Tailing pond environmental risk grade assessment method

Abstract

The invention relates to the technical field of tailing pond risk assessment, in particular to a tailing pond environment risk grade assessment method. Comprising the following steps that S1, attribute data of a plurality of tailings ponds are obtained, the attribute data of each group of tailings ponds comprise first target layer data, second target layer data and third target layer data and all comprise a plurality of related indexes, and standard attribute data are obtained; s2, obtaining attribute data of a to-be-evaluated tailing pond, obtaining weight values corresponding to first target layer data, second target layer data and third target layer data in the attribute data of the to-be-evaluated tailing pond, and calculating an evaluation judgment value of the to-be-evaluated tailing pond according to the corresponding weight values and related indexes corresponding to the to-be-evaluated tailing pond; s3, setting an environmental risk grade of the tailing pond, and obtaining the environmental risk grade of the tailing pond to be evaluated according to the evaluation judgment value of the tailing pond to be evaluated; the method improves the environmental risk grade evaluation accuracy of the tailings pond.

Description

Technical Field

[0001] The present invention relates to the technical field of tailings pond risk assessment, and in particular to a method for assessing the environmental risk level of a tailings pond. Background Art

[0002] Tailings ponds are essential components of mine processing plants, but they also present a significant risk. Tailings often contain hazardous substances such as heavy metals and chemicals. A dam failure would cause significant damage, severely impacting the local environment, life, and property. In recent years, incidents of environmental pollution, casualties, and property damage caused by tailings pond accidents have occurred frequently. Given the complexity of tailings pond environmental risks, their significant impact from natural disasters in recent years, and the prominent potential risks, it is crucial to establish a tiered environmental monitoring indicator system for tailings ponds.

[0003] Prior art CN106600153A discloses a method for tailings dam breach risk assessment, including: constructing an evaluation factor set F; constructing a comment level set U corresponding to each evaluation factor in the evaluation factor set F, and determining the weight S of each evaluation factor; obtaining the corresponding comment level of each evaluation factor and the comment level set U to obtain a fuzzy judgment matrix Ri; obtaining the tailings dam risk factor evaluation matrix Zi, and establishing a tailings dam risk evaluation matrix for each tailings dam risk factor evaluation matrix Zi, performing a secondary fuzzy comprehensive evaluation C, and determining the level of the evaluation object according to the maximum membership principle. However, in the above-mentioned tailings dam risk assessment method, the indicators that affect the tailings dam risk are not fully considered, and the accuracy of the tailings dam risk assessment is low.

[0004] Therefore, there is an urgent need to provide a tailings dam environmental risk level assessment method to improve the accuracy of tailings dam environmental risk level assessment compared with the existing technology. Summary of the Invention

[0005] The present invention solves the technical problems existing in the prior art and provides a method for evaluating the environmental risk level of a tailings pond.

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

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

[0008] S1. Acquire attribute data of multiple tailings ponds, where each set of attribute data of the tailings pond includes first target layer data, second target layer data, and third target layer data, each of which includes multiple relevant indicators, and obtain standard attribute data based on the multiple sets of attribute data;

[0009] S2. Obtain attribute data of the tailings pond to be assessed, and 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 assessed based on the standard attribute data; and calculate the assessment judgment value of the tailings pond to be assessed based on the weight values ​​corresponding to the first target layer data, the second target layer data, and the third target layer data and the relevant indicators corresponding to the tailings pond to be assessed;

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

[0011] Furthermore, the evaluation judgment value of the tailings pond to be evaluated 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 pond to be evaluated, ω1 represents the weight value of the first target layer in the attribute data of the tailings pond to be evaluated, Indicates the weight value of the m1th related indicator in the first target layer in the attribute data of the tailings pond to be evaluated, represents the score of the m1th related indicator in the first target layer in the attribute data of the tailings pond to be evaluated; ω2 represents the weight value of the second target layer in the attribute data of the tailings pond to be evaluated, Indicates the weight value of the m2th related indicator in the second target layer in the attribute data of the tailings pond to be evaluated, represents the score of the m2th related indicator in the second target layer in the attribute data of the tailings pond to be evaluated; ω3 represents the weight value of the third target layer in the attribute data of the tailings pond to be evaluated, Indicates the weight value of the m3th related indicator in the third target layer in the attribute data of the tailings pond to be evaluated, It represents the score of the m3th related indicator in the third target layer in the attribute data of the tailings pond to be evaluated.

