A method and system for safety risk assessment of coal mining working face in coal mines

Abstract

The present application provides a method and system for safety risk assessment of coal mining working faces in coal mines. The method includes: based on the constructed central point triangular whitening weight function of s risk assessment gray classes, according to the observed value of the predetermined risk assessment index of the coal mining working face, obtaining the whitening weight function value of the corresponding risk assessment gray class; wherein, the s risk assessment gray classes are obtained based on the division of the risk assessment standard of the coal mine, and s is a positive integer; there are multiple risk assessment indicators; according to the acquired risk assessment data of the coal mining working face, based on the preset combined weighting model, determining the combined weight of each risk assessment index; according to the combined weight of each risk assessment index and the whitening weight function value of the corresponding risk assessment gray class, determining the s comprehensive clustering coefficients of the coal mining working face; based on the maximum value principle, according to the s comprehensive clustering coefficients of the coal mining working face, obtaining the risk assessment result of the coal mining working face.

Description

Technical Field

[0001] The present application relates to the technical field of coal mine risk assessment, and in particular to a method and system for assessing safety risks of coal mining working faces in coal mines. Background Art

[0002] At present, coal mines usually have several working places, such as coal mining face, excavation face, transportation tunnel, return air tunnel, etc., and different working places contain different human-machine-environmental-management factors. Among them, the coal mining face has narrow and changeable space, many equipment, loud noise, concentrated personnel, complex environment, many unsafe factors, and high frequency of accidents. Moreover, the construction of coal mining face accounts for the largest proportion in the entire coal mining project and is the core of coal safety management.

[0003] With the deepening of mining depth, although the management of coal mine safety has been strengthened and safety accidents have been reduced, due to the complex and changeable environment of coal mining faces, the numerous risk factors involved, and the large amount of ambiguity and uncertainty in the coal mine production system, there are still some problems: ① In terms of the risk assessment index system for coal mining faces, due to the influence of the underground working environment of coal mines, the content of the assessment indicators is often vague during the index selection process, and it is uncertain whether the selected indicators can be used for risk assessment; moreover, the scores of the existing assessment indicators of most mines are also determined based on subjective experience, and the assessment models and methods are not universal; ② In terms of the application of safety risk assessment of coal mining faces, due to the nonlinearity, dynamics, time-varying and ambiguity of coal mining face safety management, some current assessment methods have not been well applied.

[0004] Therefore, it is necessary to provide an improved technical solution to address the above-mentioned deficiencies in the prior art. Summary of the invention

[0005] The purpose of this application is to provide a method and system for safety risk assessment of coal mining working faces in coal mines, so as to solve or alleviate the problems existing in the above-mentioned prior art.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] The present application provides a method for safety risk assessment of coal mining faces in coal mines, comprising: step S101, based on the constructed central point triangular whitening weight function of s risk assessment gray classes, according to the observed value of the predetermined risk assessment indicator of the coal mining face, obtaining the corresponding whitening weight function value of the risk assessment gray class; wherein, the s risk assessment gray classes are obtained based on the division of the risk assessment standard of the coal mine, s is a positive integer; there are multiple risk assessment indicators; step S102, according to the acquired risk assessment data of the coal mining face, based on a preset combined weighting model, determining the combined weight of each risk assessment indicator; step S103, according to the combined weight of each risk assessment indicator and its corresponding whitening weight function value of the risk assessment gray class, determining s comprehensive clustering coefficients of the coal mining face; step S104, based on the maximum value principle, according to the s comprehensive clustering coefficients of the coal mining face, obtaining the risk assessment result of the coal mining face.

[0008] Preferably, in step S101, the center point triangular whitening weight function based on the constructed s risk assessment gray classes obtains the corresponding whitening weight function value of the risk assessment gray class according to the observed value of the risk assessment indicator predetermined for the coal mining working face, including: step S111, constructing s center point triangular whitening weight functions based on the s risk assessment gray classes of the pre-divided coal mining working face; step S121, calculating the corresponding whitening weight function value of the risk assessment gray class based on the center point triangular whitening weight function and the observed value of the risk assessment indicator predetermined for the coal mining working face.

[0009] Preferably, in step S102, the risk assessment data of the coal mining face obtained is used to determine the combined weight of each risk assessment indicator based on a preset combined weighting model, including: step S112, obtaining the risk assessment data of the coal mining face based on the hierarchical analysis method; step S122, according to the formula:

[0010]

[0011] Determine the combined weight of each of the risk assessment indicators

[0012] Wherein, j represents the risk assessment index, j=1, 2, 3, ..., n, and n is a positive integer; is the subjective weight of the j-th risk assessment indicator, is the objective weight of the j-th risk assessment indicator, μ=0.5.

[0013] Preferably, in step S103, the s comprehensive clustering coefficients of the coal mining face are determined according to the combined weight of each risk assessment indicator and the corresponding whitening weight function value of the risk assessment gray class, including: step S113, determining the indicator layer clustering coefficient of each risk assessment indicator according to the combined weight of each risk assessment indicator and the corresponding whitening weight function value of the risk assessment gray class; step S123, obtaining s criterion layer comprehensive clustering coefficients of the coal mining face according to the indicator layer clustering coefficient of the risk assessment indicator and the combined weight; step S133, according to the s criterion layer comprehensive clustering coefficients of the coal mining face, respectively summing up each risk assessment gray class to obtain s comprehensive clustering coefficients of the coal mining face.

