Multi-mode control method and system for fully automatic car washing equipment based on artificial intelligence

Abstract

The invention relates to the field of automatic control technology, and is a multi-mode control method and system for fully automatic car washing equipment based on artificial intelligence. The specific method comprises: dividing the degree of completeness of the body paint of a vehicle to be washed; calculating and obtaining the paint aging coefficient of each complete body paint area; performing a first round of fuzzy cleaning on each complete body paint area according to the paint aging coefficient of each complete body paint area, and performing a second round of key cleaning on the second key cleaning area; calculating and obtaining the defect cleaning risk coefficient of the incomplete body paint area; performing a third round of defect cleaning and a fourth round of defect cleaning on the incomplete body paint area respectively; the invention solves the problem in the prior art that excessive cleaning or insufficient cleaning of some areas of the vehicle is caused by the inability to analyze cleaning feedback data in real time, while avoiding the potential risk of damage to the paint by the existing automatic car washing equipment, and ensuring the safety of the vehicle during the cleaning process.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and is a multi-mode control method and system for fully automatic car washing equipment based on artificial intelligence. Background Art

[0002] With the continuous advancement of science and technology, automated car washing equipment has been widely used in daily life. However, there are still many technical bottlenecks in the car washing process of the fully automatic car washing equipment on the market. Especially when washing the vehicle, due to the inability to analyze the cleaning feedback data in real time, some areas of the vehicle are often over-cleaned or insufficiently cleaned, which not only affects the car washing effect, but also may cause unnecessary damage to the paint. In the existing fully automatic car washing technology, most equipment adopts a single cleaning mode and fixed cleaning parameters, which cannot be flexibly adjusted according to the actual paint state and cleaning needs of the vehicle. This lack of targeted cleaning method often makes it difficult to achieve the ideal cleaning effect, and may even damage the paint due to improper cleaning strength, causing economic losses to the car owner. In addition, the existing automatic car washing equipment lacks comprehensive consideration of vehicle safety during the cleaning process. Especially when dealing with incomplete areas of the paint, due to the lack of effective risk assessment and cleaning strategy adjustment mechanism, the paint damage may be further aggravated due to improper cleaning operation, and even cause more serious safety problems. Therefore, how to analyze cleaning feedback data in real time and flexibly adjust the cleaning mode and parameters according to the actual paint status and cleaning needs of the vehicle to ensure the cleaning effect while protecting the paint safety has become an important issue that needs to be urgently solved in the current field of fully automatic car washing technology.

[0003] In the existing disclosed invention technologies, for example, the patent with application publication number CN117885698A discloses a fully automatic car washing machine that can intelligently identify the vehicle model, which specifically includes: first, capturing the real-time image of the vehicle to be washed, obtaining and analyzing the vehicle image data, extracting the vehicle features, obtaining the vehicle model recognition result, and analyzing the vehicle status based on the vehicle image data to obtain the vehicle status information; then, comprehensively analyzing the vehicle status information and the vehicle model recognition result to obtain a preliminary vehicle cleaning plan, formulating a cleaning trajectory according to the preliminary vehicle cleaning plan, and generating a preliminary cleaning operation; finally, optimizing the preliminary cleaning operation, and controlling the fully automatic car washing machine based on the optimized cleaning operation.

[0004] The above patent lacks analysis of real-time cleaning feedback data, and is unable to dynamically adjust the cleaning strategy or accurately adjust the cleaning parameters; at the same time, the cleaning mode and tool adjustment of the above patent are not detailed enough, making it difficult to cope with the complex changes in the vehicle paint condition. Summary of the invention

[0005] The technical problem to be solved by the present invention is to address the problem in the prior art that the cleaning feedback data cannot be analyzed in real time, resulting in excessive or insufficient cleaning of some areas of the vehicle. At the same time, the potential risk of damage to the vehicle paint by existing automatic car washing equipment is reduced, and a multi-mode control method and system for fully automatic car washing equipment based on artificial intelligence are proposed to ensure the safety of the vehicle during the cleaning process of the fully automatic car washing equipment.

[0006] In order to achieve the above-mentioned purpose, the technical solution of the multi-mode control method of the fully automatic car washing equipment based on artificial intelligence of the present invention comprises the following steps:

[0007] S1: Obtain a scanned image of the vehicle paint status, and divide the vehicle paint integrity of the vehicle to be washed into areas with intact paint and areas with incomplete paint;

[0008] S2: Use a spectrometer to measure the complete area of ​​the body paint of the vehicle to be washed, import the measurement results into the paint aging assessment strategy, and calculate the paint aging coefficient of each complete area of ​​the body paint of the vehicle to be washed;

[0009] S3: performing a first round of fuzzy cleaning on each intact area of ​​the vehicle body paint according to the paint aging coefficient of each intact area of ​​the vehicle body paint;

[0010] S4: extracting the first cleaning feedback data in the first round of fuzzy cleaning, and performing a second round of focused cleaning on the second cleaning focus area according to the first cleaning feedback data;

[0011] S5: Measure the incomplete paint area of ​​the vehicle body by ultrasonic equipment, import the measurement results into the defect cleaning risk assessment strategy, and calculate the defect cleaning risk coefficient of each incomplete paint area of ​​the vehicle body to be washed;

[0012] S6: According to the defect cleaning risk factor, the third round of defect cleaning and the fourth round of defect cleaning are performed on the incomplete areas of the car body paint respectively.

