Cooperative early warning method and system for fire in goaf
By obtaining multiple types of characteristic indicators underground in coal mines for spectral analysis and machine learning, the problem of inaccurate data caused by damage to goaf fire monitoring equipment was solved, accurate fire warning and automated fire extinguishing were achieved, and the reliability of goaf fire warning and the level of coal mine safety production were improved.
Patent Information
- Application Number
- CN202510996697.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-18
- Publication Date
- 2025-10-10
AI Technical Summary
In existing technologies for fire monitoring in coal mine goafs, equipment damage leads to inaccurate data collection, affecting the reliability of fire warnings.
By obtaining multiple characteristic indicators of the coal mine goaf, spectral analysis is performed to determine the fire warning characteristics, and machine learning algorithms are used to predict the fire warning level, and fire extinguishing equipment is controlled in combination with fire extinguishing strategies.
It improves the reliability of coordinated early warning of goaf fires, enables accurate identification of potential fire hazards and automatic matching of fire-fighting strategies, effectively avoids misjudgments and missed judgments, and improves the safety production level of coal mines.
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Figure CN120766418A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of fire early warning, and more particularly relates to a goaf fire cooperative early warning method and system. BACKGROUND
[0002] In underground coal mining, goaf thermal dynamic disasters have various forms, and the development speed and destructive power of different disasters are quite different. At present, the monitoring method of goaf fire is generally to combine the temperature exceeding the standard or the related gas exceeding the standard to perform early warning. However, in some extreme cases, the collection equipment may have been damaged, and it is difficult to accurately collect related data, thereby affecting the reliability of goaf fire early warning. SUMMARY
[0003] The application aims to provide a goaf fire cooperative early warning method and system to improve the reliability of goaf fire cooperative early warning.
[0004] In a first aspect, the application provides a goaf fire cooperative early warning method, including: acquiring target data, the target data including multiple types of characteristic indexes of a goaf in an underground coal mine; performing spectral analysis on the target data to determine multiple fire early warning features, the fire early warning features corresponding to the goaf regions one by one; predicting, according to each fire early warning feature, a fire early warning level of the goaf region corresponding to the fire early warning feature.
[0005] In a second aspect, the application provides a goaf fire cooperative early warning system, including: a data acquisition module configured to acquire target data, the target data including multiple types of characteristic indexes of a goaf in an underground coal mine; the goaf including multiple goaf regions; a data analysis module configured to perform spectral analysis on the target data to determine multiple fire early warning features, the fire early warning features corresponding to the goaf regions one by one; a hierarchical early warning module configured to predict, according to each fire early warning feature, a fire early warning level of the goaf region corresponding to the fire early warning feature.
[0006] In a third aspect, the application provides an electronic device including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the goaf fire cooperative early warning method described above when executing the computer program.
[0007] In a fourth aspect, the application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the goaf fire cooperative early warning method described above.
[0008] In a fifth aspect of an embodiment of the present application, a computer program product is provided, comprising a computer program or computer executable instructions. When the computer program or computer executable instructions are executed by a processor, the steps of the above-mentioned goaf fire collaborative warning method are implemented.
[0009] The beneficial effects of the goaf fire collaborative early warning method and system provided by the embodiments of the present application are: This embodiment of the application obtains multiple characteristic indicators of coal mine goafs and uses spectral analysis to determine the fire warning characteristics of each goaf, such as the absorption peak intensity of specific wavelengths and the distribution of thermal radiation energy. Based on each characteristic, the corresponding goaf fire collaborative warning level is predicted, thereby increasing data diversity and improving the reliability of goaf fire collaborative warning. BRIEF DESCRIPTION OF THE DRAWINGS
[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
[0011] Figure 1 A flow chart of a goaf fire collaborative early warning method provided in one embodiment of the present application; Figure 2 An infrared spectrum of mine disaster gases provided in one embodiment of the present application; Figure 3 A structural block diagram of a goaf fire collaborative early warning system provided in one embodiment of the present application; Figure 4 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0012] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.
[0013] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.
[0014] Please refer to Figure 1 , Figure 1A flowchart of a collaborative early warning method for goaf fire provided in an embodiment of the present application can be executed by an electronic device. The method may include: S101 to S103.
