Method, device and system for determining surface fracturing index and storage medium

Abstract

The invention discloses a method, a device and a system for determining a surface fracturing index and a storage medium. The method comprises the following steps of: acquiring an index value of a secondary index in an index system of surface fracturing; determining the membership degree of the second-level index according to the index value of the second-level index, and determining the weight of the second-level index according to the index value of the second-level index; determining an index value of the first-level index according to the membership degree of the second-level index and the weight of the second-level index; and substituting the index value of the primary index into a preset surface fracturing model to determine the surface fracturing index. By adopting the scheme provided by the invention, the accuracy of surface cracking prediction can be improved.

Description

Technical Field

[0001] This application relates to the field of coal mine monitoring technology, and in particular to a method, apparatus, system and storage medium for determining the surface fracturing index. Background Technology

[0002] Coal resources are one of my country's important energy resources, possessing significant economic and strategic importance. However, coal mining leads to the development of a robust network of overlying fissures, sometimes extending to the surface, resulting in discontinuous surface damage such as fissure clusters and step subsidence. This causes uneven surface subsidence, leading to desertification, water inrush and sandstorms, and a series of derivative disasters. Therefore, predicting surface fracturing during coal mining is crucial.

[0003] Currently, the monitoring of surface cracking caused by coal mining mostly adopts single-factor monitoring and analysis. However, the influencing factors of surface cracking are complex, and single-factor analysis cannot fully explain and predict surface cracking, thus making it impossible to accurately analyze and predict the situation of surface cracking.

[0004] Therefore, how to provide a method for determining the surface fracturing index and improve the accuracy of surface fracturing prediction has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, system, and storage medium for determining the surface fracturing index, in order to improve the accuracy of surface fracturing prediction.

[0006] This application provides a method for determining the surface fracturing index, including:

[0007] Collect the index values ​​of secondary indicators in the surface fracturing index system;

[0008] The membership degree of the secondary indicator is determined based on the indicator value of the secondary indicator, and the weight of the secondary indicator is determined based on the indicator value of the secondary indicator.

[0009] The value of the primary indicator is determined based on the membership degree and weight of the secondary indicator.

[0010] The index values ​​of the primary indicators are substituted into a preset surface fracturing model to determine the surface fracturing index.

[0011] The beneficial effects of this application are as follows: It collects the index values ​​of secondary indicators in a surface fracturing index system; determines the membership degree of the secondary indicators based on their values, and determines their weights; determines the index values ​​of primary indicators based on their membership degrees and weights; and substitutes the index values ​​of the primary indicators into a preset surface fracturing model to determine the surface fracturing index. Because a surface fracturing index system is pre-constructed, the analysis of surface fracturing through multiple indicators avoids the problem of low accuracy caused by analyzing surface fracturing using a single indicator.

[0012] In one embodiment, determining the membership degree of the secondary indicator based on the indicator value of the secondary indicator includes:

[0013] The membership degree of the secondary indicator is calculated by substituting the indicator value of the secondary indicator into a preset membership function.

[0014] In one embodiment, substituting the index value of the secondary indicator into a preset membership function to calculate the membership degree of the secondary indicator includes:

[0015] The membership degree of the secondary indicator is calculated by substituting its value into the following preset membership function:

[0016]

[0017] Where ψ is the membership degree of the secondary indicator; θ is the indicator value of the secondary indicator; θ max This represents the maximum impact of secondary indicators on surface fracturing.

[0018] In one embodiment, when the secondary indicator is a negative indicator, determining the membership degree of the secondary indicator based on its value includes:

[0019] The values ​​of the secondary indicators are positiveized to obtain positiveized indicator values;

[0020] The membership degree of the secondary indicators is determined based on the positively oriented indicator values.

[0021] In one embodiment, the positive transformation of the secondary indicator to obtain a positive indicator value includes:

[0022] Obtain the reciprocal of the value of the secondary indicator, and use the reciprocal of the value of the secondary indicator as the positive value of the secondary indicator.

[0023] In one embodiment, determining the weight of the secondary indicator based on its value includes:

[0024] The values ​​of the secondary indicators are standardized to obtain standardized secondary indicators. The standardization of the secondary indicators includes at least positiveization and normalization.

[0025] The information entropy corresponding to the secondary indicators is determined based on standardized secondary indicators;

[0026] The weights of the secondary indicators are determined based on the information entropy corresponding to the secondary indicators.

[0027] In one embodiment, substituting the index value of the primary index into a preset surface fracturing model to determine the surface fracturing index includes:

[0028] The index values ​​of the primary indicators are substituted into the following preset surface fracturing model to calculate the surface fracturing index:

[0029]

[0030] Where M is the surface fracturing index, λ i M represents the weight corresponding to the i-th primary indicator. Bi Let be the value of the i-th primary indicator.

[0031] This application also provides a device for determining the surface fracturing index, comprising:

[0032] The data acquisition module is used to collect the index values ​​of the secondary indicators in the surface fracturing index system.

[0033] The first determining module is used to determine the membership degree of the secondary indicator based on the indicator value of the secondary indicator, and to determine the weight of the secondary indicator based on the indicator value of the secondary indicator.

