Unattended car washer

Abstract

The invention discloses an unattended vehicle washing machine, which relates to the technical field of unmanned vehicle washing and comprises a vehicle detection and identification module, an environment monitoring and interference compensation module, an intelligent control and adaptive adjustment module, a sensor self-inspection and fault diagnosis module and a real-time correction and data feedback module, a high-definition camera and various sensors are used for carrying out preliminary detection and recognition on a vehicle entering the vehicle washing machine, automatic calibration of the vehicle is carried out, and the recognition precision of the sensors is ensured. Through environment perception and self-adaptive algorithm optimization, the vehicle washing system can efficiently operate under various environment conditions, the water pressure, the scrubbing angle and the cleaning time are adjusted in real time, precise cleaning is ensured, and vehicle damage is reduced. Through self-learning optimization, the system reduces the maintenance demand, prolongs the service life of equipment, reduces the maintenance frequency, reduces the operation cost, improves the rate of return on investment, and guarantees the continuous and stable service.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned car washing, and in particular to an unmanned car washing machine. Background Art

[0002] With the rapid development of the new energy vehicle industry, its unique structural design and usage characteristics have put forward new requirements for car washing equipment. Compared with traditional fuel vehicles, new energy vehicles have a power battery pack arranged in the chassis, which requires higher waterproof protection; the body often adopts innovative designs such as hidden door handles and streamlined appearance, which significantly increases the difficulty of cleaning.

[0003] Unattended car wash machines are self-service car wash equipment based on automation technology and Internet technology, which can provide cleaning services for vehicles without manual operation. Users select the required car wash service type, cleaning method, payment method, etc. through the self-service operation interface or mobile application, and the system automatically completes the steps of car body cleaning, brushing, rinsing, drying, etc. Its core technologies usually include intelligent sensors, automatic control systems, spray devices, drying systems, etc., which can be adaptively adjusted according to the different sizes and dirtiness of new energy vehicles to achieve an efficient, water-saving and environmentally friendly car washing process. In addition, Internet connection allows users to monitor the progress of car washing, query historical records, and pay fees in real time, and the management can remotely monitor the status of the equipment, perform fault diagnosis and maintenance, and further improve operational efficiency and customer experience.

[0004] The prior art has the following deficiencies:

[0005] In unattended car washes, sensor misjudgment may cause damage to the vehicle during the washing process. When the sensor is disturbed by the external environment (such as dust, dirt or rain) when detecting the vehicle's position, size or washing area, it may not be able to accurately judge the boundaries or shape of the vehicle. For example, the sensor mistakenly identifies a part of the vehicle as an obstacle, causing the washing system to clean with too high water pressure or an inappropriate brushing angle. This misjudgment may cause paint peeling, glass breakage or damage to exterior trim, and may even cause more serious mechanical failures, such as nozzle damage or body deformation. Due to the lack of manual supervision of unattended car washes, if the system does not detect and alarm in time, damage to the vehicle may cause users to face high compensation costs and seriously affect the credibility of the operator.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide an unattended car washing machine. Through dynamic sensor adjustment and adaptive control optimization of environmental perception, the car washing system operates efficiently and safely under various environmental conditions, monitors humidity, temperature and dust concentration in real time, and automatically adjusts water pressure, brushing angle and time to ensure accurate cleaning, reduce vehicle damage, and improve system stability and user experience. At the same time, the system reduces maintenance requirements, extends equipment life, reduces maintenance frequency and operating costs, improves return on investment, and ensures continuous and efficient service through self-learning and optimized feedback mechanisms to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: an unattended car washing machine, including a vehicle detection and identification module, an environment monitoring and interference compensation module, an intelligent control and adaptive adjustment module, a sensor self-test and fault diagnosis module, and a real-time correction and data feedback module:

[0009] The vehicle detection and identification module uses high-definition cameras and multiple sensors to perform preliminary detection and identification of vehicles entering the car wash machine, and automatically calibrates the vehicle to ensure the recognition accuracy of the sensor;

[0010] The environment monitoring and interference compensation module uses environmental sensors to monitor various interference factors in the working environment of the car washing machine in real time, and adjusts the sensor's sensing threshold through an adaptive algorithm to avoid the influence of the external environment on the sensor's detection accuracy;

[0011] Intelligent control and adaptive adjustment module, based on the acquired vehicle information and environmental interference data, uses an intelligent control system to adjust the working mode of the car wash machine to ensure accurate and safe vehicle washing under different environmental conditions;

[0012] The sensor self-check and fault diagnosis module performs regular self-checks on the sensor and determines whether the sensor is faulty based on the feedback from the intelligent control system. If an abnormality is found, an alarm is issued in a timely manner to ensure data accuracy during the car wash process.

[0013] The real-time correction and data feedback module monitors the sensor feedback data in real time during the car wash process. If an abnormality is detected, it will be automatically adjusted through the built-in correction algorithm, and the correction process will be uploaded to the cloud in real time for data analysis and feedback to the user, ensuring that the entire car wash process meets expectations and avoids damage to the vehicle.

[0014] Preferably, the specific steps of using a high-definition camera and a variety of sensors to perform preliminary detection and identification of vehicles entering the car wash machine and automatically calibrate the vehicle to ensure the recognition accuracy of the sensors are as follows:

[0015] By activating high-definition cameras and multiple sensors, the vehicle entering the car wash machine begins to be initially detected and located, preparing for subsequent identification and cleaning;

[0016] Combine image processing and sensor data to accurately measure the vehicle's shape, size and obstacle location, providing key information for subsequent cleaning;

[0017] Automatically calibrate the sensor based on the acquired vehicle information and adjust the vehicle position to ensure that the cleaning work is carried out in the best position;

[0018] Through multiple verifications and optimization of sensor accuracy, accurate vehicle identification is ensured, and sensor parameters are adjusted according to environmental changes.

[0019] Preferably, the specific steps of using environmental sensors to monitor various interference factors in the working environment of the car washing machine in real time and adjusting the sensing threshold of the sensor through an adaptive algorithm to avoid the influence of the external environment on the detection accuracy of the sensor are as follows:

[0020] Activate environmental sensors to monitor external environmental factors in real time and provide basic data for subsequent environmental interference compensation;

[0021] Analyze the data collected by environmental sensors to determine whether there are external interference factors that affect the sensor's detection accuracy, and prepare compensation mechanisms;

[0022] Automatically adjust the sensor's sensing threshold through an adaptive algorithm to adapt to environmental changes, ensuring that the sensor still works accurately under adverse conditions;

[0023] The vehicle cleaning process is optimized based on the adjusted sensor parameters, and the impact of environmental changes on system performance is continuously learned and recorded to optimize the adaptive algorithm.

