Laser cleaning method, device, terminal equipment, storage medium and program product

Abstract

The application provides a laser cleaning method, device, terminal equipment, storage medium and program product. The method comprises the following steps: acquiring a first image of a first object to be cleaned and spectral information of the first object; determining a first type of the first object based on the spectral information; determining gray information of the first image, and determining a first thickness of the first object based on the spectral information and the gray information; determining first information of a laser cleaning device based on the first type and the first thickness, wherein the first information is used for indicating cleaning parameters of the laser cleaning device in a cleaning process; and controlling the laser cleaning device to clean the first object based on the first information. In this way, the efficiency of laser cleaning can be improved.

Description

Technical Field

[0001] This application relates to the field of laser cleaning technology, and in particular to a laser cleaning method, apparatus, terminal equipment, storage medium, and program product. Background Technology

[0002] Laser cleaning technology can achieve non-contact cleaning of surface contaminants (such as oil, rust, coatings, etc.) on various industrial parts and cultural relics, and can avoid damaging delicate or fragile substrates.

[0003] Currently, terminal equipment can perform cleaning based on manually adjusted parameters and control the cleaning process based on visual inspection of the cleaning effect. However, among the methods described above, laser cleaning has relatively low efficiency. Summary of the Invention

[0004] This application provides a laser cleaning method, apparatus, terminal equipment, storage medium, and program product to solve the technical problem of low efficiency in laser cleaning.

[0005] In a first aspect, embodiments of this application provide a laser cleaning method, which includes:

[0006] Obtain the first image of the first object to be cleaned, as well as the spectral information of the first object;

[0007] Based on spectral information, determine the first type of the first object;

[0008] Determine the grayscale information of the first image, and determine the first thickness of the first object based on the spectral information and the grayscale information;

[0009] Based on the first type and the first thickness, the first information of the laser cleaning device is determined, and the first information is used to indicate the cleaning parameters of the laser cleaning device during the cleaning process.

[0010] Based on the first information, the laser cleaning device is controlled to clean the first object.

[0011] Based on spectral and grayscale information, the first thickness of the first object is determined, including:

[0012] Obtain the first correspondence relationship, which includes multiple thicknesses of the first object, as well as grayscale information and spectral information corresponding to each thickness of the first object;

[0013] Based on the grayscale information and spectral information of the first object, as well as the first correspondence, the first thickness of the first object is determined among multiple thicknesses of the first object.

[0014] Obtain the spectral information of the first object, including:

[0015] Obtain the distribution area of ​​the first object on the base;

[0016] The spectral sensor is controlled to irradiate the distribution area of ​​the substrate to obtain the spectral information of the first object.

[0017] According to one or more embodiments of this application, irradiating a distribution area of ​​a substrate to obtain spectral information of a first object includes:

[0018] When the distribution area on the substrate includes a first object of a single type, the distribution area is irradiated to obtain the spectral information of the first object;

[0019] When the distribution area on the substrate includes at least two types of first objects, the distribution area is irradiated to obtain mixed spectral information, and the mixed spectral information is demixed to obtain spectral information corresponding to each type of first object.

[0020] According to one or more embodiments of this application, determining a first type of a first object based on spectral information includes:

[0021] Obtain the second correspondence, which includes multiple spectral information and the object type corresponding to each spectral information;

[0022] Based on the spectral information of the first object and the second correspondence, the first type of the first object is determined from the object types corresponding to each spectral information.

[0023] According to one or more embodiments of this application, based on first information, controlling a laser cleaning device to clean a first object includes:

[0024] Based on the first information, determine the first cleaning parameters of the laser cleaning device;

[0025] Obtain the current cleaning parameters of the laser cleaning device;

[0026] Based on the preset adjustment rate, the current cleaning parameters are adjusted to the first cleaning parameters.

[0027] According to one or more embodiments of this application, the method further includes:

[0028] Acquire a second image, which is an image captured during the cleaning process of the first object;

[0029] Determine the image similarity between the second image and a preset third image, wherein the third image is an image in which the first object does not exist in the base;

[0030] When the image similarity is greater than or equal to the first threshold, the cleaning result of the first object is determined to be cleaning complete;

[0031] When the image similarity is less than the first threshold, the cleaning result of the first object is determined to be incomplete.

[0032] According to one or more embodiments of this application, the method further includes:

[0033] Obtain the second cleaning parameter corresponding to the first object. The first duration corresponding to the second cleaning parameter is less than or equal to the second threshold. The first duration is the time required to complete the cleaning of the first object based on the second cleaning parameter.

[0034] If the first object does not have stored cleaning parameters, the second cleaning parameters are stored.

[0035] A first cleaning rate corresponding to the stored cleaning parameters is determined. Based on a first duration and a first thickness, a second cleaning rate corresponding to the second cleaning parameters is determined. When the second cleaning rate is greater than the first cleaning rate, the second cleaning parameters are stored.

[0036] Secondly, embodiments of this application provide a laser cleaning apparatus, which includes an acquisition module, a first determination module, a second determination module, a third determination module, a fourth determination module, and a control module, wherein:

[0037] The acquisition module is used to acquire a first image of the first object to be cleaned, as well as the spectral information of the first object;

[0038] The first determining module is used to determine the first type of the first object based on spectral information;

[0039] The second determining module is used to determine the grayscale information of the first image;

[0040] The third determining module is used to determine the first thickness of the first object based on spectral information and grayscale information;

[0041] The fourth determining module is used to determine first information of the laser cleaning device based on the first type and the first thickness. The first information is used to indicate the cleaning parameters of the laser cleaning device during the cleaning process.

[0042] The control module is used to control the laser cleaning device to clean the first object based on the first information;

[0043] The third determining module is specifically used to obtain the first correspondence relationship, which includes multiple thicknesses of the first object, as well as grayscale information and spectral information corresponding to each thickness of the first object;

[0044] Based on the grayscale information and spectral information of the first object, as well as the first correspondence, the first thickness of the first object is determined among multiple thicknesses of the first object;

[0045] The second acquisition module is specifically used to acquire the distribution area of ​​the first object on the substrate;

[0046] The spectral sensor is controlled to irradiate the distribution area of ​​the substrate to obtain the spectral information of the first object.

[0047] Thirdly, embodiments of this application provide a terminal device, including:

[0048] At least one processor and memory;

[0049] The memory stores the instructions that the computer executes;

[0050] At least one processor executes computer execution instructions stored in memory, causing at least one processor to perform the first aspect above and various laser cleaning methods that may be involved in the first aspect.

[0051] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the first aspect above and various possible laser cleaning methods involved in the first aspect.

