阅读说明：本技术 一种自适应调整时间间隔的流场速度测量系统 (Flow field velocity measuring system capable of adaptively adjusting time interval ) 是由 杨华 尹周平 肖立 欧阳振兴 卢锦 陶成川 于 2019-10-31 设计创作，主要内容包括：本发明属于数字图像采集与处理领域,并公开了一种自适应调整时间间隔的流场速度测量系统,其包括粒子发生器、双脉冲激光器及相互通信的分幅视觉图像采集装置与图像处理子系统,粒子发生器用于在观测室中播撒流场粒子,双脉冲激光器用于发出脉冲激光以照射在流场粒子的表面,经流场粒子表面反射的光进入分幅视觉图像采集装置中予以分时采集获得两幅图像；图像处理子系统用于根据分幅视觉图像采集装置采集的两幅图像进行速度预测,并基于预测的速度计算出下一时刻的最优时间间隔,分幅视觉图像采集装置则在下一时刻根据最优时间间隔进行两幅图像的采集。本发明可在流场速度测量过程中实现时间间隔的自适应调整,具体测量精度高、测量方便等优点。(The invention belongs to the field of digital image acquisition and processing, and discloses a flow field speed measuring system capable of adaptively adjusting time intervals, which comprises a particle generator, a double-pulse laser, a framing visual image acquisition device and an image processing subsystem, wherein the framing visual image acquisition device and the image processing subsystem are communicated with each other; the image processing subsystem is used for predicting the speed according to the two images acquired by the framing visual image acquisition device and calculating the optimal time interval at the next moment based on the predicted speed, and the framing visual image acquisition device acquires the two images at the next moment according to the optimal time interval. The invention can realize the self-adaptive adjustment of the time interval in the flow field speed measurement process, and has the advantages of high specific measurement precision, convenient measurement and the like.)

1. A flow field speed measuring system capable of adaptively adjusting time intervals is characterized by comprising a particle generator (1), a double-pulse laser (2), a framing visual image acquisition device (3) and an image processing subsystem (4), wherein the framing visual image acquisition device and the image processing subsystem are communicated with each other, the particle generator (1) is used for scattering flow field particles in an observation room, the double-pulse laser (2) is used for emitting pulse laser to irradiate the surfaces of the flow field particles, and light reflected by the surfaces of the flow field particles enters the framing visual image acquisition device (3) to be acquired in a time-sharing mode to obtain two images; the image processing subsystem (4) is used for predicting the speed according to the two images acquired by the framing visual image acquisition device (3) and calculating the optimal time interval at the next moment based on the predicted speed, and the framing visual image acquisition device acquires the two images according to the optimal time interval at the next moment so as to realize the self-adaptive adjustment of the time interval in the flow field speed measurement process.

2. The flow field velocity measurement system for adaptively adjusting time interval according to claim 1, wherein the framing vision image acquisition device (3) comprises an optical lens (31), an optical component and an FPGA, which are sequentially arranged, the FPGA is connected with a trigger interface (38) and an image transmission interface (39), the trigger interface (38) is connected with the double pulse laser (2), the optical component comprises a half-transmitting and half-reflecting prism (36), a full-reflecting prism (35), a first image sensor (34) and a second image sensor (37), a part of light reflected by the surface of the flow field particle enters the optical lens (31), the other part of light is reflected into the first image sensor (34) through the half-transmitting and half-reflecting prism (36), and the other part of light is reflected into the second image sensor (37) through the full-reflecting prism (35) after being transmitted through the half-transmitting and half-reflecting prism (36), therefore, two images are acquired by the first image sensor (34) and the second image sensor (37), and the two acquired images are transmitted to the FPGA and then transmitted to the image processing subsystem for image processing through the image transmission interface (39).

3. The flow field velocity measurement system for adaptively adjusting time intervals according to claim 1, wherein the image processing subsystem (4) comprises an image receiving unit, an image transmission unit and an image processing unit which are connected in sequence, wherein the image receiving unit comprises two pairs of image transmission interfaces for receiving two images acquired by the framing visual image acquisition device; the image transmission unit comprises a main chip FPGA, a memory card and a hard disk, wherein the memory card and the hard disk are in communication connection with the main chip FPGA, the main chip FPGA is in communication connection with the image receiving unit, and the Switch chip is in communication connection with the image processing unit through an SRIO protocol.

