A method and device for early warning of coal seam outburst risk based on multi-scale microseismic information fusion

Abstract

This invention provides a method and device for early warning of coal seam outburst hazards based on multi-scale microseismic information fusion, relating to the field of early warning technology for coal seam outburst hazards. The method includes: deploying a microseismic sensor network to collect microseismic signals during the tunneling process and using filtering technology to remove interference signals; dividing the working face space into multiple regions with the center of the tunneling face as the reference point, assigning different equivalent coefficients to each region based on its distance from the tunneling face; establishing a quantitative relationship between microseismic signal energy and hazard level by combining outburst potential energy and balance coefficients, and calculating the equivalent hazard level of the microseismic signal; calculating the hazard level of the microseismic signal within a continuous time interval by summing; setting an early warning threshold using a critical value method, comparing the current hazard level with the early warning threshold in real time, and triggering an early warning when the early warning conditions are met. This invention sets reasonable equivalent values ​​based on signal distance, which can significantly improve the accuracy and effectiveness of early warning in high-risk operating areas such as coal mines.

Description

Technical Field

[0001] This invention relates to the field of coal seam outburst hazard early warning technology, and in particular to a coal seam outburst hazard early warning method and device based on multi-scale microseismic information fusion. Background Technology

[0002] Coal seam outbursts are one of the major dynamic hazards faced by coal mines, characterized by their rapid occurrence and high destructiveness. Therefore, how to accurately predict and warn of coal seam outburst risks is an important engineering and technical problem that needs to be solved in the process of coal seam mining.

[0003] Microseismic signals can be collected to predict the risk of coal seam outbursts, but current methods have limitations. They focus only on local, near-field signals or blindly cover signals from the entire region, ignoring the complexity of the disaster's occurrence mechanism, which limits the accuracy of predictions. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to provide a method and device for early warning of coal seam outburst hazards based on multi-scale microseismic information fusion, which sets reasonable equivalent values ​​according to the signal distance to improve the accuracy and effectiveness of the early warning.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] On the one hand, a method for early warning of coal seam outburst risk based on multi-scale microseismic information fusion is provided, the method comprising the following steps:

[0007] S1. Deploy a network of microseismic sensors to collect microseismic signals during the tunneling process, and use filtering techniques to remove interference signals;

[0008] S2. Using the center of the tunneling face as a reference point, the working face space is divided into multiple regions, and each region is assigned a different equivalent coefficient according to its distance from the tunneling face.

[0009] S3. Combining the prominent potential energy and the balance coefficient, establish the quantitative relationship between the energy and hazard of the microseismic signal, and calculate the equivalent hazard of the microseismic signal;

[0010] S4. Calculate the hazard level of microseismic signals within consecutive time intervals by summing up the data.

[0011] S5. Use the critical value method to set the warning threshold, compare the current danger level with the warning threshold in real time, and trigger the warning when the warning conditions are met.

[0012] Optionally, step S1 specifically includes:

[0013] The microseismic sensor network is arranged according to the length l of the tunneling face and the microseismic influence range L. The microseismic influence range L is set to twice the length l of the tunneling face, specifically, microseismic sensors are arranged within a range of 200m in front of, to both sides of and behind the tunneling face.

[0014] Optionally, step S2 specifically includes:

[0015] Using the center of the tunneling face as a reference point, a segmented concentric sphere is constructed to divide the working face space into multiple regions, each region having a different distance from the tunneling face;

[0016] Based on the distance between each region and the tunneling face, the influence of microseismic signals on the tunneling face is divided into multiple different levels, and each level is assigned an equivalent coefficient.

[0017] Optionally, in step S2, the working face space is divided into 5 regions, and each region is assigned an equivalent coefficient ξ based on its distance from the tunneling working face. Specifically, the energy of the microseismic signal is selected at the same value in the region 0-100m from the tunneling working face, 50% of the energy of the microseismic signal is selected in the region 100-200m, 6.25% of the energy of the microseismic signal is selected in the region 200-500m, 0.006% of the energy of the microseismic signal is selected in the region 500-1000m, and the influence of the microseismic signal is ignored in the region greater than 1000m.

