Energy-saving control method and apparatus for fully automatic car washing machine

Abstract

An energy-saving control method for a fully automatic car washing machine. The method comprises: step S1: acquiring configuration information of a car washing machine, and a closed-loop control loop, and determining basic device information, the closed-loop control loop comprising a sensing module-control module-execution model-based closed loop, and full-loop intervention based on a communication module; step S2: in combination with the basic device information, using dual-drive collaboration on the basis of a motion control dimension and an execution control dimension to supervise and train a dual-drive control model, the dual-drive control model being built into the control module; step S3: sensing a target vehicle and performing cyclic scanning, determining multi-source sensing data, and identifying a stain distribution point cloud of a cleaning target, each point cloud being labeled with stain characteristics; step S4: traversing the stain distribution point cloud, performing dimensional parallel decision-making and fitting optimization in combination with the dual-drive control model using low-consumption and energy-saving as a guide, to determine an energy-saving control strategy; by means of calibrating a frequency modulation loss and an execution loss, identifying necessary frequency modulation points and configuring a digital frequency modulator, to perform cleaning and braking control in response to the energy-saving control strategy. An energy-saving control apparatus for a fully automatic car washing machine is also disclosed. The energy-saving control method solves the technical problem of energy waste in a vehicle washing process caused by the lack of targeted and refined energy management measures when existing vehicle washing machine automatic control technology faces different vehicle conditions, thereby improving energy utilization efficiency in the vehicle washing process, and saving energy.

Description

An energy-saving control method and device for a fully automatic car wash machine Technical Field

[0001] This application relates to the field of automation control technology, specifically to an energy-saving control method and device for a fully automatic car wash machine. Background Technology

[0002] Fully automatic car wash machines, as efficient and convenient car washing tools, play an important role in the modern automotive service industry. With technological advancements, the automatic control technology of car wash machines is also constantly developing, aiming to improve car washing efficiency, safety, and user-friendliness.

[0003] Existing automatic control technologies for car wash machines primarily rely on programmable logic controllers (PLCs), sensor technology, and electrically controlled valves. These systems control various aspects of the car wash process through preset programs, such as water spraying, brush movement, and detergent application. While these technologies improve efficiency and safety to some extent, they still have some shortcomings. First, existing automatic control methods often lack targeted and refined energy management. This means that car wash machines typically use the same washing patterns, consuming similar energy and water resources, regardless of whether it's a large SUV or a small sedan. This approach easily leads to energy and water waste, especially when washing smaller or cleaner vehicles. Second, existing automatic control technologies cannot accurately identify the specific distribution of dirt on a vehicle. This may result in unnecessary cleaning areas being treated, further increasing energy consumption. Summary of the Invention

[0004] This application provides an energy-saving control method and device for a fully automatic car wash machine, which solves the technical problem that existing automatic control technologies for car wash machines fail to fully consider real-time vehicle conditions and specific cleaning needs, lack targeted and refined energy management measures, and thus lead to energy waste during the car wash process. This achieves the technical effect of improving energy utilization efficiency and saving energy during the car wash process.

[0005] In view of the above problems, on the one hand, this application provides an energy-saving control method for a fully automatic car wash machine. The method includes: acquiring the configuration information and closed-loop control loop of the car wash machine, determining the basic equipment information, wherein the closed-loop control loop includes a closed loop based on a sensing module-control module-execution module, and a full-loop intervention based on a communication module; combining the basic equipment information, supervising and training a dual-drive control model based on motion control and execution control dimensions, wherein the dual-drive control model is built into the control module; sensing the target vehicle and performing a cyclic scan to determine multi-source... The system senses and identifies the stain distribution point cloud of the target vehicle, where each point cloud is labeled with stain characteristics. It then traverses the stain distribution point cloud, performing parallel decision-making and fitting optimization based on low energy consumption and the dual-drive control model to determine an energy-saving control strategy. The energy-saving control strategy is then traversed, and necessary frequency tuning points are identified by calibrating frequency modulation losses and execution losses. Each necessary frequency tuning point is labeled with frequency tuning parameters. A digital frequency tuner is introduced and configured based on the necessary frequency tuning points. The execution module responds to the energy-saving control strategy by performing cleaning braking control on the target vehicle.

