A method for measuring surface pressure of a rotor model

Abstract

A kind of method suitable for the measurement of surface pressure of rotor model belongs to the field of wind tunnel test technology.To solve the problem of improving the accuracy of surface pressure measurement of rotating blade, the present application is characterized by the following steps: surface treatment of rotor blade; measurement system construction; double-frame exposure image acquisition method is constructed; temperature correlation coefficient of PSP coating is obtained based on temperature calibration test; under normal pressure and reference position, collect the reference image of rotor blade after first exposure and the reference image of rotor blade after second exposure; during the working process of test system, collect the image of blade rotation under reference position; based on the reference image of rotor blade after first exposure, carry out data processing on the image of blade rotation under reference position obtained in step S6 and the reference image of rotor blade after second exposure, and calculate the pressure data of rotating blade surface.The present application is suitable for high-speed rotation and complex flow environment.

Description

Technical Field

[0001] This invention belongs to the field of wind tunnel testing technology, specifically relating to a method for measuring surface pressure of a rotor model. Background Technology

[0002] Pressure-sensitive coating (PSP) is a non-contact optical testing technology based on the oxygen quenching mechanism, capable of acquiring distributed pressure information on model surfaces in complex aerodynamic environments. Compared with traditional point measurement methods such as pressure orifices and pressure-sensitive diaphragms, PSP offers advantages such as high spatial resolution, wide-range measurement capability, and minimal interference with the model surface. It has gradually become an important measurement tool in modern aerodynamic testing, especially suitable for pressure field identification under unsteady flow, shock wave interference, and transonic conditions.

[0003] In dual-use aerospace equipment such as helicopter rotors, tiltrotors, fan blades, and high-speed rotating components, the transient pressure distribution on the blade surface directly affects lift characteristics, load distribution, aerodynamic noise, and airframe structural safety. Especially under high-speed rotor and high-maneuverability conditions, the blade surface exhibits significant shock waves, strong three-dimensional boundary layers, and periodic dynamic stall, with its pressure distribution exhibiting strong spatiotemporal variations, making it crucial for the design of high-performance rotors and advanced weaponry. Therefore, accurately acquiring surface pressure distribution while the blade is rotating has long been a key testing capability of concern in the defense aerospace field, and PSP technology, due to its non-contact, full-field measurement characteristics, is considered an important development direction for building future rotor aerodynamic testing systems.

[0004] However, in such applications, traditional PSP methods based on intensity ratio often struggle to obtain reliable pressure information. During high-speed blade rotation, the surface is subjected to shock waves of varying intensities and locations, and may even experience unstable transonic flow, resulting in complex signal variations and degraded image quality. Furthermore, under high-speed rotation conditions, to capture complete pressure distribution information, the imaging system must complete the exposure within the coating's fluorescence lifetime. However, if the coating's fluorescence lifetime is long, it will cause significant motion blur in the image during exposure, thus affecting measurement accuracy and temporal resolution. Summary of the Invention

[0005] The problem this invention aims to solve is to improve the accuracy of measuring the surface pressure of rotating blades, and proposes a method for measuring the surface pressure of rotor models.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for measuring surface pressure of a rotor model includes the following steps:

[0008] S1. Rotor blade surface treatment, including surface cleaning, primer spraying, PSP coating spraying, and marking points;

[0009] S2. Measurement system setup, the measurement system includes the rotor blades obtained after surface treatment in step S1, motor, digital pulse signal generator, laser control box, laser, laser head beam expander, high-speed camera, and computer;

[0010] S3. Construct a dual-frame exposure image acquisition method, setting the high-speed camera to perform two exposures after the laser emits the laser, and acquire the rotor blade image after the first exposure and the rotor blade image after the second exposure;

[0011] S4. Obtain the temperature correlation coefficient of the PSP coating based on temperature calibration tests;

[0012] S5. Based on the method in step S3, under normal pressure and at the reference position, acquire the rotor blade reference image after the first exposure and the rotor blade reference image after the second exposure;

[0013] S6. Based on the method in step S3, during the operation of the test system, acquire images of the blade rotation at the reference position;

[0014] S7. Based on the rotor blade reference image obtained in step S5 after the first exposure, perform data processing on the image of the blade rotating at the reference position obtained in step S6 and the rotor blade reference image after the second exposure to calculate the pressure data on the surface of the rotating blade.