[0018] Optionally, step S3 specifically includes:

[0019] Let the protrusion potential energy Q be defined, where Q represents the energy limit that hinders the protrusion, and is derived using the gas expansion calculation formula. In the formula, p0 is the atmospheric pressure in the tunnel, p is the gas pressure, V0 is the gas content involved in the outburst, and n is the methane adiabatic coefficient.

[0020] For each microseismic signal, based on its microseismic signal energy E, equivalent coefficient ξ, outburst potential energy Q, and equilibrium coefficient λ, the formula is used. The equivalent risk level W is calculated, where the equivalent coefficient ξ is determined based on the distance between the microseismic signal and the tunneling face.

[0021] Optionally, in step S3, the equivalent hazard W of a single microseismic signal is calculated using the following formula:

[0022] ;

[0023] The balance coefficient λ is determined through accident cases. The equivalent risk level at the time of the accident is considered to be 1. λ is calculated based on the microseismic signal energy E, the equivalent coefficient ξ, and the outburst potential energy Q at that time.

[0024] Optionally, step S4 specifically includes:

[0025] Within a continuous time interval Δt, the hazard level W(Δt) of the microseismic signal is calculated by summation, where W(Δt) = ΣW, and Δt is selected based on the disturbance rheological stability time range determined by the differences in coal quality characteristics.

[0026] Optionally, step S5 specifically includes:

[0027] Based on long-term monitoring data, the maximum risk level W was determined when incidents such as blowout, stuck drill, and outburst power manifestation occurred. max With minimum value W min ;

[0028] The warning threshold is set at 0.75W. max During real-time monitoring, the currently calculated W(Δt) value is compared with the warning threshold. When the W(Δt) value is within W... min With 0.75W max When this occurs, an alert is triggered.

[0029] On the other hand, a coal seam outburst hazard early warning device based on multi-scale microseismic information fusion is provided, for implementing the method described in any of the above-mentioned embodiments, the device comprising:

[0030] The signal acquisition module is used to deploy a network of microseismic sensors to acquire microseismic signals during the tunneling process and to use filtering technology to remove interference signals.

[0031] The region division module is used to divide the working face space into multiple regions with the center of the tunneling face as the reference point. Each region is assigned a different equivalent coefficient according to its distance from the tunneling face.

[0032] The first calculation module is used to combine the protruding potential energy and the equilibrium coefficient to establish a quantitative relationship between the energy and hazard of the microseismic signal and to calculate the equivalent hazard of the microseismic signal.

[0033] The second calculation module is used to calculate the hazard level of microseismic signals within a continuous time interval by summing up the data.

[0034] The judgment and early warning module is used to set the early warning threshold using the critical value method, compare the current danger level with the early warning threshold in real time, and trigger an early warning when the early warning conditions are met.

[0035] On the other hand, an electronic device is provided, the electronic device comprising:

[0036] processor;

[0037] The memory stores computer-readable instructions, which, when loaded and executed by the processor, implement the steps of the coal seam outburst hazard early warning method described above.

[0038] On the other hand, a computer-readable storage medium is provided, wherein program code is stored in the computer-readable storage medium, and the program code can be called by a processor to execute the steps of the coal seam outburst hazard early warning method described above.

