Roadway roof stability grading evaluation method and evaluation device based on partial order set decision theory

Abstract

The invention discloses a roadway roof stability grading evaluation method and evaluation device based on a partial sequence set decision theory. The method comprises the following steps: constructing a roadway roof stability evaluation index system and a grade standard training sample; original index data of the roadway roof to be evaluated are collected and preprocessed; forming an evaluation object matrix by the preprocessed data and a grade standard training sample; partial sequence set implicit weighting processing and line-by-line comparison are carried out on the evaluation object matrix, and a comparison relation matrix is obtained; a Hase graph is drawn on the basis of a Hase algorithm; and determining a stability grading system of the to-be-evaluated roadway roof by combining the hierarchical position of the grade standard training sample in the Hasse graph. According to the method, partial sequence set implicit weighting is carried out on an evaluation object matrix, partial sequence relation visualization is realized by drawing a Hasse graph, then a grade standard training sample with a known grade is taken as a reference object, the stability grade of a to-be-evaluated roadway roof is directly reflected, and grading deviation caused by subjectivity and experience dependence of manual weighting is eliminated from the source.

Description

Technical Field

[0001] This invention relates to the field of roadway roof stability evaluation technology, and in particular to a roadway roof stability classification evaluation method and evaluation device based on partial order set decision theory. Background Technology

[0002] Roof collapse in coal mine roadways is a significant source of underground accidents. Because roof stability is influenced by a combination of factors, including geological structure, rock mass, hydrological conditions, engineering layout, mining disturbances, and the spatial structure of the goaf, traditional single-index or limited-index roof stability classification methods struggle to account for multi-dimensional information.

[0003] Existing multi-indicator evaluation methods typically rely on expert weighting or linear weighted synthesis, which have problems such as strong subjectivity of weights, high requirements for data distribution assumptions, and difficulty in showing the "superiority-disadvantage structure relationship" between samples. This results in insufficient transferability and interpretability of the grading results across different mines or regions.

[0004] Therefore, there is a need for a roof stability grading evaluation technique that can reduce the influence of subjective weighting under multiple index conditions and present the relationship between the superiority and inferiority of samples in a structured manner. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a method and device for classifying and evaluating the stability of roadway roof based on partial-order set decision theory.

[0006] This invention is achieved by the following technical solution: A method for classifying and evaluating the stability of roadway roof based on partially ordered set decision theory includes: Construct a stability evaluation index system for roadway roof, and based on the evaluation index system, construct training samples for grade standards; Collect raw index data of the roof of the roadway to be evaluated, and preprocess the raw index data; The preprocessed roadway roof index data to be evaluated and the grade standard training samples are combined to form the evaluation object matrix X; The evaluation object matrix X is subjected to implicit weighting of partial order set to obtain the cumulative transformation matrix D. The cumulative transformation matrix D is then compared row by row to obtain the comparison relation matrix R. Based on the comparison relation matrix R, the Hasse matrix is ​​generated using the Hasse algorithm, and the Hasse diagram is drawn based on the generated Hasse matrix. Based on the drawn Hasse diagram and the hierarchical position of the training samples in the Hasse diagram, the stability grading system of the roadway roof to be evaluated is determined.

[0007] By adopting the above technical solution, the index data of the roadway roof to be evaluated and the training samples of the grade standard are spliced ​​into the evaluation object matrix X, and the evaluation object matrix X is implicitly weighted by a partial order set, which eliminates the subjectivity and experience dependence of manual weighting from the root. At the same time, the partial order relationship is visualized by drawing a Hasse diagram, and the boundaries of different stability levels are clearly distinguished by the hierarchical arrangement of nodes and directed edges. Then, using the training samples of the grade standard of known grades as a reference, the stability level of the roadway roof to be evaluated can be directly reflected, avoiding the grading deviation caused by human subjective judgment.

[0008] The method for classifying and evaluating the stability of roadway roof based on partially ordered set decision theory, as described above, includes constructing a roadway roof stability evaluation index system, comprising: Factors affecting the stability of the roadway roof are divided into qualitative and quantitative indicators. The stability of the roadway roof is divided into four levels: stable, moderately stable, unstable, and extremely unstable, with scores of 0.2, 0.4, 0.6, and 0.8 for each level, respectively. Qualitative indicators are graded based on the defined roadway roof stability levels, and quantitative indicators are mapped to the corresponding level ranges using the fuzzy comprehensive evaluation method.

[0009] The method for classifying and evaluating the stability of roadway roof based on partially ordered set decision theory, as described above, includes constructing training samples for grade standards according to the evaluation index system, comprising: Based on the endpoint values ​​of the graded intervals of qualitative and quantitative indicators, the evaluation index system is divided into four levels, which correspond to the stable, moderately stable, unstable and extremely unstable levels of roadway roof stability. The evaluation index system of each level is used as the training sample for the grading standard, thereby constructing four levels of grading standard training samples.

