Coal mine soft rock roadway surrounding rock argillization degree multi-dimensional index analysis method

Abstract

The invention relates to the technical field of geotechnical engineering monitoring, in particular to a coal mine soft rock roadway surrounding rock argillization degree multi-dimensional index analysis method. The method comprises the following steps: extracting a plurality of analysis index data from a rock sample, listing an index matrix based on each analysis index data, then carrying out quantitative processing on all the rock sample index data according to the matrix, determining objective weights of all the rock sample index data through a CRITIC method, and finally determining the objective weights of all the rock sample index data according to the objective weights of all the rock sample index data. And according to the quantified roadway surrounding rock index data and the corresponding weight, calculating the evaluation score of the argillization degree of the surrounding rock, then establishing a specific grading table of the judgment value Q of the argillization degree of the roadway surrounding rock, and according to the comprehensive evaluation score of the argillization degree of the roadway surrounding rock, analyzing the grade of the argillization degree of the rock sample, and establishing a comprehensive and multi-dimensional analysis framework. Comprehensive data support can be provided for analysis of the argillization degree of the roadway surrounding rock, and multidimensional analysis of the argillization degree is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of geotechnical engineering monitoring, and particularly to a multi-dimensional index analysis method for the slime degree of surrounding rock in coal mine soft rock roadways. Background Technique

[0002] In the process of continuous deepening of coal mining operations, the problem of slime in the surrounding rock of roadways has become increasingly prominent. Especially the slime phenomenon in the surrounding rock of soft rock roadways has become a major problem faced by mine exploitation, bringing severe challenges and tests to the entire mining operation. According to relevant statistical data, about 30% of coal mine soft rock roadways in China have different degrees of slime problems in the surrounding rock, and the incidence rate of roadway instability accidents caused by this is as high as 15%, seriously threatening the lives of underground workers and the normal progress of mine exploitation. The unique properties of soft rock are one of the key factors leading to this problem. It is rich in clay minerals such as montmorillonite and kaolinite, and these minerals have a strong adsorption capacity for water. Usually, the proportion of these clay minerals in the total mass of the rock can reach 20% - 30%. When the mudstone mud mass fraction in the soft rock roadway exceeds 50%, the debris is mainly cemented by mud, and at this time the roadway will evolve into a soft rock slime roadway.

[0003] The mudstone engineering body in a water-rich environment often has a high risk of disaster. Under the action of stress redistribution after roadway excavation, soft rock is prone to plastic deformation and rheology, resulting in roadway instability, etc. The role of water is crucial in the process of slime in the surrounding rock of coal mine soft rock roadways. It not only exists in different physical states in the rock, but also weakens the rock strength by affecting the hydrogeological properties of the rock. However, the existing analysis methods for the slime degree of surrounding rock in coal mine soft rock roadways often only consider a single dimension, such as only considering the water content, the compressive strength of the rock, or the roadway support conditions, etc., lacking a comprehensive and multi-dimensional analysis framework and not being comprehensive, multi-dimensional, and real-time. Summary of the Invention

[0004] The present invention provides a multi-dimensional index analysis method for the slime degree of surrounding rock in coal mine soft rock roadways to solve the above technical problems.

[0005] The first aspect of the present invention provides a multi-dimensional index analysis method for the slime degree of surrounding rock in coal mine soft rock roadways, including:

[0006] Step S1: Sampling the surrounding rock of the roadway to obtain rock samples and obtaining multiple analysis indexes based on the rock samples;

[0007] As a further improvement of the present invention, boreholes are arranged in the surrounding rock of the roadway to be measured, rock samples in the surrounding rock of the soft rock roadway of the coal mine to be measured are obtained, the rock samples are tested for three-dimensional index, the analysis indexes of each rock sample of the roadway surrounding rock are obtained, rock sampling is carried out according to the preset borehole depth, and the slaking degree corresponding to the rock mass at different depths on the surface of the surrounding rock is obtained through testing according to the rock sampling. The preset borehole depth is divided into multiple groups of rock samples according to the preset component depth; for example, the preset borehole depth corresponding to a group of rock samples is taken as 3.5 m, and the component depth is taken as 0.5 m. The 3.5 meters of the preset borehole depth is divided into three groups according to the component depth of 0.5 m, that is, the number of rock samples corresponding to the rock samples is 3;

