Method and system for determining key parameters of a polarized imaging abrasive particle sensor

Abstract

The application belongs to the technical field of mechanical equipment wear state monitoring. A polarization imaging abrasive particle sensor key parameter determination method and system are provided, and various different parameter combinations of emission wavelengths, oil pool heights and oil deterioration degrees are constructed. For each parameter combination, the oil medium absorption coefficient and scattering coefficient are called and loaded to the oil model, so that each sub-light source of the annular linear polarization light source array emits photons, and the movement trajectory of the photons is tracked until the photons are absorbed by the oil or reach the simulated imaging target. An original photon distribution map is generated, and Gaussian blur is applied for further processing to obtain a simulated image corresponding to different parameter combinations. A preset image evaluation index is used for evaluation to determine the best parameter combination. The key parameters of the polarization imaging sensor are optimized through the Monte Carlo model, which significantly improves the imaging quality of the polarization imaging sensor in the deteriorated turbid oil and enhances the accuracy of abrasive particle feature extraction.

Description

Technical Field

[0001] This invention relates to the field of mechanical equipment wear condition detection technology, specifically to a method and system for determining key parameters of a polarization imaging abrasive sensor. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] During operation, mechanical equipment generates abrasive particles in its lubrication system. These particles contain a wealth of information related to the wear condition of the equipment, such as particle concentration, size distribution, and morphology. These characteristics can comprehensively reflect the severity, rate, and mechanism of wear. Therefore, accurately acquiring these abrasive particle characteristics is crucial for ensuring the accuracy of monitoring the wear condition of mechanical equipment.

[0004] Optical imaging sensors can comprehensively extract features such as abrasive particle concentration, size, and morphology, accurately reflecting the wear state. However, oil commonly deteriorates and becomes turbid during long-term operation. Light scattering in the oil medium leads to a decrease in sensor imaging quality, severely limiting the ability to extract abrasive particle features. Although various sensor improvement schemes have been researched for this scenario, they suffer from low oil flow rates and abrasive particle overlap, limiting their practical value. Furthermore, these studies do not analyze the light scattering mechanism in deteriorated and turbid oil, thus limiting the improvement effect on abrasive particle imaging. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for determining key parameters of a polarization imaging abrasive sensor. By optimizing the key parameters of the polarization imaging sensor using a Monte Carlo model, the imaging quality of the polarization imaging sensor in deteriorated and turbid oil is significantly improved, and the accuracy of abrasive feature extraction is enhanced, providing support for long-term reliable monitoring of the wear condition of mechanical equipment.

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

[0007] In a first aspect, the present invention provides a method for determining key parameters of a polarization imaging abrasive sensor.

[0008] A method for determining key parameters of a polarization imaging abrasive sensor, with height as the basis. A point within the oil pool is designated as the origin O, and an O-XYZ rectangular coordinate system is constructed. A simulated imaging target is positioned on the bottom surface of the oil pool, and a ring-shaped linearly polarized light source array is arranged on the plane where Z=0 of the oil pool. The simulated imaging target is located in the positive Z-axis direction. The process includes the following steps:

[0009] construct a plurality of different parameter combinations of emission wavelengths, oil pool heights and oil deterioration degrees, for each parameter combination, call oil medium absorption coefficient and scattering coefficient to load to the oil model, so that each sub-light source of the annular linear polarization light source array emits photons, and track the motion trajectory of the photons until they are absorbed by the oil or reach the simulated imaging target;

[0010] generate an original photon distribution map, and apply Gaussian blur for further processing to obtain simulated images corresponding to different parameter combinations;

[0011] evaluate the simulated images corresponding to different parameter combinations by using a preset image evaluation index, and determine the optimal parameter combination.

[0012] In a second aspect, the present application provides a polarized imaging abrasive particle sensor key parameter determination system.