[0052] Fifthly, embodiments of this application provide a computer program product, including a computer program carried on a computer-readable medium. When the computer program is executed by a processor, it implements the first aspect and various laser cleaning methods that may be involved in the first aspect.

[0053] This application provides a laser cleaning method, apparatus, terminal device, storage medium, and program product. The terminal device can acquire a first image of a first object to be cleaned and the spectral information of the first object, including: the terminal device can acquire the distribution area of ​​the first object on a substrate; the terminal device can control a spectral sensor to irradiate the distribution area of ​​the substrate to obtain the spectral information of the first object; the terminal device can determine a first type of the first object based on the spectral information; the terminal device can determine the grayscale information of the first image; the terminal device can determine a first thickness of the first object based on the spectral information and the grayscale information, including: the terminal device can acquire a first correspondence, wherein the first correspondence includes multiple thicknesses of the first object and grayscale information and spectral information corresponding to each thickness of the first object; the terminal device can determine the first thickness of the first object among the multiple thicknesses of the first object based on the grayscale information and spectral information of the first object and the first correspondence; the terminal device can determine first information of the laser cleaning apparatus based on the first type and the first thickness, wherein the first information is used to indicate the cleaning parameters of the laser cleaning apparatus during the cleaning process; the terminal device can control the laser cleaning apparatus to clean the first object based on the first information.

[0054] In the above method, since the terminal device can automatically and accurately identify the type and thickness of the contaminants, the efficiency of contaminant identification can be improved. Furthermore, the terminal device can determine the cleaning parameters in real time and accurately based on the first type and the first thickness. In this way, the laser cleaning device can clean the first object efficiently and accurately, thereby reducing the energy consumption of laser cleaning and improving the efficiency of laser cleaning. Attached Figure Description

[0055] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0056] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application;

[0057] Figure 2 A schematic flowchart of a laser cleaning method provided in an embodiment of this application;

[0058] Figure 3 A schematic flowchart illustrating a laser cleaning apparatus control method provided in an embodiment of this application;

[0059] Figure 4 A schematic flowchart illustrating a laser cleaning parameter storage method provided in an embodiment of this application;

[0060] Figure 5 This is a schematic diagram of the structure of a laser cleaning device provided in an embodiment of this application;

[0061] Figure 6 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application.

[0062] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0063] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0064] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0065] In related technologies, terminal equipment can control laser cleaning devices to clean contaminants (oil, rust, coatings, etc.) from the surface of substrates (such as aluminum alloys, titanium alloys, ceramics, etc.). Currently, terminal equipment can control the laser cleaning device through manually adjusted parameters, causing the laser to emit a laser beam onto the substrate surface, thereby cleaning the contaminants. However, in the above methods, manually set parameters are difficult to cope with the diversity of contaminant types and thickness variations, resulting in incomplete cleaning or substrate damage in some areas; power adjustment relies on manual operation and cannot respond in real time to differences in contaminant distribution, thus resulting in low cleaning efficiency; the cleaning effect relies on manual judgment and cannot provide real-time feedback on the cleaning status; parameters need to be readjusted for each cleaning operation, making the repetitive work tedious and affecting cleaning efficiency.

[0066] To address the technical problems in related technologies, this application provides a laser cleaning method. A terminal device can acquire a first image of a first object to be cleaned, as well as the spectral information of the first object. Based on the spectral information, the terminal device can determine a first type of the first object. The terminal device can determine the grayscale information of the first image. The terminal device can acquire a first correspondence, which includes multiple thicknesses of the first object and the grayscale and spectral information corresponding to each thickness of the first object. Based on the grayscale and spectral information of the first object, and the first correspondence, the terminal device can determine a first thickness of the first object from among the multiple thicknesses. Based on the first type and the first thickness, the terminal device can determine first information of the laser cleaning device. The first information is used to indicate the cleaning parameters of the laser cleaning device during the cleaning process. Based on the first information, the terminal device can control the laser cleaning device to clean the first object.

[0067] In this way, since the terminal equipment can achieve coarse localization and distribution area determination of pollutants through grayscale analysis, and achieve precise identification of the chemical composition and type of pollutants through spectral analysis, the accuracy of pollutant identification can be improved. Furthermore, since users can adaptively match cleaning parameters based on the identification results, the adaptability of cleaning parameters can be improved, thereby increasing the efficiency of laser cleaning.

[0068] Below, in conjunction with Figure 1 The application scenarios of the embodiments of this application will be described.

[0069] Figure 1This is a schematic diagram illustrating an application scenario provided by an embodiment of this application. Please refer to... Figure 1 The system comprises a terminal device, a laser cleaning unit, an image acquisition device, a spectral sensor, contaminants to be cleaned, and a substrate to be cleaned, with wired or wireless communication connections established between the various hardware components. The contaminants adhere to the substrate. The terminal device identifies the contaminants using the image acquisition device and the spectral sensor, and retrieves the corresponding initial parameters from a pre-set parameter library based on the acquired information. The terminal device can then control the power, scanning speed, and other cleaning parameters of the laser cleaning unit in real time using these initial parameters. The laser cleaning unit uses laser light to clean the contaminants. During the cleaning process, the image acquisition device acquires images of the cleaning area in real time. The terminal device compares these images with a baseline clean image and dynamically adjusts the cleaning parameters to achieve closed-loop control.

[0070] In this way, the terminal device can clean different types of contaminants. When the contaminants are different, the cleaning parameters such as the power and scanning speed of the laser cleaning device set by the terminal device will also be different, which improves the efficiency of laser cleaning. Furthermore, users can quickly and accurately obtain information about contaminants and cleaning parameters through the terminal device without manual operation. Therefore, it can improve the efficiency of laser cleaning and the accuracy of contaminant identification.

[0071] It should be noted that, Figure 1 This is an example of an application scenario for the embodiments of this application, and not a limitation thereof. The parameters of the laser cleaning device set in the terminal device may also include other parameters related to contaminants. This application does not limit this.

[0072] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0073] Figure 2 For a schematic flowchart of a laser cleaning method provided in this application, please refer to [link / reference]. Figure 2 The method may include:

[0074] S201. Obtain the first image of the first object to be cleaned, and the spectral information of the first object.

[0075] The execution entity in this application embodiment can be a terminal device or a laser cleaning device within a terminal device. The laser cleaning device can be implemented based on software, or it can be implemented based on a combination of software and hardware. This application embodiment does not limit this approach.

[0076] The first object can be an object to be cleaned on the surface of a substrate. For example, the first object can be a contaminant on the surface of the substrate. For example, the first object can include oil stains, rust, coatings, oxide layers, etc. For example, the substrate can be metal, and the first object can be rust on the surface of the metal; the substrate can be ceramic, and the first object can be a coating on the surface of the ceramic; the substrate can be a gear, and the first object can be oil stains on the surface of the gear. The embodiments of this application do not limit this.

