Automatic car washing method, device, equipment and medium

Abstract

The invention discloses an automatic car washing method, device and equipment and a medium, and relates to the technical field of car washing. The automatic car washing method comprises the steps that a to-be-washed car is obtained, and image information of the to-be-washed car is collected; using the image information of the to-be-cleaned vehicle to perform vehicle shape recognition by using an edge algorithm to obtain a vehicle feature data result; performing vehicle shape simulation analysis based on the vehicle feature data result to obtain a vehicle shape simulation analysis result; and vehicle cleaning operation is executed according to the vehicle shape simulation analysis result, and an automatic vehicle cleaning result is obtained. The full-link technology innovation from hardware collaboration, intelligent identification, precise simulation to dynamic control is achieved, intelligence, high efficiency and safety of the car washing process are finally achieved, and the car model compatibility and the cleaning quality of equipment are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle washing technology, and in particular to an automatic car washing method, apparatus, equipment and medium. Background Technology

[0002] In the current automated car wash industry, traditional equipment generally uses fixed programs to execute the car wash process. Its core flaw lies in its inability to adapt to the different exterior shapes of various car models. Whether it's the size differences between sedans and SUVs, or variations in exterior features such as the presence or absence of a sunroof, the fixed process struggles to match the curved surfaces of the car body. This not only easily leads to incomplete cleaning in certain areas but can also cause paint damage due to the hard contact between the brush rollers and the car body. Furthermore, traditional equipment often uses centralized deployment for computing power, requiring data processing to rely on a central computing center. This results in high response latency and makes it difficult to monitor and adjust operating parameters such as brush roller speed and pressure in real time during the car wash process, lacking dynamic adaptability. In addition, the sensing modules of traditional equipment are simply configured, often relying on a single sensor for basic detection. This inability to accurately collect vehicle shape data further limits the adaptability and intelligence of the car wash process, making it difficult to meet the current demand for efficient and safe car washing for diverse car models.

[0003] Therefore, there is an urgent need for an automatic car washing method, device, equipment, and medium to address the shortcomings of existing technologies. Summary of the Invention

[0004] The purpose of this invention is to propose an automatic car wash method, device, equipment, and medium to solve the problems of traditional automatic car wash equipment being unable to adapt to the differences in the appearance of different car models, having slow computing power response, resulting in incomplete cleaning, easy damage to the car body, and difficulty in real-time dynamic adjustment. The invention achieves accurate identification of vehicle features, intelligent adaptation and dynamic adjustment of car wash solutions, thereby improving car wash efficiency and adaptability.

[0005] In a first aspect, to achieve the above objectives, the present invention provides an automatic car washing method, comprising:

[0006] S1. Obtain the vehicle to be cleaned and collect image information of the vehicle to be cleaned;

[0007] S2. Using the image information of the vehicle to be cleaned, an edge algorithm is used to identify the vehicle's shape and obtain vehicle feature data results.

[0008] S3. Perform vehicle shape simulation analysis based on the vehicle feature data results, and obtain the vehicle shape simulation analysis results;

[0009] S4. Perform vehicle washing operation based on the vehicle shape simulation analysis results to obtain automatic car wash results.

[0010] Optionally, S1, acquire the vehicle to be cleaned and collect image information of the vehicle to be cleaned, including:

[0011] The ultrasonic radar and through-beam sensors are used to detect and obtain the entry status of the vehicles to be cleaned.

[0012] Determine whether the entry status of the vehicle to be cleaned is already in the entry status. If so, execute the first operation; otherwise, obtain the entry status of the vehicle to be cleaned as not in the entry status, adjust the vehicle to be cleaned, and execute the second operation.

[0013] The first operation is as follows: based on the entry status of the vehicle to be cleaned, determine whether the vehicle to be cleaned has entered the car wash area. If so, trigger multiple high-definition cameras and capture images of the vehicle to be cleaned to obtain image information of the vehicle to be cleaned. Otherwise, adjust the vehicle to be cleaned to enter the car wash area and execute the second operation.

[0014] The second operation is to use ultrasonic radar and a beam sensor to detect and obtain the entry status of the vehicle to be cleaned.

[0015] Optionally, S2, using the image information of the vehicle to be cleaned, an edge algorithm is used to perform vehicle shape recognition to obtain vehicle feature data results, including:

[0016] The image information of the vehicle to be cleaned is preprocessed to obtain the preprocessed image information of the vehicle to be cleaned.

[0017] Based on the preprocessed image information of the vehicle to be cleaned, an edge algorithm is used to extract contour features to obtain the contour features of the vehicle to be cleaned.

[0018] Clustering and fitting are performed based on the contour features of the vehicles to be cleaned to generate standardized vehicle shape contour data.

[0019] The standardized vehicle outline data to be cleaned is combined with a vehicle feature database for matching processing to obtain vehicle feature data results.

[0020] Optionally, the standardized vehicle outline data to be cleaned is combined with a vehicle feature database for matching processing to obtain vehicle feature data results, including:

[0021] Based on the standardized vehicle outline data to be cleaned, the vehicle feature database is called.

[0022] The standardized vehicle outline data to be cleaned is matched with the vehicle feature database to obtain outline similarity.

[0023] Determine whether the contour similarity is greater than a preset contour similarity threshold. If so, obtain a vehicle model contour template based on the contour similarity and the vehicle feature database. Otherwise, update the vehicle feature database using the standardized vehicle shape contour data to be cleaned and perform the third operation.

[0024] Based on the vehicle model outline template, the vehicle feature database is used to obtain vehicle feature data results;

[0025] The third operation involves matching the standardized vehicle outline data to be cleaned with the vehicle feature database to obtain outline similarity.

[0026] Optionally, S3, based on the vehicle feature data results, perform vehicle shape simulation analysis to obtain vehicle shape simulation analysis results, including:

[0027] Based on the vehicle feature data results and the vehicle feature database, obtain the vehicle shape data and basic car wash control logic of the vehicle to be cleaned.

[0028] Using the vehicle shape data of the vehicle to be cleaned, a three-dimensional network model of the vehicle is constructed.

[0029] Based on the vehicle's three-dimensional network model and the basic car wash control logic of the vehicle to be cleaned, a vehicle shape simulation analysis is performed to obtain the vehicle shape simulation analysis results.

