Heading face abnormal information detection system and method based on sound wave inversion

Abstract

The invention discloses a driving working face abnormal information detection system and method based on sound wave inversion, and relates to the technical field of coal and gas outburst early warning monitoring, and the method comprises the steps: obtaining an impact sound emission signal of a drill bit of a heading machine and a target coal mine driving working face when the heading machine carries out the driving work on the target coal mine driving working face, acquiring a coal and rock mass fracture acoustic emission signal of the target coal mine driving working face; analyzing the dissimilatory characteristics of acoustic emission waveforms of the impact acoustic emission signal and the coal and rock mass fracture acoustic emission signal; and inputting the dissimilatory characteristics of the acoustic emission waveforms into the trained classification model for inversion identification to obtain head-on geological information of the target coal mine driving working face. According to the method, the gas outburst condition in the coal seam tunneling process can be early warned in time, and the technical problems that in the prior art, in the tunneling working face abnormal information detection process, the operation difficulty is large, the real-time performance is poor, and the application range is narrow are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal and gas outburst early warning monitoring, and in particular to a system and method for detecting abnormal information of a tunneling working face based on acoustic wave inversion. Background Art

[0002] With the increase of coal mining depth, coal and gas outburst accidents occur frequently, which seriously threatens the safe production of coal mines. The coal tunnel excavation working face is the main location for coal and gas outbursts. The geological abnormal information in front of the working face, such as water-bearing areas, high-gas areas, structural areas, coal seam change areas, soft coal areas, hard coal areas, etc., are all important factors affecting the danger of coal and gas outbursts.

[0003] In response to the abnormal information of these excavation working faces, traditional detection methods mainly rely on borehole analysis. Borehole analysis extracts coal samples and gas samples, analyzes the geological characteristics and gas content of the coal seam, and thus determines the abnormal situation in front of the excavation working face. However, this method has many technical limitations. First, borehole analysis requires the planning of the drilling location and depth in advance, which is labor-intensive and time-consuming, with low detection efficiency, which is not conducive to real-time monitoring and rapid response. Secondly, the borehole test parameters can often only reflect the information of the local area around the borehole, and cannot fully and accurately reflect the overall abnormal situation in front of the excavation working face, resulting in the failure to timely warn of gas outbursts during coal seam excavation.

[0004] Conventional geophysical exploration techniques are difficult to deploy and operate under the condition of narrow space in the excavation working face, and the real-time performance is not ideal. In addition, geophysical exploration methods are usually only sensitive to certain specific abnormal information, such as electromagnetic methods mainly detect water-bearing areas and conductive abnormal bodies, and seismic methods mainly detect structural and lithological changes. It is difficult to fully identify various types of abnormal information, and the scope of application is relatively narrow.

[0005] Therefore, the existing technology for detecting abnormal information on the excavation face has been unable to meet the requirements of deep mining mine safety production for the efficiency and accuracy of detecting abnormal information on the excavation face. Summary of the invention

[0006] In order to solve the above technical problems existing in the prior art, the embodiment of the present invention provides a system and method for detecting abnormal information of a tunneling working face based on acoustic wave inversion. The technical solution is as follows:

[0007] On the one hand, a method for detecting abnormal information of a tunneling working face based on acoustic wave inversion is provided, the method comprising: based on a first acoustic emission sensor arranged in a drill bit area of ​​a tunneling machine, obtaining an impact acoustic emission signal of the drill bit of the tunneling machine and the tunneling working face of a target coal mine when the tunneling machine is tunneling on the tunneling working face of the target coal mine; based on a second acoustic emission sensor arranged at the tunneling working face of the target coal mine, obtaining an acoustic emission signal of coal rock mass fracture at the tunneling working face of the target coal mine when the tunneling machine is tunneling on the tunneling working face of the target coal mine; analyzing acoustic emission waveform alienation features of the impact acoustic emission signal and the coal rock mass fracture acoustic emission signal; inputting the acoustic emission waveform alienation features into a trained classification model for inversion identification, and obtaining the head-on geological information of the tunneling working face of the target coal mine; the head-on geological information includes abnormal geological information and non-abnormal geological information.

[0008] Furthermore, the method also includes: based on the first acoustic emission sensor, obtaining the impact acoustic emission signal set of the drill bit of the tunnel boring machine and the tunneling working face under different geological characteristics; based on the second acoustic emission sensor, obtaining the coal rock fracture acoustic emission signal set of the tunneling working face under different geological characteristics; based on the impact acoustic emission signal set and the rock fracture acoustic emission signal set, constructing an acoustic emission waveform alienation feature parameter library under different geological characteristics; based on the acoustic emission waveform alienation feature parameter library, training a preset classification model to obtain the trained classification model.