[0014] Furthermore, ω1, ω2, and ω3 are calculated according to 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 pond 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 pond 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 pond to be evaluated and the third target layer in the standard attribute data.

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

[0018]

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

[0020] Furthermore, Obtained by the following method:

[0021] S21. Using the analytic hierarchy process, a first judgment matrix, a second judgment matrix, and a third judgment matrix are constructed 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. The first-level weight value of each relevant indicator therein is obtained according to the first judgment matrix, the second judgment matrix, and the third judgment matrix;

[0022] S22. Using the entropy weight method, according to 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 pond to be evaluated;

[0023] S23, according to the first-level weight value and second-level weight value of each relevant indicator, use the distance function method to calculate

[0024] Furthermore, the method for obtaining the standard attribute data is as follows: for each set of attribute data in step S1, the corresponding standard judgment value is calculated, all the standard judgment values ​​are compared, and the attribute data corresponding to the minimum standard judgment value is set as the standard attribute data.

[0025] Furthermore, the relevant indicators in the first target layer of each set of attribute data form a first indicator data set, the relevant indicators in the second target layer form a second indicator data set, and the relevant indicators in the third target layer form a third indicator data set; the standard judgment value of 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 represents the standard judgment value of the i-th group of attribute data, B i1 represents the judgment value of the first indicator data set in the i-th group of attribute data, B i2 represents the judgment value of the second indicator data set in the i-th group of attribute data, B i3 It represents the judgment value of the third indicator data set in the i-th group of attribute data, i ranges from 1 to I, and I represents the total number of groups of attribute data processed in step S1.

[0028] Furthermore, B i1 、B i2 、B i3 Calculated according to the following formula:

[0029]

[0030] In the above formula, represents the standard score of the j1th related indicator in the first indicator data set in the i-th group of attribute data, express, The corresponding important values ​​are represents the standard score of the j2th related indicator in the second indicator data set in the i-th group of attribute data, express The corresponding important values ​​are represents the standard score of the j3th related indicator in the third indicator data set in the i-th group of attribute data, express The corresponding important value.

[0031] Furthermore, Calculated by the following formula:

[0032]

[0033] In the above formula, represents the score of the j1th related indicator in the first indicator data set in the i-th group of attribute data, Indicates the I group of attribute data The minimum value of all related indicators of the same Indicates the I group of attribute data The maximum value among all related indicators; represents the score of the j2th related indicator in the second indicator data set in the i-th group of attribute data, Indicates the I group of attribute data The minimum value of all related indicators of the same Indicates the I group of attribute data The maximum value among all related indicators; represents the score of the j3th related indicator in the third indicator data set in the i-th group of attribute data, Indicates the I group of attribute data The minimum value of all related indicators of the same Indicates the I group of attribute data The maximum value among all related indicators.

[0034] Furthermore, The method for obtaining is as follows: based on all relevant indicators in the first indicator data set in the i-th group of attribute data, a corresponding decision tree is constructed, and the information gain value of the feature corresponding to each relevant indicator in the decision tree is calculated. The information gain value corresponding to each relevant indicator is its important value;

[0035] The method for obtaining is as follows: based on all relevant indicators in the second indicator data set in the i-th group of attribute data, a corresponding decision tree is constructed, and the information gain value of the feature corresponding to each relevant indicator in the decision tree is calculated. The information gain value corresponding to each relevant indicator is its important value;

