A fully mechanized coal mining face coal and rock interface simulation and identification method and intelligent coal winning machine

Abstract

The application discloses a kind of fully mechanized coal face coal-rock interface simulation identification method and intelligent coal winning machine, 1, construct the inversion model of coal-rock interface based on the eigenvalue of radiation field;2, when coal seam thickness h=0, the radiation intensity measured by gamma ray detector in rock stratum P point is J0, is calculated to obtain relevant parameter value in the inversion model of coal-rock interface;3, set horizontal direction span Δ, set i=1, with X i Point as detection starting point detects its corresponding radiation intensity J i ;4, the radiation intensity J i It is calculated in the inversion model of coal-rock interface to predict the fluctuation slope τ of next section coal-rock interface;5, according to current detection point X i Position, predicted coal-rock interface fluctuation slope τ and horizontal direction span Δ determine next detection point X i+1 Position;6, judge whether simulation is finished or not. Realize the dynamic tracking of coal-rock interface situation, realize the coal-rock interface simulation identification of fully mechanized coal face, guide intelligent coal winning machine to cut coal.

Description

TECHNICAL FIELD

[0001] The present application relates to a fully mechanized coal face coal-rock interface simulation identification method and an intelligent coal mining machine. BACKGROUND

[0002] The "unmanned" or "few people" of the fully mechanized coal face is of great significance to realize the safe and efficient production of coal mines. As one of the key mechanical and electrical equipment of the modern fully mechanized coal face, the intelligent level of the coal mining machine directly affects the safety production and mining efficiency of the whole fully mechanized coal face. The intelligent identification technology of coal-rock interface as a key technology of intelligent mining of coal mining machine is the core of realizing intelligent coal cutting process.

[0003] The existing coal-rock identification technology of fully mechanized coal face mainly includes coal-rock image recognition technology, process signal monitoring and identification technology, and electromagnetic wave identification technology, wherein:

[0004] The coal-rock image recognition technology must be based on artificial design to work normally, and each method can usually only solve a specific problem, having the disadvantages of singleness and repetition, and cannot meet the intelligent and efficient production efficiency. In actual application, due to the differences in coal-rock types and characteristics of different coal mining faces, the robustness and generalization ability of image processing technology in coal-rock identification are limited, especially in machine learning algorithm, the problem of gradient disappearance often occurs, affecting the stability of identification.

[0005] The process signal monitoring and identification technology is a technology for evaluating the cutting state of coal and rock. By real-time monitoring and analyzing various signals of the working face, such as electrical signals, mechanical signals, coal and rock deformation signals, etc., intelligent control, safe production and improvement of coal mining efficiency can be realized. However, the vibration signal has requirements for the hardness of coal and rock, the cutting force signal has the disadvantage of large multi-axis data, the acoustic emission signal has large data volume and is easily disturbed by noise, the temperature signal is difficult to collect due to the blocking of the drum, and the current signal is easily disturbed by complex signals.

[0006] The electromagnetic wave identification technology utilizes the differences in physical properties of coal and rock, such as dielectric constant, static conductivity, and chemical properties such as chemical composition, to perceive the shape of the coal-rock interface. It does not destroy the structure of the stratum and the shape of the rock stratum, and can perform geological transparency and stratum analysis. Natural gamma rays are emitted from radioactive substances in rocks and coal seams. The content of radioactive substances in rocks is generally several tens of times that in coal seams, so the gamma ray radiation of rock strata is usually much larger than that of coal seams. The presence of coal seams has a penetrating effect on gamma rays in rock strata, so by measuring the radiation of gamma rays passing through coal seams, the thickness of the current coal seam can be determined, which can guide the intelligent coal cutting of the coal mining machine. SUMMARY

[0007] In view of the above problems, the present application provides a fully mechanized coal mining face coal-rock interface simulation identification method and an intelligent coal mining machine, based on the radiation characteristic difference between coal and immediate roof rock, a radiation intensity detection method is adopted, an inversion model of the coal-rock interface based on the radiation field characteristic value is established, the fluctuation characteristics of the coal-rock interface are intelligently analyzed, dynamic tracking of the coal-rock interface situation is realized, simulation identification of the coal-rock interface of the fully mechanized coal mining face is realized, and the intelligent coal mining machine is guided to cut coal.

[0008] Name explanation:

[0009] Coal-rock interface fluctuation slope: the slope of the line segment divided by the coal-rock interface under the simulation interface.

