CN109989740B - Coal measure stratum drilling intelligent identification system and method based on multi-source information fusion - Google Patents
Coal measure stratum drilling intelligent identification system and method based on multi-source information fusion Download PDFInfo
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Abstract
The invention relates to a coal measure stratum drilling intelligent identification system and method based on multi-source information fusion. The intelligent identification system has high construction efficiency and simple operation, effectively utilizes the parameter information inside and outside the hole to identify the stratum lithology of the current position of the drill bit and the structural information of the coal measure stratum in real time, can obtain the three-dimensional stratum prediction model of the target area, not only can provide accurate information for the intelligent identification of the coal measure stratum, but also can provide reference and guidance for the geotechnical engineering construction such as other tunnel engineering, slope engineering and the like.
Description
Technical Field
The invention relates to a coal measure stratum identification system and a method thereof, belongs to the field of geological survey, and particularly relates to an intelligent coal measure stratum drilling identification system and a method thereof based on multi-source information fusion.
Background
The geological conditions of the coal fields in China are complex, the coal-forming conditions are diverse, the period of the coal-forming era is multiple, the coal metamorphic superposition, and the structure change is multiple and complex, so that the coal mining in China faces serious geological disasters. Particularly, with the increase of the coal mining depth in recent years, the underground geomechanical environment is remarkably changed, the basic parameters necessary for mine engineering design and safety construction, such as the lithology and structural characteristics of coal rock mass, are more and more insufficient, and the reasonability of the engineering design and the safety guarantee of the mining process are greatly influenced. The method can be used for evaluating and predicting the geological conditions in the mine engineering by utilizing the drilling engineering, and can provide powerful guarantee for realizing safe, efficient and green mining of the coal in China.
At present, the characteristic identification of coal measure strata has the defects of laggard parameter extraction method, low identification precision and the like, is difficult to realize the timely and accurate identification of the stratum characteristic, and cannot effectively guide the field engineering practice. In addition, when the existing prediction method adopts single or multiple indexes and threshold values thereof for judgment, the amount of information to be relied on is small, the threshold values of various parameters of different mines are different, and when the multiple indexes are close to the threshold values in different degrees, how to comprehensively judge does not have a good solution. The study on stratum identification problems by using various information including mathematical analysis models, monitoring data analysis, drilling parameter response characteristics and the like is carried out by scholars at home and abroad, but the particularity of the mechanical-electrical-hydraulic integrated drilling machine in the operation and construction of underground coal mine roadways is less considered, the effect of a complex stratum unstructured object faced by drilling construction is ignored, and therefore the real-time and credible description on coal measure strata cannot be carried out.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
The invention aims to provide a coal measure stratum drilling intelligent identification system and a coal measure stratum drilling intelligent identification method based on multi-source information fusion, which are high in construction efficiency and simple to operate, can obtain accurate information of lithology and structural plane of a coal measure stratum drilled in real time, and can provide an effective means for scientific design and optimization of underground coal mine drilling construction parameters. The method can not only provide support for intelligent identification of coal-based strata, but also provide reference and guidance for geotechnical engineering construction such as other tunnel engineering and slope engineering.