[0014] Preferably, in step S123, according to the indicator layer clustering coefficient of the risk assessment indicator and the combination weight, s criterion layer comprehensive clustering coefficients of the coal mining working face are obtained, including: summing the indicator layer clustering coefficient of each risk assessment indicator to obtain s criterion layer clustering coefficients of the coal mining working face; according to the s criterion layer clustering coefficients and their corresponding combination weights, s criterion layer comprehensive clustering coefficients of the coal mining working face are obtained.

[0015] Preferably, in step S104, based on the maximum value principle, according to the s comprehensive clustering coefficients of the coal mining face, the risk assessment result of the coal mining face is obtained, specifically: based on the maximum value principle, the risk assessment gray class corresponding to the largest comprehensive clustering coefficient among the s comprehensive clustering coefficients is selected as the risk assessment result of the coal mining face.

[0016] Preferably, before step S101, the coal mine safety risk assessment method also includes: based on the collected verification data, respectively performing reliability verification and validity verification on a plurality of the risk assessment indicators predetermined for the coal mining working face, and determining that the credibility and feasibility of the plurality of the risk assessment indicators are respectively greater than the corresponding preset first threshold value and preset second threshold value; wherein the verification data is obtained by assigning a value to each of the risk assessment indicators in the coal mining working face.

[0017] Preferably, according to the collected verification data, reliability verification and validity verification are respectively performed on the plurality of risk assessment indicators of the predetermined coal mining working face, and it is determined that the credibility and feasibility of the plurality of risk assessment indicators are respectively greater than the corresponding preset first threshold value and preset second threshold value, including: according to the collected verification data, reliability verification is performed on the plurality of risk assessment indicators of the predetermined coal mining working face, and in response to the Cronbach coefficient of the reliability verification being greater than 0.7, it is determined that the corresponding risk assessment indicator is credible; according to the collected verification data, KMO test and Bartlett sphere test are performed on the plurality of risk assessment indicators of the predetermined coal mining working face, and in response to the KMO value in the KMO test being greater than 0.6 and the significant difference in the Bartlett sphere test being less than 0.5, it is determined that the corresponding risk assessment indicator is feasible.

[0018] Preferably, each of the risk assessment indicators is assigned a value in the coal mining face based on the Likert 5-level measurement method to obtain the verification data.

[0019] The embodiment of the present application also provides a coal mine coal mining face safety risk assessment system, including: a weight function unit, configured to be based on the constructed center point triangular whitening weight function of s risk assessment gray classes, and to obtain the corresponding whitening weight function value of the risk assessment gray class according to the observed value of the predetermined risk assessment indicator of the coal mining face; wherein, the s risk assessment gray classes are obtained based on the division of the risk assessment standard of the coal mine, and s is a positive integer; there are multiple risk assessment indicators; a weight unit, configured to determine the combined weight of each risk assessment indicator based on a preset combined weighting model according to the acquired risk assessment data of the coal mining face; a clustering unit, configured to determine s comprehensive clustering coefficients of the coal mining face according to the combined weight of each risk assessment indicator and its corresponding whitening weight function value of the risk assessment gray class; an assessment unit, configured to obtain the risk assessment result of the coal mining face according to the s comprehensive clustering coefficients of the coal mining face based on the maximum value principle.

[0020] Beneficial effects:

[0021] In the present application, by dividing the risk assessment standards of coal mines, s risk assessment gray classes of coal mines are constructed, and based on the central point triangular whitening weight function of the s risk assessment gray classes, the whitening weight function value of the corresponding risk assessment gray class is obtained according to the observed value of the predetermined risk assessment indicator of the coal mining working face; according to the acquired risk assessment data of the coal mining working face, the combined weight of each risk assessment indicator is determined based on the preset combined weighting model; then, the s comprehensive clustering coefficients of the coal mining working face are determined by the combined weight of each risk assessment indicator and its corresponding whitening weight function value of the risk assessment gray class; finally, according to the s comprehensive clustering coefficients of the coal mining working face, the risk assessment result of the coal mining working face is obtained. Therefore, on the one hand, based on the combined weighting model, the combined weight of each risk assessment indicator is determined, taking into account both subjective weighting and objective weighting, which neither relies too much on expert experience and subjective judgment, nor relies too much on mathematical calculations and ignores the correlation between multiple risk assessment indicators, making the risk assessment of coal mining working faces more accurate; on the other hand, the comprehensive clustering coefficient of the coal mining working face is determined by using the central point triangular whitening weight function to obtain the risk assessment result of the coal mining working face, which further enhances the scientific nature of the assessment process and the accuracy of the assessment results during the safety risk assessment of the coal mining working face. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. Among them:

[0023] Figure 1 A schematic flow chart of a method for assessing safety risks of a coal mining face in a coal mine provided according to some embodiments of the present application;

[0024] Figure 2 A schematic diagram of a coal mining face risk assessment system provided according to some embodiments of the present application;

[0025] Figure 3 A schematic flow chart of step S101 in a method for assessing safety risks of a coal mining face in a coal mine provided according to some embodiments of the present application;

[0026] Figure 4 A schematic flow chart of step S102 in a method for assessing safety risks of a coal mining face in a coal mine provided according to some embodiments of the present application;

[0027] Figure 5 A schematic flow chart of step S103 in a method for assessing safety risks of a coal mining face in a coal mine provided according to some embodiments of the present application;