[0013] Specifically, S1 includes the following specific steps:

[0014] S11: performing a multi-directional scan of the paint status of the vehicle to be washed in the car wash area by using a high-resolution camera, obtaining a multi-directional scan image of the paint status of the vehicle to be washed, and establishing a three-dimensional grid model of the vehicle to be washed, wherein the grid type in the three-dimensional grid model is a quadrilateral;

[0015] S12: scanning the image of the vehicle to be washed from multiple angles according to the paint state of the vehicle to be washed, searching and matching in the vehicle model database, and identifying the model data of the vehicle to be washed;

[0016] S13: preprocessing the multi-directional scanned image of the paint state of the vehicle to be washed using image processing software, wherein the preprocessing includes enhancing the contrast and clarity of the image;

[0017] S14: Use the Sobel operator to identify the edges of car paint scratches in the image, use non-maximum to suppress image noise and enhance edge continuity, and simultaneously use the image segmentation algorithm to separate the identified car paint scratches from the car paint background;

[0018] S15: extracting the car paint scratch contour through the openVC library, marking the car paint scratch area on the multi-directional scanning image of the car paint state of the vehicle to be washed, and recording the geometric feature data of each car paint scratch;

[0019] S16: Divide the body paint integrity of the vehicle to be washed according to the paint scratch area, and obtain the body paint integrity area and the body paint incomplete area, wherein the body paint integrity area is N in total, and the body paint incomplete area is M in total.

[0020] Furthermore, each vehicle body paint incomplete region includes only one paint scratch.

[0021] Specifically, S2 includes the following specific steps:

[0022] S21: measuring the complete area of ​​the body paint of the vehicle to be washed by a spectrometer to obtain a reflection spectrum of each complete area of ​​the body paint of the vehicle to be washed, wherein the abscissa of the reflection spectrum is the wavelength of visible light, and the ordinate of the reflection spectrum is the reflectivity;

[0023] S22: extracting a factory paint reflection spectrum corresponding to the model of the vehicle to be washed, comparing the factory paint reflection spectrum with the reflection spectrum obtained by measurement, and calculating the difference in reflectivity corresponding to different wavelengths between the factory paint reflection spectrum and the reflection spectrum obtained by measurement;

[0024] S23: preset a reflectivity change threshold, filter out data points whose reflectivity difference between the factory paint reflectivity spectrum and the measured reflectivity spectrum of the vehicle to be washed is greater than the reflectivity change threshold, and simultaneously mark the filtered data points as aging data points;

[0025] S24: The reflectivity of the aging data point is imported into the paint aging evaluation strategy to calculate and obtain the paint aging coefficient of each complete area of ​​the paint of the vehicle body to be washed. The paint aging evaluation strategy is as follows:

[0026] ;

[0027] in, is the paint aging coefficient of the nth complete area of ​​the car body paint;

[0028] is the total number of aging data points in the reflectance spectrum of the nth complete area of ​​the car body paint, is a subscript, indicating the Aging data points;

[0029] Indicates the reflection spectrum of the nth complete area of ​​the car body paint obtained by measurement. The reflectivity of the aging data points; In the reflection spectrum of the factory car paint, The reflectivity of the factory paint for aging data points;

[0030] In the reflection spectrum of the nth complete area of ​​the car body paint obtained by measurement, The average reflectivity of the aging data points.

[0031] Specifically, S3 includes the following specific steps:

[0032] S31: extracting the paint aging coefficient, and pre-adjusting the first round of fuzzy cleaning pressure of the standard hardness brush of the fully automatic car washing equipment according to the paint aging value of each intact area of ​​the car body paint, wherein the pre-adjustment is specifically: , The first round of fuzzy cleaning pressure of the standard hardness brush when the fully automatic car wash equipment performs the first round of fuzzy cleaning on the nth complete area of ​​the car body paint after pre-adjustment; It is the initial cleaning pressure of the standard hardness brush of the fully automatic car washing equipment;

[0033] S32: The fully automatic car washing equipment is adjusted to the first round of fuzzy cleaning mode, and the first round of fuzzy cleaning is performed on the intact area of ​​each car body paint. During the first round of fuzzy cleaning, the brush root of the standard hardness brush is evenly distributed with The cleaning trajectory pattern of the standard hardness brush follows the curve of the vehicle body.

[0034] S41: extracting the first cleaning feedback data in the first round of fuzzy cleaning, wherein the first cleaning feedback data includes: The piezoelectric sensor array collects the dynamic resistance of cleaning.

[0035] S42: Draw a function curve of the dynamic resistance of cleaning as a function of the data feedback time point , where u is a subscript, indicating the u-th piezoelectric sensor, , Indicates the data feedback time point of the nth complete area of ​​the car body paint in the first round of fuzzy cleaning;

[0036] S43: importing the cleaning dynamic resistance into the brush cleaning feedback value calculation strategy to calculate the brush cleaning feedback value ;

[0037] S44: Preset cleaning feedback threshold , the brush cleaning feedback value Greater than the cleaning feedback threshold The intact area of ​​the car body and paint is calibrated as the second key cleaning area, and the fully automatic car washing equipment is adjusted to the second round of key cleaning mode, and the second round of key cleaning is performed on the second key cleaning area. The second round of key cleaning mode includes: the cleaning trajectory mode of the standard hardness brush is circular cleaning in the second key cleaning area;

[0038] S45: Real-time calculation of the brush cleaning feedback value of the standard hardness brush in each second cleaning key area in the second round of key cleaning, when the brush cleaning feedback value of the second cleaning key area in the second round of key cleaning is less than or equal to the cleaning feedback threshold When the second cleaning key area is cleaned, the second round of cleaning is stopped; otherwise, the second round of cleaning is continued.

