A signal denoising method and system of a mine optical fiber grating pressure sensor

Abstract

The present application relates to a kind of signal denoising method and system of mine fiber grating pressure sensor, belong to mine safety monitoring technical field.The method is for the sensor signal in the complex environment of coal mine underground is susceptible to multi-parameter coupling noise interference, traditional linear filtering is difficult to handle nonlinear pressure change, leading to low monitoring accuracy.The method is: in the real-time pressure sequence of processor window buffer queue management is constructed in sliding;Approximate coefficient and detail coefficient sequence are obtained by carrying out Haar wavelet decomposition to window data;Approximate coefficient average is calculated, and soft threshold is dynamically calculated based on the minimum resolution of sensor;The number of detail coefficient that is over threshold is counted, and invalid data window disturbed by violent interference is judged and discarded, otherwise the approximate coefficient average is output as effective value.The present application can effectively filter out random and high-frequency noise, adaptively adjust the denoising intensity, while avoiding abnormal fluctuation misleading in keeping signal characteristics, improve the stability and reliability of mine pressure monitoring.

Description

Technical Field

[0001] This invention belongs to the field of mine safety monitoring technology, and relates to a method and system for signal noise reduction of a mine fiber optic pressure sensor. Background Technology

[0002] The underground environment in coal mines is extremely complex, with harsh conditions such as high humidity, high dust levels, and strong electromagnetic interference. This places extremely high demands on the reliability of mine safety monitoring equipment. Traditional power monitoring sensors are prone to failure or malfunction in these environments, making it difficult to achieve long-term stable monitoring.

[0003] In recent years, fiber Bragg grating (FBG) sensing technology has been widely used in monitoring rock strata stress, roof pressure, and support deformation in underground coal mines due to its advantages such as resistance to electromagnetic interference, corrosion resistance, long transmission distance, and high sensitivity. However, in practical applications, FBG pressure sensors still face severe technical challenges. First, temperature fluctuations, mechanical vibrations, and noise generated by equipment operation in the mine can couple with the actual pressure signal, resulting in severe multi-parameter coupling interference and causing the acquired raw data to contain a large amount of nonlinear noise.

[0004] Existing mine pressure monitoring technologies often rely too heavily on linear mathematical models when processing these complex signals, making it difficult to effectively address the nonlinear characteristics of mine pressure changes. When sensor signals are subjected to instantaneous external force impacts or sudden environmental disturbances, traditional filtering algorithms struggle to achieve high-precision noise reduction while preserving signal characteristics, easily leading to false alarms or missed alarms in the monitoring system and affecting the accuracy of fault diagnosis and real-time prediction for coal mine safety production.

[0005] Therefore, how to combine advanced signal processing algorithms with intelligent monitoring architecture, and filter out noise interference in complex environments by dynamically adjusting processing parameters to ensure the stability and reliability of pressure sensor output data has become a key issue that urgently needs to be addressed in the field of mine safety monitoring technology. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a method and system for signal noise reduction of a fiber Bragg grating pressure sensor for mining applications.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for signal noise reduction of a fiber Bragg grating pressure sensor used in mining, the method comprising the following steps: Step 1: Within the processor of the fiber Bragg grating pressure sensor host, allocate a memory area as a sliding window buffer queue, the size of which is... The bytes, and following a first-in-first-out (FIFO) method, will be used to sequence the pressure values ​​at a specific monitoring point A as monitored in real time. They are sequentially fed into the sliding window cache queue, wherein It is an even number; Step 2: After the sliding window buffer queue is full, the processor performs a first-order Haar wavelet decomposition on the data in the input window to obtain an approximate coefficient sequence. and detail coefficient sequence ,in , ; Step 3, based on the approximation coefficient sequence Calculate the average value of the sequence The formula is expressed as:

[0008] Step 4: Set the minimum resolution of the mining fiber Bragg grating pressure sensor to [value missing]. and according to the minimum resolution Set the first preset threshold Second preset threshold According to the average value of the sequence With the first preset threshold The second preset threshold Dynamic calculation of soft threshold based on relationship ; Step 5, traverse the sequence of detail coefficients. The elements in the table satisfy the absolute value of the detail coefficient. Number of Determine the number Is it greater than ; Step 6, if If the current window data is determined to be fluctuating data affected by external forces, this set of data is discarded, and the pressure value of the latest entry into the sliding window cache queue is used to cache the data in the sliding window cache queue. Cover, output As the effective pressure value of the current sensor; if Then output the average value of the sequence. This serves as the effective pressure value for the current sensor.

