Insulator pollution grade evaluation method based on multispectral analysis

Abstract

The invention provides an insulator pollution grade evaluation method based on multispectral analysis, and relates to the technical field of data analysis, and the insulator pollution grade evaluation process comprises the steps of precise alignment of a camera carried by an unmanned aerial vehicle body, multispectral camera image collection, image processing and image evaluation. The unmanned aerial vehicle body master control comprises a flight path control system, a flight attitude control system and an automatic navigation positioning calculation system, the unmanned aerial vehicle is used for carrying a multispectral camera, the multispectral camera is installed in the center of the unmanned aerial vehicle, and meanwhile a temperature detection probe and a time degree detection probe are installed on the two sides of the unmanned aerial vehicle respectively. The unmanned aerial vehicle can acquire the height image of the insulator in advance, then the unmanned aerial vehicle calculates the vertical horizontal difference from the unmanned aerial vehicle to the insulator through the automatic navigation positioning calculation system, and the unmanned aerial vehicle flies to the periphery of the insulator after accurately acquiring the position information of the insulator, so that manual information acquisition is replaced.

Description

Technical Field

[0001] The invention relates to the technical field of data analysis, and in particular to an insulator pollution level assessment method based on multi-spectral analysis. Background Art

[0002] According to a method for evaluating regional pollution based on sampling points disclosed in Chinese patent number "CN 105956377 A", based on the sampling points, the concentration of each pollutant at the sampling points and the degree of each pollutant at each estimated point are obtained; the degree of pollution at each estimated point is calculated according to the degree of pollution at the sampling points, the degree of each pollutant at the sampling points and the concentration of each pollutant at the estimated point, which effectively considers the influence of the concentration of pollutants in the background environment on the degree of pollution at the sampling points and the estimated points, and realizes accurate evaluation of regional pollution while reducing the sampling work of pollution. The method for evaluating regional pollution based on sampling points is applied to the evaluation of the degree of pollution of transmission line insulators, and the degree of pollution of the insulator of the estimated pole tower is estimated by the degree of pollution of the insulator of the sampling pole, which realizes accurate evaluation of the degree of pollution of the transmission line insulator while reducing the sampling work.

[0003] The above patent documents and prior art have the following technical problems when used:

[0004] Problem 1: At present, there are many transmission lines in the power system, and the insulators are located at higher positions on the towers. The above solutions are mainly completed manually, so the manual sampling of insulator contamination is heavy, requiring a lot of manpower and material resources, and there will be certain risks for the inspectors.

[0005] The second problem is that when the existing technology uses a spectral camera to photograph the surface of the insulator, the recognition end does not process the image in a detailed and comprehensive manner and cannot find the main features from it. It also does not perform analysis and evaluation based on the fusion of other multi-source data, resulting in inaccurate evaluation results. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides an insulator pollution level assessment method based on multi-spectral analysis, which solves the problem that manual sampling of insulator pollution levels is labor-intensive and the pollution level assessment results are not accurate enough.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an insulator contamination level assessment method based on multispectral analysis, the insulator contamination level assessment process includes precise positioning of the camera mounted on the drone body, multispectral camera image acquisition, image processing and image assessment, and the specific steps are as follows:

[0008] The main control of the UAV body includes a flight trajectory control system, a flight attitude control system and an automatic navigation and positioning calculation system. The UAV is equipped with a multispectral camera, which is installed in the center of the UAV. At the same time, a temperature detection probe and a time detection probe are installed on both sides of the UAV. The UAV will obtain the height image of the insulator in advance, and then the UAV calculates the vertical level difference from itself to the insulator through the automatic navigation and positioning calculation system, specifically, the image height h of the high-altitude high-voltage line A is calculated. a and the image height h of insulator B b As well as the image distance between the high-altitude high-voltage line A and the insulator B, the distance u between the high-altitude high-voltage line A and the insulator B and the drone a and u b As well as the angle θ between the high-altitude high-voltage line A and the insulator B relative to the imaging center, the safe distance from the drone to the transmission line plane is:

[0009]

[0010] SP2: When the built-in computing system of the drone calculates the height position of the insulator, it will be transmitted to the flight drive module of the drone, and then the drive module will control the drone body to automatically rise.