[0039] Specifically, in S43, the brush cleaning feedback value calculation strategy is as follows:

[0040] ;

[0041] in, It indicates the brush cleaning feedback value obtained by calculating the u-th piezoelectric sensor in the first round of fuzzy cleaning for the n-th complete area of ​​the car body paint;

[0042] It represents the brush cleaning feedback value of the nth complete area of ​​the car body paint in the first round of fuzzy cleaning;

[0043] It indicates the total duration of the first round of fuzzy cleaning of the nth complete area of ​​the car body paint by the fully automatic car washing equipment in the first round of fuzzy cleaning. ;

[0044] A function curve representing the change of the cleaning dynamic resistance corresponding to the piezoelectric sensor adjacent to the right side of the u-th piezoelectric sensor with the data feedback time point;

[0045] The function curve representing the change of the cleaning dynamic resistance corresponding to the piezoelectric sensor adjacent to the u-th piezoelectric sensor in the vertical direction and below as a function of the data feedback time point.

[0046] Specifically, S5 includes the following specific steps:

[0047] S51: measuring the incomplete paint area of ​​the vehicle body by using ultrasonic equipment, and recording the echo time of the ultrasonic wave in each incomplete paint area of ​​the vehicle body;

[0048] S52: extracting the geometric feature data of each paint scratch obtained in step S15, importing the geometric feature data and the echo time into the defect cleaning risk assessment strategy, and calculating and obtaining the defect cleaning risk coefficient of each incomplete paint area of ​​the body of the vehicle to be washed, wherein the defect cleaning risk assessment strategy is specifically:

[0049] ;

[0050] in, represents the cleaning risk factor of the mth incomplete paint area of ​​the vehicle body;

[0051] They represent the outline length of the paint scratch and the maximum width of the paint scratch respectively; It represents the echo time of the ultrasonic wave in the mth incomplete paint area of ​​the vehicle body.

[0052] Specifically, S6 includes the following specific steps:

[0053] S61: Extract the cleaning risk coefficient of each incomplete paint area of ​​the vehicle body and preset the cleaning risk level judgment threshold Assess the cleaning risk level of each car body paint defect area as follows:

[0054] when When , the cleaning risk level of the mth incomplete paint area of ​​the vehicle body is judged to be a slight cleaning risk level;

[0055] when When , the cleaning risk level of the mth incomplete paint area of ​​the vehicle body is judged to be the general cleaning risk level;

[0056] when When , the cleaning risk level of the mth incomplete paint area of ​​the vehicle body is judged to be a major cleaning risk level;

[0057] S62: adjusting the fully automatic car washing equipment to the third round defect cleaning mode, performing the third round of defect cleaning on the incomplete paint area of ​​the vehicle body with a cleaning risk level of a slight cleaning risk level and a general cleaning risk level, wherein the cleaning tool used in the third round defect cleaning mode includes a synthetic fiber brush and a soft wool brush, and the cleaning track mode of the cleaning brush used in the third round defect cleaning mode is to follow the car paint scratch curve;

[0058] S63: Adjust the fully automatic car washing equipment to the fourth round of defect cleaning mode, and perform the fourth round of defect cleaning on the incomplete paint area of ​​the car body with a cleaning risk level of a major cleaning risk level, wherein the cleaning tool used in the fourth round of defect cleaning mode is a clean soft cloth, and the cleaning trajectory pattern of the cleaning tool used in the fourth round of defect cleaning mode is point scrubbing.

[0059] Specifically, S62 further includes: pre-adjusting the third round defect cleaning speed of the cleaning tool of the full-automatic car washing equipment according to the cleaning risk coefficient of each incomplete paint area of ​​the car body, wherein the pre-adjustment is specifically: , The third round defect cleaning speed of the cleaning tool when the fully automatic car washing equipment performs the third round of defect cleaning on the mth incomplete paint area of ​​the car body after pre-adjustment; It is the initial cleaning speed of the fully automatic car washing equipment.

[0060] In addition, the multi-mode control system of the fully automatic car washing equipment based on artificial intelligence of the present invention includes the following modules:

[0061] Car paint status scanning module, car paint aging assessment module, first round fuzzy cleaning module, second round key cleaning module, cleaning risk assessment module, third round defect cleaning module and fourth round defect cleaning module;

[0062] The paint status scanning module is used to obtain a paint status scanning image, and divide the paint integrity of the vehicle body to be washed to obtain a complete paint area and an incomplete paint area.

[0063] The paint aging assessment module measures the complete area of ​​the body paint of the vehicle to be washed by a spectrometer, imports the measurement results into the paint aging assessment strategy, and calculates and obtains the paint aging coefficient of each complete area of ​​the body paint of the vehicle to be washed;

[0064] The first round of fuzzy cleaning module performs a first round of fuzzy cleaning on each complete area of ​​the vehicle body paint according to the paint aging coefficient of each complete area of ​​the vehicle body paint;

[0065] The second round of focused cleaning module is used to extract the first cleaning feedback data in the first round of fuzzy cleaning, and perform a second round of focused cleaning on the second cleaning focus area according to the first cleaning feedback data;

[0066] The cleaning risk assessment module measures the incomplete paint area of ​​the vehicle body by ultrasonic equipment, imports the measurement results into the defect cleaning risk assessment strategy, and calculates and obtains the defect cleaning risk coefficient of each incomplete paint area of ​​the vehicle body to be washed;

[0067] The third round defect cleaning module performs a third round defect cleaning mode on the incomplete area of ​​the vehicle body paint according to the defect cleaning risk coefficient;

[0068] The fourth round defect cleaning module performs a fourth round defect cleaning mode on the incomplete paint area of ​​the vehicle body according to the defect cleaning risk coefficient.

[0069] A storage medium stores instructions, and when a computer reads the instructions, the computer executes the multi-mode control method for fully automatic car washing equipment based on artificial intelligence.

[0070] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned multi-mode control method of a fully automatic car washing equipment based on artificial intelligence when executing the computer program.

[0071] Compared with the prior art, the technical effects of the present invention are as follows:

[0072] 1. The present invention adjusts the cleaning strategy through real-time feedback data during the cleaning process, such as dynamic cleaning resistance, to avoid the problem of excessive or insufficient cleaning.

