An acoustic-based coal temperature full-process monitoring experimental device and method

Abstract

The present invention discloses an acoustic-based full-process coal temperature monitoring experimental device and method. The acoustic temperature sensing monitoring system in the experimental device integrates an acoustic wave generator module, a full-process coupled transceiver transmission module, a signal data processing and analysis module, a control module, etc., and aims to combine active and passive detection technologies to comprehensively monitor the temperature changes of the coal body during the full cycle of heating. On the basis of the integrated and upgraded control and data acquisition system, air supply and environmental control system, and central control system, the test is based on the spatiotemporal evolution path of the high-temperature point of spontaneous combustion of large coal and the thermal incubation visualization experimental device. It can accurately capture the temperature field distribution while realizing the analysis of the natural oxidation characteristics of the whole process of coal. The present invention also aims to reduce the amount of coal samples and improve the repeatability of the experiment by optimizing the design of the device, and at the same time improve the data availability and experimental safety by integrating neural network algorithms and three-dimensional temperature field visualization technology.

Description

Technical Field

[0001] The invention relates to the field of mine safety, and relates to but is not limited to an acoustic-based full-process coal temperature monitoring experimental device and method. Background Art

[0002] Coal spontaneous combustion is a serious problem in mine safety accidents, which may lead to fire, explosion or other harmful events. During the spontaneous combustion of coal, the temperature of the coal body gradually increases, usually accompanied by the release of various gases, such as carbon monoxide, carbon dioxide and methane. Therefore, real-time monitoring of coal temperature changes is crucial for early prevention of coal spontaneous combustion.

[0003] Prior art coal temperature monitoring relies primarily on thermocouples and other contact sensors that are inserted directly into the coal pile to measure temperature. While this approach can provide temperature data, it has several limitations. First, contact sensors can be damaged by high temperatures or corrosive gases, resulting in high maintenance costs and short lifespans. Second, thermocouples can have long response times and fail to reflect rapid temperature changes in real time. Finally, when the coal pile to be measured is large or in an inaccessible location, the installation and maintenance of these sensors becomes more difficult and dangerous.

[0004] The existing coal spontaneous combustion detection technology has at least the following problems: 1) It fails to provide a continuous monitoring solution for the entire temperature range and the entire cycle, resulting in the inability to effectively predict and warn of the risk of coal spontaneous combustion. 2) The accuracy and efficiency of temperature measurement under high temperature conditions of the existing methods are limited, and it is necessary to develop a technology that can maintain high accuracy and rapid response under extreme temperatures. 3) Traditional contact temperature measuring instruments such as thermocouples are easily damaged at high temperatures, and a non-contact temperature measurement method with greater durability and lower maintenance costs is needed. 4) The traditional coal spontaneous combustion experimental method has complicated operating steps, long experimental cycles, large amounts of coal, and high costs, making it difficult to conduct large-scale or repetitive experiments. 5) It is difficult to effectively integrate and analyze the large amount of experimental data collected, and there is a lack of practical data processing and three-dimensional visualization tools to intuitively display the temperature field.

[0005] In order to overcome the above limitations, the present invention proposes an improved monitoring system that uses non-contact acoustic temperature measurement technology. Acoustic temperature measurement technology is based on the principle that the propagation speed of sound waves in the coal pile is related to temperature, and indirectly measures the temperature by transmitting and receiving sound wave signals. The advantages of this technology include: no direct contact with the coal body, reducing equipment loss; can be applied to a wide temperature range; and can achieve multi-point and comprehensive temperature monitoring. Summary of the invention

[0006] In view of this, an embodiment of the present invention provides an acoustic-based full-process coal temperature monitoring experimental device and method, which aims to monitor the temperature changes of the coal body during the spontaneous combustion process through a full cycle through non-contact technology.

[0007] The technical solution of the embodiment of the present invention is achieved as follows:

[0008] In the first aspect, an embodiment of the present invention provides an acoustic-based coal temperature full-process monitoring experimental device, including an acoustic temperature sensing monitoring system, a control and data acquisition system, an air supply and environmental control system, and a central control system, wherein the control and data acquisition system is used to collect gas, detect temperature, and transmit data; the air supply and environmental control system is used to control the air flow temperature and humidity entering the coal body; the acoustic temperature sensing monitoring system includes:

[0009] An acoustic wave generator module, used to generate acoustic wave signals within a specific frequency range, and the frequency and intensity are adjusted by the central control system;

[0010] The whole process coupled transceiver transmission module includes an active acoustic detection system and an acoustic emission sensor; wherein the active acoustic detection system is used for monitoring the early and mid-stage processes of coal spontaneous combustion, including multiple acoustic wave transducers deployed in a three-dimensional array, when one acoustic wave transducer is controlled to transmit an acoustic wave signal, the other acoustic wave transducers located on the opposite side serve as receivers to receive the acoustic wave signal; the acoustic wave transmission duct is used to propagate the acoustic wave in a directional manner; the acoustic emission sensor is used as a passive test module for monitoring the high-temperature combustion process of coal spontaneous combustion in the later stage; the central control system monitors the coal body temperature in real time to automatically switch the working modes of the active acoustic detection system and the acoustic emission sensor;

[0011] The signal processing and data analysis module is used to process the received acoustic wave signals, extract key data reflecting the temperature change of the coal body, estimate the temperature value of any point in the coal body area using the Kriging interpolation method, and perform regression analysis on the relationship between the acoustic wave propagation path and the flight time and temperature in combination with the neural network. The temperature field model is obtained by iterative optimization to predict the temperature results and reconstruct the three-dimensional temperature field inside the coal body;

[0012] The control module is used to control the operation of the acoustic temperature sensing monitoring system and manage the communication and data exchange between the modules.

[0013] In a second aspect, an embodiment of the present invention provides a method for monitoring the whole process of coal temperature using the experimental device described in the first aspect, the method comprising:

[0014] The coal samples are uniformly filled into a rectangular coal sample box and an air tightness test is carried out; the room temperature oxidation experiment from 30 degrees to 150 degrees and the high temperature oxidation experiment from 150 degrees to 900 degrees of the loose coal under constant air volume conditions are respectively completed; all acoustic transducers are numbered and the coal area is divided into logical blocks; the heating temperature threshold is set in the room temperature oxidation and high temperature oxidation experiments and the acoustic temperature sensing experiment is started; each acoustic transducer is calibrated and initialized; the threshold of the spontaneous combustion temperature stage of the coal is judged and the mode is switched during the heating process to complete the coal temperature monitoring under the active and passive test modules; the Kriging interpolation method is used to estimate the temperature value of any point in the coal area based on the obtained acoustic temperature detection data, and the relationship between the acoustic wave propagation path and the flight time and the temperature is regressed and analyzed in combination with the neural network, and the temperature field model is iteratively optimized to predict the temperature results and reconstruct the three-dimensional temperature field inside the coal body.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0016] By combining active and passive acoustic wave detection technologies, the acoustic-based full-process coal temperature monitoring experimental device provided by the present invention can cover a wide temperature range from room temperature to 900°C, thereby realizing full-cycle monitoring of the coal spontaneous combustion process. The coal temperature full-process monitoring system can automatically switch between different temperature modes without manual intervention, which increases the convenience of experimental operation. The present invention utilizes an acoustic temperature measurement method, combined with an acoustic wave generator and a transceiver acoustic wave transducer, to avoid damage and maintenance problems of thermocouples at high temperatures, while solving the limitation that infrared thermal imaging cannot penetrate deep into the coal body, thereby improving the accuracy and response speed of temperature measurement. In addition, compared with optical fiber temperature monitoring technology, the equipment of the present invention is easier to install and maintain, with lower costs, for subsequent mine temperature information detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0018] Figure 1 A system framework diagram of an acoustic-based coal temperature full-process monitoring experimental device provided in an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of the overall structure of an acoustic-based coal temperature full-process monitoring experimental device provided in an embodiment of the present invention;

[0020] Figure 3A schematic diagram of a top view of the overall structure of an acoustic-based coal temperature full-process monitoring experimental device provided in an embodiment of the present invention;

[0021] Figure 4 A schematic diagram of an acoustic temperature sensing detection device provided by an embodiment of the present invention;

[0022] Figure 5 A schematic flow chart of an acoustic-based full-process coal temperature monitoring experimental method provided in an embodiment of the present invention;

[0023] Figure 6 An algorithm flow chart for reconstructing a three-dimensional temperature field by combining a Kriging interpolation method and a neural network algorithm provided in an embodiment of the present invention;

[0024] Figure 7 An overall schematic diagram of the sound transmission path provided by an embodiment of the present invention;

[0025] Figure 8 A schematic diagram of the propagation of a movable acoustic detection path provided by an embodiment of the present invention;

[0026] Figure numerals: 1-experimental box; 2-sound wave generator; 3-sound wave transmission duct; 4-active acoustic detection system; 5-sound wave transducer; 6-stainless steel insulation box; 7-heating layer; 8-loosely stacked coal body; 9-acoustic transceiver array bracket module; 10-insulated oil layer; 11-insulated drilling hole; 12-gas collection device; 13-waste collection device; 14-gas chromatograph; 15-temperature sensor; 16-ventilation device; 17-gas cylinder cabinet; 18-blower; 19-sound wave generator insulation layer; 20-acoustic signal conditioner; 21-flow meter; 22-power amplifier; 23-data acquisition device; 24-physical parameter visualization platform; 25-ventilation device fixed bracket; 26-box control panel; 27-voltage controller; 28-transmission module bracket; 29-sliding support module; 30-fixed bracket; 31-adjustment device. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0029] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the embodiments of the present invention belong. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0030] Sound waves are weak pressure waves that show the propagation form of the mechanical vibration state of an object. Sound wave detection is an environmental parameter detection technology based on acoustic methods. It uses the sound waves emitted or reflected by the identified target in different scene environments to judge the state of the object, so as to achieve the purpose of monitoring and feedback of object information. The principle of sound wave detection is simple and the measurement accuracy is high. It can provide real-time data in the measurement area. As a non-contact detection technology, it has received extensive attention from domestic and foreign scholars in the field of physical information detection inside and outside mines.