[0009] Furthermore, the preset classification model includes an unsupervised clustering analysis model.

[0010] Furthermore, the acoustic emission waveform alienation characteristics include: intensity difference, onset difference, duration difference, main frequency difference, secondary main frequency difference, tertiary main frequency difference, and tail wave length difference.

[0011] Furthermore, the abnormal geological information includes: water-bearing areas, high-gas areas, structural areas, coal seam change areas, soft coal areas, and hard coal areas.

[0012] Furthermore, after obtaining the head-on geological information of the target coal mine excavation working face, the method also includes: if the head-on geological information is no abnormal geological information, controlling the tunnel boring machine to operate normally; if the head-on geological information is abnormal geological information, stopping the tunnel boring machine operation.

[0013] On the other hand, a tunneling face abnormal information detection system based on acoustic wave inversion is also provided, comprising: a first acoustic emission sensor, a second acoustic emission sensor, an analysis module and an identification module; wherein the first acoustic emission sensor is arranged in the drill bit area of ​​the tunneling machine, and the second acoustic emission sensor is arranged in front of the tunneling face of the target coal mine; the first acoustic emission sensor is used to obtain the impact acoustic emission signal of the drill bit of the tunneling machine and the tunneling face of the target coal mine when the tunneling machine is tunneling on the tunneling face of the target coal mine; the second acoustic emission sensor is used to obtain the coal rock fracture acoustic emission signal of the tunneling face of the target coal mine when the tunneling machine is tunneling on the tunneling face of the target coal mine; the analysis module is used to analyze the acoustic emission waveform alienation characteristics of the impact acoustic emission signal and the coal rock fracture acoustic emission signal; the identification module is used to input the acoustic emission waveform alienation characteristics into the trained classification model for inversion identification to obtain the head-on geological information of the tunneling face of the target coal mine; the head-on geological information includes abnormal geological information and non-abnormal geological information.

[0014] Furthermore, it also includes a training module, which is used to: based on the first acoustic emission sensor, obtain the impact acoustic emission signal set of the drill bit of the tunnel boring machine and the tunneling working face under different geological characteristics; based on the second acoustic emission sensor, obtain the coal rock fracture acoustic emission signal set of the tunneling working face under different geological characteristics; based on the impact acoustic emission signal set and the rock fracture acoustic emission signal set, construct an acoustic emission waveform alienation feature parameter library under different geological characteristics; based on the acoustic emission waveform alienation feature parameter library, train a preset classification model to obtain the trained classification model.

[0015] On the other hand, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the embodiment of the present invention when executing the computer program.

[0016] On the other hand, a computer-readable storage medium is provided, in which a program code is stored. The program code can be called by a processor to execute the method provided in the embodiment of the present invention.

[0017] The embodiment of the present invention provides a system and method for detecting abnormal information of a tunneling working face based on acoustic wave inversion. By utilizing machine learning analysis of the alienation characteristics of acoustic wave signals, real-time monitoring and intelligent identification of differential information in front of the tunneling working face are achieved. The system and method have the advantages of high detection efficiency, high accuracy, low cost and good real-time performance. The system and method can provide timely early warning of gas outbursts during coal seam tunneling, alleviating the technical problems of high operating difficulty, poor real-time performance and narrow application scope in the prior art for detecting abnormal information of the tunneling working face. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 It is a flow chart of a method for detecting abnormal information of a tunneling working face based on acoustic wave inversion provided by an embodiment of the present invention;

[0020] Figure 2 It is a flow chart of another method for detecting abnormal information of a tunneling working face based on acoustic wave inversion provided by an embodiment of the present invention;

[0021] Figure 3 It is a schematic diagram of a tunneling working face abnormal information detection system based on acoustic wave inversion provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0023] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0024] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0025] Embodiment 1

[0026] Figure 1 1 is a flow chart of a method for detecting abnormal information of a tunneling working face based on acoustic wave inversion according to an embodiment of the present invention. Figure 1 As shown, the method specifically comprises the following steps:

[0027] Step S102, based on a first acoustic emission sensor arranged in the drill bit area of ​​the tunnel boring machine, obtain an acoustic emission signal of the impact between the drill bit of the tunnel boring machine and the target coal mine excavation working face when the tunnel boring machine is excavating on the target coal mine excavation working face.

[0028] Step S104, based on the second acoustic emission sensor arranged at the target coal mine excavation working face, obtain the coal rock fracture acoustic emission signal of the target coal mine excavation working face when the tunnel boring machine is excavating on the target coal mine excavation working face.

