Rock stratum danger assessment database construction method and rock stratum danger assessment method

Abstract

The application discloses a rock stratum danger assessment database construction method and a rock stratum danger assessment method. The method comprises the following steps: constructing a triangular membership function of borehole comprehensive data, a triangular membership function of equipment comprehensive data and a danger judgment rule of each known rock stratum respectively, and obtaining a critical value of the borehole comprehensive data and a critical value of the equipment comprehensive data of each known rock stratum; calculating and judging the membership of a rock stratum to be identified according to the corresponding triangular membership function and the critical value; and then, assessing the danger of the rock stratum according to the danger judgment rule of the rock stratum. The method can establish a precise three-dimensional geological model of the rock stratum, obtain the distribution rule of the rock stratum, and complete the danger assessment of the corresponding rock stratum and roadway.

Description

Technical Field

[0001] This invention relates to the field of rock stratum hazard assessment technology, and in particular to a method for constructing a rock stratum hazard assessment database and a method for assessing rock stratum hazard. Background Technology

[0002] Currently, scholars both domestically and internationally have conducted extensive research on the theory, methods, and application technologies of rock stratum hazard assessment, particularly in engineering hazard assessment. This research has yielded positive results in reducing safety production accidents, ensuring personal safety, and improving the economic benefits of engineering projects. For example, some scholars have proposed the three-step accident safety assessment method and the safety pre-evaluation index method. They have also applied grey theory and fuzzy mathematics in safety assessment, proposing theories and methods such as grey relational degree evaluation method and grey clustering evaluation method. These methods have played a positive role in promoting safe production in coal mines. However, due to the limitations of each method in applying to specific rock strata, they still cannot provide accurate stratigraphic data. Furthermore, because the surrounding rock of coal mine roadways has already deformed, the rock stratum data explored in the early stages of roadway construction cannot accurately and effectively reflect the current state, resulting in hazard assessment results that are often not accurate or ideal. Summary of the Invention

[0003] In view of the defects or deficiencies of the existing technology, the present invention provides a method for constructing a rock stratum hazard assessment database.

[0004] Therefore, the rock stratum hazard assessment database provided by this invention includes hazard assessment data for various known lithologies. The hazard assessment data for any known lithology includes the triangular membership function of the borehole integrated data, the triangular membership function of the equipment integrated data, and the hazard judgment rules.

[0005] The construction method includes constructing triangular membership functions for borehole integrated data, triangular membership functions for equipment integrated data, and hazard judgment rules for various known lithologies, and obtaining critical values ​​for borehole integrated data and equipment integrated data for each known lithology stratum;

[0006] Methods for constructing triangular membership functions for borehole composite data and equipment composite data of any known lithology include:

[0007] Method A is used to construct the borehole integrated dataset and the equipment integrated dataset for the known lithology. Then, clustering methods are used to obtain the triangular membership functions of the borehole integrated dataset and the equipment integrated dataset for the known lithology, respectively, and the critical values ​​of the borehole integrated data and the equipment integrated data for the known lithology are obtained.

[0008] Method A includes:

[0009] (1) Drilling and collecting drill rod mechanical parameters, rock stratum performance parameters and drilling equipment measurement data at different drilling positions; the drill rod mechanical parameters include drilling force, tangential force between drill rod and rock stratum, frictional resistance between drill rod and rock stratum and torque of drill rod during operation; the rock stratum performance parameters include rock compressive strength, rock shear strength, rock color, rock grayness and rock porosity; the drilling equipment measurement data are the operating parameters of the drilling rig itself;

[0010] (2) The mechanical parameters of the drill pipe collected at each location are standardized to ensure that the standardized data conforms to a standard normal distribution and that each dataset is dimensionless. The data at each location are then weighted and calculated to obtain the mechanical performance data of the drill pipe at each location. The mechanical performance data of the drill pipe at all locations constitute the mechanical performance dataset. ;

