CN115471097A - Data-driven underground local area safety state evaluation method - Google Patents
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Abstract
The invention discloses a data-driven underground local area safety state evaluation method, which comprises the following steps of S1: establishing an influence index of safety state evaluation; s2: constructing a judgment matrix; s3: calculating index weight; s4: checking consistency; s5: determining a factor set, a comment set and a weight set; s6: establishing a membership function; s7: detecting actual data; s8: determining a membership matrix; s9: a confidence criterion; s10: and (6) comprehensively evaluating and outputting. The invention can solve the problem that the grade evaluation differs after the coal mine safety state is scored by depending on the self experience of experts at present.
Description
Technical Field
The invention relates to the technical field of coal mine safety production, in particular to a data-driven underground local area safety state evaluation method.
Background
With the increasing scale of coal mining, scientific and quantitative underground safety state assessment is the most fundamental requirement for ensuring the life safety of mine workers. Various environmental parameter information of the working area environment must be monitored in real time in order to guarantee the safety state of mine workers. Due to the fact that the underground working environment is severe, a large number of influencing factors exist, and therefore large uncertainty exists in mine safety state assessment. At present, researchers carry out various environmental evaluations based on a fuzzy comprehensive evaluation method and an analytic hierarchy process. The fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics, which converts qualitative evaluation into quantitative evaluation according to a membership theory in the fuzzy mathematics, namely, the fuzzy mathematics is used for making an overall evaluation on actual things which are restricted by various factors. The evaluation method has the characteristics of clear result and strong systematicness, and is suitable for solving various nondeterministic problems; referring to fig. 1, an Analytic Hierarchy Process (AHP) decomposes influence factors related to decision into a target layer, an index layer and a scheme layer, and then an expert analyzes importance of indexes on the same layer to indexes on an upper layer by constructing a hierarchical structure model of system internal evaluation indexes, thereby establishing a judgment matrix, and finally obtaining an empowerment method of a satisfactory weight result through sequencing calculation. The method is suitable for processing multi-target, multi-level and difficult-to-quantify complex problems, can quantify subjective judgment of people, conveys the subjective judgment in a mathematical form, and is a method for effectively combining qualitative and quantitative analysis. However, the judgment of the relative importance degree between the indexes also depends on the subjective factors of the experts, and the obtained index weights may be different according to different experts.
When the analytic hierarchy process establishes the judgment matrix, the method mainly comprises a construction method of 1-9 scale judgment matrix and a construction method of 0-1 scale fuzzy judgment matrix.
The construction method of the 1-9 scale judgment matrix compares the next layer of indexes with the previous layer of targets pairwise to obtain an importance degree value, and finishes scoring;
the judgment matrix importance degree table is as follows:
| scale | Means of |
| 1 | Indicates that two elements have the same importance compared |
| 3 | Means that the former is slightly more important than the latter when compared with the latter |
| 5 | Means that the former is significantly more important than the latter when compared with the two elements |
| 7 | Means that the former is more important than the latter in comparison with the two elements |
| 9 | Means that the former is extremely important than the latter in comparison with two elements |
| 2，4，6，8 | Intermediate value representing the above-mentioned adjacent importance |
The construction method of the fuzzy judgment matrix with 0-1 scale also needs to carry out consistency check to judge whether the matrix is scored reasonably. The method for judging whether the consistency test is passed is as follows:
at present, a fuzzy comprehensive evaluation method and an analytic hierarchy process are often applied to the evaluation of complex problems. For example, some experts evaluate the current situation of coal mine water, a fuzzy comprehensive evaluation method is adopted to divide the coal mine water into five grades, and 7 kinds of coal mine water in a certain area are comprehensively tested and analyzed from 14 kinds of influence factors; when some experts analyze the safety management system, personnel factors, equipment factors, environmental factors and management factors are quantified by using an analytic hierarchy process, corresponding solving measures are made aiming at the actual safety problems of the mine, the research result has certain practicability, and a basis is provided for mine safety production; when some experts evaluate the safety indexes of the mine ventilation system, the implementation of ventilation management precautionary measures is enhanced by using an analytic hierarchy process, and the normal operation of underground operation is effectively promoted; in the coal mine safety evaluation research, some experts analyze 16 influence indexes by utilizing fuzzy comprehensive evaluation and expert scoring, finally obtain the coal mine safety level and obtain a good evaluation effect; in the research of mine fire emergency rescue capability evaluation, some experts evaluate the constructed mine fire emergency rescue capability evaluation index system by adopting a method of combining fuzzy comprehensive evaluation-integrated statistical model with qualitative and quantitative, and apply the method to a coal mine.
