Method for grading geological conditions of digging and anchoring all-in-one machine

Abstract

The invention belongs to the technical field of coal mine tunneling, and aims to solve the problems of stray conventional quick tunneling judgment, poor field applicability and fixed weight distribution of a tunneling and anchoring all-in-one machine. The invention provides a method for grading geological conditions of a digging and anchoring all-in-one machine. The method comprises the following steps: acquiring n0 indexes influencing rapid digging of the digging and anchoring all-in-one machine; constructing an index framework which comprises a target layer A, a category layer B and an index layer C; forming an index layer C based on the n0 indexes, forming a category layer by n1 categories obtained by classifying the n0 indexes, and integrating the n1 categories into one target to form a target layer A; based on the fuzzy evaluation comprehensive vector corresponding to the target layer A, determining the geological condition grade of the digging and anchoring all-in-one machine of the coal mine, and after preliminary judgment, further analyzing influence factors according to the fuzzy evaluation comprehensive vectors corresponding to n1 categories in the category layer B. The method is used for quantitative analysis of geological influence factors in the early stage of rapid tunneling decision making of the tunneling and anchoring all-in-one machine, and rapid tunneling geological conditions are reasonably divided by adjusting the weights of the influence factors twice.

Description

Technical Field

[0001] The invention belongs to the technical field of coal mine excavation and provides a method for grading geological conditions of an integrated excavator and anchor machine. Background Art

[0002] Most of the underground coal mines in my country adopt mechanized excavation methods, and there are three main excavation processes, and their main equipment are cantilever excavators, continuous coal miners, and integrated excavators and anchorers. Compared with the traditional comprehensive excavation process and continuous coal miner excavation process, the fast excavation process of the integrated excavator and anchorer realizes the full-width cutting of the horizontal drum and the parallel operation of excavation and anchoring, which is the fastest-growing new equipment and new process in recent years.

[0003] At present, there are nearly 200 coal tunnel excavation working faces in China that use integrated digging and anchoring machines. Due to the wide and heavy body of the integrated digging and anchoring machine, the long system, large empty top distance and large empty side distance, and the complex and changeable geological conditions of my country's coal seams, the integrated digging and anchoring machine and process are more sensitive in terms of applicability, and the flexibility of adjustment is poor, which can easily lead to problems of inapplicability. Once the use fails, it will cause significant losses in funds, time, human resources, etc. At present, its applicability is mostly qualitatively judged through engineering analogy, on-site investigation and inspection, especially when the geological conditions are complex or in critical conditions, there are major problems with accuracy, and it is easy to miss implicit key factors.

[0004] In view of the above problems, the present invention discloses a method for classifying geological conditions of an integrated digging and anchoring machine. Starting from the actual engineering experience of the integrated digging and anchoring unit, the influencing factors are collected and sorted, classified and summarized, and the advantages of mathematics in quantitative analysis in the engineering field are fully utilized. It is one of the more practical technical methods at present, and has important guiding significance for the application of the integrated digging and anchoring unit and the decision-making of coal mining enterprises. Summary of the invention

[0005] In order to solve at least one of the above technical problems existing in the prior art, the present invention provides a method for classifying geological conditions of an integrated drilling and anchoring machine.

[0006] The present invention is implemented by the following technical solution: A method for grading geological conditions of an integrated digging and anchoring machine comprises the following steps:

[0007] S1: Obtain n0 indicators that affect the rapid excavation of the integrated excavator and anchor machine;

[0008] S2: Construct an indicator framework, including target layer A, category layer B and indicator layer C; the indicator layer C is composed of the n0 indicators mentioned above, the n1 categories obtained after the n0 indicators are classified constitute the category layer, and the n1 categories are integrated into 1 target to constitute the target layer A;

[0009] S3: Divide each indicator into m0 evaluation levels vertically, and quantify the values ​​of each indicator in different evaluation levels;

