CN109345140B - Auxiliary method for early warning of water inrush disaster of coal mine - Google Patents
Auxiliary method for early warning of water inrush disaster of coal mine Download PDFInfo
- Publication number
- CN109345140B CN109345140B CN201811293749.4A CN201811293749A CN109345140B CN 109345140 B CN109345140 B CN 109345140B CN 201811293749 A CN201811293749 A CN 201811293749A CN 109345140 B CN109345140 B CN 109345140B
- Authority
- CN
- China
- Prior art keywords
- evaluation
- index
- risk
- spatial
- file
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 125
- 239000003245 coal Substances 0.000 title claims abstract description 104
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000011156 evaluation Methods 0.000 claims abstract description 209
- 238000012544 monitoring process Methods 0.000 claims abstract description 51
- 238000013528 artificial neural network Methods 0.000 claims abstract description 22
- 238000011160 research Methods 0.000 claims abstract description 22
- 238000012896 Statistical algorithm Methods 0.000 claims abstract description 19
- 238000005553 drilling Methods 0.000 claims description 32
- 239000011435 rock Substances 0.000 claims description 31
- 238000005516 engineering process Methods 0.000 claims description 16
- 238000012549 training Methods 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000005065 mining Methods 0.000 claims description 9
- 238000012732 spatial analysis Methods 0.000 claims description 9
- 210000002569 neuron Anatomy 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000011161 development Methods 0.000 claims description 6
- 238000011002 quantification Methods 0.000 claims description 6
- 230000009471 action Effects 0.000 claims description 2
- 238000007789 sealing Methods 0.000 claims description 2
- 230000008878 coupling Effects 0.000 description 8
- 238000010168 coupling process Methods 0.000 description 8
- 238000005859 coupling reaction Methods 0.000 description 8
- 238000009826 distribution Methods 0.000 description 4
- 238000013210 evaluation model Methods 0.000 description 3
- 238000011031 large-scale manufacturing process Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- IYLGZMTXKJYONK-ACLXAEORSA-N (12s,15r)-15-hydroxy-11,16-dioxo-15,20-dihydrosenecionan-12-yl acetate Chemical compound O1C(=O)[C@](CC)(O)C[C@@H](C)[C@](C)(OC(C)=O)C(=O)OCC2=CCN3[C@H]2[C@H]1CC3 IYLGZMTXKJYONK-ACLXAEORSA-N 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 150000002500 ions Chemical class 0.000 description 1
- 230000009916 joint effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 239000002243 precursor Substances 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 239000005871 repellent Substances 0.000 description 1
- IYLGZMTXKJYONK-UHFFFAOYSA-N ruwenine Natural products O1C(=O)C(CC)(O)CC(C)C(C)(OC(C)=O)C(=O)OCC2=CCN3C2C1CC3 IYLGZMTXKJYONK-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Primary Health Care (AREA)
- Animal Husbandry (AREA)
- General Health & Medical Sciences (AREA)
- Mining & Mineral Resources (AREA)
- Marine Sciences & Fisheries (AREA)
- Agronomy & Crop Science (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Alarm Systems (AREA)
Abstract
The invention provides an auxiliary method for early warning of water inrush disasters in a coal mine, which comprises the following steps: establishing an evaluation index system of a key monitoring area, and calculating the continuity evaluation index data of any point in the research area through kriging interpolation to obtain the continuity evaluation index data of the space evaluation points in the research area; converting the discontinuous evaluation index into a continuous evaluation index through the risk index; obtaining at least one evaluation index through a statistical algorithm of the evaluation indexes; and inputting the at least one evaluation index into a BP artificial neural network model designed by ANN, outputting risk factors of each spatial evaluation point, and determining a key monitoring area. The auxiliary method for early warning of the coal mine water inrush disaster can perform grade evaluation on a coal mine water inrush early warning monitoring area, define key monitoring positions, optimize the field arrangement of an early warning system, and improve the early warning capability and the early warning accuracy rate of the early warning system.
Description
Technical Field
The invention belongs to the technical field of coal mine water inrush disaster early warning, and particularly relates to an auxiliary method for coal mine water inrush disaster early warning.
Background
Coal mine water inrush is one of five major disasters of coal mines, often causes great property loss and casualties, and has become an important influence factor for restricting coal mine safety production. The method can be used for rapidly and accurately predicting water inrush, is a guarantee for coal mine safety production, and has very important theoretical guiding significance and practical value for accurately predicting, predicting and evaluating the water inrush.
In order to reduce the occurrence of water damage of coal mines, various coal mine water inrush disaster early warning methods exist in the prior art, such as a coal mine water inrush disaster early warning technology which utilizes a photography method and a water quality monitoring method to perform early warning of undersea coal mine water inrush disasters, performs chemical early warning on coal mine water inrush disasters through a water quality monitoring sensor, and performs early warning, monitoring and arrangement and information acquisition for comprehensive monitoring based on a coal mine water inrush disaster standard discrimination and early warning level determination and a monitoring and early warning index system for dividing water inrush modes and water inrush disasters. In the aspect of monitoring early warning indexes, the coal mine water inrush disaster early warning method realizes real-time monitoring of indexes such as water temperature, water pressure, stress, strain, water barrier resistivity, acoustic emission, water chemical ions and the like, the coal mine water inrush disaster early warning method is gradually mature, and the early warning accuracy rate is gradually improved.
