Safety risk assessment method for hidden disaster-causing factors in coal mine

Abstract

The present application relates to the technical field of coal mine safety, in particular to a coal mine hidden disaster-causing factor safety risk evaluation method, 32 hidden disaster-causing factors are determined by carding and are divided into two categories and eight sub-categories, corresponding to five types of disasters, namely water disaster, fire disaster, gas disaster, roof disaster and rock burst; a three-segment unique code of disaster type code-classification level code-factor serial number code is constructed, which is used as the unified identification of the evaluation index layer. A three-level evaluation system is established based on the code, the weights of the criterion layer and the index layer are calculated by the analytic hierarchy process, and the consistency test is conducted to ensure the rationality of the weights; the risk is quantitatively scored by combining the fuzzy comprehensive evaluation, the risk grades of each disaster and the overall risk are determined, and the model stability is verified by sensitivity analysis. The classification, coding, quantitative evaluation and precise control are integrated, the hidden disaster-causing factors are traceable, quantifiable and comparable, which provides technical support for the precise prevention and control and safety decision of the hidden disaster-causing factors in coal mines.

Description

Technical Field

[0001] This invention relates to the field of coal mine safety technology, specifically a method for assessing the safety risks of hidden disaster-causing factors in coal mines. Background Technology

[0002] Hidden disaster-causing factors in coal mines are the core causes of major safety accidents such as water hazards, fires, and gas explosions. They are characterized by strong concealment, diverse types of disasters, significant dynamic changes, and multiple factors coupled together to cause disasters. Many major coal mine accidents in the past were closely related to the inability to identify, classify, or understand the disaster-causing relationships of hidden disaster-causing factors and the lack of risk quantification.

[0003] Existing technologies have three major shortcomings: First, there is a lack of unified standards for classifying hidden disaster-causing factors. The names and quantities of disaster-causing factors vary significantly in different regulatory documents, and the disaster-causing types of some factors are unclear, resulting in a lack of clear targets for surveys and control. Second, there is a lack of a scientific coding system, making it difficult to efficiently manage and accurately trace the numerous and complex hidden disaster-causing factors, which is not conducive to cross-departmental and cross-link information sharing and collaborative prevention and control. Third, the evaluation results of traditional safety risk assessment methods are difficult to quantify and have poor horizontal comparability, which cannot meet the needs of precise risk control in multi-factor coupled scenarios.

[0004] While existing weighted analysis and fuzzy quantitative evaluation methods have been applied in risk assessment, they have not been combined with the systematic classification and standardized coding system of hidden disaster-causing factors, thus failing to form an integrated solution of "classification-coding-quantitative assessment-precise control". The chaotic classification leads to blind selection of evaluation indicators, the lack of a coding system makes it difficult to trace the evaluation results, and the evaluation results are disconnected from the control measures, which cannot support the precise control of hidden disaster-causing factors in coal mines throughout the entire process. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method for assessing the safety risks of hidden disaster-causing factors in coal mines. By constructing a standardized classification system, innovating coding rules, and combining weight analysis and fuzzy quantitative evaluation, this method achieves accurate risk quantification, solving the problems of chaotic classification, missing coding, and insufficient risk quantification of hidden disaster-causing factors. This provides technical support for the accurate identification, efficient management, and scientific prevention and control of hidden disaster-causing factors in coal mines.

[0006] The technical solution adopted by this invention to solve its technical problem is:

[0007] A method for assessing the safety risks of hidden disaster-causing factors in coal mines, comprising:

[0008] S1. Identification and Standardized Classification of Hidden Disaster-Causing Factors in Coal Mines

[0009] Based on existing safety regulations, the types of disaster risks that can be caused by hidden disaster-causing factors in coal mines are clarified. Hidden disaster-causing factors are classified into multiple disaster-causing risk types and single disaster-causing risk types according to disaster risk types. At the same time, the correspondence between hidden disaster-causing factors and disaster risk types is determined.

[0010] S2. Construct a three-segment coding system

[0011] A three-segment coding structure of disaster type code - classification level code - factor sequence number code is adopted to assign values ​​to hidden disaster-causing factors one by one to form a unique code;

[0012] S3. Construct a three-level risk assessment indicator system

[0013] Establish a target layer-criteria layer-indicator layer system. The target layer is the comprehensive risk assessment of hidden disaster-causing factors, the criteria layer is the disaster risk type, and the indicator layer is the unique code of the hidden disaster-causing factors remaining after eliminating non-existent factors.

[0014] S4. Hierarchical Analysis to Determine Weights

[0015] The scaling method is used to construct the judgment matrix of the criterion layer and the judgment matrix of the indicator layer according to the importance of the factors contained in the criterion layer and the indicator layer, respectively. Based on the judgment matrix, the square root method is used to calculate the weight of the criterion layer and the weight of the indicator layer, and a consistency test is performed.

