Safety monitoring and early warning management system and method for coal mine tunnel

Abstract

The invention discloses a safety monitoring and early warning management system and method for a coal mine tunnel, and the method comprises the following steps: S1, deploying a plurality of monitoring nodes; s2, collecting real-time environment data of a plurality of monitoring nodes, and preprocessing the real-time environment data; s3, analyzing the similarity between the preprocessed environmental data and environmental data in historical accident hidden dangers, simulating and predicting the potential risk of an emergency situation, evaluating the safety state, and determining the risk level; s4, triggering of an early warning mechanism is determined, and an emergency plan is generated; and S5, visually displaying the environment data, the analysis data and the risk level, and dynamically adjusting the list of the change data to realize safety monitoring and early warning management. According to the invention, safety monitoring and early warning with real-time early warning and prediction capability can be provided, and the safety state of the coal mine tunnel can be comprehensively and accurately reflected, so that more accurate and efficient management and protection of the coal mine tunnel are realized, and the safety and production efficiency of coal mine production are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine safety monitoring, and in particular to a safety monitoring and early warning management system and method for coal mine tunnels. Background Art

[0002] In the process of coal mining, safety monitoring and early warning management of coal mine tunnels are key links to ensure production safety and prevent accidents. Traditional coal mine tunnel safety monitoring methods mainly rely on manual inspections and simple sensor monitoring, which have many limitations.

[0003] First, the accuracy of manual inspections is easily affected by subjective factors and human errors. Inspectors may fail to detect safety hazards in the tunnels in a timely manner due to fatigue, lack of concentration or lack of experience. Secondly, the frequency and scope of manual inspections are limited, making it difficult to achieve comprehensive and real-time monitoring of coal mine tunnels. In addition, although the sensor monitoring methods in the prior art can monitor some environmental parameters (such as ground pressure, displacement, stress and temperature, etc.), they can often only provide monitoring data for a single parameter and lack the ability to conduct comprehensive analysis of multiple parameters. As a result, the monitoring results may not be comprehensive enough and it is difficult to accurately reflect the safety status of coal mine tunnels.

[0004] Therefore, it is necessary to improve the deficiencies in the prior art to solve the above problems. Summary of the invention

[0005] The present invention overcomes the deficiencies of the prior art and provides a safety monitoring and early warning management system and method for coal mine tunnels.

[0006] To achieve the above object, the technical solution adopted by the present invention is: a safety monitoring and early warning management method for coal mine tunnels, comprising the following steps:

[0007] S1. Deploy multiple monitoring nodes in the coal mine tunnel;

[0008] S2, collect real-time environmental data of multiple monitoring nodes in coal mine tunnels, and pre-process the collected environmental data;

[0009] S3. Analyze the similarity between the pre-processed environmental data and the environmental data in historical accident hazards in real time, simulate and predict the potential risk of sudden situations in coal mine tunnels, and evaluate the safety status of coal mine tunnels to determine the risk level;

[0010] S4. Determine the triggering of the early warning mechanism according to the risk level, and the early warning mechanism generates an emergency plan based on the potential risks;

[0011] S5. Generate a list of real-time environmental data, analytical data and risk levels for visual display, and dynamically adjust the list of real-time environmental data, analytical data and risk levels that are changing to achieve safety monitoring and early warning management.

[0012] In a preferred embodiment of the present invention, in step S1, the monitoring node includes: a plurality of sensors for monitoring environmental parameters of coal mine tunnels, and a data transceiver point for transmitting and receiving environmental parameters;

[0013] The deployment of the monitoring node includes the following steps:

[0014] S11. Calculate the demand for the monitoring nodes according to the layout and geological conditions of the coal mine tunnels: Among them, N no is the total number of monitoring nodes; L is the length of the coal mine tunnel; W is the width of the coal mine tunnel; S a is the average service area, the total area that needs to be monitored in the coal mine tunnel; C g is the coefficient of geological structure complexity; A h is the influencing factor of accident-prone areas; A e is the influencing factor of the area affected by the engineering activities;

[0015] S12, optimizing the sensor deployment and calculating the service range R of the data transceiver point ac : Among them, P t is the transmitting power; G t and G r are the transmit antenna and receive antenna gains respectively; λ is the operating wavelength; L0 is the free space path loss; N f is the total noise factor; α is the correction coefficient;

[0016] S13, ensuring coverage of the entire service range and avoiding blind spots, determining the maximum allowable distance D between adjacent sensors max , and the total number of sensors N in each monitoring node se ;

[0017] D max =βR ac ; Where β is the correction factor;

[0018] Among them, A s is the effective monitoring area of ​​a single sensor.

