Non-coal mine natural disaster-causing risk assessment method and system

Abstract

The invention provides a non-coal mine natural disaster-causing risk assessment method and system, and the method comprises the steps: carrying out the division according to the factors of non-coal mine natural disaster-causing disasters and an assessment target, and forming a disaster-causing risk assessment system which comprises a target layer, a criterion layer and an index layer; calculating the index weight of each layer of index through an analytic hierarchy process; adopting a fuzzy analytic hierarchy process to obtain a fuzzy evaluation matrix of each layer of indexes; obtaining a comprehensive evaluation vector according to the index weight of each layer of index and the fuzzy evaluation matrix; according to the method, an occurrence possibility grade value and a consequence severity grade value are calculated according to evaluation vectors, a final risk grade is determined, influence weights of all indexes on disaster-causing risks are calculated by introducing fuzzy mathematics, contributions of different indexes to the disaster-causing risks are optimized, and the risk assessment accuracy is improved. According to the method, the defect of lack of difference in calculation of the weight of each disaster-causing index of the non-coal mine in a traditional method is overcome, the evaluation result can better conform to the actual situation, and the method has a feasible guiding effect.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular relates to a method and system for assessing the risk of natural disasters in non-coal mines. Background Art

[0002] my country's mineral resources are rich in variety and widely distributed, with diverse mineralization conditions. There are a large number of metal and non-metal mines and tailings ponds (hereinafter referred to as non-coal mines). Due to the complexity of geological conditions and the influence of climatic environment, mine safety production faces the challenges of superimposed regional risks, and the safety situation remains grim.

[0003] However, the traditional method lacks the ability to calculate the differences in the weights of various disaster-causing indicators in non-coal mines, resulting in non-objective and inapplicable assessment results, which are usually still in the theoretical stage.

[0004] Therefore, there is an urgent need for a practical, scientific and accurate technology for risk analysis of mine accidents caused by natural disasters.

[0005] The above statements are only used to provide background technical information related to this application. Unless otherwise indicated herein, the contents described in this section are not prior art for the contents of other parts of this application. Summary of the Invention

[0006] The present invention proposes a method for assessing the risk of natural disasters in non-coal mines based on the fuzzy analytic hierarchy process. Fuzzy mathematics is introduced to calculate the weight of the influence of various indicators on the risk of natural disasters in non-coal mines, thereby optimizing the contribution of different indicators to the disaster risk. This solves the shortcoming of the lack of differentiation in the traditional method of calculating the weights of various disaster-causing indicators in non-coal mines, enables the assessment results to be more in line with the actual situation, and plays a practical and feasible guiding role.

[0007] According to a first aspect of an embodiment of the present application, a method for assessing the risk of natural disasters in non-coal mines is provided, comprising the following steps: Based on the factors causing disasters in non-coal mines and the assessment targets, a disaster risk assessment system is formed. The system includes a target layer, a criterion layer, and an indicator layer. The target layer includes two assessment targets: the possibility of disaster risk and the severity of consequences. The criterion layer includes seven major indicators: engineering design, engineering status, natural disaster-prone factors, disaster defense capabilities, historical disaster events, disaster prevention and mitigation capabilities, and potential disaster victims and protected objects. The indicator layer includes sub-category indicators based on the major indicators of the criterion layer. Calculate the indicator weights of each layer of indicators through the hierarchical analysis method; The fuzzy evaluation matrix of each layer index is obtained by using fuzzy analytic hierarchy process; According to the indicator weights of each layer and the fuzzy evaluation matrix, a comprehensive evaluation vector is obtained; The occurrence probability level value and consequence severity level value are calculated respectively according to the evaluation vector, and the final risk level is determined.

[0008] In one embodiment of the present application, the weights of indicators at each level are calculated using the analytic hierarchy process, including: Construct the judgment matrix of each layer of indicators in a hierarchical manner, and the elements of the judgment matrix are the importance of the indicators of this layer to the indicators of the previous layer; The judgment matrix is ​​numerically scaled by the importance of each sub-category indicator of the indicator layer to the criterion layer and the importance of each major category indicator of the criterion layer to the evaluation target of the target layer; Calculate the maximum eigenvalue and eigenvector of the judgment matrix; Perform consistency check on the judgment matrix based on the maximum eigenvalue. If the matrix consistency is not satisfied, rebuild the judgment matrix until the matrix consistency is satisfied. The indicator weights of each layer of indicators are calculated based on the eigenvector corresponding to the maximum eigenvalue.

[0009] In one embodiment of the present application, a fuzzy evaluation system is constructed using fuzzy analytic hierarchy process, including: The two assessment targets of the target layer are divided into n risk levels according to the degree of disaster; Based on the evaluation of n risk levels by indicators at each level, a membership matrix is ​​constructed; After assigning values ​​to each indicator according to national, industry standards or actual conditions, the triangular fuzzy membership function is combined to construct the membership matrix elements and obtain the fuzzy evaluation matrix.