[0015] S101, acquiring target data, where the target data includes multiple types of characteristic indicators of a goaf in an underground coal mine; the goaf includes multiple goaf areas.
[0016] In the embodiment of the present application, the goaf is the underground space left after the coal mining operation is completed. Since coal mining changes the original geological structure and environmental conditions, this area is prone to accumulation of harmful gases, and there are safety hazards such as spontaneous combustion, gas explosion, and roof collapse. Therefore, it needs to be closely monitored.
[0017] The target data can be a variety of indicators collected by sensors installed in coal mine goafs. These sensors are used to monitor gas or environmental parameters in the goafs, facilitating subsequent fire warnings and disaster assessments. These data are collected by sensor arrays located at different locations, with different sensors being deployed in different goafs.
[0018] For example, in actual applications, data collection is achieved by deploying a sensor array in the goaf, converting physical signals into electrical or digital signals. This data is then stably transmitted to a surface monitoring center or underground data processing station using wireless transmission technologies (such as ZigBee, 4G / 5G) or wired transmission methods (optical fiber, cables).
[0019] The multiple characteristic indicators include natural characteristic indicators of residual coal oxidation in the goaf of coal mines and characteristic indicators of gas combustion and explosion, which can be gas characteristics.
[0020] Specifically, these include gas (methane) concentration, a flammable and explosive gas commonly found in coal mines. When its concentration in the air reaches a certain range, it can easily cause an explosion upon contact with a fire source; carbon monoxide concentration, a product of spontaneous or incomplete coal combustion, which serves as an important early warning indicator for fires; and oxygen concentration. Excessively low oxygen concentrations can threaten the lives of underground personnel and affect the combustion reaction. These gas parameters are detected by gas sensors, such as catalytic combustion sensors for gas concentration and electrochemical sensors for carbon monoxide and oxygen concentrations.
[0021] Other indicators may include temperature, humidity, or roof pressure. An abnormally high temperature in the goaf may indicate spontaneous combustion of coal. Humidity fluctuations can affect the coal oxidation process. A sudden increase or abnormal change in roof pressure may lead to a roof collapse. Temperature is measured using thermocouples or infrared temperature sensors, humidity is acquired using humidity sensors, and roof pressure is monitored in real time using pressure sensors.
[0022] In the embodiments of the present application, goaf data can be collected at regular intervals. This data collection cycle utilizes a dynamic adjustment strategy. In the absence of a disaster, sensors in non-critical areas collect data at a lower frequency, such as every 10-30 minutes, to reduce data transmission pressure and equipment energy consumption. In key areas, data is collected every 1-5 minutes to ensure timely monitoring of safety conditions in critical areas.
[0023] When the monitoring system discovers signs of disasters such as fire through data analysis, sensors in all areas immediately switch to high-frequency collection mode, even collecting data once per second, so as to track the development of disasters in real time and provide accurate and timely data support for emergency decision-making.
[0024] S102, performing spectral analysis on the target data to determine a plurality of fire warning features, wherein the fire warning features correspond to the goaf areas one by one.
[0025] Specifically, the method may include: constructing a spectral image of the mine based on the target data; predicting fire areas in the spectral image; and extracting fire areas from the spectral image. Based on the target data in each fire area, multiple fire warning features are determined for that fire area, where the fire area is defined as an area within the goaf.
[0026] For example, embodiments of the present application can deploy distributed spectral probes underground in coal mines to achieve gridded collection of spectral data within the three-dimensional space of goafs. After obtaining the relevant spectral data, spatial coordinates can be coupled with the spectral data to construct a two-dimensional spectral image with wavelength as the horizontal axis and spatial position as the vertical axis. Pseudo-color coding technology can be used to convert spectral absorption intensity into a visual color matrix, intuitively presenting the spatial distribution characteristics of gas concentration, which can serve as a fire warning feature for the corresponding goaf area.
[0027] The embodiments of the present application can combine the distribution law and distribution characteristics of mine alkane gases in the spectral wavenumber band to determine the unique fingerprint area location of mine alkane gases; through the mine polar gas spectral quantitative analysis model, the characteristic absorption peaks and wavenumber intervals of mine polar gases are extracted to obtain different gas characteristics.