However, coal mine water inrush is a multi-factor and nonlinear space problem, coal mine water inrush prediction relates to hydrogeology, rock mechanics, mining conditions and other factors, complex nonlinear relations exist among the factors, and the difficulty in predicting the position of a water inrush point is high. The coal mine water inrush disaster early warning method in the prior art is complex in data preprocessing, high in modeling precision requirement and unsatisfactory in modeling effect, and early warning failure is caused by the fact that precursor information of water inrush is not monitored and the monitoring position is different from the actual water inrush position. Therefore, how to provide an auxiliary method for coal mine water inrush disaster early warning is of great importance in predicting potential water inrush areas and determining key monitoring areas for coal mine water inrush disaster early warning, and is a technical problem to be solved urgently in coal mine water inrush disaster early warning at present.
Disclosure of Invention
In order to solve the problems, the invention aims to provide an auxiliary method for coal mine water inrush disaster early warning, aiming at the defects and shortcomings in the background art, and the auxiliary method can be used for carrying out grade evaluation on a coal mine water inrush early warning monitoring area, defining a key monitoring area for a coal mine water inrush disaster early warning system, optimizing the field arrangement of electrodes, sensors and the like of the early warning system, and improving the early warning capability and the early warning accuracy rate of the early warning system. Meanwhile, the auxiliary method for early warning of the water inrush disaster of the coal mine has the advantages of low implementation cost, convenience in operation and convenience for large-scale production and application.
In order to achieve the purpose, the invention adopts the technical scheme that:
an auxiliary method for early warning of water inrush disaster in coal mines comprises the following steps:
(1) establishing an evaluation index system of a key monitoring area of the coal mine water inrush disaster early warning system, wherein the evaluation index system comprises a continuity evaluation index and a discontinuity evaluation index;
(2) calculating the data of the continuity evaluation index of any point in the research area through kriging interpolation to obtain the data of the continuity evaluation index of the space evaluation point in the research area, and realizing the quantification of the continuity evaluation index;
(3) converting the discontinuous evaluation index into a continuous evaluation index through a risk index, wherein the risk index is calculated according to the formula (1):
in formula (1): k is a risk index of the space evaluation point, L is the length of the safe coal rock pillar, and d is the space distance from the space evaluation point to each evaluation index;
(4) designing data structures of continuity evaluation indexes and discontinuity evaluation index source data, and generating basic data files of all spatial evaluation points in a research area; obtaining at least one evaluation index of the continuity evaluation index and the discontinuity evaluation index of each spatial evaluation point through a statistical algorithm of the evaluation indexes; and inputting the at least one evaluation index into a BP artificial neural network model designed by ANN, outputting risk factors of each spatial evaluation point, and determining a key monitoring area.
Further, the value range of the risk index is [0,1], and the larger the value of the risk index is, the more dangerous the water inrush is.
Further, the continuity evaluation indexes comprise aquifer water pressure, aquifer water-rich property and aquifer water-proof coal rock pillar; the non-continuity evaluation indexes comprise fault risk indexes, collapse column risk indexes, old goaf risk indexes and closed bad borehole risk indexes.
Further, the fault risk indexLength L of medium-safety coal rock pillar1Is the formula (2):
in formula (2): m is the thickness or mining height of the coal bed, and P is the water head pressure; kpTensile strength of the coal; k1A safety factor is set; spatial distance d from spatial evaluation point to fault1Can be obtained by the space analysis of the midpoint and the line of the GIS, and the L is obtained1、d1A fault risk index at a spatial evaluation point is obtained by substituting the formula (1).
Further, the length L of the safe coal rock column in the danger index of the collapse column2The calculation formula is formula (3):
L2＝R-α+l(3)
in formula (3): r is a plastic radius formed by the collapse column under the combined action of stope stress and internal water pressure of the collapse column, alpha is the column radius of the collapse column, and l is the advancing development distance of the working face in the plastic zone; spatial distance d from spatial evaluation point to boundary of trapping column2Spatial evaluation points d inside the column, determined by the spatial analysis of points and polygons2When being equal to 0, L is2、d2The risk index of the collapse column at the spatial evaluation point was obtained by substituting the formula (1).
Further, the shape of the collapse column is not a nearly cylindrical body, and the safety coal rock column length L in the danger index of the collapse column2The formula (2) gives.
Further, the length L of the safe coal rock pillar in the danger index of the old goaf3The distance from the waterline to the water detecting line is extrapolated in parallel in the old empty area; spatial distance d from spatial evaluation point to boundary of goaf3The spatial evaluation point order d inside the boundary of the goaf is obtained by the spatial analysis of the point and the polygon3When being equal to 0, L is3、d3And (3) substituting the formula (1) to obtain the old goaf risk index of the space evaluation point.
Further, the safe pillar length in the blind hole risk indexL4The calculation formula is formula (4):
in formula (4): lrFor advancing development distance of mining fracture zone, raIs the radius of the drilled hole, p is the water pressure in the hole, c is the coal seam cohesion,
is the internal friction angle of the coal seam,
distance d from space evaluation point to poorly closed borehole4Can be obtained by GIS midpoint and point space analysis technology, and L is obtained4、d4And (3) substituting the formula (1) to obtain the drilling risk index of the poor sealing of the space evaluation point.
Further, the source data comprises spatial data and attribute data such as drilling data, faults, collapse columns, old dead zones and closed bad boreholes, and the data structure is realized by using a Shape file provided by MapObjects constructed by a GIS; the Shape file comprises an evaluation point file, an old dead zone statistical file, a drilling statistical file, a fault statistical file, a collapse column statistical file and a closed bad drilling statistical file, wherein the evaluation point file, the drilling statistical file and the closed bad drilling statistical file are point files, the fault statistical file is a line file, and the old dead zone statistical file and the collapse column statistical file are face files; and generating the basic data file through the vectorization of the Shape file.
