Mine early warning method and device based on dynamic simulation

Abstract

The invention relates to the technical field of gas monitoring, and provides a mine early warning method and device based on dynamic simulation. The method comprises the steps that all second roadways communicated with a first roadway are found, and the gas diffusion speed of the first roadway is calculated according to the draught fan wind speed of the first roadway and the draught fan wind speed of all the second roadways; according to the gas diffusion velocity of the first roadway, determining the change value of the gas concentration rising rate of the first roadway at the future moment; and calculating the gas concentration of the first roadway at the future moment according to the monitored gas concentrations of the first roadway at the current moment and the past moment and the change value of the gas concentration rising rate of the first roadway at the future moment. The gas diffusion velocity of the first roadway is calculated by using the roadway and the first roadway which are communicated with each other, so that the dynamic simulation of the gas concentration change in the roadway is realized based on the gas diffusion velocity, and the prediction of the future gas concentration in the roadway is finally realized.

Description

Technical Field

[0001] The present invention relates to the technical field of gas monitoring, and in particular to a mine early warning method and device based on dynamic simulation. Background Art

[0002] A mine is a general term for the shafts, chambers, equipment, ground buildings and structures that form an underground coal mine production system. Sometimes the inclined shafts, vertical shafts, horizontal shafts, etc. in underground mine development are also called mines. The size of the mine field, the production capacity and the service life of each mine are determined.

[0003] There are usually many kinds of dangerous gases in the mine. When these dangerous gases leak into the mine tunnels in large quantities, they may cause explosions or affect the workers in the mine. Therefore, it is necessary to monitor the dangerous gases in the mine to be able to warn of the impending danger.

[0004] In the prior art, the method for monitoring hazardous gases in mines is mainly to use gas monitoring equipment. However, the gas monitoring equipment can only monitor the gas concentration at the current moment and cannot predict the change in the concentration of hazardous gases, making it impossible to warn of impending hazardous gas situations. As a result, the warning effect of hazardous gas situations in mines is poor and the staff do not have sufficient time to deal with them.

[0005] In view of this, overcoming the defects of the prior art is an urgent problem to be solved in the field of this technology. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a mine early warning method and device based on dynamic simulation to achieve the prediction of future gas concentration in the tunnel.

[0007] The present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a mine early warning method based on dynamic simulation, using gas monitoring equipment in the tunnel to monitor the gas concentration in each tunnel; the method comprises:

[0009] Find each second lane connected to the first lane, and calculate the gas diffusion speed of the first lane according to the wind speed of the fans in the first lane and each second lane;

[0010] According to the gas diffusion velocity of the first lane, determining the change value of the gas concentration increase rate of the first lane at a future moment;

[0011] The gas concentration of the first lane at a future moment is calculated based on the monitored gas concentrations at the current moment and the past moment, as well as the change in the rate of increase of the gas concentration in the first lane at a future moment, so as to provide a gas concentration warning in advance based on the gas concentration in the first lane at a future moment.

[0012] Preferably, the step of finding each second lane connected to the first lane specifically includes:

[0013] Establish a connection relationship matrix in advance based on the connectivity between the tunnels in the mine Wherein, t(i,j) represents the connectivity between the ith lane and the jth lane. When the ith lane is connected to the jth lane, t(i,j) = 1, otherwise, t(i,j) = 0.

[0014] According to the connection relationship matrix, each second lane connected to the first lane is found.

[0015] Preferably, the calculating of the gas diffusion velocity in the first lane according to the wind speed of the fans in the first lane and each of the second lanes specifically includes:

[0016] Calculate the gas diffusion velocity in the first lane in, is the cross-sectional area of ​​the jth lane, m is the number of second lanes, V j is the initial diffusion velocity of the jth second lane; when the jth second lane is provided with an air inlet, V j =V j,w , V j,w is the fan speed at the air inlet of the j-th second lane. When the j-th second lane is not equipped with an air inlet, V j =0.

