A goaf monitoring method and system for coal mines

Abstract

The application discloses a kind of coal mine goaf monitoring method and system, it is related to coal mine goaf monitoring technical field, including the collection coal mine goaf subsidence characteristic data;Using sound processing technology, sound characteristic data are preprocessed, and subsidence monitoring data are denoising processed;Through weight analysis module, safety factor is calculated based on preprocessed data, and data is transmitted to terminal monitoring system in real time.The coal mine goaf monitoring method provided in the application uses sound processing technology and preprocessing algorithm, effectively preprocesses the sound characteristic data and other monitoring characteristic data collected, improves the accuracy and reliability of data;Sound processing technology includes noise elimination, sound enhancement, echo suppression and fast fourier transform, can effectively extract sound characteristics, provide strong support for subsequent analysis.The application achieves better results in reliability and analysis accuracy.

Description

Technical Field

[0001] This invention relates to the field of coal mine goaf monitoring technology, specifically a coal mine goaf monitoring method and system. Background Technology

[0002] The coal mining industry refers to the industry whose main business is the mining, processing, and sale of coal. The large-scale use of coal as an energy source and raw material began in the 18th century with the Industrial Revolution, when coal became the primary energy source for steam engines. With the acceleration of industrialization, the coal mining industry has undergone a transformation from small-scale manual mining to modern mechanized mining.

[0003] Coal mine safety is a crucial aspect of the coal mining industry. Both the government and enterprises highly value coal mine safety, implementing a series of measures to ensure safe production, establishing a comprehensive emergency response system, and improving emergency response speed and handling capabilities to minimize casualties and property losses. Because coal mining creates numerous goaf areas, these areas, lacking the support of coal seams, often pose safety hazards such as collapses and roof falls. With continuous advancements in coal mining technology and increased safety awareness, higher demands are placed on the safety monitoring of goaf areas. Real-time monitoring of geological changes, harmful gas concentrations, and other parameters is necessary to promptly identify and address safety hazards.

[0004] Traditionally, coal mining enterprises use manual inspections to monitor goaf areas. However, this method suffers from low efficiency and poor accuracy. Furthermore, manual inspections struggle to monitor changes in goaf areas in real time and are easily affected by human factors. Some existing technologies utilize visual algorithms to identify and analyze the surface of coal mine goaf areas, obtaining corresponding real-time monitoring data. Compared to traditional manual inspections, this method significantly improves monitoring efficiency, reduces labor costs, and enhances personnel safety. However, when the surface of the goaf area is complex, especially with abundant vegetation, it can significantly interfere with the necessary visual data collection, thus affecting the accuracy of subsequent real-time monitoring data. Therefore, it is necessary to propose a coal mine goaf monitoring system that collects the sounds emitted by pipes within the goaf area due to deformation or tilting caused by surface subsidence, thereby analyzing real-time data on subsidence trends and reducing interference from surface debris. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that existing technologies using manual inspection or visual algorithm monitoring methods suffer from low efficiency, poor accuracy, and poor real-time performance, as well as the problem of how to reduce interference from surface debris and achieve high-precision real-time monitoring.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for monitoring goaf areas in coal mines, comprising collecting settlement characteristic data of goaf areas in coal mines; preprocessing the sound characteristic data using sound processing technology and performing noise reduction processing on the settlement monitoring data; calculating a safety factor based on the preprocessed data through a weight analysis module, and transmitting the data to a terminal monitoring system in real time.

[0008] As a preferred embodiment of the coal mine goaf monitoring method described in this invention, the collection of coal mine goaf settlement characteristic data includes acquiring data on conveyor pipe deformation, stress changes, air velocity, and temperature in the goaf through a feature acquisition module. Sensors are installed at each monitoring point on the conveyor pipe to collect real-time sound data and stress change data when the conveyor pipe deforms or shifts. The sensors measure the air velocity and temperature within the goaf and transmit these data wirelessly to a data processing module for further processing. The selection of monitoring points is based on the layout of the conveyor pipe and the structural distribution of the goaf, with one monitoring point every 50 meters to ensure real-time monitoring covering the entire area.