[0024] Preferably, based on the acquired vehicle information and environmental interference data, the specific steps of using an intelligent control system to adjust the working mode of the car washing machine to ensure accurate and safe vehicle washing under different environmental conditions are as follows:

[0025] Combine vehicle information with environmental data for analysis to provide basic data for adjusting the working mode of the car wash machine and ensure accurate judgment of cleaning needs;

[0026] Automatically adjust water pressure, brushing angle and cleaning time based on analysis results to adapt to different environmental conditions and vehicle types to ensure the best cleaning effect;

[0027] Real-time monitoring of sensor feedback data during the washing process, timely adjustment of water pressure and brushing angle to maintain the accuracy and stability of the car washing process;

[0028] By learning the feedback data from each cleaning and optimizing the control algorithm, the ability to adapt to different environments and vehicle models is continuously improved.

[0029] Preferably, during the self-check of the sensor, the actual measured signal output is compared with the preset normal range value through a fault detection algorithm to determine whether there is an abnormality. If the actual signal output deviates from the normal operating range, the degree of abnormality of the sensor is diagnosed through calculation, a fault diagnosis parameter is generated, and it is evaluated in combination with the time change trend. The calculation expression is as follows:

[0030]

[0031] , where P fault is the sensor fault detection parameter, S actual (t i ) is the actual signal output at the i-th time point t, S normal (t i ) is the preset normal signal output at the i-th time point t, and n is the total number of time points;

[0032] If it is determined that the sensor has a fault, the intelligent control system will adjust the output signal of the sensor according to the sensor fault detection parameter P fault , and correct the signal output by adjusting the algorithm and perform weighted correction on the signal output. The correction formula is as follows: S corrected (t i ) = S actual (t i ) - k·P fault , where k is the correction factor S corrected (t) is the corrected signal output at the i-th time point t;

[0033] Finally, subsequent monitoring and alarm processing are performed according to the corrected signal. If the sensor fault cannot be repaired, it is judged whether the accuracy requirements of the car washing process can still be met according to the corrected parameters. If not, an alarm signal will be generated. The generation formula is as follows:

[0034] 烒:

[0035]

[0036] , where P alarm is the alarm signal parameter, and S threshold is the preset lowest effective signal threshold.

[0037] Preferably, during the car washing process, the data feedback by the sensor is monitored in real time. If an abnormality is detected, it is automatically adjusted through a built-in correction algorithm, and the correction process is uploaded to the cloud for data analysis and feedback to the user in real time to ensure that the entire car washing process meets the expectations and avoids damaging the vehicle. The specific steps are as follows:

[0038] During the car wash process, the sensor collects working environment data in real time and transmits it to the control system. The control system first detects the acquired data to identify whether there is any abnormality. At this time, the deviation of the water flow pressure from the set standard value and the difference between the brushing angle and the ideal angle are calculated. The calculation expression is as follows: ΔP = P actual -P target , Δθ=θ actual -θ target , where ΔP is the deviation of water flow pressure, P actual is the current actual water flow pressure, P target is the ideal water flow pressure, Δθ is the deviation of the scrubbing angle, θ actual is the current actual scrubbing angle, θ target It is the ideal brushing angle to set;

[0039] Once the water pressure and scrubbing angle are detected to be out of tolerance, a correction calculation is performed to adjust the water pressure and scrubbing angle according to the current deviation value. At this time, an adaptive correction coefficient is introduced and optimized in combination with the current environmental data to ensure smoothness and stability of the correction process. The expression is as follows: P new =P actual -k P ΔP, θ new =θ actual -k θ ·Δθ, where P new is the corrected water flow pressure, k P is the water flow pressure correction coefficient, θ new is the corrected scrubbing angle, k θ is the brushing angle correction factor;

[0040] After the initial correction is completed, the corrected water flow pressure and brushing angle are recalculated to confirm whether they meet the expected goals. All correction calculation results will be uploaded to the cloud in real time for data analysis and recording. At the same time, the correction process and real-time data will be fed back to the user through the network to ensure that the user understands the current car washing progress and correction status. The formula is as follows: ΔP final =P new -P target , Δθ final =θ new -θ target , where ΔP final is the final water flow pressure deviation, Δθ final is the final brushing angle deviation;

[0041] After the correction process is completed, the cloud will collect all the data in the correction process for in-depth analysis. Through machine learning and big data analysis, the correction algorithm and control parameters will be continuously optimized to improve the efficiency and accuracy of the subsequent cleaning process. The expression is as follows:

[0042]

[0043] , where δP optimized is the optimized water flow pressure, w j is the data weight coefficient, P new，j is the water flow pressure after the jth correction, δθ optimized is the optimized scrubbing angle, θ new，j is the brushing angle after the jth correction, and N is the number of corrections.

[0044] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0045] The present invention uses dynamic sensor adjustment based on environmental perception and adaptive control algorithm optimization, so that the car wash system can maintain efficient and safe operation under various environmental conditions. Environmental sensors monitor external factors such as humidity, temperature, dust concentration in real time, and dynamically adjust parameters such as water pressure, scrubbing angle and cleaning time to ensure the accuracy and thoroughness of the vehicle cleaning process. This adaptive adjustment can effectively cope with changes in different weather, seasons and vehicle models, avoid improper operations caused by environmental changes or vehicle characteristics, significantly improve the stability and cleaning effect of the system, reduce the risk of vehicle damage and improve user experience, while improving the long-term stability and efficiency of the system.

[0046] The present invention continuously optimizes sensor parameters and control algorithms through self-learning and optimization feedback mechanisms, significantly reducing equipment maintenance requirements and extending equipment service life. Through real-time monitoring and adjustment, the system can effectively avoid failures caused by environmental interference or equipment aging, reduce maintenance frequency and downtime, and reduce operating costs. With the accumulation of data and algorithm optimization, the operating efficiency of the system continues to improve, further improving the return on investment of the car washing machine, while ensuring that the equipment can continue to work efficiently and provide more stable and economical services. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0048] Figure 1 The figure is a schematic diagram of a module of an unattended car washing machine according to the present invention. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0050] The present invention provides Figure 1 An unattended car washing machine shown includes a vehicle detection and identification module, an environment monitoring and interference compensation module, an intelligent control and adaptive adjustment module, a sensor self-check and fault diagnosis module, and a real-time correction and data feedback module:

[0051] The vehicle detection and identification module uses high-definition cameras and multiple sensors (including ultrasonic sensors, infrared sensors, etc.) to perform preliminary detection and identification of vehicles entering the car wash machine, obtain the location, shape and size information of the vehicle, and automatically calibrate the vehicle to ensure the recognition accuracy of the sensor;

[0052] The specific steps to use high-definition cameras and multiple sensors (including ultrasonic sensors, infrared sensors, etc.) to perform preliminary detection and identification of vehicles entering the car wash machine, obtain the location, shape and size information of the vehicle, and automatically calibrate the vehicle to ensure the recognition accuracy of the sensor are as follows:

[0053] By activating high-definition cameras and multiple sensors, the vehicle entering the car wash machine begins to be initially detected and located, preparing for subsequent identification and cleaning;

[0054] When a vehicle enters the working area of ​​the car wash machine, high-definition cameras and various sensors (such as ultrasonic sensors, infrared sensors, etc.) will start working. The system automatically recognizes the signal of the vehicle entering and activates all relevant sensors and camera equipment. These sensors will first perform an environmental scan to determine the initial position and distance of the vehicle. In particular, the infrared sensor will help monitor the outer contour of the vehicle. At this time, the camera will also automatically aim at the front, side and top of the vehicle, capture the image of the vehicle in real time, and transmit it to the control system. The ultrasonic sensor will provide more detailed distance data to ensure accurate identification of the edges and obstacles of the vehicle.