[0019] In a preferred embodiment of the present invention, the step S2 includes the following sub-steps:

[0020] S21. Collect real-time environmental data at each monitoring node in the coal mine tunnel, including the ground pressure, displacement, stress and temperature parameters involved;

[0021] S22. Remove duplicate, incomplete or abnormal data items from the collected environmental data and normalize them to ensure that the data are on the same scale.

[0022] In a preferred embodiment of the present invention, the step S3 includes the following sub-steps:

[0023] S31, analyzing the data correlation between the pre-processed environmental data and the environmental data in historical accident hazards;

[0024] S32. Based on the analysis results, predict the potential risks of sudden situations;

[0025] S33. Based on the prediction results, the safety status of the coal mine tunnels is evaluated and the risk level is determined.

[0026] In a preferred embodiment of the present invention, in step S31, the similarity and correlation between each current environmental parameter data and each environmental parameter data in the historical accident potential hazard are identified, and the correlation coefficient r is calculated: Among them, x is the environmental parameter data after preprocessing; y is the environmental parameter data in historical accident hazards; x i and i are the corresponding observation values; and are the corresponding average values.

[0027] In a preferred embodiment of the present invention, in the step S32, the potential risk of an emergency is predicted using a machine learning algorithm;

[0028] Predict the potential risk of the above emergency happening (logistic regression):

[0029] Where P(Y=1Z,r) is the probability of an accident given all environmental parameters and the correlation coefficient r; Y=1 is the event of an accident; Z is a feature vector containing all relevant environmental parameters, for example: Z=[z1,z2,...,z n ], where z1 represents ground pressure, z2 represents displacement, and so on; e is the base of the natural logarithm; β0 is the intercept term; β i is the weight coefficient corresponding to each environmental parameter; β z is the weight coefficient of the correlation coefficient r; z i It is the specific value of the corresponding environmental parameter.

[0030] In a preferred embodiment of the present invention, in step S33, the risk level of the security status is evaluated as follows: Among them, 1 is low risk, 2 is medium risk, 3 is high risk, and 4 is extremely high risk; P is the predicted result of potential risk; t1, t2 and t3 are the thresholds for setting risk levels.

[0031] In a preferred embodiment of the present invention, the step S4 includes the following sub-steps:

[0032] S41. When the correlation analysis result reaches or exceeds the warning threshold, the warning mechanism is triggered and a warning message is sent to the user;

[0033] S42. Develop emergency plans based on early warning information and potential risks.

[0034] In a preferred embodiment of the present invention, the step S5 includes the following sub-steps:

[0035] S51. Generate a list of real-time monitored environmental data, the results of correlation analysis and the determined risk levels for visual display, including charts, graphs and maps;

[0036] S52. When the real-time monitored environmental data, the results of the correlation analysis, and the determined risk level change, the list is dynamically adjusted and the visual display content is updated;

[0037] S53. Combined with the visually displayed data, continuous monitoring and early warning management of the safety status of coal mine tunnels can be achieved.

[0038] The present invention provides a management system for a safety monitoring and early warning management method of a coal mine tunnel, comprising:

[0039] Monitoring node unit, used to monitor the environmental data inside the coal mine tunnel in real time and transmit the environmental data;

[0040] A data collection module, used to collect environmental data inside the coal mine tunnel;

[0041] A data processing module, used for preprocessing environmental data;

[0042] Similarity analysis module, used to perform similarity analysis between pre-processed environmental data and environmental data from historical accident hazards;

[0043] Risk prediction module, used to simulate and predict the potential risks of sudden situations in coal mine tunnels;

[0044] A risk level assessment module is used to determine the risk level of the safety status of the coal mine tunnel according to the predicted potential risks;

[0045] The early warning module is used to determine the triggering of the early warning mechanism according to the risk level and generate emergency plans based on potential risks;

[0046] The visualization module is used to display real-time environmental data, analytical data and risk levels, and to dynamically adjust the visualization according to changes in data.