[0006] On the other hand, this application also provides an energy-saving control device for a fully automatic car wash machine. The device includes: a basic equipment information acquisition unit, used to acquire the configuration information and closed-loop control circuit of the car wash machine, and determine the basic equipment information. The closed-loop control circuit includes a closed loop based on a sensing module-control module-execution module, and a full-loop intervention based on a communication module; a dual-drive control model training unit, used to combine the basic equipment information to supervise and train a dual-drive control model based on motion control and execution control dimensions, the dual-drive control model being built into the control module; and a cleaning target identification unit, used to sense target vehicles and perform cyclic scanning to determine multi-source... The system comprises: a sensor data point cloud identifying the stain distribution of the target vehicle, wherein each point cloud is labeled with stain characteristics; an energy-saving control strategy generation unit, which traverses the stain distribution point cloud, performs parallel decision-making and fitting optimization based on low energy consumption and the dual-drive control model, and determines the energy-saving control strategy; a necessary frequency tuning point identification unit, which traverses the energy-saving control strategy, identifies necessary frequency tuning points by calibrating frequency tuning loss and execution loss, and the necessary frequency tuning points are labeled with frequency tuning parameters; and an energy-saving control unit, which introduces a digital frequency tuner, configures the digital frequency tuner based on the necessary frequency tuning points, and the execution module responds to the energy-saving control strategy by performing cleaning braking control on the target vehicle.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: Acquiring the configuration information and closed-loop control circuit of the car wash machine, determining the basic equipment information, wherein the closed-loop control circuit includes a closed loop based on the sensing module-control module-execution module, and a full-loop intervention based on the communication module. This step ensures that the device can customize the control strategy according to the specific configuration and function of the car wash machine, and the use of the closed-loop control circuit improves the stability and response speed of the control. Combining the basic equipment information, a dual-drive control model is trained under supervision based on the dual-drive collaboration of motion control and execution control dimensions, and the dual-drive control model is built into the control module. By combining motion control and execution control, this model can more accurately adjust the car wash process, improving cleaning efficiency and effect. Sensing the target vehicle and performing cyclic scanning, determining multi-source sensor data and identifying the stain distribution point cloud of the cleaning target, wherein each point cloud is marked with stain characteristics. Using multi-source sensor data, the device can accurately identify the stain distribution of the vehicle, providing precise guidance for subsequent cleaning, ensuring the targeting and efficiency of cleaning, and avoiding energy waste. The system iterates through the stain distribution point cloud, prioritizing low energy consumption, and performs dimensional parallel decision-making and fitting optimization using the dual-drive control model to determine an energy-saving control strategy and optimize energy utilization during the car wash process. It iterates through the energy-saving control strategy, calibrating frequency modulation losses and execution losses to identify necessary frequency modulation points, each marked with a frequency modulation parameter. This step, by identifying and adjusting necessary frequency modulation points, further optimizes the control strategy and reduces energy waste. A digital frequency modulator is introduced and configured based on the necessary frequency modulation points. The execution module responds to the energy-saving control strategy, performing cleaning braking control on the target vehicle to ensure the accuracy and efficiency of the cleaning process.

[0008] In summary, this application, through precise stain identification, dual-drive collaborative control, energy-saving optimization decision-making, loss correction, and refined execution control, not only reduces the energy and water consumption during the operation of the car wash machine, but also improves the accuracy and efficiency of cleaning, while ensuring the quality of the car wash, thereby promoting the development of the car wash industry towards a more environmentally friendly and efficient direction.

[0009] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0010] Figure 1 is a schematic flowchart of an energy-saving control method for a fully automatic car wash machine provided in an embodiment of this application.

[0011] Figure 2 is a flowchart illustrating the process of determining the point cloud execution strategy in an energy-saving control method for a fully automatic car wash machine provided in an embodiment of this application.

[0012] Figure 3 is a flowchart illustrating the necessary frequency tuning points in an energy-saving control method for a fully automatic car wash machine provided in an embodiment of this application.

[0013] Figure 4 is a schematic diagram of the structure of an energy-saving control device for a fully automatic car wash machine provided in an embodiment of this application.

