System and method for exploring wind-oxidized coal in coal mine by comprehensive geophysical method

Abstract

The invention provides a system and method for exploring wind-oxidized coal in a coal mine through a comprehensive geophysical method, and belongs to the technical field of coal mine geological exploration, and the system comprises a data acquisition module, a data arrangement module, an algorithm processing module, an image drawing module and a result analysis and display module. The data acquisition module acquires distribution information of wind-oxidized coal in a coal mine through the central management module and the plurality of subunits; the data sorting module is used for classifying, marking and sorting the collected data; the algorithm processing module integrates various algorithm processing and analysis data; the image drawing module converts the processed data into an image result; and the result analysis and display module carries out visual display and generates an exploration report. Through comprehensive application of various detection means such as a direct current method, a transient electromagnetic method, a radio wave perspective method and an earthquake method, the limitation of a single method is effectively overcome, meanwhile, the accuracy and efficiency of detection of the wind-oxidized coal in the coal mine are improved, and a powerful guarantee is provided for safe production of the coal mine.

Description

Technical Field

[0001] The invention relates to the technical field of coal mine geological exploration, and in particular to a system and method for exploring wind-oxidized coal in coal mines using a comprehensive geophysical method. Background Art

[0002] During the coal mining process, the presence of wind-oxidized coal poses a serious threat to the safe production of coal mines. Due to long-term exposure to air, wind-oxidized coal undergoes oxidation and weathering, which leads to significant changes in its physical and chemical properties, manifested as reduced resistivity, increased water absorption and weakened mechanical strength. Therefore, accurately detecting the distribution area of ​​wind-oxidized coal in coal mines is of great significance for preventing safety accidents such as roof collapse and water damage.

[0003] At present, the detection of wind-oxidized coal in coal mines mainly adopts a single geophysical method, such as direct current method, transient electromagnetic method, radio wave perspective method or seismic method. These methods have their own advantages and disadvantages, but when used alone, they often have problems such as low detection accuracy, limited detection range or great influence of environmental factors. For example, although the direct current method can use the low resistivity characteristics of wind-oxidized coal for detection, it is easily interfered under complex geological conditions; although the transient electromagnetic method has a large detection depth, there is a detection blind spot in the shallow part; the radio wave perspective method is suitable for short-distance fine detection, but it has high requirements for the integrity of the coal seam; the seismic method has a good detection effect on deep coal seams, but the equipment is complex and the cost is high. In addition, the existing technology for the exploration of wind-oxidized coal still has the following problems: a) Limited detection accuracy: A single geophysical method is limited by its own principles and technical conditions, and it is often difficult to achieve the ideal detection accuracy, and it is difficult to meet the needs of coal mine safety production for accurate detection of wind-oxidized coal. b) Limited detection range: The detection range of different methods is different. Some methods are suitable for shallow detection, while others are suitable for deep detection, but it is difficult to achieve comprehensive coverage of the entire coal mine area. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a system and method for exploring wind-oxidized coal in coal mines using a comprehensive geophysical method. By comprehensively applying multiple detection means such as direct current method, transient electromagnetic method, radio wave perspective method and seismic method, the limitations of a single method are effectively overcome, while at the same time improving the accuracy and efficiency of detecting wind-oxidized coal in coal mines, providing a strong guarantee for safe production in coal mines.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A system for detecting oxidized coal in coal mines using an integrated geophysical method, comprising:

[0007] The data collection module, which consists of a central management module and multiple sub-units, is used to collect the distribution information of wind-oxidized coal in the coal mine;

[0008] A data sorting module, connected to the data acquisition module, for classifying, marking and sorting the data collected by the multiple sub-units;

[0009] An algorithm processing module, connected to the data sorting module, integrating multiple algorithms for processing and analyzing data;

[0010] An image drawing module, connected to the algorithm processing module, for converting the processed data into an image result;

[0011] The result analysis and display module is connected to the image drawing module and is used to visualize the image results, identify abnormal areas, and generate an exploration report.