In the process of applying the analytic hierarchy process, experts are required to score the importance degree of all the influencing factors in pairs. Since the scoring depends on the experience of experts, different experts give different scoring conditions. Therefore, the final results are different, so that the grade evaluation obtained after the same coal mine environment is scored by different experts is different.
Disclosure of Invention
Therefore, the embodiment of the invention provides a data-driven underground local area safety state evaluation method, which aims to solve the problem that the grade evaluation is different after the coal mine safety state is graded by depending on the self experience of experts at present.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions: a data-driven downhole local area safety state evaluation method comprises the following steps:
s1: establishing an influence index of safety state evaluation;
s2: constructing a judgment matrix;
s3: calculating the weight of the index;
s4: checking the consistency;
s5: determining a factor set, a comment set and a weight set;
s6: establishing a membership function;
s7: detecting actual data;
s8: determining a membership matrix;
s9: a confidence criterion;
s10: outputting comprehensive evaluation;
the influence indexes of the safety state evaluation in the S1 are as follows: wind speed, temperature, humidity, air quantity supply-demand ratio, gas concentration, carbon dioxide concentration, carbon monoxide concentration, oxygen concentration and dust concentration;
when the safety state influence factor judgment matrix is established by adopting a 1-9 scale judgment matrix construction method in S2, the judgment matrix comprises an element i and an element j, and the ratio of the importance of the element i to the importance of the element j is a ij The ratio of the importance of element j to element i is then a ji ＝1/a ij And a is a ii ＝1；
S3, calculating a weight value by adopting a square root method, wherein the calculation formula is as follows:
s31: calculating the product M of each column of the decision matrix i
S32: calculating M i Root of cubic (n times)
S33: to pair
Normalizing to obtain weight w i
The step of consistency check in S4 is as follows:
s41: solving the maximum eigenvalue of the judgment matrix:
s42: calculating a consistency index CI
S43: solving consistency ratio CR
Wherein, RI is an average consistency index and is related to n;
the factor set in S5 is: wind speed, temperature, humidity, air volume supply-demand ratio, gas concentration, carbon dioxide concentration, carbon monoxide concentration, oxygen concentration and dust concentration; the comment set is: excellent, good, qualified, dangerous; the weight is: w is a i ；
The Cauchy membership function in S6 is:
the small-sized device is as follows:
f(x)＝1x≤a；
centering:
large-scale:
f(x)＝0 x≤a；
in the Cauchy membership function formula, x is expressed as an input value, f (x) is expressed as membership, a is a middle value of each grade, alpha and beta are expressed as parameters, wherein the value of beta is 2, and the calculation formula of alpha and a is as follows:
a＝x u +x i /2
α＝4/(x u -x i ) 2 ；
s7, detecting underground test indexes such as wind speed, temperature, humidity, air quantity supply-demand ratio, gas concentration, carbon dioxide concentration, carbon monoxide concentration, oxygen concentration and dust concentration;
s8, according to the result detected in S7, a membership matrix R is established by combining a Cauchy membership function;
calculating a comment weight matrix in S9: q = w R, determining confidence criterion λ =0.6;
and S10, evaluating the safety state of the local area of the actual mine to obtain an evaluation result.
