Coal mine underground ventilation control method and device, electronic equipment and storage medium

Abstract

The invention provides an underground coal mine ventilation control method and device, electronic equipment and a storage medium, and belongs to the technical field of mine ventilation. The method comprises the steps that the type and concentration characteristics of harmful gas in a target area are determined according to data collected by a biosensor array; determining time sequence data of the harmful gas according to the concentration of the harmful gas, and determining a target concentration change rate of the harmful gas according to the time sequence data; according to the concentration of the harmful gas, the target concentration change rate and the current underground acoustic wave characteristics of the coal mine, the target air volume of the target area is determined; according to the target air volume of the target area, the ventilation resistance of the target area and the current opening degree of the ventilation valve, the opening degree of the ventilation valve is controlled, and the ventilation air volume is determined. According to the coal mine underground ventilation control method and device, the electronic equipment and the storage medium, the ventilation quantity can be accurately and timely adjusted.

Description

Technical Field

[0001] This application belongs to the field of mine ventilation technology, and more specifically, relates to a method and device for underground ventilation control in coal mines, electronic equipment, and storage medium. Background Technology

[0002] Underground ventilation in coal mines is a crucial link in ensuring safe production. It plays a vital role in preventing the accumulation of harmful gases such as methane and carbon monoxide and in creating a safe and suitable underground working environment. It is an indispensable and important component of the coal mine production system.

[0003] Currently, ventilation control in coal mines mainly relies on traditional sensors (such as electrochemical sensors and infrared sensors) to detect the concentration of harmful gases. Then, based on simple logic algorithms, the operating parameters of ventilation equipment are adjusted manually or semi-automatically to control the airflow. However, this approach has significant drawbacks. When determining the airflow, it often only considers the single factor of harmful gas concentration, ignoring the dynamic characteristics of harmful gas concentration changes and other relevant features of the underground environment. This results in a lack of dynamism and comprehensiveness in ventilation control, making it impossible to accurately and promptly adjust the ventilation volume. Summary of the Invention

[0004] The purpose of this application is to provide a method and device for controlling ventilation in coal mines, electronic equipment, and storage medium, which can accurately and timely adjust the ventilation volume according to the actual changing trend of harmful gases and complex environmental conditions in the mine.

[0005] A first aspect of this application provides a method for controlling ventilation in a coal mine, comprising: The types and concentration characteristics of harmful gases in the target area are determined based on data collected by the biosensor array. Determine the time series data of harmful gases based on their concentrations, and then determine the target concentration change rate of harmful gases based on the time series data. The target air volume for the target area is determined based on the concentration of harmful gases, the rate of change of target concentration, and the current acoustic characteristics of the coal mine. Based on the target air volume of the target area, the ventilation resistance of the target area, and the current opening degree of the ventilation valves, the opening degree of the ventilation valves is controlled to determine the ventilation air volume.

[0006] A second aspect of this application provides a coal mine underground ventilation control device, comprising: The data acquisition module is used to determine the type and concentration characteristics of harmful gases in the target area based on the data collected by the biosensor array. The target concentration change rate determination module is used to determine the time-series data of harmful gases based on their concentrations, and then determine the target concentration change rate of the harmful gases based on the time-series data. The target air volume determination module is used to determine the target air volume of the target area based on the concentration of harmful gases, the rate of change of target concentration, and the current acoustic characteristics of the coal mine. The ventilation control module is used to control the opening of the ventilation valves and determine the ventilation volume based on the target air volume of the target area, the ventilation resistance of the target area, and the current opening of the ventilation valves.

[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described coal mine underground ventilation control method.

[0008] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described coal mine underground ventilation control method.

[0009] The beneficial effects of the coal mine underground ventilation control method and device, electronic equipment, and storage medium provided in this application embodiment are as follows: This embodiment uses a biosensor array based on multiple microbial strains sensitive to harmful gases in coal mines to determine the types and concentration characteristics of harmful gases in the target area, and determines time-series data and target concentration change rate based on the concentration of harmful gases, thereby obtaining the dynamic characteristics of harmful gas concentration changes more accurately and making the ventilation control in coal mines more targeted; This embodiment combines the characteristics of harmful gas concentration with underground sound wave characteristics to determine the target air volume, and considers other relevant environmental characteristics underground, making ventilation control more comprehensive and ensuring ventilation safety in coal mines; Furthermore, based on the target concentration change rate of harmful gases, ventilation resistance, the current opening degree of ventilation valves, and the opening degree of target air volume regulating valves, multiple parameters are comprehensively calculated and controlled to achieve accurate and timely adjustment of ventilation volume in coal mines, better ensuring underground production safety and creating a suitable working environment. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic flowchart of a coal mine underground ventilation control method provided in an embodiment of this application; Figure 2 A structural block diagram of a coal mine underground ventilation control device provided in an embodiment of this application; Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] It is understood that in the embodiments of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0014] It should be noted that the terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0016] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for controlling ventilation in a coal mine according to an embodiment of this application. The method may include: S101: Determine the type and concentration characteristics of harmful gases in the target area based on data collected by the biosensor array.

