A method and system for analyzing the spectral image of contamination and wettability of silicone rubber surfaces

Abstract

The present invention discloses a method and system for analyzing the contamination and wetness spectrum of a silicone rubber surface, belonging to the technical field of high-voltage power transmission and transformation equipment status monitoring. The method comprises: preparing dry contamination samples and saturated wet contamination samples of a vulcanized silicone rubber sample; obtaining reflectance spectrum data of the dry contamination samples and the saturated wet contamination samples of the vulcanized silicone rubber sample using a near-infrared reflectance spectrum imager; normalizing the pixel grayscale values ​​of the reflectance spectrum imager using a calibration whiteboard and a standard blackboard to determine a reflectance calculation formula; respectively calculating curves of the reflectance average value versus wavelength for the dry contamination sample area and the saturated wet contamination sample area; constructing a contamination and wetness evaluation model, the model including contamination and wetness evaluation model parameters and wetness estimation coefficients; calculating the contamination and wetness of the surface of the vulcanized silicone rubber sample using the contamination and wetness evaluation model, and visually presenting the contamination and wetness.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-voltage power transmission and transformation equipment status monitoring, and more specifically, relates to a method and system for analyzing spectral images of contamination and wettability on the surface of silicone rubber. Background Art

[0002] To achieve large-scale energy transmission and optimize resource allocation, China has proposed a strategic layout for west-to-east power transmission, with high-voltage transmission lines as the backbone. This strategy aims to build a cross-regional power and energy transmission network. Insulators play a crucial role in this effort, providing not only essential mechanical support but also electrical isolation. However, due to their prolonged exposure to the outdoors, insulator surfaces are susceptible to the accumulation of contaminants from atmospheric pollution. According to power system operational data, up to 40% of external insulation failures are caused by pollution flashover. If these contamination issues are not promptly addressed, they can trigger large-scale power outages and cause severe economic losses. Surface contamination and the wetness of the contamination layer are key factors in the development of insulator pollution flashover. A wet contamination layer increases leakage current on the insulator surface, promoting arc initiation and propagation. For vulcanized silicone rubber composite insulators, a decrease in their hydrophobicity not only reduces their pollution resistance but also compromises the hydrophobicity of the contamination layer. Therefore, accurately monitoring the wetness of the contamination layer on composite insulator surfaces is crucial for understanding pollution flashover mechanisms, assessing operational risks, and optimizing insulator maintenance strategies.

[0003] However, for a long time, there has been a lack of effective detection methods for the wetness of the contamination layer. The current methods used to characterize the wetness of the contamination layer mainly include the contamination layer conductivity method, leakage current method, phase angle difference method and differential weight method.

[0004] The significantly increased conductivity of the pollution layer after being dampened can indicate the contamination wetness of the pollution layer at a fixed contamination level. However, there are currently no published literature or patents addressing how to separate the effects of contamination level and contamination wetness on conductivity from the measurement results. The leakage current method is also an effective method for characterizing the contamination wetness of the pollution layer. The ratio of the fundamental component of the leakage current to the maximum value of the total current represents the ratio of the residual pollution layer resistance to the total pollution layer resistance. However, this method has difficulty separating the contributions of contamination level and contamination wetness to the leakage current. The phase angle difference method characterizes the contamination wetness by measuring the phase angle difference between the leakage current and the applied voltage. The problem with this method is that it is difficult to measure the voltage phase and small current phase in complex electromagnetic environments. The differential weight method characterizes the contamination wetness by measuring the mass difference of the contaminated insulator before and after being damp. However, this method is difficult to apply in actual engineering sites and cannot provide the distribution characteristics of contamination wetness, making it of little significance for actually determining whether the pollution flashover voltage has decreased. In recent years, various spectral imaging methods have been used to assess the surface condition of insulators, such as infrared thermal imaging, ultraviolet discharge imaging, and hyperspectral imaging. While the former two can detect heating or discharge under severe contamination, the contamination conditions that can cause heating or discharge in practice are relatively rare. While hyperspectral imaging can reveal the distribution or composition of contamination on insulator surfaces in the laboratory, methods specifically analyzing contamination wetness are rare. While some papers have examined hyperspectral visualization of contamination wetness on glass insulator surfaces, fundamental differences in contamination accumulation mechanisms and spectral analysis methods between glass and vulcanized silicone rubber substrates make it difficult to analyze the contamination wetness of composite insulators.

[0005] In summary, the existing pollution wetness monitoring still faces problems such as low detection efficiency, difficulty in separating multiple factors, and poor feasibility of on-site application.