[0028] Figure 2 The infrared spectrum of mine disaster gas provided by an embodiment of the present application is as follows: Figure 2 As shown, it includes qualitative and quantitative analysis of 11 coal mine disaster gases (CH4, C2H6, C3H8, iC4H10, nC4H10, C2H4, C3H6, C2H2, SF6, CO and CO2) by infrared spectra.
[0029] S103: Predicting the fire warning level of the goaf area corresponding to each fire warning feature based on the fire warning feature.
[0030] Specifically, the method can include: calculating the similarity between the target fire warning feature and each standard fire warning feature, obtaining a plurality of similarities, the target fire warning feature being a feature in the plurality of fire warning features; selecting the standard fire warning feature with the largest similarity as the target standard fire warning feature; and inputting the target standard fire warning feature into a pre-trained fire grading warning model to determine the fire warning level of the goaf corresponding to the target fire warning feature.
[0031] The development cycle of a coal mine fire includes a latent period, an initial combustion period, a development period, and a fierce period, each of which corresponds to different standard fire warning features. The embodiments of the present application can establish a four-level standard feature library, each level including spectral features, gas ratio features, and spatial features, etc.
[0032] The embodiments of the present application can collect historical fire data in coal mine underground, and classify the features by K-means clustering or analytic hierarchy process (AHP) to form a standardized feature vector set.
[0033] Optionally, an LSTM or XGBoost algorithm can be used to construct a corresponding fire grading warning model, and the corresponding target standard fire warning feature is input into the fire grading warning model, which can output a corresponding warning level. The warning level can include a first warning, a second warning, a third warning, and a fourth warning. The first warning level is the highest, and the fourth warning level is the lowest. Each goaf can have a warning level.
[0034] The embodiments of the present application obtain multiple feature indicators of the goaf in the coal mine underground, determine the fire warning features of each goaf by spectral analysis, such as specific wavelength absorption peak intensity and thermal radiation energy distribution, etc. According to the features, the fire warning level of the corresponding goaf is predicted to increase the data diversity and improve the reliability of the goaf fire warning.
[0035] In an embodiment of the present application, the target data is subjected to spectral analysis to determine a plurality of fire warning features, including: constructing a spectral image of the coal mine underground based on the target data; predicting a fire area from the spectral image and extracting the fire area from the spectral image; determining a plurality of fire warning features of the fire area from the target data in each fire area, the fire area being an area in the goaf.
[0036] The spectral image of the coal mine underground is constructed based on the target data, including: If there is no target data in the first goaf area, data expansion is performed based on the target data of at least one second goaf area corresponding to the first goaf area to obtain the target data of the first goaf area, and at least one second goaf area is a goaf area in which target data exists and the distance from the first goaf area is less than a preset distance; a spectral image of the mine is constructed based on the target data of each goaf area.
[0037] An optional embodiment of the present application can determine a blank area without data acquisition equipment installed, i.e., a first goaf area, based on equipment deployment information in the coal mine goaf. Subsequently, a second goaf area surrounding the first goaf area is determined based on distance. A spatial interpolation algorithm is used to infer data from the first goaf area based on data from at least one second goaf area to obtain expanded data for the first goaf area.
[0038] In this embodiment of the present application, the original collected data and the expanded data can be fused to construct a spectral image. The original collected data is given a higher weight (e.g., a weight coefficient of 0.7-0.9) and the expanded data is given a lower weight (e.g., a weight coefficient of 0.1-0.3) to reflect the difference in data reliability.
[0039] For example, in the spectral image, the color depth of the original data points is enhanced according to the actual weight, and the color depth of the expanded data points is weakened proportionally.
[0040] In this embodiment of the present application, the fused spectral image can be used to predict the fire area and extract the abnormal area. Based on the original data and expanded data (weighted by weight) of each fire area, the fire warning characteristics of the area are determined.