[0017] The biosensor array in this embodiment is determined based on a variety of microbial strains sensitive to harmful gases in coal mines. In this embodiment, the harmful gases include methane and carbon monoxide. The data collected by the biosensor array in this embodiment can be metabolic activity data of the microorganisms, including respiration rate, metabolic product concentration, and acid production (alkali production). The concentrations of methane and carbon monoxide can inhibit or promote the metabolism of the microbial strains.

[0018] This embodiment obtains substances containing microorganisms collected from different environments underground in a coal mine, separates and samples these substances to obtain the separated microorganisms. These substances containing microorganisms can be air at different depths underground in a coal mine or deposits on the rock surface in different areas of an underground coal mine.

[0019] Based on the selected microbial strains sensitive to harmful gases (gas, carbon monoxide), corresponding biosensitive elements were prepared. The prepared biosensitive elements targeting different harmful gases (or having different response characteristics to the same harmful gas) were then arranged in a predetermined pattern to form a biosensor array.

[0020] The constructed biosensor array is calibrated. In this embodiment, the specific calibration method is as follows: configure standard gases of methane and carbon monoxide with different concentration gradients; place the biosensor array in the standard gas environment and detect its response signals (e.g., electrical signals, optical signals, etc.); establish a calibration curve based on the correspondence between the detected response signals and the standard gas concentrations, determine the response law of the biosensor array to different types and concentrations of harmful gases, and ensure the accuracy of detection.

[0021] The calibrated biosensor array is deployed to a target area underground in a coal mine. Through contact between the biosensor array and the gaseous environment of the target area, each biosensor element interacts with harmful gases (such as methane and carbon monoxide), generating corresponding response signals. The response signals generated by the biosensor array are collected and processed. Based on the calibration curve described above, the types and concentration characteristics of harmful gases in the target area are determined. In this embodiment, the types of harmful gases can be determined by the response differences of different biosensors, and the concentration characteristics can be calculated based on the intensity of the response signals and the calibration curve.

[0022] S102: Determine the time series data of harmful gases based on their concentrations, and determine the target concentration change rate of harmful gases based on the time series data.

[0023] In this embodiment, the concentration values ​​of harmful gases in the target area at each sampling time (for example, the sampling time can be once every 10 seconds) are obtained, and all the concentration values ​​of harmful gases are sorted according to the sampling time to form a set of time-series data containing the correspondence between sampling time and concentration value.

[0024] This embodiment can determine the concentration change rate per unit time based on the above time series data and the ratio of the concentration difference between adjacent collection times to the time interval. The concentration change rate per unit time is the target concentration change rate.

[0025] To more accurately determine the target concentration change rate, this embodiment can determine the target concentration change rate based on time-series data, linear regression equations, and a pre-trained long short-term memory network model. The specific method for determining the target concentration change rate in this embodiment is as follows: The sampling interval is determined based on the acquisition frequency of the time-series data; The data points of the sliding window are determined based on the sampling interval and the window length of the sliding window; The linear regression equation is determined based on the data points in the sliding window and the least squares method. The slope of the linear regression equation is taken as the rate of change of concentration at the current moment; Based on time-series data and a pre-trained long short-term memory network model, predict the predicted concentration change rate within a future preset time period; The target concentration change rate of harmful gases is determined based on the current concentration change rate, the predicted concentration change rate, and their respective weights.

[0026] In this embodiment, the sampling interval is determined based on the data acquisition frequency of the biosensor array. The sampling interval is the reciprocal of the sampling frequency. For example, if the acquisition frequency is once every 10 seconds, then the sampling interval in this embodiment is 10 seconds. This embodiment sets the current time as the end point of the window. The sliding window length can be determined according to actual needs and the specific analysis objectives in the coal mine. For example, in this embodiment, to analyze the concentration trend of harmful gases over the past 5 minutes, the window length can be set to 300 seconds. This window length can be adjusted according to the actual scenario. The data points of the sliding window are determined based on the sampling interval and the window length. In this embodiment, the calculation method for the data points of the sliding window can be as follows: ,in, n Let be the number of data points in the sliding window, l be the window length of the sliding window, and Δt be the sampling interval. Based on the calculation method for the data points in the sliding window, the number of data points in this embodiment is determined to be 30.

