CN111784136A - Impact risk dynamic early warning method based on hierarchical analysis and fuzzy mathematics - Google Patents
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Abstract
The invention discloses an impact risk dynamic early warning method based on hierarchical analysis and fuzzy mathematics, which comprises the following steps: firstly, determining an early warning object and an impact risk influence factor applicable to the early warning object, namely an early warning index; determining impact risk grades of the early warning indexes to form an impact risk early warning index system; thirdly, determining the weight of each early warning index based on an APH (analytic hierarchy process) theory to form a fuzzy weight vector A; establishing a single-factor fuzzy early warning set of each index based on a fuzzy mathematical theory to form a factor early warning fuzzy matrix R; step five, calculating a fuzzy comprehensive evaluation vector B of the early warning object as A multiplied by R; and step six, determining the impact danger level of the early warning object according to the maximum membership principle. According to the method, various rock burst early warning indexes are comprehensively considered, and the early warning model is adopted to carry out impact danger early warning on the early warning object, so that the early warning is more scientific, the early warning efficiency is higher, and the result is more reliable.
Description
Technical Field
The invention relates to the technical field of coal mine safety, in particular to an impact risk dynamic early warning method based on hierarchical analysis and fuzzy mathematics.
Background
With the increase of coal mining depth and the increase of mining intensity in China, the situation that coal mines face rock burst is more and more severe, and rock burst evaluation and real-time monitoring and early warning are effective measures for preventing and controlling rock burst. Meanwhile, the regulations of 'rules for preventing and controlling coal mine rock burst pressure fine' issued in 2018 are as follows: when a coal seam with rock burst is mined, comprehensive prevention and control measures such as rock burst risk prediction, monitoring and early warning, prevention and treatment, effect inspection, safety protection and the like must be taken; the method is characterized in that an impact risk monitoring system combining an area and a local area is required to be established in the rock burst mine, the area monitoring is required to cover a mine excavation area, the local monitoring is required to cover a rock burst risk area, the area monitoring can adopt a microseismic monitoring method and the like, and the local monitoring can adopt a drilling cutting method, a stress monitoring method, an electromagnetic radiation method and the like. However, in China, single index or combination of a simple model is mostly adopted for multi-index early warning, and the early warning efficiency and accuracy rate cannot meet the practical requirements of coal mines.
Disclosure of Invention
The invention aims to provide an impact risk dynamic early warning method based on hierarchical analysis and fuzzy mathematics, which comprehensively considers various parameters closely related to rock burst and carries out real-time early warning by combining a mathematical model and a judgment criterion, so that the early warning efficiency is higher, the result is more reliable, and the practicability of guiding the mine to carry out rock burst prevention and control work is higher.
The technical solution adopted by the invention is as follows:
a dynamic early warning method for impact danger based on hierarchical analysis and fuzzy mathematics comprises the following steps:
step one, determining an early-warning object and an impact risk early-warning index applicable to the early-warning object
Selecting an applicable impact risk early warning index set U according to the early warning object, and using UiIf each warning indicator is represented, U is equal to (U)1，u2，u3，u4…ui，…un) Wherein n is the number of early warning indicators;
step two, classifying the impact dangerousness of each early warning index to form an impact danger early warning index system
Classifying according to the early warning grades, and grading the impact risks of the early warning indexes; by establishing a hierarchical set v of each pre-warning indicatoriObtaining an impact risk early warning index system V of the early warning object;
step three, determining the weight of each early warning index to form a fuzzy weight vector
Determining the contribution degree of each early warning index to the impact risk of the early warning object according to an APH (advanced persistent threat) analytic hierarchy process and a 1-9 standard table, and further obtaining a fuzzy weight vector A formed by each index;
step four, establishing a single-factor fuzzy early warning set of each early warning index to form a factor early warning fuzzy matrix
Aiming at the monitoring result of a certain early warning index, a set of all membership degrees of the early warning index can be determined by combining an early warning membership function; and then a single-factor fuzzy early warning set corresponding to each early warning index can be obtained in sequence; the ith single-factor fuzzy early warning set RiForming a factor early warning fuzzy matrix R for the ith row;
step five, calculating a fuzzy comprehensive judgment vector of the early warning object
Calculating a factor early warning fuzzy matrix R synthesized by a fuzzy weight vector A reflecting the influence degree of each early warning index on the rock burst and all index membership degrees, thereby obtaining a fuzzy comprehensive judgment vector B of each rock burst early warning, wherein the fuzzy comprehensive judgment vector B is A × R (B)1，b2，b3，b4)；
Step six, determining the impact danger level of the early warning object
In step five (b)1，b2，b3，b4) Respectively corresponding to the membership degrees of the four judgment results (no impact risk, weak impact risk, medium impact risk and strong impact risk); according to the maximum membership principle, the impact danger level of the early warning object can be obtained.