[0006] Prior art document CN118424945A discloses a method for visualizing the contaminated moisture content on the surface of an insulator based on hyperspectral imaging technology. The method includes the following steps: S1, preparing a contaminated test sample; S2, building a hyperspectral imaging platform and a high-voltage test platform, and connecting the test sample to the high-voltage test platform; S3, measuring the moisture content of the test sample and obtaining a hyperspectral image of the test sample through the hyperspectral imaging platform; S4, processing the hyperspectral image obtained from step S3, wherein the hyperspectral three-dimensional data consists of one-dimensional spectral information (λ) and two-dimensional spatial information (x, y). For the test sample, its effective features are contained in a few characteristic bands. Based on the low-dimensional data in the characteristic bands, a quantitative solution model for the moisture content is proposed using partial least squares regression. Finally, quantitative visualization inversion of the moisture content of the test sample is achieved through pseudo-color processing. Its shortcomings are: 1. The influence of moisture on the reflectance spectrum is mainly reflected in the near-infrared band. The spectral bands in the visible light range that can effectively reflect wetness are relatively sparse and narrow. In addition, there is no detailed description of how to use the information of a few characteristic bands in hyperspectral imaging to reflect wetness, and which spectral parameters and parameter combinations can be used for modeling; 2. Vulcanized silicone rubber materials generally use RTV hydrophobic coatings, and their hydrophobicity will affect the judgment of the water content in the contamination layer. It is necessary to construct model parameters specifically for the entire dynamic range of contamination wetness on the vulcanized silicone rubber surface (i.e., from completely dry to saturated wetness); 3. Hyperspectral imaging is a general analysis method mostly used in laboratories. However, for actual line insulators, the shooting distance, angle and stability are difficult to guarantee, especially for drone mounting applications. Therefore, the complete use of hyperspectral curves for model training deviates from reality. Summary of the Invention

[0007] In order to solve the deficiencies in the prior art, the present invention provides a method and system for analyzing the spectral image of contamination and wettability on the surface of silicone rubber.

[0008] The present invention adopts the following technical solutions.

[0009] A first aspect of the present invention provides a method for analyzing the spectral image of contamination wetness on a silicone rubber surface, comprising the following steps:

[0010] Prepare dry contaminated samples and saturated wet contaminated samples of vulcanized silicone rubber specimens;

[0011] The reflectance spectrum data of the dry contaminated sample and the saturated wet contaminated sample of the vulcanized silicone rubber sample were obtained by near-infrared reflectance spectroscopic imaging in the 900nm to 1700nm band;

[0012] Use a calibration white board and a standard black board to normalize the pixel grayscale value of the reflectance spectrum imager and determine the reflectance calculation formula;

[0013] Calculate the curves of the average reflectance of the dry contaminated sample area and the saturated wet contaminated sample area versus wavelength respectively;

[0014] Constructing a pollution wetness assessment model, which includes pollution wetness assessment model parameters and wetness estimation coefficients. Based on the curve of the average reflectance of the dry pollution sample area and the saturated wet pollution sample area versus wavelength, setting and calculating the pollution wetness assessment model parameters, including the filling rate, difference rate, change rate, and change difference rate, and calculating the wetness estimation coefficients corresponding to the pollution wetness assessment model parameters;

[0015] Based on the contamination wettability assessment model parameters and wettability estimation coefficients, the contamination wettability of the surface of the vulcanized silicone rubber sample is calculated using the contamination wettability assessment model and visualized.

[0016] Optionally, the preparation of dry contaminated samples and saturated wet contaminated samples of the vulcanized silicone rubber sample comprises:

[0017] Prepare a fouling coating evenly on the surface of the vulcanized silicone rubber sample;

[0018] The contaminated samples are placed in an artificial fog chamber, and the relative humidity of the atmosphere in the artificial fog chamber is set to no higher than 5% and no lower than 95% respectively. The relative humidity of the atmosphere is no higher than 5% for dry humidity conditions, and the relative humidity of the atmosphere is no lower than 95% for saturated humidity conditions. The sample is left standing for no less than 60 minutes under dry and saturated humidity conditions.

[0019] Optionally, the reflection spectrum data includes the grayscale value P0(λ) of each pixel in the sample area, where λ is the wavelength;

[0020] The calculation formula of reflectivity R(λ) is:

[0021]

[0022] Where, P B (λ) is the standard black image data, P W (λ) is the standard white image data.