4. The adaptive time interval adjusting flow field velocity measurement system according to claim 3, wherein the image processing unit comprises two DSP processors for flow field velocity field estimation and next time flow field velocity field prediction, respectively.

5. The flow field velocity measurement system for adaptively adjusting time intervals according to claim 1, wherein the double pulse laser (2) is formed by combining two separate pulse lasers, and a trigger interface of the double pulse laser is connected with a trigger interface of the framing visual image acquisition device and is controlled by the framing visual image acquisition device.

6. The adaptive time interval adjusted flow field velocity measurement system according to claim 1, wherein the particle generator (1) is a fluidized bed type particle generator, the gas flow enters from the bottom of the generator to form bubbles, and the bubbles break after reaching the particle interface to carry the particles away from the top of the generator.

7. The flow field velocity measurement system for adaptively adjusting time interval according to claim 6, wherein an air flow branch is arranged beside the particle generator (1), and the air flow branch is respectively communicated with the air paths at the bottom and the top of the generator.

8. The system for measuring the flow field velocity with adaptive time interval adjustment according to claim 1, wherein the image processing subsystem specifically adopts the following processes to realize the prediction of the velocity and the calculation of the optimal time interval at the next moment:

s1 specifies an initial time interval Δ t₀Continuously acquiring n pairs of images at n moments by using a framing visual image acquisition device, calculating n velocity fields, and taking the average velocity value of each velocity field to form an initial velocity sequence (u)_i,v_i) Wherein i is 1,2, n-1, n;

s2 performs velocity prediction using the following equation:

wherein the content of the first and second substances,

s3 calculates an optimal time interval for the next time based on the predicted speed:

wherein, Δ t_n+1Is t_n+1The optimal time interval for a time instant, M is the sampling window size.

9. The flow field velocity measurement system for adaptively adjusting a time interval according to claim 8, wherein the fast estimation algorithm of the high-speed flow field velocity based on the correlation filtering in S1 calculates the corresponding velocity field, specifically:

s11 determining a score matrix SM for a sampling region on the current image acquired by the framing vision image acquisition device_CFWill score the matrix SM_CFConverting the current time domain into a time domain, selecting a maximum response position as displacement, and calculating according to the displacement and an optimal time interval corresponding to the current time to obtain a corresponding flow field speed;

s12 repeats step S11 to obtain the flow field velocities corresponding to all the sampling regions on the image at the current time, and all the flow field velocities constitute the velocity field at the current time.

Technical Field

The invention belongs to the field of digital image acquisition and processing, and particularly relates to a flow field velocity measuring system capable of adaptively adjusting time intervals.

Background

The high-speed vision measurement system is widely applied to measurement of a flow field velocity field due to the advantages of non-contact, no interference, transient state, large range and the like, an image acquisition device commonly used for flow field measurement is divided into a frame-crossing camera and a high-speed camera, the time interval of the frame-crossing camera can reach nanosecond level, the pixel resolution is high, but the frame rate is low; the high-speed camera has a high frame rate, and the frame rate can be improved by lower resolution, but the time interval can only reach microsecond level, and the time interval between the two is a fixed value set in advance, and is difficult to apply in a time-varying flow field.

And the flow field estimation can be related to flow field measurement in the flow field velocity field, the traditional flow field estimation algorithm generally adopts a fast Fourier transform cross-correlation algorithm which is mainly realized on MATLAB, and because the MATLAB has a nuclear acceleration function, although the phenomenon of high speed is embodied in the off-line use process, the speed of the C code can be greatly reduced due to the limitation of the window cross-correlation, and the requirement of the flow field velocity estimation instantaneity can not be met. In addition, most of the traditional flow field estimation algorithms adopt a fixed time interval delta t, if the time interval delta t is too small, the particle displacement is too small, the influence of peak noise is large, the error of a calculation result is too large, if the time interval delta t is too large, the calculation result is wrong if the particles slide out of a calculation window, and therefore the time interval needs to be adjusted in real time.