[0008] The test indexes are divided into three dimensions, namely, mineral composition content index, physical property index, and rock mechanics index. The rock samples at different depths are respectively subjected to multi-dimensional index tests;

[0009] The analysis indexes include water content ω, porosity θ, and clay mineral content x nt , swelling rate ψ, and uniaxial compressive strength σ; ω, θ, μ n and ψ are all positive indexes, and σ is a negative index; multi-dimensional index data is obtained according to each analysis index, and each multi-dimensional index data is filled into the multi-dimensional index data table of slaking degree

[0010] Step S11: Perform data state analysis on each multi-dimensional index data to obtain corresponding data optimization processing;

[0011] As a further improvement of the present invention, data state analysis is performed on each multi-dimensional index data, and the specific analysis method is as follows:

[0012] Normalize each multi-dimensional index data to obtain the data range corresponding to each multi-dimensional index data, obtain the data fluctuation value according to the data range corresponding to each multi-dimensional index data, obtain the preset data fluctuation upper limit value, and when the data fluctuation value corresponding to each multi-dimensional index data is greater than the data fluctuation upper limit value, calculate the difference between the data fluctuation value and the data fluctuation upper limit value to obtain the difference value.

[0013] Based on each multi-dimensional index data, the corresponding data range is divided into multiple data range intervals. The data range intervals include a low value interval, a standard data interval, and a high value interval. Obtain multiple actual data values corresponding to each multi-dimensional index data, and match each actual data value with the corresponding multiple data range intervals. Count the data volume corresponding to each data range interval, add the data volumes corresponding to the low value interval and the high value interval to obtain the total amount of abnormal data, and calculate the ratio of the total amount of abnormal data to the total number of multiple actual data values to obtain the abnormal data occupancy ratio.

[0014] Construct a circle with the value of the abnormal data ratio as the radius, and draw a straight line perpendicular to the circle with the center of the circle as the starting point. The length of the straight line is equal to the value of the difference value. Then construct a cone with the circle and the straight line, calculate the volume of the cone and record the value of the volume as the data status value; obtain a pre-set data status threshold, and when the data status value corresponding to the multidimensional indicator data is greater than the data status threshold, generate corresponding data optimization processing information, and perform corresponding data optimization processing according to the data optimization processing information.

[0015] Step S2: Setting surrounding rock deformation observation points in the tunnel to be tested and conducting in-situ testing to obtain the deformation from the inside of the surrounding rock to the surface of the surrounding rock in the tunnel to be tested; the in-situ testing is specifically as follows: using a multi-point displacement meter pre-arranged in the borehole, the relative displacement between any two points in the borehole along the burial direction is measured, and the deformation inside the surrounding rock is obtained according to the exponential change of the relative displacement. The deformation includes shrinkage, shear deformation and other displacement changes.

[0016] Step S3: Get the index data of the surrounding rock samples of the tunnel, and list the index matrix for all the rock sample indexes; the index array is represented as Among them, each θ i Represents an indicator group; based on the indicator array, the original feature matrix expression corresponding to each current analysis indicator is obtained

[0017]

[0018] Obtain the clay mineral content corresponding to the surrounding rock sample and use the formula The clay index μ is calculated n ; where x nt Expressed as clay mineral content; x fq 、x yk They are respectively expressed as the content of feldspar and quartz minerals, and the content of hydrochloride minerals; clay minerals are mainly composed of montmorillonite, illite, and kaolinite, among which the montmorillonite content has the greatest influence on the mudification degree of the surrounding rock. Montmorillonite has strong water absorption expansion and water loss shrinkage, so μ n It can be expressed as the ratio of the mineral content in the surrounding rock to the sum of the clay mineral, feldspar, quartz and hydrochloride mineral contents in the surrounding rock. The clay mineral content is the sum of the montmorillonite, illite and kaolinite contents.