[0010] S4: Determine the type of membership function, bring the quantitative value of each indicator into the membership function, obtain the membership of each evaluation level, and establish a judgment matrix for each indicator;

[0011] S5: Establish a judgment matrix of the indicator framework and obtain the initial weight vectors corresponding to n1 categories in the target layer A and the category layer B in the indicator framework system;

[0012] S6: Based on the membership function and the initial weight vectors corresponding to the n1 categories in the category layer B, determine the fuzzy relationship matrix and variable weight vector corresponding to the n1 categories in the category layer B, and then determine the fuzzy evaluation comprehensive vector corresponding to the n1 categories;

[0013] S7: Based on the membership function, the initial weight vector corresponding to the target layer A and the fuzzy relationship matrix corresponding to the n1 categories in the category layer B, determine the fuzzy relationship matrix and the variable weight vector corresponding to the target layer A, and then determine the fuzzy evaluation comprehensive vector corresponding to the target layer A;

[0014] S8: Determine the geological condition grade of the coal mine's anchoring and digging machine based on the fuzzy evaluation comprehensive vector corresponding to the target layer A. After preliminary judgment, further analyze the influencing factors based on the fuzzy evaluation comprehensive vector corresponding to the n1 categories in the category layer B.

[0015] Preferably, the step of obtaining the initial weight vector includes:

[0016] S501: Obtain the judgment matrix corresponding to n1 categories in category layer B and target layer A:

[0017] For n1 categories in the category layer B, the indicators in the indicator layer C belonging to the corresponding category are arranged in the first row and the first column thereafter to construct an initial square matrix, and each value in the matrix is ​​assigned a value by the importance scale to form a judgment matrix corresponding to the category;

[0018] For target layer A, the n1 categories belonging to target layer A are arranged in the first row and the first column to construct an initial square matrix, and each value in the matrix is ​​assigned a value by the importance scale to form a judgment matrix corresponding to target layer A;

[0019] S502: Multiply the elements of each row of the judgment matrix and calculate the product, and raise the calculation result to the i-th power, where i is the number of elements in each row; and then perform normalization processing to obtain a feature vector, which is the corresponding initial weight vector.

[0020] Preferably, the step of determining the fuzzy evaluation comprehensive vectors corresponding to the n1 categories includes:

[0021] S601: Determine the state influence of a single category;

[0022] S602: multiplying the initial weight vector of the category by the state influence amount;

[0023] S603: normalizing the result of S602 to obtain a variable weight vector of the category;

[0024] S604: constructing a fuzzy relationship matrix of the category based on the membership function;

[0025] S605: Multiply the transposed point of the variable weight vector by the fuzzy relationship matrix to obtain the fuzzy evaluation comprehensive vector of the category.

[0026] Preferably, the formula for determining the state influence amount is:

[0027]

[0028] Where b j is the adjustment level, that is, the threshold value at which the status value of a factor belongs to the lowest comment level; x j The membership degree of the lowest evaluation level is greater than the threshold value; S j is the state influence quantity; μ is the state value of a certain factor.

[0029] Preferably, the step of determining the fuzzy evaluation comprehensive vector corresponding to the target layer A includes:

[0030] S701: Determine the state influence amount of the target layer A; substitute the state value of each factor according to the state influence amount formula;

[0031] S702: multiplying the initial weight vector of the target layer A by the state influence amount;

[0032] S703: normalizing the result of S602 to obtain a variable weight vector of the target layer A;

[0033] S704: constructing a fuzzy relationship matrix of the target layer A based on the fuzzy evaluation comprehensive vectors corresponding to the n1 categories;

[0034] S705: Multiply the transposed point of the variable weight vector by the fuzzy relationship matrix to obtain the fuzzy evaluation comprehensive vector of the target layer A.