[0014] Explanation of reference numerals in the attached figures: Equipment basic information acquisition unit 10, dual-drive control model training unit 20, cleaning target identification unit 30, energy-saving control strategy generation unit 40, necessary frequency tuning point identification unit 50, energy-saving control unit 60. Detailed Implementation

[0015] This application provides an energy-saving control method and device for a fully automatic car wash machine, which solves the technical problem that existing automatic control technologies for car wash machines fail to fully consider real-time vehicle conditions and specific cleaning needs, lack targeted and refined energy management measures, and thus lead to energy waste during the car wash process. This achieves the technical effect of improving energy utilization efficiency and saving energy during the car wash process.

[0016] Example 1, as shown in Figure 1, provides an energy-saving control method for a fully automatic car wash machine. The method includes: Step S1: Obtaining the configuration information and closed-loop control circuit of the car wash machine, and determining the basic information of the equipment. The closed-loop control circuit includes a closed loop based on the sensing module-control module-execution module, and a full-loop intervention based on the communication module.

[0017] Specifically, the configuration information of a car wash machine refers to its hardware configuration, including but not limited to the type and quantity of sensors, actuator type, motor model and parameters, and communication module type. A closed-loop control loop is a fundamental component of the automatic control system of a car wash machine, comprising three main modules: a sensing module, a control module, and an execution module. The closed-loop system adjusts the operation of the car wash machine through continuous feedback loops to achieve predetermined goals. The sensing module collects real-time data during the car wash process, such as vehicle position, speed, and cleaning effect. The control module receives data from the sensing module, makes decisions based on preset programs and algorithms, and directs the operation of the execution module. The execution module, according to the instructions from the control module, actually controls various components of the car wash machine, such as the spray arms and brushes. The communication module is responsible for transmitting data within the closed-loop control loop, ensuring information flow between modules, such as Wi-Fi modules and 4G / 5G modules.

[0018] First, upon startup, the device automatically acquires the car wash machine's configuration information, including detailed hardware and software information, and summarizes this information into basic equipment information to help the device understand the car wash machine's functions and limitations. Next, the device initializes a closed-loop control circuit, which relies on the close cooperation of a sensing module, a control module, and an execution module. For example, the sensing module might include radar sensors for detecting vehicle position and pressure sensors for monitoring water pressure. The control module might be a computer system with a built-in PLC, used to make decisions based on received sensor data. The execution module might include electric valves and motors, operating the car wash machine's mechanical components according to instructions from the control module. Furthermore, the communication module plays a crucial role throughout the process, ensuring that data collected by the sensing module is transmitted to the control module in real time, and that instructions from the control module are quickly transmitted to the execution module. For example, the communication module might use wireless or wired networks to transmit data.

[0019] Through step S1, the device can fully grasp the hardware configuration and control logic of the car wash machine, providing accurate data support and control foundation for subsequent intelligent upgrades.

[0020] Step S2: Combining the basic equipment information, supervise the training of a dual-drive control model based on the dual-drive collaboration of motion control and execution control dimensions. The dual-drive control model is built into the control module.

[0021] Specifically, the motion control dimension refers to the parameters and variables related to controlling the movement of the car wash machine, such as the speed of the brushes, the direction and force of the water spray. The execution control dimension involves the control strategies of the hardware components that actually perform the car wash operation, such as the spray nozzles and brushes. The dual-drive control model is an algorithmic model that can automatically adjust the motion and execution control parameters based on the car wash machine's configuration information and real-time data to optimize the car wash process.

[0022] In step S2, a large amount of data from the cleaning process is first collected, including sensor data such as vehicle position and water spray pressure, feedback from the execution module such as motor current and brush speed, and cleaning results such as the degree of stain removal. Then, supervised training is performed using these datasets to train a dual-drive cooperative control model. The model inputs are sensor data and the execution module state, and the output is the optimized control signal (such as motor speed and water spray pressure). The training process can employ deep neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to capture complex input-output relationships. For example, CNNs can be used to process image data to identify stain types; recurrent neural networks (RNNs) can be used to process time-series data, such as motor current changes, to predict the optimal control strategy. Based on historical data, the model can learn to adjust the spray nozzle pressure to a specific value and the brush motor speed simultaneously when a particular type of stain is detected, in order to efficiently remove the stain. During training, model parameters, such as the learning rate and network structure, need to be continuously adjusted to improve model performance. For example, cross-validation can be used to find the optimal learning rate, ensuring that the model neither overfits nor underfits.

[0023] Finally, the trained dual-drive control model is deployed in the car wash machine's control module to achieve intelligent and precise control of the car wash process. For example, it analyzes vehicle sensor data in real time and dynamically adjusts the water spray pressure and brush speed to ensure high efficiency and energy saving under different cleaning conditions.