[0012] Preferably, the multiple sub-units are integrated in the central management module, and the multiple sub-units include a direct current detection unit, a transient electromagnetic detection unit, a radio wave perspective detection unit and a seismic detection unit. The units cooperate with each other to jointly realize the detection of the distribution of oxidized coal in coal mines.

[0013] Preferably, the DC electrical detection unit utilizes the low resistivity characteristics of wind-oxidized coal, arranges power supply electrodes and measuring electrodes in the tunnel, and applies DC current to obtain the potential distribution of the coal seam and the surrounding rock, and infers the wind-oxidized coal zone in the coal seam; the transient electromagnetic detection unit uses an overlapping loop device to send a short pulse current underground, measures the attenuation of the secondary induced electric field, and infers the depth and morphological information of the wind-oxidized coal in the coal seam; the radio wave perspective detection unit transmits high-frequency electromagnetic waves to the coal seam, and detects its distribution by utilizing the absorption and reflection characteristics of electromagnetic waves of wind-oxidized coal; the seismic detection unit determines the existence and characteristic information of wind-oxidized coal by analyzing the propagation speed and reflection characteristics of artificially excited seismic waves in the coal seam.

[0014] Preferably, the data sorting module consists of a feature extraction unit, a cluster analysis unit, an anomaly detection unit, a multi-dimensional labeling unit, a context-aware classification unit and a data quality scoring unit, which are respectively used to perform feature extraction, classification, multi-dimensional labeling and sorting on the collected multiple groups of data.

[0015] Preferably, the algorithm processing module integrates a direct current electrical inversion algorithm, a transient electromagnetic response inversion algorithm, a radio wave tomography algorithm and a seismic data processing algorithm, and is used to select a corresponding algorithm for data processing on the classified and marked data.

[0016] The present invention also provides a method for detecting wind-oxidized coal in coal mines by using a comprehensive geophysical method, which is implemented by using the above-mentioned system and includes the following steps:

[0017] S1. Start the data collection module and coordinate multiple sub-units through the central management module to collect the distribution information of oxidized coal in the coal mine;

[0018] S2, transferring the collected data to the data collation module for feature extraction, cluster analysis, anomaly detection, multi-dimensional labeling, context-aware classification and data quality scoring;

[0019] S3, inputting the classified and marked data into an algorithm processing module, and selecting a corresponding direct current inversion algorithm, transient electromagnetic response inversion algorithm, radio wave tomography algorithm or seismic data processing algorithm according to the data type for data processing;

[0020] S4, transmitting the result output by the algorithm processing module to the image drawing module, and converting the data into an image result;

[0021] S5. Visualize the image results through the result analysis and display module.

[0022] Preferably, in S2, the collected data is transmitted to a data sorting module for feature extraction, cluster analysis, anomaly detection, multi-dimensional labeling, context-aware classification and data quality scoring. The specific process is as follows:

[0023] First, based on the transmitted data, the physical and chemical properties of the air-oxidized coal are feature extracted. The physical properties of the air-oxidized coal are extracted by image processing technology, and the chemical properties of the air-oxidized coal are extracted by the formula:

[0024] Q = a·C+b·H+c·O+d·N+e;

[0025] In the formula, C, H, O, and N represent the contents of carbon, hydrogen, oxygen, and nitrogen, respectively; a, b, c, and d are coefficients of the corresponding chemical elements; and e represents the correlation coefficient with the nitrogen content N, which is determined according to the specific coal mine and experimental conditions;

[0026] Secondly, after completing the feature extraction of the data, the extracted features are clustered through the cluster analysis algorithm to identify the distribution area of ​​wind-oxidized coal. The formula is:

[0027]

[0028] In the formula, J is the total square error, k is the number of clusters, S i is the point set in the i-th cluster, μ i is the center point of the i-th cluster, and x is a point in the cluster;

[0029] Based on cluster analysis, anomaly detection algorithm is used to identify and mark data points that do not conform to the regular pattern. The formula is:

[0030]

[0031] In the formula, h(x) is the anomaly score, H is [0,1], d(x) is the path length from the root to the leaf node containing x, and D is the maximum depth of the tree; then anomaly detection algorithms are applied to identify and mark outliers in the data;