Further, the statistical algorithm of the evaluation index comprises a continuity evaluation index statistical algorithm and a non-continuity evaluation index statistical algorithm, wherein the continuity evaluation index statistical algorithm is solved through kriging interpolation according to the spatial position and the field attribute information of the drill hole in the drill hole statistical file; the non-continuity evaluation index statistical algorithm is that a buffer area is generated by taking the length of a safe coal rock pillar as a radius, the spatial position relation between the buffer area and a spatial evaluation point is analyzed by utilizing a GIS spatial analysis technology, the spatial evaluation points which are positioned on the boundary of the buffer area and outside the boundary of the buffer area have the risk indexes of 0; a space evaluation point which is positioned in the buffer area and outside the non-continuity evaluation index boundary is used for solving a corresponding risk index through the formula (1); and (3) the risk indexes of the space evaluation points positioned on and in the boundary of the non-continuity evaluation index are all 1, and the risk indexes obtained by the non-continuity evaluation index statistical algorithm are written into corresponding fields.
Further, the BP artificial neural network model comprises three layers, namely an input layer, a middle layer and an output layer; and the input layer inputs the at least one evaluation index, the output layer comprises a neuron, the output layer outputs a risk factor, the selected training sample is substituted into the BP artificial neural network model for training, and the number of the neuron in the middle layer is optimized according to multiple training results.
Further, by utilizing a GIS grading display function, each space evaluation point is graded and displayed according to the size of the risk factor, and a key monitoring area is determined.
Further, selecting a space evaluation point according to an exploration line of a research area, further optimizing a risk factor of the selected space evaluation point by combining with increasing the risk index of a discontinuous evaluation index boundary and/or an internal space evaluation point, and generating a trend surface graph and a residual value contour graph by combining with a kriging interpolation and contour drawing technology so as to define a key monitoring area.
The auxiliary method for early warning of the water inrush disaster of the coal mine has the beneficial effects that: based on a GIS (geographic information system) and ANN (artificial neural network) coupling technology, the grade evaluation can be carried out on the coal mine water inrush early warning monitoring area; by establishing a key monitoring area evaluation index system, converting discontinuous indexes into continuous indexes, quantizing the evaluation indexes and carrying out a statistical algorithm of the evaluation indexes, key monitoring positions are defined for the coal mine water inrush disaster early warning system, the field arrangement of electrodes, sensors and the like of the early warning system is optimized, and the early warning capacity and the early warning accuracy rate of the early warning system are improved. Meanwhile, the auxiliary method for early warning of the water inrush disaster of the coal mine has the advantages of low implementation cost, convenience in operation and convenience for large-scale production and application.
In a word, the invention provides an auxiliary method for coal mine water inrush disaster early warning, which has high prediction accuracy and strong practicability, and has wide application prospect.
Drawings
FIG. 1 is a schematic flow chart of an auxiliary method for early warning of water inrush disaster in coal mine according to the present invention;
FIG. 2 is a flowchart illustrating an embodiment of delineating a key monitoring area based on GIS and ANN coupled evaluation according to the present invention;
fig. 3 is a distribution diagram of the emphasized monitoring region in the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to specific examples. Note that the following described embodiments are illustrative only for explaining the present invention, and are not to be construed as limiting the present invention. The examples, where specific techniques or conditions are not indicated, are to be construed according to the techniques or conditions described in the literature in the art or according to the product specifications.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
The auxiliary method for coal mine water inrush disaster early warning of the invention is explained in detail with reference to the attached drawings as follows:
the invention provides an auxiliary method for early warning of water inrush disasters in coal mines, which comprises the following steps:
(1) establishing a key monitoring area evaluation index system
According to the analysis of hydrogeological conditions, whether a certain area is a potential water inrush danger area or not is evaluated mainly through two aspects of a water filling source and a water guide channel. The water filling source influencing factors mainly comprise water layer water pressure, water-rich property of a water-containing layer, water-proof coal rock pillars of the water-containing layer and the like; the water guide channel influence factors mainly comprise a collapse column, a fault, a poorly closed borehole and the like. According to the spatial distribution characteristics of the influence factors, the key monitoring area evaluation index system can be divided into a continuity evaluation index system and a non-continuity evaluation index system, wherein the continuity evaluation index mainly comprises aquifer water pressure, aquifer water-richness and aquifer waterproof coal pillars, and the non-continuity evaluation index mainly comprises a fault risk index, a collapse column risk index, an old goaf risk index and a closed bad borehole risk index.
(2) Continuity evaluation index quantification
The water pressure of the aquifer, the water enrichment of the aquifer and the water-proof and water-proof coal rock pillars of the aquifer are distributed continuously in space, and the area with the larger water pressure of the aquifer, the stronger water enrichment and the smaller water-proof coal rock pillars has water inrush danger, so that the area can be divided into potential water inrush danger areas, namely key monitoring areas arranged on the site of the coal mine water inrush disaster early warning system. Through technical means such as on-site drilling, hydrological holes and hydrological observation holes, relevant data of water pressure of an aquifer, water enrichment of the aquifer and a water-proof and water-proof coal rock pillar of the aquifer at any point in a research area are researched, and then continuity evaluation indexes including the water pressure of the aquifer, the water enrichment of the aquifer and the water-proof and water-proof coal rock pillar of the aquifer at space evaluation points in the research area are obtained through kriging interpolation calculation, so that quantification of the continuity evaluation indexes is achieved.
