A real-time monitoring method and system for dust concentration in mines

Abstract

The present invention relates to the technical field of dust concentration detection, and specifically to a real-time monitoring method and system for dust concentration in a mine. The method comprises: obtaining dust concentration data collected by a dust monitor, vibration data of the dust monitor, and an optical path offset in the dust monitor; determining a synchronous change relationship between a concentration mutation characteristic of the dust concentration data and the optical path offset, and obtaining a vibration dominance degree of the dust concentration data by combining the difference from the synchronous change relationship with a preset standard and the concentration mutation characteristic; and correcting the dust concentration data by utilizing the correlation between the optical influence of vibration on dust concentration and the vibration dominance degree, so that the corrected dust concentration data can eliminate the optical path offset caused by operational vibration in the mine environment, thereby significantly improving the accuracy and stability of dust concentration measurement in the mine.

Description

Technical Field

[0001] The present invention relates to the technical field of dust concentration detection, and in particular to a real-time monitoring method and system for dust concentration in a mine. Background Art

[0002] In recent years, with the development of online sensing technologies such as light scattering, real-time monitoring equipment has been applied on-site in mines. Through the principle of laser scattering, dust concentration output can be achieved in seconds. Combined with wireless or wired redundant communication, multi-point data can be aggregated to the cloud platform, realizing centralized visualization and intelligent early warning of dust concentration throughout the mine.

[0003] In existing technology, dust monitors based on the fundamental principle of light scattering (Mie scattering) are used to measure dust concentration online in mines. However, in the complex environment of underground mines, the accuracy of the instruments is easily affected. Signal processing methods such as low-pass filtering are typically required to smooth and correct the measured scattered light signals to output relatively reliable dust concentration values. However, when encountering severe vibration conditions such as drilling and blasting operations, filtering methods have difficulty eliminating transient, non-periodic optical path offsets. Under strong vibration, the laser source, lens assembly, or detector may experience micron-level or angular misalignment, resulting in sudden changes in scattered light intensity. This can cause false spikes or drops in dust concentration readings, affecting the accuracy of underground dust concentration monitoring. Summary of the Invention

[0004] In order to solve the technical problem of low accuracy of existing methods for monitoring dust concentration in mines, the present invention aims to provide a method and system for real-time monitoring of dust concentration in mines. The technical solutions adopted are as follows:

[0005] In a first aspect of the present invention, a method for real-time monitoring of dust concentration in a mine is provided, comprising:

[0006] Obtaining dust concentration data collected by a dust monitor, vibration data of the dust monitor, and optical path offset in the dust monitor;

[0007] Determine the synchronous change relationship between the concentration mutation characteristics of the dust concentration data and the optical path offset, and combine the difference from the preset standard synchronous change relationship and the concentration mutation characteristics to obtain the vibration dominance degree of the dust concentration data; the vibration dominance degree indicates the possibility of dust concentration mutation being dominated by vibration;

[0008] The optical influence of vibration on dust concentration is compensated by utilizing the correlation between the optical influence of vibration on dust concentration and the degree of dominance of the vibration, and the dust concentration data is corrected according to the compensation result; the optical influence characterizes the changing relationship between the vibration data and the optical path offset.

[0009] In an exemplary embodiment, determining the relationship between the concentration mutation characteristic of the dust concentration data and the synchronous change of the optical path offset includes:

[0010] Determine a concentration mutation feature neighborhood sequence of a neighborhood range of the concentration mutation feature at each moment, and a light path offset neighborhood sequence of a neighborhood range of the light path offset at each moment; the neighborhood range of each moment is a number of previous moments adjacent to each moment;

[0011] The correlation coefficient between the concentration mutation characteristic neighborhood sequence and the optical path offset neighborhood sequence corresponding to each moment is determined to obtain the synchronous change relationship between the optical path offset and the concentration mutation characteristic at each moment.

[0012] In an exemplary embodiment, the process of obtaining the preset standard synchronous change relationship includes:

[0013] Determining a concentration mutation feature target sequence, wherein the concentration mutation feature target sequence is composed of concentration mutation features greater than a preset concentration mutation feature threshold;

[0014] Obtaining an optical path offset at the same moment as each concentration mutation feature in the concentration mutation feature target sequence to form an optical path offset target sequence;

[0015] The correlation coefficient of the concentration mutation characteristic target sequence and the optical path offset target sequence is determined to obtain the preset standard synchronous change relationship.

[0016] In an exemplary embodiment, the method for real-time monitoring of dust concentration in a mine further includes:

[0017] Arranging the vibration data in ascending order to obtain an ascending sequence of vibration data, and sorting the optical path offsets according to the ascending sequence of vibration data to obtain an optical path offset reference sequence;

[0018] Obtaining the relative change amplitude of the vibration data of each data point within the neighborhood of each data point in the vibration data ascending sequence, and the relative change amplitude of the optical path offset in the optical path offset reference sequence;

[0019] Obtaining a relative change amplitude difference of each data point within a neighborhood range, wherein the relative change amplitude difference is a difference between the relative change amplitude of the optical path offset and the relative change amplitude of the vibration data at the same data point;

[0020] By fusing the relative change amplitude differences within the neighborhood of each data point, the optical impact of vibration on dust concentration at each moment is obtained.