Based on this, research and design are needed to obtain a flow field velocity measurement system capable of adjusting time intervals in real time, so as to measure the flow field velocity and meet the requirements of real-time performance and accuracy.

Disclosure of Invention

Aiming at the defects or improvement requirements of the prior art, the invention provides a flow field speed measuring system capable of adaptively adjusting time intervals, so that the technical problems that the time intervals are not adjustable, the precision is not high when a high-speed time-varying flow field is measured and real-time measurement cannot be carried out in the traditional flow field measuring system are solved.

In order to achieve the above purpose, the present invention provides a flow field velocity measurement system with a time interval adaptively adjusted, which includes a particle generator, a double-pulse laser, and a framing visual image acquisition device and an image processing subsystem which are communicated with each other, wherein the particle generator is used for scattering flow field particles in an observation room, the double-pulse laser is used for emitting pulse laser to irradiate the surface of the flow field particles, and light reflected by the surface of the flow field particles enters the framing visual image acquisition device to be acquired in a time-sharing manner to obtain two images; the image processing subsystem is used for predicting the speed according to the two images acquired by the framing visual image acquisition device and calculating the optimal time interval at the next moment based on the predicted speed, and the framing visual image acquisition device acquires the two images according to the optimal time interval at the next moment so as to realize the self-adaptive adjustment of the time interval in the flow field speed measurement process.

Preferably, the framing visual image acquisition device comprises an optical lens, an optical assembly and an FPGA which are sequentially arranged, the FPGA is connected with a trigger interface and an image transmission interface, the trigger interface is connected with the double-pulse laser, the optical assembly comprises a semi-transparent semi-reflective prism, a total-reflective prism, a first image sensor and a second image sensor, a part of light reflected by the surface of the flow field particle enters the optical lens and is reflected to the first image sensor through the semi-transparent semi-reflective prism, the other part of light is transmitted by the semi-transparent semi-reflective prism and is reflected to the second image sensor through the total-reflective prism, so that two images are acquired by the first image sensor and the second image sensor, the two acquired images are transmitted to the FPGA and are transmitted to the image processing subsystem through the image transmission interface for image processing.

Preferably, the image processing subsystem comprises an image receiving unit, an image transmitting unit and an image processing unit which are connected in sequence, wherein the image receiving unit comprises two pairs of image transmitting interfaces for receiving two images acquired by the framing visual image acquisition device; the image transmission unit comprises a main chip FPGA, a memory card and a hard disk, wherein the memory card and the hard disk are in communication connection with the main chip FPGA, the main chip FPGA is in communication connection with the image receiving unit, and the Switch chip is in communication connection with the image processing unit through an SRIO protocol.

Preferably, the image processing unit includes two DSP processors, which are respectively used for estimating the flow field velocity field and predicting the flow field velocity field at the next time.

Preferably, the double-pulse laser is formed by combining two independent pulse lasers, and a trigger interface of the double-pulse laser is connected with a trigger interface of the framing visual image acquisition device and is controlled by the framing visual image acquisition device.

As a further preferred, the particle generator is a fluidized bed type particle generator, the gas flow enters from the bottom of the generator to form bubbles, and the bubbles break after reaching the particle interface to carry the particles away from the top of the generator.

Preferably, the particle generator is provided with an airflow branch at its side, and the airflow branch is respectively communicated with the air passages at the bottom and top of the generator.

Preferably, the image processing subsystem specifically adopts the following processes to realize the speed prediction and the calculation of the optimal time interval at the next moment:

s1 specifies an initial time interval Δ t₀Continuously acquiring n pairs of images at n moments by using a framing visual image acquisition device, calculating n velocity fields, and taking the average velocity value of each velocity field to form an initial velocity sequence (u)_i,v_i) Wherein i is 1,2, n-1, n;

s2 performs velocity prediction using the following equation:

wherein the content of the first and second substances,

is according to t_nVelocity of time of day

Predicted t_n+1The speed of the state at the moment in time,

is t_nVelocity vector (u) of time_n,v_n)，A_nIs a state transition matrix, B_nIs a control matrix, b_nIs a control vector;

s3 calculates an optimal time interval for the next time based on the predicted speed:

wherein, Δ t_n+1Is t_n+1The optimal time interval for a time instant, M is the sampling window size.

