Flow field measurement system, method, device, apparatus, and storage medium

Abstract

The application discloses a flow field measurement system, a flow field measurement method, a device, equipment and a storage medium. The flow field measurement system comprises a processor, a backlight module and a camera. The processor is connected with the camera and the backlight module. The processor sends a pulse dimming signal to the backlight module, so as to obtain a liquid-gas interface image under backlight irradiation of a first light intensity and a flow field particle image under backlight irradiation of a second light intensity based on the dimming signal. Since the liquid-gas interface image is collected at the first light intensity, the definition of the liquid-gas interface image can be improved. Since the flow field particle image is collected at the second light intensity, by reducing the backlight intensity, a flow field particle image of higher quality can be obtained. By improving the definition of the liquid-gas interface image, the accuracy of extracting interface boundary features from the liquid-gas interface image can be improved. Finally, the interface boundary features are fused with the flow field particle image, the accuracy of liquid-gas interface boundary recognition can be improved, and the precision of flow field measurement can be improved.

Description

Technical Field

[0001] This invention belongs to the field of flow field measurement technology, and particularly relates to a flow field measurement system, a method, apparatus, equipment and storage medium for flow field measurement. Background Technology

[0002] Currently, the liquid-gas interface plays a crucial role in multiphase flow and fluid mechanics research, particularly in the study of liquid-gas interface slip phenomena. The curvature of the liquid-gas interface significantly influences the fluid slip effect. To better investigate the impact of liquid-gas interface curvature on fluid slip characteristics and flow mechanisms, existing techniques typically employ particle image velocimetry (PIV) to measure the motion of particles in the fluid, thereby capturing velocity information of the flow field.

[0003] However, due to complex factors such as the reflective properties of the liquid-gas interface and interface vibration, the image becomes blurred, and related technologies struggle to effectively separate clear boundary signals from noise. Consequently, the accuracy of liquid-gas interface boundary identification is low, resulting in low precision in flow field measurement. Summary of the Invention

[0004] This invention provides a flow field measurement system, a method, apparatus, equipment, and storage medium that can improve the accuracy of flow field measurement.

[0005] In a first aspect, embodiments of the present invention provide a flow field measurement system, comprising:

[0006] The processor, connected to the camera and backlight module, is used to send a pulse dimming signal to the backlight module upon receiving a trigger sampling signal from the camera.

[0007] The backlight module is used to periodically emit backlight with a first light intensity and a second light intensity according to the pulse dimming signal.

[0008] A camera is used to acquire, during the first time period of each sampling period at least once, an image of the liquid-gas interface of a target flow field region under backlight illumination of a first light intensity and an image of the flow field particles of the target flow field region under backlight illumination of a second light intensity during the second time period of the sampling period; the first light intensity is greater than the second light intensity.

[0009] The processor is also used to extract interface boundary features from the liquid-gas interface image based on the liquid-gas interface image and the flow field particle image acquired by the acquisition camera in the same sampling period; and to fuse the interface boundary features and the flow field particle image to obtain the fluid velocity field within the target flow field region.

[0010] In one feasible implementation, the camera is also used to send the actual backlight intensity at the time of camera exposure to the processor;

[0011] The processor is also used to calculate the deviation between the actual backlight intensity and the target backlight intensity; if the deviation is greater than the preset deviation, the signal characteristics of the pulse dimming signal are adjusted according to the deviation so that the deviation is less than or equal to the preset deviation.

[0012] In one feasible implementation, the pulse period of the pulse dimming signal is the same as the sampling period, the high-level duration of the pulse period is the same as the duration of the first time period, and the low-level duration of the pulse period is the same as the duration of the second time period.

[0013] In a second aspect, embodiments of the present invention provide a flow field measurement method applied to a processor, the processor being connected to a camera and a backlight module, the method comprising:

[0014] Upon receiving the camera's trigger sampling signal, a pulse dimming signal is sent to the backlight module so that the backlight module periodically emits backlight with a first light intensity and a second light intensity according to the pulse dimming signal.

[0015] The receiver acquires liquid-gas interface images and flow field particle images in each sampling period of at least one sampling period; the liquid-gas interface image is acquired by the camera in the first time period of the sampling period under backlight illumination of the target flow field region with the first light intensity, and the flow field particle image is acquired by the camera in the second time period of the sampling period under backlight illumination of the target flow field region with the second light intensity.

[0016] Extract interface boundary features from liquid-gas interface images;

[0017] By fusing interface boundary features and flow field particle images, the flow field velocity distribution within the target flow field region can be determined.

[0018] In one feasible implementation, extracting interface boundary features from the liquid-gas interface image includes:

[0019] The liquid-gas interface image is converted to grayscale to obtain a single-channel grayscale image;

[0020] A single-channel grayscale image is binarized to obtain a binarized image; the binarized image includes the interface region and the main body region.

[0021] The binarized image is processed to obtain the interface boundary features in the liquid-gas interface image.

[0022] In one feasible implementation, the interface boundary features include at least one of the following: boundary coordinates, local radius of curvature of the interface, interface normal vector, and interface velocity.

[0023] Processing the binarized image to obtain the interface boundary features in the liquid-gas interface image includes at least one of the following:

[0024] Morphological processing is performed on the binarized image to extract the interface boundary in the morphologically processed binarized image and obtain the boundary coordinates;

[0025] Calculate the local radius of curvature of the interface based on the grayscale gradient of adjacent pixels in the interface area;

[0026] Determine the normal vectors that are perpendicular to the interface boundary and point towards the mainstream region.

[0027] The boundary displacement is determined based on the boundary coordinates within different sampling periods; the interface velocity is determined based on the boundary displacement and the time intervals within different sampling periods, where the time intervals include the time interval between the first and last sampling periods.

