Unattended car washer

Abstract

The application discloses an unattended car washing machine, and relates to the technical field of unattended car washing, which comprises a vehicle detection and recognition module, an environment monitoring and interference compensation module, an intelligent control and self-adaptive adjustment module, a sensor self-checking and fault diagnosis module and a real-time correction and data feedback module; the vehicle detection and recognition module utilizes high-definition cameras and various sensors to preliminarily detect and recognize vehicles entering the car washing machine, automatically calibrates the vehicles and ensures the recognition accuracy of the sensors. Through environment sensing and self-adaptive algorithm optimization, the car washing system can efficiently operate under various environmental conditions, can real-timely adjust water pressure, brushing angle and cleaning time, can ensure accurate cleaning and reduce vehicle damage. Through self-learning optimization, the system can reduce maintenance requirements, prolong equipment life, reduce maintenance frequency, reduce operation cost, improve investment return rate and ensure continuous stable service.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned car washing, in particular to an unattended car washing machine. BACKGROUND

[0002] With the rapid development of new energy vehicle industry, its unique structural design and use characteristics have put forward new requirements for car washing equipment. Compared with traditional fuel vehicles, new energy vehicles have higher waterproof protection requirements due to the arrangement of power battery packs on the chassis; the cleaning difficulty is significantly increased due to innovative designs such as hidden door handles and streamlined appearance.

[0003] An unattended car washing machine is a self-service car washing equipment based on automation technology and internet technology, which can provide cleaning services for vehicles without manual operation. Users select the required car washing service type, cleaning method, payment method, etc. through a self-service operation interface or a mobile application, and the system automatically completes the steps of body cleaning, brushing, rinsing, drying, etc. Its core technologies usually include intelligent sensors, automatic control systems, spraying devices, drying systems, etc., which can adaptively adjust according to the different sizes and dirt levels of new energy vehicles, realizing an efficient, water-saving and environmentally friendly car washing process. In addition, internet connection enables users to monitor the washing progress in real time, query historical records, and pay fees, and managers can remotely monitor the equipment status, diagnose and maintain faults, further improving operational efficiency and customer experience.

[0004] The prior art has the following disadvantages:

[0005] In an unattended car washing machine, sensor misjudgment may cause damage to the vehicle during the washing process. When the sensor is disturbed by external environments such as dust, dirt or rainwater during the detection of the vehicle's position, size or cleaning area, it may not accurately determine the boundaries or shape of the vehicle. For example, the sensor incorrectly identifies a certain part of the vehicle as an obstacle, causing the cleaning system to clean with excessive water pressure or an inappropriate brushing angle. Such misjudgment may cause paint peeling, glass breakage or damage to external trim, and may even cause more serious mechanical failures such as nozzle damage or body deformation. Since the unattended car washing machine lacks manual supervision, if the system does not detect and alarm in time, the damage to the vehicle may cause the user to face high compensation costs and seriously affect the reputation of the operator.

[0006] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0007] The purpose of the present application is to provide an unattended car washer, which optimizes the washing system to run efficiently and safely under various environmental conditions through dynamic sensor adjustment and adaptive control based on environmental perception, monitors humidity, temperature and dust concentration in real time, automatically adjusts water pressure, brushing angle and time to ensure accurate cleaning and reduce vehicle damage, and improves system stability and user experience. At the same time, the system reduces maintenance requirements, prolongs equipment life, reduces maintenance frequency and operating costs, improves return on investment, and provides continuous efficient service to solve the problems in the above background art.

[0008] To achieve the above purpose, the present application provides the following technical solution: an unattended car washer, comprising a vehicle detection and recognition module, an environment monitoring and interference compensation module, an intelligent control and adaptive adjustment module, a sensor self-checking and fault diagnosis module, and a real-time correction and data feedback module:

[0009] The vehicle detection and recognition module uses high-definition cameras and various sensors to preliminarily detect and recognize vehicles entering the car washer, automatically calibrates the vehicles, and ensures the recognition accuracy of the sensors;

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

[0011] The intelligent control and adaptive adjustment module adjusts the working mode of the car washer based on the obtained vehicle information and environmental interference data, and ensures accurate and safe vehicle cleaning under different environmental conditions;

[0012] The sensor self-checking and fault diagnosis module periodically checks the sensors and judges whether the sensors have faults in combination with the intelligent control system feedback, and if an abnormality is found, an alarm is processed in time to ensure the accuracy of the data during the washing process;

[0013] The real-time correction and data feedback module monitors the data feedback by the sensors in real time during the washing process, automatically adjusts through the built-in correction algorithm if an abnormality is detected, and uploads the correction process to the cloud in real time for data analysis and feedback to the user to ensure that the entire washing process meets the expectations and avoids damaging the vehicle.