[0073] 2. The present invention uses a spectrometer to perform detailed measurements on the vehicle paint, calculates the vehicle paint aging coefficient, and uses ultrasonic equipment to perform risk assessment on vehicle paint defects, and can adjust the cleaning mode and intensity in a targeted manner.

[0074] 3. The present invention adopts multiple rounds of fuzzy cleaning, key cleaning and defect cleaning modes, and processes them separately according to different paint conditions and risk levels, with higher adaptability.

[0075] 4. The present invention performs the third and fourth rounds of defect cleaning on the incomplete areas of the vehicle paint, including using different cleaning tools and cleaning modes to reduce the cleaning risk. At the same time, the pressure and speed of the cleaning tool are pre-adjusted according to the vehicle paint aging coefficient and the cleaning risk coefficient to ensure the cleaning effect and vehicle paint safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0077] Figure 1 It is a flow chart of the multi-mode control method of the fully automatic car washing equipment based on artificial intelligence of the present invention;

[0078] Figure 2It is a structural schematic diagram of a multi-mode control system of a fully automatic car washing equipment based on artificial intelligence of the present invention; DETAILED DESCRIPTION

[0079] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0080] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0081] Embodiment 1:

[0082] like Figure 1 As shown, the multi-mode control method of the fully automatic car washing equipment based on artificial intelligence in the embodiment of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0083] S1: Obtain a scanned image of the vehicle paint status, and divide the vehicle paint integrity of the vehicle to be washed into areas with intact paint and areas with incomplete paint;

[0084] S1 includes the following specific steps:

[0085] S11: performing a multi-directional scan of the paint status of the vehicle to be washed in the car wash area by using a high-resolution camera, obtaining a multi-directional scan image of the paint status of the vehicle to be washed, and establishing a three-dimensional grid model of the vehicle to be washed, wherein the grid type in the three-dimensional grid model is a quadrilateral;

[0086] S12: scanning the image of the vehicle to be washed from multiple angles according to the paint state of the vehicle to be washed, searching and matching in the vehicle model database, and identifying the model data of the vehicle to be washed;

[0087] S13: preprocessing the multi-directional scanned image of the paint state of the vehicle to be washed using image processing software, wherein the preprocessing includes enhancing the contrast and clarity of the image;

[0088] S14: Use the Sobel operator to identify the edges of car paint scratches in the image, use non-maximum to suppress image noise and enhance edge continuity, and simultaneously use the image segmentation algorithm to separate the identified car paint scratches from the car paint background;

[0089] S15: extracting the car paint scratch contour through the openVC library, marking the car paint scratch area on the multi-directional scanning image of the car paint state of the vehicle to be washed, and recording the geometric feature data of each car paint scratch;

[0090] S16: Divide the body paint integrity of the vehicle to be washed according to the paint scratch area, and obtain the body paint integrity area and the body paint incomplete area, wherein the body paint integrity area is N in total, and the body paint incomplete area is M in total.

[0091] Furthermore, each vehicle body paint incomplete region includes only one paint scratch.

[0092] S2: Use a spectrometer to measure the complete area of ​​the body paint of the vehicle to be washed, import the measurement results into the paint aging assessment strategy, and calculate the paint aging coefficient of each complete area of ​​the body paint of the vehicle to be washed;

[0093] S2 includes the following specific steps:

[0094] S21: measuring the complete area of ​​the body paint of the vehicle to be washed by a spectrometer to obtain a reflection spectrum of each complete area of ​​the body paint of the vehicle to be washed, wherein the abscissa of the reflection spectrum is the wavelength of visible light, and the ordinate of the reflection spectrum is the reflectivity;

[0095] S22: extracting a factory paint reflection spectrum corresponding to the model of the vehicle to be washed, comparing the factory paint reflection spectrum with the reflection spectrum obtained by measurement, and calculating the difference in reflectivity corresponding to different wavelengths between the factory paint reflection spectrum and the reflection spectrum obtained by measurement;

[0096] S23: preset a reflectivity change threshold, filter out data points whose reflectivity difference between the factory paint reflectivity spectrum and the measured reflectivity spectrum of the vehicle to be washed is greater than the reflectivity change threshold, and simultaneously mark the filtered data points as aging data points;

[0097] S24: The reflectivity of the aging data point is imported into the paint aging evaluation strategy to calculate and obtain the paint aging coefficient of each complete area of ​​the paint of the vehicle body to be washed. The paint aging evaluation strategy is as follows:

[0098] ;

[0099] in, is the paint aging coefficient of the nth complete area of ​​the car body paint;

[0100] is the total number of aging data points in the reflectance spectrum of the nth complete area of ​​the car body paint, is a subscript, indicating the Aging data points;

[0101] Indicates the reflection spectrum of the nth complete area of ​​the car body paint obtained by measurement. The reflectivity of the aging data points; In the reflection spectrum of the factory car paint, The reflectivity of the factory paint for aging data points;

[0102] In the reflection spectrum of the nth complete area of ​​the car body paint obtained by measurement, The average reflectivity of the aging data points.

[0103] S3: performing a first round of fuzzy cleaning on each intact area of ​​the vehicle body paint according to the paint aging coefficient of each intact area of ​​the vehicle body paint;

[0104] S3 includes the following specific steps:

[0105] S31: extracting the paint aging coefficient, and pre-adjusting the first round of fuzzy cleaning pressure of the standard hardness brush of the fully automatic car washing equipment according to the paint aging value of each intact area of ​​the car body paint, wherein the pre-adjustment is specifically: , The first round of fuzzy cleaning pressure of the standard hardness brush when the fully automatic car wash equipment performs the first round of fuzzy cleaning on the nth complete area of ​​the car body paint after pre-adjustment; It is the initial cleaning pressure of the standard hardness brush of the fully automatic car washing equipment;

[0106] S32: The fully automatic car washing equipment is adjusted to the first round of fuzzy cleaning mode, and the first round of fuzzy cleaning is performed on the intact area of ​​each car body paint. During the first round of fuzzy cleaning, the brush root of the standard hardness brush is evenly distributed with The cleaning trajectory pattern of the standard hardness brush follows the curve of the vehicle body.

