A method and system for detecting acetylene gas production rate of oil-immersed transformer gas relay based on light scattering method

Abstract

The present invention relates to the technical field of oil-immersed transformer condition monitoring, and more specifically to a method and system for detecting the acetylene gas production rate of an oil-immersed transformer gas relay based on a light scattering method. The method comprises: determining the incident position of a light source unit, wherein a laser signal emitted by the light source unit is incident on an observation window on one side of the gas relay; a sensor unit captures scattered light intensity signals at different angles; a processing unit calculates the bubble particle size distribution of the scattered light intensity signals based on the Mie scattering principle; a machine vision unit photographs the bubble cluster in the gas relay, and monitors the bubble rise rate using a matching window correlation method in the processing unit, thereby combining the bubble particle size distribution and the bubble rise rate to obtain the corresponding gas production rate. The present invention utilizes light scattering and high-speed camera technology to accurately monitor the dynamic changes of bubbles.

Description

Technical Field

[0001] The present invention relates to the technical field of oil-immersed transformer state monitoring, and in particular to a method and system for detecting the acetylene gas production rate of a gas relay of an oil-immersed transformer based on a light scattering method. Background Art

[0002] In power systems, the operating status of oil-immersed transformers is directly related to the stability and security of the power grid. Over long-term operation, transformers can experience problems such as overheating, partial discharge, and insulation aging. The insulating oil and materials within the transformer decompose, generating a certain amount of gas. The generation and accumulation of these gases reflects the health of the transformer. Therefore, monitoring the gas generation rate within the transformer is a key indicator for assessing transformer health. Traditional gas relay monitoring primarily relies on changes in gas accumulation. While this can detect faults promptly, it struggles to accurately monitor the gas generation rate, making early warning and diagnosis of faults difficult.

[0003] In recent years, the application of optical detection technology in transformer condition monitoring has gradually attracted attention. Compared with traditional monitoring methods, optical sensing-based detection technology can detect subtle changes in gas generation rate at the early stage of a fault, thus providing earlier warning.

[0004] The invention patent with publication number CN119514141A discloses a method and system for simulating bubble movement in transformer oil considering multi-physical field coupling. The method establishes a multi-physical field model coupling electric field, thermal field and flow field, solves parameters through multiple physical field models, and thus obtains the bubble movement process in transformer oil. This detection method requires the construction of multiple models, is relatively complex, and cannot directly obtain the gas production rate. Summary of the Invention

[0005] Purpose of the Invention: To address the aforementioned technical problems, the present invention provides a method for detecting the acetylene generation rate of an oil-immersed transformer gas relay based on light scattering. This method addresses the inability to accurately monitor the gas generation rate and the resulting low monitoring accuracy of gas relays. The present invention also provides a system for detecting the acetylene generation rate of an oil-immersed transformer gas relay based on light scattering.

[0006] Technical solution: To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] In a first aspect, the present invention provides a method for detecting the acetylene gas production rate of an oil-immersed transformer gas relay based on a light scattering method, the method comprising:

[0008] Polystyrene particles with known particle size distribution were doped into transformer oil and introduced into a gas relay;

[0009] Determine the incident position of the light source unit; the laser signal emitted by the light source unit is incident on an observation window on one side of the gas relay, and the sensor unit processes the scattered light through the observation window on the other side of the gas relay to capture scattered light intensity signals at different angles. The processing unit calculates the bubble particle size distribution based on the scattered light intensity signal according to the Mie scattering principle;

[0010] The machine vision unit photographs the bubble group in the gas relay and monitors the bubble rising rate through the matching window correlation method in the processing unit, thereby obtaining the corresponding gas production rate by combining the bubble particle size distribution and the bubble rising rate. The matching window correlation method first selects the bubble group image photographed by the machine vision unit to calculate the velocity field from coarse grid to fine grid, and then tracks and measures the velocity of a single bubble after meeting the resolution requirements.

[0011] Further, including:

[0012] At least two piezoelectric ultrasonic sensors are provided on the observation window on each side. The piezoelectric ultrasonic sensors receive the sound waves generated by the bubbles and send them to the processing unit. The processing unit uses the TDOA principle to calculate the position of each bubble through the arrival time of the sound waves generated by the bubbles, thereby realizing three-dimensional target positioning of the bubble group. The sensor unit adjusts its position according to the result of the three-dimensional target positioning.

[0013] Further, including:

[0014] The sensor unit includes a CCD detector, and the position of the CCD detector is adjusted according to the result of the three-dimensional positioning, including: the center of the CCD detector is aligned with the result of the three-dimensional target positioning.

[0015] Further, including:

[0016] The processing unit calculates the bubble particle size distribution based on the scattered light intensity signal using the Mie scattering principle, including:

[0017] The processing unit performs annular segmentation on the light intensity distribution area corresponding to the scattered light intensity signals at different angles captured by the CCD detector;

[0018] The specific angle range represented by each portion is obtained according to the number N of annular segments, and the light intensity distribution signal under each portion is obtained according to the scattered light intensity signal of each portion. The light intensity distribution signal is obtained by subtracting the background signal from the scattered light intensity signal of the current portion captured. The background signal is the light intensity signal corresponding to the absence of bubbles;

[0019] Multiple light intensity distribution signals are combined to form a light intensity distribution column vector E, whose dimension is expressed as .

[0020] Further, including:

[0021] The processing unit calculates the bubble particle size distribution based on the scattered light intensity signal according to the Mie scattering principle, and further includes:

[0022] The light intensity coefficient matrix T caused by bubble scattered light is calculated according to Mie scattering theory. The light intensity coefficient matrix includes the light intensity distribution information of scattered light of bubbles of different particle sizes at different angles. It is non-reversible and the corresponding dimension is expressed as ;

[0023] According to the relationship Solving the particle size distribution matrix The solution method is: use the Tikhonov iterative algorithm to iteratively solve the initial particle size distribution matrix until the convergence condition is met, thereby obtaining the final particle size distribution matrix , the elements in the particle size distribution matrix are the proportions of bubbles corresponding to different particle sizes.

