A coal mine emergency early warning method and system for preventing emergency accidents

Abstract

The application relates to the technical field of wisdom emergency, and discloses a coal mine emergency early warning method and system for preventing emergency accidents, which comprises the following steps: deploying a plurality of sensor nodes in a coal mine; sampling environmental parameters of the coal mine through the plurality of sensor nodes, and adjusting sampling rates of the plurality of sensor nodes according to the environmental parameters of the coal mine; sending the environmental parameters of the coal mine to preset distributed edge computing nodes; calculating an emergency accident risk index according to the environmental parameters of the coal mine; and issuing an emergency early warning signal to mobile terminals of a plurality of workers in the coal mine when the emergency accident risk index is higher than a preset threshold. According to the application, through reasonable configuration of the sensor nodes and the edge computing nodes, and intelligent data management and analysis processes, the resource allocation efficiency in the coal mining process can be improved, the waste of manpower and material resources caused by false reports or delayed reports can be reduced, and the rescue cost after an accident occurs can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent emergency, and more specifically, to a coal mine emergency early warning method and system for preventing emergency accidents. BACKGROUND

[0002] In the process of coal mining, safety is the primary consideration. Due to the complexity and danger of the coal mine environment, the occurrence of emergency accidents such as gas explosion, coal dust explosion, roof collapse, etc. poses a great threat to the safety of workers.

[0003] Traditional coal mine safety monitoring methods usually rely on single sensors in fixed positions, which can detect environmental parameters such as temperature, humidity, gas concentration, etc. in a specific area. However, this type of monitoring has certain limitations: first, the sensor placement is relatively fixed and difficult to fully cover the entire coal mine environment; second, data collection and processing are often centralized on ground stations or central servers, which may cause data transmission delays and affect the real-time performance of the early warning system; third, fixed sampling rates may not adapt to changes in monitoring accuracy requirements in different environments, thereby reducing the accuracy of early warning.

[0004] Therefore, there is a need for a new coal mine emergency early warning method and system for preventing emergency accidents that combines multi-sensor networks, adaptive sampling, edge computing, and intelligent early warning mechanisms to overcome the shortcomings of existing technology and provide more reliable technical support for coal mine safety production. SUMMARY

[0005] To solve the above technical problems, the present application is proposed to provide a coal mine emergency early warning method and system for preventing emergency accidents, aiming to overcome the shortcomings of existing technology and provide more reliable technical support for coal mine safety production.

[0006] In a first aspect, the present application provides a coal mine emergency early warning method for preventing emergency accidents, comprising: deploying a plurality of sensor nodes in a coal mine; sampling environmental parameters of the coal mine through the plurality of sensor nodes and adjusting the sampling rate of the plurality of sensor nodes according to the environmental parameters of the coal mine; sending the environmental parameters of the coal mine to a pre-set distributed edge computing node through a pre-set communication protocol; calculating an emergency accident risk index on the distributed edge computing node according to the environmental parameters of the coal mine, wherein the high and low of the emergency accident risk index represents the high and low probability of occurrence of a pre-set type of emergency accident; and issuing an emergency early warning signal to the mobile terminals of a plurality of workers in the coal mine when the emergency accident risk index is higher than a pre-set threshold.

[0007] Optionally, in the aforementioned emergency early warning method for coal mines to prevent emergencies, adjusting the sampling rate of the plurality of sensor nodes according to the environmental parameters of the coal mine includes: calculating the rate of change of the environmental parameters of the coal mine, wherein the environmental parameter sampled by the i-th sensor node at time t among the plurality of sensor nodes is x. i At time t, the rate of change of the environmental parameters sampled by the i-th sensor node at time t is Δx. i (t); Calculate the sampling rate of the multiple sensor nodes. Where α is a preset adjustment coefficient, f base The preset baseline sampling rate is N, where N is the number of the plurality of sensor nodes, and w i x is the preset weight value corresponding to the i-th sensor node. i (D) is the preset reference sampling parameter of the i-th sensor node.