[0011] , ,in, i Any location within the borehole i =1,2,···,n; For position i Drill pipe mechanical performance data at that time; , , , These are the weighting coefficients, , , , The values ​​are all in the range of 0 to 1, and + + + =1; For position i Standardized data on drilling force during drill pipe operation; For position i Standardized data on the tangential force between the drill pipe and the rock formation; For position i Standardized data on the frictional resistance between the drill pipe and the rock formation; For position i Standardized data on the torque of the drill pipe during operation;

[0012] The rock strata performance parameters collected from each location were standardized to ensure the data conformed to a standard normal distribution, resulting in dimensionless datasets. Weighted calculations were then performed on the data from each location to obtain the rock strata performance data for that location. The rock strata performance data from all locations constituted a rock strata performance dataset. ;

[0013] ,in, For position i Time-bound rock strata performance dataset; , , , , These are the weighting coefficients, , , , , The values ​​are all in the range of 0 to 1, and + + + + =1; For position i Standardized data on the compressive strength of rocks at that time; For position i Standardized data on the shear strength of rock at that time; For position i Standardized data on the color intensity of rocks at that time; For position i Standardized data of rock grayscale; For position i Standardized data on rock porosity;

[0014] After standardizing the equipment measurement data, principal component analysis was used to reduce the dimensionality of the data, resulting in the first principal dataset. Second master dataset Third master dataset ;

[0015] (3) Constructing an in-hole comprehensive dataset , , ,in, For position i Comprehensive data within the time hole, , The learning factor takes the value of a real number between 0 and 1. a , b This is an adjustment coefficient, and its value ranges from 0 to 3;

[0016] Building a comprehensive device dataset , , = ,in, For position i Comprehensive data from the equipment at that time , , These are the weighting coefficients, both real numbers between 0 and 1. + + =1; , For position i The first master data of time, , For position i The second master data of time, , For position i The third master data of time;

[0017] Methods for constructing hazard assessment rules for any known lithology include:

[0018] Sort the data in the borehole composite dataset for any known lithology from smallest to largest, and then divide the sorted dataset into 7 intervals. U 1~ U 7, in order: [x1, x a+1 ), [x a+1 , x 2a+1 ), [x 2a+1 , x 3a+1 ), [x 3a+1 ,x 4a+1 ), [x 4a+1 , x 5a+1 ), [x 5a+1 , x 6a+1 ), [x 6a+1 , x n The number of data points in each interval is α, or 1 to 6 more or 1 to 6 less than α, where α is the integer quotient of the total number of data points in the comprehensive dataset within the hole divided by 7; x1 and x n x represents the minimum and maximum values ​​in the in-hole synthesis dataset, respectively. a+1、 x 2a+1、 x 3a+1、 x 4a+1、 x 5a+1、 x 6a+1 These are the endpoints of each interval, and x1 <x a+1 <x 2a+1 <x 3a+1 <x 4a+1 <x 5a+1 <x 6a+1 <x n ;

[0019] Sort the data in the comprehensive dataset of any device from smallest to largest, and then divide the sorted dataset into 7 intervals. H 1~ H 7, in order: [y1, yb+1 ), [y b+1 , y 2b+1 ), [y 2b+1 , y 3b+1 ), [y 3b+1 , y 4b+1 ), [y 4b+1 , y 5b+1 ), [y 5b+1 , y 6b+1 ), [y 6b+1 , y n The number of data points in each interval is β, which is either 1 to 6 more or 1 to 6 less than β, where β is the integer quotient of the total number of data points in the device's comprehensive dataset divided by 7; y1 and y n These are the minimum and maximum values ​​in the device's comprehensive dataset, y. b+1、 y 2b+1、 y 3b+1、 y 4b+1、 y 5b+1、 y 6b+1 These are the endpoints of each interval, and y1 <y b+1 <y 2b+1 <y 3b+1 <y 4b+1 <y 5b+1 <y 6b+1 <y n ;

[0020] Construct a hazard assessment rule for this known lithology:

[0021]

[0022] T 1~ T The numbers 5 represent different levels of rock strata hazard, in descending order: extremely low hazard, low hazard, medium hazard, high hazard, and extremely high hazard; among these, the judgment rules... T 0 is T 2~ T One of the four.