[0011] SP3: When the drone is flying, it will automatically search, detect and monitor various equipment around the insulator. During this process, the station staff will calibrate and control the flight speed, flight position and flight trajectory of the drone. During the control process, the roll angle, pitch angle and other control variables will be calculated in detail according to the attitude stability of the drone, and the flight trajectory will be controlled and adjusted according to the calculation results to ensure the stability of the drone inspection flight to the maximum extent, making the multi-spectral camera more stable when working.

[0012] SP4: The front-end imaging element of the multi-spectral camera mainly splices multiple independent filter substrates in sequence to obtain a filter array, and installs the filter array in front of the imaging device. When the multi-band light obtained by the light splitting by the filter array is dispersed to different imaging positions, multi-spectral images of corresponding bands in different strip areas are obtained, and the imaging device is a planar array detector.

[0013] Preferably, when the multispectral camera on the drone is close to the insulator, the light source is turned on and the light intensity and angle are adjusted according to the light reflection of the surface of the insulator to be evaluated. The parallel light reaches the surface of the insulator to be evaluated and generates reflected light. The multi-band image is used to collect the multi-band image of the reflected light on the surface of the insulator.

[0014] Preferably, the multispectral camera needs to be preheated for half an hour before being placed close to the insulator for image acquisition to maintain the stability of the light source, and parameter setting is performed after the preheating is completed. Due to the current of the device itself and the light source image in the environment, black and white correction is required to obtain the hyperspectral image.

[0015] Preferably, after acquiring the insulator surface image, the multispectral camera will transmit it to the image processing system, perform principal component analysis (PCA) and minimum noise factorization (MNF) dimensionality reduction analysis on the hyperspectral full-band image, find out the most obvious contamination components on the insulator surface, and then perform characteristic wavelength screening based on the competitive adaptive reweighting (CARS) algorithm and the continuous projection (SPA) algorithm respectively, and classify the degree of contamination attachment in combination with a support vector machine (SVM).

[0016] Preferably, the image processing system performs denoising on the image data, and removes noise in the image signal through wavelet denoising and convolution smoothing technology.

[0017] Preferably, after the image information noise is processed, the image processing system will perform refraction correction on the image, and use MSC for spectral analysis to effectively suppress the scattering phenomenon, thereby more accurately capturing the characteristics of each component in the "ideal spectrum" and improving the accuracy of the analysis.

[0018] Preferably, a multispectral detection diversity is built in the image processing system, and a multispectral analysis system is established based on the acquired hyperspectral image through the image evaluation system. The analysis system includes the design of the imaging system, the establishment of a classification model and software development, and the image identifies the location of contamination attachment and evaluates the contamination level through SVM.

[0019] Preferably, auxiliary detectors are installed on both sides of the multi-spectral camera on the front of the drone, and the auxiliary detectors may be temperature sensors, time sensors, pressure sensors, and flammable gas concentration sensors.

[0020] Preferably, the multi-spectral camera and the auxiliary detector jointly obtain the insulator surface information sensor f. The detection value obtained by the auxiliary detector can be obtained by x 1 , x 2 , …, x n Indicates that these test values ​​are all unbiased judgment results of the true value x, and at the same time, each test value is independent of each other. Indicates the corresponding variance, with X m (i) and w 1 , w 2 ,…,w n They represent the detection value of the mth sensor at time i and the weights of different sensors, which can determine the fused judgment value of the surrounding environment of the transmission line The sum weights must conform to the following formula.