[0039] The beneficial effects of the technical solution provided by this invention include at least the following:

[0040] In this embodiment of the invention, a microseismic sensor network is deployed in a specific area to focus on microseismic monitoring at the tunneling face. The working face space is divided into multiple areas, and a segmented concentric spherical model is innovatively constructed. Different equivalent coefficients are assigned based on the signal distance to accurately reflect the signal's contribution to the hazard. Combining outburst potential energy and balance coefficients, the equivalent hazard of the microseismic signal is calculated, establishing a quantitative relationship between microseismic signal energy and hazard. The hazard of the microseismic signal is dynamically accumulated in real time to ensure the timeliness and accuracy of the assessment. A critical value method is used to set a warning threshold, and the current hazard is compared with the warning threshold in real time, triggering a warning when the warning conditions are met. This invention overcomes the limitations of existing coal seam outburst hazard prediction methods by setting reasonable equivalent values ​​based on signal distance, which can significantly improve the accuracy and effectiveness of warnings in high-risk operating areas such as coal mines, providing strong protection for safe production. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of a coal seam outburst hazard early warning method based on multi-scale microseismic information fusion provided in an embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the working surface space division provided in an embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of the structure of the coal seam outburst hazard early warning device based on multi-scale microseismic information fusion provided in an embodiment of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0046] In embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the term "exemplary" is intended to present the concept in a specific manner.

[0047] This invention provides a method for early warning of coal seam outburst risk based on multi-scale microseismic information fusion, such as... Figure 1 As shown, the method includes the following steps:

[0048] S1. Deploy a network of microseismic sensors to collect microseismic signals during the tunneling process, and use filtering techniques to remove interference signals.

[0049] First, microseismic sensors are deployed. The deployment range of the microseismic sensors is determined by the length l of the tunneling face and the microseismic influence range L. It is generally assumed that the microseismic influence range L is a function of the tunneling face length l, and L = 2l is set. As an optional implementation, the microseismic sensors are deployed within a range of 200m in front of, to both sides of, and behind the tunneling face.

[0050] During tunneling operations, micro-vibration signals are collected during the tunneling process, and filtering technology is used to remove interference signals generated by human activities, so as to ensure that only signals reflecting the true fracture state of coal and rock are retained and focused, thereby improving the accuracy and reliability of monitoring data.

[0051] S2. Using the center of the tunneling face as a reference point, the working face space is divided into multiple regions, and each region is assigned a different equivalent coefficient according to its distance from the tunneling face.

[0052] In this step, a segmented concentric sphere is constructed with the center of the tunneling face as the reference point, dividing the working face space into multiple regions, each with a different distance from the tunneling face; based on the distance between each region and the tunneling face, the influence of the microseismic signal on the tunneling face is divided into multiple different levels, and each level is assigned an equivalent coefficient.

[0053] Specifically, refer to Figure 2As shown, the working face space is divided into 5 regions. Each region is assigned an equivalent coefficient based on its distance from the tunneling face. The basis for setting the equivalent coefficient is that the vibration signal has a smaller impact on the tunneling face the farther away it is, and it decays exponentially.

[0054] Specifically, the following settings are used: the energy of the microseismic signal is selected at the same level in the area 0-100m from the tunneling face; 50% of the energy of the microseismic signal is selected in the area 100-200m; 6.25% of the energy of the microseismic signal is selected in the area 200-500m; 0.006% of the energy of the microseismic signal is selected in the area 500-1000m; and the influence of the microseismic signal is ignored in the area greater than 1000m.

[0055] For each microseismic signal, its inherent energy E (i.e., microseismic signal energy E) is used as the basis, and an equivalent coefficient is introduced to reflect the contribution of signal characteristics to the outburst hazard. Based on the distance from the tunneling face, the influence of these concentric microseismic signals on the tunneling face is divided into five different levels, and each level is assigned an equivalent coefficient to reflect changes in signal attenuation or propagation characteristics.

[0056] by Figure 2 For example, energy decays exponentially with distance. If Level I is set to 0~100 (average 50m); Level II is 100~200 (average 150), an increase of 100m, so 100 / 50=2, and the energy is half of the original; Level III is 200~500 (average 250m), an increase of 200m, so 200 / 50=4, and the energy decay rate is 1 / 24=1 / 16=6.25%; Level IV is 500~1000 (average 750m), an increase of 700m, so 700 / 50=14, and the energy decay rate is 1 / 214=1 / 16384=0.006%; Level V is the region beyond 1000m, where energy is negligible. Therefore, the value of ξ is related to distance, taking values ​​of 1, 0.5, 0.0625, 0.00006, etc.