[0023] This invention also provides a method for assessing the hazard of rock strata. To this end, the provided method for assessing the hazard of rock strata utilizes a database constructed using the above-described method to perform a hazard assessment of the rock strata to be assessed, including:

[0024] Step 1: Construct the comprehensive borehole dataset and comprehensive equipment dataset of the rock strata to be evaluated using method A described above;

[0025] Step 2: Evaluate the lithology of the rock strata to be evaluated using the database constructed using the above method, including:

[0026] For any location of the rock stratum to be evaluated iComprehensive data inside the borehole, i =1,2,...,n; Calculate the position i The rock strata belong to various known lithologies, including, based on the borehole location of the rock strata to be evaluated. i The relationship between the critical values ​​of the borehole composite data and the borehole composite data of any known lithology is used to calculate the location using the triangular membership function of the borehole composite dataset of that known lithology. i The degree of membership of the rock strata to any known lithology is used to calculate the location. i After determining the membership degree of the rock strata to various known lithologies, select the maximum membership degree. ;

[0027] For any location of the rock stratum to be evaluated i Comprehensive equipment data, i =1,2,...,n; Calculate the position i The rock strata belong to various known lithologies, including, based on the borehole location of the rock strata to be evaluated. i The relationship between the comprehensive equipment data and the critical value of the comprehensive equipment data of any known lithology is used to calculate the position using the triangular membership function of the comprehensive equipment data set of the known lithology. i The degree of membership of the rock strata to any known lithology is used to calculate the location. i After determining the membership degrees of the rock strata to various known lithologies, select the maximum membership degree. ;

[0028] like and When they belong to the same lithology and all exceed the first threshold, then the location... i The lithology at that location is and For the lithology to which it belongs, the first threshold value ranges from [0.7, 0.8]; otherwise:

[0029] like If the value is greater than the second threshold, then the position... i The lithology at that location is The lithology to which it belongs, and the range of the second threshold value is [0.9, 0.95];

[0030] like When the value is greater than the third threshold, the position... i The lithology at that location is The lithology to which it belongs, the value range of the third threshold is [0.9, 0.95];

[0031] Step 3: Use the database constructed using the above method to assess the hazard at different borehole locations in the rock strata to be assessed, including: for any locationi， Based on the comprehensive borehole data and equipment data at that location, and according to the hazard assessment rules for the lithology to which that location belongs, determine the hazard level of any location. i The danger of the rock strata;

[0032] Step 4: The hazard assessment results of the rock stratum section are obtained by considering the hazards at multiple locations on the same cross section.

[0033] This invention further provides a method for assessing the risk of deformed roadways in coal mines. To this end, the method described above is used to assess the risk of multiple different sections of the roadway. Then, the risk assessment results of these multiple sections are used to assess the risk of the entire roadway.

[0034] This invention also provides a drilling system. The provided drilling system includes a drilling rig, a detection system, and a control system. The control system controls the drilling rig to perform drilling operations and simultaneously controls the detection system to detect the borehole environment. The control system includes the aforementioned rock stratum hazard assessment database and a rock stratum hazard assessment module. The rock stratum hazard assessment module assesses the lithological hazard during the drilling process using the aforementioned method based on the rock stratum hazard assessment database.

[0035] The method of this invention can establish an accurate three-dimensional geological model of rock strata, obtain the distribution law of rock strata, and complete the risk assessment of corresponding rock strata and tunnels. Attached Figure Description

[0036] Figure 1 This is a lithological distribution map of one of the rock strata in the example;

[0037] Figure 2 for Figure 1 The risk assessment results of a section in the middle of the mudstone layer identified in the study. Detailed Implementation

[0038] Unless otherwise specified, the scientific and technical terms and methods used in this document are based on the understanding of those skilled in the art or are implemented using relevant methods known to those skilled in the art.