[0055] The key to this stage is to ensure the activation of the sensor system and the accurate positioning of the vehicle. The combination of cameras and ultrasonic sensors can establish the distance relationship between the vehicle and the car wash machine in real time, preparing for subsequent accurate detection and cleaning. If some sensors cannot detect the vehicle during the initial startup, an alarm will be triggered to avoid system errors or vehicles not being recognized. In addition, infrared sensors can detect objects without being affected by light, avoiding interference with recognition caused by strong light during the day or at night.

[0056] Combine image processing and sensor data to accurately measure the vehicle's shape, size and obstacle location, providing key information for subsequent cleaning;

[0057] Through the images captured by the high-definition camera, the system will accurately measure the shape and size of the vehicle. Combined with the reflected signal of the ultrasonic sensor, the system can not only obtain the length, width and height of the vehicle, but also determine the position of the wheels, roof and other important parts through the infrared sensor. These data will be transmitted to the central processing unit for analysis and processing to identify the type of vehicle (such as sedans, SUVs, trucks, etc.). The process also includes the judgment of whether there are obstacles on the body of the vehicle (such as roof racks, rearview mirrors and other protrusions), so as to provide data support for selecting the best water flow pressure and brushing angle for subsequent cleaning work.

[0058] The core of this process is to ensure that the system conducts a comprehensive evaluation of the vehicle's multi-dimensional data. By integrating the camera's image processing technology and the sensor's real-time data, very accurate vehicle contour and size information can be obtained. In order to reduce errors, the vehicle's recognition algorithm will adjust parameters according to different vehicle types. For example, for taller vehicles (such as SUVs or trucks), measurement points will be automatically added to ensure that the roof and other locations are not missed. This step is crucial to the accuracy of vehicle recognition. Only by accurately obtaining the vehicle's size information can reasonable parameter settings be provided for the subsequent car washing process.

[0059] Automatically calibrate the sensor based on the acquired vehicle information and adjust the vehicle position to ensure that the cleaning work is carried out in the best position;

[0060] After obtaining the size and shape information of the vehicle, the system will automatically calibrate to ensure that all sensors can be accurately aligned and work synchronously. If the sensor deviates or has positioning errors at this time, the system will automatically adjust the sensor's sensing area to avoid improper car washing due to errors. In some cases, when the placement of the vehicle deviates from the ideal track, the vehicle identification module will issue an adjustment instruction and use the adjustment equipment to slightly move the vehicle to the optimal position to ensure that all sensors can measure accurately. At this time, the outline of the vehicle matches the working area of ​​the car wash machine, and the sensor reconfirms the vehicle to ensure that the calibration is completed before entering the next step of the washing operation.

[0061] The vehicle calibration step is to eliminate all potential sources of error and ensure that the car wash system can perform accurately in the subsequent stages. Vehicle calibration adjusts the relationship between the sensor position and the vehicle so that the distance and angle between the vehicle and the car wash machine can achieve the best match. Especially for some irregular or small vehicles (such as city cars or electric cars), the system requires more detailed calibration to ensure that the sensor can correctly measure every part of the vehicle to avoid omissions or incorrect processing during the washing process due to position deviation.

[0062] Through multiple verifications and optimization of sensor accuracy, we ensure accurate vehicle identification and adjust sensor parameters according to environmental changes;

[0063] After calibration, the vehicle identification module will perform multiple confirmations based on the aforementioned data to optimize the accuracy of the sensor and confirm whether the vehicle has been correctly identified. At this point, the system will fuse the information from multiple sensors, correct potential errors, and ensure that every part of the vehicle can be correctly identified and processed by the cleaning system. During this process, the system will also monitor environmental factors in real time to determine whether there is moisture, dust, or other substances that affect the recognition accuracy of the sensor. If the sensor error exceeds the tolerance range, the system will restart the detection until the recognition result meets the standard.

[0064] The key to this step is to eliminate all possible interference and misjudgment by verifying the accuracy of sensor data multiple times. During this process, the system evaluates the sensor's response time and measurement accuracy to ensure that all measurement results are comprehensively calculated and mutually verified. This process is not just calibration, but also involves adaptive adjustment of real-time environmental data. For example, when the air humidity or temperature changes, the system can intelligently adjust the sensor parameters to ensure that the accuracy of recognition is always maintained at a high level, thereby avoiding vehicle damage caused by misidentification or incorrect cleaning.

[0065] The environment monitoring and interference compensation module uses environmental sensors (such as humidity, temperature, and dust sensors) to monitor various interference factors in the working environment of the car washing machine in real time, and adjusts the sensor's sensing threshold through an adaptive algorithm to avoid the impact of the external environment on the sensor's detection accuracy;

[0066] Use environmental sensors (such as humidity, temperature, and dust sensors) to monitor various interference factors in the working environment of the car wash machine in real time, and adjust the sensor's sensing threshold through an adaptive algorithm to avoid the influence of the external environment on the sensor's detection accuracy. The specific steps are as follows:

[0067] Activate environmental sensors to monitor external environmental factors in real time and provide basic data for subsequent environmental interference compensation;

[0068] After the vehicle enters the car wash and is initially identified and calibrated by high-definition cameras and sensors, the system will activate environmental sensors (such as humidity, temperature, dust sensors, etc.) and begin real-time monitoring of the working environment around the car wash. Environmental sensors will continuously collect relevant environmental data, such as humidity changes, air temperature, dust concentration, etc., which provide the system with dynamic changes in the external environment. Through real-time monitoring of environmental sensors, the system can obtain the environmental status of the current operating area of ​​the car wash, ensuring that external factors will not interfere with the accuracy and recognition process of the sensors.

[0069] This step provides the system with basic data on environmental changes during the car wash process for the first time through the activation and data collection of environmental sensors. After the initial detection and identification of the vehicle, the environmental sensors will monitor the humidity and temperature changes in the air, especially in different seasons and weather conditions, which may affect the working state of the sensors. For example, moisture may affect the signal propagation speed of ultrasonic sensors, and dust will cover the lens of infrared sensors and affect their sensing range. Therefore, by continuously monitoring these external factors, the system can prepare for subsequent data compensation and adjustments.