[0023] Optionally, the curve of the average reflectivity of the dry contaminated sample area versus the wavelength λ is recorded as R0(λ); the curve of the average reflectivity of the saturated wet contaminated sample area versus the wavelength λ is recorded as R1(λ);

[0024] The calculation of the filling rate k1 includes:

[0025] k1=R p (λ1) / R max

[0026] Where R maxis the maximum absolute value of the difference between R1(λ) and R0(λ), and λ1 is the wavelength corresponding to the maximum absolute value of the difference between R1(λ) and R0(λ);

[0027] Calculating the difference rate k2 includes:

[0028] k2=R p (λ1)-R p (λ2)

[0029] Where λ2 is the wavelength corresponding to the minimum absolute value of the difference between R1(λ) and R0(λ);

[0030] Calculating the rate of change k3 includes:

[0031]

[0032] Where λ3 is the central wavelength corresponding to the maximum value of the absolute value of the difference between R1(λ) and R0(λ) at the dλ step change rate;

[0033] Calculating the change difference rate k4 includes:

[0034]

[0035] Where λ4 is the central wavelength corresponding to the minimum value of the absolute value of the difference between R1(λ) and R0(λ) at the dλ step change rate;

[0036] R p (λ) is the reflectivity of the current test result at each pixel.

[0037] Optionally, the R max The calculation includes:

[0038] R max =max(|R1(λ)-R0(λ)|)

[0039] The r max The calculation includes:

[0040]

[0041] The value of dλ is 5 times the spectral resolution of the near-infrared reflectance spectrometer.

[0042] Optionally, the calculation of the wetness estimation coefficient corresponding to the pollution wetness assessment model parameter includes:

[0043] Set the initial wettability coefficient α 0 (t):

[0044]

[0045] Among them, Yi is the i-th element of the measured pollution wetness vector, X i is the i-th row of the parameter matrix of the pollution wetness assessment model, α is the wetness estimation coefficient vector, and n is the number of samples;

[0046] Calculate the model deviation value δ of the mth iteration at time t m (t), the calculation formula is:

[0047] δ m (t) = Y - Xα m (t)

[0048] Among them, Y is the measured pollution wetness vector, X is the pollution wetness assessment model parameter matrix, α m (t) is the estimated wetness coefficient of the mth iteration at time t;

[0049] The deviation σ is defined as an indicator describing the degree of deviation of the model output:

[0050] σ=1.4826EA

[0051] Among them, EA is the average deviation absolute value, which is the absolute value of the median deviation value of all models;

[0052] Defining standardized deviation is the model deviation value δ of the mth iteration at time t m The ratio of (t) to the deviation σ:

[0053]

[0054] Calculate the indicator weight ρ of the mth iteration at time t m (t), using indicator weight ρ m (t) Perform weighted regression and update the estimated coefficient α of the corrected wetness m (t):

[0055] α m+1 (t) = (X T ρ * X) -1 X T ρ * Y

[0056] Among them, ρ * It is a diagonal matrix, and the elements on its diagonal are the indicator weights ρ m (t);

[0057] Repeat the above steps until the change in the corrected wetness estimation coefficient is less than the preset iterative convergence deviation threshold s:

[0058] |α m+1 (t)-αm (t)|

[0059] At this time, α m (t) is the final wettability estimation coefficient vector.

[0060] Optionally, the indicator weight ρ of the mth iteration at time t is calculated m (t) include:

[0061]

[0062] Among them, b is the weight scaling value, which ranges from 1.05 to 1.4.

[0063] Optionally, the calculating the contamination wettability of the surface of the vulcanized silicone rubber sample by using the contamination wettability evaluation model and visually presenting the contamination wettability specifically includes:

[0064] Extracting one-dimensional reflectance spectrum data corresponding to each pixel from the reflectance spectrum data after black and white correction;

[0065] The pollution wetness is calculated based on the calculation results of the pollution wetness evaluation model parameters k1~k4 and the corresponding wetness estimation coefficients α1~α4. The pollution wetness evaluation model is expressed as:

[0066] Pollution wetness = α1·k1+α2·k2+α3·k3+α4·k4

[0067] The wetness value of each pixel point (x, y) in the test area is calculated, and a characterization color scale is defined for different result data. The detection results obtained by all coordinates are pseudo-colored according to the color scale in turn to obtain distribution images of different wetness.

[0068] Optionally, the method further includes, when using a near-infrared reflectance spectrum imager to acquire reflectance spectrum data of the sample, using a uniform halogen light source to irradiate the surface of the sample.

