Dual-pulse laser PIV control system for mine flow field measurement

Abstract

The application relates to a double-pulse laser PIV control system for mine flow field measurement, and relates to the technical field of mine measurement, and comprises the following: a flow field feature acquisition module acquires an axial wind speed and a diffusion half-width and calculates a velocity gradient characteristic value; a pulse interval prediction module predicts an initial interval by using a working condition model; an environmental interference compensation module identifies an interference area according to a signal-to-noise ratio, compensates the interval by blowing the flow speed, and opens a high-gain image intensifier; an initial velocity field solving module collects particle images and solves the velocity field by using the axial wind speed as a constraint; a secondary compensation module performs secondary compensation according to the full-field velocity average value; and an iterative optimization module iteratively solves until convergence and outputs visualized data. The application solves the technical problem that the existing mine flow field PIV system relies on a fixed pulse interval and a single acquisition mode, and cannot capture effective particle image pairs when the wind speed gradient changes dramatically or is shielded by high dust, resulting in flow field measurement failure or loss of data precision.

Description

Technical Field

[0001] This application relates to the field of mine measurement technology, and in particular to a dual-pulse laser PIV control system for mine flow field measurement. Background Technology

[0002] The longwall face is the main work area for tunneling underground in coal mines. The ventilation system delivers fresh air to the face through ventilation ducts, forming a high-speed jet field at the duct outlet. The wind speed distribution and flow field structure of the jet field directly affect gas dilution, dust transport, and the comfort of the working environment. Accurately measuring the flow field state in this area is an important basis for mine ventilation safety assessment.

[0003] In existing technologies, mine flow field measurement mainly employs point-by-point measurement with impeller-type anemometers or indirect calculation using tracer gas methods. This only obtains wind speed values ​​at a limited number of discrete points, failing to capture the continuous velocity vector distribution across the entire field. Particle image velocimetry (PIV) is a non-contact, full-field optical flow velocity measurement technique. Applying it to mine flow field measurement can achieve online visualization of the entire field velocity vector. However, existing PIV control systems rely on manually preset fixed pulse intervals and single-shot acquisition modes. At the mine ventilation outlet, wind speed gradients change drastically. Fixed pulse intervals cause cross-correlation matching failures in high-speed regions due to excessive particle displacement, and in low-speed regions, insufficient particle displacement leads to increased velocity calculation errors. Simultaneously, the large amount of suspended dust in the mine generates strong backscattered light, severely reducing the signal-to-noise ratio of the particle image, resulting in numerous anomalous vectors in the cross-correlation calculation results, making it difficult to guarantee the integrity and accuracy of the flow field data. Summary of the Invention

[0004] This application provides a dual-pulse laser PIV control system for mine flow field measurement, which solves the technical problems of existing mine flow field PIV systems that use manually preset fixed pulse intervals, lack the ability to adaptively set pulse intervals according to the characteristics of jet field wind speed changes, and do not dynamically compensate for backscattering interference caused by underground dust and enhance image quality, resulting in poor particle image quality and difficulty in ensuring the accuracy of flow field measurement in high dust environments.

[0005] This application discloses the following technical solution: This application provides a dual-pulse laser PIV control system for measuring flow fields in mines, the system comprising: The flow field feature acquisition module is used to acquire the jet axis wind speed and jet diffusion half width of the jet field at the ventilation outlet of the tunnel face, and to calculate the characteristic value of the jet field velocity gradient. The pulse interval prediction module is used to input the characteristic value of the jet field velocity gradient into the pulse interval prediction model pre-bound to the mine ventilation condition to generate the initial pulse interval between the first pulse and the second pulse of the dual-pulse laser. The environmental interference compensation module is used to identify the backscattering interference area based on the signal-to-noise ratio of the tracer particle image at each location in the jet field space. Within the backscattering interference area, the initial pulse interval is used as a reference and the flow rate of the optical path sealing purge airflow is used as a constraint to perform the first-level compensation on the initial pulse interval, generate the first compensation pulse interval, and simultaneously turn on the high-gain image intensifier. The initial velocity field calculation module is used to acquire particle image pairs under dual-pulse laser illumination controlled by the first compensation pulse interval, and calculate the initial velocity field with the jet axis wind speed as a constraint. The secondary compensation module is used to perform a second-level compensation on the first compensation pulse interval based on the average velocity of the entire initial velocity field, generate a second compensation pulse interval, and update the pulse interval of the dual-pulse laser. The iterative optimization module is used to iteratively acquire the particle image pairs and solve the velocity field until the deviation of the mean velocity of the entire field between two adjacent solutions is less than a preset deviation threshold, output the final velocity field and generate dynamic visualization data of the flow field.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The technical solution of this application provides a dual-pulse laser PIV control system for measuring flow fields in mines. First, by collecting the axial and radial wind speed distribution at the air outlet of the ventilation duct, the characteristic value of the jet field velocity gradient is calculated using the maximum axial wind speed and the radial half-speed width. Based on this, the spacing between sensor measuring points is adaptively adjusted, thereby realizing the quantitative characterization of the drastic changes in wind speed in the jet field. This solves the problems of traditional systems lacking means to perceive wind speed gradient characteristics and the inability of sensor placement to adapt to changes in flow field scale.

[0007] Furthermore, by automatically generating the initial pulse interval based on the velocity gradient characteristic value using the pulse interval prediction model bound to the mine ventilation conditions, the transformation of the pulse interval from a manually fixed preset to an adaptive generation based on the flow field characteristics was realized, solving the problem of particle image capture failure when the fixed pulse interval changes drastically with wind speed gradient.

[0008] Next, by analyzing the signal-to-noise ratio of the tracer particle image, the backscattering interference area of ​​dust was automatically identified. The pulse interval was compensated in the first stage with the flow rate of the purge airflow constrained by the optical path sealing, and the high-gain image intensifier was turned on simultaneously. Based on the feedback of the compensated signal-to-noise ratio, the purge airflow velocity was gradually increased until the image quality met the standard. This achieved dynamic compensation for downhole dust interference and closed-loop enhancement of the image signal-to-noise ratio, solving the problems of poor particle image quality and low accuracy of flow field measurement in high dust environments.

[0009] Then, by performing normalized cross-correlation calculation on the collected particle images, and using the product of jet axis wind speed and pulse interval as reference displacement to remove abnormal vectors, interpolation is performed to fill in the gaps, thus obtaining the initial velocity field of the entire field. This achieves accurate conversion from particle images to velocity vector fields and automatic correction of abnormal vectors, solving the problem of a large number of abnormal vectors and data integrity damage in the cross-correlation calculation results under dust interference.

[0010] Furthermore, by using the average velocity of the entire initial velocity field as a benchmark, a second-level compensation is performed on the pulse interval and the laser pulse interval is updated. This allows the pulse interval to be further adapted to the overall velocity level of the current flow field based on environmental compensation, thus achieving a two-level progressive optimization of the pulse interval from initial value prediction to environmental compensation and then to velocity field feedback.

[0011] Finally, by iteratively acquiring particle image pairs and solving the velocity field, and using twice the measurement accuracy of the wind speed sensor as the convergence criterion, the iteration stops when the deviation of the average value of two adjacent velocity fields is less than a threshold, and the final velocity field is output. Visual data output is generated in the form of vector arrows, color mapping and streamline superposition, realizing iterative convergence control of velocity field solution results and online intuitive presentation of flow field state. This solves the problems of unstable results, lack of data accuracy convergence criteria and dynamic visualization output methods in traditional single acquisition methods.