[0032] After identifying regular and abnormal data, the data is labeled in multiple dimensions to adjust the data classification criteria. The formula is:

[0033] T(x)={t 1 ,t 2 ,...,t n};

[0034] Where T(x) is the label set of data point x, t n Marks for different attributes;

[0035] Then, the multi-dimensional labeled data is subjected to context-aware classification, and the formula is:

[0036]

[0037] In the formula, C(x,c) is the classification of data point x under context c, N(x,c) is the set of data points similar to x under context c, and f(y) is the classification label of y. The classification standard is adjusted according to the context information of the data to improve the accuracy and adaptability of data classification.

[0038] After completing the context-aware classification, the data is scored for data quality. If it does not meet the requirements, the above steps are repeated to clean or mark the data according to the scoring results. The formula is:

[0039] Q=w 1 Q 1 +w 2 Q 2 +...+w n Q n ;

[0040] In the formula, Q is the overall score of data quality, Q 1 ,Q 2 ,...,Q n Score the data quality in different dimensions, w 1 ,w 2 ,...,w n is the corresponding weight.

[0041] Preferably, in S3, the DC electrical inversion algorithm is:

[0042]

[0043] Where R is the resistivity, ΔV is the potential difference between the electrodes, I is the current passing through the electrodes, and d is the distance between the electrodes;

[0044] The transient electromagnetic response algorithm is converted from the frequency domain by Fourier transform:

[0045]

[0046] Where h(t) is the electromagnetic response in the time domain, I(ω) is the spectrum of the current waveform, H(ω) is the electromagnetic response in the frequency domain, and ω is the angular frequency;

[0047] The radio wave tomography algorithm is:

[0048] D·X=Y;

[0049] Where D is the line segment length matrix, X is the attenuation coefficient matrix of each grid, and Y is the constant matrix related to the measured field intensity;

[0050] The seismic data processing algorithm is:

[0051]

[0052] In the formula, F s is the sampling frequency, and T is the sampling interval.

[0053] Preferably, in S4, the image drawing module receives the processing results output by the algorithm processing module, converts the processing results into image form through graphic processing technology, obtains the potential distribution map, resistivity distribution map and seismic wave velocity distribution map of the wind-oxidized coal, and combines the image enhancement technology to enhance the clarity of the image drawing.

[0054] Preferably, in S5, it also includes identifying abnormal areas in the image through the result analysis and display module, and generating an exploration report of wind-oxidized coal in combination with coal mine information.

[0055] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0056] (1) The present invention integrates multiple geophysical detection technologies such as direct current method, transient electromagnetic method, radio wave perspective method and seismic method, and can comprehensively analyze the distribution of wind-oxidized coal in coal mines from different angles and levels. The comprehensive application of these multiple methods improves the accuracy of exploration, makes the identification of wind-oxidized coal more accurate, helps to reduce misjudgment and missed judgment, and provides more reliable geological data support for safe mining of coal mines.

[0057] (2) The data sorting module provided by the present invention can effectively classify, mark and sort the large amount of collected data. The algorithm processing module integrates a variety of advanced data processing algorithms, such as direct current electrical inversion algorithm, transient electromagnetic response inversion algorithm, etc. These algorithms can deeply analyze and process the data and extract key information, thereby improving the efficiency of data processing and the reliability of the results.

[0058] (3) The present invention converts complex data into intuitive image results through the image drawing module, and visualizes them through the result analysis and display module, so that non-professionals can quickly understand the exploration results. At the same time, the system can identify abnormal areas and generate exploration reports, providing direct decision-making support for coal mine geological exploration and improving the efficiency and scientificity of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0060] Figure 1 A schematic diagram of a system module for detecting wind-oxidized coal in a coal mine using a comprehensive geophysical method according to the present invention;

[0061] Figure 2 A schematic diagram of a data acquisition module provided in Embodiment 1 of the present invention;

[0062] Figure 3 A schematic diagram of a data sorting module provided in Embodiment 1 of the present invention;

[0063] Figure 4 The present invention is a flow chart of a method for exploring wind-oxidized coal in coal mines using a comprehensive geophysical method. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] Embodiment 1

