A mine safety monitoring system and method for confined spaces in mines

Abstract

This invention discloses a mine safety monitoring system and method for confined spaces in mines, belonging to the field of mine safety management. The method includes: using UWB positioning technology to locate workers entering the confined space and obtaining their coordinates within the confined space; marking discrete points of worker activity within the confined space and calculating discrete weights based on the number of discrete points within the scanning window; using these discrete weights to calculate safety assessment weights for key monitoring areas, regular monitoring areas, and non-key monitoring areas; outputting predicted environmental parameters; and predicting the safety risk status within the confined space. The system includes a monitoring system, a UWB positioning system, and a monitoring center. This invention significantly improves the accuracy of safety monitoring, risk identification capabilities, and emergency response speed within confined spaces in mines.

Description

Technical Field

[0001] This invention relates to the field of mine safety management, and specifically to a mine safety monitoring system and method for confined spaces in mines. Background Technology

[0002] Safety monitoring in confined spaces in mines (such as roadways, tunnels, and goafs) faces challenges including complex environments, high noise levels in sensor data, and stringent real-time requirements for risk assessment. Furthermore, risk prediction necessitates assessments of future safety risks. Existing technologies largely rely on single sensors or static thresholds, resulting in low monitoring accuracy, high false alarm rates, and an inability to adapt to dynamic environmental changes. Moreover, as mines develop and are exploited, the density of personnel activity in different areas within the confined space changes in real time. Areas with high activity density are inevitably key development areas, leading to a higher probability of mine accidents. Therefore, it is necessary to assign safety risk assessment weights to areas with varying activity densities to prevent low-risk areas from influencing the assessment of high-risk areas, thereby affecting the overall accuracy of safety risk prediction within the confined space.

[0003] Therefore, there is an urgent need to propose a new mine safety monitoring system and method for confined spaces in mines. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention provides a mine safety monitoring system and method for confined spaces in mines. Based on the density of personnel activity in different areas within the confined space as a reference, a comprehensive assessment of safety risks is conducted, with the assessment focus shifting towards areas with high personnel activity density.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0006] A method for monitoring mine safety in confined spaces is provided, comprising:

[0007] Step S1: Construct a 3D model of the mine based on 3D point cloud scanning data, mark the entrance and exit locations of the confined space, and establish the 3D space of the confined space according to the size parameters of the confined space; use UWB positioning technology to locate the workers entering the confined space and obtain the positioning coordinates of the workers in the confined space.

[0008] Step S2: Based on the location coordinates of the staff in the confined space, mark the discrete points of the staff's activities in the confined space in the 3D model, use the scanning window to scan in the confined space, calculate the discrete weight according to the number of discrete points in the scanning window, and filter the key areas, regular areas and non-key areas in the confined space.

[0009] Step S3: Merge adjacent key areas, regular areas and non-key areas within the confined space, and divide the confined space into key monitoring areas, regular monitoring areas and non-key monitoring areas. Calculate the safety assessment weights of key monitoring areas, regular monitoring areas and non-key monitoring areas using discrete weights.

[0010] Step S4: Based on the monitoring system deployed in the confined space, collect environmental parameters within the confined space, and use the Kalman filter algorithm to predict the environmental parameters within the confined space, outputting the predicted environmental parameters;

[0011] Step S5: Based on the predicted environmental parameters, calculate the safety risk coefficients for key monitoring areas, routine monitoring areas, and non-key monitoring areas within the confined space. Combined with the safety assessment weights, calculate the safety risk indicators under different environmental parameters within the confined space and predict the safety risk status within the confined space.

[0012] Further, step S1 includes:

[0013] Step S11: Construct a 3D model of the mine based on the 3D point cloud scan data, establish a 3D coordinate system on the 3D model, mark the entrance and exit positions of the confined space on the 3D model, and obtain the position coordinates (x, y, y) of the entrance and exit. n ,y n ,z n ), where n is the number of the confined space;

[0014] Step S12: Establish a three-dimensional space of the confined space on the three-dimensional model based on the size parameters of the confined space; starting from the entrance and exit of the confined space, uniformly deploy several UWB base stations along the confined space, and obtain the coordinates (x, y, y) of the UWB base stations in the three-dimensional coordinate system. i ,y i ,z i ), where i is the number of the UWB base station. Staff members carrying UWB positioning components enter the confined space from the entrance / exit.