[0047] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0048] (1) The present invention provides a safety monitoring and early warning management system and method for coal mine tunnels. By deploying multiple monitoring nodes, real-time environmental data is collected and preprocessed to achieve real-time monitoring of multiple parameters of coal mine tunnels, and multi-parameter comprehensive analysis is performed to predict potential risks in real time, determine risk levels, and trigger early warning mechanisms according to risk levels to generate emergency plans. It can provide safety monitoring and early warning with real-time early warning and prediction capabilities, comprehensively and accurately reflect the safety status of coal mine tunnels, promptly detect abnormal situations and take timely measures to reduce accident risks, thereby achieving more accurate and efficient management and protection of coal mine tunnels, greatly improving the safety and production efficiency of coal mine production.

[0049] (2) In the present invention, by deploying monitoring nodes including a variety of sensors and data receiving and transmitting points, it is possible to cover various key areas of coal mine tunnels, which not only ensures that various subtle safety changes in the tunnels can be captured, but also provides an accurate and comprehensive data source for subsequent data processing and analysis, and realizes comprehensive monitoring of environmental parameters, thereby ensuring real-time, accurate and reliable monitoring data transmission, while also achieving efficient utilization of resources and safety protection of coal mine tunnels.

[0050] (3) In the present invention, by performing correlation analysis between the pre-processed environmental data and the environmental data in previous accident hazards, the similarity between the current environmental data and the previous accident hazard data can be identified, and then the potential risks can be predicted. The triggering of the early warning mechanism can enable users to understand the potential risks in a timely manner, gain time for taking countermeasures, ensure that they can respond quickly and effectively when risks occur, realize rapid and accurate identification of abnormal conditions in the tunnels, and provide timely warnings and predictions, so that mine managers can detect abnormal conditions early and take timely measures to reduce accident risks.

[0051] (4) In the present invention, by improving the intuitive data visualization display, users can intuitively understand the changes and trends of the tunnels, and can better grasp the safety status and potential risks of the coal mine tunnels, which is convenient for users to make decisions and judgments. At the same time, dynamically adjusting the list and updating the visualization display content can enable users to grasp the latest safety information and risk status at any time, ensuring the timeliness and effectiveness of the response measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] 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 or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 It is a flow chart of a safety monitoring and early warning management method for a coal mine tunnel according to a preferred embodiment of the present invention;

[0054] Figure 2 It is a structural diagram of a safety monitoring and early warning management system for coal mine tunnels according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0055] 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.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0057] like Figure 1 As shown, a safety monitoring and early warning management method for coal mine tunnels includes the following steps:

[0058] S1. Deploy multiple monitoring nodes in the coal mine tunnel;

[0059] S2, collect real-time environmental data of multiple monitoring nodes in coal mine tunnels, and pre-process the collected environmental data;

[0060] S3. Analyze the similarity between the pre-processed environmental data and the environmental data in historical accident hazards in real time, simulate and predict the potential risk of sudden situations in coal mine tunnels, and evaluate the safety status of coal mine tunnels to determine the risk level;

[0061] S4. Determine the triggering of the early warning mechanism according to the risk level, and the early warning mechanism generates an emergency plan based on the potential risks;

[0062] S5. Generate a list of real-time environmental data, analytical data and risk levels for visual display, and dynamically adjust the list of real-time environmental data, analytical data and risk levels that are changing to achieve safety monitoring and early warning management.

[0063] In some specific implementations, in step S1, the monitoring nodes include: a variety of sensors for monitoring environmental parameters of coal mine tunnels, and a data transceiver point for transmitting and receiving environmental parameters;

[0064] The deployment of monitoring nodes includes the following steps:

[0065] S11. Calculate the demand for monitoring nodes based on the layout and geological conditions of the coal mine tunnels: Among them, N no is the total number of monitoring nodes; L is the length of the coal mine tunnel; W is the width of the coal mine tunnel; S a is the average service area, the total area that needs to be monitored in the coal mine tunnel; C g It is the coefficient of geological structure complexity. The geological complexity inside the tunnel is evaluated using the geological exploration report, such as faults, fissures, etc. The complexity is rated on a scale of 1-5, where 1 means simple and 5 means very complex. h It is the influencing factor of accident-prone areas. It collects historical accident data and determines the areas with high accident frequency. e It is the impact factor of the area affected by the engineering activities, considering the impact of current and future mining operations on the environment;

[0066] S12. Optimize sensor deployment and calculate the service range R of data transmission and reception points ac : Among them, P t is the transmitting power; G t and G r are the transmit antenna and receive antenna gains respectively; λ is the operating wavelength; L0 is the free space path loss; N f is the total noise factor; α is the correction factor (between 0.5 and 0.8);

[0067] S13. Ensure coverage of the entire service range and avoid blind spots, and determine the maximum allowable distance D between adjacent sensors. max , and the total number of sensors in each monitoring node N se ;

[0068] D max =βR ac ; Where β is the correction factor (between 0.6 and 0.9);

[0069] Among them, A s is the effective monitoring area of ​​a single sensor.