[0010] In one embodiment of the present application, triangular fuzzy membership functions are combined to construct membership matrix elements to obtain a fuzzy evaluation matrix, including: The membership matrix R is: ; in, r ij ( i =1, 2… m ; j =1, 2… n ), representing each membership matrix R Middle i The indicator in j The value on the level represents the i The index value is j The degree of membership of each level. m Represents the number of indicators in each membership matrix; n is the number of levels; According to the triangular fuzzy membership function r k1 、 rk2 、 r k3 、 r k4 Calculate the membership matrix elements to obtain the fuzzy evaluation matrix. The triangular fuzzy membership function formula is as follows: ; ; ; ; Among them, x is the normalized value of each subcategory indicator, which can be assigned to each indicator according to national, industry standards or actual conditions.

[0011] In one embodiment of the present application, a comprehensive evaluation vector is obtained based on the indicator weights of each layer of indicators and the fuzzy evaluation matrix, including: Multiply the sub-category indicator weights of the indicator layer by the corresponding fuzzy evaluation matrix to obtain the first evaluation vector T', which is: ; in, W′= { w 1, w 2, w 3··· w m}, w 1, w 2, w 3··· w m represents the weight coefficient of each sub-category indicator, and R is the fuzzy evaluation matrix; Multiply the first evaluation vector T' by the weight of the major indicators in the criterion layer to obtain the comprehensive evaluation vector T, the formula is: ; Among them, W represents the indicator weight of each major category of indicators.

[0012] In one embodiment of the present application, the occurrence probability level value and the consequence severity level value are calculated based on the evaluation vector, and the final risk level is determined, including: Calculate the probability level value L, the formula is: L = 1 + 3 × (X * Sᵀ); The formula for calculating the severity level of consequences R is: R = 1 + 3 × (Y * Sᵀ); Where X is the evaluation vector of the occurrence possibility index of non-coal mines; Y is the evaluation vector of the severity index of non-coal mine consequences; S is the median vector of the interval after normalization of the values ​​of each index, S = [0.875, 0.625, 0.375, 0.125]; Among them, the larger the value of the occurrence possibility level value L, the greater the possibility of the disaster occurring; the larger the value of the consequence severity level value R, the greater the severity of the consequence.

[0013] In one embodiment of the present application, a consistency check is performed on the judgment matrix according to the maximum eigenvalue, including: Calculate the consistency index CI, the formula is: ; in, λ max is the maximum eigenvalue of the judgment matrix; k is the order of the judgment matrix, that is, the number of indicators corresponding to each judgment matrix; Computational consistency check CR , the formula is: ; like CR <0.1, the judgment matrix satisfies the matrix consistency; in, RI is the average random consistency index, and its specific value is related to the order of the judgment matrix k It can be obtained by looking up the matrix RI value table.

[0014] According to a third aspect of an embodiment of the present application, a non-coal mine natural disaster risk assessment system is provided, comprising: Indicator library module: used to store the seven categories of indicators at the criteria layer and their extensible sub-category indicators; Assessment and analysis module: configured to implement a risk assessment method for natural disasters in non-coal mines; Dynamic early warning module: used to automatically trigger emergency plans based on the risk level output by the assessment and analysis module.

[0015] According to the third aspect of the embodiment of the present application, a non-coal mine natural disaster risk assessment device is provided, including: a storage unit for storing executable instructions; and a processing unit for connecting to the memory to execute executable instructions to complete a non-coal mine natural disaster risk assessment method.

[0016] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored; the computer program is executed by a processor to implement a method for assessing the risk of natural disasters in non-coal mines.

[0017] The non-coal mine natural disaster risk assessment method and system of the present application include dividing the disaster-causing factors and assessment targets of non-coal mine natural disasters to form a disaster risk assessment system, which includes a target layer, a criterion layer and an indicator layer; calculating the indicator weights of each layer of indicators through the hierarchical analysis method; using the fuzzy hierarchical analysis method to obtain the fuzzy evaluation matrix of each layer of indicators; obtaining a comprehensive evaluation vector based on the indicator weights of each layer of indicators and the fuzzy evaluation matrix; calculating the occurrence possibility level value and the consequence severity level value respectively according to the evaluation vector, and determining the final risk level.

[0018] This application introduces fuzzy mathematics to calculate the weight of the impact of various indicators on the disaster risk of natural disasters in non-coal mines, optimizes the contribution of different indicators to the disaster risk, and solves the shortcoming of the lack of differentiation in the traditional method of calculating the weights of various disaster-causing indicators in non-coal mines. It can make the evaluation results more in line with the actual situation and has a practical and feasible guiding role. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 : A schematic diagram showing the steps of a method for assessing the risk of natural disasters in non-coal mines according to an embodiment of the present application is shown; Figure 2 : shows an assessment flow chart of a method for assessing the risk of natural disasters in non-coal mines according to an embodiment of the present application; Figure 3 , which shows a flowchart of steps for calculating indicator weights according to the hierarchical analysis method of an embodiment of the present application; Figure 4 , which is a flowchart of the steps for obtaining a fuzzy evaluation matrix according to an embodiment of the present application; Figure 5 , which shows a flowchart of steps for obtaining a comprehensive evaluation vector according to an embodiment of the present application; Figure 6 : shows a structural schematic diagram of a non-coal mine natural disaster risk assessment system according to an embodiment of the present application; Figure 7 Schematic diagram of the structure of the non-coal mine natural disaster risk assessment device according to an embodiment of the present application is shown in FIG. DETAILED DESCRIPTION

[0020] Regarding this application, the existing assessment methods for risk analysis of mine accidents caused by natural disasters have certain limitations. The current risk assessment of natural disasters in non-coal mines generally adopts the "Technical Specifications for Risk Assessment of Natural Disasters in Non-Coal Mines" for assessment and calculation, but its calculation method uses a simple weighted average method, which ignores the differences in the risk contributions of different indicators and cannot fully analyze the risks of mine accidents caused by natural disasters.

