An insulating contamination developing method, device, terminal and medium

Abstract

The application discloses an insulation contamination developing method and device, a terminal and a medium, and is characterized in that the method comprises the following steps: collecting multiple insulation contamination images by using multiple spectral channel imaging sensors, and analyzing the dispersion of the gray scale values of the multiple insulation contamination images; if the dispersion is lower than a preset threshold, the multiple insulation contamination images are output; if the dispersion is higher than or equal to the preset threshold, the gain of the multiple spectral channel imaging sensors is dynamically updated to be lower than the preset threshold, the sensor configuration is updated by using the updated gain, and the multiple insulation contamination images are output. By means of the gain iterative optimization algorithm and the hardware cooperation, the light quantity compensation can be realized by using the gain adjustment of the imaging chip itself, and the quality of the initial image is significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems, and more particularly, to an insulator contamination development method and system. BACKGROUND

[0002] Line insulators or power equipment bushings bear mechanical support and electrical insulation between the transmission line and the transmission tower, and between the power equipment and the processing line. However, due to the long-term work of the insulators or bushings in the outdoor atmospheric environment, the insulator surface will inevitably accumulate contamination to form a contamination layer, thereby greatly increasing the risk of surface flashover of the insulator. Studies have shown that the important factors affecting the insulator contamination flashover voltage are the contamination degree and the contamination distribution. The former is mainly analyzed by using the determination of equivalent salt density and ash density, which requires obtaining the insulator contamination by simulating the tower insulator string or artificial line sampling, and evaluating the contamination value by using the equivalent leakage current value in the laboratory offline mode. The latter contamination distribution is mainly obtained by visible light or infrared image. This method can obtain data online and non-contact, but when the contamination is difficult to distinguish from the color of the insulator body or the boundary simulation in a wide spectral range, the contamination distribution will be difficult to distinguish.

[0003] The existing non-contact image-based detection method for insulator contamination state mainly includes infrared thermal imaging and ultraviolet solar blind imaging. Infrared thermal imaging collects the infrared radiation signal generated by the heat of the contaminated insulator under high voltage environment through an infrared detector and an optical imaging objective to establish an infrared thermal image of the insulator surface, converts the invisible infrared energy emitted by the object into a visible image, uses the temperature distribution characteristics of the insulator surface to construct a surface contamination analysis algorithm, and then realizes the detection of the insulator contamination state. However, the infrared thermal imager relies on the temperature difference of the target object for imaging. If the overall temperature difference of the insulator is small, the thermal image has poor resolution for the surface details. At the same time, the parameter difference itself will cause an unpredictable deviation between the imaging temperature and the true temperature, and the heat caused by the aging of the insulator will have a great impact on the detection result. The solar blind ultraviolet discharge imaging can capture the pollution discharge spot of the insulator through the solar blind ultraviolet image when the contaminated insulator has discharge conditions, and then reflect the pollution state. It can effectively and intuitively observe the discharge condition of the contaminated insulator, and provides a powerful diagnostic means for live detection. However, the small amount of information obtained by the narrow ultraviolet solar blind band is difficult to accurately represent the surface contamination characteristics of the insulator, and it is impossible to comprehensively and comprehensively evaluate the contamination state of the insulator.

[0004] Generally, the contamination degree of the insulator is difficult to cause significant surface leakage current increase or surface electric field distortion, and the serious heating and abnormal corona of the insulator often lack sufficient conditions, so the infrared imaging and ultraviolet solar blind imaging are not sensitive to the contamination of the insulator. The multi-band imaging technology is an imaging method which is widely used in forestry, agriculture, mining, medicine and other fields in recent years. At present, the application of multi-band imaging technology in insulator detection is in the initial stage, and its detection ability for insulator contamination has been verified by experiments, and a certain scale of field application has been carried out. This method shows outstanding advantages in portability, economy and diagnostic ability, but the main problem is that the adjustment of spectral channel parameters often relies on field experience, and inappropriate channel sensitivity matching will lead to failure of the development of the contaminated area of the insulator. This problem has become the main bottleneck affecting the field application of multi-band imaging.

