Remote mine monitoring method, monitoring device and remote mine monitoring system

Abstract

The application provides a remote mine monitoring method, a monitoring device and a remote mine monitoring system. The method comprises the following steps: receiving a target data packet, the target data packet being formed by an edge UPF based on environment data and worker data, and the target data packet being used to draw a monitoring image of a work area; assigning a first gray value and a second gray value to a pixel grid of a plurality of to-be-drawn images according to the target data packet to obtain a monitoring image, the first gray value being used to represent whether the environment of the work area is safe, the second gray value being used to represent whether the workers in the work area are healthy, and the pixel grid being a sub-image obtained by segmenting the to-be-drawn image; inputting each monitoring image into a preset classifier model, and determining the safety level of each work area according to the output result, the safety level being used to represent whether the work area is safe. The method solves the problems of low recognition monitoring efficiency and difficulty in comprehensively determining the overall safety of the current mine.

Description

Technical Field

[0001] This invention relates to the field of mine safety supervision technology, and more specifically, to a remote mine monitoring method, monitoring device, computer-readable storage medium, and remote mine monitoring system. Background Technology

[0002] In the existing energy industry, coal will remain one of the main energy sources for a considerable period of time. Coal production primarily comes from underground mining.

[0003] Current technologies typically rely on monitoring personnel in the monitoring room to visually identify the situation inside the monitor. Firstly, the communication method between the monitor and the monitoring room in current technologies is limited by the limited transmission rate, latency, and reliability of traditional communication systems, which severely restricts transmission efficiency. Monitoring personnel find it difficult to receive monitoring data quickly. However, with the development of mobile communication technology, edge UPF has emerged. Compared to the UPF in traditional 5G, edge UPF is closer to the client, which can meet the diverse and personalized network needs of local area network users and improve transmission rate, providing mine monitoring with the possibility of higher speed and lower latency transmission.

[0004] Even so, existing mine monitoring methods still require manual observation, which is not only inefficient but also makes it difficult to comprehensively assess the overall safety situation of multiple work areas in the mine.

[0005] Therefore, there is an urgent need in this field for a remote mine monitoring method, system, and device based on UPF. Summary of the Invention

[0006] The main objective of this application is to provide a remote mine monitoring method, monitoring device, computer-readable storage medium, and remote mine monitoring system, so as to at least solve the problems of low efficiency and difficulty in comprehensively determining the overall safety status of multiple work areas in the mine by relying on the human eye to identify the situation in the monitoring room in the existing technology.

[0007] To achieve the above objectives, according to one aspect of this application, a remote mine monitoring method is provided. The method includes: receiving a target data packet, the target data packet being constructed by an edge UPF using environmental data and worker data, the target data packet being used to draw a monitoring image of a work area, the environmental data being gas environment-related parameters of the work area underground, and the worker data including health status information and location information of workers in the work area; assigning a first grayscale value and a second grayscale value to the pixel grids of multiple images to be drawn according to the target data packet to obtain the monitoring image, the first grayscale value being used to characterize whether the environment of the work area is safe, the second grayscale value being used to characterize whether the workers in the work area are healthy, the work area corresponding one-to-one with the images to be drawn, and the pixel grid being a sub-image obtained by segmenting the images to be drawn; inputting each of the monitoring images into a preset classifier model, and determining the safety level of each work area according to the output result, the safety level being used to characterize whether the work area is safe.

[0008] Optionally, assigning a first grayscale value and a second grayscale value to the pixel grids of multiple images to be drawn according to the target data packet includes: a decoding step, decoding the target data packet to obtain multiple environmental data and worker data, wherein the environmental data includes methane concentration parameters, oxygen concentration parameters, and carbon dioxide concentration parameters, and the worker data includes location information, worker number, body temperature parameters, heart rate parameters, blood pressure parameters, and blood oxygen parameters; an acquisition step, acquiring any one of the images to be drawn; a first calculation step, calculating the first grayscale value according to the environmental data, wherein the first grayscale value corresponds one-to-one with the image to be drawn; and a second calculation step, based on the... The process involves calculating the second grayscale value from the worker data; repeating the acquisition step, the first calculation step, and the second calculation step at least once in sequence until the first grayscale value and the second grayscale value corresponding to all the work areas are obtained; a first drawing step, adjusting the grayscale value of the corresponding image to be drawn based on the first grayscale value; a second drawing step, determining the target pixel grid based on the location information, and adjusting the grayscale value of the corresponding target pixel grid based on the second grayscale value to obtain the monitoring image, wherein the target pixel grid is the pixel grid corresponding to the work area where a worker exists, and the second grayscale value corresponds one-to-one with the target pixel grid.

[0009] Optionally, calculating the first grayscale value based on the environmental data includes: determining a first weighting parameter, a second weighting parameter, and a third weighting parameter, wherein the first weighting parameter is the weight of the methane concentration parameter, the second weighting parameter is the weight of the oxygen concentration parameter, and the third weighting parameter is the weight of the carbon dioxide concentration parameter; and calculating the weighted average of the methane concentration parameter, the oxygen concentration parameter, and the carbon dioxide concentration parameter based on the first weighting parameter, the second weighting parameter, and the third weighting parameter to obtain the first grayscale value.

[0010] Optionally, the second grayscale value is calculated based on the worker data. The method includes: determining a fourth weighting parameter, a fifth weighting parameter, a sixth weighting parameter, and a seventh weighting parameter, wherein the fourth weighting parameter is the weight of the body temperature parameter, the fifth weighting parameter is the weight of the heart rate parameter, the sixth weighting parameter is the weight of the blood pressure parameter, and the seventh weighting parameter is the weight of the blood oxygen parameter; calculating a weighted average of the body temperature parameter, the heart rate parameter, the blood pressure parameter, and the blood oxygen parameter based on the fourth weighting parameter, the fifth weighting parameter, the sixth weighting parameter, and the seventh weighting parameter to obtain a grayscale variation value; and summing the first grayscale value and the grayscale variation value to obtain the second grayscale value.

[0011] Optionally, determining the target pixel grid based on the location information and adjusting the grayscale value of the corresponding target pixel grid based on the second grayscale value includes: determining the pixel grid corresponding to the work area where the worker exists as the target pixel grid based on the location information, wherein the location information corresponds one-to-one with the target pixel grid; sorting the second grayscale values ​​corresponding to the worker number based on the worker number, wherein the worker number corresponds one-to-one with the second grayscale value and the worker number corresponds one-to-one with the location information; and sequentially adjusting the grayscale value of the target pixel grid to the corresponding second grayscale value.