[0028] In one feasible implementation, the interface boundary features include: boundary coordinates, local radius of curvature of the interface, interface normal vector, and interface velocity.

[0029] By fusing interface boundary features and flow field particle images, the flow field velocity distribution within the target flow field region is obtained, including:

[0030] The flow field particle image is divided into multiple sub-images;

[0031] Multiple velocity vectors are obtained by cross-correlation calculations based on multiple sub-images;

[0032] The initial velocity field is obtained by combining multiple velocity vectors;

[0033] The interface proximity region of the initial velocity field is determined based on the interface boundary characteristics. The interface proximity region represents the area that is less than a preset distance from the interface boundary.

[0034] Search for target particles that meet the first preset condition in the target region of the initial velocity field and perform cross-correlation calculation to obtain the target velocity vector corresponding to the target particle. The first preset condition includes the target sub-image located in a range smaller than the sub-image, the interface normal vector located in a target sub-image, and the target weight being greater than the weight threshold.

[0035] The target velocity vector is optimized to obtain the optimized velocity vector.

[0036] Replace the velocity vector corresponding to the target region of the initial velocity field with the optimized velocity vector to obtain the target velocity field;

[0037] Based on the target velocity field, determine the velocity distribution of the flow field within the target flow field region.

[0038] In one feasible implementation, before performing cross-correlation calculations on target particles that satisfy the first preset condition from the target region of the initial velocity field to obtain the target velocity vector corresponding to the target particles, the method further includes:

[0039] Based on the obtained interface mask parameters, a first weight coefficient is set for the interface neighbor pixels corresponding to the target sub-image, and a second weight coefficient is set for the mainstream area pixels corresponding to the target sub-image.

[0040] In one feasible implementation, the target velocity vector is optimized to obtain an optimized velocity vector, including:

[0041] The gradient calculation weights are determined based on the local radius of curvature of the interface.

[0042] The vorticity in the target region of the initial velocity field is corrected based on the local radius of curvature of the interface to obtain the corrected vorticity.

[0043] The target velocity vector is optimized by calculating the gradient weights and the corrected vorticity to obtain the optimized velocity vector.

[0044] In one feasible implementation, the gradient calculation weights are determined based on the local radius of curvature of the interface, including:

[0045] Obtain the kinematic viscosity coefficient of the fluid;

[0046] The first parameter is obtained by multiplying the fluid kinematic viscosity coefficient by the local radius of curvature of the interface;

[0047] Divide the interface speed by the first parameter to obtain the second parameter;

[0048] The gradient calculation weights are determined based on the second parameter.

[0049] In one feasible implementation, the vorticity in the target region of the initial velocity field is corrected based on the local radius of curvature of the interface to obtain the corrected vorticity, including:

[0050] Obtain global vorticity parameters;

[0051] The third parameter is obtained by dividing the product of the global vorticity parameter and the preset correction coefficient by the local curvature radius of the interface.

[0052] Add the third parameter to the global vorticity parameter to obtain the vorticity correction parameter;

[0053] The vorticity in the target region of the initial velocity field is corrected according to the vorticity correction parameters to obtain the corrected vorticity.

[0054] Thirdly, embodiments of the present invention provide a flow field measurement device, the device including a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the flow field measurement method as described in any of the second aspects.

[0055] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the flow field measurement method as described in any of the second aspects.

[0056] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements a flow field measurement method as described in any of the second aspects.

[0057] This invention provides a flow field measurement system, a method, apparatus, device, and storage medium. The flow field measurement system includes a processor, a backlight module, and a camera. The processor is connected to the camera and the backlight module. In this invention, the processor sends a pulsed dimming signal to the backlight module to acquire a liquid-gas interface image under a first-intensity backlight illumination and a flow field particle image under a second-intensity backlight illumination, based on the dimming signal. Since the liquid-gas interface image is acquired under the first-intensity backlight illumination, the liquid-gas interface forms a high-contrast boundary due to backlight reflection / transmission, thus improving the clarity of the liquid-gas interface image. Since the flow field particle image is acquired under the second-intensity backlight illumination, reducing the backlight intensity effectively suppresses the interference of backlight scattering on particle imaging, resulting in a higher-quality flow field particle image. Because the clarity of the liquid-gas interface image is improved, the accuracy of extracting interface boundary features from the liquid-gas interface image is improved. Finally, fusing the more accurate interface boundary features with the higher-quality flow field particle image improves the accuracy of liquid-gas interface boundary recognition and the precision of flow field measurement. Attached Figure Description

[0058] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a schematic diagram of a flow field measurement system provided in an embodiment of this disclosure;

[0060] Figure 2 This is a schematic flowchart of a flow field measurement method provided in an embodiment of this disclosure;

[0061] Figure 3 This is a flowchart illustrating a method for extracting interface boundary features according to an embodiment of this disclosure;

[0062] Figure 4 This is a flowchart illustrating another method for extracting interface boundary features provided in this embodiment of the disclosure;

[0063] Figure 5 This is a schematic flowchart of a method for obtaining the velocity distribution of a flow field provided in an embodiment of this disclosure;

[0064] Figure 6 This is a flowchart illustrating a method for optimizing a target velocity vector according to an embodiment of this disclosure;

[0065] Figure 7 This is a flowchart illustrating a method for determining gradient calculation weights provided in an embodiment of this disclosure;

[0066] Figure 8 This is a schematic flowchart of a method for correcting vorticity provided in an embodiment of this disclosure;

[0067] Figure 9 This is a schematic diagram of the structure of a flow field measurement device provided in an embodiment of this disclosure. Detailed Implementation

[0068] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0069] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0070] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0071] Before describing the technical solutions provided by the embodiments of the present invention, in order to facilitate understanding of the embodiments of the present invention, the present invention will specifically explain the problems existing in the related technologies:

[0072] Currently, the liquid-gas interface plays a crucial role in multiphase flow and fluid mechanics research, particularly in the study of liquid-gas interface slip phenomena. The curvature of the liquid-gas interface significantly influences the slip effect of fluids; however, the mechanism of this influence remains not fully understood. The liquid-gas interface slip effect refers to the relative flow phenomenon between gas and liquid, i.e., the liquid undergoes slip motion under the influence of gas, especially at flow boundaries and in regions with significant curvature changes, where the slip effect is more pronounced.