[0012] In summary, this application solves the technical problem of measurement failure in existing mine flow field PIV systems with fixed pulse intervals and single acquisition modes under drastic wind speed changes and high dust environments by quantifying the velocity gradient characteristics of the jet field and adaptively generating pulse intervals. After dynamic compensation for dust interference and closed-loop enhancement of image signal-to-noise ratio, the initial velocity field is calculated, followed by secondary compensation of pulse intervals and iterative convergence of velocity field, and finally outputting dynamic visualization data of the flow field. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the structure of a dual-pulse laser PIV control system for measuring flow field in mines, provided in an embodiment of this application.

[0015] Figure 2 This is a schematic flowchart of a dual-pulse laser PIV control system for measuring flow field in mines, provided in an embodiment of this application.

[0016] Figure 3 The flowchart illustrates the pulse interval adaptive iterative optimization process of a dual-pulse laser PIV control system for mine flow field measurement provided in this application embodiment.

[0017] In the attached diagram, Figure 1 The components represented by each number are described as follows: Flow field feature acquisition module 11, pulse interval prediction module 12, environmental interference compensation module 13, initial velocity field calculation module 14, secondary compensation module 15, and iterative optimization module 16. Detailed Implementation

[0018] This application provides a dual-pulse laser PIV control system for mine flow field measurement, which solves the technical problem that existing mine flow field PIV systems lack a closed-loop control mechanism covering the entire process from pulse interval prediction, environmental interference compensation, image acquisition and calculation to iterative optimization. This makes it impossible to dynamically adjust the pulse interval and iteratively converge the measurement results according to the real-time characteristics of the jet field and the degree of dust interference, resulting in unstable flow field measurement results and insufficient visualization data accuracy to meet the needs of mine ventilation safety assessment.

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. It should be noted that the numerical values ​​in the embodiments are for illustrative purposes only and do not constitute a limitation on this application.

[0020] Example 1, as shown in the appendix Figure 1 and attached Figure 2 As shown, this application provides a dual-pulse laser PIV control system for measuring flow fields in mines, the system comprising the following modules: Flow field feature acquisition module 11: used to acquire the jet axis wind speed and jet diffusion half width of the jet field at the ventilation outlet of the tunnel face, and to calculate the characteristic value of the jet field velocity gradient.

[0021] In this embodiment, to accurately quantify the drastic changes in wind speed within the jet field, an array of wind speed sensors needs to be deployed at the outlet of the wind tunnel to simultaneously collect axial and radial wind speed distribution data. Traditional measurement methods only acquire wind speed at a single point, failing to describe the overall velocity gradient of the jet field. This module, through joint axial and radial sensor placement, dynamically adjusts the spacing between measurement points based on the jet diffusion half-width, ensuring that effective gradient information can still be captured even when the flow field scale changes. This addresses the problems of existing systems lacking quantitative sensing methods for the wind speed gradient characteristics of the jet field and having fixed sensor placement that cannot adapt to changes in the flow field scale.

[0022] In the system provided in this application embodiment, the flow field feature acquisition module is specifically used for: arranging a first wind speed sensor sequence on the axis of the air duct outlet to collect axial wind speed distribution along the axis, and using the wind speed at the location of the maximum value in the axial wind speed distribution as the jet axis wind speed; arranging a second wind speed sensor sequence on a radial section perpendicular to the air duct outlet axis to collect radial wind speed distribution, and using the radial distance corresponding to the point where the velocity value in the radial wind speed distribution drops to half of the jet axis wind speed as the jet diffusion half-width; using the ratio of the jet axis wind speed to the jet diffusion half-width as the jet field velocity gradient feature value, and dynamically adjusting the spacing between adjacent measuring points of the first and second wind speed sensor sequences according to the value of the jet diffusion half-width, wherein the spacing between adjacent measuring points is positively correlated with the jet diffusion half-width. Detailed explanation follows: In this embodiment, the jet axis velocity refers to the maximum velocity value in the axial velocity distribution along the outlet axis of the duct, located in the jet core region, and characterizing the main velocity of the jet at the duct outlet. The jet diffusion half-width refers to the radial distance corresponding to the radial velocity decreasing from its maximum value outwards to half on a radial section perpendicular to the axis, reflecting the spatial expansion range of the jet. The jet field velocity gradient characteristic value is the ratio of the jet axis velocity to the jet diffusion half-width. A larger ratio indicates a more severe velocity decay over a shorter spatial distance, a larger velocity gradient, and a higher requirement for pulse intervals.

[0023] The first anemometer sequence refers to a linear array of multiple anemometers evenly spaced along the axis of the wind tunnel outlet, used to collect axial wind speed distribution. The second anemometer sequence refers to a linear array of multiple anemometers evenly spaced radially across a cross-section perpendicular to the axis, used to collect radial wind speed distribution. The distance between adjacent measuring points refers to the physical distance between two adjacent anemometers in the same sensor sequence. Positive correlation refers to the relationship between the distance between adjacent measuring points and the jet diffusion half-width changing in the same direction; the larger the jet diffusion half-width, the larger the spatial scale of the flow field, and the greater the distance between adjacent measuring points should be to cover a larger measurement range.

[0024] In this module, to obtain the characteristic values ​​of the jet field velocity gradient and adaptively adjust the sensor placement, a first sequence of wind speed sensors is first arranged along the axis of the air outlet of the duct. Each sensor is arranged sequentially along the axis, synchronously collecting axial wind speed distribution data. The location of the maximum wind speed in the axial wind speed distribution is identified; this wind speed value is the jet axis wind speed. Then, a second sequence of wind speed sensors is arranged on a radial section perpendicular to the axis, with each sensor arranged sequentially outwards from the axis in the radial direction, synchronously collecting radial wind speed distribution data. The radial position where the wind speed decreases from the maximum axial value to half its original value is found in the radial wind speed distribution; the distance between this position and the axis is the jet diffusion half-width. Finally, the characteristic value of the jet field velocity gradient is obtained by dividing the jet axis wind speed by the jet diffusion half-width.

[0025] Furthermore, this system also includes: determining a positive correlation factor between the jet diffusion half-width and the distance between adjacent measuring points, wherein the positive correlation factor is calibrated by the diameter of the air outlet of the wind tunnel; multiplying the real-time value of the jet diffusion half-width by the positive correlation factor to obtain the target measuring point distance; sending the target measuring point distance to the stepper drive mechanisms of the first and second wind speed sensor sequences respectively to adjust the physical distance between adjacent wind speed sensors; using the jet diffusion half-width simultaneously as the axial and radial coverage boundaries of the sensor sequences to constrain the end positions of the first wind speed sensor sequence along the axial direction and the end positions of the second wind speed sensor sequence along the radial direction, respectively. A detailed explanation follows: In this embodiment, the positive correlation scaling factor refers to the linear proportionality coefficient between the jet diffusion half-width and the distance between adjacent measuring points, calibrated by the diameter of the duct outlet. The positive correlation scaling factor is calculated as follows: the positive correlation scaling factor equals the minimum physical installation spacing divided by the radius of the duct outlet. The minimum physical installation spacing is determined based on the sum of the diameter of the anemometer's housing and the width of the mounting bracket. For example, if the anemometer's housing diameter is 0.08m and the mounting bracket width is 0.07m, the minimum physical installation spacing is 0.15m. If the duct outlet radius is 0.5m, then the positive correlation scaling factor = 0.15 ÷ 0.5 = 0.3. The target measuring point spacing refers to the optimal physical spacing between sensors calculated based on the real-time value of the jet diffusion half-width and the positive correlation scaling factor. The axial coverage boundary refers to the maximum effective measurement range of the first anemometer sequence along the axial direction, and the radial coverage boundary refers to the maximum effective measurement range of the second anemometer sequence along the radial direction.