[0030] Optionally, based on the vehicle's three-dimensional network model and the basic car wash control logic of the vehicle to be cleaned, a vehicle shape simulation analysis is performed to obtain the vehicle shape simulation analysis results, including:

[0031] Import the vehicle's 3D network model and the 3D model of the car wash machine's execution part into the simulation environment, load the basic car wash control logic of the vehicle to be cleaned, and obtain the basic scene of the vehicle to be cleaned.

[0032] Based on the vehicle's three-dimensional network model and the vehicle's basic car wash control logic, avoidance rules are set for the car wash machine's execution components.

[0033] Based on the basic scenario of the vehicle to be cleaned and the avoidance rules of the car wash machine's actuators, the motion parameters of the car wash machine's actuators are set.

[0034] Based on the basic scenario of the vehicle to be cleaned and the basic car wash control logic of the vehicle to be cleaned, combined with the motion parameters of the car wash machine's execution components, an operational simulation algorithm is used to obtain the simulation results of the vehicle to be cleaned.

[0035] Determine whether the simulation results of the vehicle to be cleaned conform to the avoidance rules of the car wash machine's actuator. If so, use the simulation results of the vehicle to be cleaned to obtain the vehicle shape simulation analysis results. Otherwise, based on the simulation results of the vehicle to be cleaned and the avoidance rules of the car wash machine's actuator, adjust the motion parameters of the car wash machine's actuator and execute the fourth operation.

[0036] The fourth operation is as follows: based on the basic scenario of the vehicle to be cleaned and the basic car wash control logic of the vehicle to be cleaned, combined with the motion parameters of the car wash machine's execution components, an operational simulation algorithm is used to obtain the simulation results of the vehicle to be cleaned.

[0037] Optionally, S4, perform a vehicle washing operation based on the vehicle shape simulation analysis results to obtain automatic car wash results, including:

[0038] The simulation analysis results of the vehicle's exterior shape are analyzed to generate a set of cleaning execution instructions for the car wash machine;

[0039] The car wash machine's execution components are controlled according to the car wash machine's cleaning execution instruction set, and target data of the car wash process are collected in real time.

[0040] The target data of the car wash process is fitted with the vehicle shape simulation analysis results to obtain the car wash control results.

[0041] Determine whether the car wash control result is normal. If it is normal, obtain the car wash status of the vehicle to be washed and execute the fifth operation. Otherwise, adjust the motion parameters of the car wash machine's execution component according to the car wash control result and execute the fourth operation.

[0042] The fifth operation is as follows: determine whether the car wash status of the vehicle to be washed is the washing completed state. If so, obtain the washing completed state of the vehicle to be washed as the automatic car wash result. Otherwise, mark the vehicle to be washed as not washing completed state, update the washing execution instruction set of the car wash machine according to the not washing completed state of the vehicle to be washed, and execute the sixth operation.

[0043] The sixth operation is to control the execution components of the car wash machine according to the cleaning execution instruction set of the car wash machine, and to collect target data of the car wash process in real time.

[0044] Secondly, to achieve the above objectives, the present invention provides an automatic car wash device, comprising: a car wash machine core controller and a distributed AI intelligent analysis terminal, wherein the car wash machine core controller includes a vehicle acquisition module and a core control module, and the distributed AI intelligent analysis terminal includes a feature recognition analysis module and a simulation analysis module;

[0045] The vehicle acquisition module is used to acquire the vehicle to be cleaned and to collect image information of the vehicle to be cleaned.

[0046] The feature recognition and analysis module is used to use the image information of the vehicle to be cleaned to perform vehicle shape recognition using an edge algorithm, and obtain vehicle feature data results.

[0047] The simulation analysis module is used to perform vehicle shape simulation analysis based on the vehicle feature data results and obtain vehicle shape simulation analysis results.

[0048] The core control module is used to perform vehicle washing operations based on the vehicle shape simulation analysis results and obtain automatic car wash results.

[0049] Thirdly, to achieve the above objectives, the present invention provides an electronic device, comprising: one or more processors; and a storage device having stored one or more programs thereon, which, when executed by the one or more processors, cause the one or more processors to implement the method described in any implementation of the first aspect.

[0050] Fourthly, to achieve the above objectives, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by one or more processors, implements the method as described in any implementation of the first aspect.

[0051] Compared with the closest existing technology, the present invention has the following advantages:

[0052] This invention utilizes a collaborative hardware architecture combining an ARM-based car wash machine core controller and a distributed AI intelligent analysis terminal, coupled with edge computing algorithms, to achieve full-dimensional vehicle image acquisition and high-precision recognition of vehicle type and body features. This overcomes the limitations of traditional car wash equipment that relies on manual settings or single recognition methods, significantly improving the accuracy and efficiency of vehicle type recognition. By constructing a vehicle shape simulation model and generating customized cleaning trajectory control curves, this invention ensures that the motion trajectory of the cleaning execution components precisely matches the vehicle's curved surface. This effectively eliminates cleaning blind spots, improves equipment operating efficiency and vehicle compatibility, and addresses the shortcomings of traditional equipment in adapting to multiple vehicle models. Furthermore, through a closed-loop control mechanism of dynamic monitoring and real-time parameter adjustment during the cleaning process, this invention continuously fits and verifies actual cleaning data with simulation results, promptly avoiding problems such as excessive brush roller pressure and trajectory interference. This avoids the risk of vehicle damage and dynamically optimizes the cleaning process, improving equipment operating efficiency. This invention, through its end-to-end technological innovation from hardware collaboration, intelligent recognition, precise simulation to dynamic control, ultimately achieves intelligent, efficient, and safe car wash processes, significantly enhancing the equipment's vehicle compatibility and cleaning quality. Attached Figure Description

[0053] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0054] Figure 1 This is a flowchart of an automatic car wash method according to an embodiment of the present invention;

[0055] Figure 2 This is a schematic diagram of the structure of an automatic car wash device according to an embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram of the structure of the electronic device proposed in an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0058] The terminology used in the embodiments section of this invention is for the purpose of explaining specific embodiments of the invention only, and is not intended to limit the invention.

