CN108389135A - A kind of water bursting source method of discrimination based on Grey Relational Model - Google Patents
A kind of water bursting source method of discrimination based on Grey Relational Model Download PDFInfo
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- CN108389135A CN108389135A CN201810233684.8A CN201810233684A CN108389135A CN 108389135 A CN108389135 A CN 108389135A CN 201810233684 A CN201810233684 A CN 201810233684A CN 108389135 A CN108389135 A CN 108389135A
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- water
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- relational model
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- bursting source
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
Abstract
The invention discloses a kind of water bursting source method of discrimination based on Grey Relational Model, including：Each water-bearing layer actual measurement data of water quality in collection target area and gushing water situation are established Grey Relational Model, data normalization processing, calculate correlation coefficient, calculation of relationship degree and determining inteerelated order, are determined water bursting source, compared with actual conditions based on actual measurement data of water quality.The present invention is based on grey correlation theories, only use the leading ion in each water-bearing layer as evaluation index, analyze its association contact, model is established to be differentiated, prediction result is accurate and reliable, has certain novelty, and the present invention is easy to operate, it is easy to practical application, a kind of new approaches and method is provided for water bursting source differentiation.
Description
Technical field
The present invention relates to water bursting sources in coal mining to differentiate field, and in particular to a kind of prominent based on Grey Relational Model
Water water source method of discrimination
Background technology
One of an important factor for mine water disaster is influence Safety of Coal Mine Production, if can quickly judge after water bursting in mine prominent
Can water water source determines quickly reasonably formulate mine water disaster control method, and the tradition research to water bursting in mine water source is research
Hydrogeologic condition and complicated for operation is analyzed, the present invention is on the basis of mine surveys hydrological data, to having projective water point sampling
Analysis quickly sentences water bursting in mine water source using the Grey Relational Model method in gray theory according to Analysis Results of Water Quality
, not easy to operate, water bursting source can be directly obtained, result and the actual conditions goodness of fit are higher.
Invention content
1. the object of the invention
In consideration of it, the present invention for water bursting source problem is differentiated in coal mining, provides a kind of easy to operate, precise and high efficiency
Method of discrimination.
2. technical scheme of the present invention
To achieve the above object, present invention relates particularly to a kind of water bursting source method of discrimination based on Grey Relational Model,
This method includes：Step A collects each water-bearing layer actual measurement data of water quality in target area and gushing water situation；Step B, based on actual measurement water
Matter data, establishes Grey Relational Model；Step C, data normalization processing；Step D, calculate correlation coefficient；Step E, the degree of association
Calculate and determine inteerelated order；Step F, determines water bursting source, is compared with actual conditions.
The present invention is based on grey correlation theories, only use the leading ion in each water-bearing layer as evaluation index, analyze its pass
Connection contact, establishes model and is differentiated, prediction result is accurate and reliable, has certain novelty, and the present invention is easy to operate, is easy to
Practical application provides a kind of new approaches and method for water bursting source differentiation.
Description of the drawings
The attached drawing for being used for illustrating herein is for further explanation of the present invention, is the part of the application,
But the present invention can not be limited.
Fig. 1 is that the present invention is based on the water bursting source method of discrimination flow charts of Grey Relational Model.
Specific embodiment
Below in conjunction with the accompanying drawings and the example applied of the present invention, invention is further explained.
Fig. 1 is that the present invention is based on the water bursting source method of discrimination flow charts of Grey Relational Model.As shown in Figure 1, this method
Including：
Step A collects each water-bearing layer actual measurement data of water quality in target area and gushing water situation；
Step B establishes Grey Relational Model based on actual measurement data of water quality；
Step C, data normalization processing；
Step D, calculate correlation coefficient；
Step E, calculation of relationship degree and determining inteerelated order.
Step F, determines water bursting source, is compared with actual conditions.
Below in conjunction with specific example, above-mentioned each step is explained.
Step A collects each water-bearing layer actual measurement data of water quality in target area and gushing water situation.
In present example, illustrated in conjunction with certain mine production practices.In 2013 water inrush accident occurs for certain mine.It is below
Projective water point and each aquifer water chemical analysis results (being shown in Table 1).