[0008] Optionally, in the aforementioned emergency early warning method for coal mines to prevent emergencies, sampling the environmental parameters of the coal mine through the multiple sensor nodes includes: detecting the data transmission power of the multiple sensor nodes and the distance between them and the distributed edge computing node, wherein the data transmission power of the i-th sensor node at time t is P. i (t), the distance between the distributed edge computing node and the node is D. i (t); Detect the remaining power of the plurality of sensor nodes, wherein the remaining power of the i-th sensor node at time t is S. i (t); Detect the load L(t) of the data transmission network accessed by the multiple sensor nodes; Establish a power consumption model for the multiple sensor nodes: Where E(t) represents the total power consumption of the plurality of sensor nodes at time t, and β and γ are preset balance coefficients; the data transmission power of at least one of the plurality of sensor nodes is adjusted so that the total power consumption E(t) of the plurality of sensor nodes at time t does not exceed the preset power limit.

[0009] Optionally, in the aforementioned emergency early warning method for coal mines to prevent emergencies, before adjusting the data transmission power of at least one of the plurality of sensor nodes, the emergency early warning method for coal mines to prevent emergencies further includes: detecting the power supply status of the coal mine; detecting the status of electrical equipment in the coal mine; and setting the power upper limit based on the power supply status and the status of electrical equipment in the coal mine.

[0010] Optionally, in the aforementioned emergency prevention and early warning method for coal mine, before adjusting the data transmission power of at least one of the plurality of sensor nodes, the emergency prevention and early warning method for coal mine further comprises: detecting the data transmission quality of the plurality of sensor nodes at time t and the data transmission quality at time t-1, wherein the data transmission quality of the i-th sensor node at time t is Q(t), and the data transmission quality of the i-th sensor node at time t-1 is Q(t-1); detecting the instantaneous power consumption of the plurality of sensor nodes at time t, wherein the instantaneous power consumption of the i-th sensor node at time t is E i (t); calculating the pheromone concentration of the plurality of sensor nodes at time t, wherein the pheromone concentration of the i-th sensor node at time t is wherein T(t-1) is the pheromone concentration of the i-th sensor node at time t-1, the pheromone concentration of the i-th sensor node at the initial time is a fixed value, ρ is a preset information evaporation coefficient, 0<ρ<1, and μ is a preset proportion factor; selecting the at least one sensor node from the plurality of sensor nodes according to the pheromone concentration of the plurality of sensor nodes at time t.

[0011] Optionally, in the aforementioned emergency prevention and early warning method for coal mine, adjusting the data transmission power of at least one of the plurality of sensor nodes comprises: when it is necessary to adjust the data transmission power of the i-th sensor node, calculating the difference ΔE between the total power consumption E(t) of the plurality of sensor nodes at time t and the total power consumption E(t-1) at time t-1; calculating the data transmission power of the i-th sensor node at time t+1 wherein E max represents the upper limit of the power, and A(t) is a preset environmental sensitivity factor, which represents the influence degree of the environment of the coal mine on the data transmission efficiency.

[0012] Optionally, in the aforementioned emergency prevention and early warning method for coal mine, before calculating the data transmission power of the i-th sensor node at time t+1 the emergency prevention and early warning method for coal mine further comprises: detecting the temperature, humidity and dust concentration of the coal mine at time t; and calculating the value of the environmental sensitivity factor A(t) according to the temperature, humidity and dust concentration of the coal mine at time t.

[0013] Optionally, in the preceding coal mine emergency early warning method for preventing emergency accidents, the emergency early warning signal is sent to mobile terminals of a plurality of workers in the coal mine, including: detecting distances between the plurality of workers and potential danger sources in the coal mine; detecting roles of the plurality of workers; sorting the plurality of workers according to the distances between the plurality of workers and the potential danger sources and the roles of the plurality of workers; and sending the emergency early warning signal to the plurality of workers in turn according to the sorting result.