[0039] Lithology refers to properties that reflect the characteristics of rocks, such as color, structure, cement, cement type, and special minerals. In practical engineering applications, lithology can be classified according to the specific geological conditions of the project. The main reason for the difference in lithology is the difference in rock structure. Although the range of variation in borehole comprehensive data and equipment comprehensive data is wide and has a significant impact on the properties and hazards of rock strata, their average values ​​can still clearly reflect the influence of lithological differences on the mechanical properties of rocks, and the differences in mechanical properties between different rock types are very obvious. For example, based on rock drillability, rocks can be classified into typical weak interlayers, soft coal seams, hard coal seams, mudstone layers, and sandy mudstone layers.

[0040] The drill rod mechanical parameters and lithological performance parameters described in this invention can be acquired using an in-hole measurement system. The drill rod mechanical parameters include drilling force, tangential force, frictional resistance, and torque. The strata performance parameters include rock compressive strength, rock shear strength, rock color, grayscale, and porosity. The equipment measurement data described in this invention are the operating parameters of the drilling rig itself, including the position parameters, feed speed, and feed pressure of the drill rod feed device, and the displacement parameters, rotation speed, and rotation pressure of the drilling rig rotation device. Position i represents any position within one or more holes, and the specific value of n in each borehole is determined by selecting the sampling frequency.

[0041] The present invention employs a clustering method to obtain the triangular membership functions of the borehole comprehensive dataset and the equipment comprehensive dataset for a known lithology, and obtains the critical values ​​of the corresponding known lithology borehole comprehensive data and equipment comprehensive data. A specific example is as follows:

[0042] For any known rock stratum, the triangular membership function of its borehole integrated dataset is established as shown in equation (1):

[0043] (1)

[0044] In the formula, For position i The membership degree of the comprehensive data value from the borehole of the rock strata to the known lithology. For position i The comprehensive data values ​​inside the hole, , , These are the critical values ​​of the comprehensive borehole data for the known lithology;

[0045] The membership function of its comprehensive equipment data value is established as shown in equation (2) below:

[0046] (2)

[0047] In the formula, For position i The degree to which the comprehensive data value of the rock strata belongs to the known lithology. For position i The overall data value of the equipment, , , These are the critical values ​​of the comprehensive equipment data for the known lithology.

[0048] Similarly, triangular membership functions can be established for the borehole integrated data values ​​and equipment integrated data values ​​corresponding to other rock strata.

[0049] The hazard assessment rule for a known lithology in this invention is expressed as follows:

[0050]

[0051] The aforementioned hazard assessment rules specifically define the risk levels corresponding to the H and U intervals. For example, for any location... i， Based on the hazard assessment rules of the lithology to which this location belongs ， When the borehole integrated data and equipment integrated data at this location are respectively in the interval U 1 and interval H When the value is 1, the risk level of that location is extremely low, and the risk levels corresponding to other H and U intervals are deduced accordingly.

[0052] In the hazard assessment rules of this invention T 0 is T 2~ T One of the four options, the specific value of which can be determined based on the corresponding intervals of the comprehensive data within the borehole. U 1~ U 7. Comprehensive data of equipment in each range H 1~ H The range and relative magnitude of 7 are selected and adjusted based on human experience. For example, this can be achieved through calculation. U 1~ U 7. H 1~ H 7. The average value of the data within each interval is used to determine the risk level of each interval based on the relative magnitude of the average values ​​between intervals.

[0053] The drilling system includes a drilling rig, a detection system, and a related control system. The control system controls the drilling rig to perform drilling operations and controls the detection system to collect environmental parameters inside and around the borehole. The drilling rig, detection system, and related control system are all mature products to those skilled in the art, such as drilling rigs and related detection systems used in coal mines. The drilling system of this invention is based on existing drilling systems, and its control system integrates the rock strata hazard assessment database and rock strata hazard assessment module of this invention. The rock strata hazard assessment module assesses the lithological hazard during the drilling process using the assessment method of this invention based on the rock strata hazard assessment database.