Certain mine of table 1 each aquifer water chemical analysis synthesis result mg/L in 2012
Step B establishes Grey Relational Model based on actual measurement data of water quality.
In present example, based on field data in A, Model sequence is established, it is specific as follows：
Certain the mine water bursting source distinguishing sequence of table 2 mg/L
Step C, data normalization processing.
In order to analyze more different amounts, should water quality initial data in table 2 be subjected to unitization processing first, i.e., into rower
The processing of standardization dimensionless：
X (k) is standardization sequence, X(0)K is original data sequence.Obtain standardization sequence table 3.
Table 3 standardizes sequence table mg/L
Step D, calculate correlation coefficient.
During the data to table 2 are standardized, common property gives birth to 6 standardization sequences, if X0(k) it is female sequence
Row, Xj(k) it is subsequence, then obtains sequence X0(k) ... ..., Xj(k), wherein k is comparative factor, and j is subsequence number, upper
It states in 6 sequences and removes outside an auxiliary sequence, share 5 subsequences.
If remembering Δmin=min min | X0(k)-Xj(k) | }, Δmax=max max | X0(k)-Xj(k) | }, Δj(k)=|
X0(k)-Xj(k) |, then calculation of relationship degree formula is：εj(k)=(Δmin+ρΔmax)/(Δj(k)+ρΔmax) in formula, εj(k) it is
Auxiliary sequence X0With the incidence coefficient in kth point；Δj(k)=| X0(k)-Xj(k) |, it is kth point X0With XjAbsolute difference；Δmin=
min{min|X0(k)-Xj(k) | }, bracket is interior to indicate X0Sequence and XjSequence pair answers the lowest difference of each point, and ΔminIt indicates each
On the basis of sequence lowest difference, by j=1,2 ..., n, the lowest difference in all sequences, i.e. two-stage lowest difference, Δ are found outmaxMeaning
It is similar.ρ is resolution ratio, generally takes 0.5.
Step E, calculation of relationship degree and determining inteerelated order.
By auxiliary sequence X0(k) with subsequence Xj(k) several incidence coefficient values carry out equalization operation, are denoted as rj, define rj
For subsequence Xj (k) and auxiliary sequence X0(k) the degree of association (being shown in Table 4), wherein:
Degree of association rj (j=1,2 ...) is obtained into subsequence X by numerical values recited arrangementj(k) to auxiliary sequence X0(k) pass
Join sequence.
4 subsequence of table and auxiliary sequence association table
As shown in Table 4, it is r by the inteerelated order that subsequence and auxiliary sequence determine2＜ r3＜ r4＜ r1＜ r5。
Step F, determines water bursting source, is compared with actual conditions.
According to relating sequence in step E it is found that water sample X0With water sample X5The degree of association it is maximum, value 0.9712 is much larger than
The degree of association of other water samples, accordingly deducibility water sample X0With water sample X5Tight ness rating it is higher, it can be inferred that water sample X0Also 8 be should be
Coal seam floor plate Sandstone Water, this result be actually consistent.
The present invention is based on grey correlation theories, only use the leading ion in water-bearing layer as evaluation index, analyze its pass
Connection contact, establishes model and is differentiated, prediction result is accurate and reliable, has certain novelty, and the present invention is easy to operate, is easy to
Practical application provides a kind of new approaches and method for water bursting source differentiation.
Specific example described above, to the purpose of the present invention, process and effect are described in detail, and are not used to limit this
The restriction range of invention, all within the spiritual principles of the present invention, any modification, equivalent replacement for being made etc. should be included in
Within protection scope of the present invention.
Claims (6)
1. a kind of water bursting source method of discrimination based on Grey Relational Model, which is characterized in that this method includes：
Step A collects each water-bearing layer actual measurement data of water quality in target area and gushing water situation；
Step B establishes Grey Relational Model based on actual measurement data of water quality；
Step C, data normalization processing；
Step D, calculate correlation coefficient；
Step E, calculation of relationship degree and determining inteerelated order.
Step F, determines water bursting source, is compared with actual conditions.