[0014] Preferably, the specific steps of using high-definition cameras and various sensors to preliminarily detect and recognize vehicles entering the car washer, and automatically calibrating the vehicles to ensure the recognition accuracy of the sensors are as follows:

[0015] By activating the high-definition cameras and various sensors, the preliminary detection and positioning of the vehicles entering the car washer are started to prepare for subsequent recognition and cleaning.

[0016] Combining image processing and sensor data, accurately measure the shape, size and obstacle position of the vehicle, providing key information for subsequent cleaning;

[0017] According to the obtained vehicle information, automatically calibrate the sensor and adjust the vehicle position to ensure that the cleaning work is carried out in the best position;

[0018] Through multiple verification and optimization of sensor accuracy, ensure that the vehicle identification is accurate and correct, and adjust the sensor parameters according to the environmental changes.

[0019] Preferably, the environmental sensor is used to monitor various interference factors in the working environment of the car washer in real time, and the sensing threshold of the sensor is adjusted through an adaptive algorithm to avoid the influence of external environment on the detection accuracy of the sensor. The specific steps are as follows:

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

[0021] Analyze the data collected by the environmental sensor to determine whether there are external interference factors that affect the detection accuracy of the sensor, and prepare for the compensation mechanism;

[0022] Adjust the sensing threshold of the sensor through an adaptive algorithm to adapt to environmental changes and ensure that the sensor still works accurately under adverse conditions;

[0023] Optimize the vehicle cleaning process according to the adjusted sensor parameters, and continuously learn and record the influence of environmental changes on system performance to optimize the adaptive algorithm.

[0024] Preferably, based on the obtained vehicle information and environmental disturbance data, an intelligent control system is used to adjust the working mode of the car washer to ensure accurate and safe vehicle cleaning under different environmental conditions. The specific steps are as follows:

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

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

[0027] Monitor sensor feedback data in real time during the cleaning process to adjust water pressure and brushing angle in time to maintain the accuracy and stability of the car washing process;

[0028] Optimize the control algorithm by learning the feedback data of each cleaning to continuously improve the adaptability to different environments and vehicle types.

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

[0030] ,

[0031] In the formula, P fault is a 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 judged 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. The correction formula is as follows: S corrected (t i ) = S actual (t i )-k·P fault , in which k is a correction factor S corrected (t) is the corrected signal output at the i-th time point t.

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

[0034] alkyl:

[0035] ,

[0036] In the formula, P alarm is an alarm signal parameter, and S threshold is a preset minimum effective signal threshold.

[0037] Preferably, in the car washing process, the data fed back by the sensor is monitored in real time. If an abnormality is detected, automatic adjustment is performed through the built-in correction algorithm, and the correction process is uploaded to the cloud in real time for data analysis and feedback to the user, so as 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, sensors 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 any abnormalities. At this time, it calculates the deviation of the water flow pressure from the set standard value and the difference between the scrubbing angle and the ideal angle. 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 have deviations beyond the tolerance range, 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 a smooth and stable 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 during the correction process for in-depth analysis, and through machine learning and big data analysis, the correction algorithm and control parameters will be continuously optimized to improve the efficiency and accuracy of subsequent cleaning processes, as follows:

[0042]

[0043] 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 brushing 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 application are:

[0045] Through dynamic sensor adjustment and adaptive control algorithm optimization based on environmental perception, the car washing system can maintain efficient and safe operation under various environmental conditions. Environmental sensors monitor humidity, temperature, dust concentration and other external factors in real time, and dynamically adjust water pressure, brushing angle and cleaning time and other parameters to ensure the accuracy and thoroughness of the vehicle cleaning process. This adaptive adjustment can effectively respond to changes in different weather, seasons and vehicle models, avoiding improper operation due to environmental changes or vehicle characteristics, significantly improving the stability and cleaning effect of the system, reducing the risk of vehicle damage and improving user experience, while improving the long-term stability and efficiency of the system.