[0026] In this module, to dynamically adjust the sensor placement based on real-time changes in the flow field scale, a positive correlation scaling factor is first calibrated based on the diameter of the duct outlet. The target measurement point spacing is obtained by multiplying the real-time value of the jet diffusion half-width by the positive correlation scaling factor. This target measurement point spacing is then sent to the stepping drive mechanisms of the first and second anemometer sequences. Based on the received target measurement point spacing command, the stepping drive mechanisms drive each anemometer to move along the guide rail to a designated position, ensuring that the physical distance between adjacent anemometers equals the target measurement point spacing. Simultaneously, the jet diffusion half-width is used as the axial and radial coverage boundaries of the sensor sequences. The end position of the first anemometer sequence along the axial direction is constrained from exceeding the axial coverage boundary, and the end position of the second anemometer sequence along the radial direction is constrained from exceeding the radial coverage boundary, ensuring that the coverage range of the sensor sequences matches the flow field scale.

[0027] For example, in a tunnel boring machine face, the radius of the ventilation duct outlet is 0.5m, the measured jet axis velocity is 12m / s, and the jet diffusion half-width is 0.8m. The characteristic value of the jet field velocity gradient is... The positive correlation ratio factor = 0.15 ÷ 0.5 = 0.3, and the target measuring point spacing = 0.8 × 0.3 = 0.24m. The stepping drive mechanism of the first and second wind speed sensor sequences adjusts the adjacent spacing of each sensor to 0.24m, and both the axial coverage boundary and the radial coverage boundary are set to 0.8m.

[0028] In summary, this module collects the axial and radial wind speed distribution at the air outlet of the duct, calculates the velocity gradient characteristic value of the jet field using the maximum axial wind speed and the radial half-velocity width, and adaptively adjusts the spacing between sensor measuring points accordingly. This enables a quantitative characterization of the drastic changes in wind speed in the jet field, solving the problems of traditional systems lacking means to perceive wind speed gradient characteristics and sensor placement being unable to adapt to changes in the flow field scale.

[0029] Pulse interval prediction module 12: used to input the velocity gradient characteristic value of the jet field into the pulse interval prediction model pre-bound to the mine ventilation condition, and generate the initial pulse interval between the first pulse and the second pulse of the dual-pulse laser.

[0030] In this embodiment, based on the obtained jet field velocity gradient feature value, in order to solve the problem that the fixed pulse interval cannot adapt to wind speed changes, an initial pulse interval is automatically generated by a pre-constructed pulse interval prediction model bound to the mine ventilation conditions, according to the velocity gradient feature value. This realizes the transformation of the pulse interval from manual fixed preset to adaptive generation based on the flow field characteristics, so as to solve the problem of particle image capture failure when the fixed pulse interval changes drastically with wind speed gradient.

[0031] The system provided in this application embodiment includes the following steps for constructing the pulse interval prediction model: collecting historical jet field velocity gradient feature value records of the longwall face under different mine ventilation conditions, and collecting historical optimal pulse interval records corresponding to each of the historical jet field velocity gradient feature value records; constructing a sample dataset using the historical jet field velocity gradient feature value records and the historical optimal pulse interval records, wherein the historical optimal pulse interval records are double pulse interval calibration values ​​that maximize the cross-correlation peak value under each mine ventilation condition; dividing the sample dataset into multiple condition subsets according to the mine ventilation condition type, wherein each condition subset corresponds to a mine ventilation condition; Within each subset of working conditions, the historical jet field velocity gradient feature value records are used as input, and the historical optimal pulse interval records are used as supervision labels. For each subset of working conditions, an initial prediction function is constructed using the inverse proportional regression relationship between the historical jet field velocity gradient feature value records and the historical optimal pulse interval records. The proportional coefficient of the initial prediction function is used as the parameter to be fitted. Using the supervision labels as the fitting target, least squares fitting is performed on the parameter to be fitted in each subset of working conditions until the sum of squared fitting residuals is less than the fitting convergence threshold, thereby obtaining the working condition-bound prediction function corresponding to each mine ventilation working condition. All the working condition-bound prediction functions constitute the pulse interval prediction model. Detailed explanation is as follows: In this embodiment, the historical jet field velocity gradient characteristic value recording data refers to the set of velocity gradient characteristic values ​​recorded in previous measurements of the jet field at the ventilation outlet of the ventilation duct in a fully mechanized mine face. The historical optimal pulse interval recording data refers to the set of double-pulse interval values ​​corresponding to each historical velocity gradient characteristic value, which, after manual calibration or experimental verification, maximize the cross-correlation peak value, reflecting the optimal pulse interval under that velocity gradient. The historical optimal pulse interval recording data is obtained as follows: under various mine ventilation conditions, PIV data is collected at different pulse intervals; the cross-correlation peak value of the particle image pair under each pulse interval is calculated; and the pulse interval corresponding to the largest cross-correlation peak value is taken as the optimal pulse interval for that condition and that velocity gradient and recorded.

[0032] Mine ventilation operating condition types refer to different ventilation operation states such as normal ventilation, enhanced ventilation, and ventilation shutdown for maintenance in fully mechanized tunneling faces. Operating condition subsets refer to the subsets of the sample dataset obtained by dividing the dataset according to mine ventilation operating condition types. The inverse proportional regression relationship refers to a negative quantitative relationship where a larger velocity gradient characteristic value corresponds to a smaller pulse interval, and vice versa. The initial prediction function is the mathematical expression describing this inverse proportional relationship, using the form Δt = k ÷ G, where Δt is the pulse interval, G is the velocity gradient characteristic value, and k is the proportionality coefficient to be fitted. The parameter to be fitted refers to the proportionality coefficient k in the initial prediction function, which needs to be determined through data fitting. The fitting convergence threshold is the upper limit of the sum of squared residuals used to determine whether the fitting is complete during the least squares fitting process.

[0033] In this module, to establish a model capable of automatically predicting the optimal pulse interval based on velocity gradient feature values, historical jet field velocity gradient feature value records and corresponding historical optimal pulse interval records are first collected from the longwall face under different mine ventilation conditions, including normal ventilation, enhanced ventilation, and shutdown maintenance, to construct a sample dataset. The sample dataset is then divided into three subsets based on mine ventilation conditions: normal ventilation, enhanced ventilation, and shutdown maintenance. Within each subset, using historical jet field velocity gradient feature value records as input and historical optimal pulse interval records as supervision labels, an initial prediction function Δt = k ÷ G in the form of an inverse proportional regression is established. Using the supervision labels as the fitting target, the proportionality coefficient k within each subset is fitted using least squares. Fitting is complete when the sum of squared residuals is less than the fitting convergence threshold, yielding the proportionality coefficient values ​​for each condition, thus determining the condition-bound prediction function for each condition. All condition-bound prediction functions together constitute the pulse interval prediction model. When used online, the pulse interval prediction model calls the corresponding condition-binding prediction function according to the current mine ventilation conditions, substitutes the real-time jet field velocity gradient characteristic value, and outputs the initial pulse interval.

[0034] For example, under normal ventilation conditions, the collected historical data is fitted using least squares to obtain the proportionality coefficient k. The binding prediction function for this operating condition is Δt = 240 ÷ G. The current jet field velocity gradient eigenvalue is... Substituting into the function, we get the initial pulse interval = 240 ÷ 15 = 16 μs.

[0035] In summary, this module automatically generates the initial pulse interval based on the velocity gradient characteristic value by using a pulse interval prediction model bound to the mine ventilation conditions. This achieves the transformation of the pulse interval from a manually fixed preset to an adaptively generated one based on the flow field characteristics, solving the problem of particle image capture failure when the fixed pulse interval changes drastically with wind speed gradient.