[0059] like Figure 1 As shown, an embodiment of the present invention provides an automatic car wash method, including:

[0060] S1. Obtain the vehicle to be cleaned and collect image information of the vehicle to be cleaned;

[0061] When a vehicle waiting to be washed enters the car wash machine, the machine uses multiple high-definition cameras deployed inside and outside to capture images of the vehicle's front, sides, rear, and other areas in all dimensions, simultaneously recording the vehicle's location information to provide complete visual data support for subsequent shape recognition.

[0062] S2. Using the image information of the vehicle to be cleaned, an edge algorithm is used to identify the vehicle's shape and obtain vehicle feature data results.

[0063] Based on the acquired vehicle images, edge computing algorithms are used to process the images in real time, identifying information such as vehicle model and body structure features. Finally, the feature data results of the corresponding vehicle are obtained, providing a basis for matching subsequent car wash operations.

[0064] S3. Perform vehicle shape simulation analysis based on the vehicle feature data results, and obtain the vehicle shape simulation analysis results;

[0065] Based on the obtained vehicle feature data, the vehicle's shape is simulated and calculated using a distributed AI intelligent analysis terminal. This simulates the dynamic contact trajectory between the car wash machine's top brush and the vehicle's curved surface, and outputs simulation analysis results including a top brush control point distribution map and control curves to ensure the accuracy of subsequent cleaning operations.

[0066] S4. Perform vehicle washing operation based on the vehicle shape simulation analysis results and obtain automatic car wash results;

[0067] The core controller of the car wash machine automatically matches the cleaning logic of the corresponding car model based on the simulation analysis results of the vehicle's shape, controls the brushes and other components to perform the cleaning operation, and monitors the cleaning status in real time through sensors and makes adjustments until the car wash is completed, and obtains the automatically cleaned vehicle result.

[0068] In summary, steps S1 to S4 rely on the multi-sensor hardware and distributed AI terminal of the car wash machine to first complete the acquisition of full-dimensional images of the vehicle, then accurately identify information such as vehicle model and body features through edge algorithms, and generate a suitable cleaning trajectory control curve by combining simulation models. Finally, customized cleaning is completed through dynamic monitoring and adjustment. This method not only solves the shortcomings of traditional car wash equipment in adapting to multiple vehicle models, but also improves the thoroughness of cleaning through precise trajectory and real-time adjustment, while avoiding the risk of vehicle damage, and achieves intelligent, efficient and safe automatic car wash results.

[0069] As one possible implementation, in the above embodiments, step S1 may specifically include the following steps:

[0070] S1-1. Use ultrasonic radar and through-beam sensors to detect and obtain the entry status of the vehicles to be cleaned.

[0071] The vehicle acquisition module of the car wash machine's core controller activates the ultrasonic radar and through-beam sensors to continuously scan and detect the entrance area of ​​the car wash machine. Through data such as distance signals and obstruction signals fed back by the sensors, it obtains basic data on the entry status of vehicles to be washed in real time, providing a basis for subsequent status judgment.

[0072] S1-2. Determine whether the entry status of the vehicle to be cleaned is already in the entry status. If yes, proceed to step S1-3. Otherwise, obtain the entry status of the vehicle to be cleaned as not in the entry status, adjust the vehicle to be cleaned, and return to step S1-1.

[0073] The core control module receives the collected sensor data and, according to the preset judgment rules, determines whether the vehicle to be cleaned has actually entered the designated car wash area. If the judgment result is that the vehicle has entered, it proceeds to the subsequent car wash area position judgment stage. If the judgment result is that the vehicle has not entered, it records the vehicle's entry status as "not entered" and simultaneously issues a command to adjust the position of the vehicle to be cleaned, controls the sensor to maintain continuous detection mode, and returns to re-collect entry status data until the vehicle to be cleaned is in the "entered" state.

[0074] S1-3. Based on the entry status of the vehicle to be cleaned, determine whether the vehicle to be cleaned has entered the car wash area. If so, trigger multiple high-definition cameras and capture images of the vehicle to be cleaned to obtain image information of the vehicle to be cleaned. Otherwise, adjust the vehicle to be cleaned to enter the car wash area and return to step S1-1.

[0075] After determining that the vehicle to be washed has entered the washing area, the core control module further combines sensor data to determine whether the vehicle has completely entered the preset washing area. If the position requirements are met, it immediately triggers multiple high-definition cameras deployed inside and outside the car wash machine to simultaneously capture images of the front, sides, and rear of the vehicle, completing the acquisition and storage of image information. If the vehicle has not entered the washing area, an adjustment command is issued to guide the vehicle into the washing area. After adjustment, the process returns to step S1-1. Two high-resolution cameras are installed inside the car wash machine, and two high-resolution cameras are installed outside the car wash machine.

[0076] In summary, steps S1-1 to S1-3 first utilize ultrasonic radar and through-beam sensors to acquire the vehicle's entry status, then determine whether it has entered the washing area. If the vehicle has not entered, continuous detection continues; if it has entered, further confirmation is made regarding whether the vehicle has entered the car wash area. Once the conditions are met, multiple high-definition cameras are triggered to complete image acquisition. This process achieves accurate perception of vehicle entry and automated triggering of image acquisition, avoiding invalid acquisition and false triggering, improving the response efficiency of the car wash device and the accuracy of data acquisition, and providing a reliable data foundation for subsequent vehicle shape recognition and simulation analysis.

[0077] As one possible implementation, in the above embodiments, step S2 may specifically include the following steps:

[0078] S2-1. Preprocess the image information of the vehicle to be cleaned to obtain the preprocessed image information of the vehicle to be cleaned.

[0079] This step is the preprocessing stage for vehicle shape recognition. It mainly involves a series of processing operations, such as noise reduction, grayscale conversion, and image enhancement, on the vehicle images captured by multiple high-definition cameras to eliminate errors caused by environmental interference and hardware defects. Specifically, noise reduction algorithms are first used to filter out interference information such as environmental noise and reflective spots in the image. Then, grayscale conversion and image enhancement processing are performed to improve the contrast between the vehicle's shape features and the background. Finally, a clear and effective preprocessed vehicle image is output, laying the data foundation for subsequent edge feature extraction.

[0080] S2-2. Based on the preprocessed image information of the vehicle to be cleaned, an edge algorithm is used to extract contour features to obtain the contour features of the vehicle to be cleaned.

