Coal mine goaf monitoring method and system

Abstract

The invention discloses a goaf monitoring method and system for a coal mine, and relates to the technical field of goaf monitoring for the coal mine. Preprocessing the sound characteristic data by adopting a sound processing technology, and carrying out noise reduction processing on the settlement monitoring data; and calculating a safety coefficient based on the preprocessed data through a weight analysis module, and transmitting the data to a terminal monitoring system in real time. According to the goaf monitoring method for the coal mine, a sound processing technology and a preprocessing algorithm are adopted, collected sound feature data and other monitoring feature data are effectively preprocessed, and the accuracy and reliability of the data are improved; the sound processing technology comprises noise elimination, sound enhancement, echo suppression and fast Fourier transform, sound features can be effectively extracted, and powerful support is provided for subsequent analysis. The method has better effects in the aspects of reliability and analysis accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of goaf monitoring for coal mines, and in particular to a goaf monitoring method and system for coal mines. Background Art

[0002] The coal mining industry refers to industries whose main business is mining, processing and selling coal. The large-scale use of coal as energy and raw materials began with the Industrial Revolution in the 18th century, when coal became the main 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 one of the important backgrounds of the coal mining industry. The government and enterprises attach great importance to coal mine safety production work and have taken a series of measures to ensure safety production, establish a complete accident emergency rescue system, improve emergency response speed and processing capabilities, and minimize casualties and property losses. Because a large number of goafs will be formed during coal mining, these areas often have safety hazards such as collapse and roof falls due to the loss of support from the coal seam. With the continuous advancement of coal mining technology and the improvement of safety awareness, higher requirements are put forward for the safety monitoring of goafs. It is necessary to monitor the geological changes and harmful gas concentrations of goafs in real time in order to timely discover and deal with safety hazards.

[0004] Traditionally, coal mining enterprises use manual inspections to monitor goafs, but this method has problems such as low efficiency and poor accuracy. At the same time, manual inspections are difficult to monitor changes in goafs in real time and are easily affected by human factors. In some existing technologies, there are also visual algorithms based on which the surface of coal mine goafs is identified and analyzed to obtain corresponding real-time monitoring data. Compared with traditional manual inspections, this method greatly improves monitoring efficiency, reduces labor costs, and improves personnel safety. However, when the surface of coal mine goafs is relatively complex, if there are many surface vegetation, it will greatly interfere with the necessary feature data collected by vision, thereby affecting the accuracy of subsequent real-time monitoring data of coal mine goafs. Therefore, it is necessary to propose a goaf monitoring system for coal mines, which collects the sound emitted by the pipes inside the goafs of coal mines due to deformation or tilt caused by surface settlement, thereby analyzing and obtaining real-time data of its settlement trend changes, and reducing the interference of surface debris on monitoring data. Summary of the invention

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

[0006] Therefore, the technical problem solved by the present invention is: the existing technology uses manual inspection or visual algorithm monitoring methods, which have 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 technical problems, the present invention provides the following technical solutions: 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 the 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 solution of the method for monitoring goaf in coal mines described in the present invention, the collection of settlement characteristic data of goaf in coal mines includes obtaining the deformation, stress change, air velocity and temperature data of the conveying pipe through the characteristic collection module, setting a sensor at each monitoring point of the conveying pipe to collect the sound data and stress change data when the conveying pipe is deformed or offset in real time, measuring the air velocity and temperature in the goaf through the sensor, and transmitting the wireless signal to the data processing module for further processing. The selection of the monitoring point is based on the layout of the conveying pipe and the structural distribution of the goaf, and a monitoring point is set every 50 meters to ensure real-time monitoring covering the entire area.

[0009] As a preferred solution of the method for monitoring coal mine goaf of the present invention, the method of collecting settlement characteristic data of coal mine goaf also includes using strain gauges to measure the real-time strain of the conveying pipe in three directions, axial, radial and oblique, and calculating the stress data in each direction according to the generalized Hooke's law, which is expressed as:

[0010]

[0011] Among them, σ 0o is the axial stress, σ 90o For P is the principal stress, θ P is the principal stress direction, E is the elastic modulus, v is the Poisson's ratio, ε 0o , ε 45o , ε 90o They are axial, oblique, and radial strains, respectively;

[0012] All data are transmitted to the preprocessing module through the real-time data acquisition unit for subsequent safety assessment and analysis.

[0013] As a preferred solution of the coal mine goaf monitoring method described in the present invention, the pre-processing of sound feature data using sound processing technology includes first applying a noise elimination algorithm to remove environmental noise, then using sound enhancement technology to improve signal quality, and finally using echo suppression technology to process reflected sound waves, and using fast Fourier transform FFT to convert time domain signals into frequency domain signals.