[0029] Step S106, analyzing the acoustic emission waveform alienation characteristics of the impact acoustic emission signal and the coal-rock fracture acoustic emission signal.

[0030] Specifically, the acoustic emission signals of the impact between the drill bit and the tunneling working face and the acoustic emission signals of rock fracture are detected when the tunneling machine is operating. If the coal body on the tunneling working face contains water, gas or structure, the alienation characteristics between the above two acoustic emission signals are different. The embodiment of the present invention uses the alienation characteristics to identify abnormal tunneling working faces.

[0031] Step S108, inputting the acoustic emission waveform alienation feature into the trained classification model for inversion recognition, and obtaining the head-on geological information of the target coal mine excavation working face; the head-on geological information includes abnormal geological information and non-abnormal geological information.

[0032] In an optional implementation provided by an embodiment of the present invention, the first acoustic emission sensor is arranged on a cantilever bridge behind the drill bit, and the second acoustic emission sensor is arranged within 5 m in front of the coal wall.

[0033] Preferably, the frequency of the first acoustic emission sensor and the second acoustic emission sensor is 1kHz~100kHz, and the sampling frequency is 200kHz, that is, 200,000 sampling points are included in 1s; the first acoustic emission sensor and the second acoustic emission sensor are connected by a timer to ensure that the clock error of the two acoustic emission sensors does not exceed 1μs.

[0034] Preferably, the two acoustic emission sensors only detect the signal generated when the tunnel boring machine hits the coal body, and do not perform detection when the tunnel boring machine is not operating.

[0035] Specifically, the first acoustic emission sensor is always arranged on the drill bit of the tunnel boring machine and remains stationary during operation; the second acoustic emission sensor is arranged at a position that moves along with the tunnel boring operation of the tunnel boring machine until the tunnel is connected.

[0036] Figure 2FIG. 1 is a flow chart of another method for detecting abnormal information of a tunneling working face based on acoustic wave inversion according to an embodiment of the present invention. Figure 2 As shown, optionally, abnormal geological information includes: water-bearing areas, high-gas areas, structural areas, coal seam change areas, soft coal areas, and hard coal areas.

[0037] The acoustic emission waveform alienation feature refers to the classification of the acoustic emission signals generated during tunneling operations under different geological characteristics, and the analysis of the differences between the impact acoustic emission waveform of the tunneling machine and the acoustic emission waveform received on the coal wall.

[0038] Preferably, if Figure 2 As shown, in the embodiment of the present invention, the acoustic emission waveform alienation characteristics include: intensity difference, onset difference, duration difference, main frequency difference, secondary main frequency difference, tertiary main frequency difference, and tail wave length difference.

[0039] Specifically, the method provided in the embodiment of the present invention further includes constructing an acoustic emission waveform alienation feature parameter library and training a classification model. Specifically, the method includes the following steps:

[0040] Based on the first acoustic emission sensor, a set of impact acoustic emission signals of the drill bit of the tunnel boring machine and the tunneling working face under different geological characteristics is obtained;

[0041] Based on the second acoustic emission sensor, a set of acoustic emission signals of coal and rock mass fracture at the excavation working face under different geological characteristics is obtained;

[0042] Based on the impact acoustic emission signal set and the rock fracture acoustic emission signal set, a characteristic parameter library of acoustic emission waveform alienation under different geological characteristics is constructed;

[0043] The preset classification model is trained based on the acoustic emission waveform alienation feature parameter library to obtain a trained classification model.

[0044] In an optional implementation provided by an embodiment of the present invention, the preset classification model includes an unsupervised clustering analysis model.

[0045] Specifically, the acoustic emission waveform alienation feature parameter library refers to the use of machine learning methods to classify a large number of acoustic emission signals collected during excavation under different geological characteristics, and to construct corresponding acoustic emission waveform alienation feature parameter libraries according to different geological characteristics;

[0046] The acoustic emission waveform alienation characteristic parameter library includes acoustic emission waveform alienation characteristics in water-bearing areas, high-gas areas, structural areas, coal seam change areas, soft coal areas, and hard coal areas;

[0047] In the embodiment of the present invention, the unsupervised cluster analysis model is established on the upper layer of the acoustic emission waveform alienation feature parameter library, and the newly collected acoustic emission waveforms are automatically classified, identified, and inverted to determine the geological characteristics of the excavation site;

[0048] The unsupervised cluster analysis model can realize the probability ratio of acoustic emission waveforms corresponding to different geological features.

