Method and device for judging deformation of underground roadway of coal mine

Abstract

The invention provides a coal mine underground roadway deformation discrimination method and device, and the method comprises the steps: carrying out the simulation analysis of a roadway according to a large-section roadway mechanical model, and obtaining a large-section roadway influence factor; performing fuzzy hierarchy analysis on the influence factors of the large-section roadway, and performing gray analysis on the influence factors of the large-section roadway by using gray clustering analysis to respectively obtain risk evaluation values and gray correlation degree values corresponding to the influence factors of the large-section roadway; calculating a comprehensive weight value according to a product between the risk evaluation value and the grey correlation degree value; calculating a comprehensive weight value corresponding to each large-section roadway influence factor, and obtaining a target index value by a product of the weight factor vector and the index quantization factor; and determining a corresponding roadway support mode according to the target index value. The problems that in the prior art, coal mine stress concentration influence factors are not comprehensively analyzed and concluded, and objective factors influencing large-section stress concentration are not counted, so that roadway supporting is unreasonable are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel deformation judgment, and in particular to a method for judging the deformation of a tunnel in an underground coal mine, a device for judging the deformation of a tunnel in an underground coal mine, a computer-readable storage medium and a computer program product. Background Art

[0002] In order to build a complete underground coal transportation, gangue discharge, ventilation and other systems, and realize the safe and efficient operation of coal mines, various tunnels are connected to each other to form intersections, so tunnel intersections are widely present in coal mines. Common large-section tunnels in coal mines can be roughly divided into the following types according to their different intersection styles and uses in the plane: cross-shaped intersection tunnels, T-shaped intersection tunnels, Y-shaped intersection tunnels and L-shaped intersection tunnels, mining face opening cuts, trackless rubber-wheeled vehicle turning chambers, large electromechanical equipment chambers, etc. With the large-scale mining equipment, modernization of transportation equipment, and the gradual expansion of mining depth and scale in coal mining projects, ordinary tunnel sections can no longer meet mining needs. In order to ensure normal coal mining, production and transportation, and tunnel ventilation, the application of large-section tunnels will become the norm in future mines. According to the statistics of national unified coal mine data, various tunnel intersections in large-section tunnel roof accidents are the main locations where accidents occur frequently. Roof accidents caused by instability of the surrounding rock of the intersections account for about 30% to 40% of the total number of roadway roof accidents. Large-section roadways are a common underground roadway layout. Accidents frequently occur at roadway intersections. This is because the stress and damage laws at the intersections of large-section roadways have their own characteristics compared to ordinary roadways. The differences between them and ordinary roadways are: (1) The exposed area of ​​the roof at the intersection is larger, and the stress concentration in the broken zone of the roof is more serious, which can easily lead to further deformation and damage of the surrounding rock of the roof; (2) In the triangular area formed by the intersection of the roadways, the damage to the surrounding rock is much more serious than that of ordinary roadways. The serious deformation and damage of the surrounding rock in the area leads to the decrease of the supporting capacity of the surrounding rock in the triangular area on the roadway roof, and the roadway damage is further aggravated; (3) The construction process of the intersection roadway is complicated. To form a complete roadway intersection, multiple excavations are required at the intersection. Each roadway excavation will redistribute the stress in the original roadway surrounding rock that is already in a balanced state. Multiple stress redistributions cause the stress in the surrounding rock near the roadway intersection to overlap with each other, aggravating the concentration of stress in the surrounding rock, thereby aggravating the damage of the roadway surrounding rock and reducing the stability of the surrounding rock. It is precisely because of the above characteristics that the stress and damage of the surrounding rock at the intersection of large-section roadways are much more complicated than those of ordinary roadways, and the roadway support becomes much more difficult. Therefore, if the stress and damage laws of the surrounding rock of large-section intersection roadways are not correctly understood, the original roadway support adopted will fail, and it is easy to cause serious deformation and damage of the surrounding rock of large-section roadways, or even roof collapse and other production safety accidents, which will cause great harm to the normal production of coal mines and the safety of workers. However, there are problems in the existing technology: (1) Up to now, the coal mine system has not formed a complete method for evaluating the stress concentration of large sections in the tunnel and the support method to be selected; (2) At present, there has been no comprehensive analysis and summary of the factors affecting the stress concentration in coal mines, and there is no statistical analysis of the objective factors affecting the stress concentration of large sections.(3) The weights of various influencing factors have not yet been unified or a unified method for determining factor weights has not yet been formed. Summary of the invention

[0003] The main purpose of the present application is to provide a method for determining deformation of underground coal mine tunnels, a device for determining deformation of underground coal mine tunnels, a computer-readable storage medium and a computer program product, so as to at least solve the problem that the prior art has not yet conducted a comprehensive analysis and summary of the factors affecting stress concentration in coal mines, and there is no statistical analysis of the objective factors that affect stress concentration in large sections, leading to unreasonable tunnel support.

[0004] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for distinguishing deformation of underground coal mine tunnels is provided, including: constructing a large-section tunnel mechanical model and performing tunnel simulation analysis based on the large-section tunnel mechanical model to obtain influencing factors of the large-section tunnel, wherein the influencing factors of the large-section tunnel include the buried depth of the tunnel, the distance between the tunnel and the roof aquifer, the water richness of the roof aquifer, the size of the coal pillar left in the tunnel, the tunnel excavation process, the mining conditions around the tunnel, the integrity of the tunnel roof, the water spraying conditions of the tunnel roof, the original stress concentration conditions of the tunnel and the cross-sectional dimensions; performing fuzzy hierarchical analysis on the influencing factors of the large-section tunnel to judge the stability of the large-section tunnel, obtaining the risk judgment value corresponding to each of the influencing factors of the large-section tunnel, and using gray clustering analysis to distinguish the influencing factors of the large-section tunnel Grayscale analysis is performed on the factors to obtain the grey correlation value corresponding to each of the influencing factors of the large-section roadway, wherein the large-section roadway is a roadway in an underground coal mine whose cross section exceeds the set area; the comprehensive weight value corresponding to each of the influencing factors of the large-section roadway is calculated according to the product between the risk assessment value and the grey correlation value; the product between the comprehensive weight value corresponding to each of the influencing factors of the large-section roadway, the weight factor vector and the index quantification factor is calculated to obtain the target index value, the target index value is used to indicate the safety degree of deformation of the large-section roadway, the weight factor vector is the relative importance ranking of the influencing factors of the large-section roadway, and the index quantification factor is the reliability evaluation value of the influencing factors of the large-section roadway; the corresponding roadway support method is determined according to the target index value to carry out roadway support.

[0005] Optionally, a large-section tunnel mechanical model is constructed and a tunnel simulation analysis is performed based on the large-section tunnel mechanical model to obtain the influencing factors of the large-section tunnel, including: initializing the boundary conditions, initial conditions and condition parameters of the large-section tunnel mechanical model, the boundary conditions refer to setting the boundaries of the large-section tunnel mechanical model during the simulation process, including at least the support method of the surrounding rock, the tunnel excavation speed and the surface load, the initial conditions refer to setting the initial state of the large-section tunnel mechanical model at the beginning of the simulation, including at least the geological conditions and initial stress state of the surrounding rock, and the condition parameters are all parameters that affect the stability of the surrounding rock of the large-section tunnel; simulate the excavation of the large-section tunnel, analyze the stress characteristics and failure laws of the large-section tunnel in the triangle area of ​​the intersection point, and obtain the stress characteristics and failure laws of the surrounding rock of the large-section tunnel; analyze the stress characteristics and failure laws of the surrounding rock of the large-section tunnel in combination with the deformation and failure characteristics of the large-section tunnel to obtain the influencing factors of the large-section tunnel.

[0006] Optionally, a fuzzy hierarchical analysis is performed on the influencing factors of the large-section roadway to judge the stability of the large-section roadway, and the risk judgment value corresponding to each of the influencing factors of the large-section roadway is obtained, including: using the assignment method to scale each of the influencing factors of the large-section roadway to obtain a fuzzy judgment matrix; transforming the fuzzy judgment matrix according to the first formula to obtain a fuzzy consistency judgment matrix, and the first formula is ν i represents the sum of the elements in the i-th row of the fuzzy judgment matrix, ν j represents the sum of the elements in the jth row of the fuzzy judgment matrix, m represents the order of the fuzzy judgment matrix, r ij represents the element value of the i-th row and j-th column in the fuzzy consistency judgment matrix; the fuzzy consistency judgment matrix is ​​parsed according to the second formula to obtain the risk weight set, and the second formula is: r ik Represents the element value of the i-th row and k-th column in the fuzzy consistency judgment matrix; calculates the product between the risk weight set and the reference value matrix to obtain the evaluation values ​​corresponding to the large-section tunnels; calculates the product between the risk weight set and the corresponding evaluation values ​​to obtain the risk vector, and the elements in the risk vector represent the risk evaluation values ​​corresponding to the large-section tunnels.

