Non-coal underground mine goaf hidden disaster risk assessment method

Abstract

The invention provides a non-coal underground mine goaf hidden disaster risk assessment method, which is based on rock mass mechanics and complex network theories, disasters possibly caused by a goaf are decomposed into quantifiable hidden disaster risk factors, and a four-level risk level classification system is constructed. And a subjective and objective combination weighting method combining a CRITIC method and a Pagek matter-element analysis model is innovatively adopted, scientific distribution of hidden disaster-inducing factor weights is realized, and potential risks such as rock mass structure deterioration and goaf communication can be dynamically identified by quantitatively evaluating key indexes such as goaf stability, geological structure and treatment measures, so that the risk of hidden disaster-inducing factors can be identified, and the risk of hidden disaster-inducing factors can be identified. Scientific basis is provided for formulating targeted prevention and control strategies and optimizing safety management processes for mine enterprises, the accident rate of the goaf can be reduced, conversion of non-coal mine safety production from passive treatment to active prevention can be promoted, and remarkable social benefits and industry popularization value are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mine safety and disaster prevention, and particularly relates to a non-coal underground mine goaf hidden disaster-causing risk assessment method. BACKGROUND

[0002] The non-coal underground mine goaf has a large number of goafs formed due to long-term mining, and there are many hidden disaster-causing risks such as roof falling and rib spalling, shock wave, surface subsidence, water and mud inrush, and poor ventilation, which seriously threaten the safety production of the mine. In recent years, with the increase of the mining depth and scale of metal and non-metal mines, goaf accidents have become more and more frequent, and therefore, the general survey of the hidden disaster-causing factors of the goaf has been widely valued. A series of policies and regulations have been introduced by the state and local governments, and the mining enterprises are required to strengthen the general survey and prevention and control of the hidden disaster-causing factors. However, the traditional assessment method often relies on experience and judgment, lacks scientificity and systematicness, and is difficult to accurately assess the hidden disaster-causing risks of the goaf. SUMMARY

[0003] In view of the technical problems in the background art, the present application provides a non-coal underground mine goaf hidden disaster-causing risk assessment method. The non-coal underground mine goaf hidden disaster-causing risk assessment method realizes comprehensive risk assessment of the hidden disaster-causing types (such as roof falling and rib spalling, shock wave, surface subsidence, water and mud inrush, etc.) of the goaf through dynamic weight calculation.

[0004] In the first aspect, the embodiments of the present application provide a non-coal underground mine goaf hidden disaster-causing risk assessment method, which specifically includes the following steps:

[0005] Obtaining historical data of the underground mine goaf, analyzing the disaster types and disaster-causing factors of the underground mine goaf according to the historical data, and dividing the risk levels of the disaster-causing factors to establish a risk assessment system;

[0006] According to the influence degree of the disaster-causing factors on the disaster types and in combination with expert experience, determining the subjective weight and objective weight of each disaster-causing factor, and calculating the comprehensive weight of each disaster-causing factor according to the subjective weight and the objective weight of each disaster-causing factor;

[0007] According to the data monitoring of each disaster-causing factor of the underground mine goaf, obtaining the parameter data of each disaster-causing factor;

[0008] According to the parameter data of each disaster-causing factor and the comprehensive weight of each disaster-causing factor, determining the goaf hidden disaster-causing risk level through correlation degree calculation.

[0009] Further, in the embodiment, the evaluating the influence degree of the disaster-causing factor on the disaster type according to the historical data and combining expert experience, determining the subjective weight and the objective weight of each disaster-causing factor, and calculating the comprehensive weight of each disaster-causing factor according to the subjective weight and the objective weight of each disaster-causing factor comprises:

[0010] standardizing the disaster-causing factors and calculating the objective weight of each disaster-causing factor;

[0011] constructing a disaster type-disaster-causing factor complex network based on expert experience and the correlation between the disaster type and the disaster-causing factor, calculating a network centrality index according to the disaster type-disaster-causing factor complex network, and obtaining the subjective weight of each disaster-causing factor according to the network centrality index;

[0012] calculating the comprehensive weight of each disaster-causing factor according to the subjective weight and the objective weight of each disaster-causing factor.

[0013] Further, in the embodiment, the standardizing the disaster-causing factors and calculating the objective weight of each disaster-causing factor comprises:

[0014] standardizing the disaster-causing factors based on the CRITIC method, calculating the coefficient of variation and the independence coefficient of each disaster-causing factor, and calculating the objective weight of each disaster-causing factor through the coefficient of variation and the independence coefficient.

[0015] Further, in the embodiment, the network centrality index comprises node degree centrality, clustering coefficient, closeness centrality, and weight centrality, and the subjective weight of each disaster-causing factor is obtained by integrating the node degree centrality, the clustering coefficient, the closeness centrality, and the weight centrality.