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Example:

[0056] In this embodiment, the known lithologies in the rock strata hazard assessment database are typical weak interlayers, soft coal seams, hard coal seams, mudstone layers, and sandy mudstone layers;

[0057] The comprehensive dataset of the weak interlayer contains 2000 data points (i.e., data collected from 2000 different locations), with a variation range of [0.92~3.55); the comprehensive dataset of the equipment contains 2000 data points, with a variation range of [0.15~0.35].

[0058] The comprehensive dataset of borehole data in soft coal seams contains 2000 data points, with a variation range of [3.55~9.36); the comprehensive dataset of equipment data contains 2000 data points, with a variation range of 0.35~0.53.

[0059] The comprehensive dataset of borehole data in hard coal seams contains 2000 data points, with a variation range of [9.36–15.47); the comprehensive dataset of equipment data contains 2000 data points, with a variation range of [0.53–0.78].

[0060] The borehole comprehensive dataset for the mudstone layer contains 2000 data points, with a variation range of [15.47–21.25]; the equipment comprehensive dataset contains 2000 data points, with a variation range of [0.78–1.06].

[0061] The dataset contains 2000 data points on the in-pore mechanical properties of the sandy mudstone layer, ranging from 21.25 to 25.38. The dataset also contains 2000 data points on the equipment, ranging from 1.06 to 1.55.

[0062] Critical values ​​of comprehensive data within the borehole of weak interlayers , , The critical values ​​for the comprehensive equipment data are 3.55, 2.15, and 0.92, respectively. , , The critical values ​​for in-hole composite data in soft coal seams are 0.35, 0.24, and 0.15, respectively. , , The critical values ​​for the comprehensive equipment data are 9.36, 6.23, and 3.55, respectively. , , The critical values ​​for the comprehensive borehole data of hard coal seams are 0.53, 0.46, and 0.35, respectively. , , The critical values ​​for the comprehensive equipment data are 15.47, 11.46, and 9.36, respectively. , , The critical values ​​for the comprehensive borehole data of the mudstone layer are 0.78, 0.65, and 0.53, respectively. , , The critical values ​​for the comprehensive equipment data are 21.25, 18.63, and 15.47, respectively. , , The critical values ​​for the comprehensive borehole data of the sandy mudstone layer are 1.06, 0.93, and 0.78, respectively. , , The critical values ​​for the comprehensive equipment data are 25.38, 23.68, and 21.25, respectively. , , The values ​​are 1.55, 1.37, and 1.06 respectively; the first threshold is 0.7, the second threshold is 0.9, and the third threshold is 0.9.

[0063] Taking the mudstone layer as an example, U 1~ U The seven intervals are: [15.47, 16.32), [16.32, 17.13), [17.13, 17.98), [17.98, 18.82), [18.82, 19.61), [19.61, 20.45), [20.45, 21.25]. H 1~ H The seven intervals are: [0.78, 0.82), [0.82, 0.85), [0.85, 0.90), [0.90, 0.94), [0.94, 0.99), [0.99, 1.02), [1.02, 1.06]. The hazard assessment rules for known mudstone strata are shown in Table 1 below.

[0064] Table 1 Fuzzy Judgment Rules

[0065]

[0066] Table 1 gives the risk levels for the corresponding H and U intervals. For example, for any location... i， Based on the hazard assessment rules of the lithology to which this location belongs ， When the borehole integrated data and equipment integrated data at this location are respectively in the interval U 1 and interval HIf the value is 1, then the hazard level of that location is extremely low. Similarly, by judging the relevant datasets according to the fuzzy judgment rules in Table 1, the hazard distribution status of a single borehole can be determined, and a numerical analytical model of the hazard distribution of a single borehole can be established.