[0014] In a second aspect, the present application provides a coal mine emergency early warning system for preventing emergency accidents, including: a plurality of sensor nodes deployed in a coal mine; a data sampling module that samples environmental parameters of the coal mine through the plurality of sensor nodes and adjusts sampling rates of the plurality of sensor nodes according to the environmental parameters of the coal mine; a data transmission module that sends the environmental parameters of the coal mine to a preset distributed edge computing node through a preset communication protocol; an index calculation module that calculates an emergency accident risk index according to the environmental parameters of the coal mine on the distributed edge computing node, the level of the emergency accident risk index representing the probability of occurrence of a preset type of emergency accident; and a signal sending module that sends an emergency early warning signal to mobile terminals of a plurality of workers in the coal mine when the emergency accident risk index is higher than a preset threshold.

[0015] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0016] According to the technical solution of the present application, through reasonable configuration of sensor nodes and edge computing nodes and intelligent data management and analysis process, the resource allocation efficiency in the process of coal mining can be improved, the waste of manpower and material resources caused by false reports or delayed reports can be reduced, and the rescue cost after an accident occurs can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0017] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description of embodiments of the present application taken in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of embodiments of the present application and constitute a part of the specification, which together with the description, serve to explain the present application. The drawings are not intended to limit the present application, and the same reference numerals are generally used to represent the same elements throughout the drawings.

[0018] Figure 1 A flowchart of a coal mine emergency early warning method for preventing emergency accidents according to an embodiment of the present application;

[0019] Figure 2 A partial flowchart of a coal mine emergency early warning method for preventing emergency accidents according to an embodiment of the present application;

[0020] Figure 3 Another layout flowchart of a coal mine emergency early warning method for preventing emergency accidents according to an embodiment of the present application;

[0021] Figure 4 Still another partial flowchart of a coal mine emergency early warning method for preventing emergency accidents according to an embodiment of the present application;

[0022] Figure 5 Still another partial flowchart of a coal mine emergency early warning method for preventing emergency accidents according to an embodiment of the present application;

[0023] Figure 6 A block diagram of a coal mine emergency early warning system for preventing emergency accidents according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] Some embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art will understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0025] As shown in Figure 1 An embodiment of the present application provides a coal mine emergency early warning method for preventing emergency accidents, comprising:

[0026] Step S110, deploying a plurality of sensor nodes in the coal mine.

[0027] Step S120, sampling the environmental parameters of the coal mine by the plurality of sensor nodes, and adjusting the sampling rate of the plurality of sensor nodes according to the environmental parameters of the coal mine.

[0028] In this embodiment, by deploying a plurality of sensor nodes in the coal mine and dynamically adjusting the sampling rate according to the environmental parameters, comprehensive and real-time monitoring of the coal mine is ensured. This adaptive sampling mechanism not only improves the accuracy of data collection, but also effectively reduces unnecessary data transmission volume, thereby optimizing the operation efficiency.

[0029] Step S130, sending the environmental parameters of the coal mine to a preset distributed edge computing node through a preset communication protocol.

[0030] In this embodiment, distributed edge computing nodes are used for data processing, avoiding the delay problem that may be caused by traditional centralized processing methods. The edge computing nodes can quickly analyze and process data from the sensors, instantly evaluate the emergency accident risk index, and respond in the shortest time, greatly shortening the time interval from detection to warning.

[0031] Step S140, on the distributed edge computing node, an emergency accident risk index is calculated according to the environmental parameters of the coal mine, and the high and low of the emergency accident risk index represents the high and low of the probability of occurrence of the preset type of emergency accident.

[0032] In this embodiment, the emergency accident risk index can be automatically calculated according to the collected environmental parameters, and a reasonable threshold is set to trigger the early warning signal. This mechanism ensures the accuracy and timeliness of the early warning information, so that the staff can receive the notification before the potential danger occurs, and gain valuable time for taking risk avoidance measures.

[0033] Step S150, when the emergency accident risk index is higher than the preset threshold, an emergency early warning signal is sent to the mobile terminal of the plurality of workers in the coal mine.

[0034] According to the technical scheme of the embodiment, through reasonable configuration of the sensor nodes and the edge computing nodes, and intelligent data management and analysis process, the resource allocation efficiency in the coal mining process is improved, the waste of manpower and material resources caused by false reports or late reports is reduced, and the rescue cost after the accident is also reduced.