[0016] S5. Fuzzy Comprehensive Evaluation

[0017] A fuzzy evaluation matrix is ​​constructed for the indicator layer, and the risk score vector for the criterion layer is calculated by combining it with the weights of the indicator layer. The risk score vector for the criterion layer is combined with the weights of the criterion layer to calculate the quantitative risk score value for the target layer, thereby determining the overall risk level of hidden disaster-causing factors in coal mines.

[0018] As a preferred embodiment, a further technical solution of the present invention is:

[0019] Preferred options also include:

[0020] Select the criteria layer and indicator layer whose weights meet the requirements, adjust the weights of the criteria layer and indicator layer according to the set rules, and observe the change range of the target value risk quantification score. If the change range is less than 5%, it is determined that the stability of the evaluation indicator system meets the requirements and the evaluation result is reliable.

[0021] Preferably, the disaster risk types in S1 include floods, fires, gas, roof collapses, and rock bursts;

[0022] Multiple types of disaster risks:

[0023] Category I includes hidden disaster-causing factors: old open areas, abandoned wells, faults, folds, igneous rock intrusions, collapse columns, and igneous rocks, corresponding to all disaster risk types;

[0024] Category II includes hidden disaster-causing factors: overlying or underlying coal pillars, isolated coal pillars, areas of abnormal changes in coal seam thickness, and stress concentration areas, corresponding to gas, roof, and rockburst disaster risk types;

[0025] Category III includes hidden disaster-causing factors: ground stress and mine pressure, corresponding to roof and rockburst disaster risk types;

[0026] Single disaster risk type:

[0027] Category IV includes hidden disaster-causing factors: poorly sealed boreholes, water source wells, oil and gas wells and coalbed methane wells, collapse zones and water-conducting fracture zones, water-conducting damage zones caused by mining in the floor, skylights, surface water bodies, loose aquifers and bedrock aquifers, delamination water, wind oxidation zones, ancient riverbed scour zones, fire zones, and areas with abnormally rich water, corresponding to the type of water disaster risk;

[0028] Category V includes hidden disaster-causing factors: underground fire zones and spontaneous combustion of coal seams, corresponding to fire disaster risk types;

[0029] Category VI includes hidden disaster-causing factors: outburst-prone coal seams, outburst-prone danger zones, and high-gas-content areas, corresponding to gas disaster risk types;

[0030] Category VII includes hidden disaster-causing factors: poor coal seam roof structure, corresponding to roof disaster risk type;

[0031] Category VIII includes hidden disaster-causing factors: shock tendency, corresponding to the risk type of rockburst disaster.

[0032] Preferably, in S2, the disaster type codes use uppercase letters to represent each disaster risk type, where S-flood, H-fire, W-gas, D-roof collapse, and C-rockburst;

[0033] The classification level codes use Roman numerals to represent the subcategories contained in multiple disaster risk types and single disaster risk types, respectively. Among them, the subcategory codes I, II, and III represent the subcategories contained in multiple disaster risk types, while IV, V, VI, VII, and VIII represent the subcategories contained in single disaster risk types.

[0034] The factor number code uses lowercase letters to represent the specific hidden disaster-causing factors contained in each sub-category.

[0035] Preferably, in S3, for specific coal mine areas, in combination with the requirements of the survey cycle and routine supplementation of hidden disaster-causing factors, hidden disaster-causing factors that have been clearly eliminated through the survey are used as the indicator layer corresponding to the criterion layer, and a dynamic risk assessment indicator system specific to coal mine areas is formed by combining the three-stage coding system.

[0036] Preferably, the step in S4, which calculates the criterion layer weights and index layer weights based on the judgment matrix using the square root method and performs a consistency check, includes:

[0037] Calculate the initial weights :

[0038] ;

[0039] in, This represents the initial weight of the i-th factor in the criterion or indicator layer. This indicates the number of factors contained in the criteria layer or indicator layer. This represents the element in the i-th row and j-th column of the judgment matrix;

[0040] The initial weights are normalized to obtain the final weights. :

[0041] ;

[0042] in, The denominator represents the final weight of the i-th factor in the criteria or indicator layer, and the denominator represents the sum of all the initial weights in the criteria or indicator layer.

[0043] Calculate the largest eigenvalue of the judgment matrix :

[0044] ;

[0045] in, This represents the judgment matrix at the criterion or indicator level. Represents the weight vector of the criterion layer or indicator layer;

[0046] Calculate the consistency ratio :

[0047] ;

[0048] in, This represents the average random consistency index of the same order.

[0049] If the calculated consistency ratio is less than the set threshold, the consistency check is considered passed.