[0069] A second aspect of the present invention provides a silicone rubber surface contamination wetness spectrum image analysis system, based on the silicone rubber surface contamination wetness spectrum image analysis method described in the first aspect of the present invention, the system comprises:

[0070] Near-infrared reflectance spectrum imager, pixel grayscale value normalization module, reflectance calculation module, model parameter calculation module, wetness estimation coefficient calculation module, pollution wetness calculation module and visualization module;

[0071] Near-infrared reflectance spectrum imager, pixel grayscale value normalization module, reflectance calculation module, model parameter calculation module, wetness estimation coefficient calculation module, pollution wetness calculation module and visualization module; ​

[0072] The near-infrared reflectance spectrum imager is used to obtain the reflectance spectrum data of dry contaminated samples and saturated wet contaminated samples of vulcanized silicone rubber samples in the 900nm~1700nm band; the pixel grayscale value normalization module normalizes the pixel grayscale value of the reflectance spectrum imager; the reflectance calculation module is used to calculate the reflectance; the model parameter calculation module is used to calculate the contamination wetness evaluation model parameters; the wetness estimation coefficient calculation module is used to calculate the wetness estimation coefficient corresponding to the contamination wetness evaluation model parameters; the contamination wetness calculation module is used to calculate the contamination wetness on the surface of the vulcanized silicone rubber sample; the visualization module is used to visualize the contamination wetness on the surface of the vulcanized silicone rubber sample.

[0073] Compared with the prior art, the beneficial effects of the present invention include at least:

[0074] High detection efficiency: The present invention utilizes reflective spectrum imaging technology to quickly obtain spectral data of the contamination layer, significantly improving detection efficiency.

[0075] Strong field applicability: This method does not require complex equipment and environmental conditions and is suitable for rapid deployment and use in various field environments.

[0076] Rich information: The present invention can provide accurate quantitative analysis of the wetness of the pollution layer and visualize the distribution characteristics of the pollution wetness, providing important data support for insulator design optimization and pollution flashover risk assessment.

[0077] High precision and high stability: The reflectance spectrum imaging technology and pollution wetness assessment model algorithm adopted in the present invention can effectively reduce the impact of measurement errors and outliers, and improve the accuracy and stability of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 This is a diagram showing the visual distribution of contamination on the surface of a vulcanized silicone rubber at different contamination wetness levels according to an embodiment of the present invention;

[0079] Figure 2 It is a flow chart of a method provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0080] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0081] The present invention provides a method for analyzing the wettability of a silicone rubber surface through a spectrum image in Example 1, as shown in FIG. Figure 2 As shown, the following steps are included:

[0082] Step 1, preparing a dry contaminated sample and a saturated wet contaminated sample of a vulcanized silicone rubber sample;

[0083] In a further preferred but non-limiting embodiment, step 1 specifically comprises:

[0084] Prepare a fouling coating evenly on the surface of the vulcanized silicone rubber sample;

[0085] Place the contaminated sample in an artificial fog chamber, and set the relative humidity of the atmosphere in the artificial fog chamber to no higher than 5% (dry) and no lower than 95% (saturated). The sample should be left standing for no less than 60 minutes under dry and saturated humidity conditions.

[0086] Step 2: obtaining reflectance spectrum data of a dry contaminated sample of a vulcanized silicone rubber sample and a saturated wet contaminated sample of a vulcanized silicone rubber sample in a wavelength range of 900 nm to 1700 nm by a near-infrared reflectance spectroscopic imager;

[0087] In a further preferred but non-limiting embodiment, in step 2, the reflectance spectrum data includes the grayscale value P0(λ) of each pixel in the sample area, wherein λ is the wavelength in the range of 900 nm to 1700 nm.

[0088] In a further preferred but non-limiting embodiment, step 2 further includes, when using a near-infrared reflectance spectroscopic imager to acquire reflectance spectrum data of the sample, using a uniform halogen light source to irradiate the sample surface.

[0089] Step 3: normalize the pixel grayscale values ​​of the reflectance spectrum imager using a calibration white plate with a reflectivity greater than 95% and a standard black plate with a reflectivity less than 5% to determine a reflectivity calculation formula;

[0090] In a further preferred but non-limiting embodiment, in step 3, the calculation formula of the reflectivity R(λ) is:

[0091]

[0092] Where P0(λ) is the grayscale value of each pixel in the sample area, P B (λ) is the standard black image data, P W (λ) is the standard white image data.

[0093] Step 4, based on the reflectance spectrum data obtained in step 2 and the reflectance calculation formula determined in step 3, respectively calculate the curves of the reflectance average value of the dry contaminated sample area and the saturated wet contaminated sample area as a function of wavelength;

[0094] In a further preferred but non-limiting embodiment, in step 4, the curve of the average reflectance of the dry dirty sample area versus wavelength λ is recorded as R0(λ); the curve of the average reflectance of the saturated wet dirty sample area versus wavelength λ is calculated and recorded as R1(λ).