[0007] Optionally, gray cluster analysis is used to perform grayscale analysis on the influencing factors of the large-section roadway to obtain the gray correlation value corresponding to each of the influencing factors of the large-section roadway, including: determining a first whitening function, a second type whitening function and a third whitening function, wherein the first whitening function, the second whitening function and the third whitening function are all used to quantitatively describe the changes of the evaluation object with the size of the evaluation index and the actual sample value corresponding to the evaluation object, and the evaluation object and the evaluation index are any one of the influencing factors of the large-section roadway; calculating the cluster gray number according to the third formula, and the third formula is f qk (d pq ) represents the actual sample value d pq The whitening function value belonging to the kth gray class, the actual sample value is the sample observation value of the evaluation object with respect to the evaluation index, η qk Indicates the weight value of the qth evaluation index belonging to the kth gray class, δ pk represents the cluster gray number of the kth gray class of the pth evaluation object, p=1,2,3...,n, q=1,2,3...,n, n represents the total number of the evaluation indicators; the gray correlation value corresponding to each evaluation indicator is calculated according to the fourth formula, and the fourth formula is ξ p Represents the grey relational value corresponding to the p-th evaluation object.

[0008] Optionally, after performing grayscale analysis on the large-section roadway influencing factors using gray cluster analysis to obtain the gray correlation value corresponding to each of the large-section roadway influencing factors, the method also includes: using a rank correlation coefficient method to perform a consistency check on each of the large-section roadway influencing factors to eliminate the large-section roadway influencing factors that fail the consistency check.

[0009] Optionally, before calculating the product of the comprehensive weight value, weight factor vector and index quantization factor corresponding to each of the large-section roadway influencing factors to obtain the target index value, the method also includes: setting a corresponding index quantization standard for each of the large-section roadway influencing factors; matching the actual sample value corresponding to each of the large-section roadway influencing factors with the corresponding index quantization standard to obtain the index quantization factor corresponding to each of the large-section roadway influencing factors when calculating them.

[0010] Optionally, determining a corresponding tunnel support method according to the target index value to perform tunnel support includes: when the target index value is less than or equal to a first index value and greater than a second index value, determining that the large-section tunnel is over-supported under the current tunnel support method, and reducing tunnel support, and the first index value is greater than the second index value; when the target index value is less than or equal to the second index value and greater than a third index value, determining that the large-section tunnel is appropriately supported under the current tunnel support method, and there is no need to adjust the current tunnel support method, and the second index value is greater than the second index value; when the target index value is less than or equal to the third index value and greater than a fourth index value, determining that the large-section tunnel is appropriately supported under the current tunnel support method, and there is no need to adjust the current tunnel support method, and the third index value is greater than the fourth index value; when the target index value is less than or equal to the fourth index value and greater than the fifth index value, determining that the large-section tunnel is insufficiently supported under the current tunnel support method, and increasing tunnel support, and the fourth index value is greater than the fifth index value.

[0011] According to another aspect of the present application, a device for distinguishing deformation of underground coal mine tunnels is provided, and the device includes: a first analysis unit, which is used to construct a mechanical model of a large-section tunnel and perform tunnel simulation analysis based on the mechanical model of the large-section tunnel to obtain influencing factors of the large-section tunnel, wherein the influencing factors of the large-section tunnel include the buried depth of the tunnel, the distance between the tunnel and the roof aquifer, the water richness of the roof aquifer, the size of the coal pillar left in the tunnel, the tunnel excavation process, the mining conditions around the tunnel, the integrity of the tunnel roof, the water spraying conditions of the tunnel roof, the original stress concentration conditions of the tunnel and the cross-sectional dimensions; a second analysis unit, which is used to perform fuzzy hierarchical analysis on the influencing factors of the large-section tunnel to judge the stability of the large-section tunnel, obtain the risk judgment value corresponding to each of the influencing factors of the large-section tunnel, and grayscale the influencing factors of the large-section tunnel using gray clustering analysis. Analysis is performed to obtain the grey correlation value corresponding to each of the influencing factors of the large-section roadway, wherein the large-section roadway is a roadway in an underground coal mine whose cross section exceeds a set area; a first calculation unit is used to calculate the comprehensive weight value corresponding to each of the influencing factors of the large-section roadway according to the product between the risk assessment value and the grey correlation value; a second calculation unit is used to calculate the product between the comprehensive weight value corresponding to each of the influencing factors of the large-section roadway, the weight factor vector and the index quantification factor to obtain the target index value, the target index value is used to indicate the safety degree of deformation of the large-section roadway, the weight factor vector is the relative importance ranking of the influencing factors of the large-section roadway, and the index quantification factor is the reliability evaluation value of the influencing factors of the large-section roadway; a determination unit is used to determine the corresponding roadway support method according to the target index value to carry out roadway support.

[0012] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0013] According to another aspect of the present application, a computer program product is provided, comprising computer instructions, wherein when the computer instructions are executed by a processor, any one of the methods described above is implemented.

[0014] By applying the technical solution of the present application, in the method for distinguishing deformation of underground tunnels in coal mines, comprehensive statistics and analysis are conducted on the influencing factors of stress concentration of large sections in underground coal mines, statistical analysis is conducted on all influencing factors, and the size of each influencing factor is comprehensively judged according to the actual situation of the mine to obtain a risk assessment value, and different weights are assigned to the correlation between each influencing factor according to different influencing factors, and the target index value is calculated for all influencing factors to determine the required support method according to the target index value. The present application solves the problem that the influencing factors of stress concentration in coal mines have not been comprehensively analyzed and summarized in the prior art, and there is no statistical objective factor affecting the stress concentration of large sections, which leads to unreasonable tunnel support. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A hardware structure block diagram of a mobile terminal for executing a method for distinguishing deformation of a coal mine tunnel provided in an embodiment of the present application is shown;

[0016] Figure 2 A schematic diagram of a flow chart of a method for distinguishing deformation of a tunnel in an underground coal mine provided according to an embodiment of the present application is shown;

[0017] Figure 3 A schematic diagram of a flow chart of a method for distinguishing deformation of a tunnel in an underground coal mine provided according to an embodiment of the present application is shown;

[0018] Figure 4 A structural block diagram of a coal mine underground tunnel deformation identification device provided according to an embodiment of the present application is shown.

[0019] The above drawings include the following reference numerals:

[0020] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0021] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] As introduced in the background technology, in the prior art, up to now, the coal mine system has not formed a complete method for evaluating stress concentration in large cross-sections of tunnels and the support method that needs to be selected; at present, the factors affecting stress concentration in coal mines have not been comprehensively analyzed and summarized, and there are no statistical objective factors affecting stress concentration in large cross-sections. In order to solve the problem that in the prior art, the factors affecting stress concentration in coal mines have not been comprehensively analyzed and summarized, and there are no statistical objective factors affecting stress concentration in large cross-sections, resulting in unreasonable tunnel support, the embodiments of the present application provide a method for distinguishing deformation of underground mine tunnels, a device for distinguishing deformation of underground mine tunnels, a computer-readable storage medium and a computer program product.

[0025] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal for a method for distinguishing deformation of a coal mine tunnel according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the coal mine underground tunnel deformation discrimination method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0028] In this embodiment, a method for determining deformation of underground coal mine tunnels that runs on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0029] Figure 2 Flow chart of the method for distinguishing deformation of underground tunnels in coal mines according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0030] Step S201, construct a large-section tunnel mechanical model and perform tunnel simulation analysis based on the above-mentioned large-section tunnel mechanical model to obtain the influencing factors of the large-section tunnel. The above-mentioned large-section tunnel influencing factors include the burial depth of the tunnel, the distance between the tunnel and the roof aquifer, the water richness of the above-mentioned roof aquifer, the size of the coal pillar left in the tunnel, the tunnel excavation technology, the mining conditions around the tunnel, the integrity of the tunnel roof, the water spraying conditions of the tunnel roof, the original stress concentration of the tunnel and the cross-sectional size.

[0031] Specifically, Rhinoceros 3D software was used to construct a mechanical model of a large-section roadway (i.e., the large-section roadway mechanical model mentioned above), and then the roadway excavation was simulated, the stress characteristics of the triangular area of ​​the roadway intersection were analyzed by comparison, and the data of each possible influencing factor was visualized. Finally, the stress characteristics and failure laws of the large-section surrounding rock were obtained, and the main controlling factors affecting the stability of the large-section roadway surrounding rock were found. According to the above analysis and simulation, it was found that there are many factors affecting the large section of the underground coal mine, namely, the burial depth of the roadway, the distance between the roadway and the roof aquifer, the water richness of the above-mentioned roof aquifer, the size of the coal pillar left in the roadway, the roadway excavation technology, the mining conditions around the roadway, the integrity of the roadway roof, the water sprinkling condition of the roadway roof, the original stress concentration of the roadway and the cross-sectional size.

[0032] Step S202, performing fuzzy hierarchical analysis on the influencing factors of the large-section roadways to judge the stability of the large-section roadways, obtaining the risk judgment value corresponding to each of the influencing factors of the large-section roadways, and using gray clustering analysis to perform grayscale analysis on the influencing factors of the large-section roadways, obtaining the gray correlation value corresponding to each of the influencing factors of the large-section roadways, the large-section roadways are the roadways in the underground coal mine whose cross section exceeds the set area.

[0033] Specifically, based on the fuzziness of the potential risk of the large-section roadway roof, the traditional analytic hierarchy process (AHP) is used as the basis to quantitatively calculate the influencing factors of the large-section roadway, and the derivative fuzzy hierarchy analysis that can specifically judge the risk of the large-section roadway is optimized. The risk judgment value corresponding to each of the above-mentioned large-section roadway influencing factors is obtained according to the derived fuzzy hierarchy analysis. The influencing factors affecting the large-section roadway have obvious uncertainty and gray characteristics. The gray clustering analysis theory is used to further improve the roadway risk judgment, and the gray correlation value corresponding to each of the above-mentioned large-section roadway influencing factors is obtained.