[0034] The second determining module is used to determine the indicator value of the primary indicator based on the membership degree of the secondary indicator and the weight of the secondary indicator;

[0035] The third determining module is used to substitute the index value of the primary index into a preset surface fracturing model to determine the surface fracturing index.

[0036] In one embodiment, the second determining module includes:

[0037] The calculation submodule is used to substitute the index values ​​of the secondary indicators into a preset membership function to calculate the membership degree of the secondary indicators.

[0038] In one embodiment, the computing submodule is further configured to:

[0039] The membership degree of the secondary indicator is calculated by substituting its value into the following preset membership function:

[0040]

[0041] Where ψ is the membership degree of the secondary indicator; θ is the indicator value of the secondary indicator; θ max This represents the maximum impact of secondary indicators on surface fracturing.

[0042] In one embodiment, the second determining module includes:

[0043] The processing submodule is used to perform positive transformation on the index values ​​of the secondary indicators to obtain positive index values.

[0044] The first determining submodule is used to determine the membership degree of the secondary indicators based on the positiveized indicator values.

[0045] In one embodiment, the processing submodule is further configured to:

[0046] Obtain the reciprocal of the value of the secondary indicator, and use the reciprocal of the value of the secondary indicator as the positive value of the secondary indicator.

[0047] In one embodiment, the second determining module includes:

[0048] The standardization module is used to standardize the index values ​​of the secondary indicators to obtain standardized secondary indicators. The standardization of the secondary indicators includes at least positiveization and normalization of the secondary indicators.

[0049] The second determination submodule is used to determine the information entropy corresponding to the secondary indicators based on the standardized secondary indicators;

[0050] The third determining submodule is used to determine the weight of the secondary indicator based on the information entropy corresponding to the secondary indicator.

[0051] In one embodiment, the third determining module is further configured to:

[0052] The index values ​​of the primary indicators are substituted into the following preset surface fracturing model to calculate the surface fracturing index:

[0053]

[0054] Where M is the surface fracturing index, λ i M represents the weight corresponding to the i-th primary indicator. Bi Let be the value of the i-th primary indicator.

[0055] This application also provides a system for determining the surface fracturing index, comprising:

[0056] At least one processor; and,

[0057] A memory communicatively connected to the at least one processor; wherein,

[0058] The memory stores instructions that can be executed by the at least one processor to implement the method for determining the surface fracturing index as described in any of the above embodiments.

[0059] This application also provides a computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor corresponding to a surface fracturing index determination system, enables the surface fracturing index determination system to implement the surface fracturing index determination method described in any of the above embodiments.

[0060] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0061] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0062] The accompanying drawings are provided to further illustrate the present application and form part of the specification. They are used together with the embodiments of the present application to explain the application and do not constitute a limitation thereof. In the drawings:

[0063] Figure 1 This is a flowchart of a method for determining the surface fracturing index in one embodiment of this application;

[0064] Figure 2 This is a system architecture diagram of a surface fracturing prediction index in one embodiment of this application;

[0065] Figure 3 This is a schematic diagram of the structure of a device for determining the surface fracturing index according to an embodiment of this application;

[0066] Figure 4 This is a schematic diagram of the hardware structure of a system for determining the surface fracturing index according to an embodiment of this application. Detailed Implementation

[0067] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application.

[0068] Figure 1 This is a flowchart of a method for determining the surface fracturing index according to an embodiment of this application, as shown below. Figure 1 As shown, the method can be implemented as follows: S101-S104:

[0069] In step S101, the index values ​​of the secondary indicators in the surface fracturing index system are collected;

[0070] In step S102, the membership degree of the secondary indicator is determined based on the indicator value of the secondary indicator, and the weight of the secondary indicator is determined based on the indicator value of the secondary indicator.

[0071] In step S103, the index value of the primary index is determined according to the membership degree of the secondary index and the weight of the secondary index;

[0072] In step S104, the index value of the primary index is substituted into the preset surface fracturing model to determine the surface fracturing index.

[0073] Figure 2 This is a system architecture diagram of a surface fracturing prediction index according to an embodiment of this application. Figure 2 As shown, the primary indicators include three aspects: surface deformation, the degree of development of overlying strata fissures, and the fault spacing of the top plate. This application analyzes surface fracturing from three levels: surface deformation, the degree of development of overlying strata fissures, and the fault spacing of the top plate. Figure 2 As shown, this application pre-establishes a surface fracturing evaluation index system, which is divided into three levels:

[0074] (1) Target layer: Surface fracturing index;

[0075] (2) Criteria Level (i.e., primary indicators):

[0076] This invention mainly evaluates the surface, overlying rock, and working face from three aspects, taking into account more comprehensive factors. Specifically, the primary index includes three aspects: surface deformation cracking coefficient (B1), overlying rock fracture development degree cracking coefficient (B2), and roof fracture step distance cracking coefficient (B3).