[0031] Existing mine geological exploration technologies are mainly divided into direct exploration technology, geophysical exploration technology and chemical exploration technology. Direct exploration technology uses drilling methods to drill in the coal body area and pre-buried detectors to directly survey and collect rock and soil distribution information and physical parameters. Geophysical exploration technology uses the differentiated physical properties of the target and the environment in the coal body area to achieve target detection, mainly including seismic method, radio pit penetration method, resistivity method and infrared telemetry method; chemical exploration method uses the difference in relevant chemical composition or special reactions to detect the changes in regional chemical composition through instruments, and then judge the geological conditions of the coal body area. The commonly used method is radon measurement. Although the above technologies have made great progress in the field of detecting mine geological information, they are limited by my country's complex and changeable mine environment and technical bottlenecks. The existing detection methods are difficult to achieve accurate and real-time detection of geological information inside and outside the mine. This has been a problem that has been urgently needed to be solved.

[0032] Compared with other geophysical exploration methods, acoustic wave detection technology has certain advantages in terms of economic cost, application scenarios, applicability, etc. By filtering, denoising, spectrum analysis, and time-frequency analysis of the original acoustic wave signal, the dynamic change characteristics of the acoustic wave signal are applied to coal mine geological exploration, which can promote the construction of future intelligent dynamic monitoring systems while meeting real-time detection and decision-making.

[0033] In the field of mine safety monitoring and coal spontaneous combustion prevention, there have been many attempts to solve the problem of coal spontaneous combustion monitoring. The following are three of the closest existing technologies and their advantages and disadvantages:

[0034] The advantages of fiber optic temperature monitoring technology are: fiber optic sensors are highly resistant to environmental interference, can work stably in harsh environments, and the sensors themselves do not generate sparks, making them suitable for explosive gas environments. Fiber optic temperature monitoring systems can provide continuous temperature distribution curves, which are suitable for large-scale monitoring. Disadvantages are: the wiring and installation of fiber optic sensors are relatively complicated, especially in complex coal mine environments, where wiring and maintenance become more difficult. Fiber optic sensors are relatively expensive, and once damaged, they need to be replaced as a whole, increasing maintenance costs.

[0035] The advantage of traditional thermocouple monitoring technology is that thermocouple is a mature temperature measurement technology with the characteristics of fast response speed and wide measurement range, which is suitable for a variety of industrial process control. The disadvantage is that thermocouple needs to be in direct contact with the measured medium, and it is easy to be damaged if placed in high-temperature coal for a long time. It is also affected by coal dust coverage or chemical corrosion, and the measurement accuracy is not high. At the same time, the installation and replacement work inside the coal body is large and the risk is high.

[0036] The advantages of infrared thermal imaging monitoring technology are: infrared thermal imaging technology can realize non-contact temperature measurement, is suitable for long-distance and large-area temperature monitoring, is simple to operate, and can quickly find abnormal temperature points. Disadvantages: infrared thermal imaging technology is greatly affected by environmental factors, such as coal dust, water vapor, etc., which can interfere with the measurement results, and cannot provide deep temperature information inside the coal body, and can only monitor the surface temperature distribution.

[0037] The technical solution provided by the present invention fully considers the advantages and disadvantages of the above-mentioned technologies, and further innovates on this basis. The present invention avoids the damage and maintenance problems of thermocouples at high temperatures by combining non-contact acoustic temperature measurement technology with an acoustic wave generator, and solves the limitation that infrared thermal imaging cannot penetrate deep into the coal body. In addition, compared with optical fiber temperature monitoring technology, the equipment of the present invention is easier to install and maintain, and has lower costs, which is convenient for subsequent mine temperature information detection. Through this method, the present invention aims to provide an efficient, low-cost and safe coal spontaneous combustion monitoring solution.

[0038] Figure 1 A schematic diagram of the system framework of an acoustic-based coal temperature full-process monitoring experimental device provided in an embodiment of the present invention is shown in FIG. Figure 1As shown, the experimental device includes an acoustic temperature sensing monitoring system 100, a control and data acquisition system 200, an air supply and environmental control system 300 and a central control system 400, wherein the control and data acquisition system 200 is used to collect gas, detect temperature and transmit data; the air supply and environmental control system 300 is used to control the temperature and humidity of the airflow entering the coal body; the acoustic temperature sensing monitoring system 100 includes:

[0039] The sound wave generator module 101 is used to generate a sound wave signal within a specific frequency range, and the frequency and intensity are adjusted by the central control system;

[0040] The whole process coupled transceiver transmission module 102 includes an active acoustic detection system and an acoustic emission sensor; wherein the active acoustic detection system is used for monitoring the early and middle stages of coal spontaneous combustion, and includes a plurality of acoustic transducers deployed in a three-dimensional array, in which when one acoustic transducer is controlled to transmit an acoustic signal, the other acoustic transducers located on the opposite side serve as receivers to receive the acoustic signal; the acoustic transmission duct is used to propagate the acoustic wave in a directional manner; the acoustic emission sensor is used as a passive test module for monitoring the high-temperature combustion process of coal spontaneous combustion in the later stage; the central control system monitors the coal body temperature in real time to automatically switch the working modes of the active acoustic detection system and the acoustic emission sensor;

[0041] The signal data processing and analysis module 103 is used to amplify, filter and digitize the received sound wave signal, extract key data reflecting the temperature change of the coal body, use the Kriging interpolation method to estimate the temperature value of any point in the coal body area, and use the neural network to perform regression analysis on the relationship between the sound wave propagation path and the flight time and temperature, iteratively optimize to obtain a temperature field model to predict the temperature result, and reconstruct the three-dimensional temperature field inside the coal body;

[0042] The control module 104 is used to control the operation of the acoustic temperature sensing monitoring system and manage the communication and data exchange between the modules.

[0043] Here, the experimental device provided by the embodiment of the present invention changes the research focus from the traditional early detection of coal spontaneous combustion to the full-cycle coal temperature detection and monitoring by adding an acoustic temperature sensing monitoring system into the experimental box. The acoustic temperature sensing monitoring system integrates a variety of acoustic temperature measurement units, including an acoustic wave generator, an acoustic wave transducer that can emit and receive sound waves, an active acoustic detection system, an acoustic emission sensor and its control module. These units will be installed around and inside the experimental device to achieve real-time monitoring of the temperature and oxidation characteristics of the coal body during the full cycle of spontaneous combustion. In this way, the acoustic temperature measurement method is used to reduce the contact error that may be introduced by traditional thermocouple temperature measurement, improve the accuracy and response speed of temperature measurement, and improve the accuracy of the experiment.

[0044] The acoustic wave generator module 101 includes an acoustic wave generator and a control circuit, which can generate high-intensity acoustic wave signals within a wide frequency range. The frequency and intensity are adjusted by a central control system to ensure that the acoustic wave can effectively penetrate the coal body, and the temperature change is fed back based on the functional relationship between the coal temperature and the basic acoustic parameters, and the temperature field is further inverted through an algorithm.

[0045] The full-process coupled transceiver transmission module 102 is used to transmit and receive sound wave signals and monitor the temperature of the coal body. Among them, the active acoustic detection system is used for early and mid-term monitoring of coal spontaneous combustion, and monitors the temperature changes inside the coal body by emitting low-frequency sound waves. In implementation, multiple sound wave transducers are evenly arranged around the coal body area to form a closed rectangular layout (upper, middle, and lower), that is, a three-dimensional array. By controlling one sound wave transducer to emit a sound wave signal, and the remaining multiple sound wave receivers on the opposite side receive the returned signal, it is ensured that the sound wave signal can fully cover the coal body area inside the experimental device. The propagation speed of sound waves in the coal body is closely related to the temperature. By calculating the time difference of sound wave propagation, the temperature distribution of the coal body can be accurately calculated. It is suitable for a temperature range from room temperature to 300°C.

