data of the tailings dam to be evaluated.

[0014] Furthermore, ω1, ω2, and ω3 are calculated using the following formula:

[0015]

[0016] In the above formula, X1 represents the correlation value between the first target layer in the attribute data of the tailings dam to be evaluated and the first target layer in the standard attribute data; X2 represents the correlation value between the second target layer in the attribute data of the tailings dam to be evaluated and the second target layer in the standard attribute data; and X3 represents the correlation value between the third target layer in the attribute data of the tailings dam to be evaluated and the third target layer in the standard attribute data.

[0017] Furthermore, X1, X2, and X3 are calculated using the following formula:

[0018]

[0019] In the above formula, This represents the mean of all relevant indicators in the first target layer of the attribute data of the tailings dam to be evaluated. express The scores of relevant indicators in the corresponding standard attribute data. This represents the mean of all relevant indicators in the first target layer of the standard attribute data; This represents the mean of all relevant indicators in the second target layer of the attribute data for the tailings dam to be evaluated. express The scores of relevant indicators in the corresponding standard attribute data. This represents the mean of all relevant indicators in the second target layer of the standard attribute data; This represents the mean of all relevant indicators in the third target layer of the attribute data for the tailings dam to be evaluated. express The scores of relevant indicators in the corresponding standard attribute data. This represents the mean of all relevant indicators in the third target layer of the standard attribute data.

[0020] Furthermore, It is obtained through the following method:

[0021] S21. Using the analytic hierarchy process, construct a first judgment matrix, a second judgment matrix, and a third judgment matrix for the first target layer, the second target layer, and the third target layer in the tailings dam attribute data to be evaluated, respectively. Based on the first judgment matrix, the second judgment matrix, and the third judgment matrix, obtain the first-level weight value of each relevant indicator within them.

[0022] S22. Using the entropy weight method, based on the first judgment matrix, the second judgment matrix, and the third judgment matrix, obtain the secondary weight values ​​of the relevant indicators of the tailings dam to be evaluated.

[0023] S23. Based on the primary and secondary weight values ​​of each relevant indicator, calculate using the distance function method.

[0024] Furthermore, the method for obtaining standard attribute data is as follows: for each set of attribute data in step S1, calculate the corresponding standard judgment value, compare all the standard judgment values, and set the attribute data corresponding to the smallest standard judgment value as the standard attribute data.

[0025] Furthermore, the relevant indicators within the first target layer of each set of attribute data form the first indicator dataset, the relevant indicators within the second target layer form the second indicator dataset, and the relevant indicators within the third target layer form the third indicator dataset; the standard judgment value for each set of attribute data is calculated according to the following formula:

[0026] B i =B i1 +B i2 +B i3 ;

[0027] In the above formula, B i B represents the standard judgment value of the i-th attribute data group. i1 B represents the judgment value of the first indicator dataset in the i-th group of attribute data. i2 B represents the judgment value of the second indicator dataset in the i-th group of attribute data. i3 This represents the judgment value of the third indicator dataset in the i-th group of attribute data, where i ranges from 1 to I, and I represents the total number of attribute data groups processed in step S1.

[0028] Furthermore, B i1 B i2 B i3 The following formula is used to calculate:

[0029]

[0030] In the above formula, This represents the standard score of the j1-th related indicator in the first indicator dataset of the i-th attribute data group. express, Corresponding importance value, This represents the standard score of the j2-th related indicator in the second indicator dataset of the i-th attribute data set. express Corresponding importance value, This represents the standard score of the j3rd related indicator in the third indicator dataset of the i-th attribute data group. express The corresponding importance value.