(3) Conversion of non-continuity evaluation index into continuity index and quantification
The fault, the collapse column, the old vacant area and the sealed bad drilling hole are distributed in the space mainly by being concentrated on a certain line or a band, an area or a point, have no continuity and are discontinuous indexes. The discontinuity index is not easy to quantify and input into an evaluation model, and the influence of the discontinuity index is limited to the spatial position of the discontinuity index, is a fixed value, cannot reflect the change trend of the influenced area, and is not consistent with the actual situation. Therefore, the non-continuity evaluation index is converted into the continuity evaluation index, which is beneficial to establishing an evaluation model and improving the evaluation precision. The invention converts the discontinuous evaluation index into the continuous evaluation index, introduces the risk index, the value range of the risk index is [0,1], the larger the value is, the more the water inrush risk exists, and the calculation formula is formula (1):
in formula (1): k is the risk index of a certain spatial evaluation point EP in the space, L is the length of the safe coal rock pillar, and d is the spatial distance from the EP point to each evaluation index.
The non-continuity evaluation index can be converted into the continuity evaluation index by the formula (1). The method for solving the risk index of each non-continuity evaluation index is as follows:
fault risk index: the fault refers to a fault containing water diversion or having water inrush risk, and the length L of the safe coal pillar of the fault1Is the formula (2):
in formula (2): m is the thickness or mining height of the coal bed, and P is the water head pressure; kpTensile strength of the coal; k1A safety factor is set; taking 2-5.
Distance d from one spatial evaluation point EP to fault1Can be obtained by the space analysis of the midpoint and the line of the GIS, and the L is obtained1、d1The fault risk index of the EP point can be obtained by substituting the formula (1).
Collapse column hazard index: similar to the fault influence index, the distance d from a certain spatial evaluation point EP to the boundary of the trapping column2Spatial evaluation points d inside the column, determined by the spatial analysis of points and polygons2The length L of the collapse column safety coal rock column is equal to 02The calculation formula is formula (3):
L2＝R-α+l(3)
in formula (3): r is a plastic radius formed by the collapse column under the joint action of stope stress and water pressure inside the collapse column, alpha is the column radius of the collapse column, and l is the advancing development distance of the working face in the plastic zone.
If the shape of the collapse column is not nearly circular, L can be calculated by the formula (2), and L is calculated by the same method2、d2The risk index of the collapse column of the EP point can be obtained by substituting the formula (1).
Old goaf risk index: safety coal rock pillar length L in old goaf3Can be determined by the three-line division principle of the old goaf, L3The distance between the accumulated waterline of the goaf and the detection waterline is equal to the distance between the accumulated waterline of the goaf and the detection waterline, which is extrapolated in parallel, and is specifically shown in table 1. Distance d from one space evaluation point EP to boundary of goaf3The solving method of (1) is the same as that of the collapse column, and the point order d positioned in the boundary of the old empty area30. Mixing L with3、d3The old empty area risk index of the EP point can be obtained by substituting the formula (1).
TABLE 1 Laobai Water exploring line division basis (m)
Risk index of poorly closed borehole: distance d from one space evaluation point EP to poorly closed borehole4The length L of the coal rock pillar with poor drilling safety can be closed by solving the problem by using a GIS midpoint and point space analysis technology4The calculation formula is formula (4):
in formula (4): lrFor advancing development distance of mining fracture zone, raIs the radius of the drilled hole, p is the water pressure in the hole, c is the coal seam cohesion,
is the internal friction angle of the coal seam,
mixing L with4、d4Can be obtained by substituting the formula (1)The EP point's closure poor borehole risk index.
According to the method, through risk indexes and calculation, non-continuity evaluation indexes including fault risk indexes, collapse column risk indexes, old goaf risk indexes and closed bad borehole risk indexes are converted into continuity evaluation indexes, and quantification of the non-continuity evaluation indexes is achieved.
(4) Key monitoring area is demarcated based on GIS and ANN coupling evaluation
The method is based on a GIS and ANN coupling technology design evaluation model to determine the key monitoring area, and is specifically realized as follows:
a. selecting space evaluation points, generating various required basic data files according to the designed data structure vectorization, and solving six evaluation indexes or partial indexes of the six evaluation indexes, such as aquifer water pressure, aquifer water-rich property, aquifer waterproof and water-proof coal rock pillar thickness, fault risk index, collapse pillar risk index, old goaf risk index, closed bad borehole risk index and the like, of each space evaluation point in a research area according to the statistical algorithm of the evaluation indexes. And then extracting a space evaluation point with higher research degree from the space evaluation point, and taking the space evaluation point as a training sample and an inspection sample by participating in evaluation of the risk through a computer and experts, wherein the same space evaluation point can not be used as both the training sample and the inspection sample.
b. And designing a three-layer BP artificial neural network model by using the ANN, wherein an input layer of the model can be one or more of the six evaluation indexes according to the actual situation, an output layer is a neuron, namely a risk factor, the output value range of the value is [0,1], and the larger the value is, the larger the water inrush risk is, the more important monitoring position is. And substituting the selected training samples into a BP model for training, and optimizing the number of neurons in the middle layer according to multiple training results. And after the training convergence is finished, substituting the test samples to check the applicability of the BP model, if the field requirement is met, substituting indexes of other spatial evaluation points into the model to respectively calculate the risk factors of the model, and writing the risk factors into corresponding spatial evaluation point risk factor fields by utilizing a GIS system editing function.
c. And (4) utilizing a GIS grading display function to grade and display each space evaluation point according to the risk factor, and determining a key monitoring area.