[0027] This embodiment includes a sliding window. n consecutive time points ( n It can be option 5), with time difference as the independent variable. x This time difference is the difference between the time of the first data point and the time of subsequent data points; the gas concentration is the dependent variable. y , build n Groups of data pairs, each data pair can be represented as ( x i , y i ),in, x i Let i be the time difference of the i-th data point. y iLet be the concentration value of harmful gas at the i-th data point.

[0028] Determine separately x i and y i The average value of the time difference and the average gas concentration are used to determine the slope of the linear regression equation. This slope reflects the rate of concentration change over time. This slope is then taken as the rate of concentration change at the current moment.

[0029] Historical time-series data prior to the current moment (e.g., time-series data from the past 30 minutes) is preprocessed according to the format required by the Long Short-Term Memory (LSTM) network model. The preprocessed time-series data is then input into a pre-trained LTM network model, which outputs a predicted concentration sequence for a future preset time period (e.g., 5 minutes).

[0030] For the predicted concentration sequence, this embodiment uses a sliding window with the same rate of change of concentration as the current time to determine the linear regression equation of the predicted concentration, and its slope is the rate of change of predicted concentration in the future preset time period.

[0031] This embodiment determines the target concentration change rate of harmful gases based on the current concentration change rate, the predicted concentration change rate, and their respective weights, using a target concentration change rate calculation formula. The target concentration change rate calculation formula is as follows: ,in, R The target concentration change rate, b 1 represents the rate of change of concentration at the current moment. The weight of the rate of change of concentration at the current moment. b 2 represents the predicted rate of concentration change. The weights for predicting the rate of concentration change. Among them, , and Adjustments can be made based on the actual conditions underground in the coal mine.

[0032] S103: Determine the target air volume for the target area based on the concentration characteristics of harmful gases and the current acoustic characteristics of the coal mine.

[0033] This embodiment extracts key features of harmful gases based on time-series data. These key features include the current instantaneous concentration value, average concentration value, concentration change rate, and concentration exceeding indicators. Before determining the target air volume for the target area based on the concentration characteristics of the harmful gases and the current acoustic characteristics in the coal mine, the following steps are also included: Determine the concentration characteristic vector of harmful gases based on their concentration. Determine the acoustic feature vector of the acoustic data based on the acoustic data; Based on the concentration characteristics and vector of harmful gases and the current acoustic characteristic vector in the coal mine, the target air volume for the target area is determined, including: The concentration feature vector of harmful gases and the sound wave feature vector are input into a pre-trained fusion model to determine the initial air volume of the target area. Obtain the ventilation dynamics parameters of the target area, including actual wind speed data, ventilation pressure difference data, and air density data; Determine the actual ventilation resistance of the target area based on the ventilation dynamics parameters of the target area; Determine the ideal wind pressure for the target area based on the actual ventilation resistance and initial air volume; Adjust the initial air volume according to the initial air volume and the ideal air pressure to obtain the target air volume.

[0034] In this embodiment, the sampling frequency is set to 1 time / minute and the sampling time is set to 30 minutes. Based on the collected harmful gas concentration data of the target area, corresponding time-series data is generated. For example, this time-series data can be [c1, c2, ..., c...]. n] In this embodiment, n is 30. The concentration value collected at the latest acquisition time in the time series data is obtained and taken as the instantaneous concentration. Based on all the data in the time series data, a trend line is fitted using the least squares method, and the slope of this trend line is taken as the concentration change rate. In this embodiment, the risk quantity corresponding to the concentration at each adjacent sampling time in the time series data and the preset concentration threshold is calculated using the numerical integration method. The risk quantities in the time series data are summed to obtain the cumulative risk quantity.

[0035] In this embodiment, the feature vector of harmful gas concentration is [instantaneous concentration, concentration change rate, risk accumulation].