[0046] Through self-learning and optimization feedback mechanism, the present application continuously optimizes sensor parameters and control algorithms, significantly reduces the maintenance requirements of the equipment, and prolongs the service life of the equipment. Through real-time monitoring and adjustment, the system can effectively avoid faults caused by environmental interference or equipment aging, reduce maintenance frequency and downtime, and reduce operating costs. With data accumulation and algorithm optimization, the system's operating efficiency is continuously improved, further improving the return on investment of the car washer, while ensuring that the equipment can work efficiently and continuously, providing more stable and economical services. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings described below are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0048] Figure 1 A module schematic diagram of the unattended car washer of the present application. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many 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 this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0050] The present invention provides Figure 1 The unmanned car washing machine shown in the figure includes a vehicle detection and identification module, an environmental 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:

[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 vehicle's location, shape and size information, and automatically calibrate the vehicle to ensure sensor recognition accuracy;

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

[0053] By activating high-definition cameras and multiple sensors, it begins preliminary detection and positioning of vehicles entering the car wash machine, preparing for subsequent identification and cleaning;

[0054] When a vehicle enters the car wash's operating area, high-definition cameras and various sensors (such as ultrasonic sensors and infrared sensors) begin operating. The system automatically identifies vehicle entry signals and activates all relevant sensors and cameras. These sensors will first perform an environmental scan to determine the vehicle's initial position and distance. Infrared sensors, in particular, will help monitor the vehicle's outer contours. At this point, the camera will automatically focus on the front, sides, and top of the vehicle, capturing images of the vehicle in real time and transmitting them to the control system. Ultrasonic sensors provide more detailed distance data to ensure accurate identification of the vehicle's edges and obstacles.

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

[0056] By combining image processing and sensor data, the vehicle's shape, size, and obstacle location are accurately measured, providing key information for subsequent cleaning.

[0057] Through high-definition camera images, the system accurately measures the vehicle's shape and size. Combined with the reflection signals of ultrasonic sensors, the system not only obtains the length, width, and height of the vehicle, but also determines the positions of the wheels, roof, and other important parts through infrared sensors. These data are transmitted to the central processing unit for analysis and processing to identify the type of vehicle (such as sedan, SUV, truck, etc.). This process also includes judging whether there are obstacles on the vehicle body (such as roof racks, rearview mirrors, etc.), providing 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 comprehensively evaluates the multi-dimensional data of the vehicle. By integrating camera image processing technology and real-time sensor data, very accurate vehicle contour and size information can be obtained. To reduce errors, the vehicle recognition algorithm adjusts parameters according to different vehicle types, such as automatically increasing measurement points for higher vehicles (such as SUVs or trucks) to ensure that the cleaning of positions such as the roof is not missed. This step is crucial for the accuracy of vehicle recognition, and only by accurately obtaining the size information of the vehicle can reasonable parameter settings be provided for the subsequent car washing process.

[0059] According to the obtained vehicle information, automatically calibrate the sensors 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 accurately align and work synchronously. If there is a deviation or positioning error in the sensors at this time, the system will automatically adjust the sensing area of the sensors to avoid improper car washing due to errors. In some cases, when the vehicle is placed off the ideal track, the vehicle recognition module will issue an adjustment instruction to slightly move the vehicle to the best position using adjustment equipment to ensure that all sensors can accurately measure. At this time, the vehicle's contour matches the working area of the car washer, and the sensors reconfirm the vehicle to ensure that calibration is complete before proceeding to the next step of cleaning operation.

[0061] The vehicle calibration step is to eliminate all potential error sources, ensuring that the car washing system can accurately perform in 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 washer are optimally matched. Especially for some irregular or small vehicles (such as city small cars or electric cars), the system needs more precise calibration to ensure that the sensor can correctly measure each part of the vehicle, avoiding omissions or errors during the cleaning process due to positional deviations.

[0062] By verifying and optimizing sensor accuracy multiple times, ensure that vehicle identification is accurate and adjust sensor parameters according to environmental changes;

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

[0064] The key to this step is to verify the accuracy of sensor data multiple times to eliminate all possible interference and misjudgment. In this process, the system evaluates the response time and measurement accuracy of the sensor to ensure that all measurement results are calculated and verified. This process not only involves calibration, but also involves adaptive adjustment of real-time environmental data, such as when air humidity or temperature changes, the system can intelligently adjust the parameters of the sensor to ensure that the accuracy of identification always remains at a high level, thereby avoiding vehicle damage caused by misidentification or incorrect cleaning.