[0077] The first image can be an image of a first object. For example, the first image can be an image of the surface of the first object. For example, if the first object is oil, the first image is an image of the oily surface.

[0078] In some embodiments, the terminal device can acquire an image of the first object using an image acquisition device (such as a high-definition camera) to obtain a first image. After acquiring the first image, the terminal device can perform preprocessing operations such as denoising and enhancement to improve image quality and facilitate subsequent analysis.

[0079] For example, the substrate may be an aluminum alloy, the first object may be an oil stain on the surface of the aluminum alloy, and the first image includes an image of the oil stain on the surface of the aluminum alloy.

[0080] The spectral information can be spectral feature data. For example, the spectral information can be the reflectance spectral data of the first object. For example, the spectral information can be the reflectance spectral data of the surface of the first object. For example, the spectral information can include the reflectance of the surface of the first object at multiple wavelengths, covering at least one of the visible, near-infrared, and infrared bands. For example, the multiple wavelengths can include wavelength A, wavelength B, and wavelength C. When wavelength A illuminates the surface of the first object, reflectance a is obtained; when wavelength B illuminates the surface of the first object, reflectance b is obtained; and when wavelength C illuminates the surface of the first object, reflectance c is obtained. The terminal device can determine reflectance a, reflectance b, and reflectance c as the spectral information of the first object. The terminal device can extract the specific spectral characteristics of pollutants from the spectral curve formed by the reflectance of each wavelength, achieving accurate type identification.

[0081] For example, the first object can be oil stains, wavelength A can be a wavelength within the visible light band (e.g., 450nm blue light), wavelength B can be a wavelength within the visible to near-infrared band (e.g., 670nm red light), and wavelength C can be a wavelength within the near-infrared band (e.g., 900nm). Because oil stains (mainly composed of hydrocarbons) have absorption characteristics for specific wavelengths of light, their reflectance spectrum will exhibit corresponding characteristics. For example, near wavelength B (670nm), oil stains may exhibit low reflectance b; while in the near-infrared region of wavelength C (900nm), their reflectance c may be relatively high. The terminal device can extract the spectral characteristics used to identify the oil stains by acquiring and analyzing the spectral curve composed of reflectance a, reflectance b, reflectance c, etc. The above wavelengths are illustrative and not intended to limit this application; the detection wavelength can be adjusted according to the type of contaminant.

[0082] In some embodiments, the spectral sensor can be a device for acquiring reflectance spectral data of a first object. For example, the spectral sensor can be a fiber optic spectrometer, a multispectral camera, or a hyperspectral imager. This application does not limit this specific application.

[0083] In some embodiments, the terminal device can acquire the spectral information of the first object based on the following feasible implementation: acquiring the distribution area of ​​the first object on the substrate, controlling the spectral sensor to irradiate the distribution area of ​​the substrate, and obtaining the spectral information of the first object. This can improve the accuracy of pollutant identification.

[0084] In some embodiments, the distribution area can be an effective distribution area where the surface of the first object is located. For example, the distribution area can indicate the location of the surface of the first object. For example, the distribution area can be a rectangular area defined by its upper left corner coordinates (X1, Y1) and lower right corner coordinates (X2, Y2), in which case the location of the first object can be confirmed within the area defined by the coordinate range (X1, Y1) to (X2, Y2). Alternatively, the distribution area can be a circular area, an irregular polygonal area, etc., defined by the center coordinates (Cx, Cy) and radius R, in which case the location of the first object can be confirmed within the circular area. This application does not limit this aspect.

[0085] It should be noted that the distribution area can be a single pollutant area, multiple discrete areas of the same pollutant, multiple discrete areas of multiple pollutants, or a mixed pollutant area.

[0086] For example, the distribution area can be a region containing only one type of contaminant. For instance, on the surface of an aluminum alloy part, there is a circular oil stain. The terminal device can identify the area covered by this oil stain as a distribution area through image analysis. This application does not limit this approach.

[0087] For example, the distribution area can be the same pollutant distributed in different regions. For instance, on a ceramic piece, there can be three non-contiguous resin coatings, and the terminal device can identify three independent distribution areas. This application does not limit this.

[0088] For example, the distribution area can be multiple discrete regions of various contaminants. For instance, on the surface of an aluminum alloy part, one side has an oil stain and the other side has rust. The terminal device can identify the type of contaminant and identify the areas covered by the oil stain and rust as the distribution areas, respectively. This application does not limit this approach.

[0089] For example, the distribution area can be a mixed contaminant area. For instance, on the surface of an aluminum alloy part, there may be an area of ​​oil and a area of ​​rust in the same region, which may overlap or be regularly distributed. This application does not limit this to specific examples.

[0090] In some embodiments, the terminal device can control a spectral sensor to irradiate a distribution area of ​​the substrate to obtain spectral information of the first object. For example, when the first object is oil, the terminal device can communicate with the spectral sensor, which irradiates the oil and receives the reflectivity reflected from the surface of the oil. The spectral sensor can then send this reflectivity to the terminal device.

[0091] For example, for an aluminum alloy surface containing both oil and rust, the terminal device can identify two unconnected sets of pixels. The terminal device calculates the boundary coordinates of the oil and rust areas respectively, thereby determining two independent distribution regions. Using this coordinate information, the terminal device controls a spectral sensor to sequentially illuminate each independent distribution region and collect its respective spectral data. This application does not limit the scope of the embodiments described herein.

[0092] In some embodiments, the terminal device can irradiate the distribution area of ​​the substrate to obtain the spectral information of the first object based on the following feasible implementation methods: when the distribution area on the substrate includes a single type of first object, the distribution area is irradiated to obtain the spectral information of the first object; when the distribution area on the substrate includes at least two types of first objects, the distribution area is irradiated to obtain mixed spectral information; and the mixed spectral information is demixed to obtain the spectral information corresponding to each type of first object. This can improve the accuracy of pollutant identification.

[0093] In some embodiments, the terminal device can control a spectral sensor to irradiate a distribution area containing a first object of a single type, thereby obtaining spectral information of the first object. For example, when the first object is oil, the terminal device can communicate with the spectral sensor, which irradiates the oil and receives the reflectivity reflected from the surface of the oil. The spectral sensor can then send this reflectivity to the terminal device.

[0094] In some embodiments, the terminal device can control a spectral sensor to irradiate the distribution area of ​​at least two types of first objects to obtain mixed spectral information. For example, when a contaminant area is scanned, the terminal device records a spectral curve. This curve includes both the absorption valley characteristics of oil near 1700 nm and the low reflectivity characteristics of rust in the 800-1000 nm range, superimposed together. This application does not limit the scope of the embodiments.

