A signal control method and system for a gas sensor

Abstract

The present invention relates to the field of gas detection and analysis and processing technology, and in particular to a signal control method and system for a gas sensor. The method includes: obtaining the current data sequence of electrical signals in a mine in real time through a gas sensor; determining the data volatility of the current data sequence; reducing the window length of the current data sequence according to the data volatility of the current data sequence; determining the noise level of each data point in the reduced window; correcting the preset weight value of each data point according to the noise level of each data point in the reduced window; obtaining the filter value of each data point; converting the filter value into a gas concentration value through a gas sensor to complete signal control. Dynamically reducing the length of the window through data volatility effectively balances the sensitivity of data capture and the need for noise smoothing, ensuring that subtle changes in gas concentration can still be accurately captured in complex and changing environments.

Description

Technical Field

[0001] The present invention relates to the technical field of gas detection and analysis processing, and in particular to a signal control method and system for a gas sensor. Background Art

[0002] In the mining industry, mine safety has always been the top priority of production operations. As mining depth increases, environmental conditions in mines become increasingly complex, including unstable air flow, drastic temperature fluctuations, and widespread distribution of electromagnetic equipment, all of which pose huge challenges to the accurate monitoring of gas concentrations. The signals monitored by traditional gas sensors in complex environments are susceptible to noise interference, resulting in distortion of the measured signals and difficulty in accurately reflecting the true changes in gas concentrations. When emergencies occur in mines, such as gas leaks or spontaneous combustion of coal seams, rapid and accurate gas concentration monitoring is essential for taking timely safety measures.

[0003] The patent document with announcement number CN216437493U discloses a wireless transmission device for a gas detector. The sampling and holding circuit receives the detection signal of the gas detector. After voltage stabilization and filtering, the signal enters the sampling and holding device to hold the inverse output of the response time of the gas detector detection probe. When the normally closed contact of relay K2 is closed, the weighted average transmission circuit transmits the time control signal to the remote end through the communication module by the controller on one route and adds the other route to the gate of the field effect transistor T1. The field effect transistor T1 is turned on. After sampling and holding, the signal enters the rectifier and the average value filter for average filtering and is transmitted to the remote end through the communication module. The dynamic real-time transmission circuit uses a differential amplifier to compare the amplitude of the signal before and after the sampling and holding device. When the amplitude is large or the gas detection signal is high, the bidirectional thyristor VS1 is triggered to turn on, the relay K1 coil is energized, and the gas detection signal is transmitted to the remote end by the controller through the communication module in real time, thereby reducing the transmission power consumption and ensuring the integrity and efficiency of the signal transmission.

[0004] The above patent document adopts weighted averaging followed by average filtering. However, the complex and changeable environmental conditions in the mine, including air flow, temperature fluctuations, and interference from electromagnetic equipment, cause noise signals to be mixed into the signals collected by the gas sensor. These noise signals seriously interfere with the accuracy of the signal conversion into concentration data, limiting the sensitivity and accuracy of the gas sensor to changes in gas concentration. When processing noise signals, the traditional weighted moving average filtering method can smooth the data to a certain extent. However, when faced with sudden events in the mine, such as sudden gas leaks, spontaneous combustion of coal seams, etc., which cause the gas concentration to increase rapidly in a short period of time, it fails to accurately distinguish the changing characteristics of noise and real signals, and may mistakenly regard these important safety warning signals as noise and filter them, resulting in the inability to capture the real concentration changes while removing the noise, thereby reducing the monitoring accuracy. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a signal control method and system for a gas sensor.

[0006] In a first aspect, the present invention provides a signal control method for a gas sensor, which adopts the following technical solution:

[0007] A signal control method for a gas sensor comprises: obtaining a current data sequence of an electrical signal in a mine in real time through a gas sensor; taking the difference between the value of each data point in the current data sequence and the mean value as the deviation of each data point, determining the data volatility of the current data sequence according to the deviation, the maximum value and the minimum value in the deviation, wherein the data volatility is positively correlated with the difference between the maximum value and the minimum value in the deviation of each data point and the deviation of each data point; reducing the window length of the current data sequence according to the data volatility of the current data sequence, wherein the length of the window after reduction is positively correlated with the length of a preset window and negatively correlated with the data volatility; performing curve fitting on each data point in the window after reduction, and obtaining the data points in the window. The fitting value of each data point; determining the noise level of each data point in the reduced window, the noise level is positively correlated with the inconsistency of the difference between the data point and its two adjacent data points, and the difference between the value of the data point and its fitting value; correcting the preset weight value of each data point according to the noise level of each data point in the reduced window, the corrected weight value is positively correlated with the noise level of the data point; taking any data point in the reduced window as the target data point, and performing weighted averaging on the data points other than the target data point in the reduced window according to the corrected weight value to obtain the filtered value of the target data point; converting the filtered value into a gas concentration value through a gas sensor to complete signal control.

[0008] The beneficial effects are: by acquiring data in real time, calculating deviations, reducing window lengths, and curve fitting, the actual gas concentration can be reflected more accurately and the error caused by data volatility can be reduced; by evaluating the noise level of data points and correcting weight values, the influence of noise on the results can be effectively suppressed, and the stability and reliability of the signal can be improved; by reducing the window length according to data volatility, the filtering process can adapt to different environmental changes and improve the flexibility and accuracy of data processing; by calculating the filtering value through the weighted average method, the influence of abnormal data on the final result can be reduced while maintaining data smoothness.

[0009] Furthermore, the data volatility satisfies the following relationship:

[0010] ; In the formula, is the data volatility of the current data series, is the maximum value of the deviation of each data point in the current data sequence, is the minimum value of the deviation of each data point in the current data sequence, The first The deviation of the data points, is the number of data points in the current data sequence, is a hyperparameter.

[0011] The beneficial effects are: by combining the maximum and minimum values ​​of the deviation of the data points, the volatility measurement is closer to the variation range of the actual data, improving the calculation accuracy; through weighting and normalization, the volatility assessment is smoother, avoiding the adverse effects of extreme values ​​on the results.

[0012] Furthermore, the length of the reduced window satisfies the following relationship:

[0013] ; In the formula, is the length of the reduced window of the current data sequence, is the length of the preset window of the current data sequence, is the data volatility of the current data series, is the inverse tangent function, The round-up symbol.

[0014] The beneficial effects are: by reducing the window length according to the data volatility, when the data volatility is high, the window length is shortened accordingly, which helps to capture more detailed data features, thereby improving the sensitivity of data processing; when the data volatility is large, shortening the window can reduce the impact of noise on data processing, thereby improving the accuracy of the data, and when the volatility is small, retaining a longer preset window can integrate more data, which helps to improve the stability of the measurement results.

[0015] Furthermore, the curve fitting is performed using a least squares method.

[0016] Furthermore, the noise level satisfies the following relationship:

[0017] ; In the formula, The first The noise level of each data point is The first The value of the data point, The first The fitted value of the data points, To reduce the number of data points in the window, The first The value of the data point, The first The value of the data point, is a hyperparameter, is the standard normalization function.

[0018] The beneficial effects are: combining the difference between the data point and the fitted value and the inconsistency of the difference between adjacent data points can more accurately reflect the noise level of each data point; using a standard normalization function to standardize the noise level so that its value is within a fixed range helps to eliminate the impact of different data ranges and ensure that the comparison and processing of noise levels are more consistent and reliable.

[0019] Furthermore, the modified weight value satisfies the following relationship:

[0020] ; In the formula, The first The corrected weight value of the data point is The first The preset weight value of each data point, The first The noise level of each data point.

[0021] The beneficial effect is that by calculating the ratio of the preset weight value to the noise level, the weight of the data point can be adjusted according to the actual noise level of the data point, so that the data point with larger noise will be given a lower correction weight, thereby reducing the impact on the result; the corrected weight value can more accurately reflect the reliability of the data point, making the data processing or analysis results more stable and reliable.

[0022] Furthermore, the signal control includes: in response to any gas concentration value being greater than a preset safety threshold, determining that the gas concentration in the current period exceeds the standard, and issuing an early warning prompt to complete the signal control of the gas sensor.

[0023] In a second aspect, the present invention provides a signal control system for a gas sensor, which adopts the following technical solution:

[0024] A signal control system for a gas sensor includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned signal control method for the gas sensor is implemented.