[0024] Through the above steps, the device can automatically generate control strategies and adjust its operation according to different vehicles and cleaning needs, thereby achieving an efficient and precise car washing process.

[0025] Step S3: Sensing the target vehicle and performing a cyclic scan to determine multi-source sensor data and identify the stain distribution point cloud of the cleaning target, wherein each point cloud is labeled with stain characteristics.

[0026] Specifically, cyclic scanning refers to the continuous and periodic scanning of the target vehicle using sensors installed on the car wash machine to capture the real-time state of the vehicle's surface, including the distribution and changes in dirt. Multi-source sensor data is a comprehensive collection of information from various types of sensors, used to gain a complete understanding of the vehicle's condition. The dirt distribution point cloud is a three-dimensional point cloud model reflecting the distribution of dirt on the vehicle's surface, generated by processing data collected from sensors. Each point in the point cloud corresponds to a location on the vehicle's surface, and the point's attributes include the characteristics of the dirt at that location. These characteristics include the type, size, shape, and density of the dirt.

[0027] In step S3, firstly, multiple types of sensors on the car wash machine work together to cyclically scan the incoming vehicles. For example, infrared sensors are used to detect the vehicle's outline and position to ensure a safe distance; lidar is used to measure the physical properties of the vehicle's surface, such as its unevenness, to identify potential dirt hiding places; and a high-resolution camera is responsible for capturing images of the vehicle's surface for dirt type identification. Next, these multi-source sensor data are fused to construct a three-dimensional point cloud model of the vehicle's surface. This process can be accomplished using image processing techniques, point cloud construction algorithms, and data fusion techniques. For example, image processing algorithms identify dirt in the images captured by the camera and match its location information with the three-dimensional data from the lidar to generate point cloud data with dirt characteristics. Each point in the point cloud contains not only location information but also characteristics such as dirt type and size. Determining dirt characteristics can use deep learning techniques, such as convolutional neural networks (CNNs), for dirt type identification to improve recognition accuracy. For example, a convolutional neural network (CNN) model is trained using a large amount of vehicle image data before and after washing to learn the characteristics of dirt to accurately classify dirt types. Ultimately, the generated point cloud data will serve as an important input for subsequent steps such as cleaning strategy development, enabling the device to formulate the optimal cleaning plan based on the distribution and characteristics of dirt on the vehicle surface, achieving efficient, energy-saving, and personalized cleaning results.

[0028] Step S4: Traverse the stain distribution point cloud, and with low energy consumption as the guide, combine the dual-drive control model to perform dimensional parallel decision-making and fitting optimization to determine the energy-saving control strategy.

[0029] Specifically, after acquiring the point cloud of dirt distribution on the vehicle, the device traverses these point clouds, analyzing the characteristics of each dirt stain. This information is then input into the dual-drive control model for decision-making. With low energy consumption as the goal, the device combines the previously trained dual-drive control model to make decisions and optimize. For example, the device might use a genetic algorithm or particle swarm optimization algorithm to find the optimal cleaning strategy. In this process, the device considers multiple dimensions, such as water pressure, brush speed, and detergent usage, and seeks the best combination. For example, for small stains on the vehicle, the device might decide to use lower water pressure and a slower brush speed to reduce energy consumption. Through this parallel decision-making and optimization process, the device ultimately determines an energy-saving control strategy. This strategy considers not only cleaning efficiency but also energy consumption, thus achieving a highly efficient and energy-saving cleaning process.

[0030] Step S5: Traverse the energy-saving control strategy, identify the necessary frequency modulation points by calibrating the frequency modulation loss and execution loss, and identify the frequency modulation parameters of the necessary frequency modulation points.

[0031] Specifically, frequency adjustment loss refers to the energy loss caused by actuator frequency adjustment during the car wash process. Execution loss refers to the energy loss incurred when executing the control strategy, i.e., the energy difference before and after frequency adjustment. Necessary frequency adjustment points are the frequency adjustment operation points in the energy-saving control strategy that have a significant impact on energy consumption, and are the focus of optimization. Frequency adjustment parameters refer to the parameters used to adjust the inverter settings, such as frequency and voltage. In step S5, the device first analyzes each energy-saving control strategy generated in S4, focusing on evaluating the actual energy consumption of each strategy during execution. For example, for a strategy, the energy consumption when controlling the motor frequency change is analyzed, including energy conversion losses during frequency conversion and mechanical losses during execution. Next, through comparative analysis, necessary frequency adjustment points that have a significant impact on overall energy consumption are identified. If it is found that a certain strategy significantly increases energy consumption when controlling the brush motor frequency, this may be a key frequency adjustment point that needs optimization. For these necessary frequency adjustment points, the device further optimizes the frequency adjustment parameters.