[0095] In some embodiments, the terminal device can demix the mixed spectral information to obtain spectral information corresponding to various types of first objects. For example, the terminal device can use a spectral demixing algorithm, based on the spectral curve and using a known pure pollutant spectral database (including pure oil and pure rust spectra) as a reference, to perform mathematical decomposition. The algorithm restores the contribution of each component in the mixed signal, thereby obtaining their corresponding spectral information. For example, spectral curve A is measured at the center point of region A. The system runs a linear demixing algorithm and finds that curve B cannot be fitted by a single pure spectrum, but can be fitted by a linear combination of spectra P1 and P2. The identified components are lubricating oil (P1) and iron oxide (P2), and the abundance (contribution ratio) of each component is: lubricating oil accounts for 70%, and iron oxide accounts for 30%. This application embodiment does not limit this.

[0096] S202. Based on spectral information, determine the first type of the first object.

[0097] The first type can be the kind of the first object. For example, when the first object is an object to be cleaned, the first type can be the type of the object to be cleaned. For example, when the first object is a contaminant, the first type can be the type of the contaminant.

[0098] For example, when the first object is a contaminant, the first type may include oil stains, rust, coatings, oxide layers, etc. This application does not limit this.

[0099] In some embodiments, the terminal device may determine a first type of a first object based on spectral information in the following feasible manner: obtaining a second correspondence, the second correspondence including multiple spectral information and the object type corresponding to each spectral information; and determining the first type of the first object from among the object types corresponding to each spectral information based on the spectral information of the first object and the second correspondence. This can improve the accuracy of pollutant identification.

[0100] In some embodiments, the second correspondence can be a mapping relationship stored in a preset parameter library. For example, the second correspondence can be a type-matching correspondence. For example, the second correspondence can be a correspondence between different reflectance spectral data features and pollutant types.

[0101] For example, when the first object is oil, the reflectance spectral data of the oil shows characteristic infrared absorption peaks of hydrocarbons near 2900 cm⁻¹ and 1700 cm⁻¹. These characteristic peaks are shown exemplarily. This application does not limit this.

[0102] It should be noted that the second correspondence is established and stored in advance before the terminal device is used. The establishment process is as follows: A large number of standard samples of known pollutant types (such as oil stains, rust, specific coatings, and oxide layers) are collected experimentally. Their reflectance spectra in a specific wavelength band (e.g., 400-1000 nm) are measured. Spectral features that match this type of pollutant (e.g., characteristic absorption peaks, wavelength positions of reflection peaks, etc.) are extracted from these samples. These feature data are then matched with the corresponding pollutant types and stored in a preset parameter library. The terminal device can quickly match pollutant types through feature comparison. The aforementioned wavelength bands are illustrative and not intended to limit this application; the measurement wavelength bands can be adjusted according to the pollutant type.

[0103] S203. Determine the grayscale information of the first image, and determine the first thickness of the first object based on the spectral information and the grayscale information.

[0104] In some embodiments, grayscale information can be used to distinguish between contaminant areas and substrate areas, and to perform preliminary identification of different contaminant types. For example, grayscale information can be the grayscale value and grayscale distribution characteristics of each pixel in the first image. For example, grayscale information can be the grayscale value and grayscale distribution characteristics of each pixel in the first image, including grayscale mean, grayscale gradient, grayscale histogram, etc. For example, grayscale information can be the grayscale values ​​of different types of contaminants in the first image. For example, grayscale information can be the grayscale values ​​used to identify contaminants such as oil stains, rust, and coatings in the first image.

[0105] For example, the grayscale value of oil stains in the first image can be from 180 to 240, the grayscale value of rust can be from 30 to 80, and the grayscale value of coating can be from 100 to 160. The above values ​​are for illustrative purposes only and are not intended to limit this application. They can be adjusted according to the actual scenario. This application does not limit these values.

[0106] In some embodiments, the terminal device can perform noise reduction, enhancement, and other operations on the first image using an image acquisition device to improve image quality. If the first image is a color image, the terminal device can convert it into a grayscale image using an image acquisition device, that is, convert the color information of each pixel into a grayscale value representing brightness, and extract the distribution features that can display the characteristics of the first object (pollutant) region from the preprocessed grayscale image. The terminal device can determine the distribution features as the grayscale information of the first image.

[0107] In some embodiments, after obtaining the grayscale information of the first image, the terminal device can analyze the grayscale information. For example, the terminal device can apply an image segmentation algorithm to analyze the grayscale information. For example, the terminal device can identify the set of consecutive pixels in the image that meet preset feature conditions (e.g., grayscale values ​​below or above a specific threshold) using a grayscale histogram, and calculate their boundaries. The terminal device can then convert this boundary information into coordinates that can be used for localization. For example, by generating one or more boundary coordinates, the terminal device can determine the distribution area using these coordinates.

[0108] The first thickness can be the thickness of the first object. For example, when the first object is an object to be cleaned, the first thickness can be the thickness of the object to be cleaned. For example, when the first object is a contaminant, the first thickness can include 0.05 mm (such as a thin oil film, thin rust layer, etc.) or 0.5 mm (thick coating), and can be divided into thin type (less than or equal to 0.1 mm), medium-thick type (0.1 mm-0.5 mm), thick type (greater than or equal to 0.5 mm), etc. The embodiments of this application do not limit this.

[0109] In some embodiments, the terminal device can determine the first thickness of a first object based on spectral information and grayscale information using the following feasible implementation: obtaining a first correspondence, which includes multiple thicknesses of the first object and grayscale and spectral information corresponding to each thickness of the first object; and determining the first thickness of the first object from among the multiple thicknesses of the first object based on the grayscale and spectral information of the first object and the first correspondence. This can improve the accuracy of pollutant identification.

[0110] In some embodiments, the first correspondence can be a thickness-matching correspondence. For example, the first correspondence may include multiple thicknesses of various first objects, as well as grayscale information and reflectance spectral data corresponding to each thickness. For example, if the first object is oil stain, with a thickness of 0.05 mm, the grayscale value is D, and there is a spectral curve d with corresponding reflectance values ​​at wavelengths of 450 nm, 670 nm, 900 nm, etc.; with a thickness of 0.10 mm, the grayscale value is E, and there is a spectral curve e with corresponding reflectance values ​​at wavelengths of 450 nm, 670 nm, 900 nm, etc.; for example, if the first object is rust, with a thickness of 0.20 mm, the grayscale value is F, and there is a spectral curve f with corresponding reflectance values ​​at wavelengths of 450 nm, 670 nm, 900 nm, etc.; with a thickness of 0.50 mm, the grayscale value is G, and there is a spectral curve g with corresponding reflectance values ​​at wavelengths of 450 nm, 670 nm, 900 nm, etc. The above values ​​and wavelengths are illustrative examples. This application does not limit this.

