CN111861138A - Coal rock micro-core digital intelligent fine detection and prediction system, method and device - Google Patents
Coal rock micro-core digital intelligent fine detection and prediction system, method and device Download PDFInfo
- Publication number
- CN111861138A CN111861138A CN202010595929.9A CN202010595929A CN111861138A CN 111861138 A CN111861138 A CN 111861138A CN 202010595929 A CN202010595929 A CN 202010595929A CN 111861138 A CN111861138 A CN 111861138A
- Authority
- CN
- China
- Prior art keywords
- rock
- coal
- micro
- core
- impact
- 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.)
- Pending
Links
- 239000011435 rock Substances 0.000 title claims abstract description 224
- 239000003245 coal Substances 0.000 title claims abstract description 99
- 238000001514 detection method Methods 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000005553 drilling Methods 0.000 claims abstract description 43
- 238000004458 analytical method Methods 0.000 claims abstract description 18
- 238000004364 calculation method Methods 0.000 claims abstract description 15
- 238000012545 processing Methods 0.000 claims description 13
- 239000002245 particle Substances 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 238000010008 shearing Methods 0.000 claims description 4
- 238000003860 storage Methods 0.000 claims description 4
- 238000005065 mining Methods 0.000 abstract description 7
- 238000009412 basement excavation Methods 0.000 abstract description 5
- 238000012360 testing method Methods 0.000 description 12
- 206010017076 Fracture Diseases 0.000 description 6
- 208000010392 Bone Fractures Diseases 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 239000000523 sample Substances 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 3
- 239000002689 soil Substances 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 235000003889 Paeonia suffruticosa Nutrition 0.000 description 1
- 240000005001 Paeonia suffruticosa Species 0.000 description 1
- 238000012669 compression test Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000002330 electrospray ionisation mass spectrometry Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 230000010287 polarization Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000004901 spalling Methods 0.000 description 1
- 239000004575 stone Substances 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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Computer Hardware Design (AREA)
- General Health & Medical Sciences (AREA)
- Geometry (AREA)
- Agronomy & Crop Science (AREA)
- Primary Health Care (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mining & Mineral Resources (AREA)
- Marine Sciences & Fisheries (AREA)
- Animal Husbandry (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention provides a coal petrography micro-core digital intelligent fine detection and forecast system, a method and a device, which are characterized in that a multi-factor detection principle, an intensity calculation method and a forecast early warning standard are compiled into a calculation program language and are integrated into an intelligent analysis system, and the intelligent analysis system is configured on a coal petrography micro-core drilling device to form the coal petrography micro-core digital intelligent detection and forecast system and the device. The system and the device are simple to operate, convenient to transport, small in occupied space, high in precision and accurate in forecasting result, and are suitable for risk detection and forecasting in the coal mine roadway and tunnel surrounding rock excavation and mining process.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of mine engineering, in particular to a system, a method and a device for digital intelligent fine detection and prediction of coal rock micro-cores.
[ background of the invention ]
At present, the traditional rock burst, rockburst and collapse risk testing and forecasting technologies of coal mine engineering, tunnel engineering, geotechnical engineering and the like adopt site coring, laboratory testing and standard calculation to give corresponding grades, and then give forecasting results according to site conditions, so that the method is long in time consumption, serious in cost, high in manpower and material resource investment, lag in time and large in result deviation. The method can not meet the requirements of the modern excavation, mining and mining safety forecast work, and can not take rapid and accurate emergency treatment countermeasures for field events.
Accordingly, there is a need to develop a system, method and apparatus for digital intelligent fine coal core survey and forecast to address the deficiencies of the prior art and to solve or alleviate one or more of the above problems.
[ summary of the invention ]
In view of the above, the present invention provides a system, a method and a device for digital intelligent fine detection and prediction of coal petrography micro-core, which are implemented by compiling a multi-factor detection principle, an intensity calculation method and a prediction and pre-warning standard into a computer program language, integrating into an intelligent analysis system, and configuring the intelligent analysis system on a coal petrography micro-core drilling device to form the system and the device for digital intelligent detection and prediction of coal petrography micro-core. The system and the device are simple to operate, convenient to transport, small in occupied space, high in precision and accurate in forecasting result, and are suitable for risk detection and forecasting in the coal mine roadway and tunnel surrounding rock excavation and mining process.