[0031] Furthermore, The following formula can be used to calculate:

[0032]

[0033] In the above formula, This represents the score of the j1-th related indicator in the first indicator dataset of the i-th attribute data group. Indicates that in group I attribute data, there is... The minimum value among all the same relevant indicators, Indicates that in group I attribute data, there is... The maximum value among all the same relevant indicators; This represents the score of the j2-th related indicator in the second indicator dataset of the i-th attribute data set. Indicates that in group I attribute data, there is... The minimum value among all the same relevant indicators, Indicates that in group I attribute data, there is... The maximum value among all the same relevant indicators; This represents the score of the j3rd related indicator in the third indicator dataset of the i-th attribute data group. Indicates that in group I attribute data, there is... The minimum value among all the same relevant indicators, Indicates that in group I attribute data, there is... The maximum value among all the same relevant indicators.

[0034] Furthermore, The method for obtaining the information is as follows: Based on all relevant indicators in the first indicator dataset of the i-th group of attribute data, construct the corresponding decision tree, calculate the information gain value of the feature corresponding to each relevant indicator in the decision tree, and the information gain value corresponding to each relevant indicator is its importance value.

[0035] The method for obtaining the information is as follows: Based on all relevant indicators in the second indicator dataset of the i-th group of attribute data, construct the corresponding decision tree, calculate the information gain value of the feature corresponding to each relevant indicator in the decision tree, and the information gain value corresponding to each relevant indicator is its importance value.

[0036] The method for obtaining the information gain is as follows: Based on all relevant indicators in the third indicator dataset of the i-th group of attribute data, construct the corresponding decision tree, calculate the information gain value of the feature corresponding to each relevant indicator in the decision tree, and the information gain value corresponding to each relevant indicator is its importance value.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] This invention analyzes and processes attribute data from tailings ponds in different regions to obtain standard attribute data. When conducting environmental risk assessments on tailings ponds to be assessed, the attribute data of the tailings ponds to be assessed is processed and analyzed with the standard attribute data to obtain weight values ​​for different target layers. Then, the weight values ​​of each relevant indicator are calculated separately to obtain the assessment judgment value and conduct environmental risk assessments on the tailings ponds. The tailings pond environmental risk assessment method of this invention has high accuracy, is more comprehensive, and has authenticity and guidance. Attached Figure Description

[0039] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0040] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0041] like Figure 1 As shown, the present invention provides a method for assessing the environmental risk level of a tailings dam, comprising the following steps:

[0042] S1. Obtain attribute data from multiple tailings ponds. Each tailings pond corresponds to a set of attribute data. Obtain standard attribute data based on multiple sets of attribute data.

[0043] Each set of attribute data includes data at the first target layer, the second target layer, and the third target layer. The first target layer data corresponds to relevant indicators of the tailings dam hazard dimension, the second target layer data corresponds to relevant indicators of the tailings dam control mechanism dimension, and the third target layer data corresponds to relevant indicators of the tailings dam surrounding environment vulnerability dimension. Specifically, the first target layer data includes scores for tailings dam type, tailings dam capacity, tailings dam height, tailings dam mineral type, tailings dam operation status, and tailings dam inflow type. The second target layer data includes scores for return water method, pollution prevention facility construction, management entity, and wastewater discharge. The third target layer data includes scores for distance from river, land use type, distance from nature reserve, affected population, affected GDP, and cross-administrative boundary.

[0044] The data for the first target layer, the second target layer, and the third target layer are obtained in Table 1 below:

[0045] Table 1

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] The buffer zone is defined as follows: the maximum discharge distance of the tailings dam is obtained using existing methods, and the area within this maximum discharge distance is considered the buffer zone. The population and GDP within the buffer zone are then obtained using ArcGIS.

[0052] The numerical points involved in the indicator scoring criteria in Table 1 are all obtained by dividing the data using the existing natural data breakpoint method. That is, all relevant data of the tailings ponds obtained in step S1 are divided using the natural data breakpoint method to obtain the corresponding graded values.