Preferably, a construction method of a 0-1 scale fuzzy judgment matrix is adopted in determining the construction judgment matrix, and the fuzzy judgment matrix has a ratio of importance of an element i to an element j of r ij The ratio of the importance of element j to element i is then r ji ＝1-r ij And r is ii ＝0.5。
Preferably, when determining to construct the judgment matrix, the method includes the following steps:
step 1: randomly and uniformly taking values from 9 numbers of 0.1-0.9 fuzzy scales as an upper triangular element of a matrix R, taking 0.5 as a main diagonal element, taking 1 as a lower triangular element, and subtracting the upper triangular element at a corresponding position to form an n-order random fuzzy complementary judgment matrix;
step 2: calculating a fuzzy consistency index FCI of the obtained random fuzzy complementary judgment matrix;
and step 3: calculating FCR
The ratio of the fuzzy consistency index FCI to the mean random fuzzy consistency index FRI of the same order, called fuzzy consistency ratio, is noted as:
where the FRI value is related to the order.
Preferably, the identity of the decision matrix is theoretically acceptable when CR < 0.1.
Preferably, the consistency of the decision matrix is theoretically acceptable when FCR < 0.1.
The invention has at least the following beneficial effects:
1. according to the method, the safety state of the local area under the coal mine is evaluated by adopting a fuzzy comprehensive evaluation method, an analytic hierarchy process and a confidence criterion, so that the safety state of a working area is quickly and conveniently subjected to grade evaluation, and the problem that the grade evaluation is different after the safety state of the coal mine is graded by depending on the experience of an expert is effectively solved;
2. the invention makes up the scoring difference caused by the expert self-help scoring in the analytic hierarchy process by using the confidence criterion, provides various scoring criteria and weight calculation methods, and ensures that the final evaluation result of the safety state is more reasonable.
Drawings
In order to more clearly illustrate the prior art and the present invention, the drawings used in the description of the prior art and the embodiments of the present invention will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary and that other drawings may be derived by those of ordinary skill in the art without inventive effort from the drawings provided.
The structures, ratios, sizes, and other characteristics shown in the present specification are only used for matching with the contents disclosed in the specification, so that those skilled in the art can understand and read the present disclosure, and do not limit the conditions that the present disclosure can be implemented, and any modifications of the structures, changes of the ratio relationships, or adjustments of the sizes should fall within the scope of the present disclosure without affecting the efficacy and the achievable purpose of the present disclosure.
FIG. 1 is a diagram of the structure of an analytic hierarchy process;
FIG. 2 is a block diagram of a data-driven downhole local zone safety state assessment method according to the present invention;
FIG. 3 is a graph of an index safety rating classification function;
FIG. 4 is a block diagram of a security assessment impact indicator.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further 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 present application and are not intended to limit the present application.
In the description of the present invention, "a plurality" means two or more unless otherwise specified. The terms "first," "second," "third," "fourth," and the like (if any) in the description and claims of the present invention and in the above-described drawings are intended to distinguish between the referenced items. For a scheme with a time sequence flow, the term expression does not need to be understood as describing a specific sequence or a sequence order, and for a scheme of a device structure, the term expression does not have distinction of importance degree, position relation and the like.
Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements specifically listed, but may include other steps or elements not expressly listed that are inherent to such process, method, article, or apparatus or that are added to a further optimization scheme based on the present inventive concept.
Referring to fig. 2, the method for evaluating the safety state of a data-driven downhole local area of the invention includes the following steps:
s1: establishing an influence index of safety state evaluation;
s2: constructing a judgment matrix;
s3: calculating index weight;
s4: checking consistency;
s5: determining a factor set, a comment set and a weight set;
s6: establishing a membership function;
s7: detecting actual data;
s8: establishing a membership matrix;
s9: a confidence criterion;
s10: and (4) comprehensive evaluation output (excellent, good, qualified and dangerous).
Wherein S1-S4 are analytic hierarchy processes, and S5-S8 are fuzzy comprehensive evaluation processes.