[0009] As a preferred embodiment of the coal mine goaf monitoring method described in this invention, the collection of coal mine goaf settlement characteristic data further includes using strain gauges to perform real-time strain measurements on the conveying pipe in three directions: axial, radial, and oblique. The stress data in each direction is calculated based on the generalized Hooke's law, and expressed as follows:

[0010]

[0011] Where, σ 0o For axial stress, σ 90o For, σ P Principal stress, θ P The principal stress directions, E is the elastic modulus, v is Poisson's ratio, ε 0o ε 45o ε 90o These are axial, oblique, and radial strains, respectively.

[0012] All data is transmitted to the preprocessing module via the real-time data acquisition unit for subsequent security assessment and analysis.

[0013] As a preferred embodiment of the coal mine goaf monitoring method of the present invention, the preprocessing of sound feature data using sound processing technology includes first applying a noise cancellation algorithm to remove environmental noise, then using sound enhancement technology to improve signal quality, finally using echo suppression technology to process reflected sound waves, and using Fast Fourier Transform (FFT) to convert the time domain signal into a frequency domain signal.

[0014] As a preferred embodiment of the coal mine goaf monitoring method described in this invention, the noise reduction processing of the settlement monitoring data includes noise reduction processing of stress change data of the conveying pipe, air velocity and temperature data of the goaf, smoothing stress change data using a low-pass filter to remove high-frequency noise, and applying a Kalman filter algorithm to predict and correct air velocity and temperature data. The preprocessed data will be transmitted to the weight analysis module.

[0015] As a preferred embodiment of the coal mine goaf monitoring method of the present invention, the step of calculating the safety factor based on preprocessed data through the weight analysis module includes weight allocation of processed sound feature data and necessary settlement monitoring feature data based on fuzzy evaluation method and hierarchical analysis method, and after allocation, obtaining the safety factor data of the area based on the coal mine goaf safety factor algorithm.

[0016] As a preferred embodiment of the coal mine goaf monitoring method of the present invention, the step of transmitting data to the terminal monitoring system in real time includes transmitting the calculated safety factor data to the terminal monitoring system.

[0017] Another objective of this invention is to provide a coal mine goaf monitoring system that uses a weight analysis module to scientifically allocate weights to preprocessed data using fuzzy evaluation and analytic hierarchy process (AHP) methods, and calculates a safety factor based on a coal mine goaf safety factor algorithm. This enables accurate early warning of goaf subsidence risk and solves the problem of poor accuracy in current visual algorithm monitoring methods.

[0018] As a preferred embodiment of the coal mine goaf monitoring system of the present invention, it includes a data acquisition module, a data processing module, and an evaluation module; the data acquisition module is used to collect settlement characteristic data of the coal mine goaf; the data processing module is used to preprocess the sound characteristic data using sound processing technology and to reduce noise in the settlement monitoring data; the evaluation module is used to calculate the safety factor based on the preprocessed data through a weight analysis module and transmit the data to the terminal monitoring system in real time.

[0019] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement a method for monitoring goaf areas in coal mines.

[0020] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for monitoring goaf areas in coal mines.

[0021] The beneficial effects of this invention are as follows: The coal mine goaf monitoring method provided by this invention employs sound processing technology and preprocessing algorithms to effectively preprocess the collected sound feature data and other monitoring feature data, improving the accuracy and reliability of the data. The sound processing technology includes noise cancellation, sound enhancement, echo suppression, and fast Fourier transform, which can effectively extract sound features, providing strong support for subsequent analysis. Scientific weighting is applied to the preprocessed data, and a safety factor is calculated based on the coal mine goaf safety factor algorithm, achieving accurate early warning of goaf subsidence risk. This invention achieves better results in terms of reliability and analytical accuracy. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the 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.

[0023] Figure 1 The first embodiment of the present invention provides an overall flowchart of a method for monitoring goaf areas in coal mines. Detailed Implementation

[0024] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0025] Example 1, referring to Figure 1 As one embodiment of the present invention, a method for monitoring goaf areas in coal mines is provided, comprising:

[0026] S1: Collect data on the subsidence characteristics of coal mine goaf areas.

[0027] Furthermore, the collection of settlement characteristic data in coal mine goaf areas includes acquiring data on conveyor pipe deformation, stress changes, air velocity, and temperature in the goaf through a feature acquisition module. Sensors are installed at each monitoring point on the conveyor pipe to collect real-time sound data and stress change data when the pipe deforms or shifts. The sensors also measure the air velocity and temperature within the goaf area and transmit this data wirelessly to a data processing module for further processing. Monitoring points are selected based on the layout of the conveyor pipe and the structural distribution of the goaf, with one monitoring point every 50 meters to ensure real-time monitoring of the entire area. Simultaneously, a malfunction at a monitoring point allows for rapid troubleshooting, enabling staff to take timely measures.