[0036] The method for obtaining is: based on all relevant indicators in the third indicator data set in the i-th group of attribute data, a corresponding decision tree is constructed, and the information gain value of the feature corresponding to each relevant indicator in the decision tree is calculated. The information gain value corresponding to each relevant indicator is its important value.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention obtains standard attribute data by analyzing and processing the attribute data of tailings ponds in different regions in the past. When conducting an environmental risk assessment on the tailings pond to be assessed, the attribute data of the tailings pond to be assessed and the standard attribute data are processed and analyzed to obtain weight values ​​of different target layers. Then, the weight values ​​of each relevant indicator are calculated respectively to obtain an assessment judgment value, and an environmental risk assessment of the tailings pond is conducted. The tailings pond environmental risk assessment method of the present invention has high accuracy, is more comprehensive, and is authentic and instructive. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection 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 pond, comprising the following steps:

[0042] S1. Acquire attribute data of multiple tailings ponds, where each tailings pond corresponds to a set of attribute data, and obtain standard attribute data based on the multiple sets of attribute data.

[0043] Each set of attribute data includes first target layer data, second target layer data, and third target layer data. The first target layer data corresponds to relevant indicators of the tailings pond hazard dimension, the second target layer data corresponds to relevant indicators of the tailings pond control mechanism dimension, and the third target layer data corresponds to relevant indicators of the tailings pond surrounding environmental vulnerability dimension; specifically, the first target layer data includes the tailings pond type score, tailings pond storage capacity score, tailings pond dam height score, tailings pond ore type score, tailings pond operation status score, and tailings pond entry type score; the second target layer data includes the backwater method score, pollution control facility construction score, management subject score, and wastewater discharge score; the third target layer data includes the distance to the river score, land use type score, distance to nature reserve score, affected population score, affected GDP score, and cross-administrative boundary score.

[0044] The first target layer data, the second target layer data, and the third target layer data are specifically obtained from the following Table 1:

[0045] Table 1

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] The buffer zone was delineated using existing methods to determine the maximum downflow distance from the tailings pond. The areas within this maximum downflow distance were designated as buffer zones. The population and GDP within the buffer zones were obtained using ArcGIS.

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

[0053] The relevant indicators in the first target layer of each set of attribute data form the first indicator data set, the relevant indicators in the second target layer form the second indicator data set, and the relevant indicators in the third target layer form the third indicator data set. The standard judgment value of 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 represents the standard judgment value of the i-th group of attribute data, B i1 represents the judgment value of the first indicator data set in the i-th group of attribute data, B i2 represents the judgment value of the second indicator data set in the i-th group of attribute data, B i3 It represents the judgment value of the third indicator data set in the i-th group of attribute data, i ranges from 1 to I, and I represents the total number of groups of attribute data processed in step S1.

[0056] B i1 、B i2 、B i3 Calculated according to the following formula:

[0057]

[0058] In the above formula, represents the standard score of the j1th related indicator in the first indicator data set in the i-th group of attribute data, express, The corresponding important values ​​are represents the standard score of the j2th related indicator in the second indicator data set in the i-th group of attribute data, express The corresponding important values ​​are represents the standard score of the j3th related indicator in the third indicator data set in the i-th group of attribute data, express The corresponding important value.

[0059] Calculated by the following formula:

[0060]

[0061] In the above formula, represents the score of the j1th related indicator in the first indicator data set in the i-th group of attribute data, Indicates the I group of attribute data The minimum value of all related indicators of the same Indicates the I group of attribute data The maximum value among all related indicators; represents the score of the j2th related indicator in the second indicator data set in the i-th group of attribute data, Indicates the I group of attribute data The minimum value of all related indicators of the same Indicates the I group of attribute data The maximum value among all related indicators; represents the score of the j3th related indicator in the third indicator data set in the i-th group of attribute data, Indicates the I group of attribute data The minimum value of all related indicators of the same Indicates the I group of attribute data The maximum value among all related indicators; Obtained according to Table 1.

[0062] The method for obtaining is: based on all relevant indicators in the first indicator data set in the i-th group of attribute data, a corresponding decision tree is constructed, and the information gain value of the feature corresponding to each relevant indicator in the decision tree is calculated. The information gain value corresponding to each relevant indicator is its important value.

[0063] The method for obtaining is: based on all relevant indicators in the second indicator data set in the i-th group of attribute data, a corresponding decision tree is constructed, and the information gain value of the feature corresponding to each relevant indicator in the decision tree is calculated. The information gain value corresponding to each relevant indicator is its important value.