[0016] Further, in the embodiment, the constructing a matter element analysis model of goaf hidden disaster risk assessment based on the matter element analysis theory and determining the risk level of goaf hidden disaster according to the parameter data of each disaster-causing factor and the weight of each disaster-causing factor through correlation degree calculation comprises:

[0017] constructing a matter element analysis model of goaf hidden disaster risk assessment based on the matter element analysis theory;

[0018] determining the correlation degree between the risk level and each disaster-causing factor according to the parameter data of each disaster-causing factor and the weight of each disaster-causing factor;

[0019] determining the risk level of goaf hidden disaster based on the maximum membership principle.

[0020] Further, in the embodiment, the risk level is divided into four levels of I, II, III and IV.

[0021] The level I is a major risk, an unacceptable risk, a goaf instability, a major risk, not yet handled, a major loss of safety conditions, and a highest risk.

[0022] The level II is a larger risk, an unexpected risk, a goaf instability, a major risk, insufficient control measures, or a larger risk, not yet handled, a loss of safety conditions, and a higher risk.

[0023] The level III is a general risk, a limited acceptable risk, a basically stable goaf, a general risk, basically provided safety conditions, and a lower risk.

[0024] The level IV is a low risk, an acceptable risk, a stable goaf, no major risk, and a major risk is safely controlled or eliminated, completely provided safety conditions, and a lowest risk.

[0025] Further, in the embodiment, the method further includes determining an optimal treatment scheme according to the hidden disaster risk level of the goaf.

[0026] In a second aspect, the embodiments of the present application provide a computer readable storage medium, which is a non-volatile storage medium or a non-transitory storage medium, and has a computer program stored thereon, and the computer program is characterized in that, when the computer program is run by a processor, the steps of the non-coal underground mine goaf hidden disaster risk assessment method according to any one of the preceding embodiments are performed.

[0027] In a third aspect, the embodiments of the present application provide a computer program product, which includes computer programs / instructions, and when the computer programs / instructions are executed by a processor, the steps of the non-coal underground mine goaf hidden disaster risk assessment method according to any one of the preceding embodiments are implemented.

[0028] In a fourth aspect, the embodiments of the present application provide a non-coal underground mine goaf hidden disaster risk assessment device, which includes a memory and a processor, and the memory has a computer program stored thereon, which can be run on the processor, and when the processor runs the computer program, the steps of the non-coal underground mine goaf hidden disaster risk assessment method according to any one of the preceding embodiments are performed.

[0029] Beneficial effects: the application provides a non-coal underground mine goaf hidden disaster risk assessment method, through establishing a four-level classification system, the main disaster factors such as roof fall, shock wave and surface subsidence are refined into 13 quantifiable indexes, and the CRITIC method and Pajek matter element analysis model are combined to realize the scientific distribution of the weight of hidden disaster factors. The method realizes the scientific distribution of the weight of hidden disaster factors by combining the risk matrix model and the complex network relationship, and finally outputs the I-IV level comprehensive risk grade by combining the variation coefficient, independence coefficient and correlation degree calculation, which significantly improves the objectivity and systematization of risk assessment, solves the problem of relying on experience judgment and lacking quantitative analysis in traditional method, and provides technical support for accurate identification and grading control of mine hidden disaster risk. At the same time, the scheme can dynamically identify potential risks such as rock mass structure deterioration and goaf connection by quantitatively evaluating key indexes such as goaf stability, geological structure and treatment measures, which provides scientific basis for mine enterprises to develop targeted prevention and control strategies and optimize safety management process, not only can reduce the accident rate of goaf, but also can promote the transformation of non-coal mine safety production from passive governance to active prevention, which has significant social benefits and industry popularization value.

[0030] The above description is only a summary of the technical scheme of the application, in order to more clearly understand the technical means of the application, which can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the application more obvious and easy to understand, the following will specifically describe the embodiment of the application. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical scheme of the application, the drawings used in the application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creating labor.

[0032] Figure 1 For Figure 1 The flow chart of the non-coal underground mine goaf hidden disaster risk assessment method provided by the embodiment of the application. DETAILED DESCRIPTION

[0033] The embodiments of the technical scheme of the application will be described in detail below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical scheme of the application, therefore only as an example, and cannot limit the protection scope of the application.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this application; the use of the terms "including," "comprising," or "having" and variations thereof herein is intended to be broad and encompass the terms "consisting of" and "consisting essentially of" and variations thereof. Unless otherwise noted, the terms "including" and "comprising" are open-ended and do not exclude the presence of unrecited elements or limitations.