[0067] In the present invention; , , , Weighting coefficients , , , The values ​​are all in the range of 0 to 1, and + + + =1, the value is determined based on the site conditions of the deformed roadway, in this embodiment , , , ;

[0068] In this invention , , , , These are the weighting coefficients, , , , , The values ​​are all in the range of 0 to 1, and + + + + =1, a value determined based on the site conditions of the deformed roadway, in this embodiment. , , , , ;

[0069] , The learning factors are all real numbers between 0 and 1. (This is from an example.) =1、 ;

[0070] a , b The adjustment coefficients typically take values ​​between 0 and 3, and the specific values ​​can be chosen based on the actual conditions of the deformed roadway. In this embodiment... =0.5、 ;

[0071] , , These are the weighting coefficients, each a real number between 0 and 1. + + =1; the specific value can be determined based on the on-site conditions of the deformed roadway. In this embodiment... =0.4、 ;

[0072] The rock strata hazard assessment database of this embodiment is used to assess the hazard of rock strata in a coal mine roadway. By drilling at different locations in the coal mine roadway, the mechanical parameters of the drill rod, the performance parameters of the rock strata, and the measurement data of the drilling equipment at different locations in each hole are collected. The method A of this invention is used to construct a comprehensive dataset of the rock strata in the hole and a comprehensive dataset of the equipment.

[0073] The aforementioned database is used to identify the lithology at each location and assess the corresponding hazard. Kriging interpolation is then used to assess the lithology and hazard of borehole locations at the same cross-section of the deformed roadway, ensuring that the average relative error is less than or equal to the required error (selected as 0.01 in this embodiment). Numerical analytical models of strata distribution and hazard distribution at multiple cross-sections within the roadway are established. The lithology distribution map of one section of strata is shown below. Figure 1 As shown, Figure 1 The hazard assessment results of a section in the middle of the identified mudstone layer are as follows: Figure 2 As shown, Figure 1 In the diagram, the blue surface represents the boundary of the roadway roof, the yellow surface represents the interface between the hard coal seam and the mudstone layer, and the three-dimensional area formed by the yellow and blue surfaces is the identification area for the mudstone layer. Figure 2 In the diagram, the blue surface represents the boundary between extremely low risk and low risk, the yellow area represents the boundary between low risk and medium risk, and the three-dimensional area formed by the yellow and blue surfaces represents the low-risk identification zone.

[0074] Subsequently, the risk of the entire tunnel was assessed using the identification and judgment results of multiple cross-sections.