[0053] The relevant indicators within the first target layer of each set of attribute data form the first indicator dataset, the relevant indicators within the second target layer form the second indicator dataset, and the relevant indicators within the third target layer form the third indicator dataset. The standard judgment value for each set of attribute data is calculated using the following formula:

[0054] B i =B i1 +B i2 +B i3 ;

[0055] In the above formula, B i B represents the standard judgment value of the i-th attribute data group. i1 B represents the judgment value of the first indicator dataset in the i-th group of attribute data. i2 B represents the judgment value of the second indicator dataset in the i-th group of attribute data. i3 This represents the judgment value of the third indicator dataset in the i-th group of attribute data, where i ranges from 1 to I, and I represents the total number of attribute data groups processed in step S1.

[0056] B i1 B i2 B i3 The following formula is used to calculate:

[0057]

[0058] In the above formula, This represents the standard score of the j1-th related indicator in the first indicator dataset of the i-th attribute data group. express, Corresponding importance value, This represents the standard score of the j2-th related indicator in the second indicator dataset of the i-th attribute data set. express Corresponding importance value, This represents the standard score of the j3rd related indicator in the third indicator dataset of the i-th attribute data group. express The corresponding importance value.

[0059] The following formula can be used to calculate:

[0060]

[0061] In the above formula, This represents the score of the j1-th related indicator in the first indicator dataset of the i-th attribute data group. Indicates that in group I attribute data, there is... The minimum value among all the same relevant indicators, Indicates that in group I attribute data, there is... The maximum value among all the same relevant indicators; This represents the score of the j2-th related indicator in the second indicator dataset of the i-th attribute data set. Indicates that in group I attribute data, there is... The minimum value among all the same relevant indicators, Indicates that in group I attribute data, there is... The maximum value among all the same relevant indicators; This represents the score of the j3rd related indicator in the third indicator dataset of the i-th attribute data group. Indicates that in group I attribute data, there is... The minimum value among all the same relevant indicators, Indicates that in group I attribute data, there is... The maximum value among all the same relevant indicators; According to Table 1.

[0062] The method for obtaining the information gain is as follows: Based on all relevant indicators in the first indicator dataset of the i-th group of attribute data, construct the corresponding decision tree, calculate the information gain value of the feature corresponding to each relevant indicator in the decision tree, and the information gain value corresponding to each relevant indicator is its importance value.

[0063] The method for obtaining the information gain is as follows: Based on all relevant indicators in the second indicator dataset of the i-th group of attribute data, construct the corresponding decision tree, calculate the information gain value of the feature corresponding to each relevant indicator in the decision tree, and the information gain value corresponding to each relevant indicator is its importance value.

[0064] The method for obtaining the information gain is as follows: Based on all relevant indicators in the third indicator dataset of the i-th group of attribute data, construct the corresponding decision tree, calculate the information gain value of the feature corresponding to each relevant indicator in the decision tree, and the information gain value corresponding to each relevant indicator is its importance value.

[0065] Compare the standard judgment values ​​of all calculated attribute data, and set the attribute data corresponding to the smallest standard judgment value as the standard attribute data.

[0066] S2. Obtain the attribute data of the tailings dam to be evaluated, and combine it with the standard attribute data to obtain the evaluation judgment value of the tailings dam to be evaluated, which is specifically calculated using the following formula:

[0067]

[0068] In the above formula, P represents the evaluation judgment value of the tailings dam to be evaluated, and ω1 represents the weight value of the first target layer in the attribute data of the tailings dam to be evaluated. This represents the weight value of the m1-th relevant indicator in the first target layer of the attribute data of the tailings dam to be evaluated. ω1 represents the score of the m1-th relevant indicator in the first target layer of the attribute data of the tailings dam to be evaluated; ω2 represents the weight value of the second target layer in the attribute data of the tailings dam to be evaluated. This represents the weight value of the m2-th relevant indicator in the second target layer of the attribute data of the tailings dam to be evaluated. ω represents the score of the m2-th relevant indicator in the second target layer of the attribute data of the tailings dam to be evaluated; ω3 represents the weight value of the third target layer in the attribute data of the tailings dam to be evaluated. This represents the weight value of the m3-th relevant indicator in the third target layer of the attribute data of the tailings dam to be evaluated. This represents the score of the m3rd relevant indicator in the third target layer of the attribute data of the tailings dam to be evaluated.