[0070] It should be noted that by deploying monitoring nodes including a variety of sensors and data receiving and sending points, it is possible to cover all key areas of coal mine tunnels, which not only ensures that various subtle safety changes in the tunnels can be captured, but also provides an accurate and comprehensive data source for subsequent data processing and analysis, and realizes comprehensive monitoring of environmental parameters, thereby ensuring real-time, accurate and reliable monitoring data transmission, while also achieving efficient utilization of resources and safety protection of coal mine tunnels.

[0071] In some specific embodiments, the step of S2 includes the following sub-steps:

[0072] S21. Collect real-time environmental data at each monitoring node in the coal mine tunnel, including the ground pressure, displacement, stress and temperature parameters involved;

[0073] S22. Remove duplicate, incomplete or abnormal data items from the collected environmental data and normalize them to ensure that the data are on the same scale.

[0074] In this embodiment, in step S21, ground pressure, displacement, stress and temperature parameters are obtained by using ground pressure sensors, displacement sensors, stress sensors and temperature sensors respectively; by introducing multiple sensors, real-time monitoring of multiple parameters of coal mine tunnels is achieved, including ground pressure, displacement, stress, temperature, etc. Through multi-parameter monitoring, the safety status of the tunnel can be fully understood and potential problems and risks can be discovered in time.

[0075] In this embodiment, in step S22, it is necessary to remove duplicate data from the acquired environmental data, identify and process abnormal values: abnormal values ​​may be caused by data entry errors or special circumstances of patients, and need to be processed according to actual conditions, such as replacing them with mean values, medians or deletions; normalization processing is specifically to scale the data so that it falls into a small specific interval, such as [0, 1] or [-1, 1], which helps to eliminate the impact of the dimension on the results. The calculation formula is: Among them, X is the original data; X′ is the normalized data.

[0076] In some specific embodiments, the step of S3 includes the following sub-steps:

[0077] S31, analyzing the data correlation between the pre-processed environmental data and the environmental data in historical accident hazards;

[0078] S32. Based on the analysis results, predict the potential risks of sudden situations;

[0079] S33. Based on the prediction results, the safety status of the coal mine tunnels is evaluated and the risk level is determined.

[0080] It should be noted that by conducting a correlation analysis between the pre-processed environmental data and the environmental data in previous accident hazards, the similarities between the current environmental data and the previous accident hazard data can be identified, and then potential risks can be predicted. The triggering of the early warning mechanism can enable users to understand the potential risks in a timely manner, gain time for taking countermeasures, ensure that they can respond quickly and effectively when risks occur, quickly and accurately identify abnormal conditions in the tunnels, and provide timely warnings and predictions, so that mine managers can detect abnormal conditions early and take timely measures to reduce accident risks.

[0081] In this embodiment, in step S31, the similarity and correlation between each current environmental parameter data and each environmental parameter data in the historical accident potential hazard are identified, and the correlation coefficient r is calculated: Among them, x is the environmental parameter data after preprocessing; y is the environmental parameter data in historical accident hazards; x i and i are the corresponding observation values; and are the corresponding average values;

[0082] When the correlation coefficient r=1, it means that the environmental parameter data after pretreatment and the environmental parameter data in historical accident hazards are completely positively correlated, that is, the increase of one variable is always accompanied by the increase of another variable; when r=-1, it means that the environmental data after pretreatment and the environmental data in historical accident hazards are completely negatively correlated, that is, the increase of one variable is always accompanied by the decrease of another variable; when r=0, it means that there is no linear correlation between the environmental data after pretreatment and the environmental data in historical accident hazards; that is, the closer the absolute value of r is to 1, the stronger the linear relationship between the environmental data after pretreatment and the environmental data in historical accident hazards, and the closer it is to 0, the weaker the linear relationship.