[0028] Figure 6A schematic diagram of the structure of a coal mining face safety risk assessment system provided according to some embodiments of the present application;

[0029] Figure 7 A schematic diagram of the structure of a weight function unit in a coal mining face safety risk assessment system provided according to some embodiments of the present application;

[0030] Figure 8 A schematic diagram of the structure of a weight unit in a coal mining face safety risk assessment system provided according to some embodiments of the present application;

[0031] Fig. 9 This is a schematic diagram of the structure of a clustering unit in a coal mine working face safety risk assessment system provided according to some embodiments of the present application. DETAILED DESCRIPTION

[0032] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. Each example is provided by way of explanation of the present application and does not limit the present application. In fact, it will be clear to those skilled in the art that modifications and variations may be made in the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as a part of an embodiment may be used in another embodiment to produce yet another embodiment. Therefore, it is desired that the present application includes such modifications and variations within the scope of the appended claims and their equivalents.

[0033] Exemplary Methods

[0034] Figure 1 A schematic flow chart of a method for assessing safety risks of a coal mining face in a coal mine provided according to some embodiments of the present application; Figure 1 As shown in the figure, the safety risk assessment method of the coal mining face in the coal mine includes:

[0035] Step S101, based on the constructed central point triangular whitening weight function of s risk assessment gray classes, according to the observed value of the predetermined risk assessment index of the coal mining working face, obtain the whitening weight function value of the corresponding risk assessment gray class; wherein, the s risk assessment gray classes are obtained based on the classification of the risk assessment standard of the coal mine, s is a positive integer; there are multiple risk assessment indicators;

[0036] In the embodiment of the present application, by dividing the risk assessment standards of coal mines, s risk assessment gray categories of coal mines are obtained. Specifically, according to the risk assessment standards of coal mines, the safety risks of coal mining faces are divided into four categories: major risk, greater risk, general risk and low risk, corresponding to risk assessment gray category 1 to risk assessment gray category 4. Here, it should be noted that the scores of each risk assessment gray category are obtained through manual labeling. Specifically, the number of gray categories and the range of gray category interval scores for risk assessment gray categories are shown in Table 1. Table 1 is as follows:

[0037] Table 1 Grey classification and trust range

[0038]

[0039] In the embodiment of the present application, risk assessment indicators of coal mining working faces are selected to establish a risk assessment system for coal mining working faces in coal mines, such as Figure 2 As shown in Figure 1, the risk assessment system for coal mining faces includes: Personnel indicators B 1 , Equipment Index B 2 , Environmental Indicator B 3 and management indicators B 4 Among them, personnel indicator B 1 Including: Coal Mining Machine Driver C 11 , Pump station worker C 12 , belt conveyor driver C 13 , hydraulic support worker C 14 , Scraper conveyor driver C 15 , Dust prevention worker C 16 , Gas Inspector C 17 , End Maintenance Worker C 18 , infinite rope winch operator C 19 , Reprinting Machine Worker C 120 , Crusher C 121 ; Equipment index B 2 Includes: Coal Miner C 21 , Scraper conveyor C 22 , Belt conveyor C 23 , hydraulic support C 24 , Monomer pillar C 25 , Endless Rope Winch C 26 , transfer machine C 27 , Crusher C 28 , Emulsion Pump Station C 29 ; Environmental indicators B 3 Includes: Wind speed C 31 , dust concentration C 32 , methane concentration C 33 , carbon monoxide concentration C 34 , hydrogeological type C 35 , Gas Grade C36 , Coal seam spontaneous combustion tendency C 37 , coal seam explosion tendency C 38 , coal seam impact tendency C 39 ; Management indicator B 4 Including: Technical measures to prevent spontaneous combustion C 41 , Job Safety Production Responsibility System C 42 , Dust prevention and removal technical measures C 43 , Special design and technical measures for preventing sudden outbursts C 44 , working face machinery inspection and maintenance system C 45 , Anti-collision safety technical measures C 46 .

[0040] Figure 3 Schematic diagram of the process of step S101 in a method for assessing safety risk of a coal mining face in a coal mine according to some embodiments of the present application; Figure 3 As shown, the whitening weight function of the triangle center point of the constructed s risk assessment gray classes is used to obtain the corresponding whitening weight function value of the risk assessment gray class according to the observed value of the predetermined risk assessment index of the coal mining working face, including:

[0041] Step S111, constructing s center point triangle whitening weight functions based on the pre-divided s risk assessment gray classes of the coal mining working face;

[0042] In the embodiment of the present application, first, the value range of each risk assessment indicator j is set [a j , b j ]; Then, according to the existing risk assessment standards of coal mines, the s risk assessment gray categories are divided, and the center point of each risk assessment gray category is determined respectively. Among them, k = 1, 2, ..., s, j = 1, 2, ..., m, m is a positive integer; finally, according to the number of gray classes (s), s center point triangle whitening weight functions are constructed.