[0021] In order to solve the problem that traditional methods lack the ability to calculate the differences in the weights of various disaster-causing indicators of non-coal mines, resulting in non-objective and inapplicable evaluation results, the present invention adopts the fuzzy hierarchical analysis method to evaluate the disaster risk of natural disasters in non-coal mines. Compared with traditional methods, it can not only distinguish the impact weights of different influencing factors on non-coal mines, but also make the calculated weight results more real and credible. This method is suitable for the refined evaluation of non-coal mines, and the evaluation results are more objective.

[0022] The present invention first divides the risk factors of natural disasters in non-coal mines into seven aspects: engineering design, engineering status, natural disaster-prone factors, disaster defense capabilities, historical disaster events, disaster prevention and mitigation capabilities, and potential disaster victims and protected objects, and divides them into two parts: possibility of occurrence and severity of consequences.

[0023] Secondly, the specific indicator weights and the probability of occurrence and severity of consequences are calculated based on the hierarchical analysis method and the fuzzy hierarchical analysis method. Finally, the specific levels of probability of occurrence and severity of consequences are determined based on the level table, and the disaster risk level of natural disasters in non-coal mines is determined based on the risk level table.

[0024] In general, this application divides the disaster-causing factors and assessment targets of non-coal mine natural disasters to form a disaster risk assessment system, which includes a target layer, a criterion layer and an indicator layer; the indicator weights of each layer are calculated by the hierarchical analysis method; the fuzzy evaluation matrix of each layer is obtained by the fuzzy hierarchical analysis method; the comprehensive evaluation vector is obtained according to the indicator weights of each layer and the fuzzy evaluation matrix; the occurrence possibility level value and the consequence severity level value are calculated respectively according to the evaluation vector, and the final risk level is determined.

[0025] This application introduces fuzzy mathematics to calculate the weight of the impact of various indicators on the disaster risk of natural disasters in non-coal mines, optimizes the contribution of different indicators to the disaster risk, and solves the shortcoming of the lack of differentiation in the traditional method of calculating the weights of various disaster-causing indicators in non-coal mines. It can make the evaluation results more in line with the actual situation and has a practical and feasible guiding role.

[0026] In order to make the technical solutions and advantages of the embodiments of the present application more clearly understood, the exemplary embodiments of the present application are further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, and are not an exhaustive list of all the embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other unless they conflict.

[0027] Example 1 Figure 1 Schematic diagram of the steps of the method for assessing the risk of natural disasters in non-coal mines according to an embodiment of the present application is shown in FIG.

[0028] like Figure 1 As shown, a method for assessing the risk of natural disasters in non-coal mines is provided, comprising the following steps: S1: Based on the factors causing non-coal mine natural disasters and the assessment targets, a disaster risk assessment system is formed. The system includes the target layer, the criterion layer and the indicator layer; The target layer includes two assessment objectives: the possibility of disaster risk and the severity of consequences. The criterion layer includes seven major indicators: project design, project status, natural disaster risk factors, disaster defense capabilities, historical disaster events, disaster prevention and mitigation capabilities, and potential disaster victims and protected objects. The indicator layer includes sub-category indicators based on the major indicators of the criterion layer. S2: Calculate the indicator weights of each level through the analytic hierarchy process; S3: Use fuzzy analytic hierarchy process to obtain the fuzzy evaluation matrix of each layer indicator; S4: According to the indicator weights of each layer and the fuzzy evaluation matrix, a comprehensive evaluation vector is obtained; S5: Calculate the probability of occurrence level value and consequence severity level value respectively according to the evaluation vector, and determine the final risk level.

[0029] Figure 2 Detailed description of the evaluation flow chart of the method for evaluating the risk of natural disasters in non-coal mines according to an embodiment of the present application is shown in FIG.

[0030] like Figure 2 As shown in the figure, in the evaluation process, data is collected first, and the possibility of occurrence and severity of consequences of the current status of the project are evaluated according to the sub-category indicators of each major category. The major indicators include project design, project status, natural disaster-prone factors, disaster defense capabilities, historical disaster events, disaster prevention and mitigation capabilities, and potential disaster victims and protected objects; then, the weights of specific indicators are calculated based on the fuzzy hierarchical analysis method, and the possibility of occurrence and severity of consequences are determined, and finally the disaster risk level of natural disasters in non-coal mines is determined.

[0031] Thus, by introducing fuzzy mathematics, we can calculate the weight of the influence of each indicator on the disaster risk of natural disasters in non-coal mines, optimize the contribution of different indicators to the disaster risk, and solve the shortcoming of the lack of difference in the weights of various disaster-causing indicators of non-coal mines calculated by traditional methods. This can make the evaluation results more in line with the actual situation and play a practical guiding role.