[0013] A polarized imaging abrasive particle sensor key parameter determination system, taking a certain point in an oil pool with a height of constructs an O-XYZ rectangular coordinate system, wherein the bottom surface of the oil pool is arranged with a simulated imaging target, the plane of the oil pool Z=0 is arranged with an annular linear polarization light source array, and the simulated imaging target is located in the positive direction of the Z axis, and comprises:

[0014] The photon tracking unit is configured to construct a plurality of different parameter combinations of emission wavelengths, oil pool heights and oil deterioration degrees, for each parameter combination, call oil medium absorption coefficient and scattering coefficient to load to the oil model, so that each sub-light source of the annular linear polarization light source array emits photons, and track the motion trajectory of the photons until they are absorbed by the oil or reach the simulated imaging target;

[0015] The image generation unit is configured to generate an original photon distribution map, and apply Gaussian blur for further processing to obtain simulated images corresponding to different parameter combinations;

[0016] The parameter optimization unit is configured to evaluate the simulated images corresponding to different parameter combinations by using a preset image evaluation index, and determine the optimal parameter combination.

[0017] In a third aspect, the present application provides a computer device, comprising: a processor and a computer readable storage medium;

[0018] The processor is adapted to execute a computer program;

[0019] The computer readable storage medium has a computer program stored therein, and the computer program is executed by the processor to realize the polarized imaging abrasive particle sensor key parameter determination method according to the first aspect of the present application.

[0020] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, the computer program being adapted to be loaded and executed by a processor to implement the key parameter determination method of the polarization imaging abrasive particle sensor according to the first aspect of the present application.

[0021] In a fifth aspect, the present application provides a computer program product, which comprises a computer program, the computer program being executed by a processor to implement the key parameter determination method of the polarization imaging abrasive particle sensor according to the first aspect of the present application.

[0022] Compared with the prior art, the present application has the following beneficial effects:

[0023] The present application innovatively provides a key parameter determination method of a polarization imaging abrasive particle sensor, which optimizes the key parameters of the polarization imaging abrasive particle sensor through a Monte Carlo model, realizes a major breakthrough in technical effects in multiple key aspects, for each group of parameters, calls the oil medium absorption coefficient and scattering coefficient to load to the oil model, simulates the motion trajectory of the photon emitted by the annular linear polarization light source array sub-light source, until the photon is absorbed by the oil or reaches the simulated imaging target, highly restores the complex propagation of light in the deteriorated turbid oil, and the generated original photon distribution map is subjected to PSF Gaussian blur processing, so that the simulation image clarity and contrast are greatly improved, the imaging quality of the sensor in the harsh oil environment is significantly improved, and the problems of previous imaging blur and detail loss are effectively solved.

[0024] The present application can more accurately simulate the propagation process of the photon in the oil by initially estimating the transmittance measured at the maximum thickness and combining the optimization algorithm, thereby improving the accuracy of the oil model; by tracking the motion trajectory of the photon until it is absorbed or reaches the simulated imaging target, the behavior of the photon in the oil can be comprehensively analyzed, and reliable data basis is provided for subsequent image generation; the original photon distribution map is generated by applying PSF Gaussian blur processing, a more actual simulation image is obtained, and the simulation image is evaluated through the preset image evaluation index, so that the optimal parameter combination can be scientifically determined, and strong support is provided for the design and optimization of the polarization imaging abrasive particle sensor, and the detection performance and reliability of the sensor are improved.

[0025] The application sets the analog imaging target as a circular metal with different diameters, can comprehensively simulate the imaging ability of the sensor on different sizes of abrasive particles, makes the research more universal and practical, can meet the needs of the diversity of abrasive particle sizes in actual detection, describes the interaction between the circular metal surface and the photons by using the Fresnel reflection coefficient, accurately calculates the reflection coefficient through a given formula, the formula considers the incident angle of the photons in the oil medium and the metal surface and the complex refractive index of the circular metal, can accurately describe the interaction mechanism of light and the metal surface, provides a precise theoretical basis for subsequent photon trajectory tracking and imaging simulation, which helps to deeply understand the propagation characteristics of light in the oil-metal system, optimizes the sensor design, improves the accuracy and sensitivity of the abrasive particle detection, and further improves the performance and reliability of the sensor in oil condition monitoring and fault diagnosis.