[0069] ω1, ω2, and ω3 are calculated using the following formula:

[0070]

[0071] In the above formula, X1 represents the correlation value between the first target layer in the attribute data of the tailings dam to be evaluated and the first target layer in the standard attribute data; X2 represents the correlation value between the second target layer in the attribute data of the tailings dam to be evaluated and the second target layer in the standard attribute data; and X3 represents the correlation value between the third target layer in the attribute data of the tailings dam to be evaluated and the third target layer in the standard attribute data. This represents the mean of all relevant indicators in the first target layer of the attribute data of the tailings dam to be evaluated. express The scores of relevant indicators in the corresponding standard attribute data. This represents the mean of all relevant indicators in the first target layer of the standard attribute data; This represents the mean of all relevant indicators in the second target layer of the attribute data for the tailings dam to be evaluated. express The scores of relevant indicators in the corresponding standard attribute data. This represents the mean of all relevant indicators in the second target layer of the standard attribute data; This represents the mean of all relevant indicators in the third target layer of the attribute data for the tailings dam to be evaluated. express The scores of relevant indicators in the corresponding standard attribute data. This represents the mean of all relevant indicators in the third target layer of the standard attribute data.

[0072] It is obtained through the following method:

[0073] S21. Using the Analytic Hierarchy Process (AHP), construct a first judgment matrix, a second judgment matrix, and a third judgment matrix for the first, second, and third target layers of the tailings dam attribute data to be evaluated, respectively. Calculate the first, second, and third maximum eigenvalues ​​of each judgment matrix. Based on these eigenvalues, calculate the consistency ratio (CR) of each judgment matrix and perform a consistency check. Obtain the first-level weight value of each relevant indicator within each judgment matrix, specifically calculated using the following formula:

[0074]

[0075] In the above formula, express The corresponding first-level weight value, Indicating the first judgment matrix The corresponding feature weights, Indicates to The normalized value; express The corresponding first-level weight value, Indicating the second judgment matrix The corresponding feature weights, Indicates to The normalized value; express The corresponding first-level weight value, Indicating the third judgment matrix The corresponding feature weights, Indicates to The normalized value.

[0076] S22. Using the entropy weight method, based on the first judgment matrix, the second judgment matrix, and the third judgment matrix, the secondary weight values ​​of the relevant indicators of the tailings dam to be evaluated are obtained, specifically calculated using the following formula:

[0077]

[0078] In the above formula, express The corresponding secondary weight values, express The corresponding information entropy, express The corresponding secondary weight values, express The corresponding information entropy, express The corresponding secondary weight values, express The corresponding information entropy.

[0079] S23. Based on the primary and secondary weight values ​​of each relevant indicator, calculate using the distance function method. Specifically, it is calculated using the following formula:

[0080]

[0081]

[0082] In the above formula, Both represent weighting coefficients. express Distance between them express Distance between them express The distance between them.

[0083] S3. Set the environmental risk level of the tailings dam. The method is as follows: set a first risk value, a second risk value, and a third risk value. If the first risk value is less than the second risk value, and the second risk value is less than the third risk value, then the level is low risk if it is less than or equal to the first risk value. If it is greater than the first risk value but less than or equal to the second risk value, then the level is medium risk. If it is greater than the second risk value but less than or equal to the third risk value, then the level is high risk. If it is greater than the third risk value, then the level is extremely high risk. Compare the assessment judgment value of the tailings dam to be assessed obtained in step S2 with the first risk value, the second risk value, and the third risk value respectively to obtain the environmental risk level of the tailings dam to be assessed.