[0032] Figure 3 , which is a flowchart of the steps for calculating the indicator weights according to the hierarchical analysis method of an embodiment of the present application.

[0033] like Figure 3 As shown in Figure 2, the weights of indicators at each level are calculated using the hierarchical analysis method in S2, including: S21: Construct the judgment matrix of each layer of indicators in a hierarchical manner. The elements of the judgment matrix are the importance of the indicators of this layer to the indicators of the previous layer. S22: numerically scale the judgment matrix based on the importance of each sub-category indicator of the indicator layer to the criterion layer and the importance of each major category indicator of the criterion layer to the evaluation target of the target layer; S23: Calculate the maximum eigenvalue and eigenvector of the judgment matrix; S24: Perform consistency check on the judgment matrix according to the maximum eigenvalue. If the matrix consistency is not satisfied, reconstruct the judgment matrix until the matrix consistency is satisfied. S25: The indicator weights of the indicators at each layer are calculated based on the eigenvector corresponding to the maximum eigenvalue.

[0034] Figure 4 hereinafter is a flowchart showing the steps for obtaining a fuzzy evaluation matrix according to an embodiment of the present application.

[0035] like Figure 4 As shown in Figure 3, the fuzzy analytic hierarchy process is used in S3 to obtain the fuzzy evaluation matrix of each layer indicator, including: S31: Divide the two assessment targets of the target layer into n risk levels according to the degree of disaster; S32: Construct a membership matrix based on the evaluation of n risk levels by indicators at each level; S33: After assigning values ​​to each indicator according to national, industry standards or actual conditions, the triangular fuzzy membership function is combined to construct membership matrix elements to obtain a fuzzy evaluation matrix.

[0036] In the specific implementation, the triangular fuzzy membership function is combined to construct the membership matrix elements to obtain the fuzzy evaluation matrix, which includes the following steps: The membership matrix R is: ; in, r ij ( i =1, 2… m; j =1, 2… n ), representing each membership matrix R Middle i The indicator in j The value on the level represents the i The index value is j The degree of membership of each level; m Represents the number of indicators in each membership matrix; n is the number of levels; According to the triangular fuzzy membership function r k1 、 r k2 、 r k3 、 r k4 Calculate the membership matrix elements to obtain the fuzzy evaluation matrix. The triangular fuzzy membership function formula is as follows: ; ; ; ; Among them, x is the normalized value of each subcategory indicator, which can be assigned to each indicator according to national, industry standards or actual conditions.

[0037] Figure 5 FIG. 5 shows a flowchart of steps for obtaining a comprehensive evaluation vector according to an embodiment of the present application.

[0038] like Figure 5 As shown, next, in S4, the comprehensive evaluation vector is obtained according to the indicator weights of each layer and the fuzzy evaluation matrix, which specifically includes the following steps: S41: Multiply the sub-category indicator weights of the indicator layer by the corresponding fuzzy evaluation matrix to obtain the first evaluation vector T', which is: ; in, W′= { w 1, w 2, w 3··· w m}, w 1, w 2, w 3··· w m represents the weight coefficient of each sub-category indicator, and R is the fuzzy evaluation matrix; S42: Multiply the first evaluation vector T' by the weight of the major indicators in the criterion layer to obtain a comprehensive evaluation vector T, the formula is: ; Among them, W represents the indicator weight of each major category of indicators.

[0039] Finally, S5 calculates the probability of occurrence and severity of consequences based on the evaluation vector and determines the final risk level, which specifically includes the following process: Calculate the probability level value L, the formula is: L = 1 + 3 × (X * Sᵀ); The formula for calculating the severity level of consequences R is: R = 1 + 3 × (Y * Sᵀ); Where X is the evaluation vector of the occurrence possibility index of non-coal mines; Y is the evaluation vector of the severity index of non-coal mine consequences; S is the median vector of the interval after normalization of the values ​​of each index, S = [0.875, 0.625, 0.375, 0.125]; Among them, the larger the value of the occurrence possibility level value L, the greater the possibility of the disaster occurring; the larger the value of the consequence severity level value R, the greater the severity of the consequence.

[0040] In other embodiments, in S2, during the process of calculating the indicator weights of each layer of indicators by the hierarchical analysis method, it is necessary to perform a consistency check on the judgment matrix according to the maximum eigenvalue, which specifically includes the following steps: Calculate the consistency index CI, the formula is: ; in, λ max is the maximum eigenvalue of the judgment matrix; k is the order of the judgment matrix, that is, the number of indicators corresponding to each judgment matrix; Computational consistency check CR , the formula is: ; like CR <0.1, the judgment matrix satisfies the matrix consistency; in, RI is the average random consistency index, and its specific value is related to the order of the judgment matrix k It can be obtained by looking up the matrix RI value table.

[0041] To further illustrate the principle and process of this embodiment, a specific application example is provided below for detailed description.