[0015] Furthermore, in step S2, the rotor blades are mounted on a motor, the motor is connected to a digital pulse signal generator, the digital pulse signal generator is connected to a laser control box and a high-speed camera, the laser control box is connected to a laser, the laser is connected to a laser head beam expander, the laser emitted by the laser head beam expander illuminates the rotor blades, and the high-speed camera is positioned above the rotor blades.

[0016] Further, in step S4, the fluorescence decay time constant of the PSP coating is measured in the calibration chamber using a standard pressure source and a PSP sample. Then, the temperature of the calibration chamber is changed, and the measurement is performed again. The fluorescence decay time constant at each temperature point is recorded. Then, regression analysis is performed on the collected fluorescence decay time constants at each temperature point to obtain the first temperature correlation coefficient A(T) and the second temperature correlation coefficient B(T).

[0017] Furthermore, the reference image of the rotor blade obtained in step S5 after the first exposure is... The reference image of the rotor blade after the second exposure is as follows: .

[0018] Furthermore, the rotor blade test image obtained in step S6 after the first exposure is... The rotor blade test image after the second exposure is as follows .

[0019] Furthermore, the specific implementation method of step S7 includes the following steps:

[0020] S7.1. Image Registration: Matching of corresponding marker points is performed using dot detection and image cross-correlation methods. , All registered to The image above shows the rotor blade test image after the first exposure following registration. Experimental images of rotor blades after registration and second exposure. Reference image of the rotor blade after registration and second exposure. ;

[0021] S7.2. An adaptive region extraction method based on depth prior, for Mask M1 is obtained by mask extraction, and then... , as well as Multiplying with M1 yields a clean, background-removed image of the rotor blade after the first exposure. Clean, second-exposure image of the rotor blade after background removal. Reference image of the rotor blades after a clean second exposure, with the background removed. And a clean reference image of the rotor blades after the first exposure, with the background removed. ;

[0022] S7.3. Based on autocorrelation analysis combined with blind deconvolution algorithm, for The rotor blade test image after the first exposure was obtained by deblurring. The second exposure image of the rotor blade after deblurring. ;

[0023] Then calculate the pressure P of the rotating blade, using the following formula:

[0024]

[0025] in, The atmospheric pressure is given in step S5.

[0026] The beneficial effects of this invention are:

[0027] This invention discloses a method for measuring surface pressure on rotor models. Through improved data processing, it solves key problems in surface pressure measurement under high-speed rotor rotation conditions. Especially in the face of motion blur and complex background light variations caused by high-speed rotation, this invention employs efficient image registration and deblurring techniques, significantly improving image quality and the accuracy of marker point recognition, thereby ensuring the accuracy of pressure inversion. Adaptive region extraction and blind deblurring algorithms effectively restore image details and reduce the impact of blur. This method improves the accuracy and reliability of rotating blade surface pressure measurement and is suitable for high-speed rotation and complex flow environments. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the measurement system of the present invention, wherein 1 is a rotor blade, 2 is a motor, 3 is a digital pulse signal generator, 4 is a laser control box, 5 is a laser, 6 is a laser head beam expander, 7 is a high-speed camera, and 8 is a computer.

[0029] Figure 2 This is a schematic diagram of the dual-frame exposure image acquisition method of the present invention;

[0030] Figure 3 This is a flowchart of a method for measuring surface pressure of a rotor model according to the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention 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 for explaining the invention and are not intended to limit the invention; that is, the described specific embodiments are merely a part of the embodiments of the invention, and not all of them. The components of the specific embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations, and the invention may also have other embodiments.

[0032] Therefore, the following detailed description of specific embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected specific embodiments of the invention. All other specific embodiments obtained by those skilled in the art based on these specific embodiments without inventive effort are within the scope of protection of this invention.

[0033] To further understand the invention's content, features, and effects, the following specific embodiments are provided, along with accompanying drawings. Figure 1 - Appendix Figure 3 Detailed explanation is as follows:

[0034] Example 1:

[0035] A method for measuring surface pressure of a rotor model includes the following steps:

[0036] S1. Rotor blade surface treatment, including surface cleaning, primer spraying, PSP coating spraying, and marking points;

[0037] Furthermore, the blade surface is cleaned using a non-abrasive cleaning medium. Non-test areas are masked with tape or non-woven fabric. A primer is then applied to the cleaned blade to ensure coating adhesion and signal uniformity. The PSP coating is applied using a multi-coat, thin-layer spraying method. The first coat serves as a base coat, forming a uniform thin layer. Subsequent 1-2 coats are gradually layered to the target film thickness, allowing sufficient drying time between each coat to avoid excessive solvent leading to sagging or orange peel texture. Spraying is performed reciprocally along the chord or spanwise direction of the blade, ensuring uniform overlap. After spraying, the blade is allowed to stand in a dust-free environment. Once the coating is fully cured, marking points are evenly distributed on the blade surface; preferably, at least 10 marking points are arranged using a globally uniform strategy.