[0036] This embodiment collects acoustic signals within the target area using acoustic sensors deployed in the roadway. Doppler frequency shift analysis is performed on these signals to calculate the estimated average wind speed across the roadway cross-section. The acoustic energy of a preset high-frequency band (e.g., the turbulence characteristic band) in the acoustic data is used as a turbulence intensity index, representing the turbulence of the airflow; a higher turbulence intensity index indicates more turbulent airflow. The effective sound pressure in the target area is calculated based on the amplitude of the acoustic signal, and the sound pressure level is further calculated based on the ratio of this effective sound pressure to a reference sound pressure. This sound pressure level is used to assist in determining the operating status of large underground equipment (e.g., the sound pressure level changes when the equipment is turned on, off, or experiences abnormal vibration) and to assess the reliability of the acoustic data itself (e.g., a low sound pressure level may indicate sensor blockage or severe signal interference). Based on this acoustic signal, the acoustic feature vector in this embodiment can be [estimated average wind speed, turbulence intensity index value, sound pressure level].

[0037] In this embodiment, the gas concentration feature vector and the sound wave feature vector are concatenated into a comprehensive feature vector. This comprehensive feature vector is input into a pre-trained fusion model. Based on the historical data learned by the model, a complex nonlinear mapping from multidimensional features to optimal airflow is established. The model then outputs the initial airflow for the target area. Specifically, the nonlinear mapping between the comprehensive feature vector and the initial airflow can include the following: if the current harmful gas concentration is higher than a preset concentration threshold, the concentration change trend (slope) is positive, and the estimated average wind speed is lower than a preset wind speed, then it is determined that a large airflow is urgently needed under current conditions; if the current harmful gas concentration is lower than a preset concentration threshold, the concentration change trend (slope) is negative, and the turbulence intensity index is higher than a preset index threshold, then it is determined that an appropriate airflow is needed to stabilize the flow field under current conditions.

[0038] This embodiment combines the feature vector of harmful gas concentration with the feature vector of sound waves in coal mines. The feature vector of sound waves in coal mines can indirectly reflect the intensity of underground operations (e.g., the noise of mining equipment) and the state of airflow disturbance. Thus, the initial air volume output by the fusion model can more comprehensively cover the required air volume in the target area, avoiding energy waste caused by excessive air volume or safety risks caused by insufficient air volume.

[0039] In this embodiment, the actual average wind speed of the target area and the static pressure difference between the air inlet and outlet of the target area are collected by a physical sensor set at the center of the tunnel cross-section. The current air density is calculated based on the data collected by the temperature, humidity and absolute pressure sensors respectively.

[0040] Based on the actual average wind speed, static pressure difference and current air density of the target area, a set of ventilation dynamic parameters is determined, which is [actual average wind speed, static pressure difference, current air density].

[0041] The predicted wind speed is determined based on the initial air volume and the cross-sectional area of ​​the roadway. The predicted wind speed is compared with the actual average wind speed, and the ventilation network is judged to be unobstructed based on the comparison results.

[0042] In one possible embodiment, a camera is installed near the tunnel cross-section. Based on the captured images of the tunnel cross-section, image recognition and computer vision algorithms are used to obtain the cross-sectional area of ​​the target area. The predicted wind speed of the target area is determined based on the initial air volume, the tunnel cross-sectional area, and the predicted wind speed calculation formula. In this embodiment, the predicted wind speed calculation formula can be: ,in, v To predict wind speed, This is the initial air volume. SThe cross-sectional area of ​​the target area's tunnel is defined. The predicted wind speed is compared with the actual average wind speed. If the absolute value of the difference between the predicted wind speed and the actual average wind speed is higher than the preset wind speed difference, it is determined that there is a major blockage in the ventilation network (e.g., accidental closure of air doors, tunnel collapse). In this case, airflow cannot be adjusted under these conditions, triggering the highest level alarm.

[0043] If the absolute value of the difference between the predicted wind speed and the actual average wind speed is not higher than the preset wind speed difference, then the ventilation network is considered to be functioning smoothly.

[0044] This embodiment calculates the actual ventilation resistance based on the smooth operation of the ventilation network, static pressure difference, and actual wind speed, thereby determining the ideal wind pressure. The ideal wind pressure is compared with the preset wind pressure threshold of the ventilation network's fans: if it exceeds the threshold, a safe airflow is used as the target airflow; otherwise, the initial airflow is used directly as the target airflow. In this embodiment, the actual ventilation resistance is used to characterize the local resistance characteristics of the roadway, and the formula for calculating this ventilation resistance is: , R 1 represents the actual ventilation resistance. This is the static pressure difference between the air inlet and the air outlet.

