Coal mine safety monitoring method and system based on edge data analysis and readable medium
By using edge data analysis methods to collect and process coal mine environmental and personnel information data, the problem of data processing delay in existing technologies has been solved, enabling efficient, accurate, and timely early warning of coal mine safety monitoring and improving the level of safety management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA COAL TECH & ENG GRP CHONGQING RES INST CO LTD
- Filing Date
- 2023-12-04
- Publication Date
- 2026-07-21
AI Technical Summary
Existing coal mine safety monitoring systems are unable to achieve proactive prevention, precise control, and regional governance, resulting in data processing delays, an inability to accurately reflect the safety status of coal mines, and an inability to meet the needs of information management.
Using edge data analysis, the system acquires coal mine environmental and personnel information data through a data acquisition module, performs preprocessing, clustering, difference calculation, and PCA transformation, matches the safety level with the threshold range, and issues alarm information.
It has enabled efficient, accurate and timely information-based prediction and early warning of coal mine safety monitoring, improved the level of safety management, and ensured that operators can take timely measures.
Smart Images
Figure CN117514355B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of coal mine safety monitoring, and relates to a coal mine safety monitoring method, system and readable medium based on edge data analysis. Background Technology
[0002] Monitoring the safety status of coal mines, improving the early warning capabilities for major hazard sources, reducing the incidence of major hazard accidents, and enhancing the overall safety management level of coal mines are becoming increasingly crucial. Common coal mine safety monitoring methods primarily involve using sensors to detect environmental information in various underground areas, such as the content of hazardous gases like methane, and then directly transmitting this data to a ground server. The server then records the correlation between the current time and the hazardous gas content, thus completing the safety monitoring of the coal mine.
[0003] However, current intelligent coal mine construction technologies mainly focus on systems directly related to production, such as coal mine production and transportation. Construction in coal mine safety monitoring lags behind, remaining at a rudimentary stage of real-time monitoring of disaster parameters and alarms for exceeding related limits. Disaster prevention and control have not yet reached the level of advanced prevention, precise control, and regional governance. Due to the complexity of the coal mine environment and operational conditions, and the large amount of environmental information requiring monitoring, monitoring systems often result in excessive data loads on ground servers, leading to rapidly increasing workload and processing delays. This prevents the system from accurately reflecting the true safety status of the coal mine. Specifically, a large amount of data cannot be analyzed, shared, or exchanged, failing to meet the needs of information management and fully leveraging the advantages of information technology—speed, accuracy, and real-time performance. Therefore, it cannot meet the needs of spatial information processing in coal mines, and even less can it accurately and quickly provide robust monitoring of coal mine environmental safety. Summary of the Invention
[0004] In view of this, the purpose of the present invention is to provide a coal mine safety monitoring method, system and readable medium based on edge data analysis.
[0005] To achieve the above objectives, the present invention provides the following technical solution:
[0006] Firstly, this application provides a coal mine safety monitoring method based on edge data analysis, including:
[0007] The acquisition module obtains first data and second data. The first data is coal mine environmental information data, and the second data is personnel information data, which includes personnel location information data and personnel identity information data.
[0008] Analyze the first data, and match the security level corresponding to the first data based on the first data and the preset level matching degree;
[0009] The security level and the second data are correlated to obtain the correlation result;
[0010] The association result is compared with a preset multi-level threshold range. If the comparison result is within the threshold range, no alarm message is issued; if the comparison result is not within the threshold range, an alarm message is issued to provide a prompt.
[0011] Preferably, the collection location and direction of the first data are collected, wherein the collection location includes underground coal mining face, tunneling face, coal preparation plant above ground, coal mine power plant, coal mine substation, coal mine railway, intake airway and return airway;
[0012] The first data collection direction includes collecting wind speed, wind direction, temperature, geological distribution characteristics, and coal mine segmentation in the coal mine environment.
[0013] Preferably, the analysis of the first data includes:
[0014] The first data is preprocessed, which includes orthorectification, sharpening, mosaicking and color balancing of the first data according to different types of remote sensing image processing models, to obtain the preprocessed first data.