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

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

[0067] Activate the environmental sensor to monitor external environmental factors in real time, providing basic data for subsequent environmental interference compensation;

[0068] After the vehicle enters the car washer and is preliminarily identified and calibrated by high-definition cameras and sensors, the system will activate environmental sensors (such as humidity, temperature, dust sensors, etc.) to start real-time monitoring of the working environment around the car washer. The environmental sensors will continuously collect relevant environmental data such as humidity changes, air temperature, dust concentration, etc., which provide dynamic change information of the external environment for the system. Through real-time monitoring of the environmental sensors, the system can obtain the environmental state of the current operating area of the car washer, ensuring that external factors do not interfere with the accuracy of the sensors and the identification process.

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

[0070] Analyze the data collected by the environmental sensors to determine whether there are external interference factors that affect the detection accuracy of the sensors and prepare for the compensation mechanism;

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

[0072] After collecting environmental data, the system will perform real-time analysis to evaluate the potential impact of these factors on sensor performance. For example, high humidity may reduce the detection distance and accuracy of the sensor, affecting the accurate identification of the vehicle's position. Dust accumulation may block the emission and reception devices of the infrared sensor, affecting the identification of the vehicle's outline. At this time, the system not only evaluates environmental changes individually, but also compares these changes with vehicle information to ensure that the external environment does not adversely affect the identification and cleaning process of the vehicle. If an impact is found, the system will enter the adjustment and compensation stage.

[0073] Automatically adjust the sensing threshold of the sensor through an adaptive algorithm to adapt to environmental changes and ensure that the sensor still works accurately under adverse conditions;

[0074] After the system detects changes in the external environment and analyzes possible interference factors, it automatically adjusts the sensor's sensing threshold based on a pre-set adaptive algorithm. For example, in conditions of high humidity or excessive dust, the system automatically increases sensor sensitivity or expands its sensing range to ensure proper functioning under adverse conditions. Simultaneously, the system adjusts the infrared sensor's sensing intensity to ensure it can effectively identify the vehicle's outline and size even when covered in dust. By dynamically adjusting sensor parameters, the system maintains efficient and accurate recognition capabilities.

[0075] The core of this step is to automatically adjust the sensors through adaptive algorithms, enabling them to adapt to changing environmental conditions. These algorithms dynamically adjust the sensors' operating thresholds based on environmental data such as humidity, temperature, and dust concentration. For example, in high humidity, the system might increase the ultrasonic sensor's transmit power to ensure unimpeded signal transmission. In high dust concentrations, the system might increase the infrared sensor's receiver sensitivity to ensure accurate vehicle information even when parts of the sensor surface are covered. This real-time adjustment not only improves the accuracy of the car wash process but also prevents 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 provides real-time feedback and optimization of the vehicle recognition process based on the adjusted sensor parameters and optimization results. This process involves re-evaluating sensor accuracy and optimizing cleaning parameters (such as water pressure and brush angle) based on environmental data and vehicle detection information. The system also continuously learns and records its performance under different environmental conditions, optimizing its adaptive algorithms and enabling it to automatically adjust its operating mode based on future environmental changes, minimizing the impact of these changes on the car wash process.

[0078] After adjusting for environmental factors, the system compares the results with the actual car wash operation to ensure the entire wash process can proceed without being negatively impacted by the external environment. For example, when the ambient humidity is high, the system may reduce water pressure to prevent excessive moisture from affecting the wash effect or causing damage to the equipment. Simultaneously, the environmental compensation mechanism continuously optimizes the adaptive algorithm based on empirical data from each wash, gradually improving the system's sensitivity and ability to respond to environmental changes. Ultimately, the entire car wash process will achieve optimal cleaning results based on this continuously optimized algorithm, ensuring that the vehicle is not damaged by environmental changes.

[0079] The 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 washing machine, automatically selects 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 adjusts the working mode of the car washing machine, automatically selects appropriate water pressure, brushing angle, and cleaning time, and ensures accurate and safe vehicle cleaning under different environmental conditions. The specific steps are as follows:

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

[0082] After the vehicle is identified and calibrated, the system will integrate and analyze vehicle information (such as size, shape) and environmental data (such as humidity, temperature, dust concentration, etc.). These data provide complete background information for the intelligent control system, helping the system determine the specific needs of the current washing task. For example, in a high-humidity or high-dust environment, 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 of the vehicle will also affect the cleaning strategy of the car washing machine, especially for different vehicle types (such as small 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 washing needs. The size and shape of the vehicle will affect the distribution of water flow and the angle of brushing, so the system first needs to evaluate whether the threshold of the sensor needs to be adjusted through the humidity, temperature, and dust data provided by the environmental sensor, and then make an optimized decision on the cleaning parameters. For example, if the humidity is high, the system may adjust the water pressure to avoid excessive water washing; if the dust concentration in the environment is high, it needs to increase the water flow or change the brushing angle to ensure the cleaning effect.