[0017] Preferably, determining the change value of the rising rate of gas concentration in the first lane at a future moment according to the gas diffusion velocity in the first lane specifically includes:

[0018] Calculate the change value of the gas concentration increase rate in the first lane at time i Where S is the cross-sectional area of ​​the first tunnel, L is the tunnel length of the first tunnel, H is the tunnel height of the first tunnel, V i is the gas diffusion velocity in the first channel, C c is the historical gas concentration of the first lane at the current moment, is the gas concentration rising rate of the first lane at the current moment.

[0019] Preferably, the gas concentration rising rate of the first lane at the current moment is Among them, C cis the historical gas concentration of the first lane at the current moment, C c-1 is the historical gas concentration of the first lane at the last moment, T c The time interval between the current moment and the previous moment.

[0020] Preferably, the cross-sectional area of ​​the first tunnel is calculated by simulating the cross section of the tunnel into a corresponding regular shape, specifically including:

[0021] When the cross section of the first tunnel is simulated as a trapezoid, the tunnel cross section reference value S is calculated. f =(B1+B2)×H÷2; where B1 is the upper clear width of the cross section of the first lane, B2 is the lower clear width of the cross section of the first lane; H is the lane height of the first lane;

[0022] When the cross section of the first tunnel is simulated as a circle, the tunnel cross section reference value S is calculated. f =π×R 2 ; Wherein, R is the radius of the first lane;

[0023] When the cross section of the first tunnel is simulated as a rectangle, the tunnel cross section reference value S is calculated. f =B×H; where B is the cross-sectional width of the first lane;

[0024] According to the tunnel section reference value S f , the cross-sectional area of ​​the first tunnel is calculated to be S = S f +S a ; Among them, S a is the preset compensation value corresponding to the first lane.

[0025] Preferably, the gas concentration of the first lane at a future time is calculated based on the monitored gas concentrations of the first lane at the current time and the past time, and the change value of the rising rate of the gas concentration of the first lane at a future time, and specifically includes:

[0026] Build a gas concentration prediction model Among them, T pre is the time interval between time i and the current time, is the gas concentration in the first lane at the future time i, C c is the gas concentration in the first lane at the current moment, is the change value of the rising rate of gas concentration in the first lane at the future time i, is the gas concentration rising rate of the first lane at the current moment.

[0027] Preferably, the step of providing a gas concentration warning in advance according to the gas concentration of the first lane at a future moment specifically includes:

[0028] When the gas concentration in the first lane at a future moment is greater than a preset concentration, a gas concentration warning is issued.

[0029] In a second aspect, the present invention further provides a mine early warning device based on dynamic simulation, which is used to implement the mine early warning method based on dynamic simulation described in the first aspect, and the device comprises:

[0030] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the mine early warning method based on dynamic simulation described in the first aspect.

[0031] In a third aspect, the present invention further provides a non-volatile computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are executed by one or more processors to complete the method described in the first aspect.

[0032] In a fourth aspect, a chip is provided, comprising: a processor and an interface, for calling and running a computer program stored in the memory from a memory, and executing the method of the first aspect.

[0033] According to a fifth aspect, a computer program product comprising instructions is provided. When the instructions are executed on a computer or a processor, the computer or the processor executes the method according to the first aspect.

[0034] The present invention calculates the gas diffusion rate of the first lane by using the connected lanes and the first lane itself, thereby realizing dynamic simulation of the change of gas concentration in the lane based on the gas diffusion rate, that is, combining the gas concentration and gas diffusion rate observed in history to infer the gas concentration in the first lane in the future, thereby realizing the prediction of the future gas concentration in the lane. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 It is a flow chart of a first mine early warning method based on dynamic simulation provided by an embodiment of the present invention;

[0037] Figure 2 is a flow chart of a second mine early warning method based on dynamic simulation provided by an embodiment of the present invention;

[0038] Figure 3 is a flow chart of a third mine early warning method based on dynamic simulation provided by an embodiment of the present invention;

[0039] Figure 4 is a flow chart of a fourth mine early warning method based on dynamic simulation provided by an embodiment of the present invention;