In one aspect, the present invention provides a coal rock micro-core digital intelligent detection and prediction system, including:
the data acquisition unit is used for drilling coal rock characteristic touch parameters in the coal rock;
the data calibration unit is used for quantitatively calibrating the coal rock characteristic touch parameters into coal rock mechanical strength indexes;
the grading processing unit is used for carrying out grading calculation processing on the coal rock mechanical strength index and giving a grading standard;
And the early warning analysis unit is used for analyzing, correcting and alarming the data after the grading processing according to the grading standard.
The above-described aspects and any possible implementations further provide an implementation where the coal rock characteristic tactile parameters include particle size, porosity, water content, fracture formation, ground stress, torque, rotational speed, rate of penetration, thrust, and hydraulic power.
The above aspects and any possible implementation manners further provide an implementation manner, and the coal-rock mechanical strength index includes a coal-rock type, a principal stress, a shear stress, a rock elastic energy index, a dynamic failure time, an impact energy index, an impact energy velocity index, an impact critical softening coefficient, an impact critical stress coefficient, a compressive strength, a shear strength, a tensile strength, a rock integrity and a hardness coefficient.
The above-described aspects and any possible implementations further provide an implementation in which the classification criteria in the classification processing unit include a surrounding rock quality grade (RQD) classification criterion, a rock burst prediction criterion, a rock burst propensity criterion, and a rock burst hazard criterion.
The above aspects and any possible implementation manners further provide an implementation manner, and the analysis and calibration result in the early warning analysis unit is a forecast and early warning for coal rocks of a coal mine and surrounding rocks of a tunnel.
The above-mentioned aspects and any possible implementation manners further provide a coal rock micro-core digital intelligent detection and forecast method, which includes the following steps:
s1: drilling characteristic tactile parameters of the coal rock;
s2, quantitatively calibrating the coal rock type, the main stress, the shear stress, the rock elastic energy index, the dynamic failure time, the impact energy index, the impact energy speed index, the impact critical softening coefficient, the impact critical stress coefficient, the compressive strength, the shearing strength, the tensile strength, the rock integrity and the hardness coefficient by using the touch parameters;
s3: providing a touch parameter and coal petrography physical and mechanical quantitative calculation method according to a quantitative calibration result;
s4: establishing a surrounding rock quality grade grading standard, a rock burst prediction standard, an impact tendency standard and a rock burst danger standard;
s5: and respectively giving forecast and early warning aiming at coal mine coal rocks and tunnel surrounding rocks.
The above-mentioned aspects and any possible implementation manners further provide a digital intelligent coal-rock micro-core detection and forecast device, where the digital intelligent coal-rock micro-core detection and forecast device includes a data collector, a memory, a processor, and a processing program stored in the memory and executable on the processor, and when the processing program is executed by the processor, the processing program implements the steps of the digital intelligent coal-rock micro-core detection and forecast method.
The above aspects and any possible implementation manners further provide an implementation manner, where the data acquisition device is a coal rock micro-core drilling device, and the coal rock micro-core drilling device is connected to the processor.
The above-mentioned aspects and any possible implementation manners further provide a computer-readable storage medium, on which a processing program for digital intelligent coal-rock micro-core detection and prediction is stored, and when the processing program for digital intelligent coal-rock micro-core detection and prediction is executed by a processor, the steps of the method for digital intelligent coal-rock micro-core detection and prediction are implemented.
Compared with the prior art, the invention can obtain the following technical effects: according to the invention, quantitative values such as coal (rock) type, main stress, shear stress, rock elastic energy index, dynamic failure time, impact energy index, impact energy speed index, impact critical softening coefficient, impact critical stress coefficient, compressive strength, shear strength, tensile strength, rock integrity, hardness coefficient and the like can be calculated by using the touch parameters; according to the quantitative value and the classification standard of the quality grade (RQD) of the surrounding rock, the rock burst prediction standard, the impact tendency standard and the rock burst danger standard, the quality grade (RQD) of the surrounding rock, the rock burst grade, the impact tendency and the rock burst danger are calculated in real time, the forecasting and early warning are carried out in time, the accuracy rate of the forecasting result reaches 100%, and the early warning time is more than 5 times earlier than that of the conventional experimental test means.