[0021] In an exemplary embodiment, compensating for the optical influence includes:

[0022] Obtaining a compensation coefficient, wherein the compensation coefficient is obtained from a correlation between the vibration dominance degree and the optical influence;

[0023] The optical influence is compensated according to the compensation coefficient.

[0024] In an exemplary embodiment, the correcting dust concentration data according to the compensation result includes:

[0025] According to the compensated optical influence at each target moment, a dust concentration correction degree at each target moment is obtained; the target moment is a moment corresponding to a concentration mutation characteristic greater than a preset concentration mutation characteristic threshold;

[0026] According to the degree of dust concentration correction at each target moment and the data change trend of the dust concentration data at each target moment, the dust concentration correction coefficient at each target moment is obtained;

[0027] The dust concentration data at each target moment is corrected according to the dust concentration correction coefficient at each target moment.

[0028] In an exemplary embodiment, the process of obtaining the dust concentration correction coefficient includes:

[0029] If the data change trend of the dust concentration data at a certain target moment is that the data becomes larger, then the dust concentration correction coefficient of the dust concentration data at the target moment is equal to the difference between the value 1 and the dust concentration correction degree at the target moment; if the data change trend of the dust concentration data at the target moment is that the data becomes smaller, then the dust concentration correction coefficient of the dust concentration data at the target moment is equal to the sum of the value 1 and the dust concentration correction degree at the target moment.

[0030] In an exemplary embodiment, the method for real-time monitoring of dust concentration in a mine further includes:

[0031] The corrected dust concentration data is compared with a preset dust concentration threshold value, and if a preset number of consecutive dust concentration data are greater than the preset dust concentration threshold value, an alarm signal is output.

[0032] In an exemplary embodiment, the method for real-time monitoring of dust concentration in a mine further includes:

[0033] The difference in the degree of change of the dust concentration data at each moment and its neighborhood range is determined to obtain the concentration mutation characteristics of the dust concentration data at each moment; the neighborhood range of each moment is the previous several moments adjacent to each moment.

[0034] In a second aspect of the present invention, a real-time monitoring system for dust concentration in a mine is provided, comprising: a memory and a processor; the memory is connected to the processor; the memory is used to store program instructions; the processor is used to implement the above-mentioned real-time monitoring method for dust concentration in a mine when the program instructions are executed.

[0035] The present invention has the following beneficial effects: in addition to obtaining the dust concentration data collected by the dust monitor, the present invention also obtains the vibration data of the dust monitor and the optical path offset in the dust monitor that affect the accuracy of the dust concentration data. Due to the complex working conditions in the mine, misalignment may occur in the dust monitor under the action of vibration, resulting in changes in the intensity of scattered light, and then causing false mutations in the dust concentration data. Then, the dust concentration data is corrected according to the impact of vibration on the dust concentration data and the optical path offset, so that the corrected dust concentration data can eliminate the optical path offset caused by operational vibration in the mine environment, and significantly improve the accuracy and stability of dust concentration measurement in the mine. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a method for real-time monitoring of dust concentration in a mine provided by one embodiment of the present invention;

[0037] Figure 2 A flowchart for obtaining the relationship between the concentration mutation characteristics of dust concentration data and the synchronous change of the optical path offset provided by one embodiment of the present invention;

[0038] Figure 3 This is a flowchart for obtaining a preset standard synchronous change relationship provided by an embodiment of the present invention;

[0039] Figure 4 is a flow chart for obtaining the optical effect of vibration on dust concentration provided by one embodiment of the present invention;

[0040] Figure 5 This is a flow chart for correcting dust concentration data provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0041] To further illustrate the technical means and effects employed by the present invention to achieve its intended objectives, the following detailed description of the specific embodiments, structures, features, and effects of the present invention is provided in conjunction with the accompanying drawings and preferred embodiments. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this invention pertains. The data and information collected in this application were obtained with full consent and authorization.

[0043] Mines present unique conditions, subject to drilling and blasting, as well as mechanical vibration, which can affect dust concentration monitoring. Dust concentration is determined by a dust monitor, which uses a laser source to emit a beam of light. The intensity of the scattered light received by the detector indicates the dust concentration. Furthermore, vibration can cause internal components of the dust monitor to shift, shifting the laser beam position and causing errors in the measured scattered light intensity, which in turn affects the accuracy of dust concentration. Severe vibration in mines can also lead to false spikes in dust concentration.

[0044] This embodiment provides a method for real-time monitoring of dust concentration in a mine. Figure 1 Shown, including:

[0045] Step S1: obtaining dust concentration data collected by the dust monitor, vibration data of the dust monitor, and optical path offset in the dust monitor;

[0046] Step S2: Determine the synchronous change relationship between the concentration mutation characteristics of the dust concentration data and the optical path offset, and combine the difference from the preset standard synchronous change relationship and the concentration mutation characteristics to obtain the vibration dominance degree of the dust concentration data;

[0047] Step S3: using the correlation between the optical effect of vibration on dust concentration and the degree of vibration dominance, the optical effect is compensated, and the dust concentration data is corrected according to the compensation result.

[0048] Each step is described in detail below with reference to the accompanying drawings.

[0049] Step S1: Obtain dust concentration data collected by a dust monitor, vibration data of the dust monitor, and optical path offset in the dust monitor.