[0010] The method for classifying and evaluating the stability of roadway roof based on partially ordered set decision theory, as described above, includes the following steps: collecting the original index data of the roadway roof to be evaluated and preprocessing the original index data: Perform trend-alignment processing on the inverse indicators in the original indicator data; The original indicator data after trend homogenization is normalized to map indicator data with different dimensions to... Interval.

[0011] The method for classifying and evaluating the stability of roadway roof based on partial-order set decision theory, as described above, involves performing implicit weighting processing on the evaluation object matrix X to obtain the cumulative transformation matrix D, including: The cumulative transformation matrix D is obtained by performing a cumulative transformation on the evaluation object matrix X. The specific calculation process is as follows: ; In the formula, The cumulative transformation matrix D has m rows and n columns, where m rows represent m schemes and n columns represent n indicators.

[0012] The method for classifying and evaluating the stability of roadway roof based on partial-order set decision theory, as described above, involves comparing the cumulative transformation matrix D row by row to obtain the comparison relation matrix R, which includes: Given a partially ordered set, perform row-by-row comparisons of the row vectors of the cumulative transformation matrix D based on the given partially ordered set; If the i-th row is greater than or equal to the j-th row, then count... Otherwise, .

[0013] The method for classifying and evaluating the stability of roadway roof based on partially ordered set decision theory, as described above, includes generating a Hasse matrix based on the comparison relation matrix R using the Hasse algorithm, which includes: Perform Boolean operations on the comparison relation matrix R to obtain the transfer matrix. The specific calculation process is as follows: ; In the formula, For or, For and, and This represents the intermediate node K in the comparison relation matrix R; Based on the transfer matrix Construct a partial order matrix A, which is: ; The Hasse matrix is ​​obtained by Boolean matrix subtraction based on the partial order matrix A. The specific calculation process is as follows: ; In the formula, B is the square of the partial order matrix A.

[0014] The roadway roof stability classification and evaluation method based on partially ordered set decision theory, as described above, further includes: The weights of the evaluation indicators are determined by AHP weights; Based on the weights of the evaluation indicators, the indicators in the stability grading system of the roadway roof to be evaluated are sorted to obtain the weight percentage of each indicator.

[0015] The method for classifying and evaluating the stability of roadway roof based on partially ordered set decision theory, as described above, involves determining the weights of evaluation indicators through AHP weights, including: Establish a hierarchical structure model; The Delphi expert scoring method was used to compare the qualitative and quantitative indicators affecting the stability of the roadway roof in pairs, establish a comparison matrix, and use the 1-9 scale method to determine the element values ​​of the comparison matrix, where the larger the number, the more important it is. The evaluation index weights are obtained by calculating the largest eigenvalue of the comparison matrix and its corresponding eigenvector, normalizing the eigenvector, and performing a consistency check.

[0016] In addition, to achieve the above objectives, the present invention also provides a roadway roof stability grading and evaluation device, the roadway roof stability grading and evaluation device comprising: a memory, a processor, and a roadway roof stability grading and evaluation program stored in the memory and executable on the processor, the roadway roof stability grading and evaluation program being configured to implement the roadway roof stability grading and evaluation method as described above.

[0017] Compared with existing technologies, the roadway roof stability classification evaluation method and device proposed in this invention based on partial-order set decision theory has the following beneficial effects: 1. The roadway roof stability grading evaluation method proposed in this invention concatenates the index data of the roadway roof to be evaluated and the training samples of the grading standards into an evaluation object matrix X, and performs implicit weighting of the evaluation object matrix X with a partially ordered set, thereby eliminating the subjectivity and experience dependence of manual weighting from the root. At the same time, the partial order relationship is visualized by drawing a Hasse diagram, and the boundaries of different stability levels are clearly distinguished by the hierarchical arrangement of nodes and directed edges. Then, using the training samples of the grading standards of known levels as references, the stability level of the roadway roof to be evaluated can be directly reflected, avoiding the grading deviation caused by human subjective judgment. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0019] Figure 1 This is a flowchart of the roadway roof stability classification and evaluation method based on partial order set decision theory of the present invention. Figure 2 for Figure 1 Flowchart of the specific method for step S10; Figure 3 for Figure 1 Another specific method flowchart for step S10; Figure 4 for Figure 1 Flowchart of the specific method for step S20; Figure 5 for Figure 1Flowchart of the specific method for step S40; Figure 6 for Figure 1 Flowchart of the specific method for step S50; Figure 7 Another flowchart of the roadway roof stability classification and evaluation method based on partial order set decision theory of the present invention; Figure 8 for Figure 7 Flowchart of the specific method for step S70; Figure 9 This is a flowchart for determining the weight ranking of roadway roof stability indicators based on the AHP method; Figure 10 This is a weighting chart of various indicators in an embodiment of the present invention; Figure 11 This is a Hasse diagram from an embodiment of the present invention. Detailed Implementation