[0015] Step S13: The UWB positioning component sends positioning signals to the UWB base station in the confined space in real time. After receiving the positioning signal, the UWB base station immediately sends a request signal to the monitoring center. After receiving the request signal, the monitoring center locates the current position of the staff and outputs the staff's positioning coordinates.

[0016] Furthermore, the methods for locating staff include:

[0017] Step S131: Calculate the straight-line distance l between the UWB positioning component and the UWB base station using the transmission time t1 and reception time t2 of the positioning signal. i=λ×(t2-t1), where λ is the signal transmission speed; This yields the straight-line distance data {l1,l2,…,l...} between the UWB positioning component and each UWB base station. I}, where I is the number of UWB base stations, l I The straight-line distance between the UWB positioning component and the I-th UWB base station;

[0018] Step S132: Based on the order in which the monitoring center receives the request signals, calculate the positioning coordinates (X) of the UWB positioning component using the distances l1, l2, and l3 between the UWB base stations and the UWB positioning component corresponding to the first three received request signals. u ,Y u Z u );

[0019]

[0020] Where (x1,y1,z1), (x2,y2,z2), and (x3,y3,z3) are the coordinates of the UWB base stations corresponding to the first three request signals, respectively.

[0021] Step S133: Positioning coordinates (X) of the UWB positioning component u ,Y u Z u () serves as the positioning coordinates for staff within a confined space.

[0022] Further, step S2 includes:

[0023] Step S21: Based on the positioning coordinates (X) u ,Y u Z u Mark the discrete points where workers move within the confined space in the 3D model;

[0024] Step S22: Define a scanning window for a unit cube, and use the scanning window to scan within the confined space, recording the number α of discrete points within the scanning window at each scanning position. v And calculate the discrete weight p at each scan position. v ;

[0025]

[0026] Where V is the number of discrete points, and v is the scan position number;

[0027] Step S23: Update in real time based on the changes in the number of workers entering the confined space, the number of discrete points, and the discrete weight of each scanning position;

[0028] Step S24: Set the reference range (p1, p2) for the discrete weights, and assign the discrete weights p1, p2, and p2 to each scan position. v Compare with the reference range (p1, p2);

[0029] If p v If p2 > 2, then the scan position v is determined to be the key area;

[0030] If p1≤p v If p2 ≤ p2, then the scan position v is determined to be a regular region;

[0031] If p1 > p v If so, the scanned position v is determined to be a non-key area.

[0032] Further, step S3 includes:

[0033] Step S31: Based on the relative positions between key areas, regular areas and non-key areas, merge two adjacent areas of the same type to obtain the distribution of key areas, regular areas and non-key areas within the confined space after merging;

[0034] Step S32: Based on the two endpoints of the fused key area, regular area and non-key area, draw perpendicular lines to the three-dimensional space of the confined space, and use the two perpendicular lines as the boundary to divide the confined space into key monitoring area, regular monitoring area and non-key monitoring area;

[0035] Step S33: Calculate the safety assessment weight P for key monitoring areas, routine monitoring areas, and non-key monitoring areas based on the discrete weights within the region. w ;

[0036]

[0037] Where w represents the key monitoring area, routine monitoring area, or non-key monitoring area, s represents the discrete weight number contained within the key monitoring area, routine monitoring area, or non-key monitoring area, S represents the number of discrete weights, and p s Let be the s-th discrete weight.

[0038] Further, step S4 includes:

[0039] Step S41: Based on the monitoring system deployed within the confined space, collect data on gas concentration, oxygen concentration, ground settlement, and spatial deformation within the confined space to form an environmental parameter matrix z for the confined space. k k represents the time when environmental parameters were collected;

[0040] Step S42: Use the Kalman filter algorithm to predict the environmental parameters in the confined space, and use the environmental parameters collected at time k-1 to predict the environmental parameters at time k.

[0041] Further, step S42 includes:

[0042] Step S421: Calculate the mean and standard deviation of each environmental parameter, and establish the state matrix of the environmental parameters;

[0043] Step S422: Utilize the state matrix x at time k-1 k-1 Construct state prediction equations and covariance prediction equations;

[0044]

[0045] in, Let F be the predicted state matrix at time k, B be the state transition matrix, and U be the control input matrix. k-1 Let k be the control matrix at time k-1. Let P be the prediction covariance matrix at time k. k-1 Let Q be the covariance matrix at time k-1, and let Q be the process noise covariance matrix.