[0050] Preferably, S5 specifically includes:

[0051] Determine the risk assessment level set V = (high risk, relatively high risk, medium risk, low risk) of hidden disaster-causing factors, and divide each risk assessment level interval according to the percentage scoring standard: high risk [80, 100), relatively high risk [60, 80), medium risk [40, 60), low risk [20, 40]. Take the median of each risk assessment level interval to form the assessment level weighting vector F = [90, 70, 50, 30].

[0052] Based on the risk assessment level range, multiple risk assessment levels are scored for each factor in the indicator layer. The percentage of scores for each factor at each risk assessment level is calculated, and a fuzzy evaluation matrix is ​​then constructed.

[0053] ;

[0054] in, Indicates the first The number of factors contained in each indicator layer This represents the proportion of the j-th factor in the indicator layer that belongs to the k-th risk assessment level.

[0055] Calculate the fuzzy comprehensive evaluation vector of the criterion layer:

[0056] ;

[0057] in, Indicates the weight of the indicator layer;

[0058] Calculate the risk score vector at the criterion level:

[0059] ;

[0060] Calculate the target layer risk quantification score:

[0061] ;

[0062] in, This represents the weight of the criterion layer.

[0063] The present invention, which adopts the above technical solution, has the following prominent features compared with the prior art:

[0064] For the first time, a three-segment coding system of disaster type, classification level, and factor number was constructed to achieve standardized and traceable management of hidden disaster-causing factors and solve the problem of cross-departmental information collaboration.

[0065] The system innovatively divides the factors into two major categories and eight subcategories, totaling 32 types of hidden disaster-causing factors, clearly defining their correspondence with five types of major disasters, unifying classification standards, and solving the pain point of chaotic classification in the industry.

[0066] The integration of hierarchical analysis and fuzzy comprehensive evaluation enables multi-factor coupled quantitative evaluation, and the evaluation results are quantified, comparable horizontally, and traceable vertically.

[0067] In accordance with the requirements of the "Specifications for Survey of Hidden Disaster-Causing Factors in Mines", the evaluation results are made dynamic, and a closed loop is formed by coding, evaluation, control and dynamic updating;

[0068] It has been verified to be stable and reliable through sensitivity testing and can be widely applied to precision prevention and control in coal mines, enterprise management, scientific research data support, and government regulatory decision-making. Attached Figure Description

[0069] Figure 1 This is a schematic diagram illustrating the principle and flow of the safety risk assessment method for hidden disaster-causing factors in coal mines in this embodiment of the invention.

[0070] Figure 2 This is an example diagram of three-segment encoding in an embodiment of the present invention. Detailed Implementation

[0071] The present invention will be further illustrated below with reference to specific embodiments. The purpose of this illustration is solely to provide a better understanding of the invention. Therefore, the examples given do not limit the scope of protection of the present invention.

[0072] like Figure 1 As shown in the figure, this embodiment presents a method for assessing the safety risks of hidden disaster-causing factors in coal mines, including:

[0073] S1. Identification and Standardized Classification of Hidden Disaster-Causing Factors in Coal Mines

[0074] Based on existing safety regulations, the types of disaster risks that can be caused by hidden disaster-causing factors in coal mines are clarified. Hidden disaster-causing factors are classified into multiple disaster-causing risk types and single disaster-causing risk types according to the disaster risk type. At the same time, the correspondence between hidden disaster-causing factors and disaster risk types is determined.

[0075] During implementation, a literature review method was used to conduct statistical analysis on three core standards: the "Notice on Strengthening the Survey and Management of Hidden Disaster-Causing Factors in Coal Mines," the "Detailed Rules for Coal Mine Geological Work," and the "Specification for the Survey of Hidden Disaster-Causing Factors in Mines." The results showed that the total number of hidden disaster-causing factors specified in the relevant standards were 25, 28, and 40, respectively. Among these, 7 factors were common, 8 factors had inconsistent descriptions, and the remaining factors showed significant differences. This reflects a divergence in the current understanding of disaster-causing factors, and the disaster-causing type of some factors is unclear.

[0076] Based on the analysis and comparison of existing literature, a total of 32 hidden disaster-causing factors were identified as needing to be investigated in underground coal mines. The types of major coal mine disaster risks that these hidden disaster-causing factors may lead to include five categories: floods, fires, gas leaks, roof collapses, and rock bursts. These risks are categorized into two main types and eight subcategories: multiple disaster-causing risks (including categories I, II, and III) and single disaster-causing risks (including categories IV, V, VI, VII, and VIII).