[0095] Step 5: Based on the curves of the average reflectance of the dry contaminated sample area and the saturated wet contaminated sample area versus wavelength calculated in step 4, define and calculate the contamination wetness assessment model parameters k1 to k4;

[0096] In a further preferred but non-limiting embodiment, step 5 specifically comprises:

[0097] Based on the curve R0(λ) of the average reflectance of the dry contaminated sample area versus wavelength λ and the curve R1(λ) of the average reflectance of the saturated wet contaminated sample area versus wavelength λ calculated in step 4, it can be determined that:

[0098] At time t, within the detection band, the maximum absolute value of the difference between R1(λ) and R0(λ) is:

[0099] R max =max(|R1(λ)-R0(λ)|)

[0100] The wavelength corresponding to the maximum absolute value of the difference between R1(λ) and R0(λ) is λ1;

[0101] At time t, within the detection band, the wavelength corresponding to the minimum absolute value of the difference between R1(λ) and R0(λ) is λ2;

[0102] At time t, within the detection band, the maximum value of the absolute value of the difference between R1(λ) and R0(λ) at the dλ step change rate is:

[0103]

[0104] The central wavelength corresponding to the maximum value of the absolute value of the difference between R1(λ) and R0(λ) at the dλ step change rate is λ3; where dλ is 5 times the spectral resolution of the reflectance spectrum imager.

[0105] Step 5.4: Calculate that at time t, within the detection band, the central wavelength corresponding to the minimum value of the absolute value of the difference between R1(λ) and R0(λ) at the dλ step change rate is λ4;

[0106] Step 5.5, calculate the reflectivity of each pixel in the current test result at time t is R p (λ), define model parameters k1~k4:

[0107] Fill rate k1:

[0108] k1=R p (λ1) / R max

[0109] Difference rate k2:

[0110] k2=R p (λ1)-R p (λ2)

[0111] Rate of change k3:

[0112]

[0113] Change difference rate k4:

[0114]

[0115] Step 6, calculating the wetness estimation coefficients α1 to α4 corresponding to the pollution wetness assessment model parameters k1 to k4;

[0116] In a further preferred but non-limiting embodiment, in step 6, the calculation of the wetness estimation coefficient specifically includes:

[0117] The calculation of the wetness estimation coefficient includes:

[0118] Step 6.1, initial parameter setting:

[0119] Set the initial wettability coefficient α 0 (t):

[0120]

[0121] Among them, Y i is the i-th element of the measured pollution wetness vector, X i is the i-th row of the model parameter matrix, α is the wettability estimation coefficient vector, and n is the number of samples;

[0122] Step 6.2, model deviation value:

[0123] Calculate the model deviation value δ of the mth iteration at time t m (t), the calculation formula is:

[0124] δ m (t) = Y - Xα m (t)

[0125] Where Y is the measured pollution wetness vector, X is the model parameter matrix, X = [k1k2k3k4] T , α m(t) is the estimated wetness coefficient of the mth iteration at time t;

[0126] Step 6.3, Deviation and Standardized Deviation:

[0127] The deviation σ is defined as an indicator describing the degree of deviation of the model output:

[0128] σ=1.4826EA

[0129] Among them, EA is the average deviation absolute value, which is the absolute value of the median deviation value of all models;

[0130] Defining standardized deviation is the model deviation value δ of the mth iteration at time t m The ratio of (t) to the deviation σ:

[0131]

[0132] Step 6.4, calculate indicator weights:

[0133] Calculate the indicator weight ρ of the mth iteration at time t m (t):

[0134]

[0135] Where b is the weight scaling value, which ranges from 1.05 to 1.4;

[0136] Step 6.5, weighted regression:

[0137] Use indicator weight ρ m (t) Perform weighted regression and update the estimated coefficient α of the corrected wetness m (t):

[0138] α m+1 (t) = (X T ρ * X) -1 X T ρ * Y

[0139] Among them, ρ * It is a diagonal matrix, and the elements on its diagonal are the indicator weights ρ m (t);

[0140] Step 6.6, iterate until convergence:

[0141] Repeat steps 6.2-6.5 until the change in the corrected wettability estimation coefficient is less than the preset iterative convergence deviation threshold s:

[0142] |α m+1 (t)-αm (t)|

[0143] At this time, α m (t) is the final wetness estimation coefficient vector, α m (t) = [α1 α2 α3 α4].