[0049] The method provided in the embodiment of the present invention, after step S108, further includes:

[0050] If the geological information facing the surface shows no abnormal geological information, the tunnel boring machine is controlled to operate normally;

[0051] If the geological information facing the target is abnormal, the tunnel boring machine operation will be stopped.

[0052] Specifically, the first acoustic emission sensor and the second acoustic emission sensor are respectively arranged on the drill bit of the tunnel boring machine and the working face to be excavated to ensure equal power operation of the tunnel boring machine. After the tunneling operation begins, the acoustic emission signal parameter test is carried out. Each test lasts for 3 minutes, and the test is carried out again every 3 minutes, and the test is carried out continuously for 10 times.

[0053] The danger of the tunneling process is judged by importing all the collected acoustic emission signal waveforms into the acoustic emission waveform alienation feature parameter library for identification. The classification model automatically identifies the acoustic emission waveform alienation features and makes a decision when the probability of the identified geological features accounts for more than 70%;

[0054] When the inversion identification results are normal, maintain normal operation and detection frequency. When the geological features identified by inversion are any of the water-bearing area, high-gas area, structural area, coal seam change area, soft coal area, and hard coal area, stop the operation and take corresponding measures according to the identified geological features;

[0055] After taking relevant measures, the excavation area will be inverted and identified again. If there is no abnormality in the inversion result, normal excavation will be carried out. If there is an abnormality, classified prevention and control will continue to be carried out according to the inversion result until there is no abnormality in the inversion result.

[0056] The embodiment of the present invention provides a method for detecting abnormal information of a tunneling working face based on acoustic wave inversion. Two acoustic emission sensors are respectively arranged at the tunneling working face and the drill bit area of ​​a tunneling machine to simultaneously detect the impact acoustic emission signal of the drill bit and the tunneling working face and the rock fracture acoustic emission signal when the tunneling machine is operating. If the coal body on the tunneling working face contains water, gas or structure, the alienation characteristics between the above two acoustic emission signals are different. The present invention realizes real-time monitoring and intelligent recognition of differential information in front of the tunneling working face by machine learning analysis of the alienation characteristics of acoustic wave signals. The method has the advantages of high detection efficiency, high accuracy, low cost and good real-time performance. The method can timely warn of gas outbursts in the process of coal seam tunneling, and alleviates the technical problems of high operation difficulty, poor real-time performance and narrow application scope in the prior art for detecting abnormal information of the tunneling working face.

[0057] Embodiment 2

[0058] Figure 3 Schematic diagram of an abnormal information detection system for a tunneling working face based on acoustic wave inversion according to an embodiment of the present invention. Figure 3 As shown, the system includes: a first acoustic emission sensor 10, a second acoustic emission sensor 20, an analysis module 30 and an identification module 40; wherein the first acoustic emission sensor 10 is arranged in the drill bit area of ​​the tunnel boring machine, and the second acoustic emission sensor 20 is arranged in front of the target coal mine tunneling working face.

[0059] Specifically, the first acoustic emission sensor 10 is used to obtain the acoustic emission signal of the impact between the drill bit of the tunnel boring machine and the target coal mine excavation working face when the tunnel boring machine is excavating on the target coal mine excavation working face;

[0060] The second acoustic emission sensor 20 is used to obtain the coal rock fracture acoustic emission signal of the target coal mine excavation working face when the tunnel boring machine is excavating on the target coal mine excavation working face;

[0061] An analysis module 30 is used to analyze the acoustic emission waveform alienation characteristics of the impact acoustic emission signal and the coal-rock mass fracture acoustic emission signal;

[0062] The recognition module 40 is used to input the acoustic emission waveform alienation characteristics into the trained classification model for inversion recognition to obtain the head-on geological information of the target coal mine excavation working face; the head-on geological information includes abnormal geological information and non-abnormal geological information.

[0063] like Figure 3 As shown, the system provided by the embodiment of the present invention further includes a training module 50, which is used to:

[0064] Based on the first acoustic emission sensor, a set of impact acoustic emission signals of the drill bit of the tunnel boring machine and the tunneling working face under different geological characteristics is obtained;

[0065] Based on the second acoustic emission sensor, a set of acoustic emission signals of coal and rock mass fracture at the excavation working face under different geological characteristics is obtained;

[0066] Based on the impact acoustic emission signal set and the rock fracture acoustic emission signal set, a characteristic parameter library of acoustic emission waveform alienation under different geological characteristics is constructed;

[0067] The preset classification model is trained based on the acoustic emission waveform alienation feature parameter library to obtain a trained classification model.

[0068] The present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the embodiment of the present invention when executing the computer program.