[0046] In practice, the active acoustic detection system also includes a waveguide, a signal conditioner, a data collector, etc. The waveguide transmits acoustic wave signals to ensure that the acoustic waves can be effectively transmitted into the coal body and reduce attenuation. The material is high-sound-conducting aluminum alloy. The installation position is to connect the acoustic wave generator and the acoustic wave transmitter, and it is buried in the coal body to ensure efficient transmission of acoustic waves. Signal conditioner: amplifies, filters and denoises the received acoustic wave signal to ensure signal stability and accuracy. It is installed on the outside of the experimental device to receive signals from the acoustic wave receiver. Data collector: collects and records acoustic wave signal data and transmits it to the data processing system for analysis. The data collector model is DAQ-400, with a sampling rate of 100 kHz. Connected to the signal conditioner, the data is transmitted to the data processing system by wire or wireless.

[0047] Acoustic emission sensors are used as passive test modules to monitor the high-temperature combustion process in the later stage of coal spontaneous combustion, mainly in the high temperature range above 300°C. When the coal reaches the spontaneous combustion temperature and combustion occurs, the central control system switches to the acoustic emission sensor to monitor the temperature changes in the high temperature environment. The automatic switching mechanism of the central control system ensures the continuity and accuracy of monitoring. The most appropriate detection method is automatically selected at different temperature stages to achieve accurate monitoring in the full temperature range. The following key design points are included:

[0048] 1) Dual system integration: The acoustic temperature sensing monitoring system innovatively integrates two monitoring technologies: active acoustic detection system and acoustic emission sensor, so as to realize the temperature measurement and monitoring of coal in different temperature ranges, and can cover the temperature monitoring needs of the whole process of coal spontaneous combustion. Among them, active detection is suitable for the early and middle stages of coal spontaneous combustion (room temperature ~ about 300℃). Passive detection is suitable for high-temperature combustion state above 300℃ and the later ash formation stage. This dual system design ensures the continuity and accuracy of monitoring.

[0049] 2) Automatic switching mechanism: Through the central control system, the automatic switching function between active and passive working states is realized based on real-time temperature data, and the most appropriate detection method can be automatically selected according to the temperature stage change characteristics during the coal spontaneous combustion process.

[0050] 3) Modular design: The full-process coupled transceiver transmission module adopts modular design, which is easy to install and maintain. Each component is connected through a standard interface to ensure the flexibility and scalability of the system.

[0051] 4) Parameter settings: Active acoustic detection system transmission frequency range: 100 Hz - 1500 kHz; receiving sensitivity: -160 dB re 1 V / µPa; operating temperature range: -20℃ to 300℃; transmitting power: 10 W. Acoustic emission sensor operating frequency range: 10 kHz - 100 kHz; receiving sensitivity: -180 dB re 1 V / µPa; operating temperature range: above 300℃; transmitting power: 20 W. Central control system: Processor model: ARM Cortex-A53; storage capacity: 4 GB RAM, 64 GBSSD; control software: customized real-time operating system, supporting multi-tasking and automatic switching functions.

[0052] The signal processing and data analysis module 103 includes an amplifier, a filter, an analog-to-digital converter, and a data processing unit, which are respectively used to amplify, filter and digitize the received acoustic wave signal, extract key data reflecting the temperature change of the coal body, and also include a data processor, a memory, an algorithm software, and an analysis software to process the temperature data extracted from the acoustic wave signal, reconstruct the temperature field inside the coal body, and generate a detailed temperature distribution map. The Kriging interpolation method is used to construct a semi-variogram function and an optimization algorithm, so that the solution of the weight coefficient is more accurate. Combined with the neural network technology, the accurate temperature field is output by inputting the acoustic wave propagation path and the flight time, and the accurate prediction and real-time monitoring of the temperature distribution are realized. In particular, the introduction of the neural network method significantly improves the accuracy and robustness of the temperature field reconstruction, and optimizes the acoustic temperature measurement process of the coal spontaneous combustion process.

[0053] The control module 104 includes a central processing unit, control software, and an interface module. The control module manages the working status of the transceiver acoustic wave transducer, the active acoustic detection system, and the acoustic emission sensor through the central processing unit and the control software, ensures the coordinated operation of each module, and realizes communication and data exchange with other systems through the interface module.

[0054] The control and data acquisition system 200 includes: the industrial computer is an RS-485 / RS-232 optical isolation converter to ensure faster and more stable data transmission. The specification of the temperature sensor remains HN-EXP 2*15 I-level accuracy, but the number is increased to 50, of which 40 are used for coal sample monitoring and 10 are used for oil layer insulation monitoring. All sensors are equipped with nickel-chromium stainless steel sleeves, and the temperature resistance is increased to 1000°C.

[0055] The air supply and environmental control system 300 includes: an oil-free compressor, a flow stabilizing valve and a humidity regulating box, which ensure the precise control of the air flow temperature and humidity entering the coal body, and the air volume control range is increased to 0.4~30 m³ / h to provide more flexible oxygen supply adjustment. The oil-free compressor is used to provide pollution-free compressed air to ensure the cleanliness of the coal body air supply during the experiment. The flow stabilizing valve is used to adjust and stabilize the air volume entering the coal body. The humidity regulating box is used to adjust the air humidity entering the coal body. The parameters of key components are as follows: oil-free compressor (power: 2 kW; maximum air supply: 50 m³ / h; noise level: <60 dB); flow stabilizing valve (control range: 0.4~30 m³ / h; control accuracy: ±0.1 m³ / h); humidity regulating box (adjustment range: 10%~90% RH; control accuracy: ±2% RH).

[0056] The central control system 400 is the core part of the experimental control center, which coordinates all systems and modules and exchanges data and communicates with each subsystem through a network interface. The system consists of a central processing unit, control software, and an interface module. Key component parameters Central processing unit model: ARM Cortex-A53; processing speed: 1.4 GHz; memory: 4 GB RAM; storage: 64 GB SSD; interface: supports multiple communication protocols such as Modbus and TCP / IP. Interface module type: multi-function interface module, supporting digital and analog signal input and output. Quantity: 8 digital inputs, 8 digital outputs, 4 analog inputs, and 4 analog outputs. The central control system 40 is installed in the experimental control center and connected to each subsystem through a network interface. The central processing unit is connected to sensors, actuators and other devices of each subsystem through an interface module to realize real-time exchange and processing of data and instructions.

[0057] The embodiment of the present invention innovatively develops the design of a visualization experimental device for the spatiotemporal evolution path of high-temperature points of coal spontaneous combustion and thermal incubation. The experimental device integrates an acoustic temperature sensing monitoring system, which can accurately capture the propagation of sound waves at different depths and angles, thereby obtaining temperature information inside the coal body. The experimental device can not only withstand extreme environmental conditions, such as high temperature and humidity, but also work accurately at different temperature stages, ensuring that reliable data is collected throughout the process of coal spontaneous combustion. In addition, active acoustic wave detection is combined with passive acoustic emission technology to form a dual-path detection method. This method uses active detection at low temperatures and switches to passive detection at high temperatures, ensuring comprehensive monitoring of the entire coal spontaneous combustion process. Finally, the Kriging interpolation method is used to construct a semi-variogram function and an optimization algorithm, so that the solution of the weight coefficient is more accurate. Combined with neural network technology, by inputting the sound wave propagation path and flight time, data is collected to a set of continuous computer network systems to output an accurate temperature field, realizing accurate prediction and real-time monitoring of temperature distribution.

[0058] The experimental device provided by the embodiment of the present invention is based on the different stages of coal spontaneous combustion from low temperature to high temperature, and uses active and passive detection technologies to realize a comprehensive monitoring platform. Through accurate data collection of acoustic temperature measurement technology and dynamic non-destructive detection of acoustic emission technology, the embodiment of the present invention not only optimizes the monitoring process of coal spontaneous combustion, but also provides an effective research reference for the accurate detection of hidden high-temperature areas in goaf areas.

[0059] In some possible embodiments, the acoustic temperature sensing monitoring system also includes a movable acoustic transceiver array bracket module, which is used to carry the transceiver acoustic wave transducer module and pre-set it in the coal body, and realize the monitoring of fixed and mobile temperature sensing points to adapt to different test requirements and experimental conditions and construct a more accurate three-dimensional temperature field.

[0060] Here, in order to better adapt to different test requirements and experimental conditions, the acoustic sensing device of the present invention realizes modular movement through a sliding support module, which not only can fix the temperature sensing point in the coal body, but also can be easily moved in the experimental device space to construct a more accurate three-dimensional temperature field.

[0061] The transceiver array bracket module is built based on a fixed grid, including a sliding support module, a fixed bracket, and an adjustment device. The movable acoustic detection path based on the transceiver array bracket module is as follows Figure 8 As shown, the sliding support module 29 is made of high-strength material to ensure stability and durability; the fixed bracket 30 is used to fix the temperature sensing point in the coal body, and the sensing device is conveniently moved in space through the adjustment device 31, so as to optimize the arrangement of the measuring points and obtain more accurate temperature data.