[0026] Advantages of the additional aspects of the application will be partially given in the following description, partially become obvious from the following description, or be known by the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0027] The drawings accompanying the specification of this application form a part thereof, serve to provide further understanding of the application, and together with the description of the exemplary embodiments of the application and explanations thereof serve to explain the application, and do not constitute an improper limitation of the application.

[0028] Figure 1 Schematic diagram of the Monte Carlo imaging simulation geometric model of the sensor provided for one exemplary embodiment of the application;

[0029] Figure 2 Polarization differential image quality change under different light source wavelengths provided for one exemplary embodiment of the application;

[0030] Figure 3 Simulated polarization differential images under different oil pool heights and oil degradation degrees provided for one exemplary embodiment of the application;

[0031] Figure 4 Image RMS contrast under different oil pool heights and oil degradation degrees provided for one exemplary embodiment of the application;

[0032] Figure 5 Image SSIM under different oil pool heights and oil degradation degrees provided for one exemplary embodiment of the application;

[0033] Figure 6 Schematic diagram of the polarization imaging abrasive particle sensor key parameter determination system provided for one exemplary embodiment of the application;

[0034] Figure 7 Schematic diagram of the computer device provided for one exemplary embodiment of the application. DETAILED DESCRIPTION

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0037] This implementation proposes a method for determining key parameters of a polarization imaging abrasive sensor, optimizing these key parameters to improve the sensor's monitoring capability. The method includes the following steps:

[0038] S101: Construct a geometric physical model, which is a three-dimensional model including a ring-polarized light source, an oil medium layer, abrasive particles, and a detector.

[0039] S102: Based on the Monte Carlo polarized photon transmission simulation method, the key sensor parameters affecting imaging capability in deteriorated and turbid oil scenarios are determined.

[0040] Specifically, in S101 of this implementation, a Monte Carlo polarization imaging model is constructed, and the light source system is modeled. The core structure of the sensor is simplified into three parts: a ring-shaped linearly polarized light source, an oil medium region, and a photon detection / imaging target. The model strictly defines the light source position, direction cosine, oil refraction / scattering coefficient, detector position, and imaging target size, etc., to construct a physical model environment that can realistically reflect the photon propagation and scattering characteristics in deteriorated and turbid oil, and efficiently perform Monte Carlo simulations. Specifically, this includes:

[0041] (1) Polarization light source settings.

[0042] (1-1) Establish a ring-shaped linearly polarized light source array.

[0043] like Figure 1 As shown, in the coordinate system of Plane, settings N A point light source is evenly distributed within a radius On the circumference. Among them, the first k The spatial coordinates of a point light source can be expressed as:

[0044] (1);

[0045] (1-2) Photon emission direction constraint.

[0046] Through cone angle Limiting the initial propagation direction range of photons, based on polar angle and azimuth parametric representation, the three-dimensional component expressions of the photon velocity vector are derived, see Figure 1 , can be expressed as:

[0047] (2).

[0048] where, the term is always positive, ensuring that all photons are injected into the medium along the axis direction.

[0049] (1-3) Radiation characteristic simulation.

[0050] The Lambertian radiation model is adopted to describe the statistical characteristics of the photon emission angle through the probability density function , which explains the physical meaning of the weighted sampling to the simulation of the divergence characteristics of the light source. The probability density function is:

[0051] (3).

[0052] (1-4) Polarization state initialization.

[0053] The Stokes vector of linearly polarized photons is:

[0054] (4).

[0055] where the polarization direction is x .

[0056] (2) Oil medium setting.

[0057] According to the structural layout of the abrasive grain polarization imaging sensor, the oil medium in the Monte Carlo imaging model is set in the axis direction of the ring light source, and its geometric model is a cube with side length L , height h . The bottom of the oil medium is a white diffuse reflection interface that is not transparent to light, with a reflectivity of . The diffuse reflection follows the Lambertian model, which is ideal uniform scattering, and the bottom is set as the z=35mm plane.

[0058] The absorption coefficient and the scattering coefficient of the oil medium are the core parameters of the photon transport simulation, which directly affect the propagation process of the photons in the medium. The absorption coefficient reflects the probability of photon absorption by the medium, while the scattering coefficient reflects the probability of photon scattering. A higher absorption coefficient will result in a shorter propagation distance of the photons in the oil, reducing the number of photons reaching the detector. A larger scattering coefficient will cause the direction of the photons to change frequently, increasing the interference of scattered light and reducing the imaging quality.

[0059] In order to accurately obtain the absorption coefficient and scattering coefficient of the oil medium, the present application adopts a diffusion approximation method based on macroscopic transmission signals combined with a spectral transmittance measurement method.

[0060] The basic principle of the diffusion approximation method is that the radiative transfer equation is simplified into a diffusion equation based on the isotropic assumption of photon transmission in a highly scattering medium, and specifically, the diffusion approximation method includes:

[0061] (5);

[0062] Among them, represents the photon flow density at the position, is a light source term; is a diffusion coefficient, and the diffusion coefficient is defined as:

[0063] (6);

[0064] In the formula, is a reduced scattering coefficient; is an anisotropy factor, which represents the forwardness of light scattering.

[0065] The diffusion equation describes the distribution of photons in the medium, but in fact, in order to obtain the absorption coefficient and scattering coefficient, the photon flow density is not directly measured, but the optical parameters of the oil medium are indirectly inverted by measuring the transmittance of the oil medium, and the transmittance can be represented as:

[0066] (7);

[0067] Among them, I t is the transmitted light intensity, I 0 is the incident light intensity. According to the diffusion equation, the relationship between the transmittance and the thickness can be simplified as:

[0068] (8);

[0069] Among them, is an effective attenuation coefficient, which can comprehensively represent the equivalent attenuation effect of absorption and scattering. can be represented as:

[0070] (9);

[0071] In the formula, R is an interface reflectivity, and for a normally incident plane wave, the interface reflectivity is determined by the difference in refractive index of the medium, and can be represented as: ​

[0072] (10);

[0073] wherein, n0is the refractive index of the incident side, here air ; n1is the refractive index of the transmitted side, here the oil medium, taking the commonly used value .

[0074] By measuring the transmittance data at least two thicknesses, a nonlinear equation set is constructed. Since the transmittance is simultaneously affected by the coupling of the absorption coefficient and the scattering coefficient, it is difficult to distinguish between the two by empirical formula of single thickness or single wavelength. By Levenberg-Marquardt algorithm, the overall fitting of the transmittance data measured at all thicknesses in the least squares sense is carried out, and the objective function is the minimization of the sum of squares of residuals, which is expressed as:

[0075] (11);

[0076] wherein, is the predicted transmittance calculated according to the diffusion approximation theory (i.e. formula (8)) for the initial guess or the current iteration of the absorption coefficient and the scattering coefficient of the given thickness , and the specific calculation method is:

[0077] (12);

[0078] (13);

[0079] wherein, represents the effective reflection coefficient, represents the actual transmittance obtained by directly measuring the spectrophotometer in the spectral transmittance experiment, corresponding to the same thickness .

[0080] By minimizing the sum of squares of residuals between them, the Levenberg-Marquardt algorithm can simultaneously correct and , so that the model predicted transmittance curve and the experimental curve can be as consistent as possible at all thickness points. In this way, the physical parameters that are consistent with the multiple scattering effect and the spectral transmittance measurement can be obtained.

[0081] (3) Detector setting.

[0082] In the present implementation, the detector parameter setting is carried out, and the photon detector adopts a composite scheme of direct photon detection combined with lens point spread function (PSF) post-processing, and the PSF is modeled as a two-dimensional Gaussian function:

[0083] (14);

[0084] wherein, is the standard deviation of the Gaussian kernel, is the position coordinate.

[0085] (4) Imaging target setup.

[0086] In the present implementation, the imaging target setup is performed, and the imaging target setup is simulated as a circular metal (iron material) with different diameters, including 100 pm, 40 pm, and 10 pm, to simulate the imaging capability of the sensor for micro abrasive particles in the range of 10-100 pm. The interaction of the metal surface with the photons is described by the Fresnel reflection coefficient:

[0087] (15);

[0088] wherein, is the incident angle of the photons in the oil medium and the metal surface, is the complex refractive index of the metal, represents the s component in the Fresnel reflection coefficient.

[0089] Specifically, in the present implementation S102, a large number of simulated photons are generated by using the Monte Carlo simulation method, and their propagation paths and interactions in the oil medium are tracked. Key parameters such as the wavelength of the light source, the height of the oil pool, and the degree of oil deterioration are changed, and how these changes affect the propagation characteristics of the photons is observed, and then the influence of these changes on the imaging quality is evaluated. By statistically analyzing the polarization state and spatial distribution of the simulated photons, how these parameters affect the clarity, contrast, and target recognition capability of the image is analyzed.

[0090] In order to qualitatively and quantitatively analyze the simulated imaging quality, three image evaluation indexes, intensity profile, root mean square contrast (RMS), and structure similarity index measure (SSIM), are used.

[0091] Intensity profile: by selecting a line or path on the image, the intensity values of the pixels along the path are plotted, reflecting the transition characteristics of the target and the background, and quantifying the edge sharpness and detail retention capability of the target.

[0092] Root mean square contrast (RMS Contrast): reflects the degree of dispersion of the pixel gray value in the image relative to the average value, and the formula is:

[0093] (16);

[0094] wherein, is the intensity value of a pixel is the average intensity of the image, is the image size. Higher RMS contrast means that the bright and dark areas in the image are more different, and the details are clearer. Structural Similarity Index (SSIM): measures the structural similarity between two images, integrates brightness, contrast and structural similarity, simulates the perception of human eyes to image quality, and the formula is:

[0095]

[0096] (17); wherein,

[0097] and x represent two images to be compared, y and are the average brightness of and x , respectively, y and are the standard deviations (reflecting the contrast) of and x , respectively, y is the covariance (reflecting the structural similarity) of and x . The value range of SSIM is [-1, 1], and the closer the value is to 1, the more similar the images are, y and are constants. Specifically, the key parameter determination method based on Monte Carlo simulation proposed by the present implementation mode includes the following processes:

[0098] 1) Set the simulation parameter range.

[0099] Light source wavelength range: starting from 380 nm, every 40 nm for a group of simulation, until 700 nm. Oil pool height range: the value range of oil pool height h is set to 1.0 mm to 5.0 mm, and every 0.5 mm sets a height point; oil deterioration degree range: the oil deterioration degree c is from 0 (brand new oil) to 1.0 (completely deteriorated), every 0.2 sets a deterioration degree point.

[0100] 2) Perform Monte Carlo simulation.

[0101]

[0102] ​The Monte Carlo method was used to simulate the propagation path of photons in an oil medium. First, based on the given light source wavelength *λ* and oil degradation concentration *r*, the absorption and scattering coefficients were calculated using the diffuse approximation model established above and the Levenberg-Marquardt least squares iterative algorithm. Then, during the simulation, each photon propagated at a step size *s*. , The photon travels along a random number between [0,1] and its weight is absorbed and attenuated. Then scattering occurs, and the scattering angle and azimuth are sampled using the Mie phase function to update the photon direction and its Stokes vector until the photon is absorbed or escapes the oil region—this process achieves different... Oil tank height and degree of degradation Accurate simulations of photon scattering and absorption in oil were performed under specific conditions. Finally, the quality of the images generated by each simulation was evaluated by calculating the root mean square contrast (RMS) and structural similarity index (SSIM) to quantify the imaging quality.

[0103] 3) Analyze the impact of key parameters.

[0104] The study analyzed the variation trend of imaging quality under different wavelengths to determine the optimal imaging wavelength range; it also investigated the combined effects of oil pool height and oil degradation degree on imaging quality, and determined the influence law of oil pool height on imaging quality under different degradation degrees.

[0105] Based on simulation results, the optimal combination of key parameters for the polarization imaging sensor was determined. By comparing the imaging quality indicators under different parameter combinations, the parameter settings that achieve the best imaging effect were identified. These optimal parameters will be used to guide the design and manufacturing of actual sensors to ensure the acquisition of high-quality abrasive images in practical applications.

[0106] The following is a specific example. First, the measured values ​​of the optical parameters of the oil are given, as shown in Table 1.

[0107] Table 1: Measured values ​​of optical parameters of oil

[0108]

[0109] Based on the constructed Monte Carlo polarization imaging model, the main fixed parameters of the polarization source, oil medium, detector, and simulated imaging target are first given, as shown in Table 2.

[0110] Table 2: Monte Carlo Simulation Parameter Settings

[0111]

[0112] The specific simulation process is as follows:

[0113] S1: For each set of parameters (λ, h, c), call the oil medium absorption coefficient and scattering coefficient loaded into the oil model, then emit photons, track their movement trajectory until absorbed or reach the detector;

[0114] S2: Generate the original photon distribution map → apply PSF Gaussian blur → output the simulated image;

[0115] S3: Calculate the RMS contrast and SSIM of the image (based on the new oil low height image), determine the optimal parameter combination.

[0116] First, the light source wavelength optimization is carried out, the test conditions are: h = 2 mm, c = 40%, λ = 380 nm ~ 700 nm (step 40 nm), each group of λ is simulated 4 times, the RMS contrast and SSIM index of the simulated imaging under each wavelength are calculated and the mean value is taken, and the results are as shown in Figure 2 .

[0117] It can be seen that when λ < 660 nm, the RMS contrast increases approximately linearly with λ; when λ = 660 nm, RMS = 0.28, SSIM = 0.25, and the imaging quality is best; in order to avoid the interference of oil absorption peak when λ > 700 nm, λ = 660 nm is selected as the best wavelength.

[0118] Secondly, the oil pool height and oil degradation degree double parameter optimization is carried out, the test design is: λ = 660 nm, h = 1.0 ~ 5.0 mm (step 0.5 mm), c = 0 ~ 1.0 (step 0.2), a total of 54 groups of experiments, each group is simulated twice, through the grouping simulation experiment of two parameters, 54 groups of polarization difference images are obtained.

[0119] As shown in Figure 3 , the simulated polarization difference images under different oil pool heights and oil degradation degrees, the RMS contrast and SSIM of the above 54 groups of original images are calculated, and the results are as shown in Figure 4 and Figure 5 , the lowest values of the two image quality evaluation indexes also appear in the lower right corner of the experimental parameter combination ( h = 5.0 mm, c= 1.0), which are far lower than the minimum threshold value required for target detection. This shows that under this extreme condition, the improvement of image quality by polarization differential imaging cannot meet the basic requirements of feature extraction of the target, so the sensor oil pool height parameter or the limit of the applicable oil degradation level needs to be compromised. Since the oil pool height not only directly determines the flow rate of the oil in the sensor and the adsorption performance of the abrasive particles, but also indirectly affects the maximum oil degradation level that the sensor can adapt to. Lower oil pool height may lead to poor oil flow, affecting the effective adsorption of abrasive particles, while higher oil pool height will increase the transmission distance of light in the oil, intensify scattering and absorption, and thus reduce the imaging quality, especially when the oil degradation level is high. In order to enable the sensor to have stronger adaptability to actual working conditions and ensure that clear image features of abrasive particles can be obtained as accurately as possible during the entire oil change cycle of the equipment, the optimal oil pool height is set to h = 2.5 mm.

[0120] Under the condition that the optimal oil pool height is set to h = 2.5 mm, even if the oil degradation level is as high as 1.0, the 50 μm target in the polarization differential image can still be clearly identified, and the corresponding RMS contrast and SSIM values are 0.306 and 0.22, respectively, which can meet the basic recognition requirements of the human eye and the minimum requirements of the image quality for the subsequent abrasive particle recognition algorithm, providing a strong guarantee for the reliability and effectiveness of the sensor in practical applications. In summary, according to the model establishment and simulation result analysis, the optimal parameter combination of the sensor is the light source wavelength , the oil pool height h = 2.5 mm, which can optimize the imaging capability of abrasive particles in degraded and turbid oil under the premise of high flow rate and high abrasive particle adsorption rate.

[0121] Figure 6 A polarization imaging abrasive particle sensor key parameter determination system is shown, comprising:

[0122] A photon tracking unit is configured to construct a plurality of different parameter combinations of emission wavelengths, oil pool heights, and oil degradation levels, for each parameter combination, load the oil medium absorption coefficient and scattering coefficient to the oil model, so that each sub-light source of the annular linearly polarized light source array emits photons, and track the motion trajectory of the photons until they are absorbed by the oil or reach the simulated imaging target.

[0123] An image generation unit is configured to generate an original photon distribution map and apply a PSF Gaussian blur for further processing to obtain a simulated image corresponding to different parameter combinations.

[0124] The parameter optimization unit is configured to evaluate the simulation images corresponding to different parameter combinations by using a preset image evaluation index, and determine an optimal parameter combination.

[0125] It can be understood that the above-mentioned units can be combined into one or several other units respectively or totally, or some of the units can be further split into a plurality of units with smaller functions to constitute, which can realize the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions, and in actual application, the function of one unit can also be realized by a plurality of units, or the functions of a plurality of units are realized by one unit. In other embodiments of the present application, the system can also include other units, and in actual application, these functions can also be realized by other units and can be realized by a plurality of units in cooperation.

[0126] According to another embodiment of the present application, the system described in the embodiment can be constructed by running a computer program (including program code) capable of performing each step involved in the corresponding method of the present application on a general computing device such as a computer including processing elements and storage elements such as a Central Processing Unit (CPU), a Random Access Memory (RAM), a Read Only Memory (ROM), etc., the computer program can be recorded on a computer readable recording medium, and loaded into the above-mentioned computing device through the computer readable recording medium and run therein.

[0127] Figure 7 A computer device is shown, which includes a processor, a communication interface and a computer readable storage medium. Wherein, the processor, the communication interface and the computer readable storage medium can be connected through a bus or other means.

[0128] Wherein, the communication interface is used for receiving and sending data, the computer readable storage medium can be stored in the memory of the electronic device, the computer readable storage medium is used for storing a computer program, the computer program includes program instructions, and the processor is used for executing the program instructions stored in the computer readable storage medium.

[0129] The processor is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, and is particularly suitable for loading and executing one or more instructions to realize a corresponding method process or a corresponding function.

[0130] The processor is configured to perform the following process:

[0131] Constructing a plurality of different parameter combinations of emitting wavelengths, oil pool heights and oil deterioration degrees, for each parameter combination, calling oil medium absorption coefficient and scattering coefficient to load to the oil model, so that each sub-light source of the annular linear polarized light source array emits photons, and the motion trajectory of the photons is tracked until the photons are absorbed by the oil or reach the simulated imaging target;

[0132] Generating an original photon distribution map, and applying PSF Gaussian blur for continuous processing to obtain a simulated image corresponding to the different parameter combinations;

[0133] The simulated images corresponding to the different parameter combinations are evaluated by using a preset image evaluation index, and the best parameter combination is determined.

[0134] The application also provides a computer readable storage medium, which is a memory device in an electronic device and is used for storing programs and data. It can be understood that the computer readable storage medium herein can include a built-in storage medium in the electronic device, and of course can also include an expansion storage medium supported by the electronic device. The computer readable storage medium provides a storage space, and the storage space stores a processing system of the electronic device.

[0135] In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory; optionally, the computer readable storage medium can also be at least one computer readable storage medium located away from the aforementioned processor.

[0136] In one embodiment, the computer readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer readable storage medium to implement the following processes:

[0137] Constructing a plurality of different parameter combinations of emitting wavelengths, oil pool heights and oil deterioration degrees, for each parameter combination, calling oil medium absorption coefficient and scattering coefficient to load to the oil model, so that each sub-light source of the annular linear polarized light source array emits photons, and the motion trajectory of the photons is tracked until the photons are absorbed by the oil or reach the simulated imaging target;

[0138] Generating an original photon distribution map, and applying PSF Gaussian blur for continuous processing to obtain a simulated image corresponding to the different parameter combinations;

[0139] The simulated images corresponding to the different parameter combinations are evaluated by using a preset image evaluation index, and the best parameter combination is determined.

[0140] The application further provides a computer program product or computer program, which comprises computer instructions stored in a computer readable storage medium.

