Tailings pond flood overtopping stability evaluation method, device and electronic equipment

Abstract

The present disclosure proposes a method, device and electronic device for evaluating the flood overtopping stability of a tailings pond, including: obtaining candidate indicators of a target tailings; weighting each candidate indicator based on a hierarchical analysis method to obtain a subjective weight, and weighting each candidate indicator based on a structural entropy weight method to obtain an evaluation indicator weight; determining a comprehensive evaluation indicator weight based on the subjective weight and the evaluation indicator weight; constructing a fuzzy judgment matrix based on each candidate indicator and its corresponding comprehensive evaluation indicator weight, and evaluating the flood overtopping stability of the target tailings based on the fuzzy judgment matrix. Thus, by combining the hierarchical analysis method with the structural entropy weight method to judge the candidate indicators, the influence of human subjective judgment can be reduced, the objectivity and accuracy of the indicator weights in the fuzzy evaluation system, and the greater flexibility and applicability of the scheme can be improved, so as to improve the accuracy of evaluating the flood overtopping stability of the target tailings.

Description

Technical Field

[0001] The present invention relates to the technical field of tailings ponds, and in particular to a method, device and electronic equipment for evaluating flood overtopping stability of tailings ponds. Background Art

[0002] Tailings dam is an important facility for storing tailings in mining production. Tailings are solid waste materials after ore selection. Tailings dams provide centralized storage space for tailings by building dams to intercept valley mouths or utilizing depressions. It not only helps regulate floods and store water for reuse, but also protects the surrounding environment from pollution by harmful substances in tailings. The safe management of tailings dams is crucial, and their stability and safety must be ensured to prevent possible accident risks.

[0003] Flood overtopping is a major form of dam failure in tailings dams. When the drainage system of a tailings dam fails or an extreme flood occurs, flood overtopping will occur. Flood overtopping of tailings dams usually occurs during the flood season. Continuous rainfall and floods, insufficient dry beach length, and failure of flood discharge facilities are important causes of flood overtopping and dam failure in tailings dams. For a specific tailings dam project, the probability of flood overtopping and dam failure is mainly related to the uncertainty of flood elements, wind and wave elements, and the performance and operation of flood discharge facilities. Since the flood overtopping of tailings dams is affected by multiple factors, and the factors are uncertain, the single hierarchical analysis method is affected by subjective weights. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, an object of the present disclosure is to propose a method for evaluating the flood overtopping stability of a tailings pond.

[0006] The second objective of the present disclosure is to provide a device for evaluating the flood overtopping stability of a tailings pond.

[0007] A third objective of the present disclosure is to provide an electronic device.

[0008] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0009] A fifth object of the present disclosure is to provide a computer program product.

[0010] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes a method for evaluating the flood overtopping stability of a tailings pond, comprising: obtaining candidate indicators of a target tailings; performing weight calculation on each candidate indicator based on a hierarchical analysis method to obtain a subjective weight, and performing weight calculation on each candidate indicator based on a structural entropy weight method to obtain an evaluation indicator weight; determining a comprehensive evaluation indicator weight based on the subjective weight and the evaluation indicator weight; constructing a fuzzy judgment matrix based on each candidate indicator and its corresponding comprehensive evaluation indicator weight, and evaluating the flood overtopping stability of the target tailings based on the fuzzy judgment matrix.

[0011] According to one embodiment of the present disclosure, the weight calculation of each candidate indicator based on the hierarchical analysis method includes: establishing a hierarchical model based on the candidate indicators, the hierarchical model includes multiple layers; performing pairwise comparisons between adjacent layers of the hierarchical model, and generating a judgment matrix based on the comparison results; determining a first weight vector for each candidate indicator based on the judgment matrix, and determining the subjective weight of the corresponding candidate indicator based on the first weight vector.

[0012] According to one embodiment of the present disclosure, before determining the subjective weight of the corresponding candidate indicator based on the first weight vector, it also includes: calculating the consistency ratio of the judgment matrix and comparing the consistency ratio with a set threshold; in response to the consistency ratio being less than the set threshold, determining the subjective weight of the corresponding candidate indicator based on the first weight vector; or, in response to the consistency ratio being greater than or equal to the set threshold, returning to execute the establishment of the hierarchical model based on the candidate indicator and its subsequent steps.