[0036] Environmental interference compensation module 13: It is used to identify the backscattering interference area based on the signal-to-noise ratio of the tracer particle image at each position in the jet field space, and within the backscattering interference area, with the initial pulse interval as the reference and the optical path sealing purge airflow velocity as the constraint, to perform the first-level compensation on the initial pulse interval, generate the first compensation pulse interval, and simultaneously turn on the high-gain image intensifier.

[0037] In this embodiment, backscattered light caused by high concentrations of dust in the mine severely reduces the signal-to-noise ratio (SNR) of particle images, leading to errors in cross-correlation calculations. By identifying interference regions through pixel-by-pixel SNR analysis and density clustering, and then using pulse interval compensation and a high-gain image intensifier in combination to improve image quality, and performing closed-loop verification of the compensation effect, dynamic compensation for underground dust interference and closed-loop enhancement of image SNR are achieved, solving the problems of poor image quality and low accuracy of flow field measurement in high-dust environments.

[0038] In the system provided in this application embodiment, the environmental interference compensation module is specifically used for: acquiring a first frame tracer particle image and a second frame tracer particle image under dual-pulse laser illumination; calculating the ratio of the grayscale standard deviation to the grayscale mean of each pixel in the first frame tracer particle image and the second frame tracer particle image, as the signal-to-noise ratio (SNR) at each location; calculating the arithmetic mean of the SNR of all pixels in the entire field of view to obtain the average SNR of the entire field of view; marking pixels with an SNR lower than the average SNR of the entire field of view as low SNR pixels; using the jet diffusion half-width as the spatial aggregation radius, performing density clustering on the low SNR pixels, and identifying the connected regions formed after clustering as backscattering interference regions. Detailed explanation follows: In this embodiment, the signal-to-noise ratio (SNR) of the tracer particle image refers to the ratio of the standard deviation of the gray level of each pixel position to the mean gray level in two frames of particle images. It is used to measure the intensity of the particle signal at that position relative to the background noise. A higher SNR indicates a clearer particle signal. The mean SNR of the entire field of view refers to the arithmetic mean of the SNR of all pixels in the entire field of view, serving as a baseline for distinguishing normal pixels from low SNR pixels. Low SNR pixels are pixels with an SNR lower than the mean SNR of the entire field of view. These pixels are severely affected by dust backscattering interference, and the particle signal is submerged in the noise of scattered light. The backscattering interference region refers to the connected region formed by density clustering of low SNR pixels, reflecting the large-area interference patches caused by dust backscattering light in the image.

[0039] In this module, to automatically identify image interference areas caused by dust backscattering, the first and second frames of tracer particle images under dual-pulse laser illumination are first acquired. For each pixel location, the grayscale value of that pixel in the first and second frames is taken respectively, and the standard deviation and mean of the two grayscale values ​​are calculated. The standard deviation divided by the mean is used as the signal-to-noise ratio (SNR) at that location. All pixels in the entire field of view are traversed, and the arithmetic mean of the SNR at each location is calculated to obtain the mean SNR of the entire field of view. Pixels with an SNR lower than the mean SNR of the entire field of view are marked as low SNR pixels. Using the jet diffusion half-width as the spatial aggregation radius of density clustering, a density-based spatial clustering algorithm is used to cluster low SNR pixels, and the connected regions formed by spatially continuous low SNR pixels are identified as backscattering interference areas.

[0040] Furthermore, this system also includes: calculating the average signal-to-noise ratio (SNR) of all pixels within the backscattering interference region to obtain the average SNR of the interference region; using the ratio of the average SNR of the interference region to the average SNR of the entire field of view as an interference intensity coefficient, wherein the interference intensity coefficient is less than 1; determining a basic compensation ratio based on the flow velocity of the optical path sealing purge airflow and the wind speed along the jet axis, and obtaining a corrected compensation ratio by combining the interference intensity coefficient; determining a pulse interval compensation amount based on the initial pulse interval and the corrected compensation ratio, and adding the initial pulse interval to the pulse interval compensation amount to generate a first compensated pulse interval; during the emission of the first and second pulses by the dual-pulse laser, sending a gated trigger signal to the high-gain image intensifier, and simultaneously activating the high-gain image intensifier within the backscattering interference region. Detailed explanation follows: In this embodiment, the mean signal-to-noise ratio (SNR) of the interference region refers to the arithmetic mean of the SNR of all pixels within the backscattering interference region; a lower value indicates more severe dust interference. The interference intensity coefficient is the ratio of the mean SNR of the interference region to the mean SNR of the entire field of view, and its value is less than 1; a smaller coefficient indicates stronger interference. The base compensation ratio is the ratio of the velocity of the optical path sealing purge airflow to the jet axis wind speed. The base compensation ratio is equal to the optical path sealing purge airflow velocity divided by the jet axis wind speed, reflecting the strength of the purge airflow relative to the mainstream velocity. The correction compensation ratio is the compensation ratio obtained by dividing the base compensation ratio by the interference intensity coefficient; the stronger the interference, the larger the correction compensation ratio.

[0041] The pulse interval compensation amount refers to the value obtained by multiplying the initial pulse interval by the correction compensation ratio, used to compensate and adjust the initial pulse interval. The first compensated pulse interval is the pulse interval value after adding the pulse interval compensation amount to the initial pulse interval. In the interference area, the relative influence of scattering noise is reduced by appropriately increasing the pulse interval. A high-gain image intensifier is an optoelectronic imaging device capable of amplifying weak light signals by a high factor. It is synchronously activated during laser pulse emission by receiving a gating trigger signal, enhancing the intensity of the scattered light signal of the tracer particles and improving the signal-to-noise ratio of the particle image in the interference area.

[0042] In this module, to dynamically compensate for dust backscattering interference, the arithmetic mean of the signal-to-noise ratio (SNR) of all pixels within the backscattering interference area is first calculated to obtain the mean SNR of the interference area. The interference intensity coefficient is then obtained by dividing the mean SNR of the interference area by the mean SNR of the entire field of view. Next, the base compensation ratio is obtained by dividing the velocity of the airflow through the sealed optical path by the jet axis wind speed. The correction compensation ratio is then obtained by dividing the base compensation ratio by the interference intensity coefficient. The pulse interval compensation amount is obtained by multiplying the initial pulse interval by the correction compensation ratio, and the first compensation pulse interval is obtained by adding the pulse interval compensation amount to the initial pulse interval. During the emission of the first and second pulses by the dual-pulse laser, a gated trigger signal is sent to the high-gain image intensifier, synchronously activating the high-gain image intensifier within the backscattering interference area to enhance the particle-scattered light signal.

[0043] Furthermore, this system also includes: acquiring compensated tracer particle image pairs enhanced by the high-gain image intensifier; calculating the average signal-to-noise ratio (SNR) of the compensated tracer particle image pairs within the backscattering interference region; comparing the average SNR with the average SNR across the entire field of view; if the average SNR is lower than the average SNR across the entire field of view, then gradually increasing the optical path sealing purge airflow velocity by using 5% of the current optical path sealing purge airflow velocity as the velocity step increment; and waiting for the optical path sealing purge airflow velocity to increase. Once the flow velocity stabilizes at the increased target value, the stabilized optical path sealing purge airflow velocity is used as a constraint again. Combined with the interference intensity coefficient and the jet axis wind speed, the compensation ratio is updated and corrected, and the first compensation pulse interval is regenerated. The regenerated first compensation pulse interval controls the dual-pulse laser to emit light again, and the high-gain image intensifier is simultaneously turned on to acquire new compensated tracer particle image pairs. The average signal-to-noise ratio after compensation is recalculated until the average signal-to-noise ratio after compensation is greater than or equal to the average signal-to-noise ratio across the entire field of view.