[0034] Step S203, obtaining the comprehensive weight value corresponding to each of the above-mentioned large-section roadway influencing factors by multiplying the above-mentioned risk assessment value and the above-mentioned grey correlation value.

[0035] Specifically, the risk evaluation values ​​corresponding to the above-mentioned large-section roadway influencing factors obtained by the derived fuzzy hierarchical analysis are introduced into the correlation degree solution process of the grey clustering method to obtain a comprehensive weight value combining the subjective weight and the objective correlation degree, so that the evaluation results of the grey clustering analysis method are more accurate and reasonable, and a more accurate and effective risk evaluation of the roadway roof stability is obtained. The comprehensive weight value is expressed as: Where: is the risk assessment value of the p-th evaluation object corresponding to the n-th evaluation index, is the grey correlation value corresponding to the pth evaluation object. The evaluation objects and evaluation indicators are both expressed as the factors influencing the above-mentioned large-section roadway. Based on the 10 evaluation indicators in the above-mentioned large-section roadway influencing factors, the weight values ​​of the influence of different evaluation objects on the roadway stability can be obtained.

[0036] Step S204, calculate the product of the above-mentioned comprehensive weight value, weight factor vector and index quantization factor corresponding to each of the above-mentioned large-section tunnel influencing factors to obtain the target index value, the above-mentioned target index value is used to represent the safety degree of the above-mentioned large-section tunnel deformation, the above-mentioned weight factor vector is the relative importance ranking of the above-mentioned large-section tunnel influencing factors, and the above-mentioned index quantization factor is the reliability evaluation value of the above-mentioned large-section tunnel influencing factors.

[0037] Specifically, Figure 3 As shown, according to the formula Calculate the above target index value, K p represents the comprehensive weight value corresponding to the pth influencing factor of the large-section roadway, C p represents the quantitative index factor corresponding to the pth influencing factor of the large-section roadway, |A p '| represents the rank of the weight factor vector corresponding to the pth influencing factor of the large-section roadway mentioned above.

[0038] For example, in this evaluation analysis, based on the selected large-section tunnel influencing factors and their conventional value ranges, the evaluation system was divided into two levels for analysis, and finally the weights of the influencing factors of large-section tunnels in different tunnels were given. According to the process of determining each evaluation index by the hierarchical analysis method, the values ​​of the relative importance of the evaluation indexes were divided into ten levels from 1 to 10, as shown in Table 1 below; and a comparison value table (i.e. Table 2) was established.

[0039] Table 1 Details of tunnel disaster safety classification

[0040]

[0041] The following important features can be seen from the table below: the values ​​on the diagonal reflect the importance of each evaluation indicator to itself, so the values ​​are all 1; the product of the values ​​on both sides of the diagonal is 1. ij=3, then the value on the other side of the diagonal must be a ji =1 / 3; the matrix is ​​consistent.

[0042] Table 2 Comparison of the importance of each indicator

[0043]

[0044] The characteristic roots and eigenvectors of the judgment matrix are calculated by the single-level sorting method, that is, the product method. The specific calculation method is as follows: 1) Normalize the elements in the above table to obtain the matrix a ij , which is used as a judgment matrix, and the general items of its elements are: 2) Add the judgment matrix after column normalization row by row: 3) Vector Normalization: That is, W = [w1, w2, ..., w n ] T is the desired eigenvector. 4) Calculate the maximum eigenvalue of the judgment matrix:

[0045] Consistency test of judgment matrix: first obtain the maximum characteristic root λ max and consistency indicators Then calculate the consistency ratio When CR≤0.1, the judgment matrix is ​​considered to have "satisfactory consistency" and is acceptable; otherwise (CI≥0.1), the judgment matrix needs to be appropriately corrected.

[0046] Assume that the weight factor vector corresponding to the influencing factors of large-section tunnel is A n ': A n '=(b1,b2,b3,...,b n ), b1, b2, b3, ..., b n The values ​​can be 1, 1 / 2, 1 / 3, ..., 1 / 10, where b n is the nth evaluation index A n The weight value relative to the evaluation target represents the evaluation factor A. n The importance of the evaluation target and its status in all evaluation indicators are ranked according to the weight. n≥ 0, the evaluation target is the factor currently used as a reference among the above-mentioned large-section influencing factors; according to the actual situation of the mine, the various factors are statistically analyzed, and the proportion of the influence on the stability of the tunnel, that is, the score of the influencing factor, is established to establish a judgment matrix, as shown in Table 3 below.

[0047] Table 3 Evaluation indexes and weight distribution of tunnel stability

[0048]

[0049]

[0050] The above indicator layers are further classified to obtain the mutual importance between the evaluation indicator classification categories to form a judgment matrix. According to the above calculation principle, the eigenvector of the judgment matrix of the indicator evaluation indicator is calculated, and finally according to the formula The above target index values ​​are calculated.

[0051] Step S205, determining the corresponding tunnel support method according to the above target index value to perform tunnel support.

[0052] Specifically, according to the range of the above-mentioned target index value, the corresponding tunnel support method is determined to carry out tunnel support, so as to reduce the problems of insufficient tunnel support and excessive support.

[0053] In this embodiment, by comprehensively counting and analyzing the factors affecting stress concentration of large sections in coal mines, statistical analysis is performed on all the influencing factors, and the size of each influencing factor is comprehensively judged according to the actual situation of the mine to obtain a risk assessment value. Different weights are assigned to the correlation between each influencing factor according to different influencing factors, and all the influencing factors are calculated to obtain the target index value, so as to judge the support method required according to the target index value. This application solves the problem that the influencing factors of stress concentration in coal mines have not been comprehensively analyzed and summarized in the prior art, and there is no statistical objective factor affecting the stress concentration of large sections, which leads to unreasonable tunnel support.

[0054] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the method for distinguishing deformation of underground coal mine tunnels of the present application will be described in detail below in combination with specific embodiments.

[0055] In order to comprehensively analyze and summarize the factors affecting stress concentration in coal mines, and statistically analyze the objective factors affecting large-section stress concentration, in an optional implementation, the above step S201 includes:

[0056] Step S2011, initializing the boundary conditions, initial conditions and condition parameters of the large-section tunnel mechanical model. The boundary conditions refer to setting the boundaries of the large-section tunnel mechanical model during the simulation process, including at least the support method of the surrounding rock, the tunnel excavation speed and the surface load. The initial conditions refer to setting the initial state of the large-section tunnel mechanical model at the beginning of the simulation, including at least the geological conditions and the initial stress state of the surrounding rock. The condition parameters are all parameters that affect the stability of the surrounding rock of the large-section tunnel.

[0057] Step S2012, simulate excavation of the large-section roadway, analyze the stress characteristics and failure laws of the large-section roadway in the triangular area of ​​the intersection, and obtain the stress characteristics and failure laws of the surrounding rock of the large-section roadway;

[0058] Step S2013, analyzing the stress characteristics and failure laws of the surrounding rock of the large-section roadway in combination with the deformation and failure characteristics of the large-section roadway to obtain the influencing factors of the large-section roadway.

[0059] In the above embodiment, Rhinoceros 3D software is used to construct a mechanical model of a large-section tunnel. After numerical meshing and transcoding are completed by Griddle, the model is imported into FLAC3D. On the basis of setting the boundary conditions and initial conditions of the model, the condition parameters that may affect the stability of the surrounding rock of the large-section tunnel are input into the model. Then, the tunnel is simulated for excavation, and the stress characteristics of the triangular area at the intersection of the tunnel are compared and analyzed: (1) The stress cloud map is viewed using the built-in post-processing function of FLAC3D to intuitively display the stress distribution in the simulated area; (2) The stress data of specific nodes or units are extracted to calculate the principal stress, shear stress, and other parameters. and other key indicators to evaluate the material stability and failure mode; (3) Analyze the behavior of the material during loading, especially the stress-strain relationship when it undergoes elastic, plastic and fracture stages; (4) Use professional post-processing software, such as Paraview or MATLAB, to perform more advanced data analysis, visualize the data of various possible influencing factors, and finally obtain the stress characteristics and failure laws of the large-section surrounding rock. Combined with the deformation and failure characteristics of the large-section tunnels in the underground site, analyze the deformation and failure mechanism of the large-section tunnels, and find out the areas where the surrounding rock of the large-section tunnels needs to be strengthened. According to the above analysis and simulation, it is found that there are many factors that affect the large-section tunnels in coal mines, namely: the burial depth of the tunnel, the distance between the tunnel and the roof aquifer, the water richness of the roof aquifer, the size of the coal pillar left in the tunnel, the tunnel excavation technology, the mining conditions around the tunnel, the integrity of the tunnel roof, the water spraying of the tunnel roof, the original stress concentration and the cross-sectional size.