[0013] According to one embodiment of the present disclosure, determining the second weight vector of each candidate indicator includes: obtaining the scoring weight corresponding to each scoring round; for any candidate indicator, calculating the average recognition and cognitive blindness of the candidate indicator in all scoring rounds based on the scoring weight; and determining the second weight vector of the candidate indicator based on the average recognition and the cognitive blindness.

[0014] According to one embodiment of the present disclosure, the calculating the average recognition of the candidate indicator in all rounds of scoring based on the scoring weight includes: taking the average value of all information entropies of the candidate indicator in the information entropy matrix as the average recognition.

[0015] According to one embodiment of the present disclosure, the cognitive blindness of the candidate indicator in all rounds of scoring is calculated based on the scoring weights, including: subtracting the maximum and minimum values ​​of all information entropies of the candidate indicator in the information entropy matrix from the average cognition respectively, and calculating the average of the two differences as the cognitive blindness only.

[0016] According to one embodiment of the present disclosure, the determining of the comprehensive evaluation index weight based on the subjective weight and the evaluation index weight includes: for any candidate index, respectively obtaining a first proportion of the subjective weight and a second proportion of the evaluation index weight; multiplying the first proportion by the subjective weight to calculate a first factor, and multiplying the second proportion by the evaluation index weight to calculate a second factor; adding the first factor and the second factor to calculate the comprehensive evaluation index weight.

[0017] To achieve the above-mentioned purpose, the second aspect of the present disclosure proposes a tailings pond flood overtopping stability evaluation device, including: an acquisition module, used to obtain candidate indicators of the target tailings; a calculation module, used to perform weight calculation on each candidate indicator based on a hierarchical analysis method to obtain a subjective weight, and to perform weight calculation on each candidate indicator based on a structural entropy weight method to obtain an evaluation index weight; a determination module, used to determine the comprehensive evaluation index weight based on the subjective weight and the evaluation index weight; an evaluation module, used to construct a fuzzy judgment matrix based on each candidate indicator and its corresponding comprehensive evaluation index weight, and evaluate the flooding stability of the target tailings based on the fuzzy judgment matrix.

[0018] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the method for evaluating the flood overtopping stability of a tailings pond as described in the first aspect embodiment of the present disclosure.

[0019] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the tailings pond flood overtopping stability evaluation method as described in the first aspect embodiment of the present disclosure.

[0020] To achieve the above-mentioned purpose, the fifth aspect of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the tailings pond flood overtopping stability evaluation method as described in the first aspect of the present disclosure.

[0021] By combining the hierarchical analysis method with the structural entropy weight method to judge the candidate indicators, the influence of human subjective judgment can be reduced, the objectivity and accuracy of the indicator weights in the fuzzy evaluation system can be improved, and the flexibility and applicability of the scheme can be greater, thereby improving the accuracy of the water flooding stability evaluation of the target tailings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of a method for evaluating flood overtopping stability of a tailings pond according to an embodiment of the present disclosure;

[0023] Figure 2 is a schematic diagram of another method for evaluating flood overtopping stability of a tailings pond according to an embodiment of the present disclosure;

[0024] Figure 3 is a schematic diagram of a three-layer model of another tailings pond flood overtopping stability evaluation method according to an embodiment of the present disclosure;

[0025] Figure 4 is a schematic diagram of another method for evaluating flood overtopping stability of a tailings pond according to an embodiment of the present disclosure;

[0026] Figure 5 is a schematic diagram of another method for evaluating flood overtopping stability of a tailings pond according to an embodiment of the present disclosure;

[0027] Figure 6 is a schematic diagram of a tailings pond flood overtopping stability evaluation device according to one embodiment of the present disclosure;

[0028] Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0030] The acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of relevant laws and regulations.

[0031] Figure 1 Schematic diagram of a method for evaluating flood overtopping stability of a tailings pond according to an embodiment of the present disclosure. Figure 1 As shown in FIG. 1 , the tailings pond flood overtopping stability evaluation method includes the following steps:

[0032] S101, obtaining candidate indicators of target tailings.

[0033] The tailings pond flood overtopping stability evaluation method of the embodiment of the present application can be applied to the scenario of predicting the risk of water overtopping of tailings. The executor of the tailings pond flood overtopping stability evaluation of the embodiment of the present application can be the tailings pond flood overtopping stability evaluation device of the embodiment of the present application, and the tailings pond flood overtopping stability evaluation device can be set on an electronic device.