[0062] Specific operation instructions: During the experiment, according to the measurement requirements, the modular movement of the sensor device is realized through the sliding support module. Through the sliding support module, the sensor device can be flexibly moved on the fixed grid to adapt to different test requirements and experimental conditions. By adjusting the position of the sliding support module and the height and angle of the fixed bracket, the arrangement of the measurement points can be optimized. At the same time, the number of acoustic transmission modules can be increased or decreased. The design is a modular structure, which is convenient and flexible to install and disassemble. By adjusting the number and position of the acoustic modules, it can quickly adapt to different experimental conditions and requirements and optimize the sound wave measurement effect.

[0063] In some possible embodiments, the acoustic temperature sensing monitoring system further includes a temperature and humidity inspection module for monitoring changes in temperature and humidity in the experimental environment, and correcting the actually measured temperature based on the humidity measured at the transducer position of the humidity influence coefficient.

[0064] Here, the accuracy of the data is ensured by monitoring the temperature and humidity changes in the experimental environment of the temperature and humidity inspection module. The main components include: temperature sensor, humidity sensor, and data logger.

[0065] In some possible embodiments, when the temperature of the coal body is lower than 300°C, the acoustic temperature sensing monitoring system preferentially uses an active acoustic detection system to emit low-frequency sound waves, calculate the temperature of the coal body by using the sound wave propagation time and speed change, and monitor the temperature change of the coal body in the low temperature stage; when the temperature exceeds 300°C, it automatically switches to an acoustic emission sensor, which captures the acoustic emission sensor generated inside the coal body to provide accurate temperature data to meet the monitoring needs of high temperature environments.

[0066] Here, in the early and middle stages of coal spontaneous combustion, the temperature is usually below 300°C. At this time, the active acoustic detection system monitors the temperature changes inside the coal by emitting low-frequency sound waves. The propagation speed of sound waves in the coal is closely related to the temperature. By calculating the time difference of sound wave propagation, the temperature distribution of the coal can be accurately estimated. Through experimental observations, the sound wave propagation characteristics of coal bodies above 300°C have changed significantly. Due to the gas release and structural changes inside the coal body, the propagation speed, attenuation and reflection characteristics of the sound waves are significantly different from those in the low temperature stage. 300°C is considered to be the critical temperature at which the acoustic wave monitoring mode needs to switch from active to passive.

[0067] The passive test module is used to monitor the high-temperature combustion process in the later stage of coal spontaneous combustion, mainly in the high temperature range above 300°C. Combustion temperature (>300°C): When the coal reaches the spontaneous combustion temperature and combustion occurs, the central control system switches to the acoustic emission sensor to monitor the temperature changes in the high temperature environment. The physical and chemical changes inside the coal body at high temperatures will cause significant changes in the characteristics of the acoustic emission. The acoustic emission sensor can capture these changes and provide accurate temperature data. The temperature range threshold of 300°C is based on a large amount of experimental data and theoretical analysis, and can be dynamically adjusted according to specific circumstances during implementation.

[0068] The central control system monitors the temperature of the coal body in real time. When it detects that the temperature exceeds 300°C, it automatically stops the active acoustic detection system and starts the acoustic emission sensor. Conversely, when the temperature drops below 300°C, the system re-activates the active acoustic detection system. Under certain special experimental conditions, the active acoustic detection system and the acoustic emission sensor can work together to provide more comprehensive temperature monitoring data. For example, when the temperature approaches the critical point of 300°C, the system can use two sensors at the same time for data comparison and calibration to ensure the continuity and accuracy of temperature measurement. Through the automatic switching mechanism of the central control system, the most appropriate detection method is automatically selected at different temperature stages to achieve accurate monitoring within the full temperature range.

[0069] In some possible embodiments, the internal structure of the experimental device adopts a rectangular design with an inner diameter of 0.6 meters, which is used to accommodate loosely stacked coal and simulate the spontaneous combustion state of coal left in the goaf; the coal loading height is 1.8 meters, the outer diameter is 1.2 meters, and the total height is 4 meters to enhance the stability of the overall structure and the accuracy of monitoring; at the same time, a layered coal loading method is adopted, each layer of coal is 30 centimeters thick, and high-strength heat-resistant grids are set between layers to ensure the uniform distribution of the coal body, so as to prevent the coal body from moving during the experiment and facilitate the arrangement of sensors.

[0070] Here, the new experimental device provided by the present invention reconstructs the internal structure and sets the inner diameter to 0.6 meters, which is consistent with the parameters of the original coal spontaneous combustion test bench to ensure the comparability of the experimental conditions. The coal loading height is adjusted to 1.8 meters, the outer diameter of the experimental device is set to 1.2 meters, and the total height is increased to 4 meters to accommodate the newly added acoustic temperature sensing monitoring system. The layered coal loading method is adopted to ensure the uniform distribution of the coal body. The thickness of each layer of coal body is 30 cm, and high-strength heat-resistant grids are set between the layers to prevent the coal body from moving during the experiment and facilitate the arrangement of sensors. It can be disassembled later in the study of pore changes and collapse characteristics. This design not only increases the experimental space, but also supports a wider range of temperature monitoring, and the total volume is adjusted to 3 cubic meters. The use of a rectangular parallelepiped structure makes the device more stable, the internal space is more reasonably used, and it is conducive to the arrangement and installation of each module. The outer wall adopts a double-layer structure, the outer layer is a high-strength steel plate, the inner layer is a high-temperature resistant insulation material, a high-strength support frame is installed at the bottom, and the four corners are supported by high-strength steel columns to ensure stability in a high temperature environment. The modular design of the internal space is conducive to the layout and installation of each module. The modules are connected through standard interfaces, which is easy to install and maintain.

[0071] The above embodiments demonstrate that the experimental device proposed in the embodiments of the present invention can optimize experimental costs and efficiency: the improved device structure and monitoring method reduce the amount and cost of coal samples required for the experiment, while improving the repeatability of the experiment, which is conducive to continuous experiments on a large number of samples.

[0072] In some possible embodiments, a stainless steel insulation box is provided on the outside of the experimental device, including an insulation oil layer and a heating layer, and the original water layer insulation is replaced by oil layer insulation; the acoustic temperature sensing monitoring system is installed through the insulation drilling hole to achieve effective insulation and precise temperature control inside and outside the coal body; the insulation oil layer is wrapped with high-efficiency heat transfer oil on the outer wall of the experimental device; the heating layer is a programmable heating plate, which uses a PC-500 programmable controller, and the temperature range can be set from 20°C to 900°C, with a temperature control accuracy of ±0.5°C; a 3 mm thick thermal conductive silicone sheet is laid between the heating plate and the insulation oil layer to ensure that the heat of the heating plate is quickly transferred to the insulation oil layer, and the temperature of the heating plate and the insulation oil layer is coordinated through the central control system to ensure uniform temperature inside the entire experimental device.

[0073] Here, the oil layer temperature control method is used to replace the original water layer insulation. The temperature control layer uses high-efficiency heat transfer oil, which is wrapped around the outer wall of the experimental device to form a uniform insulation layer. The oil layer insulation not only improves the thermal efficiency, but also maintains the temperature stability during long-term experiments. An acoustic temperature sensing monitoring system is installed through the insulation drilling holes to achieve effective insulation and precise temperature control inside and outside the coal body. The outer wall is made of high-strength steel plate, the inner layer is high-temperature resistant insulation material, a high-strength support frame is installed at the bottom, and high-strength steel columns are used at the four corners to ensure stability in high temperature environments. The specific optimizations are as follows:

[0074] (1) High-efficiency heat transfer oil model HTO-500 is used, with a thermal conductivity of 0.15 W / m·K, high temperature stability, and the ability to operate for a long time at 900°C. According to the outer diameter of the experimental device and the thickness of the oil layer, the amount of heat transfer oil used is calculated to be about 60 liters. The oil layer thickness is set to 6 cm to ensure sufficient insulation effect without increasing the volume of the device too much. A circulating pump model CP-150 is used with a flow rate of 15 liters / minute to ensure that the heat transfer oil circulates evenly on the outer wall of the experimental device to form a stable temperature field. Equipped with a digital temperature controller model TC-500, the temperature control accuracy is ±1°C, and the temperature of the heat transfer oil is monitored and adjusted in real time through a temperature sensor.

[0075] (2) A programmable electric heating plate with a size of 600mm * 600mm and a thickness of 5mm is used, and the surface is covered with high thermal conductivity aluminum alloy material to ensure uniform heating. The PTC ceramic heating element is embedded inside, with good high temperature resistance, power density of 10W / cm², and total power of 3.6kW. A programmable controller with model PC-500 is used, and the temperature range can be set from 20℃ to 900℃, with a temperature control accuracy of ±0.5℃. A 3mm thick thermal conductive silicone sheet with a thermal conductivity of 2.0W / m·K is laid between the heating plate and the oil layer to ensure that the heat of the heating plate is quickly transferred to the oil layer. The temperature of the heating plate and the oil layer is coordinated through the central control system to ensure uniform temperature inside the entire experimental device and avoid local overheating. The heating plate and the oil temperature controller work together to automatically adjust the heating power according to the temperature changes during the experiment to ensure temperature control stability.