Preferably, in S1, the fast estimation algorithm of the high-speed flow field velocity based on the correlation filtering calculates the corresponding velocity field, specifically:

s11 determining a score matrix SM for a sampling region on the current image acquired by the framing vision image acquisition device_CFWill score the matrix SM_CFConverting the current time domain into a time domain, selecting a maximum response position as displacement, and calculating according to the displacement and an optimal time interval corresponding to the current time to obtain a corresponding flow field speed;

s12 repeats step S11 to obtain the flow field velocities corresponding to all the sampling regions on the image at the current time, and all the flow field velocities constitute the velocity field at the current time.

Generally, compared with the prior art, the above technical solution conceived by the present invention mainly has the following technical advantages:

1. according to the invention, by designing the flow field speed measurement system comprising the particle generator, the double-pulse laser, the framing visual image acquisition device and the image processing subsystem, the self-adaptive adjustment of the time interval in the flow field speed measurement process can be realized, the speed estimation precision aiming at the unsteady flow field can be obviously improved, and the dynamic range of the flow field measurement is improved.

2. The invention designs the framing visual image acquisition device comprising the semi-transparent semi-reflective prism, the total reflective prism and the double image sensor, and connects the framing visual image acquisition device with the double pulse laser and the image processing subsystem, and can obtain two adjacent frames of images with variable time intervals by controlling the time sequence relation between the light-emitting time of the double pulse laser and the exposure time of the double image sensor.

3. The invention can estimate and predict the flow field velocity field of the image acquired by the framing visual image acquisition device by designing the image processing system comprising the FPGA processor and the two DSP processors connected with the FPGA processor, and sends the predicted time interval back to the framing visual image acquisition device so as to realize the real-time adjustment of the image acquisition interval.

Drawings

Fig. 1 is a schematic structural diagram of a flow field velocity measurement system for adaptively adjusting time intervals according to an embodiment of the present invention;

FIG. 2 is a schematic structural diagram of a framing visual image acquisition device according to an embodiment of the present invention;

FIG. 3 is a timing diagram of exposure time and pulsed laser time for image sensor one and image sensor two;

FIG. 4 is a schematic structural diagram of an image processing subsystem provided in an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a particle generator according to an embodiment of the present invention;

fig. 6 is a schematic diagram of the generation of circulant matrices in correlation filtering, which is implemented in accordance with the present invention, illustrating only the vertical offset, and the horizontal offset, which occurs in the same manner;

fig. 7 is an overall flow of correlation filtering velocity estimation implemented in accordance with the present invention.

The same reference numbers will be used throughout the drawings to refer to the same or like elements or structures, wherein:

the system comprises a particle generator 1, a double-pulse laser 2, a framing vision image acquisition device 3, an image processing subsystem 4, an observation room 5, an air flow 11, a fluidized bed 12, a metal ceramic plate 13, a reversing valve 14, a valve 15, a ball valve 16, a pressure gauge 17, an optical lens 31, an image extender 32, an optical filter 33, an image sensor I34, a total reflection prism 35, a semi-transparent semi-reflection prism 36, an image sensor II 37, a trigger interface 38 and an image transmission interface 39.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

As shown in fig. 1, an embodiment of the present invention provides a flow field velocity measurement system capable of adaptively adjusting a time interval, which includes a particle generator 1 for scattering flow field particles, a double-pulse laser 2 for providing a flow field light source, a framing visual image acquisition device 3 and an image processing subsystem 4, wherein the framing visual image acquisition device 3 and the image processing subsystem 4 are in communication with each other, the particle generator 1 is configured to scatter the flow field particles in an observation chamber 5, the double-pulse laser 2 is configured to emit pulse laser to irradiate the surface of the flow field particles, and light reflected by the surface of the flow field particles enters the framing visual image acquisition device 3 to be acquired so as to obtain two images; the image processing subsystem 4 is used for predicting the speed according to the two images acquired by the framing visual image acquisition device 3, calculating the optimal time interval at the next moment based on the predicted speed, and acquiring the two images according to the optimal time interval at the next moment by the framing visual image acquisition device, so that the self-adaptive adjustment of the time interval is realized in the flow field speed measurement process.