[0075] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0076] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0077] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A method for constructing a rock stratum hazard assessment database, characterized in that, The rock strata hazard assessment database includes hazard assessment data for various known lithologies. Hazard assessment data for any known lithology includes triangular membership functions of borehole integrated data, triangular membership functions of equipment integrated data, and hazard judgment rules. The method includes constructing triangular membership functions for borehole integrated data, triangular membership functions for equipment integrated data, and hazard judgment rules for various known lithologies, and obtaining critical values ​​for borehole integrated data and equipment integrated data for each known lithology stratum; Methods for constructing triangular membership functions for borehole composite data and equipment composite data of any known lithology include: Method A is used to construct the borehole integrated dataset and the equipment integrated dataset for the known lithology. Then, clustering methods are used to obtain the triangular membership functions of the borehole integrated dataset and the equipment integrated dataset for the known lithology, respectively, and the critical values ​​of the borehole integrated data and the equipment integrated data for the known lithology are obtained. Method A includes: (1) Drilling and collecting drill rod mechanical parameters, rock stratum performance parameters, and drilling rig equipment measurement data at different drilling positions; the drill rod mechanical parameters include drilling force, tangential force between drill rod and rock stratum, frictional resistance between drill rod and rock stratum, and drill rod torque; the rock stratum performance parameters include rock compressive strength, rock shear strength, rock color, rock grayness, and rock porosity; the drilling rig equipment measurement data are the operating parameters of the drilling rig itself; (2) The mechanical parameters of the drill pipe collected at each location are standardized so that the standardized data conforms to the standard normal distribution and each dataset is dimensionless. The data at each location are weighted and calculated to obtain the mechanical performance data of the drill pipe at each location. The mechanical performance data of the drill pipe at all locations constitute the mechanical performance dataset F. F i ∈F, F i =u1·A i +u2·B i +u3·C i +u4·D i Where i is any position inside the borehole, i = 1, 2, ..., n; F i Here are the mechanical performance data of the drill pipe at position i; u1, u2, u3, and u4 are weighting coefficients, with values ​​ranging from 0 to 1, and u1 + u2 + u3 + u4 = 1; A i B represents the standardized data of the drilling force when the drill pipe is in operation at position i; i C represents the standardized data of the tangential force between the drill pipe and the rock strata at position i; i D represents the standardized data of the frictional resistance between the drill pipe and the rock formation at position i; i This represents the standardized data of the drill pipe torque at position i during operation. The rock strata performance parameters collected at each location were standardized to ensure that the standardized data conformed to a standard normal distribution. Each dataset was dimensionless. The data at each location were weighted and calculated to obtain the rock strata performance data at each location. The rock strata performance data at all locations constituted the rock strata performance dataset M. M i ∈M, M i =u5·E i +u6·F i +u7·G i +u8·H i +u9·J i , of which M i Let be the rock strata performance dataset at position i; u5, u6, u7, u8, and u9 are weighting coefficients, with values ​​ranging from 0 to 1, and u5 + u6 + u7 + u8 + u9 = 1; E i F represents the standardized data of rock compressive strength at position i; i C represents the standardized data for the shear strength of the rock at position i; i Here are the standardized data for rock color intensity at position i; H i J represents the standardized data of rock grayscale at location i; i Here are the standardized data for rock porosity at position i; After standardizing the equipment measurement data, principal component analysis was used to reduce the data dimensionality, resulting in the first principal dataset X, the second principal dataset Y, and the third principal dataset Z. (3) Construct the in-hole comprehensive dataset Q1, Q 1i ∈Q1, Among them, Q 1i Here is the comprehensive data inside the hole at position i, c1 and c2 are learning factors, both of which are real numbers between 0 and 1, and a and b are adjustment coefficients, both of which are real numbers between 0 and 3. Construct a comprehensive dataset of devices Q2, Q 2i ∈Q2, Q 2i =e1·X i +e2·Y i +e3·Z i , where Q 2i Let X be the comprehensive data of the device at position i, where e1, e2, and e3 are weighting coefficients, all real numbers between 0 and 1, and e1 + e2 + e3 = 1; i ∈X, X i Y is the first master data at position i. i ∈Y, Y i Z is the second master data at position i. i ∈Z, Z i This is the third master data at position i; Methods for constructing hazard assessment rules for any known lithology include: Sort the data in the borehole composite dataset for any known lithology from smallest to largest, and then divide the sorted dataset into 7 intervals U1 to U7, as follows: [x1, x... a+1 ), [x a+1 x 2a+1 ), [x 2a+1 x 3a+1 ), [x 3a+1 x 4a+1 ), [x 4a+1 x 5a+1 ), [x 5a+1 x 6a+1 ), [x 6a+1 x n The number of data points in each interval is α, or 1 to 6 more or 1 to 6 less than α, where α is the integer quotient of the total number of data points in the comprehensive dataset within the hole divided by 7; x1 and x n These are the minimum and maximum values ​​in the in-hole synthesis dataset, x and x, respectively. a+1 x 2a+1 x 3a+1 x 4a+1 x 5a+1 x 6a+1 These are the endpoints of each interval, and x1 < x2. a+1 <x 2a+1 <x 3a+1 <x 4a+1 <x 5a+1 <x 6a+1 <x n ; Sort the data in the comprehensive dataset of any device from smallest to largest, and then divide the sorted dataset into 7 intervals H1 to H7, as follows: [y1, y2, y3, y4, y5, y6, y7, y8, y9, y1, y1, y9 ... b+1 ), [y b+1 y 2b+1 ), [y 2b+1 y 3b+1 ), [y 3b+1 y 4b+1 ), [y 4b+1 y 5b+1 ), [y 5b+1 y 6b+1 ), [y 6b+1 y n The number of data points in each interval is β, which is either 1 to 6 more or 1 to 6 less than β, where β is the integer quotient of the total number of data points in the device's comprehensive dataset divided by 7; y1 and y n These are the minimum and maximum values ​​in the device's comprehensive dataset, y. b+1 y 2b+1 y 3b+1 y 4b+1 y 5b+1 y 6b+1 These are the endpoints of each interval, and y1 < y2. b+1 <y 2b+1 <y 3b+1 <y 4b+1 <y 5b+1 <y 6b+1 <y n ; Construct a hazard assessment rule for this known lithology: T1 to T5 represent different levels of rock strata hazard, namely, extremely low hazard, low hazard, medium hazard, high hazard, and extremely high hazard; among them, T0 in the judgment rule is one of T2 to T4.