[0019] Step S4: quantitatively process all the rock sample index data according to the matrix; determine the objective weights of all the rock sample index data by the CRITIC method; construct the original data matrix of the index data of the mud degree of surrounding rock of all soft rock tunnels in coal mines, normalize the data in all the original data matrices, and calculate the coefficient of variation of all the original data matrices; and obtain the weight of the mud degree corresponding to each index by comprehensive calculation of each rock sample index data;

[0020] As a further improvement of the present invention, comprehensive calculations are performed based on the index data of each rock sample. The specific calculation process is as follows:

[0021] Assume there are m samples with n indicators, and an original data matrix is constructed through the evaluation indicators:

[0022] Both n and m take positive integer values;

[0023] Normalize each multi-dimensional indicator respectively:

[0024] Among them, the positive indicator corresponds to The negative indicator corresponds to Among them, max j (α ij ) and min j (α ij ) respectively represent taking the maximum value and the minimum value of all the corresponding α ij values when i is fixed;

[0025] The standardized data matrix is B = (b ij ) 3m×n ;

[0026] Calculate the standard deviation of the indicators: Among them, is the average value of each judgment indicator and is denoted as the mean value; s j is the standard deviation of the j-th factor; μ j is the coefficient of variation;

[0027] Calculate the Pearson correlation coefficient matrix between the indicators:

[0028] Among them, r jk represents the correlation coefficient between the j-th indicator and the k-th indicator, represents the mean value of the j-th factor; and are essentially the same, both representing the mean value, corresponding to different factors;

[0029] Based on each indicator, obtain the correlation coefficient matrix corresponding to the indicators:

[0030] Using calculate the conflict of each indicator;

[0031] Using λ j = μ j × c j calculate the information content corresponding to each indicator;

[0032] Substitute the values corresponding to the conflict and information volume of each index into Calculate the weight l of the slime degree of each index j .

[0033] Step S5: Calculate the evaluation score of the slime degree of the surrounding rock according to the quantitatively processed roadway surrounding rock index data and the corresponding weights; obtain the weights corresponding to each index and the corresponding standardized data, and use the formula Calculate the comprehensive evaluation value Q corresponding to each rock sample i .

[0034] Step S6: Establish a specific classification table for the slime degree judgment value Q of the roadway surrounding rock;

[0035] Step S7: Analyze the slime degree grade of the rock sample according to the comprehensive evaluation score of the slime degree of the roadway surrounding rock; obtain the Q value corresponding to each rock sample and the slime degree classification table, and match the Q value corresponding to each rock sample with the slime degree classification table to obtain the corresponding slime degree, and use the corresponding slime degree as the slime degree of the surrounding rock of the soft rock roadway in the coal mine.

[0036] In the technical solution provided by the present invention, compared with the prior art, the beneficial effects are:

[0037] 1. In the present invention, multiple analysis index data are extracted from the rock samples, an index matrix is listed based on each analysis index data, and then all the rock sample index data are quantitatively processed according to the matrix. The objective weights of all the rock sample index data are determined by the CRITIC method. Then, according to the quantitatively processed roadway surrounding rock index data and the corresponding weights, the evaluation score of the slime degree of the surrounding rock is calculated. Then, a specific classification table for the slime degree judgment value Q of the roadway surrounding rock is established, and according to the comprehensive evaluation score of the slime degree of the roadway surrounding rock, the slime degree grade of the rock sample is analyzed, establishing a comprehensive and multi-dimensional analysis framework, which can provide comprehensive data support for the analysis of the slime degree of the roadway surrounding rock and ensure the multi-dimensionality of the slime degree analysis.

[0038] 2. In the present invention, data state analysis is performed on each multi-dimensional index data to obtain corresponding data optimization processing. By optimizing the data, the accuracy of the data used for analysis is ensured, and the influence of abnormal data on the analysis results is avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. The following drawings are not deliberately drawn to scale in actual size, and the focus is on showing the gist of the present application.