[0073] To better study the influence of the curvature of the liquid-gas interface on fluid slip characteristics and flow mechanisms, particle image velocimetry (PIV) is commonly used in related technologies to accurately capture the velocity information of the flow field by measuring the motion of particles in the fluid.

[0074] However, under complex boundary conditions such as liquid-gas two-phase flow, such as near the interface of bubbles and liquid films, reflected light can seriously affect image quality. In particular, when there is strong reflected light or boundary vibration in the experimental environment, the backlight may cause significant interference to the flow field around the liquid-gas interface, resulting in low clarity of liquid-gas mask recognition, which in turn affects the accuracy of flow field measurement.

[0075] Furthermore, in low signal-to-noise ratio environments, such as high turbulence and complex curved surface flows, existing digital mask and surface deformation algorithms have poor robustness in boundary extraction and insufficient anti-interference ability, which further limits the improvement of measurement accuracy.

[0076] In addition, existing technical solutions rely on external synchronizers to coordinate the light source, camera and timing, resulting in complex system structure and high cost. Furthermore, the collaboration of multiple devices can easily introduce signal delay or deviation, affecting measurement synchronization.

[0077] The present invention provides a flow field measurement system, a flow field measurement method, an apparatus, a device, and a storage medium, which can solve at least one of the above-mentioned technical problems existing in the related art.

[0078] This invention transmits pulsed dimming signals to the backlight module via a processor, thereby acquiring a liquid-gas interface image under a first-intensity backlight illumination and a flow field particle image under a second-intensity backlight illumination based on the dimming signals. Since the liquid-gas interface image is acquired under the first-intensity backlight illumination, the liquid-gas interface forms a high-contrast boundary due to backlight reflection / transmission, thus improving the image clarity. Since the flow field particle image is acquired under the second-intensity backlight illumination, reducing the backlight intensity effectively suppresses interference from backlight scattering on particle imaging, resulting in a higher-quality flow field particle image. Because the clarity of the liquid-gas interface image is improved, the accuracy of extracting interface boundary features from it is enhanced. Finally, fusing the more accurate interface boundary features with the higher-quality flow field particle image improves the accuracy of liquid-gas interface boundary recognition and the precision of flow field measurement.

[0079] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0080] Figure 1 This is a schematic diagram of a flow field measurement system provided in an embodiment of the present invention. Figure 1 As shown, the flow field measurement system 100 includes a processor 101, a backlight module 102, and a camera 103.

[0081] The processor 101, connected to the camera 103 and the backlight module 102, is used to send a pulse dimming signal to the backlight module upon receiving a trigger sampling signal from the camera.

[0082] The processor 101 is also used to extract interface boundary features in the liquid-gas interface image based on the liquid-gas interface image and the flow field particle image acquired by the acquisition camera 103 in the same sampling period; and to fuse the interface boundary features and the flow field particle image to obtain the fluid velocity field in the target flow field region.

[0083] In one example, processor 101 includes a microprocessor, a digital mask processing unit, and a PIV algorithm processing unit. The microprocessor can be an FPGA or an ARM chip, and includes a PID algorithm module, a trigger logic unit, and a parameter configuration interface. The microprocessor stores preset sampling periods, target backlight intensity, and digital processing parameters such as edge detection thresholds. The digital mask processing unit is used to extract a binarized mask of the liquid-gas interface based on grayscale gradients or edge detection algorithms such as the Canny operator, thereby constraining the search area for PIV particle matching. The PIV algorithm processing unit processes the flow field particle image, combines it with the interface boundary information output by the digital mask unit, optimizes the velocity field reconstruction of the adjacent region, and obtains the overall velocity vector and interface feature data.

[0084] The backlight module 102 is used to periodically emit backlight with a first light intensity and a second light intensity according to the pulse dimming signal.

[0085] In one example, the backlight module 102 may include a backlight lamp and a pulse width modulation (PWM) driving circuit. The backlight module 102 supports millisecond-level switching. The backlight lamp may be an LED array or a laser diode.

[0086] Camera 103 is used to acquire, during the first time period of each sampling period at least once, an image of the liquid-gas interface of the target flow field region under backlight illumination of a first light intensity, and an image of the flow field particles of the target flow field region under backlight illumination of a second light intensity during the second time period of the sampling period; the first light intensity is greater than the second light intensity. Camera 103 represents a special camera capable of capturing a large number of image frames in an extremely short time, such as a high-speed camera.

[0087] In one example, camera 103 uses a single-exposure mode, allowing dynamic setting of parameters such as exposure time and gain. Camera 103 is directly connected to processor 101 via a general-purpose input / output interface, providing real-time feedback of the backlight intensity signal during exposure. The trigger signal output of camera 103 is connected to the trigger input of processor 101 for synchronous control of image acquisition and backlight switching.