[0035] Preferably, the n0 indicators that affect the rapid excavation of the integrated excavator and anchor machine include burial depth C1, thickness C2, inclination angle C3, coal rock fissure development degree C4, coal seam interlayer coefficient C5, direct roof compressive strength C6, coal seam compressive strength C7, bottom plate softening coefficient C8, water-rich coefficient C9, cross-sectional area C10, tunnel slope C11, number of bottom plate fluctuations per kilometer C12, tunnel length C13, monthly advance C14, empty top distance C15, number of top anchor rod delayed support C16, empty side distance C17, number of side anchor rod delayed support C18, anchor rod spacing C19, anchor cable spacing C20, fold complexity C21, collapse column influence C22, fault influence C23, impact tendency index C24, gas outburst volume C25, and roof fall coefficient C26.

[0036] Preferably, the n1 categories in the category layer B include coal seam occurrence characteristics B1, tunnel parameters B2, and structure and safety B3;

[0037] Among them, the indicators belonging to the coal seam occurrence characteristics B1 in the index layer C are: C1-C9; the indicators belonging to the tunnel parameters B2 are:

[0038] C10-C20; indicators belonging to construction and safety B3 are: C21-C26;

[0039] One target in target layer A is: geological condition classification of integrated mining and anchoring machine; coal seam occurrence characteristics B1, tunnel parameters B2 and structure and safety B3 belong to target layer A.

[0040] Preferably, the m0 evaluation levels include but are not limited to not applicable, poor applicability, applicable, and good applicability; and the membership function types include but are not limited to large-sized, small-sized, and intermediate.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The purpose of the present invention is to provide a method for grading geological conditions of a digging and anchoring machine. The method, based on the influence of coal mine geological conditions on fast digging equipment and equipment technical parameters, layers and classifies geological elements, analyzes the geological conditions of the digging and anchoring machine, and adjusts the weight distribution to be in line with reality if there are geological factors that seriously affect the use of fast digging. The invention preliminarily solves the problem that the applicability of the fast digging of the digging and anchoring machine depends on human factors such as experience judgment and on-site surveys, the qualitative analysis is inaccurate, and the analysis results of conventional analysis may deviate from actual application and cannot effectively guide the site. It overcomes the technical difficulties of conventional judgment sparseness, poor on-site applicability, and fixed weight distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is the geological factor stratification and classification diagram of the present invention;

[0045] Figure 2 It is a flow chart of variable weight comprehensive evaluation of the present invention. DETAILED DESCRIPTION

[0046] In conjunction with the drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0047] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so that people familiar with this technology can understand and read them. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention. It should be noted that in this specification, relational terms such as first and second are only used to distinguish one entity from several other entities, and do not necessarily require or imply any actual relationship or order between these entities.

[0048] The present invention provides an embodiment:

[0049] like Figure 1 , Figure 2 As shown, a method for classifying geological conditions of a drilling and anchoring machine comprises the following steps:

[0050] S1: Obtain n0 indicators that affect the rapid excavation of the integrated excavator and anchor machine;

[0051] S2: Construct an indicator framework, including target layer A, category layer B and indicator layer C; the indicator layer C is composed of the n0 indicators mentioned above, the n1 categories obtained after the n0 indicators are classified constitute the category layer, and the n1 categories are integrated into 1 target to constitute the target layer A;

[0052] S3: Divide each indicator into m0 evaluation levels vertically, and quantify the values ​​of each indicator in different evaluation levels;

[0053] S4: Determine the type of membership function, bring the quantitative value of each indicator into the membership function, obtain the membership of each evaluation level, and establish a judgment matrix for each indicator;

[0054] S5: Establish a judgment matrix of the indicator framework and obtain the initial weight vectors corresponding to n1 categories in the target layer A and the category layer B in the indicator framework system;

[0055] S6: Based on the membership function and the initial weight vectors corresponding to the n1 categories in the category layer B, determine the fuzzy relationship matrix and variable weight vector corresponding to the n1 categories in the category layer B, and then determine the fuzzy evaluation comprehensive vector corresponding to the n1 categories;