2. A method for assessing the hazard of rock strata, characterized in that, The method utilizes the database constructed by the method of claim 1 to conduct a hazard assessment of the rock strata to be evaluated, including: Step 1: Construct a comprehensive in-hole dataset and a comprehensive equipment dataset for the rock strata to be evaluated using method A as described in claim 1; Step 2, using the database constructed by the method described in claim 1 to evaluate the lithology of the rock strata to be evaluated, includes: For any location i of the rock stratum to be evaluated, given the borehole composite data (i = 1, 2, ..., n), calculate the membership degree of the rock stratum at location i to various known lithologies. This includes calculating the membership degree of the rock stratum at location i to any known lithology based on the critical value relationship between the borehole composite data of the rock stratum at location i and the borehole composite data of any known lithology, using the triangular membership function of the borehole composite dataset of that known lithology, and selecting the maximum membership degree μ after calculating the membership degree of the rock stratum at location i to various known lithologies. 1i，max ; For any location i of the rock stratum to be evaluated using the integrated equipment data, i = 1, 2, ..., n; calculate the membership degree of the rock stratum at location i to various known lithologies. This includes calculating the membership degree of the rock stratum at location i to various known lithologies using the triangular membership function of the integrated equipment data set of the known lithology, based on the relationship between the integrated equipment data of the rock stratum at location i and the integrated equipment data of any known lithology. After calculating the membership degree of the rock stratum at location i to various known lithologies, select the maximum membership degree μ. 2i，max ; If μ 1i，max and μ 2i，max If the lithology belongs to the same type and all values ​​are greater than the first threshold, then the lithology at position i is μ. 1i，max and μ 2i，max For the lithology to which it belongs, the first threshold value ranges from [0.7, 0.8]; otherwise: If μ 1i，max When the value is greater than the second threshold, the lithology at position i is μ. 1i，max The lithology to which it belongs, and the range of the second threshold is [0.9, 0.95]; If μ 2i，max When the value is greater than the third threshold, the lithology at position i is μ. 2i，max The lithology to which it belongs, the value range of the third threshold is [0.9, 0.95]; Step 3: Using the database constructed by the method described in claim 1, assess the risk of different borehole locations in the rock strata to be assessed, including: for any location i, judging the risk of the rock strata at any location i based on the comprehensive borehole data and comprehensive equipment data at that location and the risk judgment rules of the lithology to which that location belongs; Step 4: The hazard assessment results of the rock stratum section are obtained by considering the hazards at multiple locations on the same cross section.

3. A method for assessing the risk of deformed roadways in coal mines, characterized in that, The method described in claim 2 is used to assess the hazards of multiple different sections of the roadway. Then, the hazards of the entire roadway are assessed using the hazard assessment results of the multiple sections.

4. A drilling system, comprising a drilling rig, a detection system, and a control system, wherein the control system controls the drilling rig to perform drilling operations and simultaneously controls the detection system to detect the environment inside the borehole, characterized in that... The control system includes the rock stratum hazard assessment database and the rock stratum hazard assessment module as described in claim 1. The rock stratum hazard assessment module assesses the lithological hazard during the drilling process using the method described in claim 2 based on the rock stratum hazard assessment database.

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