The invention evaluates the safety state of the local area under the coal mine, firstly, the safety evaluation method comprises the following steps of: establishing a judgment matrix by adopting an analytic hierarchy process for wind speed, temperature, humidity, air quantity supply-demand ratio, gas concentration, carbon dioxide concentration, carbon monoxide concentration, oxygen concentration and dust concentration, then comparing the next layer of indexes with the previous layer of targets pairwise by expert analysis according to a construction method of a 1-9 scale judgment matrix to obtain an importance degree value, and finishing scoring;
regarding the decision matrix, if the ratio of the importance of the element i to the element j is a ij (a ij Values from 1 to 9, and their inverse), the ratio of the importance of element j to element i is a ji ＝1/a ij And a is a ii And =1. The following table is a safety state influence factor judgment matrix established by adopting a 1-9 scale judgment matrix construction method.
The factor decision matrix is as follows:
| C1 | C2 | C3 | C4 | C5 | C6 | C7 | | C9 | |
| C1 | |||||||||
| 1 | 6 | 7 | 1/2 | 1/3 | 5 | 4 | 2 | 3 | |
| |
1/6 | 1 | 2 | 1/7 | 1/8 | 1/2 | 1/3 | 1/5 | 1/4 |
| |
1/7 | 1/2 | 1 | 1/8 | 1/9 | 1/3 | 1/4 | 1/6 | 1/5 |
| C4 | 2 | 7 | 8 | 1 | 1/2 | 6 | 5 | 3 | 4 |
| C5 | 3 | 8 | 9 | 2 | 1 | 7 | 6 | 4 | 5 |
| |
1/5 | 2 | 3 | 1/6 | 1/7 | 1 | 1/2 | 1/4 | 1/3 |
| |
1/4 | 3 | 4 | 1/5 | 1/6 | 2 | 1 | 1/3 | 1/2 |
| |
1/2 | 5 | 6 | 1/3 | 1/4 | 4 | 3 | 1 | 2 |
| |
1/3 | 4 | 5 | 1/4 | 1/5 | 3 | 2 | 1/2 | 1 |
and when the judgment matrix is constructed, the weight value of the influencing factor needs to be calculated, and the weight value is calculated by adopting a root method. The calculation formula is as follows:
s31: calculating the product M of each column of the judgment matrix A i
S32: calculating M i Root of square root of
S33: to pair
Normalizing to obtain weight w i
Consistency checks are required to ensure that their weights are reasonable. The procedure for consistency check when constructing the decision matrix using the 1-9 scale is as follows.
S41: solving the maximum eigenvalue of the judgment matrix:
s42: calculating a consistency index CI
S43: solving consistency ratio CR
Where RI is the average consistency index and is related to n. The following table shows the average consistency index values:
| |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| RI | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.26 | 1.36 | 1.41 | 1.46 | 1.49 | 1.52 | 1.54 |
when CR <0.1, the consistency of the decision matrix is considered theoretically acceptable, otherwise the corresponding index value needs to be modified. And constructing an influence factor judgment matrix of the local area and calculating a weight w, wherein the weight w is obtained according to a square root method and is = {0.1555, 0.0247, 0.0183, 0.2223, 0.3121, 0.0350, 0.0507, 0.1075 and 0.0739}. In order to ensure the reasonableness of the index weights obtained above, a consistency check must be performed on the judgment matrix. And CR =0.034 & lt 0.1 calculated by the coal face return air area judgment matrix is calculated, so that the constructed judgment matrix is considered to pass the consistency test, and the scoring is reasonable.
The construction method of the 0 to 1 scale fuzzy judgment matrix can also be adopted when determining the construction judgment matrix. The method also requires a consistency check to determine if it is reasonable to score the matrix. If the ratio of the importance of element i to element j is r ij (a ij A value of 0.1 to 0.9), the ratio of the importance of the element j to the element i is r ji ＝1-r ij And r is ii =0.5. The method comprises the following specific steps:
step 1: randomly and uniformly taking values from 9 numbers of 0.1-0.9 fuzzy scales as an upper triangular element of a matrix R, taking 0.5 as a main diagonal element, taking 1 as a lower triangular element, and subtracting the upper triangular element at a corresponding position to form an n-order random fuzzy complementary judgment matrix;
step 2: calculating a fuzzy consistency index FCI of the obtained random fuzzy complementary judgment matrix;
average random fuzzy consistency index FRI value:
and 3, step 3: calculating FCR
The ratio of the fuzzy consistency index FCI to the mean random fuzzy consistency index FRI of the same order, called fuzzy consistency ratio, is noted as:
when FCR <0.1, the consistency of the decision matrix is considered theoretically acceptable, otherwise the corresponding index value needs to be modified.