[0057] S3. Combining the prominent potential energy and the balance coefficient, establish a quantitative relationship between the energy and hazard of the microseismic signal, and calculate the equivalent hazard of the microseismic signal.

[0058] Let the protrusion potential energy Q represent the energy limit that hinders the protrusion, which is derived from the gas expansion calculation formula. In the formula, p0 is the atmospheric pressure in the roadway, p0 = 0.1 MPa; p is the gas pressure, in MPa; V0 is the gas content involved in the outburst, in cm³. 3 / g; n is the adiabatic coefficient of methane, n=1.31.

[0059] For each microseismic signal, based on its microseismic signal energy E, equivalent coefficient ξ, outburst potential energy Q, and equilibrium coefficient λ, the formula is used. The equivalent risk level W is calculated, where the equivalent coefficient ξ is determined based on the distance between the microseismic signal and the tunneling face.

[0060] Specifically, the equivalent hazard W of a single microseismic signal is calculated using the following formula:

[0061] .

[0062] Given the complex and heterogeneous nature of coal and rock structures, it is difficult to obtain a precise formula for calculating λ directly. Therefore, an approximate value based on practical experience is used as a substitute to ensure the effectiveness and practicality of the analysis.

[0063] The selection of λ is determined through accident cases (dynamic manifestation events such as blowouts, stuck drills, and outbursts). The equivalent hazard level at the time of the accident is considered to be 1. The microseismic signal energy E at this time is obtained by weighted summation. The equivalent coefficient ξ is determined according to the distance. The outburst potential energy Q is obtained by the gas expansion calculation formula, thus obtaining λ. The balance coefficient of subsequent hazard prediction can be adopted using this λ value.

[0064] For example, set 1×10 3 The equivalent hazard level of the microseismic signal energy of J is W=1, and λ=0.41 is calculated. This balance coefficient can reflect the degree of influence of mining disturbance load on outburst.

[0065] S4. Calculate the hazard level of microseismic signals within continuous time intervals by summing up the data.

[0066] Within a continuous time interval Δt, the hazard level W(Δt) of the microseismic signal is calculated by summation, where W(Δt) = ΣW, and Δt is selected based on the differences in coal quality characteristics and the range of disturbance rheological stability time.

[0067] When assessing the hazard W(Δt) of microseismic signals within a continuous time interval Δt, a cumulative summation method is used, i.e., W(Δt) = Σw (each signal). The time scale Δt is selected based on the range of disturbance rheological stability determined by differences in coal quality characteristics, typically set according to actual mine experience, ranging from 1 hour to 24 hours. This calculation process is a dynamic, continuous accumulation. For example, when Δt is set to 1 hour, W(1h) calculated at 7 o'clock represents the cumulative effect of all signals between 6 and 7 o'clock, while the calculation result at 7:10 encompasses the cumulative signal values ​​from 6:10 to 7:10, thus ensuring the timeliness and accuracy of the assessment.

[0068] S5. Use the critical value method to set the warning threshold, compare the current danger level with the warning threshold in real time, and trigger the warning when the warning conditions are met.

[0069] In this step, based on long-term monitoring data, the maximum risk level W is determined when events such as blowout, stuck drill, and protruding dynamic manifestation occur. max With minimum value W min ;

[0070] The warning threshold is set at 0.75W. max During real-time monitoring, the currently calculated W(Δt) value is compared with the warning threshold. When the W(Δt) value is within W... min With 0.75W max When this occurs, an alert is triggered.