[0050] In underground mine environments, dust monitors based on the fundamental principle of light scattering (Mie scattering) are used to collect dust concentration data. Dust monitors are deployed at key locations in the mining face and haulage tunnels. A laser source illuminates an air sample entering a detection chamber. The interaction between particles and the light beam generates scattered light, which is then converted into an electrical signal by a photodetector, which is then used to calculate the dust concentration. A dust monitor typically includes components such as a laser source, lens, air pump, and detector. 1) Light Source and Detection Chamber: A semiconductor laser diode is used to emit a parallel light beam, and a focusing lens and reflector are placed within the detection chamber to enhance the efficiency of scattered light collection. 2) Micro-Air Pump and Inlet Preconditioning: A built-in 1–5 L / min micro-blower draws the air sample into the detection chamber through a nozzle and impact plate pre-separator to remove coarse particles and prevent blockage of the chamber or piping. 3) Photodetector: A photodiode is positioned at the scattering angle (forward direction) to receive only the light scattered by particles. An adjustable gain amplifier is used to improve the signal-to-noise ratio. 4) Scattering signal acquisition: When suspended particles pass through the light beam, the intensity of the scattered light generated is proportional to the size and number of the particles; the detector converts the photocurrent into a voltage or pulse signal and sends it to the analog-to-digital conversion module for sampling.

[0051] A vibration sensor is fixed to the dust monitor housing to collect vibration data. A high-bandwidth vibration sensor (such as an integrated electronic piezoelectric accelerometer) with a frequency response of tens of kHz is used to cover the main frequency bands of drilling and blasting and mechanical vibration. The sensor is bolted directly to the dust monitor housing. The vibration sensor outputs an analog voltage or current signal, which is digitized by an on-site analog-to-digital conversion module at a sampling rate of ≥5kHz to obtain a digital vibration signal. It should be understood that due to the complex environment underground in mines, a certain amount of mechanical vibration is inevitable during mining, and the vibration value at each moment is always greater than 0.

[0052] A Position Sensitive Device (PSD) is installed within the dust monitor. This sensor uses the position change of the light spot on the dust monitor's detection surface to quantify the optical path offset, thereby determining the optical path offset within the dust monitor. The PSD is positioned in front of the dust monitor's detector to ensure that the laser spot falls in the center of the detection surface when properly aligned. After amplification, filtering, and analog-to-digital conversion of the detected analog signal, the processor calculates the light spot offset in real time and converts it into a physical displacement value (μm) using a calibration factor (e.g., mm / V).

[0053] In this embodiment, the sampling frequencies of the dust concentration data, vibration data, and optical path offset are the same and synchronous sampling is achieved to ensure that all data are on the same time baseline. Alternatively, duplicate and misplaced records in the dust concentration data, vibration data, and optical path offset are removed, multi-sensor timestamps are aligned, and missing values ​​are processed using interpolation or deletion methods to ensure that all data are on the same time baseline. In addition, various data can also be preprocessed by filtering to remove operating noise while retaining key mutation characteristics, providing a clean signal for false mutation identification and alignment compensation. Ultimately, the dust concentration data, vibration data, and optical path offset at each moment in the monitoring time period are obtained, thereby obtaining the initial sequence of dust concentration data, the initial sequence of vibration data, and the initial sequence of optical path offset, respectively.

[0054] In this embodiment, in order to facilitate subsequent data processing, the dust concentration data, vibration data, and optical path offset may be normalized to eliminate the dimension and implement processing operations between the data.

[0055] Step S2: Determine the synchronous change relationship between the concentration mutation characteristics of the dust concentration data and the optical path offset, and combine the difference from the preset standard synchronous change relationship and the concentration mutation characteristics to obtain the vibration dominance degree of the dust concentration data.

[0056] To correct for mechanical errors in dust concentration caused by vibration, it's necessary to ensure that sudden changes in dust concentration are primarily due to vibration. When the optical path deviates due to vibration, the dust concentration will also change synchronously. If the synchronous change relationship between the optical path offset and the concentration mutation characteristics of the dust concentration data within a certain dust concentration data area closely matches the preset standard synchronous change relationship, the dust concentration is primarily due to a false change caused by vibration.

[0057] First, the dust concentration data is analyzed to obtain the concentration mutation characteristics of the dust concentration data. Taking any moment as an example, the neighborhood range of that moment is determined. There is a close correlation between the neighborhood range of that moment and the moment. In an exemplary embodiment, the neighborhood range of that moment is the number of moments preceding the moment, that is, the end time of the neighborhood range of that moment is the moment before the moment. The number of moments included in the neighborhood range is set according to actual needs. In this embodiment, 5 is used as an example, that is, the neighborhood range of the moment is the first 5 moments preceding the moment.

[0058] Perform curve fitting on the initial sequence of dust concentration data to obtain a dust concentration change curve with time as the horizontal axis and dust concentration value as the vertical axis. Obtain the slope of the dust concentration value at each moment on the dust concentration change curve as the degree of data change of the dust concentration data at that moment. The larger the absolute value of the slope, the greater the degree of data change. Obtain the dust concentration value at each moment in the neighborhood of that moment, perform a straight line fit on the dust concentration values ​​at each moment in the neighborhood, and obtain the slope of the fitted line as the degree of data change of the dust concentration data in the neighborhood of that moment. The larger the absolute value of the slope of the fitted line, the greater the degree of data change of the dust concentration values ​​in the neighborhood.