Of course, it is not necessary for any one product in which the invention is practiced to achieve all of the above-described technical effects simultaneously.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method of probe forecasting provided by one embodiment of the present invention;
fig. 2 is a block diagram of a sounding forecasting system according to an embodiment of the present invention.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The invention aims to provide a system, a method and a device for digital intelligent fine detection and prediction of a coal rock micro-core, aiming at the problem that the current rock burst, rock burst and collapse risks cannot be quickly, accurately and intelligently predicted.
As shown in fig. 2, the present invention provides a digital intelligent detection and forecast system for coal and rock micro-core, which comprises:
the data acquisition unit is used for drilling coal rock characteristic touch parameters in the coal rock;
the data calibration unit is used for quantitatively calibrating the coal rock characteristic touch parameters into coal rock mechanical strength indexes;
the grading processing unit is used for carrying out grading calculation processing on the coal rock mechanical strength index and giving a grading standard;
and the early warning analysis unit is used for analyzing, correcting and alarming the data after the grading processing according to the grading standard.
The characteristic tactility parameters of the coal rock comprise particle size, porosity, water content, fracture structure, ground stress, torque, rotating speed, drilling rate, drilling thrust and hydraulic power.
The coal-rock mechanical strength indexes comprise coal-rock types, main stress, shear stress, rock elastic energy indexes, dynamic failure time, impact energy indexes, impact energy speed indexes, impact critical softening coefficients, impact critical stress coefficients, compressive strength, shearing strength, tensile strength, rock integrity and hardness coefficients.
The grading standards in the grading processing unit comprise a surrounding rock quality grade (RQD) grading standard, a rock burst prediction standard, an impact tendency standard and a rock burst danger standard.
And the analysis and correction result in the early warning analysis unit is used for forecasting and early warning coal rocks and tunnel surrounding rocks.
As shown in fig. 1, the invention provides a digital intelligent detection and forecast method for coal rock micro-core, which comprises the following steps:
s1: drilling characteristic tactile parameters of the coal rock;
s2, quantitatively calibrating the coal rock type, the main stress, the shear stress, the rock elastic energy index, the dynamic failure time, the impact energy index, the impact energy speed index, the impact critical softening coefficient, the impact critical stress coefficient, the compressive strength, the shearing strength, the tensile strength, the rock integrity and the hardness coefficient by using the touch parameters;
s3: providing a touch parameter and coal petrography physical and mechanical quantitative calculation method according to a quantitative calibration result;
S4: establishing a surrounding rock quality grade grading standard, a rock burst prediction standard, an impact tendency standard and a rock burst danger standard;
s5: and respectively giving forecast and early warning aiming at coal mine coal rocks and tunnel surrounding rocks.
The digital intelligent coal-rock micro-core detection and prediction device comprises a data collector, a memory, a processor and a processing program for the digital intelligent coal-rock micro-core detection and prediction, wherein the processing program is stored in the memory and can run on the processor, the processing program for the digital intelligent coal-rock micro-core detection and prediction is executed by the processor to realize the steps of the digital intelligent coal-rock micro-core detection and prediction method, the data collector is a coal-rock micro-core drilling device, and the coal-rock micro-core drilling device is connected with the processor.
A computer-readable storage medium, on which a processing program for digital intelligent detection and prediction of coal-rock micro-cores is stored, the processing program for digital intelligent detection and prediction of coal-rock micro-cores, when being executed by a processor, implements the steps of the digital intelligent detection and prediction method of coal-rock micro-cores.
In the invention, a coal rock micro-core drilling device drills and picks up characteristic tactile parameters (particle size, porosity, water content, fracture structure, ground stress, torque, rotating speed, drilling rate, drilling thrust and hydraulic power) of coal (rock), the tactile parameters are utilized to carry out quantitative calibration on coal (rock) type, main stress, shear stress, rock elastic energy index, dynamic failure time, impact energy index, impact energy speed index, impact critical softening coefficient, impact critical stress coefficient, compressive strength, shear strength, tensile strength, rock integrity and hardness coefficient, a tactile parameter and coal (rock) physical and mechanical quantitative calculation method is provided, a surrounding rock quality grade (RQD) grading standard, a rock burst prediction standard, an impact tendency standard and a rock burst danger standard are established, and a prediction and early warning standard aiming at coal rock and tunnel surrounding rock is respectively provided, the multi-element detection principle, the intensity calculation method and the forecast early warning standard are compiled into a calculation program language and are integrated into an intelligent analysis system, and the intelligent analysis system is configured on the coal-rock micro-core drilling device to form the coal-rock micro-core intelligent detection and forecast instrument. The device is simple to operate, convenient to transport, small in occupied space and suitable for risk detection and prediction in the coal mine roadway and tunnel surrounding rock excavation and mining process, the accuracy rate of the prediction result reaches 100%, and the early warning time is more than 5 times earlier than that of a conventional experimental test means.