[0005] In view of the above problems, there is an urgent need for an insulator contamination development method and device. SUMMARY

[0006] In order to solve the problems in the prior art, the present application provides an insulator contamination development method and device, which optimizes and adjusts the sensitivity of the target area of multiple channels to obtain different types of contamination development.

[0007] The application adopts the following technical solutions.

[0008] In a first aspect, the application relates to an insulator contamination development method, which comprises the following steps: collecting multiple insulator contamination images by using multiple spectral channel imaging sensors, analyzing the dispersion of the gray values of the multiple insulator contamination images; if the dispersion is lower than a preset threshold, outputting the multiple insulator contamination images; if the dispersion is higher than or equal to the preset threshold, dynamically updating the gain of the multiple spectral channel imaging sensors to the dispersion lower than the preset threshold, and using the gain updated sensor configuration to output the multiple insulator contamination images.

[0009] Preferably, collecting multiple insulator contamination images by using multiple spectral channel imaging sensors comprises: each of the multiple spectral channel imaging sensors uses an optical lens with a different spectral transmittance from one of the other multiple spectral channel imaging sensors.

[0010] Preferably, the spectral transmittances of the optical lenses with different spectral transmittances are determined by the radiation spectral bands of iron oxide, calcium sulfate, sodium chloride, kaolin, copper sulfate, silicon dioxide and calcium chloride; at least one of the optical lenses does not have a spectral transmittance.

[0011] Preferably, the dispersion of the gray scale values of the plurality of insulation contamination images is analyzed, including: collecting synchronous imaging data of the 1 to n spectral channel imaging sensors at the mth time sequence; using a pixel registration method to realize registration between the synchronous imaging data of the plurality of spectral channel imaging sensors to obtain a registration image; extracting a contaminated area from the plurality of registration images, calculating the total gray scale of the contaminated area in each registration image, and analyzing the dispersion of the total gray scale of the contaminated area in the plurality of registration images.

[0012] Preferably, the dispersion L of the total gray scale of the contaminated area in the plurality of registration images is m

[0013]

[0014] g m,i is the total gray scale of the ith spectral channel imaging sensor in the 1 to n spectral channel imaging sensors at the mth time sequence.

[0015] Preferably, the gain of the plurality of spectral channel imaging sensors is dynamically updated and the plurality of insulation contamination images is output, including: if the dispersion at the mth time sequence is higher than or equal to a preset threshold, the gain of the plurality of spectral channel imaging sensors is dynamically updated; wherein the gain of the ith spectral channel imaging sensor at the m+1th time sequence is described as

[0016]

[0017] g i is the total gray scale of the plurality of registration images corresponding to the ith spectral channel imaging sensor;

[0018] The dispersion at the m+1th time sequence is continuously judged whether it is higher than or equal to the preset threshold, and if not, the gain of the plurality of spectral channel imaging sensors is continuously updated in a loop until the dispersion is higher than or equal to the preset threshold.

[0019] The second aspect of the application relates to an insulation contamination developing device, which is implemented by using the method in the first aspect of the application; wherein the device comprises a replaceable filter array, an optical lens module array, a spectral imaging array, a photosensitive controller and an image processing unit connected in sequence; the replaceable filter array uses an ion sputtering film plating method to process the surface of a fused quartz glass sheet to form a plurality of different circular filter units and form a rectangular array; the light quantity of each lens in the optical lens module array is adjusted by an electric control adjustment mode, and the electric control adjustment mode uses the gain of the plurality of spectral channel imaging sensors calculated by the insulation contamination developing method as the adjustment basis.

[0020] ​​​Preferably, the thickness of the coated fused quartz glass sheet is not more than 1mm, and the minimum gap between the coated fused quartz glass sheet and the optical lens module is not more than 0.3mm; each circular filter covers the light entry area of the optical lens, and the light-absorbing material is filled around the filter.

[0021] In a third aspect, the present application provides a terminal comprising a processor and a storage medium; the storage medium is configured to store instructions; and the processor is configured to operate according to the instructions to perform the steps of the method of the first aspect.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, causes the steps of the method of the first aspect to be performed.

[0023] The present application has the beneficial effect that, compared with the prior art, the insulation contamination development method and device can obtain different types of contamination development through multi-channel target area photosensitivity optimization adjustment.