[0043] When constructing the triangular whitening weight function of s center points, set x as the observed value of risk assessment index j. First, construct the lower limit measure whitening weight function of risk assessment index j corresponding to risk assessment gray class 1. and the upper limit measure whitening weight function corresponding to the risk assessment gray class s of the risk assessment index j Then, a triangular whitening weight function of the risk assessment index j with respect to the risk assessment gray class k (k = 2, 3, 4, ... (s-1)) is constructed. Specifically, for the intermediate risk assessment gray class k, the connection point and the center point of the risk assessment gray class (k-1) (or the turning point of risk assessment gray category 1 And point and the center point of the risk assessment gray class (k+1) (or the turning point of risk assessment gray category s , we get the triangular whitening weight function of risk assessment index j on risk assessment gray class k The constructed center point triangle whitening weight function is shown in formula (1)-formula (3), and formula (1)-formula (3) is as follows:

[0044]

[0045]

[0046]

[0047] Step S121: Based on the center point triangle whitening weight function, according to the observed value of the predetermined risk assessment index of the coal mining working face, calculate the whitening weight function value of the corresponding risk assessment gray class.

[0048] In the embodiment of the present application, x is set as the observed value of the risk assessment index j. or When , the triangular whitening weight function value of the risk assessment index j belonging to risk assessment gray class 1 and risk assessment gray class s can be calculated by formula (1) and formula (2) respectively: For the observed value x of risk assessment index j, when k = 2, 3, 4, ... (s-1), the triangular whitening weight function value of risk assessment index j with respect to risk assessment gray class k can be calculated by formula (3): Among them, a j 、b j They represent the lower limit and upper limit of the observed value of risk assessment indicator j, respectively. In this application, the observed value of risk assessment indicator j is obtained by manual annotation. j =0; b j =10.

[0049] In an application scenario, the observed values ​​of each risk assessment indicator are shown in Table 2. Table 2 is as follows:

[0050] Table 2 Observed values ​​of risk assessment indicators

[0051]

[0052] Step S102: determining the combined weight of each risk assessment indicator based on the acquired risk assessment data of the coal mining face and a preset combined weighting model;

[0053] In an embodiment of the present application, when conducting a safety risk assessment on a coal mining face, a combined weighting method is used to calculate the combined weights of risk assessment indicators, taking into account both subjective weighting and objective weighting. It neither relies too much on expert experience and subjective judgment, nor relies too much on mathematical calculations and ignores the correlation between indicators, making the assessment results more accurate.

[0054] Specifically, Figure 4 As shown, according to the risk assessment data of the coal mining face obtained, based on the preset combined weighting model, the combined weight of each risk assessment indicator is determined, including:

[0055] Step S112, obtaining risk assessment data of the coal mining face based on the analytic hierarchy process;

[0056] In the embodiment of the present application, the Saaty 1-9 scaling method is used to obtain risk assessment data of the coal mining face. Specifically, the risk labeling data about the risk assessment index of the coal mining face is collected from the coal mine safety inspector through the assessment standard of the Saaty 1-9 scaling method, and the collected risk labeling data is averaged according to the risk assessment index to obtain the corresponding risk assessment index data.

[0057] Step S122: Determine the combined weight of each risk assessment indicator according to formula (4) Formula (4) is as follows:

[0058]

[0059] Wherein, j represents the risk assessment index, j=1, 2, 3, ..., n, and n is a positive integer; is the subjective weight of the j-th risk assessment indicator, is the objective weight of the j-th risk assessment indicator, μ = 0.5.

[0060] In the embodiment of the present application, the collected risk assessment data is calculated according to the risk assessment indicators by numerical calculation software (for example, Matlab software) to obtain the subjective weight of each risk assessment indicator. By non-dimensionalizing the risk assessment data according to the risk assessment indicators, the entropy weight of each risk assessment indicator is obtained, that is, the objective weight

[0061] According to formula (1)-formula (3) and the data in Table 2, the whitening weight function value of the indicator layer in this application scenario and the corresponding combination weight can be obtained, as shown in Table 3. Table 3 is as follows:

[0062] Table 3. Whitening weight function values ​​of indicator layer and corresponding combination weights

[0063]

[0064]

[0065] Step S103, determining s comprehensive clustering coefficients of the coal mining face according to the combined weight of each risk assessment indicator and the whitening weight function value of the corresponding risk assessment gray class;

[0066] In an embodiment of the present application, the indicator layer clustering coefficient of the coal mining face is calculated by the whitening weight function value of each risk assessment indicator obtained in step S101 and the combined weight of each risk assessment indicator obtained in step S102. Then, the criterion layer clustering coefficient of the coal mining face is obtained through the indicator layer clustering coefficient. The criterion layer comprehensive clustering coefficient of the coal mining face is obtained from the clustering coefficient of the criterion layer and the combined weight of the criterion layer. Finally, the comprehensive clustering coefficient of the coal mining face is obtained from the comprehensive clustering coefficient of the criterion layer.

[0067] Specifically, Figure 5 As shown in Figure 1, according to the combined weight of each risk assessment indicator and its corresponding whitening weight function value of the risk assessment gray class, s comprehensive clustering coefficients of the coal mining face are determined, including:

[0068] Step S113: determining the indicator layer clustering coefficient of each risk assessment indicator according to the combined weight of each risk assessment indicator and the whitening weight function value of the corresponding risk assessment gray class;

[0069] Specifically, the indicator layer clustering coefficient of the risk assessment indicator is determined according to formula (5): Formula (5) is as follows:

[0070]

[0071] Formula (5) indicates that the indicator layer clustering coefficient of risk assessment indicator j with respect to risk assessment gray class k (k = 2, 3, ..., s-1) is calculated by using the whitening weight function value and weight value of each risk assessment indicator: in, represents the whitening weight function of risk assessment index j with respect to risk assessment gray class k, w j is the weight of risk assessment index j in comprehensive clustering, x ij Represents the parameter value of risk assessment indicator j with respect to criterion level i.