[0034] It should be noted that the target tailings are tailings that currently need to be risk predicted. There are many methods for obtaining candidate indicators of the target tailings, and no limitation is made here.

[0035] In a possible implementation, the setting can be performed manually. Optionally, the candidate index of the target tailings can be determined by analyzing the historical data of the target tailings.

[0036] It should be noted that there can be multiple candidate indicators, and no limitation is made here. Candidate indicators can exist in levels, and low-level indicators can be the extension or lower level of high-level indicators. For example, high-level indicators can be first-level indicators, and low-level indicators can be second-level indicators. The first-level indicators include internal environmental factors A1, external environmental factors A2, and human factors A3. The second-level indicators include the internal and external slope ratio A of the dam body. 11 , average particle size A 12 , Dam height A 13 、Accumulation capacity A 14 , Permeability coefficient A 15 , Wetting line high A 16 , height difference between beach top and reservoir water level A 17 , Dry beach length A 18 Flood discharge capacity A 19 , daily rainfall A 21 、Hydrological conditions A 22 、Geographical location A 23 , Maximum earthquake intensity A 24 , Daily management coefficient A 31 , Emergency Management Coefficient A 32 , Monitoring equipment perfection coefficient A 33 There are 15 secondary indicators in total.

[0037] S102, weight calculation is performed on each candidate indicator based on the hierarchical analysis method to obtain a subjective weight, and weight calculation is performed on each candidate indicator based on the structural entropy weight method to obtain an evaluation indicator weight.

[0038] AHP, also known as analytic hierarchy process (AHP), is a multi-criteria decision analysis method. It is a decision-making tool that combines qualitative and quantitative analysis. It is widely used in the fields of economy, military, agriculture, education, medical care and environment. Especially when facing complex decision-making problems, it can help decision makers systematically organize and analyze information to make more reasonable choices. AHP can decompose complex decision-making problems into a hierarchical structure, which is easy to understand and analyze. It can process quantitative data and quantify the qualitative judgment of experts. It is suitable for situations where there is a lack of data. At the same time, the decision-making process and results of AHP are easy to explain, which increases the transparency of decision-making.

[0039] The Structural Entropy Weighting Method is a weight determination method based on the principle of information entropy. It is mainly used in multi-criteria decision analysis or multi-index comprehensive evaluation to determine the weights of various evaluation indicators. This method emphasizes the relationship and influence between the various elements within the system, rather than just the amount of information of a single indicator. The weight of the structural entropy weighting method is determined by the data itself, reducing the interference of human factors, while taking into account the relationship between indicators to make the weight distribution more reasonable.

[0040] Therefore, by combining the hierarchical analysis method with the structural entropy weight method to judge the candidate indicators, the influence of human subjective judgment can be reduced, the objectivity and accuracy of the indicator weights in the fuzzy evaluation system can be improved, and the flexibility and applicability of the scheme can be greater.

[0041] S103, determining the comprehensive evaluation index weight based on the subjective weight and the evaluation index weight.

[0042] In the embodiment of the present disclosure, after the subjective weight and the evaluation index weight are obtained, there are many methods for calculating the comprehensive evaluation index weight, which are not limited here.

[0043] In one possible implementation, the subjective weight and the evaluation index weight can be substituted into the comprehensive evaluation index weight algorithm to calculate the comprehensive evaluation index weight. It should be noted that the comprehensive evaluation index weight algorithm is designed in advance and can be changed according to actual design needs.

[0044] In another possible implementation, the subjective weight and the evaluation index weight can be input into a comprehensive evaluation index weight generation model to generate a comprehensive evaluation index weight. The comprehensive evaluation index weight generation model is trained in advance and stored in the storage space of the electronic device to facilitate retrieval and use when needed.

[0045] S104, constructing a fuzzy judgment matrix based on the candidate indicators and their corresponding comprehensive evaluation indicator weights, and evaluating the flooding stability of the target tailings based on the fuzzy judgment matrix.

[0046] It should be noted that after obtaining the fuzzy judgment matrix, the data to be predicted can be input into the fuzzy judgment matrix to generate a judgment value. When evaluating the stability of water flooding of the target tailings, the judgment value can be compared with the judgment threshold. When the judgment value exceeds the judgment threshold, it can be considered that the target tailings is at risk of water flooding.