[0028] It should be noted that the collection of settlement characteristic data for coal mine goaf areas also includes real-time strain measurements of the conveying pipe in three directions—axial, radial, and oblique—using strain gauges. Stress data in each direction are calculated based on the generalized Hooke's law, and expressed as follows:

[0029]

[0030]

[0031] Where, σ 0o For axial stress, σ 90o For, σ P Principal stress, θ P The principal stress directions, E is the elastic modulus, v is Poisson's ratio, ε 0o ε 45o ε 90o These represent axial, oblique, and radial strains, respectively.

[0032] All data is transmitted to the preprocessing module via the real-time data acquisition unit for subsequent security assessment and analysis.

[0033] S2: Sound processing technology is used to preprocess the sound feature data and to reduce the noise of the settlement monitoring data.

[0034] Furthermore, sound processing techniques are used to preprocess the sound feature data, including first applying noise cancellation algorithms to remove environmental noise, then using sound enhancement techniques to improve signal quality, finally using echo suppression techniques to process reflected sound waves, and using Fast Fourier Transform (FFT) to convert the time-domain signal into a frequency-domain signal.

[0035] It should be noted that the noise reduction processing of the settlement monitoring data includes noise reduction processing of the stress change data of the delivery pipe, the air velocity and temperature data of the goaf, smoothing the stress change data using a low-pass filter to remove high-frequency noise, and applying the Kalman filter algorithm to predict and correct the air velocity and temperature data. The preprocessed data will be transmitted to the weight analysis module.

[0036] S3: The weight analysis module calculates the safety factor based on the preprocessed data and transmits the data to the terminal monitoring system in real time.

[0037] Furthermore, the safety factor is calculated based on the preprocessed data through the weight analysis module, including the fuzzy evaluation method and the analytic hierarchy process. The processed sound feature data and the necessary settlement monitoring feature data are weighted and assigned. After the assignment is completed, the safety factor data of the region is obtained based on the safety factor algorithm for coal mine goaf.

[0038] It should be noted that the steps of the Analytic Hierarchy Process (AHP) are as follows:

[0039] Step one is to construct a hierarchical structure model. Constructing a hierarchical structure model involves dividing the decision-making object and objectives, as well as the relationships between influencing factors, into a lowest, middle, and highest level. The highest level represents the decision objective, the middle levels represent the decision criteria, and the lowest level represents alternative solutions. Between adjacent levels, the higher level is the objective level, and the lower level is the factor level.

[0040] Step two: Construct the judgment matrix. Based on the constructed hierarchical structure model, establish judgment matrices for each level of factors, assigning different values ​​to each factor to represent its importance. The judgment matrix can be represented by A. mn To represent, A ij (i = 1, 2, ..., m; j = 1, 2, ..., n) represents factor A. i For A j The relative importance of A ij =1,A ij =1 / A ji And A ij >0.

[0041] Step 3: Hierarchical sorting and consistency check, based on judgment matrix A. mn Calculate its largest eigenvalue λ max And solve the characteristic equation A mn x=λ max x, to obtain the largest eigenvalue λ max The corresponding eigenvector x = (x1, x2, ... x n ) T After normalization, the final evaluation index weight vector is obtained, denoted as W. The process of determining W is the hierarchical single ranking. Based on the single ranking, the importance weights of each element relative to the element in the previous level are calculated sequentially, which is the hierarchical overall ranking. Whether the weights determined by the hierarchical overall ranking are valid needs to be determined through a consistency check. If the judgment matrix is ​​of order n, (λ...) maxThe larger the value of -n), the stronger the inconsistency. The consistency test establishes three test parameters: consistency index (CI), random consistency index (RI), and test coefficient (CR). The formula for calculating CI is as follows:

[0042]

[0043] Where λ max is the largest eigenvalue of the judgment matrix, where n is the order of the matrix. Consistency and CI are negatively correlated; complete consistency is achieved when CI = 0. To measure the magnitude of CI and eliminate randomness in one-off tests, RI is introduced on top of CI, and the final test coefficient CR is obtained by comparing CI with RI. The calculation formula is as follows:

[0044]

[0045] The value of RI is related to the order of the judgment matrix. The higher the order of the judgment matrix, the greater the probability of random deviation, and the larger the value of RI.