[0072] In the embodiment of the present application, the indicator clustering coefficient of the risk assessment indicator can be obtained by multiplying the whitening weight function value of the indicator layer with the corresponding indicator layer combination weight. The indicator layer clustering coefficient in this application scenario can be obtained from Table 3 as shown in Table 4, which is as follows:

[0073] Table 4 Clustering coefficient of indicators

[0074]

[0075]

[0076] Step S123, obtaining s criterion layer comprehensive clustering coefficients of the coal mining working face according to the indicator layer clustering coefficient and the combination weight of the risk assessment indicator;

[0077] In the embodiment of the present application, the subjective weights of the risk assessment indicators are checked for consistency, and the subjective weights of the risk assessment indicators that pass the consistency check are arithmetic averaged to obtain the subjective weights of the criterion layer. According to the subjective weights of the criterion layer and the objective weights of the criterion layer.

[0078] According to the criteria of risk assessment indicators and original data, each risk assessment indicator is subjected to a quantitative hardening process, and then the entropy weight of each risk assessment indicator is calculated, and the indicator weights corresponding to the criterion layer indicators are summed up to obtain the entropy weight of the criterion layer, and then the objective weight of the criterion layer is determined.

[0079] Specifically, the corresponding criterion layer indicator clustering coefficient is obtained by summing up the indicator layer indicator clustering coefficient, as shown in Table 5; Table 5 is as follows:

[0080] Table 4 Clustering coefficients of criterion level indicators and corresponding combined weights

[0081]

[0082] When obtaining the s criterion layer comprehensive clustering coefficients of the coal mining face according to the indicator layer clustering coefficients and combined weights of the risk assessment indicators, first, the indicator layer clustering coefficients of each risk assessment indicator are summed to obtain the s criterion layer clustering coefficients of the coal mining face; then, according to the s criterion layer clustering coefficients and their corresponding combined weights, the s criterion layer comprehensive clustering coefficients of the coal mining face are obtained, as shown in Table 6. Table 6 is as follows:

[0083] Table 6 Comprehensive clustering coefficient of criterion layer

[0084]

[0085] Step S133: According to the s criterion layer comprehensive clustering coefficients of the coal mining working face, sum up each risk assessment gray class respectively to obtain s comprehensive clustering coefficients of the coal mining working face.

[0086] Specifically, the comprehensive clustering coefficients of each criterion layer of the coal mining face correspond to the sum of each risk assessment gray class. The comprehensive clustering coefficients of each criterion layer indicator are added together to obtain the comprehensive clustering coefficients of the coal mining face. That is, the comprehensive clustering coefficients of s criterion layers corresponding to the risk assessment gray classes are summed to obtain s comprehensive clustering coefficients of the coal mining face, as shown in Table 7. Table 7 is as follows:

[0087] Table 7 Comprehensive clustering coefficient of coal mining face

[0088]

[0089] Furthermore, according to the maximum principle, It is determined whether each risk assessment indicator j belongs to risk assessment gray class k. When multiple risk assessment indicators belong to the same gray class k, the advantages and disadvantages or rankings of each risk assessment indicator belonging to the same gray class k are further determined according to the size of the comprehensive clustering coefficient.

[0090] Step S104: Obtain risk assessment results of the coal mining face according to the s comprehensive clustering coefficients of the coal mining face.

[0091] Specifically, the risk assessment gray class corresponding to the largest comprehensive clustering coefficient among the s comprehensive clustering coefficients is selected as the risk assessment result of the coal mining face.

[0092] In an embodiment of the present application, based on the maximum value principle, the s comprehensive clustering coefficients of the coal mining working face are compared, and the risk assessment gray class corresponding to the comprehensive clustering coefficient with the largest value is used as the risk assessment result of the coal mining working face.

[0093] In some optional embodiments, before step S101, the coal mine safety risk assessment method also includes: based on the collected verification data, performing reliability verification and validity verification on a plurality of risk assessment indicators of the predetermined coal mining working face, and determining that the credibility and feasibility of the plurality of risk assessment indicators are respectively greater than a preset first threshold and a preset second threshold; wherein the verification data is obtained by assigning a value to each risk assessment indicator in the coal mining working face.

[0094] In the embodiment of the present application, based on the Likert 5-level measurement method, the verification data is obtained by assigning and collecting the safety risk assessment indicators of the coal mining face to the coal mine workers. The obtained verification data is analyzed for reliability and validity using the "Analyze (Data Reduction Factor)" function in SPSS 23.0 to determine whether the selected risk assessment indicators can be used for risk assessment research on coal mining faces.

[0095] In a specific example, reliability verification and validity verification are performed on multiple risk assessment indicators of a predetermined coal mining face according to the collected verification data, and when it is determined that the reliability and feasibility of the multiple risk assessment indicators are respectively greater than the corresponding preset first threshold and preset second threshold, reliability verification is performed on multiple risk assessment indicators of a predetermined coal mining face according to the collected verification data, and in response to the Cronbach coefficient of the reliability verification being greater than 0.7, it is determined that the corresponding risk assessment indicator is reliable; KMO test and Bartlett sphere test are performed on multiple risk assessment indicators of a predetermined coal mining face according to the collected verification data, and in response to the KMO value in the KMO test being greater than 0.6 and the significant difference in the Bartlett sphere test being less than 0.5, it is determined that the corresponding risk assessment indicator is feasible.