[0015] The preprocessed first data is then filtered and corrected for target objects, and coarsely classified according to a multi-scale adaptive feature classification algorithm.
[0016] The DBSCAN algorithm is used to cluster the first data obtained from the coarse classification to obtain the clustering results.
[0017] Based on the clustering results, the number of point clouds in each cluster is counted, and the top two clusters with the most point clouds are retained. The clusters include the clusters of coal mine environmental information data and the clusters of non-coal mine environmental information data.
[0018] Preferably, the step of counting the number of point clouds in each cluster based on the clustering results, retaining the top two clusters with the largest number of point clouds, and then including:
[0019] Extract the two clusters with the most retained point clouds, and denote them as the first cluster and the second cluster, wherein both the first cluster and the second cluster contain redundant parts;
[0020] Using principal component change detection, the first cluster and the second cluster are subjected to difference calculation to obtain change information, which is the difference information of different bands obtained;
[0021] The difference information is subjected to PCA transformation to obtain the processed first data.
[0022] Secondly, this application also provides a coal mine safety monitoring system based on edge data analysis, including an acquisition module, a matching module, an association module, and a judgment module, wherein:
[0023] Acquisition module: used to acquire first data and second data according to the acquisition module. The first data is coal mine environmental information data, and the second data is personnel information data, which includes personnel location information data and personnel identity information data.
[0024] Matching module: used to analyze the first data and match the security level corresponding to the first data based on the first data and a preset level matching degree;
[0025] Association module: used to associate the security level with the second data to obtain the association result;
[0026] Judgment module: Used to compare the correlation result with a preset multi-level threshold range. If the comparison result is within the threshold range, no alarm message is issued; if the comparison result is not within the threshold range, an alarm message is issued to provide a prompt.
[0027] Thirdly, this application also provides a coal mine safety monitoring device based on edge data analysis, comprising:
[0028] Memory, used to store computer programs;
[0029] A processor is used to implement the steps of the coal mine safety monitoring method based on edge data analysis when executing the computer program.
[0030] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described coal mine safety monitoring method based on edge data analysis.
[0031] The beneficial effects of this invention are as follows: This invention enables efficient monitoring of safety issues in underground coal mines, achieving accurate and timely automatic information-based prediction and early warning, providing effective assistance in handling safety accidents; it realizes intelligent monitoring and early warning of coal mine safety, and can perform graded alarms after analyzing and processing terminal information, enabling operators to take corresponding actions in a timely manner based on alarm information, ensuring safe operation of coal mining.
[0032] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description
[0033] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:
[0034] Figure 1 This is a schematic diagram of a coal mine safety monitoring method based on edge data analysis.
[0035] Figure 2 This is a schematic diagram of a coal mine safety monitoring device based on edge data analysis.
[0036] Reference numerals: Coal mine safety monitoring equipment 800, processor 801, memory 802, multimedia component 803, I / O interface 804, communication component 805. Detailed Implementation
[0037] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.
[0038] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.
[0039] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.
[0040] Figure 1 This is a schematic diagram of a coal mine safety monitoring method based on edge data analysis.
[0041] Figure 2 This is a schematic diagram of a coal mine safety monitoring device based on edge data analysis.
[0042] Example 1:
[0043] This embodiment provides a coal mine safety monitoring method based on edge data analysis.
[0044] Coal is a vital national energy source, and coal enterprises bear a sacred and glorious responsibility for energy development. However, in fulfilling this glorious mission, coal enterprises often experience various safety accidents due to various reasons. These accidents range from minor injuries and property damage to more serious incidents causing unnecessary sacrifices and significant losses to families and businesses. In special circumstances, such as gas explosions or water inrushes, serious casualties among coal miners and adverse social impacts can occur. This underscores the sacred nature of the work performed by coal mine workers and underscores the need for safe and reliable personal protective equipment, machinery, and lighting systems. Specifically, lighting fixtures must be explosion-proof, waterproof, and have strong smoke penetration capabilities. Of course, the working environment in coal mines is relatively crucial. Coal mines operate in a wide range of areas (underground coal mining faces, roadways, coal preparation plants, coal mine power plants, coal mine substations, coal mine railways, and other locations), and these sites are often characterized by high levels of dust, vibration, and water, and may even be flammable, explosive, toxic, prone to collapse, or subject to water infiltration. Therefore, this invention provides a coal mine safety monitoring method based on edge data analysis, which is of paramount importance.