In order to solve the problems, the scheme of the invention is as follows:
the utility model provides a coal measure stratum creeps into intelligent recognition system based on multisource information fusion, includes:
the system comprises an in-hole data detection system, a data acquisition system and a data processing system, wherein the in-hole data detection system is arranged in a drilling tool of a tunnel drilling machine and is used for measuring in-hole drilling parameter information;
the in-hole data acquisition system is used for acquiring and communicating in-hole information;
the drilling machine working condition monitoring system is used for monitoring and acquiring drilling machine operation parameter information;
the image processing system is used for acquiring the characteristic information of the orifice return slag image;
the intelligent expert system is used for obtaining drilling sensitive parameters based on the in-hole drilling parameter information, the drilling machine operation parameter information and the return slag image characteristic information, performing data fuzzification on the drilling sensitive parameters according to a membership function, and identifying the drilling hole columnar stratum characteristics and/or judging the stratum transverse trend change in real time according to a fuzzy rule;
the intelligent expert system performs comprehensive analysis and arrangement according to multi-source information, removes irrelevant features to obtain relevant drilling sensitive parameters, then maps n-dimensional feature parameter information to the minimum k-dimensional feature parameter through a principal component analysis method, wherein k is less than n, realizes dimension reduction processing on data features, constructs brand-new orthogonal k-dimensional principal component information, and performs data fuzzification on the drilling parameters according to membership functions; the membership function adopts a normal distribution function type:
where σ and x 0 The parameters are adjusted, the parameters are determined according to the actual drilling parameter level, and the parameters can be adjusted in real time according to the actual situation by utilizing a deep learning algorithm;
determining a fuzzy rule according to the mapping relation of the coal measure strata-characteristic parameters to form an expert rule set; the recognition result is output according to the fuzzy rule, the recognition result is output in a clear mode, a clear membership function adopts a trigonometric function, and parameters of the clear membership function can be adjusted in real time; and identifying and judging the characteristics of the columnar stratum of the drilled hole and/or judging the transverse trend change of the stratum in real time, completing the identification and prediction of the coal measure stratum of the target area, drawing a three-dimensional quantitative prediction model of the stratum of the target area through a three-dimensional Voronoi diagram, and displaying the result on a display.
A method of a coal measure stratum drilling intelligent identification system based on multi-source information fusion comprises the following steps:
step 1, determining rock stratum characteristics and a prediction range of a target area according to geological data of the target coal-series stratum area, constructing a drilling tool and coal-series stratum drilling dynamic model, and analyzing drilling parameters and coal-series stratum characteristic change characteristics by applying finite element simulation software;
step 2, constructing a data hole by using a tunnel drilling machine to obtain a rock sample at a position corresponding to a coal core, determining a coal rock response characteristic value through a similar simulation test and a rotary drilling test platform test, and establishing a database of drilling parameters and coal measure stratum characteristics;
step 3, drilling construction is carried out by using the underground drill rig, the operation parameter information of the drill rig is obtained through a drill rig working condition monitoring system, meanwhile, the real-time orifice return slag image information is obtained by an image processing system and is transmitted to an intelligent expert system;
where σ and x 0 The parameters are adjusted, the parameters are determined according to the actual drilling parameter level, and the parameters can be adjusted in real time according to the actual situation by utilizing a deep learning algorithm;
determining a fuzzy rule according to the mapping relation of the coal measure strata-characteristic parameters to form an expert rule set; the recognition result is output according to the fuzzy rule, the recognition result is output in a clear mode, the clear membership function adopts a trigonometric function, and the parameters of the clear membership function can be adjusted in real time; and identifying and judging the characteristics of the columnar stratum of the drilled hole and/or judging the transverse trend change of the stratum in real time, completing the identification and prediction of the coal measure stratum of the target area, drawing a three-dimensional quantitative prediction model of the stratum of the target area through a three-dimensional Voronoi diagram, and displaying the result on a display.
As apparent from the above description, the present invention has the following effects:
the method has the advantages of high construction efficiency and simplicity in operation, effectively utilizes the parameter information inside and outside the hole to identify the stratum lithology of the current position of the drill bit and the structural information of the coal measure stratum in real time, can obtain a three-dimensional stratum prediction model of a target area, can provide accurate information for intelligent identification of the coal measure stratum, and also provides reference and guidance for geotechnical engineering construction such as other tunnel engineering, slope engineering and the like.
Drawings
The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments of the present invention and, together with the description, further serve to explain the principles of the invention and to enable a person skilled in the pertinent art to make and use the disclosure.
FIG. 1 is a schematic structural diagram of an intelligent recognition system for coal measure formation drilling based on multi-source information fusion, according to the invention;
FIG. 2 is a schematic structural diagram of a drilling rig condition monitoring system of the present invention;
FIG. 3 is a schematic diagram of an in-hole data detection system according to the present invention;
FIG. 4 is a schematic diagram of the configuration of the in-hole data acquisition system of the present invention;
FIG. 5 is a schematic diagram of an intelligent expert of the present invention;
FIG. 6 is a schematic diagram of the method of the present invention.