[0025] By adopting the above technical solution, the above-mentioned signal control method of a gas sensor is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0026] The present invention has the following technical effects:

[0027] By dynamically reducing the window length according to data volatility, the sensitivity of data capture and the need for noise smoothing are effectively balanced, ensuring that subtle changes in gas concentration can still be accurately captured in complex and changing environments; at the same time, by combining curve fitting with noise level assessment, it accurately distinguishes between noise and real signal changes, avoiding the misjudgment of important safety warning signals as noise in traditional methods, and ensuring the authenticity and reliability of monitoring data; the weighted average method based on noise level correction makes the filtered data closer to the actual gas concentration, improves the accuracy of gas concentration measurement, and ensures the safety of the mine environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding numbers are the same or corresponding parts.

[0029] Figure 1 It is a method flow chart of a signal control method of a gas sensor according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0031] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0032] The embodiment of the present invention discloses a signal control method of a gas sensor, referring to Figure 1 , comprising steps S1 to S7:

[0033] S1: Obtain the current data sequence of the electrical signal in the mine in real time through the gas sensor.

[0034] By building a processing unit into the gas sensor, the electrical signal collected in real time is transmitted to the processing unit for subsequent processing. It should be noted that the gas sensor contains sensitive elements. When the target gas enters the sensor probe, the sensitive elements will interact with the target gas to generate electrical signals.

[0035] Implementers can set the collection duration and frequency of the current data sequence according to the actual implementation situation. For example, the collection duration is 1 minute and the collection frequency is 1 second / time.

[0036] S2: Determine the data volatility of the current data series.

[0037] It should be noted that as a monitoring tool, the electrical signals detected by the gas sensor can reflect the changes in gas concentration. However, due to the unstable air flow inside the mine and the widespread existence of electromagnetic equipment, these electrical signals are subject to significant noise interference. However, noise signals often appear as significant signal fluctuations in local time periods. Therefore, it is necessary to first analyze the changes in the data sequence of the electrical signals in the current time period.

[0038] The difference between the value of each data point in the current data sequence and the mean is taken as the deviation of each data point, and the data volatility of the current data sequence is determined according to the deviation, the maximum value and the minimum value in the deviation.

[0039] Specifically, the data volatility satisfies the following relationship:

[0040] ;

[0041] In the formula, is the data volatility of the current data series, is the maximum value of the deviation of each data point in the current data sequence, is the minimum value of the deviation of each data point in the current data sequence, The first The deviation of the data points, is the number of data points in the current data sequence, is a hyperparameter.

[0042] Implementers can set hyperparameters according to actual implementation conditions, for example, 0.01. The purpose of hyperparameters is to prevent situation occurs.

[0043] in, Indicates the current degree of change. The larger the value, the The greater the maximum and minimum difference between the individual data and the mean deviation, the greater the change in the current data sequence; Indicates the degree of difference of data in the current data sequence. The larger the value, the greater the deviation of multiple data from the mean in the current data sequence, which makes the final calculated deviation average larger, which means that the difference of data in the current data sequence is more obvious. In the actual mine environment, the concentration data detected in the local time is often relatively stable, and if such strong volatility occurs, it may be caused by noise signals. When the data change amplitude is large and the data difference is more obvious, it means that the data volatility in the current data sequence is strong, which means that there may be noise data in the current data sequence.

[0044] S3: Reduce the window length of the current data sequence according to the data volatility of the current data sequence.

[0045] It should be noted that due to the extreme complexity and potential instability of the mine environment, sudden events such as gas leakage and coal seam spontaneous combustion can cause significant changes in gas concentration in a short period of time. These sudden events are often accompanied by a sharp rise or fall in concentration, resulting in large data volatility. If only the overall data sequence is analyzed once, it is likely that the detailed changes in the data, especially those short-term and critical concentration fluctuations, will be ignored due to the averaging effect, resulting in the omission of important safety information. Therefore, by dynamically adjusting the window length, it is ensured that when new data is collected, the window length is reduced according to the volatility of the data sequence. If the volatility is large, it means that the current data segment may contain a lot of noise or sharp concentration changes. In order to capture these changes more accurately and avoid information loss, it is necessary to reduce the window length to ensure that different volatility areas in the data sequence are effectively divided into different windows. On the contrary, if the volatility is small, try to keep the preset window length.