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

[0015] As a preferred solution of the coal mine goaf monitoring method described in the present invention, the calculation of the safety factor based on the pre-processed data through the weight analysis module includes weight allocation of the processed data of the sound feature data and the necessary monitoring feature data of the settlement based on the fuzzy evaluation method and the hierarchical analysis method, and after the allocation is completed, the safety factor data of the area is obtained based on the coal mine goaf safety factor algorithm.

[0016] As a preferred solution of the method for monitoring goaf areas in coal mines described in the present invention, the real-time transmission of data to the terminal monitoring system includes transmitting the calculated safety factor data to the terminal monitoring system.

[0017] Another object of the present invention is to provide a coal mine goaf monitoring system, which can use fuzzy evaluation method and hierarchical analysis method through a weight analysis module to scientifically assign weights to preprocessed data, and calculate the safety factor based on the coal mine goaf safety factor algorithm, thereby achieving accurate early warning of goaf subsidence risks and solving the problem of poor accuracy of current visual algorithm monitoring methods.

[0018] As a preferred solution of the coal mine goaf monitoring system described in 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 pre-process the sound characteristic data using sound processing technology, and to perform noise reduction processing on the settlement monitoring data; the evaluation module is used to calculate the safety factor based on the pre-processed data through the weight analysis module, and transmit the data to the terminal monitoring system in real time.

[0019] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for monitoring goaf areas in coal mines.

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

[0021] Beneficial effects of the present invention: The method for monitoring goaf areas in coal mines provided by the present invention adopts sound processing technology and preprocessing algorithms to effectively preprocess the collected sound feature data and other monitoring feature data, thereby improving the accuracy and reliability of the data; the sound processing technology includes noise elimination, sound enhancement, echo suppression and fast Fourier transform, which can effectively extract sound features, provide strong support for subsequent analysis, scientifically assign weights to the preprocessed data, and calculate the safety factor based on the safety factor algorithm for goaf areas in coal mines, thereby realizing accurate early warning of goaf area settlement risks. The present invention achieves better results in terms of reliability and analysis accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use 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 ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 An overall flow chart of a method for monitoring goaf in a coal mine provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0025] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a method for monitoring goaf areas in coal mines, comprising:

[0026] S1: Collect settlement characteristic data of coal mine goaf.

[0027] Furthermore, the collection of coal mine goaf settlement characteristic data includes obtaining the conveying pipe deformation, stress change, goaf air velocity and temperature data through the characteristic collection module, setting sensors at each monitoring point of the conveying pipe to collect real-time sound data and stress change data when the conveying pipe is deformed or offset, measuring the air velocity and temperature in the goaf through sensors, and transmitting them to the data processing module through wireless signals for further processing. The selection of monitoring points is based on the layout of the conveying pipe and the structural distribution of the goaf. A monitoring point is set every 50 meters to ensure real-time monitoring of the entire area. At the same time, when a monitoring point fails, it can be quickly checked so that the staff can take corresponding measures in time.

[0028] It should be noted that the collection of settlement characteristic data of coal mine goaf also includes the use of strain gauges to measure the real-time strain in the three directions of the conveying pipe, namely, axial, radial and oblique, and calculate the stress data in each direction according to the generalized Hooke's law, which is expressed as:

[0029]

[0030]

[0031] Among them, σ 0o is the axial stress, σ 90o For P is the principal stress, θ P is the principal stress direction, E is the elastic modulus, v is the Poisson's ratio, ε 0o , ε 45o , ε 90o They are axial, oblique and radial strains respectively.

[0032] All data are transmitted to the preprocessing module through the real-time data acquisition unit for subsequent safety assessment and analysis.

[0033] S2: Use sound processing technology to pre-process the sound feature data and perform noise reduction on the settlement monitoring data.

[0034] Furthermore, the sound feature data is pre-processed using sound processing technology, including first applying a noise elimination algorithm to remove environmental noise, then using sound enhancement technology to improve signal quality, and finally using echo suppression technology to process reflected sound waves, and using fast Fourier transform FFT to convert time domain signals into frequency domain signals.

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

[0036] S3: The safety factor is calculated based on the preprocessed data through the weight analysis module, and the data is transmitted 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 hierarchical analysis method, and the weights are allocated to the processing data of the sound feature data and the necessary monitoring feature data of the settlement. After the allocation is completed, the safety factor data of the area is obtained based on the coal mine goaf safety factor algorithm.