[0047] It should be noted that the judgment threshold is set in advance and can be changed according to actual conditions, and no limitation is made here.

[0048] In the disclosed embodiment, firstly, the candidate index of the target tailings is obtained, then the weight calculation of each candidate index is performed based on the hierarchical analysis method to obtain the subjective weight, and the weight calculation of each candidate index is performed based on the structural entropy weight method to obtain the evaluation index weight, and then the comprehensive evaluation index weight is calculated based on the subjective weight and the evaluation index weight, and finally the fuzzy judgment matrix is ​​constructed based on each candidate index and its corresponding comprehensive evaluation index weight, and the water-flooding stability of the target tailings is evaluated based on the fuzzy judgment matrix. Thus, by combining the hierarchical analysis method with the structural entropy weight method to judge the candidate index, the influence of human subjective judgment can be reduced, the objectivity and accuracy of the index weight in the fuzzy evaluation system, and the greater flexibility and applicability of the scheme can be improved, so as to improve the accuracy of the water-flooding stability evaluation of the target tailings.

[0049] In the above embodiment, the weight of each candidate indicator is calculated based on the hierarchical analysis method, and the weight of each candidate indicator can also be calculated by Figure 2 Explaining further, the method includes:

[0050] S201, establishing a hierarchical model based on candidate indicators, where the hierarchical model includes multiple layers.

[0051] To establish a hierarchical model, when applying the AHP to analyze problems, the first thing is to construct a hierarchical model to organize and hierarchize the problem. A complex problem consists of several indicators, and these evaluation indicators have a hierarchy according to their importance to the final goal or the degree of inclusion between them. Figure 3 As shown, a three-layer model can be established based on candidate indicators.

[0052] S202, performing pairwise comparisons between adjacent layers of the hierarchical structure model, and generating a judgment matrix based on the comparison results.

[0053] It should be noted that the judgment matrix can be converted into specific values ​​according to the experts' cognition of the importance of the candidate indicators. For example, the conversion can be performed as shown in the following table:

[0054]

[0055]

[0056] Expert opinions were collected and a judgment matrix was constructed. After the hierarchical structure of each indicator was established, the evaluation indicators at the same level that were dominated by the same superior indicator were compared in pairs according to the level table to form a judgment matrix as shown in the following table.

[0057]

[0058] S203: determining a first weight vector for each candidate indicator based on the judgment matrix, and determining a subjective weight of the corresponding candidate indicator based on the first weight vector.

[0059] It should be noted that before determining the subjective weight of the corresponding candidate indicator based on the first weight vector, it is also necessary to calculate the consistency ratio of the judgment matrix and compare the consistency ratio with the set threshold. In response to the consistency ratio being less than the set threshold, the subjective weight of the corresponding candidate indicator is determined based on the first weight vector; or, in response to the consistency ratio being greater than or equal to the set threshold, return to execute the establishment of a hierarchical model based on the candidate indicators and its subsequent steps.

[0060] In a possible implementation, the evaluation index weight vector may be obtained first. When calculating the judgment matrix, the judgment matrix is ​​first standardized.

[0061] Calculate the product M of each row of the judgment matrix i

[0062]

[0063] Calculate M i The nth root of

[0064]

[0065] Pair Vector After normalization, we get the first-level indicator weight vector.

[0066]

[0067] Perform a consistency test on the judgment matrix. If the relative consistency CR of the judgment matrix A is less than 0.1, the judgment matrix A is considered feasible and passes the consistency test. Usually, the smaller the CR value, the better (close to 0). If the CR of the judgment matrix A does not meet the requirement of less than 0.1 and fails the consistency test, return to the step and compare two by two again to reconstruct a qualified judgment matrix A. The calculation formula of CR is as follows:

[0068]

[0069] Among them, (AW) i represents the i-th element of the vector AW, λ max It refers to the maximum eigenvalue of the judgment matrix, CI refers to the consistency index of the judgment matrix, CR refers to the consistency ratio, RI refers to the average random consistency index of the same order matrix, and n is the number of judgment indicators.

[0070] The 1st to 9th order judgment matrices can be shown in the following table.