The specific grading standard and parameter conversion calibration method of the invention is as follows:
the compression strength, the shear strength, the tensile strength and the hardness coefficient are calibrated by torque, rotating speed, drilling rate, drilling thrust and hydraulic power conversion, and the specific calculation method is as follows:
the blade is arranged at the position of the spiral drill rod close to the drill bit, and the total torque required by the drilling of the drill bit is M ═ M1+M2+M3Power required for drill bit rotation
Wherein M is1For the working torque of the drill bit, M2For blade operating torque, M3The working torque for discharging broken rock soil; omega is the rotating speed; k is a power reserve coefficient; eta is the rotation efficiency of the drilling machine.
According to the stress decomposition of each component:
M1＝f1x·d＝(q+ks)·d，M2＝f2x·D，M3＝f3x·D，F＝f1y+f2y+f3y(1)
in the formula (f)1,f2,f3The component forces in the horizontal and vertical directions are respectively f1x,f2x,f3xAnd f1y,f2y,f3y(ii) a F is the thrust on the drill rod; d is the diameter of the drill bit; d is the diameter of the drill rod; s is the footage of the drill rod rotating for one circle; q and k are parameters related to coal rock strength.
The finishing method can obtain the following steps:
F＝f1y+f2y+f3y≈(f1x+f2x+f3x)·tgα
(3)
wherein alpha is the included angle of the conical drill bit. As can be seen from the formulas (2) and (3), if the data of the torque, the rotating speed, the drilling rate, the drilling thrust and the hydraulic power are measured in the drilling process, parameters q and k related to the coal rock strength can be obtained, the coal rock strength range can be determined, and further the compressive strength, the shear strength, the tensile strength and the hardness coefficient of the coal rock can be obtained.
In the rotary drilling process of the drill rod, test signals of the push-torque sensor, the rotating speed sensor, the galvanometer and the laser range finder are collected by the data acquisition instrument through the data line, amplified and transmitted to the microcomputer for recording. The microcomputer records various sensor data signals transmitted by the data acquisition instrument, converts the various sensor data signals into corresponding torque, rotating speed, drilling rate, drilling thrust and hydraulic power through a programmed processing program, then utilizes a programmed test parameter and coal rock strength calculation program, calculates by using test data to obtain a coal rock strength numerical value, displays a relation curve of drilling rod footage and thrust, torque, rotating speed, consumed mechanical work and a quantitative relation curve of the drilling rod footage and the coal rock strength, and automatically draws a drilling coal rock strength histogram along with depth change.
And the calibration results of the coal rock type and the rock integrity are calculated according to physical parameters such as particle size, porosity, water content, fracture structure and the like.
The main stress and the shear stress are obtained by decomposing the ground stress in different directions.
And the rock elastic energy index, the dynamic failure time, the impact energy index, the impact energy speed index, the impact critical softening coefficient and the impact critical stress coefficient are obtained by performing standard test on the detected coal rock.
In the present invention, the classification criteria in the classification processing unit are specifically as follows:
1. impact susceptibility rating scale
Coal sample dynamic destruction time DT:
coal bed rock burst is the phenomenon that a large amount of elastic energy is released instantly when the coal body is suddenly unstable. Thus, the time to failure of the coal, i.e., the time elapsed from the strength limit to complete spalling, also reflects the strength of the impact propensity of the coal seam. Therefore, the time from the limit load to complete failure of the coal sample under the conventional triaxial compression test condition, namely the dynamic failure time DT (ms) of the coal sample is taken as an index for determining the impact tendency of the coal bed:
DT is less than 50, the strong impact tendency is that DT is more than or equal to 50 and less than or equal to 500, and the medium impact direction is; 500< DT, no impact tendency.