[0012] Optionally, before inputting each of the monitored images into a preset classifier model and determining the safety level of each work area based on the output results, the method further includes: determining the work area to be at a first safety level if the environment in the work area is safe and the worker is healthy; determining the work area to be at a second safety level if the environment in the work area is safe but the worker is unhealthy; determining the work area to be at a third safety level if the worker is healthy but the environment in the work area is unsafe; and determining the work area to be at a fourth safety level if the environment in the work area is unsafe and the worker is unhealthy.

[0013] Optionally, before inputting each of the monitoring images into a preset classifier model and determining the safety level of each work area based on the output results, the method includes: determining that the environment of the work area corresponding to the pixel grid is unsafe when the first gray value exceeds a first preset value or the second gray value exceeds a second preset value; and determining that the worker in the work area corresponding to the pixel grid is unhealthy when the second gray value exceeds a third preset value.

[0014] According to another aspect of this application, a remote mine monitoring device is provided. The device includes: a receiving unit for receiving a target data packet, wherein the target data packet is constructed by an edge UPF using environmental data and worker data, the target data packet being used to draw a monitoring image of a work area, the environmental data being gas environment-related parameters of the work area underground, and the worker data including health status information and location information of the workers in the work area; a drawing unit for assigning a first grayscale value and a second grayscale value to the pixel grids of multiple images to be drawn according to the target data packet, thereby obtaining the monitoring image, wherein the first grayscale value is used to characterize whether the environment of the work area is safe, the second grayscale value is used to characterize whether the workers in the work area are healthy, the work area corresponds one-to-one with the image to be drawn, and the pixel grid is a sub-image obtained by segmenting the image to be drawn; and a first determining unit for inputting each monitoring image into a preset classifier model and determining the safety level of each work area according to the output result, the safety level being used to characterize whether the work area is safe.

[0015] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0016] According to another aspect of this application, a remote mine monitoring system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any one of the methods described.

[0017] Applying the technical solution of this application, in the above-mentioned remote mine monitoring method, firstly, a target data packet is received. The target data packet is constructed by an edge UPF using environmental data and worker data. The target data packet is used to draw a monitoring image of the work area. The environmental data includes gas environment-related parameters of the work area in the mine, and the worker data includes the health status information and location information of the workers in the work area. Then, according to the target data packet, a first gray value and a second gray value are assigned to the pixel grids of multiple images to be drawn to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe, and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the image to be drawn, and the pixel grid is a sub-image obtained by segmenting the image to be drawn. Finally, each of the above monitoring images is input into a preset classifier model, and the safety level of each work area is determined according to the output result. The safety level is used to characterize whether the work area is safe. This method uses environmental data acquisition equipment and worker data acquisition equipment to collect environmental parameters and worker human body parameters in the mine in real time. Based on the environmental parameters, a first grayscale value is calculated, and the grayscale value of the image to be drawn corresponding to the work area is adjusted to the first grayscale value. Based on the human body parameters and the first grayscale value, a second grayscale value is calculated, and the grayscale value of the sub-image containing the worker in the image to be drawn is adjusted to the second grayscale value. The drawn monitoring image is input into a pre-trained classifier. Based on the grayscale values, the safety level of all work areas is determined, thereby judging the overall safety situation of the mine. Furthermore, this application extends the UPF network element to the edge of the mine's 5G base station, shortening the data transmission distance and enabling real-time monitoring of underground safety conditions. This method solves the problem in existing technologies where relying on monitoring personnel in the monitoring room to identify the situation within the monitor is inefficient and makes it difficult to comprehensively determine the overall safety situation of multiple work areas in the current mine. Attached Figure Description

[0018] Figure 1 A hardware structure block diagram of a mobile terminal for performing a remote mine monitoring method according to an embodiment of this application is shown;

[0019] Figure 2 A schematic flowchart of a remote mine monitoring method according to an embodiment of this application is shown;

[0020] Figure 3 A flowchart illustrating a specific remote mine monitoring method according to an embodiment of this application is shown;

[0021] Figure 4 A structural block diagram of a remote mine monitoring device according to an embodiment of this application is shown;

[0022] Figure 5 A schematic diagram of the composition of a remote mine monitoring system provided according to an embodiment of this application is shown. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] As described in the background section, existing mine monitoring methods still require manual observation, which is not only inefficient but also makes it difficult to comprehensively assess the overall safety situation of multiple work areas in the mine. To address the problems of low monitoring efficiency and difficulty in comprehensively assessing the overall safety situation of multiple work areas in the mine caused by relying on monitoring personnel in the monitoring room to identify the situation inside the monitor, embodiments of this application provide a remote mine monitoring method, monitoring device, computer-readable storage medium, and remote mine monitoring system.

[0027] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0028] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a remote mine monitoring method according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0029] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the device information display method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] This embodiment provides a remote mine monitoring method that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] Figure 2 This is a flowchart of a remote mine monitoring method according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0032] Step S201: Receive target data packet. The target data packet is constructed by the edge UPF using environmental data and worker data. The target data packet is used to draw a monitoring image of the work area. The environmental data is the gas environment-related parameters of the work area in the mine. The worker data includes the health status information and location information of the workers in the work area.

[0033] Specifically, the mine comprises multiple work areas, each equipped with environmental monitoring equipment. Environmental data is collected based on this equipment. Mine workers wear human body indicator monitoring devices, of which various types are used, to collect worker data. An edge UPF network element receives the environmental and human body indicator data and constructs them into data packets, which are then sent to the monitoring terminal. These data packets can be statistically analyzed every 0.5 seconds, 1 second, or 2 seconds before being sent to the monitoring terminal. The environmental monitoring equipment includes methane, oxygen, and carbon dioxide concentration detectors. Each work area is equipped with at least one of these detectors. If a work area has multiple detectors, the average value of the parameters collected by the same type of environmental monitoring equipment is calculated as the output result when calculating environmental data. The human body indicator monitoring devices include location tags, temperature sensors, heart rate monitors, blood oxygen monitors, and blood pressure monitors. The location tags can be UWB (Ultra Wideband) location tags.

[0034] Step S202: Assign a first gray value and a second gray value to the pixel grids of multiple images to be drawn according to the target data packet to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the images to be drawn and the pixel grids are sub-images obtained by segmenting the images to be drawn.

[0035] Specifically, the monitoring terminal receives data packets sent by the aforementioned edge UPF, retrieves preset images to be drawn, each image corresponding to a working area, and each image has multiple preset pixel grids. Based on the environmental data of the working area, a first grayscale value is assigned to the corresponding image to be drawn. Based on the worker data of the working area, a second grayscale value is assigned to the pixel grid of the worker corresponding to the image to be drawn, based on the first grayscale value, thus obtaining the drawn image. Using this method, the worker and environmental conditions are fully simulated in a single image, improving the monitoring accuracy of safety monitoring.

[0036] Step S203: Input each of the above-mentioned monitoring images into a preset classifier model, and determine the safety level of each working area based on the output results. The safety level is used to characterize whether the above-mentioned working area is safe.