[0064] The method for obtaining is: based on all relevant indicators in the third indicator data set in the i-th group of attribute data, a corresponding decision tree is constructed, and the information gain value of the feature corresponding to each relevant indicator in the decision tree is calculated. The information gain value corresponding to each relevant indicator is its important value.

[0065] The calculated standard judgment values ​​of all attribute data are compared, and the attribute data corresponding to the minimum standard judgment value is set as the standard attribute data.

[0066] S2. Obtain the attribute data of the tailings pond to be assessed, and combine it with the standard attribute data to obtain the assessment judgment value of the tailings pond to be assessed, which is specifically calculated by the following formula:

[0067]

[0068] In the above formula, P represents the evaluation judgment value of the tailings pond to be evaluated, ω1 represents the weight value of the first target layer in the attribute data of the tailings pond to be evaluated, Indicates the weight value of the m1th related indicator in the first target layer in the attribute data of the tailings pond to be evaluated, represents the score of the m1th related indicator in the first target layer in the attribute data of the tailings pond to be evaluated; ω2 represents the weight value of the second target layer in the attribute data of the tailings pond to be evaluated, Indicates the weight value of the m2th related indicator in the second target layer in the attribute data of the tailings pond to be evaluated, represents the score of the m2th related indicator in the second target layer in the attribute data of the tailings pond to be evaluated; ω3 represents the weight value of the third target layer in the attribute data of the tailings pond to be evaluated, Indicates the weight value of the m3th related indicator in the third target layer in the attribute data of the tailings pond to be evaluated, It represents the score of the m3th related indicator in the third target layer in the attribute data of the tailings pond to be evaluated.

[0069] ω1, ω2, and ω3 are calculated according to 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 pond 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 pond 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 pond to be evaluated and the third target layer in the standard attribute data; Represents the mean value of all relevant indicators in the first target layer of the attribute data of the tailings pond to be evaluated, express The scores of relevant indicators in the corresponding standard attribute data, Represents the mean of all relevant indicators in the first target layer in the standard attribute data; Represents the mean value of all relevant indicators in the second target layer of the attribute data of the tailings pond to be evaluated, express The scores of relevant indicators in the corresponding standard attribute data, Represents the mean of all relevant indicators in the second target layer of the standard attribute data; Represents the mean value of all relevant indicators in the third target layer in the attribute data of the tailings pond to be evaluated, express The scores of relevant indicators in the corresponding standard attribute data, Represents the mean of all relevant indicators in the third target layer in the standard attribute data.

[0072] Obtained by the following method:

[0073] S21. Using the analytic hierarchy process (AHP), 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, a first judgment matrix, a second judgment matrix, and a third judgment matrix are respectively constructed, and the first maximum eigenvalue, the second maximum eigenvalue, and the third maximum eigenvalue of the first judgment matrix, the second judgment matrix, and the third judgment matrix are respectively calculated. According to the first maximum eigenvalue, the second maximum eigenvalue, and the third maximum eigenvalue, the consistency ratio CR of each judgment matrix is ​​calculated, and a consistency test is performed. According to the first judgment matrix, the second judgment matrix, and the third judgment matrix, the first-level weight value of each relevant indicator therein is obtained, which is specifically calculated by the following formula:

[0074]

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

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

[0077]

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

[0079] S23, according to the first-level weight value and second-level weight value of each relevant indicator, use the distance function method to calculate It is calculated by the following formula:

[0080]

[0081]

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

[0083] S3. Set the environmental risk level of the tailings pond. The setting method is: set the first risk value, the second risk value, and the third risk value. The first risk value is less than the second risk value, the second risk value is less than the third risk value, and the risk level is less than or equal to the first risk value. The risk level is low, the risk level is greater than the first risk value and less than or equal to the second risk value, the risk level is medium, the risk level is greater than the second risk value and less than or equal to the third risk value, and the risk level is ultra-high; the assessment judgment value of the tailings pond to be assessed obtained in step S2 is compared with the first risk value, the second risk value, and the third risk value respectively to obtain the environmental risk level of the tailings pond to be assessed.