Because the excellent limit of the safety state is not specified clearly in the coal mine safety standard, four grades of excellent, good, qualified and dangerous indexes are adopted for dividing the safety state. It can also be divided into n levels. If the n levels are obtained, the function values of the normal distribution are divided into 1/n and 2/n. For the monitoring data research of the ventilation index, the ventilation index can be approximately considered to meet the normal distribution. The functional expression is:
in the formula: a and b are constants, and both a and b are greater than or equal to 0. This is because the actual index physical quantity has a value distribution in the positive half axis, see fig. 3, which is a graph of the index safety rating dividing function.
And (3) carrying out grade division on the nine influencing factor indexes by utilizing coal mine safety regulation, and determining the corresponding normal distribution U (x).
For example: the wind speed rating scale is shown in the following table:
and when the safety grade division of the influencing factors is completed, determining the Cauchy membership function.
The small-sized device is as follows:
f(x)＝1 x≤a；
the centering is as follows:
large-scale:
f(x)＝0 x≤a；
in the Cauchy and Cauchy membership function formula, x is represented as an input value, f (x) is represented as membership, a is a middle value of each grade, alpha and beta are represented as parameters, and the value of beta is 2. The calculation formula of α and a is as follows:
a＝x u +x i /2
α＝4/(x u -x i ) 2
therefore, the membership function corresponding to the wind speed is established as follows:
the advantages are that:
good:
or
And (4) qualification:
or
Danger: f. of 1 (x) =1x < 0.25 or x > 4
The actual test factor indexes are assumed to be: {2, 8, 20, 1, 0.5, 0, 19, 1}, and thus a membership matrix R can be obtained as follows:
| 0.9846 | 0 | 0 | 0 |
| 0 | 0.6923 | 0 | 0 |
| 0 | 0 | 0.9918 | 0 |
| 0 | 0.5 | 0.5 | 0 |
| 0 | 0.74 | 0 | 0 |
| 1 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 |
| 0 | 0 | 0 | 1 |
| 0 | 0 | 0 | 1 |
set up comment set v = { excellent, good, qualified, dangerous }, where excellent>Good effect>Qualified>And (4) danger. The above available weight set W: {0.1555, 0.0247, 0.0183, 0.2223, 0.3121, 0.0350, 0.0507, 0.1075, 0.0739}, and the comment weight matrix Q = W × R = {0.5538, 0.3591, 0.1292, 0.1814}. Carrying out normalization processing on Q to obtain a matrix M 1 The actual coal mine underground local area of 45.25% is excellent, 29.34% is good, 12.92% is qualified and 18.14% is dangerous due to the fact that {0.4525, 0.2934, 0.1059 and 0.1482} can be obtained. The invention adopts confidence coefficient lambda =0.6. So that the actual coal mine underground local areaThe domain is of good degree.
The confidence criterion is quoted to judge whether the final result is reasonable or not, so that the scientific and feasible grade evaluation is carried out on the safety state of the local area under the coal mine. For confidence criterion, if the set is evaluated { V 1 ,V 2 ,…,V n Is an ordered evaluation set, lambda is confidence coefficient, and x belongs to V i Class membership of μ x (V i ) If at V 1 >V 2 >V 3 >…>V n When the conditions are satisfied:
at V 1 <V 2 <V 3 <…<V n The following requirements are met:
the evaluation object x is considered to belong to V k0 And (4) class.
It can be seen that the confidence criterion is considered from a "strong" point of view, i.e. the more "strong" the better, and the "strong" class should be a considerable proportion. The confidence coefficient value range is usually 0.5< lambda <1, and is generally 0.6-0.8. The present invention employs λ =0.6.