[0035] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0036] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearance of the phrase in various places in the specification is not necessarily all referring to the same embodiment, or to a particular embodiment, or to a particular set of embodiments, and is not intended to exclude other embodiments or options from the application. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0037] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0038] In the description of the embodiments of the present application, the term "a plurality of" refers to two or more (including two), and similarly, "a plurality of groups" refers to two or more groups (including two groups), and "a plurality of pieces" refers to two or more pieces (including two pieces).

[0039] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the embodiments of the present application and simplifying the description, and therefore cannot be understood as limiting the embodiments of the present application, which do not indicate or imply that the indicated devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the embodiments of the present application.

[0040] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connection", "connecting", "fixing" and the like should be understood in a broad sense, for example, can be fixed connection, can also be detachable connection, or integral; can be mechanical connection, can also be electrical connection; can be direct connection, can also be indirect connection through an intermediate medium, can be internal communication of two elements or interaction relationship of two elements. For those skilled in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0041] The goaf of non-coal underground mine has a large number of empty areas due to long-term mining, and there are many hidden disaster risks such as roof falling and rib spalling, shock wave, surface subsidence, water and mud inrush, poor ventilation and the like, which seriously threaten the safety production of the mine. In recent years, with the increase of the mining depth and scale of metal and non-metal mines, goaf accidents have become more and more frequent, therefore, the general survey of hidden disaster factors in goaf has been widely valued. The state and local governments have successively introduced a series of policies and regulations, requiring mining enterprises to strengthen the general survey and prevention and control of hidden disaster factors. However, the traditional evaluation method often relies on experience judgment, lacks scientificity and systematicness, and it is difficult to accurately evaluate the hidden disaster risk of goaf.

[0042] In order to solve the technical problem that the traditional evaluation method often relies on experience judgment, lacks scientificity and systematicness, and it is difficult to accurately evaluate the hidden disaster risk of goaf, the present application provides a hidden disaster risk evaluation method for goaf of non-coal underground mine, which realizes the comprehensive risk evaluation of the hidden disaster types (such as roof falling and rib spalling, shock wave, surface subsidence, water and mud inrush and the like) of goaf through multi-source data fusion and dynamic weight calculation.

[0043] The embodiments of the present application provide a hidden disaster risk evaluation method for goaf of non-coal underground mine, which specifically includes the following steps:

[0044] S1, obtaining the historical data of the goaf of underground mine, analyzing the disaster types and disaster factors of the goaf of underground mine according to the historical data, and dividing the risk levels of the disaster factors to establish a risk evaluation system.

[0045] S2, determining the subjective weight and objective weight of each disaster factor according to the influence degree of the disaster factors on the disaster types and combining the expert experience, and calculating the comprehensive weight of each disaster factor according to the subjective weight and objective weight of each disaster factor.

[0046] S3, obtaining the parameter data of each disaster factor according to the data monitoring of each disaster factor of the goaf of underground mine.

[0047] S4. Based on the matter-element analysis theory, a matter-element analysis model for the hidden disaster risk assessment of goaf areas is constructed, and the hidden disaster risk level of goaf areas is determined through correlation calculation based on the parameter data of each disaster factor and the comprehensive weight of each disaster factor.

[0048] Specifically, in this embodiment, historical data of underground mine goafs are obtained, and by analyzing the historical data of underground mine goafs, the types of disasters that may occur in underground mine goafs and the possible disaster-causing factors corresponding to the disaster types are analyzed from the historical data, and the risk levels of the disaster-causing factors are divided to establish a risk assessment system; the degree of influence of the disaster-causing factors on the disaster types is assessed based on the historical data and combined with expert experience to determine the subjective weight and objective weight of each disaster-causing factor, and the comprehensive weight of each disaster-causing factor is calculated based on the subjective weight and objective weight of each disaster-causing factor. Weight; sensors are set up in each goaf of the underground mine, and data monitoring of various disaster-causing factors in the goaf of the underground mine is carried out through sensor detection, so as to obtain parameter data of each disaster-causing factor, and the correlation between the parameter data of each disaster-causing factor and the comprehensive weight of each disaster-causing factor is calculated through the matter-element analysis model of hidden disaster risk assessment of goaf based on matter-element analysis theory, so as to determine the hidden disaster risk level of the goaf, and accurately assess the risk level of hidden disaster types in goaf through dynamic weight calculation based on historical data of underground mine goaf and expert experience.

[0049] like Figure 1 As shown, Figure 1 This is a flow chart of a method for assessing hidden disaster risks in goaf areas of non-coal underground mines provided in an embodiment of the present application. In this embodiment, the method for assessing hidden disaster risks in goaf areas of non-coal underground mines includes the following steps:

[0050] S1. Obtain historical data on underground mine goafs, analyze the disaster types and disaster-causing factors in underground mine goafs based on the historical data, and classify the risk levels of the disaster-causing factors to establish a risk assessment system.