In the second step, the impact risk of each early warning index can be classified into 5 grades, namely none, weak, medium, strong and unsafe. And the impact risk grading number of each early warning index in the step two is not consistent with the impact risk grade division number of the final early warning object in the step six, but the application effect of the method is not influenced.
In the third step, each early warning index u is usediPairwise comparison is carried out and the value is assigned corresponding to a 1-9 scale table, a matrix can be obtained, the characteristic vector is solved aiming at the matrix, and a fuzzy weight vector A (a) is obtained1，a2，…ai，…an) And is (a ') after normalization treatment'1，a’2，…a’i，…a’n)。
In the fourth step, the membership function is shown as formula (1):
in the formula (1), x is a test value, S1、S2、S3And S4And the four grading threshold values respectively correspond to the early warning indexes.
In the sixth step, the maximum membership rule refers to the comparison of b1～b4Size, if maximum value is biIf i takes values of 1, 2, 3 and 4, the early warning object is the ith-level impact risk; impact hazard classes fall into four categories: the impact danger level belongs to level 1 and is no impact danger; the impact risk grade belongs to grade 2 and is a weak impact risk; the impact risk rating is class 3, which is a medium impact risk; the impact hazard classification belongs to class 4 and is a strong impact hazard.
The beneficial technical effects of the invention are as follows:
according to the early warning method, various rock burst early warning indexes are comprehensively considered, different weight values are given through the mathematical model according to different working conditions, and then the early warning model is adopted to carry out impact risk early warning on the early warning object, so that the early warning is more scientific, the early warning efficiency is higher, and the result is more reliable. In addition, the early warning method is suitable for early warning equipment for real-time monitoring in engineering, namely, the early warning result of the impact danger is real-time and dynamic. The early warning method can be used for guiding the coal mine to carry out rock burst prevention and control work, and has important practical significance for guaranteeing the safe production of the coal mine.
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The invention is further described with reference to the following figures and detailed description:
FIG. 1 is a flow chart of an impact risk dynamic warning method based on hierarchical analysis and fuzzy mathematics according to the present invention;
fig. 2 is a schematic diagram of an impact risk early warning index system established in the embodiment of the present invention.
Detailed Description
As shown in fig. 1, a dynamic early warning method for impact risk based on hierarchical analysis and fuzzy mathematics, which is applicable to coal mines, specifically comprises the following steps:
step one, determining an early warning object and an impact risk influence factor applicable to the early warning object, namely an early warning index
The early warning object can be a range including a mine, a mining area (panel area or level), a stope face and a roadway, and can also be a section of roadway and a monitoring point. Taking a monitoring point as an example, selecting an applicable impact risk early warning index set U according to an early warning object, and using UiIf each warning indicator is represented, U is equal to (U)1，u2，u3，u4…ui，…un) And n is the number of early warning indexes, including mining stress, roadway displacement, roof separation amount, anchor rod working load, anchor cable working load, support resistance and the like.
Step two, classifying the impact dangerousness of each early warning index to form an impact danger early warning index system
Classifying according to the early warning grades, classifying the impact risk of each early warning index into 5 grades which are respectively none, weak, medium, strong and unsafe. Taking mining induced stress sigma as an example, the early warning index grading set v1(none, weak, medium, strong, unsafe) ═ 1.5 σIs normal，1.5σIs normal＜σ≤3σIs normal，3σIs normal＜σ≤4.5σIs normal，4.5σIs normal＜σ≤6σIs normal，σ＞6σIs normal). By establishing a hierarchical set v of each pre-warning indicatoriAnd obtaining an impact risk early warning index system V of the early warning object such as a monitoring point.
Step three, determining the weight of each early warning index to form a fuzzy weight vector
And the weight represents the contribution degree of each early warning index to the impact risk of the monitoring point, such as the weight occupied by the impact risk influence of mining stress, roadway displacement and the like on the monitoring point.
According to the corresponding relation between monitoring data such as mining stress, roadway displacement and the like and impact display in the past, and in combination with an APH (advanced persistent threat) analytic hierarchy process and a 1-9 standard table, the contribution degree of each early warning index to the impact risk of a monitoring point is determined, and then a fuzzy weight vector A formed by each index is obtained.