[0021]

[0022] Preferably, the threshold value of the external safety state of the insulator detected by the auxiliary detector is specifically set as follows:

[0023] Highly safe state: temperature is not higher than 15℃, humidity is not higher than 5%, pressure is not higher than 30MPa, flammable gas concentration is not higher than 80mg / m 3 ;

[0024] Moderate safety status: temperature is not higher than 30℃, humidity is not higher than 20%, pressure is not higher than 60MPa, flammable gas concentration is not higher than 120mg / m 3 ;

[0025] Critical safety state: temperature is not higher than 45℃, humidity is not higher than 35%, pressure is not higher than 90MPa, flammable gas concentration is not higher than 160mg / m 3 Moderately dangerous state: temperature not higher than 60℃, humidity not higher than 50%, pressure not higher than 120MPa, flammable gas concentration not higher than 190mg / m 3 .

[0026] Highly dangerous state: temperature not higher than 75℃, humidity not higher than 65%, pressure not higher than 150MPa, flammable gas concentration not higher than 220mg / m 3 .

[0027] Beneficial Effects

[0028] The present invention provides an insulator contamination level assessment method based on multi-spectral analysis. It has the following beneficial effects:

[0029] 1. The insulator pollution level assessment process of the present invention includes precise positioning of the drone body mounted camera, multi-spectral camera image acquisition, image processing and image evaluation. The drone is equipped with a multi-spectral camera to collect images around the insulator. The drone includes a flight trajectory control system, a flight attitude control system and an automatic navigation and positioning calculation system. After accurately obtaining the insulator position information, it will fly to the vicinity of the insulator to replace manual information collection.

[0030] 2. The present invention performs denoising and refraction correction preprocessing on the collected images to make the collected images clearer. A multispectral detection diversity is built in the image processing system. A multispectral analysis system is established based on the acquired hyperspectral images through the image evaluation system. The analysis system includes the design of the imaging system, the establishment of the classification model and the software development, and the image identifies the attachment site of contamination and evaluates the contamination level through SVM.

[0031] 3. The present invention evaluates the contamination on the surface of insulators mainly through multi-spectral image analysis and information fusion of auxiliary detectors. The contamination on the surface of insulators is evaluated from the fused information. When evaluating the contamination on the surface of insulators, it is mainly divided into three levels according to the degree of pollution. At the same time, the system can detect and evaluate the safety status of the insulator through four auxiliary detectors during the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the UAV body of the present invention;

[0033] Figure 2 It is a detection process framework diagram of the present invention;

[0034] Figure 3 This is a structural diagram of the main control system of the drone body of the present invention;

[0035] Figure 4 It is a schematic diagram of the detection process of the multi-spectral camera of the present invention;

[0036] Figure 5 This is a diagram showing the internal module structure of the multi-spectral camera of the present invention;

[0037] Figure 6 It is a flow chart of the insulator spectrum analysis system of the present invention;

[0038] Figure 7 It is a framework diagram of the online evaluation method of the present invention;

[0039] Figure 8 It is a denoising flow chart of the present invention;

[0040] Fig. 9 This is a multi-source data fusion flow chart of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment one:

[0043] like Figure 1-9 As shown in the figure, the insulator contamination level assessment method based on multispectral analysis, the insulator contamination level assessment process includes precise positioning of the camera mounted on the drone body, multispectral camera image acquisition, image processing and image assessment. The specific steps are as follows:

[0044] The main control of the drone body includes a flight trajectory control system, a flight attitude control system, and an automatic navigation and positioning calculation system. The drone is equipped with a multispectral camera, which is installed in the center of the drone. At the same time, temperature detection probes and time detection probes are installed on both sides of the drone. The drone will obtain the height image of the insulator in advance, and then the drone calculates the vertical level difference from itself to the insulator through the automatic navigation and positioning calculation system, specifically calculating the image height h of the high-altitude high-voltage line A. a and the image height h of insulator B b As well as the image distance between the high-altitude high-voltage line A and the insulator B, the distance u between the high-altitude high-voltage line A and the insulator B and the drone a and u b As well as the angle θ between the high-altitude high-voltage line A and the insulator B relative to the imaging center, the safe distance from the drone to the transmission line plane is:

[0045]

[0046] SP2: When the drone's built-in computing system calculates the height position of the insulator, it will be transmitted to the drone's flight drive module, and then the drive module will control the drone body to automatically rise.