[0083] In this embodiment, in step S32, the potential risk of an emergency is predicted, and a machine learning algorithm is used to enable it to automatically learn and discover laws and patterns from data, and then use these laws and patterns to perform prediction, classification, clustering, and dimensionality reduction tasks. Preferably, a deep learning model is used, combined with modeling and simulation performed by an artificial intelligence algorithm, and regression learning is performed using a neural network tool;

[0084] Predicting the potential risk of an emergency (logistic regression):

[0085] Where P(Y=1|Z,r) is the probability of an accident given all environmental parameters and the correlation coefficient r; Y=1 is the event of an accident; Z is a feature vector containing all relevant environmental parameters, for example: Z=[z1,z2,…,zn ], where z1 represents ground pressure, z2 represents displacement, and so on; e is the base of the natural logarithm; β0 is the intercept term; β i is the weight coefficient corresponding to each environmental parameter; β z is the weight coefficient of the correlation coefficient r, which is used to measure the impact on the risk; i It is the specific value of the corresponding environmental parameter.

[0086] It should be noted that the weight β corresponding to each environmental parameter i and the weight β of the correlation coefficient r z , combined with the entropy weight method for calculation, the entropy weight method is an objective weight allocation method, which determines the weight based on the discrete degree of the data, rather than based on subjective judgment. The combination of the two ensures the distribution of weights. Specifically: Calculate the proportion of indicators: For the normalized data, calculate the proportion p of each indicator m in each sample n ij , where y ij is the value of the i-th sample on the j-th influencing factor;

[0087] Calculate information entropy: Calculate the information entropy of each indicator using the weight in, a is the sample size;

[0088] Calculate the coefficient of variation and weight: Coefficient of variation D j =1-E j , calculate the weight according to the coefficient of difference

[0089] In this embodiment, in step S33, the risk level of the security status is evaluated as follows: Among them, 1 is low risk, 2 is medium risk, 3 is high risk, and 4 is extremely high risk; P is the predicted result of potential risk; t1, t2 and t3 are the thresholds for setting risk levels, which can be determined based on historical data or expert opinions.

[0090] In some specific embodiments, the step of S4 includes the following sub-steps:

[0091] S41. When the correlation analysis result reaches or exceeds the warning threshold, the warning mechanism is triggered and a warning message is sent to the user;

[0092] S42. Develop emergency plans based on early warning information and potential risks.

[0093] In this embodiment, in step S41, the triggering of the early warning mechanism:

[0094] Among them, True is triggered; False is not triggered; rth It is a set relevance threshold that can be determined based on historical data or expert opinion.

[0095] In this embodiment, in step S42, after the early warning mechanism is triggered, a corresponding emergency plan is generated according to different risk levels and specific circumstances to help the user take further early warning measures and conduct comprehensive analysis and decision-making.

[0096] In some specific embodiments, the step of S5 includes the following sub-steps:

[0097] S51. Generate a list of real-time monitored environmental data, the results of correlation analysis and the determined risk levels for visual display, including charts, graphs and maps;

[0098] S52. When the real-time monitored environmental data, the results of the correlation analysis, and the determined risk level change, the list is dynamically adjusted and the visual display content is updated;

[0099] S53. Combined with the visually displayed data, continuous monitoring and early warning management of the safety status of coal mine tunnels can be achieved.

[0100] In this embodiment, in step S51, after obtaining the real-time monitored environmental data, the results of the correlation analysis and the determined risk level, a list is created, which includes information such as the real-time monitoring data, the correlation analysis results and the risk level. Each row of the list can represent a monitoring node or a group of monitoring data, and the columns correspond to different data types (such as timestamp, ground pressure value, displacement value, correlation coefficient, risk level, etc.);

[0101] Chart form: Line chart, used to show the changing trend of real-time monitoring data over time, such as the fluctuation of parameters such as ground pressure and displacement; Bar chart, used to compare the risk level or correlation coefficient of different monitoring nodes or different time periods; Pie chart, used to show the distribution of risk levels, such as the proportion of low risk, medium risk and high risk;

[0102] Graphical form: Scatter plot, used to display the results of correlation analysis, such as comparing current monitoring data with previous accident hazard data to observe their distribution and aggregation; heat map, used to display the distribution of risk levels in coal mine tunnels, with color depth indicating risk size;

[0103] Map form: Geographic Information System map, which marks the location of monitoring nodes on the map and uses different colors or icons to indicate risk levels; three-dimensional map, for complex coal mine tunnel structures, three-dimensional maps can be used to more intuitively display the risk distribution and location of monitoring nodes.