[0035] As Figure 2 shown, another embodiment of the present application also provides an emergency early warning method for preventing emergency accidents in coal mines. Compared with the foregoing embodiment, the emergency early warning method for preventing emergency accidents in coal mines of the present embodiment, step S120 comprises:

[0036] Step S210, calculating the change rate of the environmental parameters of the coal mine, wherein the environmental parameter sampled by the i-th sensor node in the plurality of sensor nodes at time t is x i (t), the change rate of the environmental parameter sampled by the i-th sensor node at time t is Δx i (t).

[0037] In this embodiment, by calculating the change rate (Δx_i(t)) of the environmental parameter of each sensor node at a specific time point, the instantaneous change trend of the environmental parameter of the coal mine can be more sensitively captured. This not only helps to identify potential risks earlier, but also provides information about the rate of environmental change, providing more detailed data support for evaluating the likelihood of emergency accidents.

[0038] Step S220, calculating the sampling rate of the plurality of sensor nodes wherein a is a preset adjustment coefficient, f base is a preset baseline sampling rate, N is the number of the plurality of sensor nodes, w i is a preset weight value corresponding to the i-th sensor node, x i (D) is a preset baseline sampling parameter of the i-th sensor node.

[0039] According to the technical scheme of the embodiment, the sampling rate dynamic adjustment formula based on the environmental parameters and the change rate thereof is introduced, and the importance (weight value) of each sensor node, the current environmental parameter and the change thereof relative to the reference value, and the preset adjustment coefficient and the reference sampling rate are comprehensively considered. This method ensures that the sampling frequency can be flexibly adjusted according to the actual situation on the premise of maintaining data integrity and real-time, so as to realize effective utilization of resources and maximization of performance.

[0040] As shown in Figure 3 Another embodiment of the present application also provides a coal mine emergency early warning method for preventing emergency accidents. Compared with the foregoing embodiment, the coal mine emergency early warning method for preventing emergency accidents of the present embodiment further comprises the following steps in step S120:

[0041] In step S310, the data transmission power of the plurality of sensor nodes and the distance between the distributed edge computing nodes are detected, wherein the data transmission power of the i th sensor node at time t is P i (t), and the distance between the distributed edge computing nodes is D i (t).

[0042] In step S320, the residual capacity of the plurality of sensor nodes is detected, wherein the residual capacity of the i th sensor node at time t is S i (t).

[0043] In step S330, the load L(t) of the data transmission network accessed by the plurality of sensor nodes is detected.

[0044] In the present embodiment, by considering the distance from the sensor node to the edge computing node and the network load, the data transmission strategy of each node can be optimized without affecting the data integrity. For example, the transmission power is appropriately reduced in high-load or long-distance transmission, and the power is increased in low-load or short-distance transmission, so as to maintain the best data transmission rate and quality, and reduce the risk of interference.

[0045] In step S340, a power consumption model is established for the plurality of sensor nodes: Wherein E(t) represents the total power consumption of the plurality of sensor nodes at time t, and β and γ are preset balance coefficients.

[0046] In this embodiment, the power consumption model takes into account data transmission power, distance, network load and remaining power, and introduces a balance coefficient to ensure the effectiveness and flexibility of the model. Through this model, the total power consumption of multiple sensor nodes at any time can be accurately calculated, thereby achieving fine-grained control of energy consumption. This not only helps to reduce unnecessary power waste, but also effectively prolongs the endurance of the entire early warning mechanism, especially in the case of limited power supply.

[0047] In step S350, the data transmission power of at least one of the plurality of sensor nodes is adjusted so that the total power consumption E(t) of the plurality of sensor nodes at time t does not exceed the preset upper limit of power.

[0048] In this embodiment, the data transmission power of the sensor nodes is dynamically adjusted to ensure that the total power consumption does not exceed the preset upper limit of power, avoiding equipment failure or downtime problems caused by excessive power consumption. In addition, through real-time monitoring of power supply conditions and electrical equipment conditions, the power upper limit can be set more scientifically and reasonably to ensure stable and reliable operation in complex and variable working environments.