[0081] Based on the preprocessed image, lightweight edge algorithms, such as the Canny algorithm and the Sobel operator, are used to scan the front, sides, and rear of the vehicle pixel by pixel. This accurately identifies and extracts key contour information such as edge lines and feature inflection points of the vehicle's shape, forming a vehicle contour feature dataset containing edge feature points and contour lines of the vehicle's shape. The edge algorithm first identifies areas with drastic grayscale changes in the image through gradient calculation, then retains the clearest edge lines through non-maximum suppression, and finally removes false edges through double thresholding. This accurately extracts key contour features such as the vehicle's outer contour, window frames, and tire edges, providing core data for subsequent contour fitting.

[0082] S2-3. Based on the contour features of the vehicle to be cleaned, perform cluster fitting to generate standardized vehicle shape contour data.

[0083] For the extracted vehicle contour feature dataset, a clustering algorithm (such as DBSCAN) is used to classify and aggregate discrete feature points, remove invalid noise features, and then the least squares method or Bézier curve is used to fit each group of feature points to generate a continuous and smooth vehicle shape contour curve. Subsequently, the fitted contour data is mapped to a preset standardized coordinate system to eliminate the size differences caused by different shooting angles and distances, and finally obtains standardized shape contour data that is proportional to the real vehicle, providing a unified benchmark for subsequent matching processing.

[0084] S2-4. The standardized vehicle outline data to be cleaned is combined with the vehicle feature database for matching processing to obtain vehicle feature data results.

[0085] The generated standardized shape contour data is matched with the vehicle feature database stored locally at the edge. By calculating the similarity score of the contour, the most matching vehicle template is quickly located. Then, the corresponding vehicle feature information is retrieved from the template, and finally, the complete vehicle feature data result is integrated and synchronized to the core controller of the car wash machine, providing an accurate basis for subsequent dynamic adaptation of the cleaning process.

[0086] In summary, steps S2-1 to S2-4 first perform preprocessing on the acquired original vehicle images, including noise reduction, grayscale conversion, and enhancement, to filter out interference information and improve image clarity. Then, based on the preprocessed images, an edge detection algorithm is called to extract the contour lines and contour feature points of the vehicle's shape. Subsequently, clustering fitting is used to transform the discrete feature points into smooth and continuous standardized shape contour data. Finally, the data is matched with a preset vehicle feature database to generate vehicle feature data results. This process, through phased and refined processing, significantly improves the accuracy of vehicle shape recognition, effectively avoids recognition errors caused by image noise and contour discrepancies, provides high-quality data support for subsequent vehicle shape simulation analysis, and thus ensures the reliability of the customized cleaning strategy.

[0087] As one possible implementation, in the above embodiments, step S2-4 may specifically include the following steps:

[0088] S2-4-1. Based on the standardized vehicle outline data to be cleaned, call the vehicle feature database;

[0089] After obtaining standardized vehicle outline data, the system triggers a call to the local vehicle feature database at the edge. This database pre-stores standardized outline templates for various common vehicle models, basic car wash control logic and corresponding vehicle parameters, body structure features and accessory configuration information. By calling this database, reference data is provided for subsequent outline matching, ensuring that the matching process has a clear reference basis and avoiding blind comparison without a benchmark.

[0090] S2-4-2. Match the standardized vehicle outline data to be cleaned with the vehicle feature database to obtain outline similarity.

[0091] Using contour matching algorithms, such as iterative nearest-point algorithm and template matching algorithm, the standardized shape contour data of the vehicle to be cleaned is compared one by one with all pre-stored vehicle contour templates in the database. By calculating indicators such as the overlap of contour curves, the number of matched key feature points, and the curve fitting degree, a quantified contour similarity value between the vehicle to be cleaned and each database template is generated. This step is the core link to achieve accurate vehicle identification. The quantified similarity index provides an objective and quantifiable basis for subsequent matching result judgments.

[0092] S2-4-3. Determine whether the contour similarity is greater than the preset contour similarity threshold. If so, obtain the vehicle model contour template based on the contour similarity and the vehicle feature database. Otherwise, update the vehicle feature database using the standardized vehicle shape contour data to be cleaned, and proceed to step S2-4-2.

[0093] The calculated contour similarity is compared with a preset threshold. If the similarity is higher than the threshold, it means that the current vehicle contour highly matches a certain vehicle model template already in the database, and that template can be directly retrieved to proceed to the next step of feature extraction. If the similarity is lower than the threshold, the current vehicle is determined to be a new vehicle model not included in the database. The system will automatically add its standardized contour data to the vehicle feature database and then re-execute the matching step. This design ensures rapid matching of existing vehicle models while also enabling dynamic iteration and self-learning of the database, continuously improving the system's compatibility with new vehicle models.

[0094] S2-4-4. Based on the vehicle model outline template, use the vehicle feature database to obtain vehicle feature data results;

[0095] Using a successfully matched vehicle profile template as an index, the system retrieves all associated information from the vehicle feature database, including the basic vehicle model (such as sedan, SUV, pickup truck), additional exterior features (such as sunroof, roof rack, spare tire), and car wash adaptation parameters. This information is then integrated to generate structured vehicle feature data. This result is directly synchronized to the car wash machine's core controller, providing precise decision-making support for subsequent dynamic adaptation of the washing process and ensuring a perfect match between the car wash action and the vehicle's exterior shape.

[0096] In summary, steps S2-4-1 to S2-4-4 call the vehicle feature database to calculate the similarity between the standardized vehicle outline data to be cleaned and the vehicle model templates in the database. Then, the matching result is judged based on a preset threshold. If the match is successful, the corresponding vehicle model outline template is directly obtained and the vehicle feature data result is generated. If the match fails, the new outline data is entered into the updated database and the matching is re-matched. This process realizes accurate vehicle model identification and dynamic iterative optimization of the database. It not only solves the problem of poor adaptability of traditional fixed template matching to niche vehicle models, but also improves the matching efficiency and accuracy through threshold judgment, providing high-quality and highly adaptable vehicle feature data support for subsequent simulation analysis.

[0097] As one possible implementation, in the above embodiments, step S3 may specifically include the following steps:

[0098] S3-1. Based on the vehicle feature data results and the vehicle feature database, obtain the vehicle shape data and basic car wash control logic of the vehicle to be cleaned.