[0104] In this embodiment, in step S52, the trigger of dynamic adjustment is:

[0105] Among them, True means triggering; False means not triggering; ΔRL and Δr are the changes in risk level assessment and correlation coefficient respectively; δ RL and δ r They are the preset change thresholds used to determine whether to trigger an update operation.

[0106] It should be noted that by improving the intuitive data visualization display, users can intuitively understand the changes and trends in the tunnels, and can better grasp the safety status and potential risks of coal mine tunnels, which is convenient for users to make decisions and judgments. At the same time, dynamically adjusting lists and updating visual display content can enable users to grasp the latest safety information and risk conditions at any time, ensuring the timeliness and effectiveness of response measures.

[0107] like Figure 2 As shown, a management system for a safety monitoring and early warning management method for a coal mine tunnel includes:

[0108] Monitoring node unit, used to monitor the environmental data inside the coal mine tunnel in real time and transmit the environmental data;

[0109] A data collection module, used to collect environmental data inside the coal mine tunnel;

[0110] A data processing module, used for preprocessing environmental data;

[0111] Similarity analysis module, used to perform similarity analysis between pre-processed environmental data and environmental data from historical accident hazards;

[0112] Risk prediction module, used to simulate and predict the potential risks of sudden situations in coal mine tunnels;

[0113] A risk level assessment module is used to determine the risk level of the safety status of the coal mine tunnel according to the predicted potential risks;

[0114] The early warning module is used to determine the triggering of the early warning mechanism according to the risk level and generate emergency plans based on potential risks;

[0115] The visualization module is used to display real-time environmental data, analytical data and risk levels, and to dynamically adjust the visualization according to changes in data.

[0116] It should be noted that the safety monitoring and early warning management system for coal mine tunnels can implement the steps in the safety monitoring and early warning management method for coal mine tunnels in the above embodiment, and can achieve the same technical effects. Refer to the description in the above embodiment and no detailed description will be given here.

[0117] The above is based on the ideal embodiment of the present invention. Through the above description, it is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the attached claims rather than the above description, and it is intended to include all changes within the meaning and scope of the equivalent elements of the claims. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0118] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A safety monitoring and early warning management method for coal mine tunnels, characterized in that: The following steps are involved: S1. Deploy multiple monitoring nodes in the coal mine tunnel; S2, collect real-time environmental data of multiple monitoring nodes in coal mine tunnels, and pre-process the collected environmental data; S3. Analyze the similarity between the pre-processed environmental data and the environmental data in historical accident hazards in real time, simulate and predict the potential risk of sudden situations in coal mine tunnels, and evaluate the safety status of coal mine tunnels to determine the risk level; S4. Determine the triggering of the early warning mechanism according to the risk level, and the early warning mechanism generates an emergency plan based on the potential risks; S5. Generate a list of real-time environmental data, analytical data and risk levels for visual display, and dynamically adjust the list of real-time environmental data, analytical data and risk levels that are changing to achieve safety monitoring and early warning management.

2. A method for safety monitoring and early warning management of coal mine tunnels according to claim 1, characterized in that: In the step S1, the monitoring nodes include: a variety of sensors for monitoring environmental parameters of coal mine tunnels, and a data transceiver point for transmitting and receiving environmental parameters; The deployment of the monitoring node includes the following steps: S11. Calculate the demand for the monitoring nodes according to the layout and geological conditions of the coal mine tunnels: Among them, N no is the total number of monitoring nodes; L is the length of the coal mine tunnel; W is the width of the coal mine tunnel; S a is the average service area, the total area that needs to be monitored in the coal mine tunnel; C g is the coefficient of geological structure complexity; A h is the influencing factor of accident-prone areas; A e is the influencing factor of the area affected by the engineering activities; S12, optimizing the sensor deployment and calculating the service range R of the data transceiver point ac : Among them, P t is the transmitting power; G t and G r are the transmit antenna and receive antenna gains respectively; λ is the operating wavelength; L0 is the free space path loss; N f is the total noise factor; α is the correction coefficient; S13, ensuring coverage of the entire service range and avoiding blind spots, determining the maximum allowable distance D between adjacent sensors max , and the total number of sensors N in each monitoring node se ; D max =βR ac ; Where β is the correction factor; Among them, A s is the effective monitoring area of ​​a single sensor.