[0060] In addition, before conducting an analysis based on the mechanical model of the large-section tunnel, a detailed investigation is conducted on the hydrogeological conditions, support quality, deformation and damage of the surrounding rock, and nearby mining disturbance of the underground large-section tunnel, and the current status of the underground large-section tunnel is summarized and classified; for large-section tunnels with severe deformation, a peep instrument is used to understand the damage status of the internal structure of the surrounding rock of the large-section tunnel, and the main influencing factors of the deformation and damage of the surrounding rock and the deformation and damage characteristics of the tunnel are analyzed; for large-section tunnels with severe damage, core sampling is performed on the top and bottom plates of the tunnel, and laboratory tests of physical and mechanical parameters are carried out, including true density, apparent density, porosity, uniaxial tensile strength, uniaxial compressive strength, deformation modulus, Poisson's ratio, cohesion, internal friction angle, etc. of the surrounding rock, to provide basic data for theoretical analysis and numerical calculation during the implementation of the project.

[0061] In order to preliminarily and accurately determine the weights of the factors affecting the large-section roadway, in an optional implementation manner, the above step S202 includes:

[0062] Step S2021, using the assignment method to scale the influencing factors of the above-mentioned large-section tunnels to obtain a fuzzy judgment matrix;

[0063] Step S2022, transform the above fuzzy judgment matrix according to the first formula to obtain a fuzzy consistency judgment matrix. The above first formula is ν i represents the sum of the elements in the i-th row of the above fuzzy judgment matrix, ν j represents the sum of the elements in the jth row of the above fuzzy judgment matrix, m represents the order of the above fuzzy judgment matrix, r ij represents the element value of the i-th row and j-th column in the above fuzzy consistency judgment matrix;

[0064] Step S2023, the fuzzy consistency judgment matrix is ​​analyzed according to the second formula to obtain a risk weight set. The second formula is: r ik represents the element value of the i-th row and k-th column in the above fuzzy consistency judgment matrix;

[0065] Step S2024, calculating the product between the risk weight set and the reference value matrix to obtain the evaluation values ​​corresponding to each major cross-section laneway;

[0066] Step S2025, calculate the product between the above risk weight set and the corresponding above evaluation value to obtain a risk vector, and the elements in the above risk vector represent the above risk evaluation value corresponding to the above large-section tunnel.

[0067] In the above embodiment, as shown in Table 4 below, 0-1 assignment is used for scaling to obtain the fuzzy judgment matrix V.

[0068] Table 4 Fuzzy simplified 0-1 scale

[0069]

[0070] The fuzzy judgment matrix V is: Elements j and i both represent parameters in the factors affecting large-section tunnels. There are 10 parameters in the factors affecting large-section tunnels, so m = 10, ν 11 Indicates the importance of the first element in the influencing factors of large-section tunnels compared with itself, ν 12 It indicates the importance of the first and second elements in the factors affecting large-section tunnels. The other factors are the same and will not be elaborated on. The conversion formula is as follows: According to the transformation formula, the fuzzy judgment matrix is ​​transformed to obtain the fuzzy consistency judgment matrix R: The risk weight set W of the factor layer is [w1,w2,w3,…,w m ]The analytical formula is: m is the order of the matrix.

[0071] Since the factors affecting the stability of the roof in large-section tunnels are difficult to quantify, the fuzzy unquantifiable index factors are assigned a scoring method to obtain the evaluation value, and this type of problem is converted into an evaluation value matrix P for analysis, that is, each element value in the fuzzy consistency judgment matrix is ​​assigned a specific appropriate parameter value, where: Where: n is the number of influencing factors of large-section tunnels; m is the matrix order.

[0072] In the process of risk assessment of large-section tunnel roof, it is difficult to directly classify the factors affecting the stability of the large-section tunnel roof. For the target problem with multi-layer structure, the risk of the element layer is firstly assessed, and the evaluation value G of the element layer for each evaluation index of the criterion layer is summarized, where After obtaining the judgment value G, the criterion layer is solved again for the target layer to obtain the risk judgment values ​​of the influencing factors of the major section tunnels. After sorting, the risk level in the roof judgment system can be obtained. The corresponding risk level varies with h. n The value increases. That is, the risk vector is H total =W·G=[h1,h2,h3,…,h n ], where h1,h2,h3,…,h n It indicates the risk assessment value corresponding to the influencing factors of large-section tunnels, and is sorted in ascending order according to the risk level.

[0073] In order to calculate the correlation between the factors affecting the large-section roadway to further accurately determine the weights of the factors affecting the large-section roadway, in an optional implementation, the above step S202 further includes:

[0074] Step S2026, determining a first whitening function, a second type whitening function and a third whitening function, wherein the first whitening function, the second whitening function and the third whitening function are all used to quantitatively describe how the evaluation object changes with the size of the evaluation index and the actual sample value corresponding to the evaluation object, and the evaluation object and the evaluation index are any one of the influencing factors of the large-section roadway;

[0075] Step S2027, calculate the cluster gray number according to the third formula, the third formula is f qk (d pq ) represents the actual sample value d pqThe whitening function value belonging to the kth gray class, the actual sample value is the sample observation value of the evaluation object for the evaluation index, η qk Indicates the weight value of the qth evaluation index belonging to the kth gray class, δ pk represents the clustering gray number of the kth gray class of the pth evaluation object, p=1,2,3...,n, q=1,2,3...,n, n represents the total number of the evaluation indicators;

[0076] Step S2028, calculate the grey correlation value corresponding to each of the above evaluation indicators according to the fourth formula, the fourth formula is: ξ p It represents the grey relational value corresponding to the pth evaluation object.

[0077] In the above embodiment, the factors affecting the stability of the large-section tunnel roof have obvious uncertainty and gray characteristics, and the gray clustering analysis theory is used to further improve the risk assessment. First, the evaluation index is determined to be q, q = 1, 2, ..., n; the evaluation object is p, p = 1, 2, ..., n; the clustering gray class is k, k = 1, 2, 3 (indicator characteristic value classification), the evaluation index can be understood as the index when analyzing between different levels in the multi-level analysis, and the evaluation object can be understood as the index when analyzing between the same level. The whitening function of the q index belonging to the k gray class is f qk (x), the whitening function quantitatively describes the change of a certain evaluation object with the value of the evaluation index and the actual sample point of the evaluation index. The indicators of this evaluation are divided into three gray classes (levels), namely large, medium and small; the gray classes to be evaluated are also classified according to the risk of large, medium and small. The expression of the whitening clustering weight function is as follows.

[0078] Belongs to the large type whitening function f q1 (i.e. the first whitening function mentioned above) is: Membership medium type whitening function f q2 (i.e. the second whitening function mentioned above) is: Membership small type whitening function f q3 (i.e. the third whitening function mentioned above) is: Where: d ij is the actual sample value; H q 、M q , L q are the thresholds of each evaluation index belonging to large, medium and small gray classes respectively. The clustering gray number δ of the kth gray class of the pth evaluation object pk Calculated by the following formula: The grey correlation values ​​corresponding to the above evaluation indicators are calculated according to the fourth formula: The grey correlation value reflects the similarity of each evaluation object. The greater the correlation, the higher the correlation between each evaluation object and the standard sequence in the target problem, and the higher the final risk assessment value.

[0079] In order to improve the accuracy of the roadway stability assessment, in an optional implementation, after the above step S203, the method further includes:

[0080] Step S301, using the rank correlation coefficient method to perform consistency check on the above-mentioned large-section roadway influencing factors, so as to eliminate the above-mentioned large-section roadway influencing factors that fail the consistency check.

[0081] In the above embodiment, in order to make the correlation between risk assessment indicators closely related and make risk identification and risk focus response more practical, the Spearman rank correlation coefficient is used to perform consistency test on the integration degree of key risk indicators. Here, it is assumed that the number of elements of risk variables X and Y are both N. When taking the i-th value, X i , Y i Represents. Sorting two random variables X and Y respectively, we can get the sorted sets x and y of the elements in the two random variables. Among them, the element x i For X i The ordering of X, y i Y i The order in Y. Then the Spearman rank correlation coefficient ρ is: Among them, d i =x i -y i , (1≤i≤N). Generally, |ρ|>0.95 indicates significant correlation; |ρ|≥0.8 indicates high correlation; 0.5≤|ρ|<0.8 indicates moderate correlation; 0.3≤|ρ|<0.5 indicates low correlation; |ρ|<0.3 indicates extremely weak correlation and is considered irrelevant. After the consistency test, irrelevant large-section roadway influencing factors are eliminated.

[0082] In order to calculate the indicator quantification factor to improve the accuracy of risk assessment, in an optional implementation manner, after the above step S204, the method further includes:

[0083] Step S401, setting corresponding index quantification standards for each of the above-mentioned large-section tunnel influencing factors;

[0084] Step S402, matching the actual sample values ​​corresponding to the above-mentioned large-section roadway influencing factors with the corresponding index quantification standards, and obtaining the above-mentioned index quantification factors corresponding to the above-mentioned large-section roadway influencing factors in the calculation.

[0085] In the above embodiment, the index quantification standard corresponding to each of the above large-section roadway influencing factors is set, as shown in the column of the index quantification standard in Table 5 below, and the actual sample value corresponding to each of the above large-section roadway influencing factors is matched with the corresponding index quantification standard, that is, the index quantification factor C corresponding to each of the above large-section roadway influencing factors is obtained. p .