Step four, establishing a single-factor fuzzy early warning set of each early warning index to form a factor early warning fuzzy matrix
Fuzzy mathematics is applied to determine the degree of each early warning index which belongs to different early warning grades in the early warning grade set, namely the degree of membership, and r is used as the degree of membershipijAnd (4) showing. Aiming at a monitoring result (actual test result) of a certain early warning index, a set of all membership degrees of the early warning index can be determined by combining an early warning membership function, namely a single-factor early warning matrix; and then a single-factor fuzzy early warning matrix or an early warning set corresponding to each early warning index can be obtained in sequence. Arranging the ith single-factor fuzzy early warning set R according to different early warning indexes and each row from top to bottomiAnd the ith row is formed into a factor early warning fuzzy matrix R. For example, the factor warning ambiguity matrix R may be n rows and 4 columns.
Step five, calculating a fuzzy comprehensive judgment vector of the early warning object
The fuzzy comprehensive early warning considers the influence of all factors on monitoring points, and a fuzzy weight vector A reflecting the influence degree of each early warning index on the rock burst and a factor early warning fuzzy matrix R synthesized by all index membership degrees are operated, so that a fuzzy comprehensive judgment vector B of each rock burst early warning is obtained, wherein the fuzzy comprehensive judgment vector B is A × R (B is B1，b2，b3，b4)。
Step six, determining the impact danger level of the early warning object
In step five (b)1，b2，b3，b4) Respectively corresponding to the membership degrees of the four judgment results (no impact risk, weak impact risk, medium impact risk and strong impact risk). According to the maximum membership principle, the impact risk level reflected by the monitoring data of the monitoring point for a period of time can be obtained.
In the third step, the scale of 1-9 is shown in table 1 below.
TABLE 1
| Scale | Means for indicating |
| 1 | Element i and element j are equally important |
| 3 | Element i is slightly more important than element j |
| 5 | Element i is significantly more important than element j |
| 7 | Element i is more strongly important than element j |
| 9 | Element i is extremely important than element j |
| 2,4,6,8 | Between the above scales |
| Reciprocal of the | Denotes aijAnd aijReciprocal of each other |
By applying each pre-warning index uiPairwise comparison and assignment are carried out corresponding to the table 1, a matrix G can be obtained, the characteristic vector is solved aiming at the matrix G, and a fuzzy weight matrix A is obtained (a)1，a2，…ai，…an) And is (a ') after normalization treatment'1，a’2，…a’i，…a’n)。
In step four, the membership rijThe membership function is calculated and obtained, and the membership function is shown as a formula (1).
In the formula (1), x is a test value, S1、S2、S3And S4And the four grading threshold values respectively correspond to the early warning indexes.
In the sixth step, the principle of maximum membership degree means that b is compared1～b4Magnitude, if maximum value biAnd if the value of i is 1, 2, 3 or 4, the early warning object is the ith-level impact risk. Impact hazard classes are divided into four categories; the impact danger level belongs to level 1 and is no impact danger; the impact risk grade belongs to grade 2 and is a weak impact risk; the impact risk rating is class 3, which is a medium impact risk; the impact hazard classification belongs to class 4 and is a strong impact hazard.
The dynamic early warning method for the impact risk based on the hierarchical analysis and the fuzzy mathematics is further introduced by taking a certain coal mine in Henan as a specific application example.
Background: a certain coal mine in Henan is a typical rock burst mine, and multiple rock burst accidents occur in the process of mining 2-3 coals. A stress meter and a displacement meter are installed in the roadway driving process of the 13200 working face for real-time monitoring, and the method is adopted for carrying out real-time early warning on impact danger in the roadway driving process.
(1) Determining an early warning object and an impact risk early warning index applicable to the early warning object: and determining a 500-600 m section of the early warning object 13200 at the upper roadway distance inlet according to the installation positions of the stress gauge and the displacement gauge, and determining the impact danger early warning index as stress gradient σ and displacement gradient.
(2) And determining the impact risk grades of the early warning indexes to form an impact risk early warning index system, as shown in fig. 2.
(3) Determining the weight of each early warning index to form a fuzzy weight vector: according to the comparison of the importance degrees of the early warning indexes, combining an APH theory and a 1-9 standard table, and obtaining a fuzzy weight matrix A ═ (a)1’，a2’)＝(0.1667，0.8333)。
(4) And establishing a single-factor fuzzy early warning set of each index to form a factor early warning fuzzy matrix. Wherein the grading threshold value of each early warning index refers to fig. 2, and the factor early warning fuzzy matrix R is obtained by calculation
(5) Calculating a fuzzy comprehensive evaluation vector of the early warning object: b ═ axr ═ (0.9603, 0.0397, 0, 0).
(6) Determining the impact risk level of the early warning object: the fuzzy comprehensive evaluation index of the measuring point is 0.9603 and the impact risk grade is none according to the maximum membership principle.
According to the field drilling cutting quantity monitoring result and the power display condition, the drilling cutting quantity in the monitoring time period does not exceed the standard, the field is displayed without power, and the monitoring point is free of impact danger and is consistent with the result obtained by the dynamic impact danger early warning method based on the hierarchical analysis and fuzzy mathematics.