[0044] In this module, to verify the compensation effect and perform closed-loop adjustment, compensated tracer particle image pairs enhanced by a high-gain image intensifier are acquired, and the average signal-to-noise ratio (SNR) after compensation is calculated. The average SNR after compensation is compared with the average SNR of the entire field of view. If the average SNR after compensation is lower than the average SNR of the entire field of view, the flow rate is gradually increased by 5% of the flow rate of the optical path sealing purge airflow. After the flow rate stabilizes, the correction compensation ratio and the first compensation pulse interval are recalculated with the new flow rate. The light is emitted again for acquisition and evaluation, forming a closed-loop feedback until the average SNR after compensation reaches or exceeds the average SNR of the entire field of view.

[0045] For example, the average signal-to-noise ratio (SNR) across the entire field of view is 18 dB, and the average SNR in a certain backscattering interference area is 12 dB. The interference intensity coefficient is approximately 0.67 (12 ÷ 18). The airflow velocity of the optical path sealing purge is 0.5 m / s, the jet axis wind speed is 12 m / s, the basic compensation ratio is approximately 0.5 ÷ 12 ≈ 0.042, and the correction compensation ratio is approximately 0.042 ÷ 0.67 ≈ 0.063. The initial pulse interval is 16 μs, the pulse interval compensation is approximately 16 × 0.063 ≈ 1.0 μs, and the first compensation pulse interval is 17 μs. A high-gain image intensifier is simultaneously activated. After compensation, the average SNR in this area rises to 17.5 dB, but is still below 18 dB. Therefore, the purge velocity is increased to 0.525 m / s in 5% increments, the correction compensation ratio and the first compensation pulse interval are recalculated, and the light output is evaluated again.

[0046] In summary, this module automatically identifies dust backscattering interference areas by analyzing the signal-to-noise ratio of tracer particle images. It performs first-stage compensation on the pulse interval with the purge airflow velocity constrained by the optical path sealing as the constraint, and simultaneously activates the high-gain image intensifier. Based on the feedback of the compensated signal-to-noise ratio, the purge airflow velocity is gradually increased until the image quality meets the standard. This achieves dynamic compensation for downhole dust interference and closed-loop enhancement of the image signal-to-noise ratio, solving the problems of poor particle image quality and low flow field measurement accuracy in high dust environments.

[0047] Initial velocity field calculation module 14: used to acquire particle image pairs under dual-pulse laser illumination controlled by the first compensation pulse interval, and calculate the initial velocity field with the jet axis wind speed as a constraint.

[0048] In this embodiment, based on the first compensation pulse interval and the acquisition of particle image pairs, an accurate full-field initial velocity distribution is obtained by performing adaptive cross-correlation analysis on the particle image pairs and using jet axis wind speed constraints to eliminate abnormal displacement vectors. This solves the problem that the cross-correlation solution results of traditional systems contain a large number of abnormal vectors and the integrity and accuracy of flow field data are damaged under dust interference.

[0049] In the system provided in this application embodiment, the initial velocity field calculation module is specifically used for: dividing the first and second frame particle images in the particle image pair into multiple interrogation windows arranged in the same way; for each interrogation window, moving the interrogation window pixel by pixel between the interrogation window of the first frame particle image and the search window corresponding to the second frame particle image and calculating the normalized similarity at each moving position to form a similarity distribution plane; extracting the offset of the position with the maximum similarity in the similarity distribution plane relative to the center of the search window as the particle displacement vector of the interrogation window; using the product of the jet axis wind speed and the first compensation pulse interval as the axial reference displacement, projecting the particle displacement vector of each interrogation window onto the jet axis direction, and removing interrogation windows whose absolute value of the axial projection displacement exceeds three times the axial reference displacement; filling the missing interrogation windows after removal by linear interpolation using the particle displacement vectors of adjacent valid interrogation windows to obtain a complete particle displacement vector distribution across the entire field; dividing the particle displacement vectors of all interrogation windows by the first compensation pulse interval to obtain the initial velocity field. Detailed explanation follows: In this embodiment, the interrogation window refers to dividing the particle image into several small rectangular regions of the same size. Each interrogation window contains a set of tracer particle images and is the basic processing unit for cross-correlation analysis. The search window refers to a larger rectangular region in the second frame particle image corresponding to the interrogation window. It is used to search for the new position of the particle in the second frame after the particle has moved within the interrogation window in the first frame. The size of the search window is usually two to four times the size of the interrogation window. Normalized similarity is a standardized index that measures the degree of grayscale matching between the interrogation window and the candidate windows at the current position in the search window. The value ranges from 0 to 1, and the closer the value is to 1, the more similar the particle images in the two windows are. The similarity distribution plane is a two-dimensional distribution matrix formed after the interrogation window moves pixel by pixel within the search window and the normalized similarity of each position is calculated. The particle displacement vector is the offset of the maximum similarity position relative to the center of the search window, representing the average displacement of the tracer particles within the pulse interval in the interrogation window. The axial reference displacement is the product of the jet axis wind speed and the first compensation pulse interval, representing the theoretical displacement value of the particle moving at the jet axis wind speed along the axial direction within the pulse interval.

[0050] In this module, to calculate the overall velocity vector from particle image pairs, the first and second frame particle images are first divided into multiple interrogation windows arranged in the same manner. For each interrogation window, a search window centered at the corresponding position of the interrogation window is determined in the second frame particle image. The interrogation window is moved pixel by pixel within the search window, and at each moving position, the normalized similarity between the gray value sequence within the interrogation window and the gray value sequence of the corresponding candidate window within the search window is calculated, forming a similarity distribution plane for that interrogation window. The maximum value of the normalized similarity is found in the similarity distribution plane, and the offset of the coordinate position corresponding to this maximum value relative to the center of the search window is the particle displacement vector of that interrogation window.

[0051] Then, the axial reference displacement is obtained by multiplying the jet axis wind speed by the first compensation pulse interval. The particle displacement vectors of each interrogation window are projected onto the jet axis direction to obtain the axial projected displacement. Interrogation windows whose absolute value of axial projected displacement exceeds three times the axial reference displacement are identified as abnormal interrogation windows and removed; the particle displacement vectors of abnormal interrogation windows are not accepted. For the missing interrogation window positions after removal, linear interpolation is performed using the particle displacement vectors of the surrounding valid interrogation windows to fill in the gaps, obtaining a complete particle displacement vector distribution across the entire field. Finally, the particle displacement vectors of all valid windows are divided by the first compensation pulse interval to obtain the velocity vectors at each window position, thus forming the initial velocity field.

[0052] Furthermore, the system also includes: extracting the grayscale values ​​of each pixel position within the query window in the first frame particle image to form a query window grayscale value sequence; taking each movement position of the query window within the search window as the current matching position, extracting the grayscale values ​​of each pixel within the same spatial coverage area as the query window in the second frame particle image to form a candidate window grayscale value sequence; calculating the dot product of the query window grayscale value sequence and the candidate window grayscale value sequence as the similarity numerator of the current matching position; using the geometric mean of the sum of squares of the query window grayscale value sequence and the candidate window grayscale value sequence as the similarity denominator of the current matching position; determining the normalized similarity of the current matching position based on the similarity numerator and the similarity denominator; traversing all movement positions of the query window within the search window, and constructing the similarity distribution plane with the normalized similarity at each movement position.

[0053] In this module, the normalized similarity is calculated as follows: Let the grayscale value sequence of the query window be A, and the grayscale value sequence of the candidate window be B. The sum of the products of all elements in A and the corresponding elements in B is used as the numerator of the similarity score. The square root of the product of the sum of squares of all elements in A and the sum of squares of all elements in B is used as the denominator of the similarity score. The normalized similarity score is equal to the numerator divided by the denominator. The value of this normalized similarity score ranges from 0 to 1, with the peak value corresponding to the best matching position of the query window within the search window.