[0040] Figure 1It is the method flow chart of a multi - dimensional index analysis method for the slime degree of the surrounding rock of a soft - rock roadway in a coal mine according to the present invention;

[0041] Figure 2 It is the multi - dimensional index data table of the slime degree of the surrounding rock of a soft - rock roadway in a coal mine according to the multi - dimensional index analysis method of the present invention;

[0042] Figure 3 It is the slime degree classification table of a multi - dimensional index analysis method for the slime degree of the surrounding rock of a soft - rock roadway in a coal mine according to the present invention. Detailed implementation manners

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0044] For ease of understanding, the specific process of the embodiments of the present invention will be described below. Please refer to Figures 1-3 In an embodiment of a multi - dimensional index analysis method for the slime degree of the surrounding rock of a soft - rock roadway in a coal mine in the embodiments of the present invention, the following steps are included:

[0045] S1: Sampling the surrounding rock of the roadway to obtain rock samples and obtaining multiple analysis indexes according to the rock samples; arranging boreholes in the surrounding rock of the roadway to be measured, obtaining rock samples in the surrounding rock of the soft - rock roadway in the coal mine to be measured, testing the rock samples for three - dimensional index tests, and obtaining the analysis indexes of each rock sample of the surrounding rock of the roadway. Specifically: Rock sampling is carried out according to the preset borehole depth, and the slime degree corresponding to the rock mass at different depths on the surface of the surrounding rock is obtained through testing according to the rock sampling. The preset borehole depth is divided into multiple groups of rock samples according to the preset component depth. For example, for a group of rock samples, the preset borehole depth is taken as 3.5 m, and the component depth is taken as 0.5 m. The 3.5 - meter preset borehole depth is divided into three groups according to the component depth of 0.5 m, that is, the number of rock samples corresponding to the rock samples is 3;

[0046] The analysis indexes include water content ω, porosity θ, clay mineral content x nt 、swelling rate ψ and uniaxial compressive strength σ; ω, θ, μ nand ψ are both positive indicators, and σ is a negative indicator; the moisture content of the rock sample is measured by the drying and weighing method, the porosity is measured by the mercury injection method, the uniaxial compressive strength of the rock corresponding to each uniaxial compression standard specimen is obtained by experiment using a press, and the expansion rate is measured using a rock expansion pressure tester; the average value of multiple groups of rock strengths is calculated as the compressive strength index of the surrounding rock of the soft rock tunnel of the coal mine to be tested; multidimensional indicator data are obtained according to each analysis indicator, and each multidimensional indicator data is filled into the multidimensional indicator data table of mudification degree.

[0047] S11: Perform data status analysis on each multi-dimensional indicator data to obtain corresponding data optimization processing;

[0048] The data status analysis of each multi-dimensional indicator data is carried out in the following specific analysis methods:

[0049] Normalize each multidimensional indicator data to obtain the data range corresponding to each multidimensional indicator data, obtain the data fluctuation value according to the data range corresponding to each multidimensional indicator data, obtain the pre-set data fluctuation upper limit value, and when the data fluctuation value corresponding to each multidimensional indicator data is greater than the data fluctuation upper limit value, calculate the difference between the data fluctuation value and the data fluctuation upper limit value to obtain the difference value.

[0050] Based on each multidimensional indicator data, the corresponding data range is divided into multiple data range intervals, and the data range intervals include a low value interval, a standard data interval and a high value interval. Multiple actual data values ​​corresponding to each multidimensional indicator data are obtained, and each actual data value is matched with the corresponding multiple data range intervals. The data volume corresponding to each data range interval is counted, and the data volume corresponding to the low value interval and the high value interval are added and summed to obtain the total amount of abnormal data. The total amount of abnormal data is compared with the total number of multiple actual data values ​​to obtain the abnormal data proportion value.

[0051] Construct a circle with the value of the abnormal data ratio as the radius, and draw a straight line perpendicular to the circle with the center of the circle as the starting point. The length of the straight line is equal to the value of the difference value. Then construct a cone with the circle and the straight line, calculate the volume of the cone and record the value of the volume as the data status value; obtain a pre-set data status threshold, and when the data status value corresponding to the multidimensional indicator data is greater than the data status threshold, generate corresponding data optimization processing information, and perform corresponding data optimization processing according to the data optimization processing information.