[0111] It should be noted that the first correspondence is established and stored in advance before the terminal device is used. The establishment process is as follows: A large number of standard samples of known pollutant types are collected experimentally, forming thickness gradients (e.g., 0.05 mm, 0.1 mm, 0.2 mm). Their reflectance spectra in specific wavelength bands (e.g., 400-1000 nm) are measured, and grayscale images of their surfaces are simultaneously acquired. Spectral features (e.g., characteristic absorption peaks, wavelength positions and intensities of reflection peaks) are extracted from the reflectance spectrum data, and grayscale distribution features (e.g., grayscale histograms) are extracted from the grayscale images. The terminal device can match these thickness values, the corresponding spectral feature sets, and the grayscale feature sets, and store them in a preset parameter library according to the pollutant type. The aforementioned thickness and wavelength bands are illustrative examples and not limitations of this application; they can be adjusted according to the pollutant type.

[0112] In some embodiments, the terminal device can determine the first thickness of the first object from among multiple thicknesses of the first object based on the grayscale information and spectral information of the first object, as well as the first correspondence. For example, a pixel in region B has been analyzed, and its grayscale value is 115. The spectral information (after demixing) shows that it contains two substances: lubricating oil and iron oxide, with lubricating oil accounting for 70% and iron oxide accounting for 30%. The spectral characteristics of the lubricating oil obtained from demixing (such as the absorption depth at 1700 nm) are input into the "lubricating oil spectrum-thickness model." The model outputs a basic thickness value based on the spectral characteristics, for example, a thickness value of 55 nm. The information that iron oxide accounts for 30% after demixing is also input into the model. In the mixed pixel, the corresponding thickness is approximately 210 nm. The terminal device can find the corresponding thickness among multiple thicknesses corresponding to lubricating oil and iron oxide based on the calculated thickness. This application embodiment does not limit this.

[0113] S204. Based on the first type and the first thickness, determine the first information of the laser cleaning device, which is used to indicate the cleaning parameters of the laser cleaning device during the cleaning process.

[0114] In some embodiments, the first information may be cleaning control information. For example, the first information may be used to indicate cleaning parameters of the laser cleaning device during the cleaning process. For example, the first information may be cleaning parameters such as laser power, scanning speed, PID algorithm parameters, and pulse frequency retrieved from a preset parameter library. For example, the first information may be an initial power retrieved from a preset parameter library based on the type and thickness of the contaminant (e.g., 80 to 100W for thick rust and 20 to 40W for thin oil stains). The above power values ​​are illustrative and not intended to limit this application; they can be adjusted according to the substrate and the type of contaminant.

[0115] In some embodiments, the PID algorithm can be a control algorithm for real-time adjustment of the power of a laser cleaning device. For example, the PID algorithm can obtain the amount of contaminant residue reflected by the real-time acquisition of image grayscale value changes, calculate the error between the current cleaning effect and the target state, and adjust the cleaning parameters such as the power and scanning speed of the laser cleaning device according to the error.

[0116] It should be noted that the PID algorithm can achieve rapid response and stable adjustment of cleaning parameters through the combined action of three parameters: proportional (P), integral (I), and derivative (D).

[0117] For example, when cleaning rust, the terminal device can control the laser cleaning device to start based on first information (such as an initial power of 80W). The terminal device can dynamically calculate and adjust the power based on the real-time detected changes in image grayscale values ​​(reflecting the reduction of residual contaminants) using a PID algorithm. For instance, during the cleaning process, the terminal device can dynamically adjust the power from the initial 80W to between 70W and 90W according to the actual cleaning efficiency, protecting the substrate while removing contaminants. The power values ​​mentioned above are illustrative examples. This application does not limit the scope of the embodiments.

[0118] It should be noted that the PID algorithm, through the combined action of three parameters—proportional (P), integral (I), and derivative (D)—can achieve a rapid response (response speed less than or equal to 10ms) and stable power. For example, when cleaning a thick layer of rust, the power can be automatically adjusted based on the reduction of residue (e.g., dynamically adjusted from 20W to 40W to 80W) from an initial power setting (e.g., 80W) to ensure that contaminants are removed while avoiding damage to the substrate. The aforementioned response speed and power values ​​are illustrative examples. This application does not limit this aspect.

[0119] S205. Based on the first information, control the laser cleaning device to clean the first object.

[0120] In some embodiments, the terminal device can control the laser cleaning device to clean the first object based on the first information in the following feasible manner: determining the first cleaning parameters of the laser cleaning device based on the first information, obtaining the current cleaning parameters of the laser cleaning device, and adjusting the current cleaning parameters to the first cleaning parameters based on a preset adjustment rate. This can improve the efficiency of laser cleaning.

[0121] In some embodiments, the terminal device can send first information (e.g., a set of instructions for parameters such as laser power, scanning speed, and pulse frequency) to the control system of the laser cleaning apparatus. Based on the parameters in the first information, the terminal device can control the laser generator to output a specified power and control its scanning speed, so that the laser beam acts on the distribution area where the first object is located. The laser beam precisely targets the effective distribution area of ​​the contaminants, avoiding substrate damage and energy waste caused by indiscriminate irradiation.

[0122] It should be noted that the terminal device can detect images of the cleaning area in real time through image acquisition equipment (such as acquiring one frame every 100ms), and assess the amount of pollutant residue by analyzing the changes in image grayscale values.

[0123] In some embodiments, based on the real-time data, the terminal device can call a PID control algorithm to dynamically calculate and output a power adjustment command to fine-tune the laser power in order to achieve an adaptive cleaning process. The adjustment response speed can reach the order of 10ms. The above acquisition interval and response speed are illustrative examples and are not intended to limit this application. They can be adjusted according to cleaning requirements.

[0124] This embodiment provides a control method for a laser cleaning device. A terminal device can acquire a first image of a first object to be cleaned, as well as the spectral information of the first object. Based on the spectral information, the terminal device can determine a first type of the first object. The terminal device can also determine the grayscale information of the first image and, based on the spectral and grayscale information, determine a first thickness of the first object. Based on the first type and first thickness, the terminal device can determine first information for the laser cleaning device. Based on this first information, the terminal device can control the laser cleaning device to clean the first object. Thus, since the terminal device can determine the corresponding cleaning parameters based on the type and thickness of the contaminants, the efficiency of contaminant cleaning can be improved.