As shown in fig. 2, the framing visual image acquisition device 3 includes an optical lens 31, an optical component and an FPGA, which are sequentially arranged, the FPGA is connected to a trigger interface 38 and an image transmission (Camera Link) interface 39, the optical component includes a half-transmitting and half-reflecting prism 36, a total-reflecting prism 35, a first image sensor 34 and a second image sensor 37, a part of light reflected by the surface of the flow field particle enters the optical lens 31, and is reflected into the first image sensor 34 by the half-transmitting and half-reflecting prism 36, and another part of light is transmitted by the half-transmitting and half-reflecting prism 36 and is reflected into the second image sensor 37 by the total-reflecting prism 35, so that two images are acquired by the first image sensor 34 and the second image sensor 37, and the two acquired images are transmitted to the FPGA and are transmitted to the image processing subsystem for image processing by the image transmission interface 39. An image extender 32 is connected to an end of the optical lens 31 adjacent to the half-mirror 36 in order to extend the flange distance, and an optical filter is connected to an end of the image extender 32 adjacent to the half-mirror 36 in order to filter foreign light except for the reflected green light, reduce interference of external environment light, and create a dark background.

Specifically, the first image sensor 34, the second image sensor 37, the trigger interface 38, the image transmission interface 39 and the FPGA are integrated on a hardware board card, the trigger interface 38 is connected with the double-pulse laser, and the image transmission interface 39 is connected with the image processing subsystem. After two images at a certain time interval are acquired by the framing visual image acquisition device, the two images are transmitted to the image processing subsystem through a Camera Link protocol, wherein the Camera Link protocol is a high-speed protocol specially aiming at industrial visual image data transmission and comprises a power supply signal, an image data signal, a Camera control signal and a serial communication signal. The Camera Link interface has three configurations, namely a Base mode, a Medium mode and a Full mode, and the Full mode needs to be selected for transmission due to the huge image data volume, so that the highest transmission speed reaches 7.8 Gbps.

Fig. 3 is a time sequence relationship between exposure times of the first image sensor and the second image sensor and the double-pulse laser, where the light emitting time of the laser 1 is in the first exposure ending interval of the image sensor, the light emitting time of the laser 2 is in the second exposure starting interval of the image sensor, and since the framing visual image acquisition device is in a dark environment and the pulse width of the pulse laser is narrow in other time periods, it can be considered that the images acquired by the first image sensor and the second image sensor are images of the light emitting time of the double-pulse laser, and the time interval is the time interval of the pulse laser emitted by the double-pulse laser. The FPGA acquires two adjacent frames of images with a certain time interval by controlling the time sequence relation between the exposure time of the image sensor I and the exposure time of the image sensor II and the pulse laser emitting time of the double-pulse laser.

As shown in fig. 4, the image processing subsystem 4 includes an image receiving unit, an image transmitting unit and an image processing unit, which are connected in sequence, wherein the image receiving unit includes two pairs of image transmitting interfaces for receiving two images acquired by the framing visual image acquisition device; the image transmission unit takes an FPGA (field Programmable Gate array) as a main chip, is assisted by a memory card (DDR3) and a hard disk (SSD) to perform image caching and transmission, and realizes interconnection of the FPGA and a plurality of DSPs through a sub-chip (Switch) to estimate a flow field speed field and predict the speed of the next frame of image. Specifically, the image transmission unit comprises a main chip FPGA, a memory card and a hard disk, wherein the memory card and the hard disk are in communication connection with the main chip FPGA, the main chip FPGA is in communication connection with the image receiving unit and used for receiving two images, and the main chip FPGA is also in communication connection with the image processing unit through a Switch chip and used for transmitting the two received images to the image processing unit. Specifically, the image processing unit includes two dsp (digital Signal processor) processors, which are respectively used for estimating the flow field velocity field and predicting the flow field velocity field at the next time. The framing visual image acquisition device acquires two continuous adjacent frames of images at a certain time interval and transmits the two continuous adjacent frames of images to the image processing subsystem through a CameraLink interface, an FPGA and a DSP in the image processing subsystem are interconnected through a Switch chip, image data are transmitted to the DSP through an SRIO protocol, the DSP estimates the flow field speed by processing the two images, substitutes the calculated flow field speed into a speed prediction algorithm to update the speed field, predicts the flow field speed of the next period at the same time, adjusts the time interval of the next moment of the predicted flow field speed, feeds back time interval parameters to the framing visual image acquisition device through serial port communication, and adjusts the time interval so as to improve the measurement precision and the dynamic range of the flow field speed.