[0084] S4. After determining the environmental risk level of the tailings pond to be assessed, the attribute data of the assessed tailings pond is brought into step S1, and step S1 is repeated to update the standard attribute data.

[0085] The present invention obtains standard attribute data by analyzing and processing the attribute data of tailings ponds in different regions in the past. When conducting an environmental risk assessment on the tailings pond to be assessed, the attribute data of the tailings pond to be assessed and the standard attribute data are processed and analyzed to obtain weight values ​​of different target layers. Then, the weight values ​​of each relevant indicator are calculated respectively to obtain an assessment judgment value, and an environmental risk assessment of the tailings pond is conducted. The tailings pond environmental risk assessment method of the present invention has high accuracy, is more comprehensive, and is authentic and instructive.

[0086] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for assessing the environmental risk level of a tailings pond, characterized in that: The following steps are involved: S1. Acquire attribute data of multiple tailings ponds, where each set of attribute data of the tailings pond includes first target layer data, second target layer data, and third target layer data, each of which includes multiple relevant indicators, and obtain standard attribute data based on the multiple sets of attribute data; S2. Obtain attribute data of the tailings pond to be assessed, and 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 assessed based on the standard attribute data; and calculate the assessment judgment value of the tailings pond to be assessed based on the weight values ​​corresponding to the first target layer data, the second target layer data, and the third target layer data and the relevant indicators corresponding to the tailings pond to be assessed; S3. Set the environmental risk level of the tailings pond, including low risk level, medium risk level, high risk level and ultra-high risk level, and set the first risk value, second risk value and third risk value. The risk level is less than or equal to the first risk value, the risk level is medium, the risk level is high, the risk level is greater than the second risk value and less than or equal to the third risk value, and the risk level is ultra-high; According to the assessment judgment value of the tailings pond to be assessed obtained in step S2, the environmental risk level of the tailings pond to be assessed is obtained.

2. A tailings pond environmental risk level assessment method according to claim 1, characterized in that: The assessment judgment value of the tailings pond to be assessed in step S2 is calculated according to the following formula: In the above formula, P represents the evaluation judgment value of the tailings pond to be evaluated, ω1 represents the weight value of the first target layer in the attribute data of the tailings pond to be evaluated, Indicates the weight value of the m1th related indicator in the first target layer in the attribute data of the tailings pond to be evaluated, represents the score of the m1th related indicator in the first target layer in the attribute data of the tailings pond to be evaluated; ω2 represents the weight value of the second target layer in the attribute data of the tailings pond to be evaluated, Indicates the weight value of the m2th related indicator in the second target layer in the attribute data of the tailings pond to be evaluated, represents the score of the m2th related indicator in the second target layer in the attribute data of the tailings pond to be evaluated; ω3 represents the weight value of the third target layer in the attribute data of the tailings pond to be evaluated, Indicates the weight value of the m3th related indicator in the third target layer in the attribute data of the tailings pond to be evaluated, It represents the score of the m3th related indicator in the third target layer in the attribute data of the tailings pond to be evaluated.

3. A tailings dam environmental risk level assessment method according to claim 2, characterized in that: ω1, ω2, and ω3 are calculated according to the following formula: In the above formula, X1 represents the correlation value between the first target layer in the attribute data of the tailings pond 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 pond 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 pond to be evaluated and the third target layer in the standard attribute data.

4. A method for assessing the environmental risk level of a tailings dam according to claim 3, characterized in that: X1, X2, and X3 are calculated according to the following formula: In the above formula, Represents the mean value of all relevant indicators in the first target layer of the attribute data of the tailings pond to be evaluated, express The scores of relevant indicators in the corresponding standard attribute data, Represents the mean of all relevant indicators in the first target layer in the standard attribute data; Represents the mean value of all relevant indicators in the second target layer of the attribute data of the tailings pond to be evaluated, express The scores of relevant indicators in the corresponding standard attribute data, Represents the mean of all relevant indicators in the second target layer in the standard attribute data; Represents the mean value of all relevant indicators in the third target layer of the attribute data of the tailings pond to be evaluated, express The scores of relevant indicators in the corresponding standard attribute data, Represents the mean of all relevant indicators in the third target layer in the standard attribute data.