[0051] For example, in this embodiment, taking a non-coal underground mine as an example, historical data of the goaf of the non-coal underground mine is obtained. From the analysis of the historical data, it can be seen that the risk types of hidden disasters that may occur in the goaf mainly include: roof collapse, rock burst, surface collapse, water and mud burst, and poor ventilation; the hidden disaster factors that cause the above risk types include the rock structure of the goaf, geological structure, rock quality, height, goaf volume, exposure area, ore body safety factor, span, burial depth, exposure time, water accumulation in the goaf, adjacent goaf conditions, and governance and management measures, a total of 13 indicators.

[0052] The disaster types and disaster-causing factors of the goaf of an underground mine are obtained, and the risk levels of the disaster-causing factors are divided, which are divided into four levels of I, II, III and IV.

[0053] The I level is a major risk, an unacceptable risk, the goaf is unstable, there is a major risk, it has not been handled, the safety condition is majorly lost, and the risk is the highest.

[0054] The II level is a relatively large risk, an unexpected risk, the goaf is unstable, there is a major risk, and the control measures are insufficient; or there is a relatively large risk, it cannot be handled yet, the safety condition is lost, and the risk is relatively high.

[0055] The III level is a general risk, a limited acceptable risk, the goaf is basically stable, there is a general risk, the safety condition is basically met, and the risk is relatively low.

[0056] The IV level is a low risk, an acceptable risk, the goaf is stable, there is no major risk, and the relatively large risk has been safely controlled or eliminated, the safety condition is completely met, and the risk is the lowest.

[0057] According to the above risk levels, the disaster-causing degrees of the disaster-causing factors are classified, and a risk assessment system is established.

[0058] S2, according to the historical data, the influence degree of the disaster-causing factors on the disaster types is evaluated, the subjective weight and the objective weight of each disaster-causing factor are determined according to the expert experience, and the comprehensive weight of each disaster-causing factor is calculated according to the subjective weight and the objective weight of each disaster-causing factor.

[0059] In this embodiment, the specific steps of obtaining the comprehensive weight of each disaster-causing factor include:

[0060] S21, the disaster-causing factors obtained from the historical data analysis are standardized based on the CRITIC method, and the variation coefficient and the independence coefficient of each disaster-causing factor are calculated, and the initial weight of each disaster-causing factor is calculated through the variation coefficient and the independence coefficient.

[0061] Specifically, an evaluation matrix X=(x kj ) n×m is established, where x is the measured value or the score value of the jth hidden disaster risk factor in the ith goaf to be evaluated; m is the number of goafs to be evaluated; and n is the number of hidden disaster factors.

[0062] The elements of the original evaluation matrix X are converted according to the standardization formula

[0063]

[0064] Wherein: is the mean value of the jth disaster-causing factor index, S j is the standard deviation.

[0065] The Pearson correlation coefficient between each evaluation disaster factor is calculated by using the standardized matrix X * kj n×m

[0066] The independence coefficient η of each evaluation disaster factor is calculated by the following formula j :

[0067]

[0068] According to the variation coefficient and the independence coefficient of each evaluation disaster factor obtained above, the importance coefficient C of each evaluation disaster factor is calculated by the following formula j :

[0069]

[0070] Finally, the objective weight wj of each evaluation disaster factor is calculated

[0071]

[0072] S22, a disaster type-disaster factor complex network is constructed based on expert experience and the correlation between disaster types and disaster factors, a network centrality index is calculated according to the disaster type-disaster factor complex network, and a subjective weight of each disaster factor is obtained according to the network centrality index;

[0073] Specifically, a disaster type-disaster factor complex network is constructed based on expert experience and the correlation between disaster types and disaster factors, and the node degree, clustering coefficient, closeness and importance of each node in the disaster type-disaster factor complex network are calculated by Pajek software.

[0074] The node degree of each node in the network is calculated

[0075] k i =∑ j∈N a ij

[0076] where k i is the node in-degree and out-degree, a ij represents the number of connection edges between node vi and node vj, and N is the number of nodes in the disaster network.

[0077] The clustering coefficient of each node is calculated

[0078]

[0079] C i ∈[0,1],C i ​​C i = 1 indicates that the other nodes connected to node vi are connected to each other.

[0080] Calculate the clustering coefficient of the entire network:

[0081]

[0082] According to the closeness centrality of each node in the network, including the inner and outer centrality:

[0083] Inner centrality:

[0084]

[0085] Outer centrality:

[0086]

[0087] Use eigenvector centrality to measure the importance of each node,

[0088] x(t) = cAx(t-1), t = 1, 2,…

[0089] x = λ -1 Ax

[0090] Where x is the importance measure value of the node, c is the proportionality constant, and A is the adjacency matrix.