[0077] (3) Indicator layer (i.e., secondary indicators):

[0078] Secondary indicators are specific indicators that influence various aspects of primary indicators. It should be noted that the selection of secondary indicators can be done either manually or by establishing a simulation model corresponding to the geological strata and obtaining multiple sets of experimental data from the simulation model. These multiple sets of experimental data are obtained by adjusting multiple parameters according to preset step sizes, resulting in preset indicator values ​​and surface cracking indicator values. Both the preset indicator values ​​and the surface cracking indicator values ​​are pre-selected monitoring values. Then, correlation analysis is performed between the multiple sets of experimental data and the surface cracking indicator values ​​to obtain the corresponding correlation coefficients. Indicators with correlation coefficients greater than preset values ​​are selected as secondary indicators. Finally, cluster analysis is performed on the secondary indicators to determine their correspondence with the primary indicators, thus forming a surface cracking indicator system. The indicator system constructed using this method can automatically adjust according to changes in circumstances, making it more flexible and practical.

[0079] In one specific embodiment of this application, the following parameters are selected as secondary indicators for each primary indicator.

[0080] ① Internal indices of the surface deformation cracking coefficient (B1): vertical surface displacement (C1) and horizontal surface displacement (C2).

[0081] In coal mining, vertical surface displacement refers to the change or movement of the surface in the vertical direction caused by mining activities; horizontal surface displacement refers to the movement or deformation of the surface in the horizontal direction. It is generally believed that the greater the vertical and horizontal surface displacements, the greater the likelihood of surface cracking.

[0082] ②Internal indices of the degree of development of overburden fractures and the fracture coefficient (B2): mining thickness (C3), working face length (C4), mining depth (C5), core recovery rate (C6), and fractal dimension value (C7).

[0083] In coal mining, "mining thickness" refers to the thickness of the coal seam being mined. "Working face length" refers to the length of the working face in a mine. "Mining depth" refers to the vertical distance from the surface to the bottom of the mine or the working face during mining or underground construction. Generally, the greater the mining thickness, working face length, and mining depth, the greater the impact on the underground rock strata, leading to greater stress on the surface rock strata and increasing the likelihood of surface cracking. Core recovery rate refers to the ratio of the actual length of the core sample obtained during geological exploration to the expected target length. A low core recovery rate, meaning less rock is actually extracted, may lead to an increase in underground cavities, affecting the stability of the underground rock strata and increasing the risk of surface cracking. Fractal dimension is a parameter describing the complexity of the coal seam structure. It can be used to characterize the internal spatial structure and geometric features of the coal seam; a larger fractal dimension may indicate a more complex surface crack structure.

[0084] ③ Internal indices of the fracture step distance of the top plate (B3): fracture distance (C8), fracture thickness (C9), subsidence of fractured rock block (C10), delamination length (C11), contact length between the pseudo-top and the unbroken rock beam of the direct top (C12), and contact length between the direct top and the unbroken rock beam of the main top (C13).

[0085] Breaking distance refers to the distance a rock mass or rock fractures or breaks under stress. Generally, a larger breaking distance indicates a greater extent and degree of surface cracking. Breaking rock block subsidence refers to the vertical distance a rock or rock mass subsides after fracturing or breaking. When a rock or rock mass fractures and forms subsidence blocks, it may cause uneven settlement or deformation of the surface, leading to surface cracking. The greater the subsidence of the broken rock block, the greater the likelihood of surface cracking. Delamination length refers to the length of horizontal or inclined cracks in a rock mass or rock. When there are long or dense delamination cracks in a rock mass or rock, these cracks may be transmitted to the surface when they deform or displace, leading to surface cracking. Generally, a larger delamination length indicates a lower likelihood of surface cracking. The contact length between the unbroken rock beam and the immediate roof refers to the length during coal mining where the unbroken rock beam contacts and supports the mine roof (i.e., the rock strata above the coal seam). When the contact length between the false top and the unbroken rock beam of the immediate top is relatively long, the connection between the rock beams is strong, the overall stability of the rock mass is good, and the risk of surface cracking is relatively low. Conversely, when the contact length between the false top and the unbroken rock beam of the immediate top is short, the connection between the rock beams is weak, the overall stability of the rock mass is poor, and the risk of surface cracking is relatively high. The contact length between the unbroken rock beam of the immediate top and the main top refers to the length within the rock mass where the rock beams between the immediate top and the main top are in contact without breaking. When the contact length between the unbroken rock beam of the immediate top and the main top is long, the connection strength between the rock beams is high, the overall stability of the rock mass is good, and the risk of surface cracking is relatively low. Conversely, when the contact length between the unbroken rock beam of the immediate top and the main top is short, the connection between the rock beams is weak, the overall stability of the rock mass is poor, and the risk of surface cracking is relatively high.

[0086] When predicting the degree of surface fracturing, the index values ​​of secondary indicators in the surface fracturing index system are collected; the membership degree of the secondary indicators is determined based on the index values ​​of the secondary indicators, and the weight of the secondary indicators is determined based on the index values ​​of the secondary indicators; the index values ​​of primary indicators are determined based on the membership degree and the weight of the secondary indicators; the index values ​​of the primary indicators are substituted into a preset surface fracturing model to determine the surface fracturing index.

[0087] (1) Collect the index values ​​of secondary indicators in the index system for surface fracturing.

[0088] In this application, all secondary indicators except for the fractal dimension can be directly measured. The fractal dimension value can be obtained through indoor similar simulation experiments. The fractal dimension value of the overburden fracture network varies with different working face advance distances, which is a common method in this field, and therefore will not be elaborated upon in this application.

[0089] (2) Determine the membership degree of the secondary indicators based on their values, and determine the weight of the secondary indicators based on their values.