[0096] In the embodiment of the present application, when the reliability of the risk assessment index is verified using the collected verification data, through analysis-scaling-reliability analysis-Cronbanh's Alpha value test, when the Cronbanh's Alpha value of the verification data is greater than 0.7, it means that the risk assessment index is credible; when the risk assessment index is tested for validity using the collected verification data, when KMO is greater than 0.6 and Sig significance is less than 0.5, it means that the risk assessment index is feasible.

[0097] In the embodiment of the present application, based on the combined weighting model, the combined weight of each risk assessment indicator is determined, taking into account both subjective weighting and objective weighting. It neither relies too much on expert experience and subjective judgment, nor relies too much on mathematical calculations and ignores the correlation between multiple risk assessment indicators, thereby making the risk assessment of the coal mining face more accurate; the comprehensive clustering coefficient of the coal mining face is determined by using the center point triangular whitening weight function to obtain the risk assessment result of the coal mining face, which further enhances the scientific nature of the assessment process and the accuracy of the assessment results during the safety risk assessment of the coal mining face.

[0098] Exemplary Systems

[0099] Figure 6 Schematic diagram of a safety risk assessment system for a coal mining face provided according to some embodiments of the present application; Figure 6 As shown, the coal mine working face safety risk assessment system includes: a weight function unit 601, a weight unit 602, a clustering unit 603 and an assessment unit 604.

[0100] The weight function unit 601 is configured to obtain the whitening weight function value of the corresponding risk assessment gray class based on the central point triangulation weight function of the constructed s risk assessment gray classes according to the observed value of the predetermined risk assessment indicator of the coal mining working face; wherein the s risk assessment gray classes are obtained based on the division of the risk assessment standards of the coal mine, s is a positive integer; and there are multiple risk assessment indicators.

[0101] The weight unit 602 is configured to determine the combined weight of each risk assessment indicator based on the acquired risk assessment data of the coal mining working face and based on a preset combined weighting model.

[0102] The clustering unit 603 is configured to determine s comprehensive clustering coefficients of the coal mining face according to the combined weight of each risk assessment indicator and the whitening weight function value of the corresponding risk assessment gray class.

[0103] The evaluation unit 604 is configured to obtain a risk evaluation result of the coal mining face according to the s comprehensive clustering coefficients of the coal mining face.

[0104] Figure 7 Schematic diagram of the structure of a weight function unit in a coal mining face safety risk assessment system provided in accordance with some embodiments of the present application; Figure 7 As shown, the weight function unit 601 includes: a function construction subunit 611, configured to construct s center point triangular whitening weight functions based on s risk assessment gray classes of the pre-divided coal mining working face; a function value calculation subunit 621, configured to calculate the whitening weight function value of the corresponding risk assessment gray class based on the center point triangular whitening weight function and according to the observed value of the predetermined risk assessment indicator of the coal mining working face.

[0105] Figure 8 Schematic diagram of the structure of a weight unit in a coal mining face safety risk assessment system provided in accordance with some embodiments of the present application; Figure 8 As shown, the weight unit 602 includes: a risk data acquisition subunit 612, configured to acquire risk assessment data of the coal mining face based on the hierarchical analysis method; a combined weight calculation subunit 622, configured to calculate the combined weight according to the formula:

[0106]

[0107] Determine the combined weight of each risk assessment indicator

[0108] Wherein, j represents the risk assessment index, j=1, 2, 3, ..., n, and n is a positive integer; is the subjective weight of the j-th risk assessment indicator, is the objective weight of the j-th risk assessment indicator, μ = 0.5.

[0109] Fig. 9 Schematic diagram of the structure of a clustering unit in a coal mining face safety risk assessment system provided according to some embodiments of the present application; Fig. 9 As shown, the clustering unit 603 includes: an indicator layer clustering subunit 613, configured to determine the indicator layer clustering coefficient of each risk assessment indicator according to the combined weight of each risk assessment indicator and the whitening weight function value of its corresponding risk assessment gray class; a criterion layer clustering subunit 623, configured to obtain s criterion layer comprehensive clustering coefficients of the coal mining working face according to the indicator layer clustering coefficient and the combined weight of the risk assessment indicator; a comprehensive clustering subunit 633, configured to sum up the s criterion layer comprehensive clustering coefficients of the coal mining working face corresponding to each risk assessment gray class respectively, to obtain s comprehensive clustering coefficients of the coal mining working face.

[0110] The coal mine mining face safety risk assessment system provided in the embodiment of the present application can implement the steps and processes of any of the above-mentioned coal mine mining face safety risk assessment method embodiments, and achieve the same technical effects, which will not be repeated here one by one.

[0111] Example Applications

[0112] Here, this exemplary application is explained based on the above-mentioned coal mine mining face safety risk assessment method.

[0113] (1) Select coal mining face risk assessment indicators and establish a coal mining face risk assessment indicator system, such as Figure 2 As shown;

[0114] (2) Conduct reliability and validity verification on the evaluation index system, specifically:

[0115] A credibility survey on the safety risk assessment indicators of coal mining working faces was conducted among mine staff. The Likert 5-level measurement method was used to assign values ​​to risk assessment indicators. The “Analyze (Data Reduction Factor)” function in SPSS 23.0 was used to conduct reliability and validity analysis on the collected verification data to determine whether the selected risk assessment indicators can be used for risk assessment research on coal mining working faces.