[0070] Analyze the data collected by environmental sensors to determine whether there are external interference factors that affect the sensor's detection accuracy, and prepare compensation mechanisms;

[0071] Based on the real-time data provided by the environmental sensors, the system analyzes the external environment to determine whether there are factors that may affect the sensor's detection accuracy. For example, when the air humidity is detected to be abnormally high, it may cause interference with the sensor signal; or when the dust concentration is detected to be too high, the accuracy of the infrared sensor may be reduced. The system will evaluate these environmental changes and activate the corresponding compensation mechanism as needed. At this time, the system will also compare with the data provided by the vehicle detection module to ensure that environmental factors have not affected the vehicle recognition accuracy.

[0072] After environmental data is collected, the system will conduct real-time analysis to evaluate the potential impact of these factors on sensor performance. For example, high humidity may cause the sensor's detection distance and accuracy to decrease, affecting the accurate identification of the vehicle's position. Dust accumulation may block the infrared sensor's transmitting and receiving devices, thereby affecting the identification of the vehicle's outline. At this point, the system not only evaluates environmental changes alone, but also needs to compare these changes with vehicle information to ensure that the external environment does not have an adverse effect on the vehicle's identification and cleaning process. If an impact is found, the system will enter the adjustment and compensation stage.

[0073] Automatically adjust the sensor's sensing threshold through an adaptive algorithm to adapt to environmental changes, ensuring that the sensor still works accurately under adverse conditions;

[0074] When the system detects changes in the external environment and analyzes possible interference factors, the system will then automatically adjust the sensor's sensing threshold according to a preset adaptive algorithm. For example, in the case of high humidity or excessive dust, the system will automatically enhance the sensor's sensitivity or expand its sensing range to ensure that the sensor can still work properly under adverse conditions. At the same time, the system will also adjust the sensing intensity of the infrared sensor to ensure that the sensor can still effectively identify the vehicle's outline and size even if it is covered with dust. By dynamically adjusting the sensor's parameters, the system can maintain efficient and accurate recognition capabilities.

[0075] The core of this step is to achieve automatic adjustment of the sensor through an adaptive algorithm so that it can adapt to changing environmental conditions. The adaptive algorithm will dynamically adjust the operating threshold of the sensor based on environmental data such as humidity, temperature, and dust concentration. For example, in a high humidity environment, the system may increase the transmission power of the ultrasonic sensor to ensure that the propagation of the signal is not affected; when the dust concentration is high, the system will increase the receiving sensitivity of the infrared sensor to ensure that even if part of the sensor surface is covered, the system can still obtain accurate vehicle information. This real-time adjustment not only improves the accuracy of the car washing process, but also avoids sensor failure or recognition errors caused by environmental factors.

[0076] Optimize the vehicle cleaning process based on the adjusted sensor parameters, continuously learn and record the impact of environmental changes on system performance, and optimize the adaptive algorithm;

[0077] Finally, the system will provide real-time feedback and optimization of the vehicle identification process based on the adjusted sensor parameters and optimization results. This process includes re-evaluating the sensor accuracy and optimizing the cleaning parameters (such as water pressure, brushing angle, etc.) in combination with environmental data and vehicle detection information. The system will also continue to learn and record the working performance under different environmental conditions, optimize the adaptive algorithm, so that the system can automatically adjust the working mode according to future environmental changes to avoid the impact of environmental changes on the car washing process.

[0078] After completing the adjustment of environmental factors, the system will compare the adjustment results with the actual car washing operation to ensure that the entire washing process can continue without being negatively affected by the external environment. For example, when the ambient humidity is high, the system may reduce the water pressure to prevent excessive water vapor from affecting the washing effect or causing equipment damage. At the same time, the environmental compensation mechanism will continuously optimize the adaptive algorithm based on the empirical data of each washing, gradually improving the system's sensitivity and response capabilities to environmental changes. Ultimately, the entire car washing process will achieve the best cleaning effect based on the continuously optimized algorithm to ensure that the vehicle will not be damaged by environmental changes.

[0079] Intelligent control and adaptive adjustment module, based on the acquired vehicle information and environmental interference data, uses an intelligent control system to adjust the working mode of the car wash machine, automatically selects the appropriate water pressure, brushing angle and cleaning time, and ensures accurate and safe vehicle cleaning under different environmental conditions;

[0080] Based on the acquired vehicle information and environmental interference data, the intelligent control system is used to adjust the working mode of the car wash machine, automatically select the appropriate water pressure, brushing angle and cleaning time, and ensure accurate and safe vehicle cleaning under different environmental conditions. The specific steps are as follows:

[0081] Combine vehicle information with environmental data for analysis to provide basic data for adjusting the working mode of the car wash machine and ensure accurate judgment of cleaning needs;

[0082] After the vehicle is identified and calibrated, the system will integrate and analyze the vehicle information (such as size and shape) and environmental data (such as humidity, temperature, dust concentration, etc.). These data provide complete background information for the intelligent control system to help the system determine the specific needs of the current car washing task. For example, in an environment with high humidity or high dust, the system may need to adjust the working mode of the car washing machine to cope with the potential impact of the environment on the cleaning process. The size and shape information of the vehicle will also affect the cleaning strategy of the car washing machine, especially for different models (such as compact cars and SUVs), the system will automatically select different cleaning methods.

[0083] Detailed explanation:

[0084] By integrating vehicle information and environmental data, the system can fully understand the current car washing needs. The size and shape of the vehicle will affect the distribution of water flow and the angle of brushing, so the system must first evaluate whether the sensor threshold needs to be adjusted through the humidity, temperature and dust data provided by the environmental sensor, and then make an optimized decision on the washing parameters. For example, if the humidity is high, the system may adjust the water flow pressure to avoid excessive washing; if the dust concentration in the environment is high, it is necessary to increase the water flow or change the brushing angle to ensure the washing effect.

[0085] Automatically adjust water pressure, brushing angle and cleaning time based on analysis results to adapt to different environmental conditions and vehicle types to ensure the best cleaning effect;

[0086] Based on the analysis results, the intelligent control system automatically selects the appropriate water pressure, brushing angle and cleaning time. For example, when the humidity is high, the system will appropriately reduce the water pressure to avoid instability or equipment failure caused by excessive moisture during the car wash process; in dusty environments, the system may increase the water flow and increase the cleaning time to ensure that dust is completely removed. The brushing angle will also be dynamically adjusted according to the shape of the vehicle. For example, for tall SUVs or vans, the brushing angle will be increased to ensure thorough cleaning of the roof and sides.