Energy index PES：
The energy index is the specific method proposed by the Bolan mining research institute, which is to use the test piece to perform uniaxial compression and loading-unloading deformation test and calculate P according to the following formulaES(kj/m3) Value of
PESThe value representing the strain energy stored in the rock per unit volume, EuRepresenting the unloading modulus, energy index P of the rock specimenESThe polarization standard of is PES<50, no impact tendency; p is more than or equal to 50ESP is less than or equal to 100, and the medium impact tendency is that P is less than or equal to 100ES< 200, strong impact tendency, PESMore than or equal to 200, and strong impact tendency.
Elastic energy index PESIs commonly used to determine the impact propensity of rock (except for partially soft rock) and for coal seams, due to its uniaxial compressive strength eeP, which is smaller than that of stone (except soft rock), and thus determined according to equation (4)ESThe value is small, and the method is not suitable for judging the impact tendency of the coal seam according to the grading standard.
Impact hazard class criteria
TABLE 1
| Influencing factor | I | II | III | IV |
| σ1/Rc | <0.15 | 0.15～0.20 | 0.20～0.40 | ＞0.40 |
| σθ/Rc | <0.20 | 0.20～0.30 | 0.30～0.55 | ＞0.55 |
| Rc/Rt | <15 | 15～18 | 18～22 | ＞22 |
| Wet | <2.0 | 2.0～3.5 | 3.5～5.0 | ＞5.0 |
| Kv | <0.55 | 0.55～0.60 | 0.60～0.80 | ＞0.80 |
TABLE 2
Wherein, the table 1 is the coal bed impact risk index, the table 2 is the rock burst five-factor comprehensive criterion and the rock burst classification, and the sigma is1Is the maximum principal stress, sigma, of the wall of the surrounding rockθIs the maximum tangential stress of the wall of the surrounding rock, RcIs uniaxial compressive strength, R, of rocktIs uniaxial tensile strength of rock, WetIs the elastic energy index of rock, KvIs the integrity coefficient of the rock mass.
2. Tunnel surrounding Rock Quality (RQD) grading standard
The test principle is as follows: and (3) drilling the coal Rock micro core to take out the core, obtaining Rock Quality index Rock Quality Designation (RQD) by using the percentage of the accumulated length of the core of more than 10cm in the drilling depth, and grading the Rock mass Quality (the integrity degree of the Rock mass) according to the RQD index. The RQD primarily reflects the degree of rock integrity, i.e., the extent of fracture development in the formation at that location. The rock quality was divided into five categories according to the RQD value, as shown in Table 3:
| categories | RQD(％) | Quality of rock |
| 1 | >90 | Superior food |
| 2 | ＝75～90 | Good wine |
| 3 | ＝50～75 | Good taste |
| 4 | ＝25～50 | Difference (D) |
| 5 | <25 | Is very poor |
TABLE 3
The technical indexes are as follows: the structural morphology of the rock is judged according to the aspects of the freshness and integrity of the rock, the structural condition, the development of joint fractures, weak structural surfaces, fault zones and the like, and the distribution of bad bodies is identified and forecasted, and the result is shown in table 4.
TABLE 4
Integrity of rock mass: according to the RQD index and the structural morphology distribution condition of the rock, the integrity of the rock is graded, and the grading standard is shown in the table 5:
| quality grading | RQD index/%) | Structural form classification |
| Good effect | 90-100 | Class I |
| Good taste | 75-90 | Class II |
| Medium and high grade | 50-75 | Class III |
| Difference (D) | 25-50 | Class IV |
| Extreme difference | 5-25 | Class V |
TABLE 5
And (3) evaluating the stability of surrounding rocks: evaluating the stability of the excavated surrounding rock from two aspects of the mechanical condition of instability of the excavated surrounding rock and the structural integrity of the rock, wherein the mechanical requirement of instability of the excavated surrounding rock is sigma1The soil surrounding rock instability disaster prediction and early warning method is characterized in that the soil surrounding rock instability disaster classification threshold is not less than 0.15Rc, the integrity requirement is that the rock integrity coefficient RQD is not more than 0.55, the stable state of the surrounding rock is analyzed by using the mechanical conditions and the integrity conditions of the excavated surrounding rock, meanwhile, the rock stress real-time monitoring technology is adopted, prediction and early warning are carried out on the instability disaster of the excavated surrounding rock, and the classification threshold of the instability disaster of the excavated surrounding rock is.