[0045] See Figure 1 The figure shows that the method includes steps S100, S200, S300 and S400.
[0046] S100. Obtain first data and second data according to the acquisition module. The first data is coal mine environmental information data, and the second data is personnel information data, which includes personnel location information data and personnel identity information data.
[0047] Understandably, the sources of coal mine edge data analysis, in the traditional sense, mainly include data stored in the databases of coal mine enterprise management information systems, paper-based information, audio and video images, etc., which are mostly individual, small-scale, heterogeneous data. Coal mine edge data analysis encompasses these traditional data and their related and extended source data, covering all data throughout the entire lifecycle of a coal mine enterprise's production and operation, as well as their temporal and spatial relationships. The sources of coal mine edge data analysis can be summarized into three aspects: First, daily operational data generated by traditional coal mine enterprise management information systems. Coal mine enterprise management information systems have accumulated a large amount of data on mining machine operation, sales, safety, finance, operations, environment, etc., mostly stored in the form of tables, graphs, audio, video, logs, etc.; Second, real-time data obtained through the application of sensing technology and Internet of Things technology in the construction of smart mines. This type of data includes comprehensive automation information in the production process (such as intelligent equipment sensing and control data, machine operating parameters, etc.) and engineering digital information (such as various underground monitoring and surveillance data, personnel positioning, mining footage, geological changes, etc.); Third, external data related to coal mine operations. This refers to external information related to the production and operation activities of coal mining enterprises, such as data on geology, coal distribution, credit, finance, consumption, energy policies, and macroeconomic factors predicting product markets. In this embodiment, coal mine environmental information data can include the working environment when coal miners enter the coal mine to begin work, as well as historical working environments over a certain period. Personnel information data includes personnel identity verification and location parameters when entering the coal mine to begin work.
[0048] It is understandable that in this step S100, the first data collection location and collection direction include the following: underground coal mining face, tunneling face, coal preparation plant above ground, coal mine power plant, coal mine substation, coal mine railway, intake airway and return airway.
[0049] The first data collection direction includes collecting wind speed, wind direction, temperature, geological distribution characteristics, and coal mine segmentation in the coal mine environment.
[0050] S200. Analyze the first data, and match the security level corresponding to the first data based on the first data and the preset level matching degree.
[0051] It is understood that step S200 includes S201, S202, S203, and S204, wherein:
[0052] S201. Preprocess the first data, the preprocessing including orthorectification, sharpening, mosaicking and color balancing of the first data according to different types of remote sensing image processing models, to obtain the preprocessed first data.
[0053] S202. The preprocessed first data is screened and corrected for target objects, and the screened and corrected first data is coarsely classified according to the multi-scale adaptive feature classification algorithm.
[0054] S203. The DBSCAN algorithm is used to cluster the first data obtained from the coarse classification to obtain the clustering results;
[0055] S204. Based on the clustering results, count the number of point clouds in each cluster, and retain the top two clusters with the most point clouds. The clusters include the clusters of the coal mine environmental information data and the clusters of the non-coal mine environmental information data.
[0056] It should be noted that the point cloud data obtained by coarse clustering is subjected to point cloud neighborhood density statistics, and the point cloud density less than the set threshold is selected as the minimum expected cluster m; Eps is determined by a heuristic method, and DBSCAN clustering is performed on the point cloud data obtained by coarse clustering based on the values of MinPts and Eps.
[0057] It is understandable that step S204 is followed by S2041, S2042, and S2043, where:
[0058] S2041. Extract the two clusters with the largest number of retained point clouds, and denot them as the first cluster and the second cluster, wherein both the first cluster and the second cluster contain redundant parts;
[0059] It is understood that the clusters include the clusters of the coal mine environmental information data and the clusters of the non-coal mine environmental information data. The remote sensing images being detected have multiple bands, and there are redundant parts between the information of each band. Removing the redundant parts makes the efficiency higher.