[0091] By integrating the node degree, clustering coefficient, closeness and importance of each node in the disaster type-cause complex network, the subjective weight of each disaster-causing factor is obtained.

[0092] S23, according to the objective weight and subjective weight of each disaster-causing factor, calculate the comprehensive weight of each disaster-causing factor.

[0093] Specifically, the objective weight and subjective weight of each disaster-causing factor are collected to form a weight set, and a linear combination coefficient a p = (a1a2…a3) is introduced, and the combination weight is calculated by combining the linear combination coefficient with the weight set:

[0094]

[0095] Where, The entire represents the possible weight vector set, and α p is the combination coefficient,

[0096] To get a game theory combination weight vector closer to the ideal value, the comprehensive weight is determined by minimizing the deviation of the combination weight, the subjective weight and the objective weight.

[0097] Get the countermeasure model:

[0098]

[0099] According to the properties of combined differentials, the optimal first-order inverse condition of the above game model is derived:

[0100]

[0101] Normalize the obtained combination coefficients

[0102]

[0103] The comprehensive weight vector is as follows:

[0104]

[0105] S3. Based on the matter-element analysis theory, a matter-element analysis model for the hidden disaster risk assessment of goaf areas is constructed, and the hidden disaster risk level of goaf areas is determined through correlation calculation based on the comprehensive weight of each disaster-causing factor.

[0106] Specifically, a matter-element analysis model for hidden disaster risk assessment in goaf is constructed based on the matter-element analysis theory. The matter-element analysis model is an efficient method for dealing with the problem of contradictory compatibility and the coexistence of qualitative and quantitative indicators. Generally, the characteristic matter element of each evaluation object is defined as: R = (P, C, V) as the basic element to describe the evaluation object, specifically including the evaluation object set, that is, the risk level P = [P1, P2, ..., P n ], the evaluation index set is the hidden disaster factor C = [C1, C2, ..., C m ] and the hidden disaster factor value set V = [V1, V2, ..., V m ];

[0107]

[0108] Where: R j is a classical domain matter-element, with n groups corresponding to n levels of risk level; P j is the evaluation level j of the object, j = (1, 2, 3, ... n); C i They correspond to i hidden disaster factors, i = (1, 2, 3, ... m); the classic domain is the value range corresponding to the hidden disaster level j (a mj , b mj ).

[0109]

[0110] Where: R P is the domain element, P is the total hidden disaster risk level of the mine; V Pare hidden disaster factors C1, C2, ..., C m The total value range of V is the domain of P. Pi =(a pi , b pi ) is the hidden disaster factor C i The value range of a pi and b pi C is the hidden hazard factor of mines i upper and lower limits.

[0111] Setting a correlation function to describe the correlation between the hidden disaster risk factor and the corresponding disaster level t, and determining the correlation between the risk level and each disaster factor according to the weight of each disaster factor;

[0112]

[0113] Where, v ti is the standardized domain of the i-th hidden disaster risk factor when the hidden disaster risk level of mine safety is level t; ρ(u ij ,u pi ) and ρ(u ij , v ti ) represent u ij The distance to.

[0114] R P The comprehensive correlation degree L of the hidden disaster risk level t of the mine goaf takes the maximum value, and the risk level is determined according to the maximum value:

[0115]

[0116] After obtaining the correlation of goaf risk levels, the hidden disaster risk level of goaf is determined based on the maximum membership principle.

[0117] In some embodiments, the method for assessing hidden disaster risks in goafs of non-coal underground mines also includes the following steps: determining the optimal control plan based on the hidden disaster risk level of the goaf; after determining the hidden disaster risk level of the goaf of the underground mine, designing a control plan based on the hidden disaster risk level and expert review; after determining the control plan, implementing the project in the goaf of the underground mine according to the control plan; after completing the project construction, monitoring and verifying the construction site to verify the effectiveness of the control plan; dynamically matching the optimal control plan based on the hidden disaster risk level, realizing the transition from passive response to active prevention and control, and ultimately achieving the comprehensive control goal of "controllable risks, affordable costs, and sustainable ecology."

[0118] It can be understood that in the present embodiment, the governance scheme can be determined by expert experience and characteristics of different risk levels, and thus the effectiveness of the goaf in the non-coal underground mine can be effectively governed.

[0119] For example, the characteristics of the I-level risk: the correlation is significantly higher than other levels, there are serious safety hazards, and it may cause sudden disasters (such as collapse, water inrush, etc.). Its governance scheme mainly includes immediate shutdown, stopping work in the goaf and surrounding area, evacuating personnel and equipment, and closing the dangerous area, setting warning signs; laying high-density monitoring network (surface displacement, underground stress, hydrology, etc.), real-time data transmission, organizing multidisciplinary expert team to develop long-term governance scheme, and taking blasting collapse to eliminate the goaf if necessary.