[0042] Firstly, the non-coal mine natural disaster risk assessment system is divided into target layer, criterion layer and indicator layer. The target layer refers to whether the possibility of natural disaster risk in non-coal mines is high enough and whether the consequences are serious; the criterion layer includes seven major indicators such as engineering design, engineering status, and natural disaster-prone factors; and the indicator layer includes specific sub-category indicators under the seven criterion layers.

[0043] Each criterion layer can be filled with specific indicators to form an indicator layer based on actual conditions. For example: the engineering design category should include the mine design scale grade, designed mining height or excavation depth, etc.; the engineering status category should include the current mining scale grade, mining height and spoil dump volume stacking height, etc.; the natural disaster risk factor category should include altitude type, permafrost type, rock structure type and earthquake activity, etc.; the disaster defense capacity category should include the mine construction site category, the comprehensive seismic defense capacity of mining area buildings and the building structure type of mining area living and office area; the historical disaster event category should include the number of disaster events and the impact of disaster events caused by natural disasters such as earthquakes, geological disasters and floods; the disaster prevention and mitigation capacity category should include the mine rescue team category, the number of full-time mine rescue teams and the number of mine safety technicians, etc.; the potential victims and protected objects category should include the maximum number of on-duty people in a single shift, the distance between the final slope bottom line of the spoil dump and the protected object, etc.

[0044] The specific indicators are divided into two directions: possibility of occurrence and severity of consequences, and the possibility of occurrence and severity of consequences are divided into four levels: low, general, large and serious.

[0045] The probability of occurrence index is affected by project design, project status, natural disaster-prone factors, disaster prevention capabilities and historical disaster events; the severity of the consequences is affected by disaster prevention and mitigation capabilities and potential victims and protected objects.

[0046] Next, the probability and severity of non-coal mine accidents caused by natural disasters were calculated using the analytic hierarchy process and fuzzy analytic hierarchy process.

[0047] Regarding the calculation of the weights of indicators at each level through the hierarchical analysis method, according to the nine-scale method, see Table 1, and construct the judgment matrix corresponding to the criterion layer and the indicator layer by comparing the indicators , the formula is: ; In the formula a ij Refers to major indicators i and major indicators j The importance comparison of the same category of indicators, or the sub-category indicators in the same category of indicators i and subcategory indicators j The importance of comparison. It has the following prescribed principles:a ij > 0; a ij =1 / a ij ( i ≠ j ); a ij = 1( i = j = 1,2, 3, …, n ). .

[0048] Table 1 Values ​​and meanings of the nine-scale method

[0049] In Table 1, a ij For indicators i and indicators j Comparison of importance; a ji For indicators j and indicators i Comparison of importance.

[0050] Secondly, the eigenvalue method is used to solve the eigenvectors of the judgment matrix of the criterion layer and the seven indicator layers of the non-coal mine natural disaster risk assessment system. Then, the eigenvectors are normalized to obtain the criterion layer and the weights of each indicator layer of the non-coal mine natural disaster risk assessment system. W and W′ , the formula is as follows: ; Where: w =[ w 1 , w 2 , …, w n ] is the eigenvector of the judgment matrix of the two target layers (possibility of occurrence and severity of consequences) and the seven criterion layers (engineering design, engineering status, natural disaster-prone factors, etc.). λ max is the corresponding judgment matrix A The maximum eigenvalue of .

[0051] In order to ensure the logical consistency of the evaluation system, the judgment matrix A Perform consistency checks to avoid logical contradictions between indicators. The consistency check formula is: ; like CR <0.1, the judgment matrix passed the consistency test; in CIis the consistency index, and the formula is: ; Where: λ max is a matrix A The maximum eigenvalue of k is a matrix A The order of is the number of indicators corresponding to each judgment matrix.

[0052] RI is the average random consistency index, and its specific value is related to the order of the judgment matrix k related, k is the corresponding number of indicators in each judgment matrix, see Table 2.

[0053] Table 2 "1-9" order matrix RI value

[0054] Finally, the indicator weights of each layer are calculated in sequence through the hierarchical analysis method.

[0055] The fuzzy analytic hierarchy process is used to optimize the index weights.

[0056] First, construct the membership matrix R By constructing a membership matrix, the subjectivity and uncertainty of experts' scoring of various indicators of natural disaster risk in non-coal mines can be effectively dealt with.

[0057] Constructing the membership matrix R as follows: ; Where: r ij ( i =1, 2… m ; j =1, 2… n ), representing each membership matrix R Middle i The indicator in j The value on the level represents the i The index value is j The degree of membership of each level. m Represents the number of indicators in each membership matrix; n is the number of levels, representing the number of levels of likelihood and severity of consequences. In this embodiment n =4.

[0058] The triangular fuzzy membership function is used to construct the membership matrix elements, and the corresponding membership function r k1 、 rk2 、 r k3 、 r k4 as follows: ; ; ; ; Where: x It is the normalized value of each subcategory indicator, which can be assigned to each indicator according to national, industry standards or actual conditions.

[0059] The weights calculated by the analytic hierarchy process are combined with the fuzzy evaluation matrix to obtain the comprehensive evaluation vector T′ , the formula is: (10) Where: W′= { w 1, w 2, w 3··· w m}, represents the weight of each sub-category indicator, and the weight coefficient is calculated using the hierarchical analysis method based on the expert scoring results. are the membership degrees of each indicator to the four levels.