[0038] It should be noted that the steps of the hierarchical analysis method are as follows:

[0039] Step 1: Construct a hierarchical model. To construct a hierarchical model, we need to divide the decision objects and goals, as well as the relationships between the influencing factors, into the lowest level, the middle level, and the highest level. The highest level is the purpose of decision-making, the middle level is the decision criteria, and the lowest level is the alternative plan. Between two adjacent levels, the higher level is the target level, and the lower level is the factor level.

[0040] Step 2: Construct a judgment matrix. Based on the constructed hierarchical model, establish a judgment matrix for each level of factors and assign different values ​​to each factor to represent its importance. The judgment matrix can be expressed as A mn To indicate that 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 test, according to the judgment matrix A mn Calculate its maximum characteristic root λ max , and solve the characteristic equation A mn x=λ max x, get the maximum characteristic root λ max The corresponding eigenvector x=(x 1 ,x 2 ,...x n ) T , and normalize it to get the final evaluation index weight vector, denoted as W. The process of determining W is the hierarchical single sorting, and the importance weights of each layer element to the previous layer element are calculated on the basis of the single sorting, which is the hierarchical total sorting. Whether the weight results determined by the hierarchical total sorting are valid needs to be determined by consistency test. If the judgment matrix is ​​of order n, (λ max-n) is larger, the stronger its inconsistency is. The consistency test sets up three test parameters: consistency index (CI), random consistency index (RI) and test coefficient (CR). The calculation formula of CI is as follows:

[0042]

[0043] where λ max is the maximum characteristic root of the judgment matrix, and n is the order of the matrix. The consistency and CI values ​​are negatively correlated, and they are completely consistent when CI = 0. In order to measure the size of CI and eliminate the randomness in the one-time test, RI is introduced on the basis of CI, and CI is compared with RI to obtain the final test coefficient CR. 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] Further, the steps of 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, U = (u 1 ,u 2 , ..., u n The evaluation set refers to the set of possible evaluation results for the evaluation object, denoted by V, V = (v 1 , v 2 , ..., v m ),m={3,4,...,10}.

[0048] Step 2: Determine the weight of each indicator. In the comprehensive evaluation, the influence of each factor on the evaluation object is different, that is, there are differences in importance. Therefore, it is necessary to give weights to each factor to reflect this difference. The set of weights of each factor is denoted as A, A = (a 1 , a 2 , ..., a n ).

[0049] Step 3: Single factor fuzzy evaluation and construction of 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 all elements in the factor set are fuzzily evaluated, the evaluation results of each single factor can be obtained.1 , R 2 , ..., R n The matrix R nm , that is, the fuzzy comprehensive evaluation matrix.

[0050] Step 4: Fuzzy comprehensive evaluation. Based on the evaluation matrix and combined with the indicator weight A, the final evaluation result can be obtained through fuzzy transformation.

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

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

[0053] Step 2: According to the weight setting criteria of the hierarchical analysis method, the indicators at each level are compared pairwise and the judgment matrix is ​​constructed based on the judgment scale.

[0054] Step three, derive its fuzzy comprehensive evaluation matrix based on the normal membership function.

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

[0056] Example 2, an 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, three steps are used for monitoring: collection of settlement characteristic data, data preprocessing, and safety factor calculation and transmission.

[0058] A data acquisition module is set up inside the coal mine goaf, with a monitoring point installed every 50 meters. The monitoring point is equipped with strain gauges, temperature and flow rate sensors, and sound collection units, which can detect the deformation, stress change, air flow rate and temperature of the conveying pipe in real time. The data collected by the sensor is transmitted to the data processing module via wireless signals to ensure real-time monitoring of regional coverage. During data collection, settlement changes are simulated, and the pipeline is tilted or deformed by artificially introduced surface displacement, triggering sound data collection for further settlement monitoring.

[0059] The collected data enters the preprocessing module, and the sound feature data is processed using noise elimination, sound enhancement and echo suppression technology. The sound signal is converted to the frequency domain through the fast Fourier transform (FFT) algorithm for subsequent analysis. For the stress, flow rate and temperature data of settlement monitoring, low-pass filtering is used for noise reduction and the Kalman filter algorithm is used for data correction to make the data smoother and more reliable. The processed data is transmitted to the weight analysis module.

[0060] The weight analysis module is based on fuzzy evaluation method and hierarchical analysis method, and combines sound characteristics and settlement characteristics data for weighted analysis to generate safety factors. A hierarchical model is established to hierarchically process decision goals, criteria and alternative plans, build a judgment matrix and perform consistency tests to ensure the validity of the data. Finally, the safety factor of the area is obtained based on the safety factor algorithm of the coal mine goaf, and the results are transmitted to the terminal monitoring system in real time for reference by on-site managers.