[0120] The characteristics of the II-level risk are that there are obvious hidden dangers, and the risk needs to be controlled in a short period of time, so the II-level risk governance scheme can be planned mainly by local limited production: suspending work in high-risk areas, and limiting speed mining in other areas; dynamic monitoring and early warning, increasing monitoring frequency (once a day), combining with microseismic monitoring system to capture rock mass cracking signals, and establishing early warning threshold to trigger automatic alarm. Anchor cables, steel supports and other temporary supports are used for roof and surrounding rock; and grouting is carried out in the fracture development area, and artificial pillars or expanded original pillar size are supplemented, and low-strength filling (such as waste rock filling) is carried out in key parts.

[0121] The III-level risk is a general risk, which is limited to accept risk, the goaf is basically stable, there is a general risk, the safety condition is basically met, the risk is low, the hidden danger is controllable but needs long-term attention, so when dealing with the III-level risk, the methods of mining while governing, regular monitoring, engineering optimization and local reinforcement can be used, and the governance measures are implemented simultaneously in the normal production process, and the mining sequence is adjusted to avoid the exposure of the goaf; anchor rod + metal net support is installed for unstable roof, and settlement observation pile is set on the ground for regular re-measurement.

[0122] The IV-level risk is a low risk, which is acceptable, the goaf is stable, there is no major risk, and the major risk has been safely controlled or eliminated, the safety condition is completely met, the risk is the lowest, and it is temporarily controllable. When dealing with the goaf with IV-level risk, the surface displacement and underground stress, goaf boundary fracture development, water seepage, ground sound anomaly and other phenomena can be monitored by means of regular monitoring, daily inspection, preventive maintenance and updating risk level every quarter combined with monitoring data, and preventive maintenance is carried out.

[0123] Exemplary, in this embodiment, taking a certain underground mine as an example, the historical data of the goaf of the underground mine are obtained, and the goaf of the underground mine is investigated, the investigation result shows that there are more than 140 goafs, the total amount of goaf is about 3.64 million cubic meters, among them, the filling goaf is about 0.63 million cubic meters, the caving filling goaf is about 3 million cubic meters, the 1956 layer 17 line through the vein roadway is currently in the stage of ore mining, the stope has not been filled, the volume of the goaf is about 4000m 3 ;

[0124] Due to the existence of goaf, the possible risk types of hidden disaster may mainly include: roof caving and rib spalling A1, rock burst A2, surface subsidence A3, water and mud inrush A4, poor ventilation A5.

[0125] The hidden disaster factors causing the above risk types include 13 indexes of rock mass structure X1, geological structure X2, rock quality X3, height X4, goaf volume X5, exposed area X6, ore body safety factor X7, span X8, buried depth X9, exposure time X10, goaf water X11, adjacent goaf situation X12, management and management measures X13.

[0126] The risk index system of goaf is established, and the risk level is divided into I, II, III and IV four levels, I level is major risk, unacceptable risk, the highest risk; II level is larger risk, unexpected risk, higher risk; III level is general risk, limited acceptance risk, lower risk; IV level is low risk, acceptable risk, the lowest risk. The risk level is shown in the following table 1:

[0127] Table 1 is the risk level division result

[0128]

[0129] According to the above classification standard, the qualitative and quantitative index classification and classification standard of goaf danger evaluation shown in table 2 are proposed:

[0130]

[0131] Based on CRITIC method, the data of goaf hidden disaster factors are standardized, and the correlation coefficient is obtained, as shown in table 3;

[0132] Table 3

[0133] X1 X2 X3 X4 X5 X6 X7 X8 X9 x10 X11 X12 X13 X1 1.00000 -0.29434 0.55140 -0.20664 0 -0.13018 0 -0.05408 -0.02225 0 0 0.31693 0 X2 -0.29434 1.00000 0.14525 -0.12478 0 -0.16541 0 -0.14954 -0.25917 0 0 0.08221 0 X3 0.55140 0.14525 1.00000 -0.4026 0 -0.31052 0 -0.19331 -0.05308 0 0 0.39049 0 X4 -0.20664 -0.12478 -0.4026 1.00000 0 0.77072 0 0.55922 0.21485 0 0 0.24015 0 X5 0 0 0 0 1.00000 0 0 0 0 0 0 0 0 X6 -0.13018 -0.16541 -0.31052 0.77072 0 1.00000 0 0.57167 -0.0208 0 0 -0.2044 0 X7 0 0 0 0 0 0 1.00000 0 0 0 0 0 0 X8 -0.05408 -0.14954 -0.19331 0.55922 0 0.57167 0 1.00000 0 0 0 -0.2016 0 X9 -0.02225 -0.25917 -0.05308 0.21485 0 -0.0208 0 0.31945 1.00000 0 0 -0.2822 0 X10 0 0 0 0 0 0 0 0 0 1.00000 0 0 0 X11 0 0 0 0 0 0 0 0 0 0 1.00000 0 0 X12 0.31693 0.08221 0.39049 0.24015 0 -0.20444 0 -0.2016 -0.2822 0 0 1.00000 0 X13 0 0 0 0 0 0 0 0 0 0 0 0 1.00000