[0085] According to the analysis results, automatically adjust the water pressure, brushing angle, and cleaning time to adapt to different environmental conditions and vehicle types, ensuring the best cleaning effect;

[0086] Based on the analysis results, the intelligent control system will automatically select the appropriate water pressure, brushing angle, and cleaning time. For example, in high humidity, the system will appropriately reduce the water pressure to avoid excessive water causing instability or equipment failure during the car washing process; in a dusty environment, the system may increase the water flow intensity and increase the cleaning time to ensure thorough removal of dust. The brushing angle will also be dynamically adjusted according to the vehicle's shape, for example, for tall SUVs or vans, the brushing angle will be increased to ensure thorough cleaning of the roof and both sides.

[0087] In this step, the system will automatically select the best working mode based on the comprehensive analysis of the data. Water pressure is one of the most critical parameters in the car washing process, too high water pressure may damage the vehicle surface, too low water pressure cannot achieve effective cleaning effect. The adjustment of 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 influence), to ensure that each part of the vehicle body can be fully cleaned. The adjustment of cleaning time is also dynamic, the system will fine-tune according to the dirt level of the vehicle and environmental conditions, to ensure the cleaning effect without wasting resources.

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

[0089] During the car washing process, the system will monitor the data feedback from the sensors in real time to ensure the accuracy of the cleaning effect. For example, the system will monitor the dirt level of the vehicle body and possible abnormalities during the brushing process, such as excessive water flow pressure or improper brushing angle, through ultrasonic sensors and infrared sensors. If the system detects abnormal conditions, it will immediately correct by adjusting the water pressure or brushing angle, and feedback the adjustment results to the control center, to ensure that the car washing process is always in the best state.

[0090] The focus of this process is real-time feedback and dynamic adjustment. Through real-time monitoring, the system can timely find the deviation that may occur during the car washing process, such as excessive water pressure, improper brushing angle, etc. If the sensor detects that the water flow pressure is too high, the system will automatically reduce the water pressure; if the brushing angle deviates from the standard, the system will re-adjust the angle to ensure that each part of the vehicle is properly cleaned during the entire car washing 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, and the adaptability to different environments and vehicle types is continuously improved;

[0092] The system will continuously optimize the control algorithm through feedback and adjustment results from each cleaning process. After each cleaning, the system will record parameters such as water pressure, brushing angle, cleaning time, as well as environmental conditions and vehicle conditions. These data will be used to train the machine learning model, so that the system can more accurately select the best cleaning parameters in future cleaning tasks. At the same time, with the accumulation of more data, 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 continuously learns and evolves. By continuously optimizing the control algorithm, the system not only improves its adaptability to different environments and vehicle models, but also responds 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 to predict the best car washing solution under different environmental conditions, gradually improving car washing effectiveness and system reliability.

[0094] The sensor self-checking and fault diagnosis module regularly checks the sensors and judges whether the sensors have failed in combination with the intelligent control system feedback. If abnormalities are found, timely alarm processing is performed to ensure the accuracy of data during the car washing process.

[0095] The specific steps for regularly checking the sensors and judging whether the sensors have failed in combination with the intelligent control system feedback are as follows:

[0096] During the sensor self-checking 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 degree of sensor abnormality is diagnosed through calculation, fault diagnosis parameters are generated, and the time change trend is evaluated. The calculation expression is as follows:

[0097] ,

[0098] In the formula, 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 faultIf the value of P exceeds the threshold value, it indicates that the abnormal amplitude of the sensor output signal is large, and the system will consider that the sensor has failed or needs to be calibrated. This step helps to quickly diagnose sensor problems by comparing the deviation of the actual signal from the preset standard signal.

[0100] 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, with the correction formula as follows: S corrected (t i )=S actual (t i )-k·P fault , where k is the correction factor for controlling the correction amplitude, 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, and is usually optimized by the learning model of the system according to historical data. Through this step, the system can correct the detected abnormal value to a signal output closer to the real environment, so that the subsequent cleaning process is not affected by the sensor fault.