TABLE 6
Grade of tunnel surrounding rock: according to rock strength, engineering geological characteristics, rock mass structure morphology, rock mass integrity and surrounding rock stability and RQD indexes, the surrounding rock is divided into six grades as shown in a table 7:
TABLE 7
According to the invention, quantitative values such as coal (rock) type, main stress, shear stress, rock elastic energy index, dynamic failure time, impact energy index, impact energy speed index, impact critical softening coefficient, impact critical stress coefficient, compressive strength, shear strength, tensile strength, rock integrity, hardness coefficient and the like can be calculated by using the touch parameters; according to the quantitative value and the classification standard of the quality grade (RQD) of the surrounding rock, the rock burst prediction standard, the impact tendency standard and the rock burst danger standard, the quality grade (RQD) of the surrounding rock, the rock burst grade, the impact tendency and the rock burst danger are calculated in real time, the forecasting and early warning are carried out in time, the accuracy rate of the forecasting result reaches 100%, and the early warning time is more than 5 times earlier than that of the conventional experimental test means.
The system of the invention comprises: the device comprises a coal rock micro-core drilling device, a multi-factor detection principle, an intensity calculation method, a forecast early warning standard and an intelligent analysis system. The coal rock micro-core drilling device drills and picks up coal (rock) characteristic tactile parameters (particle size, porosity, water content, fracture structure, ground stress, torque, rotating speed, drilling rate, drilling thrust and hydraulic power), the tactile parameters are utilized to carry out quantitative calibration on coal (rock) type, main stress, shear stress, rock elastic energy index, dynamic failure time, impact energy index, impact energy speed index, impact critical softening coefficient, impact critical stress coefficient, compressive strength, shear strength, tensile strength, rock integrity and hardness coefficient, a tactile parameter and coal (rock) physical and mechanical quantitative calculation method is provided, a surrounding rock quality grade (RQD) grading standard, a rock burst prediction standard, an impact tendency standard and a rock burst danger standard are established, a prediction and early warning standard aiming at coal mine and tunnel surrounding rock is provided respectively, a multi-element detection principle is adopted, and the method is adopted, The intensity calculation method and the forecast early warning standard are compiled into a calculation program language, are combined into an intelligent analysis system, and the intelligent analysis system is configured on the coal rock micro-core drilling device to form the intelligent coal rock micro-core detection and forecast instrument. The device is simple to operate, convenient to transport, small in occupied space, high in precision and accurate in forecasting result, and is suitable for risk detection and forecasting in the coal mine roadway and tunnel surrounding rock excavation and mining process.
When the coal rock micro-core digital intelligent detection and prediction system is used for detecting and predicting, the following steps are sequentially carried out:
step 1: investigating the basic information data of the tunnel;
step 2: grading the overall risk of the tunnel;
and step 3: performing advanced prediction and micro-drilling fine prediction;
and 4, step 4: performing classification bad body prediction on tunnel surrounding rocks;
and 5: selecting a monitoring project according to actual surrounding rock registration, risk registration and construction design schemes;
step 6: monitoring data for a long time, and evaluating the safety of the tunnel structure based on a Monte Carlo random simulation thought;
and 7: selecting reasonable control measures and optimizing a construction design scheme according to the evaluation result of the step 6;
and 8: and reasonably early warning the threshold value, and sending early warning information to a mobile terminal, such as a mobile phone, a tablet and the like.
The system, the method and the device for digital intelligent fine detection and prediction of the coal rock micro-core provided by the embodiment of the application are introduced in detail. The above description of the embodiments is only for the purpose of helping to understand the method of the present application and its core ideas; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
As used in the specification and claims, certain terms are used to refer to particular components. As one skilled in the art will appreciate, manufacturers may refer to a component by different names. This specification and claims do not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. "substantially" means within an acceptable error range, and a person skilled in the art can solve the technical problem within a certain error range to substantially achieve the technical effect. The description which follows is a preferred embodiment of the present application, but is made for the purpose of illustrating the general principles of the application and not for the purpose of limiting the scope of the application. The protection scope of the present application shall be subject to the definitions of the appended claims.
It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a commodity or system that includes the element.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The foregoing description shows and describes several preferred embodiments of the present application, but as aforementioned, it is to be understood that the application is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the application as described herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the application, which is to be protected by the claims appended hereto.