[0144] In a further preferred but non-limiting embodiment, the measured contamination wetness can be obtained in a variety of ways, such as using a precision electronic scale to monitor the mass difference of the insulator compared to the dry state, or estimating the moisture content of the contaminant after equilibrium by monitoring the relative humidity of the environment.

[0145] Step 7: Substitute the pollution wetness evaluation model parameters k1-k4 and wetness estimation coefficients α1-α4 into the pollution wetness evaluation model, calculate the pollution wetness of the surface of the vulcanized silicone rubber sample, and visualize it.

[0146] In a further preferred but non-limiting embodiment, in step 7, calculating the contamination wettability of the surface of the vulcanized silicone rubber sample and visually presenting the contamination wettability specifically includes:

[0147] Extracting one-dimensional reflectance spectrum data corresponding to each pixel from the reflectance spectrum data after black and white correction;

[0148] According to the calculation results of the four model parameters (k1~k4) and the corresponding wetness estimation coefficients α1~α4, the pollution wetness is calculated:

[0149] Pollution wetness = α1·k1+α2·k2+α3·k3+α4·k4

[0150] The wetness value of each pixel point (x, y) in the test area is calculated, and a characterization color scale is defined for different result data. The detection results obtained by all coordinates are pseudo-colored according to the color scale in turn to obtain distribution images of different wetness.

[0151] The object of this embodiment is vulcanized silicone rubber samples. These insulators have accumulated a certain degree of dirt on their surfaces. Our goal is to detect the dirt wetness of the dirt layer on the surface of these insulators using reflectance spectroscopy imaging technology.

[0152] (a) Implementation environment: A reflectance spectroscopy imaging platform was built in the laboratory, including a reflectance spectroscopy imager, a set of symmetrical light sources, and an optical darkroom.

[0153] Implementation steps:

[0154] (b) Sample preparation:

[0155] ​A clean vulcanized silicon rubber sheet with a diameter of 4 cm was used as a substrate material to prepare an artificial pollution sample according to the method of the present invention.

[0156] (c) Reflectance spectrum data collection:

[0157] The prepared contamination sample is placed on a reflectance spectrum imaging platform, and a reflectance spectrum imager is used to obtain near-infrared reflectance spectrum data of the sample in the 900-1700 nm band.

[0158] (d) Pollution wetness reflection model parameter set:

[0159] Taking a certain pixel as an example, the difference index (DI), normalized index (NI) and band ratio index (BRI) are calculated according to the method provided by the present invention. The wavelengths involved in the calculation of the four model parameters are as follows:

[0160]

[0161] (e) Pollution wetness assessment model:

[0162] For time t,

[0163] Use the least squares method to get the initial coefficient α (0) ;

[0164] Calculate the model deviation value δ and deviation σ, and calculate the standardized deviation σ N .

[0165] Calculate the indicator weight ρ for weighted regression, where b is 1.3224.

[0166] Perform weighted regression and continuously iteratively update the revised wetness estimation coefficient vector α m+1 .

[0167] Iterate until convergence to obtain the final wetness estimation coefficient vector α. Taking a certain pixel as an example, assuming that the wetness estimation coefficient vector α obtained after iteration of the robust regression model is: α1=0.12, α2=0.11, α3=0.38, α4=0.09

[0168] (f) Quantitative inversion and visualization of pollution wetness:

[0169] Calculate the pollution wetness data wetness (x,y) corresponding to each pixel point (x,y) at time t. Take a certain pixel point as an example:

[0170] (x,y)=α1·k1+α2·k2+α3·k3+α4·k4=0.12×0.331+0.11×0.0726+0.38×0.465+0.09×0.0331=0.2274

[0171] According to the above calculation, the estimated wettability of the dirt layer on the surface of the vulcanized silicone rubber is 0.2274. Figure 1 As shown in the figure, by pseudo-coloring the wetness data of all pixels, a visual image of the wetness of the contamination layer is obtained, which provides important information for further analysis and maintenance of insulators.

[0172] In Example 3, the present invention provides a silicone rubber surface contamination wetness spectrum image analysis system. Based on the silicone rubber surface contamination wetness spectrum image analysis method described in Example 1, the system includes:

[0173] Near-infrared reflectance spectrum imager, pixel grayscale value normalization module, reflectance calculation module, model parameter calculation module, wetness estimation coefficient calculation module, pollution wetness calculation module and visualization module;

[0174] The near-infrared reflectance spectrum imager is used to obtain the reflectance spectrum data of dry contaminated samples and saturated wet contaminated samples of vulcanized silicone rubber samples in the 900nm~1700nm band; the pixel grayscale value normalization module normalizes the pixel grayscale value of the reflectance spectrum imager; the reflectance calculation module is used to calculate the reflectance; the model parameter calculation module is used to calculate the contamination wetness assessment model parameters k1~k4; the wetness estimation coefficient calculation module is used to calculate the wetness estimation coefficients α1~α4 corresponding to the contamination wetness assessment model parameters k1~k4; the contamination wetness calculation module is used to calculate the contamination wetness on the surface of the vulcanized silicone rubber sample; the visualization module is used to visualize the contamination wetness on the surface of the vulcanized silicone rubber sample.