[0041] This embodiment of the application constructs spectral images and extracts fire area characteristics, converting abstract data into visual image analysis to accurately locate abnormal areas. It also uses surrounding data interpolation to expand data in areas without equipment, and combines weight distribution to enhance the reliability of the original data and fill monitoring blind spots.
[0042] In one embodiment of the present application, after predicting the fire warning level of the goaf area corresponding to each fire warning feature according to the fire warning feature, the method further includes: Determine the disaster type and disaster level based on the fire warning level; The fire extinguishing strategy is determined according to the disaster type and disaster level, and the target equipment is controlled to extinguish the fire according to the fire extinguishing strategy. The target equipment includes various types of fire extinguishing equipment installed in the coal mine.
[0043] The specific steps are as follows: First, based on the fire warning level, refer to the preset level-disaster mapping table to determine the specific disaster type (such as smoldering, open fire) and disaster level (low, medium, high).
[0044] Then, based on the obtained disaster type and level, a pre-prepared strategy library is called to select an appropriate fire extinguishing strategy.
[0045] Finally, the system converts the fire extinguishing strategy into control instructions to accurately control the fire extinguishing equipment in the coal mine, such as starting the automatic sprinkler device and releasing fire extinguishing gas, to achieve efficient and automated fire extinguishing, thereby reducing the harm of coal mine fire.
[0046] The corresponding fire extinguishing strategy can be determined in combination with the disaster type and disaster level to control the corresponding fire extinguishing equipment.
[0047] Specifically, if the determination is a gas explosion disaster, different control methods can be determined based on different levels of disaster.
[0048] For example, when the determination is a low-level risk, the strategy of strengthening ventilation is preferred, which uses underground ventilation equipment such as main ventilators and local ventilators to increase the air volume in the goaf and reduce the gas concentration to within a safe range. At the same time, all fire sources are strictly prohibited from entering the area to prevent the gas from being ignited.
[0049] When the determination is a medium-level risk, inert gas fire extinguishing method can be used. By injecting nitrogen, carbon dioxide and other inert gases into the goaf, the oxygen concentration is diluted to suppress the combustion reaction and achieve the purpose of fire extinguishing. At this time, the inert gas delivery pipeline and release device pre-installed in the underground are started to operate according to the calculated gas injection amount and speed.
[0050] When the determination is a high-level risk, a comprehensive fire extinguishing strategy is adopted. First, use the fireproof wall to close the goaf to cut off the oxygen supply and prevent the spread of fire. Then, use the foam fire extinguishing equipment to inject high-multiple foam into the closed area to cover the surface of the burning material, isolate air and reduce temperature. In addition, dry powder fire extinguishing system can be used to extinguish local open fires and control the fire.
[0051] Specifically, if the determination is a coal spontaneous combustion disaster, different control methods can also be determined based on different levels of disaster.
[0052] For example, when the determination is a low-level risk, mud and fly ash slurry can be prepared on the ground to fill the coal seam gaps, isolate oxygen and inhibit coal oxidation and spontaneous combustion. At the same time, the water injection amount in the goaf can be appropriately increased to reduce the temperature of the coal body.
[0053] When the determination is a medium-level risk, in addition to continuing grouting, a retardant fire extinguishing method can also be introduced. The retardant solution is sprayed or injected onto the surface and inside of the coal body to prevent the oxidation process of the coal and slow down the spontaneous combustion rate. Special retardant spraying equipment can be used to operate according to a certain concentration and spraying amount.
[0054] When the decision is a level risk, a composite inert gas-gel fire extinguishing strategy is adopted. First, inert gas is injected to reduce the oxygen concentration, and then composite gel is injected, which can form a solid heat and oxygen isolation layer on the surface of the coal body, effectively extinguishing the fire and preventing rekindling. At the same time, cooling equipment such as an underground refrigeration unit is used to reduce the temperature of the goaf environment to assist in extinguishing the fire.