[0037] Specifically, the classifier mentioned above can be a random forest classifier or a nearest neighbor classifier, etc.

[0038] In this embodiment, firstly, a target data packet is received. This target data packet is constructed by an edge UPF using environmental data and worker data. The target data packet is used to draw a monitoring image of the work area. The environmental data includes gas environment parameters related to the work area in the mine, and the worker data includes the health status and location information of the workers in the work area. Then, based on the target data packet, a first gray value and a second gray value are assigned to the pixel grids of multiple images to be drawn to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe, and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the image to be drawn, and the pixel grid is a sub-image obtained by segmenting the image to be drawn. Finally, each of the monitoring images is input into a preset classifier model, and the safety level of each work area is determined based on the output result. The safety level is used to characterize whether the work area is safe. This method uses environmental data acquisition equipment and worker data acquisition equipment to collect environmental parameters and worker human body parameters in the mine in real time. Based on the environmental parameters, a first grayscale value is calculated, and the grayscale value of the image to be drawn corresponding to the work area is adjusted to the first grayscale value. Based on the human body parameters and the first grayscale value, a second grayscale value is calculated, and the grayscale value of the sub-image containing the worker in the image to be drawn is adjusted to the second grayscale value. The drawn monitoring image is input into a pre-trained classifier. Based on the grayscale values, the safety level of all work areas is determined, thereby judging the overall safety situation of the mine. Furthermore, this application extends the UPF network element to the edge of the mine's 5G base station, shortening the data transmission distance and enabling real-time monitoring of underground safety conditions. This method solves the problem in existing technologies where relying on monitoring personnel in the monitoring room to identify the situation within the monitor is inefficient and makes it difficult to comprehensively determine the overall safety situation of multiple work areas in the current mine.

[0039] In order to obtain an image for describing the safety situation of workers and the work environment, in an optional implementation, step S202 above includes:

[0040] Step S2021, decoding step, decode the above target data packet to obtain multiple above environmental data and above worker data. The above environmental data includes methane concentration parameters, oxygen concentration parameters and carbon dioxide concentration parameters. The above worker data includes the above location information, worker number, body temperature parameters, heart rate parameters, blood pressure parameters and blood oxygen parameters.

[0041] Specifically, after receiving the data packet at the monitoring end, it is necessary to decode the data packet and extract the relevant parameters in the data packet. The environmental data includes methane concentration parameters, oxygen concentration parameters, and carbon dioxide concentration parameters, while the worker data includes location information, worker number, body temperature parameters, heart rate parameters, blood pressure parameters, and blood oxygen parameters.

[0042] Step S2022, the acquisition step, acquire any one of the above-mentioned images to be drawn;

[0043] Specifically, in the solution of this application, multiple blank images to be drawn are stored on the monitoring terminal, corresponding to each working area. When drawing, the blank images can be directly called.

[0044] Step S2023, First calculation step, calculate the first gray value based on the above environmental data, the first gray value corresponds one-to-one with the above image to be drawn;

[0045] Specifically, based on the environmental data monitored in the work area corresponding to the blank image, a first gray value corresponding to the environmental data is determined. The first gray value is used to simulate the environmental data of the work area where the worker is located, so as to truly represent the realism of the image in terms of environmental simulation and improve the accuracy of the final safety level determination.

[0046] Step S2024, the second calculation step, calculates the second grayscale value based on the worker data;

[0047] Specifically, based on the worker data monitored in the work area corresponding to the blank image, a second grayscale value corresponding to the worker data is determined. The second grayscale value is used to simulate the worker's health status.

[0048] Step S2025: Repeat the steps, repeating the above acquisition step, the above first calculation step and the above second calculation step at least once, until the above first gray value and the above second gray value corresponding to all the above working areas are obtained.

[0049] Specifically, after completing the above acquisition step, the first calculation step, and the second calculation step, a first grayscale value and a second grayscale value corresponding to a working area are obtained. Repeating the above steps will yield the first grayscale value and the second grayscale value corresponding to each working area.

[0050] Step S2026, First drawing step, adjust the gray value of the image to be drawn according to the first gray value;

[0051] Specifically, after obtaining the first grayscale value, the grayscale value of the image to be drawn is adjusted to the first grayscale value. Furthermore, in the step of assigning the first grayscale value to the image to be drawn based on the environmental data of the above-mentioned working area, the grayscale value of each pixel in the above-mentioned drawing area is adjusted to the first grayscale value.

[0052] Step S2027, the second drawing step, determines the target pixel grid according to the above location information, and adjusts the gray value of the corresponding target pixel grid according to the above second gray value to obtain the above monitoring image. The above target pixel grid is the pixel grid corresponding to the above working area where a worker exists. The above second gray value corresponds one-to-one with the above target pixel grid.

[0053] Specifically, after obtaining the second grayscale value, the grayscale value of the pixel grid corresponding to the worker in the image to be drawn, which was adjusted to the first grayscale value, is adjusted to the second grayscale value, thus completing the image drawing.

[0054] To obtain the aforementioned first grayscale value, in one optional implementation, step S2023 includes:

[0055] Step S20231: Determine the first weight parameter, the second weight parameter, and the third weight parameter. The first weight parameter is the weight of the methane concentration parameter, the second weight parameter is the weight of the oxygen concentration parameter, and the third weight parameter is the weight of the carbon dioxide concentration parameter.

[0056] Specifically, before performing the calculations, relevant parameters are first set. Let δ1, δ2, and δ3 represent the weighting parameters corresponding to the methane concentration, oxygen concentration, and carbon dioxide concentration parameters, respectively. Oxygen is beneficial to workers, so the weighting parameter corresponding to the oxygen concentration parameter is negative to improve the accuracy of the final plotted image in terms of environmental simulation.

[0057] Step S20232: Based on the first weighting parameter, the second weighting parameter, and the third weighting parameter, calculate the weighted average of the methane concentration parameter, the oxygen concentration parameter, and the carbon dioxide concentration parameter to obtain the first gray value.

[0058] Specifically, let CH4 represent the methane concentration parameter, O2 represent the oxygen concentration parameter, CO2 represent the carbon dioxide concentration parameter, and h1 represent the first gray value. Then, the formula for calculating the above weighted average is as follows:

[0059] h1=δ1CH4+δ2O2+δ3CO2.

[0060] To obtain the aforementioned second grayscale value, in one optional implementation, step S2024 includes:

[0061] Step S20241: Determine the fourth weight parameter, the fifth weight parameter, the sixth weight parameter and the seventh weight parameter. The fourth weight parameter is the weight of the body temperature parameter, the fifth weight parameter is the weight of the heart rate parameter, the sixth weight parameter is the weight of the blood pressure parameter, and the seventh weight parameter is the weight of the blood oxygen parameter.