[0090] (2-1) Determining the membership degree of secondary indicators

[0091] In one embodiment of this application, the expression for the membership function of the secondary index is:

[0092]

[0093] Where ψ is the membership degree of the secondary indicator; θ is the indicator value of the secondary indicator; θ max This represents the maximum impact of secondary indicators on surface fracturing.

[0094] It should be noted that each indicator typically has a reasonable range of values, which can be pre-determined and stored manually. Positive indicators can be calculated using the formula mentioned above; that is, the larger the value, the greater the probability of surface cracking. Examples include vertical surface displacement, horizontal surface displacement, and mining thickness.

[0095] For negative indicators—that is, indicators whose larger values ​​correspond to a lower probability of surface cracking, such as core recovery rate and delamination length—various methods can be used to positively transform the indicators, replacing the original values ​​in calculations. In one embodiment of this application, negative indicators are positively transformed by taking the reciprocal of the indicator value.

[0096]

[0097] in, θ represents the index value after positive transformation, and θ represents the collected monitoring value.

[0098] The membership expression for the secondary indicators is constructed using the following formula:

[0099]

[0100] Where ψ is the membership degree of the secondary indicator; θ is the indicator value of the secondary indicator; θ min This represents the minimum value within the range of secondary indicators.

[0101] In another embodiment of this application, negative indicators are positiveened by the difference between the indicator and the maximum preset value, i.e.

[0102]

[0103] in, The index value after positive transformation, θ max θ represents the maximum value within the range of secondary indicators, and θ is the collected monitoring value.

[0104] The expression for the membership degree of each secondary indicator is as follows:

[0105]

[0106] Where ψ is the membership degree of the secondary indicator; θ is the indicator value of the secondary indicator; θ max θ represents the maximum value within the range of values ​​for the secondary indicator. min This represents the minimum value within the range of values ​​for the secondary indicator.

[0107] (2-2) Determining the weights of secondary indicators

[0108] Method 1: Determining the weights of each secondary indicator based on the entropy weight method

[0109] Determine the standardized evaluation matrix for each secondary indicator: that is, standardize the indicator values ​​of the secondary indicators to obtain standardized secondary indicators. The standardization of the secondary indicators includes at least positiveization and normalization.

[0110] First, the obtained secondary indicator values. Assume a total of i sets of secondary indicator data were obtained, and for a given primary indicator, the value of the j-th secondary indicator is x. ij Let be the specific value of the j-th evaluation indicator for the i-th data set.

[0111] Then, the data is positiveized. Specifically, by subtracting the minimum value from the index value and dividing by the index amplitude, the positiveized matrix Z = (z ij ) n×m The formula is as follows:

[0112]

[0113] Among them, z ij Let x be the positively oriented value of the j-th evaluation index in the i-th data set. ij Let x be the specific value of the j-th evaluation indicator in the i-th data set. max For x ij The maximum value of x min For x ij The minimum value, where n is the number of data sets collected, and m is the number of evaluation indicators.

[0114] Furthermore, the normalized matrix is ​​normalized to obtain the standardized matrix Y = (y ij ) n×m The calculation formula is as follows:

[0115]

[0116] Among them, y ij z is the standardized value of the j-th evaluation index in the i-th data set, i.e., the value after positive transformation and then normalization. ij Let x be the positively oriented value of the j-th evaluation index in the i-th data set. ij Let be the specific value of the j-th evaluation indicator for the i-th data set.

[0117] The information utility value corresponding to each secondary indicator is determined based on the standardized evaluation matrix.

[0118] Next, the information entropy of each indicator is calculated based on the standardized matrix Y. Specifically, the formula for calculating information entropy is as follows:

[0119]

[0120] Among them, e j Let k be the information entropy of the j-th index, and k be the value to ensure that e j The coefficient can be in the interval [0,1] when the sample values ​​of the indicator are exactly the same. The information entropy can be set between [0,1]. The smaller the entropy value, the greater the amount of information.

[0121] The weights of the secondary indicators are determined based on the information entropy corresponding to the secondary indicators. Specifically, the information utility value (information entropy redundancy) of the secondary indicators is first determined based on the information entropy, and the calculation method is as follows:

[0122] d j =1-e j

[0123] Where, d j This represents the information utility value; the greater the information utility, the greater the amount of information.

[0124] Normalizing the information utility values ​​yields the weight of each indicator:

[0125]

[0126] Among them, w j Let be the weight of the j-th indicator.

[0127] Furthermore, we can obtain the weights W = (w1, w2, ..., w) of each secondary indicator corresponding to a certain primary indicator. m ).

[0128] Method 2: Calculate the weights of each secondary indicator based on the Analytic Hierarchy Process (AHP). The specific steps are as follows:

[0129] Construct a judgment matrix for mutual comparison of various secondary indicators.