[0055] In the embodiments of the present application, after determining the disaster type and level and the corresponding fire extinguishing strategy, the monitoring system transmits control instructions to the fire extinguishing equipment control terminal through an underground communication network such as an industrial Ethernet network or a wireless communication network. After receiving the instructions, the control terminal starts the corresponding fire extinguishing equipment according to the requirements of the fire extinguishing strategy. For example, for inert gas fire extinguishing, the control terminal will open the inert gas storage tank valve and start the gas delivery pump to deliver inert gas to the goaf at a set flow rate and pressure; for foam fire extinguishing equipment, the control terminal will start the foam generator and adjust the mixing ratio of foam liquid and water to produce appropriate foam and deliver it to the fire area through the pipeline.
[0056] In addition, during the operation of the fire extinguishing equipment, sensors on the equipment collect real-time operating parameters such as gas flow, foam production, equipment pressure, etc., and feed them back to the monitoring system. The monitoring system analyzes these parameters in real time, and if it finds that the equipment is operating abnormally or the fire extinguishing effect is not as expected, it adjusts the equipment operating parameters or switches to a backup device in a timely manner.
[0057] After a period of fire extinguishing operation, the fire extinguishing effect is evaluated by collecting data such as gas parameters and temperature in the goaf. If the disaster is effectively controlled, the operating intensity of the fire extinguishing equipment is gradually reduced; if the fire extinguishing effect is not good, the disaster situation is reanalyzed, the fire extinguishing strategy is adjusted, other fire extinguishing equipment is enabled or the operating intensity of the existing equipment is increased, until the fire is completely extinguished.
[0058] The embodiments of the present application take into account that the goaf in a coal mine underground is prone to spontaneous combustion due to factors such as closed environment and accumulation of residual coal, which threatens safety in production. The embodiments of the present application accurately identify potential fire hazards and determine the disaster type and level by obtaining multiple characteristic index data such as temperature, oxygen concentration, and carbon monoxide concentration in the goaf and using deep analysis with big data and machine learning algorithms. For different disaster scenarios, the system automatically matches fire extinguishing strategies such as nitrogen inertization, gel plugging, and water injection cooling, and drives distributed fire extinguishing equipment such as sprinkler devices, grouting pumps, and inert gas generators to work cooperatively. Real-time monitoring and feedback mechanisms continuously verify the fire extinguishing effect, dynamically optimize the strategy, effectively avoid misjudgment and missed judgment, contain the fire in the incubation stage, and significantly improve the reliability of goaf fire extinguishing early warning and the level of safety in production of coal mines.
[0059] In an embodiment of the present application, after controlling the target device according to the fire extinguishing strategy, the method further comprises: obtaining monitoring data, the monitoring data comprising a plurality of characteristic indexes of the coal mine underground goaf during the fire extinguishing process; determining whether the fire extinguishing is up to standard according to the monitoring data; if the fire extinguishing is up to standard, maintaining the current fire extinguishing strategy; if the fire extinguishing is not up to standard, adjusting the fire extinguishing strategy according to the monitoring data, controlling the target device according to the adjusted fire extinguishing strategy to extinguish the fire, and performing the step of obtaining monitoring data.
[0060] Specifically, determining whether the fire extinguishing is up to standard according to the monitoring data comprises: if a target characteristic index is less than or equal to a threshold corresponding to the target characteristic index, marking the target characteristic index as a standard characteristic index, the target characteristic index being an index in the plurality of characteristic indexes in the monitoring data; if a proportion of the standard characteristic indexes is greater than or equal to a preset proportion, determining that the fire extinguishing is up to standard; if the proportion of the standard characteristic indexes is less than the preset proportion, determining that the fire extinguishing is not up to standard.
[0061] In an embodiment of the present application, before determining whether the fire extinguishing is up to standard according to the monitoring data, the method further comprises: supplementing data of a second region based on monitoring data of a first region to obtain supplemented data of the second region, the first region being a region in the coal mine underground goaf where the collection device is not damaged, and the second region being a region in the coal mine underground goaf where the collection device is damaged.
[0062] Correspondingly, determining whether the fire extinguishing is up to standard according to the monitoring data comprises: determining whether the fire extinguishing is up to standard according to the monitoring data of the first region and the supplemented data of the second region.