[0052] S2: Set up surrounding rock deformation observation points in the tunnel to be tested and conduct in-situ testing to obtain the deformation from the inside of the surrounding rock to the surface of the surrounding rock in the tunnel to be tested; the in-situ testing is specifically as follows: the relative displacement between any two points in the borehole along the burial direction is measured by a multi-point displacement meter pre-arranged in the borehole, and the deformation inside the surrounding rock is obtained according to the exponential change of the relative displacement. The deformation includes shrinkage, shear deformation and other displacement changes.

[0053] S3: Obtain the index data of the roadway surrounding rock samples, and list an index matrix for all the rock sample indexes; the index array is expressed as where each θ i represents an index group; obtain the original feature matrix expression corresponding to each current analysis index based on the index array

[0054]

[0055] Obtain the clay mineral content corresponding to the surrounding rock sample, and use the formula to calculate the clayification index μ n ; where x nt represents the clay mineral content; x fq , x yk respectively represent the feldspar and quartz mineral content, and the hydrochloride mineral content; the clay minerals mainly consist of montmorillonite, illite, and kaolinite, among which the montmorillonite content has the greatest influence on the slaking degree of the surrounding rock. Montmorillonite has strong water absorption expansion and water loss shrinkage properties. Therefore, μ n can be expressed as the ratio of the mineral content in the surrounding rock to the sum of the clay minerals, feldspar and quartz, and hydrochloride minerals in the surrounding rock. The clay mineral content is the sum of the montmorillonite, illite, and kaolinite contents.

[0056] S4: Quantitatively process all the rock sample index data according to the matrix; determine the objective weights of all the rock sample index data by the CRITIC method;

[0057] Construct the original data matrix of all the slaking degree index data of the surrounding rock of the coal mine soft rock roadway, normalize the data in all the original data matrices, and calculate the coefficient of variation of all the original data matrices;

[0058] It can be understood that the CRITIC method is selected to objectively assign weights to multi-dimensional indexes. This method reflects the discreteness between samples and the conflict between factors through the standard deviation and the correlation coefficient. The magnitude of the standard deviation is proportional to the degree of discreteness and the factor weight; different from the subjective weight assignment method, the CRITIC method determines the weight according to the variability of the index itself and the conflict between indexes. The subjective weight assignment method means, for example, determining the weight by expert scoring in the analytic hierarchy process.

[0059] Suppose there are m samples with n indexes, and construct the original data matrix through the evaluation indexes:

[0060] Both n and m take positive integer values;

[0061] Normalize each of the multi-dimensional indexes:

[0062] Among them, the positive index corresponds to The negative index corresponds to Among them, max j (α ij ), min j (α ij ) respectively represent taking the maximum value and the minimum value of all α ij values corresponding to all j with i fixed.

[0063] The standardized data matrix is B = (b ij ) 3m×n ;

[0064] Calculate the standard deviation of the indicators: Among them, is the average value of each judgment indicator and is denoted as the mean value; s j is the standard deviation of the j-th factor; μ j is the coefficient of variation.

[0065] Calculate the Pearson correlation coefficient matrix between the indicators:

[0066] Among them, r jk represents the correlation coefficient between the j-th indicator and the k-th indicator, represents the mean value of the j-th factor; and are essentially the same, both representing the mean value, just corresponding to different factors.

[0067] Based on each indicator, obtain the correlation coefficient matrix corresponding to the indicator:

[0068] Use to calculate the conflict of each indicator;

[0069] Use λ j = μ j × c j to calculate the information amount corresponding to each indicator;

[0070] Substitute the values of the conflict and the information amount corresponding to each indicator into to calculate the weight l j of the slime degree of each indicator.

[0071] S5: According to the roadway surrounding rock index data after quantitative processing and the corresponding weights, calculate the evaluation score of the surrounding rock slime degree;

[0072] Obtain the weights corresponding to each indicator and the corresponding standardized data, and use the formula to calculate the comprehensive evaluation value Q corresponding to each rock samplei 。

[0073] S6: Establish a specific grading table for the judgment value Q of the slaking degree of the roadway surrounding rock; obtain the slaking degree grade of the surrounding rock of the soft rock roadway in the coal mine based on the evaluation score of the slaking degree of the surrounding rock.