[0024] The present application also has the beneficial effects that:

[0025] 1. Compared with the multi-spectral contamination imaging method based on fixed gain, the present application adjusts and optimizes the photosensitivity according to the contamination area (rather than the entire imaging target), maximizes the use of the photosensitive performance of each channel imaging sensor, and is not affected by the photosensitive effect of the background area, thereby significantly improving the spectral image contrast of the contamination area;

[0026] 2. Compared with the existing universal multi-spectral camera parameter control method, the present application cooperates the photosensitive controller and the image processing unit, iteratively feeds back to keep the gray intensity of each channel contamination target area consistent, and provides guarantee for the quality of spectral image reconstruction;

[0027] 3. Compared with the existing digital image post-processing method, the gain iterative optimization algorithm and hardware cooperation can utilize the gain adjustment of the imaging chip itself to realize light quantity compensation, and the quality of the initial image is significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 FIG. 1 is a schematic diagram of a multi-channel spectral imaging device in the insulation contamination development method of the present application;

[0029] Figure 2A schematic diagram of a photosensitive control and image processing process in the insulator contamination developing method of the present application;

[0030] Figure 3 A schematic diagram of the multispectral contamination imaging effect of the fixed gain in the insulator contamination developing method of the present application;

[0031] Figure 4 A schematic diagram of the multispectral contamination imaging effect of the iterative gain in the insulator contamination developing method of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. The embodiments described in the present application are only a part of the embodiments of the present application, but not all the embodiments. Based on the spirit of the present application, all other embodiments not described in the present application obtained by those skilled in the art without creative labor according to the embodiments described in the present application should belong to the protection scope of the present application.

[0033] The main detection objects of infrared temperature measurement imaging and solar blind ultraviolet imaging are the heat generation or discharge caused by insulator contamination, not the contamination target itself. It is worth mentioning that contamination is one of the inducing factors of heat generation and discharge, and under general conditions, it does not manifest as heat generation or discharge phenomenon. Therefore, the above two imaging detection technologies cannot effectively judge the contamination condition in the early stage of insulator heat generation and discharge failure.

[0034] At the same time, the multi-band imaging technology has been proved by experiments in insulator detection and has developed preliminary application, but its main problem is that the spectral channel parameter adjustment often relies on field experience, and inappropriate channel photosensitivity matching will lead to failure of insulator contamination area development, and the photosensitivity or gain of each channel of the existing non-coaxial multispectral imaging device is mostly a fixed value, and will not be adaptively adjusted according to the contamination target area itself, which is not conducive to the later multispectral image reconstruction and quantitative evaluation of the contamination area.

[0035] Figure 1 A schematic diagram of a multispectral imaging device in the insulator contamination developing method of the present application. As shown in Figure 1 The first aspect of the present application relates to an insulator contamination developing method, which comprises steps 1 to 2.

[0036] Step 1, using a plurality of spectral channel imaging sensors to collect a plurality of insulator contamination images, analyzing the dispersion of the gray scale values of the plurality of insulator contamination images.

[0037] The multiple spectral channel imaging sensors are used to collect multiple insulating contamination images, wherein each of the multiple spectral channel imaging sensors adopts an optical lens with a different spectral transmittance from other sensors.

[0038] The application relates to an insulating contamination developing method for multi-channel target area sensitivity optimization adjustment, which comprises a set of multi-channel spectral imaging devices with adjustable sensitivity and a set of target area sensitivity control logic.

[0039] The replaceable filter array adopts an ion sputtering film coating method to process the surface of a fused quartz glass sheet, forms multiple different circular filter units, and constitutes a rectangular array, and the filter optical parameters are selected according to the reflection spectrum of the shooting target; the thickness of the fused quartz glass sheet after film coating is not more than 1 mm, and the minimum gap between the optical lens module is not more than 0.3 mm, and each circular filter should effectively cover the light entrance area of the optical lens; the periphery of the filter is filled with light-absorbing material to prevent light interference caused by the overflow of incident light from the filter.