[0032] Through step S5, the device not only determines the optimal energy-saving control strategy, but also further reduces energy consumption and improves the energy efficiency of the car wash process.

[0033] Step S6: Introduce a digital frequency tuner, configure the digital frequency tuner based on the necessary frequency tuning point, and the execution module responds to the energy-saving control strategy to perform cleaning braking control on the target vehicle.

[0034] Specifically, a digital frequency modulator is an electronic device used to control the operating frequency of actuators such as motors to achieve precise speed control. First, based on the necessary frequency adjustment point determined in step S5, the control parameters of the digital frequency modulator are configured to adjust the operating frequency of the actuator module. For example, if 45Hz is identified as the optimal frequency for cleaning the sides of a vehicle, the digital frequency modulator will be set to automatically adjust the motor frequency to 45Hz when a side-cleaning command is detected. Next, the digital frequency modulator connects to the actuator module to ensure that the actuator module accurately responds to the energy-saving control strategy. For example, when the cleaning strategy indicates that side cleaning is required, the digital frequency modulator will automatically adjust the motor frequency to the preset value, ensuring that the brushes work at the optimal speed. Through this precise control method, the control parameters of the car wash machine are automatically adjusted according to real-time vehicle information and cleaning needs, thereby achieving a highly efficient and energy-saving cleaning process.

[0035] Furthermore, step S3 in this embodiment of the application also includes: Step S31: Establishing a three-dimensional architecture of the target vehicle, wherein the three-dimensional architecture is located in a spatial coordinate system.

[0036] Step S32: Traverse the multi-source sensing data, perform multi-source data fitting based on the same structural position, and determine the fitted sensing data.

[0037] Step S33: Traverse the fitted sensing data, extract stain features and perform distributed identification based on the three-dimensional architecture to determine the stain distribution point cloud.

[0038] Specifically, the device first uses sensors such as cameras and laser scanners to capture the vehicle's external shape and dimensions, and integrates this information into a 3D model to establish the target vehicle's 3D architecture. For example, the device might use a laser scanner to create a precise 3D point cloud of the vehicle, which represents high-precision geometric information about the vehicle's surface.

[0039] Next, data from different sensors in the car wash machine are integrated into a unified coordinate system to ensure data accuracy and consistency. For example, the location of stains identified in camera images is matched with the location information in LiDAR point cloud data to ensure consistency and accuracy. The fitted sensor data is traversed, and stain features are extracted from the fitted data. These extracted stain features are then distributed and labeled to construct a stain distribution point cloud. For example, image processing algorithms, such as edge detection and image segmentation, are used to identify the boundaries and shapes of stains. Combined with point cloud data, their precise locations in the 3D architecture are determined. Simultaneously, machine learning models, such as convolutional neural networks (CNNs), are used to classify stain types. For each identified stain, not only is its type and size recorded, but this information is also associated with its corresponding location in the 3D architecture, generating point cloud data with stain characteristics. In this way, each stain not only has a precise location in 3D space but also a detailed characteristic description, providing accurate data support for the subsequent cleaning process.

[0040] Furthermore, step S4 in this embodiment of the application also includes: Step S41: Introducing a temporal coordinate axis, performing execution control decisions and motion control decisions on the stain distribution point cloud, and determining the execution control strategy and motion control strategy.

[0041] Step S42: Fit the execution control strategy and the motion control strategy, and perform dimensional coordination time stamp constraints to generate the energy-saving control strategy.