[0040] Figure 5 is a schematic diagram of a mine early warning method based on dynamic simulation provided by an embodiment of the present invention;

[0041] Figure 6 is a schematic diagram of a mine early warning method based on dynamic simulation provided by an embodiment of the present invention;

[0042] Figure 7 It is a schematic diagram of the architecture of a mine early warning device based on dynamic simulation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0044] Unless the context requires otherwise, throughout the specification and claims, the term "including" is to be interpreted as open inclusion, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples" and the like are intended to indicate that specific features, structures, materials or characteristics associated with the embodiment or example are included in at least one embodiment or example of the present disclosure. The schematic representation of the above terms does not necessarily refer to the same embodiment or example. In addition, the specific features, structures, materials or characteristics may be included in any one or more embodiments or examples in any appropriate manner, that is, although they may be carried in the embodiments or examples of the above terms due to reasons such as the order and position of appearance, it is not limited to that they can be carried in combination by one embodiment or example.

[0045] In the description of the present invention, the terms "first" and "second" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise specified, the meaning of "multiple" is two or more. In addition, for example, the same type of nouns may be described as two independent individuals by adding "A" and "B" at the end. In this case, the corresponding features defined as "A" and "B" are only used to distinguish the same type of individuals for description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features.

[0046] In the description of the present invention, the expression "A and / or B" (where A and B are used to formally represent specific characteristic contents) will be involved, and the corresponding expressions include the following three combinations: only A, only B, and a combination of A and B.

[0047] As used herein, "about," "substantially," or "approximately" includes the stated value and an average value that is within an acceptable range of deviation from the particular value as determined by one of ordinary skill in the art taking into account the measurements in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0048] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0049] Embodiment 1:

[0050] Embodiment 1 of the present invention provides a mine early warning method based on dynamic simulation, using gas monitoring equipment in the tunnel to monitor the gas concentration in each tunnel; Figure 1 As shown, the method includes:

[0051] In step 201, each second lane connected to the first lane is found, and the gas diffusion rate of the first lane is calculated according to the wind speed of the fans in the first lane and each second lane; the monitored gas may be methane, carbon monoxide, carbon dioxide, hydrogen sulfide, sulfur dioxide, etc. When there are multiple gases that need to be monitored and warned, the method described in this embodiment is executed for each gas.

[0052] In step 202, based on the gas diffusion rate in the first lane, the change value of the gas concentration rising rate in the first lane at a future moment is determined; the gas diffusion rate can be understood as the propagation speed of the gas in the lane, the gas concentration rising rate is the rate of change of the gas concentration in the lane, and the change value of the gas concentration rising rate is the amount of change of the gas concentration changing rate over time.

[0053] In step 203, the gas concentration of the first lane at a future moment is calculated based on the monitored gas concentrations of the first lane at the current moment and the past moment, as well as the change in the rate of increase of the gas concentration in the first lane at a future moment, so as to provide a gas concentration warning in advance based on the gas concentration in the first lane at a future moment.

[0054] The past moments and future moments are described based on the current moment. If the current moment is t, the past moments may be t-1, t-2, etc., and the future moments may be t+1, t+2, etc. The specific moments as the past moments and the future moments are determined by technical personnel in this field based on experience and demand analysis.

[0055] This embodiment calculates the gas diffusion rate of the first lane by using the connected lanes and the first lane itself, thereby realizing a dynamic simulation of the change of gas concentration in the lane based on the gas diffusion rate, that is, combining the historically observed gas concentration and gas diffusion rate to infer the future gas concentration of the first lane, thereby realizing the prediction of the future gas concentration in the lane.

[0056] In an optional embodiment, the second lanes connected to the first lane are found, such as Figure 2 As shown, specifically including:

[0057] In step 301, a connection relationship matrix is ​​established in advance based on the connection relationship between the tunnels in the mine. Among them, t(i,j) represents the connectivity between the ith lane and the jth lane. When the ith lane is connected to the jth lane, t(i,j) = 1, otherwise, t(i,j) = 0, that is, n is the total number of lanes, i is a positive integer, i is less than or equal to n, j is a positive integer, j is less than or equal to n.