[0020] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0021] Existing multi-indicator evaluation methods typically rely on expert weighting or linear weighted synthesis, which have problems such as strong subjectivity of weights, high requirements for data distribution assumptions, and difficulty in showing the "superiority-disadvantage structure relationship" between samples. This results in insufficient transferability and interpretability of the grading results across different mines or regions.

[0022] To address the aforementioned technical problems, this invention proposes a solution: First, a roadway roof stability evaluation index system is constructed, and a grade standard training sample is created based on this system. Then, original index data of the roadway roof to be evaluated is collected and preprocessed. Next, the preprocessed roadway roof index data and the grade standard training sample are combined to form an evaluation object matrix X. Then, the evaluation object matrix X undergoes partial-order set implicit weighting to obtain a cumulative transformation matrix D. The cumulative transformation matrix D is then compared row by row to obtain a comparison relation matrix R. Based on the comparison relation matrix R, a Hasse matrix is ​​generated using the Hasse algorithm, and a Hasse diagram is drawn based on the generated Hasse matrix. Finally, based on the drawn Hasse diagram and the hierarchical position of the grade standard training sample in the Hasse diagram, the stability grading system of the roadway roof to be evaluated is determined.

[0023] The above scheme concatenates the index data of the roadway roof to be evaluated and the training samples of the grading standards into an evaluation object matrix X, and performs implicit weighting of the evaluation object matrix X with a partial order set, which eliminates the subjectivity and experience dependence of manual weighting from the root. At the same time, the partial order relationship is visualized by drawing a Hasse diagram, and the boundaries of different stability levels are clearly distinguished by the hierarchical arrangement of nodes and directed edges. Then, using the training samples of the grading standards of known levels as a reference, the stability level of the roadway roof to be evaluated can be directly reflected, avoiding the grading bias caused by human subjective judgment.

[0024] Based on the above, please refer to Figure 1 As shown in the embodiments of this specification, a method for classifying and evaluating the stability of roadway roof based on partial-order set decision theory is proposed, including steps S10-S60, wherein: S10. Construct a stability evaluation index system for the roadway roof and, based on the evaluation index system, construct training samples for grade standards.

[0025] The evaluation index system consists of qualitative and quantitative indicators. The qualitative indicators are those that cannot be directly quantified by numerical values ​​and require discretization based on expert experience or on-site investigation to evaluate the stability of the roadway roof. The quantitative indicators are those that can be measured by instruments, tested by experiments, etc., to obtain specific values ​​and are used to accurately quantify and analyze the stability of the roadway roof.

[0026] Existing solutions often use quantitative indicators to evaluate the stability of the tunnel roof, while neglecting qualitative indicators such as geological structure and rock mass structure, which are difficult to quantify and reflect the actual state of the tunnel roof. This results in a large deviation between the evaluation results and the actual engineering.

[0027] In this embodiment, the roadway roof stability evaluation system constructed through qualitative and quantitative indicators integrates measurable physical parameters and unmeasurable geological features, forming a comprehensive evaluation system that can fully and accurately reflect the stability of the roadway roof. The training samples of the grade standards constructed based on this system can cover the typical characteristics of different stability levels of the roadway roof, providing a benchmark reference for the stability classification of the roadway roof to be evaluated.

[0028] S20: Collect raw index data of the roof of the roadway to be evaluated and preprocess the raw index data.

[0029] The collected raw indicator data includes, but is not limited to, qualitative indicators such as geological structure, rock mass structure, and engineering layout, as well as quantitative indicators such as burial depth, rock compressive strength, and exposed area of ​​the roof.

[0030] In this embodiment, in order to obtain the stability grading evaluation results of the roadway roof to be evaluated, and to accurately reflect the actual stability of the roadway roof to be evaluated, the collected data of the roadway roof to be evaluated must at least include the index data corresponding to the roadway roof stability evaluation index system constructed above. Secondly, the collected raw index data may have problems such as missing data, anomalies (such as data deviation caused by measurement errors), and inconsistent dimensions, which may affect the evaluation stability. Therefore, it is necessary to preprocess the raw index data to transform it into standard data that meets the evaluation requirements, so as to provide a reliable data foundation for the subsequent construction and analysis of the evaluation object matrix.

[0031] S30, the preprocessed roadway roof index data to be evaluated and the grade standard training samples are combined to form the evaluation object matrix X.