[0107] S4: extracting the first cleaning feedback data in the first round of fuzzy cleaning, and performing a second round of focused cleaning on the second cleaning focus area according to the first cleaning feedback data;

[0108] S4 includes the following specific steps:

[0109] S41: extracting the first cleaning feedback data in the first round of fuzzy cleaning, wherein the first cleaning feedback data includes: The piezoelectric sensor array collects the dynamic resistance of cleaning.

[0110] S42: Draw a function curve of the dynamic resistance of cleaning as a function of the data feedback time point , where u is a subscript, indicating the u-th piezoelectric sensor, , Indicates the data feedback time point of the nth complete area of ​​the car body paint in the first round of fuzzy cleaning;

[0111] S43: importing the cleaning dynamic resistance into the brush cleaning feedback value calculation strategy to calculate the brush cleaning feedback value ;

[0112] S44: Preset cleaning feedback threshold , the brush cleaning feedback value Greater than the cleaning feedback threshold The intact area of ​​the car body and paint is calibrated as the second key cleaning area, and the fully automatic car washing equipment is adjusted to the second round of key cleaning mode, and the second round of key cleaning is performed on the second key cleaning area. The second round of key cleaning mode includes: the cleaning trajectory mode of the standard hardness brush is circular cleaning in the second key cleaning area;

[0113] S45: Real-time calculation of the brush cleaning feedback value of the standard hardness brush in each second cleaning key area in the second round of key cleaning, when the brush cleaning feedback value of the second cleaning key area in the second round of key cleaning is less than or equal to the cleaning feedback threshold When the second cleaning key area is cleaned, the second round of cleaning is stopped; otherwise, the second round of cleaning is continued.

[0114] In S43, the brush cleaning feedback value calculation strategy is specifically as follows:

[0115] ;

[0116] in, It indicates the brush cleaning feedback value obtained by calculating the u-th piezoelectric sensor in the first round of fuzzy cleaning for the n-th complete area of ​​the car body paint;

[0117] It represents the brush cleaning feedback value of the nth complete area of ​​the car body paint in the first round of fuzzy cleaning;

[0118] It indicates the total duration of the first round of fuzzy cleaning of the nth complete area of ​​the car body paint by the fully automatic car washing equipment in the first round of fuzzy cleaning. ;

[0119] A function curve representing the change of the cleaning dynamic resistance corresponding to the piezoelectric sensor adjacent to the right side of the u-th piezoelectric sensor with the data feedback time point;

[0120] The function curve representing the change of the cleaning dynamic resistance corresponding to the piezoelectric sensor adjacent to the u-th piezoelectric sensor in the vertical direction and below as a function of the data feedback time point.

[0121] S5: Measure the incomplete paint area of ​​the vehicle body by ultrasonic equipment, import the measurement results into the defect cleaning risk assessment strategy, and calculate the defect cleaning risk coefficient of each incomplete paint area of ​​the vehicle body to be washed;

[0122] S5 includes the following specific steps:

[0123] S51: measuring the incomplete paint area of ​​the vehicle body by using ultrasonic equipment, and recording the echo time of the ultrasonic wave in each incomplete paint area of ​​the vehicle body;

[0124] S52: extracting the geometric feature data of each paint scratch obtained in step S15, importing the geometric feature data and the echo time into the defect cleaning risk assessment strategy, and calculating and obtaining the defect cleaning risk coefficient of each incomplete paint area of ​​the body of the vehicle to be washed, wherein the defect cleaning risk assessment strategy is specifically:

[0125] ;

[0126] in, represents the cleaning risk factor of the mth incomplete paint area of ​​the vehicle body;

[0127] They represent the outline length of the paint scratch and the maximum width of the paint scratch respectively; It represents the echo time of the ultrasonic wave in the mth incomplete paint area of ​​the vehicle body.

[0128] S6: According to the defect cleaning risk factor, the third round of defect cleaning and the fourth round of defect cleaning are performed on the incomplete areas of the car body paint respectively.

[0129] S6 includes the following specific steps:

[0130] S61: Extract the cleaning risk coefficient of each incomplete paint area of ​​the vehicle body and preset the cleaning risk level judgment threshold Assess the cleaning risk level of each car body paint defect area as follows:

[0131] when When , the cleaning risk level of the mth incomplete paint area of ​​the vehicle body is judged to be a slight cleaning risk level;

[0132] when When , the cleaning risk level of the mth incomplete paint area of ​​the vehicle body is judged to be the general cleaning risk level;

[0133] when When , the cleaning risk level of the mth incomplete paint area of ​​the vehicle body is judged to be a major cleaning risk level;

[0134] S62: adjusting the fully automatic car washing equipment to the third round defect cleaning mode, performing the third round of defect cleaning on the incomplete paint area of ​​the vehicle body with a cleaning risk level of a slight cleaning risk level and a general cleaning risk level, wherein the cleaning tool used in the third round defect cleaning mode includes a synthetic fiber brush and a soft wool brush, and the cleaning track mode of the cleaning brush used in the third round defect cleaning mode is to follow the car paint scratch curve;

[0135] S63: Adjust the fully automatic car washing equipment to the fourth round of defect cleaning mode, and perform the fourth round of defect cleaning on the incomplete paint area of ​​the car body with a cleaning risk level of a major cleaning risk level, wherein the cleaning tool used in the fourth round of defect cleaning mode is a clean soft cloth, and the cleaning trajectory pattern of the cleaning tool used in the fourth round of defect cleaning mode is point scrubbing.