[0071] Table 3 Average random consistency index value table

[0072]

[0073]

[0074] In the disclosed embodiment, a hierarchical model is first established based on candidate indicators, the hierarchical model includes multiple layers, and then adjacent layers of the hierarchical model are compared in pairs, and a judgment matrix is ​​generated based on the comparison results, and finally the first weight vector of each candidate indicator is determined based on the judgment matrix, and the subjective weight of the corresponding candidate indicator is determined based on the first weight vector. Thus, by calculating the weight of each candidate indicator through the hierarchical analysis method, the weight analysis of the candidate indicators can be performed from the aspect of hierarchical analysis, providing a data basis for the subsequent determination of the weight of the comprehensive evaluation indicators.

[0075] In the above embodiment, the weight of each candidate indicator is calculated based on the structural entropy weight method, and the weight of each candidate indicator can also be calculated by Figure 4 Explaining further, the method includes:

[0076] S401, performing multiple rounds of scoring on each candidate indicator, and generating an importance matrix based on the scoring results.

[0077] It should be noted that there may be many types of multiple rounds of scoring, which are not limited here. In one possible implementation, multiple rounds of scoring may be performed by multiple experts on each candidate indicator, and the scoring of each expert constitutes one round of scoring.

[0078] In the disclosed embodiment, expert opinions may be collected first, and the importance of the secondary indicators may be scored by experts in the tailings pond field to determine the importance ranking of each indicator.

[0079] It should be noted that the importance matrix can be converted into specific values ​​according to the experts' perception of the importance of the candidate indicators. For example, the conversion can be performed as shown in the following table:

[0080]

[0081]

[0082] S402, performing normalization processing based on the importance matrix to generate an information entropy matrix.

[0083] It should be noted that the information entropy in the embodiment of the present disclosure is a parameter indicating the degree of influence of the candidate indicator on the target tailings water flooding stability. The greater the influence of the candidate indicator on the target tailings water flooding stability, the smaller the corresponding information entropy.

[0084] In the embodiments of the present disclosure, there may be multiple methods for generating an information entropy matrix, which are not limited here.

[0085] In a possible implementation, the importance matrix can be transformed by a transformation model to generate an information entropy matrix. The transformation model is trained in advance and stored in a storage space of the electronic device to facilitate retrieval and use when needed.

[0086] S403: Determine a second weight vector for each candidate indicator based on the information entropy matrix, and determine an evaluation indicator weight corresponding to the candidate indicator based on the second weight vector.

[0087] In the embodiment of the present disclosure, the second weight vector of each candidate indicator is determined based on the information entropy matrix. First, the scoring weights corresponding to each round of scoring are obtained. Then, for any candidate indicator, the average recognition and cognitive blindness of the candidate indicator in all rounds of scoring are calculated based on the scoring weights. Finally, the second weight vector of the candidate indicator is determined based on the average recognition and cognitive blindness.

[0088] It should be noted that the average recognition of the candidate indicators in all rounds of scoring is calculated based on the scoring weights, and the average of all information entropies of the candidate indicators in the information entropy matrix is ​​taken as the average recognition.

[0089] The cognitive blindness of the candidate indicators in all rounds of scoring is calculated based on the scoring weights. The maximum and minimum values ​​of all information entropies of the candidate indicators in the information entropy matrix are subtracted from the average cognition respectively, and the average of the two differences is calculated as the cognitive blindness.

[0090] In a possible implementation, the indicators can be first blindly analyzed and the indicator set is recorded as U = {u1, u2, ..., u n}; Let the sorted group be a ij , then the ranking matrix of each indicator is A=(a ij ) k×n , where k is the number of experts, n is the number of evaluation indicators, and a ij Refers to the ranking number of the i-th expert on the j-th indicator.

[0091] Calculate the membership function b(a ij ), and obtain the membership matrix D = b(a ij ) k×n , calculate the evaluation vector of each secondary indicator.

[0092]

[0093] Where j is the number of indicators, m is the conversion parameter, m=j+2

[0094] The k experts are assigned to the index μ j The consistent understanding of is called the average understanding, denoted by b j .

[0095] b j =(μ 1j +μ 2j +…+μ kj ) / k

[0096] Calculate cognitive blindness Q j :

[0097]

[0098] Then k experts have j Overall awareness of j for:

[0099] m j =b j (1-Q j ), m j >0

[0100] Thus, we can get the k experts’ opinions on b j The evaluation vector M = {m j}, j = 1, 2,…, n.

[0101] Normalize the evaluation vector M and obtain ρ j Include weights for each secondary indicator.