2. a kind of water bursting source method of discrimination based on Grey Relational Model according to claim 1, which is characterized in that step
In rapid B, based on actual measurement data of water quality, Grey Relational Model is established, detailed process is as follows：
On the basis of all previous Analysis Results of Water Quality of statistical analysis mine, using the main chemical compositions of each water sample as grey correlation
The initial data of analysis, in conjunction with the grey relational grade analysis method in gray system theory by sample point water quality and water sample to be studied
Water quality degree of being associated is analyzed, using projective water point water sample as auxiliary sequence (X0), remaining each water-bearing layer water sample is as subsequence (Xj),j
=1,2,3....
3. a kind of water bursting source method of discrimination based on Grey Relational Model according to claim 1, which is characterized in that step
Rapid C, data normalization processing, detailed process are as follows：
In order to analyze more different amounts, should water quality initial data be subjected to unitization processing first, that is, be standardized immeasurable
Guiding principle processing：
X (k) is standardization sequence, X(0)K is original data sequence.
4. a kind of water bursting source method of discrimination based on Grey Relational Model according to claim 1, which is characterized in that step
Rapid D, calculate correlation coefficient, detailed process are as follows：
εj(k)=(Δmin+ρΔmax)/(Δj(k)+ρΔmax) in formula, εj(k) it is auxiliary sequence X0With the incidence coefficient in kth point；
Δj(k)=| X0(k)-Xj(k) |, it is kth point X0With XjAbsolute difference；Δmin=min min | X0(k)-Xj(k) | }, bracket
Interior expression X0Sequence and XjSequence pair answers the lowest difference of each point, and ΔminIt indicates on the basis of each sequence lowest difference, by j=1,
2 ..., n find out the lowest difference in all sequences, i.e. two-stage lowest difference, ΔmaxMeaning is similar.ρ is resolution ratio, is generally taken
0.5。
5. a kind of water bursting source method of discrimination based on Grey Relational Model according to claim 1, which is characterized in that step
Rapid E, calculation of relationship degree and determining inteerelated order, water bursting source, detailed process are as follows：
By auxiliary sequence X0(k) with subsequence Xj(k) several incidence coefficient values carry out equalization operation, are denoted as rj, and it is son to define rj
Sequence X j (k) and auxiliary sequence X0(k) the degree of association, wherein:
Degree of association rj (j=1,2 ...) is obtained into subsequence X by numerical values recited arrangementj(k) to auxiliary sequence X0(k) association
Sequence.
6. a kind of water bursting source method of discrimination based on Grey Relational Model according to claim 1, which is characterized in that step
Rapid F, determines water bursting source, is compared with actual conditions, and concrete operations are as follows：
In incidence degree sequence, the maximum degree of association is water bursting source.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110261560A (en) * | 2019-07-05 | 2019-09-20 | 安徽大学 | The water source recognition methods of complex hydrologic geology water bursting in mine and system |
| CN111553591A (en) * | 2020-04-27 | 2020-08-18 | 上海市水务规划设计研究院 | Plain river network water resource early warning regulation and control method, early warning regulation and control system and electronic equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103617147A (en) * | 2013-11-27 | 2014-03-05 | 中国地质大学（武汉） | Method for identifying mine water-inrush source |
| WO2016187347A1 (en) * | 2015-05-18 | 2016-11-24 | Corcept Therapeutics, Inc. | Methods for diagnosing and assessing treatment for cushing's syndrome |
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2018
- 2018-03-21 CN CN201810233684.8A patent/CN108389135A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103617147A (en) * | 2013-11-27 | 2014-03-05 | 中国地质大学（武汉） | Method for identifying mine water-inrush source |
| WO2016187347A1 (en) * | 2015-05-18 | 2016-11-24 | Corcept Therapeutics, Inc. | Methods for diagnosing and assessing treatment for cushing's syndrome |
Non-Patent Citations (1)
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| 郝彬彬等: "灰色关联度在矿井突水水源判别中的应用", 《中国煤炭》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110261560A (en) * | 2019-07-05 | 2019-09-20 | 安徽大学 | The water source recognition methods of complex hydrologic geology water bursting in mine and system |
| CN111553591A (en) * | 2020-04-27 | 2020-08-18 | 上海市水务规划设计研究院 | Plain river network water resource early warning regulation and control method, early warning regulation and control system and electronic equipment |
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