[0062] Specifically, before performing the calculation, relevant parameters are first set. Let δ4, δ5, δ6 and δ7 represent the weight parameters of body temperature, heart rate, blood pressure and blood oxygen, respectively.

[0063] Step S20242: Based on the fourth, fifth, sixth and seventh weighting parameters, calculate the weighted average of the body temperature parameter, heart rate parameter, blood pressure parameter and blood oxygen parameter to obtain the grayscale variation value.

[0064] Specifically, let Δ represent the grayscale variation value, T represent the body temperature parameter, H represent the heart rate parameter, P represent the blood pressure parameter, and S represent the blood oxygen parameter. Then, the formula for calculating the weighted average of the above parameters is as follows:

[0065] Δ=δ4T+δ5H+δ6P+δ7S.

[0066] Step S20243: Summing the first gray value and the gray value variation to obtain the second gray value.

[0067] Specifically, the second grayscale value can be obtained by adding the grayscale variation value to the first grayscale value.

[0068] In order to adjust the grayscale value of the target pixel grid to the second grayscale value, in an optional implementation, step S2027 includes:

[0069] Step S20271: Based on the above location information, the pixel grid corresponding to the above work area where the worker exists is determined as the above target pixel grid, and the above location information corresponds one-to-one with the above target pixel grid.

[0070] Specifically, the location of the worker in the work area is located based on the location information, and the corresponding pixel grid in the image to be drawn can be determined based on the location correspondence.

[0071] Step S20272: Sort the second gray value corresponding to the worker number according to the worker number, the worker number and the second gray value are in one-to-one correspondence, and the worker number and the location information are in one-to-one correspondence.

[0072] Specifically, there is a one-to-one correspondence between worker ID and location information. Therefore, based on the correspondence, the pixel cell corresponding to the worker and the second grayscale value corresponding to the worker data can be determined according to the worker ID. Thus, the second grayscale values ​​can be sorted according to the worker ID.

[0073] Step S20273: The gray values ​​of the target pixel grids are adjusted to the corresponding second gray values ​​in sequence.

[0074] Specifically, the gray values ​​of the aforementioned pixel grids are adjusted one by one according to the above sorting to make them equal to the corresponding second gray value.

[0075] To determine the security level corresponding to different monitoring images, in one optional implementation, before inputting each of the aforementioned monitoring images into a preset classifier model and determining the security level of each working area based on the output results, the method further includes:

[0076] Step S301: If the environment of the work area is safe and the workers are healthy, determine that the work area is at the first safety level.

[0077] Specifically, the first safety level indicates that there are no abnormalities, meaning that the working environment and the health of the workers are both safe.

[0078] Step S302: If the environment in the work area is safe, but the worker is unhealthy, the work area is determined to be at the second safety level.

[0079] Specifically, the second safety level indicates a worker's abnormality, meaning that the worker's health condition has deteriorated and requires medical attention.

[0080] Step S303: If the worker is healthy in the above-mentioned work area, but the above-mentioned environment is unsafe, determine that the above-mentioned work area is at the third safety level;

[0081] Specifically, the third safety level indicates an abnormal working environment, meaning that the working environment may pose a hazard to workers or construction, requiring the evacuation of workers and treatment of the environment.

[0082] Step S304: If the environment in the work area is unsafe and the worker is unhealthy, the work area is determined to be at the fourth safety level.

[0083] Specifically, Level 4 safety indicates that both the workers and the work environment are abnormal, requiring the evacuation of workers and special treatment for workers with health problems.

[0084] In order to determine the working environment and worker health status based on monitoring images, in one optional implementation, before inputting each of the aforementioned monitoring images into a preset classifier model and determining the safety level of each work area based on the output results, the method further includes:

[0085] Step S401: If the first gray value exceeds the first preset value or the second gray value exceeds the second preset value, it is determined that the environment of the working area corresponding to the pixel grid is unsafe.

[0086] Specifically, in cases where the work environment is abnormal, there may be two situations: either only the work environment is abnormal or both the work environment and the worker's health are abnormal. Therefore, two thresholds are set for these two situations, with the first preset value being less than the second preset value.

[0087] Step S402: If the second grayscale value exceeds the third preset value, it is determined that the worker in the work area corresponding to the pixel grid is unhealthy.

[0088] Specifically, in cases of abnormal worker health status, there may be two scenarios: either all workers are in abnormal health status or both the work environment and worker health status are in abnormal health status. However, since environmental data has a greater impact on grayscale values ​​than worker health status data, only one threshold is set.

[0089] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the remote mine monitoring method of this application will be described in detail below with reference to specific embodiments.

[0090] This embodiment relates to a specific remote mine monitoring method, such as... Figure 3 As shown, it includes the following steps:

[0091] Step S1: Environmental data collection. The mine includes multiple working areas, and each of these working areas is equipped with environmental monitoring equipment. Environmental data is collected based on the environmental monitoring equipment.

[0092] Step S2: Worker data collection. Mine workers wear human body indicator monitoring devices, which include various types. Human body indicator data is collected based on the human body indicator monitoring devices.

[0093] Step S3: Edge UPF forwarding: Receive the above environmental data and human body indicator data, construct them into a data packet, and send the data packet to the monitoring terminal;

[0094] Step S4: Image drawing. The monitoring terminal receives the data packet sent by the edge UPF and calls the preset image to be drawn. The image to be drawn includes multiple regions to be drawn. Each region to be drawn has multiple pixel grids. The regions to be drawn correspond one-to-one with the working areas. Based on the environmental data of the working areas, a first gray value is assigned to the corresponding region to be drawn. Based on the human body index data of the workers in the working areas, a second gray value is assigned to the pixel grids of the corresponding workers in the regions to be drawn, based on the first gray value, to obtain the drawn image.

[0095] Step S5: Security level determination. Input the above-drawn image into the pre-trained classifier to output the security level parameters.

[0096] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0097] This embodiment provides a remote mine monitoring method that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0098] This application also provides a remote mine monitoring device. It should be noted that the remote mine monitoring device of this application can be used to execute the remote mine monitoring method provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0099] The following describes the remote mine monitoring device provided in the embodiments of this application.

[0100] Figure 4 This is a schematic diagram of a remote mine monitoring device according to an embodiment of this application. Figure 4 As shown, the device includes:

[0101] The receiving unit 10 is used to receive target data packets, which are constructed by the edge UPF using environmental data and worker data. The target data packets are used to draw monitoring images of the work area. The environmental data are gas environment-related parameters of the work area in the mine. The worker data includes health status information and location information of the workers in the work area.