[0059] The absolute value of the slope of the dust concentration value at that moment is subtracted from the absolute value of the slope of the fitted line in the neighborhood at that moment, and the resulting difference is normalized. The resulting difference is the concentration mutation characteristic of the dust concentration data at that moment. Therefore, this difference may be positive, negative, or 0. Regardless of whether the difference is positive, negative, or 0, the following logic is satisfied: the larger the value of the difference, the higher the degree of change in the dust concentration value at that moment is compared to the degree of change in the neighborhood at that moment, the more obvious the concentration mutation characteristic of the dust concentration data at that moment, and the more it indicates that the dust concentration data at that moment has deviated from the original change trend, which may be caused by mechanical misalignment caused by vibration. The normalization method here can be a sigmoid function.

[0060] When encountering severe vibration conditions such as drilling and blasting operations, the laser source, lens group or detector inside the dust monitor will be misaligned under strong vibration, resulting in a sudden change in the scattered light intensity, which will cause a false increase or decrease in the dust concentration reading. Therefore, when the dust concentration data shows a sudden change feature, it may be affected by vibration. Then, the relationship between the concentration sudden change feature of the dust concentration data and the synchronous change of the optical path offset is obtained. Figure 2 As shown, in an exemplary embodiment, a specific process for determining the relationship between the concentration mutation characteristics of dust concentration data and the synchronous change of the optical path offset is given as follows:

[0061] Step S21: determining a concentration mutation feature neighborhood sequence within a neighborhood range of the concentration mutation feature at each moment, and a light path offset neighborhood sequence within a neighborhood range of the light path offset at each moment.

[0062] For any moment, the concentration mutation characteristics of each moment within the neighborhood of that moment are obtained to form a neighborhood sequence of concentration mutation characteristics for that moment. The neighborhood sequence of concentration mutation characteristics for that moment includes the concentration mutation characteristics of each moment within the neighborhood of that moment. Similarly, the optical path offsets of each moment within the neighborhood of that moment are obtained to form a neighborhood sequence of optical path offsets for that moment. The neighborhood sequence of optical path offsets for that moment includes the optical path offsets of each moment within the neighborhood of that moment.

[0063] Step S22: determining the correlation coefficient between the neighborhood sequence of concentration mutation characteristics and the neighborhood sequence of optical path offset corresponding to each moment, and obtaining the synchronous change relationship between the optical path offset and the concentration mutation characteristics at each moment.

[0064] Obtain a correlation coefficient between the concentration mutation characteristic neighborhood sequence at that moment and the optical path offset neighborhood sequence at that moment. The correlation coefficient represents the correlation between the changes in the two neighborhood sequences. In one exemplary embodiment, the correlation coefficient is specifically a Pearson correlation coefficient, that is, the Pearson correlation coefficient between the concentration mutation characteristic neighborhood sequence at that moment and the optical path offset neighborhood sequence at that moment is calculated. Since the Pearson correlation coefficient has a numerical range of -1 to 1, this embodiment normalizes the Pearson correlation coefficient using the following normalization method: (Pearson correlation coefficient + 1) / 2.

[0065] The normalized result of the Pearson correlation coefficient between the neighborhood sequence of the concentration mutation feature at that moment and the neighborhood sequence of the optical path offset at that moment is taken as the synchronous change relationship between the optical path offset and the concentration mutation feature at that moment.

[0066] The synchronous relationship between the optical path offset and the concentration mutation characteristic at that moment is then compared with a preset standard synchronous relationship. The preset standard synchronous relationship serves as a synchronous relationship between the optical path offset and the concentration mutation characteristic to characterize a dust concentration mutation caused by vibration. The smaller the difference between the synchronous relationship between the optical path offset and the concentration mutation characteristic at that moment and the preset standard synchronous relationship, the more likely the dust concentration mutation was primarily caused by vibration. In other words, the greater the likelihood that the dust concentration mutation was caused by vibration, and the higher the degree of vibration dominance.

[0067] The preset standard synchronous change relationship can be a preset benchmark value obtained through a large number of historical experiments. In an exemplary embodiment, Figure 3 As shown, a specific process of obtaining the preset standard synchronous change relationship is given below:

[0068] Step S23: Determine the concentration mutation characteristic target sequence.

[0069] Compare the concentration mutation characteristics at each moment with a preset concentration mutation characteristic threshold. The preset concentration mutation characteristic threshold is used to determine whether the concentration mutation characteristic at each moment is high. The preset concentration mutation characteristic threshold has a numerical range of 0-1. The specific value is set according to actual judgment needs. For example, if the judgment is more stringent, the preset concentration mutation characteristic threshold can be set to a smaller value. In this embodiment, 0.7 is used as an example.

[0070] From the concentration mutation features at each moment, concentration mutation features greater than a preset concentration mutation feature threshold are obtained. These concentration mutation features greater than the preset concentration mutation feature threshold constitute a target concentration mutation feature sequence. It should be understood that the target concentration mutation feature sequence is a sequence arranged in time sequence. Simultaneously, the moments corresponding to each concentration mutation feature greater than the preset concentration mutation feature threshold are obtained.

[0071] Step S24: obtaining the optical path offset at the same time as each concentration mutation feature in the concentration mutation feature target sequence to form an optical path offset target sequence.