[0038] S2. Measurement system setup, the measurement system includes the rotor blades obtained after surface treatment in step S1, motor, digital pulse signal generator, laser control box, laser, laser head beam expander, high-speed camera, and computer;

[0039] Furthermore, in step S2, the rotor blades are mounted on a motor, the motor is connected to a digital pulse signal generator, the digital pulse signal generator is connected to a laser control box and a high-speed camera, the laser control box is connected to a laser, the laser is connected to a laser head beam expander, the laser emitted by the laser head beam expander illuminates the rotor blades, and the high-speed camera is positioned above the rotor blades.

[0040] Furthermore, the rotor blades are surface-treated according to method S1, and the motor is a rotary blade drive motor with an encoder. Precise angle and position feedback is achieved through an integrated rotary encoder, which returns a signal for each revolution. The digital pulse signal generator receives signals from the motor and triggers the laser control box to emit light from the laser, as well as triggering the high-speed camera to perform exposure and acquire real-time images of the rotating blades. The laser is used to irradiate the coating surface, exciting fluorescent molecules in the PSP coating. The associated laser head beam expander is used for beam adjustment and optimization, ensuring that the laser beam can uniformly irradiate the coating surface and improving measurement accuracy and consistency. The high-speed camera is used to acquire images of the rotating blades. The computer is used to record and process the images of the rotating blades to obtain the pressure field on the surface of the rotating blades.

[0041] S3. Construct a dual-frame exposure image acquisition method, setting the high-speed camera to perform two exposures after the laser emits the laser, and acquire the rotor blade image after the first exposure and the rotor blade image after the second exposure;

[0042] Furthermore, when the encoder of the motor described in step S2 gives a signal, the high-speed camera performs its first exposure, and during this exposure time, the laser emits light to excite the fluorescent molecules on the PSP coating, thereby acquiring an image. . The light intensity captured in the image is primarily the background light intensity related to laser irradiation. During the first exposure, the fluorescence intensity of the PSP coating is not yet affected by the pressure change and is mainly used as the reference light intensity. After a short delay, the high-speed camera performs a second exposure to acquire the image. The fluorescence decay process of the PSP coating after laser irradiation was captured, and the relationship between fluorescence intensity decay and pressure in the coating was described. The higher the local pressure, the faster the fluorescence decay rate and the faster the fluorescence intensity decayed.

[0043] S4. Obtain the temperature correlation coefficient of the PSP coating based on temperature calibration tests;

[0044] Further, in step S4, the fluorescence decay time constant of the PSP coating is measured in the calibration chamber using a standard pressure source and a PSP sample. Then, the temperature of the calibration chamber is changed, and the measurement is performed again. The fluorescence decay time constant at each temperature point is recorded. Then, regression analysis is performed on the collected fluorescence decay time constants at each temperature point to obtain the first temperature correlation coefficient A(T) and the second temperature correlation coefficient B(T).

[0045] Furthermore, the temperature field required for the calibration test is provided by a constant-temperature oil bath system. The temperature control accuracy of the constant-temperature oil bath is better than ±0.1 ℃, and it can maintain long-term stability throughout the entire operating temperature range. The oil bath medium is required to be non-volatile, non-decomposing, and chemically stable within the operating temperature range; generally, silicone oil that does not react with the calibration chamber material is selected. The outer wall of the calibration chamber is connected to the constant-temperature oil bath through an oil bath jacket, thereby forming a uniform and controllable temperature field inside the chamber.

[0046] The calibration pressure is provided by a high-pressure gas source, requiring a stable and adjustable output pressure. The preferred working medium is dry air or an inert gas such as nitrogen to avoid adverse effects on the coating. The high-pressure gas passes sequentially through a filter (for oil and particle removal) and a dryer (for moisture adsorption) to ensure the gas entering the calibration chamber is clean and dry; subsequently, a two-stage or higher pressure reduction system enables precise control of the chamber pressure.