[0056] S7: Based on the membership function, the initial weight vector corresponding to the target layer A and the fuzzy relationship matrix corresponding to the n1 categories in the category layer B, determine the fuzzy relationship matrix and the variable weight vector corresponding to the target layer A, and then determine the fuzzy evaluation comprehensive vector corresponding to the target layer A;

[0057] S8: Determine the geological condition grade of the coal mine's anchoring and digging machine based on the fuzzy evaluation comprehensive vector corresponding to the target layer A. After preliminary judgment, further analyze the influencing factors based on the fuzzy evaluation comprehensive vector corresponding to the n1 categories in the category layer B.

[0058] The specific steps of the present invention are as follows:

[0059] 1. Constructing the index system and determining the initial weights:

[0060] 1. Collect factors that affect fast excavation: By visiting coal mine technicians, surveying the underground geological conditions of major mining areas, realistically recording the industrial tests of complete sets of equipment, summarizing and analyzing the successful or failed application cases of the complete set of excavator and anchor equipment, and finding and discussing the characteristics of related equipment and processes, the various influencing factors are listed as indicators on this basis. Currently, 26 indicators are included, which can be changed or increased in the future according to geological conditions and equipment development.

[0061] The n0 indicators that affect the rapid excavation of the integrated excavator and anchor machine include burial depth C1, thickness C2, inclination angle C3, coal rock fissure development degree C4, coal seam interlayer coefficient C5, direct roof compressive strength C6, coal seam compressive strength C7, bottom plate softening coefficient C8, water-rich coefficient C9, cross-sectional area C10, tunnel slope C11, number of bottom plate fluctuations per kilometer C12, tunnel length C13, monthly advance C14, empty top distance C15, number of top anchor rod delayed support C16, empty side distance C17, number of side anchor rod delayed support C18, anchor rod spacing C19, anchor cable spacing C20, fold complexity C21, collapse column influence C22, fault influence C23, impact tendency index C24, gas outburst volume C25, and roof fall coefficient C26.

[0062] 2. Construct the indicator framework: The indicator layer C is composed of 26 indicators, and the category layer B is constructed after the indicators are classified and sorted, and finally the target layer A of the indicator system is formed, such as Figure 1 shown.

[0063] The n1 categories in the category layer B include coal seam occurrence characteristics B1, tunnel parameters B2 and structure and safety B3; among them, the indicators belonging to the coal seam occurrence characteristics B1 in the indicator layer C are: C1-C9; the indicators belonging to the tunnel parameters B2 are: C10-C20; the indicators belonging to the structure and safety B3 are: C21-C26;

[0064] One target in target layer A is: geological condition classification of integrated mining and anchoring machine; coal seam occurrence characteristics B1, tunnel parameters B2 and structure and safety B3 belong to target layer A.

[0065] 3. Comment set classification: Each indicator is divided into 4 levels vertically, namely, not applicable, poor applicability, applicable, and good applicability.

[0066] 4. Quantification of evaluation indicators: Quantify the values ​​of each indicator of indicator layer C in different evaluation levels. For example, {not applicable, poor applicability, applicable, good applicability} of coal seam burial depth correspond to {≥800, 600~800, 300~600, ≤300} respectively. For indicators that cannot be directly quantified, it is necessary to refer to relevant formulas or reference standards for calculation, such as the floor softening coefficient, which is calculated by the ratio of the uniaxial compressive strength of the water-saturated specimen of the floor rock to the uniaxial compressive strength of the dry specimen. The four levels correspond to {<0.1, 0.1~0.5, 0.5~0.7, >0.7} respectively.

[0067] 5. Establish membership function: Determine the reasonable membership function A(x) type based on the large type (the larger the value, the better the evaluation effect), the small type (the smaller the value, the better the evaluation effect), and the intermediate type (the value is in the middle, the evaluation effect is good), substitute the determined indicator value into A(x) to obtain the membership degree of each evaluation level, and establish the indicator judgment matrix R = {r ij} m×n , (i=1,2,3,…,m;j=1,2,3,…,n). To avoid the situation where the index membership is 0, a sufficiently small amount σ (σ=0.0001) is taken to replace 0, and the increased amount is deducted from other non-zero memberships accordingly to ensure that the sum of the memberships of each index is 1.