[0060] Finally, the comprehensive evaluation vector of the target layer is calculated based on the weights of the major indicators of the criterion layer. T， The formula is: ; Where: W It represents the weight coefficient of each major indicator, which is calculated using the hierarchical analysis method based on the expert scoring results.

[0061] According to different indicator systems of different mines, T Represents the evaluation vector of the occurrence possibility index of non-coal mine disaster risk X or consequence severity index evaluation vector Y .

[0062] Finally, the likelihood of occurrence and severity of consequences are calculated.

[0063] After the values ​​of each indicator are taken, the probability of occurrence and the severity of the consequences are calculated. Occurrence probability level value L and severity rating of consequences R The calculation method is as follows: Occurrence probability level value of non-coal minesL Calculate using the following formula based on the project design, project status, natural disaster risk factors, disaster prevention capabilities, and historical disaster event information; ; Consequence severity rating R Based on the disaster prevention and mitigation capabilities and information on potential disaster victims and protected objects, the following formula is used for calculation: ; Where: L is the probability level value of occurrence, the larger the value, the greater the possibility of occurrence; R is the severity level of the consequences. The larger the value, the greater the severity of the consequences. X is the evaluation vector of the occurrence possibility index of non-coal mines; Y is the evaluation vector of the severity index of non-coal mine consequences; S is the median vector of the interval after normalizing the values ​​of each indicator, S= [0.875, 0.625, 0.375, 0.125].

[0064] Then, determine the probability level and consequence severity level according to Table 3.

[0065] Table 3 Classification criteria for likelihood of occurrence and severity of consequences

[0066] Finally, determine the risk level of natural disasters in non-coal mines.

[0067] The risk level of natural disasters in mines is divided into four levels: low (Level IV), general (Level III), major (Level II), and serious (Level I). The risk level of natural disasters in mines should be determined according to the risk level matrix (Table 4) based on the probability of occurrence and the severity of the consequences.

[0068] Table 4 Disaster risk level matrix

[0069] This embodiment first divides the risk factors of natural disasters in non-coal mines into seven aspects: engineering design, engineering status, natural disaster-prone factors, disaster defense capabilities, historical disaster events, disaster prevention and mitigation capabilities, and potential disaster victims and protected objects, and divides them into two parts: possibility of occurrence and severity of consequences.

[0070] Secondly, the weights of specific indicators and the level values ​​of occurrence possibility and consequence severity are calculated based on the hierarchical analysis method and the fuzzy hierarchical analysis method. Finally, the specific levels of occurrence possibility and consequence severity are determined based on the level table, and the disaster risk level of natural disasters in non-coal mines is determined based on the risk level table.

[0071] In summary, the non-coal mine natural disaster risk assessment method of the embodiment of the present application is adopted to divide the disaster-causing factors and assessment targets of non-coal mine natural disasters into categories to form a disaster risk assessment system, which includes a target layer, a criterion layer and an indicator layer; the indicator weights of the indicators of each layer are calculated by the hierarchical analysis method; the fuzzy evaluation matrix of the indicators of each layer is obtained by the fuzzy hierarchical analysis method; the comprehensive evaluation vector is obtained according to the indicator weights of the indicators of each layer and the fuzzy evaluation matrix; the occurrence possibility level value and the consequence severity level value are calculated respectively according to the evaluation vector, and the final risk level is determined.

[0072] This application introduces fuzzy mathematics to calculate the weight of the impact of various indicators on the disaster risk of natural disasters in non-coal mines, optimizes the contribution of different indicators to the disaster risk, and solves the shortcoming of the lack of differentiation in the traditional method of calculating the weights of various disaster-causing indicators in non-coal mines. It can make the evaluation results more in line with the actual situation and has a practical and feasible guiding role.

[0073] Example 2 This embodiment provides a non-coal mine natural disaster risk assessment system. For details not disclosed in the non-coal mine natural disaster risk assessment system of this embodiment, please refer to the specific implementation content of the non-coal mine natural disaster risk assessment scheme in other embodiments.

[0074] Figure 6 Detailed description of the structure of a non-coal mine natural disaster risk assessment system according to an embodiment of the present application is shown in FIG.

[0075] like Figure 6 As shown, a non-coal mine natural disaster risk assessment system includes: A non-coal mine natural disaster risk assessment system according to an embodiment of the present application includes: Indicator library module 10: used to store the seven categories of indicators at the criteria layer and their extensible sub-category indicators; Assessment and analysis module 20: configured to execute a non-coal mine natural disaster risk assessment method; Dynamic warning module 30: used to automatically trigger the emergency plan according to the risk level output by the assessment and analysis module.

[0076] The non-coal mine natural disaster risk assessment system of the present application is adopted, and the indicator library module 10 is used to store the seven categories of indicators of the criterion layer and the expandable sub-category indicators. The assessment and analysis module 20 is configured to execute the non-coal mine natural disaster risk assessment method, including dividing the disaster-causing factors and assessment targets of non-coal mine natural disasters to form a disaster risk assessment system, which includes a target layer, a criterion layer and an indicator layer; calculating the indicator weights of the indicators of each layer through the hierarchical analysis method; using the fuzzy hierarchical analysis method to obtain the fuzzy evaluation matrix of the indicators of each layer; obtaining a comprehensive evaluation vector based on the indicator weights of the indicators of each layer and the fuzzy evaluation matrix; calculating the occurrence possibility level value and the consequence severity level value according to the evaluation vector, and determining the final risk level.