[0045] The ideal air pressure required to achieve the initial target air volume is determined based on the actual ventilation resistance, the initial target air volume, and the ideal air pressure calculation formula. This ideal air pressure characterizes the theoretical total resistance that the fans in the ventilation network need to overcome to achieve the initial target air volume. The ideal air pressure calculation formula is as follows: ,in, The ideal air pressure required for the initial target air volume.

[0046] In this embodiment, the calculated ideal air pressure is compared with the preset air pressure threshold of the ventilation network's fans. If the ideal air pressure exceeds the preset air pressure threshold, it is determined that the ideal air pressure is outside the safe range. In this case, the initial target air volume will be abandoned, and a safe air volume within the fan's capacity will be determined and used as the target air volume. If the ideal air pressure does not exceed the preset air pressure threshold, it is determined that the ideal air pressure is within the safe range. In this case, the initial air volume will be used as the target air volume.

[0047] S104: Based on the target concentration change rate of harmful gases, the ventilation resistance of the target area, the current opening degree of the ventilation valve, and the target air volume, adjust the opening degree of the ventilation valve to determine the current ventilation air volume.

[0048] In this embodiment, the air volume deviation is determined based on the difference between the target air volume and the current actual air volume. This air volume deviation is used to characterize the gap between the current state and the ideal state.

[0049] The air volume deviation, target concentration change rate, ventilation resistance, and current valve opening are used as input parameters. These input parameters are then fed into the valve control algorithm (e.g., a PID controller). The PID controller determines the basic valve opening adjustment amount, thereby determining the current ventilation air volume.

[0050] Before adjusting the opening of the ventilation valves and determining the current ventilation volume, the process also includes: Input the target air volume into the pre-trained valve opening adjustment model and output the target valve flow coefficient; Convert the valve flow coefficient into the target valve opening. Adjust the opening of the ventilation valves to determine the current airflow, including: Adjust the opening of the ventilation valve according to the target valve opening and the current opening of the ventilation valve; The current ventilation volume is determined based on the adjusted opening degree of the ventilation valve.

[0051] In this embodiment, the target air volume is input into a pre-trained valve opening adjustment model. This model has learned the nonlinear mapping relationship between the target air volume and the valve flow coefficient in a complex mine ventilation network environment. The model outputs the target valve flow coefficient. This target valve flow coefficient reflects the valve's ability to conduct airflow; the larger the coefficient, the greater the ventilation volume at the same opening degree.

[0052] This embodiment establishes a correspondence table between valve flow coefficient and valve opening based on a preset characteristic curve of the current ventilation valve. This preset characteristic curve can be provided by the valve manufacturer, or it can be determined after correction using actual downhole test data. The correspondence table shows that a valve flow coefficient of 0.6 corresponds to a valve opening of 40%; a valve flow coefficient of 0.8 corresponds to a valve opening of 60%. Based on the target valve flow coefficient and the corresponding table obtained above, the target valve opening of the current ventilation valve can be determined.

[0053] This embodiment adjusts the opening of the current ventilation valve based on the difference between the obtained target valve opening and the current opening of the ventilation valve. Specifically, the adjustment method for the current ventilation valve opening can be as follows: if the current ventilation valve opening is less than the target valve opening, a command to increase the valve opening is sent to the ventilation valve, gradually increasing the opening to the target valve opening. In this embodiment, the valve opening is increased by no more than 5% each time to avoid airflow turbulence caused by sudden changes in air volume.

[0054] If the current opening of the ventilation valve is greater than the target valve opening, a command to reduce the valve opening is sent to the ventilation valve, gradually reducing the opening to the target valve opening. In this embodiment, the valve opening is reduced by no more than 5% each time to avoid airflow turbulence caused by sudden changes in air volume.

[0055] If the current opening of the ventilation valve is greater than the target valve opening, no adjustment is needed, and the current state can be maintained.

[0056] In this embodiment, after the valve opening is adjusted to the target value and the system has been running stably for 5-10 minutes (ensuring that the airflow reaches a steady state), an air meter (e.g., a mechanical air meter or an electronic air meter) can be used to measure the wind speed of the roadway cross-section in the target area. The average wind speed is determined based on the measured wind speed, and the current ventilation volume is determined by multiplying the average wind speed by the roadway cross-section.

[0057] In this embodiment, the adjustment of the ventilation valve opening is based on the difference between the target valve opening and the current opening, rather than directly jumping to the target opening. This avoids downhole airflow disturbances (e.g., local negative pressure, gas stratification and accumulation) caused by sudden changes in valve opening, thereby ensuring the stability of the ventilation system operation, reducing mechanical wear of the valve actuator, and extending the service life of the equipment.