[0084] S4. After determining the environmental risk level of the tailings dam to be assessed, bring the attribute data of the assessed tailings dam into step S1, repeat step S1, and update the standard attribute data.

[0085] This invention analyzes and processes attribute data from tailings ponds in different regions to obtain standard attribute data. When conducting environmental risk assessments on tailings ponds to be assessed, the attribute data of the tailings ponds to be assessed is processed and analyzed with the standard attribute data to obtain weight values ​​for different target layers. Then, the weight values ​​of each relevant indicator are calculated separately to obtain the assessment judgment value and conduct environmental risk assessments on the tailings ponds. The tailings pond environmental risk assessment method of this invention has high accuracy, is more comprehensive, and has authenticity and guidance.

[0086] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A method for assessing the risk level of an environment of a tailings pond, characterized by, Comprise the following steps: S1, obtain the attribute data of a plurality of tailings ponds, each set of attribute data of the tailings ponds includes first target layer data, second target layer data, and third target layer data, the first target layer data, the second target layer data, and the third target layer data all include a plurality of related indexes, and standard attribute data is obtained according to a plurality of sets of attribute data; the method for obtaining the standard attribute data is that, for each set of attribute data in the S1 step, a corresponding standard judgment value is calculated, all the standard judgment values are compared, and the attribute data corresponding to the smallest standard judgment value is set as the standard attribute data; the first target layer data includes tailings pond type score, tailings pond capacity score, tailings pond dam height score, tailings pond ore type score, tailings pond operation status score, and tailings pond storage type score, the second target layer data includes backwater mode score, pollution prevention and control facility construction score, management subject score, and wastewater discharge score, and the third target layer data includes distance from river score, land use type score, distance from nature reserve score, affected population score, affected GDP score, and cross-administrative boundary score; The related indexes in the first target layer in each set of attribute data form a first index data set, the related indexes in the second target layer form a second index data set, and the related indexes in the third target layer form a third index data set; the standard judgment value of each set of attribute data is calculated according to the following formula: In the above formula, a standard judgment value representing the i-th set of attribute data, a judgment value representing a first index data set in the i-th set of attribute data, a judgment value representing a second index data set in the i-th set of attribute data, a judgment value representing a third index data set in the i-th set of attribute data, i is 1 to I, and I represents the total number of sets of attribute data processed in the S1 step. , , was calculated according to the following formula: In the above formulae, denotes the standard score of the jth relevant indicator in the first indicator data set in the ith set of attribute data, denotes, the corresponding importance value, denotes the standard score of the jth relevant indicator in the second indicator data set in the ith set of attribute data, denotes, the corresponding importance value, denotes the standard score of the jth relevant indicator in the third indicator data set in the ith set of attribute data, denotes, the corresponding importance value;​​​ , , The acquisition method is: based on all related indicators in the first indicator data set, all related indicators in the second indicator data set, and all related indicators in the third indicator data set in the i-th group of attribute data, respectively, a corresponding decision tree is constructed, the information gain value of the feature corresponding to each related indicator in the decision tree is calculated, and the information gain value corresponding to each related indicator is its importance value. S2, obtain attribute data of a tailings pond to be evaluated, obtain weight values corresponding to the first target layer data, the second target layer data, and the third target layer data in the attribute data of the tailings pond to be evaluated according to the standard attribute data, and calculate an evaluation judgment value of the tailings pond to be evaluated according to the weight values corresponding to the first target layer data, the second target layer data, and the third target layer data and corresponding related indexes of the tailings pond to be evaluated; the evaluation judgment value of the tailings pond to be evaluated in the S2 step is calculated according to the following formula: In the above formula, This indicates the assessment value of the tailings dam to be evaluated. This represents the weight value of the first target layer in the attribute data of the tailings dam to be evaluated. This indicates the first target layer of the attribute data of the tailings dam to be evaluated. The weight values ​​of each relevant