[0134] The variation coefficient, independence coefficient and weight of each factor are calculated, as shown in table 4:

[0135] Table 4

[0136]

[0137] The node degree, clustering coefficient, closeness, and weight center of each index and disaster-causing factor were calculated by Pajek software, as shown in Table 5:

[0138] Table 5

[0139]

[0140]

[0141] After integration, the subjective weights of indexes of hidden disaster-causing factors X1, X2, …, X13 calculated by Pajek complex network are shown in Table 6.

[0142] Table 6

[0143] Sequence number X1 X2 X3 X4 X5 Subjective weight 0.12086015 0.01633078 0.08234294 0.06208529 0.07862733 Sequence number X6 X7 X8 X9 X10 Subjective weight 0.07862733 0.09974374 0.07902607 0.01633078 0.05202862 Sequence number X11 X12 X13 Subjective weight 0.12007921 0.06988783 0.12402987

[0144] Based on the combination weight algorithm, the comprehensive weights of each hidden disaster-causing factor are shown in Table 7.

[0145] Table 7

[0146]

[0147]

[0148] According to the matter element model, the classical field of the hidden disaster risk of the goaf is:

[0149]

[0150] According to the matter element model, the correlation degree of the hidden disaster-causing factors of the goaf of the mine is shown in Table 8.

[0151] Table 8

[0152]

[0153]

[0154] The correlation degree of the risk level of the goaf is shown in Table 9.

[0155] Table 9

[0156] Risk level Major risk Greater risk General risk Low risk Grade Ⅰ Ⅱ Ⅲ IV Correlation degree 0.111001682 0.260178648 0.504227322 0.415940502

[0157] In summary, according to the maximum membership degree principle, the hidden disaster risk level of the goaf of the mine is grade III, belonging to general risk.

[0158] The embodiment establishes a four-level classification system to refine main disaster factors such as roof caving and rib spalling, shock wave, and surface subsidence into 13 quantifiable indexes, and innovatively adopts a subjective and objective combined weighting method combining CRITIC method and Pajek matter element analysis model to realize scientific distribution of weights of hidden disaster factors. The method significantly improves the objectivity and systematization of risk assessment by constructing a risk matrix model and a complex network relationship, combining the variation coefficient, the independence coefficient and the correlation degree calculation, and finally outputting the I-IV level comprehensive risk grade, solves the problem that the traditional method relies on experience judgment and lacks quantitative analysis, and provides technical support for accurate identification and grading control of hidden disaster risks in non-coal underground mines. At the same time, the scheme can dynamically identify potential risks such as rock mass structure degradation and goaf connection by quantitatively evaluating key indexes such as goaf stability, geological structure and treatment measures, and provides a scientific basis for formulating targeted prevention and control strategies and optimizing safety management processes for mining enterprises, which not only can reduce the accident rate of goaf, but also can promote the transformation of safety production in non-coal mines from passive governance to active prevention, and has significant social benefits and industry popularization value.

[0159] In a second aspect, the embodiment of the present application provides a computer readable storage medium, which is a non-volatile storage medium or a non-transitory storage medium, and has stored thereon a computer program. The computer program is characterized in that when executed by a processor, the computer program performs the steps of the non-coal underground mine goaf hidden disaster risk assessment method according to any one of the above embodiments.

[0160] In a third aspect, the embodiment of the present application provides a computer program product, which includes computer programs / instructions. The computer programs / instructions are characterized in that when executed by a processor, the computer programs / instructions perform the steps of the non-coal underground mine goaf hidden disaster risk assessment method according to any one of the above embodiments.

[0161] In a fourth aspect, the embodiment of the present application provides a non-coal underground mine goaf hidden disaster risk assessment device, which includes a memory and a processor. The memory has stored thereon a computer program that can be run on the processor. The computer program is characterized in that when the processor runs the computer program, the processor performs the steps of the non-coal underground mine goaf hidden disaster risk assessment method according to any one of the above embodiments.