[0074] S7: Analyze the slaking degree grade of the rock sample according to the comprehensive evaluation score of the slaking degree of the roadway surrounding rock; match the Q value corresponding to each rock sample with the slaking degree grading table to obtain the corresponding slaking degree, and use the corresponding slaking degree as the slaking degree of the surrounding rock of the soft rock roadway in the coal mine.

[0075] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-dimensional index analysis method for the mudification degree of surrounding rock in soft rock tunnels in coal mines, characterized in that: The steps include: Step S1: sampling the surrounding rock of the tunnel to obtain a rock sample and obtaining a plurality of analysis indicators based on the rock sample; Step S11: Perform data status analysis on each multi-dimensional indicator data to obtain corresponding data optimization processing; Step S2: setting surrounding rock deformation observation points in the tunnel to be tested and performing in-situ testing to obtain the deformation from the inside of the surrounding rock to the surface of the surrounding rock in the tunnel to be tested; Step S3: obtaining index data of surrounding rock samples of the tunnel, and listing an index matrix for all the rock sample indexes; Step S4: quantify all the rock sample index data according to the matrix; determine the objective weights of all the rock sample index data by the CRITIC method; Step S5: calculating the surrounding rock mudification degree evaluation score according to the tunnel surrounding rock index data after quantitative processing and the corresponding weight; Step S6: Establishing a specific classification table for the judgment value Q of the mudification degree of the tunnel surrounding rock; Step S7: Analyze the mud degree level of the rock sample according to the comprehensive evaluation score of the mud degree of the tunnel surrounding rock.

2. The method for multi-dimensional index analysis of the mudification degree of surrounding rock of soft rock tunnel in coal mine according to claim 1 is characterized in that: The rock samples are obtained by sampling the surrounding rocks of the tunnel and multiple analysis indicators are obtained according to the rock samples, which are specifically: Drill holes are arranged in the surrounding rock of the roadway to be tested to obtain rock samples in the surrounding rock of the soft rock roadway to be tested in the coal mine, and the rock samples are tested for three-dimensional indicators to obtain the analytical indicators of each rock sample of the roadway surrounding rock. Rock sampling is performed according to a preset preset drilling depth, and the mudification degree corresponding to the rock mass at different depths on the surrounding rock surface is obtained according to the rock sampling. The preset drilling depth is divided according to the preset component depth to obtain multiple groups of rock samples; The test indicators are divided into three dimensions, namely, mineral content index, physical property index, and rock mechanics index. Multi-dimensional index tests are conducted on rock samples at different depths. The analysis indicators include water content ω, porosity θ, clay mineral content x nt , expansion rate ψ and uniaxial compressive strength σ; ω, θ, μ n and ψ are both positive indicators, and σ is a negative indicator. Multidimensional indicator data are obtained according to each analysis indicator, and each multidimensional indicator data is filled into the multidimensional indicator data table of mudification degree.

3. The multi-dimensional index analysis method for the mudification degree of surrounding rock of soft rock tunnel in coal mine according to claim 1 is characterized in that: The data status analysis of each multi-dimensional indicator data is carried out in the following specific analysis methods: Normalizing each multidimensional indicator data to obtain the data range corresponding to each multidimensional indicator data, obtaining the data fluctuation value according to the data range corresponding to each multidimensional indicator data, obtaining the pre-set data fluctuation upper limit value, and when the data fluctuation value corresponding to each multidimensional indicator data is greater than the data fluctuation upper limit value, performing a difference calculation between the data fluctuation value and the data fluctuation upper limit value to obtain a difference value; Based on each multidimensional indicator data, the corresponding data range is divided into multiple data range intervals, the data range intervals include a low value interval, a standard data interval and a high value interval, multiple actual data values ​​corresponding to each multidimensional indicator data are obtained, and each actual data value is matched with the corresponding multiple data range intervals, the data volume corresponding to each data range interval is counted, the data volume corresponding to the low value interval and the data volume corresponding to the high value interval are added and summed to obtain the total amount of abnormal data, and the total amount of abnormal data is compared with the total number of multiple actual data values ​​to obtain the abnormal data proportion value; Construct a circle with the value of the abnormal data ratio as the radius, and draw a straight line perpendicular to the circle with the center of the circle as the starting point. The length of the straight line is equal to the value of the difference value. Then construct a cone with the circle and the straight line, calculate the volume of the cone and record the value of the volume as the data status value; obtain a pre-set data status threshold, and when the data status value corresponding to the multidimensional indicator data is greater than the data status threshold, generate corresponding data optimization processing information, and perform corresponding data optimization processing according to the data optimization processing information.