[0024] Further, including:

[0025] The machine vision unit photographs the bubble group in the gas relay and monitors the bubble rising rate by a matching window correlation method, wherein the matching window correlation method includes:

[0026] Converting the multiple consecutively captured images into grayscale images, subtracting a background image without gas flow from the grayscale image to obtain a grayscale image containing only bubble information, and performing noise reduction on the grayscale image;

[0027] Select a rectangular window centered on the target bubble group in the image , select four windows B, C, D, and E of the same size with the four corners of the current window as the center, and calculate the grayscale distribution matrix corresponding to windows B, C, D, and E respectively. The grayscale distribution matrix is a matrix formed by extracting the grayscale value of each pixel block in the corresponding window;

[0028] Perform sliding matching on the grayscale distribution matrix of the four windows B, C, D, and E in the current frame and the matrix of the same-sized area near the window in the next frame, so as to find the window with the highest cross-correlation with the local window of the current frame in the next frame. The position of the cross-correlation peak is the coordinate offset of the current window. The same-sized area near the window is the initial search radius with the theoretical maximum displacement as the initial search radius, and slides in the four directions of up, down, left, and right with a step size of one pixel.

[0029] The velocities of the corresponding windows are obtained based on the coordinate offsets of windows B, C, D, and E, and the velocities of the four windows B, C, D, and E are used as the velocities of the four corners of window A.

[0030] The maximum velocity of bubbles in oil is used as the velocity threshold. If the velocity of any one or more of the four corners of window A is greater than the velocity threshold, it is discarded, and the window size is expanded and the corresponding velocity is recalculated. Otherwise, the next step of fine grid velocity measurement is carried out.

[0031] Further, including:

[0032] The method steps corresponding to the fine grid velocity measurement include:

[0033] In rectangular window A, select a rectangular window smaller than window A with the target bubble as the center. , the rectangular window Contains at most one bubble;

[0034] The size of the fine grid is , the corresponding center coordinates are , then the normalized coordinates of the fine grid in the rectangular window A are expressed as: ;

[0035] Calculate the weights corresponding to the four corners of window A according to the normalized coordinates, and then obtain the speed of the current fine grid according to the speed corresponding to the four corners of window A;

[0036] The obtained fine grid velocity is used to predict the corresponding fine grid displacement, and the obtained displacement is used as the search radius and one pixel is used as the step size, so that the rectangular window Make the position shift up, down, left and right, and find the peak value of the cross-correlation function after quadratic fitting. The offset window with the highest matching degree is obtained, and the rectangular window is obtained and the contour length of the corresponding bubble in the window with the highest matching degree. If the contour length change rate is greater than 10% or the fine grid speed is greater than the corresponding threshold, the target bubble is reselected for velocity measurement;

[0037] If the fine grid includes more than one bubble, the size of the fine grid is further reduced to perform velocity measurement until a single bubble is located for velocity measurement.

[0038] Further, including:

[0039] The matching window correlation method further includes:

[0040] Track and measure a certain number of bubbles in the field of view to obtain the corresponding velocity distribution, and calculate the average velocity of the corresponding bubbles as the velocity of the bubble group , expressed as: ;

[0041] in, is the number of bubbles being tracked.

[0042] Further, including:

[0043] The method of combining the bubble size distribution and the bubble rise rate to obtain the corresponding gas production rate includes:

[0044] The weighted average method is used to calculate the average bubble particle size, which is expressed as: ;

[0045] in, m is the number of particle size bins, For the j The particle size of the bubbles, The particle size is The proportion of bubbles, which is derived from the particle size distribution matrix get;

[0046] Calculate the gas production rate: ;

[0047] Where N is the total number of bubbles in the field of view.

[0048] On the other hand, the present invention also provides an acetylene gas production rate detection system for an oil-immersed transformer gas relay based on a light scattering method, the system comprising: a gas relay, a light source unit, a sensor unit, a machine vision unit and a processing unit;

[0049] doping polystyrene particles with known particle size distribution into transformer oil and introducing the particles into the gas relay;

[0050] Determining the incident position of the light source unit, and causing the laser signal emitted by the light source unit to be incident on an observation window on one side of the gas relay;

[0051] The sensor unit processes the scattered light at the observation window on the other side of the gas relay, thereby capturing scattered light intensity signals at different angles, and the processing unit calculates the bubble particle size distribution based on the scattered light intensity signals according to the Mie scattering principle;

[0052] The machine vision unit photographs the bubble group in the gas relay and monitors the bubble rising rate through the matching window correlation method in the processing unit, thereby obtaining the corresponding gas production rate by combining the bubble particle size distribution and the bubble rising rate. The matching window correlation method first selects the bubble group image photographed by the machine vision unit to calculate the velocity field from coarse grid to fine grid, and then tracks and measures the velocity of a single bubble after meeting the resolution requirements.

[0053] Finally, the present invention also provides a storage medium comprising computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described above.

[0054] Compared with the existing technology, this application has the following beneficial effects:

[0055] The present invention uses a machine vision unit, a sensor unit, and a light source unit connected to a gas relay to perform data processing, that is, utilizing the non-contact and high sensitivity of optical sensing technology, so that the method can still work stably under high load and harsh environment;

[0056] The light source unit of the present invention is mainly a laser. When the laser passes through a group of bubbles, bubbles of different particle sizes have different scattering abilities to the laser. By using the Mie scattering principle to analyze scattering signals at different angles, the particle size distribution of the bubbles can be accurately calculated, thereby improving the measurement accuracy of the bubble particle size distribution. At the same time, the movement trajectory and rising speed of the bubbles are recorded by the machine vision unit, and combined with the calculation of the particle size distribution, the gas generation rate can be accurately inferred, thereby improving the detection accuracy of the bubble rising rate; therefore, the present invention adopts a light scattering method and high-speed camera technology to achieve accurate monitoring of the dynamic changes of bubbles.