[0136] S62 also includes: pre-adjusting the third round defect cleaning speed of the cleaning tool of the full-automatic car washing equipment according to the cleaning risk coefficient of each incomplete paint area of ​​the car body, wherein the pre-adjustment is specifically: , The third round defect cleaning speed of the cleaning tool when the fully automatic car washing equipment performs the third round of defect cleaning on the mth incomplete paint area of ​​the car body after pre-adjustment; It is the initial cleaning speed of the fully automatic car washing equipment.

[0137] Embodiment 2:

[0138] like Figure 2 As shown, the multi-mode control system of the fully automatic car washing equipment based on artificial intelligence in the embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules:

[0139] Car paint status scanning module, car paint aging assessment module, first round fuzzy cleaning module, second round key cleaning module, cleaning risk assessment module, third round defect cleaning module and fourth round defect cleaning module;

[0140] The paint status scanning module is used to obtain a paint status scanning image, and divide the paint integrity of the vehicle body to be washed to obtain a complete paint area and an incomplete paint area.

[0141] The paint aging assessment module measures the complete area of ​​the body paint of the vehicle to be washed by a spectrometer, imports the measurement results into the paint aging assessment strategy, and calculates and obtains the paint aging coefficient of each complete area of ​​the body paint of the vehicle to be washed;

[0142] The first round of fuzzy cleaning module performs a first round of fuzzy cleaning on each complete area of ​​the vehicle body paint according to the paint aging coefficient of each complete area of ​​the vehicle body paint;

[0143] The second round of focused cleaning module is used to extract the first cleaning feedback data in the first round of fuzzy cleaning, and perform a second round of focused cleaning on the second cleaning focus area according to the first cleaning feedback data;

[0144] The cleaning risk assessment module measures the incomplete paint area of ​​the vehicle body by ultrasonic equipment, imports the measurement results into the defect cleaning risk assessment strategy, and calculates and obtains the defect cleaning risk coefficient of each incomplete paint area of ​​the vehicle body to be washed;

[0145] The third round defect cleaning module performs a third round defect cleaning mode on the incomplete area of ​​the vehicle body paint according to the defect cleaning risk coefficient;

[0146] The fourth round defect cleaning module performs a fourth round defect cleaning mode on the incomplete paint area of ​​the vehicle body according to the defect cleaning risk coefficient.

[0147] Embodiment three:

[0148] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0149] The processor executes the above-mentioned multi-mode control method of the fully automatic car washing equipment based on artificial intelligence by calling the computer program stored in the memory.

[0150] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the multi-mode control method for fully automatic car washing equipment based on artificial intelligence provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0151] Embodiment 4:

[0152] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0153] When the computer program runs on a computer device, the computer device executes the above-mentioned multi-mode control method of a fully automatic car washing device based on artificial intelligence.

[0154] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0155] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0156] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0157] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0158] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0159] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0160] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0161] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0162] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0163] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0164] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0165] 1. The present invention adjusts the cleaning strategy through real-time feedback data during the cleaning process, such as dynamic cleaning resistance, to avoid the problem of excessive or insufficient cleaning.

[0166] 2. The present invention uses a spectrometer to perform detailed measurements on the vehicle paint, calculates the vehicle paint aging coefficient, and uses ultrasonic equipment to perform risk assessment on vehicle paint defects, and can adjust the cleaning mode and intensity in a targeted manner.

[0167] 3. The present invention adopts multiple rounds of fuzzy cleaning, key cleaning and defect cleaning modes, and processes them separately according to different paint conditions and risk levels, with higher adaptability.

[0168] 4. The present invention performs the third and fourth rounds of defect cleaning on the incomplete areas of the vehicle paint, including using different cleaning tools and cleaning modes to reduce the cleaning risk. At the same time, the pressure and speed of the cleaning tool are pre-adjusted according to the vehicle paint aging coefficient and the cleaning risk coefficient to ensure the cleaning effect and vehicle paint safety.

[0169] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A multi-mode control method for fully automatic car washing equipment based on artificial intelligence, characterized in that: The method comprises the following specific steps: S1: Obtain a scanned image of the vehicle paint status, and divide the vehicle paint integrity of the vehicle to be washed into areas with intact paint and areas with incomplete paint; S11: performing a multi-directional scan of the paint status of the vehicle to be washed in the car wash area by using a high-resolution camera, obtaining a multi-directional scan image of the paint status of the vehicle to be washed, and establishing a three-dimensional grid model of the vehicle to be washed; S12: scanning the image of the vehicle to be washed from multiple angles according to the paint state of the vehicle to be washed, searching and matching in the vehicle model database, and identifying the model data of the vehicle to be washed; S13: Preprocessing the multi-directional scanned image of the paint state of the vehicle to be washed using image processing software; S14: Use the Sobel operator to identify the edges of car paint scratches in the image, use non-maximum to suppress image noise and enhance edge continuity, and simultaneously use the image segmentation algorithm to separate the identified car paint scratches from the car paint background; S15: extracting the car paint scratch contour through the openVC library, marking the car paint scratch area on the multi-directional scanning image of the car paint state of the vehicle to be washed, and recording the geometric feature data of each car paint scratch; S16: dividing the body paint integrity of the vehicle to be washed according to the paint scratch area, and obtaining the body paint integrity area and the body paint incomplete area, wherein the body paint integrity area has N areas in total, and the body paint incomplete area has M areas in total; S2: Use a spectrometer to measure the complete area of ​​the body paint of the vehicle to be washed, import the measurement results into the paint aging assessment strategy, and calculate the paint aging coefficient of each complete area of ​​the body paint of the vehicle to be washed; S3: performing a first round of fuzzy cleaning on each intact area of ​​the vehicle body paint according to the paint aging coefficient of each intact area of ​​the vehicle body paint; S4: extracting the first cleaning feedback data in the first round of fuzzy cleaning, and performing a second round of focused cleaning on the second cleaning focus area according to the first cleaning feedback data; S5: Measure the incomplete paint area of ​​the vehicle body by ultrasonic equipment, import the measurement results into the defect cleaning risk assessment strategy, and calculate the defect cleaning risk coefficient of each incomplete paint area of ​​the vehicle body to be washed; S6: According to the defect cleaning risk factor, the third round of defect cleaning and the fourth round of defect cleaning are performed on the incomplete areas of the car body paint respectively.