[0087] In this step, the system will automatically select the best working mode based on the comprehensive analysis data. Water pressure is one of the most critical parameters in the car washing process. Too high water pressure may damage the surface of the vehicle, while too low water pressure cannot achieve effective cleaning. The adjustment of the brushing angle not only needs to consider the size of the vehicle, but also needs to be optimized according to different environments (such as humidity and dust) to ensure that all parts of the car body can be fully cleaned. The adjustment of the washing time is also dynamic. The system will make fine adjustments according to the degree of dirtiness of the vehicle and environmental conditions to ensure the cleaning effect without wasting resources.

[0088] Real-time monitoring of sensor feedback data during the washing process, timely adjustment of water pressure and brushing angle to maintain the accuracy and stability of the car washing process;

[0089] During the car wash process, the system monitors the data fed back by the sensors in real time to ensure the accuracy of the washing effect. For example, the system uses ultrasonic sensors and infrared sensors to monitor the degree of dirtiness of the car body and possible abnormalities during the washing process, such as excessive water pressure or improper washing angle. If the system detects an abnormality, it will immediately make corrections by adjusting the water pressure or washing angle, and feed back the adjustment results to the control center to ensure that the car wash process is always in the best state.

[0090] The key to this process is real-time feedback and dynamic adjustment. Through real-time monitoring, the system can promptly detect possible deviations during the car wash process, such as excessive water pressure, inappropriate brushing angle, etc. If the sensor detects that the water pressure is too high, the system will automatically reduce the water pressure; if the brushing angle deviates from the standard, the system will readjust the angle to ensure that every part of the vehicle is properly cleaned during the entire car wash process. This step not only ensures the cleaning effect, but also helps to save energy and reduce emissions, avoiding unnecessary waste of resources.

[0091] By learning the feedback data of each cleaning, the control algorithm is optimized to continuously improve the adaptability to different environments and vehicle models;

[0092] The system will continuously optimize the control algorithm through feedback and adjustment results of each cleaning process. After each cleaning, the system will record parameters such as water pressure, brushing angle, cleaning time, as well as data on environmental conditions and vehicle conditions. This data will be used to train the machine learning model, allowing the system to more accurately select the best cleaning parameters in future cleaning tasks. At the same time, as more data is accumulated, the system will be able to gradually improve its adaptability to different environmental conditions and different vehicle models, thereby achieving long-term performance optimization.

[0093] The purpose of this step is to ensure that the system continues to learn and evolve. By continuously optimizing the control algorithm, the system can not only improve its adaptability to different environments and vehicle models, but also respond quickly to changing weather and environmental conditions. For example, the system may increase water flow in high temperature environments and reduce water pressure in cold environments. Through machine learning, the system will accumulate more data, predict the best car washing solutions under different environmental conditions, and gradually improve the car washing effect and system reliability.

[0094] The sensor self-check and fault diagnosis module performs regular self-checks on the sensor and determines whether the sensor is faulty based on the feedback from the intelligent control system. If an abnormality is found, an alarm is issued in a timely manner to ensure data accuracy during the car wash process.

[0095] Regularly perform self-checks on the sensor and determine whether the sensor is faulty based on the feedback from the intelligent control system. If an abnormality is found, issue an alarm in a timely manner. The specific steps to ensure the accuracy of data during the car wash process are as follows:

[0096] During the sensor self-test process, the fault detection algorithm compares the actual measured signal output with the preset normal range value to determine whether there is an abnormality. If the actual signal output deviates from the normal working range, the abnormality of the sensor is diagnosed by calculation, and the fault diagnosis parameters are generated and evaluated in combination with the time change trend. The calculation expression is as follows:

[0097]

[0098] , where P fault is the sensor fault detection parameter, S actual (t i ) is the actual signal output at the i-th time point t, S normal (t i ) is the preset normal signal output at the i-th time point t, and n is the total number of time points;

[0099] If P faultThe value exceeds the threshold, indicating a large abnormal amplitude of the sensor output signal. The system will consider that the sensor has a fault or needs calibration. This step helps to quickly diagnose sensor problems by comparing the deviation between the actual signal and the preset standard signal.

[0100] If it is determined that the sensor has a fault, the intelligent control system will, according to the sensor fault detection parameter P fault , correct the output signal of the sensor through an adjustment algorithm, perform weighted correction on the signal output, and the correction formula is as follows: S corrected (t i ) = S actual (t i ) - k·P fault , where k is the correction factor used to control the correction amplitude, and S corrected (t) is the corrected signal output at the i-th time point t;

[0101] The correction factor k is dynamically adjusted according to the sensitivity of the system, usually optimized by the learning model of the system based on historical data. Through this step, the system can correct the detected abnormal values to a signal output closer to the real environment, so that the subsequent cleaning process is not affected by sensor faults.

[0102] Finally, perform subsequent monitoring and alarm processing according to the corrected signal. If the sensor fault cannot be repaired, judge whether it can still meet the accuracy requirements of the car washing process according to the corrected parameters. If not, generate an alarm signal, and the generation formula is as follows:

[0103] 烒:

[0104]

[0105] , where P alarm is the alarm signal parameter, and S threshold is the preset lowest effective signal threshold.

[0106] If P alarm exceeds the threshold, indicating that the gap between the corrected signal and the lowest effective signal threshold S threshold is too large, the system will send an alarm signal, indicating that the sensor needs more in-depth repair or replacement. This step ensures that the system can effectively judge faults according to real-time feedback and avoid incorrect execution during the car washing process.

[0107] Real-time correction and data feedback module. During the car washing process, it monitors the data fed back by the sensor in real time. If an abnormality is detected (such as too high water flow pressure or improper brushing angle), it automatically adjusts through the built-in correction algorithm and uploads the correction process to the cloud in real time for data analysis and feedback to the user to ensure that the entire car washing process meets the expectations and avoids damaging the vehicle;

[0108] During the car wash process, the sensor feedback data is monitored in real time. If an abnormality is detected (such as excessive water pressure or improper brushing angle), the built-in correction algorithm is used to automatically adjust and upload the correction process to the cloud in real time for data analysis and feedback to users, ensuring that the entire car wash process meets expectations and avoids damage to the vehicle. The specific steps are as follows:

[0109] During the car wash process, sensors (including water flow pressure sensors, brushing angle sensors, etc.) collect working environment data in real time and transmit it to the control system. The control system first detects the acquired data to identify whether there are abnormal conditions, such as excessive water flow pressure, improper brushing angle, etc. At this time, the deviation of the water flow pressure from the set standard value, as well as the difference between the brushing angle and the ideal angle, is calculated. The calculation expression is as follows: ΔP = P actual -P target , Δθ=θ actual -θ target , where ΔP is the deviation of water flow pressure, P actual is the current actual water flow pressure, P target is the ideal water flow pressure, Δθ is the deviation of the scrubbing angle, θ actual is the current actual scrubbing angle, θ target It is the ideal brushing angle to set;

[0110] The core task of this step is to detect and calculate the errors of water flow pressure and brushing angle in real time to prepare for subsequent corrections.