[0086] Table 5 Safety reliability evaluation indexes of large-section tunnel roof

[0087]

[0088]

[0089] In order to reduce insufficient and excessive tunnel support, in an optional implementation, the above step S205 includes:

[0090] Step S2051, when the target index value is less than or equal to the first index value and greater than the second index value, it is determined that the large-section roadway is over-supported under the current roadway support mode, and the roadway support is reduced, and the first index value is greater than the second index value;

[0091] Step S2052, when the target index value is less than or equal to the second index value and greater than the third index value, it is determined that the support of the large-section roadway under the current roadway support mode is appropriate, and there is no need to adjust the current roadway support mode, and the second index value is greater than the third index value;

[0092] Step S2053, when the target index value is less than or equal to the third index value and greater than the fourth index value, it is determined that the support of the large-section roadway under the current roadway support mode is appropriate, and there is no need to adjust the current roadway support mode, and the third index value is greater than the fourth index value;

[0093] Step S2054, when the target index value is less than or equal to the fourth index value and greater than the fifth index value, it is determined that the large-section tunnel is insufficiently supported under the current tunnel support method, and the tunnel support is increased, and the fourth index value is greater than the fifth index value.

[0094] In the above embodiment, the roof disaster safety classification is divided into 4 levels, namely, level I to level IV, as shown in the following Table 6.

[0095] Table 6 Roof disaster safety classification details

[0096]

[0097] 90 < A ≤ 100, high safety factor, excessive support; 80 < A ≤ 90, relatively high safety factor, suitable for the current situation; 70 < A ≤ 80, medium safety factor, suitable for the current situation; 0 < A ≤ 70, low safety factor, need to strengthen support. 100, 90, 80, 70, and 0 are the first index value, the second index value, the third index value, the fourth index value, and the fifth index value respectively. Those skilled in the art can set the specific values according to the actual situation.

[0098] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0099] The embodiment of the present application also provides a device for judging the deformation of a coal mine roadway. It should be noted that the device for judging the deformation of a coal mine roadway in the embodiment of the present application can be used to execute the method for judging the deformation of a coal mine roadway provided by the embodiment of the present application. The device is used to implement the above-mentioned embodiment and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0100] The following introduces the device for judging the deformation of a coal mine roadway provided by the embodiment of the present application.

[0101] Figure 4 is a structural block diagram of the device for judging the deformation of a coal mine roadway according to the embodiment of the present application. As Figure 4 shown, the device includes:

[0102] The first analysis unit 10 is used to construct a mechanical model of a large-section roadway and perform roadway simulation analysis according to the above mechanical model of the large-section roadway to obtain the influencing factors of the large-section roadway. The above influencing factors of the large-section roadway include the burial depth of the roadway, the distance between the roadway and the roof aquifer, the water-richness of the above roof aquifer, the size of the coal pillar left in the roadway, the roadway driving process, the surrounding mining and excavation conditions of the roadway, the integrity of the roadway roof, the water spraying condition of the roadway roof, the original stress concentration condition of the roadway, and the section size.

[0103] Specifically, Rhinoceros 3D software was used to construct a mechanical model of a large-section roadway (i.e., the large-section roadway mechanical model mentioned above), and then the roadway excavation was simulated, the stress characteristics of the triangular area of ​​the roadway intersection were analyzed by comparison, and the data of each possible influencing factor was visualized. Finally, the stress characteristics and failure laws of the large-section surrounding rock were obtained, and the main controlling factors affecting the stability of the large-section roadway surrounding rock were found. According to the above analysis and simulation, it was found that there are many factors affecting the large section of the underground coal mine, namely, the burial depth of the roadway, the distance between the roadway and the roof aquifer, the water richness of the above-mentioned roof aquifer, the size of the coal pillar left in the roadway, the roadway excavation technology, the mining conditions around the roadway, the integrity of the roadway roof, the water sprinkling condition of the roadway roof, the original stress concentration of the roadway and the cross-sectional size.

[0104] The second analysis unit 20 is used to perform fuzzy hierarchical analysis on the influencing factors of the above-mentioned large-section roadways to judge the stability of the large-section roadways, obtain the risk judgment value corresponding to each of the influencing factors of the above-mentioned large-section roadways, and use gray clustering analysis to perform grayscale analysis on the influencing factors of the above-mentioned large-section roadways to obtain the gray correlation value corresponding to each of the influencing factors of the above-mentioned large-section roadways. The above-mentioned large-section roadways are roadways in underground coal mines whose cross-section exceeds the set area.

[0105] Specifically, based on the fuzziness of the potential risk of the large-section roadway roof, the traditional analytic hierarchy process (AHP) is used as the basis to quantitatively calculate the influencing factors of the large-section roadway, and the derivative fuzzy hierarchy analysis that can specifically judge the risk of the large-section roadway is optimized. The risk judgment value corresponding to each of the above-mentioned large-section roadway influencing factors is obtained according to the derived fuzzy hierarchy analysis. The influencing factors affecting the large-section roadway have obvious uncertainty and gray characteristics. The gray clustering analysis theory is used to further improve the roadway risk judgment, and the gray correlation value corresponding to each of the above-mentioned large-section roadway influencing factors is obtained.

[0106] The first calculation unit 30 is used to calculate the comprehensive weight value corresponding to each of the above-mentioned large-section tunnel influencing factors according to the product between the above-mentioned risk evaluation value and the above-mentioned grey correlation value.

[0107] Specifically, the risk evaluation values ​​corresponding to the above-mentioned large-section roadway influencing factors obtained by the derived fuzzy hierarchical analysis are introduced into the correlation degree solution process of the grey clustering method to obtain a comprehensive weight value combining the subjective weight and the objective correlation degree, so that the evaluation results of the grey clustering analysis method are more accurate and reasonable, and a more accurate and effective risk evaluation of the roadway roof stability is obtained. The comprehensive weight value is expressed as: Where: is the risk assessment value of the p-th evaluation object corresponding to the n-th evaluation index, is the grey correlation value corresponding to the pth evaluation object. The evaluation objects and evaluation indicators are both expressed as the factors influencing the above-mentioned large-section roadway. Based on the 10 evaluation indicators in the above-mentioned large-section roadway influencing factors, the weight values ​​of the influence of different evaluation objects on the roadway stability can be obtained.

[0108] The second calculation unit 40 is used to calculate the product of the above-mentioned comprehensive weight value, weight factor vector and index quantization factor corresponding to each of the above-mentioned large-section tunnel influencing factors to obtain a target index value, the above-mentioned target index value is used to represent the safety degree of the above-mentioned large-section tunnel deformation, the above-mentioned weight factor vector is the relative importance ranking of the above-mentioned large-section tunnel influencing factors, and the above-mentioned index quantization factor is the reliability evaluation value of the above-mentioned large-section tunnel influencing factors.

[0109] Specifically, Figure 3 As shown, according to the formula Calculate the above target index value, K p represents the comprehensive weight value corresponding to the pth influencing factor of the large-section roadway, C p represents the quantitative index factor corresponding to the pth influencing factor of the large-section roadway, |A p '| represents the rank of the weight factor vector corresponding to the pth influencing factor of the large-section roadway mentioned above.

[0110] The determination unit 50 is used to determine the corresponding tunnel support method according to the above target index value to perform tunnel support.

[0111] Specifically, according to the range of the above-mentioned target index value, the corresponding tunnel support method is determined to carry out tunnel support, so as to reduce the problems of insufficient tunnel support and excessive support.

[0112] In this embodiment, by comprehensively counting and analyzing the factors affecting stress concentration of large sections in coal mines, statistical analysis is performed on all the influencing factors, and the size of each influencing factor is comprehensively judged according to the actual situation of the mine to obtain a risk assessment value. Different weights are assigned to the correlation between each influencing factor according to different influencing factors, and all the influencing factors are calculated to obtain the target index value, so as to judge the support method required according to the target index value. This application solves the problem that the influencing factors of stress concentration in coal mines have not been comprehensively analyzed and summarized in the prior art, and there is no statistical objective factor affecting the stress concentration of large sections, which leads to unreasonable tunnel support.

[0113] In order to comprehensively analyze and summarize the factors affecting stress concentration in coal mines, and statistically analyze the objective factors affecting large-section stress concentration, in an optional implementation manner, the first analysis unit includes:

[0114] An initialization module is used to initialize the boundary conditions, initial conditions and condition parameters of the large-section tunnel mechanical model. The boundary conditions refer to the setting of the boundary of the large-section tunnel mechanical model during the simulation process, including at least the support method of the surrounding rock, the tunnel excavation speed and the surface load. The initial conditions refer to the setting of the initial state of the large-section tunnel mechanical model at the beginning of the simulation, including at least the geological conditions and the initial stress state of the surrounding rock. The condition parameters are all parameters that affect the stability of the surrounding rock of the large-section tunnel.

[0115] The first analysis module simulates the excavation of the large-section roadway, analyzes the stress characteristics and failure laws of the large-section roadway in the triangular area of ​​the intersection, and obtains the stress characteristics and failure laws of the surrounding rock of the large-section roadway;

[0116] The second analysis module analyzes the surrounding rock stress characteristics and failure laws of the above-mentioned large-section tunnel in combination with the deformation and failure characteristics of the large-section tunnel to obtain the influencing factors of the above-mentioned large-section tunnel.