[0130] A preset text is obtained, which records the degree of influence of each secondary indicator on surface cracking. Since the relative importance of each indicator on surface cracking is generally agreed upon in the industry (e.g., indicator a is more important than indicator b, and indicators b and c are equally important), the degree of influence of each secondary indicator on surface cracking can be pre-stored in the preset text. This degree of influence can be a specific value determined by pre-set rules, or it can be a pre-stored relative importance with other indicators, such as relatively important, very important, or extremely important. Then, the secondary indicators are compared pairwise according to the preset text to determine their relative importance; the greater the influence of a secondary indicator on surface cracking, the greater its relative importance. The comparison results of each secondary indicator are then assigned values ​​based on their relative importance. For example, equal importance is assigned a value of 1, slightly important is assigned a value of 2, and relatively important is assigned a value of 3, etc. Finally, the assigned values ​​corresponding to the comparison results are used to construct a judgment matrix for each secondary indicator.

[0131] Specifically, when determining the judgment matrix, a scaling method can be used to obtain a judgment matrix for comparing evaluation indicators. This involves comparing the importance of two evaluation indicators based on experience, assigning values ​​from 1 to 9 according to the former's relative importance to the latter. Higher values ​​indicate greater importance of the former compared to the latter. For example, 1 indicates that one indicator is equally important to the other; 3 indicates that one indicator is slightly more important; 5 indicates that one indicator is significantly more important; 7 indicates that one indicator is very important; 9 indicates that one indicator is extremely important; 2, 4, 6, and 8 can represent the importance at a compromise between adjacent scales. The reciprocals of the above numbers represent inverse comparisons.

[0132] When determining the judgment matrix, other methods can be used to first determine the importance of each secondary indicator, and then the ratio of the importance of the two indicators can be used as elements of the judgment matrix. For example, since the coefficient of variation is the ratio of the standard deviation of the original data to the mean of the original data, it can reflect the magnitude of data change. Therefore, the coefficient of variation method can be used to determine the importance of each secondary indicator. This involves obtaining multiple sets of historical data for each secondary indicator, determining the coefficient of variation for each set of data, and using these coefficients as the importance of each indicator. The importance of each indicator is then compared to obtain the corresponding elements of the judgment matrix. For example, using two indicators with importance S... i and Sj Then compared to the obtained This represents the relative importance of the two indicators.

[0133] ② Determine the eigenvectors of the judgment matrix, normalize the eigenvectors, and determine the weights of each indicator from the normalized eigenvectors.

[0134] In this application, before calculating the eigenvectors of the judgment matrix, a consistency check is performed to verify the correctness of the constructed judgment matrix and reduce the possibility of logical errors in the judgment matrix, such as A being more important than B, B being more important than C, but C being more important than A. This is achieved through a formula. The consistency index of the judgment matrix is ​​calculated; a larger value indicates poorer consistency. In the formula, CI is the consistency index, λmax is the largest eigenvalue, n is the number of eigenvalues, CR is the consistency ratio index (CI is the same as above), and RI is the average random consistency index. When CR < 0.10, the judgment matrix satisfies the consistency test; otherwise, it needs to be readjusted to achieve satisfactory consistency.

[0135] After the consistency check, the largest eigenvalue and the corresponding eigenvector of the judgment matrix are calculated, and the eigenvector is normalized. The normalized eigenvector is then used as the weight set Q = (q1, q2, ..., q...). m ).

[0136] Method 3: Comprehensively determine the weights of evaluation indicators

[0137] After obtaining the weights of the evaluation indicators using Method 1 and Method 2 respectively, the weights obtained by the two methods are combined to obtain a comprehensive weight, which is then used as the weight of the evaluation indicators. In one embodiment, the comprehensive weight is obtained by calculating the geometric mean of the two weights. That is, the comprehensive weight is determined using the following formula:

[0138]

[0139] Among them, z j w is the comprehensive weight of the j-th indicator. j Let q be the weight of the evaluation index obtained by method one. j The weights of the evaluation indicators obtained by method two.

[0140] Furthermore, we can obtain the index weight set Z = [z1, z2, z3, ..., z m ].

[0141] Of course, the comprehensive weight of the indicators can also be determined by weighted summation, and this application does not limit this method.

[0142] (3) Determine the index value of the primary index based on the membership degree and weight of the secondary index.

[0143] (3-1) Determination of the indicator values ​​of primary indicators

[0144] Obtain the weights and membership degrees of the secondary indicators corresponding to the primary indicators; substitute the fuzzy matrix of the weight set of the secondary indicators into the preset evaluation model to obtain the indicator values ​​of the primary indicators.

[0145] For example, the index value of the primary index, the surface deformation cracking coefficient (B1), can be obtained through the index values ​​of the vertical surface displacement (C1) and the horizontal surface displacement (C2) and their membership degrees.

[0146]

[0147] Among them, M B1 The index value of the surface deformation-induced cracking coefficient, a primary index, is z. Ci ψ represents the weight of the secondary indicator Ci. Ci represents the membership degree of the secondary indicator Ci.

[0148] Assuming the weight corresponding to the vertical displacement of the Earth's surface is a%, and the weight corresponding to the horizontal displacement of the Earth's surface is b%, then we have:

[0149]

[0150] Among them, M B1 The index value of the surface deformation-induced cracking coefficient is a primary indicator, where a% represents the weight of vertical surface displacement, b% represents the weight of horizontal surface displacement, and l 垂直 The monitored value of vertical displacement of the earth's surface, l 水平 The monitored value of horizontal displacement of the ground surface, l 垂直max l represents the maximum value within the range of vertical displacement at the Earth's surface. 水平max This represents the maximum value within the range of horizontal displacement values ​​on the Earth's surface.