[0054] For example, the query window size is 32 pixels by 32 pixels, and the search window size is 64 pixels by 64 pixels. After a query window moves pixel by pixel within the search window and the normalized similarity is calculated, the maximum normalized similarity is 0.92. The corresponding offset relative to the center of the search window is Δx = 4.2 pixels and Δy = -1.8 pixels, which is the particle displacement vector of that query window. The first compensation pulse interval is 17μs, and the axial reference displacement = 12m / s × 17μs = 0.204mm. The particle displacement vectors of each query window are projected onto the axial direction, and abnormal query windows with axial projection displacements exceeding 3 times the axial reference displacement (0.612mm) are removed. Linear interpolation is used to fill in the missing query windows. Finally, the displacement vector is divided by 17μs to obtain the velocity vector at that point. The velocity vectors of all query windows constitute the initial velocity field.

[0055] In summary, this module performs normalized cross-correlation calculations on the acquired particle images, and after removing anomalous vectors by using the product of the jet axis wind speed and pulse interval as the reference displacement, it performs interpolation to fill in the gaps, thus obtaining the initial velocity field of the entire field. This achieves accurate conversion from particle images to velocity vector fields and automatic correction of anomalous vectors, solving the problem of a large number of anomalous vectors and data integrity loss in the cross-correlation calculation results under dust interference.

[0056] Secondary compensation module 15: Used to perform a second-level compensation on the first compensation pulse interval based on the average velocity of the entire initial velocity field, generate a second compensation pulse interval, and update the pulse interval of the dual-pulse laser.

[0057] In this embodiment, based on the obtained initial velocity field, to ensure that the particle displacement is within the optimal dynamic range of cross-correlation analysis across the entire field of view, the pulse interval needs to be adjusted a second time according to the average velocity of the entire field. In this module, the arithmetic mean of the magnitudes of the velocity vectors of all interrogation windows in the initial velocity field is first calculated to obtain the average velocity of the entire field. The preset optimal particle displacement pixel value is divided by the average velocity of the entire field to obtain the reference pulse interval. The preset optimal particle displacement pixel value is determined based on the size of the interrogation window, typically taking one-quarter to one-third of the interrogation window's side length. The average of the reference pulse interval and the first compensated pulse interval is used as the adjusted pulse interval value; that is, the second compensated pulse interval is equal to the sum of the first compensated pulse interval and the reference pulse interval divided by two. The pulse interval parameters of the dual-pulse laser are updated with the second compensated pulse interval, making the particle displacement in the subsequently acquired particle images more compatible with the dynamic range of cross-correlation analysis. This secondary compensation, based on the preceding environmental interference compensation, further adapts the pulse interval to the overall velocity level of the current flow field, improving the peak cross-correlation signal-to-noise ratio and velocity calculation accuracy.

[0058] Iterative optimization module 16: This module iteratively acquires the particle image pairs and calculates the velocity field until the deviation between the average velocity values ​​of two adjacent velocity field calculations is less than a preset deviation threshold. It then outputs the final velocity field and generates dynamic flow field visualization data. The pulse interval adaptive iterative optimization process in this step is as follows: Figure 3 As shown.

[0059] In this embodiment, based on the obtained second compensation pulse interval, the stable velocity field is gradually approximated through iterative loops, and the iteration is terminated by a quantitative convergence criterion. Finally, the results are transformed into visual graphics to solve the problems of unstable results, lack of data accuracy convergence criteria, and lack of dynamic visualization output methods in traditional single acquisition methods.

[0060] In the system provided in this application embodiment, the iterative optimization module is specifically used for: using the second compensation pulse interval as the current pulse interval, controlling the dual-pulse laser to emit light according to the current pulse interval, acquiring particle image pairs of the current round, solving the velocity field of the current round and calculating the average velocity of the entire field of the current round velocity field; reading the average velocity of the entire field of the previous round velocity field, calculating the absolute value of the difference between the average velocity of the current round velocity field and the average velocity of the entire field of the previous round velocity field, as the average velocity deviation; using twice the measurement accuracy of the first wind speed sensor sequence as the preset deviation threshold; when the average velocity deviation is greater than or equal to the preset deviation threshold, using the current round velocity... The average velocity of the entire velocity field is used to replace the average velocity of the initial velocity field. The second compensation pulse interval is then regenerated based on the replaced average velocity. This regenerated second compensation pulse interval replaces the current pulse interval. Particle image pairs are reacquired and the velocity field is calculated. When the deviation of the average velocity is less than the preset deviation threshold, iteration stops, and the velocity field calculated in the current round is taken as the final velocity field. The final velocity field is divided into multiple visualization sub-intervals according to the jet diffusion half-width. Within each visualization sub-interval, dynamic visualization data of the flow field is generated using velocity vector arrows, velocity color mapping, and streamline overlay, and output to the user interface. Detailed explanation follows: In this embodiment, the current-round velocity field refers to the velocity field obtained by acquiring particle image pairs after the laser emits light at the current pulse interval, and using the same cross-correlation solution method as the initial velocity field. The mean velocity of the current-round velocity field is the arithmetic mean of the magnitudes of the velocity vectors of all query windows in the velocity field. The mean velocity of the previous-round velocity field refers to the mean velocity of the velocity field obtained in the previous iteration. The mean velocity deviation is the absolute value of the difference between the mean velocity of the current round and the mean velocity of the previous round, used to measure the overall change in the velocity field between two iterations.

[0061] The preset deviation threshold refers to the upper limit of the deviation used to determine whether the velocity field has converged. It is set to twice the measurement accuracy of the first anemometer sequence. For example, if the sensor measurement accuracy is ±0.1 m / s, the preset deviation threshold is 0.2 m / s. Visualized sub-regions divide the final velocity field into several sub-regions based on the jet diffusion half-width. The size of each sub-region is related to the jet diffusion half-width. Velocity vector arrows are vector graphics where the arrow direction represents the velocity direction and the arrow length represents the velocity magnitude. Velocity color mapping maps the velocity magnitude to different colors using a preset color scale and overlays them onto the velocity field image. Streamline overlay display draws streamlines on the velocity field to track particle trajectories, visually displaying the flow field structure.

[0062] In this module, to obtain a stable final velocity field through iterative convergence, the second compensation pulse interval is first used as the current pulse interval. The dual-pulse laser is controlled to emit light at this interval to acquire particle image pairs for the current round. The current round velocity field is solved using the same cross-correlation solution method as the initial velocity field solution module, and the mean velocity of the current round velocity field is calculated. The mean velocity of the previous round velocity field is read, and the absolute value of the difference between the mean velocity of the current round and the mean velocity of the previous round is calculated as the mean velocity deviation. Twice the measurement accuracy of the first wind speed sensor sequence is used as the preset deviation threshold.

[0063] When the deviation of the mean velocity across the entire field is greater than or equal to a preset deviation threshold, it indicates that the velocity field has not yet converged. The mean velocity of the initial velocity field is replaced with the mean velocity of the current velocity field. Secondary compensation is then performed again according to the replaced mean velocity to generate a new second compensation pulse interval. The current pulse interval is replaced with the new second compensation pulse interval, and particle images are reacquired to calculate the velocity field, thus entering the next iteration. When the deviation of the mean velocity across the entire field is less than the preset deviation threshold, it indicates that the velocity field has converged and stabilized. Iteration stops, and the velocity field calculated in the current round is taken as the final velocity field.

[0064] After obtaining the final velocity field, it is divided into multiple visualization sub-intervals according to the value of the jet diffusion half-width. Within each visualization sub-interval, arrows represent the direction and magnitude of the velocity vector, and a preset color scale maps the velocity magnitude to different colors for overlay display. A streamline tracing algorithm is used to draw streamlines overlaid on the velocity field image to generate dynamic flow field visualization data, which is then output to the user terminal display interface for use in mine ventilation safety assessment.