[0047] The calibration chamber is primarily constructed of stainless steel to meet structural safety requirements under the highest calibration pressure. The chamber is connected to a constant-temperature oil bath via an external oil bath jacket. Quartz optical windows are installed on the side walls for laser excitation and camera imaging; the windows have an average transmittance greater than 90% in both the excitation and emission wavelengths. High-pressure dry gas enters the calibration chamber after depressurization, filtration, and drying. The internal pressure is monitored in real-time by a pressure sensor mounted on the chamber and stabilized via a pressure regulating valve.

[0048] S5. Based on the method in step S3, under normal pressure and at the reference position, acquire the rotor blade reference image after the first exposure and the rotor blade reference image after the second exposure;

[0049] Furthermore, the reference image of the rotor blade obtained in step S5 after the first exposure is... The reference image of the rotor blade after the second exposure is as follows: .

[0050] Furthermore, specifically at a given atmospheric pressure When the blade is not rotating, the blade under test is moved to a position with a better viewing angle by means of a motor control. According to step S3, a trigger signal is manually given to the digital pulse signal generator to trigger the laser to emit light, triggering the high-speed camera to take two exposures and collect data. The following dual-frame image and .

[0051] S6. Based on the method in step S3, during the operation of the test system, acquire images of the blade rotation at the reference position;

[0052] Furthermore, the rotor blade test image obtained in step S6 after the first exposure is... The rotor blade test image after the second exposure is as follows .

[0053] Furthermore, initially, the digital pulse signal generator is triggered directly by the code value returned by the motor without delay, which in turn triggers the laser to emit light and the high-speed camera to expose. In camera preview mode, the delay of the digital pulse signal generator is adjusted so that the position of the blade in the acquired image is aligned with... Consistent with the above, then following the steps described in S3, acquire images of the blade during the two exposure times while it is rotating. and

[0054] S7. Based on the rotor blade reference image obtained in step S5 after the first exposure, perform data processing on the image of the blade rotating at the reference position obtained in step S6 and the rotor blade reference image after the second exposure to calculate the pressure data on the surface of the rotating blade.

[0055] Furthermore, the specific implementation method of step S7 includes the following steps:

[0056] S7.1. Image Registration: Matching of corresponding marker points is performed using dot detection and image cross-correlation methods. , All registered to The image above shows the rotor blade test image after the first exposure following registration. Experimental images of rotor blades after registration and second exposure. Reference image of the rotor blade after registration and second exposure. ;

[0057] Furthermore, matching of corresponding marker points is performed using dot detection and image cross-correlation methods. When the blade rotates, the coating's fluorescence lifetime is longer, which leads to... Both have a certain degree of blurriness, and the corresponding markers in a blurry image may not be accurately identified. Therefore, the first step is to... Coarse-grained deblurring is performed to obtain an image that amplifies noise but has clear markers. .

[0058] The specific steps are as follows: First, the PSF (Point Spread Function) is roughly estimated by comparing the frequency domain of the blurred image and the clear image. Then, a regularization constraint is added to estimate the PSF by minimizing the error. The PSF in the frequency domain is transformed back to spatial format using an Inverse Fourier Transform (IFFT), and the result is centered. The obtained PSF is cropped, selecting a small region based on the degree of image blur. A Hanning window function is used to smooth the edges of the PSF and reduce frequency domain ringing effects. To ensure that image brightness is not affected, the PSF is normalized to maintain constant image brightness while avoiding brightness overflow due to an overly strong PSF. Finally, the blurred image is recovered using deconvolution, and Laplacian regularization is used to suppress noise.

[0059] based on , and Correlation of images is used to perform corresponding point matching to obtain the matched corresponding points. , and By combining affine transformation methods , and ,Will , Registration Obtain the registered image , .