[0111] Once the water pressure and scrubbing angle are detected to be out of tolerance, a correction calculation is performed. According to the current deviation value, the water pressure and scrubbing angle are adjusted to approach the set value. At this time, an adaptive correction coefficient is introduced and optimized in combination with the current environmental data to ensure smoothness and stability of the correction process. The expression is as follows: P new =P actual -k P ΔP, θ new =θ actual -k θ ·Δθ, where P new is the corrected water flow pressure, k P is the water flow pressure correction coefficient, θ new is the corrected scrubbing angle, k θ is the brushing angle correction factor;

[0112] Correction factor k P and k θ Dynamically adjust according to environmental sensor data (such as humidity, temperature, dust concentration, etc.) to ensure the accuracy of the correction process. For example, in a high humidity environment, the water flow pressure may be low, so the correction factor k needs to be increased.P To increase water pressure.

[0113] After the initial correction is completed, the corrected water flow pressure and brushing angle are recalculated to confirm whether they meet the expected goals. All correction calculation results will be uploaded to the cloud in real time for data analysis and recording. At the same time, the correction process and real-time data will be fed back to the user through the network to ensure that the user understands the current car washing progress and correction status. The formula is as follows: ΔP final =P new -P target , Δθ final =θ new -θ target , where ΔP final is the final water flow pressure deviation, Δθ final is the final brushing angle deviation;

[0114] By calculating the final deviation in real time, it can be determined whether the correction effect has achieved the expected goal. If the final deviation still exceeds the set tolerance range, further correction will be made and the correction value will be transmitted to the cloud for users to view.

[0115] After the correction process is completed, the cloud will collect all the data in the correction process (such as water flow pressure, brush angle correction value, etc.) for in-depth analysis. Through machine learning and big data analysis, the correction algorithm and control parameters will be continuously optimized to improve the efficiency and accuracy of the subsequent washing process. These data are not only used to optimize the car washing process, but also provide users with detailed feedback information to help users understand the car washing quality and system status. The expression is as follows:

[0116]

[0117] , where δP optimized is the optimized water flow pressure, w j is the data weight coefficient, P new，j is the water flow pressure after the jth correction, δθ optimized is the optimized scrubbing angle, θ new，j is the brushing angle after the jth correction, and N is the number of corrections.

[0118] Through the weighted average method, the optimal parameters can be extracted from the data of multiple correction processes to continuously optimize the correction algorithm and improve the accuracy and efficiency of subsequent car washing processes.

[0119] Implementation method 1: In this implementation method, the working performance of the car washing machine is highly dependent on changes in environmental conditions. Therefore, after identifying and calibrating the vehicle, the system dynamically adjusts the sensor working mode by integrating the data of the environmental sensors. Specifically, the car washing machine uses a variety of sensors such as high-definition cameras, infrared sensors, and ultrasonic sensors to conduct a comprehensive inspection of the vehicle entering the car washing machine. Vehicle information includes size, shape, location, etc., while the data provided by the environmental sensors include humidity, dust concentration, temperature, etc. These data will be analyzed and correlated through intelligent algorithms to determine the sensor's sensing threshold and working mode.

[0120] When a vehicle enters the working area of ​​the car washer, the sensor and environmental monitoring module are first activated. The high-definition camera performs visual recognition of the vehicle and transmits the vehicle's outline and size data to the control system. At the same time, the system activates environmental sensors (such as humidity, temperature, dust sensors, etc.) to monitor the environmental factors in the working area of ​​the car washer in real time. The sensors will collect data such as humidity, dust concentration, and air temperature, which reflect the current environmental conditions of the car washer's operating area.

[0121] Based on the collected vehicle information and environmental data, the system will conduct a comprehensive analysis. For example, when the humidity is too high, the sensing ability of the infrared sensor may be affected, resulting in perception errors. The system will dynamically adjust the sensitivity of the infrared sensor to improve its accuracy in humid environments. In cases with high dust concentrations, the detection signal of the ultrasonic sensor may be reflected or scattered, and the system will increase the sensor's transmission power to ensure that the signal can be accurately transmitted and fed back to the sensor. At the same time, the system will calculate the specific position and shape of the vehicle, and adjust the sensor's working mode based on environmental data to ensure the accuracy of the detection results.

[0122] As environmental data changes, the system will adjust the working mode of the sensor in real time. For example, in high humidity or foggy weather, the spread of water flow will be more difficult. The system can automatically lower the water flow pressure and adjust the angle of the nozzle to avoid unnecessary damage that may be caused by high-pressure water flow. At this time, the sensitivity of the sensor will increase to ensure that every part of the vehicle surface is correctly identified and cleaned. If more dust is detected on the surface of the vehicle, the system will automatically increase the cleaning time or increase the water flow to ensure that the dust and dirt on the vehicle are thoroughly removed. The core of adaptive adjustment is to ensure that the impact of environmental factors on the cleaning effect is minimized, thereby maintaining the stability and efficiency of the system.

[0123] After each car wash task is completed, the system will evaluate the adjustment effect of the sensor and feed the data of this operation back to the control system. Through machine learning algorithms, the system will optimize its working mode according to the performance of the sensor under different environmental conditions. Long-term data accumulation will enable the system to respond quickly under similar environmental conditions and automatically select the most appropriate sensor parameter settings, further improving work efficiency and cleaning results.

[0124] The key to this implementation is to dynamically adjust the working threshold of the sensor through real-time monitoring and analysis of environmental factors (such as humidity, temperature, dust, etc.), so that the car washing machine can maintain the best working state under various environmental conditions, ensuring accurate identification and efficient cleaning of the vehicle.

[0125] Implementation 2: The core of this implementation is that the intelligent control system can dynamically adjust the working parameters of the car washing machine such as water pressure, brushing angle, and cleaning time according to the vehicle identification information and environmental interference data to ensure the efficiency and safety of each cleaning task. The car washing machine automatically selects and optimizes every link in the car washing process by combining the size, shape, surface dirtiness, and environmental data of the vehicle, thereby ensuring that the car washing effect can be optimized regardless of the vehicle type or environmental changes.

[0126] After detecting the vehicle, the car washer first obtains information such as the size, position, and shape of the vehicle through high-definition cameras and sensors, and performs automatic calibration. At the same time, environmental sensors also monitor factors such as humidity, temperature, and dust concentration in the working area in real time. The system will integrate and analyze these two parts of data to determine the cleaning needs of the vehicle. For example, if the surface of the vehicle is covered with a lot of dust, the system will adjust the water flow and brushing force to ensure that all dust is removed. If the weather is humid, the system may reduce the water flow pressure to avoid excessive accumulation of water.