Example (c):
referring to fig. 4, the safety state of the local area of the coal face of the actual underground coal mine is evaluated, and first, the influence indexes (wind speed c1, temperature c2, humidity c3, air volume supply-demand ratio c4, gas concentration c5, carbon dioxide concentration c6, carbon monoxide concentration c7, oxygen concentration c8, and dust concentration c 9) of the safety state evaluation are considered.
Secondly, comparing the next layer of indexes with the previous layer of targets pairwise according to a construction method of a 1-9 scale judgment matrix by expert analysis to obtain an importance degree value, wherein the following table shows a judgment matrix constructed by the influence indexes of safety state evaluation:
| C1 | C2 | C3 | C4 | C5 | C6 | C7 | | C9 | |
| C1 | |||||||||
| 1 | 6 | 7 | 1/2 | 1/3 | 5 | 4 | 2 | 3 | |
| |
1/6 | 1 | 2 | 1/7 | 1/8 | 1/2 | 1/3 | 1/5 | 1/4 |
| |
1/7 | 1/2 | 1 | 1/8 | 1/9 | 1/3 | 1/4 | 1/6 | 1/5 |
| C4 | 2 | 7 | 8 | 1 | 1/2 | 6 | 5 | 3 | 4 |
| C5 | 3 | 8 | 9 | 2 | 1 | 7 | 6 | 4 | 5 |
| |
1/5 | 2 | 3 | 1/6 | 1/7 | 1 | 1/2 | 1/4 | 1/3 |
| |
1/4 | 3 | 4 | 1/5 | 1/6 | 2 | 1 | 1/3 | 1/2 |
| |
1/2 | 5 | 6 | 1/3 | 1/4 | 4 | 3 | 1 | 2 |
| |
1/3 | 4 | 5 | 1/4 | 1/5 | 3 | 2 | 1/2 | 1 |
the weight formula is calculated according to the root method, and W = {0.1555, 0.0247, 0.0183, 0.2223, 0.3121, 0.0350, 0.0507, 0.1075, and 0.0739} can be obtained by calculating the weight of each influence index. To further verify the validity of their scoring, a consistency check is required on the calculated weights. The scoring consistency ratio CR =0.034 was constructed as 0.1, and therefore it was determined that the scoring was reasonable.
According to a fuzzy comprehensive evaluation method, a factor set, a weight set and a comment set are firstly required to be established. Wherein the factor set U = { wind speed, temperature, humidity, air volume supply-demand ratio, gas concentration, carbon dioxide concentration, carbon monoxide concentration, oxygen concentration and dust concentration }. Weight set W = {0.1555, 0.0247, 0.0183, 0.2223, 0.3121, 0.0350, 0.0507, 0.1075, 0.0739}. As the excellent limit of safety state evaluation is not specified clearly in the coal mine safety standard, four grades of excellent, good, qualified and dangerous indexes are adopted in the division. The panel of comments is therefore set to V = { excellent, good, qualified, dangerous }, where excellent > good > qualified > dangerous.