[0010] In order to achieve the above technical purpose and achieve the above technical effect, the present application realizes the following technical scheme:

[0011] A fully mechanized coal mining face coal-rock interface simulation identification method, comprising the following steps:

[0012] Step 1, constructing an inversion model of the coal-rock interface based on the radiation field characteristic value;

[0013] Step 2, when the coal seam thickness h=0, the radiation intensity measured by the gamma ray detector at the P point in the rock layer is J0, which is brought into the inversion model of the coal-rock interface to calculate the relevant parameter value;

[0014] Step 3, setting a horizontal direction span Δ, setting i=1, taking X i point as the detection starting point to detect the corresponding radiation intensity J i ;

[0015] Step 4, bringing the radiation intensity J i into the inversion model of the coal-rock interface to calculate the coal-rock interface fluctuation slope τ of the next segment;

[0016] Step 5, determining the next detection point X i position according to the current detection point X i+1 position, the predicted coal-rock interface fluctuation slope τ and the horizontal direction span Δ;

[0017] Step 6, judging whether the simulation is finished:

[0018] If the simulation is not finished, i+1, detecting the corresponding radiation intensity J i+1 of the next detection point determined in step 5, and entering step 4;

[0019] If the simulation is finished, sequentially connecting the detection points with straight lines and outputting as the simulation interface.

[0020] Preferably, the radiation intensity detected at the point X i is J i , and a Ji The calculation model is:

[0021]

[0022] In engineering practice and on-site testing, Ignore;

[0023] Assuming μ(τw+Δh)=x, the inversion model of the coal-rock interface is constructed as follows:

[0024]

[0025] Where K' is the proportional coefficient, q is the concentration of radioactive substances in the rock, ρ is the density of the gangue, k is the attenuation coefficient of the radioactive substance's γ-ray in the rock, Φ(x) is the ratio e -x The Jinge function with a faster decrease, μ is the attenuation coefficient of the radioactive material γ ray in the coal seam, τ is the i The slope of the coal-rock interface in the next section is predicted, w is the actual slope of the coal-rock interface, Δh is the thickness of the thin coal seam left between the predicted simulation interface and the actual coal-rock interface, is the maximum angle that the gamma-ray detector can detect, and l is the thickness of the direct top.

[0026] Preferably, in step 2, when the coal seam thickness h=0, then x=0, Substitute into the calculation to get The value of

[0027] Preferably, the horizontal span Δ is the horizontal distance traveled by the intelligent coal mining machine in 0.1 seconds.

[0028] Correspondingly, an intelligent coal mining machine includes a coal mining machine body, which uses any of the above-mentioned coal-rock interface simulation and identification methods for a fully mechanized mining working face to identify the coal-rock interface and mine coal.

[0029] Preferably, the gamma-ray detector is arranged just above or in front of the shearer drum.

[0030] The beneficial effects of the present invention are:

[0031] First, the present invention analyzes the characteristics of the coal-rock interface to form a representation of the interface. Based on this, a model formula is developed to calculate the radiation value generated at a specific point on the working face, thereby determining the parameters affecting the radiation field distribution. An inversion model is then used to establish a spatiotemporal model of the cross-correlation between the characteristic parameters of the coal-rock interface, such as the slope, and the changes in radiation field intensity. This model identifies characteristic parameters for changes in coal-rock interface morphology, establishes a method for inverting radiation intensity into the coal-rock interface, and forms a final coal-rock interface simulation by continuously and consistently predicting the slope of the next section of the interface.

[0032] Second, the coal-rock interface inversion mathematical model based on the radiation field eigenvalue established by the present application can intelligently analyze the undulating characteristics of the coal-rock interface, realize dynamic tracking of the coal-rock interface situation, and form a natural gamma ray-based intelligent recognition method for the shape of the coal-rock interface.

[0033] Third, the present application realizes coal-rock interface recognition of a fully mechanized coal mining face, and can be used to guide intelligent coal cutting of a coal mining machine, which has important significance for intelligentization of the coal mining machine and safe and efficient production of coal mines. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 is a schematic diagram of the undulating slope of the coal-rock interface of the present application;

[0035] Figure 2 is a distribution schematic diagram of the coal-rock interface and the simulated interface of the present application;

[0036] Figure 3 is a schematic diagram of the sensitive area position of the intelligent coal mining machine of the present application. DETAILED DESCRIPTION

[0037] The technical solutions of the present application will be further described in detail below in combination with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it, but the embodiments are not limiting to the present application.

[0038] The coal-rock interface mainly includes a natural coal-rock interface (i.e. a coal seam and a direct roof in direct contact with it) and a fault generated by later crustal movement. According to investigations, in most cases, the distribution of the coal-rock interface has the characteristics of gentle change and curved nonlinearity. In view of the distribution characteristics of the coal-rock interface, a characteristic parameter needs to be found to describe the coal-rock interface.

[0039] Firstly, the characteristic parameter should be convenient to obtain through monitoring the natural gamma radiation field intensity.

[0040] Secondly, the characteristic parameter should have simplicity, and the coal-rock interface needs to be described simply and clearly.