[0102] Specifically, the mine comprises multiple work areas, each equipped with environmental monitoring equipment. Environmental data is collected based on this equipment. Mine workers wear human body indicator monitoring devices, of which various types are used, to collect worker data. An edge UPF network element receives the environmental and human body indicator data and constructs them into data packets, which are then sent to the monitoring terminal. These data packets can be statistically analyzed every 0.5 seconds, 1 second, or 2 seconds before being sent to the monitoring terminal. The environmental monitoring equipment includes methane, oxygen, and carbon dioxide concentration detectors. Each work area is equipped with at least one of these detectors. If a work area has multiple detectors, the average value of the parameters collected by the same type of environmental monitoring equipment is calculated as the output result when calculating environmental data. The human body indicator monitoring devices include location tags, temperature sensors, heart rate monitors, blood oxygen monitors, and blood pressure monitors. The location tags can be UWB (Ultra Wideband) location tags.

[0103] The drawing unit 20 is used to assign a first gray value and a second gray value to the pixel grids of multiple images to be drawn according to the target data packet to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe, and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the images to be drawn, and the pixel grid is a sub-image obtained by segmenting the images to be drawn.

[0104] Specifically, the monitoring terminal receives data packets sent by the aforementioned edge UPF, retrieves preset images to be drawn, each image corresponding to a working area, and each image has multiple preset pixel grids. Based on the environmental data of the working area, a first grayscale value is assigned to the corresponding image to be drawn. Based on the worker data of the working area, a second grayscale value is assigned to the pixel grid of the worker corresponding to the image to be drawn, based on the first grayscale value, thus obtaining the drawn image. Using this method, the worker and environmental conditions are fully simulated in a single image, improving the monitoring accuracy of safety monitoring.

[0105] The first determining unit 30 is used to input each of the above-mentioned monitoring images into a preset classifier model and determine the safety level of each working area based on the output results. The safety level is used to characterize whether the above-mentioned working area is safe.

[0106] Specifically, the classifier mentioned above can be a random forest classifier or a nearest neighbor classifier, etc.

[0107] In this embodiment, the receiving unit receives a target data packet, which is constructed by an edge UPF using environmental data and worker data. The target data packet is used to draw a monitoring image of the work area. The environmental data includes gas environment-related parameters of the work area in the mine, and the worker data includes the health status and location information of the workers in the work area. The drawing unit assigns a first gray value and a second gray value to the pixel grids of multiple images to be drawn according to the target data packet to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe, and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the image to be drawn, and the pixel grid is a sub-image obtained by segmenting the image to be drawn. The first determining unit inputs each of the monitoring images into a preset classifier model and determines the safety level of each work area according to the output result. The safety level is used to characterize whether the work area is safe. This device collects environmental parameters and worker human body parameters in the mine in real time using environmental data acquisition equipment and worker data acquisition equipment. Based on the environmental parameters, it calculates a first grayscale value and adjusts the grayscale value of the image to be drawn corresponding to the work area to the first grayscale value. Based on the human body parameters and the first grayscale value, it calculates a second grayscale value and adjusts the grayscale value of the sub-image containing the worker in the image to the second grayscale value. The completed monitoring image is input into a pre-trained classifier. Based on the grayscale values, the safety level of all work areas is determined, thereby judging the overall safety situation of the mine. Furthermore, this application extends the UPF network element to the edge of the mine's 5G base station, shortening the data transmission distance and enabling real-time monitoring of underground safety conditions. This device solves the problem in existing technologies where relying on monitoring personnel in the monitoring room to identify the situation within the monitor is inefficient and makes it difficult to comprehensively determine the overall safety situation of multiple work areas in the current mine.

[0108] In order to obtain images for describing worker and workplace safety conditions, in one optional embodiment, the above-mentioned drawing unit includes:

[0109] The decoding module is used to perform the decoding steps, decode the target data packet to obtain multiple environmental data and worker data. The environmental data includes methane concentration parameters, oxygen concentration parameters and carbon dioxide concentration parameters. The worker data includes the location information, worker number, body temperature parameters, heart rate parameters, blood pressure parameters and blood oxygen parameters.

[0110] Specifically, after receiving the data packet at the monitoring end, it is necessary to decode the data packet and extract the relevant parameters in the data packet. The environmental data includes methane concentration parameters, oxygen concentration parameters, and carbon dioxide concentration parameters, while the worker data includes location information, worker number, body temperature parameters, heart rate parameters, blood pressure parameters, and blood oxygen parameters.

[0111] The acquisition module is used to perform the acquisition steps and acquire any one of the above-mentioned images to be drawn.

[0112] Specifically, in the solution of this application, multiple blank images to be drawn are stored on the monitoring terminal, corresponding to each working area. When drawing, the blank images can be directly called.

[0113] The first calculation module is used to perform the first calculation step, calculate the first gray value based on the environmental data, and the first gray value corresponds one-to-one with the image to be drawn.

[0114] Specifically, based on the environmental data monitored in the work area corresponding to the blank image, a first gray value corresponding to the environmental data is determined. The first gray value is used to simulate the environmental data of the work area where the worker is located, so as to truly represent the realism of the image in terms of environmental simulation and improve the accuracy of the final safety level determination.

[0115] The second calculation module is used to perform the second calculation step, which calculates the second grayscale value based on the worker data.

[0116] Specifically, based on the worker data monitored in the work area corresponding to the blank image, a second grayscale value corresponding to the worker data is determined. The second grayscale value is used to simulate the worker's health status.

[0117] The repeating module is used to perform repeating steps, repeating the above acquisition step, the above first calculation step and the above second calculation step at least once, until the above first gray value and the above second gray value corresponding to all the above working areas are obtained.

[0118] Specifically, after completing the above acquisition step, the first calculation step, and the second calculation step, a first grayscale value and a second grayscale value corresponding to a working area are obtained. Repeating the above steps will yield the first grayscale value and the second grayscale value corresponding to each working area.

[0119] The first drawing module is used to perform the first drawing step, adjusting the gray value of the corresponding image to be drawn according to the first gray value.

[0120] Specifically, after obtaining the first grayscale value, the grayscale value of the image to be drawn is adjusted to the first grayscale value. Furthermore, in the step of assigning the first grayscale value to the image to be drawn based on the environmental data of the above-mentioned working area, the grayscale value of each pixel in the above-mentioned drawing area is adjusted to the first grayscale value.

[0121] The second drawing module is used to perform the second drawing step, determine the target pixel grid according to the above location information, and adjust the gray value of the corresponding target pixel grid according to the above second gray value to obtain the above monitoring image. The target pixel grid is the pixel grid corresponding to the above working area where a worker exists, and the above second gray value corresponds one-to-one with the above target pixel grid.

[0122] Specifically, after obtaining the second grayscale value, the grayscale value of the pixel grid corresponding to the worker in the image to be drawn, which was adjusted to the first grayscale value, is adjusted to the second grayscale value, thus completing the image drawing.