[0009] Furthermore, in step 1, during the initial power-on state, the number of pressure values ​​in the sliding window buffer queue is 0, and the pressure value collected for the first time after the sensor is preheated is... Fill the entire window.

[0010] Furthermore, in step 1, the pressure values ​​collected for the second and subsequent times are sequentially placed into the window according to the first-in-first-out method. Each time, the latest collected pressure value is placed at the end of the queue, while the earliest data entering the window is removed from the queue.

[0011] Furthermore, in step 2, the approximation coefficient The calculation formula is:

[0012] The detail factor The calculation formula is: .

[0013] Furthermore, step 2 also includes processing the detail coefficient sequence. All elements in the array are set to 0 for noise reduction, and the reconstructed sequence is obtained by reconstructing the signal using the Haar wavelet method.

[0014] Furthermore, in step 4, the first preset threshold The second preset threshold .

[0015] Furthermore, the soft threshold The calculation logic is as follows: like ,but ; like ,but The calculation formula is: ; like ,but .

[0016] A smart mine pressure monitoring system based on the method, the system comprising a mine fiber Bragg grating pressure sensor, a processor, and a memory.

[0017] Furthermore, the processor is used to allocate a memory region as the sliding window cache queue, and to perform the Haar wavelet decomposition and the soft thresholding. Dynamic adjustment calculations are performed to achieve real-time prediction of rock strata stress, roof pressure, and support deformation in coal mines.

[0018] The beneficial effects of this invention are as follows: (1) By constructing a sliding window cache queue in the processor and adopting a first-in-first-out data management method, combined with sliding window smoothing technology, the present invention can effectively eliminate random noise in the underground coal mine environment and ensure the stability and reliability of the pressure sensor output signal.

[0019] (2) In response to the coupling interference such as temperature fluctuations and mechanical vibrations in mines, this invention utilizes Haar wavelet (Haar) decomposition technology to accurately identify and filter high-frequency noise in pressure signals, thus solving the technical bottleneck of traditional linear models being unable to cope with complex nonlinear pressure changes in mines.

[0020] (3) By introducing a dynamic threshold calculation mechanism based on the minimum resolution of the sensor, this invention can dynamically adjust the soft threshold parameter according to the average value of the pressure sequence monitored in real time. This adaptive threshold adjustment method enables the system to maintain a high-precision noise reduction effect in different pressure ranges.

[0021] (4) This invention establishes an effective data evaluation system by counting the number of data points exceeding the dynamic soft threshold in the statistical detail coefficients. When a monitoring point is subjected to a severe external impact, causing a large fluctuation in the data, the system can automatically identify and discard the invalid data and replace it with the latest valid value, thereby avoiding the misleading effect of abnormal fluctuations on the mine safety monitoring results.

[0022] (5) This invention integrates fiber optic grating sensing technology with intelligent algorithms, enabling real-time and high-precision monitoring of rock strata stress, roof pressure and support deformation in coal mines, providing solid data support for real-time prediction and fault diagnosis of mine safety status.

[0023] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0024] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the fiber optic pressure sensor noise reduction model of the present invention; Figure 2 This is a flowchart of the fiber Bragg grating pressure value noise removal process of the present invention. Detailed Implementation

[0025] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0026] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0027] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0028] Example 1 This embodiment details a method for denoising signals from a fiber Bragg grating pressure sensor used in mining. This method is primarily applied in underground coal mine environments, and by processing the raw data collected by the fiber Bragg grating pressure sensor, it achieves high-precision denoising and smooth output.