[0102] Finally, according to the corrected signal, subsequent monitoring and alarm processing are performed, and if the sensor fault cannot be repaired, it is determined whether the accuracy requirements of the car washing process can still be met according to the corrected parameters, and if not, an alarm signal is generated, with the generation formula as follows:

[0103] alkane:

[0104] ,

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

[0106] If P alarm exceeds the threshold value, it indicates that the corrected signal and the minimum effective signal threshold S threshold are too different, and the system will issue an alarm signal to indicate that the sensor needs to be repaired or replaced. This step ensures that the system can effectively judge the fault 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, real-time monitoring of sensor feedback data, if an abnormality is detected (such as excessive water flow pressure or improper brushing angle), automatic adjustment is performed through the built-in correction algorithm, and the correction process is uploaded to the cloud in real time for data analysis and feedback to the user, ensuring that the entire car washing process meets expectations and avoids damaging the vehicle;

[0108] During the car washing process, real-time monitoring of sensor feedback data, if abnormal (such as water flow pressure is too high or brush angle is not appropriate), through the built-in correction algorithm for automatic adjustment, and the correction process is uploaded 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 damage to the vehicle. The specific steps are as follows:

[0109] During the car washing process, sensors (including water flow pressure sensors, brush angle sensors, etc.) collect real-time working environment data and transmit them to the control system. The control system first detects the acquired data to identify whether there are abnormal conditions, such as water flow pressure being too high, brush angle being inappropriate, etc. At this time, the deviation of water flow pressure from the set standard value and the difference between the brush angle and the ideal angle are calculated, and the expressions are 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 set ideal water flow pressure, Δθ is the deviation of the brush angle, θ actual is the current actual brush angle, and θ target is the set ideal brush angle.

[0110] The core task of this step is to detect and calculate the errors of water flow pressure and brush angle in real time, preparing for subsequent correction.

[0111] Once the water flow pressure and brush angle are detected to have deviations beyond the tolerance range, correction calculation is performed. According to the current deviation value, the water flow pressure and brush angle are adjusted to approach the set value. At this time, an adaptive correction coefficient is introduced to optimize the correction process based on the current environmental data, ensuring the smoothness and stability of the correction process. The expressions are 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 brush angle, and k θ is the brush angle correction coefficient.

[0112] The correction coefficients k P and k θ are dynamically adjusted based on 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 coefficient kP to increase the water flow pressure.

[0113] After the initial correction is completed, the corrected water flow pressure and brushing angle are recalculated to confirm whether the desired target is met. All correction calculation results are uploaded to the cloud for data analysis and recording in real time. The correction process and real-time data are also fed back to the user through the network to ensure that the user understands the current car washing progress and correction situation. 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, and 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 meets the expected target. If the final deviation still exceeds the set tolerance range, further correction will be performed, and the correction value will be transmitted to the cloud for the user 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, brushing 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 subsequent cleaning processes. These data not only optimize the car washing process but also provide detailed feedback information for users to 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 brushing 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 in multiple correction processes to continuously optimize the correction algorithm and improve the accuracy and efficiency of subsequent car washing processes.

[0119] In this embodiment, the working performance of the car washing machine is highly dependent on changes in environmental conditions, so the system dynamically adjusts the sensor working mode by integrating environmental sensor data after identifying and calibrating the vehicle. Specifically, the car washing machine uses high-definition cameras, infrared sensors, ultrasonic sensors, and other sensors to comprehensively detect the vehicle entering the washing machine. Vehicle information includes size, shape, position, etc., while environmental sensor data includes humidity, dust concentration, temperature, etc. These data will be analyzed by intelligent algorithms and correlated with each other to determine the sensor's sensing threshold and working mode.

[0120] When the car washing machine detects a vehicle entering the working area, it first activates the sensor and environmental monitoring module. 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 starts the environmental sensors (such as humidity, temperature, dust sensors, etc.) to monitor the environmental factors in the car washing machine working area in real time. The sensors will collect humidity, dust concentration, air temperature, etc. These data reflect the current environmental conditions of the car washing machine operating area.

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

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

[0123] After each car washing task, 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 based on the performance of the sensor under different environmental conditions. Long-term data accumulation will enable the system to quickly respond under similar environmental conditions, automatically select the most appropriate sensor parameter settings, and further improve work efficiency and cleaning effect.

[0124] The key of this embodiment lies in dynamically adjusting 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 washer can maintain the best working state under various environmental conditions, ensuring accurate identification and efficient cleaning of vehicles.

