Structure surface temperature testing system based on color difference identification of photosensitive paint

Abstract

The invention relates to the technical field of temperature testing, in particular to a structure surface temperature testing system based on photosensitive coating chromatic aberration recognition, which comprises a chromatic aberration feature extraction module, a heat conduction track analysis module and a temperature gradient reconstruction module. According to the method, the space-time evolution characteristics of a chromaticity field are captured through a dynamic gradient analysis technology, the non-uniform thermal field recognition sensitivity is improved, the stable temperature resolution is kept under complex illumination, a transient thermal shock propagation path is recognized and analyzed based on a heat conduction track cluster motion mode, the system response delay is shortened, and a nonlinear temperature-sensitive characteristic compensation mechanism is adopted; measurement deviation caused by coating process difference is overcome, higher-precision temperature reconstruction of a curved surface structure is realized, space correction and direction weighting are combined to enhance the spatial analysis capability of a temperature gradient field, a tiny heat affected area is accurately identified, multi-dimensional coupling analysis remarkably reduces temperature field reconstruction errors of a large-scale structure, and the engineering applicability is superior to that of a traditional method.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature testing, and in particular to a structure surface temperature testing system based on photosensitive coating color difference recognition. Background Art

[0002] The field of temperature testing technology includes methods and devices for detecting and monitoring temperature changes in different environments or structures. The core content of this technical field is to accurately obtain the temperature information of the target object to evaluate its operating status, changes in material properties and safety control. In general, temperature testing technology mainly covers contact and non-contact temperature measurement methods. The former includes the use of sensors such as thermocouples and thermistors, while the latter often uses principles such as infrared temperature measurement and thermal imaging detection to sense the temperature of the target. In engineering applications, structural surface temperature testing is not only an important means of health monitoring during operation, but also plays a key role in the construction stage. For example, in the process of composite material molding, early concrete curing or thermal spraying construction, accurate acquisition of the temperature field is crucial for quality control. Therefore, a temperature sensing system applicable to the entire process (including the construction period and the operation period) has significant engineering value.

[0003] The structural surface temperature measurement system based on photosensitive coating color difference recognition refers to a testing device and method that indirectly determines the surface temperature of a structure by applying a specific photosensitive coating to the surface and utilizing the color change patterns exhibited by the coating at different temperatures, combined with an optical image acquisition device to obtain surface color difference information. The system includes the screening and formulation of photosensitive coating materials, analysis of the impact of coating processes on temperature-sensitive properties, quantitative establishment of the relationship between color difference changes and temperature, spectral analysis and processing of image data, and temperature calculation based on image feature extraction. The process typically involves capturing a color image of the structural surface using a standard light source. By analyzing the differences in color components in the target area of ​​the image and referencing the known temperature response curve of the coating, the numerical identification of the structural surface temperature is ultimately achieved.

[0004] Traditional photosensitive coating temperature measurement methods are limited by the static color difference analysis framework, making it difficult to effectively handle the spatiotemporal correlation characteristics of dynamic temperature fields and prone to losing critical heat conduction information in scenarios with rapid temperature changes. Existing colorimetry-temperature mapping models fail to account for the nonlinear characteristics of material thermal responses, resulting in missed detection of key temperature nodes in the curing monitoring of multilayer composite materials. Discrete contact temperature measurement methods are limited by fixed point distribution and cannot fully capture the temperature gradient distribution characteristics of curved structures. Colorimetry processing methods based on global averaging ignore the differences in local heat conduction paths, making it difficult to accurately distinguish between the thermal evolution patterns of the interior and surface layers in the maintenance monitoring of large-volume concrete structures. Existing systems lack a dynamic compensation mechanism for coating aging effects, and measurement accuracy continues to decline during long-term monitoring due to material performance degradation. Furthermore, traditional methods do not establish a correlation model between colorimetric changes and thermophysical processes, making it impossible to effectively separate the combined effects of conductive and radiant heat in scenarios where complex heat sources coexist. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a structure surface temperature testing system based on photosensitive coating color difference recognition.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solution: a structure surface temperature measurement system based on photosensitive coating color difference recognition, comprising: The color difference feature extraction module is used to obtain the color distribution data of the pixels on the coating surface through the imaging device, calculate the brightness gradient of adjacent pixels using the sliding window difference method, generate the color gradient vector, mark the color difference response feature points that meet the threshold, and pass it to the heat conduction trajectory analysis module; A heat conduction trajectory analysis module is used to perform time-series analysis on the chromaticity gradient vector, calculate the consistency of the motion trajectory of the color difference response feature point cluster using a vector field clustering algorithm, generate a main heat conduction direction field, and simultaneously calculate the change in pixel chromaticity distribution data per unit time to generate a chromaticity change rate field, which is then passed to the temperature gradient reconstruction module; The temperature gradient reconstruction module is used to construct a spatial correction coordinate system based on the main heat conduction direction field, and use an adaptive weight distribution algorithm to perform direction weighted fusion on the chromaticity change rate field. A temperature gradient distribution tensor is generated, and the coordinates of abnormal regions of the temperature gradient distribution tensor are extracted and transferred to a coating parameter optimization module.

[0007] As a further solution of the present invention, in the chromaticity-temperature mapping relationship, Indicates the lightness value in the CIELab* color space, Indicates the temperature measurement value, is the coefficient of the quadratic term, is the coefficient of the first-order term, is the reference brightness compensation value; The color difference response feature points are specifically gradient amplitude, spatial coordinates, and response timestamps. The main heat conduction direction field includes direction vector matrix, cluster center trajectory, and heat flux density distribution. The chromaticity change rate field specifically refers to the rate gradient tensor, time differential parameter, and abnormal fluctuation area, and the temperature gradient distribution tensor includes anisotropy coefficient, heat conduction rate matrix, and temperature mutation threshold; In the heat flux distribution calculation, represents the thermal conductivity of the material, represents the temperature gradient vector.

[0008] As a further solution of the present invention, the specific implementation process of the sliding window difference method is as follows: setting a 3×3 pixel detection window, calculating the brightness gradient between the center pixel of the window and the adjacent pixels in the CIELab* color space, ,when And the hue angle changes When , it is marked as a valid color difference response feature point; The vector field clustering algorithm uses dynamic time warping (DTW) to calculate the consistency coefficient of the feature point cluster motion trajectory ,in Represents the coordinate vector of the i-th feature point in the k-th time frame, is the speed standard deviation threshold, when When it is determined to be a homologous heat conduction trajectory; The maximum detection speed of the system The critical speed value is obtained through calibration experiments. The experimental method is: the heat source moving speed is increased in steps of 0.1 mm / s on the surface of the standard specimen, and the critical speed value is recorded when the recognition rate of the color difference response feature point is lower than 95%.

[0009] As a further solution of the present invention, the time differential parameter is specifically: The chroma sampling interval is 1 second, and the rate gradient tensor is calculated using the central difference method: , synchronously calculate the chromaticity change acceleration , when the absolute value of acceleration exceeds When marked as abnormal fluctuation area; The correlation between the abnormal fluctuation area determination and temperature mutation is established by establishing a partial differential equation Verify, where is the material density, is the specific heat capacity, is the thermal conductivity, is the heat source term.

[0010] As a further solution of the present invention, the method for constructing the space correction coordinate system is: taking the maximum vector direction of the main heat conduction direction field as the X' axis, establishing the deviation angle with the direction field The rotating coordinate system of The adaptive weight allocation algorithm adopts an exponential decay function ,in Indicates the difference between the current pixel chromaticity change rate and the neighborhood average rate, is the rate standard deviation; The neighborhood average rate is calculated using a mean filtering algorithm with a 3×3 pixel window.

[0011] As a further solution of the present invention, the method for determining the temperature mutation threshold is: statistically analyzing the standard deviation of the historical temperature gradient distribution tensor ,set up is the abnormal temperature mutation threshold. When the thermal conductivity matrix value of the detection area exceeds When the warning is triggered.

[0012] As a further solution of the present invention, the generation logic of the coating thickness control instruction set includes: according to the temperature gradient amplitude of the abnormal area Thermal resistance of coating benchmark Calculate the thickness adjustment ,in is the material thermal conductivity compensation coefficient; The thermal conductivity compensation coefficient of the material is obtained through a thermal resistance experimental calibration curve. The experimental method is: measuring the thermal resistance change rate of coatings of different thicknesses under a reference temperature gradient, and taking the slope of the linear regression curve as the K value.

[0013] As a further embodiment of the present invention, the system further comprises: A coating parameter optimization module is used to analyze the coordinates of the abnormal area, match the thermal response characteristics database of the photosensitive material, execute the thickness control strategy logic judgment, and generate a coating thickness control instruction set; The coating thickness control instruction set specifically includes a thickness adjustment value, a material thermal resistance parameter, and spraying path coordinates corresponding to the coordinates of the B-spline curve control points.

[0014] As a further solution of the present invention, the method for generating the spray path coordinates includes: projecting the coordinates of the abnormal area onto a three-dimensional surface model reconstructed by acquiring point cloud data through laser scanning, using B-spline curve interpolation to generate a continuous spray path, and controlling the point spacing. , radius of curvature .

[0015] As a further solution of the present invention, the matching method of the material thermal resistance parameters is: according to the anisotropy coefficient in the temperature gradient distribution tensor ,when Select thermal resistance value when high-resistance material, when Select thermal resistance value when Conventional materials; The anisotropy coefficient is determined by a heat conduction comparison test, wherein the test method is as follows: heat sources in different directions are set on the surface of a standard specimen, and a critical mutation point of the ratio of the anisotropic heat conduction rate is measured.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the spatiotemporal evolution characteristics of the chromaticity field are captured by dynamic gradient analysis technology, which significantly improves the recognition sensitivity of non-uniform thermal fields and maintains stable temperature resolution under complex lighting conditions. Cluster motion pattern recognition based on heat conduction trajectories effectively analyzes the propagation path of transient thermal shocks and shortens system response delays. A nonlinear temperature-sensitive characteristic compensation mechanism is adopted to overcome the measurement deviation caused by coating process differences and achieve higher-precision temperature reconstruction in curved structure detection. The combination of spatial correction and directional weighting technology enhances the spatial resolution capability of the temperature gradient field and accurately identifies tiny heat-affected areas. This processing logic significantly reduces the temperature field reconstruction error in large-scale structure monitoring through multi-dimensional coupling analysis of the chromaticity field and the heat conduction path, and has better engineering applicability than traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is an overall flow chart of the structural surface temperature testing system based on photosensitive coating color difference recognition of the present invention; DETAILED DESCRIPTION

[0018] To make the purpose, technical solutions and advantages of the present invention clearer, the following is a detailed description of the technical solutions based on software implementation in conjunction with the system architecture diagram and embodiments. It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present invention and do not constitute a limitation on the scope of protection.

[0019] In the description of this invention, the system architecture relationships or data processing flows indicated by terms such as "layer," "module," "interface," "data flow," "client," and "server" are defined based on the architecture diagrams or flow charts corresponding to the embodiments. This expression is intended solely to clarify the logical relationships between the various elements of the technical solution and does not limit the physical deployment form. The term "plurality" encompasses two or more technical units, including but not limited to scalable elements such as multiple data nodes, processing threads, service instances, or functional components. The specific number will be determined based on the actual business scenario and requires special explanation.

[0020] See also Figure 1The present invention provides a technical solution: a structure surface temperature testing system based on photosensitive coating color difference recognition includes: The color difference feature extraction module uses an imaging device to acquire pixel color distribution data on the coating surface. Specifically, for example, on a high-temperature gas turbine blade test platform, the blade surface was pre-sprayed with a LCRHalcrest TLC R75C10W photosensitive temperature-sensitive coating. A Basleraceac A1920-155µm industrial camera paired with a Computar M2514-MP2 megapixel fixed-focus lens (focal length 25mm, aperture F1.4) was used. The camera was connected to an image acquisition card via a Gigabit Ethernet interface. The camera lens was vertically aligned with the blade area to be measured, and the working distance was set to 300mm to ensure that the imaging field of view covered key blade locations, such as the leading edge or pressure surface. The camera exposure time was set to 500µs, the gain to 0dB, and the frame rate to 20 frames / second. During thermal shock tests on the blade, such as those simulating ignition and flameout conditions, the camera continuously captured a sequence of images of the coating surface. After each frame of image is captured, the image data (e.g. 2048×1080 pixel resolution) is transferred to the computer memory in the original Bayer format. Subsequently, the original image data is white-balanced, and the correction parameters are calibrated under the test environment lighting based on the standard color palette. Next, the color space conversion is performed to convert the RGB (red, green, blue) three-channel pixel values ​​after white balance correction into the CIELab color space pixel by pixel. (brightness), (red / green axis), (yellow / blue axis) three components. For example, the RGB value collected at a certain pixel is (210,120,80), and the corresponding value is calculated using the standard RGB to CIELab conversion formula (based on the D65 standard light source). The value is 75.3, The value is 30.2, The value is 40.5. , , The values ​​together constitute the pixel chromaticity distribution data of the coating surface at that moment.

[0021] After obtaining the chromaticity distribution data, the sliding window difference method is used to calculate the brightness gradient of adjacent pixels. Set a 3×3 pixel detection window, which is located in the entire Slide pixel by pixel on the image. For each 3×3 area covered by the window, take the center pixel as the reference and calculate the distance between it and the surrounding 8 adjacent pixels. For example, the value of the center pixel P0 of the current detection window is The value is , its right adjacent pixel P1 The value is Then the brightness gradient component between P0 and P1 is Similarly, calculate the brightness gradient of P0 and the other 7 adjacent pixels. These gradient values ​​together represent the intensity and direction of the brightness change near the central pixel P0. For example, if the central pixel , the right pixel , then the absolute value of the brightness difference between the two is .

[0022] Then, a chromaticity gradient vector is generated. The chromaticity gradient vector here is not a traditional mathematical gradient vector. , but refers to the comprehensive consideration of the characteristics of brightness gradient and hue angle change within the detection window. Specifically, for the center pixel of the detection window, in addition to calculating the brightness gradient between it and the adjacent pixels In addition, the hue angle between it and the adjacent pixels needs to be calculated Changes For example, the hue angle of the central pixel P0 is , the hue angle of its adjacent pixel P1 is , then the hue angle changes This group consists of and The constructed features are used for subsequent feature point marking.

[0023] Next, mark the color difference response feature points that meet the threshold. For each central pixel of the 3×3 detection window, calculate the brightness gradient based on its brightness gradient with the 8 adjacent pixels. and hue angle changes When the brightness gradient between the central pixel and any of its adjacent pixels And the corresponding hue angle change between these two pixels When , the central pixel is marked as a valid color difference response feature point. These two threshold parameters and The setting is based on the following: Luminance gradient threshold : Reference: In CIELab* color space, (Total color difference) usually takes 2.3 as a reference value for a perceptible difference (Just Noticeable Difference, JND). , but the principle is similar: the human eye can only stably perceive when the brightness change reaches a certain level. This threshold is designed to filter out visually discernible coating brightness changes caused by temperature changes, while excluding background noise or weak, non-significant color fluctuations.

[0024] Experimental verification process: Experimental materials: The same photosensitive coating as used in actual applications (such as LCRHalcrestTLCR75C10W) was evenly sprayed on 10 standard nickel-based high-temperature alloy specimens (size 50mm×50mm×2mm), and the coating thickness was controlled at 50±5μm.

[0025] Experimental equipment: precision temperature controlled heating table (temperature control accuracy ±0.1 ), the aforementioned imaging system (Basler camera and Computar lens), and a standard D65 light source illumination box.

[0026] Experimental steps: 1. Place the specimen in the lighting box and use the imaging system to focus on the specimen surface. 2. Set the initial reference temperature, for example, 750 , record the pixel points on the coating surface at this time 3. Take 0.2 The step length is used to slowly increase or decrease the temperature of the heating stage. After each step length change, it is stabilized for 30 seconds. The image is collected and the new value of each pixel is calculated. value, get 4. At the same time, invite 5 observers who have been trained in color recognition to judge whether there is a perceptible brightness difference between the current coating color and the coating color at the reference temperature under uniform observation conditions. Record the temperature difference when the observer first perceives the difference and the corresponding average 5. Repeat steps 3 and 4 to cover the main color change temperature range of the paint.

[0027] Experimental data and results: Table 1: Summary of threshold calibration experimental data; ; As shown in Table 1, based on multiple experimental data, When the value reaches about 2.3, the observer can stably identify the brightness change. Considering the robustness of the system application, 2.3 is selected as judgment threshold.

[0028] Hue angle change threshold : Reference: Changes in hue angle directly reflect changes in the color's hue, such as from orange to yellow. A change in hue angle typically indicates a hue shift that is discernible to the naked eye, helping to distinguish true material color changes from light and dark changes caused solely by factors such as uneven lighting.

[0029] Experimental verification process: Experimental materials and equipment Threshold calibration experiment.

[0030] Experimental steps: same Calibrate experimental steps 1 and 2.

[0031] With 0.2 As the step size, slowly change the temperature of the heating stage, collect images and calculate the pixel 、 Value, and then calculate the hue angle and its variation .

[0032] Ask the observer to judge whether there is a perceptible difference in "color" rather than just "lightness" between the current coating color and the coating color at the reference temperature. Record the temperature difference when the observer first perceives the difference in hue and the corresponding average value.

[0033] Experimental data and results: Table 2: Summary of threshold calibration experimental data; ; As shown in Table 2, the experiment shows that the average The hue angle change can be stably perceived as a change in hue. As judgment threshold.

[0034] Example: Assume that in a 3×3 detection window, the chromaticity value of the central pixel P0 is The adjacent pixel P1 on the right has a chromaticity value of Calculate the difference between P0 and P1 : . Calculate the hue angle of P0 : (Use the inverse tangent function and convert the result into an angle.) Calculate the hue angle of P1 : Calculate the hue angle change between P0 and P1 : . Judgment: Due to and , so the center pixel P0 (or its boundary with P1) is marked as a valid color difference response feature point. The coordinates of these marked feature points (e.g., (x, y) pixel coordinates) and the corresponding marking time (frame number) are recorded and passed to the subsequent heat conduction trajectory analysis module. For example, if the coordinates of P0 are (1024, 530) and the current image is the 100th frame, the data (1024, 530, 100) is output as the feature point information.

[0035] See also Figure 1 The heat conduction trajectory analysis module receives the color difference response feature point data passed by the color difference feature extraction module. This data typically includes the feature point's 2D coordinates (x, y) in the image and the time frame number t in which it was identified. For example, analyzing 100 consecutive image frames (each frame is 0.05 seconds apart, for a total of 5 seconds of data), thousands of such feature points may be detected.

[0036] First, the color difference response feature point data is analyzed in time series. This process associates the feature points of the same physical thermal event at different time frames to form a motion trajectory. The set of feature points identified in the frame image ,in , the system will try to match it with the Feature point set in the frame image Matching is based on spatial proximity and motion continuity. For example, if In the Frame, then in Find the most likely corresponding point within a preset search radius of the frame (e.g. 5 pixels) Through this frame-by-frame matching, a time series of feature points, namely the trajectory, can be obtained. ,in It's a trajectory In the The coordinate vector of the time frame (if the trajectory exists in this frame).

[0037] Then, the vector field clustering algorithm is used to calculate the consistency of the motion trajectory of the color difference response feature point cluster. Here, a variant of the dynamic time warping (DTW) algorithm is used to measure the consistency of the two trajectories. and The formula for calculating the consistency coefficient is: Parameter details: :Indicates the The trajectory and The consistency coefficient between the two trajectories. Its value range is [0,1]. The closer the value is to 1, the more similar or consistent the two trajectories are.

[0038] : Indicates the number of time frames that two trajectories have in common when compared. For example, if the trajectory Exists in frames 1 to 10, trajectory exists in frames 3 to 12, and they all need to be compared on these frames, then is the number of frames where they overlap, which is 8 frames here (from frame 3 to frame 10). More precisely, it is the number of point pairs on the DTW alignment path. But the formula here is more like a direct frame-by-frame comparison, so is the number of frames in the common time period of the two trajectories.

[0039] : The index of the summation symbol, representing the sequence number of the time frame, from 1 to .

[0040] :Indicates the The track in coordinate vectors on a common time frame. For example, , that is, the pixel coordinates of the feature point in the image.

[0041] :Indicates the The track in coordinate vectors on a common time frame, .

[0042] :Indicates that On the time frame, the trajectory Feature points and trajectories The square of the Euclidean distance between the feature points. This term calculates the difference between the spatial positions of two points.

[0043] : Speed ​​standard deviation threshold, which actually plays a role of a spatial scale parameter (distance unit) in this formula, used to normalize the square of the distance. Its value is .

[0044] : The maximum detection speed of the system, obtained through calibration experiments.

[0045] Acquisition process: 1. Experimental preparation: Spray a photosensitive coating consistent with the actual application uniformly on the surface of a standard specimen (e.g., Inconel 718 alloy, 100mm×100mm×3mm). Set up a point heat source with precisely controlled movement speed (e.g., a 20W semiconductor laser with a spot diameter of 1mm). The imaging system (same as above) is facing the specimen surface. 2. Experimental execution: The point heat source is sprayed with an initial speed of Move in a straight line on the surface of the specimen. The imaging system acquires images at a fixed frame rate (e.g. 20fps). The color difference feature extraction module identifies the color difference response feature points generated by the heat source movement trajectory. Record the number of successfully identified feature points and compare it with the number of feature points that should be generated theoretically (based on the heat source path length and feature point density estimation) to calculate the recognition rate. 3. Speed ​​increase: Increase the heat source movement speed in steps of 0.1mm / s and repeat step 2. 4. Critical speed determination: When the color difference response feature point recognition rate is lower than 95% for the first time, record the heat source movement speed at this time as .but The value is this or slightly below it.

[0046] Experimental data example: Table 3: Calibration experimental data | Heat source moving speed (mm / s); ; As shown in Table 3, when the heat source moving speed is 1.5 mm / s, the recognition rate drops to 94.6%, which is lower than 95%. Therefore, the critical speed value is 1.4 mm / s. .

[0047] Calculation and setting of values: .here The unit of is mm / s. However, is the square of the distance (units such as pixels² or mm²). Therefore, Should be a distance unit. Refers to the maximum movement speed allowed for feature points on the image, then It can be understood as a time frame interval A measure of the spatial deviation allowed between points on the same trajectory. Assume that the pixel size of the imaging system is (mm / pixel), the camera frame rate is (fps), then Then the maximum moving distance of the feature point in one frame interval is At this point, you can Set to .For example, Camera frame rate ,but . .but If the camera field of view is 100mm, corresponding to 2000 pixels, then 1 pixel is approximately 0.05mm. .therefore, The actual value is set to Pixels. (pixel²). (pixel²).

[0048] The calculation logic of the formula: For two trajectories and , in each pair of time frames where they coexist , calculate the square of the spatial distance between their corresponding feature points. This square distance is calculated by the Gaussian function in the form of ) is converted into a similarity value between 0 and 1: the smaller the distance, the closer the value is to 1; the larger the distance, the closer the value is to 0. Controls the sensitivity of this conversion, smaller This makes the similarity more sensitive to distance changes. Finally, these similarity values ​​over all common time frames are averaged to obtain the overall consistency coefficient. .

[0049] Example: Assume there are two trajectories and , they are in 3 consecutive time frames ( ) have characteristic points. pixels, pixel².

[0050] Timeframe 1: pixels, pixels; pixel²; ; Timeframe 2: pixels, pixels; pixel²; ; Timeframe 3: pixels, pixels; pixel²; ; .

[0051] The benefit of the formula is that, through the combination of exponential decay function and distance square, the weight of close points is much greater than that of distant points. The introduction of parameters allows adjusting the sensitivity of the judgment according to the system's maximum detection speed (reflecting the typical motion scale of feature points), thereby more robustly identifying trajectory clusters with similar motion patterns.

[0052] when It is determined to be a homologous heat conduction trajectory.

[0053] The threshold value of 0.85 is set based on: Reference: This threshold is usually obtained by conducting experiments under known conditions of multiple independent heat sources and a single extended heat source, and analyzing the relationship between homologous trajectories. Distribution and heterogeneous trajectories distribution, choose a value that can better distinguish the two.

[0054] Experimental verification process: 1. Scenario 1 (same source): Use an area heat source (such as a 5mm×5mm square heater) or a slowly moving point heat source to generate a large number of closely related feature point trajectories. Calculate the distance between these trajectories. 2. Scenario 2 (heterogeneous sources): Use two or more spatially separated, independently controlled point heat sources. The feature point trajectories they generate should belong to different sources. Calculate the distance between trajectories from different heat sources. 3. Data analysis: Collect a large amount of data from scenario 1 and scenario 2. For example, collect 1000 pairs of trajectories value.

[0055] For scenario 1 (same source), The values ​​are expected to be high, for example, most are distributed between 0.8 and 0.99.

[0056] For scenario 2 (heterogeneous source), The value is expected to be low, for example, most of the distribution is between 0.4 and 0.7. 4. Threshold selection: Select a threshold that can maximize the distinction between the two types of trajectories. For example, if the homologous trajectories The value is greater than 0.85 for more than 95% of the heterologous trajectories. More than 95% of the values ​​are less than 0.85, so 0.85 is a reasonable threshold.

[0057] Setting the value to 0.85 requires that two trajectories have, on average, a high degree of spatial proximity over their common time frame in order to be considered homologous.

[0058] In the above example, .because , so the trajectory and In this example, it is not determined to be a homologous heat conduction trajectory.

[0059] By calculating the By applying threshold judgment, the trajectories can be divided into different clusters. Each cluster represents an independent heat conduction event or area. Subsequently, based on these clustering results, a main heat conduction direction field is generated. For each cluster, the average displacement vector (i.e., velocity vector) of all trajectories within the cluster is calculated at each time frame. These average velocity vector fields are smoothed in space and time (for example, using Gaussian filtering or mean filtering) to form a continuous main heat conduction direction field. This field is represented by a two-dimensional vector at each spatial location (or grid cell), indicating the main direction and relative rate of heat propagation at that location.

[0060] The chromaticity distribution data change per unit time is synchronously calculated to generate the chromaticity change rate field. This process is for each pixel (or its neighborhood) The time differential parameter is calculated as the value changes with time. The chroma sampling interval in seconds.

[0061] The setting basis: Reference: Choosing this sampling interval requires balancing the system's ability to respond to rapid thermal events with the data processing burden. The response time of photosensitive coatings (the time it takes for a color change to stabilize) typically ranges from tens to hundreds of milliseconds. For typical industrial monitoring scenarios, the temperature change rate can range from a few degrees Celsius per second to hundreds of degrees Celsius per second.

[0062] Experimental verification process: 1. Select a typical photosensitive coating whose response curve under specific temperature excitation is known ( 2. Apply step temperature excitation or fast linear temperature rise excitation, use a high frame rate camera (e.g. 100-200fps, i.e. = 0.01s or 0.005s) record 3. Downsample the benchmark data to simulate different (such as 0.02s, 0.05s, 0.1s, 0.2s). 4. Compare and use different Calculated Compared with the calculation using high-resolution benchmark data 5. Choose one , which can fully capture the expected fastest Changing features (e.g., peak rate, changing trend) without causing data redundancy and a surge in computational complexity due to oversampling.

[0063] For example, for a coating with a response time of about 50ms, if you want to capture at least 5 data points of its change process, the sampling interval needs to be However, if you are interested in more macroscopic temperature trends (such as events lasting several seconds), a sampling interval of 0.1 seconds (10fps) or 0.05 seconds (20fps) is usually sufficient. seconds, that is, the camera frame rate is 20 frames / second.

[0064] The rate gradient tensor is calculated using the central difference method: .

[0065] Formula parameter description: :Indicates the time When the pixel brightness The first derivative with respect to time, The rate of change.

[0066] : In time The first sampling moment after Plus a sampling interval )’s pixel brightness value.

[0067] : In time The first sampling moment before Subtract one sampling interval )’s pixel brightness value.

[0068] : The time span used in the central difference method.

[0069] Operation logic: This formula takes the current moment The two sampling points before and after The difference between the two sampling points is divided by the time interval between the two sampling points. , to estimate The instantaneous rate of change at a given moment. This is a numerical differentiation method with second-order accuracy.

[0070] Example: Assume Seconds, at a certain pixel, Second; ; ; ; this That is, the pixel at 1.00 seconds The rate at which the value changes; constitutes an element of the chromaChangeRate field.

[0071] Synchronously calculate the chromaticity change acceleration The calculation formula is usually: ;

[0072] Formula parameter description: :Indicates the time When the pixel brightness The second derivative with respect to time, The change of acceleration.

[0073] : At the current moment The pixel brightness value.

[0074] , , :Same as above.

[0075] Operational logic: This formula compares A moment and two moments before and after The relationship between the value of the acceleration is used to estimate the acceleration. The second-order central difference of the value series is performed.

[0076] Example: Continue using seconds, at the same pixel, Second; (Add the current time value); (To show non-zero acceleration, we adjust The value of ), .

[0077] When the absolute value of acceleration exceeds The time is marked as abnormal fluctuation area.

[0078] Acceleration threshold The setting basis is: Reference: This threshold is used to identify The value of the area where the value changes sharply and rapidly may indicate a very rapid temperature shock or potential measurement data anomaly (such as light source flickering, image artifacts caused by severe vibration). This value should be much larger than the value caused by normal heat conduction process. acceleration.

[0079] Experimental verification process: 1. Scenario 1 (normal / slow thermal change): Apply slow and uniform heating or cooling to the coated specimen and record Data and calculation . Usually these values ​​are small. 2. Scenario 2 (rapid thermal shock): Use pulsed laser or rapid contact with high / low temperature objects to apply rapid thermal shock to the specimen locally and record Data and calculation . These values ​​will increase significantly. 3. Scenario 3 (interference): Introduce known interference during the imaging process, such as periodic flickering of the light source, slight vibration of the camera, etc., and record Data and calculation 4. Data analysis: Under normal circumstances, Probably between 0-500 within the range.

[0080] Under known interference, May reach 500-2000 .

[0081] Under real rapid thermal shocks of particular concern (e.g., critical heat flux where phase change or damage of the material may occur), Probably more than 3,000 5. Threshold selection: Choose a threshold high enough to filter out most operational noise and normal variations while still capturing truly severe thermal events. It is an empirical value determined based on such experiments, used to mark areas where the rate of change itself is also changing rapidly.

[0082] In the above example, the calculated acceleration value is .because , so the pixel is not marked as an abnormal fluctuation area at this moment. If the acceleration calculated by another pixel is , the point will be marked.

[0083] The correlation between abnormal fluctuation area determination and temperature mutation is established by establishing a partial differential equation verify.

[0084] Formula parameter description: : Material density (kg / m³). For example, for the base material Inconel718 under the coating, .

[0085] : Material specific heat capacity (J / (kg·K)). For example, Inconel718 is .

[0086] :Temperature (K or ).

[0087] : time(s).

[0088] : The rate of change of temperature with time.

[0089] : Divergence operator.

[0090] : Material thermal conductivity (W / (m·K)). For example, Inconel718 is For anisotropic materials, is a tensor.

[0091] : Temperature gradient vector, which represents the rate and direction of temperature change in space.

[0092] : Internal heat source power per unit volume (W / m³). If there is no internal heat source, Q=0.

[0093] Operational logic and purpose: This is the heat conduction equation, which describes the temporal and spatial distribution of the temperature field during the unsteady heat conduction process. Represents the rate of change of the internal energy of the object over time (heat storage term), the first term on the right represents the net incoming heat flow due to the temperature difference (divergence form of Fourier's law of heat conduction), and the second term Represents an internal heat source.

[0094] Verification process: When the chromaticity change acceleration analysis marks an area as an "abnormal fluctuation area", it means that the area The value undergoes a sharp acceleration in a short period of time, which may correspond to a sharp change in temperature. In order to confirm that this “abnormal fluctuation” is indeed caused by a real and physically reasonable “temperature jump” rather than measurement noise or non-ideal response of the coating itself, the temperature information of the area (via -T relationship conversion, see subsequent modules) into this heat conduction equation. 1. Obtain physical property parameters: query or measure the material under study (usually the substrate under the coating) 2. Estimate boundary conditions and heat sources: Based on the experimental settings or actual working conditions, estimate the boundary conditions (e.g., heat flux density, convection heat transfer coefficient) and possible internal heat sources around the abnormal fluctuation area. 3. Numerical solution: Use numerical methods such as finite difference method (FDM), finite element method (FEM) or finite volume method (FVM) to simulate the temperature field evolution of the area. The temperature field obtained by inversion is used as the initial condition or reference. 4. Result comparison: Compare the temperature change rate obtained by numerical simulation and temperature acceleration (if necessary), with If an area is marked as abnormal fluctuation (corresponding to high ), and its corresponding The value is also very large, and this If the anomalous fluctuation can be explained by the heat conduction equation under reasonable physical parameters and boundary conditions, then the anomalous fluctuation is confirmed to be a real temperature jump. Conversely, if the equation cannot explain such a drastic temperature change (for example, it requires unrealistic heat flux or thermal conductivity), then the anomalous fluctuation may be caused by other non-thermal factors.

[0095] The benefit of the formula is that it provides a verification mechanism based on physical laws, which can link the observed drastic changes in color to the thermophysical properties of the material itself, thereby enhancing the reliability of the judgment of temperature mutation events and distinguishing between real rapid thermal phenomena and potential measurement artifacts.

[0096] Example: Assume that in a certain abnormal fluctuation area (for example, 1mm×1mm), The change reflects the temperature s from Sudden increase to . Then the average The base material is Inconel718: , (values ​​taken at high temperature), (Values ​​taken at high temperature). This means that to achieve such a heating rate, the net input power per unit volume (from conduction or internal heat source) needs to reach Next, we analyze If the surface of this area is subjected to a strong external heat flow (such as laser heating), this heat flow will be converted into term (if volume absorption) or through boundary conditions By estimating the maximum heat flux that can actually be achieved (e.g. ), and considering the heat penetration depth, it can be judged Is the energy level the same as If they do not match, the authenticity of the “abnormal fluctuation” is questionable.

[0097] The heat conduction trajectory analysis module ultimately passes the generated main heat conduction direction field and the calculated chromaticity change rate field data to the temperature gradient reconstruction module for further processing.

[0098] See also Figure 1 The temperature gradient reconstruction module receives the main heat conduction direction field data and chromaticity change rate field data transmitted by the heat conduction trajectory analysis module. For example, the main heat conduction direction field is represented by a It is expressed in vector matrix form, where is the pixel resolution of the imaging area or the number of grid cells divided, each vector Indicates the location The main direction and relative intensity of heat propagation at the location. The chromaticity change rate field is a A scalar matrix with each element express Department The rate of change of the value, such as the one calculated above .

[0099] First, a spatial correction coordinate system is constructed based on the main heat conduction direction field. The specific construction method is: analyze all the direction vectors in the input main heat conduction direction field, and count the modulus and frequency of occurrence of each direction vector. The direction vector with the largest modulus (or the most significant after weighted average) is selected as the dominant direction. For example, in the analysis of the thermal shock of a blade leading edge, it is found that most of the strong heat flow lines point to the blade span direction, and their average direction is parallel to the horizontal axis of the image. Angle. Then take the average direction ( ) as the direction of the new coordinate system X' axis. The original image coordinate system is XY, and the new space correction coordinate system is X'Y'. The transformation from XY to X'Y' is achieved through a rotation matrix, and the rotation angle is The condition here is "the deviation angle from the direction field The "rotating coordinate system" means that if there are multiple significant conduction directions and the angle between them is less than , the average direction can be taken as the X' axis; if there are multiple dominant directions with large angles, it may be necessary to process them in different regions or adopt a more complex non-Cartesian coordinate system; Here, it is assumed that there is a clear dominant direction, whose angle with the X-axis of the original image coordinate system is . Then any original coordinate Transform to new coordinate system The formula is: For example, the coordinates of a point P in the original image are (100,50) pixels; ; Pixels; Pixels; The coordinates of point P in the new coordinate system are (107.16, 31.88) pixels.

[0100] Subsequently, the adaptive weight distribution algorithm is used to perform direction-weighted fusion on the chromaticity change rate field. The purpose of this step is to adjust the contribution of the chromaticity change rate of each pixel according to the main direction of heat propagation to more accurately reflect the temperature change along the direction of heat flow. The weight calculation formula is . Detailed description of parameters: : The weight assigned to the current pixel's chroma change rate, with a value range of (0,1].

[0101] : The absolute value of the difference between the chroma change rate of the current pixel and the average chroma change rate of its neighborhood. The neighborhood average rate is calculated using the mean filter algorithm of a 3×3 pixel window. For example, for pixel , and its chromaticity change rate is The chromaticity change rates of the 9 pixels in its 3×3 neighborhood (including itself) are The average rate of the neighborhood is . .

[0102] : Rate standard deviation, set to Here It is related to the maximum detection speed of the system in the heat conduction trajectory analysis module (the maximum moving speed of the feature point on the image), but is applied to the chromaticity change rate field here. (unit: mm / s or pixel / s) is converted to the chromaticity change rate (unit: / s) related scale. A more reasonable explanation is that It is the typical standard deviation of the chromaticity change rate obtained from a large number of experimental data statistics, or a The adjustment parameters are set based on the dynamic range of the value and the expected maximum rate of change. Assume that through calibration experiments, in a typical rapid thermal event, the observed The standard deviation of the rate of change of values ​​is approximately . You can set Or, if you follow The original definition and try to relate: If Represents the movement of feature points on the image per second pixels, and the movement of each pixel corresponds to change units (this depends on the temperature gradient and -T curve), then Can be This relationship is more indirect. Here we assume that It is independently calibrated based on the statistical characteristics of the chromaticity change rate. The setting basis is: Reference: This parameter controls the sensitivity of the weight function to rate deviation. Make the weight More sensitive to changes.

[0103] Experimental verification process: 1. Collect multiple sets of chromaticity change rate field data under different thermal conditions. 2. Calculate the and its 3×3 neighborhood average ,get 3. Count these The standard deviation of the values ​​is denoted as 4. Settings for A multiple of, for example ,in is a tuning factor (e.g. between 0.5 and 2) that is optimized by the quality of subsequent fusion results. Or, according to the above If it can be reasonably converted to the scale of chromaticity change rate, then use .

[0104] Assume that through experimental analysis, for the thermal event of interest, The change rate is mainly distributed in , the standard deviation of the average neighborhood difference is approximately Then set .

[0105] Example: A pixel of (Calculated result from the previous module). The chromaticity change rate of its 3×3 neighborhood (unit: ( ) / s)is: ; ; ; set up , . Then the weighted chromaticity change rate of the pixel is This weighted fusion is not directly used Multiply , but will This is used to allocate weights for subsequent information fusion in the main heat conduction direction. For example, when calculating the gradient along the X' axis in the main heat conduction direction, if the chromaticity change rate of a point is significantly different from that of its neighbors ( A more specific fusion method may be to use the gradient of each point along the X' direction (the main heat conduction direction) to calculate the gradient of each point. Values ​​and corresponding weights to compute a weighted average rate, or to use these weights when computing the temperature gradient.

[0106] Next, according to the chromaticity-temperature mapping relationship Generates the temperature gradient distribution tensor.

[0107] Formula parameter description: : Lightness value in CIELab* color space.

[0108] :Temperature measurement value (unit: or K).

[0109] :Quadratic term coefficient (unit: or ).

[0110] :Linear coefficient (unit: or ).

[0111] : Reference brightness compensation value ( Unit), representing Time Value, or the start of the calibration temperature zone value.

[0112] coefficient Determination of (calibration experiment): Experimental materials: The same photosensitive paint as used in actual applications is evenly sprayed on a standard test piece with known thermophysical properties (for example, the size and material are similar to the actual workpiece).

[0113] Experimental equipment: precision programmable temperature controlled heating table or temperature box (temperature control accuracy ), thermocouple (contact temperature measurement, accuracy ), the aforementioned imaging system, standard D65 light source.

[0114] Experimental steps: 1. Place the coated specimen on a heating platform with the thermocouple close to the specimen surface (or embedded near the specimen surface) to obtain the actual temperature. 2. arrive ), select multiple (e.g. 10-20) stable temperature points. 3. At each temperature point, after the temperature stabilizes (e.g. 5 minutes), use the imaging system to capture images of the coating surface and calculate the average temperature of the selected area. Value. Simultaneously record the thermocouple reading 4. Collect the corresponding data of all temperature points Data pair.

[0115] Data processing and coefficient calculation: The collected Data pairs (where , is the number of calibration points), and the least squares method is used to fit the quadratic polynomial , thus obtaining the best Coefficient value.

[0116] Experimental data and examples: Table 4: -T relationship calibration experimental data example (for a certain photosensitive coating); ; As shown in Table 4, 9 groups were obtained. Data. By fitting a quadratic polynomial to these data (for example, using the polyfit function in MATLAB or the trendline function in Excel), the coefficients can be obtained. Assume that the fitting result is: Then the chromaticity-temperature mapping relationship of the paint is: .

[0117] The benefit of the formula is that the quadratic polynomial can better describe the nonlinear response characteristics of many photosensitive coatings over a wide temperature range than a linear relationship, thereby improving the accuracy of temperature measurement.

[0118] Generate temperature gradient distribution tensor: 1. Temperature field Acquisition: For each pixel in the chromaticity change rate field after direction weighted fusion ,That The value is known (either from the original image or after time averaging). Substitute the value into the inverse solution -T relationship to obtain the temperature of the point .because It is a The quadratic equation , which is solved as follows: . Usually based on the characteristics of the coating (such as temperature increase, Lower) Select the appropriate root.

[0119] Example: A pixel , using the above coefficients: , here appears , indicating the coefficients of the fit or the given The value has no real solution under this model, or the coefficients I chose do not fit the example data points exactly. This emphasizes the importance of accurate calibration. Let's rescale the coefficients so that hour It has good performance near , and ensures that the discriminant is positive. Suppose that another set of coefficients is obtained through accurate calibration and fitting, for example: (Note: Photosensitive coatings Follow The behavior of the changes varies, and this is just to construct a solvable example. Follow Rising and falling, the parabola opening downward corresponds to )when ,but . Discriminant . It is still a negative number. This shows the sensitivity of the quadratic equation solution to the coefficients. In practical applications, the selection and fitting quality of the calibration curve are very important. To continue the example, we assume a and The inverse function between It has been directly obtained by table lookup or piecewise linear interpolation, or a polynomial that performs well within the calibration range is used. For example, suppose that the inverse function is directly calculated to get .

[0120] Temperature gradient Calculation: After obtaining the entire temperature field Then, the temperature gradient is calculated in the spatially corrected coordinate system X'Y'. . These partial derivatives can be computed using the central difference method: in and Is the pixel spacing or grid spacing in the corrected coordinate system. The temperature gradient distribution tensor actually refers to the There is a temperature gradient vector .

[0121] The main heat conduction direction field also includes the direction vector matrix, cluster center trajectory, heat flux density distribution .

[0122] Parameter Description: : Heat flux density vector (W / m²), which indicates the rate and direction of heat transfer per unit area.

[0123] : Material thermal conductivity (W / (m·K)). This is the thermal conductivity of the base material under the coating, or the equivalent thermal conductivity that takes into account the effect of the coating. This value is usually a function of temperature and needs to be consulted in the material manual or determined experimentally. For example, for nickel-based high-temperature alloys hour, .

[0124] : The temperature gradient vector obtained by the above calculation.

[0125] Operational logic: Fourier's law of heat conduction describes that in a steady-state or transient heat conduction process, the heat flux density is proportional to the temperature gradient.

[0126] Example: The temperature gradient calculated at a certain point is (Assuming pixel size has been converted to physical size). Material thermal conductivity . . Then the heat flux density vector at this point is Its module length .

[0127] The benefit of the formula is that it links the directly measurable temperature gradient to a key engineering thermophysical parameter, heat flux density, enabling a quantitative assessment of the thermal load state at each point on the surface of the structure.

[0128] Finally, the coordinates of the abnormal area of ​​the temperature gradient distribution tensor are extracted and transferred to the coating parameter optimization module. The method for determining the temperature mutation threshold is: the standard deviation of the historical temperature gradient distribution tensor is calculated. For example, under similar working conditions, the gradient of a large amount of collected temperature field data is calculated, and then these gradient values ​​(such as gradient modulus) are statistically analyzed. ). Assuming that historical data shows The mean of , standard deviation Then set When the current temperature gradient modulus of a region is (or directly ), the temperature gradient in this area is considered abnormal. More directly, if refers to the fluctuation of temperature itself rather than the gradient, then the standard deviation of the statistical historical temperature gradient distribution tensor " should be understood as the standard deviation of the historical temperature data itself, or more likely the temperature change rate The standard deviation of . Assume that the temperature change rate .pass Rate of change and Relationships can be obtained Statistical History Standard deviation of the values .For example, . Set the abnormal temperature mutation threshold to .

[0129] When the thermal conductivity matrix of the detection area (i.e. Field) value exceeds When the warning is triggered.

[0130] Warning threshold The setting basis is: Reference: This threshold is usually set based on the thermal shock resistance of the material, the safe operating window of a specific process, or the critical heating rate at which thermal damage to the structure may occur.

[0131] Experimental verification process: 1. Conduct thermal cycle or thermal shock tests on the same materials / components as in actual applications. 2. Gradually increase the heating / cooling rate. 3. At each rate, check whether the material has micro cracks, yielding, coating peeling or other forms of damage. 4. Record the critical temperature change rate at which unacceptable damage or performance degradation begins to occur. 5. Warning threshold It should be set below the critical rate with a safety margin (e.g., 70-80% of the critical rate).

[0132] Experimental data example: A thermal shock test was conducted on a turbine blade material and it was found that when the heating rate reached After a certain period of time, micro cracks begin to appear on the leading edge of the blade. To ensure safety, the warning threshold is set to .

[0133] For example, the calculated .because , the area triggers the warning and its coordinates (for example, the center pixel coordinates The coordinate list of these abnormal areas is passed to the coating parameter optimization module.

[0134] See also Figure 1 The coating parameter optimization module receives the coordinates of the abnormal region passed by the temperature gradient reconstruction module. For example, the input data may be a set of lists, where each element contains the identifier of the abnormal region, the coordinates of the center point, the approximate range of the region, or a set of contour points. .

[0135] First, the coordinates of the abnormal area are parsed. This process converts the input coordinate information into the data structure required for internal processing of the module. For example, if the input is the pixel coordinates of the area, it needs to be converted into physical size coordinates (if the subsequent spray path requires physical units). If the camera calibration parameters are known (such as the conversion coefficient of mm / pixel), then .

[0136] Then, the photosensitive material thermal response characteristics database is matched. The database stores the thermal response characteristics of different photosensitive materials. Curve parameters (as mentioned above ), thermal conductivity, specific heat, density, response time and stability at different temperatures, adhesion to different substrates, and corresponding thermal resistance characteristics. For each abnormal area analyzed, according to the temperature range of the area (given by The detailed characteristics of the most suitable or currently used coating material are searched in the database based on information such as the inversion result, temperature change rate, etc. This step mainly confirms the known parameters of the current coating and provides a basis for subsequent thickness control.

[0137] Next, the thickness control strategy logic is executed. The core of this logic is to determine the thickness of the abnormal area according to its temperature characteristics (such as the temperature gradient amplitude). or temperature change rate ) and the performance parameters of the coating itself (such as the benchmark thermal resistance ) to compare and determine if and how to adjust the coating thickness.

[0138] The coating thickness control instruction set specifically includes the thickness adjustment value, material thermal resistance parameters, and the spray path coordinates corresponding to the B-spline curve control point coordinates. The calculation logic is: .

[0139] Formula parameter description: : Coating thickness adjustment amount (mm). A positive value indicates an increase in thickness, a negative value indicates a decrease in thickness (if the process allows, otherwise it usually refers to the difference between the target thickness when re-spraying and the current thickness).

[0140] :Material thermal conductivity compensation coefficient (mm·s / or mm·m²·K / (W·s) if The unit of this coefficient should be the same as and The units match so that The unit of is length. The original unit is .

[0141] : A value representing the temperature characteristics of the abnormal area. It can be the temperature gradient amplitude (unit ), or the rate of temperature change (unit ), or simply a temperature difference (unit ). From the context of "the temperature gradient amplitude in the abnormal area "Look, here Should refer to the temperature gradient magnitude or a temperature difference associated with the gradient. is the rate of temperature change, then Unit is appropriate, the result is (mm).

[0142] :Coating base thermal resistance (m²·K / W). This is a standard unit of thermal resistance. Not a standard thermal resistance unit, but some dimensionless or related Unit matching parameters, then The units of also need to be adjusted accordingly. Assume that is a simplified parameter with the same unit as (If ) cancel each other out, making Units can be exported directly (mm). For example, if yes ( ),and is a reference rate value (e.g., the maximum acceptable rate ),at this time The unit is mm. But more formally, is the thermal resistance. The thermal resistance of the coating ,in is the thickness, is the thermal conductivity of the coating.

[0143] Material thermal conductivity compensation coefficient Obtaining: References: The value reflects the sensitivity of coating thickness changes to inhibiting temperature changes (or temperature gradients).

[0144] Experimental verification process (obtaining thermal resistance experimental calibration curve): 1. Experimental materials: Select the target photosensitive coating and prepare a series of coating specimens with different thicknesses (for example, thickness from 20μm to 100μm, step length 10μm) on a standard substrate. 2. Experimental equipment: Heat flow meter (used to measure the heat flux density through the specimen) ), precision temperature controlled heat source and cold plate, thermocouple. 3. Experimental steps: a. For each specimen of different thickness, apply a known, constant heat flux at one end Or maintain a constant temperature difference (Temperature difference between the upper and lower surfaces of the coating). b. Measure the temperature response under steady-state or specific transient conditions. For example, measure the thermal resistance of coatings of different thicknesses under a reference temperature gradient (or temperature change rate). c. More in line with the formula middle The unit may be a measure of the "driving force" at a specific temperature (e.g. the difference between substrate temperature and ambient temperature), different coating thickness How to influence the temperature change rate of the substrate surface or the final temperature gradient d. Assuming that different thicknesses are measured To maintain the substrate surface temperature At a target value (relative to a reference temperature There is a temperature difference ) Some kind of "effort" required or some kind of "effect" produced. e. The description of "measuring the rate of change of thermal resistance of coatings of different thicknesses and taking the slope of the linear regression curve as the K value" may be drawing (Thickness) vs Or similar indirect relationship diagram.

[0145] One possible calibration is to apply a standard rapid thermal shock to the back of the substrate and measure the maximum temperature change rate of the substrate surface under the front coating. With coating thickness Changes. Draw vs If this relationship is approximately linear, its slope can be used as part of.

[0146] Taking into account The unit is , The unit is (assuming it is an equivalent temperature difference caused by temperature difference or gradient), If it is a dimensionless factor or a time-dependent quantity, then In mm.

[0147] More specific experiments: Under standard heat shock (e.g., heat source power , action time ), testing different coating thicknesses Record the maximum temperature rise on the surface of each specimen. If you want to reduce the temperature rise by increasing the thickness, represents a reference, acceptable temperature rise (or a quantity related to temperature rise), then may be .

[0148] because Given, we accept this value.

[0149] Example: Assume that the temperature gradient analysis of an abnormal area yields an equivalent temperature driving force (For example, this is a quantity that combines the effects of the gradient magnitude and duration.) Baseline thermal resistance of the coating (Here according to The unit may not be the standard thermal resistance unit .if yes , yes ,to get (mm), then The unit should be s (seconds). This means May represent a benchmark response time or thermal breakthrough time. Assume seconds (this is a benchmark thermal characteristic time constant). This means that the coating thickness needs to be increased by 1.0mm. This value is too large. Usually the thickness of thermal barrier coating is in the range of microns to hundreds of microns. Let’s re-examine Units and .if is the rate of temperature change, in units of .For example (From the aforementioned warning value). .like ,but The unit is mm. must be dimensionless. Assume (dimensionless normalization factor). . Still a bit too big.

[0150] Anisotropy coefficient Determination of heat conduction (comparative heat conduction test): 1. Experimental materials: A substrate identical to the actual structure, with the coating material to be evaluated sprayed onto the surface. 2. Experimental equipment: Two or more independently controllable point heat sources (e.g., lasers), and an imaging system. 3. Experimental steps: a. Apply a heat source to the specimen surface along one direction (e.g., the X-axis) and measure the heat propagation rate along the X-axis and Y-axis (or the ratio of the major and minor axes of the resulting isothermal ellipse). b. Change the direction of heat application (e.g., the Y-axis) and repeat the measurement. c. Anisotropy coefficient It is defined as the ratio of the thermal conductivity of a material in different directions. For example, ,in and is the effective thermal conductivity in two orthogonal directions. Or, more simply, by observing the shape of the hot spot expansion, if the hot spot expands significantly faster in one direction than in other directions, it is considered that anisotropy exists. d. "The critical mutation point of measuring the ratio of isotropic thermal conductivity" may refer to the critical point where the material changes from isotropy to anisotropy when certain parameters (such as temperature, stress) are changed, or the critical point where the degree of anisotropy changes significantly. It is assumed here that is a directly measured ratio.

[0151] Example: In the test, it was found that the effective thermal conductivity along the main conduction direction (such as the rolling direction) is , and the thermal conductivity perpendicular to this direction is Then the anisotropy coefficient .

[0152] Material selection logic: when When selecting thermal resistance high-resistance material.

[0153] when When selecting thermal resistance of conventional materials.

[0154] Threshold The setting basis is: Reference: 1.2 is generally considered a rule of thumb for distinguishing mild anisotropy from significant anisotropy. When the ratio exceeds 1.2, it means that the thermal conductivity of the material in one direction is at least 20% higher than in another direction.

[0155] Experimental verification: Through thermal performance testing and structural reliability analysis of a series of materials with different known anisotropy degrees, a When this value is exceeded, the impact of anisotropy on structural thermal management or thermal stress distribution becomes non-negligible, and a coating material with more uniform thermal conductivity (i.e., better isotropy or higher thermal resistance in a specific direction) is needed to compensate or adjust it.

[0156] Thermal resistance threshold and The setting basis is: Reference: These values ​​represent the thermal resistance range of typical high thermal resistance and conventional thermal resistance coating materials.

[0157] Experimental verification: Based on application requirements (e.g., maximum allowable operating temperature, desired thermal protection level), determine the required thermal resistance range through heat transfer calculations and experiments. For example, for components that require strong thermal insulation, select For components that allow a certain amount of heat transfer or require rapid heat dissipation, select These values ​​usually correspond to the performance parameters of the actual selectable materials in the material library.

[0158] In the example, .because , so the system will filter out the thermal resistance value from the database High thermal resistance photosensitive coatings are selected as candidates.

[0159] Generation of spray path coordinates: 1. Project the coordinates of the abnormal area onto the three-dimensional surface model reconstructed by point cloud data obtained through laser scanning.

[0160] Obtaining a 3D model: Scan the actual workpiece (e.g. turbine blade) in advance using a high-precision 3D laser scanner (e.g. Creaform HandySCAN BLACK Elite, with an accuracy of up to 0.025mm) to obtain dense point cloud data of its surface. The point cloud data contains the 3D coordinates of millions of points. .

[0161] Model reconstruction: Use professional point cloud processing software (such as Geomagic Design X or PolyWorks Modeler) to process the point cloud data, including denoising, registration (if multiple scans), meshing and surface reconstruction, and ultimately generate an accurate 3D CAD model of the workpiece (for example, saved in .STL, .STEP, or .IGES format).

[0162] Coordinate projection: pixel coordinates of the abnormal area in the identified two-dimensional image It is necessary to use the camera calibration parameters (internal and external parameter matrices) and the workpiece's position in the camera coordinate system to reversely project them onto the 3D model surface to obtain the corresponding 3D space coordinates. This process involves the conversion from 2D image points to 3D space points. For example, if the abnormal area is a rectangle, the pixel coordinates of its four corner points are converted to four corresponding points on the 3D model.

[0163] B-spline curve interpolation is used to generate continuous spraying paths.

[0164] The abnormal area contour points or internal filling point sets projected onto the 3D surface are interpolated or fitted using the B-spline curve / surface algorithm to generate a smooth spray tool center point (TCP) path.

[0165] Control point spacing :B-spline curve is defined by a series of control points. The distance between control points of adjacent path segments (or the spacing between raster scan lines) on the generated spray path is no more than 2mm.

[0166] The setting of this parameter depends on the spray width of the spray equipment, the coating uniformity requirements and the size of the abnormal area. Smaller spacing can improve the uniformity and accuracy of spray coverage, but it will increase the spraying time and data volume.

[0167] Experimental Verification: By testing spray paths with different control point spacings and examining the resulting coating thickness uniformity and coverage integrity, a spacing value that achieves high efficiency while meeting quality requirements is selected. A 2mm spacing is typically suitable for repairs or localized reinforcement spraying requiring high precision.

[0168] Radius of curvature : The local curvature radius of the generated B-spline spray path at any position is not less than 5mm.

[0169] The setting of this parameter is mainly based on the kinematic limitations of the spray robot or actuator and the requirements of the spray process itself (for example, avoiding paint accumulation or excessive thinning in sharp turns). A too small curvature radius may cause the robot to move unsteadily or exceed its acceleration / jerk limits.

[0170] Experimental Verification: Test the spray equipment's motion smoothness and spray quality at different path curvatures. Record the curvature radius at which spray defects (such as spatter, sag, and uneven thickness) or abnormal robot motion begin to appear. Then, set a minimum curvature radius greater than this critical value, with a safety margin. A 5mm radius is a common requirement for spray paths on small, complex curved surfaces.

[0171] Example: For a circular abnormal area with a diameter of 10 mm projected onto the blade surface, the B-spline path planning algorithm will generate a series of spiral or reciprocating path segments to ensure that the area is completely covered. For example, the three-dimensional coordinates of the first control point of the path are mm, the second control point is mm. These point lists form part of the spraying instructions.

[0172] Finally, the generated coating thickness control instruction set (including target area, calculated thickness adjustment amount) , the selected material thermal resistance parameters (or material model), and the detailed B-spline spray path three-dimensional coordinate sequence) are output to guide subsequent coating repair or preparation equipment (such as automated spraying robots) to perform operations. For example, the instruction may be: "Area identifier: Area_XYZ, target thickness increment: +0.05mm, selected material: HT-Coating-TypeA (thermal resistance ), spraying path point sequence: ”.

[0173] The above examples illustrate preferred implementations of the present invention. Any equivalent adjustments to the technical solutions based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithmic logic using different programming languages, service-oriented reconfiguration of functional modules, adjusting data interaction protocols, optimizing resource scheduling strategies, and other technical improvements. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture level that does not depart from the core technology of the present invention shall be deemed to be within the scope of protection defined by the claims of the present invention.

Claims

1. A structural surface temperature measurement system based on color difference recognition of photosensitive coatings, characterized in that: The system comprises: The color difference feature extraction module is used to obtain the color distribution data of the pixels on the coating surface through the imaging device, calculate the brightness gradient of adjacent pixels using the sliding window difference method, generate the color gradient vector, mark the color difference response feature points that meet the threshold, and pass it to the heat conduction trajectory analysis module; A heat conduction trajectory analysis module is used to perform time-series analysis on the chromaticity gradient vector, calculate the consistency of the motion trajectory of the color difference response feature point cluster using a vector field clustering algorithm, generate a main heat conduction direction field, and simultaneously calculate the change in pixel chromaticity distribution data per unit time to generate a chromaticity change rate field, which is then passed to the temperature gradient reconstruction module; The temperature gradient reconstruction module is used to construct a spatial correction coordinate system based on the main heat conduction direction field, and use an adaptive weight distribution algorithm to perform direction weighted fusion on the chromaticity change rate field. A temperature gradient distribution tensor is generated, and the coordinates of abnormal regions of the temperature gradient distribution tensor are extracted and transferred to a coating parameter optimization module.

2. The structure surface temperature measurement system based on photosensitive coating color difference recognition according to claim 1 is characterized in that: In the chromaticity-temperature mapping relationship, Indicates the lightness value in the CIELab* color space, Indicates the temperature measurement value, is the coefficient of the quadratic term, is the coefficient of the first-order term, is the reference brightness compensation value; The color difference response feature points are specifically gradient amplitude, spatial coordinates, and response timestamps. The main heat conduction direction field includes direction vector matrix, cluster center trajectory, and heat flux density distribution. The chromaticity change rate field specifically refers to the rate gradient tensor, time differential parameter, and abnormal fluctuation area, and the temperature gradient distribution tensor includes anisotropy coefficient, heat conduction rate matrix, and temperature mutation threshold; In the heat flux distribution calculation, represents the thermal conductivity of the material, represents the temperature gradient vector.

3. The structure surface temperature measurement system based on photosensitive coating color difference recognition according to claim 2 is characterized in that: The specific implementation process of the sliding window difference method is as follows: set a 3×3 pixel detection window, calculate the brightness gradient between the center pixel of the window and the adjacent pixels in the CIELab* color space ,when And the hue angle changes When , it is marked as a valid color difference response feature point; The vector field clustering algorithm uses dynamic time warping (DTW) to calculate the consistency coefficient of the feature point cluster motion trajectory ,in Represents the coordinate vector of the i-th feature point in the k-th time frame, is the speed standard deviation threshold, when When it is determined to be a homologous heat conduction trajectory, represents the trajectory consistency coefficient between the i-th feature point and the j-th feature point, Indicates the total number of time frames, Represents the coordinate vector of the i-th feature point in the k-th time frame, represents the speed standard deviation threshold, Indicates the maximum detection speed of the system; The maximum detection speed of the system The critical speed value is obtained through calibration experiments. The experimental method is: the heat source moving speed is increased in steps of 0.1 mm / s on the surface of the standard specimen, and the critical speed value is recorded when the recognition rate of the color difference response feature point is lower than 95%.

4. The structure surface temperature measurement system based on photosensitive coating color difference recognition according to claim 3 is characterized in that: The time differential parameter is specifically: The chroma sampling interval is 1 second, and the rate gradient tensor is calculated using the central difference method: , synchronously calculate the chromaticity change acceleration , when the absolute value of acceleration exceeds When is marked as abnormal fluctuation area, represents the rate of change of the brightness component over time, Indicates the chroma sampling time interval, represents the brightness value at time t+Δt, represents the brightness value at time t-Δt, The second derivative representing the change in brightness; The correlation between the abnormal fluctuation area determination and temperature mutation is established by establishing a partial differential equation Verify, where is the material density, is the specific heat capacity, is the thermal conductivity, is the heat source term.

5. The structure surface temperature testing system based on photosensitive coating color difference recognition according to claim 4 is characterized in that: The method for constructing the spatial correction coordinate system is as follows: taking the maximum vector direction of the main heat conduction direction field as the X' axis, establishing the deviation angle with the direction field The rotating coordinate system of The adaptive weight allocation algorithm adopts an exponential decay function ,in, represents the adaptive weight value, Indicates the difference between the current pixel chromaticity change rate and the neighborhood average rate, represents the standard deviation of the rate, Indicates the maximum detection speed of the system; The neighborhood average rate is calculated using a mean filtering algorithm with a 3×3 pixel window.

6. The structure surface temperature measurement system based on photosensitive coating color difference recognition according to claim 5 is characterized in that: The method for determining the temperature mutation threshold is: statistically analyzing the standard deviation of the historical temperature gradient distribution tensor ,set up is the abnormal temperature mutation threshold. When the thermal conductivity matrix value of the detection area exceeds When the warning is triggered, represents the standard deviation of the historical temperature gradient distribution tensor, Indicates the deviation of the current temperature gradient from the historical average.

7. The structure surface temperature measurement system based on photosensitive coating color difference recognition according to claim 6 is characterized in that: The generation logic of the coating thickness control instruction set includes: Thermal resistance of coating benchmark Calculate the thickness adjustment ,in, Indicates the coating thickness adjustment amount, represents the temperature gradient amplitude in the abnormal area, Indicates the benchmark thermal resistance value of the coating, Indicates the thermal conductivity compensation coefficient of the material; The thermal conductivity compensation coefficient of the material is obtained through a thermal resistance experimental calibration curve. The experimental method is: measuring the thermal resistance change rate of coatings of different thicknesses under a reference temperature gradient, and taking the slope of the linear regression curve as the K value.

8. The structure surface temperature measurement system based on photosensitive coating color difference recognition according to claim 7 is characterized in that: The system further comprises: A coating parameter optimization module is used to analyze the coordinates of the abnormal area, match the thermal response characteristics database of the photosensitive material, execute the thickness control strategy logic judgment, and generate a coating thickness control instruction set; The coating thickness control instruction set specifically includes a thickness adjustment value, a material thermal resistance parameter, and spraying path coordinates corresponding to the coordinates of the B-spline curve control points.

9. The structure surface temperature measurement system based on photosensitive coating color difference recognition according to claim 8, characterized in that: The method for generating the spraying path coordinates includes: projecting the abnormal area coordinates onto a three-dimensional surface model reconstructed by acquiring point cloud data through laser scanning, generating a continuous spraying path using B-spline curve interpolation, and controlling the point spacing. , radius of curvature , where the control point spacing represents the straight-line distance between adjacent spray path control points, and the curvature radius represents the minimum curvature radius of the curved section of the spray path.

10. The structure surface temperature measurement system based on photosensitive coating color difference recognition according to claim 9 is characterized in that: The matching method of the material thermal resistance parameters is: according to the anisotropy coefficient in the temperature gradient distribution tensor ,when Select thermal resistance value when high-resistance material, when Select thermal resistance value when Conventional materials, among which It represents the anisotropy coefficient in the temperature gradient distribution tensor, which is defined as the ratio of the maximum heat conduction rate to the minimum heat conduction rate; The anisotropy coefficient is determined by a heat conduction comparison test, wherein the test method is as follows: heat sources in different directions are set on the surface of a standard specimen, and a critical mutation point of the ratio of the anisotropic heat conduction rate is measured.

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