[0123] To obtain the aforementioned first grayscale value, in one optional implementation, the first calculation module includes:

[0124] The first determining submodule is used to determine a first weight parameter, a second weight parameter, and a third weight parameter, wherein the first weight parameter is the weight of the methane concentration parameter, the second weight parameter is the weight of the oxygen concentration parameter, and the third weight parameter is the weight of the carbon dioxide concentration parameter.

[0125] Specifically, before performing the calculations, relevant parameters are first set. Let δ1, δ2, and δ3 represent the weighting parameters corresponding to the methane concentration, oxygen concentration, and carbon dioxide concentration parameters, respectively. Oxygen is beneficial to workers, so the weighting parameter corresponding to the oxygen concentration parameter is negative to improve the accuracy of the final plotted image in terms of environmental simulation.

[0126] The first calculation submodule is used to calculate the weighted average of the methane concentration parameter, the oxygen concentration parameter, and the carbon dioxide concentration parameter based on the first weight parameter, the second weight parameter, and the third weight parameter, to obtain the first gray value.

[0127] Specifically, let CH4 represent the methane concentration parameter, O2 represent the oxygen concentration parameter, CO2 represent the carbon dioxide concentration parameter, and h1 represent the first gray value. Then, the formula for calculating the above weighted average is as follows:

[0128] h1=δ1CH4+δ2O2+δ3CO2.

[0129] To obtain the aforementioned second grayscale value, in one optional implementation, the second calculation module includes:

[0130] The second determining submodule is used to determine the fourth weight parameter, the fifth weight parameter, the sixth weight parameter and the seventh weight parameter, wherein the fourth weight parameter is the weight of the body temperature parameter, the fifth weight parameter is the weight of the heart rate parameter, the sixth weight parameter is the weight of the blood pressure parameter and the seventh weight parameter is the weight of the blood oxygen parameter.

[0131] Specifically, before performing the calculation, relevant parameters are first set. Let δ4, δ5, δ6 and δ7 represent the weight parameters of body temperature, heart rate, blood pressure and blood oxygen, respectively.

[0132] The second calculation submodule is used to calculate the weighted average of the body temperature parameter, heart rate parameter, blood pressure parameter and blood oxygen parameter based on the fourth weight parameter, the fifth weight parameter, the sixth weight parameter and the seventh weight parameter, and obtain the grayscale variation value.

[0133] Specifically, let Δ represent the grayscale variation value, T represent the body temperature parameter, H represent the heart rate parameter, P represent the blood pressure parameter, and S represent the blood oxygen parameter. Then, the formula for calculating the weighted average of the above parameters is as follows:

[0134] Δ=δ4T+δ5H+δ6P+δ7S.

[0135] The third calculation submodule is used to sum the first gray value and the gray value variation to obtain the second gray value.

[0136] Specifically, the second grayscale value can be obtained by adding the grayscale variation value to the first grayscale value.

[0137] In order to adjust the grayscale value of the target pixel grid to the second grayscale value, in an optional implementation, the second drawing module includes:

[0138] The third determining submodule is used to determine the pixel grid corresponding to the work area where the worker exists as the target pixel grid based on the above location information, and the above location information corresponds one-to-one with the above target pixel grid.

[0139] Specifically, the location of the worker in the work area is located based on the location information, and the corresponding pixel grid in the image to be drawn can be determined based on the location correspondence.

[0140] The sorting submodule is used to sort the second gray value corresponding to the worker number according to the worker number. The worker number and the second gray value are in one-to-one correspondence, and the worker number and the location information are in one-to-one correspondence.

[0141] Specifically, there is a one-to-one correspondence between worker ID and location information. Therefore, based on the correspondence, the pixel cell corresponding to the worker and the second grayscale value corresponding to the worker data can be determined according to the worker ID. Thus, the second grayscale values ​​can be sorted according to the worker ID.

[0142] The drawing submodule is used to sequentially adjust the grayscale values ​​of the above target pixel grids to the corresponding second grayscale values.

[0143] Specifically, the gray values ​​of the aforementioned pixel grids are adjusted one by one according to the above sorting to make them equal to the corresponding second gray value.

[0144] In order to determine the security level corresponding to different monitoring images, in one optional embodiment, the above-mentioned device further includes:

[0145] The second determining unit is used to determine that the work area is at the first safety level before inputting each of the above-mentioned monitoring images into the preset classifier model and determining the safety level of each work area based on the output results, provided that the environment of the above-mentioned work area is safe and the workers are healthy.

[0146] Specifically, the first safety level indicates that there are no abnormalities, meaning that the working environment and the health of the workers are both safe.

[0147] The third determining unit is used to determine that the work area is at the second safety level when the environment in the work area is safe but the worker is unhealthy.

[0148] Specifically, the second safety level indicates a worker's abnormality, meaning that the worker's health condition has deteriorated and requires medical attention.

[0149] The fourth determining unit is used to determine that the work area is at the third safety level when the worker is healthy but the environment is unsafe.

[0150] Specifically, the third safety level indicates an abnormal working environment, meaning that the working environment may pose a hazard to workers or construction, requiring the evacuation of workers and treatment of the environment.

[0151] The fifth determining unit is used to determine that the work area is at the fourth safety level when the environment in the work area is unsafe and the worker is unhealthy.

[0152] In order to determine the working environment and worker health status based on monitoring images, in one optional embodiment, the above-mentioned device further includes:

[0153] The sixth determining unit is used to determine that the environment of the working area corresponding to the pixel grid is unsafe before inputting each of the above-mentioned monitoring images into a preset classifier model and determining the safety level of each working area based on the output result, if the first gray value exceeds the first preset value or the second gray value exceeds the second preset value.

[0154] Specifically, in cases where the work environment is abnormal, there may be two situations: either only the work environment is abnormal or both the work environment and the worker's health are abnormal. Therefore, two thresholds are set for these two situations, with the first preset value being less than the second preset value.

[0155] The seventh determining unit is used to determine that the worker in the work area corresponding to the pixel grid is unhealthy when the second gray value exceeds the third preset value.

[0156] Specifically, in cases of abnormal worker health status, there may be two scenarios: either all workers are in abnormal health status or both the work environment and worker health status are in abnormal health status. However, since environmental data has a greater impact on grayscale values ​​than worker health status data, only one threshold is set.

[0157] Specifically, Level 4 safety indicates that both the workers and the work environment are abnormal, requiring the evacuation of workers and special treatment for workers with health problems.

[0158] The aforementioned remote mine monitoring device includes a processor and a memory. The receiving unit, drawing unit, and first determining unit, etc., are all stored as program units in the memory. The processor executes the program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0159] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, comprehensive and efficient monitoring of the mine can be achieved.

[0160] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0161] This invention provides a computer-readable storage medium including a stored program, wherein the program, when running, controls the device containing the computer-readable storage medium to execute the remote mine monitoring method.