[0076] The internal structure of the experimental device for the spatiotemporal evolution path of the high-temperature point of spontaneous combustion of coal and the visualization of thermal incubation designed in the embodiment of the present invention is designed as a rectangular parallelepiped, with a stainless steel insulation box on the outside, and the insulation method and temperature control system are optimized, thereby improving the accuracy and efficiency of the experiment. The problems of the traditional experiment with long cycle, large amount of coal, high cost and difficulty in repeating the experiment are solved. By adding a programmable heating plate, a seamless combination of low-temperature oxidation and high-temperature oxidation experiments is achieved, thereby comprehensively monitoring the oxidation behavior of the entire process of spontaneous combustion of coal.

[0077] Figure 2The overall structure diagram of a coal temperature whole process monitoring experimental device provided by the embodiment of the present invention is as follows: Figure 2 As shown, it includes an experimental box 1, an acoustic wave transducer 5, a stainless steel insulation box 6, a heating layer 7 (for high-temperature programmed temperature rise oxidation work), a loosely stacked coal body 8 (simulating the state of coal accumulation in the goaf), an acoustic transceiver array bracket 9 (inserted into the coal body in advance, equipped with an acoustic sensor device to realize fixed movement of the temperature sensing point, which is convenient for later construction of a three-dimensional temperature field), an insulating oil layer 10 (used for internal coal body insulation), an insulating borehole 11 (convenient for inserting an acoustic temperature sensor system, a thermocouple temperature measurement and other in-box physical information detection devices), a gas collection device 12 (collecting gas generated by the internal coal body), a waste collection device 13, a gas chromatograph 14, a temperature sensor 15, a ventilation device 16, a gas cylinder cabinet 17, a blower 18, an acoustic signal conditioner 20, a flow meter 21, a power amplifier 22, a data collector 23, a physical parameter visualization platform 24, a ventilation device fixing bracket 25, a box control panel 26, a voltage controller 27, and a transmission module bracket 28. The overall structure of the experimental device is shown in the top view of the cross-section diagram as follows Figure 3 shown.

[0078] The gas collection device 12 is installed on the outside of the experimental box 1, close to the sampling point. The sampling system is connected to the inside of the coal body through a pipeline, and the data analysis software is connected to the data processing and analysis system. The automatic gas sample collection system is retained and upgraded to an SP-3510 gas chromatograph. The sensitivity of the gas chromatograph analyzer 14 is improved to expand the minimum detection concentration to 20 to 40 times the original, and to enhance the ability to detect subtle gas changes. It can be compared and analyzed with acoustic temperature measurement, sensor temperature measurement, and gas threshold temperature perception. The temperature sensor 15 in the experimental device, i.e., the thermocouple resistance temperature detector, can retain the detection results of the thermocouple and compare it with the acoustic temperature sensing three-dimensional temperature field results to achieve more accurate temperature monitoring and data analysis.

[0079] Figure 4 A schematic diagram of an acoustic temperature sensing detection device provided in an embodiment of the present invention, such as Figure 4 As shown, it includes an acoustic wave generator 2, an acoustic wave transmission duct 3, an active acoustic detection system 4, and an acoustic wave generator insulation layer 19. Through the intelligent active and passive acoustic detection system, it provides an accurate and reliable technical solution for coal temperature monitoring in a complex coal environment. It not only maintains stable operation in extreme environments, but also ensures the accuracy of operation and the safety of equipment through a central control system and multiple safety protection measures.

[0080] The embodiment of the present invention provides an acoustic-based full-process coal temperature monitoring experimental device, which can cover a wide temperature range and realize full-process monitoring of coal spontaneous combustion from room temperature to 900°C. The new acoustic temperature sensing monitoring system will adopt high-temperature resistant non-contact acoustic temperature measurement technology, combined with an acoustic wave generator, a receiver and a control module to achieve high-precision and high-efficiency temperature monitoring. In addition, the embodiment of the present invention also reduces the amount of coal samples and improves experimental repeatability by optimizing the device design, and at the same time improves the data availability and experimental safety by integrating advanced data processing algorithms and three-dimensional visualization technology.

[0081] Figure 5 A flow chart of an acoustic-based coal temperature monitoring experimental method for the entire process provided by the embodiment of the present invention is shown in FIG. Figure 5 As shown, the method comprises the following steps:

[0082] Step S110, uniformly filling the rectangular coal sample box with coal samples and performing air tightness detection.

[0083] Here, the screened coal samples are uniformly filled based on the experimental box 1. The coal samples are pre-treated to ensure that the coal samples in the thermal insulation oil layer 10 have sufficient looseness to simulate the natural stacking state. The experimental box 1 is checked using an air tightness detection device to ensure the air tightness of the coal sample box under high temperature conditions, avoiding interference from external gases and leakage of internal gases, especially at the interface of the thermal insulation borehole 11.

[0084] Step S120, respectively completing a room temperature oxidation experiment from 30 degrees to 150 degrees and a high temperature oxidation experiment from 150 degrees to 900 degrees of the loose coal under a constant air volume condition.

[0085] Here, in the normal temperature oxidation experiment, the aeration device 16 cooperates with the blower 18 and the flow meter 21 to perform a normal temperature oxidation experiment on the loosely stacked coal body 8 in the range of 30 to 150°C. During the experiment, the temperature distribution of the coal body is monitored by the temperature sensor 15, and the oxygen supply is continuously monitored and adjusted by the temperature sensor and the aeration device to evaluate the spontaneous combustion starting point and oxidation rate of the coal sample, and record the oxidation reaction data of the coal body at different temperatures.

[0086] High temperature oxidation experiment: The heating layer 7 raises the temperature of the loosely stacked coal 8 to conduct a high temperature oxidation experiment. During the entire heating process, the temperature sensor 15 continuously monitors the temperature of the coal, while the gas chromatograph 14 evaluates the gas composition generated by high temperature oxidation to simulate the entire process from coal spontaneous combustion to coal fire.

[0087] Step S130: number all the acoustic wave transducers and divide the coal body area into logical blocks.

[0088] Here, the acoustic wave transducers 5 are numbered according to their positions to ensure accurate attribution of the test data, and the loosely deposited coal body 8 in the experimental box 1 is divided into logical blocks to facilitate subsequent data analysis and temperature field reconstruction.

[0089] Step S140, setting a heating temperature threshold in the normal temperature oxidation and high temperature oxidation experiments and starting an acoustic temperature sensing experiment.

[0090] Here, under the regulation of the voltage controller 27, the lower limit of the heating temperature of the coal sample is set, the electric heating wire is turned on, and the gas chromatograph 14 is prepared at the same time. After reaching the temperature rising standard, the temperature is gradually raised according to the predetermined program, and the gas chromatograph is turned on to monitor the changes in gas composition.

[0091] The main function of this step is to start the acoustic temperature sensing experiment by setting a preliminary heating temperature threshold to verify the response characteristics of the system at different temperatures. This process helps to calibrate the acoustic temperature sensing system to ensure that it can accurately monitor the temperature changes of the coal body in subsequent experiments. In the normal temperature and high temperature oxidation experiments, by setting different temperature thresholds, the reaction characteristics of the coal body in various temperature ranges can be observed, including the sound wave propagation time, attenuation characteristics, etc. These experimental data are crucial for determining a more accurate temperature threshold in the subsequent step S160. By analyzing these data, an accurate threshold can be determined to guide the subsequent experiments.

[0092] Step S150, calibrating and initializing each acoustic wave transducer.

[0093] Here, the plurality of acoustic wave transducers 5 are calibrated and initialized to ensure that they work in the best state before the experiment begins, so as to ensure the accuracy of the measurement data.

[0094] Step S160, during the temperature rise process, the threshold value of the spontaneous combustion temperature of the coal is judged and the mode is switched to complete the coal temperature monitoring under the active and passive test modules.

[0095] Here, the temperature of the coal sample is monitored in real time during the heating process, and the temperature of the coal sample is continuously monitored using the temperature sensor 15. Once the preset temperature threshold is reached, the box control panel 26 instructs the active acoustic detection system and the acoustic emission sensor to switch to the appropriate mode. Once it is detected that the temperature reaches the set heating temperature threshold, the system adjusts the heating rate or switches to a different temperature measurement mode.

[0096] When the active acoustic detection system is working, the acoustic transducer 5 is used to monitor the temperature change of the coal body in the low temperature stage, and the temperature of the coal body is calculated by the acoustic wave propagation time and velocity change. When the temperature of the coal sample exceeds 900°C, it is switched to the passive test module, and the acoustic emission sensor is used to monitor the temperature based on the acoustic emission signal generated by the coal body during the combustion process.

[0097] Step S170, for the acquired acoustic temperature detection data, the temperature value of any point in the coal body area is estimated by using the Kriging interpolation method, and the relationship between the acoustic wave propagation path and the flight time and the temperature is regressed and analyzed in combination with the neural network, and the temperature field model is obtained by iterative optimization to predict the temperature result and reconstruct the three-dimensional temperature field inside the coal body.

[0098] Here, the data collector 23 collects all acoustic temperature detection data, uses dedicated software to construct a three-dimensional temperature field, and presents it to the physical parameter visualization platform 24 to form a dynamic image of the temperature distribution inside the coal body.