[0046] Step S423: Based on the predicted state matrix Establish the prediction equation for environmental parameters at time k;

[0047]

[0048] Where H is the observation matrix, which maps the predicted state matrix to the predicted parameter matrix. middle;

[0049] Step S424: Calculate the Kalman gain R is the noise covariance matrix of the sensors in the monitoring system;

[0050] Step S425: Utilize Kalman gain K k Establish the state update equation and the covariance update equation;

[0051]

[0052] in, For the updated state matrix at time k, Let I be the covariance matrix at the updated time k, and let I be the identity matrix.

[0053] Step S426: Output the environmental parameter matrix at time k predicted using the Kalman filter algorithm. This allows us to obtain the predicted environmental parameters at time k.

[0054] Further, step S5 includes:

[0055] Step S51: Based on the predicted environmental parameters at time k, calculate the safety risk coefficient W for the key monitoring area, the regular monitoring area, and the non-key monitoring area within the confined space at time k. 1 W 2 W 3 ;

[0056]

[0057] Where A represents the number of sensors in the key monitoring area, a represents the sensor number in the key monitoring area, B represents the number of sensors in the routine monitoring area, b represents the sensor number in the routine monitoring area, C represents the number of sensors in the non-key monitoring area, and c represents the sensor number in the non-key monitoring area. To monitor environmental parameters collected by sensors in key monitoring areas, These are environmental parameters collected by sensors within the routine monitoring area. For environmental parameters collected by sensors in non-key monitoring areas, z0 is the threshold value of the environmental parameter;

[0058] Step S52: Calculate the safety risk coefficients of different environmental parameters for key monitoring areas, routine monitoring areas, and non-key monitoring areas. e represents the type of environmental parameter; based on the safety risk coefficient. Safety risk indicators under different environmental parameters within a confined space and safety assessment weight calculation P1, P2, and P3 are the safety assessment weights for key monitoring areas, routine monitoring areas, and non-key monitoring areas, respectively.

[0059] Step S53: Set security risk indicator thresholds like If the e-th environmental parameter in the confined space is too high, it is determined that there is a safety risk; otherwise, the e-th environmental parameter in the confined space is determined to be normal.

[0060] A mine safety monitoring system for confined spaces in mines is provided, which executes the aforementioned mine safety monitoring method for confined spaces in mines, comprising:

[0061] The monitoring system includes several environmental parameter acquisition sensors, including a gas concentration sensor for acquiring gas concentration in the confined space, an oxygen concentration sensor for acquiring oxygen concentration in the confined space, a displacement sensor for acquiring ground subsidence data, and a deformation sensor for acquiring spatial deformation data.

[0062] UWB positioning system, including UWB positioning base stations installed in confined spaces and UWB positioning components carried by staff;

[0063] The monitoring center communicates with the UWB positioning system and monitoring system.

[0064] The beneficial effects of this invention are as follows: This invention utilizes UWB positioning technology to locate the activities of workers within confined spaces in mines, thereby obtaining the activity density of workers in different areas within the confined space. This identifies key areas for safety risk monitoring and assessment, ensuring that safety risk index calculations focus on environmental parameters related to high worker activity, avoiding the over-averaging of high-risk areas by low-risk indicators in sparsely populated areas, which would affect the accuracy of safety risk prediction within confined spaces. This invention employs a Kalman filter algorithm to fuse the collected environmental parameters and predict future environmental parameters within the confined space, thus achieving safety risk assessment and prediction within the confined space. This significantly improves the accuracy of safety monitoring, risk identification capabilities, and emergency response speed within confined spaces in mines. Attached Figure Description

[0065] Figure 1 A flowchart for mine safety monitoring methods in confined spaces.

[0066] Figure 2 This is a schematic diagram illustrating the principle of scanning within a confined space using a scanning window.