[0162] Specifically, remote mine monitoring methods include:

[0163] Step S201: Receive target data packet. The target data packet is constructed by the edge UPF using environmental data and worker data. The target data packet is used to draw a monitoring image of the work area. The environmental data is the gas environment-related parameters of the work area in the mine. The worker data includes the health status information and location information of the workers in the work area.

[0164] Step S202: Assign a first gray value and a second gray value to the pixel grids of multiple images to be drawn according to the target data packet to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the images to be drawn and the pixel grids are sub-images obtained by segmenting the images to be drawn.

[0165] Step S203: Input each of the above-mentioned monitoring images into a preset classifier model, and determine the safety level of each working area based on the output results. The safety level is used to characterize whether the above-mentioned working area is safe.

[0166] This invention provides a processor for running a program, wherein the program executes the remote mine monitoring method.

[0167] Step S201: Receive target data packet. The target data packet is constructed by the edge UPF using environmental data and worker data. The target data packet is used to draw a monitoring image of the work area. The environmental data is the gas environment-related parameters of the work area in the mine. The worker data includes the health status information and location information of the workers in the work area.

[0168] Step S202: Assign a first gray value and a second gray value to the pixel grids of multiple images to be drawn according to the target data packet to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the images to be drawn and the pixel grids are sub-images obtained by segmenting the images to be drawn.

[0169] Step S203: Input each of the above-mentioned monitoring images into a preset classifier model, and determine the safety level of each working area based on the output results. The safety level is used to characterize whether the above-mentioned working area is safe.

[0170] This invention provides a remote mine monitoring system, such as... Figure 5 As shown, the remote mine monitoring system includes environmental monitoring equipment, human body indicator monitoring equipment, a 5G base station, an edge UPF, a monitoring terminal, a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0171] Step S201: Receive target data packet. The target data packet is constructed by the edge UPF using environmental data and worker data. The target data packet is used to draw a monitoring image of the work area. The environmental data is the gas environment-related parameters of the work area in the mine. The worker data includes the health status information and location information of the workers in the work area.

[0172] Step S202: Assign a first gray value and a second gray value to the pixel grids of multiple images to be drawn according to the target data packet to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the images to be drawn and the pixel grids are sub-images obtained by segmenting the images to be drawn.

[0173] Step S203: Input each of the above-mentioned monitoring images into a preset classifier model, and determine the safety level of each working area based on the output results. The safety level is used to characterize whether the above-mentioned working area is safe.

[0174] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0175] Step S201: Receive target data packet. The target data packet is constructed by the edge UPF using environmental data and worker data. The target data packet is used to draw a monitoring image of the work area. The environmental data is the gas environment-related parameters of the work area in the mine. The worker data includes the health status information and location information of the workers in the work area.

[0176] Step S202: Assign a first gray value and a second gray value to the pixel grids of multiple images to be drawn according to the target data packet to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the images to be drawn and the pixel grids are sub-images obtained by segmenting the images to be drawn.

[0177] Step S203: Input each of the above-mentioned monitoring images into a preset classifier model, and determine the safety level of each working area based on the output results. The safety level is used to characterize whether the above-mentioned working area is safe.

[0178] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0179] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0180] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0181] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0182] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0183] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0184] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0185] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0186] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0187] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0188] 1) The remote mine monitoring method of this application firstly receives a target data packet, which is constructed by an edge UPF using environmental data and worker data. The target data packet is used to draw a monitoring image of the work area. The environmental data consists of gas environment-related parameters of the work area in the mine, and the worker data includes the health status and location information of the workers in the work area. Then, according to the target data packet, a first gray value and a second gray value are assigned to the pixel grids of multiple images to be drawn to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe, and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the image to be drawn, and the pixel grid is a sub-image obtained by segmenting the image to be drawn. Finally, each of the monitoring images is input into a preset classifier model, and the safety level of each work area is determined according to the output result. The safety level is used to characterize whether the work area is safe. This method uses environmental data acquisition equipment and worker data acquisition equipment to collect environmental parameters and worker human body parameters in the mine in real time. Based on the environmental parameters, a first grayscale value is calculated, and the grayscale value of the image to be drawn corresponding to the work area is adjusted to the first grayscale value. Based on the human body parameters and the first grayscale value, a second grayscale value is calculated, and the grayscale value of the sub-image containing the worker in the image to be drawn is adjusted to the second grayscale value. The drawn monitoring image is input into a pre-trained classifier. Based on the grayscale values, the safety level of all work areas is determined, thereby judging the overall safety situation of the mine. Furthermore, this application extends the UPF network element to the edge of the mine's 5G base station, shortening the data transmission distance and enabling real-time monitoring of underground safety conditions. This method solves the problem in existing technologies where relying on monitoring personnel in the monitoring room to identify the situation within the monitor is inefficient and makes it difficult to comprehensively determine the overall safety situation of multiple work areas in the current mine.

[0189] 2) In this embodiment of the remote mine monitoring device of this application, the receiving unit receives a target data packet, which is constructed by an edge UPF using environmental data and worker data. The target data packet is used to draw a monitoring image of the work area. The environmental data is related parameters of the gas environment of the work area in the mine. The worker data includes the health status information and location information of the workers in the work area. The drawing unit assigns a first gray value and a second gray value to the pixel grids of multiple images to be drawn according to the target data packet to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe. The second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the image to be drawn. The pixel grid is a sub-image obtained by segmenting the image to be drawn. The first determining unit inputs each of the monitoring images into a preset classifier model and determines the safety level of each work area according to the output result. The safety level is used to characterize whether the work area is safe. This device collects environmental parameters and worker human body parameters in the mine in real time using environmental data acquisition equipment and worker data acquisition equipment. Based on the environmental parameters, it calculates a first grayscale value and adjusts the grayscale value of the image to be drawn corresponding to the work area to the first grayscale value. Based on the human body parameters and the first grayscale value, it calculates a second grayscale value and adjusts the grayscale value of the sub-image containing the worker in the image to the second grayscale value. The completed monitoring image is input into a pre-trained classifier. Based on the grayscale values, the safety level of all work areas is determined, thereby judging the overall safety situation of the mine. Furthermore, this application extends the UPF network element to the edge of the mine's 5G base station, shortening the data transmission distance and enabling real-time monitoring of underground safety conditions. This device solves the problem in existing technologies where relying on monitoring personnel in the monitoring room to identify the situation within the monitor is inefficient and makes it difficult to comprehensively determine the overall safety situation of multiple work areas in the current mine.