[0072] After obtaining the time instants corresponding to each concentration mutation feature in the target concentration mutation feature sequence, the optical path offsets at these time instants are obtained. Specifically, the optical path offsets at the same time instants as each concentration mutation feature in the target concentration mutation feature sequence are obtained. The obtained optical path offsets at these time instants form a target optical path offset sequence. It should be understood that the target optical path offset sequence is also a sequence arranged in time sequence.

[0073] Step S25: Determine the correlation coefficient between the concentration mutation characteristic target sequence and the optical path offset target sequence to obtain a preset standard synchronous change relationship.

[0074] Obtain a correlation coefficient between the concentration mutation characteristic target sequence and the optical path offset target sequence. In one exemplary embodiment, the correlation coefficient is specifically a Pearson correlation coefficient, i.e., the Pearson correlation coefficient between the concentration mutation characteristic target sequence and the optical path offset target sequence is calculated. Since the Pearson correlation coefficient ranges from -1 to 1, the Pearson correlation coefficient is also normalized here using the formula: (Pearson correlation coefficient + 1) / 2.

[0075] The result of normalizing the Pearson correlation coefficient of the concentration mutation characteristic target sequence and the optical path offset target sequence is used as the preset standard synchronous change relationship.

[0076] Obtain the difference between the synchronous change relationship between the concentration mutation characteristic and the optical path offset at that moment and the synchronous change relationship of the preset standard, and the difference is specifically the absolute value of the difference. Then, based on the difference and the concentration mutation characteristic at that moment, obtain the vibration dominance of the dust concentration data at that moment, and the vibration dominance characterizes the possibility of the dust concentration mutation dominated by vibration. Among them, the smaller the difference, the higher the vibration dominance of the dust concentration data at that moment, and the vibration dominance is inversely correlated with the difference; the larger the concentration mutation characteristic at that moment, the higher the vibration dominance of the dust concentration data at that moment, and the vibration dominance is positively correlated with the concentration mutation characteristic at that moment. In an exemplary embodiment, a specific quantification method of the vibration dominance is given as follows:

[0077] ;

[0078] in, Indicates the The degree of vibration dominance of the dust concentration data at each moment, Indicates the The concentration mutation characteristics of dust concentration data at each moment; Indicates the The relationship between the concentration mutation characteristics at each moment and the synchronous change of the optical path offset, Indicates the synchronous change relationship of the preset standard. Through the above method, the vibration dominance degree of the dust concentration data at each moment is obtained.

[0079] Step S3: using the correlation between the optical effect of vibration on dust concentration and the degree of vibration dominance, the optical effect is compensated, and the dust concentration data is corrected according to the compensation result.

[0080] The measurement of dust concentration depends on the effective overlap area between the laser beam and the suspended particles in the airflow. When vibration causes the beam to deviate from the center of the original "observation volume," the number of particles passing through the beam per unit time decreases, causing the intensity of scattered light recorded by the detector to decrease, thereby underestimating the actual dust concentration. Therefore, based on the corresponding changes in vibration data and optical path offset, the optical impact of vibration on dust concentration is analyzed to characterize the changing relationship between vibration data and optical path offset. In an exemplary embodiment, Figure 4 As shown, the real-time monitoring method for dust concentration in a mine provided by this embodiment also includes a specific process for obtaining the optical effect of vibration on dust concentration as follows:

[0081] Step S31: Arrange the vibration data in ascending order to obtain an ascending sequence of vibration data, and sort the optical path offsets according to the ascending sequence of vibration data to obtain a reference sequence of optical path offsets.

[0082] The vibration data at each moment is the vibration amplitude at that moment. The vibration amplitudes at each moment are arranged in ascending order from smallest to largest to obtain an ascending vibration data sequence. Multiple vibration amplitudes with the same value are randomly arranged in order. The moments of each vibration data in the ascending vibration data sequence are then obtained to form a moment sequence corresponding to the ascending vibration data sequence. The optical path offsets are sorted according to the moment sequence to obtain an optical path offset reference sequence. Therefore, the ascending vibration data sequence corresponds one-to-one to the optical path offset reference sequence at each moment.

[0083] Step S32: obtaining the relative change amplitude of the vibration data of each data point in the vibration data ascending sequence and the relative change amplitude of the optical path offset in the optical path offset reference sequence for each data point within the neighborhood range of each data point.

[0084] Since the ascending sequence of vibration data and the optical path offset reference sequence are not arranged in time sequence, the various moments in the sequence are not obtained in time sequence. Therefore, in order to facilitate the distinction between each moment, each data point is used to represent each position in the ascending sequence of vibration data and the optical path offset reference sequence.

[0085] Similar to the neighborhood range at any moment, for any data point, the neighborhood range of the data point is obtained, that is, the neighborhood range of the data point is the first 5 data points adjacent to the data point.

[0086] For any data point within the neighborhood of the data point, set as the neighborhood data point, the relative amplitude of vibration change of the neighborhood data point in the ascending sequence of vibration data is obtained. The relative amplitude of vibration change of the neighborhood data point is calculated by calculating the absolute value of the difference between the vibration data of the neighborhood data point and the next neighboring data point after the neighborhood data point, and then dividing the difference by the vibration data of the neighborhood data point, resulting in the relative amplitude of vibration change of the neighborhood data point. It should be understood that when obtaining the relative amplitude of vibration change of the last neighborhood data point, the absolute value of the difference between the vibration data of the data point and the last neighborhood data point is calculated, and then divided by the vibration data of the last neighborhood data point, resulting in the relative amplitude of vibration change of the last neighborhood data point.