[0046] Specifically, the length of the reduced window satisfies the following relationship:

[0047] ;

[0048] In the formula, is the length of the reduced window of the current data sequence, is the length of the preset window of the current data sequence, is the data volatility of the current data series, is the inverse tangent function, The round-up symbol.

[0049] Implementers can set the length of the preset window according to actual implementation conditions. For example, the window length is 60, which includes all data in the current data sequence.

[0050] in, Indicates the length of the reduced window. If the current sequence data is more volatile, The closer it is to 0, the smaller the length of the reduced window is. This is because the greater the volatility, the more likely the current data sequence contains noise data that results in sharp changes in concentration, and the window size needs to be reduced to better capture local details.

[0051] S4: Determine the noise level of each data point in the reduced window.

[0052] It should be noted that due to the complexity of the mine environment, if the concentration increases significantly due to real events (gas leakage, spontaneous combustion of coal seams, etc.), the electrical signals detected in the local time period also show significant fluctuations, which increases the difficulty of accurately judging the noise signal. Therefore, it is necessary to analyze the data features in the reduced window to distinguish the fluctuations caused by real events from pure noise signals. By combining the difference between the value of the data point and the fitted value, and the comprehensive evaluation of the inconsistency of the difference between the two adjacent data points of the data point, the real noise data can be identified more accurately. For the inconsistency of the two adjacent data points of the first or last data point in the data sequence, it is preset to be equal to the value of the first or last data point.

[0053] Perform curve fitting on each data point in the reduced window to obtain the fitting value of each data point in the window; determine the noise level of each data point in the reduced window.

[0054] Specifically, the curve fitting is to perform curve fitting on the data series in the reduced window and the corresponding time series by using the least square method.

[0055] Specifically, the noise level satisfies the following relationship:

[0056] ;

[0057] In the formula, The first The noise level of each data point is The first The value of the data point, The first The fitted value of the data points, To reduce the number of data points in the rear window, The first The value of the data point, The first The value of the data point, is a hyperparameter, is the standard normalization function.

[0058] Implementers can set hyperparameters according to actual implementation conditions, for example, 0.1. The purpose of hyperparameters is to prevent situation occurs.

[0059] in, Indicates The ratio of the difference between the value of a data point and its fitted value to the sum of the differences of all data in the entire window. The larger the value, the larger the deviation of the data is relative to other data, which increases the possibility that the data point is a noise. Indicates The inconsistency between the difference between a data point and its adjacent data points If the value of a data point is opposite to the sign of the difference between the data before and after it, the larger the difference between the two points, the greater the difference. A data point may be a mutation point in the data sequence, which means that the data point may be noise data; in summary, when the When the values ​​of both data points are larger, it is considered that the The greater the noise level of the data point.

[0060] S5: Modify the preset weight value of each data point according to the noise level of each data point in the reduced window.

[0061] It should be noted that when noise signals are mixed with actual signals, it is difficult for traditional weighted average filtering methods to distinguish between the two. The method gives a larger weight to recent data and then obtains the filter value of each data by weighted average. If for any data, there is noise data in the recent period, a higher weight is given to obtain the filter value of the data by averaging, causing the filtered data to deviate from the true value. Therefore, it is necessary to correct the weight value of each data in combination with the degree of noise when considering it, so as to reduce the impact of noise data on the filtering result.

[0062] Specifically, the modified weight value satisfies the following relationship:

[0063] ;

[0064] In the formula, The first The corrected weight value of the data point is The first The preset weight value of each data point, The first The noise level of each data point.

[0065] Among them, data points with high noise levels should be assigned lower weights, while data points with low noise levels should be assigned higher weights; if the preset weight value remains unchanged, the higher the noise level, the smaller the value, and the smaller the corrected weight.

[0066] Implementers can set weight values ​​according to actual implementation conditions, for example, using empirical values ​​in the field or allocating weight values ​​based on the time position of data points.

[0067] S6: Obtain the filter value of each data point.

[0068] Any data point in the reduced window is taken as the target data point, and the data points other than the target data point in the reduced window are weighted averaged according to the modified weight value to obtain the filtered value of the target data point.

[0069] S7: Convert the filtered value into a gas concentration value through the gas sensor to complete signal control.