[0117] In the above embodiment, Rhinoceros 3D software is used to construct a mechanical model of a large-section tunnel. After numerical meshing and transcoding are completed by Griddle, the model is imported into FLAC3D. On the basis of setting the boundary conditions and initial conditions of the model, the condition parameters that may affect the stability of the surrounding rock of the large-section tunnel are input into the model. Then, the tunnel is simulated for excavation, and the stress characteristics of the triangular area at the intersection of the tunnel are compared and analyzed: (1) The stress cloud map is viewed using the built-in post-processing function of FLAC3D to intuitively display the stress distribution in the simulated area; (2) The stress data of specific nodes or units are extracted to calculate the principal stress, shear stress, and other parameters. and other key indicators to evaluate the material stability and failure mode; (3) Analyze the behavior of the material during loading, especially the stress-strain relationship when it undergoes elastic, plastic and fracture stages; (4) Use professional post-processing software, such as Paraview or MATLAB, to perform more advanced data analysis, visualize the data of various possible influencing factors, and finally obtain the stress characteristics and failure laws of the large-section surrounding rock. Combined with the deformation and failure characteristics of the large-section tunnels in the underground site, analyze the deformation and failure mechanism of the large-section tunnels, and find out the areas where the surrounding rock of the large-section tunnels needs to be strengthened. According to the above analysis and simulation, it is found that there are many factors that affect the large-section tunnels in coal mines, namely: the burial depth of the tunnel, the distance between the tunnel and the roof aquifer, the water richness of the roof aquifer, the size of the coal pillar left in the tunnel, the tunnel excavation technology, the mining conditions around the tunnel, the integrity of the tunnel roof, the water spraying of the tunnel roof, the original stress concentration and the cross-sectional size.

[0118] In addition, before conducting an analysis based on the mechanical model of the large-section tunnel, a detailed investigation is conducted on the hydrogeological conditions, support quality, deformation and damage of the surrounding rock, and nearby mining disturbance of the underground large-section tunnel, and the current status of the underground large-section tunnel is summarized and classified; for large-section tunnels with severe deformation, a peep instrument is used to understand the damage status of the internal structure of the surrounding rock of the large-section tunnel, and the main influencing factors of the deformation and damage of the surrounding rock and the deformation and damage characteristics of the tunnel are analyzed; for large-section tunnels with severe damage, core sampling is performed on the top and bottom plates of the tunnel, and laboratory tests of physical and mechanical parameters are carried out, including true density, apparent density, porosity, uniaxial tensile strength, uniaxial compressive strength, deformation modulus, Poisson's ratio, cohesion, internal friction angle, etc. of the surrounding rock, to provide basic data for theoretical analysis and numerical calculation during the implementation of the project.

[0119] In order to preliminarily and accurately determine the weights of the factors affecting the large-section roadway, in an optional implementation manner, the second analysis unit includes:

[0120] The scaling module uses the value assignment method to scale the influencing factors of the above-mentioned large-section tunnels to obtain a fuzzy judgment matrix;

[0121] The conversion module converts the above fuzzy judgment matrix according to the first formula to obtain a fuzzy consistency judgment matrix. The above first formula is ν i represents the sum of the elements in the i-th row of the above fuzzy judgment matrix, ν j represents the sum of the elements in the jth row of the above fuzzy judgment matrix, m represents the order of the above fuzzy judgment matrix, r ij represents the element value of the i-th row and j-th column in the above fuzzy consistency judgment matrix;

[0122] The analysis module analyzes the fuzzy consistency judgment matrix according to the second formula to obtain the risk weight set. The second formula is: r ik represents the element value of the i-th row and k-th column in the above fuzzy consistency judgment matrix;

[0123] The first calculation module calculates the product between the risk weight set and the reference value matrix to obtain the evaluation values ​​corresponding to the major cross-section tunnels;

[0124] The second calculation module calculates the product between the above risk weight set and the corresponding above evaluation value to obtain a risk vector, and the elements in the above risk vector represent the above risk evaluation value corresponding to the above large-section tunnel.

[0125] In the above embodiment, as shown in Table 4 below, 0-1 assignment is used for scaling to obtain the fuzzy judgment matrix V.

[0126] Table 4 Fuzzy simplified 0-1 scale

[0127]

[0128] The fuzzy judgment matrix V is: Elements j and i both represent parameters in the factors affecting large-section tunnels. There are 10 parameters in the factors affecting large-section tunnels, so m = 10, ν 11 Indicates the importance of the first element in the influencing factors of large-section tunnels compared with itself, ν 12 It indicates the importance of the first and second elements in the factors affecting large-section tunnels. The other factors are the same and will not be elaborated on. The conversion formula is as follows: According to the transformation formula, the fuzzy judgment matrix is ​​transformed to obtain the fuzzy consistency judgment matrix R: The risk weight set W of the factor layer is [w1,w2,w3,…,w m ]The analytical formula is: m is the order of the matrix.

[0129] Since the factors affecting the stability of the roof in large-section tunnels are difficult to quantify, the fuzzy unquantifiable index factors are assigned a scoring method to obtain the evaluation value, and this type of problem is converted into an evaluation value matrix P for analysis, that is, each element value in the fuzzy consistency judgment matrix is ​​assigned a specific appropriate parameter value, where: Where: n is the number of influencing factors of large-section tunnels; m is the matrix order.

[0130] In the process of risk assessment of large-section tunnel roof, it is difficult to directly classify the factors affecting the stability of the large-section tunnel roof. For the target problem with multi-layer structure, the risk degree of the element layer is firstly assessed, and the evaluation value G of the element layer for each evaluation index of the criterion layer is summarized. After obtaining the judgment value G, the criterion layer is solved again for the target layer to obtain the risk judgment values ​​of the influencing factors of the major section tunnels. After sorting, the risk level in the roof judgment system can be obtained. The corresponding risk level varies with h. n The value increases. That is, the risk vector is H total =W·G=[h1,h2,h3,…,h n ], where h1,h2,h3,…,h n It indicates the risk assessment value corresponding to the influencing factors of large-section tunnels, and is sorted in ascending order according to the risk level.

[0131] In order to calculate the correlation between the factors affecting the large-section roadway to further accurately determine the weights of the factors affecting the large-section roadway, in an optional implementation manner, the second analysis unit further includes:

[0132] A determination module is used to determine a first whitening function, a second type whitening function and a third whitening function, wherein the first whitening function, the second whitening function and the third whitening function are all used to quantitatively describe how the evaluation object changes with the size of the evaluation index and the actual sample value corresponding to the evaluation object, and the evaluation object and the evaluation index are any one of the influencing factors of the large-section roadway;

[0133] The third calculation module calculates the cluster gray number according to the third formula. The third formula is: f qk (d pq ) represents the actual sample value d pq The whitening function value belonging to the kth gray class, the actual sample value is the sample observation value of the evaluation object for the evaluation index, η qk Indicates the weight value of the qth evaluation index belonging to the kth gray class, δ pk represents the clustering gray number of the kth gray class of the pth evaluation object, p=1,2,3...,n, q=1,2,3...,n, n represents the total number of the evaluation indicators;

[0134] The fourth calculation module calculates the grey correlation value corresponding to each of the above evaluation indicators according to the fourth formula. The fourth formula is: ξ p It represents the grey relational value corresponding to the pth evaluation object.

[0135] In the above embodiment, the factors affecting the stability of the large-section tunnel roof have obvious uncertainty and gray characteristics, and the gray clustering analysis theory is used to further improve the risk assessment. First, the evaluation index is determined to be q, q = 1, 2, ..., n; the evaluation object is p, p = 1, 2, ..., n; the clustering gray class is k, k = 1, 2, 3 (indicator characteristic value classification), the evaluation index can be understood as the index when analyzing between different levels in the multi-level analysis, and the evaluation object can be understood as the index when analyzing between the same level. The whitening function of the q index belonging to the k gray class is f qk (x), the whitening function quantitatively describes the change of a certain evaluation object with the value of the evaluation index and the actual sample point of the evaluation index. The indicators of this evaluation are divided into three gray classes (levels), namely large, medium and small; the gray classes to be evaluated are also classified according to the risk of large, medium and small. The expression of the whitening clustering weight function is as follows.

[0136] Belongs to the large type whitening function f q1 (i.e. the first whitening function mentioned above) is: Membership medium type whitening function f q2 (i.e. the second whitening function mentioned above) is: Membership small type whitening function f q3(i.e. the third whitening function mentioned above) is: Where: d ij is the actual sample value; H q 、M q , L q are the thresholds of each evaluation index belonging to large, medium and small gray classes respectively. The clustering gray number δ of the kth gray class of the pth evaluation object pk Calculated by the following formula: The grey correlation values ​​corresponding to the above evaluation indicators are calculated according to the fourth formula: The grey correlation value reflects the similarity of each evaluation object. The greater the correlation, the higher the correlation between each evaluation object and the standard sequence in the target problem, and the higher the final risk assessment value.

[0137] In order to improve the accuracy of the roadway stability assessment, in an optional implementation, the device further includes:

[0138] The inspection unit is used to perform grayscale analysis on the above-mentioned large-section roadway influencing factors using gray clustering analysis, and after obtaining the gray correlation value corresponding to each of the above-mentioned large-section roadway influencing factors, use the rank correlation coefficient method to perform consistency inspection on each of the above-mentioned large-section roadway influencing factors to eliminate the above-mentioned large-section roadway influencing factors that fail the consistency check.