[0125] exist Figure 2 Based on the illustrated embodiment, after cleaning the first object, the above-described laser cleaning method further includes a method for controlling the laser cleaning device. The following will be discussed in conjunction with... Figure 3The method for controlling the laser cleaning device using terminal equipment is explained in detail.

[0126] Figure 3 Please refer to the flowchart illustrating a laser cleaning apparatus control method provided in this application embodiment. Figure 3 ,include:

[0127] S301, Obtain the second image.

[0128] In some embodiments, the second image may be an image of the first object during the laser cleaning process. For example, the second image may be an image of the cleaning area acquired in real time by an image acquisition device (such as a high-definition camera) during the laser cleaning process. For example, the second image may include an image of the distribution of contaminants at the initial stage of cleaning, an image of the distribution of contaminants during the middle stage of cleaning, or an image of a small amount of contaminants remaining before the cleaning reaches the standard. This application does not limit this aspect.

[0129] S302. Determine the image similarity between the second image and the preset third image, wherein the third image is an image in which the first object does not exist in the base.

[0130] In some embodiments, the third image can be a standard clean image stored in a preset parameter library that does not contain a second image. For example, the third image can be an image that does not include contaminants, and the substrate does not include the first object when the image of the substrate is acquired. The third image serves as a reference clean image for the corresponding substrate, providing a standard reference for determining the cleaning effect.

[0131] For example, when the substrate is aluminum, the third image can be an image of the aluminum surface without contaminants; when the substrate is ceramic, the third image can be an image of the ceramic surface without coating. This application does not limit this.

[0132] S303. When the image similarity is greater than or equal to the first threshold, the cleaning result of the first object is determined to be cleaning complete.

[0133] In some embodiments, the terminal device can determine the cleaning result using an image similarity algorithm. For example, the terminal device can determine the cleaning result using a structural similarity (SSIM) algorithm. This algorithm calculates a similarity value between 0 and 1 by comprehensively comparing the similarity of two images in three dimensions: brightness, contrast, and structure.

[0134] For example, when the similarity between the second image and the third image is greater than or equal to a preset judgment threshold (e.g., 95%), the terminal device determines that the cleaning result meets the standard.

[0135] It should be noted that the above similarity values ​​and thresholds are for illustrative purposes only and are not intended to limit this application. They can be adjusted according to the cleaning accuracy requirements.

[0136] In some embodiments, the terminal device can control the laser cleaning apparatus based on the cleaning results. For example, the terminal device can control the start / stop, power, and scanning speed of the laser cleaning apparatus based on whether the cleaning results meet the standards. If the image grayscale value or spectral data is detected to reach a preset threshold for the substrate during the cleaning process, the power will be automatically reduced or the cleaning will be paused even if the cleaning does not meet the standards, to protect the substrate.

[0137] For example, if the cleaning result is determined to meet the standard (e.g., image similarity ≥ 95%), the terminal device sends a stop command to the laser cleaning device. This command controls the laser cleaning device to stop emitting light and controls the scanning device to stop working according to a preset process, thereby safely ending the entire cleaning process.

[0138] S304. When the image similarity is less than the first threshold, the cleaning result of the first object is determined to be that the cleaning is incomplete.

[0139] In some embodiments, the terminal device can determine the cleaning result using an image similarity algorithm. For example, the terminal device can determine the cleaning result using a structural similarity (SSIM) algorithm. This algorithm calculates a similarity value between 0 and 1 by comprehensively comparing the similarity of two images in three dimensions: brightness, contrast, and structure.

[0140] For example, in the initial stage of cleaning, the second image shows a large distribution of contaminants. The content and texture of this second image differ significantly from the third image (the baseline clean image), resulting in a low similarity calculated by the image similarity algorithm (e.g., 30%). This cleaning result indicates that the cleaning has not met the standards and that the amount of contaminants remaining is high.

[0141] For example, during the cleaning process, a second image shows that the contaminants have been partially removed, but some residual areas remain. The similarity between this second image and the third image increases but does not reach a threshold (e.g., 95%). This cleaning result indicates that cleaning is in progress and has not yet met the target.

[0142] For example, before the cleaning meets the standard, the second image shows a small amount of residual contaminants, and its overall appearance is close to clean. The similarity between the second image and the third image is close to a preset threshold, indicating that the cleaning result is about to meet the standard, but there are still trace amounts of residue.

[0143] It should be noted that the above similarity values ​​and thresholds are for illustrative purposes only and are not intended to limit this application. They can be adjusted according to the cleaning accuracy requirements.

[0144] In some embodiments, the terminal device can control the laser cleaning apparatus based on the cleaning results. For example, the terminal device can control the start / stop, power, and scanning speed of the laser cleaning apparatus based on whether the cleaning results meet the standards. If the image grayscale value or spectral data is detected to reach a preset threshold for the substrate during the cleaning process, the power will be automatically reduced or the cleaning will be paused even if the cleaning does not meet the standards, to protect the substrate.

[0145] For example, if the cleaning result is determined to be substandard (e.g., image similarity <95%), the terminal device controls the laser cleaning device to continue cleaning. The terminal device can dynamically calculate and adjust parameters such as laser power and scanning speed based on the real-time detected contaminant residue, using a PID control algorithm to improve laser cleaning efficiency, until it is determined to meet the standard in subsequent periodic inspections. The aforementioned similarity threshold is illustrative and not intended to limit this application.

[0146] This embodiment provides a control method for a laser cleaning device. The terminal device can acquire a second image, determine the image similarity between the second image and a preset third image, and determine that the cleaning result of a first object is complete when the image similarity is greater than or equal to a first threshold, or incomplete when the image similarity is less than the first threshold. The terminal device can then control the laser cleaning device based on the cleaning results. In this method, since the terminal device can automatically determine whether the cleaning meets the standard by comparing the similarity between the cleaning area and a standard cleaning image, the efficiency of laser cleaning can be improved.

[0147] exist Figure 2 Based on the embodiments shown, the above-described laser cleaning method further includes a method for storing laser cleaning parameters. The following will describe this method in conjunction with... Figure 4 The method for storing laser cleaning parameters in terminal equipment is explained in detail.

[0148] Figure 4 This is a schematic flowchart illustrating a laser cleaning parameter storage method provided in an embodiment of this application. Please refer to [link / reference]. Figure 4 The method may include:

[0149] S401. Obtain the second cleaning parameter corresponding to the first object.

[0150] In some embodiments, the second cleaning parameters may include cleaning parameters corresponding to the cleaning of the first object. For example, the second cleaning parameters may include cleaning power, scanning speed, thickness, and cleaning time when cleaning oil stains.