[0060] S2042. Using principal component change detection, perform difference calculation on the first cluster and the second cluster to obtain change information, wherein the change information is the difference information of different bands obtained.
[0061] S2043. Perform PCA transformation on the difference information to obtain the processed first data.
[0062] Understandably, Principal Component Analysis (PCA) for change detection is based on the mathematical concept of dimensionality reduction. It combines multiple indicators and methods for solving real-world problems into one or a few new comprehensive indicators, using a small number of principal components to reflect as much original information as possible. This improves processing efficiency and solves current problems. PCA used for change detection includes, but is not limited to, three forms of principal components: principal component difference method, differential principal component method, and multi-band principal component transformation method.
[0063] S300. Associate the security level with the second data to obtain the association result.
[0064] It is understandable that in this step S300, the security level and the second data are associated. Based on the preset association information, the association result is obtained, and the corresponding relationship can be known.
[0065] S400. The association result is compared with a preset multi-level threshold range. If the comparison result is within the threshold range, no alarm message is issued. If the comparison result is not within the threshold range, a corresponding alarm message is issued to provide a prompt.
[0066] It is understandable that this embodiment also considers temperature monitoring in coal mine environmental monitoring, taking into account that different external ambient temperatures will affect the ambient temperature at the monitoring point. Here, the external environment typically refers to the overall environment. The external ambient temperature can be measured by setting up temperature sensors in the external environment, or it can be obtained directly from data from a weather forecast platform or weather forecast app. Of course, using real-time measurement with temperature sensors provides higher accuracy. The monitoring point can be understood as the ambient temperature at which personnel enter the coal mine, or the ambient temperature next to the coal mine's conveying pipeline. When the external ambient temperature changes, assuming other conditions remain unchanged, the ambient temperature at the monitoring point will inevitably be affected and change accordingly. Therefore, to improve the accuracy of the standard database and make correct judgments, it is necessary to establish a correspondence between the external ambient temperature and the ambient temperature at the monitoring point. Specifically, using a large amount of historical data—that is, the ambient temperature and the ambient temperature at the monitoring point—a curve relationship is established between them, with the external ambient temperature as the X-axis and the ambient temperature at the monitoring point as the Y-axis. Therefore, there are also threshold ranges for comparing ambient temperature. The results are obtained based on the curve relationship, and the results are compared with the threshold ranges to determine whether the temperature is within the range.
[0067] When the end server outputs the security level result to the security level output terminal, different audible and / or visual alarm methods can be matched according to the security level classification. For example, when the security level result is less than the preset security level limit, the alarm light is green, representing "no risk, safe" or "low risk, safe"; when the security level result is equal to the preset security level limit, the alarm light can be set to yellow, representing "low risk, investigation recommended"; when the security level result is greater than the preset security level limit, the alarm light can be set to orange or red, representing "medium risk, investigation required" and "high risk, evacuation required," respectively. Those skilled in the art can select the alarm method that matches the security level according to actual needs.
[0068] Example 2:
[0069] This embodiment provides a coal mine safety monitoring system based on edge data analysis. The system includes an acquisition module, a matching module, an association module, and a judgment module, wherein:
[0070] Acquisition module: used to acquire first data and second data according to the acquisition module. The first data is coal mine environmental information data, and the second data is personnel information data, which includes personnel location information data and personnel identity information data.
[0071] Matching module: used to analyze the first data and match the security level corresponding to the first data based on the first data and a preset level matching degree;
[0072] Association module: used to associate the security level with the second data to obtain the association result;
[0073] Judgment module: Used to compare the correlation result with a preset multi-level threshold range. If the comparison result is within the threshold range, no alarm message is issued; if the comparison result is not within the threshold range, an alarm message is issued to provide a prompt.
[0074] Specifically, the acquisition location and acquisition direction of the first data are collected, including a first acquisition unit and a second acquisition unit, wherein:
[0075] The first data acquisition unit is used to collect data from underground coal mining faces, tunneling faces, surface coal preparation plants, coal mine power plants, coal mine substations, coal mine railways, intake airways, and return airways.