[0058] As can be seen from the above, this embodiment determines the type and concentration characteristics of harmful gases in the target area based on the biosensor array, which helps to accurately identify the characteristics of harmful gases. This embodiment calculates the concentration change rate at the current moment by combining time-series data, and predicts the predicted concentration change rate within a preset time period through a long short-term memory network model. Based on the concentration change rate at the current moment and the predicted concentration change rate, the target concentration change rate is determined. Through a dual-dimensional analysis method of real-time monitoring and future prediction, risks such as sudden increases and accumulation of harmful gas concentrations (e.g., sudden gas outbursts from coal seams) can be identified in advance, avoiding accidents such as explosions and poisoning caused by delayed adjustment. This embodiment also combines the characteristics of harmful gas concentration with the acoustic characteristics of underground coal mines to avoid energy waste caused by excessively high air volume or safety risks caused by excessively low air volume. The initial air volume is dynamically adjusted based on the difference between the ventilation dynamic parameters and the ideal air pressure to determine the target air volume, ensuring that the target air volume meets both safety requirements and the actual carrying capacity of the underground coal mine ventilation network, and improving the standardization and accuracy of ventilation valve control.

[0059] In one embodiment of this application, converting the valve flow coefficient into a target valve opening includes: The first adjacent flow coefficient, the first valve opening corresponding to the first adjacent flow coefficient, the second adjacent flow coefficient, and the second valve opening corresponding to the second adjacent flow coefficient are determined according to the preset valve opening curve. The preset valve opening curve is used to characterize the correspondence between the ventilation valve opening and the valve flow coefficient. The target valve opening is calculated based on the valve flow coefficient, the first valve opening, the first adjacent flow coefficient, the second adjacent flow coefficient, the second valve opening, and the linear interpolation formula. The formula for linear interpolation is as follows: ,in, For the target valve opening, This represents the first valve opening. This is the second valve opening. The valve flow coefficient, The first adjacent flow coefficient, This is the second adjacent flow coefficient.

[0060] In this embodiment, the valve flow coefficients in the preset valve opening curve are sorted in ascending order. Based on the target valve flow coefficient and the sorted valve flow coefficient data, the first adjacent flow coefficient and the second adjacent flow coefficient that meet the preset conditions are determined. The preset conditions can be: the first adjacent flow coefficient is less than the target valve flow coefficient, and the target valve flow coefficient is less than the second adjacent flow coefficient.

[0061] The first valve opening and the second valve opening corresponding to the first adjacent flow coefficient and the second adjacent flow coefficient are determined respectively.

[0062] This embodiment determines the target valve opening based on the target valve flow coefficient, the first adjacent flow coefficient, the second adjacent flow coefficient, the first valve opening, and the second valve opening, combined with the linear interpolation method calculation formula (target valve opening calculation formula). The linear interpolation method calculation formula can be: ,in, For the target valve opening, This represents the first valve opening. This is the second valve opening. For valve flow coefficient, The first adjacent flow coefficient, This is the second adjacent flow coefficient.

[0063] Based on the same inventive concept, this application also provides a coal mine underground ventilation control device for implementing the coal mine underground ventilation control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the coal mine underground ventilation control device provided below can be found in the limitations of the coal mine underground ventilation control method described above, and will not be repeated here.

[0064] This application provides an underground ventilation control device for coal mines, such as... Figure 2As shown, the underground ventilation control device 20 in the coal mine includes: a data acquisition module 21, a target concentration change rate determination module 22, a target air volume determination module 23, and a ventilation control module 24.

[0065] In one embodiment of this application, the data acquisition module 21 is used to determine the type and concentration characteristics of harmful gases in the target area based on the data collected by the biosensor array; The target concentration change rate determination module 22 is used to determine the time series data of harmful gases based on the concentration of harmful gases, and to determine the target concentration change rate of harmful gases based on the time series data. The target air volume determination module 23 is used to determine the target air volume of the target area based on the concentration characteristics of harmful gases and the current acoustic characteristics of the coal mine. The ventilation control module 24 is used to control the opening of the ventilation valve and determine the ventilation volume based on the target air volume of the target area, the ventilation resistance of the target area, and the current opening of the ventilation valve.

[0066] In one embodiment of this application, when determining the target concentration change rate of a harmful gas based on time-series data, the target concentration change rate determination module 22 is specifically used for: Determine the concentration change rate at the current moment based on time-series data; Based on time-series data and a pre-trained long short-term memory network model, predict the predicted concentration change rate within a future preset time period; The target concentration change rate of harmful gases is determined based on the current concentration change rate, the predicted concentration change rate, and their respective weights.