[0060] S7.2. An adaptive region extraction method based on depth prior, for Mask M1 is obtained by mask extraction, and then... , as well as Multiplying with M1 yields a clean, background-removed image of the rotor blade after the first exposure. Clean, second-exposure image of the rotor blade after background removal. Reference image of the rotor blades after a clean second exposure, with the background removed. And a clean reference image of the rotor blades after the first exposure, with the background removed. ;

[0061] Furthermore, the depth-prior adaptive region extraction method first performs Otsu thresholding binarization on the registered reference image to obtain a preliminary foreground mask; then, the final mask is obtained through morphological opening and closing operations and maximum connected component filtering. Following the above method... Mask M1 is obtained by mask extraction;

[0062] S7.3. Based on autocorrelation analysis combined with blind deconvolution algorithm, for The rotor blade test image after the first exposure was obtained by deblurring. The second exposure image of the rotor blade after deblurring. ;

[0063] Then calculate the pressure P of the rotating blade, using the following formula:

[0064]

[0065] in, The atmospheric pressure is given in step S5.

[0066] Furthermore, the specific implementation method of step S7.3 includes the following steps:

[0067] S7.3.1. Quantitatively assess the degree of image blur through autocorrelation analysis;

[0068] First, select a local region in the image and calculate the correlation between this region and its neighboring regions. A higher autocorrelation value indicates less texture variation and a clearer image in that region. When the autocorrelation decreases with increasing sliding distance, it reflects varying degrees of blurring in the image. To visually describe this relationship, plot the function curve of sliding distance versus similarity, i.e., the autocorrelation curve. The autocorrelation curve of a clear image typically exhibits a steep decline; while the autocorrelation curve of a blurred image declines slowly due to weakened neighborhood details, presenting a "wide and flat" shape. When the similarity (autocorrelation) decreases to approximately 1 / e ≈ 0.37, the contribution of the sliding distance beyond this point to the overall similarity is considered negligible. Therefore, this sliding distance can be defined as the blur diffusion length of the image (i.e., the spatial influence range of the point spread function (PSF)). Since the point spread function (PSF) is usually symmetrically distributed in space, the effective length of the PSF is approximately twice the sliding distance corresponding to the autocorrelation decreasing to 1 / e.

[0069] S7.3.2. Construct a blind deblurring strategy;

[0070] For an image with a mask extracted, several rows (or columns) are processed. First, the autocorrelation function of each row (or column) is calculated. The autocorrelation function is defined as:

[0071]

[0072] in, This represents the discrete autocorrelation function value of the selected image row (or column) under a translation amount k. To represent the pixel intensity value at the i-th sampling position in the selected image row (or column), To represent the pixel intensity value at a location k sampling intervals away from the i-th sampling position, N represents the total number of sampling points in the selected image row (or column);

[0073] Based on the autocorrelation function, find the length of the PSF, i.e., the autocorrelation function is reduced to... Position at time. PSF length L estimated. Where idx is the autocorrelation function decreasing to The index position is determined. After point spread function (PSF) estimation, a blind deblurring algorithm is used to progressively recover the image and optimize the PSF. The image and PSF are continuously optimized through an iterative process. The algorithm primarily minimizes the error by updating the image and PSF and performing iterative optimization. The iterative formula of the algorithm is:

[0074]

[0075] in, It is the recovered image of the (k+1)th iteration. It is a blurry image. It is the point spread function (PSF). It is a constant used to prevent division by zero;

[0076] In each iteration, through The ratio is used to adjust the image restoration value and gradually optimize the PSF. To avoid the problem of PSF dimension mismatch, a row-first, column-later deblurring strategy is adopted. This strategy first deblurs each row of the image, and then deblurs the deblurred image in the column direction.

[0077] For each row of the image, we have:

[0078]

[0079] in, This represents the image in row r. This indicates the r-th row of the blurred image, and * denotes a convolution operation. After deblurring in the row direction, the result is used as input for deblurring in the column direction. Column deblurring follows the same principle as row-wise deblurring, and iterative optimization is performed using the same method. The PSF size is adjusted according to the length of the current column to avoid calculation errors caused by an excessively large PSF size. The formula is as follows:

[0080]

[0081] in, This represents the image in column c. This is the blurred image in column c. By performing deblurring first in rows and then in columns, the problem of PSF size mismatch is avoided, further improving the image restoration quality and preventing the PSF dimension mismatch issue.