[0071] In this embodiment of the invention, a microseismic sensor network is deployed in a specific area to focus on microseismic monitoring at the tunneling face. The working face space is divided into multiple areas, and a segmented concentric spherical model is innovatively constructed. Different equivalent coefficients are assigned based on the signal distance to accurately reflect the signal's contribution to the hazard. Combining outburst potential energy and balance coefficients, the equivalent hazard of the microseismic signal is calculated, establishing a quantitative relationship between microseismic signal energy and hazard. The hazard of the microseismic signal is dynamically accumulated in real time to ensure the timeliness and accuracy of the assessment. A critical value method is used to set a warning threshold, and the current hazard is compared with the warning threshold in real time, triggering a warning when the warning conditions are met. This invention overcomes the limitations of existing coal seam outburst hazard prediction methods by setting reasonable equivalent values ​​based on signal distance, which can significantly improve the accuracy and effectiveness of warnings in high-risk operating areas such as coal mines, providing strong protection for safe production.

[0072] Accordingly, embodiments of the present invention also provide a coal seam outburst hazard early warning device based on multi-scale microseismic information fusion, such as... Figure 3 As shown, the device includes:

[0073] The signal acquisition module 201 is used to deploy a network of microseismic sensors to acquire microseismic signals during the tunneling process and to use filtering technology to remove interference signals.

[0074] The region division module 202 is used to divide the working face space into multiple regions with the center of the tunneling working face as the reference point, and each region is assigned a different equivalent coefficient according to its distance from the tunneling working face.

[0075] The first calculation module 203 is used to combine the protruding potential energy and the balance coefficient to establish a quantitative relationship between the energy and hazard of the microseismic signal and to calculate the equivalent hazard of the microseismic signal.

[0076] The second calculation module 204 is used to calculate the hazard of microseismic signals within a continuous time interval by summing up the data.

[0077] The judgment and early warning module 205 is used to set the early warning threshold using the critical value method, compare the current danger level with the early warning threshold in real time, and trigger an early warning when the early warning conditions are met.

[0078] For ease of explanation, Figure 3 Only the main components of the device are shown. The device of this embodiment can be used to perform... Figure 1 The technical solutions of the method embodiments shown are similar in principle and in effect, and will not be described again here.

[0079] In an exemplary embodiment, the present invention also provides an electronic device, the electronic device comprising:

[0080] processor;

[0081] The memory stores computer-readable instructions, which, when loaded and executed by the processor, implement the steps of the coal seam outburst hazard early warning method described above.

[0082] In an exemplary embodiment, the present invention also provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the steps of the coal seam outburst hazard early warning method described above. For example, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc.

[0083] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0084] The use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0085] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0086] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0087] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply 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.

[0088] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0089] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0090] 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.

[0091] If the aforementioned functions are implemented as 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0092] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for early warning of coal seam outburst risk based on multi-scale microseismic information fusion, characterized in that, Includes the following steps: S1. Deploy a network of microseismic sensors to collect microseismic signals during the tunneling process, and use filtering techniques to remove interference signals; S2. Using the center of the tunneling face as a reference point, the working face space is divided into multiple regions, and each region is assigned a different equivalent coefficient according to its distance from the tunneling face. S3. Combining the prominent potential energy and the balance coefficient, establish the quantitative relationship between the energy and hazard of the microseismic signal, and calculate the equivalent hazard of the microseismic signal; S4. Calculate the hazard level of microseismic signals within consecutive time intervals by summing up the data. S5. Use the critical value method to set the warning threshold, compare the current danger level with the warning threshold in real time, and trigger the warning when the warning conditions are met. Step S2 specifically includes: Using the center of the tunneling face as a reference point, a segmented concentric sphere is constructed to divide the working face space into multiple regions, each region having a different distance from the tunneling face; Based on the distance between each region and the tunneling face, the influence of microseismic signals on the tunneling face is divided into multiple different levels, and each level is assigned an equivalent coefficient. The equivalent coefficients are set based on the following criteria: the farther the vibration signal is, the smaller its impact on the tunneling face, and it decays exponentially. Step S1 specifically includes: The microseismic sensor network is arranged according to the length l of the tunneling face and the microseismic influence range L. The microseismic influence range L is set to twice the length l of the tunneling face, specifically, microseismic sensors are arranged within a range of 200m in front of, to both sides of and behind the tunneling face. Step S3 specifically includes: Let the protrusion potential energy Q be defined, where Q represents the energy limit that hinders the protrusion, and is derived using the gas expansion calculation formula. In the formula, p0 is the atmospheric pressure in the tunnel, p is the gas pressure, V0 is the gas content involved in the outburst, and n is the methane adiabatic coefficient. For each microseismic signal, the equivalent hazard level W is calculated using the formula W=f(ξ,E,λ,Q) based on its microseismic signal energy E, equivalent coefficient ξ, outburst potential energy Q, and balance coefficient λ. The equivalent coefficient ξ is determined based on the distance between the microseismic signal and the tunneling face, and the balance coefficient λ is determined through accident cases.