[0058] In step 302, the second lanes connected to the first lane are found according to the connection relationship matrix. For example, if the kth lane is used as the first lane and the second lane connected to the kth lane needs to be found, t(k,j) or t(i,k) is accessed in sequence. When the value of t(k,j) or t(i,k) is 1, it means that the jth lane or the ith lane is connected to the first lane.

[0059] This embodiment also provides a specific implementation method for a specific application scenario, that is, calculating the gas diffusion velocity in the first lane according to the wind speed of the fans in the first lane and each second lane, specifically includes:

[0060] Calculate the gas diffusion velocity in the first lane in, is the cross-sectional area of ​​the jth lane, m is the number of second lanes, V j is the initial diffusion velocity of the jth second lane; when the jth second lane is provided with an air inlet, V j =V j,w , V j,w is the fan speed at the air inlet of the j-th second lane. When the j-th second lane is not equipped with an air inlet, V j =0.

[0061] In some embodiments, determining the change value of the rate of increase of gas concentration in the first lane at a future moment according to the gas diffusion velocity in the first lane specifically includes:

[0062] Calculate the change value of the gas concentration increase rate in the first lane at time i Where S is the cross-sectional area of ​​the first tunnel, L is the tunnel length of the first tunnel, H is the tunnel height of the first tunnel, V i is the gas diffusion velocity in the first channel, C c is the historical gas concentration of the first lane at the current moment, is the gas concentration rising rate of the first lane at the current moment.

[0063] in, To prepare a preset model, technicians in this field collect the cross-sectional area (i.e., cross-sectional area) of each tunnel, tunnel length, tunnel height, gas diffusion speed at different times, gas concentration at different times, gas concentration rising rate at different times, and change value of gas concentration rising rate at different times (the change value is calculated based on the gas concentration rising rate at different times) of each tunnel in advance as historical data, and use the historical data to train the deep learning model to obtain the preset model. The preset model receives input S, L, H, V i , C c and Output

[0064] Among them, the gas concentration rising rate of the first lane at the current moment is Among them, C c is the historical gas concentration of the first lane at the current moment, C c-1 is the historical gas concentration of the first lane at the last moment, T c The time interval between the current moment and the previous moment.

[0065] The cross-sectional area of ​​the first tunnel is calculated by simulating the cross section of the tunnel into a corresponding regular shape, such as Figure 3 As shown, specifically including:

[0066] In step 401, when the cross section of the first tunnel is simulated as a trapezoid, the tunnel cross section reference value S is calculated. f =(B1+B2)×H÷2; wherein, B1 is the upper clear width of the cross section of the first lane, B2 is the lower clear width of the cross section of the first lane; H is the lane height of the first lane.

[0067] In step 402, when the cross section of the first tunnel is simulated as a circle, the tunnel cross section reference value S is calculated. f =π×R 2 ; Wherein, R is the radius of the first lane.

[0068] In step 403, when the cross section of the first tunnel is simulated as a rectangle, the tunnel cross section reference value S is calculated. f =B×H; wherein B is the cross-sectional width of the first lane; wherein the specific shape of the cross-section of the first lane is simulated is determined by technical personnel in this field based on empirical analysis.

[0069] In step 404, according to the tunnel section reference value S f , the cross-sectional area of ​​the first tunnel is calculated to be S = S f +S a ; Among them, S a is the preset compensation value corresponding to the first lane, which is obtained by those skilled in the art based on empirical analysis and is used to correct the S caused by the incomplete regularity of the lane shape. f Calculate the deviation. That is, the cross-sectional area S of the jth tunnel.

[0070] In a specific application scenario, the gas concentration of the first lane at a future time is calculated based on the monitored gas concentrations of the first lane at the current time and the past time, and the change value of the rising rate of the gas concentration of the first lane at a future time, and specifically includes:

[0071] Build a gas concentration prediction model Among them, T pre is the time interval between time i and the current time, is the future gas concentration in the first lane at the future time i, C c is the gas concentration in the first lane at the current moment, is the change value of the rising rate of gas concentration in the first lane at the future time i, is the gas concentration rising rate of the first lane at the current moment.