The reference numerals of the drawings have the following meanings:
the system comprises an intelligent expert system 1, an in-hole data acquisition system 2, an image processing system 3, a drilling machine working condition monitoring system 4, a tunnel drilling machine 5, an in-hole data detection system 6, a data acquisition device 7, a data integrator 8, a torque sensor 9, a vibration sensor 10, a rotation speed sensor 11, a tilt angle sensor 12, a signal amplifier 13, a data converter I14, an intelligent drill rod 15, a data converter II 16, an input device 17, a microprocessor 18, a storage 19, an expert model 20, an output device 21, an alarm device 22, a 23-bit display and a power supply device 24.
Detailed Description
Examples
Referring to fig. 1, the coal measure formation drilling intelligent recognition system based on multi-source information fusion provided by the embodiment includes a drilling machine working condition monitoring system 4, an in-hole data detection system 6, an in-hole data acquisition system 2, an image processing system 3 and an intelligent expert system 1.
The in-hole data detection system 6 is arranged in a drilling tool of the tunnel drilling machine 5 and is mainly used for measuring drilling parameter information in the hole, and transmitted to the in-hole data acquisition system 2 in real time, the in-hole data acquisition system 2 is mainly used for in-hole information acquisition and communication, recording, converting and transmitting the obtained drilling parameters to the intelligent expert system 1, outputting the characteristic information of the orifice return slag image to the intelligent expert system 1 by the image processing system 3, the drilling machine working condition monitoring system 4 outputs the drilling machine operation parameter information to the intelligent expert system 1, the intelligent expert system 1 comprehensively analyzes and collates the in-hole real-time information, the out-hole drilling machine monitoring information and the slag return image information, identifies the characteristics of the columnar stratum of the drilled hole in real time, and predicting the stratum distribution characteristics of the target area, judging the horizontal trend change of the rock stratum, and drawing a three-dimensional visual stratum prediction model.
Referring to fig. 2, the drilling machine working condition monitoring system 4 mainly comprises a data acquisition unit 7 and a data integrator 8, wherein the data acquisition unit 7 mainly acquires parameters of the drilling machine, such as drilling speed, drilling depth, drilling pressure, drilling machine rotating speed, drilling tool position and the like, and the data integrator 8 is mainly used for extracting and fusing main data at an orifice.
Referring to fig. 3, the in-hole data detection system 6 includes a torque sensor 9, a vibration sensor 10, a rotation speed sensor 11, and a tilt sensor 12. And the device is respectively used for detecting drilling parameters such as the torque of the drill bit in the drilling hole, the vibration of the near drill bit, the rotating speed of the drill bit, the position of the drill bit and the like.
Referring to fig. 4, the data acquisition system 2 in the hole comprises a signal amplifier 13, a first data converter 14, an intelligent drill pipe 15 and a second data converter 16. The signal amplifier 13 is mainly used for collecting and amplifying signals, the first data converter 14 is mainly used for recoding, modulating and the like of data, the intelligent drill rod 15 is mainly used for integrating and transmitting data, and the second data converter 16 is mainly used for demodulating, analyzing and fusing data.
The intelligent drilling rod 15 is through dedicated drilling rod joint threaded connection to at the public, increase insulating elastomer in female electricity connects, the elasticity can be guaranteed public, the inseparable compaction of female electricity connection, the inside transmission cable that is provided with the laminating formula structure of drilling rod, adopt the flat conductor form, drilling rod effort distributes evenly, the contact of drilling fluid with the drilling rod inner wall has been blocked, can prolong the drilling rod life-span, the transmission wire welding that is continuous in the female electricity connects with insulating elastomer, the transmission cable in transmission wire and the drilling rod links to each other, thereby guarantee the cyclic transmission of data.