[0069] The present invention further provides a computer-readable storage medium, in which program codes are stored. The program codes can be called by a processor to execute the method provided in the embodiment of the present invention.

[0070] It should be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0071] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0072] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0073] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0074] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0075] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0076] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0078] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0079] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for detecting abnormal information of a tunneling working face based on acoustic wave inversion, characterized in that: The method comprises: Based on a first acoustic emission sensor disposed in a drill bit area of ​​a tunnel boring machine, an acoustic emission signal of an impact between the drill bit of the tunnel boring machine and the tunnel boring working face of a target coal mine is obtained when the tunnel boring machine is tunneling on the tunnel boring working face of the target coal mine; Based on a second acoustic emission sensor disposed on the target coal mine excavation working face, an acoustic emission signal of coal rock mass fracture at the target coal mine excavation working face is obtained when the excavator is excavating at the target coal mine excavation working face; Analyzing the acoustic emission waveform alienation characteristics of the impact acoustic emission signal and the coal-rock mass fracture acoustic emission signal; The acoustic emission waveform alienation feature is input into the trained classification model for inversion identification to obtain the head-on geological information of the target coal mine excavation working face; the head-on geological information includes abnormal geological information and non-abnormal geological information; the abnormal geological information includes: water-bearing areas, high-gas areas, structural areas, coal seam change areas, soft coal areas, and hard coal areas.

2. The method according to claim 1, characterized in that The method further comprises: Based on the first acoustic emission sensor, a set of impact acoustic emission signals of the drill bit of the tunnel boring machine and the tunneling working face under different geological characteristics is obtained; Based on the second acoustic emission sensor, a set of acoustic emission signals of coal and rock mass fracture at the excavation working face under different geological characteristics is obtained; Based on the impact acoustic emission signal set and the rock mass fracture acoustic emission signal set, construct an acoustic emission waveform alienation characteristic parameter library under different geological characteristics; The preset classification model is trained based on the acoustic emission waveform alienation feature parameter library to obtain the trained classification model.

3. The method according to claim 2, characterized in that The preset classification model includes an unsupervised cluster analysis model.

4. The method according to claim 1, characterized in that: The acoustic emission waveform alienation characteristics include: intensity difference, onset difference, duration difference, main frequency difference, secondary main frequency difference, tertiary main frequency difference, and tail wave length difference.

5. The method according to claim 1, characterized in that After obtaining the geological information of the target coal mine excavation working face, the method further includes: If the head-on geological information is no abnormal geological information, controlling the tunnel boring machine to operate normally; If the head-on geological information is abnormal geological information, the tunnel boring machine operation is stopped.

6. A tunneling face abnormal information detection system based on acoustic wave inversion, characterized in that: include: A first acoustic emission sensor, a second acoustic emission sensor, an analysis module and an identification module; wherein the first acoustic emission sensor is arranged in the drill head area of ​​the tunnel boring machine, and the second acoustic emission sensor is arranged in front of the target coal mine tunneling working face; The first acoustic emission sensor is used to obtain an acoustic emission signal of an impact between the drill bit of the tunnel boring machine and the tunnel boring working face of the target coal mine when the tunnel boring machine is tunneling on the tunnel boring working face of the target coal mine; The second acoustic emission sensor is used to obtain an acoustic emission signal of coal rock fracture at the target coal mine excavation working face when the tunnel boring machine is excavating at the target coal mine excavation working face; The analysis module is used to analyze the acoustic emission waveform alienation characteristics of the impact acoustic emission signal and the coal-rock mass fracture acoustic emission signal; The identification module is used to input the acoustic emission waveform alienation characteristics into the trained classification model for inversion identification to obtain the head-on geological information of the target coal mine excavation working face; the head-on geological information includes abnormal geological information and non-abnormal geological information; the abnormal geological information includes: water-bearing areas, high-gas areas, structural areas, coal seam change areas, soft coal areas, and hard coal areas.

7. The system according to claim 6, characterized in that Also includes training modules for: Based on the first acoustic emission sensor, a set of impact acoustic emission signals of the drill bit of the tunnel boring machine and the tunneling working face under different geological characteristics is obtained; Based on the second acoustic emission sensor, a set of acoustic emission signals of coal and rock mass fracture at the excavation working face under different geological characteristics is obtained; Based on the impact acoustic emission signal set and the rock mass fracture acoustic emission signal set, construct an acoustic emission waveform alienation characteristic parameter library under different geological characteristics; The preset classification model is trained based on the acoustic emission waveform alienation feature parameter library to obtain the trained classification model.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 5.

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