[0077] Specifically, disaster risk types include floods, fires, gas leaks, roof collapses, and rock bursts;

[0078] Multiple types of disaster risks:

[0079] Category I includes hidden disaster-causing factors: old open areas, abandoned wells, faults, folds, igneous rock intrusions, collapse columns, and igneous rocks, corresponding to all disaster risk types;

[0080] Category II includes hidden disaster-causing factors: overlying or underlying coal pillars, isolated coal pillars, areas of abnormal changes in coal seam thickness, and stress concentration areas, corresponding to gas, roof, and rockburst disaster risk types;

[0081] Category III includes hidden disaster-causing factors: ground stress and mine pressure, corresponding to roof and rockburst disaster risk types;

[0082] Single disaster risk type:

[0083] Category IV includes hidden disaster-causing factors: poorly sealed boreholes, water source wells, oil and gas wells and coalbed methane wells, collapse zones and water-conducting fracture zones, water-conducting damage zones caused by mining in the floor, skylights, surface water bodies, loose aquifers and bedrock aquifers, delamination water, wind oxidation zones, ancient riverbed scour zones, fire zones, and areas with abnormally rich water, corresponding to the type of water disaster risk;

[0084] Category V includes hidden disaster-causing factors: underground fire zones and spontaneous combustion of coal seams, corresponding to fire disaster risk types;

[0085] Category VI includes hidden disaster-causing factors: outburst-prone coal seams, outburst-prone danger zones, and high-gas-content areas, corresponding to gas disaster risk types;

[0086] Category VII includes hidden disaster-causing factors: poor coal seam roof structure, corresponding to roof disaster risk type;

[0087] Category VIII includes hidden disaster-causing factors: shock tendency, corresponding to the risk type of rockburst disaster.

[0088] Based on this, the correspondence between each type of hidden disaster-causing factor and the five major disaster risk types in multiple or single disaster risk types is shown in Table 1 below:

[0089] Table 1. Relationship between Hidden Disaster-Causing Factors and Disaster Risk Types

[0090]

[0091] S2. Construct a three-segment coding system

[0092] A three-segment coding structure of disaster type code - classification level code - factor sequence number code is adopted to assign values ​​to hidden disaster-causing factors one by one to form a unique code.

[0093] The specific coding rules are as follows:

[0094] The disaster type codes use uppercase letters to represent the various disaster risk types, where S-flood, H-fire, W-gas, D-roof collapse, and C-rockburst.

[0095] The classification level codes use Roman numerals to represent the subcategories contained in multiple disaster risk types and single disaster risk types, respectively. Among them, the subcategory codes I, II, and III represent the subcategories contained in multiple disaster risk types, while IV, V, VI, VII, and VIII represent the subcategories contained in single disaster risk types.

[0096] The factor number code uses lowercase letters to represent the specific hidden disaster-causing factors contained in each sub-category.

[0097] Based on the above rules, each hidden disaster-causing factor is assigned a unique code. For example, the old goaf area is coded as "SⅠa", the collapse column is coded as "SⅠf", the water-rich anomaly area is coded as "SⅣl", and the coal seam spontaneous combustion is coded as "HⅤb", etc.

[0098] Encoding allows for the direct identification of the classification and corresponding disaster risk type of hidden disaster-causing factors, enabling rapid tracing and management of these factors. For example, see... Figure 2 Example code SⅠa can be directly identified as "Flood (S) - Multiple Disaster Risk Type I - Old Goaf Area (a)". In addition, referring to the example in Table 1, the obvious code HⅤb can be identified as "Fire (H) - Single Disaster Risk Type V - Coal Seam Spontaneous Combustion (b)". This facilitates the rapid synchronization of core information on disaster-causing factors by geological exploration departments, safety supervision departments, and production and operation departments, enabling rapid query and traceability, and improving the efficiency of cross-departmental collaborative prevention and control.

[0099] S3. Construct a three-level risk assessment indicator system

[0100] A target layer-criteria layer-indicator layer system is established. The target layer is for the comprehensive risk assessment of hidden disaster-causing factors, the criteria layer is for the disaster risk type, and the indicator layer is for the unique code of the hidden disaster-causing factors remaining after eliminating non-existent factors.

[0101] In practice, for specific coal mines, in conjunction with the requirements of the survey cycle and routine supplementation of hidden disaster-causing factors, the survey can eliminate hidden disaster-causing factors that have been clearly eliminated, and use the remaining hidden disaster-causing factors as the indicator layer corresponding to the criterion layer. Combined with the three-stage coding system, a dynamic risk assessment indicator system specific to coal mines can be formed.

[0102] Specific implementation, target level: comprehensive risk assessment of hidden disaster-causing factors in a certain coal mine;

[0103] Criterion layers: Flood (U1), Fire (U2), Gas (U3), Roof (U4), Rockburst (U5);

[0104] Indicator Layer: After excluding 11 factors, including abandoned shafts and burnt rocks that are clearly not present in the general survey of the mine field, the indicator layers corresponding to each criterion layer are determined. The indicator system is shown in Table 2 below:

[0105] Table 2. Three-level risk assessment indicator system

[0106]

[0107] S4. Hierarchical Analysis to Determine Weights

[0108] The scaling method is used to construct the judgment matrix of the criterion layer and the judgment matrix of the indicator layer according to the importance of the factors contained in the criterion layer and the indicator layer, respectively. Based on the judgment matrix, the square root method is used to calculate the weight of the criterion layer and the weight of the indicator layer, and a consistency test is performed.