[0067] Figure 3 A schematic diagram illustrating the principle of dividing the monitoring area. Detailed Implementation

[0068] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0069] like Figure 1 As shown, a mine safety monitoring method for confined spaces in a mine includes:

[0070] Step S1: Construct a 3D model of the mine based on 3D point cloud scan data, mark the entrance and exit locations of the confined space, and establish the 3D space of the confined space according to its size parameters; use UWB positioning technology to locate workers entering the confined space and obtain their coordinates within the confined space. Step S1 specifically includes:

[0071] Step S11: Construct a 3D model of the mine based on the 3D point cloud scan data, establish a 3D coordinate system on the 3D model, mark the entrance and exit positions of the confined space on the 3D model, and obtain the position coordinates (x, y, y) of the entrance and exit. n ,y n ,z n), where n is the number of the confined space;

[0072] Step S12: Establish a three-dimensional space of the confined space on the three-dimensional model based on the size parameters of the confined space; starting from the entrance and exit of the confined space, uniformly deploy several UWB base stations along the confined space, and obtain the coordinates (x, y, y) of the UWB base stations in the three-dimensional coordinate system. i ,y i ,z i ), where i is the number of the UWB base station. Staff members carrying UWB positioning components enter the confined space from the entrance / exit.

[0073] Step S13: The UWB positioning component sends positioning signals to the UWB base station within the confined space in real time. Upon receiving the positioning signal, the UWB base station immediately sends a request signal to the monitoring center. Upon receiving the request signal, the monitoring center locates the worker's current position and outputs the worker's coordinates. The worker's positioning methods include:

[0074] Step S131: Calculate the straight-line distance l between the UWB positioning component and the UWB base station using the transmission time t1 and reception time t2 of the positioning signal. i =λ×(t2-t1), where λ is the signal transmission speed; This yields the straight-line distance data {l1,l2,…,l...} between the UWB positioning component and each UWB base station. I}, where I is the number of UWB base stations, l I The straight-line distance between the UWB positioning component and the I-th UWB base station;

[0075] Step S132: Based on the order in which the monitoring center receives the request signals, calculate the positioning coordinates (X) of the UWB positioning component using the distances l1, l2, and l3 between the UWB base stations and the UWB positioning component corresponding to the first three received request signals. u ,Y u Z u );

[0076]

[0077] Where (x1,y1,z1), (x2,y2,z2), and (x3,y3,z3) are the coordinates of the UWB base stations corresponding to the first three request signals, respectively.

[0078] The sooner the monitoring center receives the request signal from the UWB base station, the lower the response delay between receiving the positioning signal and sending the request signal, the lower the failure rate of the UWB base station, and the closer the UWB base station is to the UWB positioning component, the more accurate the calculated positioning coordinates of the UWB positioning component.

[0079] Step S133: Positioning coordinates (X) of the UWB positioning component u ,Y u Z u () serves as the positioning coordinates for staff within a confined space.

[0080] S2: Based on the worker's location coordinates within the confined space, mark the discrete points of the worker's activities within the confined space in the 3D model. Use a scanning window to scan the confined space. Calculate the discrete weights based on the number of discrete points within the scanning window, and then filter out key areas, regular areas, and non-key areas within the confined space. Step S2 specifically includes:

[0081] Step S21: Based on the positioning coordinates (X) u ,Y u Z u Mark the discrete points where workers move within the confined space in the 3D model;

[0082] Step S22: Define a scanning window for a unit cube, and use the scanning window to scan within the confined space, recording the number α of discrete points within the scanning window at each scanning position. v And calculate the discrete weight p at each scan position. v ;

[0083]

[0084] Where V is the number of discrete points, and v is the scan position number;

[0085] Step S23: Update in real time based on the changes in the number of workers entering the confined space, the number of discrete points, and the discrete weight of each scanning position;

[0086] like Figure 2 As shown in the example, this embodiment takes mine roadways and tunnels as an example. When workers enter a confined space to work, the magnitude of the discrete weight value can represent the number of times the workers need to reach different locations in the confined space. The magnitude of the discrete weight value represents the distribution density of discrete points. The larger the discrete weight value, the more times the workers in the tunnel reach that location, and the more important the safety monitoring of that area is.

[0087] In this embodiment, fixed UWB base stations (spaced 50-100 meters apart, depending on the environment) are installed on the top or sidewall of the tunnel or alleyway. Personnel entering the tunnel or alleyway wear UWB positioning components (explosion-proof type) with UWB tags. The linkage between the UWB base stations and the UWB positioning components provides strong anti-multipath interference capability and high accuracy (0.1-0.3 meters). The positioning signal update frequency of the UWB positioning components is maintained at 10Hz to ensure dynamic tracking.