[0047] SP3: When the drone is flying, it will automatically search, detect and monitor various equipment around the insulator. During this process, the station staff will calibrate and control the flight speed, flight position and flight trajectory of the drone. During the control process, the roll angle, pitch angle and other control variables will be calculated in detail according to the attitude stability of the drone, and the flight trajectory will be controlled and adjusted according to the calculation results to ensure the stability of the drone inspection flight to the maximum extent, making the multi-spectral camera more stable when working.

[0048] SP4: The front-end imaging element of the multi-spectral camera mainly splices multiple independent filter substrates in sequence to obtain a filter array, and installs the filter array in front of the imaging device. When the multi-band light obtained by the light splitting by the filter array is dispersed to different imaging positions, multi-spectral images of corresponding bands in different strip areas are obtained. The imaging device is a planar array detector.

[0049] In the present invention, a multispectral camera is used to collect dirt images on the surface of the insulator, and then a drone is used to fly to the insulator. In order to facilitate stable collection of the spectral camera, a series of control methods for the drone are provided above to make the collection more stable. Specific embodiment 2:

[0051] like Figure 1-9As shown, when the multispectral camera on the drone is close to the insulator, the light source is turned on and the light intensity and angle are adjusted according to the light reflection on the surface of the insulator to be evaluated. The parallel light reaches the surface of the insulator to be evaluated and generates reflected light. The multi-band image is used to collect the multi-band image of the reflected light on the insulator surface.

[0052] The multispectral camera needs to be preheated for half an hour before being placed close to the insulator for image acquisition to keep the light source stable. Parameters can be set after the preheating is completed. Due to the current of the device itself and the light source image in the environment, black and white correction is required to obtain the hyperspectral image.

[0053] After acquiring the insulator surface image, the multispectral camera will transmit it to the image processing system, and perform principal component analysis (PCA) and minimum noise separation (MNF) dimensionality reduction analysis on the hyperspectral full-band image to find the most obvious contamination components on the insulator surface, and then perform characteristic wavelength screening based on the competitive adaptive reweighting (CARS) algorithm and the continuous projection (SPA) algorithm, and classify the degree of contamination adhesion in combination with the support vector machine (SVM). (Finally, the images corresponding to the characteristic wavelengths are combined into multi-wavelength images, and MNF transformation is performed. The result is similar to the transformation of the full band, proving that images based on characteristic wavelengths can replace full-band images for early detection of hidden contamination of insulators.

[0054] The image processing system will remove noise from the image data. The image data collected by electronic devices will be mixed with some irrelevant signals and noise, which may drown out the useful signals. The data preprocessing step can clean up the data, reduce noise, reduce data dimensions, solve data type problems, etc., and provide a better data foundation for subsequent modeling and analysis. The noise in the image signal can be removed through wavelet denoising and convolution smoothing technology. Figure 8 It is a wavelet threshold denoising process. It decomposes the signal into multiple frequency sub-bands based on the principle of wavelet transform. Then, for each sub-band signal, a threshold is applied to process the signal. The impact of noise on the signal is reduced by threshold processing. Finally, the processed sub-band signal is subjected to inverse wavelet transform to complete signal denoising.