[0067] like Figure 1 As shown, the present invention provides a system for exploring wind-oxidized coal in coal mines using a comprehensive geophysical method, comprising:

[0068] The data collection module, which consists of a central management module and multiple sub-units, is used to collect the distribution information of wind-oxidized coal in the coal mine;

[0069] A data sorting module, connected to the data acquisition module, is used to classify, mark and sort the data collected by the multiple sub-units;

[0070] The algorithm processing module is connected to the data sorting module and integrates multiple algorithms for processing and analyzing data;

[0071] An image drawing module, connected to the algorithm processing module, is used to convert the processed data into image results;

[0072] The result analysis and display module is connected to the image drawing module to visualize the image results, identify abnormal areas, and generate exploration reports.

[0073] Reference Figure 2 , multiple sub-units are integrated in the central management module. The multiple sub-units include direct current detection unit, transient electromagnetic detection unit, radio wave perspective detection unit and seismic detection unit. Each unit cooperates with each other to jointly realize the exploration of the distribution of wind-oxidized coal in coal mines.

[0074] Among them, the DC electrical detection unit utilizes the low resistivity characteristics of wind-oxidized coal, arranges power supply electrodes and measuring electrodes in the tunnel, and applies DC current to obtain the potential distribution of the coal seam and surrounding rock, and infers the wind-oxidized coal zone in the coal seam. Specific methods include mine DC advanced detection technology and mine DC electrical perspective method. The former uses a point power source near the head of excavation to form a full-space current field for advance detection, and the latter uses a steady current field in the working face and its top and bottom plate rock layers for perspective detection.

[0075] The transient electromagnetic detection unit uses an overlapping loop device to send a short pulse current underground and measure the attenuation of the secondary induced electric field. The wind-oxidized coal has a strong conductivity and will produce a large secondary electric field response during the induction process, thereby providing depth and morphology information of the wind-oxidized coal body.

[0076] The radio wave perspective detection unit transmits high-frequency electromagnetic waves to the coal seam and uses the absorption and reflection characteristics of electromagnetic waves of wind-oxidized coal to detect its distribution. Radio wave perspective can be used for short-distance fine detection, especially when there are many joints and fissures in the coal seam, and can effectively determine the distribution of wind-oxidized coal.

[0077] The seismic detection unit determines the presence and characteristic information of wind-oxidized coal by analyzing the propagation speed and reflection characteristics of artificially excited seismic waves in coal seams. The seismic method has a high resolution for the detection of deep coal seams and is suitable for providing three-dimensional imaging of coal seam structure and wind-oxidized coal distribution. In this way, through the combination of the above methods, the potential distribution of wind-oxidized coal in coal mines can be accurately identified, and wind-oxidized coal at different depths and in different areas of coal mines can be covered to ensure that all potential wind-oxidized coal areas are detected without omission.

[0078] Reference Figure 3 ,The data sorting module consists of a feature extraction unit, a cluster analysis unit, an omaly detection unit, a multi-dimensional labeling unit, a context-aware classification unit and a data quality scoring unit, which are used to perform feature extraction, classification, multi-dimensional labeling and sorting of the collected multiple groups of data, respectively.

[0079] In addition, the algorithm processing module integrates the direct current electrical inversion algorithm, the transient electromagnetic response inversion algorithm, the radio wave tomography algorithm and the seismic data processing algorithm, which is used to select the corresponding algorithm for data processing after classification and marking.

[0080] In addition, refer to Figure 4 The present invention also provides a method for detecting wind-oxidized coal in coal mines using a comprehensive geophysical method, which is implemented using the above-mentioned system and includes the following steps:

[0081] S1. Start the data collection module and coordinate multiple sub-units through the central management module to collect the distribution information of oxidized coal in the coal mine;

[0082] S2, transferring the collected data to the data collation module for feature extraction, cluster analysis, anomaly detection, multi-dimensional labeling, context-aware classification and data quality scoring;

[0083] S3, inputting the classified and marked data into an algorithm processing module, and selecting a corresponding direct current inversion algorithm, transient electromagnetic response inversion algorithm, radio wave tomography algorithm or seismic data processing algorithm according to the data type for data processing;

[0084] S4, transmitting the result output by the algorithm processing module to the image drawing module, and converting the data into an image result;

[0085] S5. Visualize the image results through the result analysis and display module.