The data structure based on GIS and ANN coupling evaluation in the step (4) of the auxiliary method for coal mine water inrush disaster early warning is as follows:
the source data required by the statistical calculation evaluation index mainly comprise space data and attribute data such as drilling data, faults, collapse columns, old dead zones and closed bad drilling holes, for example, the source data faults are spatially distributed as space data, and the corresponding length of the safe coal rock columns is attribute data. The GIS is utilized to build the Shape file provided by MapObjects, so that the efficient management of the spatial data and the attribute data can be realized. The Shape file is composed of a series of files, wherein necessary basic files comprise a coordinate file (. shp), an index file (. shx) and an attribute file (. dbf), the shp file stores the spatial position of each primitive, the dbf file stores the attribute information of each primitive, the shp file establishes the spatial position of each primitive in the shp file and the attribute information of each corresponding primitive in the dbf file, and unified management of the spatial data and the attribute data is achieved.
The type of Shape file can be divided into point, line and surface files, and a point object, a linear object and a surface object on a space are respectively stored. The invention designs six Shape files according to each index of a space evaluation point: the method comprises the following steps of obtaining a space evaluation point file, an old dead zone statistical file, a drilling statistical file, a fault statistical file, a collapse column statistical file and a closed bad drilling statistical file, wherein the space evaluation point file, the drilling statistical file and the closed bad drilling statistical file are point files, the fault statistical file is a line file, and the old dead zone statistical file and the collapse column statistical file are surface files. The structure of the database table of the dbf file is shown in a table 2 and comprises seven fields: risk factor (WXZS), aquifer hydraulic pressure (HSCSY), aquifer water-richness (hscsx), aquifer water-repellent coal-rock column thickness (FSMYZHD), fault risk index (DCWXZS), collapse column risk index (XLZWXZS), goaf risk index (LKQWXZS), and blind hole risk index (BLZKWXZS).
Table 2 spatial evaluation points dbf file database table structure
The hole statistics file dbf file contains 3 fields: aquifer water pressure (HSCSY), aquifer coal pillar thickness (HSCMYHD), water enrichment index (FSXZS), field types are double precision, fault statistics file, goaf statistics file, collapse pillar statistics file and seal bad borehole statistics file dbf file has only 1 field: the length (L) of the safe coal rock pillar is double-precision.
According to the auxiliary method for coal mine water inrush disaster early warning, a basic data file based on GIS and ANN coupling evaluation in step (4) is generated as follows:
respectively and newly building a space evaluation point file, an old dead zone statistical file, a drilling statistical file, a fault statistical file, a collapse column statistical file and a closed bad drilling statistical file by using an algorithm system, and then realizing the vectorization of the data file by the following method:
spatial evaluation point file: and (3) introducing the CAD format of the research area into a project plan, and uniformly generating spatial evaluation points every 10m or 20m in the research area. At this time, each field of the spatial evaluation point is empty.
Drilling hole statistical file: creating an EXCEL file, respectively establishing five columns of x, y, HSCSY, HSCMYHD and FSXZS, counting relevant data of drilling holes inside or around a research area, writing the relevant data into the corresponding column, and if the drilling holes have no information of relevant fields, assigning the field to be-1. And generating a statistical point space primitive by using x and y by reading the EXCEL data file, and then reading corresponding HSCSY, HSCMYHD and FSXZS information to assign values to fields related to the statistical point space primitive, thereby completing the batch drilling vectorization. The vectorization method of the statistical file of the badly closed drill holes is the same as the vectorization method of the drill holes.
Fault statistical file, old dead zone statistical file, collapse column statistical file: importing a mining engineering plan in a CAD format in a research area into a system, then respectively drawing all faults, old dead zones and collapse columns in the plan in a fault statistical file, an old dead zone statistical file and a collapse column statistical file, calculating and solving the lengths of the corresponding safe coal rock columns by the method, and then writing the lengths into corresponding fields of the faults, the old dead zones or the collapse columns by utilizing an attribute interactive editing function.
The statistical algorithm of the evaluation indexes based on GIS and ANN coupling evaluation in the step (4) of the auxiliary method for coal mine water inrush disaster early warning is as follows:
for three continuity evaluation indexes of water pressure of an aquifer, water richness of the aquifer and water-proof coal-rock pillar thickness of the aquifer, the three continuity evaluation indexes can be obtained by solving through kriging interpolation according to corresponding drilling space positions and corresponding field attribute information in the drilling statistical file. For example, the aquifer water pressure at a certain spatial evaluation point EP can be obtained by establishing a kriging spatial interpolation model from the coordinates of all the boreholes and the aquifer water pressure (except for the water pressure of-1) in the borehole statistical file, and then substituting x and y of the EP point into the model.
And generating buffer areas of all elements by taking L as a radius for four non-continuity evaluation indexes of a fault risk index, a collapse column risk index, an old goaf risk index and a closed bad drilling risk index. And analyzing the spatial position relation between the buffer area and the spatial evaluation points by using a GIS spatial analysis technology, wherein the risk indexes of the buffer area boundary and the spatial evaluation points except the buffer area boundary are 0. And (3) calculating the distance d from each spatial evaluation point to the element boundary by using a spatial analysis technology at the spatial evaluation points positioned inside the buffer area and outside the element boundary, then substituting d and L into the formula (1) to solve the corresponding risk index, and writing the risk index into a field corresponding to each spatial evaluation point. The corresponding risk index of spatial evaluation points located on and within the boundaries of the elements, such as fault lines, inside old goafs, inside sunken columns, and spatial evaluation points coinciding with poorly closed boreholes, is assigned a value of 1. For example, if a certain spatial evaluation point EP is affected by a plurality of faults at the same time, a plurality of fault risk indexes are obtained, and then the maximum value is written in the DCWXZS field of the EP point.