[0029] Combination Figure 1 As shown, the signal processing logic in this embodiment is deployed within the processor of the fiber Bragg grating pressure sensor host. The processor first allocates a memory area as a sliding window buffer queue, the size of which is set to Z bytes. This queue is managed strictly according to the First In First Out (FIFO) principle. When the sensor starts working, the system will read the real-time pressure value sequence of monitoring point A. The sliding windows are sequentially sent in. In this embodiment, the window length is set. .

[0030] Upon initial power-on, the system performs a warm-up process to ensure rapid algorithm startup. The initial number of pressure values ​​in the window is 0. After the sensor has warmed up and stabilized, the first pressure value is collected. And fill the entire window with this value, that is, the initial sequence in the window at this time is Subsequently, whenever a new stress value is collected, the system places it at the end of the queue and removes the data at the front of the queue.

[0031] Combination Figure 2 As shown, once the sliding window is filled, the processor begins executing the core denoising process: The first step is to perform a first-level Haar wavelet decomposition. The processor performs pairwise calculations on the 10 pressure values ​​within the window. The approximation coefficients are calculated as follows: The calculation method for detail coefficients is as follows: This calculation yields an approximate coefficient sequence containing 5 elements. And a sequence of detail coefficients containing 5 elements .

[0032] The second step is to calculate the sequence average. The processor calculates the approximation coefficient sequence. average This average value represents the overall level of the pressure signal within the current monitoring period.

[0033] The third step is dynamic threshold calculation and logical judgment. The minimum resolution of this fiber Bragg grating pressure sensor is known. MPa. The system sets the first preset threshold. MPa, second preset threshold MPa. If the currently calculated It is 0.8 MPa, because Then set a soft threshold. MPa.

[0034] The fourth step is data validity verification. The processor iterates through the sequence of detail coefficients. Count the number of elements whose absolute value is greater than 1.0 MPa. If the statistical results (Right now This indicates that the signal fluctuation is within the normal range, and the output should be... This is the effective pressure value of the current sensor. If If the data is found to have been subjected to severe underground blasting or mechanical impact interference, the data set is considered invalid and discarded, and the system directly outputs the latest raw value. This is used as the current valid value to ensure real-time monitoring response.

[0035] Example 2 This embodiment, based on Embodiment 1, further demonstrates the soft threshold under different pressure environments. The dynamic adjustment process demonstrates the adaptability of this invention to nonlinear pressure changes.

[0036] In this embodiment, the mine pressure monitoring system is in the stage of rapid increase in support pressure. After the sliding window is filled with data, the processor calculates the average value of the approximate coefficient sequence. MPa.

[0037] According to the dynamic threshold calculation formula, since at this time (Right now ), soft threshold Calculated using linear interpolation:

[0038] At this point, the processor will use the updated... MPa is used as the criterion. Traverse the sequence of detail coefficients. If the number of them with an absolute value exceeding 2.6 MPa More than half of the windows If this occurs, the data discarding mechanism is triggered. This dynamic adjustment mechanism allows the system to automatically relax threshold requirements in environments with high pressure and potentially increased signal fluctuations, thus avoiding misinterpreting normal rapid pressure increases as interference noise.

[0039] Example 3 This embodiment illustrates an intelligent mine pressure monitoring system based on the above method. The system includes a mine fiber optic pressure sensor deployed at the coal mine working face, and a processor and memory located in the monitoring center or underground substation.

[0040] The workflow is as follows: 1. The sensor transmits wavelength signals to the processor via an optical fiber link.

[0041] 2. The processor's internal memory pre-stores Haar wavelet transform instructions and dynamic threshold logic algorithms.

[0042] 3. The processor uses the signal noise reduction method of the mining fiber Bragg grating pressure sensor to establish a sliding window by allocating memory space and perform a first-level decomposition of the pressure value after real-time wavelength conversion.

[0043] 4. During data processing, the processor sets all detail coefficients to 0 for reconstruction. It then compares the reconstructed features with the original sequence, combining... Determine whether the current mine pressure is in a dangerous state of sudden change.