[0065] For example, in the first iteration, the average velocity of the current velocity field is 6.5 m / s, while the average velocity of the previous iteration was 6.3 m / s, with a deviation of 0.2 m / s. The measurement accuracy of the first wind speed sensor sequence is ±0.1 m / s, and the preset deviation threshold is 0.2 m / s. Since the deviation equals the threshold and convergence has not occurred, the second compensation pulse interval is replaced with 6.5 m / s and regenerated, proceeding to the next iteration. In the second iteration, the average velocity of the entire field is 6.68 m / s, with a deviation of 0.18 m / s from the previous iteration's 6.5 m / s, which is less than 0.2 m / s. Therefore, the iteration stops, and the final velocity field is output. The velocity field is divided into multiple visual sub-intervals based on a jet diffusion half-width of 0.8 m, generating dynamic visualization data of the flow field containing velocity vector arrows, color mapping, and streamline overlay.

[0066] In summary, this module iteratively acquires particle image pairs and calculates the velocity field. Using twice the measurement accuracy of the wind speed sensor as the convergence criterion, it stops iterating and outputs the final velocity field when the deviation between the average values ​​of two adjacent velocity fields is less than a threshold. The module generates visualized data output using vector arrows, color mapping, and streamline overlay, thus achieving iterative convergence control of the velocity field calculation results and online intuitive presentation of the flow field state. This solves the problems of unstable results, lack of data accuracy convergence criteria, and lack of dynamic visualization output methods in traditional single acquisition methods.

[0067] The embodiments of this application, through the specific implementation methods described above, achieve the following technical effects: This application proposes a dual-pulse laser PIV control system for mine flow field measurement. It calculates the velocity gradient characteristic values ​​of the jet field by measuring the axial and radial wind speed distribution at the ventilation duct outlet and adaptively adjusts the sensor spacing. An initial pulse interval is generated using a pulse interval prediction model bound to the operating conditions. Dust backscattering interference areas are identified through signal-to-noise ratio analysis, and first-level pulse interval compensation and closed-loop image intensifier enhancement are performed. Normalized cross-correlation calculations are performed on the particle images, and outlier vectors are eliminated using axial reference displacement constraints to obtain the initial velocity field. Second-level pulse interval compensation is performed based on the average velocity across the entire field. Finally, iterative acquisition and calculation are performed until the velocity field converges, outputting the final velocity field and generating visualized data. The coordinated operation of these modules effectively solves the technical problem of measurement failure in existing mine flow field PIV systems with fixed pulse intervals and single-shot acquisition modes under conditions of drastic wind speed changes and high dust levels.

[0068] It should be noted that the order of the embodiments in this application is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The above content describes specific embodiments of this specification. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

Claims

1. A dual-pulse laser PIV control system for measuring flow fields in mines, characterized in that, The system includes: The flow field feature acquisition module is used to acquire the jet axis wind speed and jet diffusion half width of the jet field at the ventilation outlet of the tunnel face, and to calculate the characteristic value of the jet field velocity gradient. The pulse interval prediction module is used to input the characteristic value of the jet field velocity gradient into the pulse interval prediction model pre-bound to the mine ventilation condition to generate the initial pulse interval between the first pulse and the second pulse of the dual-pulse laser. The environmental interference compensation module is used to identify the backscattering interference area based on the signal-to-noise ratio of the tracer particle image at each location in the jet field space. Within the backscattering interference area, the initial pulse interval is used as a reference and the flow rate of the optical path sealing purge airflow is used as a constraint to perform the first-level compensation on the initial pulse interval, generate the first compensation pulse interval, and simultaneously turn on the high-gain image intensifier. The initial velocity field calculation module is used to acquire particle image pairs under dual-pulse laser illumination controlled by the first compensation pulse interval, and calculate the initial velocity field with the jet axis wind speed as a constraint. The secondary compensation module is used to perform a second-level compensation on the first compensation pulse interval based on the average velocity of the entire initial velocity field, generate a second compensation pulse interval, and update the pulse interval of the dual-pulse laser. The iterative optimization module is used to iteratively acquire the particle image pairs and solve the velocity field until the deviation of the mean velocity of the entire field between two adjacent solutions is less than a preset deviation threshold, output the final velocity field and generate dynamic visualization data of the flow field.

2. The dual-pulse laser PIV control system for mine flow field measurement according to claim 1, characterized in that, Obtain the jet axis velocity and jet spread half-width at the ventilation outlet of the tunnel face, and calculate the characteristic values ​​of the jet field velocity gradient, including: A first wind speed sensor sequence is arranged on the axis of the air outlet of the air duct to collect the axial wind speed distribution along the axis direction, and the wind speed at the location where the maximum value of the axial wind speed distribution is located is taken as the wind speed of the jet axis. A second wind speed sensor sequence is arranged on a radial section perpendicular to the air outlet axis of the air duct to collect radial wind speed distribution. The radial distance corresponding to when the speed value in the radial wind speed distribution drops to half of the wind speed of the jet axis is taken as the jet diffusion half width. The ratio of the jet axis wind speed to the jet diffusion half-width is used as the characteristic value of the jet field velocity gradient, and the spacing between adjacent measuring points of the first wind speed sensor sequence and the second wind speed sensor sequence is dynamically adjusted according to the value of the jet diffusion half-width, wherein the spacing between adjacent measuring points is positively correlated with the jet diffusion half-width.

3. The dual-pulse laser PIV control system for mine flow field measurement according to claim 2, characterized in that, Dynamically adjusting the spacing between adjacent measuring points of the first and second wind speed sensor sequences according to the value of the jet diffusion half-width includes: Determine the positive correlation ratio factor between the jet diffusion half-width and the distance between adjacent measuring points, wherein the positive correlation ratio factor is calibrated by the diameter of the air outlet of the duct; The distance between target measuring points is obtained by multiplying the real-time value of the jet diffusion half-width by the positive correlation scaling factor. The target measurement point spacing is sent to the stepping drive mechanism of the first wind speed sensor sequence and the second wind speed sensor sequence respectively to adjust the physical spacing between adjacent wind speed sensors; The jet diffusion half-width is used as both the axial and radial coverage boundaries of the sensor sequence, constraining the end positions of the first wind speed sensor sequence along the axial direction and the end positions of the second wind speed sensor sequence along the radial direction, respectively.

4. The dual-pulse laser PIV control system for mine flow field measurement according to claim 1, characterized in that, The steps for constructing the pulse interval prediction model include: Collect historical jet field velocity gradient characteristic value records of the tunneling face under different mine ventilation conditions, and collect historical optimal pulse interval records corresponding to each historical jet field velocity gradient characteristic value record; The historical jet field velocity gradient characteristic value record data and the historical optimal pulse interval record data constitute a sample dataset, wherein the historical optimal pulse interval record data is the double pulse interval calibration value that maximizes the cross-correlation peak value under each mine ventilation condition; The sample dataset is divided into multiple condition subsets according to the type of mine ventilation conditions, wherein each condition subset corresponds to a type of mine ventilation condition. Within each subset of operating conditions, the historical jet field velocity gradient characteristic value record data is used as input, and the historical optimal pulse interval record data is used as supervision label; For each of the aforementioned working condition subsets, an initial prediction function is constructed based on the inverse proportional regression relationship between the historical jet field velocity gradient characteristic value record data and the historical optimal pulse interval record data, and the proportional coefficient of the initial prediction function is used as the parameter to be fitted. Using the supervision label as the fitting target, least squares fitting is performed on the parameters to be fitted in each of the working condition subsets until the sum of squared fitting residuals is less than the fitting convergence threshold, thereby obtaining the working condition binding prediction function corresponding to each of the mine ventilation working conditions, and the pulse interval prediction model is constructed using all the working condition binding prediction functions.