[0139] In the above embodiment, in order to make the correlation between risk assessment indicators closely related and make risk identification and risk focus response more practical, the Spearman rank correlation coefficient is used to perform consistency test on the integration degree of key risk indicators. Here, it is assumed that the number of elements of risk variables X and Y are both N. When taking the i-th value, X i , Y i Represents. Sorting two random variables X and Y respectively, we can get the sorted sets x and y of the elements in the two random variables. Among them, the element x i For X i The ordering of X, y i Y i The order in Y. Then the Spearman rank correlation coefficient ρ is: Among them, d i =x i -y i , (1≤i≤N). Generally, |ρ|>0.95 indicates significant correlation; |ρ|≥0.8 indicates high correlation; 0.5≤|ρ|<0.8 indicates moderate correlation; 0.3≤|ρ|<0.5 indicates low correlation; |ρ|<0.3 indicates extremely weak correlation and is considered irrelevant. After the consistency check, irrelevant large-section tunnel influencing factors are eliminated. In order to calculate the indicator quantification factor to improve the accuracy of risk assessment, in an optional implementation, the device also includes:

[0140] A setting unit, used for setting corresponding index quantization standards for each of the above-mentioned large-section roadway influencing factors before calculating the product of the above-mentioned comprehensive weight value, weight factor vector and index quantization factor corresponding to each of the above-mentioned large-section roadway influencing factors to obtain the target index value;

[0141] The matching unit is used to match the actual sample values ​​corresponding to the above-mentioned large-section roadway influencing factors with the corresponding index quantification standards to obtain the above-mentioned index quantification factors corresponding to the above-mentioned large-section roadway influencing factors.

[0142] In the above embodiment, the index quantification standard corresponding to each of the above large-section roadway influencing factors is set, as shown in the column of the index quantification standard in Table 5 below, and the actual sample value corresponding to each of the above large-section roadway influencing factors is matched with the corresponding index quantification standard, that is, the index quantification factor C corresponding to each of the above large-section roadway influencing factors is obtained. p .

[0143] Table 5 Safety reliability evaluation indexes of large-section tunnel roof

[0144]

[0145]

[0146] In order to reduce insufficient and excessive tunnel support, in an optional implementation, the determination unit includes:

[0147] A first determination module determines that the large-section roadway has excessive support under the current roadway support mode and reduces roadway support when the target index value is less than or equal to the first index value and greater than the second index value, and the first index value is greater than the second index value;

[0148] The second determination module determines that the support of the large-section roadway under the current roadway support mode is appropriate, and there is no need to adjust the current roadway support mode, and the second indicator value is greater than the third indicator value when the target indicator value is less than or equal to the second indicator value and greater than the third indicator value;

[0149] A third determination module determines that the support of the large-section roadway under the current roadway support mode is appropriate, and there is no need to adjust the current roadway support mode, and the third indicator value is greater than the fourth indicator value, when the target indicator value is less than or equal to the third indicator value and greater than the fourth indicator value;

[0150] A fourth determination module determines that the support of the large cross-section roadway is insufficient under the current roadway support method and increases the roadway support when the above-mentioned target index value is less than or equal to the above-mentioned fourth index value and greater than the fifth index value, and the fourth index value is greater than the fifth index value.

[0151] In the above embodiment, the safety grading of roof disasters is divided into 4 levels, namely levels I to IV, as shown in Table 6 below.

[0152] Table 6 Details of the safety grading of roof disasters

[0153]

[0154] 90 < A ≤ 100, high safety level, excessive support; 80 < A ≤ 90, relatively high safety level, suitable for the current situation; 70 < A ≤ 80, medium safety level, suitable for the current situation; 0 < A ≤ 70, low safety level, support needs to be strengthened. 100, 90, 80, 70, and 0 are the above-mentioned first index value, the above-mentioned second index value, the above-mentioned third index value, the above-mentioned fourth index value, and the above-mentioned fifth index value respectively, and specific values can be set by those skilled in the art according to the actual situation.

[0155] The above-mentioned coal mine underground roadway deformation discrimination device includes a processor and a memory. The above-mentioned first analysis unit, second analysis unit, first calculation unit, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to implement corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned each module is located in different processors in any combination form.

[0156] The processor contains a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and by adjusting the kernel parameters, the problem that the influencing factors of coal mine stress concentration have not been comprehensively analyzed and summarized in the prior art, and the objective factors affecting the stress concentration of large cross-sections have not been statistically analyzed, resulting in unreasonable roadway support, can be solved.

[0157] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one storage chip.

[0158] An embodiment of the present invention provides a computer-readable storage medium. The above-mentioned computer-readable storage medium includes a stored program, wherein when the above-mentioned program runs, it controls the device where the above-mentioned computer-readable storage medium is located to execute the above-mentioned coal mine underground roadway deformation discrimination method.

[0159] An embodiment of the present invention provides a processor, which is used to run a program, wherein the above-mentioned method for distinguishing deformation of underground coal mine tunnels is executed when the above-mentioned program is run.

[0160] An embodiment of the present invention provides a coal mine underground tunnel deformation discrimination system. The coal mine tunnel deformation discrimination system includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, at least the following steps are implemented:

[0161] Step S201, constructing a large-section tunnel mechanical model and performing tunnel simulation analysis based on the large-section tunnel mechanical model to obtain influencing factors of the large-section tunnel, wherein the influencing factors of the large-section tunnel include the buried depth of the tunnel, the distance between the tunnel and the roof aquifer, the water richness of the roof aquifer, the size of the coal pillar left in the tunnel, the tunnel excavation process, the mining conditions around the tunnel, the integrity of the tunnel roof, the water spraying conditions of the tunnel roof, the original stress concentration of the tunnel and the cross-sectional size;

[0162] Step S202, fuzzy hierarchical analysis is performed on the influencing factors of the large-section roadway to judge the stability of the large-section roadway, and the risk judgment value corresponding to each of the influencing factors of the large-section roadway is obtained, and gray cluster analysis is used to perform gray analysis on the influencing factors of the large-section roadway to obtain the gray correlation value corresponding to each of the influencing factors of the large-section roadway. The large-section roadway is a roadway in the coal mine underground whose cross section exceeds the set area;

[0163] Step S203, calculating the comprehensive weight value corresponding to each of the above-mentioned large-section roadway influencing factors according to the product between the above-mentioned risk evaluation value and the above-mentioned grey correlation value;

[0164] Step S204, calculating the product of the comprehensive weight value, the weight factor vector and the index quantization factor corresponding to each of the above-mentioned large-section roadway influencing factors to obtain a target index value, wherein the above-mentioned target index value is used to indicate the safety degree of the above-mentioned large-section roadway deformation, the above-mentioned weight factor vector is the relative importance ranking of the above-mentioned large-section roadway influencing factors, and the above-mentioned index quantization factor is the reliability evaluation value of the above-mentioned large-section roadway influencing factors;

[0165] Step S205, determining the corresponding tunnel support method according to the above target index value to perform tunnel support.

[0166] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0167] Step S201, constructing a large-section tunnel mechanical model and performing tunnel simulation analysis based on the large-section tunnel mechanical model to obtain influencing factors of the large-section tunnel, wherein the influencing factors of the large-section tunnel include the buried depth of the tunnel, the distance between the tunnel and the roof aquifer, the water richness of the roof aquifer, the size of the coal pillar left in the tunnel, the tunnel excavation process, the mining conditions around the tunnel, the integrity of the tunnel roof, the water spraying conditions of the tunnel roof, the original stress concentration of the tunnel and the cross-sectional size;

[0168] Step S202, fuzzy hierarchical analysis is performed on the influencing factors of the large-section roadway to judge the stability of the large-section roadway, and the risk judgment value corresponding to each of the influencing factors of the large-section roadway is obtained, and gray cluster analysis is used to perform gray analysis on the influencing factors of the large-section roadway to obtain the gray correlation value corresponding to each of the influencing factors of the large-section roadway. The large-section roadway is a roadway in the coal mine underground whose cross section exceeds the set area;

[0169] Step S203, calculating the comprehensive weight value corresponding to each of the above-mentioned large-section roadway influencing factors according to the product between the above-mentioned risk evaluation value and the above-mentioned grey correlation value;

[0170] Step S204, calculating the product of the comprehensive weight value, the weight factor vector and the index quantization factor corresponding to each of the above-mentioned large-section roadway influencing factors to obtain a target index value, wherein the above-mentioned target index value is used to indicate the safety degree of the above-mentioned large-section roadway deformation, the above-mentioned weight factor vector is the relative importance ranking of the above-mentioned large-section roadway influencing factors, and the above-mentioned index quantization factor is the reliability evaluation value of the above-mentioned large-section roadway influencing factors;

[0171] Step S205, determining the corresponding tunnel support method according to the above target index value to perform tunnel support.

[0172] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0173] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0174] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0175] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0176] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0177] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0178] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0179] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0180] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0181] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0182] 1) The coal mine underground tunnel deformation identification method of the present application comprehensively counts and analyzes the factors affecting the stress concentration of large sections in the coal mine, performs statistical analysis on all the influencing factors, comprehensively judges the size of each influencing factor according to the actual situation of the mine, obtains the risk evaluation value, assigns different weights to the correlation between each influencing factor according to different influencing factors, calculates all the influencing factors to obtain the target index value, and judges the support method required according to the target index value. The present application solves the problem that the influencing factors of coal mine stress concentration have not been comprehensively analyzed and summarized in the prior art, and there is no statistical objective factor affecting the stress concentration of large sections, which leads to unreasonable tunnel support.