[0076] The second acquisition unit is used to acquire the first data. The acquisition direction includes acquiring wind speed, wind direction, temperature, geological distribution characteristics, and coal segmentation in the coal mine environment.
[0077] Specifically, the analysis of the first data includes a preprocessing unit, a classification unit, a clustering unit, and a statistical unit, wherein:
[0078] Preprocessing unit: used to preprocess the first data, the preprocessing including orthorectification, sharpening, mosaicking and color balancing of the first data according to different types of remote sensing image processing models, to obtain preprocessed first data;
[0079] Classification unit: used to filter and correct the target objects in the preprocessed first data, and to perform coarse classification on the filtered and corrected first data according to the multi-scale adaptive feature classification algorithm;
[0080] Clustering unit: used to cluster the first data obtained from the coarse classification using the DBSCAN algorithm to obtain clustering results;
[0081] Statistical unit: used to count the number of point clouds in each cluster based on the clustering results, and retain the top two clusters with the most point clouds. The clusters include the clusters of coal mine environmental information data and the clusters of non-coal mine environmental information data.
[0082] Specifically, the statistical unit subsequently includes an extraction unit, a calculation unit, and a transformation unit, wherein:
[0083] Extraction unit: used to extract the two clusters with the most retained point clouds, denoted as the first cluster and the second cluster, wherein both the first cluster and the second cluster contain redundant parts;
[0084] Calculation unit: used to perform difference calculation on the first cluster and the second cluster using principal component change detection to obtain change information, wherein the change information is the difference information of different bands obtained;
[0085] Transformation unit: used to perform PCA transformation on the difference information to obtain the processed first data.
[0086] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.
[0087] Example 3:
[0088] Corresponding to the above method embodiments, this embodiment also provides a coal mine safety monitoring device based on edge data analysis. The coal mine safety monitoring device based on edge data analysis described below and the coal mine safety monitoring method based on edge data analysis described above can be referred to in correspondence.
[0089] Figure 2 This is a block diagram illustrating a coal mine safety monitoring device 800 based on edge data analysis, according to an exemplary embodiment. Figure 2 As shown, the coal mine safety monitoring device 800 based on edge data analysis includes a processor 801 and a memory 802. The coal mine safety monitoring device 800 also includes one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.
[0090] The processor 801 controls the overall operation of the edge data analysis-based coal mine safety monitoring device 800 to complete all or part of the steps in the aforementioned edge data analysis-based coal mine safety monitoring method. The memory 802 stores various types of data to support the operation of the edge data analysis-based coal mine safety monitoring device 800. This data may include, for example, instructions for any application or method operating on the edge data analysis-based coal mine safety monitoring device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, or buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the edge data analysis-based coal mine safety monitoring device 800 and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.
[0091] In an exemplary embodiment, the coal mine safety monitoring device 800 based on edge data analysis may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the aforementioned coal mine safety monitoring method based on edge data analysis.
[0092] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, these program instructions implement the steps of the coal mine safety monitoring method based on edge data analysis described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above. These program instructions may be executed by the processor 801 of the coal mine safety monitoring device 800 based on edge data analysis to complete the coal mine safety monitoring method based on edge data analysis described above.
[0093] Example 4:
[0094] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the coal mine safety monitoring method based on edge data analysis described above.
[0095] A computer program is stored on a readable storage medium, and when the computer program is executed by a processor, it implements the steps of the coal mine safety monitoring method based on edge data analysis described in the above method embodiments.
[0096] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.