The image processing system 3 can obtain the characteristic parameters of the rock such as color, gray scale, block degree and the like by acquiring the orifice slag return diagram in real time.
Referring to fig. 5, the intelligent expert system 1 mainly includes an input device 17, a microprocessor 18, a memory 19, an expert model 20, an output device 21, an alarm device 22, a display 23, and a power supply device 24.
The input device 17 is used for inputting a basic drilling data source and a real-time drilling data source to the expert model 20, wherein the basic drilling data source comprises a database corresponding to characteristic parameters and drilling parameters of a coal measure stratum, and a typical coal rock image characteristic and historical similar drilling parameter database; the real-time drilling data source mainly comprises real-time drilling parameters in the hole obtained by the in-hole data detection system 6, operation parameters of the drill outside the hole collected by the drill working condition monitoring system 4 and real-time hole return slag images obtained by the image processing system 3.
The expert model 20 mainly includes a PDC drill stress model, a coal measure stratum mechanics calculation model, a target area stratum analysis model, and a prediction model. The PDC drill bit stress model mainly reads relevant parameters, obtains a drilling tool structure, a drill bit type and a drilling tool length, and calculates key drilling parameters such as cutting force and horizontal force applied to the drill bit in real time. The coal measure stratum mechanical model is mainly used for reading characteristic parameters of a coal measure stratum, obtaining coal measure stratum differentiation characteristics and establishing a grading basis. The target area stratum analysis model is mainly used for conducting stratum demarcation on the change trend reflected by real-time drilling parameters to obtain stratum characteristic distribution in the drilling process, and the prediction model is used for comprehensively estimating and analyzing the stratum trend of the target area by utilizing the columnar change of discrete drilling stratum and applying a generalized prediction method to automatically calculate and recognize the stratum lithology of the target area.
The PDC drill bit stress model mainly comprises an axial force F of PDC cutting teeth a And tangential force F t 。
The contact pressure F can be obtained by integrating the infinitesimal contact stress along the y-axis direction N1 Comprises the following steps:
in the formula: l is F N2 The maximum pressing depth generated in the direction can be simply calculated according to the definition of the contact rigidity to obtain the contact pressure F N2 Comprises the following steps:
in the formula: d 'is the average penetration depth of the front end face of the PDC cutting tooth, and d' is l/2.
According to the principle of equivalent force system, the axial force F can be obtained after simplification a And tangential force F t Comprises the following steps:
F a ＝F N1 (cosθ+fsinθ)+F N2 (sinθ+fcosθ) (3)
in the formula: theta is an included angle between the axial direction of the PDC cutting tooth and the rock contact surface and is defined as a cut-in angle; e is the equivalent elastic modulus of the rock at the bottom of the hole,
MPa; a is the area of the cylindrical contact surface in mm 2 (ii) a d is the normal pressing depth, mm; s is horizontal tangential displacement, mm; beta is a constant and is determined by the shape of a contact surface, and the circular section is 1; f is the sliding friction coefficient of the rock; f s For friction between the bottom of the PDC cutters and the rock, P N Is the contact stress of the tangential contact surface.
The stress rule of the PDC drill bit can be obtained through the formulas (3) and (4), namely the stress state of the PDC cutting teeth is obtained through the operation parameters of the drilling machine.
The coal measure stratum mechanics calculation model is used for pressing, cutting and crushing rock for the PDC drill bit under the action of axial pressure and torsional moment provided by the drilling machine. According to the mechanical characteristics of rock, because most of the cutting and crushing processes of coal-based stratum rock are brittle fracture, the deformation of the action area of the cutting tooth before crushing is small, and the time is short, the basic forms of the PDC cutting tooth drilling rock crushing can be divided into 2 types: firstly, the pressing-in crushing is formed by feeding under the action of axial force, and a certain pressing-in depth is generated; the second is shear fracture formed by rotary motion under the action of cutting force.