[0151] In some embodiments, after a single or multiple cleaning tasks are completed, the terminal device can collect data of the cleaning process, compare the cleaning time with an internally preset time threshold, filter out parameter combinations whose cleaning time is less than or equal to the preset threshold, and determine them as the second cleaning parameters corresponding to the first object. When filtering, the terminal device takes into account both cleaning efficiency and substrate protection, and excludes parameter combinations that may cause damage to the substrate.

[0152] Wherein, the first duration corresponding to the second cleaning parameter is less than or equal to the second threshold, and the first duration is the time required to complete cleaning of the first object based on the second cleaning parameter. The second threshold is used to filter out qualified second cleaning parameters. For example, when the second threshold is 10 minutes, only second cleaning parameters with a cleaning duration of less than or equal to 10 minutes can be stored. This application embodiment does not limit this.

[0153] S402. If the first object does not have stored cleaning parameters, store the second cleaning parameters.

[0154] In some embodiments, the terminal device can determine whether a second cleaning parameter for the first object is stored based on a preset parameter library. For example, the terminal device can compare the cleaning parameters of the first object and the second cleaning parameter in the preset parameter library; if there is no duplicate cleaning parameter, the second cleaning parameter is stored. For example, the terminal device can input the second cleaning parameter for querying; if no result is found, the second cleaning parameter corresponding to the first object is stored in the preset parameter library.

[0155] S403. Determine the first cleaning rate corresponding to the stored cleaning parameters, and based on the first duration and the first thickness, determine the second cleaning rate corresponding to the second cleaning parameters. When the second cleaning rate is greater than the first cleaning rate, store the second cleaning parameters.

[0156] In some embodiments, the first cleaning rate can be data stored in a preset parameter library. For example, it could be the ratio of the contaminant thickness of the first object to the total time required to complete the cleaning. This application does not limit this to specific parameters.

[0157] For example, when the terminal device performs cleaning task A, for the contaminant-substrate combination of "thick rust-steel plate", the cleaning parameters used are: laser power 500W, scanning speed 100mm / s, rust thickness removed 10.0mm, and cleaning time to achieve the target (image similarity ≥95%) is 30 seconds. When performing cleaning task B, for the same "thick rust-steel plate" combination, the parameters used are: laser power 800W, scanning speed 150mm / s, rust thickness removed 14.4mm, and cleaning time to achieve the target (image similarity ≥98%) is 20 seconds. The cleaning rate of parameter group A is 3.33mm / s, and the cleaning rate of parameter group B is 7.20mm / s. Parameter group B has a higher cleaning rate and faster cleaning. The terminal device can determine the parameter group corresponding to this cleaning rate as the second cleaning parameter corresponding to "thick rust-steel plate". The above parameters and times are illustrative and not intended to limit this application.

[0158] This embodiment provides a laser cleaning parameter storage method. The terminal device can acquire second cleaning parameters corresponding to a first object. If no existing cleaning parameters exist for the first object, the terminal device can store the second cleaning parameters. The terminal device can determine a first cleaning rate corresponding to the stored cleaning parameters. Based on a first duration and a first thickness, the terminal device can determine a second cleaning rate corresponding to the second cleaning parameters. If the second cleaning rate is greater than the first cleaning rate, the second cleaning parameters are stored. In this method, since the terminal device can store cleaning parameters for different contaminant types using a preset parameter library, and simultaneously associate them with the substrate type of the first object, the cost of manual operation can be reduced. Furthermore, since the terminal device can reduce repetitive debugging work through the reuse and self-updating functions of the parameter library, the efficiency of laser cleaning can be improved.

[0159] Figure 5 A schematic diagram of a laser cleaning apparatus 500 provided in this application. Please refer to [link / reference]. Figure 5 The laser cleaning device includes a first acquisition module 501, a second acquisition module 502, a first determination module 503, a second determination module 504, a third determination module 505, a fourth determination module 506, a control module 507, a third acquisition module 508, and a fourth acquisition module 509, wherein:

[0160] The first acquisition module 501 is used to acquire the first image of the first object to be cleaned;

[0161] The second acquisition module 502 is used to acquire the spectral information of the first object to be cleaned;

[0162] The first determining module 503 is used to determine the first type of the first object based on spectral information;

[0163] The second determining module 504 is used to determine the grayscale information of the first image;

[0164] The third determining module 505 is used to determine the first thickness of the first object based on spectral information and grayscale information;

[0165] The fourth determining module 506 is used to determine first information of the laser cleaning device based on the first type and the first thickness. The first information is used to indicate the cleaning parameters of the laser cleaning device during the cleaning process.

[0166] The control module 507 is used to control the laser cleaning device to clean the first object based on the first information.

[0167] According to one or more embodiments of this application, the third determining module 505 is specifically used for:

[0168] Obtain the first correspondence relationship, which includes multiple thicknesses of the first object, as well as grayscale information and spectral information corresponding to each thickness of the first object;

[0169] Based on the grayscale information and spectral information of the first object, as well as the first correspondence, the first thickness of the first object is determined among multiple thicknesses of the first object.

[0170] According to one or more embodiments of this application, the second acquisition module 502 is specifically used for:

[0171] Obtain the distribution area of ​​the first object on the base;

[0172] The spectral sensor is controlled to irradiate the distribution area of ​​the substrate to obtain the spectral information of the first object.

[0173] According to one or more embodiments of this application, the second acquisition module 502 is specifically used for:

[0174] When the distribution area on the substrate includes a first object of a single type, the distribution area is irradiated to obtain the spectral information of the first object;

[0175] When the distribution area on the substrate includes at least two types of first objects, the distribution area is irradiated to obtain mixed spectral information, and the mixed spectral information is demixed to obtain spectral information corresponding to each type of first object.

[0176] According to one or more embodiments of this application, the first determining module 503 is specifically used for:

[0177] Obtain the second correspondence, which includes multiple spectral information and the object type corresponding to each spectral information;

[0178] Based on the spectral information of the first object and the second correspondence, the first type of the first object is determined from the object types corresponding to each spectral information.

[0179] According to one or more embodiments of this application, the control module 507 is specifically used for:

[0180] Based on the first information, determine the first cleaning parameters of the laser cleaning device;

[0181] Obtain the current cleaning parameters of the laser cleaning device;

[0182] Based on the preset adjustment rate, the current cleaning parameters are adjusted to the first cleaning parameters.

[0183] According to one or more embodiments of this application, the third acquisition module 508 is specifically used for:

[0184] Acquire a second image, which is an image captured during the cleaning process of the first object;

[0185] Determine the image similarity between the second image and a preset third image, wherein the third image is an image in which the first object does not exist in the base;

[0186] When the image similarity is greater than or equal to the first threshold, the cleaning result of the first object is determined to be cleaning complete;

[0187] When the image similarity is less than the first threshold, the cleaning result of the first object is determined to be incomplete.