[0077] This application introduces fuzzy mathematics to calculate the weight of the impact of various indicators on the disaster risk of natural disasters in non-coal mines, optimizes the contribution of different indicators to the disaster risk, and solves the shortcoming of the lack of differentiation in the traditional method of calculating the weights of various disaster-causing indicators in non-coal mines. It can make the evaluation results more in line with the actual situation and has a practical and feasible guiding role.

[0078] Example 3 This embodiment provides a non-coal mine natural disaster risk assessment device. For details not disclosed in the non-coal mine natural disaster risk assessment device of this embodiment, please refer to the specific implementation content of the non-coal mine natural disaster risk assessment method or system in other embodiments.

[0079] Figure 7 Schematic diagram of the structure of the non-coal mine natural disaster risk assessment device 400 according to an embodiment of the present application is shown in FIG.

[0080] like Figure 7 As shown, the non-coal mine natural disaster risk assessment device 400 includes: a storage unit 402: for storing executable instructions; and a processing unit 401: for connecting with the storage unit 402 to execute executable instructions to complete the non-coal mine natural disaster risk assessment method.

[0081] Those skilled in the art will understand that Figure 7 It is only an example of the non-coal mine natural disaster risk assessment device 400 and does not constitute a limitation of the non-coal mine natural disaster risk assessment device 400. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the non-coal mine natural disaster risk assessment device 400 may also include input and output devices, network access equipment, buses, etc.

[0082] The so-called processing unit 401 (Central Processing Unit, CPU) can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processing unit 401 can also be any conventional processor, etc. The processing unit 401 is the control center of the non-coal mine natural disaster risk assessment device 400, and uses various interfaces and lines to connect various parts of the entire non-coal mine natural disaster risk assessment device 400.

[0083] Storage unit 402 can be used to store computer-readable instructions. Processing unit 401 implements the various functions of non-coal mine natural disaster risk assessment device 400 by running or executing the computer-readable instructions or modules stored in storage unit 402 and accessing the data stored in storage unit 402. Storage unit 402 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of non-coal mine natural disaster risk assessment device 400. Furthermore, storage unit 402 may include a hard disk, memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, a read-only memory (ROM), a random access memory (RAM), or other non-volatile or volatile storage devices.

[0084] If the modules integrated into the non-coal mine natural disaster risk assessment device 400 are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the processes in the above-mentioned method embodiments by instructing the relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium. When executed by a processor, the computer-readable instructions can implement the steps of each of the above-mentioned method embodiments.

[0085] Example 5 This embodiment provides a computer-readable storage medium on which a computer program is stored; the computer program is executed by a processor to implement the non-coal mine natural disaster risk assessment method in other embodiments.

[0086] The non-coal mine natural disaster risk assessment device and storage medium of the present application include dividing the disaster-causing factors and assessment targets of non-coal mine natural disasters to form a disaster risk assessment system, which includes a target layer, a criterion layer and an indicator layer; calculating the indicator weights of each layer of indicators through the hierarchical analysis method; using the fuzzy hierarchical analysis method to obtain the fuzzy evaluation matrix of each layer of indicators; obtaining a comprehensive evaluation vector based on the indicator weights of each layer of indicators and the fuzzy evaluation matrix; calculating the occurrence possibility level value and the consequence severity level value respectively according to the evaluation vector, and determining the final risk level.

[0087] This application introduces fuzzy mathematics to calculate the weight of the impact of various indicators on the disaster risk of natural disasters in non-coal mines, optimizes the contribution of different indicators to the disaster risk, and solves the shortcoming of the lack of differentiation in the traditional method of calculating the weights of various disaster-causing indicators in non-coal mines. It can make the evaluation results more in line with the actual situation and has a practical and feasible guiding role.

[0088] Those skilled in the art will appreciate that the terms used in the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. As used in the present invention and the appended claims, the singular forms "a," "the," and "the" are intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.

[0089] It should be understood that although the terms "first," "second," "third," etc. may be used in the present invention to describe various information, such information should not be limited to these terms. These terms are merely used to distinguish information of the same type from one another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. Depending on the context, the term "if" as used herein may be interpreted as "when," "when," or "in response to determining."

[0090] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0091] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for assessing the risk of natural disasters in non-coal mines, characterized in that: The following steps are involved: Based on the factors causing disasters in non-coal mines and the assessment targets, a disaster risk assessment system is formed, which includes a target layer, a criterion layer and an indicator layer. The target layer includes two assessment targets: the possibility of occurrence of disaster risk and the severity of consequences. The criterion layer includes seven major indicators: engineering design, engineering status, natural disaster-prone factors, disaster defense capabilities, historical disaster events, disaster prevention and mitigation capabilities, and potential disaster victims and protected objects. The indicator layer includes sub-category indicators based on the major indicators of the criterion layer. Calculate the indicator weights of each layer of indicators through the hierarchical analysis method; The fuzzy evaluation matrix of each layer index is obtained by using fuzzy analytic hierarchy process; According to the indicator weights of each layer and the fuzzy evaluation matrix, a comprehensive evaluation vector is obtained; The occurrence probability level value and the consequence severity level value are calculated respectively according to the evaluation vector, and the final risk level is determined.