Because the traditional flow field estimation algorithm is complex in calculation and time-consuming when the calculation is carried out on a general-purpose computer, the requirement for measuring the flow field speed in real time cannot be met, and the FPGA can accelerate certain parts in the algorithm in parallel based on the characteristics of parallel calculation to accelerate the calculation process of the algorithm. The image processing subsystem adopts a mode of interconnecting the FPGA and the two DSPs to improve the image processing speed, the FPGA and the DSPs adopt an SRIO (Serial Rapid IO) protocol for mutual communication, an SRIO data exchange chip is adopted as a data center for connecting the FPGA and the DSPs, and the image processing subsystem has good processing expansion capacity by adopting the design of the processing system with the distributed processor architecture.

Further, the double-pulse laser 2 is formed by combining two independent pulse lasers, the two independent pulse lasers are combined into a unit, two beams of high-energy narrow-pulse-width pulse lasers can be continuously emitted at an extremely short time interval, the pulse laser wavelength is 532nm, each laser is controlled by two paths of pulse signals, a trigger interface of the double-pulse laser is connected with a trigger interface of the framing visual image acquisition device, and the framing visual image acquisition device is used for controlling the trigger interfaces.

As shown in fig. 5, the particle generator 1 is a fluidized bed type particle generator, and an air flow 11 (compressed air) enters the generator from the bottom of the generator to form air bubbles in the particle bed material, and the air bubbles break after reaching the particle interface to carry the particles away from the top of the generator, thereby effectively solving the agglomeration effect of the particles. Specifically, the fluidized bed comprises a fluidized bed 12, a metal ceramic plate 13 arranged in the fluidized bed, an air flow inlet communicated with the bottom of the fluidized bed and an air flow outlet communicated with the top of the fluidized bed, solid particle particles are placed on the metal ceramic plate 13, air flow 11 enters a generator from the air flow inlet at the bottom of the generator and forms air bubbles in a solid particle bed material, and the air bubbles are broken after reaching the interface of the solid particle particles, so that the solid particle particles are taken away by the air flow from the air flow outlet at the top of the generator. In order to adjust and control the ratio of the gas flow entering the fluidized bed 12 to the total gas flow, a diverter valve 14 is arranged on the gas flow inlet, and a valve 15 is connected to the gas flow outlet. To control the particle generator on and off, a ball valve 16 is connected to the air outflow. In order to monitor the current pressure, the air outflow path is also connected with a pressure gauge 17. In addition, the particle generator 1 is further provided with an airflow branch at its side, which is respectively communicated with the air paths (i.e. airflow inlet path and airflow outlet path) at the bottom and top of the generator, i.e. the airflow is divided into two paths, one path is led to the fluidized bed and the other path is used as a bypass, thereby realizing the adjustable concentration of particle scattering.

When the device is used for measurement, a sealed lightless space is formed between the framing visual image acquisition device and the observation chamber, when the inflow of compressed air (airflow) flows from the inlet of the observation chamber, the particle generator is started, the airflow forms bubbles in the solid particle bed material, the bubbles break after reaching the solid particle interface, so that the solid particles are taken away by the airflow, the particles are scattered in the observation chamber along with the compressed air, the framing visual image acquisition device sends a trigger signal through the trigger interface to control the double-pulse laser to send pulse laser, meanwhile, the pulse laser illuminates the flow field area, the light reflected by the particle surface enters the light splitting area through the optical lens 1, the light path is split into two parts by the combination of the semi-transparent semi-reflecting prism and the total reflecting prism, the two parts irradiate onto the image sensor I and the image sensor II, the image sensor I and the image sensor II convert the optical signals into electric signals and then are collected and, the left image and the right image are transmitted to an image processing subsystem through a CameraLink transmission interface to be subjected to image processing operation, meanwhile, the image processing subsystem carries out flow field speed field estimation operation and speed prediction estimation according to the two images transmitted by the framing visual image acquisition device, then the optimal time interval at the next moment is calculated according to the predicted speed, and finally the optimal time interval is transmitted back to the framing visual image acquisition device to realize time interval self-adaptive adjustment.