[0041] Based on this, the present application selects the undulating slope of the coal-rock interface to describe the characteristic of the coal-rock interface. As shown in the simulated interface, the simulated interface is formed by a plurality of straight lines, and the length of each straight line is a detection range. The detection range is required to be determined according to the measurement accuracy. The smaller the detection range, the higher the measurement accuracy, and vice versa. Figure 1

[0042] ​The present invention defines the coal-rock interface undulation slope as follows: the slopes of the multiple line segments that the coal-rock interface is divided into below the simulated interface. Obviously, a gamma-ray detector must be installed below the coal-rock interface. The gamma-ray detector can detect the intensity of the natural gamma-ray radiation field in real time. The coal-rock interface undulation slope can be measured based on the natural gamma-ray radiation intensity received by the gamma-ray detector.

[0043] The slope of the coal-rock interface can be positive or negative, such as Figure 1 If the horizontal height of point B is higher than that of point A, the undulation slope between points AB is positive. On the contrary, the undulation slope between points DE is negative. In addition to being positive or negative, the undulation slope can also be large or small. Figure 1 The horizontal height difference between points B and A is greater than the horizontal height difference between points C and B, and their detection ranges are the same. Therefore, the slope between points AB is greater because it is steeper. Similarly, between points DE and EF, it is clear that the slope between DE is greater (their slopes are both negative).

[0044] A method for simulating and identifying the coal-rock interface in a fully-mechanized mining working face of the present invention comprises the following steps:

[0045] Step 1: Construct an inversion model of the coal-rock interface based on the radiation field characteristic value. i The radiation intensity detected at is J i , construct J i The calculation model is:

[0046]

[0047] Since the angle of the coal-rock interface is greater than 70° or even 80° in most cases, Perform analysis:

[0048] In the above formula:

[0049] (1) Φ(x) is the ratio of e -x A function that decreases faster;

[0050] (2) The value of is usually less than 0.25, The value of is usually greater than 4;

[0051] (3) Generally speaking The value of is 1 / 30 of the value of Φ(μ(τw+Δh));

[0052] (4) The absolute value of is generally 1 / 25 of Φ(μ(τw+Δh))-Φ(μ(τw+Δh)+kl), and then multiplied by This coefficient, In most cases, it is 1 / 100 of Φ(μ(τw+Δh))-Φ(μ(τw+Δh)+kl);

[0053] (5), in engineering practice and field detection, can be Negligible.

[0054] Therefore, let μ(τw+Δh) = x, the inversion model of coal and rock interface is constructed as:

[0055]

[0056] In the formula, K' is a proportional coefficient (considering the radiation detection efficiency, different γ-ray detectors have different efficiencies, and the same γ-ray detector is a constant under the same geological conditions, and the value of K' will change under different geological conditions, which needs to be measured), q is the concentration of radioactive substances in rock, ρ is the density of gangue, k is the attenuation coefficient of radioactive γ-ray in rock, Φ(x) is the specific e -x Decreasing faster gold function, μ is the attenuation coefficient of radioactive γ-ray in coal seam, τ is the predicted next coal and rock interface fluctuation slope at point X i , w is the real fluctuation slope of coal and rock interface, Δh is the thickness of a thin coal seam left between the predicted simulation interface and the real coal and rock interface, The maximum angle that can be detected by the γ-ray detector, l is the thickness of the immediate roof.

[0057] Step 2, when the coal seam thickness h = 0, the radiation intensity measured by the γ-ray detector at point P in the rock layer is J0, which is brought into the inversion model of coal and rock interface to calculate the relevant parameter values.

[0058] Preferably, in step 2, when the coal seam thickness h = 0, x = 0, The value of is

[0059] Step 3, set the horizontal span Δ, set i = 1, and take X i Point as the starting point of detection, and detect its corresponding radiation intensity J i . Preferably, the horizontal span Δ is the horizontal distance walked by the intelligent coal mining machine in 0.1 seconds.

[0060] Step 4, bring the radiation intensity J i Into the inversion model of coal and rock interface to calculate its predicted next coal and rock interface fluctuation slope τ.

[0061] Step 5, according to the current detection point X iThe position, the predicted coal-rock interface fluctuation slope tau and the horizontal direction span delta determine the next detection point X i+1 Position.

[0062] Step 6, judging whether the simulation is ended or not:

[0063] If the simulation is not ended, i+1, the next detection point determined in step 5 is detected to obtain the corresponding radiation intensity J i+1 , and step 4 is entered;

[0064] If the simulation is ended, the detection points are sequentially connected by straight lines and output as a simulation interface.

[0065] Taking Figure 2 for example, A point is a detection starting point, the position of the next detection point B is determined according to the coal-rock interface fluctuation slope and the horizontal direction span delta obtained according to the inversion model, the position of the next detection point C is determined based on the position of B point, and the process is repeated until the simulation is ended, and then the detection points are sequentially connected by straight lines and output as a simulation interface.