[0082] It should be noted that 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 one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0083] Although this application has been described above with reference to specific embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of this application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in this application can be combined with each other in any way. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, this application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for measuring surface pressure of a rotor model, characterized in that, Includes the following steps: S1. Rotor blade surface treatment, including surface cleaning, primer spraying, PSP coating spraying, and marking points; S2. Measurement system setup, the measurement system includes the rotor blades obtained after surface treatment in step S1, motor, digital pulse signal generator, laser control box, laser, laser head beam expander, high-speed camera, and computer; S3. Construct a dual-frame exposure image acquisition method, setting the high-speed camera to perform two exposures after the laser emits a laser, and acquire the rotor blade image after the first exposure and the rotor blade image after the second exposure; S4. Obtain the temperature correlation coefficient of the PSP coating based on temperature calibration tests; S5. Based on the method in step S3, under normal pressure and at the reference position, acquire the rotor blade reference image after the first exposure and the rotor blade reference image after the second exposure; S6. Based on the method in step S3, during the operation of the test system, acquire images of the blade rotation at the reference position; S7. Based on the rotor blade reference image obtained in step S5 after the first exposure, perform data processing on the image of the blade rotating at the reference position obtained in step S6 and the rotor blade reference image after the second exposure to calculate the pressure data on the surface of the rotating blade. The specific implementation method of step S7 includes the following steps: S7.

1. Image Registration: Matching of corresponding marker points is performed using dot detection and image cross-correlation methods. , All registered to The image above shows the rotor blade test image after the first exposure following registration. Experimental images of rotor blades after registration and second exposure. Reference image of the rotor blade after registration and second exposure. ; S7.

2. An adaptive region extraction method based on depth prior, for Mask M1 is obtained by mask extraction, and then... , as well as Multiplying with M1 yields a clean, background-removed image of the rotor blade after the first exposure. Clean, second-exposure image of the rotor blades after background removal. Reference image of the rotor blades after a clean second exposure, with the background removed. And a clean reference image of the rotor blades after the first exposure, with the background removed. ; S7.

3. Based on autocorrelation analysis combined with blind deconvolution algorithm, for The rotor blade test image after the first exposure was obtained by deblurring. The second exposure image of the rotor blade after deblurring. ; First, calculate the autocorrelation function of the image rows or columns; the autocorrelation function is defined as: in, This represents the discrete autocorrelation function value of the selected image row or column under the translation amount k; To represent the pixel intensity value at the i-th sampling position in the selected row or column of the image, To represent the pixel intensity value at a location k sampling intervals away from the i-th sampling position, N represents the total number of sampling points in the selected image row or column; Based on the autocorrelation function, find the length of the PSF, i.e., the autocorrelation function is reduced to... Position at time; PSF length L estimation, Where idx is the autocorrelation function decreasing to The index position is determined; after the point spread function (PSF) is estimated, a blind deblurring algorithm is used to gradually recover the image and optimize the PSF; through an iterative process, the image and PSF are continuously optimized; the error is minimized by updating the image and PSF, and iterative optimization is performed; the iterative formula of the algorithm is: in, It is the recovered image of the (k+1)th iteration. It is a blurry image. It is the point spread function (PSF). It is a constant that prevents division by zero; In each iteration, through The ratio is used to adjust the image restoration value and gradually optimize the PSF.

2. The method for measuring surface pressure of a rotor model according to claim 1, characterized in that, In step S2, the rotor blades are mounted on a motor, which is connected to a digital pulse signal generator. The digital pulse signal generator is connected to a laser control box and a high-speed camera. The laser control box is connected to a laser, which is connected to a laser head beam expander. The laser emitted by the laser head beam expander illuminates the rotor blades. The high-speed camera is positioned above the rotor blades.

3. The method for measuring surface pressure of a rotor model according to claim 2, characterized in that, In step S4, the fluorescence decay time constant of the PSP coating is measured using a standard pressure source and a PSP sample in the calibration chamber. Then, the temperature of the calibration chamber is changed, and the measurement is performed again. The fluorescence decay time constant at each temperature point is recorded. Then, regression analysis is performed on the collected fluorescence decay time constants at each temperature point to obtain the first temperature correlation coefficient A(T) and the second temperature correlation coefficient B(T).

4. The method for measuring surface pressure of a rotor model according to claim 3, characterized in that, The reference image of the rotor blade obtained in step S5 after the first exposure is The reference image of the rotor blade after the second exposure is as follows: .

5. The method for measuring surface pressure of a rotor model according to claim 4, characterized in that, The rotor blade test image obtained in step S6 after the first exposure is as follows The rotor blade test image after the second exposure is as follows .

6. The method for measuring surface pressure of a rotor model according to claim 5, characterized in that, The specific formula for calculating the pressure P on the rotating blades is as follows: in, The atmospheric pressure is given in step S5.

Images