[0151] Similarly, assuming the weight of mining thickness (C3) is c1%, the weight of working face length (C4) is c2%, the weight of mining depth (C5) is c3%, the weight of core recovery rate (C6) is d1%, and the weight of fractal dimension value (C7) is d2%, the index value of the fractal coefficient of overburden fracture development is:

[0152]

[0153] Among them, M B2 h is the index value of the fracture coefficient, which is a primary indicator of the degree of development of overburden fractures. 平均采厚 For the thickness monitoring value, h 平均面长 h is the monitoring value of the working face length. 平均采深D is the monitoring value of the mining depth, U is the monitoring value of the core recovery rate, and h is the monitoring value of the fractal dimension. 采厚max h represents the maximum value within the range of vertical displacements at the Earth's surface. 面长max h represents the maximum value within the range of horizontal displacements on the Earth's surface. 最大采深 D represents the maximum mining depth. max U represents the maximum core recovery rate. max This represents the maximum value of the fractal dimension.

[0154] Assuming the weights of fracture distance (C8) are e1%, fracture thickness (C9) is e2%, fracture block subsidence (C10) is f1%, delamination length (C11) is f2%, contact length between the pseudo-roof and the unfractured rock beam of the immediate roof (C12) is g1%, and contact length between the immediate roof and the unfractured rock beam of the main roof (C13) is g2%, then the index value of the fracture step distance initiation coefficient of the roof is:

[0155]

[0156] Where: M B3 The index value of the fracture step distance initiation coefficient of the roof is the primary index. 平均破断 The average fracture distance of the fractured rock strata; l 破断max The maximum fracture of the fractured rock strata; h 平均厚度 h represents the average fracture thickness of the fractured rock strata. 厚度max W represents the maximum fracture thickness of the fractured rock strata; W represents the subsidence of the fractured rock block; W max The maximum subsidence of the fractured rock block; l 伪直接触长度 The contact length between the false top and the unbroken rock beam of the main top; l 伪直接触长度max The maximum contact length between the false top and the unbroken rock beam of the main top; l 直基接触长度 The length of direct contact with the unbroken rock block at the base; l 直基接触长度max This is the maximum contact length directly with the unbroken rock block at the base.

[0157] It is understandable that in the process of calculating the values ​​of the above primary indicators, for negative secondary indicators, the value of the secondary indicator is the value after positive conversion.

[0158] (3-2) Determining the weights of primary indicators

[0159] The calculation process for the weights of the secondary indicators can be based on methods such as the analytic hierarchy process (AHP) and the entropy weight method to determine the weights corresponding to the primary indicators. This application will not elaborate further on this.

[0160] (4) Substitute the index values ​​of the primary index into the preset surface fracturing model to determine the surface fracturing index.

[0161] Assume the weights corresponding to the primary indicators are λ1, λ2, and λ3, and the indicator values ​​are M. B1 M B2 and M B3 Then the surface fracturing index M is:

[0162] M=λ1M B1 +λ2M B2 +λ3M B3 .

[0163] The surface cracking prediction coefficient M is used to determine whether the surface will crack. When the M value is in (0.7, 1), the surface will definitely crack; when the M value is in (0.5, 0.7), the surface is likely to crack; when the M value is in (0.4, 0.5), the surface may crack; when the M value is less than 0.4, the surface will not crack.

[0164] All data in the method of this invention are derived from actual field data through scientific calculation. An indicator evaluation system is established using primary, secondary, and tertiary indicators. The entire analytical calculation model first calculates each sub-item, then determines the membership degree of each sub-item based on its weight, and finally calculates the total membership degree by summing the results across all sub-items. The weights of each sub-item are obtained from actual field measurements, ensuring high accuracy, strong reference value, and consistency with reality.

[0165] The beneficial effects of this application are as follows: It collects the index values ​​of secondary indicators in a surface fracturing index system; determines the membership degree of the secondary indicators based on their values, and determines their weights; determines the index values ​​of primary indicators based on their membership degrees and weights; and substitutes the index values ​​of the primary indicators into a preset surface fracturing model to determine the surface fracturing index. Because a surface fracturing index system is pre-constructed, the analysis of surface fracturing through multiple indicators avoids the problem of low accuracy caused by analyzing surface fracturing using a single indicator.

[0166] In one embodiment, the step S102 above, which involves determining the membership degree of the secondary indicator based on the indicator value of the secondary indicator, can be implemented as the following step A1:

[0167] In step A1, the index value of the secondary index is substituted into a preset membership function to calculate the membership degree of the secondary index.

[0168] In one embodiment, step A1 above can be implemented as step A2 as follows:

[0169] In step A2, the index values ​​of the secondary indicators are substituted into the following preset membership function to calculate the membership degree of the secondary indicators:

[0170]

[0171] Where ψ is the membership degree of the secondary indicator; θ is the indicator value of the secondary indicator; θ max This represents the maximum impact of secondary indicators on surface fracturing.

[0172] In one embodiment, when the secondary indicator is a negative indicator, the step S102 above, which involves determining the membership degree of the secondary indicator based on its value, can also be implemented as steps B1-B2:

[0173] In step B1, the index values ​​of the secondary index are positiveized to obtain positiveized index values;

[0174] In step B2, the membership degree of the secondary index is determined based on the positive index value.