[0067] In one embodiment of this application, when determining the concentration change rate at the current moment based on time-series data, the target concentration change rate determination module 22 is specifically used for: The sampling interval is determined based on the acquisition frequency of the time-series data; The data points of the sliding window are determined based on the sampling interval and the window length of the sliding window; The linear regression equation is determined based on the data points in the sliding window and the least squares method. The slope of the linear regression equation is used as the rate of change of concentration at the current moment.

[0068] In one embodiment of this application, before determining the target air volume for a target area based on the concentration characteristics of harmful gases and the current acoustic characteristics of the coal mine, the target air volume determination module 23 is specifically used for: Determine the concentration characteristic vector of harmful gases based on their concentration. Determine the acoustic feature vector of the acoustic data based on the acoustic data; When determining the target air volume for a target area based on the concentration characteristics of harmful gases and the current acoustic characteristic vector in the coal mine, the target air volume determination module 23 is specifically used for: The concentration feature vector of harmful gases and the sound wave feature vector are input into a pre-trained fusion model to determine the initial air volume of the target area. Obtain the ventilation dynamics parameters of the target area, including actual wind speed data, ventilation pressure difference data, and air density data; The initial air volume is adjusted based on the ventilation dynamics parameters of the target area to obtain the target air volume for the target area.

[0069] In one embodiment of this application, when adjusting the initial airflow according to the ventilation dynamics parameters of the target area to obtain the target airflow of the target area, the target airflow determination module 23 is specifically used for: Determine the actual ventilation resistance of the target area based on the ventilation dynamics parameters of the target area; Determine the ideal wind pressure for the target area based on the actual ventilation resistance and initial air volume; Adjust the initial air volume according to the initial air volume and the ideal air pressure to obtain the target air volume.

[0070] In one embodiment of this application, before controlling the opening degree of the ventilation valve and determining the ventilation volume, the ventilation control module 24 is specifically used for: Input the target air volume into the pre-trained valve opening adjustment model and output the target valve flow coefficient; Convert the valve flow coefficient into the target valve opening. When controlling the opening degree of the ventilation valve and determining the ventilation volume, the ventilation control module 24 is specifically used for: Adjust the opening of the ventilation valve according to the target valve opening and the current opening of the ventilation valve; The current ventilation volume is determined based on the adjusted opening degree of the ventilation valve.

[0071] In one embodiment of this application, when converting the valve flow coefficient into the target valve opening, the ventilation control module 24 is specifically used for: The first adjacent flow coefficient, the first valve opening corresponding to the first adjacent flow coefficient, the second adjacent flow coefficient, and the second valve opening corresponding to the second adjacent flow coefficient are determined according to the preset valve opening curve. The preset valve opening curve is used to characterize the correspondence between the ventilation valve opening and the valve flow coefficient. The target valve opening is calculated based on the valve flow coefficient, the first valve opening, the first adjacent flow coefficient, the second adjacent flow coefficient, the second valve opening, and the linear interpolation formula. The formula for linear interpolation is as follows: ,in, For the target valve opening, This represents the first valve opening. This is the second valve opening. For valve flow coefficient, The first adjacent flow coefficient, This is the second adjacent flow coefficient.

[0072] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of the data acquisition module 21, the target concentration change rate determination module 22, the target air volume determination module 23, and the ventilation control module 24 are shown.

[0073] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0074] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0075] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store information such as the concentration of harmful gases, the rate of change of target concentration, the target air volume in the target area, and the ventilation air volume.

[0076] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the coal mine underground ventilation control method provided in the embodiments of this application, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.

[0077] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0078] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0079] Those skilled in the art will recognize that the modules / units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0080] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0081] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules, units, or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or modules / units, or it may be an electrical, mechanical, or other form of connection.

[0082] The modules / units described as separate components may or may not be physically separate. Similarly, the components shown as modules / units may or may not be physical modules / units; they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0083] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated modules / units described above can be implemented in hardware or in the form of software functional modules / units.

[0084] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling ventilation in coal mines, characterized in that, include: The types and concentration characteristics of harmful gases in the target area are determined based on data collected by the biosensor array. The time-series data of the harmful gas are determined based on the concentration of the harmful gas, and the target concentration change rate of the harmful gas is determined based on the time-series data. The target air volume for the target area is determined based on the concentration of the harmful gas, the rate of change of the target concentration, and the current acoustic characteristics of the coal mine. Based on the target air volume of the target area, the ventilation resistance of the target area, and the current opening degree of the ventilation valve, the opening degree of the ventilation valve is controlled to determine the ventilation air volume.