[0116] ① Reliability test: analysis-scaling-reliability analysis-Cronbanh's Alpha value test. The Cronbanh's Alpha value of the verification data is greater than 0.7, which means that the indicator is credible;

[0117] ② Validity test: factor KMO and Bartlett sphericity test. The KMO of the verification data is greater than 0.6 and the Sig significance is less than 0.5, which indicates that the evaluation index is feasible.

[0118] Table 8 Reliability and validity test values

[0119]

[0120] It can be seen from Table 8 that the constructed risk assessment index system is credible and feasible, and can be used in the research on safety risk assessment of coal mining faces in coal mines.

[0121] (3) Classification. According to the existing risk assessment standards for coal mines, risk assessment is divided into four categories: major risk, relatively large risk, general risk and low risk, corresponding to risk assessment gray category 1 to risk assessment gray category 4. The range of different risk assessment gray categories is expressed by a score of 0-10, with major risk being [0-4], relatively large risk being (4-7], general risk being (7-9], and low risk being (9-10]. The specific risk assessment gray category classification is shown in Table 1.

[0122] (4) Construct the triangular whitening weight function of the center point of each risk assessment gray class:

[0123] According to Table 2, it can be concluded that when the risk assessment gray category is a major risk: when k = 1, When the gray category is a greater risk: k = 2, When the risk assessment gray category is general risk: k = 3, When the risk assessment gray category is low risk, k = 4, The specific gray-whitening weight function formulas for each risk assessment are shown in Formula 6 to Formula 9 below:

[0124]

[0125]

[0126]

[0127]

[0128] Where: and are the central point triangular whitening weight functions of indicator j for risk assessment gray class 1, risk assessment gray class 2, risk assessment gray class 3 and risk assessment gray class 4 respectively.

[0129] (5) Risk assessment data collection: Risk assessment data of risk assessment indicators of coal mining working faces are collected from coal mine safety inspectors. The risk assessment data collection is a judgment matrix between various indicators, which can be evaluated using the Saaty1-9 scaling method. After the collection is completed, the mean of each indicator is used as the data source for calculating the subjective weight of the risk assessment indicator.

[0130] (6) Combined weighting to calculate the weight of each risk assessment indicator:

[0131] ① Subjective weight: Use Matlab software to calculate the subjective weight value of each indicator in the risk assessment data, and then take the arithmetic average of the weight values ​​that pass the consistency test to obtain the criterion layer indicator weight.

[0132] ②Objective weight: By collecting safety inspection record data, each risk assessment indicator is dimensionless and the entropy weight of each risk assessment indicator is calculated.

[0133] ③ Use formula 4 to calculate the weight of each risk assessment indicator.

[0134] (7) Using the whitening weight function values ​​and combined weights of each risk assessment indicator obtained by (4) and (6), the clustering coefficient value of the index layer of the coal mining working face is calculated according to Formula 5:

[0135] Table 9 Clustering coefficient values ​​of index layers of coal mining working face

[0136]

[0137]

[0138] (8) The clustering coefficient of the criterion layer is obtained by summing up the clustering coefficient of the index layer obtained in (7), and the comprehensive clustering coefficient of the criterion layer of the coal mining working face is obtained through the clustering coefficient of the criterion layer and its combined weight.

[0139] Table 10 Clustering coefficient and combination weight of criterion level indicators

[0140]

[0141] Table 11 Comprehensive clustering coefficient of standard layer indicators of coal mining working face

[0142]

[0143]

[0144] (9) According to the comprehensive clustering coefficient of the criterion layer, the comprehensive clustering coefficient of the coal mining face is obtained by summing up the corresponding risk assessment gray categories. The principle of the maximum value of the comprehensive clustering coefficient of the coal mining face is adopted, and the largest gray category is selected as the result of the safety risk assessment. At the same time, the risk assessment status of each risk assessment indicator in (8) can also be obtained.

[0145] Table 12 Comprehensive clustering coefficient of coal mining face

[0146]

[0147] According to Table 6, the risk assessment of the coal mining face is gray category 3, which is a general risk. The personnel index B of this area can also be obtained based on the assessment index system. 1 、Equipment index B 2 、Environmental indicators B 3 and management indicators B 4 All are at the general risk level.