[0087] Similarly, the relative change amplitude of the optical path offset of the neighborhood data point in the optical path offset reference sequence is obtained. The relative change amplitude of the optical path offset of the neighborhood data point is calculated by calculating the absolute value of the difference between the optical path offset of the neighborhood data point and the next neighboring data point after the neighborhood data point, and then dividing the difference by the optical path offset of the neighborhood data point. The result obtained is the relative change amplitude of the optical path offset of the neighborhood data point. It should be understood that when obtaining the relative change amplitude of the optical path offset of the last neighborhood data point, the absolute value of the difference between the optical path offset of the data point and the last neighborhood data point is calculated, and then divided by the optical path offset of the last neighborhood data point. The result obtained is the relative change amplitude of the optical path offset of the last neighborhood data point.

[0088] Step S33: Obtain the relative change amplitude difference of each data point within the neighborhood range.

[0089] For any neighborhood data point, the relative change amplitude of the optical path offset is subtracted from the relative change amplitude of the vibration data of that neighborhood data point. The difference is the relative change amplitude difference of the neighborhood data point. The larger the relative change amplitude difference of the neighborhood data point, the greater the relative change amplitude of the optical path offset of that neighborhood data point is than the relative change amplitude of the vibration data of that neighborhood data point. This means that when the vibration data has a relatively small change amplitude, the optical path offset will produce a relatively large change amplitude, and the optical impact of vibration on dust concentration will be more obvious.

[0090] Step S34: The relative change amplitude differences within the neighborhood of each data point are integrated to obtain the optical impact of the vibration on the dust concentration at each moment.

[0091] The relative amplitude differences of the neighboring data points within the neighborhood of the data point are integrated. Specifically, the average of the relative amplitude differences of all the neighboring data points within the neighborhood of the data point is calculated and normalized. The normalized result is the optical impact of the vibration of the data point on the dust concentration. The normalization method here can be a sigmoid function.

[0092] At this point, the optical influence of vibration of each data point on dust concentration is obtained. Although the data points are not arranged in time sequence, there is a one-to-one correspondence between each data point and each moment. Therefore, after obtaining the optical influence of vibration of each data point on dust concentration, the optical influence of vibration on dust concentration at each moment is also obtained according to the corresponding relationship.

[0093] It is impossible to accurately infer the false changes in dust concentration based on the optical path offset. The environment in the mine is complex, and the concentration error is also coupled with multiple environmental factors such as particle refractive index, particle size distribution, humidity and airflow velocity. Therefore, it is necessary to obtain the pure mechanical error through the relationship between the vibration data of the dust monitor and the optical path offset. In the mine, the greater the optical impact of vibration on dust concentration, the greater the concentration mutation characteristics corresponding to the dust concentration, thereby reflecting the error amplitude of the dust concentration reading. Through the optical impact of vibration on dust concentration, the mapping relationship of vibration to dust concentration is analyzed, that is, according to the optical impact of vibration on dust concentration at each moment and the degree of vibration dominance, the degree of correlation between the optical impact and the degree of vibration dominance is obtained. In an exemplary embodiment, the optical impact of vibration on dust concentration at each moment is sorted in time sequence to obtain an optical impact sequence, and the degree of vibration dominance at each moment is sorted in time sequence to obtain a vibration dominance sequence. Then the degree of correlation between the optical impact sequence and the vibration dominance sequence is obtained. In an exemplary embodiment, a specific method for obtaining the degree of correlation is given as follows:

[0094] ;

[0095] in, It indicates the degree of correlation between the optical influence and the degree of vibration dominance, that is, the mapping relationship between vibration and spurious changes in dust concentration; represents the optical impact sequence; Indicates the vibration dominance degree sequence. Indicates the DTW (Dynamic Time Warping) distance between the optical influence sequence and the vibration dominance sequence. Represents an exponential function with the natural constant e as the base. The larger the DTW distance, the greater the difference between the optical influence sequence and the vibration dominant degree sequence. right Perform negative correlation normalization so that Characterizes the degree of correlation between the optical influence and the degree of vibration dominance. The greater the correlation, the more synchronized the changes of the two sequences are.

[0096] The optical impact of vibration on dust concentration is compensated based on the degree of correlation between the optical impact and the degree of vibration dominance. Specifically, a compensation coefficient is obtained based on the degree of correlation between the optical impact and the degree of vibration dominance. In an exemplary embodiment, the degree of correlation between the optical impact and the degree of vibration dominance is used as the compensation coefficient. Then, the optical impact at each moment is compensated based on the compensation coefficient. In an exemplary embodiment, for any moment, the optical impact at that moment is multiplied by the compensation coefficient, and the result obtained is the compensated optical impact at that moment. The compensated optical impact at that moment represents the vibration compensation amplitude of the dust concentration data at that moment, indicating the amplitude of the sudden change in dust concentration caused by the optical impact of vibration on dust concentration, and represents the amplitude of the false change in dust concentration caused by the indirect effect of vibration on dust concentration.