[0099] Based on the identified vehicle feature data, the system performs deep correlation matching with the vehicle feature database, retrieving complete vehicle exterior data corresponding to the model from the database. This includes body surface parameters, spatial coordinates and dimensions of key components (sunroof, roof rack, wheel rims), and simultaneously extracts the pre-stored basic car wash control logic for the model from the database. This logic covers the appropriate brushing zone sequence, brushing pressure thresholds for different body areas, spray flow levels, and car wash brush movement speed range. The accurate retrieval of this data and logic provides a highly vehicle-matched basic input for subsequent 3D modeling and simulation analysis, ensuring the relevance and effectiveness of the simulation process.

[0100] S3-2. Using the vehicle shape data of the vehicle to be cleaned, construct a three-dimensional network model of the vehicle;

[0101] Based on the acquired detailed vehicle shape data, the built-in lightweight 3D modeling engine is invoked. The retrieved vehicle shape data is used as the modeling input, and the vehicle is meshed at a 1:1 scale. By fitting the complex curved surface structure of the vehicle body with triangular mesh surfaces, the spatial shape of the roof curvature, bumper corners, window frames, and additional components is accurately reproduced. At the same time, redundant details that do not affect the planning of the car wash brush's motion trajectory are eliminated. Finally, a lightweight 3D mesh model of the vehicle is generated. This model not only ensures the accuracy of the vehicle body shape reproduction but also takes into account the computational efficiency of subsequent simulation analysis, providing an intuitive 3D carrier for the simulation verification of the car wash control logic.

[0102] S2-3. Based on the vehicle's three-dimensional network model and the basic car wash control logic of the vehicle to be cleaned, perform vehicle shape simulation analysis and obtain the vehicle shape simulation analysis results.

[0103] Based on the existing 3D network model of the vehicle, combined with the basic car wash control logic of the vehicle to be cleaned, the contact trajectory, pressure distribution and movement path of the car wash brush and the vehicle body surface are simulated. JSON format data containing the top brush control point distribution map and control curves are generated to obtain the vehicle shape simulation analysis results, providing a basis for the precise control of the car wash machine in the future.

[0104] In summary, steps S3-1 to S3-3 firstly obtain the corresponding vehicle's exterior data and basic car wash control logic through deep matching of vehicle feature data with the database; then, based on this exterior data, a lightweight 3D vehicle network model that balances accuracy and efficiency is constructed; finally, relying on this model and combined with the basic car wash control logic, the contact trajectory, pressure distribution, and motion path of the car wash brush are simulated, generating JSON-formatted simulation results containing control point distribution maps and control curves. This series of processes not only ensures the targeting and adaptability of the car wash control logic, but also significantly improves the accuracy and operating efficiency of the car wash machine through simulation verification, effectively avoiding cleaning dead spots or equipment collision risks caused by improper vehicle model adaptation.

[0105] As one possible implementation, in the above embodiments, step S3-3 may specifically include the following steps:

[0106] S3-3-1. Import the vehicle's three-dimensional network model and the three-dimensional model of the car wash machine's execution part into the simulation environment, load the basic car wash control logic of the vehicle to be cleaned, and obtain the basic scene of the vehicle to be cleaned.

[0107] The completed 3D mesh model of the vehicle and the 3D models of the car wash machine's actuators (including the dimensions, structure, and motion constraints of key components such as the wash brushes, gantry, and spray assembly) are simultaneously imported into the edge-end lightweight simulation engine to realize the physical construction of the virtual car wash scene. At the same time, the basic car wash control logic precisely matched to the vehicle model is loaded. This logic includes preset brush washing zone order, brush pressure thresholds for each area, spray flow levels, and other core parameters. Through the collaborative loading of the model and control logic, a simulation base scene corresponding to the shape of the vehicle to be cleaned and the basic cleaning rules is built, providing a core carrier for the subsequent parameter setting and motion simulation of the car wash machine's actuators.

[0108] S3-3-2. Based on the vehicle's three-dimensional network model and the vehicle's basic car wash control logic, set avoidance rules for the car wash machine's execution components;

[0109] By combining the spatial coordinates of protruding body parts (such as roof racks, rearview mirrors, and rear spoilers) in the vehicle's 3D mesh model, and the protection requirements for sensitive areas of the body in the basic car wash control logic, we specifically set motion constraints (such as motion range boundaries and motion speed thresholds) and contact parameter thresholds (such as the upper limit of the contact pressure between the brush and the body) for the actuators such as car wash brushes and spray arms. At the same time, we clearly define the minimum safe distance, pressure control standards, and motion direction restriction rules when the actuators approach protruding body parts and sensitive paint areas. From the perspective of simulation parameters, we avoid the risk of hard collisions between car wash actuators and the body, ensuring the safety and rationality of the subsequent vehicle shape simulation analysis process.

[0110] S3-3-3. Based on the basic scenario of the vehicle to be cleaned and the avoidance rules of the car wash machine's execution components, set the motion parameters of the car wash machine's execution components;

[0111] Based on the established simulation scenario, and following the pre-defined avoidance rules for the actuators, the motion parameters of the core actuators of the car wash machine are initialized. These parameters include the swing angle range, extension stroke range, and motion speed gradient of the car wash brush, as well as the movement path and start / stop nodes of the gantry. This ensures that the motion parameters of each actuator meet both the cleaning requirements of the basic car wash control logic and the safety constraints of the avoidance rules.

[0112] S3-3-4. Based on the basic scenario of the vehicle to be cleaned and the basic car wash control logic of the vehicle to be cleaned, combined with the motion parameters of the car wash machine's execution components, an operational simulation algorithm is used to obtain the simulation results of the vehicle to be cleaned.

[0113] In the established simulation scenario, the basic car wash control logic and the initial motion parameters of the execution components (such as correcting the brush roller trajectory coordinates and adjusting the upper limit of contact pressure) are integrated. The kinematic simulation algorithm is called to drive the execution components of the car wash machine to simulate the dynamic contact trajectory between the top brush of the car wash machine and the curve of the car body. The motion trajectory of the car wash brush, the contact pressure with the car body, and the coverage of the cleaning area are recorded in real time. Finally, the initial simulation results containing trajectory curves, pressure distribution, and coverage range are generated.