After receiving the two pictures sent by the framing visual image acquisition device, the image processing subsystem performs preprocessing on the two pictures by the FPGA, such as processing operations of Gaussian filtering, image registration and the like, removes image noise, aligns the two pictures, transmits the two pictures to one of the DSP processors through an SRIO protocol after preprocessing, and performs related filtering calculation (speed field estimation) on the two pictures by the DSP. The image processing subsystem specifically adopts the following processes to realize the prediction of speed and the calculation of the optimal time interval at the next moment:

the first is the initialization of the velocity sequence, i.e. the initial acquisition time interval Δ t for a given framing vision image acquisition device₀(namely the time interval for acquiring two images at the same time), continuously acquiring n pairs of images at n times, calculating n velocity fields by using a high-speed flow field velocity fast estimation algorithm (CF-PIV) based on correlation filtering, and forming an initial velocity sequence (u) containing n velocity vectors by taking the average velocity values of the velocity fields_i,v_i) Wherein i is 1,2, n-1, n;

then, at t_nFlow field speed prediction based on a Kalman predictor and calculation of an optimal time interval are carried out at the moment, and the principle is as follows:

the two basic equations of the kalman predictor are the state equation and the observation equation:

w_n+1＝A_nw_n+B_nb_n+q_n

W_n+1＝H_n+1w_n+1+d_n+1

wherein w_n+1Is at time t_n+1Predicted speed of (W)_n+1Using a flow field velocity measurement based on correlation filtering, A_nIs a state transition matrix, B_nIs a control matrix, b_nIs a control vector, q_nIs system noise, H_n+1Is a measurement matrix, d_n+1Is the measurement noise;

state transition matrix A_nControl matrix B_nAccording to initial velocity sequence fitting and by combining with the prior knowledge of a flow field, u is represented_i，v_iFunctional relationship between:

for example, u is found_iAnd v_iThe following relationships exist:

u_n＝au_n-1+bv_n-1+c

v_n＝eu_n+fv_n+d

then

H_n+1Generally, the method is related to errors brought by measurement technology, is difficult to estimate and observe, and is generally regarded as an identity matrix.

Control vector b_nSet generally as unit vector, system noise q_nAnd measuring noise d_n+1Is gaussian noise:

q_n～N(0,Q_n)

d_n+1～N(0,D_n+1)

wherein D is_n+1Is to measure the noise covariance as a preset value, Q_nIs the system noise covariance, which is a preset value.

Specifically, the following formula is adopted for prediction:

the whole Kalman prediction process is a continuous loop of 'prediction-correction-prediction-correction …', and the prediction equation is as follows:

wherein the content of the first and second substances,

is according to t_nVelocity of time of day

Predicted t_n+1The speed of the state at the moment in time,

i.e. the time t_nVelocity vector (u) of_n,v_n)；

Is at t_nAccording to time of day

And Q_nThe covariance of the prediction is determined by the prediction,

is the covariance, given an initial value and then iteratively updated according to a formula, typically initially set as the identity matrix.

Then, based on the predicted speed

Can calculate the value at t_n+1Optimum time interval at of time_n+1Considering the limitation of the sampling window size in CF-PIV, at t_n+1Optimum time interval at of time_n+1The method comprises the following steps:

where the coefficient here is 1/3 rather than 1/4 in the conventional PIV algorithm because CF-PIV can still estimate velocity accurately at large displacements, M is the sampling window size.

Further, t is transmitted through serial port communication_n+1Optimum time interval at of time_n+1Feeding back the parameters to the framing visual image acquisition device, wherein the framing visual image acquisition device is at t_n+1Taking a pair of images [ I (t) at time_n+1),I(t′_n+1)]Then, the pair of images is used for estimating the flow field velocity at t by adopting a correlation filtering-based flow field velocity estimation algorithm (CF-PIV)_n+1A two-dimensional velocity field of time of day.