2. The coal mine underground ventilation control method as described in claim 1, characterized in that, Determining the target concentration change rate of harmful gases based on the time-series data includes: The concentration change rate at the current moment is determined based on the time series data; Based on the time series data and the pre-trained long short-term memory network model, predict the predicted concentration change rate within a future preset time period; The target concentration change rate of harmful gases is determined based on the current concentration change rate, the predicted concentration change rate, and their respective weights.

3. The coal mine underground ventilation control method as described in claim 2, characterized in that, Determining the concentration change rate at the current moment based on the time-series data includes: The sampling interval is determined based on the acquisition frequency of the time-series data; The data points of the sliding window are determined according to the sampling interval and the window length of the sliding window; The linear regression equation is determined based on the data points in the sliding window and the least squares method. The slope of the linear regression equation is taken as the rate of change of concentration at the current moment.

4. The coal mine underground ventilation control method as described in claim 1, characterized in that, Before determining the target air volume for the target area based on the concentration characteristics of the harmful gas and the current acoustic characteristics in the coal mine, the method further includes: The concentration feature vector of the harmful gas is determined based on the concentration of the harmful gas. Determine the acoustic feature vector of the acoustic data based on the acoustic data; The step of determining the target air volume for the target area based on the concentration characteristics of the harmful gas and the current acoustic characteristic vector in the coal mine includes: The concentration feature vector of the harmful gas and the sound wave feature vector are input into a pre-trained fusion model to determine the initial air volume of the target area. Obtain the ventilation dynamics parameters of the target area, including actual wind speed data, ventilation pressure difference data, and air density data; The initial air volume is adjusted according to the ventilation dynamics parameters of the target area to obtain the target air volume for the target area.

5. The coal mine underground ventilation control method as described in claim 4, characterized in that, The step of adjusting the initial air volume according to the ventilation dynamics parameters of the target area to obtain the target air volume for the target area includes: Determine the actual ventilation resistance of the target area based on the ventilation dynamics parameters of the target area; The ideal wind pressure for the target area is determined based on the actual ventilation resistance and the initial air volume. The initial air volume is adjusted according to the initial air volume and the ideal air pressure to obtain the target air volume.

6. The coal mine underground ventilation control method as described in claim 5, characterized in that, Before controlling the opening degree of the ventilation valve and determining the ventilation volume, the method further includes: The target air volume is input into a pre-trained valve opening adjustment model, and the target valve flow coefficient is output. Convert the valve flow coefficient into the target valve opening. The control of the opening degree of the ventilation valve to determine the ventilation volume includes: Adjust the opening of the ventilation valve according to the target valve opening and the current opening of the ventilation valve; The current ventilation volume is determined based on the adjusted opening degree of the ventilation valve.

7. The coal mine underground ventilation control method as described in claim 6, characterized in that, The step of converting the valve flow coefficient into the target valve opening includes: The first adjacent flow coefficient, the first valve opening corresponding to the first adjacent flow coefficient, the second adjacent flow coefficient, and the second valve opening corresponding to the second adjacent flow coefficient are determined according to the preset valve opening curve. The preset valve opening curve is used to characterize the correspondence between the ventilation valve opening and the valve flow coefficient. The target valve opening is calculated based on the valve flow coefficient, the first valve opening, the first adjacent flow coefficient, the second adjacent flow coefficient, the second valve opening, and the linear interpolation formula. The formula for calculating the linear interpolation method is as follows: ,in, For the target valve opening, This represents the first valve opening. This is the second valve opening. For valve flow coefficient, The first adjacent flow coefficient, This is the second adjacent flow coefficient.

8. A ventilation control device for underground coal mines, characterized in that, include: The data acquisition module is used to determine the type and concentration characteristics of harmful gases in the target area based on the data collected by the biosensor array. The target concentration change rate determination module is used to determine the time-series data of the harmful gas based on the concentration of the harmful gas, and to determine the target concentration change rate of the harmful gas based on the time-series data. The target air volume determination module is used to determine the target air volume of the target area based on the concentration of the harmful gas, the target concentration change rate, and the current acoustic characteristics of the coal mine. The ventilation control module is used to control the opening of the ventilation valve and determine the ventilation volume based on the target air volume of the target area, the ventilation resistance of the target area, and the current opening of the ventilation valve.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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