[0088] This invention transmits pulsed dimming signals to the backlight module via a processor, thereby acquiring a liquid-gas interface image under a first-intensity backlight illumination and a flow field particle image under a second-intensity backlight illumination based on the dimming signals. Since the liquid-gas interface image is acquired under the first-intensity backlight illumination, the liquid-gas interface forms a high-contrast boundary due to backlight reflection / transmission, thus improving the image's clarity. Since the flow field particle image is acquired under the second-intensity backlight illumination, reducing the backlight intensity effectively suppresses interference from backlight scattering on particle imaging, resulting in a higher-quality flow field particle image. Because the clarity of the liquid-gas interface image is improved, the accuracy of extracting interface boundary features from it is enhanced. Finally, fusing the more accurate interface boundary features with the higher-quality flow field particle image improves the accuracy of liquid-gas interface boundary recognition and the precision of flow field measurement.

[0089] In addition, the flow field measurement system in this embodiment of the invention achieves flow field measurement by signal interaction between the camera, processor, and backlight module within the system. Compared with the prior art, it does not require an external synchronizer to coordinate the backlight module, camera, and timing, thus simplifying the components of the flow field measurement system and reducing costs. Furthermore, since the flow field measurement system in this embodiment of the invention does not require an external synchronizer to coordinate the backlight module, camera, and timing, it reduces the probability of signal delay or deviation caused by multi-device collaboration, thereby improving measurement synchronization.

[0090] In one embodiment, the camera 103 is further configured to send the actual backlight intensity at the time of camera exposure to the processor 101; the processor 101 is further configured to calculate the deviation between the actual backlight intensity and the target backlight intensity; if the deviation is greater than a preset deviation, the signal characteristics of the pulse dimming signal are adjusted according to the deviation so that the deviation is less than or equal to the preset deviation. The target backlight intensity represents the preset illumination intensity emitted by the backlight module and can be adjusted in real time.

[0091] It is understood that the embodiments of the present invention provide real-time feedback of the actual backlight intensity to the processor via a high-speed camera, which ensures the stability of the backlight under different conditions, thereby guaranteeing the reliability of the interface image quality.

[0092] In one embodiment, the high-speed camera feeds back the actual backlight intensity during exposure to the processor 101 in real time via the GPIO interface; the PID algorithm module built into the processor 101 calculates the dimming error and dynamically adjusts the pulse width modulation duty cycle, frequency and phase shift of the backlight to stabilize the backlight intensity at the target backlight intensity ±ΔI (ΔI≤5%I_target).

[0093] In one example, the expression for calculating the dimming error is shown in formula (1).

[0094] e(t) = I_target - S_feedback (1)

[0095] Where e(t) represents the dimming error, I_target represents the target backlight intensity, and S_feedback represents the actual backlight intensity.

[0096] In one embodiment, the pulse period of the pulse dimming signal is the same as the sampling period, the high-level duration of the pulse period is the same as the duration of the first time period, and the low-level duration of the pulse period is the same as the duration of the second time period.

[0097] In this embodiment of the invention, by controlling the pulse period of the pulse dimming signal to be the same as the sampling period, the acquired image is captured under the backlight intensity corresponding to the pulse dimming signal, thereby improving the consistency between the acquired image and the pulse dimming signal.

[0098] Figure 2 This invention provides an application to... Figure 1 The diagram shows a flow field measurement method for the flow field measurement system. Figure 2 The method is applied to a processor that is connected to a camera and a backlight module, and may include steps S210-S240.

[0099] S210: Upon receiving a trigger sampling signal from the camera, a pulse dimming signal is sent to the backlight module so that the backlight module periodically emits backlight with a first light intensity and a second light intensity according to the pulse dimming signal.

[0100] Among them, the trigger sampling signal represents the image sampling trigger signal, which can be, for example, a signal generated when image information of the target flow field region is acquired. The pulse dimming signal represents a periodic electrical signal.

[0101] In one embodiment, the processor can use a receiving component to receive a trigger sampling signal from the camera. After receiving the trigger sampling signal from the camera, a pulse dimming signal is sent to the backlight module so that the backlight module dynamically adjusts the pulse width modulation duty cycle, frequency, and phase shift of the backlight module according to the pulse dimming signal, and periodically emits backlight with a first light intensity and a second light intensity.

[0102] S220: Receives liquid-gas interface images and flow field particle images acquired by the camera in each sampling period of at least one sampling period.

[0103] Among them, the liquid-gas interface image is acquired by the camera in the first time period of the sampling cycle under the backlight illumination of the target flow field region with the first light intensity, and the flow field particle image is acquired by the camera in the second time period of the sampling cycle under the backlight illumination of the target flow field region with the second light intensity.

[0104] In one embodiment, the processor may utilize the receiving component to receive liquid-gas interface images and flow field particle images acquired by the camera in each sampling period of at least one sampling period.

[0105] In one example, the camera acquires a frame of liquid-gas interface image in the first time period of the sampling period and a frame of flow field particle image in the second time period of the sampling period, thus acquiring two frames of images in the sampling period and sending them to the processor.

[0106] In the process flow of this embodiment of the invention, the processor acquires liquid-gas interface images and flow field particle images collected in each sampling period of at least one sampling period. Since the liquid-gas interface image is acquired under backlight illumination of the first light intensity, the liquid-gas interface forms a high-contrast boundary due to backlight reflection / transmission, thus improving the clarity of the liquid-gas interface image. Since the flow field particle image is acquired under backlight illumination of the second light intensity, by reducing the backlight intensity, the interference of backlight scattering on particle imaging can be effectively suppressed, resulting in higher quality flow field particle images.

[0107] S230: Extract interface boundary features from the liquid-gas interface image.

[0108] Among them, the interface boundary characteristics are features that reflect the intermolecular forces, thermodynamic properties, and physicochemical properties of the liquid-gas interface.