[0057] The matching window correlation method adopted in the present invention uses a progressive search strategy from coarse to fine. After sliding matching and calculation of mutual correlation coefficients on fine grids, a velocity field distribution with lower resolution can be obtained. Finally, a single bubble is located to measure the velocity. This method improves spatial resolution while reducing computational complexity.

[0058] At least two piezoelectric ultrasonic sensors are provided on each side observation window of the gas relay of the present invention. The piezoelectric ultrasonic sensors receive the sound waves generated by the bubbles and send them to the processing unit. The processing unit uses the TDOA principle to calculate the position of each bubble through the arrival time of the sound waves generated by the bubbles, thereby realizing three-dimensional target positioning of the bubble group. The sensor unit adjusts its position according to the result of the three-dimensional target positioning. This method not only makes the position of the sensor more accurate, but also uses the acoustic signal as a trigger source, so that the optical system is started only when bubbles occur, avoiding misalignment between the sampling window and the dynamic timing of the bubbles, thereby improving the accuracy of subsequent detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 This is a flow chart of a method for detecting acetylene production rate of an oil-immersed transformer gas relay based on a light scattering method according to an embodiment of the present invention;

[0061] Figure 2 This is a schematic structural diagram of an acetylene gas production rate detection device for an oil-immersed transformer gas relay based on a light scattering method according to an embodiment of the present invention;

[0062] Figure 3 Schematic diagram of the arrangement of the piezoelectric ultrasonic sensor according to an embodiment of the present invention;

[0063] Figure 4 A schematic diagram of a relationship structure between a fine grid and a coarse grid according to an embodiment of the present invention;

[0064] The accompanying drawings include: a machine vision unit 1, a light source unit 2, a gas relay 3, a sensor unit 4, and a processing unit 5. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0066] Example 1: Figure 1 As shown, this embodiment provides a method for detecting the acetylene gas production rate of an oil-immersed transformer gas relay based on a light scattering method, the method comprising the following steps:

[0067] S1 mixes polystyrene particles with known particle size distribution into transformer oil and introduces the particles into the gas relay 3.

[0068] Given that it is difficult to generate bubbles with a stable particle size distribution to verify the reliability of the device, this embodiment uses polystyrene particles with a known particle size distribution to conduct experiments to verify the accuracy of the system in measuring particle size and its distribution; a gas flow rate with different gradients is used to conduct a gas introduction experiment on the gas relay 3 to simulate the gas generation rate under different fault conditions.

[0069] S2 determines the incident position of the light source unit 2. The laser signal emitted by the light source unit 2 is incident on the observation window on one side of the gas relay 3. The sensor unit 4 processes the scattered light at the observation window on the other side of the gas relay 3, thereby capturing scattered light intensity signals at different angles. The processing unit 5 calculates the bubble particle size distribution based on the scattered light intensity signal according to the Mie scattering principle.

[0070] In this embodiment, the light source unit 2 includes a laser and a laser beam expander. The laser is positioned between the adjustment lever and the heavy gas reed contact of the gas relay 3. The laser preferably uses a wavelength of 1531 nm, a power of 10 mW, and adjustable laser power. In this embodiment, the gas relay 3 is provided with an observation window to allow light to pass through. The interior of the gas relay 3 is oil-filled and includes oil inlet and outlet channels, which are connected to the oil-immersed transformer through the oil inlet and outlet channels, thereby allowing oil circulation and bubble transport. The sensor unit 4 in this embodiment includes a CCD detector, which can collect scattered light intensities at different angles.

[0071] The S3 machine vision unit 1 photographs the bubble group in the gas relay 3 and monitors the bubble rising rate through the matching window correlation method in the processing unit 5, thereby obtaining the corresponding gas production rate by combining the bubble particle size distribution and the bubble rising rate. The matching window correlation method first selects the bubble group image photographed by the machine vision unit 1 to calculate the velocity field from coarse grid to fine grid, and then tracks and measures the velocity of a single bubble after meeting the resolution requirements.

[0072] In this embodiment, the machine vision unit 1 includes a high-speed camera, which is used to capture bubble motion images and transmit them to the processing unit 5. The processing unit 5 performs analysis and calculation to obtain a measured value of the gas production rate and compares it with the theoretical value to verify the accuracy of the method. The processing unit 5 of this embodiment can be a host computer.

[0073] In summary, the implementation of the method in this embodiment is based on the acetylene gas production rate detection device of the oil-immersed transformer gas relay based on the light scattering method. The structural relationship between the machine vision unit 1, the light source unit 2, the gas relay 3, the sensor unit 4 and the processing unit 5 involved in the device is as follows: Figure 2 As shown, specifically, the laser emitted by the light source unit 2 is incident on the bubble group in the gas relay 3. Bubbles of different particle sizes have different scattering abilities to the laser. The sensor unit 4 is used to process the scattered light and capture scattered light intensity signals at different angles. The scattered light intensity at different angles of the sensor unit 4 is analyzed and processed to calculate the bubble particle size distribution. The machine vision unit 1 is used to photograph the bubble group in the gas relay 3, and the processing unit 5 tracks the bubbles through continuous photography, monitors the bubble rising rate, and calculates the gas production rate by combining the bubble particle size distribution and the bubble rising rate.

[0074] In this embodiment, the light source unit 2 emits a laser with a known wavelength, which is then emitted into a bubble group after beam expansion. The sensor unit 4 focuses the scattered light and selectively enhances the scattered light of the incident frequency, and then receives the scattered light intensity at multiple angles. The machine vision unit 1 is used to track the bubbles and detect the bubble rising rate. When the processing unit 5 calculates the gas production rate, it first calculates the bubble particle size distribution based on the light intensity signals at different angles based on the Mie scattering principle, and then obtains the bubble rising rate based on the image measured by the machine vision unit 1. The gas production rate is calculated by combining the two results.