F a ＝η 1 σ k S k1 (5)
In the formula: eta 1 The axial wear coefficient of PDC cutting teeth; sigma k The uniaxial compressive strength of rock is MPa; s k1 Is axial press-in area of PDC cutting teeth, mm 2 (ii) a n is the rotating speed of the PDC anchor rod drill bit, r/min;
the internal friction angle, (°), of the coal-based formation rock; and c is contact stiffness.
The crushing conditions of the coal measure stratum rock can be obtained through the formulas (5) and (6), and the change rule of the operation parameters of the drilling machine and the crushing of the coal measure stratum rock is established by combining the formulas (3) and (4).
The microprocessor 18 mainly performs fast calculation and analysis of the expert model 20, and the power supply device 24 is mainly used for providing uninterruptible power supply for the microprocessor 18 and the display 23. The display 23 is mainly used for displaying a coal measure stratum identification result and a three-dimensional visual prediction model, and the alarm device 22 is used for displaying and transmitting an alarm signal when a result parameter exceeds a preset threshold value.
Referring to fig. 6, the method for the intelligent recognition system for coal measure formation drilling based on multi-source information fusion provided in this embodiment specifically includes:
step 1, determining rock stratum characteristics and a prediction range of a target area according to geological data of the target coal-series stratum area, constructing a drilling tool-coal-series stratum drilling dynamic model, and performing drilling parameter-coal-series stratum characteristic change characteristic analysis by using finite element simulation software.
And 2, constructing a data hole by using a tunnel drilling machine to obtain a rock sample at the position corresponding to the coal core, determining a coal rock response characteristic value through a similar simulation test and a rotary drilling test platform test, and establishing a database of drilling parameters and coal measure stratum characteristics.
And 3, drilling construction is carried out by using the tunnel drilling machine, the operation parameter information of the drilling machine is obtained through a drilling machine working condition monitoring system, and meanwhile, the real-time hole return slag image information is obtained by an image processing system and is transmitted to an intelligent expert system together.
And 4, carrying out real-time detection on the information in the hole by using the in-hole data detection system, collecting and communicating the main parameters by using the in-hole data collection system, and transmitting the main parameters to the intelligent expert system. And 5, rapidly extracting real-time information in the hole and monitoring information of the drilling machine outside the hole by the intelligent expert system, and processing the returned slag image information.
And 6, performing comprehensive analysis and arrangement by the intelligent expert system according to the multi-source information, removing irrelevant features to obtain relevant drilling sensitive parameters, mapping n-dimensional feature parameter information to the minimum k-dimensional feature parameter (k is less than n) by a PCA (principal component analysis) method to realize dimension reduction processing on the data features, constructing brand-new orthogonal k-dimensional principal component information, and performing data fuzzification on the drilling parameters according to a membership function. The membership function adopts a normal distribution function type:
where σ and x 0 The parameters are determined, either as determined, or adjusted in real time as a function of actual conditions using a deep learning algorithm, to adjust the parameters.
And determining a fuzzy rule according to the mapping relation of the coal measure strata-characteristic parameters to form an expert rule set. And (3) outputting the identification result according to a fuzzy rule, and clearly outputting the identification result (stratum property, strength and the like), wherein the clear membership function adopts a trigonometric function, and the parameters of the clear membership function can be determined or adjusted in real time. And identifying and judging the characteristics of the columnar strata of the drilled hole and/or judging the transverse trend change of the strata in real time, completing the identification and prediction of the coal measure strata of the target area, drawing a three-dimensional quantitative prediction model of the strata of the target area through a three-dimensional Voronoi diagram, and displaying the result on a display.