[0099] In some possible embodiments, Figure 6 As shown, the above step S170 is implemented by the following steps:

[0100] Step S171, 12 acoustic wave transducers are evenly arranged on each two-dimensional plane of the three-dimensional experimental box to form a closed rectangular parallelepiped layout.

[0101] Here, one transducer is controlled to transmit an acoustic wave signal, and the remaining transducers located on the opposite side act as receivers to receive the acoustic wave signal.

[0102] Suppose that a mathematical model of acoustic temperature measurement with three-dimensional temperature field reconstruction is constructed. The three-dimensional experimental box contains three two-dimensional planes to construct the sound propagation route. On each plane, 12 sound wave transducers are evenly arranged on the boundary to form a closed rectangular layout (upper, middle, and lower). Each transducer can transmit and receive sound waves. In each plane, each transducer transmits a sound wave, which is received by the remaining 11 transducers in the plane. Therefore, each transducer will have 11 sound wave propagation paths. At the same time, consider the sound wave propagation paths perpendicular to these planes, that is, the transducer on each plane can also receive sound waves from the transducers on the other two planes. Each transducer in each plane transmits a sound wave, which is received by the remaining 11 transducers in the plane. There are a total of 12×11 =132 paths. The transducers on each plane of the vertical plane receive sound waves from the transducers on the other two planes. Assuming there are 12 main paths across the plane, there are 12×3×2=72 paths (across two planes), so there are a total of 132×3+72=468 paths in the three-dimensional experimental box, such as Figure 7 shown.

[0103] Step S172, collecting the sound wave propagation paths and flight times corresponding to each pair of transducer combinations and calculating the propagation speed of each path, and determining the average temperature of each path in combination with the sound speed-temperature relationship.

[0104] Here, the basic principle of acoustic temperature measurement can be briefly described as follows: the propagation speed of sound waves in a gas medium is a function of the temperature of the gas medium and is also related to the gas components. Under most application conditions, the gas components and their relative contents are known and vary within a very small range. Therefore, the propagation speed of sound waves can be regarded as the first function of the temperature of the gas medium. Based on the acoustic wave equation, the motion equation and the ideal gas state equation, the propagation speed calculation formula can be obtained as follows:

[0105] Formula (1);

[0106] Where v is the speed of sound; γ is the gas adiabatic index (the ratio of the gas's specific heat at constant pressure to its specific heat at constant volume); R is the ideal gas constant; T is the thermodynamic temperature; and M is the molar mass of the gas.

[0107] Under the single-path condition, the length between each pair of acoustic transducers is a constant that can be measured in advance, set as L. According to the single-valued function relationship between the medium temperature and the acoustic wave velocity, the average temperature T on the acoustic wave path can be obtained. The following formula (2), where t is the acoustic wave propagation time:

[0108] Formula (2);

[0109] The acoustic wave transit time data is collected from each acoustic wave transducer on the three planes. The acoustic velocity of each path is calculated based on the transit time and the known propagation distance. The average temperature of each path is calculated using the known acoustic velocity-temperature relationship, as shown in the following formulas (3) and (4):

[0110] Formula (3);

[0111] Formula (4);

[0112] in, , , In the coal body area Click to The average temperature, sound propagation distance, and sound wave flight time on the propagation path between points.

[0113] It is worth noting that moisture will be released during the heating process of the coal body. The change in humidity will affect the propagation speed of sound waves in the medium, thereby affecting the temperature measurement. The humidity impact analysis formula is:

[0114] Formula (5);

[0115] Among them, c ijklmn The actual coal area Click to The speed of sound on the propagation path between points, c ijklmn0 For no humidity Click to The reference speed of sound on the propagation path between points; β is the humidity influence coefficient; H ijklmn is the measured or estimated humidity at each transducer location.

[0116] The acoustic wave attenuation function is introduced, the absorption coefficient and scattering effect of the coal body are considered, and the final sound velocity calculation formula is updated as follows:

[0117] Formula (6);

[0118] in, is the sound wave attenuation function, d is the path length, ω is the sound wave frequency, and T is the temperature.

[0119] Step S173, constructing a temperature data vector and a semi-variogram matrix.

[0120] Here, according to the average temperature T ijklmn The semi-variogram γ(h) is constructed, and γ(h) describes the spatial autocorrelation of temperature data at distance h. γ(h) is constructed based on the spherical model, as shown in the following formula (7):

[0121] Formula (7);

[0122] Among them, C0 is the base, C is the variance, and a is the range.

[0123] For the point x0 to be measured in the coal body area, there are already measuring points x1, x2...x n , n is the total number of measurement points, that is, the number of temperature data points actually measured in the entire study area. Each measurement point corresponds to a specific three-dimensional coordinate and the temperature T(x i ), construct temperature data vector T = [ T ( x 1 ) , T ( x 2 ) , …， T ( x n ) ] T . Construct the semivariogram matrix:

[0124] Γ = [ γ ( 0 ) γ ( h 12 ) ⋯ γ ( h 1 n ) γ ( h 21 ) γ ( 0 ) ⋯ γ ( h 2 n ) ⋮ ⋮ ⋱ ⋮ γ ( h n 1 ) γ ( h n 2 ) ⋯ γ ( 0 ) ] Formula (8);

[0125] in, For two known measuring points x i and x j The semivariogram between the two locations usually represents the expected value of the temperature difference corresponding to the distance between the two locations, or the quadratic statistic of the temperature difference. In Kriging interpolation, the value of the semivariogram is used to weigh the influence of different measurement points to estimate the value of the unknown point. is the value of the semivariogram at zero distance.

[0126] The point to be measured x0 and the existing measured point x i The semi-variant function vector of : γ = [ γ （ h 01 ）, γ （ h 02 ） , ⋯ , γ （ h 0n ） ] T , weight coefficient vector: λ = [ λ 1 , λ 2 ⋯ , λ n ] T .

[0127] Step S174, estimating the temperature of any grid point in the coal body region according to the Kriging interpolation method.

[0128] Here, the weight coefficient λ is solved for the measured point ijklmn Minimize the estimation error and construct the system equation. is the semi-variant function matrix, μ is the Lagrange multiplier, The point to be measured x0 and the existing measured point x i The semi-variant function vector of :

[0129] [ Γ 1 1 T 0 ] [ λ i j k l m n μ ] = [ γ 1 ] Formula (9);

[0130] Introduce μ as the Lagrange multiplier and construct the objective function:

[0131] Formula (10);

[0132] For λ ijklmn Take the derivative and set it equal to zero:

[0133] Formula (11);

[0134] Formula (12);

[0135] In order to meet the normalization condition , and finally get:

[0136] Formula (13);

[0137] in, is the temperature of the test point x0 in the coal body area; In the coal area Click to The average temperature along the propagation path between points; is the weight coefficient, which is obtained by solving the system equations.

[0138] Apply Kriging interpolation to all test points and use the obtained weight coefficient to calculate the temperature of each point The final temperature field reconstruction formula is as follows:

[0139] Formula (14);

[0140] At the same time, an error evaluation and feedback mechanism is introduced to verify the reconstructed temperature field using measured data, and the model parameters are adjusted to minimize the reconstruction error. The error minimization formula is as follows:

[0141] Formula (15);

[0142] Among them, T measured,ijk is the actual measured temperature, T predicted,ijk The reconstructed temperature field is graphically displayed using a 3D visualization tool to analyze the changing trend of the temperature field and identify high-temperature areas and potential spontaneous combustion risk points.

[0143] Step S175, introducing a neural network algorithm, performing regression analysis on the relationship between the acoustic wave propagation path, the flight time and the temperature, and predicting the average temperature on the acoustic wave propagation path.

[0144] Here, in order to improve the accuracy of 3D temperature field reconstruction, a machine learning algorithm can be introduced for regression analysis, the acoustic wave propagation path and flight time are collected, the path average temperature is calculated, and the acoustic wave propagation path and flight time are used as input data to train the temperature field model using a machine learning algorithm neural network. Through iterative optimization, the parameters of the neural network are adjusted so that it can accurately predict the temperature changes on the acoustic wave propagation path.

[0145] Step S176, combining the neural network prediction results and the Kriging interpolation calculation results, using dedicated software to reconstruct a high-precision three-dimensional temperature field inside the coal body and perform visual analysis.

[0146] Here, based on the machine learning prediction results, Kriging interpolation is further used to optimize the temperature field reconstruction.

[0147] In the above embodiment, non-contact temperature measurement is used to reduce the sensor failure rate, extend the service life of the equipment, and reduce the complexity of maintenance. In addition, by using multiple acoustic transducers working simultaneously, real-time temperature monitoring of multiple positions of the coal pile is achieved, which improves the comprehensiveness and reliability of the data. Finally, the acoustic temperature sensing system automatically switches between different temperature monitoring modes without manual intervention, which increases the convenience of experimental operation.