[0109] In implementation, the criteria layer includes five factors: flood (U1), fire (U2), gas (U3), roof collapse (U4), and rock burst (U5). These factors remain fixed. For the aforementioned mine field, the Saaty 1-9 scaling method is used to construct a judgment matrix. The scales 1-9 represent "equally important" to "extremely important," and their reciprocals represent the inverse importance relationship between corresponding indicators. Scaling each pair of elements ultimately forms a 5×5 judgment matrix.

[0110]

[0111] The judgment matrix for each indicator layer is constructed according to the corresponding criterion layer. The flood indicator layer contains 12 factors forming a 12×12 judgment matrix. The specific judgment matrix A1 is as follows:

[0112]

[0113] The fire indicator layer comprises 6 factors forming a 6×6 judgment matrix, as shown in the specific judgment matrix A2 below:

[0114]

[0115] The gas index layer comprises 11 factors forming an 11×11 judgment matrix, as shown in the following judgment matrix A3:

[0116]

[0117] The top-level indicator layer contains 12 factors forming a 12×12 judgment matrix, as shown in the specific judgment matrix A4 below:

[0118]

[0119] The rockburst index layer comprises 12 factors forming a 12×12 judgment matrix, as shown in the specific judgment matrix A5 below:

[0120]

[0121] Accordingly, the steps of calculating the criterion layer weights and index layer weights using the square root method based on the judgment matrix, and performing consistency checks, include:

[0122] Calculate the initial weights :

[0123] ;

[0124] in, This represents the initial weight of the i-th factor in the criterion or indicator layer. This indicates the number of factors contained in the criteria layer or indicator layer. This represents the element in the i-th row and j-th column of the judgment matrix of the criterion layer or indicator layer.

[0125] The initial weights are normalized to obtain the final weights. :

[0126] ;

[0127] in, The denominator represents the final weight of the i-th factor in the criteria or indicator layer, and the denominator represents the sum of all the initial weights in the criteria or indicator layer.

[0128] Calculate the largest eigenvalue of the judgment matrix :

[0129] ;

[0130] in, This represents the judgment matrix at the criterion or indicator level. Represents the weight vector of the criterion layer or indicator layer;

[0131] Calculate the consistency ratio :

[0132] ;

[0133] in, This represents the average random consistency index of the same order.

[0134] If the calculated consistency ratio is less than the set threshold, the consistency check is considered passed.

[0135] Both the criterion-level weights and the indicator-level weights are calculated using the above formulas and procedures, and a consistency check is performed during the calculation process. corresponding The values ​​are shown in Table 3 below:

[0136] Table 3. Average Random Consistency Index (RI) Values ​​of the Same Rank

[0137]

[0138] Taking the judgment matrix A above as an example, the weights of the criterion layer are calculated as w=[28.186%,16.188%,33.855%,18.106%,3.666%], and the consistency test CR value is 0.031;

[0139] Taking the judgment matrix A1 above as an example, the weights of the flood index layer are calculated as w1=[20.551%,4.873%,5.118%,5.118%,20.551%,5.118%,6.372%,2.530%,2.530%,10.967%,0.809%,15.463%], and the consistency test CR value is 0.066;

[0140] Taking the judgment matrix A2 as an example, the weights of the fire index layer are calculated as w2=[30.427%,16.145%,4.412%,5.064%,2.829%,41.123%], and the consistency test CR value is 0.089;

[0141] Taking the judgment matrix A3 as an example, the weights of the gas index layer are calculated as w3=[33.638%,13.231%,1.934%,1.934%,13.231%,4.894%,4.546%,4.546%,5.645%,8.200%,8.200%], and the consistency test CR value is 0.052;

[0142] Taking the judgment matrix A4 as an example, the weights of the top plate index layer are calculated as w4=[2.536%,21.878%,6.391%,1.839%,6.610%,4.187%,4.187%,4.246%,4.110%,10.902%,10.902%,22.213%], and the consistency test CR value is 0.056;

[0143] Taking the judgment matrix A5 as an example, the weights of the rockburst index layer are calculated as w5=[2.567%,3.692%,3.692%,3.692%,1.680%,3.692%,7.523%,8.712%,8.044%,26.797%,26.797%,3.110%], and the consistency test CR value is 0.064.

[0144] All CR values ​​in the consistency test were less than 0.1, and all passed the consistency test.