[0042] Specifically, the temporal coordinate axis is a reference axis established in the time dimension for the cleaning process, used to precisely control the timing and duration of cleaning actions. First, the temporal coordinate axis is introduced to establish a time-dimensional reference for the cleaning process. For example, the temporal coordinate axis is set from the start to the end of cleaning, divided in seconds, assigning a precise timestamp to each cleaning action. Next, execution control and motion control decisions are made based on the stain distribution point cloud. Based on the stain distribution point cloud, the timing, duration, and intensity of cleaning actions such as water spraying and brushing are determined, as well as the movement path and speed of cleaning equipment such as nozzles and brushes, ensuring cleaning efficiency and energy saving. For example, for heavy stains on the front of a vehicle, the device might decide to start at the 10th second and continue for 30 seconds, using high-pressure water spray combined with rapid brushing; simultaneously, the movement path and speed of the equipment are determined to ensure the equipment accurately reaches the stain location for efficient stain removal.

[0043] The device further fits the execution control strategy and motion control strategy, synchronizing the execution and motion control strategies on the time-phase coordinate axis to ensure coordinated operation and generate an energy-saving control strategy. For example, for the cleaning task mentioned above, the device precisely matches the timestamps of the water spraying and brushing actions with the timestamps of the equipment movement, ensuring that the equipment has accurately reached the position when the cleaning action begins. The generated strategy is as follows: at the 10th second, the equipment reaches the heavily soiled area at the front of the vehicle and begins high-pressure water spraying and rapid brushing, lasting for 30 seconds; at the 40th second, the equipment reaches the lightly soiled area on the side and begins medium-pressure water spraying and medium-speed brushing, lasting for 20 seconds.

[0044] Through coordinated control on the time-phase coordinate axis, Zhuangzhai can ensure that each cleaning action is executed at the most appropriate time and location, avoiding unnecessary waiting and energy consumption, and achieving a precise and energy-saving cleaning process.

[0045] Furthermore, step S41 in this embodiment of the application further includes: Step S411: Determine the full execution steps, wherein the full execution steps are accompanied by a sequence identifier and an execution component identifier.

[0046] Step S412: Traverse all execution steps and set a preset execution standard. The preset execution standard corresponds to a preset stain characteristic state. The preset execution standard is a reference standard and meets the requirements of low power consumption and energy saving.

[0047] Step S413: Based on the preset execution criteria, traverse the stain distribution point cloud to determine the point cloud execution strategy, wherein the point cloud execution strategy corresponds one-to-one with the stain distribution point cloud.

[0048] Specifically, the full execution steps refer to all the operational steps that the car wash machine needs to perform during the cleaning process, including spraying water, scrubbing, and applying cleaning agents. Preset execution standards refer to the standards set to achieve specific stain cleaning effects, and these standards take energy-saving requirements into account.

[0049] First, the device determines the entire execution process, including all necessary cleaning operations. Each operation step is labeled with a sequence identifier and an execution component identifier to ensure the order and accuracy of the cleaning process. The sequence identifier indicates the order in which each step is executed during the cleaning process. The execution component identifier identifies the specific equipment or component performing each step, such as a spray head or brush. For example, step one: the spray head applies medium-pressure water to the front of the vehicle, labeled 1-Sprayer; step two: the brush rapidly scrubs the front of the vehicle, labeled 2-Brush.

[0050] Next, the device iterates through all execution steps, setting preset execution standards. These standards are set based on the characteristics of the stains on the vehicle and guide the operation of each step. For example, for medium-pressure water spraying, the standard is a water pressure of 3 Bar and a duration of 10 seconds; for fast scrubbing, the standard is a brush speed of 100 rpm and a duration of 15 seconds. These preset execution standards correspond to preset stain characteristics, such as light, medium, and heavy stains, while also meeting the requirements of low energy consumption. For dirtier areas, the device may set higher water pressure and brush speed; for cleaner areas, it may set lower water pressure and brush speed.

[0051] Finally, based on preset execution standards, the device traverses the stain distribution point cloud and determines how to adjust the cleaning operation according to the location and characteristics of each stain point cloud, thus determining the point cloud execution strategy. For example, for identified moderate stains, the device refers to the preset execution standards and decides to use a combination of medium-pressure water spray and rapid brushing, that is, to apply a 1-Sprayer and 2-Brush combination strategy, with specific parameters of water spray pressure of 3 Bar and duration of 10 seconds; and brush speed of 100 rpm and duration of 15 seconds.

[0052] By employing a point cloud execution strategy based on the point cloud generated from the stain distribution, the device can adopt the most suitable cleaning strategy for stains of different locations and degrees, avoiding over-cleaning and energy waste, and achieving a precise and energy-saving cleaning process.