[0114] S3-3-5. Determine whether the simulation result of the vehicle to be cleaned conforms to the avoidance rules of the car wash machine's actuator. If so, use the simulation result of the vehicle to be cleaned to obtain the vehicle shape simulation analysis result. Otherwise, adjust the motion parameters of the car wash machine's actuator based on the simulation result of the vehicle to be cleaned and the avoidance rules of the car wash machine's actuator, and execute step S3-3-4.

[0115] The initial simulation results are compared with the preset avoidance rules for the actuators to determine whether the car wash brush's trajectory meets the safety distance requirements and whether the contact pressure is below the threshold limit. If the simulation results fully comply with the avoidance rules, the trajectory, pressure, coverage, and other data are directly integrated to generate vehicle shape simulation analysis results containing a top brush control point distribution map and control curve JSON format data. If the simulation results violate the avoidance rules, the motion parameters such as the speed, extension stroke, or gantry movement path of the car wash machine's actuators are adjusted based on the location and type of the violation area. Then, the kinematic simulation analysis is re-executed until the simulation results meet all avoidance rule requirements.

[0116] In summary, steps S3-3-1 to S3-3-5 first import the vehicle's 3D mesh model, the 3D model of the car wash machine's execution components, and the basic car wash control logic into the simulation environment to build a basic scenario. Then, based on the vehicle body features and sensitive area protection requirements, avoidance rules for the car wash machine's execution components are set. Subsequently, the motion parameters of the execution components are initialized according to the basic scenario and avoidance rules. Initial simulation results are generated through kinematic simulation algorithms. Finally, based on whether the simulation results conform to the avoidance rules, it is decided whether to directly output the results or iteratively update the motion parameters and re-simulate. This process, through the collaborative import of the 3D model and control logic, constructs a simulation environment consistent with the actual car wash scenario, ensuring the authenticity of the simulation analysis. The targeted setting of avoidance rules avoids the collision risk between the car wash brush and protruding parts and sensitive areas of the vehicle body from the source. The iterative optimization mechanism of motion parameters achieves precise calibration of the car wash brush's motion trajectory and pressure parameters, effectively eliminating cleaning blind spots. The final output simulation analysis results can provide the car wash machine with optimal execution parameters, significantly improving the intelligence and safety of automatic car washing, while also improving cleaning efficiency and vehicle adaptability.

[0117] As one possible implementation, in the above embodiments, step S4 may specifically include the following steps:

[0118] S4-1. Analyze the simulation analysis results of the vehicle's shape to generate a set of cleaning execution instructions for the car wash machine;

[0119] After receiving the vehicle shape simulation analysis results, the core controller of the car wash machine first analyzes the structured data, including the optimized motion trajectory, contact pressure threshold of the actuator, action timing table, and avoidance rules. It then converts the virtual parameters at the simulation level into instructions that can directly drive the hardware, generating a set of car wash machine cleaning execution instructions that includes spray flow level, brush motion coordinates, action triggering timing, and safety avoidance logic. This provides a precise control basis for subsequent actual cleaning operations.

[0120] S4-2. Control the execution components of the car wash machine according to the cleaning execution instruction set of the car wash machine, and collect the target data of the car wash process in real time;

[0121] The core controller of the car wash machine, based on the cleaning execution instruction set, sends control signals to the brushes, spray arms and other execution components of the car wash machine, driving each component to act according to preset timing and parameters. At the same time, through pressure sensors, position sensors and flow sensors on the car wash machine body, it collects target data such as brush contact pressure, spray flow rate, vehicle position coordinates and component movement speed in real time during the car wash process, providing real-time feedback for subsequent closed-loop verification.

[0122] S4-3. Fit the target data of the car wash process with the vehicle shape simulation analysis results to obtain the car wash control results;

[0123] The core controller of the car wash machine will collect the target data of the car wash process in real time and compare it with the preset parameter thresholds, motion trajectory curves and pressure change models in the vehicle shape simulation analysis results. By calculating the deviation value between the actual data and the simulation data, it will generate car wash control results including quantitative indicators such as trajectory overlap, pressure compliance rate and timing matching degree, so as to evaluate the degree of fit between the actual cleaning action and the simulation plan.

[0124] S4-4. Determine whether the car wash control result is normal. If it is normal, obtain the car wash status of the vehicle to be washed and execute step S4-5. Otherwise, adjust the motion parameters of the car wash machine's execution component according to the car wash control result and return to execute step S3-3-4.

[0125] The core controller of the car wash machine compares various indicators in the car wash control results with preset normal operating thresholds. If indicators such as trajectory overlap and pressure compliance rate are within the acceptable range, the car wash control result is determined to be normal. The current car wash status of the vehicle to be washed is then acquired, and subsequent operations are initiated. If the result is abnormal, the motion parameters of the car wash machine's actuators are automatically adjusted based on the deviation data in the car wash control results. The process then returns to step S3-3-4 to re-execute the kinematic simulation analysis. The car wash status of the vehicle to be washed includes both an incomplete washing state and a completed washing state.

[0126] S4-5. Determine whether the car wash status of the vehicle to be washed is the washing completed state. If yes, obtain the washing completed state of the vehicle to be washed as the automatic car wash result. Otherwise, mark the vehicle to be washed as not washing completed state, update the car wash machine's washing execution instruction set according to the not washing completed state of the vehicle to be washed, and execute step S4-2.

[0127] If the car wash control result is normal, the core controller of the car wash machine will determine whether the vehicle to be washed has completed all the washing processes. If it has, the washing completion status will be output as the final automatic car wash result. If it is determined that the vehicle is still in the state of not completing the washing, it will be marked as not cleaning completed. The washing machine's washing execution instruction set will be dynamically updated by combining the current washing progress and the target data collected in real time. Then, it will return to the execution step S4-2 to re-execute the car wash machine's execution component control and data acquisition operations to continue to advance the remaining washing process.

[0128] In summary, steps S4-1 to S4-5 generate a cleaning execution instruction set by analyzing the vehicle's exterior shape simulation results. This sets the instructions to drive the car wash machine's components and collect process data in real time. The actual data is then compared with the simulation results using a closed-loop fitting verification. Through state determination and parameter adjustment, a closed-loop control system encompassing simulation, execution, verification, and optimization is achieved. This process, through the closed-loop linkage between simulation and actual operation, ensures the accuracy and adaptive adjustment of car wash control, effectively guaranteeing the consistency between the cleaning actions and the simulation plan, and improving the reliability and cleaning quality of the automatic car wash.