[0162] It should be noted that the present application is not limited to the above embodiments. The above embodiments are only examples, and embodiments having the same technical idea and playing the same role and effect within the scope of the technical solutions of the present application are all included in the technical scope of the present application. In addition, within the scope of the main idea of the present application, various modifications that can be thought of by those skilled in the art, and other ways constructed by combining part of the components of the embodiments are also included in the scope of the present application.

Claims

1. A method for assessing hidden disaster risk in goaf areas of non-coal underground mines, characterized in that: The specific steps include: Obtaining historical data of underground mine goaf areas, analyzing the types of disasters and hazard factors in the underground mine goaf areas based on the historical data, and classifying the risk levels of the hazard factors to establish a risk assessment system; Assessing the degree of influence of the hazard factors on the disaster type based on the historical data and combining expert experience to determine the subjective weight and objective weight of each hazard factor, and calculating the comprehensive weight of each hazard factor based on the subjective weight and objective weight of each hazard factor; The hidden disaster risk level of the goaf is determined by calculating the correlation based on the comprehensive weight of each disaster-causing factor.

2. The method for assessing hidden disaster risk in goaf areas of non-coal underground mines according to claim 1 is characterized in that: The step of evaluating the degree of influence of the hazard factors on the disaster type based on the historical data and combining expert experience to determine the subjective weight and objective weight of each hazard factor, and calculating the comprehensive weight of each hazard factor based on the subjective weight and objective weight of each hazard factor includes: Standardizing the disaster-causing factors and calculating the objective weight of each disaster-causing factor; constructing a disaster type-hazard factor complex network based on expert experience and the correlation between the disaster type and the hazard factor, calculating a network centrality index based on the disaster type-hazard factor complex network, and obtaining a subjective weight of each hazard factor based on the network centrality index; The comprehensive weight of each disaster factor is calculated according to the subjective weight of each disaster factor and the objective weight.

3. The method for assessing hidden disaster risk in goaf areas of non-coal underground mines according to claim 2, characterized in that: The step of normalizing the disaster-causing factors and calculating the objective weight of each disaster-causing factor includes: The disaster-causing factors are standardized based on the CRITIC method, and the coefficient of variation and the coefficient of independence of each disaster-causing factor are calculated. The objective weight of each disaster-causing factor is calculated through the coefficient of variation and the coefficient of independence.

4. The method for assessing hidden disaster risk in goaf areas of non-coal underground mines according to claim 2, characterized in that: The network centrality index includes the node degree, clustering coefficient, proximity and importance of each node. The subjective weight of each disaster factor is obtained by integrating the node degree, clustering coefficient, proximity and importance of each node.

5. The method for assessing hidden disaster risk in goaf areas of non-coal underground mines according to claim 1 is characterized in that: The method of determining the hidden disaster risk level of the goaf area by correlation calculation based on the weights of the various disaster-causing factors includes: Based on the matter-element analysis theory, a matter-element analysis model for risk assessment of hidden disasters in goaf areas was constructed; Determining the correlation between the risk level and each of the hazard factors according to the comprehensive weight of each of the hazard factors; Based on the maximum membership principle, the risk level corresponding to the maximum correlation is determined, and then the hidden disaster risk level of the goaf is determined.

6. The method for assessing hidden disaster risk in goaf areas of non-coal underground mines according to claim 1, characterized in that: The risk levels are divided into four levels: I, II, III, and IV; Among them, Level I is a major risk, unacceptable risk, goaf instability, major risks, untreated, major lack of safety conditions, and the highest risk; Level II is a relatively large risk, an unexpected risk, the goaf is unstable, there are major risks, and the control measures are insufficient; or there are relatively large risks that cannot be handled, there are missing safety conditions, and the risk is relatively high; Level III is general risk, with limited risk acceptance. The goaf is basically stable, with general risks, and the safety conditions are basically in place, so the risk is relatively low. Level IV is low risk, acceptable risk, the goaf is stable, there is no major risk, and the major risks have been safely controlled or eliminated. The safety conditions are fully met and the risk is the lowest.

7. The method for assessing hidden disaster risk in goaf areas of non-coal underground mines according to claim 1, characterized in that: Also includes: Determine the control plan based on the hidden disaster risk level of the goaf.

8. A computer-readable storage medium, wherein the computer-readable storage medium is a non-volatile storage medium or a non-transient storage medium, and a computer program is stored thereon, wherein: When the computer program is executed by a processor, the steps of the method for assessing hidden disaster risks in goaf areas of non-coal underground mines according to any one of claims 1 to 7 are executed.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method for assessing hidden disaster risks in goaf areas of non-coal underground mines according to any one of claims 1 to 7 are implemented.

10. A device for assessing hidden disaster risks in goaf areas of non-coal underground mines, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor runs the computer program, the processor executes the steps of the method for assessing hidden disaster risks in goaf areas of non-coal underground mines according to any one of claims 1 to 7.

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