Claims (9)
1. A coal rock micro-core digital intelligent detection and prediction system is characterized by comprising:
the data acquisition unit is used for drilling coal rock characteristic touch parameters in the coal rock;
the data calibration unit is used for quantitatively calibrating the coal rock characteristic touch parameters into coal rock mechanical strength indexes;
The grading processing unit is used for carrying out grading calculation processing on the coal rock mechanical strength index and giving a grading standard;
and the early warning analysis unit is used for analyzing, correcting and alarming the data after the grading processing according to the grading standard.
2. The sounding forecasting system of claim 1, wherein the coal-rock characteristic tactile parameters include particle size, porosity, water content, fracture configuration, torque, rotation speed, drilling rate, drilling thrust and hydraulic power.
3. The sounding and forecasting system of claim 1, wherein the coal-rock mechanical strength index includes coal-rock type, principal stress, shear stress, rock elastic energy index, dynamic failure time, impact energy index, impact energy velocity index, impact critical softening coefficient, impact critical stress coefficient, compressive strength, shear strength, tensile strength, rock integrity and hardness coefficient.
4. The sounding forecasting system of claim 1, wherein the grading criteria in the grading processing unit include a surrounding rock quality grade grading criterion, a rock burst prediction criterion, an impact tendency criterion, and a rock burst risk criterion.
5. The sounding prediction system of claim 1, wherein the analysis and calibration result in the early warning analysis unit is a prediction early warning for coal mine rocks and tunnel surrounding rocks.
6. A coal rock micro-core digital intelligent detection and forecast method based on the detection and forecast system of any one of the claims 1-5, characterized in that the method comprises the following steps:
s1: drilling characteristic tactile parameters of the coal rock;
s2, quantitatively calibrating the coal rock type, the main stress, the shear stress, the rock elastic energy index, the dynamic failure time, the impact energy index, the impact energy speed index, the impact critical softening coefficient, the impact critical stress coefficient, the compressive strength, the shearing strength, the tensile strength, the rock integrity and the hardness coefficient by using the touch parameters;
s3: providing a touch parameter and coal petrography physical and mechanical quantitative calculation method according to a quantitative calibration result;
s4: establishing a surrounding rock quality grade grading standard, a rock burst prediction standard, an impact tendency standard and a rock burst danger standard;
s5: and respectively giving forecast and early warning aiming at coal mine coal rocks and tunnel surrounding rocks.
7. A coal-rock micro-core digital intelligent detection and forecast device is characterized by comprising a data acquisition device, a memory, a processor and a processing program for coal-rock micro-core digital intelligent detection and forecast, wherein the processing program is stored in the memory and can be operated on the processor, and the steps of the coal-rock micro-core digital intelligent detection and forecast method according to claim 6 are realized when the processing program for coal-rock micro-core digital intelligent detection and forecast is executed by the processor.
8. The digital intelligent coal-rock micro-core detection and forecast device according to claim 7, wherein the data collector is a coal-rock micro-core drilling device, and the coal-rock micro-core drilling device is connected with the processor.
9. A computer-readable storage medium, wherein the computer-readable storage medium stores thereon a processing program for digital intelligent coal-rock micro-core detection and prediction, and when the processing program is executed by a processor, the processing program implements the steps of the digital intelligent coal-rock micro-core detection and prediction method according to claim 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010595929.9A CN111861138A (en) | 2020-06-28 | 2020-06-28 | Coal rock micro-core digital intelligent fine detection and prediction system, method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010595929.9A CN111861138A (en) | 2020-06-28 | 2020-06-28 | Coal rock micro-core digital intelligent fine detection and prediction system, method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111861138A true CN111861138A (en) | 2020-10-30 |
Family
ID=72989456
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010595929.9A Pending CN111861138A (en) | 2020-06-28 | 2020-06-28 | Coal rock micro-core digital intelligent fine detection and prediction system, method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111861138A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115098914A (en) * | 2022-06-20 | 2022-09-23 | 北京科技大学 | Intelligent extraction method and system for sensitive elements of urban road collapse hidden danger |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140019047A1 (en) * | 2011-03-30 | 2014-01-16 | China University Of Mining And Technology | Multi-information coupling prediction method of coal and gas outburst danger |