[0148] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for assessing safety risks of coal mining working faces in a coal mine, characterized in that: include: Step S101: Based on the constructed The triangular whitening weight function of the center point of the risk assessment gray class is obtained according to the observed value of the predetermined risk assessment index of the coal mining working face, and the corresponding whitening weight function value of the risk assessment gray class is obtained; wherein, The risk assessment gray categories are obtained based on the risk assessment standards for coal mines. is a positive integer; there are multiple risk assessment indicators; Step S102: determining the combined weight of each risk assessment indicator according to the acquired risk assessment data of the coal mining face and based on a preset combined weighting model; Step S103: Determine the risk of the coal mining face according to the combined weight of each risk assessment indicator and the corresponding whitening weight function value of the risk assessment gray class. A comprehensive clustering coefficient; Step S104: Based on the maximum value principle, according to the coal mining face A comprehensive clustering coefficient is obtained to obtain the risk assessment result of the coal mining face; The step of determining the combined weight of each risk assessment indicator based on the acquired risk assessment data of the coal mining working face and a preset combined weighting model comprises: Step S112, obtaining risk assessment data of the coal mining face based on the analytic hierarchy process; Step S122, according to the formula: ； Determine the combined weight of each of the risk assessment indicators ; in, represents the risk assessment indicator, , is a positive integer; For the The subjective weight of each of the risk assessment indicators, For the The objective weight of each of the risk assessment indicators, ; The risk assessment index of the coal mining face is determined according to the combined weight of each risk assessment index and the whitening weight function value of the corresponding risk assessment gray class. A comprehensive clustering coefficient, including: Step S113, determining the indicator layer clustering coefficient of each risk assessment indicator according to the combined weight of each risk assessment indicator and the whitening weight function value of the corresponding risk assessment gray class; Step S123, obtaining a criterion layer comprehensive clustering coefficient of the coal mining working face according to the indicator layer clustering coefficient of the risk assessment indicator and the combination weight; Step S133: According to the comprehensive clustering coefficient of the criterion layer of the coal mining working face, sum each of the risk assessment gray categories to obtain the risk of the coal mining working face. A comprehensive clustering coefficient; The step of obtaining the criterion layer comprehensive clustering coefficient of the coal mining face according to the index layer clustering coefficient of the risk assessment index and the combined weight includes: The index layer clustering coefficient of each risk assessment index is summed to obtain the criterion layer clustering coefficient of the coal mining working face; According to the criterion layer clustering coefficient and its corresponding combination weight, the coal mining face The comprehensive clustering coefficient of the criterion layer.

2. The method for safety risk assessment of coal mining working face in a coal mine according to claim 1, characterized in that: In step S101, the constructed The triangular whitening weight function of the center point of the risk assessment gray class is obtained according to the observed value of the predetermined risk assessment index of the coal mining working face, and the corresponding whitening weight function value of the risk assessment gray class is obtained, including: Step S111: based on the pre-divided coal mining face The risk assessment gray category is constructed The center point triangle whitening weight function; Step S121, based on the center point triangle whitening weight function, according to the observed value of the predetermined risk assessment indicator of the coal mining working face, calculate the corresponding whitening weight function value of the risk assessment gray class.

3. The method for safety risk assessment of coal mining working face in a coal mine according to claim 1, characterized in that: In step S104, based on the maximum value principle, according to the coal mining working face The comprehensive clustering coefficient is used to obtain the risk assessment result of the coal mining face, which is specifically: Based on the maximum value principle, select The risk assessment gray class corresponding to the largest comprehensive clustering coefficient among the comprehensive clustering coefficients is the risk assessment result of the coal mining working face.

4. The method for safety risk assessment of coal mining working face according to any one of claims 1 to 3, characterized in that: Before step S101, the coal mining face safety risk assessment method further includes: According to the collected verification data, reliability verification and validity verification are performed on the plurality of risk assessment indicators predetermined for the coal mining face, and it is determined that the credibility and feasibility of the plurality of risk assessment indicators are respectively greater than the corresponding preset first threshold value and preset second threshold value; wherein the verification data is obtained by assigning a value to each of the risk assessment indicators in the coal mining face.

5. The method for safety risk assessment of coal mining working face in a coal mine according to claim 4, characterized in that: According to the collected verification data, respectively performing reliability verification and validity verification on a plurality of the risk assessment indicators of the predetermined coal mining working face, and determining that the reliability and feasibility of the plurality of the risk assessment indicators are respectively greater than the corresponding preset first threshold value and preset second threshold value, including: According to the collected verification data, reliability verification is performed on a plurality of the risk assessment indicators of the predetermined coal mining working face, and in response to the Cronbach coefficient of the reliability verification being greater than 0.7, it is determined that the corresponding risk assessment indicator is credible; Based on the collected verification data, KMO test and Bartlett sphere test are performed on the predetermined multiple risk assessment indicators of the coal mining working face. In response to the KMO value in the KMO test being greater than 0.6 and the significant difference in the Bartlett sphere test being less than 0.5, the corresponding risk assessment indicator is determined to be feasible.

6. The method for safety risk assessment of coal mining working face in a coal mine according to claim 4, characterized in that: Based on the Likert 5-level measurement method, each risk assessment indicator is assigned a value in the coal mining face to obtain the verification data.

7. A coal mining face safety risk assessment system, the system being used to execute the steps of a coal mining face safety risk assessment method according to any one of claims 1 to 6, characterized in that: include: The weight function unit is configured based on the construction The triangular whitening weight function of the center point of the risk assessment gray class is obtained according to the observed value of the predetermined risk assessment index of the coal mining working face, and the corresponding whitening weight function value of the risk assessment gray class is obtained; wherein, The risk assessment gray categories are obtained based on the risk assessment standards for coal mines. is a positive integer; there are multiple risk assessment indicators; A weighting unit, configured to determine the combined weight of each of the risk assessment indicators based on the acquired risk assessment data of the coal mining working face and a preset combined weighting model; The clustering unit is configured to determine the risk of the coal mining face according to the combined weight of each risk assessment indicator and the whitening weight function value of the corresponding risk assessment gray class. A comprehensive clustering coefficient; The evaluation unit is configured to evaluate the coal mining face based on a maximum value criterion. A comprehensive clustering coefficient is obtained to obtain the risk assessment result of the coal mining face.

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