[0109] In one embodiment, after obtaining the liquid-gas interface image, the interface boundary features in the liquid-gas interface image can be extracted using a preset algorithm.

[0110] S240: The interface boundary features and the flow field particle image are fused to determine the flow field velocity distribution within the target flow field region.

[0111] In one embodiment, after obtaining the interface boundary features, the interface boundary features and the flow field particle image can be input into the PIV algorithm processing unit for fusion, thereby determining the flow field velocity distribution within the target flow field region based on the output of the PIV algorithm processing unit.

[0112] This invention, through sending pulsed dimming signals to a backlight module, acquires a liquid-gas interface image under a first-intensity backlight illumination and a flow field particle image under a second-intensity backlight illumination. Since the liquid-gas interface image is acquired under the first-intensity backlight illumination, the liquid-gas interface forms a high-contrast boundary due to backlight reflection / transmission, thus improving the image's clarity. Since the flow field particle image is acquired under the second-intensity backlight illumination, reducing the backlight intensity effectively suppresses interference from backlight scattering on particle imaging, resulting in a higher-quality flow field particle image. Because the clarity of the liquid-gas interface image is improved, the accuracy of extracting interface boundary features from it is enhanced. Finally, fusing the more accurate interface boundary features with the higher-quality flow field particle image improves the accuracy of liquid-gas interface boundary recognition and the precision of flow field measurement.

[0113] In one embodiment, interface boundary features are extracted from the liquid-gas interface image, such as... Figure 3 As shown, steps S231-S233 may be included.

[0114] S231: Perform grayscale processing on the liquid-gas interface image to obtain a single-channel grayscale image.

[0115] In one embodiment, the liquid-gas interface image can be grayscale processed to convert the pixel values ​​in the liquid-gas interface image into grayscale values, thereby obtaining a single-channel grayscale image.

[0116] In the steps of this invention embodiment, since the liquid-gas interface often experiences sudden changes in brightness due to refraction and reflection under illumination, grayscale processing of the liquid-gas interface image can accurately reflect this sudden change trend.

[0117] S232: Binarize the single-channel grayscale image to obtain a binary image.

[0118] The binarized image includes the interface region and the main body region.

[0119] In one embodiment, after obtaining a single-channel grayscale image, the pixel values ​​in the single-channel grayscale image can be compared with a preset grayscale threshold. The values ​​of pixels that are greater than or equal to the preset grayscale threshold are set as a first value, and the values ​​of pixels that are less than the preset grayscale threshold are set as a second value, thereby obtaining a binarized image.

[0120] In the steps of this embodiment of the invention, the single-channel grayscale image is binarized, which simplifies the areas on both sides of the interface into a binary distribution and directly highlights the interface outline.

[0121] S233: Process the binarized image to obtain the interface boundary features in the liquid-gas interface image.

[0122] In one embodiment, after obtaining the binarized image, contour extraction, boundary feature calculation, and other processing can be performed on the binarized image to obtain the interface boundary features in the liquid-gas interface image.

[0123] The embodiments of the present invention perform multiple processing on the liquid-gas interface image, such as grayscale processing and binarization processing, to avoid the low accuracy of extracting interface boundary features caused by a single processing, thereby enabling accurate extraction of interface boundary features in the liquid-gas interface image.

[0124] In one embodiment, the interface boundary features include at least one of the following: boundary coordinates, local radius of curvature of the interface, interface normal vector, and interface velocity; the binarized image is processed to obtain the interface boundary features in the liquid-gas interface image, such as... Figure 4 As shown, it includes at least one of the following steps S2331-S2334.

[0125] S2331: Perform morphological processing on the binarized image, extract the interface boundary in the morphologically processed binarized image, and obtain the boundary coordinates.

[0126] Morphological processing includes erosion, dilation, opening, and closing operations. Erosion is used to remove tiny protrusions at interface edges and separate adhering small bubbles. Dilation is used to fill tiny voids in liquid areas and connect broken interface contours. Opening operations are used to eliminate isolated noise outside the liquid-gas interface (such as bright spots in the background). Closing operations are used to repair small pores within liquid areas (such as black noise inside bubbles).

[0127] In one embodiment, morphological processing can be performed on the binarized image, that is, noise in the liquid-gas interface can be processed to improve the continuity of the contour, thereby extracting the interface boundary in the morphologically processed binarized image and obtaining the boundary coordinates.

[0128] S2332: Calculate the local radius of curvature of the interface based on the grayscale gradient of adjacent pixels in the interface area.

[0129] In this embodiment, the local radius of curvature of the interface can be calculated based on the grayscale gradient of adjacent pixels in the interface region.

[0130] S2333: Determine the normal vector perpendicular to the interface boundary and pointing towards the mainstream region to obtain the interface normal vector.

[0131] In this embodiment, the normal vector perpendicular to the interface boundary and pointing towards the mainstream area can be determined to obtain the interface normal vector.

[0132] S2334: Determine the boundary displacement based on the boundary coordinates within different sampling periods; determine the interface velocity based on the boundary displacement and the time interval within different sampling periods.

[0133] The time intervals in different sampling periods include the time interval between the first sampling period and the last sampling period.

[0134] In this embodiment, the boundary displacement can be determined based on the boundary coordinates within different sampling periods. After determining the boundary displacement, the interface velocity is determined based on the boundary displacement and the time intervals within different sampling periods.