[0129] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of an automatic car wash device, which is similar to... Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0130] like Figure 2 As shown, an automatic car wash device in this embodiment includes: a car wash machine core controller and a distributed AI intelligent analysis terminal. The car wash machine core controller includes a vehicle acquisition module and a core control module, and the distributed AI intelligent analysis terminal includes a feature recognition analysis module and a simulation analysis module.

[0131] The vehicle acquisition module is used to acquire the vehicle to be cleaned and to collect image information of the vehicle to be cleaned.

[0132] This module serves as the data entry point for the device. It is responsible for sensing the entry status of vehicles to be cleaned by relying on the ultrasonic radar, through-beam sensors, and other hardware configured in the car wash machine. When a vehicle is detected entering the car wash area, it triggers multiple high-definition cameras deployed inside and outside the car wash machine to complete the collection and preliminary preprocessing of the vehicle's full-dimensional image information, providing a complete and effective visual data foundation for subsequent shape recognition.

[0133] The feature recognition and analysis module is used to use the image information of the vehicle to be cleaned to perform vehicle shape recognition using an edge algorithm, and obtain vehicle feature data results.

[0134] This module is the device's intelligent recognition unit, equipped with an edge computing model. After receiving image information from the vehicle acquisition module, it uses edge computing technology to analyze the vehicle's shape in real time, accurately identifying key information such as vehicle type (e.g., sedan, SUV) and body structure features (e.g., whether it has a sunroof or roof rack). It generates standardized vehicle feature data results and feeds them back to the core control module, providing a core basis for subsequent simulation analysis.

[0135] The simulation analysis module is used to perform vehicle shape simulation analysis based on the vehicle feature data results and obtain vehicle shape simulation analysis results.

[0136] This module is the strategy generation unit of the device. Based on the vehicle feature data output by the feature recognition and analysis module, it runs the vehicle shape simulation model, constructs a 1:1 vehicle three-dimensional mesh model, and uses the operation simulation algorithm to simulate the dynamic contact trajectory between the car wash machine top brush and the vehicle body surface. Finally, it generates simulation analysis results including the top brush control point distribution map and control curve, providing the core control module with a customized cleaning strategy adapted to the vehicle.

[0137] The core control module is used to perform vehicle washing operations based on the vehicle shape simulation analysis results and obtain automatic car wash results.

[0138] This module is the scheduling center of the device, responsible for the process coordination and instruction issuance of various modules. It receives image data from the vehicle acquisition module and vehicle feature results from the feature recognition and analysis module, and issues simulation requests to the simulation analysis module. Finally, based on the simulation analysis results, it controls the washing machine's execution components, such as brushes, to perform the corresponding cleaning operations. At the same time, it combines the cleaning status feedback from sensors to realize dynamic parameter adjustment, ensuring the orderly operation of the car wash process.

[0139] In summary, this automatic car wash device uses a core controller and a distributed AI intelligent analysis terminal as its core architecture. The vehicle acquisition module detects the vehicle's entry and acquires full-dimensional images. The edge detection algorithm of the feature recognition and analysis module accurately identifies the vehicle's external features. The simulation analysis module then constructs a 3D model of the vehicle based on the recognition results and generates a customized cleaning trajectory. Finally, the core control module coordinates the collaborative operation of all modules and drives the execution components to complete the cleaning process. This device breaks away from the limitations of traditional car wash equipment's fixed processes, achieving intelligent adaptation to multiple vehicle models. It improves cleaning efficiency and cleanliness while avoiding vehicle damage through precise trajectory planning, achieving efficient, safe, and intelligent car wash technology.

[0140] In this embodiment, the specific processing of an automatic car wash device and its resulting technical effects can be referred to separately. Figure 1 The relevant descriptions of steps S1, S2, S3 and S4 in the corresponding embodiments will not be repeated here.

[0141] It should be noted that the implementation details and technical effects of each module and unit in the device provided in the embodiments of this disclosure can be referred to the description of other embodiments in this disclosure, and will not be repeated here.

[0142] The following is for reference. Figure 3 It shows a schematic diagram of the structure of a computer system 500 suitable for implementing the electronic device of the present disclosure. Figure 3 The computer system 500 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0143] like Figure 3 As shown, the computer system 500 may include a processing device 501 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM 502 or a program loaded from storage device 508 into random access RAM 503. RAM 503 also stores various programs and data required for the operation of the computer system 500. The processing device 501, ROM 502, and RAM 503 are interconnected via bus 504. I / O interface 505 is also connected to bus 504.

[0144] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows computer system 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 A computer system 500 with various electronic 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.

[0145] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure 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 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0146] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. 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 disclosure, 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 disclosure, 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.

[0147] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0148] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the following functions: Figure 1 The illustrated embodiments and their alternative implementations demonstrate an automatic car wash method.

[0149] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language 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).

[0150] 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 disclosure. 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.

[0151] The units or modules described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units or modules do not necessarily limit the unit itself; for example, an acquisition module can also be described as "acquiring preset prompts, including modality fusion prompts, attention mechanism prompts, and / or time-related prompts."

[0152] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure 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 disclosed in this disclosure that have similar functions.

Claims

1. An automatic car wash method, characterized in that, include: S1. Obtain the vehicle to be cleaned and collect image information of the vehicle to be cleaned; S2. Using the image information of the vehicle to be cleaned, an edge algorithm is used to identify the vehicle's shape and obtain vehicle feature data results. S3. Perform vehicle shape simulation analysis based on the vehicle feature data results, and obtain the vehicle shape simulation analysis results; S4. Perform vehicle washing operation based on the vehicle shape simulation analysis results to obtain automatic car wash results.

2. The automatic car wash method according to claim 1, characterized in that, S1. Obtain the vehicle to be cleaned and acquire its image information, including: The ultrasonic radar and through-beam sensors are used to detect and obtain the entry status of the vehicles to be cleaned. Determine whether the entry status of the vehicle to be cleaned is already in the entry status. If so, execute the first operation; otherwise, obtain the entry status of the vehicle to be cleaned as not in the entry status, adjust the vehicle to be cleaned, and execute the second operation. The first operation is as follows: based on the entry status of the vehicle to be cleaned, determine whether the vehicle to be cleaned has entered the car wash area. If so, trigger multiple high-definition cameras and capture images of the vehicle to be cleaned to obtain image information of the vehicle to be cleaned. Otherwise, adjust the vehicle to be cleaned to enter the car wash area and execute the second operation. The second operation is to use ultrasonic radar and a beam sensor to detect and obtain the entry status of the vehicle to be cleaned.