[0061] Table 1 Experimental data comparison table

[0062]

[0063] According to Table 1, the data of each monitoring point demonstrates the stability and responsiveness of the system. First, the deformation and stress values ​​of each monitoring point reflect the stress conditions of different parts inside the goaf. Combined with the air velocity and temperature data, it is helpful to deeply analyze the differences in the internal environment of the region. In addition, the change in sound frequency can reflect the change in the acoustic wave characteristics caused by the deformation or settlement of the pipeline. The signal strength after noise elimination shows that the system can still provide clear sound data under noise interference, reflecting the advantage of the preprocessing technology of the present invention in filtering out interference.

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

[0065] Among them, the data acquisition module is used to collect the settlement characteristic data of the coal mine goaf. The data processing module is used to pre-process the sound characteristic data using sound processing technology and perform noise reduction on the settlement monitoring data. The evaluation module is used to calculate the safety factor based on the pre-processed data through the weight analysis module and transmit the data to the terminal monitoring system in real time.

[0066] If the function is implemented in the form of 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 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 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 of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

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

[0068] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0069] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in 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 rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for monitoring goaf in a coal mine, characterized in that: include: Collect settlement characteristic data of coal mine goaf; Use sound processing technology to pre-process the sound feature data and perform noise reduction on the settlement monitoring data; The safety factor is calculated based on the preprocessed data through the weight analysis module, and the data is transmitted to the terminal monitoring system in real time.

2. The method for monitoring goaf in a coal mine according to claim 1, characterized in that: The collection of coal mine goaf settlement characteristic data includes obtaining the conveying pipe deformation, stress change, goaf air velocity and temperature data through the characteristic collection module, setting a sensor at each monitoring point of the conveying pipe to collect the sound data and stress change data when the conveying pipe is deformed or offset in real time, measuring the air velocity and temperature in the goaf through the sensor, and transmitting it to the data processing module through wireless signals for further processing. The selection of the monitoring point is based on the layout of the conveying pipe and the structural distribution of the goaf, and a monitoring point is set every 50 meters to ensure real-time monitoring covering the entire area.

3. The method for monitoring goaf in a coal mine according to claim 2, characterized in that: The collection of settlement characteristic data of the coal mine goaf also includes using strain gauges to measure the real-time strain of the conveying pipe in three directions, axial, radial and oblique, and calculating the stress data in each direction according to the generalized Hooke's law, which is expressed as: Among them, σ 0o is the axial stress, σ 90o For P is the principal stress, θ P is the principal stress direction, E is the elastic modulus, v is the Poisson's ratio, ε 0o , ε 45o , ε 90o They are axial, oblique, and radial strains, respectively; All data are transmitted to the preprocessing module through the real-time data acquisition unit for subsequent safety assessment and analysis.

4. The method for monitoring goaf in a coal mine according to claim 3, characterized in that: The preprocessing of the sound feature data using sound processing technology includes first applying a noise elimination algorithm to remove environmental noise, then using sound enhancement technology to improve signal quality, and finally using echo suppression technology to process reflected sound waves, and using fast Fourier transform FFT to convert time domain signals into frequency domain signals.

5. The method for monitoring goaf in a coal mine according to claim 4, characterized in that: The noise reduction processing of the settlement monitoring data includes performing noise reduction processing on the stress change data of the conveying pipe, the air flow rate and temperature data of the goaf, using a low-pass filter to smooth the stress change data and remove high-frequency noise, and applying the Kalman filter algorithm to predict and correct the air flow rate and temperature data. The pre-processed data will be transmitted to the weight analysis module.

6. The method for monitoring goaf in a coal mine according to claim 5, characterized in that: The calculation of the safety factor based on the preprocessed data through the weight analysis module includes weighting the processed data of the sound feature data and the necessary monitoring feature data of the settlement based on the fuzzy evaluation method and the hierarchical analysis method, and after the allocation is completed, the safety factor data of the area is obtained based on the coal mine goaf safety factor algorithm.

7. The method for monitoring goaf in a coal mine according to claim 6, characterized in that: The real-time transmission of data to the terminal monitoring system includes transmitting the calculated safety factor data to the terminal monitoring system.

8. A system using the method for monitoring goaf in a coal mine according to any one of claims 1 to 7, characterized in that: Including data acquisition module, data processing module and evaluation module; The data acquisition module is used to collect settlement characteristic data of coal mine goaf; The data processing module is used to pre-process the sound feature data using sound processing technology and to perform noise reduction processing on the settlement monitoring data; The evaluation module is used to calculate the safety factor based on the pre-processed data through the weight analysis module, and transmit the data to the terminal monitoring system in real time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for monitoring goaf in a coal mine according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for monitoring goaf in a coal mine according to any one of claims 1 to 7 are implemented.

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