[0053] Furthermore, as shown in Figure 2, step S413 of this embodiment further includes: Step S413-1: Traverse the stain distribution point cloud and identify the stain features of the point cloud.

[0054] Step S413-2: Traverse the point cloud stain features and perform differential calibration with the preset stain feature state to determine the preset difference value, wherein the preset difference value has a positive or negative sign, and the preset difference value corresponds one-to-one with the point cloud stain features.

[0055] Step S413-3: Traverse the preset differences to determine the parameter control frequency ratio.

[0056] Step S413-4: Traverse the parameter control frequency ratio, initialize the preset execution standard, and determine the point cloud execution strategy.

[0057] Specifically, the device first traverses the point cloud of stain distribution to identify the stain features of each point. Next, it traverses the point cloud stain features and performs differential calibration with preset stain feature states to determine a preset difference value. The preset difference value is the difference between the point cloud stain features and the preset stain feature state, marked with a positive or negative sign to reflect the degree of deviation of the stain features.

[0058] Then, the preset differences are iterated to determine the control frequency ratio. The control frequency ratio is determined based on the preset differences and is used to adjust the frequency control proportion of the execution standard. A positive control frequency ratio means increasing the execution intensity, such as increasing water spray pressure or scrubbing speed; a negative control frequency ratio means decreasing the execution intensity. This process first requires establishing a set of preset mapping rules between the differences and the control frequency ratio. For example, for stain size, the preset standard is 20cm. 2 If the actual stain size is 22cm 2 The difference is then +2cm 2 The corresponding frequency modulation ratio is +3%; if the stain size is 18cm 2 The difference is -2cm 2 The corresponding parameter control frequency modulation ratio is -2%. This mapping rule can be established based on historical data and experimental results through data analysis and machine learning algorithms. Then, the features of each point cloud stain are analyzed, and the difference between them and the preset state is calculated. According to the difference and the preset mapping rule, the system determines the parameter control frequency modulation ratio. Based on the determined parameter control frequency modulation ratio, the system adjusts the parameters in the preset execution standard. Finally, the device iterates through the parameter control frequency modulation ratios, and for each execution standard, adjusts it according to the corresponding parameter control frequency modulation ratio to generate a point cloud execution strategy for a specific stain point.

[0059] For example, for a stain on the front of the vehicle, the device identifies its characteristics as: medium size, oil-based, and located on the left side of the hood. For a medium-sized oil-based stain, the preset value is: size 20cm. 2 Oil-based type; the device calculated the difference between the stain characteristics at this point and the preset state as: size deviation 2cm. 2 (Positive deviation), oil-based type match. For example, for a size deviation of 2cm.2 For oil-based stains, the device may set the control frequency ratio to +5% (positive deviation, increasing water spray pressure and brushing speed). For the aforementioned oil-based stains, the device adjusts the preset medium-pressure water spray and rapid brushing strategy based on the +5% control frequency ratio. The resulting point cloud execution strategy is: water spray pressure 3.15 Bar (3 Bar + 5%), duration 10.5 seconds; brush speed 105 rpm (100 rpm + 5%), duration 15 seconds.

[0060] Furthermore, as shown in Figure 3, step S5 of this embodiment further includes: Step S51: Traverse the energy-saving control strategy and identify the frequency modulation strategy node.

[0061] Step S52: Determine the frequency modulation loss based on the frequency modulation strategy node and the execution loss of the frequency modulation control section, and make a frequency modulation necessity determination. The execution loss is the energy consumption difference before and after frequency modulation.

[0062] Step S53: If the frequency modulation loss is greater than or equal to the execution loss, the frequency modulation strategy node is set as a non-essential frequency modulation point; if the frequency modulation loss is less than the execution loss, the frequency modulation strategy node is set as an essential frequency modulation point.