[0040] The spectral transmittances of the optical lenses with different spectral transmittances are determined by the reflection spectrum bands of iron oxide, calcium sulfate, sodium chloride, kaolin, copper sulfate, silicon dioxide and calcium chloride; at least one of the optical lenses is not provided with a spectral transmittance.

[0041] The optical lens module array adopts multiple optical lenses with different spectral transmittances to constitute a rectangular array, the spectral range of each lens should be determined according to the sensitive reflection spectrum band of the insulating contamination, and the spectral range should be not more than 300nm-790nm, the transmittance in the spectral range should be greater than 90%, the light flux F of each lens in the array can be adjusted by the electric control, and the F adjustment range covers 1 / 1.4-1 / 22.

[0042] The spectral imaging array is made of imaging sensors with the same technical parameters and constitutes a same-plane array, and the number of imaging sensor units is n. The spectral response range of the sensor should cover 300nm-790nm, the light quantum efficiency (QE) should be higher than 30%, the pixel (P) should be not less than 640x480px, the dark noise (DN) should be lower than 5e, and the signal-to-noise ratio (SNR) should be not less than 40dB. The photosensitive controller is controlled by the upper image processing unit and executes the gain adjustment logic flow issued by the image processing unit; the working principle is to convert the gain value sequence issued by the image processing unit into an imaging sensor gain level control signal.

[0043] The dispersion of the gray scale values of the plurality of insulating contamination images is analyzed, including: collecting synchronous imaging data of 1-n spectral channel imaging sensors at the mth time sequence; using a pixel registration method to realize registration between the synchronous imaging data of the plurality of spectral channel imaging sensors to obtain a registration image; extracting a contamination area from the plurality of registration images, calculating the total gray scale of the contamination area in each registration image, and analyzing the dispersion of the total gray scale of the contamination area in the plurality of registration images.

[0044] A deviation allowance value s is set, and the mth synchronous imaging data in the 1-nth spectral channel imaging sensor is read by the image processing unit. The 1-nth image data is pixel-registered by the upper image processing unit, and the registration rate should not be lower than 95%. The contamination area in the 1-nth image is extracted by the upper image processing unit and the total gray scale value vector G of the contamination area is calculated m .

[0045] G m =[g m,1 ,g m,2 ,…g m,n ]

[0046] wherein the total gray scale value g m,i of the contamination area in the ith image is

[0047]

[0048] The dispersion L of the total gray scale of the contamination area in the plurality of registration images is m :

[0049]

[0050] and, g m,i is the total gray scale of the ith spectral channel imaging sensor in the 1-nth spectral channel imaging sensor at the mth time sequence.

[0051] Step 2, if the dispersion is lower than a preset threshold, output the plurality of insulating contamination images; if the dispersion is higher than or equal to the preset threshold, dynamically update the gain of the plurality of spectral channel imaging sensors to the dispersion being lower than the preset threshold, and update and output the plurality of insulating contamination images using the gain.

[0052] Updating and outputting the plurality of insulating contamination images using the gain includes: if the dispersion at the mth time sequence is higher than or equal to a preset threshold, dynamically updating the gain of the plurality of spectral channel imaging sensors; wherein the gain of the ith spectral channel imaging sensor at the m+1th time sequence is described as and:

[0053]

[0054] g i is the total gray level of multiple registered images corresponding to the i-th spectral channel imaging sensor;

[0055] Continue to determine whether the dispersion at the (m + 1)-th time sequence is higher than or equal to the preset threshold. If not, continue to loop and update the gains of the multiple spectral channel imaging sensors until the dispersion is higher than or equal to the preset threshold.

[0056] When RSDm < s, retain the image data of each spectral channel from No. 1 to No. n, and end the process; when RSDm ≥ s, update the gain value sequence of the imaging sensors from No. 1 to No. n according to the total gray level values of the images from No. 1 to No. n ( i = 1 to n). Update the gain value sequence of the imaging sensors ( i = 1 to n; m = m + 1), and the image processing unit issues this gain value sequence to the photosensitive controller.

[0057] Figure 2 is a schematic diagram of the photosensitive control and image processing process in an insulating contamination development method of the present invention. As Figure 2 shown, the photosensitive controller converts the sensor gain value sequence into an imaging gain level control signal and adjusts the gains of each imaging sensor. Repeat the above optimization iteration loop until RSD m < s.