[0125] Embodiment Two: The core of this embodiment is that the intelligent control system can dynamically adjust the water pressure, brushing angle, cleaning time and other working parameters of the car washer according to the identification information of the vehicle and the environmental interference data, ensuring efficient and safe cleaning every time. The car washer automatically selects and optimizes every link in the car washing process by combining the size, shape, surface dirt level of the vehicle and environmental data, ensuring that the washing effect is optimal regardless of the type of vehicle or changes in the environment.

[0126] After detecting the vehicle, the car washer first obtains the size, position, shape and other information 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 vehicle surface 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 water accumulation.

[0127] According to the size, shape and current environmental data of the vehicle, the system will automatically select 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 side of the vehicle are thoroughly cleaned; for small cars, the system will reduce the water pressure appropriately to avoid damage to the vehicle surface caused by excessive water flow. The adjustment of 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 dirt level of the vehicle body to ensure the precision and thoroughness of the cleaning process.

[0128] During the car washing process, the system will monitor the feedback data from sensors in real-time, such as water pressure, brushing angle, cleaning time, etc. Based on the actual situation, the system will make fine adjustments to the cleaning process. If it finds that the water pressure is too high or the brushing angle is not suitable during the process, the system will immediately adjust it. For example, if the sensor feedback shows that the 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 scratch the surface of the vehicle, so the system will immediately adjust the brushing angle to reduce the potential risk. Through this real-time feedback mechanism, the car washing machine can ensure that each step is accurately executed.

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

[0130] This embodiment optimizes and dynamically adjusts the intelligent control system, so that the car washing machine can make fine adjustments in each cleaning step, not only ensuring thorough cleaning of the vehicle surface, but also minimizing the risk of damage caused by inappropriate cleaning parameters.

[0131] Embodiment Three: The innovation of this embodiment lies in 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 cleaning needs of various vehicle types. Through continuous optimization of control strategy, the system not only improves work efficiency, but also ensures that each cleaning of the vehicle achieves the best effect and maximizes the avoidance of operation deviation caused by environmental changes.

[0132] After each car washing 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) will be uploaded to the central control system in real time. The system analyzes these historical data to evaluate the performance of each car washing task, including cleaning effect, time efficiency and sensor accuracy. Through these data, the system can identify which environmental conditions certain settings can better optimize the cleaning effect.

[0133] Based on historical data, the system uses adaptive machine learning algorithms to continuously optimize control strategies. Through analysis and summary of data, the system automatically adjusts various working parameters. For example, in high-temperature weather, the system finds that extending the cleaning time of some vehicles helps remove more dirt, or in low-humidity environments, water flow pressure can be appropriately increased for better results. As the amount of data increases, the system gradually learns to automatically adjust water flow, brushing angle, cleaning time, and other parameters under different conditions to achieve optimal cleaning results.

[0134] In new cleaning tasks, the system automatically adjusts working parameters based on the optimized adaptive control algorithm. This process not only adjusts parameters based on environmental data, but more importantly, the system can perceive changes in vehicles and environments in real time and dynamically adjust operation modes. For example, in

[0135] In harsh weather conditions such as heavy rain or extremely cold weather, the system automatically adjusts water temperature, brushing intensity, and cleaning duration to ensure cleaning effectiveness and vehicle safety. In the process of continuously accumulating experience and optimization, the system can maximize resource waste reduction and achieve optimal cleaning results under different environmental conditions.

[0136] Over time, the system can more accurately assess the performance of each cleaning through continuous self-learning and data feedback. Each adjustment and optimization process is recorded and serves as a basis for future decisions. The system's self-learning mechanism not only improves its adaptability to complex environments but also continuously improves the accuracy of vehicle recognition and the stability of cleaning results. Through continuous learning and data feedback, the system will gradually evolve over time and provide more accurate and personalized cleaning solutions in different scenarios.

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

[0138] This invention optimizes the car washing system through dynamic sensor adjustment and adaptive control algorithm based on environmental perception, enabling the system to operate efficiently and safely under various environmental conditions. Environmental sensors monitor external factors such as humidity, temperature, and dust concentration in real time and dynamically adjust parameters such as water pressure, brushing angle, and cleaning time to ensure the accuracy and thoroughness of the vehicle cleaning process. This adaptive adjustment effectively responds to changes in different weather, seasons, and vehicle models, avoiding improper operation due to environmental changes or vehicle characteristics, significantly improving the stability and cleaning effectiveness of the system, reducing the risk of vehicle damage, and improving user experience, while enhancing the long-term stability and efficiency of the system.