4. The method for multi-dimensional index analysis of the mudification degree of surrounding rock of soft rock tunnel in coal mine according to claim 1 is characterized in that: The in-situ test is specifically as follows: the relative displacement between any two points in the borehole along the embedding direction is measured by a multi-point displacement meter pre-arranged in the borehole, and the deformation inside the surrounding rock is obtained according to the exponential change of the relative displacement.

5. The method for multi-dimensional index analysis of the mudification degree of surrounding rock of soft rock tunnel in coal mine according to claim 1 is characterized in that: The index data of the surrounding rock samples of the tunnel are obtained, and an index matrix is ​​listed for all the rock sample indicators, which is specifically: The index array is represented as Among them, each θ i Represents an indicator group; based on the indicator array, the original feature matrix expression corresponding to each current analysis indicator is obtained Obtain the clay mineral content corresponding to the surrounding rock sample and use the formula The clay index μ is calculated n ; where x nt Expressed as clay mineral content; x fq 、x yk Expressed as feldspar and quartz mineral content, and hydrochloride mineral content respectively.

6. The method for multi-dimensional index analysis of the mudification degree of surrounding rock of soft rock tunnel in coal mine according to claim 1 is characterized in that: The quantitative processing of all the rock sample index data is performed according to the matrix; the objective weights of all the rock sample index data are determined by the CRITIC method; specifically: The original data matrix of the mudification degree index data of all coal mine soft rock tunnel surrounding rocks is constructed, and the data in all the original data matrices are normalized, and the coefficient of variation of all the original data matrices is calculated; and the weight of the mudification degree corresponding to each index is obtained by comprehensive calculation of the index data of each rock sample; Comprehensive calculation is performed through the index data of each rock sample, and the specific calculation process is as follows: Assuming there are m samples with n indicators, the original data matrix is ​​constructed by evaluation indicators: The values ​​of n and m are both positive integers; Normalize the multidimensional indicators separately: Among them, the positive indicator corresponds to Negative indicators correspond to Among them, max j (α ij ), min j (α ij ) are respectively expressed as α corresponding to all j when i is fixed ij The value takes the maximum and minimum values; The standardized data matrix is ​​B = (b ij ) 3m×n ; Calculate the standard deviation of the indicator: in, is the average value of each judgment index and is recorded as the mean; s j is the standard deviation of the jth factor; μ j is the coefficient of variation; Calculate the Pearson correlation coefficient matrix between each indicator: Among them, r jk represents the correlation coefficient between the jth index and the kth index, represents the mean of the jth factor; and They are essentially the same, both represent means and correspond to different factors; Based on each indicator, the correlation coefficient matrix corresponding to the indicator is obtained: use Calculate the conflict of each indicator; Using Lambda j =μ j ×c j Calculate the amount of information corresponding to each indicator; Substitute the values ​​of conflict and information corresponding to each indicator into Calculate the weight of each index of mud degree l j ; S5: Calculating the surrounding rock mudification degree evaluation score according to the tunnel surrounding rock index data after quantitative processing and the corresponding weight; Get the weights corresponding to each indicator and the corresponding data after standardization, and use the formula Calculate the comprehensive evaluation value Q corresponding to each rock sample i .

7. The method for multi-dimensional index analysis of the mudification degree of surrounding rock of soft rock tunnel in coal mine according to claim 1 is characterized in that: According to the comprehensive evaluation score of the mudification degree of the tunnel surrounding rock, the mudification degree grade of the rock sample is analyzed: the Q value corresponding to each rock sample and the mudification degree classification table are obtained, and the Q value corresponding to each rock sample is matched with the mudification degree classification table to obtain the corresponding mudification degree, and the corresponding mudification degree is used as the mudification degree of the surrounding rock of the soft rock tunnel in the coal mine.

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