[0088] Step S24: Set the reference range (p1, p2) for the discrete weights, and assign the discrete weights p1, p2, and p2 to each scan position. v Compare with the reference range (p1, p2);

[0089] If p v If p2 > 2, then the scan position v is determined to be the key area;

[0090] If p1≤p v If p2 ≤ p2, then the scan position v is determined to be a regular region;

[0091] If p1 > p v If so, the scanned position v is determined to be a non-key area.

[0092] S3: Merge adjacent key areas, regular areas, and non-key areas within the confined space, and divide the confined space into key monitoring areas, regular monitoring areas, and non-key monitoring areas. Calculate the safety assessment weights for key monitoring areas, regular monitoring areas, and non-key monitoring areas using discrete weights. Step S3 specifically includes:

[0093] Step S31: Based on the relative positions between key areas, regular areas and non-key areas, merge two adjacent areas of the same type to obtain the distribution of key areas, regular areas and non-key areas within the confined space after merging;

[0094] Step S32: Based on the two endpoints of the fused key area, regular area and non-key area, draw perpendicular lines to the three-dimensional space of the confined space, and use the two perpendicular lines as the boundary to divide the confined space into key monitoring area, regular monitoring area and non-key monitoring area;

[0095] like Figure 3 As shown in the figure, this embodiment takes a mine roadway or tunnel as an example. The scanning window scans along the depth direction of the roadway or tunnel. The continuous scanning window is not fully shown in the figure. The length of the scanning window is set according to the actual height of the roadway or tunnel, and is generally at least more than half the height of the roadway or tunnel. Based on the boundary of the key area, regular area and non-key area after fusion, the key monitoring area, regular monitoring area and non-key monitoring area in the roadway or tunnel are divided by the roadway or tunnel section where the boundary is located.

[0096] Step S33: Calculate the safety assessment weight P for key monitoring areas, routine monitoring areas, and non-key monitoring areas based on the discrete weights within the region. w ;

[0097]

[0098] Where w represents the key monitoring area, routine monitoring area, or non-key monitoring area, s represents the discrete weight number contained within the key monitoring area, routine monitoring area, or non-key monitoring area, S represents the number of discrete weights, and p s Let be the s-th discrete weight.

[0099] S4: Based on the monitoring system deployed within the confined space, collect environmental parameters within the confined space, and use the Kalman filter algorithm to predict the environmental parameters within the confined space, outputting the predicted environmental parameters. Step S4 specifically includes:

[0100] Step S41: Based on the monitoring system deployed within the confined space, collect data on gas concentration, oxygen concentration, ground settlement, and spatial deformation within the confined space to form an environmental parameter matrix z for the confined space. k k represents the time when environmental parameters were collected;

[0101] Step S42: Predict environmental parameters within the confined space using the Kalman filter algorithm, and predict the environmental parameters at time k using the environmental parameters collected at time k-1. Specific methods include:

[0102] Step S421: Calculate the mean and standard deviation of each environmental parameter, and establish the state matrix of the environmental parameters;

[0103] Step S422: Utilize the state matrix x at time k-1 k-1 Construct state prediction equations and covariance prediction equations;

[0104]

[0105] in, Let F be the predicted state matrix at time k, B be the state transition matrix, and U be the control input matrix. k-1 Let k be the control matrix at time k-1. Let P be the prediction covariance matrix at time k. k-1 Let Q be the covariance matrix at time k-1, and let Q be the process noise covariance matrix.

[0106] The control matrix contains key parameters for controlling environmental parameters. For example, the key parameters for controlling gas and oxygen concentrations are the power of ventilation equipment in the roadway or tunnel. The key parameters for controlling ground settlement data are the drainage efficiency of groundwater in the roadway or tunnel, the depth and width of drainage ditches on both sides, etc. The key parameters for controlling spatial deformation data are vibration load, spatial support thickness and strength data, etc.

[0107] Step S423: Based on the predicted state matrix Establish the prediction equation for environmental parameters at time k;

[0108]

[0109] Where H is the observation matrix, which maps the predicted state matrix to the predicted parameter matrix. middle;

[0110] Step S424: Calculate the Kalman gain R is the noise covariance matrix of the sensors in the monitoring system;

[0111] Step S425: Utilize Kalman gain K k Establish the state update equation and the covariance update equation;

[0112]

[0113] in, For the updated state matrix at time k, Let I be the covariance matrix at the updated time k, and let I be the identity matrix.