[0055] Convolution smoothing technology is an efficient data processing tool that uses the least squares method to process complex signals, thereby reducing random noise and retaining valuable information. It can effectively suppress the influence of "burrs", making the image smoother. The smoothing window width used in this method in the present invention is 2m+1, that is, the window contains the number of original data points mm (n=2m+1). Usually, the user needs to use an odd-numbered window in order to select a part of the measured original data set as a window instead of including it all. By performing SG convolution smoothing on each subset, the noise in the signal can be effectively reduced and a more accurate analysis result can be obtained. It is assumed that the original data points in the window can be fitted with a k-1 degree polynomial:

[0056] y i =a 0 +a 1 i+a 2 i 2 +…+a k-1 i k-1 Specific embodiment three:

[0058] like Figure 1-9 As shown in the figure, if the image information noise is processed well, the image processing system will correct the refraction of the image. The use of MSC for spectral analysis can effectively suppress the scattering phenomenon, thereby more accurately capturing the characteristics of each component in the "ideal spectrum" and improving the accuracy of the analysis. Therefore, in order to obtain more accurate results, it is necessary to first establish an "ideal spectrum" in order to better capture the characteristics of each component. This method can achieve ideal results by adjusting the NIRS of each sample, which can not only correct the baseline deviation, but also provide more accurate results. The algorithm analysis and implementation principle of MSC are shown in the following formula:

[0059] Compute the average spectrum:

[0060]

[0061] Univariate linear regression:

[0062]

[0063] Multi-source scatter correction:

[0064]

[0065] The matrix A is the average spectral vector based on the original spectra of all samples, which is a calibrated spectrum matrix of n×p dimensions, where n is the number of samples, p is the number of wavelength points corresponding to the sample spectra, and A i Represents the spectral vector of a single sample, which is a 1×p-dimensional matrix. i and bi It represents the relative offset and translation between the NIRS of each sample and the average spectrum after univariate linear regression.

[0066] After correction, feature screening is performed. In the present invention, a competitive adaptive reweighting algorithm is selected for feature screening. Based on the idea of ​​survival of the fittest, a competitive adaptive reweighting sampling method is generated by respectively utilizing the feature screening method of the PLS-DA coefficient and Monte Carlo sampling.

[0067] The Monte Carlo sampling formula is as follows:

[0068] Specific embodiment four:

[0070] like Figure 1-9 As shown, a multispectral detection system is built in the image processing system. A multispectral analysis system is established based on the acquired hyperspectral image through the image evaluation system. The analysis system includes the design of the imaging system, the establishment of the classification model and the software development. The image identifies the contamination attachment site and evaluates the contamination level through SVM. Specific embodiment five:

[0072] like Figure 1-9 As shown, auxiliary detectors are installed on both sides of the multi-spectral camera on the front of the drone. The auxiliary detectors can be temperature sensors, time sensors, pressure sensors and flammable gas concentration sensors.

[0073] The multi-spectral camera and the auxiliary detector jointly obtain the insulator surface information sensor f. The detection value obtained by the auxiliary detector can be obtained by x 1 , x 2 , …, x n Indicates that these test values ​​are all unbiased judgment results of the true value x, and at the same time, each test value is independent of each other. Indicates the corresponding variance, with X m (i) and w 1 , w 2 ,…,w n They represent the detection value of the mth sensor at time i and the weights of different sensors, which can determine the fused judgment value of the surrounding environment of the transmission line The sum weights must conform to the following formula.

[0074]

[0075] In the present invention, the assessment of insulator surface contamination is mainly carried out through multi-spectral image analysis and information fusion of auxiliary detectors. The insulator surface contamination is assessed from the fused information. When assessing the insulator surface contamination, it is mainly divided into three levels according to the degree of contamination:

[0076] Level I: The surface of the insulator is free of pollution or only slightly polluted. The main pollutants at this level are fine dust and slight scale, which can be wiped away with fine sand cloth or cotton cloth.

[0077] Level II: There are moderate levels of contaminants deposited on the surface of the insulator, such as wax, gum or bird droppings. The main contaminants at this level are heavy scale and oil.