[0049] Specifically, the power upper limit can be calculated as follows:

[0050] (1) Detect the power supply condition of the coal mine.

[0051] (2) Detect the electrical equipment condition in the coal mine.

[0052] (3) Set the power upper limit according to the power supply condition and electrical equipment condition of the coal mine.

[0053] In this embodiment, through comprehensive analysis of the power supply condition and electrical equipment condition in the coal mine, the power upper limit can be set more accurately, thereby achieving optimal allocation of limited resources. This not only helps to ensure the normal operation of the early warning mechanism, but also provides necessary power support for other critical equipment, improving the safety and economic benefits of overall production and operation.

[0054] According to the technical solution of this embodiment, by introducing advanced power consumption management and adjustment mechanism, not only the energy consumption problem existing in the traditional early warning scheme is solved, but also the reliability, stability and intelligent level of the system are improved, providing a more solid technical guarantee for coal mine safety production.

[0055] As shown in Figure 4 Another embodiment of the present application provides a coal mine emergency early warning method for preventing emergency accidents. Compared with the previous embodiment, the coal mine emergency early warning method for preventing emergency accidents of this embodiment further comprises:

[0056] Step S410, detecting the data transmission quality of the plurality of sensor nodes at time t and the data transmission quality at time t-1, wherein the data transmission quality of the i-th sensor node at time t is Q(t), and the data transmission quality of the i-th sensor node at time t-1 is Q(t-1).

[0057] In this embodiment, by monitoring the data transmission quality of the plurality of sensor nodes, the data transmission performance of each node can be evaluated in real time. This helps to discover and solve possible data transmission problems such as signal interference or packet loss in a timely manner, thereby ensuring the accuracy and integrity of data transmission.

[0058] Step S420, detecting the instantaneous power consumption of the plurality of sensor nodes at time t, wherein the instantaneous power consumption of the i-th sensor node at time t is E i (t).

[0059] In this embodiment, detecting the instantaneous power consumption of each sensor node can provide key parameters for subsequent pheromone concentration calculation. Combined with power consumption data analysis, it not only helps to further refine energy management strategies, but also dynamically adjusts the task allocation of nodes according to the current working state, avoiding overheating or failure of devices due to excessive use of some nodes.

[0060] Step S430, calculating the pheromone concentration of the plurality of sensor nodes at time t, wherein the pheromone concentration of the i-th sensor node at time t is wherein T(t-1) is the pheromone concentration of the i-th sensor node at time t-1, the pheromone concentration of the i-th sensor node at the initial time is a fixed value, ρ is a preset pheromone evaporation coefficient, 0<ρ<1, and μ is a preset proportion factor.

[0061] In this embodiment, the concept of pheromone concentration is introduced, and the behavior pattern of ants foraging in nature is simulated through formula calculation. This method can make intelligent decisions based on the historical performance (pheromone concentration) and current performance (data transmission quality and instantaneous power consumption) of the nodes, and preferentially select those nodes with good transmission quality and low energy consumption to participate in key tasks, achieving optimal allocation of resources.

[0062] Step S440, selecting at least one sensor node from the plurality of sensor nodes according to the pheromone concentration of the plurality of sensor nodes at time t.

[0063] According to the technical solution of the embodiment, through continuous monitoring of data transmission quality and instantaneous power consumption, the best sensor node can be quickly screened out in a place close to the data source (i.e., an edge computing node). This not only reduces unnecessary data transmission delay, but also improves the speed and efficiency of overall data processing, ensuring that early warning information can be timely conveyed to relevant personnel, thereby gaining valuable time for taking effective response measures.

[0064] As shown in Figure 5 Another embodiment of the present application also provides a coal mine emergency early warning method for preventing emergency accidents. Compared with the foregoing embodiment, the coal mine emergency early warning method for preventing emergency accidents of the present embodiment comprises the following steps:

[0065] Step S510, when it is necessary to adjust the data transmission power of the i-th sensor node, calculating the difference AE between the total power consumption E(t) of the plurality of sensor nodes at time t and the total power consumption E(t-1) at time t-1.