[0097] Finally, 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 present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A coal mine safety monitoring method based on edge data analysis, characterized in that, include: The acquisition module obtains first data and second data. The first data is coal mine environmental information data, and the second data is personnel information data, which includes personnel location information data and personnel identity information data. Analyze the first data, and match the security level corresponding to the first data based on the first data and the preset level matching degree; The security level and the second data are correlated to obtain the correlation result; The association result is compared with a preset multi-level threshold range. If the comparison result is within the threshold range, no corresponding alarm information is issued. If the comparison result is not within the threshold range, a corresponding alarm message will be issued to provide a prompt. The analysis of the first data includes: The first data is preprocessed, which includes orthorectification, sharpening, mosaicking and color balancing of the first data according to different types of remote sensing image processing models, to obtain the preprocessed first data. The preprocessed first data is then filtered and corrected for target objects, and coarsely classified according to a multi-scale adaptive feature classification algorithm. The DBSCAN algorithm is used to cluster the first data obtained from the coarse classification to obtain the clustering results. Based on the clustering results, the number of point clouds in each cluster is counted, and the two clusters with the largest number of point clouds are retained. The clusters include the clusters of the coal mine environmental information data and the clusters of the coal mine environmental information data. Based on the clustering results, the number of point clouds in each cluster is counted, and the two clusters with the largest number of point clouds are retained. The following steps are then performed: Extract the two clusters with the most retained point clouds, and denote them as the first cluster and the second cluster, wherein both the first cluster and the second cluster contain redundant parts; Using principal component change detection, the first cluster and the second cluster are subjected to difference calculation to obtain change information, which is the difference information of different bands obtained; The difference information is subjected to PCA transformation to obtain the processed first data.
2. The coal mine safety monitoring method based on edge data analysis according to claim 1, characterized in that, The collection location and direction of the first data are collected, including the underground coal mining face, the tunneling face, the coal preparation plant above ground, the coal mine power plant, the coal mine substation, the coal mine railway, the intake airway, and the return airway; The first data collection direction includes collecting wind speed, wind direction, temperature, geological distribution characteristics, and coal mine segmentation in the coal mine environment.
3. A coal mine safety monitoring system based on edge data analysis, characterized in that, include: Acquisition module: used to acquire first data and second data according to the acquisition module. The first data is coal mine environmental information data, and the second data is personnel information data, which includes personnel location information data and personnel identity information data. Matching module: used to analyze the first data and match the security level corresponding to the first data based on the first data and a preset level matching degree; Association module: used to associate the security level with the second data to obtain the association result; Judgment module: used to compare the correlation result with a preset multi-level threshold range. If the comparison result is within the threshold range, no corresponding alarm information is issued. If the comparison result is not within the threshold range, a corresponding alarm message will be issued to provide a prompt. The analysis of the first data includes: Preprocessing unit: used to preprocess the first data, the preprocessing including orthorectification, sharpening, mosaicking and color balancing of the first data according to different types of remote sensing image processing models, to obtain preprocessed first data; Classification unit: used to filter and correct the target objects in the preprocessed first data, and to perform coarse classification on the filtered and corrected first data according to the multi-scale adaptive feature classification algorithm; Clustering unit: used to cluster the first data obtained from the coarse classification using the DBSCAN algorithm to obtain clustering results; Statistical unit: used to count the number of point clouds in each cluster based on the clustering results, and retain the top two clusters with the most point clouds. The clusters include the clusters of the coal mine environmental information data and the clusters of the coal mine environmental information data. The statistical unit then includes: Extraction unit: used to extract the two clusters with the most retained point clouds, denoted as the first cluster and the second cluster, wherein both the first cluster and the second cluster contain redundant parts; Calculation unit: used to perform difference calculation on the first cluster and the second cluster using principal component change detection to obtain change information, wherein the change information is the difference information of different bands obtained; Transformation unit: used to perform PCA transformation on the difference information to obtain the processed first data.
4. The coal mine safety monitoring system based on edge data analysis according to claim 3, characterized in that, The acquisition location and direction of the first data are collected, including: The first data acquisition unit is used to collect data from underground coal mining faces, tunneling faces, surface coal preparation plants, coal mine power plants, coal mine substations, coal mine railways, intake airways, and return airways. The second acquisition unit is used to acquire the first data. The acquisition direction includes acquiring wind speed, wind direction, temperature, geological distribution characteristics, and coal mine segmentation in the coal mine environment.
5. A coal mine safety monitoring device based on edge data analysis, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the coal mine safety monitoring method based on edge data analysis as described in any one of claims 1 to 2.
6. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the coal mine safety monitoring method based on edge data analysis as described in any one of claims 1 to 2.