[0188] According to one or more embodiments of this application, the fourth acquisition module 509 is specifically used for:

[0189] Obtain the second cleaning parameter corresponding to the first object. The first duration corresponding to the second cleaning parameter is less than or equal to the second threshold. The first duration is the time required to complete the cleaning of the first object based on the second cleaning parameter.

[0190] If the first object does not have stored cleaning parameters, the second cleaning parameters are stored.

[0191] A first cleaning rate corresponding to the stored cleaning parameters is determined. Based on a first duration and a first thickness, a second cleaning rate corresponding to the second cleaning parameters is determined. When the second cleaning rate is greater than the first cleaning rate, the second cleaning parameters are stored.

[0192] The laser cleaning device provided in this embodiment can perform the method provided in the above-described method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0193] Figure 6 This is a schematic diagram of the structure of a terminal device provided in this embodiment. Please refer to [link / reference]. Figure 6 The diagram illustrates a structural schematic of a terminal device 600 suitable for implementing this embodiment. The terminal device may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, personal digital assistants (PDAs), portable Android devices (PADs), portable media players (PMPs), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0194] like Figure 6 As shown, the terminal device 600 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from storage device 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the terminal device 600. The processing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0195] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows terminal device 600 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 A terminal device 600 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0196] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, it performs the functions defined in the methods of the embodiments of this application.

[0197] It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0198] The aforementioned computer-readable medium may be included in the aforementioned terminal device; or it may exist independently and not assembled into the terminal device.

[0199] The aforementioned computer-readable medium carries one or more programs, which, when executed by the terminal device, cause the terminal device to perform the method shown in the above embodiments.

[0200] This application provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements various methods that may be involved in the above embodiments.

[0201] This application provides a computer program product, including a computer program that, when executed by a processor, implements various methods that may be involved in the above embodiments.

[0202] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0203] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0204] The units described in the embodiments of this application can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0205] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0206] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0207] It should be noted that the terms "a" and "a plurality of" used in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0208] The names of the messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0209] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and provisions. The data may include information, parameters and messages, such as flow switching indication information.

[0210] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0211] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Multitasking and parallel processing may be advantageous in certain contexts. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments. Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely exemplary forms of implementing the claims.

Claims

1. A laser cleaning method, characterized in that, include: Acquire a first image of the first object to be cleaned, and the spectral information of the first object; Based on the spectral information, the first type of the first object is determined; Determine the grayscale information of the first image, and based on the spectral information and the grayscale information, determine the first thickness of the first object; Based on the first type and the first thickness, first information of the laser cleaning device is determined, and the first information is used to indicate the cleaning parameters of the laser cleaning device during the cleaning process. Based on the first information, the laser cleaning device is controlled to clean the first object; Determining the first thickness of the first object based on the spectral information and the grayscale information includes: Obtain a first correspondence relationship, which includes multiple thicknesses of the first object, as well as grayscale information and spectral information corresponding to each thickness of the first object; Based on the grayscale information and spectral information of the first object, and the first correspondence, the first thickness of the first object is determined among the multiple thicknesses of the first object; Obtaining the spectral information of the first object includes: Obtain the distribution area of ​​the first object on the substrate; The spectral sensor is controlled to irradiate the distribution area of ​​the substrate to obtain the spectral information of the first object.

2. The method according to claim 1, characterized in that, Irradiating the distribution area of ​​the substrate to obtain the spectral information of the first object includes: When the distribution area on the substrate includes a first object of a single type, the distribution area is irradiated to obtain the spectral information of the first object; When the distribution area on the substrate includes at least two types of first objects, the distribution area is irradiated to obtain mixed spectral information, and the mixed spectral information is demixed to obtain spectral information corresponding to each type of first object.

3. The method according to claim 1, characterized in that, Based on the spectral information, determining the first type of the first object includes: Obtain a second correspondence, which includes multiple spectral information and the object type corresponding to each spectral information; Based on the spectral information of the first object and the second correspondence, the first type of the first object is determined from the object types corresponding to each spectral information.

4. The method according to any one of claims 1-3, characterized in that, Based on the first information, the laser cleaning device is controlled to clean the first object, including: Based on the first information, the first cleaning parameters of the laser cleaning device are determined; Obtain the current cleaning parameters of the laser cleaning device; Based on a preset adjustment rate, the current cleaning parameters are adjusted to the first cleaning parameters.

5. The method according to any one of claims 1-3, characterized in that, The method further includes: Acquire a second image, which is an image captured during the cleaning process of the first object; Determine the image similarity between the second image and a preset third image, wherein the third image is an image in which the first object does not exist in the base image; When the image similarity is greater than or equal to a first threshold, the cleaning result of the first object is determined to be cleaning complete; When the image similarity is less than the first threshold, the cleaning result of the first object is determined to be that the cleaning is incomplete.

6. The method according to claim 5, characterized in that, The method further includes: Obtain the second cleaning parameter corresponding to the first object. The first duration corresponding to the second cleaning parameter is less than or equal to the second threshold. The first duration is the time required to complete the cleaning of the first object based on the second cleaning parameter. If the first object does not have stored cleaning parameters, the second cleaning parameters are stored. A first cleaning rate corresponding to the stored cleaning parameters is determined. Based on the first duration and the first thickness, a second cleaning rate corresponding to the second cleaning parameters is determined. When the second cleaning rate is greater than the first cleaning rate, the second cleaning parameters are stored.

7. A laser cleaning device, characterized in that, It includes a first acquisition module, a second acquisition module, a first determination module, a second determination module, a third determination module, a fourth determination module, and a control module, wherein: The first acquisition module is used to acquire a first image of the first object to be cleaned; The second acquisition module is used to acquire the spectral information of the first object to be cleaned; The first determining module is used to determine a first type of the first object based on the spectral information; The second determining module is used to determine the grayscale information of the first image; The third determining module is used to determine the first thickness of the first object based on the spectral information and the grayscale information; The fourth determining module is used to determine first information of the laser cleaning device based on the first type and the first thickness. The first information is used to indicate the cleaning parameters of the laser cleaning device during the cleaning process. The control module is used to control the laser cleaning device to clean the first object based on the first information; The third determining module is specifically used to obtain a first correspondence relationship, which includes multiple thicknesses of the first object, as well as grayscale information and spectral information corresponding to each thickness of the first object; Based on the grayscale information and spectral information of the first object, and the first correspondence, the first thickness of the first object is determined among the multiple thicknesses of the first object; The second acquisition module is specifically used to acquire the distribution area of ​​the first object on the substrate; The spectral sensor is controlled to irradiate the distribution area of ​​the substrate to obtain the spectral information of the first object.

8. A terminal device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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