In this embodiment, while, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as may be understood by those of ordinary skill in the art.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It is noted that references in the specification to "one embodiment," "an example embodiment," "some embodiments," etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The utility model provides a coal measure stratum creeps into intelligent recognition system based on multisource information fusion which characterized in that includes:
the in-hole data detection system is arranged in a drilling tool of the tunnel drilling machine and used for measuring in-hole drilling parameter information;
the in-hole data acquisition system is used for acquiring and communicating in-hole information;
the drilling machine working condition monitoring system is used for monitoring and acquiring drilling machine operation parameter information;
the image processing system is used for acquiring the characteristic information of the orifice return slag image;
the intelligent expert system is used for obtaining drilling sensitive parameters based on the in-hole drilling parameter information, the drilling machine operation parameter information and the return slag image characteristic information, performing data fuzzification on the drilling sensitive parameters according to a membership function, and identifying the characteristics of the columnar stratum of the drilled hole and/or judging the transverse trend change of the stratum in real time according to a fuzzy rule;
the intelligent expert system performs comprehensive analysis and arrangement according to multi-source information, removes irrelevant features to obtain relevant drilling sensitive parameters, then maps n-dimensional feature parameter information to the minimum k-dimensional feature parameter through a principal component analysis method, wherein k is less than n, realizes dimension reduction processing on data features, constructs brand-new orthogonal k-dimensional principal component information, and performs data fuzzification on the drilling parameters according to membership functions; the membership function adopts a normal distribution function type:
where σ and x 0 The parameters are adjusted, the parameters are determined according to the actual drilling parameter level, and the parameters are adjusted in real time according to the actual situation by utilizing a deep learning algorithm;
determining a fuzzy rule according to the mapping relation of the coal measure strata-characteristic parameters to form an expert rule set; outputting the identification result according to a fuzzy rule, clearly outputting the identification result, wherein a clear membership function adopts a trigonometric function, and parameters of the clear membership function are also adjusted in real time; and identifying and judging the characteristics of the columnar stratum of the drilled hole and/or judging the transverse trend change of the stratum in real time, completing the identification and prediction of the coal measure stratum of the target area, drawing a three-dimensional quantitative prediction model of the stratum of the target area through a three-dimensional Voronoi diagram, and displaying the result on a display.
2. The intelligent recognition system for coal measure formation drilling based on multi-source information fusion of claim 1, wherein the in-hole data detection system comprises a torque sensor, a vibration sensor, a rotation speed sensor and an inclination sensor, which are respectively used for detecting a torque parameter of a drill bit in a drill hole, a vibration parameter of a near drill bit, a rotation speed parameter of the drill bit and an orientation parameter of the drill bit.
3. The coal measure formation drilling intelligent identification system based on multi-source information fusion is characterized in that the in-hole data acquisition system comprises a signal amplifier, a first data converter, an intelligent drill rod and a second data converter, the signal amplifier is mainly used for signal acquisition and amplification, the first data converter is used for data recoding and modulation, the intelligent drill rod is used for data integration and transmission, and the second data converter is used for data demodulation, analysis and fusion.
4. The coal measure formation drilling intelligent recognition system based on multi-source information fusion of claim 1,
the drilling machine working condition monitoring system comprises a data acquisition unit and a data integrator, wherein the data acquisition unit is used for acquiring drilling speed, drilling depth, drilling pressure, drilling machine rotating speed and drilling tool position parameters of a drilling machine, and the data integrator is used for extracting and fusing main data outside a hole.
5. The intelligent recognition system for coal measure formation drilling based on multi-source information fusion as claimed in claim 1, wherein the image processing system obtains characteristic parameters of color, gray scale and block degree of rock by collecting orifice slag return diagram in real time.
6. The intelligent recognition system for coal measure stratum drilling based on multi-source information fusion as claimed in claim 1, wherein the intelligent expert system comprises:
the PDC drill bit stress model is used for acquiring the structure of the drilling tool, the category of the drill bit and the length of the drilling tool and calculating key drilling parameters of cutting force and horizontal force borne by the drill bit in real time;
the coal measure stratum mechanical model is mainly used for reading characteristic parameters of a coal measure stratum, obtaining coal measure stratum differentiation characteristics and establishing a grading basis;
the target area stratum analysis model is used for carrying out stratum demarcation on the change trend reflected by the real-time drilling parameters to obtain stratum characteristic distribution in the drilling process;
the prediction model is used for comprehensively estimating and analyzing the stratum trend of the target area by utilizing the columnar change of the discrete drilling stratum and applying a generalized prediction method, and automatically calculating and identifying the lithology of the stratum of the target area;
fuzzy expert rule set, fuzzification of drilling parameters based on membership function,
and (4) carrying out clear output on the identification result according to a fuzzy rule by using the mapping relation of the coal measure strata and the characteristic parameters.