[0068] 6. Determine the initial weights of the constants.

[0069] Will Figure 1 The indicator system is constructed into a judgment matrix. Taking the B2 layer as an example: the 11 C-layer indicators belonging to it are arranged in the first row and the first column to construct the initial square matrix. Each value in the matrix is ​​assigned by the importance scale, that is, two elements are compared two by two, which one is more important and how much more important it is. The assignment range is 1 to 9, forming an 11×11 judgment matrix B. 2-C According to the above method, three more matrices are constructed, namely B 1-C ,B 3-C ,A -B Then, the n elements of each row of the square judgment matrix are multiplied and added, and then the product is raised to the nth power; after normalization, the feature vector is obtained, which is the initial weight vector, which is A A-B ,A B1-C ,A B2-C ,A B3-C .

[0070] 2. Comprehensive analysis of variable weights at B1-C levels:

[0071] 1. Determine the state influence quantity S according to the membership variable weight function B1-C As shown in the formula, where b j is the adjustment level, that is, the threshold value of the membership of a factor's status value to the lowest comment level is adjusted. When the membership of the factor's status value is greater than b j Adjust when b j =0.3, that is, when a certain indicator belongs to the lowest comment level "not applicable" level and the degree of belonging is greater than 0.3, the status is already very bad and should be adjusted. j The lowest comment level has a membership greater than 0.3. j The corresponding values ​​can be adjusted according to the actual situation of each factor. The formula and threshold can be adjusted according to the actual situation. Here is an example.

[0072] The formula for determining the state influence is:

[0073]

[0074] Where b j is the adjustment level, that is, the threshold value at which the status value of a factor belongs to the lowest comment level; x j The membership degree of the lowest evaluation level is greater than the threshold value; S j is the state influence quantity; μ is the state value of a certain factor.

[0075] 2. The initial weight vector A B1-C The state influence based on the membership degree is S B1-C Quantity multiplication.

[0076] 3. Normalize the results obtained in the previous step to obtain the variable weight vector W B1-C .

[0077] 4. According to the membership function, construct the fuzzy relationship matrix R B1-C .

[0078] 5. Determine the final fuzzy evaluation comprehensive vector Y B1-C , that is, its variable weight vector W B1-C The transposed point product of the fuzzy relation matrix R B1-C .

[0079] 3. Comprehensive analysis of B2-C layer and B3-C variable weights:

[0080] Similarly, repeat the above steps to obtain the fuzzy comprehensive evaluation vectors of tunnel parameters B2, structure and safety B3, which are Y B2-C With Y B3-C .

[0081] 4. AB layer variable weight comprehensive evaluation and analysis results:

[0082] 1. Determine the state influence S A-B . According to the state influence formula, substitute the state value of each factor.

[0083] 2. The initial weight vector A A-B and the state influence vector S obtained based on the membership degree A-B Multiply the product.

[0084] 3. Normalize the results obtained in the previous step to obtain the variable weight vector W A-B .

[0085] 4. According to Y B1-C ,Y B2-C ,Y B3-C Construct fuzzy relationship matrix RA-B .

[0086] 5. Determine the final fuzzy evaluation comprehensive vector Y A-B , that is, its variable weight vector W A-B The transposed point product of the fuzzy relation matrix R A-B .

[0087] 6. Comprehensive evaluation vector Y A-B The four values ​​correspond to not applicable, poor applicability, applicable, and good applicability. According to the principle of maximum membership, the geological condition level of the coal mine is determined. After preliminary judgment, the comprehensive evaluation vector Y of category layer B can be used. B1-C ,Y B2-C ,Y B3-C Further analyze the main influencing factors and put forward targeted improvement suggestions.