[0046] Furthermore, the steps of the fuzzy evaluation method are as follows:

[0047] Step 1: Establish the factor set and evaluation set for comprehensive evaluation. The factor set refers to the set of elements that have an impact on the evaluation object, denoted as U, where U = (u1, u2, ..., u...). n The evaluation set refers to the set of possible evaluation results for an evaluated object, denoted as V, where V = (v1, v2, ..., v...). m ), m={3,4,...,10}.

[0048] Step two: Determine the weights of each indicator. In the comprehensive evaluation, the degree of influence of each factor on the evaluation object is different, that is, they differ in importance. Therefore, it is necessary to assign weights to each factor to reflect this difference. The set of weights of each factor is denoted as A, A = (a1, a2, ..., a...). n ).

[0049] Step 3: Single-factor fuzzy evaluation and construction of the evaluation matrix. Single-factor fuzzy evaluation refers to the degree of membership of a certain element in the factor set to each element in the evaluation set, denoted as Ri, R. i ={r i1 r i2 ,...,r im If fuzzy evaluation is performed on all elements in the factor set, the evaluation results for each single factor, R1, R2, ..., R, can be obtained. n The matrix R formed by them nm That is, the fuzzy comprehensive evaluation matrix.

[0050] Step four, fuzzy comprehensive evaluation: Based on the evaluation matrix and combined with the index weights A, the final evaluation result can be obtained through fuzzy transformation.

[0051] The steps for calculating the safety factor of coal mine goaf are as follows:

[0052] Step 1: Determine the factor set based on the necessary monitoring characteristics of coal mine goaf subsidence, and determine the model membership degree through a normal membership function.

[0053] Step two: Based on the weight setting criteria of the analytic hierarchy process, compare the indicators of each level pairwise, and construct a judgment matrix based on the judgment scale.

[0054] Step 3: Derive the fuzzy comprehensive evaluation matrix based on the normal membership function.

[0055] Furthermore, the calculated safety factor data is transmitted to the terminal monitoring system.

[0056] Example 2, one embodiment of the present invention, provides a method for monitoring goaf areas in coal mines. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0057] First, monitoring is carried out in three steps: collecting settlement characteristic data, data preprocessing, and calculating and transmitting safety factors.

[0058] Data acquisition modules were installed within the coal mine goaf, with monitoring points set up every 50 meters. Each monitoring point was equipped with strain gauges, temperature and flow velocity sensors, and sound acquisition units, enabling real-time detection of pipe deformation, stress changes, air velocity, and temperature. Data collected by the sensors was transmitted wirelessly to a data processing module to ensure real-time monitoring of the covered area. During data acquisition, simulated settlement changes were implemented by artificially introduced surface displacement causing pipe tilting or deformation, triggering sound data acquisition for further settlement monitoring.

[0059] The collected data enters the preprocessing module, where noise cancellation, sound enhancement, and echo suppression techniques are used to process the sound feature data. The sound signal is converted to the frequency domain using a Fast Fourier Transform (FFT) algorithm for easier subsequent analysis. For the stress, flow velocity, and temperature data from settlement monitoring, low-pass filtering is applied for noise reduction, and a Kalman filter algorithm is used for data correction, making the data smoother and more reliable. The processed data is then transmitted to the weighting analysis module.

[0060] The weighted analysis module, based on fuzzy evaluation and analytic hierarchy process (AHP), combines sound and settlement characteristic data for weighted analysis to generate safety factors. A hierarchical model is established, processing decision objectives, criteria, and alternative solutions at different levels. A judgment matrix is ​​constructed and consistency checks are performed to ensure data validity. Finally, the regional safety factor is obtained based on the coal mine goaf safety factor algorithm, and the results are transmitted in real-time to the terminal monitoring system for reference by on-site management personnel.