[0135] This invention provides an embodiment of the method that obtains boundary coordinates by performing morphological processing on a binarized image, enabling the determination of feature information reflecting the macroscopic integrity of the boundary. By calculating the local radius of curvature of the interface based on the grayscale gradient of adjacent pixels in the interface region, local geometric details can be revealed. The interface normal vector is obtained by determining the normal perpendicular to the interface boundary and pointing towards the mainstream region, providing directional features. The interface velocity is determined based on the boundary coordinates within different sampling periods, introducing a temporal dimension. In summary, this invention can acquire multiple interface boundary features in liquid-gas interface images, enhancing feature diversity and thus improving the accuracy of subsequent flow field measurements based on these features.

[0136] In one embodiment, the interface boundary features include: boundary coordinates, local radius of curvature of the interface, interface normal vector, and interface velocity; fusing the interface boundary features and the flow field particle image yields the flow field velocity distribution within the target flow field region, such as... Figure 5 As shown, steps S241-S248 may be included.

[0137] S241: Divide the flow field particle image into multiple sub-images.

[0138] There are overlapping areas between sub-images to avoid vector discontinuities caused by discrete block division.

[0139] S242: Perform cross-correlation calculations on multiple sub-images to obtain multiple velocity vectors.

[0140] In one embodiment, after dividing the flow field particle image into multiple sub-images, the cross-correlation function of the particles in the sub-images under different displacements can be calculated based on the multiple sub-images, and multiple velocity vectors can be obtained based on the cross-correlation function.

[0141] S243: Combine multiple velocity vectors to obtain the initial velocity field.

[0142] Among them, the velocity field represents that at a certain moment, each point in the space occupied by the fluid corresponds to a velocity vector.

[0143] S244: Determine the interface-adjacent region of the initial velocity field based on the interface boundary characteristics.

[0144] Among them, the interface adjacent area represents the area that is less than a preset distance from the interface boundary.

[0145] In one example, the region near the interface can be a boundary region with a center distance of ≤3δ from the interface boundary, where δ is the diameter of the particle image.

[0146] S245: Search for target particles that meet the first preset conditions in the target region of the initial velocity field, perform cross-correlation calculations, and obtain the target velocity vector corresponding to the target particles.

[0147] The first preset condition includes a target sub-image located within a range smaller than the sub-image, and a target weight greater than a weight threshold located at the interface normal vector. The target weight represents the relevance weight corresponding to the particle.

[0148] In one embodiment, target particles that meet the first preset conditions can be obtained from the target region of the initial velocity field, and the cross-correlation function can be calculated. The target velocity vector corresponding to the target particle can then be determined based on the cross-correlation function.

[0149] In one example, the matching search window for particles in the main region can be narrowed from the sub-image to the target sub-image in the interface normal direction, and the search can only be allowed within the 3δ range of the interface normal where the target weight is greater than the weight threshold. This is equivalent to finding particles with a target weight greater than the weight threshold while limiting the search range and search direction.

[0150] In the steps of this invention embodiment, due to the viscous constraint of the tangential flow at the interface, the tangential displacement of the particles is significantly smaller than the normal displacement, and the interface normal vector n determines the dominant direction of particle motion. Therefore, when calculating the cross-correlation function, only the displacement component along the n direction is calculated, ignoring the tangential component, thus reducing mismatches caused by the local curvature radius of the interface. Furthermore, acquiring particles with target weights greater than the weight threshold highlights the influence of interface constraints on particle matching, improving the matching accuracy of particle pairs.

[0151] S246: Optimize the target velocity vector to obtain the optimized velocity vector.

[0152] S247: Replace the velocity vector corresponding to the target region of the initial velocity field with the optimized velocity vector to obtain the target velocity field.

[0153] S248: Determine the velocity distribution of the flow field within the target flow field region based on the target velocity field.

[0154] In this embodiment of the invention, by dividing the flow field particle image into multiple sub-images to construct an initial velocity field based on the acquired velocity vector, and then optimizing the velocity vector in the interface-adjacent region of the initial velocity field, the integrity of the overall flow field structure can be guaranteed, while the measurement accuracy of the interface-adjacent region can be improved through physical constraints and adaptive resolution.

[0155] In one embodiment, before searching for target particles that meet the first preset conditions from the target region of the initial velocity field, performing cross-correlation calculations, and obtaining the target velocity vector corresponding to the target particles, the flow field measurement method further includes step S310.

[0156] S310: Based on the obtained interface mask parameters, set the first weight coefficient for the interface neighboring pixels corresponding to the target sub-image, and set the second weight coefficient for the mainstream area pixels corresponding to the target sub-image.

[0157] In one example, the relevance of particle pairs within the search window can be weighted using the interface mask M(x,y), where the weight of pixels near the interface (M(x,y)=1) is set to w=1, and the weight of pixels in the mainstream area (M(x,y)=0) is set to w=0.3.

[0158] In this embodiment of the invention, weights are assigned to the neighboring pixels and mainstream pixels of the target sub-image based on the interface mask, highlighting the influence of interface constraints on particle matching and improving matching accuracy.

[0159] In one embodiment, the target velocity vector is optimized to obtain an optimized velocity vector, such as... Figure 6 As shown, steps S2461-S2463 may be included.

[0160] S2461: Determine the gradient calculation weights based on the local radius of curvature of the interface;

[0161] S2462: Correct the vorticity in the target region of the initial velocity field based on the local curvature radius of the interface to obtain the corrected vorticity.

[0162] S2463: Optimize the target velocity vector based on the gradient calculation weights and the corrected vorticity to obtain the optimized velocity vector.

[0163] This invention determines the gradient calculation weights by using the local curvature radius of the interface and corrects the vorticity based on the local curvature radius of the interface to obtain the corrected vorticity. This allows for optimization of the target velocity vector from multiple optimization perspectives based on the gradient calculation weights and the corrected vorticity, thereby improving the quality of the target velocity vector.

[0164] In one embodiment, the velocity at the liquid-gas interface can also be optimized by one-dimensional linear interpolation along the normal direction.