[0066] In the present embodiment, by calculating the difference between the total power consumptions of the plurality of sensor nodes, the change trend of the power consumption is mastered in real time. This helps to make more accurate decisions when it is necessary to adjust the data transmission power, avoiding system instability or exceeding the preset power upper limit due to sudden changes in power consumption

[0067] Step S520, calculating the data transmission power of the i-th sensor node at time t+1 E(t+1) = E(t) + AE + A(t) * (Pmax - E(t)) max wherein, E(t) represents the power upper limit, A(t) is a preset environmental sensitivity factor, indicating the influence degree of the environment of the coal mine on the data transmission efficiency.

[0068] In the present embodiment, the dynamic adjustment formula allows the data transmission power of each sensor node to be flexibly adjusted according to the actual power consumption change and environmental conditions. This adaptive strategy not only improves the energy utilization efficiency, but also maximizes the reduction of unnecessary power consumption while maintaining the communication quality.

[0069] Before step S520, the temperature, humidity and dust concentration of the coal mine at time t can be detected; and the value of the environmental sensitivity factor A(t) is calculated according to the temperature, humidity and dust concentration of the coal mine at time t.

[0070] In the present embodiment, the environmental sensitivity factor is introduced, and its value is calculated according to parameters such as the temperature, humidity and dust concentration of the coal mine. This can better cope with complex and variable working environments. For example, under high humidity or high dust concentration conditions, appropriately reducing the data transmission power can reduce signal interference and improve the stability and accuracy of data transmission; and under suitable environment, the power can be appropriately increased to speed up the data transmission rate.

[0071] According to the technical scheme of the embodiment, by introducing the total power consumption difference value calculation and the environment sensitive factor, the problems of energy consumption and insufficient environmental adaptability in the traditional early warning system are solved, and the intelligent level, reliability and response speed are improved, thereby providing more solid technical support for coal mine safety production. This method opens up a new path for safety management and technical innovation in the coal mining industry, and has important application value and development prospect.

[0072] Another embodiment of the present application also provides a coal mine emergency early warning method for preventing emergency accidents. Compared with the previous embodiment, the coal mine emergency early warning method for preventing emergency accidents of the present embodiment comprises the following steps:

[0073] (1) Detect the distance between the multiple workers and the potential danger source in the coal mine environment; and detect the roles of the multiple workers.

[0074] In the present embodiment, the distance between the multiple workers and the potential danger source is detected, and the role information of the workers is combined to make a more detailed and personalized assessment of the danger level faced by each worker. This position-based and role-based assessment method ensures the pertinence of the early warning information, so that personnel with different roles can take the most appropriate risk avoidance measures according to their own situation.

[0075] (2) According to the distance between the multiple workers and the potential danger source and the roles of the multiple workers, the multiple workers are sorted.

[0076] (3) The emergency early warning signal is sent to the multiple workers in sequence according to the sorting result.

[0077] According to the technical scheme of the embodiment, the sorting strategy based on distance and role gives stronger intelligent and humanized characteristics. It not only improves the reliability of the early warning system, but also makes the miners feel more considerate safety protection service, thereby enhancing their trust and satisfaction.

[0078] As shown in Figure 6 An embodiment of the present application provides a coal mine emergency early warning system for preventing emergency accidents, which comprises:

[0079] A plurality of sensor nodes 610 are deployed in the coal mine.

[0080] A data sampling module 620 samples the environmental parameters of the coal mine through the plurality of sensor nodes, and adjusts the sampling rate of the plurality of sensor nodes according to the environmental parameters of the coal mine.

[0081] In this embodiment, by deploying multiple sensor nodes in the coal mine and dynamically adjusting the sampling rate according to the environmental parameters, comprehensive and real-time monitoring of the coal mine is ensured. This adaptive sampling mechanism not only improves the accuracy of data collection, but also effectively reduces unnecessary data transmission, thereby optimizing the operation efficiency.