[0061] Table 1 Comparison of Experimental Data

[0062]

[0063] As shown in Table 1, the data from each monitoring point demonstrates the system's stability and responsiveness. Firstly, the deformation and stress values ​​at each monitoring point reflect the stress conditions at different locations within the goaf. Combined with air velocity and temperature data, this facilitates a deeper analysis of the environmental differences within the area. Furthermore, changes in sound frequency reflect changes in acoustic characteristics caused by pipe deformation or settlement. The signal strength after noise cancellation indicates that the system can still provide clear sound data under noise interference, demonstrating the advantages of the preprocessing technology of this invention in filtering out interference.

[0064] Example 3, one embodiment of the present invention, provides a coal mine goaf monitoring system, including a data acquisition module, a data processing module, and an evaluation module.

[0065] The data acquisition module collects settlement characteristic data from coal mine goaf areas. The data processing module preprocesses the sound characteristic data using sound processing technology and reduces noise in the settlement monitoring data. The evaluation module calculates the safety factor based on the preprocessed data using the weight analysis module and transmits the data to the terminal monitoring system in real time.

[0066] If a function is implemented as a software functional unit and sold or used as an independent product, it 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 of 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.

[0067] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0068] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0069] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0070] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for monitoring goaf areas in coal mines, characterized in that, include: Collect data on the subsidence characteristics of coal mine goaf areas; Sound processing technology is used to preprocess the sound feature data and to reduce the noise in the settlement monitoring data. The weight analysis module calculates the safety factor based on the preprocessed data and transmits the data to the terminal monitoring system in real time. The collection of coal mine goaf settlement characteristic data includes acquiring data on conveyor pipe deformation, stress change, air velocity and temperature in the goaf through a feature acquisition module, setting sensors at each monitoring point of the conveyor pipe to collect sound data and stress change data when the conveyor pipe deforms or shifts in real time, measuring air velocity and temperature in the goaf through sensors, and transmitting the data to the data processing module for further processing via wireless signals. The selection of monitoring points is based on the layout of the conveying pipe and the structural distribution of the goaf. One monitoring point is set up every 50 meters to ensure real-time monitoring covering the entire area. The collection of coal mine goaf settlement characteristic data also includes real-time strain measurement of the conveying pipe in three directions (axial, radial, and oblique) using strain gauges, and calculation of stress data in each direction based on the generalized Hooke's law, expressed as follows: ( + ) ( + ) ( + ) ； ； in, For axial stress, Radial stress, Principal stress, The directions of principal stresses are given, where E is the elastic modulus and v is Poisson's ratio. , , These are axial, oblique, and radial strains, respectively. All data is transmitted to the preprocessing module via the real-time data acquisition unit for subsequent security assessment and analysis. The preprocessing of sound feature data using sound processing technology includes first applying a noise cancellation algorithm to remove environmental noise, then using sound enhancement technology to improve signal quality, finally using echo suppression technology to process reflected sound waves, and using Fast Fourier Transform (FFT) to convert the time-domain signal into a frequency-domain signal. The noise reduction process for the settlement monitoring data includes noise reduction of stress change data in the delivery pipe, air velocity and temperature data in the goaf, smoothing stress change data using a low-pass filter to remove high-frequency noise, and using a Kalman filter algorithm to predict and correct air velocity and temperature data. The preprocessed data will be transmitted to the weight analysis module.

2. The method for monitoring goaf areas in coal mines as described in claim 1, characterized in that: The calculation of the safety factor based on preprocessed data by the weight analysis module includes weighting the processed sound feature data and the necessary settlement monitoring feature data based on fuzzy evaluation method and hierarchical analysis method. After the weighting is completed, the safety factor data of the region is obtained based on the safety factor algorithm of coal mine goaf.

3. The method for monitoring goaf areas in coal mines as described in claim 2, characterized in that: The real-time transmission of data to the terminal monitoring system includes transmitting the calculated security factor data to the terminal monitoring system.

4. A system employing the coal mine goaf monitoring method as described in any one of claims 1 to 3, characterized in that: It includes a data acquisition module, a data processing module, and an evaluation module; The data acquisition module is used to collect subsidence characteristic data of coal mine goaf areas; The data processing module is used to preprocess the sound feature data using sound processing technology and to reduce the noise of the settlement monitoring data. The evaluation module is used to calculate the safety coefficient based on the preprocessed data through the weight analysis module, and transmit the data to the terminal monitoring system in real time.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the coal mine goaf monitoring method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the coal mine goaf monitoring method according to any one of claims 1 to 3.

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