[0190] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A remote mine monitoring method, characterized by, The method includes: Receive target data packets, which are constructed by edge UPF using environmental data and worker data. The target data packets are used to draw monitoring images of the work area. The environmental data are gas environment-related parameters of the work area in the mine. The worker data includes health status information and location information of the workers in the work area. According to the target data packet, a first gray value and a second gray value are assigned to the pixel grids of multiple images to be drawn to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe, and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the images to be drawn, and the pixel grid is a sub-image obtained by segmenting the image to be drawn. Each of the monitoring images is input into a preset classifier model, and the safety level of each working area is determined based on the output results. The safety level is used to characterize whether the working area is safe. Assigning a first grayscale value and a second grayscale value to the pixels of multiple images to be drawn based on the target data packet includes: a decoding step, decoding the target data packet to obtain multiple environmental data and worker data, wherein the environmental data includes methane concentration parameters, oxygen concentration parameters, and carbon dioxide concentration parameters, and the worker data includes location information, worker number, body temperature parameters, heart rate parameters, blood pressure parameters, and blood oxygen parameters; an acquisition step, acquiring any one of the images to be drawn; a first calculation step, calculating the first grayscale value based on the environmental data, wherein the first grayscale value corresponds one-to-one with the image to be drawn; and a second calculation step, assigning a first grayscale value to the pixels of multiple images to be drawn based on the environmental data. The human data is used to calculate the second grayscale value; the steps are repeated, with the acquisition step, the first calculation step, and the second calculation step repeated at least once in sequence until the first grayscale value and the second grayscale value corresponding to all the working areas are obtained; a first drawing step is taken to adjust the grayscale value of the corresponding image to be drawn according to the first grayscale value; a second drawing step is taken to determine the target pixel grid according to the location information, and adjust the grayscale value of the corresponding target pixel grid according to the second grayscale value to obtain the monitoring image, wherein the target pixel grid is the pixel grid corresponding to the working area where a worker exists, and the second grayscale value corresponds one-to-one with the target pixel grid.

2. The method of claim 1, wherein, Calculating the first grayscale value based on the environmental data includes: A first weighting parameter, a second weighting parameter, and a third weighting parameter are determined, wherein the first weighting parameter is the weight of the methane concentration parameter, the second weighting parameter is the weight of the oxygen concentration parameter, and the third weighting parameter is the weight of the carbon dioxide concentration parameter; Based on the first weighting parameter, the second weighting parameter, and the third weighting parameter, the weighted average of the methane concentration parameter, the oxygen concentration parameter, and the carbon dioxide concentration parameter is calculated to obtain the first gray value.

3. The method of claim 2, wherein, The method for calculating the second grayscale value based on the worker data includes: A fourth weighting parameter, a fifth weighting parameter, a sixth weighting parameter, and a seventh weighting parameter are determined, wherein the fourth weighting parameter is the weight of the body temperature parameter, the fifth weighting parameter is the weight of the heart rate parameter, the sixth weighting parameter is the weight of the blood pressure parameter, and the seventh weighting parameter is the weight of the blood oxygen parameter; Based on the fourth, fifth, sixth, and seventh weighting parameters, the weighted average of the body temperature parameter, heart rate parameter, blood pressure parameter, and blood oxygen parameter is calculated to obtain the grayscale variation value; The second gray value is obtained by summing the first gray value and the gray value variation.

4. The method of claim 3, wherein, Determining the target pixel grid based on the location information, and adjusting the grayscale value of the corresponding target pixel grid based on the second grayscale value, includes: Based on the location information, the pixel grid corresponding to the work area where the worker is located is determined as the target pixel grid, and the location information corresponds one-to-one with the target pixel grid; The second grayscale value corresponding to the worker number is sorted according to the worker number, and the worker number corresponds one-to-one with the second grayscale value and one-to-one with the location information; The grayscale values ​​of the target pixel grids are sequentially adjusted to the corresponding second grayscale values.

5. The method of claim 4, wherein, Before inputting each of the monitored images into a preset classifier model and determining the safety level of each working area based on the output results, the method further includes: If the environment in the work area is safe and the worker is healthy, the work area is determined to be at the first safety level. If the environment in the work area is safe, but the worker is unhealthy, the work area is determined to be at the second safety level. If the worker is healthy in the work area, but the environment is unsafe, the work area is determined to be at the third safety level. If the environment in the work area is unsafe and the worker is unhealthy, the work area is determined to be at the fourth safety level.

6. The method of claim 5, wherein, Before inputting each of the monitored images into a preset classifier model and determining the safety level of each working area based on the output results, the method includes: If the first gray value exceeds a first preset value or the second gray value exceeds a second preset value, it is determined that the environment of the working area corresponding to the pixel grid is unsafe. If the second grayscale value exceeds a third preset value, it is determined that the worker in the work area corresponding to the pixel grid is unhealthy.

7. A remote mine monitoring device, characterised in that, The device includes: The receiving unit is used to receive target data packets, which are constructed by the edge UPF using environmental data and worker data. The target data packets are used to draw monitoring images of the work area. The environmental data are gas environment-related parameters of the work area in the mine. The worker data includes health status information and location information of the workers in the work area. The drawing unit is used to assign a first gray value and a second gray value to the pixel grids of multiple images to be drawn according to the target data packet to obtain the monitoring image. The first gray value is used to characterize whether the environment of the work area is safe, and the second gray value is used to characterize whether the workers in the work area are healthy. The work area corresponds one-to-one with the images to be drawn, and the pixel grid is a sub-image obtained by segmenting the image to be drawn. The first determining unit inputs each of the monitoring images into a preset classifier model and determines the safety level of each working area based on the output results. The safety level is used to characterize whether the working area is safe. The drawing unit includes: a decoding module, used to perform a decoding step, decoding the target data packet to obtain multiple environmental data and worker data, wherein the environmental data includes methane concentration parameters, oxygen concentration parameters, and carbon dioxide concentration parameters, and the worker data includes location information, worker number, body temperature parameters, heart rate parameters, blood pressure parameters, and blood oxygen parameters; an acquisition step, acquiring any one of the images to be drawn; a first calculation module, used to perform a first calculation step, calculating a first grayscale value based on the environmental data, wherein the first grayscale value corresponds one-to-one with the image to be drawn; a second calculation module, used for a second calculation step, calculating a second grayscale value based on the worker data; repeating... The module is used to perform repetitive steps, repeating the acquisition step, the first calculation step, and the second calculation step at least once in sequence until the first grayscale value and the second grayscale value corresponding to all the working areas are obtained; the first drawing module is used to perform a first drawing step, adjusting the grayscale value of the corresponding image to be drawn according to the first grayscale value; the second drawing module is used to perform a second drawing step, determining the target pixel grid according to the location information, and adjusting the grayscale value of the corresponding target pixel grid according to the second grayscale value to obtain the monitoring image, wherein the target pixel grid is the pixel grid corresponding to the working area where a worker exists, and the second grayscale value corresponds one-to-one with the target pixel grid.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 6.

9. A remote mine monitoring system, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 6.

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