2. The multi-mode control method for fully automatic car washing equipment based on artificial intelligence according to claim 1 is characterized in that: S2 includes the following specific steps: S21: measuring the complete area of ​​the body paint of the vehicle to be washed by a spectrometer to obtain a reflection spectrum of each complete area of ​​the body paint of the vehicle to be washed, wherein the abscissa of the reflection spectrum is the wavelength of visible light, and the ordinate of the reflection spectrum is the reflectivity; S22: extracting a factory paint reflection spectrum corresponding to the model of the vehicle to be washed, comparing the factory paint reflection spectrum with the measured reflection spectrum of the vehicle to be washed, and calculating the difference in reflectivity corresponding to different visible light wavelengths between the factory paint reflection spectrum and the measured reflection spectrum of the vehicle to be washed; S23: preset a reflectivity change threshold, filter out data points whose reflectivity difference between the factory paint reflectivity spectrum and the measured reflectivity spectrum of the vehicle to be washed is greater than the reflectivity change threshold, and simultaneously mark the filtered data points as aging data points; S24: The reflectivity of the aging data point is imported into the paint aging evaluation strategy to calculate and obtain the paint aging coefficient of each complete area of ​​the paint of the vehicle body to be washed. The paint aging evaluation strategy is as follows: ； in, is the paint aging coefficient of the nth complete area of ​​the car body paint; is the total number of aging data points in the reflectance spectrum of the nth complete area of ​​the car body paint, is a subscript, indicating the Aging data points; Indicates the reflection spectrum of the nth complete area of ​​the car body paint obtained by measurement. The reflectivity of the aging data points; In the reflection spectrum of the factory car paint, The reflectivity of the factory paint for aging data points; In the reflection spectrum of the nth complete area of ​​the car body paint obtained by measurement, The average reflectivity of the aging data points.

3. The multi-mode control method for fully automatic car washing equipment based on artificial intelligence according to claim 2 is characterized in that: S3 includes the following specific steps: S31: extracting the paint aging coefficient, and pre-adjusting the first round of fuzzy cleaning pressure of the standard hardness brush of the fully automatic car washing equipment according to the paint aging value of each complete area of ​​the car body paint, wherein the pre-adjustment is specifically: , The first round of fuzzy cleaning pressure of the standard hardness brush when the fully automatic car wash equipment performs the first round of fuzzy cleaning on the nth complete area of ​​the car body paint after pre-adjustment; The initial cleaning pressure of the standard hardness brush of the fully automatic car washing equipment; S32: The fully automatic car washing equipment is adjusted to the first round of fuzzy cleaning mode, and the first round of fuzzy cleaning is performed on the intact area of ​​each car body paint. During the first round of fuzzy cleaning, the brush root of the standard hardness brush is evenly distributed with The cleaning trajectory pattern of the standard hardness brush follows the curve of the vehicle body.

4. The multi-mode control method for fully automatic car washing equipment based on artificial intelligence according to claim 3 is characterized in that: S4 includes the following specific steps: S41: extracting the first cleaning feedback data in the first round of fuzzy cleaning, wherein the first cleaning feedback data includes: The dynamic resistance of cleaning collected by the piezoelectric sensor array, S42: Draw a function curve of the dynamic resistance of cleaning as a function of the data feedback time point , where u is a subscript, indicating the u-th piezoelectric sensor, , Indicates the data feedback time point of the nth complete area of ​​the car body paint in the first round of fuzzy cleaning; S43: importing the cleaning dynamic resistance into the brush cleaning feedback value calculation strategy to calculate the brush cleaning feedback value ; S44: Preset cleaning feedback threshold , the brush cleaning feedback value Greater than the cleaning feedback threshold The intact area of ​​the car body and paint is calibrated as the second key cleaning area, and the fully automatic car washing equipment is adjusted to the second round of key cleaning mode, and the second round of key cleaning is performed on the second key cleaning area. The second round of key cleaning mode includes: the cleaning trajectory mode of the standard hardness brush is circular cleaning in the second key cleaning area; S45: Real-time calculation of the brush cleaning feedback value of the standard hardness brush in each second cleaning key area in the second round of key cleaning, when the brush cleaning feedback value of the second cleaning key area in the second round of key cleaning is less than or equal to the cleaning feedback threshold When the second cleaning key area is cleaned, the second round of cleaning is stopped; otherwise, the second round of cleaning is continued.

5. The multi-mode control method for fully automatic car washing equipment based on artificial intelligence according to claim 4 is characterized in that: In S43, the brush cleaning feedback value calculation strategy is specifically as follows: ； in, It indicates the brush cleaning feedback value obtained by calculating the u-th piezoelectric sensor in the first round of fuzzy cleaning for the n-th complete area of ​​the car body paint; It represents the brush cleaning feedback value of the nth complete area of ​​the car body paint in the first round of fuzzy cleaning; It indicates the total duration of the first round of fuzzy cleaning of the nth complete area of ​​the car body paint by the fully automatic car washing equipment in the first round of fuzzy cleaning. ; A function curve representing the change of the cleaning dynamic resistance corresponding to the piezoelectric sensor adjacent to the right side of the u-th piezoelectric sensor with the data feedback time point; The function curve representing the change of the cleaning dynamic resistance corresponding to the piezoelectric sensor adjacent to the u-th piezoelectric sensor in the vertical direction and below as a function of the data feedback time point.