[0066] Correspondingly, an intelligent coal mining machine comprises a coal mining machine body, and the coal-rock interface of a fully-mechanized coal mining face is identified and coal mining is performed by using the coal-rock interface simulation identification method of any one of the above.

[0067] The sensitive area is closer to the position of the drum of the coal mining machine under the condition of meeting the basic condition, and the data obtained is more accurate, therefore, the gamma ray detector is preferably arranged above or in front of the drum of the coal mining machine (the drum is located at the end of the rocker arm, the drum breaks and transports the coal gangue in a rotating manner, and the mining progress of the coal mine is accelerated), as shown in Figure 3 .

[0068] The beneficial effects of the present application are:

[0069] Firstly, the present application forms the representation of the coal-rock interface by analyzing the characteristics of the coal-rock interface, gives a model formula for calculating the radiation value formed at a certain point of the working face on this basis, determines the parameters affecting the distribution of the radiation field, and establishes the mutual correlation space-time model of the representation parameters such as the coal-rock interface fluctuation slope and the radiation field intensity change through the inversion model, determines the identification characteristic parameters of the coal-rock interface morphological change, establishes a method of inverting the radiation intensity into the coal-rock interface, and forms the final coal-rock simulation interface by continuously and continuously predicting the coal-rock interface fluctuation slope of the next section.

[0070] Secondly, the coal-rock interface inversion mathematical model based on the radiation field characteristic value established by the application can intelligently analyze the undulating characteristics of the coal-rock interface, realize dynamic tracking of the coal-rock interface situation, and form a natural gamma ray-based intelligent identification method for the coal-rock interface shape.

[0071] Thirdly, the application realizes the coal-rock interface identification of the fully-mechanized coal mining face, and can be used for guiding the intelligent coal cutting of the coal winning machine, which has important significance for the intelligentization of the coal winning machine and the safe and efficient production of the coal mine.

[0072] The above is only the preferred embodiment of the application, and does not limit the patent range of the application, and any equivalent structure or equivalent flow transformation using the content of the specification and drawings of the application, or direct or indirect application in other related technical fields, are also included in the patent protection range of the application.

Claims

1. A fully mechanized coal mining face coal-rock interface simulation identification method, characterized in that, It comprises the following steps: Step 1, constructing the inversion model of coal-rock interface based on the eigenvalue of radiation field; Step 2, when the thickness of coal seam h=0, the radiation intensity measured by the gamma ray detector at point P in the rock layer is J0, which is brought into the inversion model of coal-rock interface to calculate the relevant parameter values; Step 3, set horizontal direction span Δ, set i = 1, take X i point as detection starting point, detect its corresponding radiation intensity as J i ; Step 4, calculate the radiation intensity J i The calculated radiation intensity J is brought into the inversion model of the coal-rock interface to calculate the predicted fluctuation slope τ of the coal-rock interface of the next section. Step 5, determine next detection point X according to current detection point X i Position, predicted coal-rock interface fluctuation slope τ and horizontal direction span Δ i+1 Position; Step 6, judging whether the simulation is finished: If the simulation is not finished, i+1, the next detection point determined in step 5 is detected for its corresponding radiation intensity J i+1 and step 4 is entered. If the simulation is finished, sequentially connecting the detection points with straight lines and outputting as the simulated interface; The radiation intensity detected at point X is J i , the construction of the calculation model for J i is as follows: i ​ In engineering practice and on-site detection, will Neglect; Let μ(τw+Δh)=x, then the inversion model of coal-rock interface is constructed as follows: wherein K' is a proportional coefficient, q is the concentration of radioactive material in the rock, p is the density of the gangue, k is the attenuation coefficient of the gamma rays of the radioactive material in the rock, and Φ(x) is the ratio e -x The gold function decreases more quickly, μ is the attenuation coefficient of the gamma rays of the radioactive material in the coal seam, τ is the slope of the coal-rock interface fluctuation at the point X i wherein w is the real slope of the coal-rock interface fluctuation, Δh is the thickness of the thin coal seam left between the predicted simulation interface and the real coal-rock interface, is the maximum angle that can be detected by the gamma ray detector, and l is the thickness of the immediate roof. In step 2, when the coal seam thickness h = 0, then x = 0, Substituting the calculation gives a value of ​ 2. The coal-rock interface simulation and identification method of a fully mechanized coal mining face according to claim 1, characterized in that, The horizontal distance Δ is the horizontal distance walked by the intelligent coal mining machine in 0.1 seconds.

3. A shearer loader comprising a shearer loader body, characterised in that, The coal mining machine body adopts the coal-rock interface simulation identification method of the fully-mechanized coal mining face according to any one of the above claims 1-2 to identify and mine the coal-rock interface.

4. A shearer as claimed in claim 3 wherein, The gamma ray detector is arranged above or in front of the drum of the coal mining machine.

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