[0082] The data transmission module 630 transmits the environmental parameters of the coal mine to the preset distributed edge computing node through a preset communication protocol.

[0083] In this embodiment, the distributed edge computing node is used for data processing, which avoids the delay problem that may be caused by the traditional centralized processing method. The edge computing node can quickly analyze and process data from the sensor, assess the emergency risk index in real time, and respond in the shortest time, greatly shortening the time interval from detection to warning.

[0084] The index calculation module 640 calculates the emergency risk index according to the environmental parameters of the coal mine on the distributed edge computing node. The high and low of the emergency risk index represents the high and low probability of occurrence of the preset type of emergency.

[0085] In this embodiment, the emergency risk index can be automatically calculated according to the collected environmental parameters, and a reasonable threshold is set to trigger the warning signal. This mechanism ensures the accuracy and timeliness of the warning information, so that the staff can receive the notification before the potential danger occurs, and gain valuable time for taking risk avoidance measures.

[0086] The signal sending module 650 sends an emergency warning signal to the mobile terminal of multiple workers in the coal mine when the emergency risk index is higher than the preset threshold.

[0087] According to the technical scheme of this embodiment, through reasonable configuration of sensor nodes and edge computing nodes, and intelligent data management and analysis process, the resource allocation efficiency in the process of coal mining can be improved, the waste of manpower and material resources caused by false alarm or late report can be reduced, and the rescue cost after the accident is also reduced.

[0088] The basic principles of the application are described above in conjunction with specific embodiments, but it should be noted that the advantages, advantages, effects and the like mentioned in the application are only examples and not limitations, and these advantages, advantages, effects and the like cannot be considered as each embodiment of the application must have. In addition, the above specific details are only for the purpose of example and understanding, and are not limited to the above specific details to realize the application.

[0089] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0090] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0091] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0092] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. An emergency early warning method for preventing emergency accidents in a coal mine, comprising: deploying a plurality of sensor nodes in the coal mine; sampling environmental parameters of the coal mine by the plurality of sensor nodes, and adjusting sampling rates of the plurality of sensor nodes according to the environmental parameters of the coal mine; sending the environmental parameters of the coal mine to a preset distributed edge computing node through a preset communication protocol; calculating an emergency accident risk index according to the environmental parameters of the coal mine on the distributed edge computing node, the emergency accident risk index representing a probability of occurrence of a preset type of emergency accident; when the emergency accident risk index is higher than a preset threshold, sending an emergency early warning signal to mobile terminals of a plurality of workers in the coal mine.

2. The emergency warning method for preventing emergency accidents in a coal mine according to claim 1, wherein, Adjusting the sampling rates of the plurality of sensor nodes according to the environmental parameters of the coal mine comprises: Calculate the rate of change of the environmental parameters of the coal mine, wherein the environmental parameter sampled by the i-th sensor node at time t is x. i At time t, the rate of change of the environmental parameters sampled by the i-th sensor node at time t is Δx. i (t); calculating a sampling rate of the plurality of sensor nodes wherein a is a preset adjustment coefficient, f base is a preset reference sampling rate, N is a number of the plurality of sensor nodes, w i is a preset weight value corresponding to the ith sensor node, x i (D) is a preset reference sampling parameter of the ith sensor node.

3. The coal mine emergency warning method for preventing emergency accidents according to claim 2, wherein, sampling the environmental parameters of the coal mine by the plurality of sensor nodes comprises: detecting data transmission power of the plurality of sensor nodes, and distance from the distributed edge computing node, wherein the data transmission power of the ith sensor node at time t is P i (t), and the distance from the distributed edge computing node is D i (t). detecting the remaining power of the plurality of sensor nodes, wherein the remaining power of the ith sensor node at time t is S i (t); detecting a load L(t) of a data transmission network accessed by the plurality of sensor nodes; establishing a power consumption model for the plurality of sensor nodes: wherein E(t) represents total power consumption of the plurality of sensor nodes at time t, and β and γ are preset balance coefficients. adjusting a data transmission power of at least one sensor node in the plurality of sensor nodes, so that a total power consumption E(t) of the plurality of sensor nodes at time t does not exceed a preset upper limit of power.