[0148] In some possible embodiments, the above step S175 is further implemented through the following process: collecting the propagation distance and flight time data of each sound wave propagation path as a training sample set; inputting the training sample set into the constructed neural network structure to obtain temperature data for forward calculation; wherein the neural network structure includes an input layer, a hidden layer and an output layer, and defines a layer-to-layer weight matrix and a bias vector; based on the temperature data and the corresponding true temperature value, using the mean square error as the loss function; using the back propagation algorithm to calculate the gradient, and using the gradient descent method to update the weight matrix and the bias vector until the convergence condition is met to obtain a trained temperature field model; and predicting the average temperature of each path in the coal body area through the temperature field model.

[0149] Here, in order to improve the accuracy of 3D temperature field reconstruction, a machine learning algorithm can be introduced for regression analysis. First, training data is prepared. The input data includes the propagation distance d of each path. ijk and the flight time t ijk : ; The output data is the average temperature T of each path ijk The neural network structure includes an input layer, a hidden layer, and an output layer. Assume that a three-layer neural network is used: the data in the input layer includes the propagation distance d ijk and the flight time t ijk ; Hidden layer: contains several neurons; output layer outputs average temperature T ijk .

[0150] Define the weight matrix from the input layer to the hidden layer as W1 and the bias vector as b1, the weight matrix from the hidden layer to the output as W2 and the bias vector as b2. The hidden layer activation function σ can be a ReLU function or a Sigmoid function: , the output layer calculates: , using mean square error (MSE) as the loss function, defined as , where T true is the true temperature value, which can be determined by calculating the propagation velocity and combining it with the speed of sound-temperature relationship.

[0151] The gradient is calculated using the back-propagation algorithm, and the weight matrix and bias vector are updated using the gradient descent method or its variant (Adam optimizer), as shown in formula (16):

[0152] Formula (16);

[0153] Where η is the learning rate.

[0154] In the above embodiment, the machine learning algorithm is combined with the process of three-dimensional temperature field reconstruction: the acoustic wave propagation path and flight time are collected, and the average temperature of the path is calculated. The temperature field model is trained using a machine learning algorithm neural network. The trained model is used to predict the temperature field of the coal body area. Based on the machine learning prediction results, Kriging interpolation is further used to optimize the temperature field reconstruction.

[0155] In some possible embodiments, the method further includes: comparing the acoustic temperature detection data with the temperature data obtained by the traditional physical temperature detection method to achieve more accurate temperature monitoring and data analysis.

[0156] Here, the collected acoustic temperature data are compared and analyzed with the data obtained by conventional thermocouple detection15 to verify the accuracy and efficiency of acoustic temperature measurement, as well as the applicability and advantages of the detection method for monitoring the coal spontaneous combustion process.

[0157] The key design points of the acoustic-based coal temperature full-process monitoring experimental device provided in the embodiment of the present invention are as follows:

[0158] 1. Design of a large-scale experimental device for the spatiotemporal evolution path of high-temperature points of spontaneous combustion of coal and visualization of thermal incubation: The device adopts the method of combining oil layer temperature control with programmable heating plates, and ensures precise temperature control in the range of 20°C to 900°C through high-efficiency thermal oil and PTC ceramic heating elements. It is equipped with air supply and environmental control systems and central control systems to achieve precise adjustment of the temperature and humidity of the airflow entering the coal body. The system automatically adjusts the heating power and environmental parameters through the central control system to ensure stable temperature and humidity during the experiment, effectively improving the accuracy and efficiency of the experiment.

[0159] 2. Research and development of full-process acoustic temperature sensing detection device: This invention adds an acoustic temperature sensing monitoring system to the experimental box, which changes the research focus from the traditional early detection of coal spontaneous combustion to full-cycle coal temperature detection and monitoring. The new device can retain the detection results of the thermocouple and compare them with the acoustic temperature sensing three-dimensional temperature field results to achieve more accurate temperature monitoring and data analysis.

[0160] 3. Experimental device combining active and passive detection technologies: This experimental device combines active and passive detection technologies, of which active detection is applicable to the early and middle stages of coal spontaneous combustion (room temperature ~ about 300°C). Passive detection is applicable to high-temperature combustion states above 300°C and the later ash formation stage. The core innovation lies in combining acoustic temperature measurement technology with acoustic emission technology to achieve coal temperature measurement and monitoring in different temperature ranges.

[0161] 4. Full-process coupled acoustic transmission module design: Through the intelligent active and passive acoustic detection system, it provides an accurate and reliable technical solution for coal temperature monitoring in complex coal environments. This module not only maintains stable operation in extreme environments, but also ensures the accuracy of operation and the safety of equipment through a central control system and multiple safety protection measures.

[0162] 5. Movable acoustic transceiver array module: In order to better adapt to different test requirements and experimental conditions, the acoustic sensing device of the present invention realizes modular movement through a sliding support module, which not only can fix the temperature sensing point in the coal body, but also can be easily moved in space to construct a more accurate three-dimensional temperature field.

[0163] 6. Autonomous switching function of working state: The acoustic detection system of the present invention can automatically select the most appropriate detection mode according to the temperature stage change characteristics during the coal spontaneous combustion process through the built-in temperature threshold automatic switching function.

[0164] 7. Formula innovation and optimization: By calculating the flight time and propagation distance of the sound wave, optimizing the relationship formula between the speed of sound and the temperature, and using the Kriging interpolation method to construct a semi-variogram function and an optimization algorithm, the weight coefficient solution is made more accurate. Combined with neural network technology, by inputting the sound wave propagation path and flight time, the data is collected into a continuous computer network system to output an accurate temperature field, thereby achieving accurate prediction and real-time monitoring of the temperature distribution. The present invention introduces detailed formula derivation and optimization algorithms into the acoustic temperature measurement technology. By innovatively combining the derivation of the relationship between the speed of sound and the temperature, the calculation of the path average temperature, the optimization of the Kriging interpolation method, the solution of the weight coefficient, and the combination with the neural network algorithm, the accuracy and robustness of the temperature field reconstruction are significantly improved.

[0165] The use of the acoustic-based coal temperature full-process monitoring experimental device provided in the embodiment of the present invention to monitor coal temperature has the following beneficial effects:

[0166] 1. Full-cycle and full-temperature range monitoring: By combining active and passive acoustic wave detection technologies, the present invention can cover a wide temperature range from room temperature to 900°C, thereby achieving full-cycle monitoring of the coal spontaneous combustion process.

[0167] 2. Improve experimental accuracy: The present invention uses an acoustic temperature measurement method to reduce the contact error that may be introduced by traditional thermocouple temperature measurement, and improves the accuracy and response speed of temperature measurement.

[0168] 3. Optimize experimental cost and efficiency: The improved device structure and monitoring method reduce the amount and cost of coal samples required for the experiment, while improving the repeatability of the experiment, which is conducive to continuous experiments on a large number of samples.

[0169] 4. Enhanced safety: The experimental device is designed with multiple safety control measures, such as air-tightness detection and thermal insulation measures, to ensure safety under high temperature or possible harmful gas leakage conditions during the experiment.

[0170] 5. In-depth formula derivation and algorithm optimization: It can accurately calculate the temperature of different positions of the coal body, thereby improving the accuracy of temperature monitoring. By introducing the neural network method, the accuracy and robustness of temperature field reconstruction are significantly improved, and the acoustic temperature measurement process of coal spontaneous combustion is optimized.