The following indexes are graded by coal mine safety regulations, and the corresponding normal distribution U (x) is determined as follows:
wind speed grading
Temperature grading
Humidity rating
Grading of air quantity supply-demand ratio
Gas concentration grading
| Index (%) | 0～0.45 | 0.45～0.7 | 0.7～1 | >1 |
| Rank of | Excellent (4) | Good (3) | Qualified (2) | Danger (1) |
Carbon dioxide concentration grading
| Index (%) | 0～0.55 | 0.55～0.85 | 0.85～1.5 | >1.5 |
| Rank of | Excellence (4) | Good (3) | Qualified (2) | Danger (1) |
Carbon monoxide concentration grading
| Index (%) | 0～0.001 | 0.001～0.0017 | 0.0017～0.0024 | >0.0024 |
| Rank of | Excellence (4) | Good (3) | Qualified (2) | Danger (1) |
Oxygen concentration grading
Dust concentration grading
| Index (mg/m) 3 ) | 0～0.9 | 0.9～1.4 | 1.4～2 | >2 |
| Rank of | Excellence (4) | Good (3) | Qualified (2) | Danger (1) |
The Cauchy membership function is established as follows:
wind speed
The method has the advantages that:
good:
or
And (4) qualification:
or
Danger: f. of 1 (x) =1x < 0.25 or x > 4
Temperature of
The method has the advantages that:
good:
or
And (4) qualification:
or
Danger: f. of 1 (x) =1x < 2 or x > 26
Humidity of air
The advantages are that:
good:
or
And (4) qualification:
or
Danger: f. of 1 (x) =1x < 15 or x > 95
Air quantity supply-demand ratio
The method has the advantages that:
good:
or
And (4) qualification:
or
Danger: f. of 1 (x) =1x <1 or x > 1.2
Gas concentration
The advantages are that:
good:
and (4) qualification:
danger: f. of 1 (x)＝1x＞1
Carbon dioxide concentration
The advantages are that:
good:
and (4) qualification:
danger: f. of 1 (x)＝1 x＞1.5
Concentration of carbon monoxide
The advantages are that:
good:
and (4) qualification:
danger: f. of 1 (x)＝1 x＞0.0024
Oxygen concentration
The method has the advantages that:
good:
or
And (4) qualification:
or
Danger:f 1 (x) =1x < 19.5 or x > 23.5
Dust concentration
The advantages are that:
good:
and (4) qualification:
danger: f. of 1 (x)＝1 x＞2
It is assumed that the actual test factor indexes are: {2, 8, 20, 1, 0.5, 0, 19, 1}, establishing a membership matrix R according to the cauchy membership function as:
| 0.9846 | 0 | 0 | 0 |
| 0 | 0.6923 | 0 | 0 |
| 0 | 0 | 0.9918 | 0 |
| 0 | 0.5 | 0.5 | 0 |
| 0 | 0.74 | 0 | 0 |
| 1 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 |
| 0 | 0 | 0 | 1 |
| 0 | 0 | 0 | 1 |
and calculating a comment weight matrix Q and carrying out normalization processing. Comment weight matrix Q = w × R = {0.5538, 0.3591, 0.1292, 0.1814}. Carrying out normalization processing on Q to obtain a matrix M 1 = 0.4525, 0.2934, 0.1059, 0.1482, finally passes the confidence criterion λ =0.6, so that the evaluation of the safety status of the actual mine local area is of good quality.
The above specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
All the technical features of the above embodiments can be arbitrarily combined (as long as there is no contradiction between the combinations of the technical features), and for brevity of description, all the possible combinations of the technical features in the above embodiments are not described; these examples, which are not explicitly described, should be considered to be within the scope of the present description.
The present invention has been described in considerable detail by the general description and the specific examples given above. It should be noted that numerous variations and modifications could be made to the specific embodiments described without departing from the inventive concept, and such are intended to be included within the scope of the appended claims. Therefore, the protection scope of the present patent application shall be subject to the appended claims.