[0175] It is worth noting that the technical means adopted in the present invention include: 1. Using 900nm~1700nm near-infrared hyperspectral imaging data as the source of parameter extraction, which can more sensitively reflect the moisture content; 2. For vulcanized silicone rubber, finding the optimal parameters and parameter combinations in the entire range of dry pollution and saturated wet pollution, providing more robust parameter input for model establishment; 3. The bands and band combinations corresponding to the obtained parameters can be used separately, and distortion can also be achieved using multispectral imaging with a limited number of bands, providing a reference for practical applications.

[0176] Based on this technical means, the technical effects actually achieved by the present invention include at least: 1. Improving the efficiency, accuracy and dynamic range of the visual inversion of the wettability of the contamination layer on the surface of silicone rubber insulators; 2. Reducing the general requirements for the extraction of characteristic spectral parameters on performance such as high spectral resolution and spectral response range; 3. The proposed spectral parameters and combinations are conducive to improving the robustness of the wettability analysis model.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing the wettability of silicone rubber surface contamination spectrum, characterized in that: The steps include: Prepare dry contaminated samples and saturated wet contaminated samples of vulcanized silicone rubber specimens; The reflectance spectrum data of the dry contaminated sample and the saturated wet contaminated sample of the vulcanized silicone rubber sample were obtained by near-infrared reflectance spectroscopic imaging in the 900nm to 1700nm band; Use a calibration white board and a standard black board to normalize the pixel grayscale value of the reflectance spectrum imager and determine the reflectance calculation formula; Calculate the curves of the average reflectivity of the dry contaminated sample area and the saturated wet contaminated sample area versus wavelength respectively. The curve of the average reflectivity of the dry contaminated sample area versus wavelength λ is recorded as R0(λ); Calculate the curve of the average reflectivity of the saturated wet contaminated sample area versus wavelength λ and record it as R1(λ); The calculation of filling rate k1 include: k1=R p (λ1) / R max Where R max is the maximum absolute value of the difference between R1(λ) and R0(λ), and λ1 is the wavelength corresponding to the maximum absolute value of the difference between R1(λ) and R0(λ); Calculating the difference rate k2 includes: k2=R p (λ1)-R p (λ2) Where λ2 is the wavelength corresponding to the minimum absolute value of the difference between R1(λ) and R0(λ); Calculating the rate of change k3 includes: Where λ3 is the maximum value of the absolute value of the difference between R1(λ) and R0(λ) at the dλ step change rate r max The corresponding central wavelength; Calculating the change difference rate k4 includes: Where λ4 is the central wavelength corresponding to the minimum value of the absolute value of the difference between R1(λ) and R0(λ) at the dλ step change rate; R p (λ) is the reflectivity of the current test result at each pixel; Constructing a pollution wetness assessment model, which includes pollution wetness assessment model parameters and wetness estimation coefficients. Based on the curve of the average reflectance of the dry pollution sample area and the saturated wet pollution sample area versus wavelength, setting and calculating the pollution wetness assessment model parameters, including the filling rate, difference rate, change rate, and change difference rate, and calculating the wetness estimation coefficients corresponding to the pollution wetness assessment model parameters; Based on the contamination wetness assessment model parameters and the wetness estimation coefficient, the contamination wetness of the surface of the vulcanized silicone rubber sample is calculated by the contamination wetness assessment model and visualized. The wetness estimation coefficient corresponding to the contamination wetness assessment model parameters includes: Set the initial wettability coefficient α 0 (t): Among them, Y i is the i-th element of the measured pollution wetness vector, X i is the i-th row of the parameter matrix of the pollution wetness assessment model, α is the wetness estimation coefficient vector, and n is the number of samples; Calculate the model deviation value δ of the mth iteration at time t m (t), the calculation formula is: δ m (t)=Y-Xα m (t) Among them, Y is the measured pollution wetness vector, X is the pollution wetness assessment model parameter matrix, α m (t) is the estimated wetness coefficient of the mth iteration at time t; The deviation σ is defined as an indicator describing the degree of deviation of the model output: σ=1.4826EA Among them, EA is the average deviation absolute value, which is the absolute value of the median deviation value of all models; Defining standardized deviation is the model deviation value δ of the mth iteration at time t m The ratio of (t) to the deviation σ: Calculate the indicator weight ρ of the mth iteration at time t m (t), using indicator weight ρ m (t) Perform weighted regression and update the estimated coefficient α of the corrected wetness m (t): a m+1 (t)=(X T r * X) -1 X T r * Y Among them, ρ * It is a diagonal matrix, and the elements on its diagonal are the indicator weights ρ m (t); Repeat the above steps until the change in the corrected wetness estimation coefficient is less than the preset iterative convergence deviation threshold s: |a m+1 (t)-a m (t)| <s At this time, α m (t) is the final wettability estimation coefficient vector.