[0032] In this embodiment, the preprocessed roadway roof index data vector to be evaluated and the grade standard training sample matrix are mathematically vertically concatenated to form an evaluation object matrix X. Each row of the evaluation object matrix X corresponds to an independent evaluation object, specifically including all index data of the roadway roof to be evaluated and all index data of the grade training samples. Each column corresponds to a specific index (a qualitative or quantitative index) in the evaluation index system. This integrates the scattered index data to be evaluated and the grade standard training sample data into a structured matrix, eliminating the dimensional differences between the roadway roof to be evaluated and the grade standard samples, making them mathematically comparable. Secondly, the matrix structure can simultaneously accommodate the synchronous comparative analysis of multiple index data of the roadway roof to be evaluated and multiple grade standard training samples, providing comprehensive data support for the stability grading of the roadway roof to be evaluated.

[0033] S40. The evaluation object matrix X is subjected to implicit weighting of partial order set to obtain the cumulative transformation matrix D. The cumulative transformation matrix D is compared row by row to obtain the comparison relation matrix R.

[0034] Among them, the partial order set implicit weighting refers to a weighting method that does not require manual subjective setting of the weight coefficients of each evaluation indicator, but rather relies on the distribution characteristics and inherent correlation of the standardized data in the evaluation object matrix X itself, and objectively implies the weight of each indicator's influence on the stability of the roadway roof in the transformation result through a mathematical operation of cumulative transformation.

[0035] In this embodiment, by using implicit weighting of partial-order sets, the evaluation object matrix X will objectively determine the contribution of indicators based on its ability to distinguish differences in stability levels during the cumulative transformation process. The higher the indicator data has in terms of the ability to distinguish stability levels, the greater its contribution to the evaluation result after the cumulative transformation. This achieves objective weighting, fundamentally eliminating the subjectivity and experience dependence of manual weighting, and improving the scientificity and objectivity of the evaluation results.

[0036] S50. Based on the comparison relation matrix R, generate the Hasse matrix using the Hasse algorithm, and draw the Hasse diagram based on the generated Hasse matrix.

[0037] The Hasse algorithm is an algorithm in partial order set theory used to simplify partial order relationships and extract direct partial order associations. In this embodiment, based on the known comparison relationship matrix R, redundant indirect partial order relationships between evaluation objects are eliminated, and finally a Hasse matrix with superior and inferior hierarchical topological results is generated. The Hasse graph is a visual partial order relationship graph constructed based on the Hasse matrix. The Hasse graph uses nodes to intuitively represent each evaluation object and directional line segments to represent direct partial order relationships between evaluation objects (arrows point from superior objects to inferior objects). The hierarchical arrangement of nodes intuitively presents the stability superiority and inferiority levels of all evaluation objects.

[0038] In this embodiment, the partial order relationship is visualized by drawing a Hasse diagram, which intuitively presents the hierarchical relationship in the form of nodes and directed edges. Compared with the abstract matrix form, engineers can quickly grasp the stability hierarchy of each evaluation object just by using the visualization diagram.

[0039] S60. Based on the drawn Hasse diagram and the hierarchical position of the training samples of the grading standard in the Hasse diagram, determine the stability grading system of the roadway roof to be evaluated.

[0040] In this embodiment, since the Hasse diagram clearly distinguishes the boundaries of different stability levels in the hierarchical arrangement of nodes and directed edges, and uses the known level standard training samples as a reference, it can directly reflect the stability level of the roadway roof to be evaluated, avoiding classification deviations caused by human subjective judgment. Secondly, the stability classification of the roadway roof to be evaluated is directly related to the hierarchical relationship with the level standard training samples in the Hasse diagram, which facilitates the verification and traceability of the evaluation results and enhances the persuasiveness of the stability classification system.

[0041] Alternatively, please refer to Figure 2 As shown, in step S10, the construction of the roadway roof stability evaluation index system also includes steps S11-S13, wherein: S11 divides the factors affecting the stability of the roadway roof into qualitative and quantitative indicators; S12 classifies the stability of the roadway roof into four levels: stable, moderately stable, unstable, and extremely unstable, with scores of 0.2, 0.4, 0.6, and 0.8 for each level, respectively. S13. Qualitative indicators are graded according to the defined roadway roof stability levels, and quantitative indicators are mapped to the corresponding level intervals using the fuzzy comprehensive evaluation method.

[0042] In this embodiment, the qualitative indicators (A) include geological structure, rock mass structure, hydrological conditions, mine groundwater, engineering layout, impact of mining disturbance, pillar size and layout, protective measures, and distribution of adjacent goaf areas (represented by A1 to A9 respectively). The quantitative indicators (B) include burial depth, rock compressive strength, roof exposed area, rock quality indicators, goaf burial depth, goaf span, goaf area, goaf height, goaf span-to-height ratio, coal seam dip angle, and mining depth-to-thickness ratio (represented by B1 to B11 respectively).