3. The automatic car wash method according to claim 1, characterized in that, S2. Using the image information of the vehicle to be cleaned, an edge algorithm is used to recognize the vehicle's shape, and vehicle feature data results are obtained, including: The image information of the vehicle to be cleaned is preprocessed to obtain the preprocessed image information of the vehicle to be cleaned. Based on the preprocessed image information of the vehicle to be cleaned, an edge algorithm is used to extract contour features to obtain the contour features of the vehicle to be cleaned. Clustering and fitting are performed based on the contour features of the vehicles to be cleaned to generate standardized vehicle shape contour data. The standardized vehicle outline data to be cleaned is combined with a vehicle feature database for matching processing to obtain vehicle feature data results.

4. The automatic car wash method according to claim 3, characterized in that, The standardized vehicle outline data to be cleaned is combined with a vehicle feature database for matching processing to obtain vehicle feature data results, including: Based on the standardized vehicle outline data to be cleaned, the vehicle feature database is called. The standardized vehicle outline data to be cleaned is matched with the vehicle feature database to obtain outline similarity. Determine whether the contour similarity is greater than a preset contour similarity threshold. If so, obtain a vehicle model contour template based on the contour similarity and the vehicle feature database. Otherwise, update the vehicle feature database using the standardized vehicle shape contour data to be cleaned and perform the third operation. Based on the vehicle model outline template, the vehicle feature database is used to obtain vehicle feature data results; The third operation involves matching the standardized vehicle outline data to be cleaned with the vehicle feature database to obtain outline similarity.

5. An automatic car wash method according to claim 3, characterized in that, S3. Based on the vehicle feature data results, perform vehicle shape simulation analysis to obtain vehicle shape simulation analysis results, including: Based on the vehicle feature data results and the vehicle feature database, obtain the vehicle shape data and basic car wash control logic of the vehicle to be cleaned. Using the vehicle shape data of the vehicle to be cleaned, a three-dimensional network model of the vehicle is constructed. Based on the vehicle's three-dimensional network model and the basic car wash control logic of the vehicle to be cleaned, a vehicle shape simulation analysis is performed to obtain the vehicle shape simulation analysis results.

6. The automatic car wash method according to claim 5, characterized in that, Based on the vehicle's 3D network model and the basic car wash control logic of the vehicle to be cleaned, a vehicle shape simulation analysis is performed to obtain the vehicle shape simulation analysis results, including: Import the vehicle's 3D network model and the 3D model of the car wash machine's execution part into the simulation environment, load the basic car wash control logic of the vehicle to be cleaned, and obtain the basic scene of the vehicle to be cleaned. Based on the vehicle's three-dimensional network model and the vehicle's basic car wash control logic, avoidance rules are set for the car wash machine's execution components. Based on the basic scenario of the vehicle to be cleaned and the avoidance rules of the car wash machine's actuators, the motion parameters of the car wash machine's actuators are set. Based on the basic scenario of the vehicle to be cleaned and the basic car wash control logic of the vehicle to be cleaned, combined with the motion parameters of the car wash machine's execution components, an operational simulation algorithm is used to obtain the simulation results of the vehicle to be cleaned. Determine whether the simulation results of the vehicle to be cleaned conform to the avoidance rules of the car wash machine's actuator. If so, use the simulation results of the vehicle to be cleaned to obtain the vehicle shape simulation analysis results. Otherwise, based on the simulation results of the vehicle to be cleaned and the avoidance rules of the car wash machine's actuator, adjust the motion parameters of the car wash machine's actuator and execute the fourth operation. The fourth operation is as follows: based on the basic scenario of the vehicle to be cleaned and the basic car wash control logic of the vehicle to be cleaned, combined with the motion parameters of the car wash machine's execution components, an operational simulation algorithm is used to obtain the simulation results of the vehicle to be cleaned.

7. An automatic car wash method according to claim 6, characterized in that, S4. Based on the vehicle shape simulation analysis results, perform a vehicle washing operation to obtain the automatic car wash results, including: The simulation analysis results of the vehicle's exterior shape are analyzed to generate a set of cleaning execution instructions for the car wash machine; The car wash machine's execution components are controlled according to the car wash machine's cleaning execution instruction set, and target data of the car wash process are collected in real time. The target data of the car wash process is fitted with the vehicle shape simulation analysis results to obtain the car wash control results. Determine whether the car wash control result is normal. If it is normal, obtain the car wash status of the vehicle to be washed and execute the fifth operation. Otherwise, adjust the motion parameters of the car wash machine's execution component according to the car wash control result and execute the fourth operation. The fifth operation is as follows: determine whether the car wash status of the vehicle to be washed is the washing completed state. If so, obtain the washing completed state of the vehicle to be washed as the automatic car wash result. Otherwise, mark the vehicle to be washed as not washing completed state, update the washing execution instruction set of the car wash machine according to the not washing completed state of the vehicle to be washed, and execute the sixth operation. The sixth operation is to control the execution components of the car wash machine according to the cleaning execution instruction set of the car wash machine, and to collect target data of the car wash process in real time.

8. An automatic car wash device, employing the method as described in any one of claims 1-7, characterized in that, include: The car wash machine core controller and the distributed AI intelligent analysis terminal include a vehicle acquisition module and a core control module, and the distributed AI intelligent analysis terminal includes a feature recognition analysis module and a simulation analysis module. The vehicle acquisition module is used to acquire the vehicle to be cleaned and to collect image information of the vehicle to be cleaned. The feature recognition and analysis module is used to use the image information of the vehicle to be cleaned to perform vehicle shape recognition using an edge algorithm, and obtain vehicle feature data results. The simulation analysis module is used to perform vehicle shape simulation analysis based on the vehicle feature data results and obtain vehicle shape simulation analysis results. The core control module is used to perform vehicle washing operations based on the vehicle shape simulation analysis results and obtain automatic car wash results.

9. An electronic device, characterized in that, include: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by one or more processors, implements the method as described in any one of claims 1-7.

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