[0063] Specifically, supplementing data of the second region based on the monitoring data of the first region to obtain the supplemented data of the second region comprises: if an area of the first region is greater than an area of the second region, supplementing data of a second target collection point according to the monitoring data of a first target collection point and a first influencing factor to obtain monitoring data of the second target collection point; if the area of the first region is less than or equal to the area of the second region, supplementing data of the second target collection point according to the monitoring data of the first target collection point and a second influencing factor to obtain monitoring data of the second target collection point; wherein the first target collection point is a collection point in the first region closest to the second target collection point, and the second target collection point is a collection point in the second region; the first influencing factor comprises a distance between the first target collection point and the second target collection point and a ventilation trend of the second target collection point; and the second influencing factor comprises a time factor of the first target collection point and a fire spread speed.
[0064] In the embodiment, the monitoring data of the first region is essentially the monitoring data of the first target collection point, and the monitoring data of the second region is essentially the monitoring data of the second target collection point.
[0065] In the embodiment, when the area of the first region is greater than the area of the second region, the monitoring data of the second target collection point can be determined by the first formula.
[0066] The first formula comprises:
[0067] wherein S2 is the monitoring data of the second target collection point, S1 is the monitoring data of the first target collection point, d represents the distance between the first target collection point and the second target collection point, k represents the attenuation coefficient, and a represents the ventilation correction coefficient. The ventilation trend can be characterized according to the wind speed, for example:
[0068] wherein v represents the actual wind speed, v0 represents the reference wind speed, and β is the ventilation influence coefficient, which is positive when the wind is judged to be favorable and negative when the wind is judged to be adverse, and is usually valued at (-0.3, 0.5).
[0069] In the embodiment, when the area of the first region is less than or equal to the area of the second region, the monitoring data of the second target collection point can be determined by the second formula.
[0070] The second formula comprises:
[0071] wherein, represents the monitoring data of the second target collection point at time t, represents the change rate of the monitoring data of the first target collection point at time t, represents the monitoring data of the first target collection point at time t, represents the trend attenuation factor, when the fire is closer to the second target collection point, tends to 1.
[0072] The embodiment dynamically switches the supplement strategy according to the area relationship, focuses on the spatial distance attenuation when the area is large, and focuses on the time trend prediction when the area is small, thereby improving the data reliability in different scenarios. Through the ventilation trend and the spread speed parameters, the static space supplement is combined with the dynamic development of the fire, which is more consistent with the actual scene of the goaf fire extinguishing. The scientific supplement method avoids the misjudgment of the fire extinguishing effect caused by the missing of the second region data, and provides comprehensive data support for decision-making.
[0073] Corresponding to the goaf fire cooperative early warning method of the above embodiment, Figure 3 A structural block diagram of a goaf fire cooperative warning system provided by an embodiment of the present application is shown. For ease of illustration, only parts related to the embodiments of the present application are shown. Referring to Figure 3 The goaf fire cooperative warning system 20 includes a data acquisition module 201, a data analysis module 202, and a hierarchical warning module 203.
[0074] The data acquisition module 201 is configured to acquire target data, the target data including multiple types of feature indicators of a goaf underground of a coal mine; the goaf includes multiple goaf areas; The data analysis module 202 is configured to perform spectral analysis on the target data to determine multiple fire warning features, the fire warning features corresponding one-to-one to the goaf areas. The hierarchical warning module 203 is configured to predict, according to each fire warning feature, a fire warning level of the goaf area corresponding to the fire warning feature.
[0075] In an embodiment of the present application, the hierarchical warning module 203 is specifically configured to calculate similarities between a target fire warning feature and each standard fire warning feature, to obtain multiple similarities, the target fire warning feature being a feature in the multiple fire warning features; select a standard fire warning feature with the largest similarity as a target standard fire warning feature; and input the target standard fire warning feature into a pre-trained fire hierarchical warning model to determine the fire warning level of the goaf area corresponding to the target fire warning feature.
[0076] In an embodiment of the present application, the data analysis module 202 is specifically configured to construct a spectral image underground of the mine based on the target data. perform fire area prediction on the spectral image to extract a fire area in the spectral image; determine, according to target data in each fire area, multiple fire warning features of the fire area, the fire area being an area in the goaf area.