[0058] Figure 3 is a schematic diagram of the imaging effect of fixed-gain multispectral contamination for an insulating contamination development method of the present invention. In the prior art, using the fixed-gain multispectral contamination imaging method, the contamination states of insulators of different materials cannot be accurately obtained.

[0059] Figure 4 is a schematic diagram of the imaging effect of iterative-gain multispectral contamination for an insulating contamination development method of the present invention. As Figure 4 shown, the number of imaging sensor units n = 7, and the spectral ranges of each lens are respectively: 350nm ± 25nm, 435nm ± 25nm, 485nm ± 25nm, 535nm ± 25nm, 590nm ± 25nm, 645nm ± 25nm, 705nm ± 25nm, and the deviation allowable value s = 2%.

[0060] In the m-th imaging, the contamination image data of the silicone rubber surface in 7 spectral channels can be obtained, and pixel registration is performed on the contamination image data in 7 spectral channels to ensure that the registration rate is not lower than 95%. If RSD m < 2%, retain the image data of all spectral channels and end the process. If RSD m ≥ 2%, update the gain value of each imaging sensor: i = 1 to 7. Among them is the gain value of the i-th imaging sensor in the current m-th imaging. The updated gain value sequence is sent to the photosensitive controller. The photosensitive controller converts the gain value sequence into an imaging gain level control signal and adjusts the gain of each imaging sensor. The above steps are repeated until the RSD m <2%.

[0061] In this embodiment, in the first imaging (m = 1), the following total gray value vector is obtained:

[0062] G1 = [150.1, 160.3, 155.1, 145.0, 140.2, 164.9, 150.0]

[0063] The average value is calculated:

[0064]

[0065] Calculate Lm:

[0066]

[0067] Update the gain value:

[0068] Send the gain instruction and adjust the gain: send the updated gain value sequence to the photosensitive controller.

[0069] Repeat the optimization iteration: repeat the above steps until the RSD m <2%.

[0070] The image processing unit processes and feeds back the image information of the imaging sensor array, and the process includes image registration, total gray value calculation of the contaminated area, deviation judgment, imaging sensor gain calculation and gain value sequence sending. The above process adopts a feedback closed loop mode until the target value is reached.

[0071] The contrast of each type of single contamination or mixed contamination area on the insulator sample obtained by the present application, including Fe2O3, CaSO4, NaCl, Kaolin, CuSO4, SiO2, CaCl2, etc., is significantly improved.

[0072] Compared with the multispectral pollution imaging method based on fixed gain, the photosensitivity optimization of the application is not dependent on the whole image, but on the pollution area itself, the contrast of the pollution area in each channel imaging can be optimized, thereby improving the spectral image reconstruction and quantitative evaluation effect of the insulation pollution area; compared with the existing general multispectral camera parameter control method, the application ensures the consistency of the gray intensity of the pollution target area through the iteration of the photosensitive controller and the image processing unit, and provides guarantee for the stability of the spectral image reconstruction quality; compared with the existing digital image post-processing method, the gain iteration optimization algorithm and hardware can be used to realize the light quantity compensation by using the gain adjustment of the imaging chip itself, and the quality of the initial image is significantly improved.

[0073] In the second aspect of the application, an insulation pollution developing device is provided, which is implemented by using the method in the first aspect of the application; the device comprises a replaceable filter array, an optical lens module array, a spectral imaging array, a photosensitive controller and an image processing unit connected in sequence; the replaceable filter array is processed by using an ion sputtering film coating method to form a plurality of different circular filter units and form a rectangular array; the light quantity of each lens in the optical lens module array is adjusted by using an electric control adjustment mode, and the electric control adjustment mode is adjusted based on the gain of the plurality of spectral channel imaging sensors calculated by the insulation pollution developing method.

[0074] The thickness of the coated fused quartz glass sheet is not more than 1 mm, and the minimum gap between the coated fused quartz glass sheet and the optical lens module is not more than 0.3 mm; each circular filter covers the light inlet area of the optical lens, and the surrounding of the filter is filled with light-absorbing material.