[0072] In an actual application scenario, the gas concentration warning is performed in advance according to the gas concentration of the first lane at a future moment, specifically including:

[0073] When the gas concentration in the first lane at a future moment is greater than a preset concentration, a gas concentration warning is issued. The preset concentration and the specific future moment to be used for the gas concentration warning are determined by those skilled in the art based on demand analysis.

[0074] Embodiment 2:

[0075] The present invention is based on the method described in Example 1, combined with specific application scenarios, and uses technical descriptions in related scenarios to illustrate the implementation process of the present invention in characteristic scenarios.

[0076] The mine early warning method based on dynamic simulation provided in this embodiment is as follows: Figure 4 and Figure 5 As shown, specifically including:

[0077] In step 501, various data in the mine are collected and processed, including tunnel three-dimensional model data and gas monitoring equipment data, wherein the gas monitoring equipment data includes concentration data of key gases such as methane, carbon monoxide, carbon dioxide, hydrogen sulfide and sulfur dioxide, as well as wind speed data.

[0078] In step 502, data modeling is performed on the mine tunnel according to the three-dimensional model structure of the tunnel.

[0079] (1) Encode each tunnel in sequence as Tunnel_1, Tunnel_2, Tunnel_3, ..., Tunnel_n.

[0080] (2) Basic information of each tunnel includes: tunnel section type Type, tunnel length L, tunnel height H, whether it is an air inlet The types of tunnels include: trapezoidal, rectangular, circular, and irregular. Further information collected based on different tunnel section types includes:

[0081] Trapezoid: Net width on the upper section is B1, net width on the lower section is B2;

[0082] Rectangular, irregular shape: section width B;

[0083] Circle: Radius R.

[0084] (3) Construct a connection relationship matrix between lanes to identify whether different lanes are connected. Example: in

[0085] In step 503, the cross-sectional area of ​​each tunnel is calculated, S = S f +S a , where S fIt is the initial value calculated based on the tunnel section type and its basic information; in order to more accurately reflect the actual situation, S a As a compensation value, it is used to correct the S caused by the irregular shape of the roadway. f Calculate the deviation.

[0086] (1) S calculated according to different tunnel section types f for:

[0087] 1) Trapezoid: S f =(B1+B2)×H÷2

[0088] 2) Rectangular, irregular shape: S f =B×H

[0089] 3) Round: S f =π×R 2

[0090] (2) Deviation compensation S caused by the irregular shape of the roadway a The calculation is based on the AI ​​algorithm and historical data to perform data training and compensation value fitting calculation.

[0091] In step 504, the diffusion velocity of each gas in each lane is calculated. Where V j is the gas diffusion velocity in the jth lane, is the cross-sectional area of ​​the jth lane, m is the number of lanes (i.e., the second lane in Example 1) connected to lane i (i.e., the first lane in Example 1), and the specific value is determined according to the lane connection relationship matrix, where when lane A=1, V i =V w , V w is the fan speed.

[0092] Establish a prediction model for the concentration of each gas in each tunnel in is the predicted concentration of the current gas in the current lane (i.e., the first lane in Example 1) at time i in the future, C c is the concentration of the current gas in the current lane monitored at the current moment, is the rising rate of the current gas in the current lane at the current moment, is the rising rate change value of the current gas in the current lane to the future time i, T pre is the time interval to the future time i.

[0093] Calculate the rising rate of the current gas concentration in the current lane at the current moment Among them C i-1 is the gas concentration of the current gas in the current lane at the last moment, Tc It is the time interval between the current moment and the last monitored gas concentration data.

[0094] Calculate the change in the rising rate of the current gas in the current lane to the future time i The prediction model is established based on the cross-sectional area, width, height, gas diffusion velocity at different times, gas concentration, and rising rate of each tunnel as characteristic indicators combined with historical data training.