[0097] Uncompensated sudden shifts in dust concentration can cause false peaks or valleys, affecting early warning decisions. Therefore, real-time correction is required to eliminate systematic errors caused by vibration interference while maintaining a second-level response, while retaining true concentration changes and providing reliable data for mine safety warnings. Therefore, the dust concentration data is corrected based on the compensated optical effects. In an exemplary embodiment, Figure 5 As shown, a specific correction process for dust concentration data is given as follows:

[0098] Step S35: Obtaining the dust concentration correction degree at each target moment according to the compensated optical influence at each target moment.

[0099] This embodiment corrects the dust concentration data at the moment when the concentration mutation characteristic is larger. Then, the concentration mutation characteristic at each moment is compared with the preset concentration mutation characteristic threshold, and the moments corresponding to the concentration mutation characteristics greater than the preset concentration mutation characteristic threshold are obtained, and these moments are defined as target moments.

[0100] For any target moment, the compensated optical impact at that target moment represents the vibration compensation amplitude of the dust concentration data at that target moment. The greater the vibration compensation amplitude at that target moment, the greater the degree of dust concentration correction at that target moment. In one exemplary embodiment, the compensated optical impact at that target moment is used as the degree of dust concentration correction at that target moment.

[0101] Step S36: Obtaining a dust concentration correction coefficient at each target moment according to the degree of dust concentration correction at each target moment and the data change trend of the dust concentration data at each target moment.

[0102] If the data change trend of the dust concentration data at the target moment is that the data becomes larger, that is, the slope of the dust concentration data at the target moment is greater than 0, then the relevant vibration compensation needs to be subtracted. Therefore, the dust concentration correction coefficient of the dust concentration data at the target moment is equal to the difference between the value 1 and the dust concentration correction degree at the target moment; if the data change trend of the dust concentration data at the target moment is that the data becomes smaller, that is, the slope of the dust concentration data at the target moment is less than 0, then the relevant vibration compensation needs to be added, and the dust concentration correction coefficient of the dust concentration data at the target moment is equal to the sum of the value 1 and the dust concentration correction degree at the target moment.

[0103] Step S37: Correcting the dust concentration data at each target moment according to the dust concentration correction coefficient at each target moment.

[0104] First Taking the dust concentration data at the target moment as an example, if The data change trend of the dust concentration data at the target moment is that the data becomes larger, so the correction formula is as follows:

[0105] ;

[0106] in, Indicates the The corrected dust concentration data at each target moment, Indicates the The degree of dust concentration correction at each target moment, Indicates the The dust concentration data before correction at the target time. In this case, Dust concentration correction factor at each target moment.

[0107] Jordi The data change trend of the dust concentration data at the target moment is that the data is decreasing, so the correction formula is as follows:

[0108] ;

[0109] in, In this case, Dust concentration correction factor at each target moment.

[0110] By the above method, the corrected dust concentration data at each target time is obtained. The dust concentration data at other times except the target time is not corrected, which can also be understood as: the dust concentration correction coefficient at these times is 1. In this way, the dust concentration data at each time is corrected.

[0111] In subsequent applications, a dust concentration threshold is preset. The preset dust concentration threshold is used to determine whether the corrected dust concentration data is high. Then, the specific value of the preset dust concentration threshold is set according to actual judgment needs. The preset dust concentration threshold can be a general safety threshold for dust concentration in the mine. Compare the corrected dust concentration data at each moment with the preset dust concentration threshold. If a preset number of consecutive corrected dust concentration data is greater than the preset dust concentration threshold, an alarm signal is output. The alarm signal may include sound and light alarms, text messages, or push notifications from the mine dispatching system, and the abnormal period data is marked and archived simultaneously, so that management personnel can quickly locate and take ventilation or dust suppression measures to ensure that the mine dust concentration can still be accurately and continuously monitored and a rapid response can be achieved under complex vibration conditions. Among them, the preset number is set according to actual needs, such as 4.

[0112] This embodiment also provides a real-time monitoring system for dust concentration in a mine, including: a memory and a processor; the memory is connected to the processor, and the memory is used to store program instructions; the processor is used to implement the steps in the above-mentioned embodiment of the real-time monitoring method for dust concentration in a mine when the program instructions are executed.

[0113] In an exemplary embodiment, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the above-mentioned embodiment of the method for real-time monitoring of dust concentration in a mine.