3. A method for safety monitoring and early warning management of coal mine tunnels according to claim 1, characterized in that: The step S2 includes the following sub-steps: S21. Collect real-time environmental data at each monitoring node in the coal mine tunnel, including the ground pressure, displacement, stress and temperature parameters involved; S22. Remove duplicate, incomplete or abnormal data items from the collected environmental data and normalize them to ensure that the data are on the same scale.

4. A method for safety monitoring and early warning management of coal mine tunnels according to claim 1, characterized in that: The step S3 includes the following sub-steps: S31, analyzing the data correlation between the pre-processed environmental data and the environmental data in historical accident hazards; S32. Based on the analysis results, predict the potential risks of sudden situations; S33. Based on the prediction results, the safety status of the coal mine tunnels is evaluated and the risk level is determined.

5. A method for safety monitoring and early warning management of coal mine tunnels according to claim 4, characterized in that: In the step S31, the similarity and correlation between each current environmental parameter data and each environmental parameter data in the historical accident potential hazard are identified, and the correlation coefficient r is calculated: Among them, x is the environmental parameter data after preprocessing; y is the environmental parameter data in historical accident hazards; x i and i are the corresponding observation values; and are the corresponding average values.

6. A safety monitoring and early warning management system and method for coal mine tunnels according to claim 4, characterized in that: In the step S32, the potential risk of an emergency is predicted using a machine learning algorithm; Predict the potential risk of the above emergency happening (logistic regression): Where P(Y=1Z,r) is the probability of an accident given all environmental parameters and the correlation coefficient r; Y=1 is the event of an accident; Z is a feature vector containing all relevant environmental parameters, for example: Z=[z1,z2,...,z n ], where z1 represents ground pressure, z2 represents displacement, and so on; e is the base of the natural logarithm; β0 is the intercept term; β i is the weight coefficient corresponding to each environmental parameter; β z is the weight coefficient of the correlation coefficient r; z i It is the specific value of the corresponding environmental parameter.

7. A method for safety monitoring and early warning management of coal mine tunnels according to claim 4, characterized in that: In the step S33, the risk level of the safety status is evaluated as follows: Among them, 1 is low risk, 2 is medium risk, 3 is high risk, and 4 is extremely high risk; P is the predicted result of potential risk; t1, t2 and t3 are the thresholds for setting risk levels.

8. A method for safety monitoring and early warning management of coal mine tunnels according to claim 1, characterized in that: The step S4 includes the following sub-steps: S41. When the correlation analysis result reaches or exceeds the warning threshold, the warning mechanism is triggered and a warning message is sent to the user; S42. Develop emergency plans based on early warning information and potential risks.

9. A method for safety monitoring and early warning management of coal mine tunnels according to claim 1, characterized in that: The step S5 includes the following sub-steps: S51. Generate a list of real-time monitored environmental data, the results of correlation analysis and the determined risk levels for visual display, including charts, graphs and maps; S52. When the real-time monitored environmental data, the results of the correlation analysis, and the determined risk level change, the list is dynamically adjusted and the visual display content is updated; S53. Combined with the visually displayed data, continuous monitoring and early warning management of the safety status of coal mine tunnels can be achieved.

10. A management system based on a safety monitoring and early warning management method for a coal mine tunnel according to any one of claims 1 to 9, characterized in that: include: Monitoring node unit, used to monitor the environmental data inside the coal mine tunnel in real time and transmit the environmental data; A data collection module, used to collect environmental data inside the coal mine tunnel; A data processing module, used for preprocessing environmental data; Similarity analysis module, used to perform similarity analysis between pre-processed environmental data and environmental data from historical accident hazards; Risk prediction module, used to simulate and predict the potential risks of sudden situations in coal mine tunnels; A risk level assessment module is used to determine the risk level of the safety status of the coal mine tunnel according to the predicted potential risks; The early warning module is used to determine the triggering of the early warning mechanism according to the risk level and generate emergency plans based on potential risks; The visualization module is used to display real-time environmental data, analytical data and risk levels, and to dynamically adjust the visualization according to changes in data.

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