[0095] Will C c Each value is substituted into the gas concentration prediction model to calculate the gas concentration at the future time i.

[0096] In step 505, the predicted gas concentration is compared with the corresponding gas concentration threshold set. When it is greater than the set threshold (i.e., the preset concentration in Example 1), an early warning is issued to inform that the gas concentration will reach the threshold at the next time i so that preventive measures can be taken in time.

[0097] Embodiment 3:

[0098] This embodiment also provides a mine emergency dispatch system based on dynamic simulation on the basis of Embodiment 1 and Embodiment 2. Figure 6 As shown, it includes: intelligent monitoring platform, intelligent decision-making platform and emergency dispatch platform.

[0099] The intelligent monitoring platform is mainly responsible for collecting and processing data from various monitoring devices in the mine, covering multiple aspects of safety monitoring needs, including:

[0100] Gas monitoring equipment: real-time collection and analysis of concentration data and wind speed data of key gases such as methane, carbon monoxide, carbon dioxide, hydrogen sulfide and sulfur dioxide to ensure that the gas environment is within a safe range.

[0101] Personnel positioning equipment: Tracks and records the information of all underground personnel, including personal identity, type of work performed and precise location, to facilitate personnel management and rapid response in emergency situations.

[0102] Camera equipment: provides real-time monitoring images of important areas underground, helping managers to detect abnormal situations in time and take corresponding measures.

[0103] The core responsibility of the intelligent decision-making platform is to conduct in-depth analysis and simulation of the data collected by the intelligent monitoring platform to identify potential abnormal situations and generate effective emergency response plans based on them. By comprehensively evaluating multi-dimensional data such as gas concentration, personnel location, and real-time monitoring images, hidden dangers can be discovered in a timely manner, and decision makers can be assisted in formulating scientific and reasonable response strategies, thereby improving the safety management level and emergency response efficiency of the mine. The main steps include:

[0104] (1) 3D modeling of the mine

[0105] Based on the specific conditions of the on-site survey, the mine is modeled in 3D to accurately reproduce the actual state of the mine, including details such as the geological structure, tunnel layout and equipment location within the mine.

[0106] (2) Based on the historical data collected by the monitoring platform, simulation is performed in combination with a three-dimensional model and a fluid mechanics algorithm to fit the diffusion path and diffusion speed of the gas in the mine under different conditions, and then a prediction model for the mine gas circulation is established. The gas concentration in the tunnel at a future time is predicted based on the prediction model. The specific implementation method has been described in detail in Examples 1 and 2 and will not be repeated here.

[0107] (3) Based on the actual data change trend monitored by the sensor and combined with AI intelligent algorithms, the constructed mine gas circulation prediction model is corrected and fed back in real time to improve the prediction accuracy.

[0108] (4) Based on the prediction results and personnel location information, the best escape route is planned for the staff, and the best rescue route is determined for the rescue personnel. On this basis, the distribution of materials and the location of the rescue personnel are comprehensively considered to generate the best dispatch plan, which is pushed to the emergency dispatch platform for real-time dispatch by the dispatch personnel. At the same time, based on the personnel location, sensor monitoring data and rescue plan, the most relevant video surveillance images are automatically pushed to assist the dispatch personnel to grasp the on-site situation in real time and ensure the efficiency and pertinence of the emergency response.

[0109] The main function of the emergency dispatch platform is to formulate scientific and reasonable rescue plans based on the emergency events and handling suggestions pushed by the intelligent decision-making platform, and to conduct unified command and dispatch of the accident site. By efficiently coordinating rescue resources, it ensures that the emergency response is rapid and orderly, minimizes the impact of accidents, and ensures the safety of personnel. It mainly includes:

[0110] (1) Early warning

[0111] The intelligent decision-making platform predicts the future trend of monitoring indicator data by analyzing the trend of monitoring data reported by the intelligent monitoring platform and integrating the monitoring indicator prediction model constructed by AI algorithm and historical data. It helps the platform to pre-identify the time point when the monitoring indicator is about to reach the alarm value, and push the warning information to the emergency dispatch platform in time so that intervention measures can be taken quickly to effectively prevent the incident. In addition, the intelligent decision-making platform will continuously adjust and improve the prediction model based on the latest monitoring data reported by the intelligent monitoring platform and the decision-making instructions issued by the platform itself, so as to continuously improve the accuracy and reliability of the prediction.