[0043] Specifically, this embodiment divides the stability of the roadway roof into four levels: stable, moderately stable, unstable, and extremely unstable, and sets corresponding scores for each level, so that different stability levels have comparable quantitative basis. In the process of constructing the roadway roof stability evaluation index system, the stability level of each index data of the qualitative index is determined according to the actual roadway roof stability characteristics it represents, and a score corresponding to that level is attached. For the quantitative index, the fuzzy comprehensive evaluation method is adopted, which uses the continuous numerical characteristics and boundary fuzziness of the quantitative index to map the precise measurement value of each index in the quantitative index to the corresponding stability level range.

[0044] Alternatively, please refer to Figure 3 As shown, in step S10, constructing the level standard training samples according to the evaluation index system, the method also includes steps S14-S15, wherein: S14. According to the endpoint values ​​of the graded intervals of qualitative and quantitative indicators, the evaluation index system is divided into four levels, which correspond to the stable, moderately stable, unstable and extremely unstable levels of roadway roof stability. S15 uses the evaluation index system of each level as training samples for the grade standard, thereby constructing four levels of grade standard training samples.

[0045] In this embodiment, the stability level score corresponding to each indicator data in the qualitative indicators is used as the interval endpoint, and indicator data with the same score are classified into the same stability level. For each indicator data in the quantitative indicators, the corresponding critical value of each level's adaptation interval is determined by the fuzzy comprehensive evaluation method. Thus, the entire evaluation indicator system is divided into four level standard training samples corresponding to the stability level of the roadway roof. This avoids the sample error caused by the fuzzy indicator basis and subjective selection of sample parameters in the traditional sample construction. Moreover, the four level standard training samples are all constructed based on the hierarchical division of the same evaluation indicator system that includes both qualitative and quantitative indicators. This ensures that the indicator dimensions (covering all 20 indicators) and parameter standards of each level sample are completely consistent, avoiding the confusion of evaluation benchmarks due to different construction basis of different level samples. This provides a benchmark reference for the subsequent stability classification of the roadway roof to be evaluated.

[0046] Alternatively, please refer to Figure 4 As shown, in step S20, the raw index data of the roadway roof to be evaluated is collected, and the raw index data is preprocessed. This also includes steps S21-S22, wherein: S21, convert the reverse indicators in the original indicator data to the same trend; S22, normalizes the original indicator data after trend convergence to map indicator data of different dimensions to... Interval.

[0047] In this embodiment, since the collected raw indicator data includes both positive and negative indicator data, where a larger positive indicator value indicates better stability of the roadway roof, and a smaller negative indicator value indicates better stability of the roadway roof, to avoid discrepancies in indicator trends that could lead to contradictions between the evaluation results and actual conditions in subsequent partial-order set analysis, the negative indicators in the raw indicator data are processed to achieve a common trend. This ensures that a larger value indicates better stability of the roadway roof. The common trend processing utilizes the formula... The reverse indicator data is processed in reverse. To eliminate the influence of different units among the various indicator data, which could lead to significant deviations in the evaluation results, it is necessary to normalize the trend-coordinated indicator data. The specific normalization formula is as follows: .

[0048] in, This refers to the data corresponding to the j-th evaluation indicator for the i-th evaluation object; The data after dimensionless processing is the data for the j-th evaluation index of the i-th evaluation object; The minimum value of all indicator data under the j-th evaluation indicator for the i-th evaluation object; It represents the maximum value of all indicator data under the j-th evaluation indicator for the i-th evaluation object.

[0049] It should be noted that the positive and negative indicator data mentioned above are determined by the characteristics of each indicator data itself. For example, in the quantitative indicator of rock compressive strength, the larger the value, the higher the strength, the stronger the rock mass bearing capacity, that is, the better the stability. Another example is the roof exposed area, the larger the value, the larger the exposed area, the larger the pressure relief range, that is, the easier it is to collapse, and the worse the stability. Other indicator data will not be elaborated here in this embodiment.

[0050] As a preferred implementation, in step S40, the evaluation object matrix X undergoes partial-order set implicit weighting processing to obtain the cumulative transformation matrix D. An exemplary partial-order set implicit weighting method is given: the cumulative transformation matrix D is obtained by performing a cumulative transformation calculation on the evaluation object matrix X. The specific calculation process is as follows:

[0051] In the formula, The cumulative transformation matrix D has m rows and n columns, where m rows represent m schemes and n columns represent n indicators.