[0114] Step S426: Output the environmental parameter matrix at time k predicted using the Kalman filter algorithm. This allows us to obtain the predicted environmental parameters at time k.

[0115] S5: Based on the predicted environmental parameters, calculate the safety risk coefficients for key monitoring areas, routine monitoring areas, and non-key monitoring areas within the confined space. Combined with safety assessment weights, calculate the safety risk indicators under different environmental parameters within the confined space, and predict the safety risk status within the confined space. Step S5 specifically includes:

[0116] Step S51: Based on the predicted environmental parameters at time k, calculate the safety risk coefficient W for the key monitoring area, the regular monitoring area, and the non-key monitoring area within the confined space at time k. 1 W 2 W 3 ;

[0117]

[0118] Where A represents the number of sensors in the key monitoring area, a represents the sensor number in the key monitoring area, B represents the number of sensors in the routine monitoring area, b represents the sensor number in the routine monitoring area, C represents the number of sensors in the non-key monitoring area, and c represents the sensor number in the non-key monitoring area. To monitor environmental parameters collected by sensors in key monitoring areas, These are environmental parameters collected by sensors within the routine monitoring area. For environmental parameters collected by sensors in non-key monitoring areas, z0 is the threshold value of the environmental parameter;

[0119] Step S52: Calculate the safety risk coefficients of different environmental parameters for key monitoring areas, routine monitoring areas, and non-key monitoring areas. e represents the type of environmental parameter; based on the safety risk coefficient. Safety risk indicators under different environmental parameters within a confined space and safety assessment weight calculation P1, P2, and P3 are the safety assessment weights for key monitoring areas, routine monitoring areas, and non-key monitoring areas, respectively.

[0120] Step S53: Set security risk indicator thresholds like If the e-th environmental parameter in the confined space is too high, it is determined that there is a safety risk; otherwise, the e-th environmental parameter in the confined space is determined to be normal.

[0121] Specifically, in this embodiment, regarding the risk of gas explosion, the following conditions must be met: first, the gas concentration must be within the explosion limit, generally 5%-16%; second, the oxygen concentration in the gas mixture must be no less than 12%; and third, there must be a high-temperature ignition source with sufficient energy, generally 650℃-750℃. The higher the gas concentration and the higher the oxygen concentration, the greater the risk of a gas explosion in the confined space, and the greater the risk of ignition in areas with denser personnel activity. During the gas explosion risk assessment, safety risk indicators calculated based on gas and oxygen concentrations are used simultaneously. The threshold for gas concentration is set at 5%, and the threshold for oxygen concentration is set at 12%, to comprehensively assess the risk of a gas explosion. When the safety risk indicators corresponding to both gas and oxygen concentrations are determined to indicate a safety risk, a risk warning for a possible gas explosion in the confined space is issued.

[0122] A mine safety monitoring system for confined spaces in mines, comprising:

[0123] The monitoring system includes several environmental parameter acquisition sensors, including a gas concentration sensor for acquiring gas concentration in the confined space, an oxygen concentration sensor for acquiring oxygen concentration in the confined space, a displacement sensor for acquiring ground subsidence data, and a deformation sensor for acquiring spatial deformation data.

[0124] UWB positioning system, including UWB positioning base stations installed in confined spaces and UWB positioning components carried by staff;

[0125] The monitoring center communicates with the UWB positioning system and monitoring system.

[0126] This invention utilizes UWB positioning technology to locate the activities of workers within confined spaces in mines, thereby obtaining the activity density of workers in different areas of the confined space. This identifies key areas for safety risk monitoring and assessment, ensuring that safety risk index calculations focus on environmental parameters related to high worker activity, avoiding the over-averaging of high-risk areas by low-risk indicators in sparsely populated areas, which could negatively impact the accuracy of safety risk prediction within confined spaces. This invention employs a Kalman filter algorithm to fuse the collected environmental parameters and predict future environmental parameters within the confined space, thus achieving safety risk assessment and prediction within confined spaces. This significantly improves the accuracy of safety monitoring, risk identification capabilities, and emergency response speed within confined spaces in mines.