[0145] Weighted analysis shows that the relatively important risk types in a certain mine are gas (33.855%), flood (28.186%), roof collapse (18.106%), and fire (16.188%). The key disaster-causing factors in each criterion layer are: for floods, old goaf areas (20.55%), collapse columns (20.55%), and abnormally water-rich areas (15.46%); for fires, spontaneous combustion of coal seams (41.12%) and old goaf areas (30.43%); for gas, old goaf areas (33.64%), faults (13.23%), and collapse columns (13.23%); for roof collapse, poor roof structure of coal seams (22.21%) and faults (21.88%); and for rockbursts, in-situ stress (26.80%) and mine pressure (26.80%). The main hidden disaster-causing factors in coal mining areas over the next five years are determined to be: old goaf areas, faults, collapse columns, ground stress, mine pressure, abnormally water-rich areas, spontaneous combustion of coal seams, and poor roof structure of coal seams.

[0146] S5. Fuzzy Comprehensive Evaluation

[0147] A fuzzy evaluation matrix is ​​constructed for the indicator layer, and the risk score vector for the criterion layer is calculated by combining it with the weights of the indicator layer. The risk score vector for the criterion layer is combined with the weights of the criterion layer to calculate the quantitative risk score value for the target layer, thereby determining the overall risk level of hidden disaster-causing factors in coal mines.

[0148] During implementation, a risk assessment level set V = (high risk, relatively high risk, medium risk, low risk) for hidden disaster-causing factors is determined, and each risk assessment level interval is divided according to a percentage scoring standard: high risk [80, 100), relatively high risk [60, 80), medium risk [40, 60), low risk [20, 40]. The median of each risk assessment level interval is taken to form the assessment level weighting vector F = [90, 70, 50, 30].

[0149] Based on the risk assessment level range, multiple risk assessment levels are scored for each factor in the indicator layer. The percentage of scores for each factor at each risk assessment level is calculated, and a fuzzy evaluation matrix is ​​then constructed.

[0150] ;

[0151] in, Indicates the first The number of factors contained in each indicator layer This represents the proportion of the j-th factor in the indicator layer that belongs to the k-th risk assessment level.

[0152] Calculate the fuzzy comprehensive evaluation vector of the criterion layer:

[0153] ;

[0154] in, Indicates the weight of the indicator layer;

[0155] Calculate the risk score vector at the criterion level:

[0156] ;

[0157] Calculate the target layer risk quantification score:

[0158] ;

[0159] in, This represents the weight of the criterion layer.

[0160] In practice, the fuzzy evaluation matrix of the flood disaster index layer for a certain mine is as follows:

[0161]

[0162] The fuzzy evaluation matrix for fire index layer is as follows:

[0163]

[0164] The fuzzy evaluation matrix for the gas index layer is as follows:

[0165]

[0166] The fuzzy evaluation matrix of the top plate index layer is as follows:

[0167]

[0168] The fuzzy evaluation matrix for the rockburst index layer is as follows:

[0169]

[0170] The fuzzy comprehensive evaluation vector of the criterion layer and the risk score vector of each criterion layer are calculated according to the above formula, as shown in Table 4 below:

[0171] Table 4. Risk Assessment at the Standard Level

[0172]

[0173] Ultimately, the scoring vectors for the risks of hidden disaster-causing factors such as floods, fires, gas leaks, roof collapses, and rock bursts at the criterion level can be determined as Z=[63.35, 49.33, 60.94, 64.10, 34.78]. The risk levels are respectively high risk, medium risk, high risk, high risk, and low risk. The comprehensive risk score at the target level is y=59.35, with a risk level of medium risk.

[0174] Finally, based on the evaluation results, targeted control measures can be proposed:

[0175] 1) For core disaster-causing factors such as old open areas, faults, and collapse columns, adopt the combined detection technology of "geophysical exploration + drilling" to improve the accuracy of the survey.

[0176] 2) For the three high-risk types of floods, gas, and roof collapses, strengthen special governance investment: for floods, focus on the prevention and control of old water-filled and water-rich abnormal areas, and build an integrated prevention and control system of "monitoring-early warning-governance"; for gas, optimize the ventilation system and strengthen dynamic monitoring; for roof collapses, strengthen the support design, focus on monitoring thick and hard roofs and stress concentration areas, and strengthen the implementation of fault crossing measures.

[0177] 3) Although rockburst is a low-risk event, it is necessary to continuously monitor changes in ground stress and mine pressure to avoid dynamic risk escalation.