[0088] The present invention can realize dynamic adjustment of variable weights, highlight the impact of individual extreme values ​​on the whole, and enhance applicability:

[0089] The key to this method is that it can reflect the impact of a certain indicator on the whole through dynamic adjustment of variable weights, thus achieving a more reasonable analysis. For example, when the coal seam inclination is 20°, it belongs to the lowest comment level "not applicable", but if it is still based on the original weight of 0.07, it is obviously contrary to the fact that the fast excavation system is no longer applicable. The analysis results may ignore the impact of individual extreme values ​​on the whole. At this time, it is necessary to adjust on the basis of the original weight to highlight the impact of extreme data and redistribute the remaining weights.

[0090] The present invention can provide a complete evaluation index system for the applicability of the integrated digging and anchoring unit:

[0091] Through a large number of successful and failed cases of rapid excavation of integrated drilling and anchoring machines, 26 factors related to their applicability were statistically analyzed horizontally and classified, and quantitative indicators corresponding to 4 levels for each factor were determined vertically, providing complete guidance for coal mining enterprises' advance decision to introduce integrated drilling and anchoring units to prevent the occurrence of missing items and omissions.

[0092] The present invention can achieve scientific quantitative analysis and prevent unnecessary losses:

[0093] This method is based on the characteristics of the integrated excavation and anchoring unit and a large amount of on-site front-line experience, and takes into account factors closely related to the fast excavation equipment set. The analysis process is rigorous and close to the site, realizing quantitative assessment and analysis. Through relevant feasibility analysis, unnecessary losses caused by management decision-making errors can be prevented.

[0094] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for classifying geological conditions of a drilling and anchoring machine, characterized in that: The following steps are involved: S1: Obtain n0 indicators that affect the rapid excavation of the integrated excavator and anchor machine; S2: Construct an indicator framework, including target layer A, category layer B and indicator layer C; the indicator layer C is composed of the n0 indicators mentioned above, the n1 categories obtained after the n0 indicators are classified constitute the category layer, and the n1 categories are integrated into 1 target to constitute the target layer A; S3: Divide each indicator into m0 evaluation levels vertically, and quantify the values ​​of each indicator in different evaluation levels; S4: Determine the type of membership function, bring the quantitative value of each indicator into the membership function, obtain the membership of each evaluation level, and establish a judgment matrix for each indicator; S5: Establish a judgment matrix of the indicator framework and obtain the initial weight vectors corresponding to n1 categories in the target layer A and the category layer B in the indicator framework system; S6: Based on the membership function and the initial weight vectors corresponding to the n1 categories in the category layer B, determine the fuzzy relationship matrix and variable weight vector corresponding to the n1 categories in the category layer B, and then determine the fuzzy evaluation comprehensive vector corresponding to the n1 categories; S7: Based on the membership function, the initial weight vector corresponding to the target layer A and the fuzzy relationship matrix corresponding to the n1 categories in the category layer B, determine the fuzzy relationship matrix and the variable weight vector corresponding to the target layer A, and then determine the fuzzy evaluation comprehensive vector corresponding to the target layer A; S8: Determine the geological condition grade of the coal mine's anchoring and digging machine based on the fuzzy evaluation comprehensive vector corresponding to the target layer A. After preliminary judgment, further analyze the influencing factors based on the fuzzy evaluation comprehensive vector corresponding to the n1 categories in the category layer B.