[0052] Alternatively, please refer to Figure 5 As shown, in step S40, the cumulative transformation matrix D is compared row by row to obtain the comparison relation matrix R. The process also includes steps S41-S42, where: S41, Given a partially ordered set, perform row-by-row comparisons of the row vectors of the cumulative transformation matrix D based on the given partially ordered set; S42, if the i-th row is greater than or equal to the j-th row, then count... Otherwise, the calculation .

[0053] Specifically, let the given partially ordered set be... Where A is the set of evaluation objects, containing the index data of the roof of the roadway to be evaluated and the training samples of the grading standards. For a partial order relation on the set of evaluation objects X, which satisfies reflexivity, antisymmetry, and transitivity, during the row-by-row comparison of the cumulative transformation matrix D, for ,like This indicates that the i-th and j-th rows of the cumulative transformation matrix D satisfy reflexivity, antisymmetry, and transitivity. Let be the inequality. ;like or and Incomparable, the i-th row and j-th row of the surface cumulative transformation matrix D do not satisfy antisymmetry and transitivity, or do not satisfy reflexivity, denoted as The final matrix R is called The comparison matrix.

[0054] It should be noted that the given partially ordered set of the evaluation object matrix X and the order structure (the mathematical structure formed by the evaluation object set and the binary dominance relation) implied in the cumulative transformation matrix D are not additional external input data, but rather the mathematical objects implied in the evaluation object matrix X and the cumulative transformation matrix D themselves, used to describe the objective decision-making reality of multi-index fusion and non-fully ordered comparability in the evaluation of roadway roof stability.

[0055] Alternatively, please refer to Figure 6As shown, in step S50, generating the Hasse matrix based on the comparison relation matrix R using the Hasse algorithm, steps S51-S53 are also included, where: S51, perform Boolean operations on the comparison relation matrix R to obtain the transfer matrix. The specific calculation process is as follows: ; In the formula, For or, For and, and This represents the intermediate node K in the comparison relation matrix R; S52, based on the transfer matrix Construct a partial order matrix A, which is: ; S53. Based on the partial order matrix A, the Hasse matrix is ​​obtained by Boolean matrix subtraction. The specific calculation process is as follows: ; In the formula, B is the square of the partial order matrix A.

[0056] In this embodiment, since the comparison relation matrix R cannot intuitively reflect the partial order relation, a Hasse diagram needs to be drawn to visualize the partial order relation. Before drawing the Hasse diagram, a Hasse matrix needs to be established. After performing Boolean operations on the comparison relation matrix, the Hasse matrix can be obtained as follows: This allows for a visual representation of hierarchical relationships using nodes and directed edges. Compared to an abstract matrix, engineers can quickly grasp the stability hierarchy of each evaluation object simply through a visual diagram.

[0057] As a preferred implementation method, please refer to Figure 7 As shown, the roadway roof stability grading evaluation method further includes steps S70-S80, wherein: S70, determine the weight of the evaluation indicators through AHP weighting; S80. Based on the weight of the evaluation index, the indicators in the stability classification system of the roadway roof to be evaluated are sorted to obtain the weight ratio of each indicator.

[0058] Among them, AHP weight refers to the explicit weight coefficient vector calculated by the analytic hierarchy process to characterize the relative importance of each evaluation indicator data (qualitative and quantitative indicators); evaluation indicator weight refers to the quantitative coefficient characterizing the degree of influence of each evaluation indicator data on the roadway roof stability classification result. The larger the weight value, the more significant the influence of the indicator data on the stability level determination. It can be used to measure the importance of the indicator or to explain the dominant factor of the evaluation result.

[0059] Optional, please refer to Figure 8 As shown, in step S70, determining the weights of the evaluation indicators through AHP weights also includes steps S71-S73, where: S71, Establish a hierarchical structure model; S72 uses the Delphi expert scoring method to compare the qualitative and quantitative indicators that affect the stability of the roadway roof in pairs, establish a comparison matrix, and use the 1-9 scale method to determine the element values ​​of the comparison matrix, where the larger the number, the more important it is. S73: By calculating the largest eigenvalue of the comparison matrix and its corresponding eigenvector, the eigenvector is normalized and a consistency check is performed to obtain the evaluation index weights.

[0060] Among them, Delphi expert scoring is a method that uses multiple rounds of anonymous expert opinion collection and feedback to gradually converge the opinions of the expert group, thereby forming a unified and reliable expert consensus and providing authoritative experience support for the evaluation results.