Claims (5)
1. A data-driven downhole local area safety state evaluation method is characterized by comprising the following steps of:
s1: establishing an influence index of safety state evaluation;
s2: constructing a judgment matrix;
s3: calculating index weight;
s4: checking consistency;
s5: determining a factor set, a comment set and a weight set;
s6: establishing a membership function;
s7: detecting actual data;
s8: establishing a membership matrix;
s9: a confidence criterion;
s10: outputting comprehensive evaluation;
the influence indexes of the safety state evaluation in the S1 are as follows: wind speed, temperature, humidity, air volume supply-demand ratio, gas concentration, carbon dioxide concentration, carbon monoxide concentration, oxygen concentration and dust concentration;
when a 1-9 scale judgment matrix construction method is adopted to establish the safety state influence factor judgment matrix in S2, the judgment matrix comprises an element i and an element j, and the ratio of the importance of the element i to the importance of the element jIs a ij The ratio of the importance of element j to element i is then a ji ＝1/a ij And a is a ii ＝1；
S3, calculating a weight value by adopting a square root method, wherein the calculation formula is as follows:
s31: calculating the product M of each column of the decision matrix i
S32: calculating M i Root of cubic (n times)
S33: to pair
Normalizing to obtain weight w i
The step of consistency check in S4 is as follows:
s41: solving the maximum eigenvalue of the judgment matrix:
s42: calculating a consistency index CI
S43: solving consistency ratio CR
Wherein, RI is an average consistency index and is related to n;
the set of factors in S5 is: wind speed, temperature, humidity, air quantity supply-demand ratio, gas concentration, carbon dioxide concentration, carbon monoxide concentration, oxygen concentration and dust concentration; the comment set is: excellent, good, qualified, dangerous; the weight is: w is a i ；
The Cauchy membership function in S6 is:
the small-sized device is as follows:
f(x)＝1 x≤a；
the centering is as follows:
large-scale:
f(x)＝0 x≤a；
in the Cauchy membership function formula, x is expressed as an input value, f (x) is expressed as a membership, a is an intermediate value of each grade, alpha and beta are expressed as parameters, wherein the value of beta is 2, and the calculation formula of alpha and a is as follows:
a＝x u +x i /2
α＝4/(x u -x i ) 2 ；
s7, detecting underground test indexes such as wind speed, temperature, humidity, air quantity supply-demand ratio, gas concentration, carbon dioxide concentration, carbon monoxide concentration, oxygen concentration and dust concentration;
s8, according to the result detected in S7, determining a membership matrix R by combining a Cauchy membership function;
calculating a comment weight matrix in S9: q = w R, determining confidence criterion λ =0.6;
and S10, evaluating the safety state of the local area of the actual mine to obtain an evaluation result.
2. A data-driven downhole local area safety status evaluation method according to claim 1, wherein a construction method of a fuzzy judgment matrix with a scale of 0 to 1 is adopted in determining the construction judgment matrix, and with respect to the fuzzy judgment matrix, the ratio of the importance of an element i to the importance of an element j is r ij The ratio of the importance of element j to element i is then r ji ＝1-r ij And r is ii ＝0.5。
3. A data-driven downhole local area safety state evaluation method according to claim 2, wherein in determining the configuration decision matrix, comprising the steps of:
step 1: randomly and uniformly taking values from 9 numbers of fuzzy scales of 0.1-0.9 as upper triangular elements of a matrix R, taking the main diagonal elements as 0.5, taking the lower triangular elements as 1, and subtracting the upper triangular elements at corresponding positions to form an n-order random fuzzy complementary judgment matrix;
step 2: calculating a fuzzy consistency index FCI of the obtained random fuzzy complementary judgment matrix;
and 3, step 3: calculating FCR
The ratio of the fuzzy consistency index FCI to the mean random fuzzy consistency index FRI of the same order, called fuzzy consistency ratio, is noted as:
where the FRI value is related to the order.
4. A data-driven downhole local zone safety status assessment method according to claim 1, wherein the consistency of the decision matrix is theoretically acceptable when CR < 0.1.
5. A data-driven downhole local zone safety state evaluation method according to claim 3, wherein the consistency of the decision matrix is theoretically acceptable when FCR < 0.1.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116089787A (en) * | 2023-03-08 | 2023-05-09 | 中国人民解放军海军工程大学 | Ship subsystem running state analysis method and system based on analytic hierarchy process |
| CN116227982A (en) * | 2022-12-30 | 2023-06-06 | 中国矿业大学(北京) | Quantification method and device for pollution degree of coal dust |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116227982A (en) * | 2022-12-30 | 2023-06-06 | 中国矿业大学(北京) | Quantification method and device for pollution degree of coal dust |
| CN116227982B (en) * | 2022-12-30 | 2023-10-31 | 中国矿业大学(北京) | Quantification method and device for pollution degree of coal dust |
| CN116089787A (en) * | 2023-03-08 | 2023-05-09 | 中国人民解放军海军工程大学 | Ship subsystem running state analysis method and system based on analytic hierarchy process |
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