[0175] In one embodiment, step B1 above can also be implemented as the following steps:

[0176] Obtain the reciprocal of the value of the secondary indicator, and use the reciprocal of the value of the secondary indicator as the positive value of the secondary indicator.

[0177] In one embodiment, the step S102 above, which involves determining the weight of the secondary indicator based on the indicator value of the secondary indicator, can be implemented as the following steps C1-C3:

[0178] In step C1, the index values ​​of the secondary indicators are standardized to obtain standardized secondary indicators. The standardization of the secondary indicators includes at least positiveization and normalization.

[0179] In step C2, the information entropy corresponding to the secondary indicators is determined based on the standardized secondary indicators;

[0180] In step C3, the weights of the secondary indicators are determined based on the information entropy corresponding to the secondary indicators.

[0181] In one embodiment, step S104 above can be implemented as follows:

[0182] The index values ​​of the primary indicators are substituted into the following preset surface fracturing model to calculate the surface fracturing index:

[0183]

[0184] Where M is the surface fracturing index, λ i M represents the weight corresponding to the i-th primary indicator. Bi Let be the value of the i-th primary indicator.

[0185] Figure 3 This is a schematic diagram of a device for determining the surface fracturing index according to an embodiment of this application, as shown below. Figure 3 As shown, it includes:

[0186] The data acquisition module 301 is used to acquire the index values ​​of the secondary indicators in the surface fracturing index system;

[0187] The first determining module 302 is used to determine the membership degree of the secondary indicator based on the indicator value of the secondary indicator, and to determine the weight of the secondary indicator based on the indicator value of the secondary indicator.

[0188] The second determining module 303 is used to determine the indicator value of the primary indicator based on the membership degree of the secondary indicator and the weight of the secondary indicator;

[0189] The third determining module 304 is used to substitute the index value of the primary index into a preset surface fracturing model to determine the surface fracturing index.

[0190] In one embodiment, the second determining module includes:

[0191] The calculation submodule is used to substitute the index values ​​of the secondary indicators into a preset membership function to calculate the membership degree of the secondary indicators.

[0192] In one embodiment, the computing submodule is further configured to:

[0193] The membership degree of the secondary indicator is calculated by substituting its value into the following preset membership function:

[0194]

[0195] Where ψ is the membership degree of the secondary indicator; θ is the indicator value of the secondary indicator; θ max This represents the maximum impact of secondary indicators on surface fracturing.

[0196] In one embodiment, the second determining module includes:

[0197] The processing submodule is used to perform positive transformation on the index values ​​of the secondary indicators to obtain positive index values.

[0198] The first determining submodule is used to determine the membership degree of the secondary indicators based on the positiveized indicator values.

[0199] In one embodiment, the processing submodule is further configured to:

[0200] Obtain the reciprocal of the value of the secondary indicator, and use the reciprocal of the value of the secondary indicator as the positive value of the secondary indicator.

[0201] In one embodiment, the second determining module includes:

[0202] The standardization module is used to standardize the index values ​​of the secondary indicators to obtain standardized secondary indicators. The standardization of the secondary indicators includes at least positiveization and normalization of the secondary indicators.

[0203] The second determination submodule is used to determine the information entropy corresponding to the secondary indicators based on the standardized secondary indicators;

[0204] The third determining submodule is used to determine the weight of the secondary indicator based on the information entropy corresponding to the secondary indicator.

[0205] In one embodiment, the third determining module is further configured to:

[0206] The index values ​​of the primary indicators are substituted into the following preset surface fracturing model to calculate the surface fracturing index:

[0207]

[0208] Where M is the surface fracturing index, λ i M represents the weight corresponding to the i-th primary indicator. Bi Let be the value of the i-th primary indicator.

[0209] Figure 4 This is a schematic diagram of the hardware structure of a system for determining the surface fracturing index according to an embodiment of this application, as shown below. Figure 4 As shown, the system for determining the surface fracturing index includes:

[0210] At least one processor 420; and,

[0211] Memory 404 communicatively connected to the at least one processor 420; wherein,

[0212] The memory 404 stores instructions that can be executed by the at least one processor 420 to implement the method for determining the surface fracturing index as described in any of the above embodiments.

[0213] Reference Figure 4 The surface fracturing index determination system 400 may include one or more of the following components: processing component 402, memory 404, power supply component 406, multimedia component 408, audio component 410, input / output (I / O) interface 412, sensor component 414, and communication component 416.

[0214] Processing component 402 typically controls the overall operation of the surface fracturing index determination system 400. Processing component 402 may include one or more processors 420 to execute instructions to complete all or part of the steps of the method described above. Furthermore, processing component 402 may include one or more modules to facilitate interaction between processing component 402 and other components. For example, processing component 402 may include a multimedia module to facilitate interaction between multimedia component 408 and processing component 402.

[0215] Memory 404 is configured to store various types of data to support the operation of the surface fracturing index determination system 400. Examples of this data include instructions for any application or method operating on the surface fracturing index determination system 400, such as text, images, videos, etc. Memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0216] Power supply component 406 provides power to various components of the surface fracturing index determination system 400. Power supply component 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the vehicle control system 400.