[0102]

[0103] In the disclosed embodiment, each candidate indicator is first scored multiple times, and an importance matrix is ​​generated based on the scoring results, and then normalization is performed based on the importance matrix to generate an information entropy matrix, and finally, a second weight vector of each candidate indicator is determined based on the information entropy matrix, and the evaluation indicator weight of the corresponding candidate indicator is determined based on the second weight vector. Thus, by calculating the weight of each candidate indicator through the structural entropy weight method, the weight analysis of the candidate indicator can be performed from the level of information entropy, providing a data basis for the subsequent determination of the weight of the comprehensive evaluation indicator.

[0104] In the above embodiment, the comprehensive evaluation index weight is calculated based on the subjective weight and the evaluation index weight. Figure 5 Explaining further, the method includes:

[0105] S501: For any candidate indicator, respectively obtain a first proportion of the subjective weight and a second proportion of the evaluation indicator weight.

[0106] It should be noted that the first proportion of the subjective weight and the second proportion of the evaluation index weight are designed in advance and can be changed according to actual design needs, and no limitation is made here. The sum of the first proportion and the second proportion is 1. For example, the first proportion can be 0.3 and the second proportion can be 0.7.

[0107] S502: multiply the first proportion by the subjective weight to calculate and obtain the first factor, and multiply the second proportion by the evaluation index weight to calculate and obtain the second factor.

[0108] S503: Add the first factor and the second factor to calculate and obtain the weight of the comprehensive evaluation index.

[0109] In the embodiment of the present disclosure, the formula for calculating the weight of the comprehensive evaluation index can be as follows:

[0110]

[0111] Among them, n is the comprehensive evaluation index weight of the nth candidate index, W n is the subjective weight of the nth candidate indicator, p n is the evaluation index weight of the nth candidate index, θ is the first proportion, and (1-θ) is the second proportion.

[0112] In the embodiment of the present disclosure, after obtaining the comprehensive evaluation index weights, a fuzzy judgment matrix is ​​constructed based on each candidate index and its corresponding comprehensive evaluation index weight. An evaluation set may be established first. For example, it may be as follows:

[0113] U = {low risk, lower risk, medium risk, higher risk, high risk}. The expert evaluation method is used to judge the various indicators of tailings dam breach risk and establish the fuzzy judgment matrix R i .

[0114] Then, fuzzy comprehensive evaluation is performed, for example, as follows:

[0115] Calculate the tailings pond risk index evaluation matrix B according to the formula i , then the tailings pond flood overtopping and dam breach risk assessment matrix is:

[0116] B i =θ i ×R i

[0117] The overall evaluation matrix A is obtained according to the formula, where θ i is the comprehensive evaluation index weight of the nth candidate index.

[0118] A=WB=(ρ j ×B i )

[0119] Finally, the flood overtopping dam breach risk assessment value is calculated. In order to more intuitively see the risk of dam breach, the results of fuzzy comprehensive evaluation are converted into specific values ​​according to the formula.

[0120] F=AX T

[0121] X is a vector composed of the score values ​​corresponding to each risk level in the evaluation set. The dam break risk level and its correspondence can be shown in the following table.

[0122]

[0123] Corresponding to the tailings pond flood overtopping stability evaluation methods provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides a tailings pond flood overtopping stability evaluation device. Since the tailings pond flood overtopping stability evaluation device provided in the embodiment of the present disclosure corresponds to the tailings pond flood overtopping stability evaluation methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned tailings pond flood overtopping stability evaluation methods are also applicable to the tailings pond flood overtopping stability evaluation device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0124] Figure 6 Schematic diagram of a tailings pond flood overtopping stability evaluation device according to one embodiment of the present disclosure. Figure 6 As shown, the tailings pond flood overtopping stability evaluation device 600 includes: an acquisition module 610, a calculation module 620, a determination module 630 and an evaluation module 640.

[0125] The acquisition module 610 is used to acquire candidate indicators of the target tailings.

[0126] The calculation module 620 is used to calculate the weight of each candidate indicator based on the hierarchical analysis method to obtain the subjective weight, and to calculate the weight of each candidate indicator based on the structural entropy weight method to obtain the evaluation indicator weight.

[0127] The determination module 630 is used to determine the comprehensive evaluation index weight based on the subjective weight and the evaluation index weight.

[0128] The evaluation module 640 is used to construct a fuzzy judgment matrix based on each candidate index and its corresponding comprehensive evaluation index weight, and to evaluate the water flooding stability of the target tailings based on the fuzzy judgment matrix.