The principle of the flow field velocity estimation algorithm (CF-PIV) based on the correlation filtering is as follows:

first, a sampling window is used to sample an image I (t)_n+1) And (3) sampling by translating and sliding upwards, and generating a circular matrix R for the current sampling region R:

R＝C(r)

the circulant matrix fully utilizes the samples in the sampling area in a dense sampling mode, so that the characteristics of the input image are fully utilized, and simultaneously, the samples in the filter model are greatly increased, so that the filter has better discrimination capability.

In the circulant matrix, the number of samples is M × M, the size of each sample is consistent with the size of the sampling region r, and the regression model of the correlation filtering is as follows:

f(r)＝g^Tr

a gaussian response is then generated that is as large as the sampling region, and the gaussian response matrix z is a discrete form of a two-dimensional gaussian distribution:

where x and y are the image coordinates in the sampling region r,

and

is at t_n+1Predicted speed (initial) at time coordinate (x, y)And

set to 0), σ is the variance of the gaussian kernel in the x-direction and y-direction, and M is the sampling window size.

The object functions in the regression model are:

wherein r is^k(k 1.., mxm) isOne sample of the circulant matrix R, λ is the regularization coefficient of the regression model, which is used to ensure the generalization of the classifier, and can be expressed directly in a matrix manner:

let the derivative be 0, one can get:

g＝(R^TR+λI)^-1R^Tz

where g is the filter and I is the identity matrix.

Considering that the circulant matrix may become a diagonal matrix in the frequency domain and the convolution in the time domain may become a dot product in the frequency domain, it may become:

wherein the content of the first and second substances,

and

is the fourier transform of the filter g and the gaussian response matrix z,

is that

The transpose of (a) is performed,

is the Fourier transform of r;

applying the correlation filter g to the second graph I (t)_n′₊₁) To obtain a score matrix SM_CF：

This scoring matrix SM_CFConverting the time domain into a time domain, selecting the maximum response position as displacement like the traditional PIV algorithm, and then selecting the maximum response position as the displacement according to the optimal time interval delta t of two frames_n+1To obtain t_n+1Taking the estimated value of the two-dimensional velocity field of the time flow field as t_n+1Measurement of the velocity of the flow field at the moment, velocity ═ displacement/Δ t_n+1And ifft is an inverse fourier transform,

is the sampling area of the second image

The transposing of (1).

The above operation is performed for each sampling region on the image to obtain the current time (i.e. t)_n+1) And (3) flow field speeds corresponding to all sampling areas on the image, wherein all the flow field speeds form a speed field at the moment.

Finally, t is added_n+1The velocity vector W is obtained by averaging the two-dimensional velocity field of the moment_n+1The speed sequence is updated by taking the speed sequence as a parameter, and the principle is as follows:

first, the residual error is calculated

And Kalman gain K_n+1：

The velocity sequence and covariance matrix are then updated:

wherein I is an identity matrix;

will be provided withAs (u)_n+1|n+1,v_n+1|n+1) The speed sequence is updated, and the speed sequence after updating is (u)_i,v_i) ( i 2,3.., n, n +1), and the next time t is repeated_n+2Is estimated.

In addition, in order to realize the function of human-computer interaction, a main processor FPGA of the image processing subsystem is connected with an Intel board card through a serial port, the Intel board card is provided with a capacitive display screen, an operator can input related parameters through the capacitive display screen to complete the configuration of the framing visual image acquisition device and the image processing subsystem, and meanwhile, the processed image of the flow field velocity field can also be displayed on the capacitive display screen.

According to the invention, an FPGA processor receives two adjacent frames of images acquired by a framing visual image acquisition device through a Camera Link interface, image data are transmitted to a DSP processor through an SRIO Link to carry out flow field velocity field estimation processing, the velocity estimated by the DSP processor is substituted into the initial velocity in a Kalman predictor to update the velocity field, the velocity of the next cycle of the flow field velocity field is calculated through Kalman prediction, a proper time interval required by the framing visual image acquisition device is further obtained, a time interval parameter is sent to the framing visual image acquisition device through serial port communication, and the framing visual image acquisition device is adjusted accordingly, so that the flow field velocity measurement precision and the dynamic range are improved.

It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.