[0077] In an embodiment of the present application, the data analysis module 202 is specifically configured to, if a first goaf area has no target data, perform data augmentation on target data of at least one second goaf area corresponding to the first goaf area to obtain target data of the first goaf area, the at least one second goaf area being a goaf area having target data and being less than a preset distance from the first goaf area. construct a spectral image underground of the mine based on the target data of each goaf area.
[0078] In an embodiment of the present application, the system 20 can further include: a fire decision module configured to, after predicting, according to each fire warning feature, a fire warning level of the goaf area corresponding to the fire warning feature, determine a disaster type and a disaster level according to the fire warning level. The fire extinguishing strategy is determined according to the disaster type and disaster level, and the target equipment is controlled to extinguish the fire according to the fire extinguishing strategy. The target equipment includes various types of fire extinguishing equipment installed in the coal mine.
[0079] In one embodiment of the present application, the system 20 may further include: The monitoring module is used to obtain monitoring data after controlling the target equipment to extinguish the fire according to the fire extinguishing strategy. The monitoring data includes multiple types of characteristic indicators of the coal mine goaf during the fire extinguishing process; judge whether the fire extinguishing meets the standards based on the monitoring data; if the fire extinguishing meets the standards, maintain the current fire extinguishing strategy; if the fire extinguishing does not meet the standards, adjust the fire extinguishing strategy according to the monitoring data, control the target equipment to extinguish the fire according to the adjusted fire extinguishing strategy, and execute the steps of obtaining monitoring data.
[0080] In one embodiment of the present application, the monitoring module is further configured to mark the target characteristic indicator as a qualified characteristic indicator if the target characteristic indicator is less than or equal to a threshold value corresponding to the target characteristic indicator, where the target characteristic indicator is an indicator among multiple types of characteristic indicators in the monitoring data; If the proportion of characteristic indicators that meet the standards is greater than or equal to the preset proportion, the fire extinguishing is determined to meet the standards; If the proportion of characteristic indicators that meet the standards is less than the preset proportion, it is determined that the fire extinguishing fails to meet the standards.
[0081] See also Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 4 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 3 The functions of the data acquisition module 201, the data analysis module 202 and the disaster decision module 203 are shown.
[0082] It should be understood that, in the embodiments of the present application, the processor 301 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.
[0083] The input device 302 can include a touchpad, a fingerprint collection sensor (for collecting fingerprint information and direction information of a fingerprint of a user), a microphone, etc., and the output device 303 can include a display (LCD, etc.), a speaker, etc.
[0084] The memory 304 can include read-only memory and random access memory, and provide instructions and data for the processor 301. A part of the memory 304 can also include non-volatile random access memory.
[0085] In specific implementations, the processor 301, the input device 302 and the output device 303 described in the embodiments of the present application can execute the implementation manners described in the goaf fire cooperative warning method provided by the embodiments of the present application, and can also execute the implementation manners of the electronic device described in the embodiments of the present application, which will not be described here.
[0086] In another embodiment of the present application, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. The computer program includes program instructions, and the program instructions are executed by a processor to implement all or part of the processes of the above-mentioned embodiment methods. The computer program can also be used to instruct related hardware to complete, and the computer program can be stored in a computer readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.
[0087] The computer readable storage medium can be an internal storage unit of the electronic device of any of the preceding embodiments, such as a hard disk or a memory of the electronic device. The computer readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, or the like provided on the electronic device. Further, the computer readable storage medium can include both the internal storage unit and the external storage device of the electronic device. The computer readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer readable storage medium can also be used to temporarily store data that has been output or will be output.
[0088] The computer program product includes computer executable instructions or a computer program stored in a computer readable storage medium. The processor of the electronic device reads the computer executable instructions from the computer readable storage medium. The processor executes the computer executable instructions to cause the electronic device to perform the goaf fire cooperative early warning method described above in the embodiments of the present application.
[0089] Those skilled in the art can understand that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.
[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic device and the units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.
[0091] In several embodiments provided in the present application, it should be understood that the disclosed electronic device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There can be another division manner in actual implementation. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces or units, and can also be electrical, mechanical or other forms of connection.
[0092] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.
[0093] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.