[0183] 2) The coal mine underground tunnel deformation discrimination device of the present application comprehensively counts and analyzes the factors affecting the stress concentration of large sections in the coal mine, performs statistical analysis on all the influencing factors, comprehensively judges the size of each influencing factor according to the actual situation of the mine, obtains the risk evaluation value, assigns different weights to the correlation between each influencing factor according to different influencing factors, calculates all the influencing factors to obtain the target index value, and judges the support method required according to the target index value. The present application solves the problem that the influencing factors of coal mine stress concentration have not been comprehensively analyzed and summarized in the prior art, and there is no statistical objective factor affecting the stress concentration of large sections, which leads to unreasonable tunnel support.

[0184] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining deformation of underground coal mine tunnels, characterized in that: include: Constructing a large-section tunnel mechanical model and performing tunnel simulation analysis based on the large-section tunnel mechanical model to obtain influencing factors of the large-section tunnel, wherein the influencing factors of the large-section tunnel include the buried depth of the tunnel, the distance between the tunnel and the roof aquifer, the water richness of the roof aquifer, the size of the coal pillar left in the tunnel, the tunnel excavation process, the mining conditions around the tunnel, the integrity of the tunnel roof, the water spraying conditions of the tunnel roof, the original stress concentration of the tunnel and the cross-sectional size; Performing fuzzy hierarchical analysis on the influencing factors of the large-section roadway to judge the stability of the large-section roadway, obtaining the risk judgment value corresponding to each of the influencing factors of the large-section roadway, and performing grayscale analysis on the influencing factors of the large-section roadway using gray cluster analysis to obtain the gray correlation value corresponding to each of the influencing factors of the large-section roadway, wherein the large-section roadway is a roadway in an underground coal mine whose cross section exceeds a set area; The comprehensive weight value corresponding to each of the large-section roadway influencing factors is calculated based on the product between the risk assessment value and the grey correlation value; Calculate the product of the comprehensive weight value, weight factor vector and index quantification factor corresponding to each of the large-section roadway influencing factors to obtain a target index value, wherein the target index value is used to indicate the safety degree of deformation of the large-section roadway, the weight factor vector is the relative importance ranking of the large-section roadway influencing factors, and the index quantification factor is the reliability evaluation value of the large-section roadway influencing factors; The corresponding tunnel support method is determined according to the target index value to carry out tunnel support.

2. The method according to claim 1, characterized in that A large-section tunnel mechanical model is constructed and tunnel simulation analysis is performed based on the large-section tunnel mechanical model to obtain the influencing factors of the large-section tunnel, including: Initializing the boundary conditions, initial conditions and conditional parameters of the large-section tunnel mechanical model, wherein the boundary conditions refer to setting the boundaries of the large-section tunnel mechanical model during the simulation process, including at least the support method of the surrounding rock, the tunnel excavation speed and the surface load, and the initial conditions refer to setting the initial state of the large-section tunnel mechanical model at the beginning of the simulation, including at least the geological conditions and the initial stress state of the surrounding rock, and the conditional parameters are all parameters that affect the stability of the surrounding rock of the large-section tunnel; Simulate excavation of the large-section tunnel, analyze the stress characteristics and failure laws of the large-section tunnel in the triangular area of ​​the intersection, and obtain the stress characteristics and failure laws of the surrounding rock of the large-section tunnel; The stress characteristics and failure laws of the surrounding rock of the large-section roadway are combined with the deformation and failure characteristics of the large-section roadway to analyze and obtain the influencing factors of the large-section roadway.

3. The method according to claim 1, characterized in that The fuzzy hierarchical analysis is performed on the influencing factors of the large-section roadway to judge the stability of the large-section roadway, and the risk judgment value corresponding to each of the influencing factors of the large-section roadway is obtained, including: The influencing factors of each large-section roadway are scaled by using the assignment method to obtain a fuzzy judgment matrix; The fuzzy judgment matrix is ​​transformed according to the first formula to obtain a fuzzy consistency judgment matrix. The first formula is: ν i represents the sum of the elements in the i-th row of the fuzzy judgment matrix, ν j represents the sum of the elements in the jth row of the fuzzy judgment matrix, m represents the order of the fuzzy judgment matrix, r ij represents the element value of the i-th row and j-th column in the fuzzy consistency judgment matrix; The fuzzy consistency judgment matrix is ​​analyzed according to the second formula to obtain the risk weight set. The second formula is: r ik represents the element value of the i-th row and the k-th column in the fuzzy consistency judgment matrix; Calculate the product between the risk weight set and the reference value matrix to obtain the evaluation values ​​corresponding to each major section of the tunnel; The product between the risk weight set and the corresponding evaluation value is calculated to obtain a risk vector, and the elements in the risk vector represent the risk evaluation value corresponding to the large-section tunnel.

4. The method according to claim 1, characterized in that: The gray scale analysis of the influencing factors of the large-section roadway is performed using gray cluster analysis to obtain the gray correlation values ​​corresponding to the influencing factors of the large-section roadway, including: Determine a first whitening function, a second type whitening function and a third whitening function, wherein the first whitening function, the second whitening function and the third whitening function are all used to quantitatively describe the change of the evaluation object with the size of the evaluation index and the actual sample value corresponding to the evaluation object, and the evaluation object and the evaluation index are any one of the influencing factors of the large-section roadway; The clustering gray number is calculated according to the third formula, which is: Indicates the actual sample value d pq The whitening function value belonging to the kth gray class, the actual sample value is the sample observation value of the evaluation object with respect to the evaluation index, η qk Indicates the weight value of the qth evaluation index belonging to the kth gray class, δ pk represents the clustering gray number of the kth gray class of the pth evaluation object, p=1,2,3...,n, q=1,2,3...,n, n represents the total number of the evaluation indicators; The grey correlation value corresponding to each evaluation index is calculated according to the fourth formula, and the fourth formula is: ξ p Represents the grey relational value corresponding to the p-th evaluation object.

5. The method according to claim 4, characterized in that After performing grayscale analysis on the influencing factors of the large-section roadway by using gray cluster analysis to obtain the gray correlation value corresponding to each of the influencing factors of the large-section roadway, the method further includes: The rank correlation coefficient method is used to perform consistency check on the influencing factors of the large-section tunnel, so as to eliminate the influencing factors of the large-section tunnel that fail the consistency check.

6. The method according to claim 1, characterized in that Before calculating the product of the comprehensive weight value, the weight factor vector and the index quantization factor corresponding to each of the large-section roadway influencing factors to obtain the target index value, the method further includes: Setting corresponding quantitative index standards for each of the influencing factors of the large-section tunnel; The actual sample values ​​corresponding to the influencing factors of the large-section roadway are matched with the corresponding quantitative standards of the indicators to obtain the quantitative factors of the indicators corresponding to the influencing factors of the large-section roadway.

7. The method according to claim 1, characterized in that Determining a corresponding tunnel support method according to the target index value to perform tunnel support includes: In the case where the target index value is less than or equal to the first index value and greater than the second index value, it is determined that the large-section roadway is over-supported under the current roadway support mode, and the roadway support is reduced, and the first index value is greater than the second index value; When the target index value is less than or equal to the second index value and greater than the third index value, it is determined that the support of the large-section roadway under the current roadway support mode is appropriate, there is no need to adjust the current roadway support mode, and the second index value is greater than the third index value; When the target index value is less than or equal to the third index value and greater than the fourth index value, it is determined that the support of the large-section tunnel under the current tunnel support mode is appropriate, and there is no need to adjust the current tunnel support mode, and the third index value is greater than the fourth index value; When the target index value is less than or equal to the fourth index value and greater than the fifth index value, it is determined that the large-section tunnel is insufficiently supported under the current tunnel support method, and the tunnel support is increased, and the fourth index value is greater than the fifth index value.

8. A device for distinguishing deformation of underground coal mine tunnels, characterized in that: The device comprises: The first analysis unit is used to construct a large-section tunnel mechanical model and perform tunnel simulation analysis based on the large-section tunnel mechanical model to obtain influencing factors of the large-section tunnel, wherein the influencing factors of the large-section tunnel include the buried depth of the tunnel, the distance between the tunnel and the roof aquifer, the water-richness of the roof aquifer, the size of the coal pillar left in the tunnel, the tunnel excavation process, the mining conditions around the tunnel, the integrity of the tunnel roof, the water spraying conditions of the tunnel roof, the original stress concentration of the tunnel and the cross-sectional size; The second analysis unit is used to perform fuzzy hierarchical analysis on the influencing factors of the large-section roadway to judge the stability of the large-section roadway, obtain the risk judgment value corresponding to each of the influencing factors of the large-section roadway, and use gray clustering analysis to perform gray analysis on the influencing factors of the large-section roadway to obtain the gray correlation value corresponding to each of the influencing factors of the large-section roadway, wherein the large-section roadway is a roadway in an underground coal mine whose cross section exceeds a set area; A first calculation unit is used to calculate the comprehensive weight value corresponding to each of the large-section roadway influencing factors according to the product between the risk evaluation value and the grey correlation value; A second calculation unit is used to calculate the product of the comprehensive weight value, the weight factor vector and the index quantization factor corresponding to each of the large-section roadway influencing factors to obtain a target index value, wherein the target index value is used to indicate the safety degree of deformation of the large-section roadway, the weight factor vector is the relative importance ranking of the large-section roadway influencing factors, and the index quantization factor is the reliability evaluation value of the large-section roadway influencing factors; A determination unit is used to determine a corresponding tunnel support method according to the target index value to perform tunnel support.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images