[0127] Based on the size and shape of the vehicle and the current environmental data, the system automatically selects the most appropriate water pressure, brushing angle and cleaning time. For example, for tall SUVs or trucks, the system will increase the brushing angle and water flow pressure to ensure that the roof and sides of the vehicle are thoroughly cleaned; while for small cars, the system will moderately reduce the water pressure to avoid excessive water flow causing damage to the vehicle surface. The adjustment of the brushing angle is also based on the specific structure of the vehicle to ensure that every corner can be effectively cleaned. At the same time, the system will adjust the cleaning time according to the degree of dirtiness of the vehicle body to ensure a detailed and thorough cleaning process.

[0128] During the car wash process, the system will fine-tune the cleaning process according to the actual situation by real-time monitoring of sensor feedback data (such as water pressure, brushing angle, cleaning time, etc.). If the water pressure is too high or the brushing angle is inappropriate during the process, the system will make adjustments immediately. For example, if the sensor feedback water pressure is too high, the system will automatically reduce the water pressure; if the brushing angle is too sharp, it may cause damage to the brush head or scratches on the vehicle surface. The system will immediately adjust the brushing angle to reduce potential risks. Through this real-time feedback mechanism, the car wash machine can ensure that each step is executed accurately.

[0129] After each car wash task, the system will collect data on water pressure, brushing angle, washing time, etc., and compare this data with environmental sensor data to evaluate the washing effect. By continuously accumulating this feedback data, the system can identify the best washing parameters under different environmental conditions. As the amount of data increases, the system will be able to automatically select the most suitable parameters according to environmental changes and vehicle types in future washing processes, thereby improving overall efficiency and accuracy.

[0130] This embodiment enables the car washing machine to make fine adjustments in each cleaning step through optimization and dynamic adjustment of the intelligent control system, which not only ensures that the vehicle surface is thoroughly cleaned, but also minimizes the risk of damage caused by inappropriate cleaning parameters.

[0131] Implementation method 3: The innovation of this implementation method is that the car washing machine gradually optimizes the control algorithm through self-learning and feedback mechanism, and continuously adjusts the working mode based on historical data, so that the system can adapt to different environmental conditions, weather changes and the cleaning needs of various models. By continuously optimizing the control strategy, the system can not only improve work efficiency, but also ensure that each vehicle cleaning achieves the best effect, and can minimize the operation deviation caused by environmental changes.

[0132] After each car wash task is completed, the system will collect data on vehicle identification, environmental monitoring, sensor feedback, etc. In particular, data related to environmental conditions (such as humidity, temperature, dust concentration) and cleaning parameters (such as water pressure, brushing angle), this information will be uploaded to the central control system in real time. By analyzing these historical data, the system evaluates the performance of each car wash task, including cleaning effect, time efficiency and sensor accuracy. Through these data, the system can identify under which environmental conditions certain settings can better optimize the cleaning effect.

[0133] Based on historical data, the system uses an adaptive machine learning algorithm to continuously optimize the control strategy. By analyzing and summarizing the data, the system automatically adjusts various working parameters. For example, in hot weather, the system will find that extending the washing time of some vehicles will help remove more dirt, or in a low humidity environment, the water flow pressure can be appropriately increased, and the effect will be better. As the amount of data increases, the system gradually learns to automatically adjust parameters such as water flow, brushing angle, and washing time under different conditions to achieve the best cleaning effect.

[0134] In the new cleaning task, the system will automatically adjust the working parameters according to the optimized adaptive control algorithm. This process is not only to adjust the parameters according to the environmental data, but more importantly, the system can perceive the changes of the vehicle and the environment in real time and dynamically adjust the operation mode.

[0135] In adverse weather conditions (such as heavy rain or extremely cold weather), the system will automatically adjust the water temperature, scrubbing force and cleaning time to ensure cleaning effect and vehicle safety. In the process of continuous accumulation of experience and optimization, the system can minimize resource waste and achieve the best cleaning effect under different environmental conditions.

[0136] Over time, the system will be able to more accurately evaluate the performance of each cleaning through continuous self-learning and data feedback. Each adjustment and optimization process will be recorded and used as a basis for future decisions. The system's self-learning mechanism not only improves its ability to adapt to complex environments, but also continuously improves the accuracy of vehicle recognition and the stability of cleaning effects. Through continuous learning and data feedback, the system will gradually evolve during long-term use and provide more accurate and personalized cleaning solutions in different scenarios.

[0137] In summary, this implementation method enables the system to maintain high adaptability and stability under various environmental conditions through self-learning and data feedback mechanisms. As time goes by, the system will become more efficient and accurate.

[0138] The present invention uses dynamic sensor adjustment based on environmental perception and adaptive control algorithm optimization, so that the car wash system can maintain efficient and safe operation under various environmental conditions. Environmental sensors monitor external factors such as humidity, temperature, dust concentration in real time, and dynamically adjust parameters such as water pressure, scrubbing angle and cleaning time to ensure the accuracy and thoroughness of the vehicle cleaning process. This adaptive adjustment can effectively cope with changes in different weather, seasons and vehicle models, avoid improper operations caused by environmental changes or vehicle characteristics, significantly improve the stability and cleaning effect of the system, reduce the risk of vehicle damage and improve user experience, while improving the long-term stability and efficiency of the system.

[0139] The present invention continuously optimizes sensor parameters and control algorithms through self-learning and optimization feedback mechanisms, significantly reducing equipment maintenance requirements and extending equipment service life. Through real-time monitoring and adjustment, the system can effectively avoid failures caused by environmental interference or equipment aging, reduce maintenance frequency and downtime, and reduce operating costs. With the accumulation of data and algorithm optimization, the operating efficiency of the system continues to improve, further improving the return on investment of the car washing machine, while ensuring that the equipment can continue to work efficiently and provide more stable and economical services.

[0140] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0141] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0142] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0143] 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.

[0144] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein 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 this application.

[0145] 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.

[0146] 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.

[0147] In addition, each functional unit in each embodiment of the present application 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.

[0148] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0149] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An unattended car washing machine, characterized in that: It includes vehicle detection and identification module, environment monitoring and interference compensation module, intelligent control and adaptive adjustment module, sensor self-check and fault diagnosis module, and real-time correction and data feedback module: The vehicle detection and identification module uses high-definition cameras and multiple sensors to perform preliminary detection and identification of vehicles entering the car wash machine, and automatically calibrates the vehicle to ensure the recognition accuracy of the sensor; The environment monitoring and interference compensation module uses environmental sensors to monitor various interference factors in the working environment of the car washing machine in real time, and adjusts the sensor's sensing threshold through an adaptive algorithm to avoid the influence of the external environment on the sensor's detection accuracy; Intelligent control and adaptive adjustment module, based on the acquired vehicle information and environmental interference data, uses an intelligent control system to adjust the working mode of the car wash machine to ensure accurate and safe vehicle washing under different environmental conditions; The sensor self-check and fault diagnosis module performs regular self-checks on the sensor and determines whether the sensor is faulty based on the feedback from the intelligent control system. If an abnormality is found, an alarm is issued in a timely manner to ensure data accuracy during the car wash process. The real-time correction and data feedback module monitors the sensor feedback data in real time during the car wash process. If an abnormality is detected, it will be automatically adjusted through the built-in correction algorithm, and the correction process will be uploaded to the cloud in real time for data analysis and feedback to the user, ensuring that the entire car wash process meets expectations and avoids damage to the vehicle.