6. The multi-mode control method for fully automatic car washing equipment based on artificial intelligence according to claim 5 is characterized in that: S5 includes the following specific steps: S51: measuring the incomplete paint area of ​​the vehicle body by using ultrasonic equipment, and recording the echo time of the ultrasonic wave in each incomplete paint area of ​​the vehicle body; S52: extracting the geometric feature data of each paint scratch obtained in step S15, importing the geometric feature data and the echo time into the defect cleaning risk assessment strategy, and calculating and obtaining the defect cleaning risk coefficient of each incomplete paint area of ​​the body of the vehicle to be washed, wherein the defect cleaning risk assessment strategy is specifically: ； in, represents the cleaning risk factor of the mth incomplete paint area of ​​the vehicle body; They represent the outline length of the paint scratch and the maximum width of the paint scratch respectively; It represents the echo time of the ultrasonic wave in the mth incomplete paint area of ​​the vehicle body.

7. The multi-mode control method for fully automatic car washing equipment based on artificial intelligence according to claim 6 is characterized in that: S6 includes the following specific steps: S61: Extract the cleaning risk coefficient of each incomplete paint area of ​​the vehicle body and preset the cleaning risk level judgment threshold , assess the cleaning risk level of each car body paint incomplete area, as follows: when When , the cleaning risk level of the mth incomplete paint area of ​​the vehicle body is judged to be a slight cleaning risk level; when When , the cleaning risk level of the mth incomplete paint area of ​​the vehicle body is judged to be the general cleaning risk level; when When , the cleaning risk level of the mth incomplete paint area of ​​the vehicle body is judged to be a major cleaning risk level; S62: adjusting the fully automatic car washing equipment to the third round defect cleaning mode, performing the third round of defect cleaning on the incomplete paint area of ​​the vehicle body with a cleaning risk level of a slight cleaning risk level and a general cleaning risk level, wherein the cleaning tool used in the third round defect cleaning mode includes a synthetic fiber brush and a soft wool brush, and the cleaning track mode of the cleaning brush used in the third round defect cleaning mode is to follow the car paint scratch curve; S63: Adjust the fully automatic car washing equipment to the fourth round of defect cleaning mode, and perform the fourth round of defect cleaning on the incomplete paint area of ​​the car body with a cleaning risk level of a major cleaning risk level, wherein the cleaning tool used in the fourth round of defect cleaning mode is a clean soft cloth, and the cleaning trajectory pattern of the cleaning tool used in the fourth round of defect cleaning mode is point scrubbing.

8. The multi-mode control method for fully automatic car washing equipment based on artificial intelligence according to claim 7 is characterized in that: S62 also includes: pre-adjusting the third round defect cleaning speed of the cleaning tool of the full-automatic car washing equipment according to the cleaning risk coefficient of each incomplete paint area of ​​the car body, wherein the pre-adjustment is specifically: , The third round defect cleaning speed of the cleaning tool when the fully automatic car washing equipment performs the third round of defect cleaning on the mth incomplete paint area of ​​the car body after pre-adjustment; It is the initial cleaning speed of the fully automatic car washing equipment.

9. A multi-mode control system for a fully automatic car wash equipment based on artificial intelligence, which is implemented based on a multi-mode control method for a fully automatic car wash equipment based on artificial intelligence as claimed in any one of claims 1 to 8, characterized in that: The system includes the following modules: Car paint status scanning module, car paint aging assessment module, first round fuzzy cleaning module, second round key cleaning module, cleaning risk assessment module, third round defect cleaning module and fourth round defect cleaning module; The paint status scanning module is used to obtain a paint status scanning image, and divide the paint integrity of the vehicle body to be washed to obtain a complete paint area and an incomplete paint area. The paint aging assessment module measures the complete area of ​​the body paint of the vehicle to be washed by a spectrometer, imports the measurement results into the paint aging assessment strategy, and calculates and obtains the paint aging coefficient of each complete area of ​​the body paint of the vehicle to be washed; The first round of fuzzy cleaning module performs a first round of fuzzy cleaning on each complete area of ​​the vehicle body paint according to the paint aging coefficient of each complete area of ​​the vehicle body paint; The second round of focused cleaning module is used to extract the first cleaning feedback data in the first round of fuzzy cleaning, and perform a second round of focused cleaning on the second cleaning focus area according to the first cleaning feedback data; The cleaning risk assessment module measures the incomplete paint area of ​​the vehicle body by ultrasonic equipment, imports the measurement results into the defect cleaning risk assessment strategy, and calculates and obtains the defect cleaning risk coefficient of each incomplete paint area of ​​the vehicle body to be washed; The third round defect cleaning module performs a third round defect cleaning mode on the incomplete area of ​​the vehicle body paint according to the defect cleaning risk coefficient; The fourth round defect cleaning module performs a fourth round defect cleaning mode on the incomplete paint area of ​​the vehicle body according to the defect cleaning risk coefficient.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-mode control method for a fully automatic car washing equipment based on artificial intelligence as described in any one of claims 1 to 8 is implemented.

11. An electronic device, characterized in that: include: A memory for storing instructions; A processor is used to execute the instruction so that the device performs the operation of implementing the multi-mode control method of fully automatic car washing equipment based on artificial intelligence as described in any one of claims 1-8.

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