[0061] For details, please refer to Figure 9 As shown, a hierarchical model is first established, which is divided into a target layer, a criterion layer, and an indicator layer. In this embodiment, the target layer is the stability of the roadway roof, the criterion layer is the calculation of indicator weights under a single criterion, and the indicator layer is the data of various indicators affecting the stability of the roadway roof, including the aforementioned qualitative and quantitative indicators. Then, using the Delphi expert scoring method, a total of 20 indicators, including both qualitative and quantitative indicators, are compared pairwise to establish a comparison matrix. The 1-9 scaling method is used to determine the element values ​​of the comparison matrix. Then, the maximum eigenvalue of the comparison matrix and its corresponding eigenvector are calculated. Specific calculation methods can include the square root method, the stacking method, etc. This embodiment uses the square root method, and the specific calculation formula is as follows: ; In the formula, This indicates the element in the x-th row and y-th column of the comparison matrix, where n represents the order of the comparison matrix. This represents the product value of each indicator data in the comparison matrix.

[0062] Table 1 shows the product values ​​of the above-mentioned indicator data in this embodiment. .

[0063] Table 1. Product values ​​of various indicator data

[0064] Then calculate the product of the data for each indicator. The nth root is calculated as follows: ; In the formula, n is 20 (the specific value of n depends on the type of indicator data). It represents the largest eigenvalue of each indicator data.

[0065] Table 2 shows the maximum eigenvalues ​​of each indicator data calculated based on Table 1. .

[0066] Table 2. Maximum eigenvalues ​​of each indicator data

[0067] Then find the largest eigenvalue of each indicator data. Normalization yields the evaluation index weights for each indicator under this criterion. The specific calculation process is as follows: ; A consistency test is performed on the relative weights of the normalized indicator data to determine whether the elements in the comparison matrix satisfy logical consistency. This is specifically performed using the consistency index CI and the random consistency index RI. The calculation process for the consistency index CI is as follows: ; In the formula, The largest eigenvalue in the comparison matrix is ​​n, where n represents the order of the comparison matrix.

[0068] When looking up the random consistency index RI, the value of RI is found in the corresponding table based on the order of the comparison matrix. The table shows that the average random consistency index RI for a 20th-order matrix is ​​1.629. Combining the consistency index CI and the random consistency index RI, the consistency ratio CR is calculated. The specific calculation process is as follows: ; Specifically, if CR < 0.1, the consistency check is considered to have passed; otherwise, the consistency check fails.

[0069] The maximum eigenvalue can be calculated from the above data in this embodiment. The consistency index CI is 0.1227 and the consistency ratio CR is 0.0753 < 0.1, which meets the consistency test requirements.

[0070] Table 3 shows the weighted sorting of each indicator data according to the weight of the evaluation indicator.

[0071] Table 3. Weighting of each indicator data

[0072] It should be noted that the above definitions of each element value are as follows: 1 indicates that the two indicator data are equally important, 3 indicates that one indicator data is slightly more important than the other, 5 indicates that one indicator data is significantly more important than the other, 7 indicates that one indicator data is strongly more important than the other, 9 indicates that one indicator data is extremely more important than the other, and 2, 4, 6, and 8 are the intermediate values ​​of the above adjacent judgments.

[0073] In this embodiment, AHP weights provide expert experience to rank the various indicators in the grading system for the stability of the roadway roof under evaluation, obtaining the weight percentage of each indicator. This verifies whether the evaluation results obtained from the Hasse diagram after the implicit weighting of the partial-order set are consistent with the importance judgment of the core indicators, thus enhancing the reliability of the evaluation results. At the same time, the weight percentage ranking results of each indicator data enable engineering technicians not only to know the specific evaluation conclusion of the roadway roof under evaluation, but also to intuitively obtain the influence weight of each indicator, which is conducive to the subsequent development of targeted support optimization schemes and risk control measures for important indicators, thereby enhancing the interpretability of the evaluation results.

[0074] This specification also proposes a roadway roof stability grading and evaluation device, which includes: a memory, a processor, and a roadway roof stability grading and evaluation program stored in the memory and executable on the processor. The roadway roof stability grading and evaluation program is configured to implement the roadway roof stability grading and evaluation method as described above.

[0075] It is worth noting that, since the roadway roof stability grading and evaluation device of the present invention is used to implement the above-mentioned roadway roof stability grading and evaluation method, the embodiments of the roadway roof stability grading and evaluation device of the present invention include all the technical solutions of all embodiments of the above-mentioned roadway roof stability grading and evaluation method, and the technical effects achieved are exactly the same, so they will not be repeated here.