Parts not described in the above modes can be realized by adopting or referring to the prior art.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (4)
1. A dynamic early warning method for impact danger based on hierarchical analysis and fuzzy mathematics is characterized by comprising the following steps:
step one, determining an early-warning object and an impact risk early-warning index applicable to the early-warning object
Selecting an applicable impact risk early warning index set U according to the early warning object, and using UiIf each warning indicator is represented, U is equal to (U)1，u2，u3，u4…ui，…un) Wherein n is the number of early warning indicators;
step two, classifying the impact dangerousness of each early warning index to form an impact danger early warning index system
Classifying according to the early warning grades, and grading the impact risks of the early warning indexes; by establishing a hierarchical set v of each pre-warning indicatoriObtaining an impact risk early warning index system V of the early warning object;
step three, determining the weight of each early warning index to form a fuzzy weight vector
Determining the contribution degree of each early warning index to the impact risk of the early warning object according to an APH (advanced persistent threat) analytic hierarchy process and a 1-9 standard table, and further obtaining a fuzzy weight vector A formed by each index;
step four, establishing a single-factor fuzzy early warning set of each early warning index to form a factor early warning fuzzy matrix
Aiming at the monitoring result of a certain early warning index, a set of all membership degrees of the early warning index can be determined by combining an early warning membership function; and then a single-factor fuzzy early warning set corresponding to each early warning index can be obtained in sequence; the ith single-factor fuzzy early warning set RiForming a factor early warning fuzzy matrix R for the ith row;
step five, calculating a fuzzy comprehensive judgment vector of the early warning object
Calculating a factor early warning fuzzy matrix R synthesized by a fuzzy weight vector A reflecting the influence degree of each early warning index on the rock burst and all index membership degrees, thereby obtaining a fuzzy comprehensive judgment vector B of each rock burst early warning, wherein the fuzzy comprehensive judgment vector B is A × R (B)1，b2，b3，b4)；
Step six, determining the impact danger level of the early warning object
In step five (b)1，b2，b3，b4) Respectively corresponding to the membership degrees of the four judgment results (no impact risk, weak impact risk, medium impact risk and strong impact risk); according to the maximum membership principle, the impact danger level of the early warning object can be obtained.
2. The dynamic early warning method for impact risk based on hierarchical analysis and fuzzy mathematics as claimed in claim 1, wherein: in the third step, each early warning index u is usediPairwise comparison is carried out and the value is assigned corresponding to a 1-9 scale table, a matrix can be obtained, the characteristic vector is solved aiming at the matrix, and a fuzzy weight vector A (a) is obtained1，a2，…ai，…an) And is (a ') after normalization treatment'1，a’2，…a’i，…a’n)。
3. The dynamic early warning method for impact risk based on hierarchical analysis and fuzzy mathematics as claimed in claim 1, wherein: in the fourth step, the membership function is shown as formula (1):
in the formula (1), x is a test value, S1、S2、S3And S4And the four grading threshold values respectively correspond to the early warning indexes.
4. The dynamic early warning method for impact risk based on hierarchical analysis and fuzzy mathematics as claimed in claim 1, wherein: in the sixth step, the principle of maximum membership degree means that b is compared1～b4Size, if maximum value is biIf i takes values of 1, 2, 3 and 4, the early warning object is the ith-level impact risk; impact hazard grade fractionIs classified into four types: the impact danger level belongs to level 1 and is no impact danger; the impact risk grade belongs to grade 2 and is a weak impact risk; the impact risk rating is class 3, which is a medium impact risk; the impact hazard classification belongs to class 4 and is a strong impact hazard.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115496342A (en) * | 2022-09-05 | 2022-12-20 | 煤炭科学技术研究院有限公司 | Rock burst early warning method and device based on subjective and objective dynamic weights |
| CN115860582A (en) * | 2023-02-28 | 2023-03-28 | 山东科技大学 | Intelligent impact risk early warning method based on self-adaptive lifting algorithm |
| 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 |
-
2020
- 2020-06-22 CN CN202010577804.3A patent/CN111784136A/en active Pending
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| 张宁博 等: "基于一孔多点式应力与位移监测系统的掘进巷道冲击危险性评价", 煤炭学报, pages 140 - 149 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115496342A (en) * | 2022-09-05 | 2022-12-20 | 煤炭科学技术研究院有限公司 | Rock burst early warning method and device based on subjective and objective dynamic weights |
| 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 |
| CN115860582A (en) * | 2023-02-28 | 2023-03-28 | 山东科技大学 | Intelligent impact risk early warning method based on self-adaptive lifting algorithm |
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