[0129] In one possible implementation, the calculation module 620 is also used to: establish a hierarchical model based on the candidate indicators, the hierarchical model includes multiple layers; compare adjacent layers of the hierarchical model in pairs, and generate a judgment matrix based on the comparison results; determine a first weight vector for each candidate indicator based on the judgment matrix, and determine the subjective weight of the corresponding candidate indicator based on the first weight vector.

[0130] In one possible implementation, the calculation module 620 is also used to: calculate the consistency ratio of the judgment matrix and compare the consistency ratio with a set threshold; in response to the consistency ratio being less than the set threshold, determine the subjective weight of the corresponding candidate indicator based on the first weight vector; or, in response to the consistency ratio being greater than or equal to the set threshold, return to execute the establishment of a hierarchical model based on the candidate indicators and its subsequent steps.

[0131] In one possible implementation, the computing module 620 is also used to: perform multiple rounds of scoring for each candidate indicator, and generate an importance matrix based on the scoring results; perform normalization processing based on the importance matrix to generate an information entropy matrix; determine a second weight vector for each candidate indicator based on the information entropy matrix, and determine the evaluation indicator weight of the corresponding candidate indicator based on the second weight vector.

[0132] In one possible implementation, the calculation module 620 is also used to: obtain the scoring weights corresponding to each round of scoring; for any candidate indicator, calculate the average recognition and cognitive blindness of the candidate indicator in all rounds of scoring based on the scoring weights; and determine the second weight vector of the candidate indicator based on the average recognition and cognitive blindness.

[0133] In a possible implementation, the calculation module 620 is further used to: calculate the average value of all information entropies of the candidate indicators in the information entropy matrix as the average recognition degree.

[0134] In a possible implementation, the calculation module 620 is further used to: subtract the maximum value and the minimum value of all information entropies of the candidate indicator in the information entropy matrix from the average cognition, and calculate the average value of the two differences as the cognitive blindness.

[0135] In one possible implementation, the evaluation module 640 is also used to: for any candidate indicator, obtain a first proportion of the subjective weight and a second proportion of the evaluation indicator weight, respectively; multiply the first proportion and the subjective weight to calculate the first factor, and multiply the second proportion and the evaluation indicator weight to calculate the second factor; add the first factor and the second factor to calculate the comprehensive evaluation indicator weight.

[0136] Therefore, by combining the hierarchical analysis method with the structural entropy weight method to judge the candidate indicators, the influence of human subjective judgment can be reduced, the objectivity and accuracy of the indicator weights in the fuzzy evaluation system can be improved, and the flexibility and applicability of the scheme can be greater, thereby improving the accuracy of the water flooding stability evaluation of the target tailings.

[0137] In order to implement the above embodiment, the present disclosure further provides an electronic device 700, Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 7 As shown, the electronic device 700 includes: a processor 701 and a memory 702 that is communicatively connected to the processor, the memory 702 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 701 to implement the present disclosure. Figure 1-Figure 5 The tailings pond flood overtopping stability evaluation method of the embodiment.

[0138] In order to implement the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the above embodiments. Figure 1-Figure 5 The tailings pond flood overtopping stability evaluation method of the embodiment.

[0139] In order to implement the above embodiments, the present disclosure also provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the above embodiments. Figure 1-Figure 5 The tailings pond flood overtopping stability evaluation method of the embodiment.

[0140] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0141] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0142] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0143] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0144] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0145] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that contains, stores, communicates, propagates or transmits a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0146] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0147] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0148] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0149] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for evaluating flood overtopping stability of a tailings pond, characterized in that: include: Obtain candidate indicators for target tailings; The weight of each candidate indicator is calculated based on the hierarchical analysis method to obtain the subjective weight, and the weight of each candidate indicator is calculated based on the structural entropy weight method to obtain the evaluation indicator weight; Determine the comprehensive evaluation index weight based on the subjective weight and the evaluation index weight; A fuzzy judgment matrix is ​​constructed based on each candidate index and its corresponding comprehensive evaluation index weight, and the water flooding stability of the target tailings is evaluated based on the fuzzy judgment matrix; The step of determining the comprehensive evaluation index weight based on the subjective weight and the evaluation index weight comprises: For any candidate indicator, respectively obtain the first proportion of the subjective weight and the second proportion of the evaluation indicator weight; The first proportion is multiplied by the subjective weight to calculate a first factor, and the second proportion is multiplied by the evaluation index weight to calculate a second factor; Adding the first factor and the second factor to calculate the comprehensive evaluation index weight; The formula for calculating the weight of comprehensive evaluation index can be as follows: Among them, n is the comprehensive evaluation index weight of the nth candidate index, W n is the subjective weight of the nth candidate indicator, p n is the evaluation index weight of the nth candidate index, θ is the first proportion, and (1-θ) is the second proportion.