[0139] The application continuously optimizes sensor parameters and control algorithms through self-learning and feedback mechanisms, significantly reducing maintenance requirements and extending the service life of the equipment. 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 data accumulation and algorithm optimization, the system's operating efficiency is continuously improved, further improving the return on investment of the car washer, while ensuring that the equipment can work efficiently and continuously, providing more stable and economical services.

[0140] The above formulas are dimensionless values calculated, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the latest real situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0141] The above only describes some exemplary embodiments of the application by way of illustration, and it is self-evident that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the application. Therefore, the above figures and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the application.

[0142] It should be noted that in this paper, if there are relationship terms such as first and second, 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 the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "includes a" does not exclude the presence of another identical element in the process, method, article or equipment including the element.

[0143] It should be understood that in various embodiments of the present application, the size of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0144] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0145] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0146] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiment according to actual needs.

[0147] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically, or two or more units can 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 skilled person in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0149] The above only describes some exemplary embodiments of the present application by way of illustration, and it is needless to say that those skilled in the art can modify the described embodiments in various ways without deviating from the spirit and scope of the present application. Therefore, the above figures and descriptions are illustrative in nature and should not be understood as limiting the scope of protection of the claims of the present application.

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-test 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 sensors; The environmental monitoring and interference compensation module uses environmental sensors to monitor various interference factors in the car wash machine's working environment 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; The intelligent control and adaptive adjustment module uses an intelligent control system to adjust the car wash machine's operating mode based on acquired vehicle information and environmental interference data, ensuring accurate and safe vehicle cleaning under different environmental conditions; The sensor self-test and fault diagnosis module regularly performs self-tests 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 will be issued in a timely manner to ensure data accuracy during the car wash process. The real-time correction and data feedback module monitors sensor data in real time during the car wash process. If an anomaly is detected, it automatically adjusts using a built-in correction algorithm. The correction process is 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. 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 operating range, the degree of sensor abnormality is diagnosed through calculation, and fault diagnosis parameters are generated. These parameters are evaluated based on the time change trend. The calculation expression 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 , by adjusting the algorithm to correct the output signal of the sensor, the signal output is weighted and corrected. 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; Finally, subsequent monitoring and alarm processing are performed based on the corrected signal. If the sensor failure cannot be repaired, the corrected parameters are used to determine whether the accuracy requirements of the car wash process can still be met. If not, an alarm signal will be generated. The generation formula is as follows: Yu: ， Where, P alarm is the alarm signal parameter, S threshold It is the preset minimum valid signal threshold.

2. The unmanned car washing machine according to claim 1, characterized in that: The specific steps for 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, it begins preliminary detection and positioning of vehicles entering the car wash machine, preparing for subsequent identification and cleaning; Combining image processing and sensor data, it accurately measures 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 optimizations of sensor accuracy, we ensure accurate vehicle recognition and adjust sensor parameters according to environmental changes.

3. The unmanned car washing machine according to claim 1, characterized in that: The specific steps for using environmental sensors to monitor various interference factors in the car wash machine's working environment in real time and adjusting the sensor's sensing threshold through an adaptive algorithm to prevent the external environment from affecting 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 unmanned car washing machine according to claim 1, characterized in that: Based on the acquired vehicle information and environmental interference data, an intelligent control system is used to adjust the car wash machine's operating mode to ensure accurate and safe vehicle cleaning under different environmental conditions. The specific steps are as follows: Analyze vehicle information and environmental data to provide basic data for adjusting the working mode of the car wash machine and ensure accurate judgment of cleaning needs; Automatically adjusts water pressure, brushing angle and cleaning time based on analysis results to adapt to different environmental conditions and vehicle types to ensure optimal cleaning results; During the cleaning process, sensor feedback data is monitored in real time, and water pressure and brush angle are adjusted in a timely manner to maintain the accuracy and stability of the car washing process; By learning the feedback data from each cleaning, the control algorithm is optimized and the adaptability to different environments and vehicle models is continuously improved.

5. The unmanned car washing machine according to claim 1, characterized in that: During the car wash process, sensor data is monitored in real time. If an anomaly is detected, the system automatically adjusts the data using a built-in correction algorithm. The correction process is 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. The specific steps are as follows: During the car wash process, sensors 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 any abnormalities. At this time, it calculates the deviation of the water flow pressure from the set standard value and the difference between the scrubbing angle and the ideal angle. 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 have deviations beyond the tolerance range, 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 a smooth and stable 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 from 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: 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.

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