[0076] It should be noted that the sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0078] Those skilled in the art should understand that the above description is one embodiment provided in conjunction with specific content, and does not imply that the specific implementation of the present invention is limited to these descriptions. Furthermore, due to differences in industry naming conventions, the invention is not limited to the above names or English names. Any methods or structures similar to or identical to those of the present invention, or any technical deductions or substitutions made based on the concept of the present invention, should be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating the stability of a roadway roof based on the partial order set decision theory, characterized in that, include: Construct a stability evaluation index system for roadway roof, and based on the evaluation index system, construct training samples for grade standards; Collect raw index data of the roof of the roadway to be evaluated, and preprocess the raw index data; The preprocessed roadway roof index data to be evaluated and the grade standard training samples are combined to form the evaluation object matrix X; The evaluation object matrix X is subjected to implicit weighting of partial order set to obtain the cumulative transformation matrix D. The cumulative transformation matrix D is then compared row by row to obtain the comparison relation matrix R. Based on the comparison relation matrix R, the Hasse matrix is ​​generated using the Hasse algorithm, and the Hasse diagram is drawn based on the generated Hasse matrix. Based on the drawn Hasse diagram and the hierarchical position of the training samples in the Hasse diagram, the stability grading system of the roadway roof to be evaluated is determined.

2. The method according to claim 1, characterized in that, The constructed roadway roof stability evaluation index system includes: Factors affecting the stability of the roadway roof are divided into qualitative and quantitative indicators. The stability of the roadway roof is divided into four levels: stable, moderately stable, unstable, and extremely unstable, with scores of 0.2, 0.4, 0.6, and 0.8 for each level, respectively. Qualitative indicators are graded based on the defined roadway roof stability levels, and quantitative indicators are mapped to the corresponding level ranges using the fuzzy comprehensive evaluation method.

3. The method according to claim 2, characterized in that, The construction of graded standard training samples based on the evaluation index system includes: Based on the endpoint values ​​of the graded intervals of qualitative and quantitative indicators, the evaluation index system is divided into four levels, which correspond to the stable, moderately stable, unstable and extremely unstable levels of roadway roof stability. The evaluation index system of each level is used as the training sample for the grading standard, thereby constructing four levels of grading standard training samples.

4. The method according to claim 1, characterized in that, The process of collecting raw index data of the roadway roof to be evaluated and preprocessing the raw index data includes: Perform trend-alignment processing on the inverse indicators in the original indicator data; The original index data after the same trend is normalized to map the index data of different dimensions to interval.

5. The method according to claim 1, characterized in that, The step of performing implicit weighting on the evaluation object matrix X using a partial order set to obtain the cumulative transformation matrix D includes: The cumulative transformation matrix D is obtained by performing a cumulative transformation on the evaluation object matrix X. The specific calculation process is as follows: ； In the formula, The cumulative transformation matrix D has m rows and n columns, where m rows indicate m schemes, and n columns indicate n indexes.

6. The method according to claim 1, characterized in that, The comparison of the cumulative transformation matrix D row by row yields the comparison relation matrix R, which includes: Given a partially ordered set, perform row-by-row comparisons of the row vectors of the cumulative transformation matrix D based on the given partially ordered set; If the ith row is greater than or equal to the jth row, then count , otherwise count .

7. The method according to claim 1, characterized in that, The step of generating the Hasse matrix based on the comparison relation matrix R using the Hasse algorithm includes: Boolean operation is performed on the comparison relation matrix R to obtain a transfer matrix The specific calculation process is as follows: ； wherein is or is and and denotes an intermediate node K in the comparison relation matrix R; Based on the transfer matrix , a partial order matrix A is constructed, the partial order matrix A is: ; The Hasse matrix is ​​obtained by Boolean matrix subtraction based on the partial order matrix A. The specific calculation process is as follows: ； In the formula, B is the square of the partial order matrix A.

8. The method according to claim 1, characterized in that, The method for classifying and evaluating the stability of the roadway roof also includes: The weights of the evaluation indicators are determined by AHP weights; Based on the weights of the evaluation indicators, the indicators in the stability grading system of the roadway roof to be evaluated are sorted to obtain the weight percentage of each indicator.

9. The method according to claim 8, characterized in that, The process of determining the weights of evaluation indicators through AHP weights includes: Establish a hierarchical structure model; The qualitative indexes and quantitative indexes influencing the stability of the roadway roof are compared two by two by using the Delphi expert scoring method, a comparison matrix is established, and a 1-9 scale method is used to determine the element values of the comparison matrix, wherein the larger the number is, the more important it is; The evaluation index weight is obtained by calculating the maximum eigenvalue of the comparison matrix and the corresponding eigenvector, normalizing the eigenvector, and performing consistency checking.

10. A device for evaluating the stability of a roadway roof, characterized in that, The roadway roof stability grading evaluation device includes a memory, a processor, and a roadway roof stability grading evaluation program stored on the memory and executable on the processor, and the roadway roof stability grading evaluation program is configured to implement the roadway roof stability grading evaluation method in any one of claims 1 to 9.

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