[0075] The spectral imaging array is made of imaging sensors with the same technical parameters and forms a same plane array, and the number of the imaging sensor units is n; the spectral response range of the sensor should cover the range of 300 nm to 790 nm, the quantum efficiency (QE) should be higher than 30%, the pixel (P) should be not less than 640*480 px, the dark noise (DN) should be lower than 5e, and the signal-to-noise ratio (SNR) should be not less than 40 dB.

[0076] In the third aspect of the application, a terminal is provided, which comprises a processor and a storage medium; the storage medium is used for storing instructions; the processor is used for operating according to the instructions to perform the steps of the method in the first aspect of the application.

[0077] In the fourth aspect of the application, a computer readable storage medium is provided, which stores a computer program; when the program is executed by a processor, the steps of the method in the first aspect of the application are implemented.

[0078] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit it, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for developing insulating contaminants, characterized in that, The method includes the following steps: Multiple images of insulation contamination were acquired using a multi-spectral channel imaging sensor, and the dispersion of grayscale values ​​in the multiple insulation contamination images was analyzed. The method of acquiring multiple images of insulation contamination using a multi-spectral channel imaging sensor includes: Each of the plurality of spectral channel imaging sensors employs an optical lens with a different spectral transmittance than one of the other plurality of spectral channel imaging sensors; the spectral transmittance of the optical lenses with different spectral transmittance is determined by the radiometric spectral bands of iron oxide, calcium sulfate, sodium chloride, kaolin, copper sulfate, silicon dioxide, and calcium chloride, respectively; at least one of the optical lenses does not have a set spectral transmittance. The analysis of the dispersion of grayscale values ​​from multiple insulating contamination images includes: Acquire synchronous imaging data from 1 to n spectral channel imaging sensors at the m-th time sequence; use pixel registration method to register the synchronous imaging data between multiple spectral channels of imaging sensors to obtain a registered image; extract contaminated areas from multiple registered images, calculate the total gray level of the contaminated areas in each image to be registered, and analyze the dispersion of the total gray level of the contaminated areas in multiple registered images. The dispersion L of the total gray level of the contaminated area in multiple registered images m for: and, g m,i Let be the total gray level of the i-th spectral channel imaging sensor among 1 to n spectral channel imaging sensors at the m-th time sequence; If the dispersion is lower than a preset threshold, multiple images of insulation contamination are output. If the dispersion is higher than or equal to a preset threshold, the gain of multiple spectral channel imaging sensors is dynamically updated until the dispersion is lower than the preset threshold, and the sensor configuration is updated using the gain and multiple images of insulation and dirt are output. The process of updating and outputting multiple insulating dirt images using the gain includes: If the dispersion at the nth time series is higher than or equal to a preset threshold, the gains of multiple spectral channel imaging sensors are dynamically updated; where the gain of the i-th spectral channel imaging sensor at the (m+1)th time series is described as follows: and: g i The total grayscale of the multiple registered images corresponding to the i-th spectral channel imaging sensor is given; the discreteness at the (m+1)-th time sequence is further determined to be higher than or equal to a preset threshold. If not, the gain of the multiple spectral channel imaging sensors is continuously updated until the discreteness is higher than or equal to the preset threshold.

2. An insulating dirt developing device, characterized in that: The apparatus is implemented using the method described in claim 1; wherein... The device includes a replaceable filter array, an optical lens module array, a spectral imaging array, a photosensor controller, and an image processing unit connected in sequence. The replaceable filter array uses an ion sputtering coating method to treat the surface of a fused silica glass sheet, forming multiple different circular filter units, which are then arranged in a rectangular array. The light transmittance of each lens in the optical lens module array is electronically controlled, and the electronic control is based on the gain of multiple spectral channel imaging sensors calculated in an insulating dirt development method.

3. The insulating dirt developing device according to claim 2, characterized in that: The thickness of the coated fused silica glass sheet does not exceed 1 mm, and the minimum gap between it and the optical lens module does not exceed 0.3 mm. Each circular filter covers the light-gathering area of ​​the optical lens, and the area around the filter is filled with light-absorbing material.

4. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to claim 1.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method of claim 1.

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