2. The method for classifying geological conditions of a drilling and anchoring machine according to claim 1, characterized in that: The steps to obtain the initial weight vector include: S501: Obtain the judgment matrix corresponding to n1 categories in category layer B and target layer A: For n1 categories in the category layer B, the indicators in the indicator layer C belonging to the corresponding category are arranged in the first row and the first column thereafter to construct an initial square matrix, and each value in the matrix is ​​assigned a value by the importance scale to form a judgment matrix corresponding to the category; For target layer A, the n1 categories belonging to target layer A are arranged in the first row and the first column to construct an initial square matrix, and each value in the matrix is ​​assigned a value by the importance scale to form a judgment matrix corresponding to target layer A; S502: Multiply the elements of each row of the judgment matrix and calculate the product, and raise the calculation result to the i-th power, where i is the number of elements in each row; and then perform normalization processing to obtain a feature vector, which is the corresponding initial weight vector.

3. The method for classifying geological conditions of a drilling and anchoring machine according to claim 2, characterized in that: The steps of determining the fuzzy evaluation comprehensive vectors corresponding to the n1 categories include: S601: Determine the state influence of a single category; S602: multiplying the initial weight vector of the category by the state influence amount; S603: normalizing the result of S602 to obtain a variable weight vector of the category; S604: constructing a fuzzy relationship matrix of the category based on the membership function; S605: Multiply the transposed point of the variable weight vector by the fuzzy relationship matrix to obtain the fuzzy evaluation comprehensive vector of the category.

4. The method for classifying geological conditions of a drilling and anchoring machine according to claim 3, characterized in that: The formula for determining the state influence is: Where b j is the adjustment level, that is, the threshold value at which the status value of a factor belongs to the lowest comment level; x j The membership degree of the lowest evaluation level is greater than the threshold value; S j is the state influence quantity; μ is the state value of a certain factor.

5. The method for classifying geological conditions of a drilling and anchoring machine according to claim 3, characterized in that: The steps of determining the fuzzy evaluation comprehensive vector corresponding to the target layer A include: S701: Determine the state influence amount of the target layer A; substitute the state value of each factor according to the state influence amount formula; S702: multiplying the initial weight vector of the target layer A by the state influence amount; S703: normalizing the result of S602 to obtain a variable weight vector of the target layer A; S704: constructing a fuzzy relationship matrix of the target layer A based on the fuzzy evaluation comprehensive vectors corresponding to the n1 categories; S705: Multiply the transposed point of the variable weight vector by the fuzzy relationship matrix to obtain the fuzzy evaluation comprehensive vector of the target layer A.

6. The method for classifying geological conditions of a drilling and anchoring machine according to claim 1, characterized in that: The n0 indicators that affect the rapid excavation of the integrated excavator and anchor machine include burial depth C1, thickness C2, inclination angle C3, coal rock fissure development degree C4, coal seam interlayer coefficient C5, direct roof compressive strength C6, coal seam compressive strength C7, bottom plate softening coefficient C8, water-rich coefficient C9, cross-sectional area C10, tunnel slope C11, number of bottom plate fluctuations per kilometer C12, tunnel length C13, monthly advance C14, empty top distance C15, number of top anchor rod delayed support C16, empty side distance C17, number of side anchor rod delayed support C18, anchor rod spacing C19, anchor cable spacing C20, fold complexity C21, collapse column influence C22, fault influence C23, impact tendency index C24, gas outburst volume C25, and roof fall coefficient C26.

7. The method for classifying geological conditions of a drilling and anchoring machine according to claim 6, characterized in that: The n1 categories in the category layer B include coal seam occurrence characteristics B1, roadway parameters B2, and structure and safety B3; Among them, the indicators belonging to the coal seam occurrence characteristics B1 in the index layer C are: C1-C9; the indicators belonging to the tunnel parameters B2 are: C10-C20; indicators belonging to construction and safety B3 are: C21-C26; One target in target layer A is: geological condition classification of integrated mining and anchoring machine; coal seam occurrence characteristics B1, tunnel parameters B2 and structure and safety B3 belong to target layer A.

8. The method for classifying geological conditions of a drilling and anchoring machine according to claim 1, characterized in that: The m0 evaluation levels include but are not limited to not applicable, poor applicability, applicable, and good applicability; the membership function types include but are not limited to large-sized, small-sized, and intermediate.

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