[0086] Specifically, in S2, the collected data is transmitted to the data sorting module for feature extraction, cluster analysis, anomaly detection, multi-dimensional labeling, context-aware classification and data quality scoring. The specific process is as follows:

[0087] First, based on the transmitted data, the physical and chemical properties of the air-oxidized coal are feature extracted. The physical properties of the air-oxidized coal are extracted by image processing technology, and the chemical properties of the air-oxidized coal are extracted by the formula:

[0088] Q = a·C+b·H+c·O+d·N+e;

[0089] In the formula, C, H, O, and N represent the contents of carbon, hydrogen, oxygen, and nitrogen, respectively; a, b, c, and d are coefficients of the corresponding chemical elements; and e represents the correlation coefficient with the nitrogen content N, which is determined according to the specific coal mine and experimental conditions;

[0090] Secondly, after completing the feature extraction of the data, the extracted features are clustered through the cluster analysis algorithm to identify the distribution area of ​​wind-oxidized coal. The formula is:

[0091]

[0092] In the formula, J is the total square error, k is the number of clusters, S i is the point set in the i-th cluster, μ i is the center point of the i-th cluster, and x is a point in the cluster;

[0093] Based on cluster analysis, anomaly detection algorithm is used to identify and mark data points that do not conform to the regular pattern. The formula is:

[0094]

[0095] In the formula, h(x) is the anomaly score, H is [0,1], d(x) is the path length from the root to the leaf node containing x, and D is the maximum depth of the tree; then anomaly detection algorithms are applied to identify and mark outliers in the data;

[0096] After identifying regular and abnormal data, the data is labeled in multiple dimensions to adjust the data classification criteria. The formula is:

[0097] T(x)={t 1 ,t 2 ,...,t n};

[0098] Where T(x) is the label set of data point x, t n Marks for different attributes;

[0099] Then, the multi-dimensional labeled data is subjected to context-aware classification, and the formula is:

[0100]

[0101] In the formula, C(x,c) is the classification of data point x under context c, N(x,c) is the set of data points similar to x under context c, and f(y) is the classification label of y. The classification standard is adjusted according to the context information of the data to improve the accuracy and adaptability of data classification.

[0102] After completing the context-aware classification, the data is scored for data quality. If it does not meet the requirements, the above steps are repeated to clean or mark the data according to the scoring results. The formula is:

[0103] Q=w 1 Q 1 +w 2 Q 2 +...+w n Q n ;

[0104] In the formula, Q is the overall score of data quality, Q 1 ,Q 2 ,...,Q n Score the data quality in different dimensions, w 1 ,w 2 ,...,w n is the corresponding weight.

[0105] Specifically, in S3, the DC electrical inversion algorithm is:

[0106]

[0107] Where R is the resistivity, ΔV is the potential difference between the electrodes, I is the current passing through the electrodes, and d is the distance between the electrodes;

[0108] The transient electromagnetic response algorithm is converted from the frequency domain through Fourier transform:

[0109]

[0110] Where h(t) is the electromagnetic response in the time domain, I(ω) is the spectrum of the current waveform, H(ω) is the electromagnetic response in the frequency domain, and ω is the angular frequency;

[0111] The radio wave tomography algorithm is:

[0112] D·X=Y;

[0113] Where D is the line segment length matrix, X is the attenuation coefficient matrix of each grid, and Y is the constant matrix related to the measured field intensity;

[0114] The seismic data processing algorithm is:

[0115]

[0116] In the formula, F s is the sampling frequency, and T is the sampling interval.

[0117] Finally, in S4, the image drawing module receives the processing results output by the algorithm processing module, converts the processing results into image form through graphic processing technology, obtains the potential distribution map, resistivity distribution map and seismic wave velocity distribution map of wind-oxidized coal, and combines image enhancement technology to enhance the clarity of image drawing. In S5, it also includes identifying abnormal areas in the image through the result analysis and display module, and generating a wind-oxidized coal exploration report in combination with coal mine information.