[0078] Level III: A large amount of dirt is deposited on the surface of the insulator. If it is not cleaned in time, it will cause a flashover accident. Some common pollutants include sea salt, sand, dust and sludge. Specific embodiment six:

[0080] like Figure 1-9 As shown, the auxiliary detectors in the present invention are installed on both sides of the front of the drone with a dedicated power supply system. During the evaluation, the safety status of the insulator can be detected and evaluated through four auxiliary detectors. The threshold value of the external safety status of the insulator detected by the auxiliary detector is specifically set as follows:

[0081] Highly safe state: temperature is not higher than 15℃, humidity is not higher than 5%, pressure is not higher than 30MPa, flammable gas concentration is not higher than 80mg / m 3 ;

[0082] Moderate safety status: temperature is not higher than 30℃, humidity is not higher than 20%, pressure is not higher than 60MPa, flammable gas concentration is not higher than 120mg / m 3 ;

[0083] Critical safety state: temperature is not higher than 45℃, humidity is not higher than 35%, pressure is not higher than 90MPa, flammable gas concentration is not higher than 160mg / m 3 ;

[0084] Moderately dangerous state: temperature not higher than 60℃, humidity not higher than 50%, pressure not higher than 120MPa, flammable gas concentration not higher than 190mg / m 3 ;

[0085] Highly dangerous state: temperature not higher than 75℃, humidity not higher than 65%, pressure not higher than 150MPa, flammable gas concentration not higher than 220mg / m 3 .

[0086] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0087] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An insulator contamination level assessment method based on multi-spectral analysis is characterized by: The insulator pollution level assessment process includes precise positioning of the drone body-mounted camera, multi-spectral camera image acquisition, image processing and image assessment. The specific steps are as follows: SP1: The main control of the drone body includes a flight trajectory control system, a flight attitude control system and an automatic navigation and positioning calculation system. The drone is equipped with a multispectral camera, which is installed in the center of the drone. At the same time, temperature detection probes and time detection probes are installed on both sides of the drone. The drone will obtain the height image of the insulator in advance, and then the drone calculates the vertical level difference from itself to the insulator through the automatic navigation and positioning calculation system, specifically calculating the image height h of the high-altitude high-voltage line A. a and the image height h of insulator B b As well as the image distance between the high-altitude high-voltage line A and the insulator B, the distance u between the high-altitude high-voltage line A and the insulator B and the drone a and u b As well as the angle θ between the high-altitude high-voltage line A and the insulator B relative to the imaging center, the safe distance between the drone and the transmission line plane is: SP2: When the built-in computing system of the drone calculates the height position of the insulator, it will be transmitted to the flight drive module of the drone, and then the drive module will control the drone body to automatically rise; SP3: When the UAV is flying, it will automatically search, detect and monitor various equipment around the insulator. During this process, the station staff will calibrate and control the flight speed, flight position and flight trajectory of the UAV aircraft; during the control process, the roll angle, pitch angle and other control variables will be calculated in detail according to the attitude stability of the UAV aircraft, and the flight trajectory will be controlled and adjusted according to the calculation results, so as to maximize the stability of the UAV aircraft inspection flight and make the multi-spectral camera more stable when working; SP4: The front-end imaging element of the multi-spectral camera mainly splices multiple independent filter substrates in sequence to obtain a filter array, and installs the filter array in front of the imaging device. When the multi-band light obtained by the light splitting by the filter array is dispersed to different imaging positions, multi-spectral images of corresponding bands in different strip areas are obtained, and the imaging device is a planar array detector.

2. The insulator contamination level assessment method based on multi-spectral analysis according to claim 1 is characterized in that: When the multispectral camera on the drone is close to the insulator, the light source is turned on and the light intensity and angle are adjusted according to the light reflection situation on the surface of the insulator to be evaluated. The parallel light reaches the surface of the insulator to be evaluated and generates reflected light. The multi-band image is used to collect the multi-band image of the reflected light on the insulator surface.