Claims

1. A method for monitoring mine safety in confined spaces, characterized in that, include: Step S1: Construct a 3D model of the mine based on 3D point cloud scanning data, mark the entrance and exit locations of the confined space, and establish the 3D space of the confined space according to the size parameters of the confined space; use UWB positioning technology to locate the workers entering the confined space and obtain the positioning coordinates of the workers in the confined space. Step S2: Based on the location coordinates of the staff in the confined space, mark the discrete points of the staff's activities in the confined space in the 3D model, use the scanning window to scan in the confined space, calculate the discrete weight according to the number of discrete points in the scanning window, and filter the key areas, regular areas and non-key areas in the confined space. Step S3: Merge adjacent key areas, regular areas and non-key areas within the confined space, and divide the confined space into key monitoring areas, regular monitoring areas and non-key monitoring areas. Calculate the safety assessment weights of key monitoring areas, regular monitoring areas and non-key monitoring areas using discrete weights. Step S4: Based on the monitoring system deployed in the confined space, collect environmental parameters within the confined space, and use the Kalman filter algorithm to predict the environmental parameters within the confined space, outputting the predicted environmental parameters; Step S5: Based on the predicted environmental parameters, calculate the safety risk coefficients of key monitoring areas, regular monitoring areas and non-key monitoring areas within the confined space, and combine them with the safety assessment weights to calculate the safety risk indicators under different environmental parameters within the confined space, and predict the safety risk status within the confined space. Step S1 includes: Step S11: Construct a 3D model of the mine based on the 3D point cloud scan data, establish a 3D coordinate system on the 3D model, mark the entrance and exit positions of the confined space on the 3D model, and obtain the position coordinates of the entrance and exit. , n The numbering of the confined space; Step S12: Establish a three-dimensional space of the confined space on the three-dimensional model based on the size parameters of the confined space; starting from the entrance and exit of the confined space, uniformly deploy several UWB base stations along the confined space, and obtain the coordinates of the UWB base stations in the three-dimensional coordinate system. , i The UWB base station is numbered, and staff carrying UWB positioning components enter the confined space from the entrance / exit. Step S13: The UWB positioning component sends positioning signals to the UWB base station in the confined space in real time. After receiving the positioning signal, the UWB base station immediately sends a request signal to the monitoring center. After receiving the request signal, the monitoring center locates the current position of the staff and outputs the staff's positioning coordinates. Step S2 includes: Step S21: Based on positioning coordinates Mark the discrete points where workers move within the confined space in the 3D model; Step S22: Define a scanning window for a unit cube, and use the scanning window to scan within the confined space, recording the number of discrete points within the scanning window at each scanning position. And calculate the discrete weights for each scan position. ; ； in, V The number of discrete points. v This refers to the scan location number; Step S23: Update in real time based on the changes in the number of workers entering the confined space, the number of discrete points, and the discrete weight of each scanning position; Step S24: Set the reference range for discrete weights Discrete weights for each scan position Reference range Compare; like Then determine the scanning position. v Key areas; like Then determine the scanning position. v This is a regular area; like Then determine the scanning position. v This is a non-priority area.

2. The mine safety monitoring method for confined spaces in mines according to claim 1, characterized in that, The methods for locating the staff include: Step S131: Utilize the transmission time of the positioning signal With the timing of receiving the positioning signal Calculate the straight-line distance between the UWB positioning component and the UWB base station. , The signal transmission speed is used to obtain the straight-line distance data between the UWB positioning component and each UWB base station. , I The number of UWB base stations, For the distance of the UWB positioning component to the first I The straight-line distance between UWB base stations; Step S132: Based on the order in which the monitoring center receives the request signals, use the distance between the UWB base station and the UWB positioning component corresponding to the first three received request signals. Calculate the positioning coordinates of the UWB positioning component ; ； in, These are the coordinates of the UWB base stations corresponding to the first three request signals; Step S133: Positioning coordinates of the UWB positioning component Used as a coordinate system for staff within a confined space.