[0171] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0172] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0173] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0174] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on 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 invention. In addition, the functional units in the embodiments of the present invention may all be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0175] The above is only an embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An acoustic-based coal temperature full-process monitoring experimental device, characterized in that: It includes an acoustic temperature sensing monitoring system, a control and data acquisition system, an air supply and environmental control system and a central control system, wherein the control and data acquisition system is used to collect gas, detect temperature and transmit data; the air supply and environmental control system is used to control the air flow temperature entering the coal body, the cleanliness of the air supply and the air humidity to simulate the real mine environment; the acoustic temperature sensing monitoring system includes: An acoustic wave generator module, used to generate acoustic wave signals within a specific frequency range, and the frequency and intensity are adjusted by the central control system; The whole process coupled transceiver transmission module includes an active acoustic detection system and an acoustic emission sensor; wherein the active acoustic detection system is used for early and mid-stage process monitoring of coal spontaneous combustion, including multiple acoustic wave transducers deployed in a three-dimensional array, when one acoustic wave transducer is controlled to transmit an acoustic wave signal, the other acoustic wave transducers located on the opposite side serve as receivers to receive the acoustic wave signal; the acoustic emission sensor is used as a passive test module for monitoring the high-temperature combustion process of coal spontaneous combustion in the later stage; the central control system monitors the coal body temperature in real time, and the automatic switching mechanism is used to switch between the coal body and the coal body; When the temperature is lower than 300℃, the control gives priority to the use of the active acoustic detection system, which emits low-frequency sound waves, calculates the temperature of the coal body by using the sound wave propagation time and speed changes, and monitors the temperature changes of the coal body in the low-temperature stage; when the temperature approaches the critical point of 300℃, the active acoustic detection system and the acoustic emission sensor are used simultaneously for data comparison and calibration; when the temperature exceeds 300℃, the active acoustic detection system is automatically stopped and switched to the acoustic emission sensor to capture the acoustic wave emission characteristics generated inside the coal body. When the temperature drops below 300℃, the active acoustic detection system is reactivated; A movable acoustic transceiver array support module, including a sliding support module, a fixed support, and an adjustment device, is used to carry the multiple acoustic wave transducers and pre-place them in the coal body, and realize the monitoring of fixed and mobile temperature sensing points to adapt to different test requirements and experimental conditions and construct a more accurate three-dimensional temperature field; The signal processing and data analysis module is used to process the received sound wave signal, extract key data reflecting the temperature change of the coal body, calculate the average temperature on each sound wave propagation path by using the known relationship between sound speed and temperature, construct the semivariogram function matrix by using the Kriging interpolation method and determine the temperature field reconstruction formula to estimate the temperature value of any point in the coal body area, and perform regression analysis on the relationship between the sound wave propagation path and the flight time and temperature by combining the neural network, iteratively optimize to obtain the temperature field model to predict the temperature result, and reconstruct the three-dimensional temperature field inside the coal body; wherein, the temperature field model is obtained by collecting the propagation distance and flight time of each sound wave transmission path in the three-dimensional array as a training sample set, and defining the mean square error between the temperature data and the corresponding true temperature value as the loss function for training; The temperature and humidity inspection module is used to monitor the temperature and humidity changes in the experimental environment. The humidity measured at the transducer position based on the humidity influence coefficient corrects the actual measured temperature. The humidity influence analysis formula is: ； in, The actual coal area Click to The speed of sound along the propagation path between points, For no humidity Click to The reference speed of sound on the propagation path between points; β is the humidity influence coefficient; is the humidity measured or estimated at each transducer location; A control module, used to control the operation of the acoustic temperature sensing monitoring system and manage the communication and data exchange between the modules; Among them, in the signal processing and data analysis module, the test points in the coal body area are x 0 , existing measurement points x 1 , x 2 ……x n , n is the total number of measurement points, and the constructed temperature data vector is , the constructed semi-variogram matrix is: ； In the formula, For two known measuring points x i and x j The semivariogram between is used to describe the spatial autocorrelation of data between different locations. is the value of the semivariogram at zero distance; the point to be measured x 0 With known measuring points x i The semivariant function vector between ; The temperature field reconstruction formula finally determined by Kriging interpolation method is as follows: ； ； In the formula, The coal body area is the test point x 0 Temperature; In the coal area Click to The average temperature along the propagation path between points; is the weight coefficient, and by solving the system equation We get is the semi-variant function matrix, μ is the Lagrange multiplier, For the test point x 0 With existing measurement points x i The semi-variant function vector of .

2. The experimental device according to claim 1, characterized in that: The internal structure of the experimental device adopts a rectangular design with an inner diameter of 0.6 meters, which is used to accommodate loosely stacked coal and simulate the spontaneous combustion state of coal left in the goaf; the coal loading height is 1.8 meters, the outer diameter is 1.2 meters, and the total height is 4 meters, so as to enhance the stability of the overall structure and the accuracy of monitoring; at the same time, the coal is loaded in layers, and each layer of coal is 30 centimeters thick. High-strength heat-resistant grids are set between layers to ensure the uniform distribution of the coal body, so as to prevent the coal body from moving during the experiment and facilitate the arrangement of sensors.

3. The experimental device according to claim 1, characterized in that: The outside of the experimental device is provided with a stainless steel insulation box, including an insulation oil layer and a heating layer, and the original water layer insulation is replaced by the oil layer insulation; The insulating oil layer is wrapped with high-efficiency heat-conducting oil on the outer wall of the experimental device; the heating layer is a programmable heating plate, which adopts a PC-500 programmable controller, and the temperature range can be set from 20°C to 900°C, with a temperature control accuracy of ±0.5°C; a 3 mm thick thermal conductive silicone sheet is laid between the heating plate and the insulating oil layer to ensure that the heat of the heating plate is quickly transferred to the insulating oil layer, and the temperatures of the heating plate and the insulating oil layer are coordinated through the central control system to ensure that the temperature inside the entire experimental device is uniform.

4. A method for monitoring the whole process of coal temperature using the experimental device according to any one of claims 1 to 3, characterized in that: The method comprises the following steps: Fill the rectangular coal sample box with coal samples evenly and conduct air tightness test; Complete the loose coal body oxidation experiment from 30 to 150 degrees at room temperature and from 150 to 900 degrees at high temperature under constant air volume conditions; Number all acoustic transducers and divide the coal area into logical blocks; Set the heating temperature threshold in the room temperature oxidation and high temperature oxidation experiments and start the acoustic temperature sensing experiment; Calibrate and initialize each acoustic transducer; During the heating process, the threshold value of the spontaneous combustion temperature stage of the coal is judged and the mode is switched to complete the coal temperature monitoring under the active and passive test modules; Monitor the temperature and humidity changes in the experimental environment, and correct the actual measured temperature based on the humidity measured at the transducer position based on the humidity influence coefficient; the humidity influence analysis formula is: ;in, The actual coal area Click to The speed of sound along the propagation path between points, For no humidity Click to The reference speed of sound on the propagation path between points; β is the humidity influence coefficient; is the humidity measured or estimated at each transducer location; According to the acquired acoustic temperature detection data, the semivariogram matrix is ​​constructed by the Kriging interpolation method and the temperature field reconstruction formula is determined to estimate the temperature value of any point in the coal body area. The relationship between the acoustic wave propagation path and the flight time and the temperature is regressed and analyzed in combination with the neural network. The temperature field model is obtained by iterative optimization to predict the temperature result and reconstruct the three-dimensional temperature field inside the coal body. The temperature field model is obtained by collecting the propagation distance and the flight time of each acoustic wave transmission path in the three-dimensional array as a training sample set, and defining the mean square error between the temperature data and the corresponding true temperature value as the loss function for training. Among them, the test points in the coal area x 0 , existing measurement points x 1 ,x 2 ……x n , n is the total number of measurement points, and the constructed temperature data vector is , the constructed semi-variogram matrix is: ； In the formula, For two known measuring points x i and x j The semivariogram between is used to describe the spatial autocorrelation of data between different locations. is the value of the semivariogram at zero distance; Points to be tested x 0 With existing measurement points x i The semivariant function vector between ; The temperature field reconstruction formula finally determined by Kriging interpolation method is as follows: ； ； In the formula, The coal body area is the test point x 0 Temperature; In the coal area Click to The average temperature along the propagation path between points; is the weight coefficient, and by solving the system equation We get is the semi-variant function matrix, μ is the Lagrange multiplier, For the test point x 0 With existing measurement points x i The semi-variant function vector of .

5. The method according to claim 4, characterized in that The Kriging interpolation method is used to estimate the temperature value of any point in the coal body area based on the acoustic temperature detection data obtained, and the relationship between the acoustic wave propagation path and the flight time and the temperature is regressed and analyzed in combination with the neural network technology. The temperature field model is obtained by iterative optimization to predict the temperature result, and the three-dimensional temperature field inside the coal body is reconstructed, including: 12 acoustic wave transducers are evenly arranged on each two-dimensional plane of the three-dimensional experimental box to form a closed square layout; one of the acoustic wave transducers is controlled to transmit an acoustic wave signal, and the remaining acoustic wave transducers located on the opposite side serve as receivers to receive the acoustic wave signal; Collect the sound wave propagation path and flight time corresponding to each pair of transducer combinations and calculate the propagation speed of each path, and determine the average temperature of each path based on the sound speed-temperature relationship; Construct temperature data vector and semi-variogram matrix; According to the Kriging interpolation method, the temperature of any grid point in the coal body area is estimated; A neural network algorithm is introduced to perform regression analysis on the relationship between the acoustic wave propagation path, flight time and temperature, and predict the average temperature on the acoustic wave propagation path. Combining the prediction results of the neural network and the calculation results of the Kriging interpolation method, special software is used to reconstruct the high-precision three-dimensional temperature field inside the coal body and perform visual analysis.

6. The method according to claim 5, characterized in that The neural network algorithm is introduced to perform regression analysis on the relationship between the acoustic wave propagation path, the flight time and the temperature, and predict the average temperature on the acoustic wave propagation path, including: Collect the propagation distance and flight time data of each sound wave propagation path as a training sample set; Input the training sample set into the constructed neural network structure to obtain temperature data for forward calculation; wherein the neural network structure includes an input layer, a hidden layer and an output layer, and defines a layer-to-layer weight matrix and a bias vector; Based on the temperature data and the corresponding true temperature value, using mean square error as a loss function; The gradient is calculated by using a back propagation algorithm, and the weight matrix and the bias vector are updated by using a gradient descent method until a convergence condition is met to obtain a trained temperature field model; The temperature field model is used to predict the average temperature of each path in the coal body area.

7. The method according to claim 5 or 6, characterized in that: The average temperature of each path is determined by the following formula: ； in, , , In the coal body area Click to The average temperature, sound propagation distance, and sound wave flight time on the propagation path between points. is the gas adiabatic index; R is the ideal gas constant; T is the thermodynamic temperature; M is the molar mass of the gas.

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