According to the auxiliary method for coal mine water inrush disaster early warning, a key monitoring area is defined based on GIS and ANN coupling evaluation in the step (4), a space evaluation point can be preferentially selected according to a survey line of a research area, then a risk factor of the selected space evaluation point is further optimized by combining optimization technologies such as increasing the risk index of element boundaries such as a collapse column and the like or internal space evaluation points, a trend surface graph and a residual value contour graph are generated by combining kriging interpolation and contour drawing technologies, and the key monitoring area is further defined.
Examples
In the embodiment, the monitoring and early warning of the bottom plate of the stope face are taken as an example, the field arrangement of a coal mine water inrush disaster early warning system is optimized for defining key monitoring areas, and the auxiliary method for coal mine water inrush disaster early warning provided by the invention is used for evaluating a certain working face monitoring area.
The mining of the working face is mainly influenced by an aquifer and a collapse column, and sufficient aquifer water-rich data does not exist, so that only four evaluation indexes of aquifer water pressure, aquifer water-proof coal rock columns, fault danger indexes and collapse column danger indexes are selected in the evaluation process.
According to main drilling data (see table 3) in and around the working surface and the drawings in the CAD format of the research area, drilling statistical files, fault statistical files and collapse column statistical files are respectively established according to the method, and then uniform statistical points are generated in the research area every 10 m.
TABLE 3 Main borehole data in and around recovery face
The method comprises the steps of automatically counting field values of HSCSY, FSMYZHD, DCWXZS and XLZWXZS of each spatial evaluation point by the aid of the method, selecting training samples and test samples, designing a 3-layer BP artificial neural network model, using HSCSY, FSMYZHD, DCWXZS and XLZWXZS as input layer neurons and WXZS as output layer neurons, obtaining risk factors of each spatial evaluation point through steps of training, solving and the like, writing the risk factors into WXZS fields of the corresponding spatial evaluation points, optimizing the spatial evaluation points and the WXZS values, and generating a key monitoring area distribution map by means of kriging interpolation and isoline drawing technology, wherein the key monitoring area distribution map is shown in figure 3.
As can be seen from fig. 3, there are 7 important monitoring areas in the research area, namely 1# to 7# areas, wherein 4# to 7# areas are located outside the stope line, so 1#, 2# and 3# areas are listed as important monitoring areas. In the key monitoring area, key monitoring is realized by means of arranging electrodes in an encrypted manner, embedding sensors in construction drill holes and the like. Meanwhile, when the coal face approaches a key monitoring area, the index acquisition interval is shortened, the acquisition index signal is encrypted, and the safe stoping of the face is realized. Through early warning practice, the defined key monitoring area is the area with the most abnormal occurrence, and the auxiliary method for coal mine water inrush disaster early warning provided by the invention is proved to be feasible and effective.
The auxiliary method for coal mine water inrush disaster early warning of the sealed seed source is based on a GIS (geographic information system) and ANN (artificial neural network) coupling technology, and can be used for carrying out grade evaluation on a coal mine water inrush early warning monitoring area; by establishing a key monitoring area evaluation index system, converting discontinuous indexes into continuous indexes, quantizing the evaluation indexes and carrying out a statistical algorithm of the evaluation indexes, key monitoring positions are defined for the coal mine water inrush disaster early warning system, the field arrangement of electrodes, sensors and the like of the early warning system is optimized, and the early warning capacity and the early warning accuracy rate of the early warning system are improved. Meanwhile, the auxiliary method for early warning of the water inrush disaster of the coal mine has the advantages of low implementation cost, convenience in operation and convenience for large-scale production and application.
In a word, the invention provides an auxiliary method for coal mine water inrush disaster early warning, which has high prediction accuracy and strong practicability, and has wide application prospect.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Claims (7)
1. An auxiliary method for early warning of water inrush disaster in coal mines is characterized by comprising the following steps: the method comprises the following steps:
(1) establishing an evaluation index system of a key monitoring area of the coal mine water inrush disaster early warning system, wherein the evaluation index system comprises a continuity evaluation index and a discontinuity evaluation index; the continuity evaluation indexes comprise water pressure of an aquifer, water enrichment of the aquifer and a water-proof coal rock pillar of the aquifer; the non-continuity evaluation indexes comprise fault risk indexes, collapse column risk indexes, old goaf risk indexes and closed bad borehole risk indexes;
(2) calculating the data of the continuity evaluation index of any point in the research area through kriging interpolation to obtain the data of the continuity evaluation index of the space evaluation point in the research area, and realizing the quantification of the continuity evaluation index;
(3) converting the discontinuous evaluation index into a continuous evaluation index through a risk index, wherein the risk index is calculated according to the formula (1):
in formula (1): k is a risk index of the space evaluation point, L is the length of the safe coal rock pillar, and d is the space distance from the space evaluation point to each evaluation index;