3. The mine safety monitoring method for confined spaces in mines according to claim 1, characterized in that, Step S3 includes: Step S31: Based on the relative positions between key areas, regular areas and non-key areas, merge two adjacent areas of the same type to obtain the distribution of key areas, regular areas and non-key areas within the confined space after merging; Step S32: Based on the two endpoints of the fused key area, regular area and non-key area, draw perpendicular lines to the three-dimensional space of the confined space, and use the two perpendicular lines as the boundary to divide the confined space into key monitoring area, regular monitoring area and non-key monitoring area; Step S33: Calculate the safety assessment weights for key monitoring areas, routine monitoring areas, and non-key monitoring areas based on the discrete weights within the region. ; ； in, w Areas categorized as key monitoring areas, routine monitoring areas, or non-key monitoring areas s For key monitoring areas, routine monitoring areas, or non-key monitoring areas, a discrete weight number is assigned. S The number of discrete weights, For the first s Each discrete weight.

4. The mine safety monitoring method for confined spaces in mines according to claim 3, characterized in that, Step S4 includes: Step S41: Based on the monitoring system deployed within the confined space, collect data on gas concentration, oxygen concentration, ground subsidence, and spatial deformation within the confined space to form an environmental parameter matrix for the confined space. , k The time when environmental parameters were collected; Step S42: Predict environmental parameters within the confined space using the Kalman filter algorithm. k Prediction of environmental parameters collected at time -1 k Environmental parameters at any given time.

5. The mine safety monitoring method for confined spaces in mines according to claim 4, characterized in that, Step S42 includes: Step S421: Calculate the mean and standard deviation of each environmental parameter, and establish the state matrix of the environmental parameters; Step S422: Utilize time k -1 state matrix Construct state prediction equations and covariance prediction equations; ； ； in, For a moment k The predicted state matrix, Here is the state transition matrix. To control the input matrix, For a moment k -1 control matrix, For a moment k The predicted covariance matrix, For a moment k The covariance matrix of -1 The process noise covariance matrix; Step S423: Based on the predicted state matrix Establishment Time k Prediction equations for environmental parameters; ； in, The observation matrix is ​​used to map the predicted state matrix to the predicted parameter matrix. middle; Step S424: Calculate the Kalman gain ; To monitor the noise covariance matrix of sensors within the system; Step S425: Utilize Kalman gain Establish the state update equation and the covariance update equation; ； ； in, For the moment of update k The state matrix, For the moment of update k The covariance matrix, It is the identity matrix; Step S426: Output the prediction using the Kalman filter algorithm k Environmental parameter matrix at time Thus, the prediction is obtained. k Environmental parameters at any given time.

6. The mine safety monitoring method for confined spaces in mines according to claim 5, characterized in that, Step S5 includes: Step S51: Based on the prediction k Environmental parameters at time, calculation k Safety risk coefficients in key monitoring areas, routine monitoring areas, and non-key monitoring areas within a confined space. ; ； in, A To determine the number of sensors in the key monitoring area, a The sensor numbering for the key monitoring area. B The number of sensors in the routine monitoring area. b This refers to the sensor number within the routine monitoring area. C This refers to the number of sensors in non-key monitoring areas. c This refers to the sensor numbering in non-key monitoring areas. To monitor environmental parameters collected by sensors in key monitoring areas, These are environmental parameters collected by sensors within the routine monitoring area. These are environmental parameters collected by sensors in non-key monitoring areas. z 0 represents the threshold value for environmental parameters; Step S52: Calculate the safety risk coefficients of different environmental parameters for key monitoring areas, routine monitoring areas, and non-key monitoring areas. , e Types of environmental parameters; based on safety risk coefficients Safety risk indicators under different environmental parameters within a confined space and safety assessment weight calculation , The safety assessment weights are respectively for key monitoring areas, routine monitoring areas, and non-key monitoring areas; Step S53: Set security risk indicator thresholds ,like Then determine the first one in the confined space e If the environmental parameters are too high, there is a safety risk; otherwise, the confined space is deemed to be in a confined space with the first environmental parameter being too high. e All environmental parameters are normal.

7. A mine safety monitoring system for confined spaces in mines, comprising the mine safety monitoring method for confined spaces as described in any one of claims 1-6, characterized in that, include: The monitoring system includes several environmental parameter acquisition sensors, including a gas concentration sensor for acquiring gas concentration in the confined space, an oxygen concentration sensor for acquiring oxygen concentration in the confined space, a displacement sensor for acquiring ground subsidence data, and a deformation sensor for acquiring spatial deformation data. UWB positioning system, including UWB positioning base stations installed in confined spaces and UWB positioning components carried by staff; The monitoring center communicates with the UWB positioning system and monitoring system.

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