4. The emergency warning method for preventing emergency accidents in a coal mine according to claim 3, wherein, Before adjusting the data transmission power of at least one sensor node in the plurality of sensor nodes, the emergency early warning method for preventing emergency accidents in a coal mine further comprises: detecting a power supply condition of the coal mine; detecting an electrical equipment condition in the coal mine; setting the upper limit of power according to the power supply condition and the electrical equipment condition of the coal mine.

5. The emergency warning method for preventing emergency accidents in a coal mine according to claim 3, wherein, Before adjusting the data transmission power of at least one sensor node in the plurality of sensor nodes, the emergency early warning method for preventing emergency accidents in a coal mine further comprises: detecting a data transmission quality of the plurality of sensor nodes at time t and a data transmission quality of the plurality of sensor nodes at time t-1, wherein the data transmission quality of the i-th sensor node at time t is Q(t), and the data transmission quality of the i-th sensor node at time t-1 is Q(t-1); detecting an instantaneous power consumption of the plurality of sensor nodes at time t, wherein the instantaneous power consumption E i (t); calculating the pheromone concentration of the plurality of sensor nodes at time t, wherein the pheromone concentration of the i-th sensor node at time t is wherein T(t-1) is the pheromone concentration of the i-th sensor node at time t-1, the pheromone concentration of the i-th sensor node at an initial time is a fixed value, p is a preset pheromone evaporation coefficient, 0 < p < 1, and m is a preset proportional factor; selecting the at least one sensor node from the plurality of sensor nodes according to a pheromone concentration of the plurality of sensor nodes at time t.

6. The emergency warning method for preventing emergency accidents in a coal mine according to claim 3, wherein, Adjusting the data transmission power of at least one sensor node in the plurality of sensor nodes comprises: when the data transmission power of the i-th sensor node needs to be adjusted, calculating a difference ΔE between a total power consumption E(t) of the plurality of sensor nodes at time t and a total power consumption E(t-1) of the plurality of sensor nodes at time t-1; calculating data transmission power of the ith sensor node at time t+1 wherein E max represents the power upper limit, A(t) is a preset environment sensitive factor, indicating the influence degree of the environment of the coal mine on data transmission efficiency.

7. The emergency warning method for preventing emergency accidents in a coal mine according to claim 6, wherein, calculating the data transmission power of the ith sensor node at time t+1 Before the coal mine emergency early warning method for preventing emergency accidents further comprises: detecting environmental parameters of the coal mine at time t, the environmental parameters of the coal mine including temperature, humidity, and dust concentration; calculating a value of the environmental sensitive factor A(t) according to the environmental parameters of the coal mine at time t.

8. The coal mine emergency warning method for preventing emergency accidents according to claim 1, wherein, Sending an emergency early warning signal to mobile terminals of a plurality of workers in the coal mine comprises: detecting distances of the plurality of workers from potential dangerous sources in the coal mine; detecting roles of the plurality of workers; According to the distance between the plurality of workers and the potential dangerous source and the roles of the plurality of workers, the plurality of workers are sorted; According to the sorting result, the emergency warning signal is sent to the plurality of workers in turn.

9. A coal mine emergency warning system for preventing emergency accidents, comprising: a plurality of sensor nodes deployed in a coal mine; a data sampling module that samples environmental parameters of the coal mine through the plurality of sensor nodes and adjusts a sampling rate of the plurality of sensor nodes according to the environmental parameters of the coal mine; a data transmission module that transmits the environmental parameters of the coal mine to a preset distributed edge computing node through a preset communication protocol; an index calculation module that calculates an emergency accident risk index according to the environmental parameters of the coal mine on the distributed edge computing node, the level of the emergency accident risk index representing the probability of occurrence of a preset type of emergency accident; a signal sending module that sends an emergency warning signal to mobile terminals of a plurality of workers in the coal mine when the emergency accident risk index is higher than a preset threshold.

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