[0178] The implementation also includes: selecting criterion and indicator layers with acceptable weights, adjusting the weights of the criterion and indicator layers according to set rules, and observing the change in the target value risk quantification score. If the change is less than 5%, the stability of the evaluation indicator system is considered acceptable, and the evaluation results are reliable. For example, to verify the stability of the evaluation model, sensitivity analysis was conducted on gas (the highest weight criterion layer) and old workings (high-weight indicators across multiple criterion layers): the weight of the gas criterion layer was adjusted by ±10%, and the weight of the old workings indicator was adjusted by ±15%, and the change in the comprehensive score of the target layer was observed. The results showed that the score changes were 2.87% and 3.52%, respectively, both less than 5%, indicating good model stability and reliable evaluation results.

[0179] The technical solution of this invention, through literature research and standardization, clarifies 32 hidden disaster-causing factors in coal mines, and innovatively classifies them into "multiple disaster-causing risk types" (3 subcategories) and "single disaster-causing risk types" (5 subcategories). It clarifies their correspondence with five major disasters: floods, fires, gas, roof collapses, and rock bursts, and constructs a three-level evaluation system of "target layer - criterion layer - indicator layer". This solves the problems of chaotic classification of disaster-causing factors and unclear correspondence between disaster-causing factors and disaster types in existing research.

[0180] The weights of each level were determined using the analytic hierarchy process (AHP), and their rationality was verified by a consistency test (CR < 0.1). Based on the weights of each indicator level, the main hidden disaster-causing factors for the mining area in the next five years were comprehensively determined to be: old goaf areas, faults, collapse columns, ground stress, mine pressure, water-rich abnormal areas, coal seam spontaneous combustion, and poor coal seam roof structure.

[0181] The fuzzy comprehensive evaluation method was used to conduct a safety risk assessment of hidden disaster-causing factors in a mine. The risk levels of the criterion layer were as follows: water disaster (higher risk, 63.35 points), fire (medium risk, 49.33 points), gas disaster (higher risk, 60.94 points), roof fall disaster (higher risk, 64.10 points), and rock burst disaster (low risk, 34.78 points). The comprehensive score of the target layer was 59.35 points, which belongs to medium risk. The evaluation results are highly consistent with the actual safety conditions of the mine.

[0182] The evaluation method based on the combination of hierarchical analysis and fuzzy comprehensive evaluation overcomes the subjective defects of traditional evaluation and realizes the quantitative evaluation of disasters caused by multiple coupled factors, providing a basis for risk identification, precise prevention and control, differentiated management and control and scientific decision-making of hidden disaster-causing factors in coal mines.

[0183] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the present invention. All equivalent changes made based on the description and drawings of the present invention are included within the scope of the present invention.

Claims

1. A method for assessing the safety risks of hidden disaster-causing factors in coal mines, characterized in that, include: S1. Identification and Standardized Classification of Hidden Disaster-Causing Factors in Coal Mines Based on existing safety regulations, the types of disaster risks that can be caused by hidden disaster-causing factors in coal mines are clarified. Hidden disaster-causing factors are classified into multiple disaster-causing risk types and single disaster-causing risk types according to disaster risk types. At the same time, the correspondence between hidden disaster-causing factors and disaster risk types is determined. S2. Construct a three-segment coding system A three-segment coding structure of disaster type code - classification level code - factor sequence number code is adopted to assign values ​​to hidden disaster-causing factors one by one to form a unique code; S3. Construct a three-level risk assessment indicator system Establish a target layer-criteria layer-indicator layer system. The target layer is the comprehensive risk assessment of hidden disaster-causing factors, the criteria layer is the disaster risk type, and the indicator layer is the unique code of the hidden disaster-causing factors remaining after eliminating non-existent factors. S4. Hierarchical Analysis to Determine Weights The scaling method is used to construct the judgment matrix of the criterion layer and the judgment matrix of the indicator layer according to the importance of the factors contained in the criterion layer and the indicator layer, respectively. The weights of the criterion layer and the index layer are calculated using the square root method based on the judgment matrix, and a consistency check is performed. S5. Fuzzy Comprehensive Evaluation A fuzzy evaluation matrix is ​​constructed for the indicator layer, and the risk score vector for the criterion layer is calculated by combining it with the weights of the indicator layer. The risk score vector for the criterion layer is combined with the weights of the criterion layer to calculate the quantitative risk score value for the target layer, thereby determining the overall risk level of hidden disaster-causing factors in coal mines.

2. The method for assessing the safety risks of hidden disaster-causing factors in coal mines according to claim 1, characterized in that, Also includes: Select the criteria layer and indicator layer whose weights meet the requirements, adjust the weights of the criteria layer and indicator layer according to the set rules, and observe the change range of the target value risk quantification score. If the change range is less than 5%, it is determined that the stability of the evaluation indicator system meets the requirements and the evaluation result is reliable.