| CN106437853A (en) * | 2016-09-27 | 2017-02-22 | 西安科技大学 | Method for early warning against coal rock burst dynamic disasters |
| CN106644724A (en) * | 2016-12-30 | 2017-05-10 | 中国矿业大学 | Grouting reinforcement effect detection method for coal-mine weathered broken rich water surrounding rocks |
| CN108663269A (en) * | 2018-03-30 | 2018-10-16 | 山东大学 | Underground engineering wall rock digital rig partition method based on equivalent rock mass strength |
-
2020
- 2020-06-28 CN CN202010595929.9A patent/CN111861138A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140019047A1 (en) * | 2011-03-30 | 2014-01-16 | China University Of Mining And Technology | Multi-information coupling prediction method of coal and gas outburst danger |
| CN106437853A (en) * | 2016-09-27 | 2017-02-22 | 西安科技大学 | Method for early warning against coal rock burst dynamic disasters |
| CN106644724A (en) * | 2016-12-30 | 2017-05-10 | 中国矿业大学 | Grouting reinforcement effect detection method for coal-mine weathered broken rich water surrounding rocks |
| CN108663269A (en) * | 2018-03-30 | 2018-10-16 | 山东大学 | Underground engineering wall rock digital rig partition method based on equivalent rock mass strength |
Non-Patent Citations (3)
| Title |
|---|
| ZHANG, QL; LIU, ZY; TAN, JR: ""Prediction of geological conditions for a tunnel boring machine using big operational data"", 《AUTOMATION IN CONSTRUCTION》, vol. 100, 30 April 2019 (2019-04-30) * |
| 宋朝阳;纪洪广;孙利辉;: "煤岩冲击倾向性指标试验研究进展", 地下空间与工程学报, no. 2, 15 December 2015 (2015-12-15) * |
| 杜哲;: "某煤矿煤岩冲击倾向性试验及分析", 现代矿业, no. 04, 25 April 2020 (2020-04-25) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115098914A (en) * | 2022-06-20 | 2022-09-23 | 北京科技大学 | Intelligent extraction method and system for sensitive elements of urban road collapse hidden danger |
| CN115098914B (en) * | 2022-06-20 | 2023-03-10 | 北京科技大学 | Intelligent extraction method and system for sensitive elements of urban road collapse hidden danger |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Khandelwal et al. | Correlating static properties of coal measures rocks with P-wave velocity | |
| CN108663269B (en) | Underground engineering surrounding rock digital drilling zoning method based on equivalent rock mass strength | |
| Khaksar et al. | Rock strength from core and logs, where we stand and ways to go | |
| CN107605536B (en) | Coal and gas prominent real-time early warning device and method based on Multi-source Information Fusion | |
| Jian et al. | Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines | |
| Villaescusa et al. | Stress measurements from oriented core | |
| Rajesh Kumar et al. | Prediction of uniaxial compressive strength, tensile strength and porosity of sedimentary rocks using sound level produced during rotary drilling | |
| CN108519284A (en) | Underground engineering wall rock digital rig real-time graded method | |
| CN108710759B (en) | Method for judging impact tendency by measuring softening modulus index of coal body on site | |
| Glamheden et al. | Rock mechanics Forsmark. Site descriptive modelling Forsmark stage 2.2 | |
| WO2016168882A1 (en) | Statistical method for micro-scale rock damage quantification and characterisation using x-ray micro-tomography | |
| CN112504838B (en) | TBM-loaded rock mechanics comprehensive test and information evaluation system | |
| Tuncay et al. | Relation between Kaiser effect levels and pre-stresses applied in the laboratory | |
| Mark et al. | New developments with the coal mine roof rating | |
| Wang et al. | A strain based method for determining the crack closure and initiation stress in compression tests | |
| CN111861138A (en) | Coal rock micro-core digital intelligent fine detection and prediction system, method and device | |
| CN112100842B (en) | Novel method for identifying abnormal region of crustal stress and measuring crustal stress in large range | |
| CN114297950A (en) | Method for calculating distribution of water loss compression amount of pressure-bearing water-containing loose layer region | |
| CN100414283C (en) | Method for testing compressive strength of pit sand concrete in high strength grade through rebound tester without damage | |
| He | A case review of the deformation modulus of rock mass: scale effect | |
| CN117350144A (en) | Rock mass strength characteristic prediction method based on machine learning | |
| Govindaraj | A new approach for estimation of properties of metamorphic rocks | |
| Smith | Improvements in blast fragmentation using measurement while drilling parameters | |
| Vásárhelyi et al. | Determining the failure envelope of the intact granitic rocks from Bátaapáti | |
| CN108614947B (en) | Method for discriminating weathering and unloading value of rock mass |
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 |