length L of safe coal rock pillar in fault danger index1Is the formula (2):
in formula (2): m is the thickness or mining height of the coal bed, and P is the water head pressure; kpTensile strength of the coal; k1A safety factor is set; spatial distance d from spatial evaluation point to fault1Through the spatial analysis of the midpoint and the line of the GIS, L is obtained1、d1Substituting formula (1) to obtain a fault risk index of a space evaluation point;
safe coal rock column length L in danger index of collapse column2The calculation formula is formula (3):
L2＝R-α+l (3)
in formula (3): r is a plastic radius formed by the collapse column under the combined action of stope stress and internal water pressure of the collapse column, alpha is the column radius of the collapse column, and l is the advancing development distance of the working face in the plastic zone; spatial distance d from spatial evaluation point to boundary of trapping column2Spatial evaluation points d inside the column, determined by the spatial analysis of points and polygons2When being equal to 0, L is2、d2Substituting formula (1) to obtain the danger index of the collapse column of the space evaluation point;
the length L of the safe coal rock pillar in the danger index of the old goaf3The distance from the waterline to the water detecting line is extrapolated in parallel in the old empty area; spatial distance d from spatial evaluation point to boundary of goaf3The position in the air is obtained by the space analysis of points and polygonsSpatial evaluation points within zone boundaries3When being equal to 0, L is3、d3Substituting an expression (1) to obtain the risk index of the old goaf at the space evaluation point;
safe coal rock pillar length L in the risk index of poor closed borehole4The calculation formula is formula (4):
in formula (4): lrFor advancing development distance of mining fracture zone, raIs the radius of the drilled hole, p is the water pressure in the hole, c is the coal seam cohesion,
is the internal friction angle of the coal seam,
distance d from space evaluation point to poorly closed borehole4Calculated by GIS midpoint and point space analysis technology, and L is calculated4、d4Substituting an expression (1) and calculating the risk index of the drilling hole with poor sealing of the space evaluation point;
(4) designing data structures of continuity evaluation indexes and discontinuity evaluation index source data, and generating basic data files of all spatial evaluation points in a research area; obtaining at least one evaluation index of the continuity evaluation index and the discontinuity evaluation index of each spatial evaluation point through a statistical algorithm of the evaluation indexes; and inputting the at least one evaluation index into a BP artificial neural network model designed by ANN, outputting risk factors of each spatial evaluation point, and determining a key monitoring area.
2. The auxiliary method for coal mine water inrush disaster warning as claimed in claim 1, wherein: the value range of the risk index is [0,1], and the larger the value of the risk index is, the more dangerous is the water inrush.
3. The auxiliary method for coal mine water inrush disaster warning as claimed in claim 2, wherein: the source data comprises spatial data and attribute data such as drilling data, faults, collapse columns, old dead zones and closed bad boreholes, and the data structure is realized by using a Shape file provided by MapObjects constructed by a GIS; the Shape file comprises an evaluation point file, an old dead zone statistical file, a drilling statistical file, a fault statistical file, a collapse column statistical file and a closed bad drilling statistical file, wherein the evaluation point file, the drilling statistical file and the closed bad drilling statistical file are point files, the fault statistical file is a line file, and the old dead zone statistical file and the collapse column statistical file are face files; and generating the basic data file through the vectorization of the Shape file.
4. The auxiliary method for coal mine water inrush disaster warning as claimed in claim 3, wherein: the statistical algorithm of the evaluation index comprises a continuity evaluation index statistical algorithm and a non-continuity evaluation index statistical algorithm, wherein the continuity evaluation index statistical algorithm is solved through kriging interpolation according to the corresponding drill hole space position and field attribute information in the drill hole statistical file; the non-continuity evaluation index statistical algorithm is that a buffer area is generated by taking the length of a safe coal rock pillar as a radius, the spatial position relation between the buffer area and a spatial evaluation point is analyzed by utilizing a GIS spatial analysis technology, the spatial evaluation points which are positioned on the boundary of the buffer area and outside the boundary of the buffer area have the risk indexes of 0; a space evaluation point which is positioned in the buffer area and outside the non-continuity evaluation index boundary is used for solving a corresponding risk index through the formula (1); and (3) the risk indexes of the space evaluation points positioned on and in the boundary of the non-continuity evaluation index are all 1, and the risk indexes obtained by the non-continuity evaluation index statistical algorithm are written into corresponding fields.
5. The auxiliary method for coal mine water inrush disaster warning as claimed in claim 1, wherein: the BP artificial neural network model comprises three layers, namely an input layer, a middle layer and an output layer; and the input layer inputs the at least one evaluation index, the output layer comprises a neuron, the output layer outputs a risk factor, the selected training sample is substituted into the BP artificial neural network model for training, and the number of the neuron in the middle layer is optimized according to multiple training results.
6. The auxiliary method for coal mine water inrush disaster warning as claimed in claim 5, wherein: and (4) utilizing a GIS grading display function to grade and display each space evaluation point according to the size of the risk factor, and determining a key monitoring area.