5. A method for assessing the environmental risk level of a tailings dam according to claim 2, characterized in that: Obtained by the following method: S21. Using the analytic hierarchy process, a first judgment matrix, a second judgment matrix, and a third judgment matrix are constructed 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. The first-level weight value of each relevant indicator therein is obtained according to the first judgment matrix, the second judgment matrix, and the third judgment matrix; S22. Using the entropy weight method, according to 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 pond to be evaluated; S23, according to the first-level weight value and second-level weight value of each relevant indicator, use the distance function method to calculate 6. A method for assessing the environmental risk level of a tailings dam according to claim 1, characterized in that: The method for obtaining standard attribute data is as follows: for each set of attribute data in step S1, the corresponding standard judgment value is calculated, all the standard judgment values ​​are compared, and the attribute data corresponding to the minimum standard judgment value is set as the standard attribute data.

7. A method for assessing the environmental risk level of a tailings dam according to claim 6, characterized in that: The relevant indicators in the first target layer of each set of attribute data form the first indicator data set, the relevant indicators in the second target layer form the second indicator data set, and the relevant indicators in the third target layer form the third indicator data set. The standard judgment value of each set of attribute data is calculated according to the following formula: B i =B i1 +B i2 +B i3 ; In the above formula, B i represents the standard judgment value of the i-th group of attribute data, B i1 represents the judgment value of the first indicator data set in the i-th group of attribute data, B i2 represents the judgment value of the second indicator data set in the i-th group of attribute data, B i3 It represents the judgment value of the third indicator data set in the i-th group of attribute data, i ranges from 1 to I, and I represents the total number of groups of attribute data processed in step S1.

8. A method for assessing the environmental risk level of a tailings dam according to claim 7, characterized in that: B i1 、B i2 、B i3 Calculated according to the following formula: In the above formula, represents the standard score of the j1th related indicator in the first indicator data set in the i-th group of attribute data, express, The corresponding important values ​​are represents the standard score of the j2th related indicator in the second indicator data set in the i-th group of attribute data, express The corresponding important values ​​are represents the standard score of the j3th related indicator in the third indicator data set in the i-th group of attribute data, express The corresponding important value.

9. A method for assessing the environmental risk level of a tailings dam according to claim 8, characterized in that: Calculated by the following formula: In the above formula, represents the score of the j1th related indicator in the first indicator data set in the i-th group of attribute data, Indicates the I group of attribute data The minimum value of all related indicators of the same Indicates the I group of attribute data The maximum value among all related indicators; represents the score of the j2th related indicator in the second indicator data set in the i-th group of attribute data, Indicates the I group of attribute data The minimum value of all related indicators of the same Indicates the I group of attribute data The maximum value among all related indicators; represents the score of the j3th related indicator in the third indicator data set in the i-th group of attribute data, Indicates the I group of attribute data The minimum value of all related indicators of the same Indicates the I group of attribute data The maximum value among all related indicators.

10. A method for assessing the environmental risk level of a tailings dam according to claim 9, characterized in that: The method for obtaining is as follows: based on all relevant indicators in the first indicator data set in the i-th group of attribute data, a corresponding decision tree is constructed, and the information gain value of the feature corresponding to each relevant indicator in the decision tree is calculated. The information gain value corresponding to each relevant indicator is its important value; The method for obtaining is as follows: based on all relevant indicators in the second indicator data set in the i-th group of attribute data, a corresponding decision tree is constructed, and the information gain value of the feature corresponding to each relevant indicator in the decision tree is calculated. The information gain value corresponding to each relevant indicator is its important value; The method for obtaining is: based on all relevant indicators in the third indicator data set in the i-th group of attribute data, a corresponding decision tree is constructed, and the information gain value of the feature corresponding to each relevant indicator in the decision tree is calculated. The information gain value corresponding to each relevant indicator is its important value.

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