[0165] The expression for calculating linear interpolation is shown in formula (2).

[0166]

[0167] Where u(x) b ,y b ) and u(x i ,y i ) represents velocity, (x i ,y i ) represents the particle coordinates in the region adjacent to the interface, w i These are the interpolation weights.

[0168] Understandably, since the interface is a free surface with no actual particle distribution, the velocity at the interface needs to be obtained by interpolating the particle velocity field in the mainstream region, thereby improving the accuracy of subsequent flow field measurements.

[0169] In one embodiment, the gradient calculation weights are determined based on the local radius of curvature of the interface, such as... Figure 7 As shown, steps S410-S440 may be included.

[0170] S410: Obtain the kinematic viscosity coefficient of the fluid.

[0171] S420: The first parameter is obtained by multiplying the fluid kinematic viscosity coefficient by the local radius of curvature of the interface.

[0172] S430: Divide the interface speed by the first parameter to obtain the second parameter.

[0173] S440: Determine the gradient calculation weights based on the second parameter.

[0174] In one example, the velocity gradient is in the interface normal direction. According to u b The formula / v·R is determined, where u b Let ν be the interface velocity, ν be the fluid kinematic viscosity coefficient, and R be the local radius of curvature of the interface.

[0175] Understandably, traditional gradient calculations are susceptible to noise interference near interfaces. This invention introduces the local radius of curvature of the interface as a regularization term to adjust the weights of gradient calculations. This relationship constrains gradient calculations, suppressing the amplification effect of noise on the gradient.

[0176] In one embodiment, the vorticity in the target region of the initial velocity field is corrected based on the local radius of curvature of the interface to obtain the corrected vorticity, such as... Figure 8 As shown, steps S510-S540 may be included.

[0177] S510: Obtain global vorticity parameters.

[0178] S520: The third parameter is obtained by dividing the product of the global vorticity parameter and the preset correction coefficient by the local curvature radius of the interface.

[0179] S530: Add the third parameter to the global vorticity parameter to obtain the vorticity correction parameter.

[0180] S540: Correct the vorticity in the target region of the initial velocity field according to the vorticity correction parameter to obtain the corrected vorticity.

[0181] In one embodiment, after calculating the global vorticity parameters, vorticity correction parameters are determined based on the global vorticity parameters in order to correct the vorticity in the target region.

[0182] In one example, the expression for calculating the vorticity correction parameter is shown in formula (3).

[0183] ω 修正 =ω 全局 +K·ω 全局 / R (3)

[0184] Where, ω 修正 Characterizing the vorticity correction parameter. ω 全局 Characterizes the global vorticity parameter. K is a correction coefficient, reflecting the effect of curvature on enhancing or attenuating vorticity. R characterizes the local radius of curvature at the interface.

[0185] This invention improves the accuracy of subsequent flow field measurements by obtaining global vorticity parameters, determining vorticity correction parameters, and performing correction processing based on these parameters.

[0186] Figure 9 A schematic diagram of the flow field measurement hardware structure provided in an embodiment of the present invention is shown.

[0187] The flow field measurement device may include a processor 801 and a memory 802 storing computer program instructions.

[0188] Specifically, the processor 801 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of the present invention.

[0189] Memory 802 may include mass storage for data or instructions. For example, and not limitingly, memory 802 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 802 may include removable or non-removable (or fixed) media. Where appropriate, memory 802 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 802 is non-volatile solid-state memory.

[0190] Memory 802 may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0191] The processor 801 reads and executes computer program instructions stored in the memory 802 to implement any of the flow field measurement methods in the above embodiments.

[0192] In one example, the flow field measurement device may also include a communication interface 803 and a bus 804. For example, Figure 8 As shown, the processor 801, memory 802, and communication interface 803 are connected through bus 804 and complete communication with each other.

[0193] The communication interface 803 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of the present invention.

[0194] Bus 804 includes hardware, software, or both, that couples components of an online data flow metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 804 may include one or more buses. Although specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect. Additionally, in conjunction with the flow field measurement method in the above embodiments, embodiments of the invention also provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when executed by a processor, the computer program instructions implement any of the flow field measurement methods described in the above embodiments.

[0195] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements any of the flow field measurement methods described in the above embodiments.

[0196] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0197] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact disc read-only memory (CD-ROM), optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0198] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0199] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor may be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions. The above are merely specific embodiments of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the scope of protection of this invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this invention, and such modifications or substitutions should all be covered within the scope of protection of this invention.

Claims

1. A flow field measurement method, characterized in that, Applied to a processor connected to a camera and a backlight module, the method includes: Upon receiving a trigger sampling signal from the camera, a pulse dimming signal is sent to the backlight module so that the backlight module periodically emits backlight with a first light intensity and a second light intensity according to the pulse dimming signal. The camera receives liquid-gas interface images and flow field particle images acquired in each sampling period of at least one sampling period; the liquid-gas interface image is acquired by the camera in the first time period of the sampling period under backlight illumination of the target flow field region with the first light intensity, and the flow field particle image is acquired by the camera in the second time period of the sampling period under backlight illumination of the target flow field region with the second light intensity. Extract the interface boundary features from the liquid-gas interface image; The interface boundary features and the flow field particle image are fused to determine the flow field velocity distribution within the target flow field region; The interface boundary features include: boundary coordinates, local radius of curvature of the interface, interface normal vector, and interface velocity. The step of fusing the interface boundary features and the flow field particle image to obtain the flow field velocity distribution within the target flow field region includes: The flow field particle image is divided into multiple sub-images; Multiple velocity vectors are obtained by performing cross-correlation calculations on the multiple sub-images; The initial velocity field is obtained by combining the multiple velocity vectors. The interface proximity region of the initial velocity field is determined based on the interface boundary characteristics, and the interface proximity region represents the region that is less than a preset distance from the interface boundary. Search for target particles that meet the first preset conditions from the target region of the initial velocity field and perform cross-correlation calculation to obtain the target velocity vector corresponding to the target particles. The first preset conditions include target sub-images located in a range smaller than the sub-image, and target sub-images located in the interface normal vector and target weights greater than weight thresholds. The target velocity vector is optimized to obtain the optimized velocity vector; Replace the velocity vector corresponding to the target region of the initial velocity field with the optimized velocity vector to obtain the target velocity field; Based on the target velocity field, determine the velocity distribution of the flow field within the target flow field region.