indicator, This indicates the first target layer of the attribute data of the tailings dam to be evaluated. The scores of each relevant indicator; This represents the weight value of the second target layer in the attribute data of the tailings dam to be evaluated. This indicates the second target layer in the attribute data of the tailings dam to be evaluated. The weight values ​​of each relevant indicator, This indicates the second target layer in the attribute data of the tailings dam to be evaluated. The scores of each relevant indicator; This represents the weight value of the third target layer in the attribute data of the tailings dam to be evaluated. This indicates the third target layer in the attribute data of the tailings dam to be evaluated. The weight values ​​of each relevant indicator, This indicates the third target layer in the attribute data of the tailings dam to be evaluated. The scores of each relevant indicator; , , was calculated according to the following formula: In the above formula, represents a correlation value of the first target layer in the attribute data of the tailing pond to be evaluated and the first target layer in the standard attribute data, represents a correlation value of the second target layer in the attribute data of the tailing pond to be evaluated and the second target layer in the standard attribute data, represents a correlation value of the third target layer in the attribute data of the tailing pond to be evaluated and the third target layer in the standard attribute data. , , was calculated according to the following formula: in the above formula, denotes the mean value of all relevant indicators in the first target layer in the attribute data of the tailings pond to be evaluated, denotes the score of the relevant indicator in the corresponding standard attribute data, denotes the mean value of all relevant indicators in the first target layer in the standard attribute data; denotes the mean value of all relevant indicators in the second target layer in the attribute data of the tailings pond to be evaluated, denotes the score of the relevant indicator in the corresponding standard attribute data, denotes the mean value of all relevant indicators in the second target layer in the standard attribute data; denotes the mean value of all relevant indicators in the third target layer in the attribute data of the tailings pond to be evaluated, denotes the score of the relevant indicator in the corresponding standard attribute data, denotes the mean value of all relevant indicators in the third target layer in the standard attribute data 、 、 By the following method: S21, use the analytic hierarchy process to construct a first judgment matrix, a second judgment matrix, and a third judgment matrix for the first target layer, the second target layer, and the third target layer in the attribute data of the tailings pond to be evaluated respectively, and obtain a first-level weight value of each related index in the first judgment matrix, the second judgment matrix, and the third judgment matrix respectively; S22, use the entropy weight method to obtain a second-level weight value of the related indexes of the tailings pond to be evaluated according to the first judgment matrix, the second judgment matrix, and the third judgment matrix; S23, according to the first level weight value, the second level weight value of each relevant index, using distance function method respectively calculate 、 、 ; S3, set tailings pond environmental risk levels, including a low risk level, a medium risk level, a high risk level, and an ultra-high risk level, set a first risk value, a second risk value, and a third risk value, the low risk level is less than or equal to the first risk value, the medium risk level is greater than the first risk value and less than or equal to the second risk value, the high risk level is greater than the second risk value and less than or equal to the third risk value, and the ultra-high risk level is greater than the third risk value; According to the evaluation judgment value of the tailings pond to be evaluated obtained in the S2 step, the environmental risk level of the tailings pond to be evaluated is obtained.

2. The method for evaluating the environmental risk grade of a tailings pond according to claim 1, characterized in that, , , By calculation from the following formula: In the above formula, This indicates the first indicator in the i-th group of attribute data. The scores of each relevant indicator Indicates that in group I attribute data, there is... The minimum value among all the same relevant indicators, Indicates that in group I attribute data, there is... The maximum value among all the same relevant indicators; This indicates that the second indicator in the i-th group of attribute data is the first... The scores of each relevant indicator Indicates that in group I attribute data, there is... The minimum value among all the same relevant indicators, Indicates that in group I attribute data, there is... The maximum value among all the same relevant indicators; This indicates the third indicator in the i-th group of attribute data. The scores of each relevant indicator Indicates that in group I attribute data, there is... The minimum value among all the same relevant indicators, Indicates that in group I attribute data, there is... The maximum value among all the same relevant indicators.

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