[0044] 5. Finally, the processed smooth pressure curve is displayed on the ground monitoring terminal via industrial Ethernet to enable real-time prediction of rock strata stress, roof pressure and support deformation in coal mines.

[0045] As can be seen from the above embodiments, the present invention, by combining a sliding window with a dynamic threshold wavelet algorithm, not only achieves effective signal smoothing, but also solves the problem of nonlinear noise interference in complex mining environments through intelligent adjustment of soft thresholds.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for signal noise reduction in a mine fiber Bragg grating pressure sensor, characterized in that: The method includes the following steps: Step 1: Within the processor of the fiber Bragg grating pressure sensor host, allocate a memory area as a sliding window buffer queue, the size of which is... The bytes, and following a first-in-first-out (FIFO) method, will be used to sequence the pressure values ​​at a specific monitoring point A as monitored in real time. They are sequentially fed into the sliding window cache queue, wherein It is an even number; Step 2: After the sliding window buffer queue is full, the processor performs a first-order Haar wavelet decomposition on the data in the input window to obtain an approximate coefficient sequence. and detail coefficient sequence ,in , ; Step 3, based on the approximation coefficient sequence Calculate the average value of the sequence The formula is expressed as: Step 4: Set the minimum resolution of the mining fiber Bragg grating pressure sensor to [value missing]. and according to the minimum resolution Set the first preset threshold Second preset threshold According to the average value of the sequence With the first preset threshold The second preset threshold Dynamic calculation of soft threshold based on relationship ; Step 5, traverse the sequence of detail coefficients. The elements in the table satisfy the absolute value of the detail coefficient. Number of Determine the number Is it greater than ; Step 6, if If the current window data is determined to be fluctuating data affected by external forces, this set of data is discarded, and the pressure value of the latest entry into the sliding window cache queue is used to cache the data in the sliding window cache queue. Cover, output As the effective pressure value of the current sensor; if Then output the average value of the sequence. This serves as the effective pressure value for the current sensor.

2. The signal noise reduction method for a mining fiber Bragg grating pressure sensor according to claim 1, characterized in that: In step 1, during the initial power-on state, the number of pressure values ​​in the sliding window buffer queue is 0, and the pressure value is the first one collected after the sensor has been preheated. Fill the entire window.

3. The signal noise reduction method for a mining fiber Bragg grating pressure sensor according to claim 1, characterized in that: In step 1, the pressure values ​​collected for the second and subsequent times are sequentially placed into the window using a first-in-first-out method. Each time, the latest collected pressure value is placed at the end of the queue, while the earliest data entering the window is removed from the queue.

4. The signal noise reduction method for a mining fiber Bragg grating pressure sensor according to claim 1, characterized in that: In step 2, the approximation coefficient The calculation formula is: The detail factor The calculation formula is: 。 5. The signal noise reduction method for a mine fiber Bragg grating pressure sensor according to claim 4, characterized in that: Step 2 also includes the detail coefficient sequence All elements in the array are set to 0 for noise reduction, and the reconstructed sequence is obtained by reconstructing the signal using the Haar wavelet method.

6. The signal noise reduction method for a mining fiber Bragg grating pressure sensor according to claim 1, characterized in that: In step 4, the first preset threshold The second preset threshold .

7. The signal noise reduction method for a mine fiber Bragg grating pressure sensor according to claim 6, characterized in that: The soft threshold The calculation logic is as follows: like ,but ; like ,but The calculation formula is: ; like ,but .

8. An intelligent mine pressure monitoring system based on the method of any one of claims 1 to 7, characterized in that: The system includes a mining fiber Bragg grating pressure sensor, a processor, and a memory.

9. The intelligent mine pressure monitoring system according to claim 8, characterized in that: The processor is used to allocate a memory region as the sliding window cache queue, and to perform the Haar wavelet decomposition and the soft thresholding. Dynamic adjustment calculations are performed to achieve real-time prediction of rock strata stress, roof pressure, and support deformation in coal mines.

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