2. The unattended car washing machine according to claim 1, characterized in that: The specific steps of using high-definition cameras and multiple sensors to perform preliminary detection and identification of vehicles entering the car wash machine and automatically calibrate the vehicle to ensure the recognition accuracy of the sensors are as follows: By activating high-definition cameras and multiple sensors, the vehicle entering the car wash machine begins to be initially detected and located, preparing for subsequent identification and cleaning; Combine image processing and sensor data to accurately measure the vehicle's shape, size and obstacle location, providing key information for subsequent cleaning; Automatically calibrate the sensor based on the acquired vehicle information and adjust the vehicle position to ensure that the cleaning work is carried out in the best position; Through multiple verifications and optimization of sensor accuracy, accurate vehicle identification is ensured, and sensor parameters are adjusted according to environmental changes.

3. The unattended car washing machine according to claim 1, characterized in that: The specific steps of using environmental sensors to monitor various interference factors in the working environment of the car wash machine in real time and adjusting the sensor's sensing threshold through an adaptive algorithm to avoid the influence of the external environment on the sensor's detection accuracy are as follows: Activate environmental sensors to monitor external environmental factors in real time and provide basic data for subsequent environmental interference compensation; Analyze the data collected by environmental sensors to determine whether there are external interference factors that affect the sensor's detection accuracy, and prepare compensation mechanisms; Automatically adjust the sensor's sensing threshold through an adaptive algorithm to adapt to environmental changes, ensuring that the sensor still works accurately under adverse conditions; The vehicle cleaning process is optimized based on the adjusted sensor parameters, and the impact of environmental changes on system performance is continuously learned and recorded to optimize the adaptive algorithm.

4. The unattended car washing machine according to claim 1, characterized in that: Based on the acquired vehicle information and environmental interference data, the intelligent control system is used to adjust the working mode of the car wash machine to ensure accurate and safe vehicle washing under different environmental conditions. The specific steps are as follows: Analyze by combining vehicle information and environmental data to provide basic data for adjusting the working mode of the car wash machine and ensure the accurate judgment of cleaning requirements; Automatically adjust the water pressure, brushing angle and cleaning time according to the analysis results to adapt to different environmental conditions and vehicle types and ensure the best cleaning effect; During the cleaning process, monitor the sensor feedback data in real time and adjust the water pressure and brushing angle in a timely manner to maintain the accuracy and stability of the car wash process; Optimize the control algorithm by learning the feedback data of each cleaning to continuously improve the adaptability to different environments and vehicle models.

5. The unattended car washing machine according to claim 1, characterized in that: During the sensor self-check process, use a fault detection algorithm to compare the actually measured signal output with the preset normal range value to determine whether there is an abnormality. If the actual signal output deviates from the normal working range, calculate the degree of abnormality of the sensor, generate fault diagnosis parameters, and evaluate them in combination with the time change trend. The calculation formula is as follows: Where P fault is the sensor fault detection parameter, S actual (t i ) is the actual signal output at the i-th time point t, S normal (t i ) is the preset normal signal output at the i-th time point t, and n is the total number of time points; If the sensor is judged to be faulty, the intelligent control system will detect the fault according to the sensor fault detection parameter P fault , the output signal of the sensor is corrected by adjusting the algorithm, and the signal output is weighted and corrected. The correction formula is as follows: S correted (t i )=S actual (t i )-k·P fault , where k is the correction factor S corrected (t) is the corrected signal output at the i-th time point t; Finally, perform subsequent monitoring and alarm processing based on the corrected signal. If the sensor failure cannot be repaired, judge whether the accuracy requirements of the car wash process can still be met according to the corrected parameters. If not, generate an alarm signal. The generation formula is as follows: Shi: Where P alarm is the alarm signal parameter, S threshold It is the preset minimum valid signal threshold.

6. The unattended car washing machine according to claim 1, characterized in that: During the car wash process, monitor the data feedback by the sensor in real time. If an abnormality is detected, automatically adjust it through the built-in correction algorithm, and upload the correction process to the cloud for data analysis and feedback to the user in real time to ensure that the entire car wash process meets the expectations and avoid damaging the vehicle. The specific steps are as follows: During the car wash process, the sensor collects working environment data in real time and transmits it to the control system. The control system first detects the acquired data to identify whether there is any abnormality. At this time, the deviation of the water flow pressure from the set standard value and the difference between the brushing angle and the ideal angle are calculated. The calculation expression is as follows: ΔP = P actual -P target , Δθ=θ actual -θ target , where ΔP is the deviation of water flow pressure, P actual is the current actual water flow pressure, P target is the ideal water flow pressure, Δθ is the deviation of the scrubbing angle, θ actual is the current actual scrubbing angle, θ target It is the ideal brushing angle to set; Once the water pressure and scrubbing angle are detected to be out of tolerance, a correction calculation is performed to adjust the water pressure and scrubbing angle according to the current deviation value. At this time, an adaptive correction coefficient is introduced and optimized in combination with the current environmental data to ensure smoothness and stability of the correction process. The expression is as follows: P new =P actual -k P ΔP, θ new =θ actual -k θ ·Δθ, where P new is the corrected water flow pressure, k P is the water flow pressure correction coefficient, θ new is the corrected scrubbing angle, k θ is the brushing angle correction factor; After the initial correction is completed, the corrected water flow pressure and brushing angle are recalculated to confirm whether they meet the expected goals. All correction calculation results will be uploaded to the cloud in real time for data analysis and recording. At the same time, the correction process and real-time data will be fed back to the user through the network to ensure that the user understands the current car washing progress and correction status. The formula is as follows: ΔP final =P new -P target , Δθ final =θ new -θ target , where ΔP final is the final water flow pressure deviation, Δθ final is the final brushing angle deviation; After the correction process is completed, the cloud will collect all the data during the correction process for in-depth analysis. Through machine learning and big data analysis, the correction algorithm and control parameters will be continuously optimized to improve the efficiency and accuracy of the subsequent cleaning process. The expression is as follows: In the formula, δP optimized is the optimized water flow pressure, w j is the data weight coefficient, P new，j is the water flow pressure after the jth correction, δθ optimized is the optimized scrubbing angle, θ new，j is the brushing angle after the jth correction, and N is the number of corrections.

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