Claims
1. A goaf fire collaborative early warning method, characterized in that: include: Acquire target data, wherein the target data includes multiple characteristic indicators of a goaf in a coal mine; the goaf includes multiple goaf areas; Performing spectral analysis on the target data to determine a plurality of fire warning features, wherein the fire warning features correspond one-to-one to the goaf areas; The fire warning level of the goaf area corresponding to each fire warning feature is predicted based on the fire warning feature.
2. The goaf fire collaborative early warning method according to claim 1, characterized in that: The method of predicting the fire warning level of the goaf area corresponding to each fire warning feature according to the fire warning feature includes: Calculating similarities between a target fire warning feature and each standard fire warning feature to obtain a plurality of similarities, wherein the target fire warning feature is a feature among the plurality of fire warning features; Select the standard fire warning feature with the greatest similarity as the target standard fire warning feature; The target standard fire characteristics are input into a pre-trained fire grade warning model to determine the fire warning level of the goaf area corresponding to the target fire warning characteristics.
3. The goaf fire collaborative early warning method according to claim 1, characterized in that: Perform spectral analysis on the target data to determine multiple fire warning features, including: constructing a spectral image of the mine based on the target data; Predicting a fire area on the spectral image, and extracting the fire area in the spectral image; A plurality of fire warning features of each fire area are determined based on target data of the fire area, where the fire area is an area within the goaf.
4. The goaf fire collaborative early warning method according to claim 3, characterized in that: The constructing of a spectral image of the mine based on the target data includes: If there is no target data in the first goaf area, data expansion is performed based on the target data of at least one second goaf area corresponding to the first goaf area to obtain the target data of the first goaf area, wherein the at least one second goaf area is a goaf area that has target data and is less than a preset distance from the first goaf area; A spectral image of the mine is constructed based on the target data of each goaf area.
5. The goaf fire collaborative early warning method according to claim 1, characterized in that: After predicting the fire warning level of the goaf area corresponding to each fire warning feature according to the fire warning feature, the method further includes: Determine the disaster type and disaster level according to the fire warning level; A fire extinguishing strategy is determined according to the disaster type and the disaster level, and target equipment is controlled to extinguish the fire according to the fire extinguishing strategy. The target equipment includes multiple types of fire extinguishing equipment installed in the coal mine.
6. The goaf fire collaborative early warning method according to claim 5, characterized in that: After controlling the target device to extinguish the fire according to the fire extinguishing strategy, the method further includes: Acquiring monitoring data, wherein the monitoring data includes multiple characteristic indicators of a goaf in an underground coal mine during a fire extinguishing process; Determining whether the fire extinguishing meets the standards based on the monitoring data; If the fire extinguishing meets the standards, the current fire extinguishing strategy will be maintained; If the fire extinguishing fails to meet the standards, the fire extinguishing strategy is adjusted according to the monitoring data, the target device is controlled to extinguish the fire according to the adjusted fire extinguishing strategy, and the step of obtaining the monitoring data is executed.
7. The goaf fire coordinated early warning method according to claim 6, characterized in that: The determining whether the fire extinguishing meets the standards based on the monitoring data includes: If the target characteristic indicator is less than or equal to the threshold value corresponding to the target characteristic indicator, the target characteristic indicator is marked as a qualified characteristic indicator, wherein the target characteristic indicator is an indicator among the multiple types of characteristic indicators in the monitoring data; If the proportion of characteristic indicators that meet the standards is greater than or equal to the preset proportion, the fire extinguishing is determined to meet the standards; If the proportion of characteristic indicators that meet the standards is less than the preset proportion, it is determined that the fire extinguishing fails to meet the standards.
8. A goaf fire collaborative early warning system, characterized in that: include: A data acquisition module is used to acquire target data, wherein the target data includes multiple characteristic indicators of a goaf in an underground coal mine; the goaf includes multiple goaf areas; a data analysis module, configured to perform spectral analysis on the target data to determine a plurality of fire warning features, wherein the fire warning features correspond one-to-one to the goaf areas; The hierarchical warning module is used to predict the fire warning level of the goaf area corresponding to each fire warning feature based on the fire warning feature.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.