[0075] Therefore, the present invention achieves real-time monitoring and rapid response, because the optical detection method has extremely high sensitivity and can react at the early stage of gas generation and detect potential faults in time.

[0076] In this embodiment, in order to obtain a more accurate position of the CCD detector and to solve the following problems: the non-trigger optical system requires periodic sampling, such as scanning once every 10ms, and bubbles that appear between two scans cannot be captured; and the single optical probe has a limited field of view angle, usually within 120°, and bubbles at the edge of the oil chamber are easily missed. In this embodiment, at least two piezoelectric ultrasonic sensors are provided on the observation window on each side of the gas relay 3. The piezoelectric ultrasonic sensors receive the sound waves generated by the bubbles and send them to the processing unit 5, such as Figure 3 As shown, the left side is a window of the gas relay 3, which is the laser incident side, and has two piezoelectric ultrasonic sensors at different positions, namely the small squares and triangles in the figure, which are the laser input positions. At least two piezoelectric ultrasonic sensors are also provided on the right window. This embodiment does not limit the positions and numbers of the two piezoelectric ultrasonic sensors, but in order to obtain the three-dimensional coordinates, at least two piezoelectric ultrasonic sensors are required on each side.

[0077] Because the amplitude of bubble sound waves is typically 5-10 times higher than background noise, the acoustic signal trigger threshold is set to 5 times the background, and the signal frequency trigger range is set to 50-200 kHz. When the piezoelectric ultrasonic sensor receives a signal within this frequency range and with an amplitude exceeding the trigger threshold, it triggers the optical system detection. The sensor serves only as a receiver, detecting the sound waves generated by the bubbles themselves, such as those caused by rupture and oscillation. The sound waves generated by the bubbles range from 35-120 kHz and propagate as spherical waves. The four sensors calculate the three-dimensional coordinates of the bubbles by recording the absolute time difference between the arrival of the sound waves and combining it with the speed of sound, which is approximately 1480 m / s in oil. In this embodiment, the three-dimensional coordinates of the bubbles are primarily calculated using the TDOA principle based on the arrival time of the sound waves generated by the bubbles, thereby achieving three-dimensional target positioning of the bubble group. The sensor unit 4 adjusts its position based on the results of the three-dimensional target positioning. Specifically, in this embodiment, a piezoelectric ultrasonic sensor is integrated with gas relay 3. Acoustic signals serve as a trigger source. For example, bubble oscillation, bubble burst, bubble fragmentation, and bubble coalescence generate 50-200 kHz acoustic waves, enabling the optical system to activate only when bubbles occur, thus avoiding misalignment between the sampling window and the dynamic timing of the bubbles. Furthermore, in this embodiment, four piezoelectric ultrasonic sensors are integrated. The bubble positions are calculated based on the arrival times of the acoustic waves, with an accuracy of ±0.1 mm, achieving three-dimensional positioning of the bubble cluster. Based on the three-dimensional positioning results, i.e., the bubble positions, the CCD rotation or translation is adjusted.

[0078] In this embodiment, the CCD detector is adjusted in position according to the result of the three-dimensional positioning, including: the signal line of the piezoelectric ultrasonic sensor is connected to the CCD detector, and the center of the CCD detector is adjusted to align with the result of the three-dimensional target positioning.

[0079] In this embodiment, in the specific step S2, the processing unit 5 calculates the bubble particle size distribution based on the scattered light intensity signal according to the Mie scattering principle, such as Figure 4 As shown, the specific steps include:

[0080] S21: The processing unit 5 performs annular segmentation on the light intensity distribution area corresponding to the scattered light intensity signals at different angles captured by the CCD detector.

[0081] In this embodiment, the light intensity distribution area captured by the sensor unit 4 is divided into annular shapes. This embodiment does not limit the light intensity distribution area, as long as the annular division is performed. In this embodiment, the distribution area is divided into 30 parts at equal intervals, each part represents a specific angle range, and the background signal is subtracted from the captured light intensity signal to obtain the light intensity distribution column vector at different angles.

[0082] S22 obtains the specific angle range represented by each portion according to the number N of annular divisions, and obtains the light intensity distribution signal of the portion according to the scattered light intensity signal of each portion. The light intensity distribution signal is obtained by subtracting the background signal from the scattered light intensity signal of the current portion captured. The background signal is the light intensity signal corresponding to the absence of bubbles.

[0083] In this embodiment, the light intensity distribution signal includes a light intensity signal with bubbles and a light intensity signal without bubbles. Specifically, since bubbles have a scattering effect on light, this will also cause the light intensity to weaken after passing through the solution. Therefore, the light intensity signal with bubbles will generally have a significantly different value than the light intensity signal without bubbles.

[0084] S23 combines multiple light intensity distribution signals to form a light intensity distribution column vector E, whose dimension is expressed as .

[0085] S24 calculates the light intensity coefficient matrix T caused by bubble scattered light according to Mie scattering theory. The light intensity coefficient matrix includes the light intensity distribution information of scattered light of bubbles of different particle sizes at different angles. It is non-reversible and the corresponding dimension is expressed as ; S25 according to the relationship

[0086] S25 According to the relationship The particle size distribution matrix W is solved by iteratively solving the initial particle size distribution matrix using the Tikhonov iterative algorithm until the convergence condition is met, thereby obtaining the final particle size distribution matrix. , the elements in the particle size distribution matrix are the proportions of bubbles corresponding to different particle sizes.

[0087] In this embodiment, an initial value W is first given, and then iterations are performed until a convergence condition is met, thereby obtaining a particle size distribution matrix W. in, is the proportion of bubbles of different particle sizes, and M is the total number of particle sizes of different levels.

[0088] In this embodiment, the corresponding calculation results are shown in Table 1, and the particle size distribution errors are all within 10%, thereby verifying the accuracy of the monitoring method.