7. The auxiliary method for coal mine water inrush disaster warning as claimed in claim 6, wherein: selecting a space evaluation point according to an exploration line of a research area, further optimizing a risk factor of the selected space evaluation point by combining with increasing the risk index of a discontinuous evaluation index boundary and/or an internal space evaluation point, and generating a trend surface graph and a residual value contour graph by combining with a kriging interpolation and contour drawing technology so as to define a key monitoring area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811293749.4A CN109345140B (en) | 2018-11-01 | 2018-11-01 | Auxiliary method for early warning of water inrush disaster of coal mine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811293749.4A CN109345140B (en) | 2018-11-01 | 2018-11-01 | Auxiliary method for early warning of water inrush disaster of coal mine |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109345140A CN109345140A (en) | 2019-02-15 |
| CN109345140B true CN109345140B (en) | 2021-08-27 |
Family
ID=65313013
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811293749.4A Expired - Fee Related CN109345140B (en) | 2018-11-01 | 2018-11-01 | Auxiliary method for early warning of water inrush disaster of coal mine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109345140B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112241835B (en) * | 2020-09-30 | 2024-06-14 | 北京科技大学 | Multi-source information evaluation method for flood disaster in deep shaft engineering |
| CN113217103B (en) * | 2021-05-18 | 2022-09-09 | 华北科技学院(中国煤矿安全技术培训中心) | Method for identifying separation layer water inrush |
| CN113250613B (en) * | 2021-05-25 | 2022-10-25 | 华北科技学院(中国煤矿安全技术培训中心) | Directional drilling and checking method for coal seam in small coal kiln goaf |
| CN113361213B (en) * | 2021-06-08 | 2023-04-21 | 华北科技学院(中国煤矿安全技术培训中心) | Water source identification method based on hydraulic and hydro-chemical coupling |
| CN115439287B (en) * | 2022-06-08 | 2024-09-13 | 自然资源陕西省卫星应用技术中心 | Geological disaster risk evaluation method based on machine learning |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102194056A (en) * | 2011-05-05 | 2011-09-21 | 中国矿业大学(北京) | BN-GIS (Bayesian Network-Geographic Information System) method for evaluating and predicting water inrush danger of coal-seam roof and floor |
| CN104992455A (en) * | 2015-07-29 | 2015-10-21 | 中国神华能源股份有限公司 | Collapse pit detecting method and apparatus |
| CN105179014A (en) * | 2015-08-24 | 2015-12-23 | 兖矿集团有限公司 | Coal mine water inrush disaster pre-warning method and device |
| CN105354365A (en) * | 2015-10-12 | 2016-02-24 | 内蒙古上海庙矿业有限责任公司 | Comprehensive evaluation method and system for water inrush risk of indirect water filling aquifer |
| CN105808818A (en) * | 2016-01-28 | 2016-07-27 | 中煤科工集团唐山研究院有限公司 | Method for evaluating foundation stability of coal mining subsidence area |
-
2018
- 2018-11-01 CN CN201811293749.4A patent/CN109345140B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102194056A (en) * | 2011-05-05 | 2011-09-21 | 中国矿业大学(北京) | BN-GIS (Bayesian Network-Geographic Information System) method for evaluating and predicting water inrush danger of coal-seam roof and floor |
| CN104992455A (en) * | 2015-07-29 | 2015-10-21 | 中国神华能源股份有限公司 | Collapse pit detecting method and apparatus |
| CN105179014A (en) * | 2015-08-24 | 2015-12-23 | 兖矿集团有限公司 | Coal mine water inrush disaster pre-warning method and device |
| CN105354365A (en) * | 2015-10-12 | 2016-02-24 | 内蒙古上海庙矿业有限责任公司 | Comprehensive evaluation method and system for water inrush risk of indirect water filling aquifer |
| CN105808818A (en) * | 2016-01-28 | 2016-07-27 | 中煤科工集团唐山研究院有限公司 | Method for evaluating foundation stability of coal mining subsidence area |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109345140A (en) | 2019-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109345140B (en) | Auxiliary method for early warning of water inrush disaster of coal mine | |
| CN112945139B (en) | Shield engineering auxiliary system combining three-dimensional scanning with BIM technology | |
| WO2023185735A1 (en) | Mine stress field twin modeling assimilation system for full space-time mining process, and method | |
| Li et al. | Risk assessment of water inrush in karst tunnels and software development | |
| WO2020119177A1 (en) | Wall continuous mining and continuous filling water-preserved coal mining method, and water resource migration monitoring and water disaster early warning method | |
| CN111553000A (en) | Intelligent construction method of tunnel by drilling and blasting method | |
| CN105785471A (en) | Impact danger evaluation method of mine pre-exploiting coal seam | |
| CN104408323A (en) | Method for advanced forecasting of roof separation water disaster of stope based on multi-source information fusion | |
| CN108827233B (en) | Prediction method for ground settlement of two-layer goaf | |
| CN110630274B (en) | Shield tunnel composite stratum section implementation system and implementation method | |
| Liu et al. | Fractal characterization for the mining crack evolution process of overlying strata based on microseismic monitoring technology | |
| CN102852524B (en) | Method for utilizing hydraulic support to improve bauxite recovery rate | |
| CN107798189A (en) | Accurate dynamic outburst prevention method based on transparent space geophysical | |
| CN114357750A (en) | Goaf water filling state evaluation method | |
| CN116950708A (en) | Intelligent management and control platform for one-ventilation three-prevention of coal mine | |
| CN116402387A (en) | System and method for mining gas extraction parameters and evaluating standard | |
| Liu et al. | Study of roof water inrush forecasting based on EM-FAHP two-factor model | |
| CN117910112A (en) | Intelligent design method for TBM tunnel rock burst section supporting scheme | |
| Zhan et al. | Quantitative prediction of the impact of deep extremely thick coal seam mining on groundwater | |
| CN107784437B (en) | Stress concentration-based coal roadway driving face outburst danger identification method | |
| Guo et al. | Risk assessment of shield tunneling crossing building based on variable weight theory and cloud model | |
| Li et al. | AHP‐FSE‐Based Risk Assessment and Mitigation for Slurry Balancing Shield Tunnel Construction | |
| Zeng | Research on risk evaluation methods of groundwater bursting from aquifers underlying coal seams and applications to coalfields of North China | |
| CN114837657A (en) | Coal mine underground gas extraction borehole failure evaluation system | |
| CN114066271A (en) | Tunnel water inrush disaster monitoring and management system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210827 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |