Method and device for measuring ice thickness of ice storage coil pipe based on visual perception

Abstract

The invention discloses a method and a device for measuring the ice thickness of an ice storage coil pipe based on visual perception. The method comprises the step of arranging a circular or sector-ring-shaped calibration disc on the ice storage coil pipe. The calibration disc is provided with a plurality of concentric stripe areas which are arranged along the radial direction and have different colors. Judging whether each stripe region is completely covered by the ice layer or not according to the brightness change of the calibration disc covered by the ice layer in the image, and dividing each stripe region into a completely covered region, a partially covered region and an uncovered region; and for the partial coverage interval, obtaining the ice layer coverage proportion of the partial coverage interval, thereby obtaining the ice layer thickness. The ice thickness detection accuracy is provided through staged ice thickness analysis, specifically, the ice layer completely covered by the ice layer is determined by utilizing segmentation and brightness of stripe areas with different colors, and then the ice thickness detection accuracy is improved for the partially covered stripe areas. And performing coverage proportion calculation by using the comprehensive edge strength integrating the gradient edge strength and the phase consistency edge strength.

Description

Technical Field

[0001] The present invention belongs to the technical field of ice thickness measurement of ice storage devices, and particularly relates to a method and device for measuring ice thickness of ice storage coils based on visual perception. Background Art

[0002] In ice storage equipment applications such as building energy systems and district cooling stations, accurate measurement of ice thickness on the outer wall of the ice storage coil directly impacts system energy efficiency optimization and safe operation control. Current mainstream technologies have the following significant limitations:

[0003] 1. Inherent drawbacks of contact sensing technology: Contact measurement devices, such as temperature sensors and capacitance sensors, must be directly attached to the coil surface. In low-temperature environments (below -30°C), they are susceptible to interference from heat conduction through the icing interface, leading to measurement drift. Furthermore, the complex wiring structure and installation process significantly increase deployment costs and maintenance difficulties, making them difficult to adapt to the multi-point monitoring needs of large-scale energy stations.

[0004] 2. Insufficient environmental adaptability of image measurement technology: (1) Turbid underwater media causes increased scattering of incident light, resulting in severe attenuation of image contrast and difficulty in effectively extracting ice edge features; (2) The ice transmission effect and the water surface reflection superimpose on each other, causing color information distortion and reducing the reliability of color segmentation-based recognition; (3) Low-temperature frost forms irregular attachments on the coil surface, interfering with the determination of the actual ice contour. The above factors increase the false detection rate of traditional edge detection algorithms (such as the Canny operator) in complex underwater environments, making it difficult to achieve submillimeter precision measurement.

[0005] 3. System stability challenges in low-temperature environments: Existing technologies lack targeted compensation mechanisms for low-temperature operating conditions. Geometric distortion caused by lens material shrinkage, color recognition deviations caused by light source color temperature drift, and parameter drift during long-term operation all reduce the long-term stability of the measurement system. Traditional linear calibration models struggle to effectively fit actual nonlinear distortion, further limiting measurement accuracy.

[0006] In summary, the existing technology is limited by defects such as weak environmental interference suppression ability, poor dynamic adaptability and insufficient calibration robustness. There is an urgent need to develop a high-precision, strong anti-interference non-contact ice thickness monitoring method. Summary of the Invention

[0007] The object of the present invention is to provide a method and device for measuring ice thickness of ice storage coils based on visual perception.

[0008] In a first aspect, the present invention provides a method for measuring ice thickness of an ice storage coil based on visual perception, comprising:

[0009] A circular or sector-shaped calibration disk is placed on the ice storage coil. The disk is provided with multiple concentrically and radially arranged stripes of different colors. A camera is used to capture an image of the disk outside the iced area. Color segmentation is used to extract the stripe regions from the image.

[0010] Extract the brightness gradient amplitude of the pixel and brightness gradient direction Through the dual thresholds of different sizes, the first category of undetermined pixels and the second category of undetermined pixels are extracted from the pixels of all stripe areas. Verify the direction consistency of the second type of undetermined pixels.

[0011] Based on the brightness gradient amplitude and the directional consistency verification results of the two types of undetermined pixels, the gradient edge strength of each undetermined pixel is obtained. The phase consistency edge strength is calculated for each undetermined pixel. The gradient edge strength and phase consistency edge strength are normalized and then weighted summed to obtain the comprehensive edge strength.

[0012] Whether each stripe area is completely covered by ice is judged based on the brightness change after the calibration disk in the image is covered by ice. Each stripe area is divided into an inner fully covered interval, a partially covered interval in the middle occupying one stripe area, and an outermost uncovered interval.

[0013] For partially covered areas, the ice coverage ratio is calculated by comparing the integrated edge intensity of each pixel in the corresponding stripe area with the integrated edge intensity reference value. The ice thickness is calculated based on the inner and outer radii of the fully covered area and the ice coverage ratio of the partially covered area.

[0014] Preferably, the image is used to extract the brightness gradient amplitude and brightness gradient direction Gaussian filtering is performed before.

[0015] As a preferred method, the process of extracting the first and second types of undetermined pixels is as follows: setting different lower and upper thresholds, taking pixels with brightness gradient amplitude greater than the upper threshold as the first type of undetermined pixels, and taking the brightness gradient amplitude greater than the upper threshold as the second type of undetermined pixels. Pixels between the lower threshold and the upper threshold (including the upper threshold and the lower threshold) are considered as the second type of undetermined pixels. The ratio of the lower threshold to the upper threshold is 1:3.

[0016] As an advantage, the process of direction consistency verification is as follows: extract the brightness gradient directions of eight adjacent pixels of the second type of undetermined pixel points. . Establish verification relationship If the verification relationship is satisfied, it means that the second type of undetermined pixel has passed the direction consistency verification. If the verification relationship is not satisfied, it means that the second type of undetermined pixel has failed the direction consistency verification.

[0017] As an advantage, the gradient edge intensity of the type of pixel to be determined is The gradient edge intensity expression of the second type of undetermined pixel points is: ;in, is the brightness gradient amplitude.

[0018] Preferably, the expression of the phase consistency edge strength is:

[0019]

[0020] Among them, ϕ n is the local frequency phase, is the average phase of all pixels in the image.

[0021] As a preference, the ice coverage ratio of the partially covered interval The expression is as follows:

[0022]

[0023] in, is the sum of the comprehensive edge intensities of all pixels in the partially covered interval; k is the brightness correction coefficient. is the comprehensive edge strength benchmark value, which is obtained by extracting the comprehensive edge strength of the corresponding stripe area on the unfrozen image and summing it up; is the difference between the mean brightness of the corresponding stripe area and the mean brightness of the background. is the maximum brightness value in the image.

[0024] Preferably, the images captured by the camera undergo preprocessing. The preprocessing process includes underwater scattering correction, color correction, and multi-scale denoising. The underwater scattering correction process involves calculating the transmittance based on the underwater scattering coefficient and the distance from the calibration disk to the camera lens, and correcting the pixel value of each pixel using the transmittance and background scattered light intensity. The multi-scale denoising includes non-local mean filtering, distortion correction, and contrast enhancement.

[0025] In a second aspect, the present invention provides an ice storage coil ice thickness measurement device for performing the aforementioned ice storage coil ice thickness measurement method. The ice storage coil ice thickness measurement device includes a calibration disk, an image acquisition module, and a processor. The calibration disk is concentrically fixed to one of the straight sections of the ice storage coil being measured. The image acquisition module is fixed to the inner wall of the ice storage tank. The image acquisition module includes a camera and a fill light. The fill light is a ring light that surrounds the outer periphery of the camera and faces the calibration disk. The fill light is a polarized fill light.

[0026] As a preference, the saturation and brightness of the color of each stripe area are both 100%, and the hue , where n is the serial number of the stripe area from the inside to the outside, and N is the number of stripe areas.

[0027] The present invention has the following beneficial effects.

[0028] 1. This invention fixes a calibration disk with multiple striped areas on the ice storage tray. By image recognition, the ice thickness of each striped area is dynamically estimated, enabling non-contact, stepless, and precise measurement of ice thickness in the ice storage coil. This overcomes the traditional sensor's reliance on contact measurement and improves deployment efficiency and environmental adaptability.

[0029] 2. This invention improves ice thickness detection accuracy through a phased ice thickness analysis. Specifically, it uses the segmentation and brightness of different colored stripe regions to determine the ice layer completely covered by ice. For partially covered stripe regions, the coverage ratio is calculated using a combined edge strength that integrates gradient edge strength and phase consistency edge strength, taking into account the characteristic that ice-covered areas are more blurred than uncovered areas.

[0030] 3. This invention innovatively integrates non-local mean denoising, wavelet threshold denoising, and block histogram equalization to specifically address the problems of image blur and contrast degradation caused by underwater turbidity and low-temperature frost, thereby improving the accuracy, robustness, and adaptability of ice thickness detection on ice storage coils. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of the overall structure of Example 1 of the present invention.

[0032] Figure 2 This is a schematic structural diagram of the calibration disk in Example 1 of the present invention.

[0033] Figure 3 This is a flow chart of Example 2 of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described below with reference to the accompanying drawings.

[0035] Example 1

[0036] like Figure 1 As shown, an ice thickness measurement device for ice storage coils based on visual perception includes a calibration disk 1, an image acquisition module and a processor 2.

[0037] The calibration disk 1 is fixed near the end of one of the straight sections of the ice storage coil 5 being tested. The calibration disk 1 is in the shape of a sector ring, specifically a semi-ring in this embodiment. The inner semicircular edge of the calibration disk 1 is fixed to the corresponding straight section of the ice storage coil 5. The center of the calibration disk 1 coincides with the axial center of the straight section of the ice storage coil 5.

[0038] The image acquisition module is fixed to the inner wall of the ice storage tank. The image acquisition module is located between the area where the ice storage coil 5 is located and the inner wall of the ice storage tank. The location of the image acquisition module will not freeze during the entire ice storage process. The image acquisition module is positioned directly opposite the calibration disk 1.

[0039] The image acquisition module is used to capture images of the ice ring and the calibration disk 1. The processor 2 obtains the thickness of the ice ring by detecting the radial position of the outer edge of the ice ring on the calibration disk 1 in the image.

[0040] In some embodiments, the calibration disk 1 is provided with multiple striped areas on its side. Each striped area is arranged in a concentric, fan-shaped pattern, from the inside out. Different striped areas have different colors, which assists the image acquisition module in determining the location of the outer edge of the ice ring. Of two adjacent striped areas, the outer diameter of the inner striped area is equal to the inner diameter of the outer striped area. In some further embodiments, the width of the striped area (i.e., the difference between the inner and outer radii) is 5 mm.

[0041] like Figure 2As shown, the colors of each stripe area should be significantly different. In this embodiment, each stripe area is set in the HSV space. The three primary colors in the HSV space are red (H∈[0,10]∪[350,360], S>0.8, V>0.7); yellow (H∈[20,120], S>0.7, V>0.6); and cyan (H∈[180,200], S>0.65, V>0.5). Color stability in low temperature environments was verified using the CIELAB color difference formula (ΔE*<5). In this embodiment, there are six stripe areas, and the colors of the six stripe areas are pure red with an HSV value of (0.000°, 100%, 100%), pure yellow with an HSV value of (60°, 100%, 100%), pure green with an HSV value of (120°, 100%, 100%), pure cyan with an HSV value of (180°, 100%, 100%), pure blue with an HSV value of (240°, 100%, 100%), and pure purple with an HSV value of (300°, 100%, 100%).

[0042] In some embodiments, the calibration disk 1 is made of a low-temperature resistant material that can withstand -30°C, such as high-density polyethylene.

[0043] In some embodiments, the inner radius of the calibration disk 1 is equal to the outer radius of the ice storage coil 5. The difference between the inner and outer radii of the calibration disk 1 is greater than the maximum ice thickness of the ice storage coil 5. In some embodiments, the maximum ice thickness of the ice storage coil 5 is 50 mm; the difference between the inner and outer diameters of the calibration disk 1 should be the maximum ice thickness of the coil plus some margin. For example, if the maximum ice thickness of the ice storage coil 5 is 50 mm, the difference between the inner and outer diameters of the calibration disk 1 is set to 60 mm.

[0044] In some embodiments, the outer surface of the calibration disk 1 is provided with a hydrophobic coating. In some further embodiments, the hydrophobic coating is made of a fluorosilane-based nanomaterial with a contact angle of 107° to 117° and a sliding angle of <8°. The hydrophobic coating helps reduce ice crystal adhesion.

[0045] The image acquisition module includes a camera 3 and a fill light 4. The fill light 4 is a ring light, which is fixed around the periphery of the camera 3 and faces the calibration disk 1.

[0046] In some embodiments, the camera 3 uses an industrial-grade waterproof and low-temperature resistant camera 3, supports long-term underwater operation at -30°C, and has IP68 protection.

[0047] In some embodiments, the fill light 4 is a circular polarized fill light 4, which can weaken the reflection of the water surface through polarized light separation technology and improve the clarity of underwater imaging.

[0048] In some embodiments, the processor 2 is arranged outside the ice storage tank and is connected to the image acquisition module via a cable.

[0049] Example 2

[0050] like Figure 3 As shown, a method for measuring the ice thickness of an ice storage coil 5 based on visual perception is provided, using the ice thickness measuring device for an ice storage coil 5 provided in Example 1. The method comprises the following steps:

[0051] Step 1: Image acquisition and preprocessing:

[0052] The fill light 4 is turned on, and the camera 3 is used to continuously collect images in the RGB space and upload them to the processor 2. The processor 2 pre-processes the images.

[0053] In order to avoid problems such as severe color deviation and low contrast caused by turbid water, the following pre-processing process is provided:

[0054] (1) Underwater scattering model correction:

[0055] Based on the Jaffe-McGlamery scattering model, the expression of transmittance t(x) is established as follows:

[0056] t(x)=e −βd(x)

[0057] Where β is the underwater scattering coefficient; d(x) is the distance from calibration disk 1 to the lens of camera 3.

[0058] According to the transmittance t(x), the background scattered light interference is removed to obtain the true pixel value J(x) of each pixel:

[0059]

[0060] Wherein, I(x) is the pixel value received by the camera 3, and A is the background scattered light intensity in the RGB space. The value is determined through experiments. In this embodiment, A=(0.20, 0.45, 0.60).

[0061] (2) Color correction:

[0062] For images corrected by the underwater scattering model, an underwater white balance algorithm is introduced to compensate for color cast based on the difference between the red and green channels:

[0063]

[0064] in, is the red channel pixel value after supplementation; k is the compensation coefficient; ε is the preset parameter used to prevent division by zero; R, G, and B are the pixel values ​​of the red, green, and blue channels corrected by the underwater scattering model, respectively.

[0065] (3) Multi-scale denoising:

[0066] The color-corrected image is subjected to non-local mean filtering, distortion correction and contrast enhancement in sequence.

[0067] 1) Non-local mean filtering: Set the following parameters: search window to 21 × 21, similarity window to 5 × 5, and smoothing parameter h = 1.2σ, ​​where σ is the standard value of noise. The smoothing parameter h is used to suppress Gaussian noise.

[0068] 2) Lower wave threshold denoising: using db4 wavelet basis function, 3-layer decomposition, soft threshold , suppress underwater scattering noise; is the total number of pixels.

[0069] 3) The distortion correction process involves generating a mapping table based on the Zhang Zhengyou calibration method to compensate for distortion caused by lens material shrinkage at low temperatures. A lens temperature compensation model is also established. This model is used to compensate for focal length changes at low temperatures. In this example, the temperature change amplitude ΔT = -30°C, and the focal length change rate Δ(f / f0) = -0.08%.

[0070] 4) Contrast Enhancement: The image is processed using a block histogram equalization method to improve the visibility of the ice edge. In the block histogram equalization method, the block size is set to 8×8 and the contrast ratio CL is set to 3.

[0071] Step 2: Color segmentation:

[0072] In this step, the pre-processed image is further processed in HSV space. When there is no ice on the disk, a dynamic color threshold is set to segment the stripe areas of each color. The dynamic threshold algorithm breaks through the limitations of the fixed threshold through the color temperature compensation model as follows:

[0073]

[0074] Where, ΔH(T)=0.5° / °C × ΔT; 、 They are the hues of the pixels before and after color temperature compensation respectively; 、 They are the saturation of the pixel before and after color temperature compensation; 、 The brightness of the pixel before and after color temperature compensation respectively; by compensating the color shift caused by low temperature, the area range of different stripe areas is segmented , where n represents the ordinal number of the stripe region. After using color shift correction, the error rate of stripe region segmentation is reduced.

[0075] Step 3: Determine coverage status:

[0076] (1) Constructing an improved Canny operator:

[0077] Use Gaussian filter function Smooth image noise and enhance edge continuity, parameter: σ=1.5:

[0078]

[0079] in, is the smoothed pixel value; is the coordinate of the image after color segmentation The pixel value at ; σ is the Gaussian filter function The parameter is 1.5,

[0080] Use the Sobel operator to calculate the brightness gradient amplitude of the smoothed image and brightness gradient direction :

[0081]

[0082]

[0083] (2) Dual threshold setting:

[0084]

[0085]

[0086] in, 、 are the upper threshold and the lower threshold respectively. is the maximum value of the brightness gradient amplitude of all pixels. In this embodiment, the ratio of the lower threshold to the upper threshold is 1:3.

[0087] (3) Preliminary classification of pixel types:

[0088] For each pixel of the smoothed image, the following judgment is performed:

[0089] ①. If the brightness gradient amplitude of a pixel Greater than the upper threshold , then the pixel is determined to be a strong edge and is regarded as a type of undetermined pixel.

[0090] ②. If the brightness gradient amplitude of a pixel In [ , ] interval, that is, the gradient amplitude Greater than or equal to the lower threshold , then the pixel is determined to be a weak edge and is considered as a second-class undetermined pixel, which needs to be verified for direction consistency later.

[0091] ③. If the brightness gradient amplitude of a pixel Less than the lower threshold , then the pixel is determined to be non-edge and deleted directly instead of being considered as a boundary pixel.

[0092] (4) Calculation of gradient edge intensity of undetermined pixel points:

[0093] Gradient edge strength of a class of undetermined pixel points The expression is:

[0094]

[0095] The direction consistency of the second type of undetermined pixels is verified. The specific process is as follows:

[0096] The direction consistency verification is introduced for the eight adjacent pixels of the weak edge pixel. The verification relationship is as follows:

[0097]

[0098] in, Calculate the average value; is the brightness gradient direction of the i-th adjacent pixel of the weak edge pixel; i=1,2,...,8; is the brightness gradient direction of weak edge pixels; The direction angle limit value is 22.5° in this embodiment.

[0099] If the verification relationship is satisfied, it means that the weak edge pixel has passed the direction consistency verification. If the verification relationship is not satisfied, it means that the weak edge pixel has failed the direction consistency verification.

[0100] After completing the direction consistency verification, the gradient edge strength is calculated for all the second-class undetermined pixels. as follows:

[0101]

[0102] (5) Phase Congruency (PC) detection of undetermined pixel points:

[0103] For the first and second types of undetermined pixels, the phase consistency edge strength is calculated in the frequency domain. :

[0104]

[0105] Among them, ϕ nis the local frequency phase, is the average phase of all pixels in the image.

[0106] Phase congruency edge strength The larger it is, the more significant the pixel is as an edge.

[0107] (6) Joint inspection:

[0108] Gradient edge intensity for each undetermined pixel and phase congruency edge strength , respectively, and normalized:

[0109]

[0110] in, 、 are the normalized gradient edge strength and phase consistency edge strength, respectively.

[0111] Weighted calculation of the comprehensive edge strength of each undetermined pixel as follows:

[0112]

[0113] Among them, w1 and w2 are the weights of gradient edge strength and phase consistency edge strength respectively; w1+w2=1 is satisfied, and the specific values ​​are measured by experiments.

[0114] (7) Multi-feature fusion judgment:

[0115] ①. Brightness contrast: Calculate the mean brightness difference between each stripe area and the background area ; 、 are the brightness mean values ​​of the nth stripe area and the background area in HSV space respectively. The background area refers to the area outside the calibration disk 1 in the image. , then it is considered that the nth stripe area is not completely covered by ice. , then the nth stripe area is considered to be completely covered by ice. Based on the judgment results, three intervals are formed: the completely covered interval located at the innermost side, the partially covered interval located in the middle, which occupies a stripe area, and the uncovered interval located at the outermost side. is the proportional coefficient corresponding to the nth stripe area; Through experimental determination, the value range is generally 0.5 to 1; is the maximum brightness of the image.

[0116] In this embodiment, all stripe areas within the outermost stripe area determined to be completely covered by the ice layer constitute a completely covered interval; the outer adjacent stripe areas of the outermost stripe area determined to be completely covered by the ice layer constitute a covered interval; and the remaining stripe areas constitute an uncovered interval.

[0117] ②. Coverage ratio calculation:

[0118] The ice coverage ratio of the striped area belonging to the fully covered interval .

[0119] The ice coverage ratio of the stripe area belonging to the uncovered interval .

[0120] Calculate the sum of the edge intensities of the stripe area that belongs to the partially covered interval as follows:

[0121]

[0122] in, It is a striped area partially covered by ice.

[0123] Calculate the ice cover fraction of the streak area that is partially covered by ice as follows:

[0124]

[0125] Among them, k is the brightness correction coefficient, which is measured through experiments and has a negative value with a small absolute value. It is the comprehensive edge strength benchmark value, obtained through experiments before freezing. The term "brightness correction" overcomes the limitations of single features by fusing edge intensity and brightness, improving the accuracy and robustness of ice cover percentage measurements in complex underwater environments. It not only compensates for the uncertainty of edge intensity measurements but also adapts to different types of ice, significantly improving system reliability, especially in situations with poor underwater image quality.

[0126] The principle of using comprehensive edge strength to detect ice coverage ratio in this embodiment is that the area covered by ice in the image is more blurred than the area not covered by ice, and the lower the edge strength.

[0127] The comprehensive edge strength reference value The acquisition process is as follows:

[0128] When the water body is not frozen, collect the image of the calibration disk 1 and perform color segmentation, and calculate the comprehensive edge intensity of each pixel according to steps (2) to (6) of step 3. ; For each stripe area , calculate the sum of the comprehensive edge strength in the unfrozen state in the stripe area as the comprehensive edge strength reference value of each stripe area as follows:

[0129]

[0130] Where n represents the ordinal number of the stripe area, and its values ​​are 1, 2, 3, 4, 5, and 6.

[0131] The ice coverage ratio covering different stripes is calculated using the above method (0~1), combined with color offset to correct misjudgment.

[0132] In this step, to address the problem of high noise in underwater images and the susceptibility of traditional Canny to isolated noise interference, a directional consistency constraint is introduced (the difference in the gradient direction of adjacent pixels is ≤ 22.5°) and the PC results are fused and weighted. First, the directional consistency constraint suppresses noise edges: the directional consistency constraint reduces the interference of isolated noise; second, edge continuity is enhanced: the PC supplements high-frequency details and reduces the fracture rate of the ice boundary.

[0133] In addition, this step can accurately extract the ice cover area M_covered by combining brightness contrast ΔI and edge intensity weighted fusion, dynamic threshold segmentation and morphological optimization, and provide a reliable coverage ratio for thickness calculation. The method shows strong robustness in underwater low-temperature environments, especially in dealing with uneven ice coverage and frost interference.

[0134] Step 4: Thickness calculation:

[0135] The radial pixel radius of the coverage area is calculated by the coverage ratio and the upper and lower boundary radii of the stripes:

[0136]

[0137] in, is the pixel thickness of the ice layer, 、 is the pixel radius of the lower and upper boundaries of the nth stripe, and its value is obtained by collecting images and performing color segmentation before freezing.

[0138] The actual thickness of the ice layer D is calculated as follows:

[0139]

[0140] Where L(·) is the mapping function between pixel radius and actual radius.

[0141] In this embodiment, the calibration process of the camera 3 is as follows:

[0142] (1) Internal reference calibration: Use a 9×6 checkerboard calibration plate (grid spacing 20 mm) to obtain the focal length and distortion coefficient.

[0143] (2) Spatial mapping calibration:

[0144] The calibration disk 1 is photographed in an ice-free state, and the linear relationship L(r) between the pixel length r and the actual length L(r) is fitted to establish a piecewise cubic spline interpolation model:

[0145]

[0146] Among them, r t is the critical radius, and the segmentation points are determined by cross-validation; a1, b1, c1, and d1 are the four fitting coefficients when the pixel length r is less than the critical radius; a2, b2, c2, and d2 are the four fitting coefficients when the pixel length r is greater than or equal to the critical radius.

[0147] The reasons for choosing the cubic spline interpolation model are as follows:

[0148] 1) Smoothness: The second-order derivative is continuous, avoiding sudden changes in linear interpolation.

[0149] 2) Flexibility: Adapt to nonlinear distortion of the calibration disk 1 (e.g., larger edge distortion).

[0150] 3) Computational efficiency: Compared with high-order polynomials, the computational complexity is low (response time < 5s)

[0151] (3) The role of critical radius rt

[0152] Necessity of segmented fitting: Calibrate the central area of ​​disk 1 (r <r t ) is less affected by lens distortion, and using a low curvature (small a1, b1) polynomial can avoid overfitting while maintaining smoothness; the edge area (r ≥ r t ) is significantly distorted, and a high curvature (larger a2 and b2) is required to accurately fit the complex bends caused by the distortion.

[0153] The critical radius is determined by selecting r through cross-validation t , minimizing the overall fitting error (RMSE < 0.3 mm).

[0154] (4) Periodic calibration:

[0155] Initial calibration acquires the intrinsic parameters and spatial mapping relationship of Camera 3. Periodic online calibration compensates for parameter drift during long-term operation. Trigger conditions: monthly execution or detection of abnormal thickness fluctuations. Spatial mapping calibration begins once the trigger conditions are met.

[0156] In this embodiment, the error is optimized by the following measures:

[0157] (1) Reduce the interference of transparent ice layer: Polarized light source separates the surface reflected light and reduces the color distortion caused by transmission.

[0158] (2) Vibration suppression: Hardware vibration reduction design + short exposure time (1 / 2000s) reduces motion blur.

[0159] (3) Robustness enhancement: multi-frame fusion voting judgment, abnormal thickness threshold alarm, and incremental learning to update the classifier.

[0160] (4) Calibration disk 1 design: Transparent acrylic material with integrated radial fluorescent color stripes (large HSV spatial difference), and a hydrophobic coating on the surface to reduce interference from ice adhesion.

[0161] (5) Multi-algorithm image processing framework: integrating non-local mean denoising, wavelet threshold denoising, distortion correction and CLAHE enhancement to improve underwater image quality.

[0162] (6) Dynamic calibration mechanism: Combining initial calibration with periodic online calibration to compensate for the impact of environmental changes on camera 3 parameters and spatial mapping.

[0163] (7) Thickness calculation model: Based on geometric mapping and linear interpolation, accurate conversion of ice coverage to thickness is achieved.

Claims

1. A method for measuring ice thickness of ice storage coils based on visual perception, characterized in that: include: A circular or sector-shaped calibration disk is provided on the ice storage coil; the calibration disk is provided with a plurality of striped areas of different colors arranged concentrically and radially; Use a camera to capture images of the calibration disk outside the ice area; extract each stripe area in the image through color segmentation; Extract the brightness gradient amplitude of the pixel and brightness gradient direction ; Through the double thresholds of different sizes, the first type of undetermined pixels and the second type of undetermined pixels are extracted from the pixels of all stripe areas; according to the brightness gradient direction Verify the direction consistency of the second type of undetermined pixels; Based on the brightness gradient amplitude and the direction consistency verification results of the two types of undetermined pixels, the gradient edge intensity of each undetermined pixel is obtained; the phase consistency edge intensity is calculated for the undetermined pixel; the gradient edge intensity and the phase consistency edge intensity are normalized and weighted summed to obtain the comprehensive edge intensity; Based on the brightness change of the calibration disk after being covered by ice in the image, it is determined whether each stripe area is completely covered by ice. Each stripe area is divided into an inner fully covered area, a middle partially covered area occupying one stripe area, and an outermost uncovered area. For the partially covered interval, the ice coverage ratio of the partially covered interval is obtained by comparing the comprehensive edge intensity of each pixel point in the corresponding stripe area with the comprehensive edge intensity reference value; the ice thickness is obtained based on the inner and outer radii of the fully covered interval and the ice coverage ratio of the partially covered interval.

2. The ice storage coil ice thickness measurement method according to claim 1, characterized in that: The image is used to extract the brightness gradient amplitude and brightness gradient direction Gaussian filtering is performed before.

3. The ice thickness measurement method of the ice storage coil according to claim 1, characterized in that: The process of extracting the first and second types of undetermined pixels is as follows: set different lower and upper thresholds, take the pixels whose brightness gradient amplitude is greater than the upper threshold as the first type of undetermined pixels, and take the pixels whose brightness gradient amplitude is greater than the upper threshold as the second type of undetermined pixels. The pixels between the lower threshold and the upper threshold are regarded as the second category of undetermined pixels.

4. The ice thickness measurement method of the ice storage coil according to claim 1, characterized in that: The process of direction consistency verification is as follows: extracting the brightness gradient directions of eight adjacent pixels of the second type of undetermined pixel points ; Establish verification relationship If the verification relationship is satisfied, it means that the second type of undetermined pixel has passed the direction consistency verification; if the verification relationship is not satisfied, it means that the second type of undetermined pixel has not passed the direction consistency verification.

5. The ice thickness measurement method of the ice storage coil according to claim 1, characterized in that: The gradient edge intensity of a type of undetermined pixel point The gradient edge intensity expression of the second type of undetermined pixel points is: ;in, is the brightness gradient amplitude.

6. The ice thickness measurement method of the ice storage coil according to claim 1, characterized in that: The expression of the phase consistency edge strength is: Among them, ϕ n is the local frequency phase, is the average phase of all pixels in the image.

7. The ice thickness measurement method of the ice storage coil according to claim 1, characterized in that: Ice cover ratio in partially covered areas The expression is as follows: in, is the sum of the comprehensive edge intensities of all pixels in the partially covered interval; k is the brightness correction coefficient; is the comprehensive edge strength benchmark value, which is obtained by extracting the comprehensive edge strength of the corresponding stripe area on the unfrozen image and summing it up; is the difference between the mean brightness of the corresponding stripe area and the mean brightness of the background; is the maximum brightness value in the image.

8. The ice thickness measurement method of the ice storage coil according to claim 1, characterized in that: The images captured by the camera are preprocessed; the preprocessing process includes underwater scattering correction, color correction and multi-scale denoising; the underwater scattering correction process is as follows: the transmittance is calculated based on the underwater scattering coefficient and the distance from the calibration disk to the camera lens, and the pixel value of each pixel point is corrected using the transmittance and the background scattered light intensity; the multi-scale denoising includes non-local mean filtering, distortion correction and contrast enhancement.

9. An ice storage coil ice thickness measuring device, characterized by: Used to execute the ice thickness measurement method of the ice storage coil as claimed in claim 1; the ice thickness measurement device of the ice storage coil comprises a calibration disk (1), an image acquisition module and a processor (2); the calibration disk (1) is concentrically fixed to one of the straight pipe sections of the ice storage coil (5) to be measured; the image acquisition module is fixed to the inner wall of the ice storage tank; the image acquisition module comprises a camera (3) and a fill light (4); the fill light (4) is a ring light, which is fixed around the outer periphery of the camera (3) and faces the calibration disk (1); the fill light (4) is a polarized fill light.

10. The ice thickness measuring device for ice storage coils according to claim 9, characterized in that: The saturation and brightness of the color in each stripe area are 100%, and the hue , where n is the serial number of the stripe area from the inside to the outside, and N is the number of stripe areas.

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