[0089] Table 1 Particle size distribution true value, measured value and percentage error

[0090]

[0091] In this embodiment, step S3 specifically includes:

[0092] Nitrogen with different velocity gradients was introduced into S31 to simulate the gas production rate under different fault conditions. The bubble size and distribution were first measured using the above method.

[0093] The S32 machine vision unit 1 captures the movement of bubbles at a rate of 1000 frames per second and stores the resulting image sequence in the camera memory. The image sequence is then transmitted to the processing unit 5 for visualization and image analysis. The matching window correlation method is used to process two consecutive frames of images to achieve the counting of bubbles in the image and the tracking measurement of the velocity of some bubbles. The main steps include:

[0094] By processing two frames of images taken continuously, the counting of bubbles in the image and the tracking measurement of the velocity of some bubbles are achieved.

[0095] The method is based on the classic particle image velocimetry (PIV) technique: by taking two images at a defined time interval, the trajectory of a single bubble is tracked, and its displacement from one image to the next is measured, and the velocity is calculated.

[0096] The main reasons why traditional PIV technology cannot obtain accurate bubble velocity fields are: (1) it becomes difficult to capture a single bubble when the bubble overlap increases; (2) the bubble motion is complex; and (3) the bubbles have unstable deformations, which causes irregular flickering in the captured images. Therefore, based on the above problems, the matching window correlation method described in this embodiment includes the following key steps:

[0097] S321 processes the multiple images captured continuously, converting them to grayscale. The output image has 256 grayscale levels, ranging from 0 (black) to 255 (white), and also prepares a grayscale version of the background image. Subtracting the background image from the continuous flow image enhances image contrast. This process eliminates any information unrelated to the gas phase or color variations, resulting in a grayscale image containing only bubble information. In this embodiment, the continuous flow image is an image containing both liquid and gas, while the background image corresponds to a snapshot of stagnant liquid with no gas flow.

[0098] A median filter is applied to the above image, and each pixel of the image is replaced by the median of its adjacent pixels to obtain a grayscale image of the bubble after noise reduction.

[0099] S322 repeats the above correlation calculations from a large query area to a smaller query area, reducing the computational burden and increasing the spatial data output density in the final calculation results. In multiscale bubble velocity analysis, the cross-correlation algorithm needs to find the displacement with the most accurate grayscale distribution, that is, the highest peak in the cross-correlation function. If the search range is too large, secondary peaks may be misidentified. The previous velocity field provides macroscopic flow trends, while a finer grid requires more accurate displacement predictions.

[0100] In this example, a relatively sparse velocity distribution is initially obtained using a large query area. The previous results guide the next search, which is then processed a second time using a smaller query area. This process is repeated, continuously refining the sampling grid and reducing the query window size, ultimately extracting final data with good spatial resolution.

[0101] In a preferred solution of this embodiment, the specific implementation method of step S322 is:

[0102] Step 1: Select a rectangular window centered on the target bubble group in the image. , four windows B, C, D, and E of the same size are selected with the four corners of the current window as the center, and the grayscale distribution matrices corresponding to windows B, C, D, and E are calculated respectively. The grayscale distribution matrix in this embodiment is a matrix formed by extracting the grayscale value of each pixel block in the corresponding window.

[0103] As in this embodiment, a rectangular window A is selected with the target bubble group as the center, such as a 64×64 window, and four windows (B, C, D, E) of the same size of 64×64 are selected with the four corners of the current window as the center, and the grayscale distribution matrices corresponding to the four windows are extracted.

[0104] Step 2: Sliding match the grayscale distribution matrix of the four windows B, C, D, and E in the current frame with the matrix of the same-sized area near the window in the next frame, so as to find the window with the highest cross-correlation with the local window of the current frame in the next frame. At this time, the position of the cross-correlation peak is the coordinate offset of the current window. The same-sized area near the window is the initial search radius with the theoretical maximum displacement, and slides in the four directions of up, down, left, and right with a step size of one pixel.

[0105] This embodiment first searches for the cross-correlation peak value of the local grayscale distribution, and the cross-correlation is defined as follows: in, f and g Indicates grayscale, subscript i and j is the corresponding digitized image location, and M and N are the sizes of the query region. The grayscale used in the formula is subtracted from the local average grayscale of each query region to evaluate the unique similarity between the two images. The maximum correlation coefficient reflects the confidence level of the match, and the cross-correlation peak for the same bubble should be significantly higher than that for other locations.

[0106] The two images in this formula are obtained based on sliding matching. In this embodiment, the initial search radius is set according to the theoretical maximum displacement. For example, the search radius is selected as ±20, and windows B, C, D, and E are slid up, down, left, and right in four directions with a step size of one pixel, that is, a whole pixel step size is used to define a larger search window in the corresponding area of the next frame image, for example: 20 pixels up, down, left, and right, that is, 104×104. In the second frame image, the window with the same position as the window to be matched in the first frame is slid pixel by pixel within the search window, so as to find the window with the highest degree of cross-correlation with the local window of the first frame in the second frame image. The position of the obtained cross-correlation peak is the coordinate offset of the current window, which is expressed as .

[0107] Step 3: Get the speed of the corresponding window according to the coordinate offset of the B, C, D, and E windows, and use the speed of the four windows B, C, D, and E as the speed of the four corners of the A window.

[0108] Step 4: Use the maximum velocity of the bubble in the oil as the velocity threshold. If the velocity of any one or more of the four corners of window A is greater than the threshold, discard it, expand the window size, and recalculate the corresponding velocity. Otherwise, proceed to the next step of fine grid velocity measurement.

[0109] In this example, because the displacement from the previous step can be used as an estimate for the next higher-resolution level, the outlier detection criteria should be stricter than those used in single-step analysis to prevent the new estimate from deviating from the true value. The maximum velocity of bubbles in oil can be determined from fluid properties and used as a velocity threshold to discard outliers and recalculate with a wider window. Although widening the window reduces resolution, it can provide more information.