3. The method for assessing safety risks of hidden disaster-causing factors in coal mines according to claim 1, characterized in that, The disaster risk types in S1 include floods, fires, gas, roof collapses, and rock bursts; Multiple types of disaster risks: Category I includes hidden disaster-causing factors: old open areas, abandoned wells, faults, folds, igneous rock intrusions, collapse columns, and igneous rocks, corresponding to all disaster risk types; Category II includes hidden disaster-causing factors: overlying or underlying coal pillars, isolated coal pillars, areas of abnormal changes in coal seam thickness, and stress concentration areas, corresponding to gas, roof, and rockburst disaster risk types; Category III includes hidden disaster-causing factors: ground stress and mine pressure, corresponding to roof and rockburst disaster risk types; Single disaster risk type: Category IV includes hidden disaster-causing factors: poorly sealed boreholes, water source wells, oil and gas wells and coalbed methane wells, collapse zones and water-conducting fracture zones, water-conducting damage zones caused by mining in the floor, skylights, surface water bodies, loose aquifers and bedrock aquifers, delamination water, wind oxidation zones, ancient riverbed scour zones, fire zones, and areas with abnormally rich water, corresponding to the type of water disaster risk; Category V includes hidden disaster-causing factors: underground fire zones and spontaneous combustion of coal seams, corresponding to fire disaster risk types; Category VI includes hidden disaster-causing factors: outburst-prone coal seams, outburst-prone danger zones, and high-gas-content areas, corresponding to gas disaster risk types; Category VII includes hidden disaster-causing factors: poor coal seam roof structure, corresponding to roof disaster risk type; Category VIII includes hidden disaster-causing factors: shock tendency, corresponding to the risk type of rockburst disaster.

4. The method for assessing the safety risks of hidden disaster-causing factors in coal mines according to claim 3, characterized in that, In S2, disaster type codes use uppercase letters to represent various disaster risk types, including S-flood, H-fire, W-gas, D-roof collapse, and C-rockburst. The classification level codes use Roman numerals to represent the subcategories contained in multiple disaster risk types and single disaster risk types, respectively. Among them, the subcategory codes I, II, and III represent the subcategories contained in multiple disaster risk types, while IV, V, VI, VII, and VIII represent the subcategories contained in single disaster risk types. The factor number code uses lowercase letters to represent the specific hidden disaster-causing factors contained in each sub-category.

5. The method for assessing safety risks of hidden disaster-causing factors in coal mines according to claim 1, characterized in that, S3, for specific coal mine areas, takes into account the requirements of the survey cycle and routine supplementation of hidden disaster-causing factors. Through the survey, hidden disaster-causing factors that have been clearly eliminated are excluded, and the remaining hidden disaster-causing factors are used as the indicator layer corresponding to the criterion layer. Combined with the three-stage coding system, a dynamic risk assessment indicator system exclusive to coal mine areas is formed.

6. The method for assessing the safety risks of hidden disaster-causing factors in coal mines according to claim 1, characterized in that, The steps in S4 for calculating the criterion layer weights and index layer weights using the square root method based on the judgment matrix, and then performing consistency checks, include: Calculate the initial weights : ； in, This represents the initial weight of the i-th factor in the criterion or indicator layer. This indicates the number of factors contained in the criteria layer or indicator layer. This represents the element in the i-th row and j-th column of the judgment matrix; The initial weights are normalized to obtain the final weights. : ； in, The denominator represents the final weight of the i-th factor in the criteria or indicator layer, and the denominator represents the sum of all the initial weights in the criteria or indicator layer. Calculate the largest eigenvalue of the judgment matrix : ； in, This represents the judgment matrix at the criterion or indicator level. Represents the weight vector of the criterion layer or indicator layer; Calculate the consistency ratio : ； in, This represents the average random consistency index of the same order. If the calculated consistency ratio is less than the set threshold, the consistency check is considered passed.

7. The method for assessing safety risks of hidden disaster-causing factors in coal mines according to claim 6, characterized in that, S5 specifically includes: Determine the risk assessment level set V = (high risk, relatively high risk, medium risk, low risk) of hidden disaster-causing factors, and divide each risk assessment level interval according to the percentage scoring standard: high risk [80, 100), relatively high risk [60, 80), medium risk [40, 60), low risk [20, 40]. Take the median of each risk assessment level interval to form the assessment level weighting vector F = [90, 70, 50, 30]. Based on the risk assessment level range, multiple risk assessment levels are scored for each factor in the indicator layer. The percentage of scores for each factor at each risk assessment level is calculated, and a fuzzy evaluation matrix is ​​then constructed. ； in, Indicates the first The number of factors contained in each indicator layer This represents the proportion of the j-th factor in the indicator layer that belongs to the k-th risk assessment level. Calculate the fuzzy comprehensive evaluation vector of the criterion layer: ； in, Indicates the weight of the indicator layer; Calculate the risk score vector at the criterion level: ； Calculate the target layer risk quantification score: ； in, This represents the weight of the criterion layer.

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