[0114] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0115] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A real-time monitoring method for dust concentration in a mine, characterized by: include: Obtaining dust concentration data collected by a dust monitor, vibration data of the dust monitor, and optical path offset in the dust monitor; Determine the synchronous change relationship between the concentration mutation characteristics of the dust concentration data and the optical path offset, and combine the difference from the preset standard synchronous change relationship and the concentration mutation characteristics to obtain the vibration dominance degree of the dust concentration data; the vibration dominance degree indicates the possibility of dust concentration mutation being dominated by vibration; Using the correlation between the optical effect of vibration on dust concentration and the degree of dominance of the vibration, the optical effect is compensated, and the dust concentration data is corrected according to the compensation result; The optical impact represents the changing relationship between the vibration data and the optical path offset; Determining the relationship between the concentration mutation characteristics of the dust concentration data and the synchronous change of the optical path offset includes: Determine a concentration mutation feature neighborhood sequence of a neighborhood range of the concentration mutation feature at each moment, and a light path offset neighborhood sequence of a neighborhood range of the light path offset at each moment; the neighborhood range of each moment is a number of previous moments adjacent to each moment; Determine the correlation coefficient between the concentration mutation feature neighborhood sequence and the optical path offset neighborhood sequence corresponding to each moment, and obtain the synchronous change relationship between the optical path offset and the concentration mutation feature at each moment; The process of acquiring the synchronous change relationship includes: normalizing the Pearson correlation coefficient between the concentration mutation feature neighborhood sequence and the light path offset neighborhood sequence at each moment to determine the synchronous change relationship corresponding to each moment; The method for real-time monitoring of dust concentration in a mine further comprises: Arranging the vibration data in ascending order to obtain an ascending sequence of vibration data, and sorting the optical path offsets according to the ascending sequence of vibration data to obtain an optical path offset reference sequence; Obtaining the relative change amplitude of the vibration data of each data point within the neighborhood of each data point in the vibration data ascending sequence, and the relative change amplitude of the optical path offset in the optical path offset reference sequence; Obtaining a relative change amplitude difference of each data point within a neighborhood range, wherein the relative change amplitude difference is a difference between the relative change amplitude of the optical path offset and the relative change amplitude of the vibration data at the same data point; The relative change amplitude differences within the neighborhood of each data point are integrated to obtain the optical impact of vibration on dust concentration at each moment; The process of obtaining the optical effect of vibration on dust concentration includes: normalizing the average value of the relative change amplitude differences of all neighboring data points within the neighborhood range of the data point corresponding to each moment to determine the optical effect of vibration on dust concentration at each moment; The compensating for the optical influence comprises: Obtaining a compensation coefficient, wherein the compensation coefficient is obtained from a correlation between the vibration dominance degree and the optical influence; Compensating for the optical influence according to the compensation coefficient; The calculation formula for the vibration dominance degree includes: ;in, Indicates the The degree of vibration dominance of the dust concentration data at each moment, Indicates the The concentration mutation characteristics of dust concentration data at each moment; Indicates the The relationship between the concentration mutation characteristics at each moment and the synchronous change of the optical path offset, Indicates the preset standard synchronous change relationship; The vibration dominance at each moment is sorted in time sequence to obtain a vibration dominance sequence; the optical impact of the vibration on the dust concentration at each moment is sorted in time sequence to obtain an optical impact sequence; the DTW distance between the vibration dominance sequence and the optical impact sequence is negatively correlated to obtain a compensation coefficient.

2. A method for real-time monitoring of dust concentration in a mine as claimed in claim 1, characterized in that: The process of obtaining the preset standard synchronous change relationship includes: Determining a concentration mutation feature target sequence, wherein the concentration mutation feature target sequence is composed of concentration mutation features greater than a preset concentration mutation feature threshold; Obtaining an optical path offset at the same moment as each concentration mutation feature in the concentration mutation feature target sequence to form an optical path offset target sequence; Determining the correlation coefficient between the concentration mutation characteristic target sequence and the optical path offset target sequence to obtain the preset standard synchronous change relationship; The process of acquiring the preset standard synchronous change relationship includes normalizing the Pearson correlation coefficient of the concentration mutation characteristic target sequence and the optical path offset target sequence to determine the corresponding preset standard synchronous change relationship.

3. A method for real-time monitoring of dust concentration in a mine as claimed in claim 1, characterized in that: The method of correcting the dust concentration data according to the compensation result includes: According to the compensated optical influence at each target moment, a dust concentration correction degree at each target moment is obtained; the target moment is a moment corresponding to a concentration mutation characteristic greater than a preset concentration mutation characteristic threshold; According to the degree of dust concentration correction at each target moment and the data change trend of the dust concentration data at each target moment, the dust concentration correction coefficient at each target moment is obtained; The dust concentration data at each target moment is corrected according to the dust concentration correction coefficient at each target moment.

4. A method for real-time monitoring of dust concentration in a mine as claimed in claim 3, characterized in that: The process of obtaining the dust concentration correction coefficient includes: If the data change trend of the dust concentration data at a certain target moment is that the data becomes larger, then the dust concentration correction coefficient of the dust concentration data at the target moment is equal to the difference between the value 1 and the dust concentration correction degree at the target moment; if the data change trend of the dust concentration data at the target moment is that the data becomes smaller, then the dust concentration correction coefficient of the dust concentration data at the target moment is equal to the sum of the value 1 and the dust concentration correction degree at the target moment.

5. The method for real-time monitoring of dust concentration in a mine according to claim 1, wherein: The method for real-time monitoring of dust concentration in a mine further comprises: The corrected dust concentration data is compared with a preset dust concentration threshold value, and if a preset number of consecutive dust concentration data are greater than the preset dust concentration threshold value, an alarm signal is output.

6. A method for real-time monitoring of dust concentration in a mine as claimed in claim 1, characterized in that: The method for real-time monitoring of dust concentration in a mine further comprises: The difference in the degree of change of the dust concentration data at each moment and its neighborhood range is determined to obtain the concentration mutation characteristics of the dust concentration data at each moment; the neighborhood range of each moment is the previous several moments adjacent to each moment.

7. A real-time monitoring system for dust concentration in a mine, comprising: Memory and processor; The memory is connected to the processor; The memory is used to store program instructions; The processor is configured to implement the method for real-time monitoring of dust concentration in a mine according to any one of claims 1 to 6 when the program instructions are executed.

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