[0110] Since the rectangular window contains multiple bubbles, the above method still cannot obtain the velocity of a single bubble. To improve the accuracy of measuring the rising velocity of bubbles or measure smaller bubbles, after obtaining the velocities of the four corners of the coarse grid A, the coarse grid can be divided into finer grids, and the displacement of the fine grid can be predicted. After sliding matching and calculating the mutual correlation coefficient of the fine grid, a velocity field distribution with a lower resolution can be obtained, and then the velocity of a single bubble can be located for measurement.

[0111] Step 5. In rectangular window A, select a rectangular window smaller than window A with the target bubble as the center. , the rectangular window It includes at most one bubble, such as a 32×32 window, and uses bilinear interpolation to map the coarse-scale velocity field to a fine grid to predict the current displacement. The core idea is to calculate the velocity of the target point by weighted average of the velocities of the four known pixels in the large window A. The weight is determined by the horizontal and vertical distances between the target point and the adjacent points.

[0112] The size of the fine grid is , the corresponding center coordinates are , then the normalized coordinates of the fine grid in the rectangular window A are expressed as: The weights corresponding to the four corners of window A are calculated according to the normalized coordinates, and the speed of the current fine grid is obtained according to the speed corresponding to the four corners of window A.

[0113] In this embodiment, each coarse grid unit, , corresponding to four fine grid cells, , the center coordinates of the coarse grid cell are ( x c , y c ), the center coordinates of the fine grid cells are ( x f , y f ), the normalized coordinates in the coarse grid are: Example: Figure 4 As shown in , if the fine grid is located just below and to the right of the center of the coarse grid unit, in this embodiment, the origin is located in the upper left corner, then x =0.75, y = 0.75. Calculate the weights of the four coarse grid points based on the normalized coordinates: Therefore, the fine grid point velocity is calculated by weighting the coarse grid point velocity, and the lower right corner (w 11 ) has the largest weight, and the fine grid speed is: .

[0114] Step 6: Use the obtained fine grid velocity to predict the corresponding fine grid displacement, use the obtained displacement as the search radius, and take one pixel as the step size so that the rectangular window Make the position shift up, down, left and right, and find the peak value of the cross-correlation function after quadratic fitting. The offset window with the highest matching degree is obtained, and the rectangular window is obtained and the contour length of the corresponding bubble in the window with the highest matching degree. If the contour length change rate is greater than 10% or the fine grid speed is greater than the corresponding threshold, the target bubble is reselected for velocity measurement;

[0115] Otherwise, if the fine grid includes more than one bubble, the size of the fine grid is further reduced to perform velocity measurement until a single bubble is located for velocity measurement.

[0116] In this embodiment, the calculated fine grid velocity is used to predict the fine grid displacement. This displacement data is used to offset the interrogation windows. The interrogation window is 32×32. A quadratic fit is performed on the peak of the cross-correlation function. After finding the window with the highest degree of matching, the bubble contour lengths of the front and rear windows are measured and read directly from the image. If the contour length change rate is greater than 10% or the measured speed is greater than the corresponding threshold, which is the theoretical maximum speed, the target bubble is reselected for velocity measurement.

[0117] In this embodiment, the window size can be adaptively adjusted, and the number of iterations may be more than one fine grid as in the embodiment. If a single bubble cannot be detected, the size of the fine grid must be reduced, and further cycles must be performed on this basis until a single bubble is located for speed measurement, and on this basis, multiple bubbles in the field of view are tracked and measured.

[0118] Step 7: Track and measure a certain number of bubbles in the field of view to obtain the corresponding velocity distribution, and calculate the average velocity of the corresponding bubbles as the velocity of the bubble group. , expressed as: in, is the number of bubbles being tracked.

[0119] In this embodiment, half of the bubbles in the field of view are tracked according to the above steps to obtain the velocity distribution, and the average velocity is calculated as the velocity of the bubble group.

[0120] S323 counts the bubbles in the field of view: counts the number of bubbles in the image containing only bubble information and calculates the average value to obtain the total number of bubbles in the field of view.

[0121] In this embodiment, the weighted average method is used to calculate the average bubble particle size, which is expressed as: in, m is the number of particle size bins, For the j The particle size of the bubbles, The particle size is The proportion of bubbles, which is derived from the particle size distribution matrix get;

[0122] Calculate the gas production rate: Where N is the total number of bubbles in the field of view.

[0123] The comparative relationship between the measured and theoretical values of the gas production rate obtained by calculation is shown in Table 2. As can be seen from Table 2, the percentage errors of the measured gas production rate values are all less than 15%.

[0124] Table 2 Measured value, theoretical value and percentage error of gas production rate

[0125]

[0126] In summary, the present invention provides a method for detecting the acetylene gas production rate of an oil-immersed transformer gas relay based on light scattering. This method involves calculating the bubble size distribution based on the scattered light intensity after a laser is incident on a bubble cluster, using the Mie scattering principle. High-speed video recording of the bubble cluster's motion is used to monitor the bubble's rise velocity, and the gas production rate is calculated based on the bubble size and rise velocity. This method utilizes laser optical sensing technology to monitor the bubble's motion velocity and distribution in real time, effectively inferring the gas production rate.

[0127] Embodiment 2: The present invention also provides an acetylene gas production rate detection system for an oil-immersed transformer gas relay 3 based on a light scattering method, the system comprising: a gas relay 3, a light source unit 2, a sensor unit 4, a machine vision unit 1 and a processing unit 5;

[0128] Polystyrene particles with known particle size distribution are doped into transformer oil and introduced into the gas relay 3;

[0129] Determine the incident position of the light source unit 2, and make the laser signal emitted by the light source unit 2 incident on the observation window on one side of the gas relay 3;

[0130] The sensor unit 4 processes the scattered light at the observation window on the other side of the gas relay 3, thereby capturing scattered light intensity signals at different angles. The processing unit 5 calculates the bubble particle size distribution based on the scattered light intensity signals according to the Mie scattering principle.