2. The method according to claim 1, characterized in that The weight calculation of each candidate indicator based on the hierarchical analysis method includes: Establishing a hierarchical model based on the candidate indicators, wherein the hierarchical model includes multiple layers; Comparing adjacent layers of the hierarchical structure model in pairs, and generating a judgment matrix based on the comparison results; A first weight vector of each candidate indicator is determined based on the judgment matrix, and a subjective weight of the corresponding candidate indicator is determined based on the first weight vector.

3. The method according to claim 2, characterized in that Before determining the subjective weight of the corresponding candidate indicator based on the first weight vector, the method further includes: Calculating the consistency ratio of the judgment matrix, and comparing the consistency ratio with a set threshold; In response to the consistency ratio being less than the set threshold, determining a subjective weight of a corresponding candidate indicator based on the first weight vector; or, In response to the consistency ratio being greater than or equal to the set threshold, returning to execute the step of establishing a hierarchical structure model based on the candidate indicators and subsequent steps.

4. The method according to claim 1, characterized in that: The weight calculation of each candidate indicator based on the structural entropy weight method includes: Conduct multiple rounds of scoring for each candidate indicator and generate an importance matrix based on the scoring results; Performing normalization processing based on the importance matrix to generate an information entropy matrix; Based on the information entropy matrix, a second weight vector of each candidate indicator is determined, and based on the second weight vector, an evaluation indicator weight corresponding to the candidate indicator is determined.

5. The method according to claim 4, characterized in that The determining, based on the information entropy matrix, a second weight vector of each candidate indicator comprises: Get the scoring weights corresponding to each round of scoring; For any candidate indicator, the average recognition and cognitive blindness of the candidate indicator in all rounds of scoring are calculated based on the scoring weight; A second weight vector of the candidate indicators is determined based on the average cognition and the cognition blindness.

6. The method according to claim 5, characterized in that The calculating the average recognition of the candidate indicator in all rounds of scoring based on the scoring weights includes: The average of all the information entropies of the candidate indicators in the information entropy matrix is ​​taken as the average recognition degree.

7. The method according to claim 5, characterized in that The cognitive blindness of the candidate indicator in all rounds of scoring is calculated based on the scoring weights, including: The maximum value and the minimum value of all the information entropies of the candidate indicator in the information entropy matrix are respectively subtracted from the average cognition, and the average value of the two differences is calculated as the cognitive blindness.

8. A tailings pond flood overtopping stability evaluation device, characterized in that: include: An acquisition module, used to obtain candidate indicators of target tailings; A calculation module is used to calculate the weight of each candidate indicator based on the hierarchical analysis method to obtain the subjective weight, and to calculate the weight of each candidate indicator based on the structural entropy weight method to obtain the evaluation indicator weight; A determination module, used to determine the comprehensive evaluation index weight based on the subjective weight and the evaluation index weight; An evaluation module, used to construct a fuzzy judgment matrix based on each candidate index and its corresponding comprehensive evaluation index weight, and to evaluate the flooding stability of the target tailings based on the fuzzy judgment matrix; The determination module is further configured to obtain, for any candidate indicator, a first proportion of the subjective weight and a second proportion of the evaluation indicator weight; The first proportion is multiplied by the subjective weight to calculate a first factor, and the second proportion is multiplied by the evaluation index weight to calculate a second factor; Adding the first factor and the second factor to calculate the comprehensive evaluation index weight; The formula for calculating the weight of comprehensive evaluation index can be as follows: Among them, n is the comprehensive evaluation index weight of the nth candidate index, W n is the subjective weight of the nth candidate indicator, p n is the evaluation index weight of the nth candidate index, θ is the first proportion, and (1-θ) is the second proportion.

9. An electronic device, characterized in that: Including memory and processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method according to any one of claims 1 to 7.

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