3. The insulator contamination level assessment method based on multi-spectral analysis according to claim 1 is characterized in that: The multispectral camera needs to be preheated for half an hour before being placed close to the insulator for image acquisition to maintain the stability of the light source. Parameters can be set after the preheating is completed. Due to the current of the device itself and the light source image in the environment, black and white correction is required to obtain the hyperspectral image.

4. The insulator contamination level assessment method based on multi-spectral analysis according to claim 1 is characterized in that: After acquiring the insulator surface image, the multispectral camera transmits it to the image processing system, performs principal component analysis (PCA) and minimum noise factorization (MNF) dimensionality reduction analysis on the hyperspectral full-band image, finds the most obvious contamination components on the insulator surface, and then performs characteristic wavelength screening based on the competitive adaptive reweighting (CARS) algorithm and the continuous projection (SPA) algorithm, and combines the support vector machine (SVM) to classify the degree of contamination attachment.

5. The insulator contamination level assessment method based on multi-spectral analysis according to claim 1 is characterized in that: The image processing system will perform denoising on the image data, and remove the noise in the image signal through wavelet denoising and convolution smoothing technology.

6. The insulator contamination level assessment method based on multi-spectral analysis according to claim 5 is characterized in that: After the image information noise is processed, the image processing system will perform refraction correction on the image, and use MSC for spectral analysis to effectively suppress the scattering phenomenon, thereby more accurately capturing the characteristics of each component in the "ideal spectrum" and improving the accuracy of the analysis.

7. The insulator contamination level assessment method based on multi-spectral analysis according to claim 1 is characterized in that: The image processing system is built with a multi-spectral detection diversity, and a multi-spectral analysis system is established based on the acquired hyperspectral image through an image evaluation system. The analysis system includes the design of an imaging system, the establishment of a classification model and software development, and the image identifies the location of contamination attachment and uses SVM to evaluate the contamination level.

8. The insulator contamination level assessment method based on multi-spectral analysis according to claim 1 is characterized in that: Auxiliary detectors are installed on both sides of the multi-spectral camera on the front of the drone. The auxiliary detectors can be temperature sensors, time sensors, pressure sensors and flammable gas concentration sensors.

9. The method for evaluating insulator contamination level based on multi-spectral analysis according to claim 8, characterized in that: The multi-spectral camera and the auxiliary detector jointly obtain the insulator surface information sensor f. The detection value obtained by the auxiliary detector can be obtained by x1, x2, ..., x n Indicates that these test values ​​are all unbiased judgment results of the true value x, and at the same time, each test value is independent of each other. Indicates the corresponding variance, with X m (i) and w1, w2, …, w n They represent the detection value of the mth sensor at time i and the weights of different sensors, which can determine the fused judgment value of the surrounding environment of the transmission line The sum weights must conform to the following formula:

10. The insulator contamination level assessment method based on multi-spectral analysis according to claim 9, characterized in that: The auxiliary detector detects the external safety state threshold of the insulator as follows: Highly safe state: temperature is not higher than 15℃, humidity is not higher than 5%, pressure is not higher than 30MPa, flammable gas concentration is not higher than 80mg / m 3 ; Moderate safety status: temperature is not higher than 30℃, humidity is not higher than 20%, pressure is not higher than 60MPa, flammable gas concentration is not higher than 120mg / m 3 ; Critical safety state: temperature is not higher than 45℃, humidity is not higher than 35%, pressure is not higher than 90MPa, flammable gas concentration is not higher than 160mg / m 3 Moderately dangerous state: temperature not higher than 60℃, humidity not higher than 50%, pressure not higher than 120MPa, flammable gas concentration not higher than 190mg / m 3 . Highly dangerous state: temperature not higher than 75℃, humidity not higher than 65%, pressure not higher than 150MPa, flammable gas concentration not higher than 220mg / m 3 .

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