5. The dual-pulse laser PIV control system for mine flow field measurement according to claim 1, characterized in that, Based on the signal-to-noise ratio of tracer particle images at various locations in the jet field, backscattering interference regions are identified, including: The first frame and the second frame of tracer particle images under dual-pulse laser illumination are acquired. The ratio of the standard deviation of gray level to the mean gray level of each pixel in the first frame and the second frame of tracer particle images is calculated as the signal-to-noise ratio at each position. The arithmetic mean of the signal-to-noise ratio of all pixels within the entire field of view is calculated to obtain the mean signal-to-noise ratio of the entire field of view. Pixels with a signal-to-noise ratio lower than the average signal-to-noise ratio of the entire field of view are marked as low signal-to-noise ratio pixels; Using the jet diffusion half-width as the spatial aggregation radius, density clustering is performed on the low signal-to-noise ratio pixels, and the connected regions formed after clustering are identified as backscattering interference regions.

6. The dual-pulse laser PIV control system for mine flow field measurement according to claim 1, characterized in that, Within the backscattering interference region, based on the initial pulse interval and constrained by the optical path sealing purge airflow velocity, a first-stage compensation is performed on the initial pulse interval to generate a first compensated pulse interval, and a high-gain image intensifier is simultaneously activated, including: Calculate the average signal-to-noise ratio of all pixels within the backscattering interference region to obtain the average signal-to-noise ratio of the interference region; The interference intensity coefficient is the ratio of the average signal-to-noise ratio of the interference region to the average signal-to-noise ratio of the entire field of view, wherein the interference intensity coefficient is less than 1. Based on the airflow velocity of the optical path sealing purge airflow and the wind speed of the jet axis, the basic compensation ratio is determined, and combined with the interference intensity coefficient, the corrected compensation ratio is obtained. Based on the initial pulse interval and the correction compensation ratio, the pulse interval compensation amount is determined, and the initial pulse interval and the pulse interval compensation amount are added together to generate the first compensated pulse interval; During the emission of the first and second pulses by the dual-pulse laser, a gated trigger signal is sent to the high-gain image intensifier, and the high-gain image intensifier is simultaneously activated within the backscattering interference region.

7. The dual-pulse laser PIV control system for mine flow field measurement according to claim 6, characterized in that, During the emission of the first and second pulses by the dual-pulse laser, a gated trigger signal is sent to the high-gain image intensifier, and after the high-gain image intensifier is synchronously activated within the backscattering interference region, the process further includes: Acquire compensated tracer particle image pairs enhanced by the high-gain image intensifier, and calculate the average signal-to-noise ratio of the compensated tracer particle image pairs within the backscattering interference region; The average signal-to-noise ratio after compensation is compared with the average signal-to-noise ratio across the entire field of view. If the average signal-to-noise ratio after compensation is lower than the average signal-to-noise ratio across the entire field of view, then the flow rate of the optical path sealing purge airflow is increased step by step by five percent of the current optical path sealing purge airflow rate. Wait for the flow rate of the optical path sealing purge airflow to stabilize at the increased target value, and use the stabilized optical path sealing purge airflow rate as a constraint again. Combine the interference intensity coefficient and the jet axis wind speed to update the correction compensation ratio and regenerate the first compensation pulse interval. The dual-pulse laser is controlled to emit light again with the newly generated first compensation pulse interval, the high-gain image intensifier is turned on simultaneously, new compensated tracer particle image pairs are acquired, and the average signal-to-noise ratio after compensation is calculated again until the average signal-to-noise ratio after compensation is greater than or equal to the average signal-to-noise ratio of the entire field of view.

8. The dual-pulse laser PIV control system for mine flow field measurement according to claim 1, characterized in that, Acquire particle image pairs under dual-pulse laser illumination controlled by the first compensated pulse interval, and calculate the initial velocity field using the jet axis wind speed as a constraint, including: The first and second frame particle images in the particle image pair are respectively divided into multiple query windows arranged in the same way; For each query window, between the query window in the first frame particle image and the search window corresponding to the second frame particle image, the query window is moved pixel by pixel and the normalized similarity at each moving position is calculated to form a similarity distribution plane. The offset of the position of the maximum similarity in the similarity distribution plane relative to the center of the search window is extracted and used as the particle displacement vector of the query window; Using the product of the jet axis wind speed and the first compensation pulse interval as the axial reference displacement, the particle displacement vector of each interrogation window is projected onto the jet axis direction, and interrogation windows whose absolute value of axial projection displacement exceeds three times the axial reference displacement are eliminated. For the empty query windows after removal, linear interpolation is used to fill them in using the particle displacement vectors of adjacent valid query windows to obtain the complete particle displacement vector distribution of the entire field; The initial velocity field is obtained by dividing the particle displacement vector of all query windows by the first compensation pulse interval.

9. The dual-pulse laser PIV control system for mine flow field measurement according to claim 8, characterized in that, For each query window, between the query window in the first frame particle image and the search window corresponding to the second frame particle image, the query window is moved pixel by pixel and the normalized similarity at each moving position is calculated to form a similarity distribution plane, including: Extract the grayscale value of each pixel position in the query window from the first frame particle image to form a grayscale value sequence of the query window; Taking each movement position of the query window within the search window as the current matching position, extract the gray values ​​of each pixel within the same spatial coverage area as the query window in the second frame particle image to form a candidate window gray value sequence. Calculate the dot product between the query window grayscale value sequence and the candidate window grayscale value sequence, and use it as the similarity numerator for the current matching position; The geometric mean of the sum of squares of the gray values ​​of the query window and the gray values ​​of the candidate window is used as the denominator of the similarity score for the current matching position. Based on the similarity numerator and the similarity denominator, the normalized similarity of the current matching position is determined; The query window is traversed through all its movement positions within the search window, and the normalized similarity at each movement position is used to construct the similarity distribution plane.

10. The dual-pulse laser PIV control system for mine flow field measurement according to claim 1, characterized in that, Iteratively acquire the particle image pairs and calculate the velocity field until the deviation of the mean velocity across the entire field between two adjacent calculations is less than a preset deviation threshold. Output the final velocity field and generate dynamic flow field visualization data, including: Using the second compensated pulse interval as the current pulse interval, the dual-pulse laser is controlled to emit light according to the current pulse interval, the particle image pairs of the current round are acquired, the velocity field of the current round is solved, and the average velocity of the entire field of the current round is calculated. Read the average speed of the entire field in the previous round of the speed field, and calculate the absolute value of the difference between the average speed of the current round of the speed field and the average speed of the entire field in the previous round of the speed field, as the deviation of the average speed of the entire field. The preset deviation threshold is set to twice the measurement accuracy of the first wind speed sensor sequence. When the deviation of the mean velocity across the entire field is greater than or equal to the preset deviation threshold, the mean velocity across the entire field of the initial velocity field is replaced with the mean velocity across the entire field of the current round velocity field. The second compensation pulse interval is regenerated according to the replaced mean velocity across the entire field. The current pulse interval is replaced with the regenerated second compensation pulse interval. Particle image pairs are reacquired and the velocity field is solved. When the mean deviation of the overall velocity is less than the preset deviation threshold, the iteration stops and the velocity field calculated in the current round is taken as the final velocity field. The final velocity field is divided into multiple visualization sub-intervals according to the value of the jet diffusion half-width. Within each visualization sub-interval, the dynamic visualization data of the flow field is generated by displaying velocity vector arrows, velocity color mapping, and streamline overlay, and then output to the user terminal display interface.

Images