[0118] Therefore, the system and method of using the above-mentioned comprehensive geophysical method to explore the wind-oxidized coal in coal mines effectively overcomes the limitations of a single method by comprehensively applying multiple detection methods such as direct current method, transient electromagnetic method, radio wave perspective method and seismic method, while improving the accuracy and efficiency of detecting wind-oxidized coal in coal mines, providing strong guarantee for safe production in coal mines.

[0119] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A system for detecting wind-oxidized coal in coal mines using a comprehensive geophysical method, characterized in that: include: The data collection module, which consists of a central management module and multiple sub-units, is used to collect the distribution information of wind-oxidized coal in the coal mine; A data sorting module, connected to the data acquisition module, for classifying, marking and sorting the data collected by the multiple sub-units; An algorithm processing module, connected to the data sorting module, integrating multiple algorithms for processing and analyzing data; An image drawing module, connected to the algorithm processing module, for converting the processed data into an image result; The result analysis and display module is connected to the image drawing module and is used to visualize the image results, identify abnormal areas, and generate an exploration report.

2. A system for detecting wind-oxidized coal in coal mines by using a comprehensive geophysical method according to claim 1, characterized in that: The multiple sub-units are integrated in the central management module, and the multiple sub-units include a direct current detection unit, a transient electromagnetic detection unit, a radio wave perspective detection unit and a seismic detection unit. The units cooperate with each other to jointly realize the detection of the distribution of oxidized coal in the coal mine.

3. A system for detecting wind-oxidized coal in coal mines by using a comprehensive geophysical method according to claim 2, characterized in that: The DC electrical detection unit utilizes the low resistivity characteristics of wind-oxidized coal, arranges power supply electrodes and measuring electrodes in the tunnel, and applies DC current to obtain the potential distribution of the coal seam and the surrounding rock, and infers the wind-oxidized coal zone in the coal seam; the transient electromagnetic detection unit uses an overlapping loop device to send a short pulse current underground, measures the attenuation of the secondary induced electric field, and infers the depth and morphological information of the wind-oxidized coal in the coal seam; the radio wave perspective detection unit transmits high-frequency electromagnetic waves to the coal seam, and detects its distribution by utilizing the absorption and reflection characteristics of electromagnetic waves of wind-oxidized coal; the seismic detection unit determines the existence and characteristic information of wind-oxidized coal by analyzing the propagation speed and reflection characteristics of artificially excited seismic waves in the coal seam.

4. A system for detecting wind-oxidized coal in coal mines by using a comprehensive geophysical method according to claim 1, characterized in that: The data sorting module consists of a feature extraction unit, a cluster analysis unit, an anomaly detection unit, a multi-dimensional labeling unit, a context-aware classification unit and a data quality scoring unit, which are respectively used to perform feature extraction, classification, multi-dimensional labeling and sorting on multiple sets of collected data.

5. A system for detecting wind-oxidized coal in coal mines by using a comprehensive geophysical method according to claim 1, characterized in that: The algorithm processing module integrates a direct current inversion algorithm, a transient electromagnetic response inversion algorithm, a radio wave tomography algorithm and a seismic data processing algorithm, and is used to select a corresponding algorithm for data processing after classification and labeling.

6. A method for detecting wind-oxidized coal in coal mines by using a comprehensive geophysical method, characterized in that: A system for detecting wind-oxidized coal in a coal mine using a comprehensive geophysical method as described in any one of claims 1 to 5 comprises the following steps: S1. Start the data collection module and coordinate multiple sub-units through the central management module to collect the distribution information of oxidized coal in the coal mine; S2, transferring the collected data to the data collation module for feature extraction, cluster analysis, anomaly detection, multi-dimensional labeling, context-aware classification and data quality scoring; S3, inputting the classified and marked data into an algorithm processing module, and selecting a corresponding direct current inversion algorithm, transient electromagnetic response inversion algorithm, radio wave tomography algorithm or seismic data processing algorithm according to the data type for data processing; S4, transmitting the result output by the algorithm processing module to the image drawing module, and converting the data into an image result; S5. Visualize the image results through the result analysis and display module.