[0217] The multimedia component 408 includes a screen that provides an output interface between the surface fracturing index determination system 400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 408 may also include a front-facing camera and / or a rear-facing camera. When the surface fracturing index determination system 400 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0218] Audio component 410 is configured to output and / or input audio signals. For example, audio component 410 includes a microphone (MIC) configured to receive external audio signals when the surface fissile index determination system 400 is in an operating mode, such as alarm mode, recording mode, voice recognition mode, and voice output mode. The received audio signals may be further stored in memory 404 or transmitted via communication component 416. In some embodiments, audio component 410 also includes a speaker for outputting audio signals.

[0219] I / O interface 412 provides an interface between processing component 402 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0220] Sensor assembly 414 includes one or more sensors for providing various aspects of the state assessment of the surface fracturing index determination system 400. For example, sensor assembly 414 may include a sound sensor. Additionally, sensor assembly 414 can detect the on / off state of the surface fracturing index determination system 400, the relative positioning of components (e.g., the display and keypad of the surface fracturing index determination system 400), and the operating state of the surface fracturing index determination system 400 or one of its components, such as the operating state of the air distribution plate, structural state, and discharge scraper, as well as the orientation or acceleration / deceleration of the surface fracturing index determination system 400 and temperature changes of the surface fracturing index determination system 400. Sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 414 may further include an accelerometer, a gyroscope, a magnetic sensor, a pressure sensor, a material buildup thickness sensor, or a temperature sensor.

[0221] Communication component 416 is configured to enable the surface fracturing index determination system 400 to provide wired or wireless communication capabilities with other devices and cloud platforms. The surface fracturing index determination system 400 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 416 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 416 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0222] In an exemplary embodiment, the surface fracturing index determination system 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the surface fracturing index determination method described in any of the above embodiments.

[0223] This application also provides a computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor corresponding to a surface fracturing index determination system, enables the surface fracturing index determination system to implement the surface fracturing index determination method described in any of the above embodiments.

[0224] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0225] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0226] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0227] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0228] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for determining the surface fracturing index, characterized in that, include: Collect the index values ​​of secondary indicators in the surface fracturing index system; The membership degree of the secondary indicator is determined based on the indicator value of the secondary indicator, and the weight of the secondary indicator is determined based on the indicator value of the secondary indicator. The value of the primary indicator is determined based on the membership degree and weight of the secondary indicator. The index values ​​of the primary indicators are substituted into a preset surface fracturing model to determine the surface fracturing index.

2. The method as described in claim 1, characterized in that, The step of determining the membership degree of the secondary indicator based on the indicator value of the secondary indicator includes: The membership degree of the secondary indicator is calculated by substituting the indicator value of the secondary indicator into a preset membership function.

3. The method as described in claim 2, characterized in that, The step of substituting the index value of the secondary indicator into a preset membership function to calculate the membership degree of the secondary indicator includes: The membership degree of the secondary indicator is calculated by substituting its value into the following preset membership function: Where ψ is the membership degree of the secondary indicator; θ is the indicator value of the secondary indicator; θ max This represents the maximum impact of secondary indicators on surface fracturing.

4. The method as described in claim 1, characterized in that, When the secondary indicator is a negative indicator, determining the membership degree of the secondary indicator based on its value includes: The values ​​of the secondary indicators are positiveized to obtain positiveized indicator values; The membership degree of the secondary indicators is determined based on the positively oriented indicator values.

5. The method as described in claim 4, characterized in that, The positive transformation of the secondary indicators to obtain positive indicator values ​​includes: Obtain the reciprocal of the value of the secondary indicator, and use the reciprocal of the value of the secondary indicator as the positive value of the secondary indicator.

6. The method as described in claim 1, characterized in that, The step of determining the weight of the secondary indicator based on the indicator value of the secondary indicator includes: The values ​​of the secondary indicators are standardized to obtain standardized secondary indicators. The standardization of the secondary indicators includes at least positiveization and normalization. The information entropy corresponding to the secondary indicators is determined based on standardized secondary indicators; The weights of the secondary indicators are determined based on the information entropy corresponding to the secondary indicators.

7. The method as described in claim 1, characterized in that, The step of substituting the index value of the primary index into a preset surface fracturing model to determine the surface fracturing index includes: The index values ​​of the primary indicators are substituted into the following preset surface fracturing model to calculate the surface fracturing index: Where M is the surface fracturing index, λ i M represents the weight corresponding to the i-th primary indicator. Bi Let be the value of the i-th primary indicator.

8. A device for determining the surface fracturing index, characterized in that, include: The data acquisition module is used to collect the index values ​​of the secondary indicators in the surface fracturing index system. The first determining module is used to determine the membership degree of the secondary indicator based on the indicator value of the secondary indicator, and to determine the weight of the secondary indicator based on the indicator value of the secondary indicator. The second determining module is used to determine the indicator value of the primary indicator based on the membership degree of the secondary indicator and the weight of the secondary indicator; The third determining module is used to substitute the index value of the primary index into a preset surface fracturing model to determine the surface fracturing index.

9. A system for determining the surface fracturing index, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor to implement the method for determining the surface fracturing index as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor corresponding to the surface fracturing index determination system, the surface fracturing index determination system is able to implement the surface fracturing index determination method as described in any one of claims 1-7.

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