2. The method for early warning of coal seam outburst risk based on multi-scale microseismic information fusion according to claim 1, characterized in that, In step S2, the working face space is divided into 5 regions. Each region is assigned an equivalent coefficient ξ based on its distance from the tunneling face. Specifically, the energy of the microseismic signal is selected at the same value in the region 0-100m from the tunneling face, 50% of the energy of the microseismic signal is selected in the region 100-200m, 6.25% of the energy of the microseismic signal is selected in the region 200-500m, 0.006% of the energy of the microseismic signal is selected in the region 500-1000m, and the influence of the microseismic signal is ignored in the region greater than 1000m.

3. The method for early warning of coal seam outburst risk based on multi-scale microseismic information fusion according to claim 1, characterized in that, In step S3, the equivalent hazard W of a single microseismic signal is calculated using the following formula: The balance coefficient λ is determined through accident cases. The equivalent risk level at the time of the accident is considered to be 1. λ is calculated based on the microseismic signal energy E, the equivalent coefficient ξ, and the outburst potential energy Q at that time.

4. The method for early warning of coal seam outburst risk based on multi-scale microseismic information fusion according to claim 1, characterized in that, Step S4 specifically includes: Within a continuous time interval Δt, the hazard level W(Δt) of the microseismic signal is calculated by summation, where W(Δt) = ΣW, and Δt is selected based on the disturbance rheological stability time range determined by the differences in coal quality characteristics.

5. The method for early warning of coal seam outburst risk based on multi-scale microseismic information fusion according to claim 1, characterized in that, Step S5 specifically includes: Based on long-term monitoring data, the maximum risk level W was determined when incidents such as blowout, stuck drill, and outburst power manifestation occurred. max With minimum value W min ; The warning threshold is set at 0.75W. max During real-time monitoring, the currently calculated W(Δt) value is compared with the warning threshold. When the W(Δt) value is between Wmin and 0.75W... max When this occurs, an alert is triggered.

6. A coal seam outburst hazard early warning device based on multi-scale microseismic information fusion, the device being used to implement the method as described in any one of claims 1 to 5, characterized in that, The device includes: The signal acquisition module is used to deploy a network of microseismic sensors to acquire microseismic signals during the tunneling process and to use filtering technology to remove interference signals. The region division module is used to divide the working face space into multiple regions with the center of the tunneling face as the reference point. Each region is assigned a different equivalent coefficient according to its distance from the tunneling face. The first calculation module is used to combine the protruding potential energy and the equilibrium coefficient to establish a quantitative relationship between the energy and hazard of the microseismic signal and to calculate the equivalent hazard of the microseismic signal. The second calculation module is used to calculate the hazard level of microseismic signals within a continuous time interval by summing up the data. The judgment and early warning module is used to set an early warning threshold using the critical value method, compare the current hazard level with the early warning threshold in real time, and trigger an early warning when the early warning conditions are met.

7. An electronic device, characterized in that, The electronic device includes: processor; A memory storing computer-readable instructions that, when loaded and executed by the processor, implement the method as described in any one of claims 1 to 5.

Images