7. The intelligent recognition system for coal measure formation drilling based on multi-source information fusion as claimed in claim 6, wherein the PDC bit force model calculates the axial force F of PDC cutting teeth based on the following formula a And tangential force F t ：
F a ＝F N1 (cosθ+fsinθ)+F N2 (sinθ+fcosθ)
In the formula: theta is an included angle between the axial direction of the PDC cutting tooth and the rock contact surface and is defined as a cut-in angle; e * Is the equivalent elastic modulus of the rock at the bottom of the hole,
MPa; a is the area of the cylindrical contact surface in mm 2 (ii) a d is the normal pressing depth, mm; s is horizontal tangential displacement, mm; beta is a constant and is determined by the shape of a contact surface, and the circular section is 1; f is the sliding friction coefficient of the rock; f s For friction between the bottom of the PDC cutters and the rock, P N Is contact stress of tangential contact surface, in which the contact pressure F N1 Comprises the following steps:
in the formula, contact pressure F N2 Comprises the following steps:
in the formula: l is F N2 The maximum pressing depth generated in the direction is d 'which is the average pressing depth of the front end face of the PDC cutting tooth, and d' is l/2.
8. A method for a coal measure stratum drilling intelligent identification system based on multi-source information fusion is characterized by comprising the following steps:
step 1, determining rock stratum characteristics and a prediction range of a target area according to geological data of the target coal measure stratum area, constructing a drilling tool and coal measure stratum drilling dynamic model, and analyzing drilling parameters and coal measure stratum characteristic change characteristics by applying finite element simulation software;
step 2, constructing a data hole by using a tunnel drilling machine to obtain a rock sample at a position corresponding to a coal core, determining a coal rock response characteristic value through a similar simulation test and a rotary drilling test platform test, and establishing a database of drilling parameters and coal measure stratum characteristics;
step 3, drilling construction is carried out by using the underground drill rig, the operation parameter information of the drill rig is obtained through a drill rig working condition monitoring system, meanwhile, the image processing system obtains real-time orifice return slag image information, and the real-time orifice return slag image information are transmitted to an intelligent expert system together;
step 4, utilizing an in-hole data detection system to detect in-hole information in real time, acquiring and communicating main parameters through an in-hole data acquisition system, and transmitting the main parameters to an intelligent expert system;
step 5, the intelligent expert system rapidly extracts real-time information in the hole and monitoring information of the drilling machine outside the hole, and simultaneously processes the returned slag image information;
step 6, the intelligent expert system performs comprehensive analysis and arrangement according to the multi-source information, removes irrelevant features to obtain relevant drilling sensitive parameters, then maps n-dimensional feature parameter information to the minimum k-dimensional feature parameter through a principal component analysis method, wherein k is less than n, realizes dimension reduction processing on data features, constructs brand-new orthogonal k-dimensional principal component information, and performs data fuzzification on the drilling parameters according to a membership function; the membership function adopts a normal distribution function type:
where σ and x 0 The parameters are adjusted, the parameters are determined according to the actual drilling parameter level, and the parameters are adjusted in real time according to the actual situation by utilizing a deep learning algorithm;
determining a fuzzy rule according to the mapping relation of the coal measure strata-characteristic parameters to form an expert rule set; outputting the identification result according to a fuzzy rule, clearly outputting the identification result, wherein a clear membership function adopts a trigonometric function, and parameters of the clear membership function are also adjusted in real time; and identifying and judging the characteristics of the columnar stratum of the drilled hole and/or judging the transverse trend change of the stratum in real time, completing the identification and prediction of the coal measure stratum of the target area, drawing a three-dimensional quantitative prediction model of the stratum of the target area through a three-dimensional Voronoi diagram, and displaying the result on a display.
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