2. The method for analyzing the wettability of a silicone rubber surface by a spectrum image according to claim 1, wherein: The dry contaminated sample and the saturated wet contaminated sample of the vulcanized silicone rubber sample are prepared as follows: Prepare a fouling coating evenly on the surface of the vulcanized silicone rubber sample; The contaminated samples are placed in an artificial fog chamber, and the relative humidity of the atmosphere in the artificial fog chamber is set to no higher than 5% and no lower than 95% respectively. The relative humidity of the atmosphere is no higher than 5% for dry humidity conditions, and the relative humidity of the atmosphere is no lower than 95% for saturated humidity conditions. The sample is left standing for no less than 60 minutes under dry and saturated humidity conditions.

3. The method for analyzing the wettability of a silicone rubber surface by a spectrum image according to claim 1, wherein: The reflection spectrum data includes the gray value P0(λ) of each pixel in the sample area, where λ is the wavelength; The calculation formula of reflectivity R(λ) is: Where, P B (λ) is the standard black image data, P W (λ) is the standard white image data.

4. The method for analyzing the surface contamination wetness of silicone rubber according to claim 1, wherein: The R max The calculation includes: R max =max(|R1(λ)-R0(λ)|) The r max The calculation includes: The value of dλ is 5 times the spectral resolution of the near-infrared reflectance spectrometer.

5. The method for analyzing the wettability of a silicone rubber surface by a spectrum image according to claim 1, wherein: The calculation of the index weight ρ of the mth iteration at time t m (t) include: Among them, b is the weight scaling value, which ranges from 1.05 to 1.

4.

6. The method for analyzing the wettability of a silicone rubber surface by a spectrum image according to claim 1, wherein: The calculation of the contamination wettability of the surface of the vulcanized silicone rubber sample by the contamination wettability evaluation model and the visualization thereof specifically include: Extracting one-dimensional reflectance spectrum data corresponding to each pixel from the reflectance spectrum data after black and white correction; The pollution wetness is calculated based on the calculation results of the pollution wetness evaluation model parameters k1~k4 and the corresponding wetness estimation coefficients α1~α4. The pollution wetness evaluation model is expressed as: Pollution wetness = α1·k1+α2·k2+α3·k3+α4·k4 The wetness value of each pixel point (x, y) in the test area is calculated, and a characterization color scale is defined for different result data. The detection results obtained by all coordinates are pseudo-colored according to the color scale in turn to obtain distribution images of different wetness.

7. The method for analyzing the surface contamination wetness of silicone rubber according to claim 1, wherein: The method further includes, when using the near-infrared reflectance spectrum imager to obtain reflectance spectrum data of the sample, using a uniform halogen light source to irradiate the surface of the sample.

8. A silicone rubber surface contamination wetness spectrum image analysis system, based on the silicone rubber surface contamination wetness spectrum image analysis method according to any one of claims 1 to 7, comprising: Near-infrared reflectance spectrum imager, pixel grayscale value normalization module, reflectance calculation module, model parameter calculation module, wetness estimation coefficient calculation module, pollution wetness calculation module and visualization module; The near-infrared reflectance spectrum imager is used to obtain the reflectance spectrum data of dry contaminated samples and saturated wet contaminated samples of vulcanized silicone rubber samples in the 900nm~1700nm band; the pixel grayscale value normalization module normalizes the pixel grayscale value of the reflectance spectrum imager; the reflectance calculation module is used to calculate the reflectance; the model parameter calculation module is used to calculate the contamination wetness evaluation model parameters; the wetness estimation coefficient calculation module is used to calculate the wetness estimation coefficient corresponding to the contamination wetness evaluation model parameters; the contamination wetness calculation module is used to calculate the contamination wetness on the surface of the vulcanized silicone rubber sample; the visualization module is used to visualize the contamination wetness on the surface of the vulcanized silicone rubber sample.

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