[0112] (2) Real-time scheduling

[0113] 1) Solution Planning

[0114] In response to emergency events that have occurred, the intelligent decision-making platform responds quickly, accurately locking the location of the event and the diffusion trajectory based on real-time monitoring data and the mine gas circulation prediction model. At the same time, combined with personnel positioning information and the three-dimensional model of the mine, the optimal escape route for personnel inside the mine is intelligently planned. In addition, the intelligent decision-making platform will plan the most efficient rescue action plan based on the distribution of rescue personnel and materials outside the mine.

[0115] 2) Real-time display

[0116] The emergency dispatch platform uses the three-dimensional model inside the mine to present in real time the escape routes of mine personnel and the routes of rescue personnel provided by the intelligent decision-making platform. It also displays the real-time location of relevant personnel and their actual movement trajectory, so that dispatchers can quickly grasp the distribution of personnel, accurately judge their locations, and correct path deviations in a timely manner when necessary to ensure the safety of personnel.

[0117] In addition, the intelligent decision-making platform will push relevant video surveillance images to the emergency dispatch platform in real time based on the location of the incident, providing dispatch personnel with more intuitive and realistic on-site conditions, helping them make more accurate and effective dispatch decisions.

[0118] 3) Result feedback

[0119] The intelligent decision-making platform will dynamically adjust the escape and rescue plans based on the reported monitoring data, video surveillance analysis results and the actual operational decisions of the emergency dispatch platform, and optimize the prediction model of mine gas circulation in real time, thereby continuously improving the prediction accuracy and the accuracy of plan push.

[0120] Embodiment 4:

[0121] like Figure 7 , which is a schematic diagram of the architecture of a mine warning device based on dynamic simulation according to an embodiment of the present invention. The mine warning device based on dynamic simulation according to this embodiment includes one or more processors 21 and a memory 22. Figure 7 A processor 21 is taken as an example.

[0122] The processor 21 and the memory 22 may be connected via a bus or other means. Figure 7 The example of connecting through bus is taken in the following.

[0123] The memory 22 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs and non-volatile computer executable programs, such as the mine early warning method based on dynamic simulation in Example 1. The processor 21 executes the mine early warning method based on dynamic simulation by running the non-volatile software programs and instructions stored in the memory 22.

[0124] The memory 22 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 22 may optionally include a memory remotely arranged relative to the processor 21, and these remote memories may be connected to the processor 21 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0125] The program instructions / modules are stored in the memory 22, and when executed by the one or more processors 21, the mine early warning method based on dynamic simulation in the above-mentioned embodiment 1 is executed.

[0126] It is worth noting that the information interaction, execution process, etc. between the modules and units within the above-mentioned devices and systems are based on the same concept as the processing method embodiment of the present invention. The specific contents can be found in the description of the method embodiment of the present invention and will not be repeated here.

[0127] A person skilled in the art may understand that all or part of the steps in the various methods of the embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0128] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A mine early warning method based on dynamic simulation, characterized in that: Use gas monitoring equipment in the tunnel to monitor the gas concentration in each tunnel; the method includes: Find each second lane connected to the first lane, and calculate the gas diffusion speed of the first lane according to the wind speed of the fans in the first lane and each second lane; According to the gas diffusion velocity of the first lane, determining the change value of the gas concentration increase rate of the first lane at a future moment; The gas concentration of the first lane at a future moment is calculated based on the monitored gas concentrations at the current moment and the past moment, as well as the change in the rate of increase of the gas concentration in the first lane at a future moment, so as to provide a gas concentration warning in advance based on the gas concentration in the first lane at a future moment.