[0070] Specifically, the signal control includes:

[0071] In response to any gas concentration value being greater than a preset safety threshold, the gas concentration in the current period is deemed to be excessive, and an early warning is issued to complete the signal control of the gas sensor.

[0072] Implementers can set safety thresholds based on actual implementation conditions. For example, the safety threshold for a carbon monoxide gas sensor is 0.0024%, and the safety threshold for an ammonia gas sensor is 0.004%.

[0073] After the calculation and analysis of the current data sequence is completed, the first data outside the reduced window is used as the first data of the next current data sequence, and steps S1 to S7 are repeated.

[0074] The present invention utilizes data volatility to dynamically reduce the length of the window to balance data capture and noise smoothing, utilizes curve fitting to obtain the fitting value of each data point in the reduced window, and introduces the difference between the value corresponding to the data and its fitting value, as well as the inconsistency of the difference between adjacent data of the data point to evaluate the noise level of the data, so as to accurately distinguish noise from real signal changes; the weight value of the traditional algorithm is corrected in combination with the noise level of the data in the reduced window, and the weighted average is performed using the corrected weight to obtain the filtered value of each data point in the reduced window, i.e., the filtered value, and then the filtered value is converted into a gas concentration value through the signal and concentration conversion relationship of the gas sensor, and finally, through real-time comparison with the safety threshold, once the concentration exceeds the preset safety line, the early warning system is immediately triggered, and an early warning message is issued to relevant personnel for processing, so as to ensure the safety of mine operations.

[0075] An embodiment of the present invention further discloses a signal control system for a gas sensor, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a signal control method for a gas sensor according to the present invention is implemented.

[0076] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0077] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0078] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0079] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A signal control method for a gas sensor, characterized in that: include: The current data sequence of the electrical signal in the mine is obtained in real time through the gas sensor; The difference between the value of each data point in the current data sequence and the mean is taken as the deviation of each data point. The data volatility of the current data sequence is determined according to the deviation, the maximum value and the minimum value in the deviation. The data volatility satisfies the following relationship: ; In the formula, is the data volatility of the current data series, is the maximum value of the deviation of each data point in the current data sequence, is the minimum value of the deviation of each data point in the current data sequence, The first The deviation of the data points, is the number of data points in the current data sequence, is a hyperparameter. The data volatility is positively correlated with the difference between the maximum and minimum values ​​of the deviations of each data point and the deviations of each data point. The window length of the current data sequence is reduced according to the data volatility of the current data sequence. The length of the window after reduction satisfies the following relationship: ; In the formula, is the length of the reduced window of the current data sequence, is the length of the preset window of the current data sequence, is the inverse tangent function, The sign of rounding up is positively correlated with the length of the preset window and negatively correlated with the data volatility; curve fitting is performed on each data point in the reduced window to obtain the fitting value of each data point in the window; the noise level of each data point in the reduced window is determined, and the noise level satisfies the following relationship: ; In the formula, The first The noise level of each data point is The first The value of the data point, The first The fitted value of the data points, To reduce the number of data points in the rear window, The first The value of the data point, The first The value of the data point, is a hyperparameter, is a standard normalization function. The noise level is positively correlated with the inconsistency of the difference between the data point and its two adjacent data points, and the difference between the value of the data point and its fitted value. The preset weight value of each data point is corrected according to the noise level of each data point in the reduced window. The corrected weight value satisfies the following relationship: ; In the formula, The first The corrected weight value of the data point is The first The preset weight value of each data point, the modified weight value is positively correlated with the noise level of the data point; Taking any data point in the reduced window as the target data point, performing weighted averaging on the data points other than the target data point in the reduced window according to the modified weight value to obtain a filtered value of the target data point; The filtered value is converted into a gas concentration value through a gas sensor to complete signal control.

2. A signal control method for a gas sensor according to claim 1, characterized in that: The curve fitting is performed using the least square method.

3. The signal control method of a gas sensor according to claim 1, characterized in that: The signal control is completed by: In response to any gas concentration value being greater than a preset safety threshold, the gas concentration in the current period is deemed to be excessive, and an early warning is issued to complete the signal control of the gas sensor.

4. A signal control system for a gas sensor, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a signal control method for a gas sensor according to any one of claims 1 to 3 is implemented.

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