[0131] The machine vision unit 1 photographs the bubble group in the gas relay 3 and monitors the bubble rising rate through the matching window correlation method in the processing unit 5, thereby obtaining the corresponding gas production rate by combining the bubble particle size distribution and the bubble rising rate. The matching window correlation method first selects the bubble group image photographed by the machine vision unit 1 to calculate the velocity field from coarse grid to fine grid, and then tracks and measures the velocity of a single bubble after meeting the resolution requirements.

[0132] Other technical features of the acetylene gas production rate detection system for an oil-immersed transformer gas relay based on the light scattering method described in this embodiment are similar to the corresponding acetylene gas production rate detection method for an oil-immersed transformer gas relay based on the light scattering method, and are not repeated here.

[0133] Finally, the present invention also provides a storage medium comprising computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described above.

[0134] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. "Multiple" means two or more, unless otherwise specifically defined.

[0135] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0136] In the present invention, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediary. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0137] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0138] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0139] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (not exhaustive) of computer-readable media include: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0140] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0141] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0142] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0143] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for detecting the acetylene gas production rate of an oil-immersed transformer gas relay based on a light scattering method, characterized in that: The method includes: Polystyrene particles with known particle size distribution were doped into transformer oil and introduced into a gas relay; Determining the incident position of the light source unit, and the laser signal emitted by the light source unit is incident on an observation window on one side of the gas relay. The sensor unit processes the scattered light through the observation window on the other side of the gas relay, thereby capturing scattered light intensity signals at different angles. The processing unit calculates the bubble particle size distribution based on the scattered light intensity signal according to the Mie scattering principle; The machine vision unit photographs the bubble group in the gas relay and monitors the bubble rise rate through a matching window correlation method in the processing unit, thereby combining the bubble particle size distribution and the bubble rise rate to obtain the corresponding gas production rate. The matching window correlation method first selects the bubble group image photographed by the machine vision unit to calculate the velocity field from coarse grid to fine grid, and then tracks and measures the velocity of a single bubble after the resolution requirement is met. The matching window correlation method specifically includes: Converting the multiple consecutively captured images into grayscale images, subtracting a background image without gas flow from the grayscale image to obtain a grayscale image containing only bubble information, and performing noise reduction on the grayscale image; Select a rectangular window centered on the target bubble group in the image , select four windows B, C, D, and E of the same size with the four corners of the current window as the center, and calculate the grayscale distribution matrix corresponding to windows B, C, D, and E respectively. The grayscale distribution matrix is a matrix formed by extracting the grayscale value of each pixel block in the corresponding window; Perform sliding matching on the grayscale distribution matrix of the four windows B, C, D, and E in the current frame and the matrix of the same-sized area near the window in the next frame, so as to find the window with the highest cross-correlation with the local window of the current frame in the next frame. The position of the cross-correlation peak is the coordinate offset of the current window. The same-sized area near the window is the initial search radius with the theoretical maximum displacement as the initial search radius, and slides in the four directions of up, down, left, and right with a step size of one pixel. The velocities of the corresponding windows are obtained based on the coordinate offsets of windows B, C, D, and E, and the velocities of the four windows B, C, D, and E are used as the velocities of the four corners of window A. The maximum velocity of the bubble in the oil is used as the velocity threshold. If the velocity of any one or more of the four corners of the A window is greater than the velocity threshold, it is discarded and the window size is expanded and the corresponding velocity is recalculated. Otherwise, the next step of fine grid velocity measurement is carried out. The method steps corresponding to the fine grid velocity measurement include: In rectangular window A, select a rectangular window smaller than window A with the target bubble as the center. , the rectangular window Contains at most one bubble; The size of the fine grid is , the corresponding center coordinates are , then the normalized coordinates of the fine grid in the rectangular window A are expressed as: ; Calculate the weights corresponding to the four corners of window A according to the normalized coordinates, and then obtain the speed of the current fine grid according to the speed corresponding to the four corners of window A; The obtained fine grid velocity is used to predict the corresponding fine grid displacement, and the obtained displacement is used as the search radius and one pixel is used as the step size, so that the rectangular window Make the position shift up, down, left and right, and find the peak value of the cross-correlation function after quadratic fitting. The offset window with the highest matching degree is obtained, and the rectangular window is obtained and the contour length of the corresponding bubble in the window with the highest matching degree. If the contour length change rate is greater than 10% or the fine grid speed is greater than the corresponding threshold, the target bubble is reselected for velocity measurement; If the current fine grid includes more than one bubble, the size of the current fine grid is further reduced to perform velocity measurement until a single bubble is located and the velocity is measured.

2. The method for detecting acetylene gas production rate of an oil-immersed transformer gas relay based on light scattering method according to claim 1, characterized in that: At least two piezoelectric ultrasonic sensors are provided on the observation window on each side. The piezoelectric ultrasonic sensors receive the sound waves generated by the bubbles and send them to the processing unit. The processing unit uses the TDOA principle to calculate the position of each bubble through the arrival time of the sound waves generated by the bubbles, thereby realizing three-dimensional target positioning of the bubble group. The sensor unit adjusts its position according to the result of the three-dimensional target positioning.

3. The method for detecting acetylene gas production rate of an oil-immersed transformer gas relay based on light scattering method according to claim 2, characterized in that: The sensor unit includes a CCD detector, and the position of the CCD detector is adjusted according to the result of the three-dimensional target positioning, including: the center of the CCD detector is aligned with the result of the three-dimensional target positioning.

4. The method for detecting acetylene gas production rate of an oil-immersed transformer gas relay based on light scattering method according to claim 3, characterized in that: The processing unit calculates the bubble particle size distribution based on the scattered light intensity signal using the Mie scattering principle, including: The processing unit performs annular segmentation on the light intensity distribution area corresponding to the scattered light intensity signals at different angles captured by the CCD detector; The specific angle range represented by each portion is obtained according to the number N of annular segments, and the light intensity distribution signal under each portion is obtained according to the scattered light intensity signal of each portion. The light intensity distribution signal is obtained by subtracting the background signal from the scattered light intensity signal of the current portion captured. The background signal is the light intensity signal corresponding to the absence of bubbles; Multiple light intensity distribution signals are combined to form a light intensity distribution column vector E, whose dimension is expressed as .