2. The risk assessment method according to claim 1, characterized in that: The calculation of the indicator weights of each layer of indicators by the hierarchical analysis method includes: Construct the judgment matrix of each layer of indicators in a hierarchical manner, and the elements of the judgment matrix are the importance of the indicators of this layer to the indicators of the previous layer; The judgment matrix is ​​numerically scaled according to the importance of each sub-category indicator of the indicator layer to the criterion layer and the importance of each major category indicator of the criterion layer to the evaluation target of the target layer; Calculating the maximum eigenvalue and eigenvector of the judgment matrix; Performing a consistency check on the judgment matrix according to the maximum eigenvalue, and if the matrix consistency is not satisfied, reconstructing the judgment matrix until the matrix consistency is satisfied; The indicator weights of the indicators at each layer are calculated based on the eigenvector corresponding to the maximum eigenvalue.

3. The risk assessment method according to claim 1, characterized in that: The fuzzy evaluation matrix of each layer index obtained by adopting the fuzzy analytic hierarchy process includes: The two assessment targets of the target layer are divided into n risk levels according to the degree of disaster; Based on the evaluation of n risk levels by indicators at each level, a membership matrix is ​​constructed; After assigning values ​​to each indicator according to national, industry standards or actual conditions, the triangular fuzzy membership function is combined to construct the membership matrix elements and obtain the fuzzy evaluation matrix.

4. The risk assessment method according to claim 3, characterized in that: The triangular fuzzy membership function is combined to construct the membership matrix elements to obtain the fuzzy evaluation matrix, including: The membership matrix R is: ； in, r ij ( i =1, 2… m ; j =1, 2… n ), representing each membership matrix R Middle i The indicator in j The value on the level represents the i The index value is j The degree of membership of each level; m Represents the number of indicators in each membership matrix; n is the number of levels; According to the triangular fuzzy membership function r k1 、 r k2 、 r k3 、 r k4 Calculate the membership matrix elements to obtain the fuzzy evaluation matrix. The triangular fuzzy membership function formula is as follows: ； ； ； ； Among them, x is the normalized value of each subcategory indicator, which can be assigned to each indicator according to national, industry standards or actual conditions.

5. The risk assessment method according to claim 1, characterized in that: The comprehensive evaluation vector is obtained based on the indicator weights of each layer and the fuzzy evaluation matrix, including: Multiply the sub-category indicator weights of the indicator layer by the corresponding fuzzy evaluation matrix to obtain the first evaluation vector T', which is: ； in, W′= { w 1, w 2, w 3··· w m }, w 1, w 2, w 3··· w m represents the weight coefficient of each sub-category indicator, and R is the fuzzy evaluation matrix; The first evaluation vector T' is multiplied by the weight of the major indicators in the criterion layer to obtain the comprehensive evaluation vector T, the formula is: ； Among them, W represents the indicator weight of each major category of indicators.

6. The risk assessment method according to claim 1, characterized in that: Calculating the occurrence probability level value and the consequence severity level value based on the evaluation vector and determining the final risk level includes: Calculate the probability level value L, the formula is: L = 1 + 3 × (X * Sᵀ); The formula for calculating the severity level of consequences R is: R = 1 + 3 × (Y * Sᵀ); Where X is the evaluation vector of the non-coal mine occurrence possibility index; Y is the evaluation vector of the non-coal mine consequence severity index; T is the comprehensive evaluation vector; S is the median vector of the interval after normalization of the values ​​of each index, S = [0.875, 0.625, 0.375, 0.125]; Among them, the larger the value of the occurrence possibility level value L, the greater the possibility of the disaster occurring; the larger the value of the consequence severity level value R, the greater the severity of the consequence.

7. The risk assessment method according to claim 2, characterized in that: The performing consistency check on the judgment matrix according to the maximum eigenvalue includes: Calculate the consistency index CI, the formula is: ; in, λ max is the maximum eigenvalue of the judgment matrix; k is the order of the judgment matrix, that is, the number of indicators corresponding to each judgment matrix; Computational consistency check CR , the formula is: ； like CR < 0.1, the judgment matrix satisfies matrix consistency; in, RI is the average random consistency index, and its specific value is related to the order of the judgment matrix k It can be obtained by looking up the matrix RI value table.

8. A non-coal mine natural disaster risk assessment system, characterized by: include: Indicator library module: used to store the seven categories of indicators at the criteria layer and their extensible sub-category indicators; Evaluation and analysis module: configured to execute the method according to any one of claims 1 to 7; Dynamic early warning module: used to automatically trigger emergency plans based on the risk level output by the assessment and analysis module.

9. A non-coal mine natural disaster risk assessment device, characterized in that: include: a storage unit for storing executable instructions; as well as A processing unit, configured to be connected to the memory to execute executable instructions to complete the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon; the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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