7. The method for detecting oxidized coal in coal mines by using a comprehensive geophysical method according to claim 6, characterized in that: In S2, the collected data is transferred to the data sorting module for feature extraction, cluster analysis, anomaly detection, multi-dimensional labeling, context-aware classification and data quality scoring. The specific process is as follows: First, based on the transmitted data, the physical and chemical properties of the air-oxidized coal are feature extracted. The physical properties of the air-oxidized coal are extracted by image processing technology, and the chemical properties of the air-oxidized coal are extracted by the formula: Q = a·C+b·H+c·O+d·N+e; In the formula, C, H, O, and N represent the contents of carbon, hydrogen, oxygen, and nitrogen, respectively; a, b, c, and d are coefficients of the corresponding chemical elements; and e represents the correlation coefficient with the nitrogen content N, which is determined according to the specific coal mine and experimental conditions; Secondly, after completing the feature extraction of the data, the extracted features are clustered through the cluster analysis algorithm to identify the distribution area of ​​wind-oxidized coal. The formula is: In the formula, J is the total square error, k is the number of clusters, S i is the point set in the i-th cluster, μ i is the center point of the i-th cluster, and x is a point in the cluster; Based on cluster analysis, anomaly detection algorithm is used to identify and mark data points that do not conform to the regular pattern. The formula is: In the formula, h(x) is the anomaly score, H is [0,1], d(x) is the path length from the root to the leaf node containing x, and D is the maximum depth of the tree; then anomaly detection algorithms are applied to identify and mark outliers in the data; After identifying regular and abnormal data, the data is labeled in multiple dimensions to adjust the data classification criteria. The formula is: T(x)={t1,t2,...,t n }; Where T(x) is the label set of data point x, t n Marks for different attributes; Then, the multi-dimensional labeled data is subjected to context-aware classification, and the formula is: In the formula, C(x,c) is the classification of data point x under context c, N(x,c) is the set of data points similar to x under context c, and f(y) is the classification label of y. The classification standard is adjusted according to the context information of the data to improve the accuracy and adaptability of data classification. After completing the context-aware classification, the data is scored for data quality. If it does not meet the requirements, the above steps are repeated to clean or mark the data according to the scoring results. The formula is: Q=w1·Q1+w2·Q2+...+w n ·Q n ; Where Q is the total score of data quality, Q1, Q2, ..., Q n Score the data quality of different dimensions, w1,w2,...,w n is the corresponding weight.

8. The method for detecting oxidized coal in coal mines by using a comprehensive geophysical method according to claim 6, characterized in that: In S3, the DC inversion algorithm is: Where R is the resistivity, ΔV is the potential difference between the electrodes, I is the current passing through the electrodes, and d is the distance between the electrodes; The transient electromagnetic response algorithm is converted from the frequency domain by Fourier transform: Where h(t) is the electromagnetic response in the time domain, I(ω) is the spectrum of the current waveform, H(ω) is the electromagnetic response in the frequency domain, and ω is the angular frequency; The radio wave tomography algorithm is: D·X=Y; Where D is the line segment length matrix, X is the attenuation coefficient matrix of each grid, and Y is the constant matrix related to the measured field intensity; The seismic data processing algorithm is: In the formula, F s is the sampling frequency, and T is the sampling interval.

9. The method for detecting oxidized coal in coal mines by using a comprehensive geophysical method according to claim 6, characterized in that: In S4, the image drawing module receives the processing results output by the algorithm processing module, converts the processing results into image form through graphic processing technology, obtains the potential distribution map, resistivity distribution map and seismic wave velocity distribution map of the wind-oxidized coal, and combines the image enhancement technology to enhance the clarity of the image drawing.

10. The method for detecting oxidized coal in coal mines by using a comprehensive geophysical method according to claim 6, characterized in that: In S5, it also includes identifying abnormal areas in the image through the result analysis and display module, and generating an exploration report of wind-oxidized coal in combination with coal mine information.

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