2. The method according to claim 1, characterized in that, The extraction of interface boundary features from the liquid-gas interface image includes: The liquid-gas interface image is converted to grayscale to obtain a single-channel grayscale image; The single-channel grayscale image is binarized to obtain a binarized image; the binarized image includes an interface region and a main body region; The binarized image is processed to obtain the interface boundary features in the liquid-gas interface image.

3. The method according to claim 2, characterized in that, The interface boundary features include at least one of the following: boundary coordinates, local radius of curvature of the interface, interface normal vector, and interface velocity; The process of processing the binarized image to obtain the interface boundary features in the liquid-gas interface image includes at least one of the following: The binarized image is subjected to morphological processing to extract the interface boundary in the morphologically processed binarized image, and the boundary coordinates are obtained. Calculate the local radius of curvature of the interface based on the grayscale gradient of adjacent pixels in the interface region; Determine the normal vector of the interface by pointing perpendicular to the interface boundary and pointing towards the main region; The boundary displacement is determined based on the boundary coordinates within different sampling periods; the interface velocity is determined based on the boundary displacement and the time intervals within different sampling periods, wherein the time intervals within different sampling periods include the time interval between the first and last sampling periods within the different sampling periods.

4. The method according to claim 1, characterized in that, Before performing cross-correlation calculations on target particles that satisfy the first preset condition from the target region of the initial velocity field to obtain the target velocity vector corresponding to the target particles, the method further includes: Based on the obtained interface mask parameters, a first weighting coefficient is set for the interface neighbor pixels corresponding to the target sub-image, and a second weighting coefficient is set for the mainstream area pixels corresponding to the target sub-image.

5. The method according to claim 1, characterized in that, The optimization process for the target velocity vector to obtain the optimized velocity vector includes: The gradient calculation weights are determined based on the local radius of curvature of the interface. The vorticity in the target region of the initial velocity field is corrected based on the local curvature radius of the interface to obtain the corrected vorticity. The target velocity vector is optimized based on the gradient calculation weights and the corrected vorticity to obtain the optimized velocity vector.

6. The method according to claim 5, characterized in that, The step of determining the gradient calculation weights based on the local radius of curvature of the interface includes: Obtain the kinematic viscosity coefficient of the fluid; The first parameter is obtained by multiplying the fluid kinematic viscosity coefficient by the local radius of curvature of the interface; Divide the interface speed by the first parameter to obtain the second parameter; The gradient calculation weights are determined based on the second parameter.

7. The method according to claim 5, characterized in that, The step of correcting the vorticity in the target region of the initial velocity field based on the local radius of curvature of the interface to obtain the corrected vorticity includes: Obtain global vorticity parameters; The third parameter is obtained by dividing the product of the global vorticity parameter and the preset correction coefficient by the local curvature radius of the interface. The third parameter is added to the global vorticity parameter to obtain the vorticity correction parameter; The vorticity in the target region of the initial velocity field is corrected according to the vorticity correction parameters to obtain the corrected vorticity.

8. A flow field measurement system, characterized in that, The system, applied to the flow field measurement method as described in any one of claims 1-7, comprises: The processor, connected to the camera and the backlight module, is used to send a pulse dimming signal to the backlight module upon receiving a trigger sampling signal from the camera. A backlight module is used to periodically emit backlights of a first light intensity and a second light intensity according to the pulse dimming signal. A camera is used to acquire, during a first time period of each sampling period at least once, an image of the liquid-gas interface of a target flow field region under backlight illumination of a first light intensity and an image of flow field particles of the target flow field region under backlight illumination of a second light intensity during a second time period of the sampling period; wherein the first light intensity is greater than the second light intensity. The processor is further configured to extract interface boundary features from the liquid-gas interface image based on the liquid-gas interface image and the flow field particle image acquired by the camera in the same sampling period; and to fuse the interface boundary features and the flow field particle image to obtain the fluid velocity field within the target flow field region.

9. The flow field measurement system according to claim 8, characterized in that, The camera is also used to send the actual backlight intensity during camera exposure to the processor; The processor is also used to calculate the deviation between the actual backlight intensity and the target backlight intensity; if the deviation is greater than a preset deviation, the processor adjusts the signal characteristics of the pulse dimming signal according to the deviation so that the deviation is less than or equal to the preset deviation.

10. The flow field measurement system according to claim 8, characterized in that, The pulse period of the pulsed dimming signal is the same as the sampling period, the high-level duration of the pulse period is the same as the duration of the first time period, and the low-level duration of the pulse period is the same as the duration of the second time period.

11. A flow field measurement device, characterized in that, The device includes: a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the flow field measurement method as described in any one of claims 1-7.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the flow field measurement method as described in any one of claims 1-7.

13. A computer program product, characterized in that, When the instructions in the computer program product are executed by a processor in an electronic device, the electronic device causes the electronic device to perform the flow field measurement method as described in any one of claims 1-7.

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