2. The mine early warning method based on dynamic simulation according to claim 1 is characterized in that: The step of finding each second lane connected to the first lane specifically includes: Establish a connection relationship matrix in advance based on the connectivity between the tunnels in the mine Wherein, t(i,j) represents the connectivity between the ith lane and the jth lane. When the ith lane is connected to the jth lane, t(i,j) = 1, otherwise, t(i,j) = 0. According to the connection relationship matrix, each second lane connected to the first lane is found.

3. The mine early warning method based on dynamic simulation according to claim 1, characterized in that: The step of calculating the gas diffusion velocity in the first lane according to the wind speed of the fans in the first lane and each of the second lanes specifically includes: Calculate the gas diffusion velocity in the first lane in, is the cross-sectional area of ​​the jth lane, m is the number of second lanes, V j is the initial diffusion velocity of the jth second lane; when the jth second lane is provided with an air inlet, V j =V j,w , V j,w is the fan speed at the air inlet of the j-th second lane. When the j-th second lane is not equipped with an air inlet, V j =0.

4. The mine early warning method based on dynamic simulation according to claim 1, characterized in that: The step of determining the change value of the rising rate of gas concentration in the first lane at a future moment according to the gas diffusion velocity in the first lane specifically includes: Calculate the change value of the gas concentration increase rate in the first lane at time i Where S is the cross-sectional area of ​​the first tunnel, L is the tunnel length of the first tunnel, H is the tunnel height of the first tunnel, V i is the gas diffusion velocity in the first channel, C c is the historical gas concentration of the first lane at the current moment, is the gas concentration rising rate of the first lane at the current moment.

5. The mine early warning method based on dynamic simulation according to claim 4 is characterized in that: The gas concentration rising rate of the first lane at the current moment Among them, C c is the historical gas concentration of the first lane at the current moment, C c-1 is the historical gas concentration of the first lane at the last moment, T c The time interval between the current moment and the previous moment.

6. The mine early warning method based on dynamic simulation according to claim 4 is characterized in that: The cross-sectional area of ​​the first tunnel is calculated by simulating the cross-section of the tunnel into a corresponding regular shape, specifically including: When the cross section of the first tunnel is simulated as a trapezoid, the tunnel cross section reference value S is calculated. f =(B1+B2)×H÷2; where B1 is the upper clear width of the cross section of the first lane, B2 is the lower clear width of the cross section of the first lane; H is the lane height of the first lane; When the cross section of the first tunnel is simulated as a circle, the tunnel cross section reference value S is calculated. f =π×R 2 ; Wherein, R is the radius of the first lane; When the cross section of the first tunnel is simulated as a rectangle, the tunnel cross section reference value S is calculated. f =B×H; where B is the cross-sectional width of the first lane; According to the tunnel section reference value S f , the cross-sectional area of ​​the first tunnel is calculated to be S = S f +S a ; Among them, S a is the preset compensation value corresponding to the first lane.

7. The mine early warning method based on dynamic simulation according to claim 1, characterized in that: The gas concentration of the first lane at a future time is calculated based on the monitored gas concentration of the first lane at a current time and a past time, and the change value of the gas concentration increase rate of the first lane at a future time, specifically including: Build a gas concentration prediction model Among them, T pre is the time interval between time i and the current time, is the gas concentration in the first lane at the future time i, C c is the gas concentration in the first lane at the current moment, is the change value of the rising rate of gas concentration in the first lane at the future time i, is the gas concentration rising rate of the first lane at the current moment.

8. The mine early warning method based on dynamic simulation according to any one of claims 1 to 7, characterized in that: The step of providing a gas concentration warning in advance according to the gas concentration of the first lane at a future time specifically includes: When the gas concentration in the first lane at a future moment is greater than a preset concentration, a gas concentration warning is issued.

9. A non-volatile computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, which are executed by one or more processors to complete the mine early warning method based on dynamic simulation as described in any one of claims 1-8.

10. A mine early warning device based on dynamic simulation, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the mine early warning method based on dynamic simulation as described in any one of claims 1-8.

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