[0141] A plurality of different parameter combinations of emission wavelengths, oil pool heights and oil deterioration degrees are constructed, for each parameter combination, the oil medium absorption coefficient and scattering coefficient are loaded to the oil model, so that each sub-light source of the annular linearly polarized light source array emits photons, and the motion trajectory of the photons is tracked until the photons are absorbed by the oil or reach the simulated imaging target;

[0142] An original photon distribution map is generated, and a PSF Gaussian blur is applied for continuous processing to obtain a simulated image corresponding to the different parameter combinations;

[0143] The simulated images corresponding to the different parameter combinations are evaluated by using a preset image evaluation index, and the best parameter combination is determined.

[0144] Those skilled in the art can be aware that the units and algorithm steps of the examples described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0145] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in or transmitted by a computer readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (for example, coaxial cable, optical fiber, digital line) or wireless (for example, infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium that can be accessed by a computer or a data processing device such as a server, data center, etc. integrated with one or more available media. The available media can be magnetic media (for example, floppy disk, hard disk, magnetic tape), optical media (for example, DVD), or semiconductor media (for example, solid state disk) and the like.

[0146] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A polarization imaging abrasive particle sensor key parameter determination method, characterized in that; with a height of A point in the oil pool is taken as the origin O, and an O-XYZ rectangular coordinate system is constructed, wherein the bottom surface of the oil pool is arranged with a simulated imaging target, an annular linearly polarized light source array is arranged on the plane of Z=0 of the oil pool, and the simulated imaging target is located in the positive direction of the Z axis, comprising the following processes: The parameter combinations of different emission wavelengths, oil pool heights and oil deterioration degrees are constructed, for each parameter combination, the oil medium absorption coefficient and scattering coefficient are called to load to the oil model, each sub-light source of the annular linear polarization light source array emits photons, the motion trajectory of the photons is tracked until the photons are absorbed by the oil or reach the simulated imaging target, the propagation path of the photons in the oil medium is simulated by using the Monte Carlo method; An original photon distribution map is generated, and a Gaussian blur is applied for further processing to obtain a simulated image corresponding to each parameter combination; The simulated images corresponding to different parameter combinations are evaluated by using a preset image evaluation index to determine the best parameter combination.

2. The polarimetric imaging abrasive particle sensor key parameter determination method of claim 1, wherein ； An annular linear polarization light source array is arranged on the plane of the oil pool Z=0, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target is located in the positive direction of the Z axis, and the simulated imaging target set up N A point light source is evenly distributed within a radius. On the circumference, the first k The spatial coordinates of the point light source are: ； ​ ； wherein , representing a cone angle of the photons, representing a polar angle, representing an azimuthal angle.

3. The polarimetric imaging abrasive particle sensor key parameter determination method of claim 1 wherein ； ​ ； wherein, represents a predicted transmittance calculated from the first thickness value, represents a real transmittance corresponding to the first thickness value, represents the number of thickness values.

4. The polarimetric imaging abrasive particle sensor key parameter determination method of claim 1 wherein ； ​ ； wherein is the angle of incidence of the photons at the oil medium and the metal surface, is the complex refractive index of the circular metal.

5. The polarimetric imaging abrasive particle sensor key parameter determination method of claim 1 wherein ； ​ 6. A polarized imaging abrasive particle sensor key parameter determination system characterized by; with a height of A point in the oil pool with a height of 0.5 m is taken as the origin O, and an O-XYZ rectangular coordinate system is constructed, wherein the bottom surface of the oil pool is arranged with a simulated imaging target, an annular linearly polarized light source array is arranged on the plane of Z=0 of the oil pool, and the simulated imaging target is located in the positive direction of the Z axis, comprising: ​ ​ ​ 7. The polarimetric imaging abrasive particle sensor key parameter determination system of claim 6, wherein ； ​ ； wherein, represents a predicted transmittance calculated from the first represents a real transmittance corresponding to the first represents the number of thickness values.​​ 8. A computer device, comprising: ​ ​ ​ ​ 9. A computer-readable storage medium, characterized in that, ​ 10. A computer program product, characterised in that, The computer program product comprises a computer program which, when executed by a processor, implements the method for determining key parameters of a polarisation imaging abrasive particle sensor according to any one of claims 1 to 5.

Images