5. The method for detecting acetylene gas production rate of an oil-immersed transformer gas relay based on light scattering method according to claim 4, characterized in that: The processing unit calculates the bubble particle size distribution based on the scattered light intensity signal according to the Mie scattering principle, and further includes: The light intensity coefficient matrix T caused by bubble scattered light is calculated according to Mie scattering theory. The light intensity coefficient matrix includes the light intensity distribution information of scattered light of bubbles of different particle sizes at different angles. It is non-reversible and the corresponding dimension is expressed as ; According to the relationship Solving the particle size distribution matrix The solution method is: use the Tikhonov iterative algorithm to iteratively solve the initial particle size distribution matrix until the convergence condition is met, thereby obtaining the final particle size distribution matrix , the elements in the particle size distribution matrix are the proportions of bubbles corresponding to different particle sizes.

6. The method for detecting acetylene gas production rate of an oil-immersed transformer gas relay based on light scattering method according to claim 5, characterized in that: The matching window correlation method further includes: Track and measure a certain number of bubbles in the field of view to obtain the corresponding velocity distribution, and calculate the average velocity of the corresponding bubbles as the velocity of the bubble group , expressed as: ;in, is the number of bubbles being tracked.

7. The method for detecting acetylene gas production rate of an oil-immersed transformer gas relay based on light scattering method according to claim 6, characterized in that: The method of combining the bubble size distribution and the bubble rise rate to obtain the corresponding gas production rate includes: The weighted average method is used to calculate the average bubble particle size, which is expressed as: ; in, m is the number of particle size bins, For the j The particle size of the bubbles, The particle size is The proportion of bubbles, which is derived from the particle size distribution matrix get; Calculate the gas production rate: ; Where N is the total number of bubbles in the field of view.

8. A light scattering method-based acetylene gas production rate detection system for oil-immersed transformer gas relay, characterized in that: The system includes: a gas relay, a light source unit, a sensor unit, a machine vision unit and a processing unit; doping polystyrene particles with known particle size distribution into transformer oil and introducing the particles into the gas relay; Determining the incident position of the light source unit, and causing the laser signal emitted by the light source unit to be incident on an observation window on one side of the gas relay; The sensor unit processes the scattered light at the observation window on the other side of the gas relay, thereby capturing scattered light intensity signals at different angles, and the processing unit calculates the bubble particle size distribution based on the scattered light intensity signals according to the Mie scattering principle; The machine vision unit photographs the bubble group in the gas relay and monitors the bubble rise rate through the matching window correlation method in the processing unit, thereby combining the bubble particle size distribution and the bubble rise rate to obtain the corresponding gas production rate. The matching window correlation method first selects the bubble group image photographed by the machine vision unit to calculate the velocity field from coarse grid to fine grid, and then tracks and measures the velocity of a single bubble after meeting the resolution requirement; The matching window correlation method specifically includes: Converting the multiple consecutively captured images into grayscale images, subtracting a background image without gas flow from the grayscale image to obtain a grayscale image containing only bubble information, and performing noise reduction on the grayscale image; Select a rectangular window centered on the target bubble group in the image , select four windows B, C, D, and E of the same size with the four corners of the current window as the center, and calculate the grayscale distribution matrix corresponding to windows B, C, D, and E respectively. The grayscale distribution matrix is a matrix formed by extracting the grayscale value of each pixel block in the corresponding window; Perform sliding matching on the grayscale distribution matrix of the four windows B, C, D, and E in the current frame and the matrix of the same-sized area near the window in the next frame, so as to find the window with the highest cross-correlation with the local window of the current frame in the next frame. The position of the cross-correlation peak is the coordinate offset of the current window. The same-sized area near the window is the initial search radius with the theoretical maximum displacement as the initial search radius, and slides in the four directions of up, down, left, and right with a step size of one pixel. The velocities of the corresponding windows are obtained based on the coordinate offsets of windows B, C, D, and E, and the velocities of the four windows B, C, D, and E are used as the velocities of the four corners of window A. The maximum velocity of the bubble in the oil is used as the velocity threshold. If the velocity of any one or more of the four corners of the A window is greater than the velocity threshold, it is discarded and the window size is expanded and the corresponding velocity is recalculated. Otherwise, the next step of fine grid velocity measurement is carried out. The method steps corresponding to the fine grid velocity measurement include: In rectangular window A, select a rectangular window smaller than window A with the target bubble as the center. , the rectangular window Contains at most one bubble; The size of the fine grid is , the corresponding center coordinates are , then the normalized coordinates of the fine grid in the rectangular window A are expressed as: ; Calculate the weights corresponding to the four corners of window A according to the normalized coordinates, and then obtain the speed of the current fine grid according to the speed corresponding to the four corners of window A; The obtained fine grid velocity is used to predict the corresponding fine grid displacement, and the obtained displacement is used as the search radius and one pixel is used as the step size, so that the rectangular window Make the position shift up, down, left and right, and find the peak value of the cross-correlation function after quadratic fitting. The offset window with the highest matching degree is obtained, and the rectangular window is obtained and the contour length of the corresponding bubble in the window with the highest matching degree. If the contour length change rate is greater than 10% or the fine grid speed is greater than the corresponding threshold, the target bubble is reselected for velocity measurement; If the current fine grid includes more than one bubble, the size of the current fine grid is further reduced to perform velocity measurement until a single bubble is located and the velocity is measured.

9. A storage medium containing computer-executable instructions, characterized in that: When the computer-executable instructions are executed by a processor, the processor performs the method of any one of claims 1 to 7.

Images