[0063] Specifically, the device first traverses the energy-saving control strategies to identify frequency modulation strategy nodes. Frequency modulation strategy nodes are key operation points in the energy-saving control strategy involving frequency conversion control and have a significant impact on energy consumption. For example, in a strategy for cleaning the sides of a vehicle, the operation point involving adjusting the motor frequency to optimize the cleaning effect is a frequency modulation strategy node. Next, the device evaluates the frequency modulation loss and execution loss of each frequency modulation strategy node. Frequency modulation loss is the additional energy consumption incurred due to frequency adjustment during the execution of the frequency modulation strategy node. Execution loss is the difference in energy consumption before and after frequency modulation for performing the same cleaning task, reflecting the impact of frequency modulation on actual energy consumption. Based on the evaluation results, the necessity of the frequency modulation strategy node is determined. If the frequency modulation loss is greater than or equal to the execution loss, the corresponding frequency modulation strategy node is set as a non-essential frequency modulation point; if the frequency modulation loss is less than the execution loss, the corresponding frequency modulation strategy node is set as a necessary frequency modulation point. For example, if the frequency modulation loss is 0.5 kWh, while the execution loss is only 0.2 kWh, the frequency modulation strategy node will be determined as a non-essential frequency modulation point because the benefits of frequency modulation are not significant. Conversely, if the frequency modulation loss is 0.3 kWh and the execution loss is 0.5 kWh, then this node will be set as the necessary frequency modulation point because frequency modulation can significantly improve energy efficiency. Through the above steps, the device can identify which frequency modulation strategy nodes truly need adjustment, thereby optimizing the energy use of the car wash machine and achieving energy-saving effects.

[0064] Through the above steps, the device generates a highly personalized execution strategy based on the characteristics of each point cloud stain, ensuring that the cleaning process is not only efficient and energy-saving, but also adaptable to the cleaning needs of different stains and vehicle surfaces.

[0065] Furthermore, after determining the energy-saving control strategy in step S4 of this application embodiment, the method further includes: obtaining the fault-tolerant configuration based on the motion control dimension and the execution control dimension, and setting the fault-tolerant interval; performing inward processing on the fault-tolerant interval, and traversing the energy-saving control strategy to perform strategy fault-tolerant node matching, performing strategy fault-tolerant processing, and performing strong limiting processing on the energy-saving control strategy using risk characteristics, and determining the calibration energy-saving control strategy.

[0066] Specifically, fault-tolerant configuration is a pre-defined tolerance range based on motion control and execution control dimensions, used to handle uncertainties during actual execution. The fault-tolerant range refers to the specific numerical range of the fault-tolerant configuration, such as the allowable fluctuation range of water spray pressure or brushing speed. Risk characteristics refer to factors that may lead to instability or malfunction of the car wash machine. Strong limit handling refers to restricting strategies that may exceed the fault-tolerant range to prevent malfunctions of the car wash machine.

[0067] First, the device retrieves pre-defined fault tolerance parameters from the equipment configuration file or database. These parameters define under what conditions the device can continue to operate unaffected by a failure. For example, the device might set a fault tolerance range for water pressure to ensure that the water pressure remains within an acceptable range in the event of certain sensor failures or actuator malfunctions. Based on the obtained fault tolerance configuration, a range, or fault tolerance range, is set, which defines the acceptable performance difference between normal operation and failure.

[0068] Next, the device narrows the fault tolerance range to improve its stability and reliability. For example, the device might narrow the water pressure fault tolerance range from 50-100 Pascals to 45-95 Pascals. Then, the device iterates through the energy-saving control strategy, identifies fault-tolerant nodes, performs strategy-based fault-tolerant node matching, and checks whether each control node is within the fault tolerance range to ensure normal operation. A fault-tolerant node refers to a specific node in the energy-saving control strategy that requires fault tolerance handling. If a control node is found to be outside the fault tolerance range, the device will take measures such as adjusting control parameters or switching to a backup control mode to handle potential anomalies. For example, if the device detects a fault in an actuator, it might automatically switch to a backup actuator and adjust control parameters to adapt to the new execution conditions. Finally, based on potential risk factors, i.e., risk characteristics, the device applies strong limiting to the energy-saving control strategy, restricting strategies that might exceed the fault tolerance range to prevent device failure. Based on the adjusted fault tolerance range and strategy, the energy-saving control strategy is redefined, i.e., calibrated. This means that the device, for example, may limit the maximum water pressure to avoid damaging the vehicle.

[0069] Through the above steps, the device can ensure stable operation under various conditions while achieving energy-saving effects.

[0070] In summary, the energy-saving control method for a fully automatic car wash machine provided in this application has the following technical effects: It acquires the configuration information and closed-loop control circuit of the car wash machine, and determines the basic equipment information. The closed-loop control circuit includes a closed loop based on a sensing module-control module-execution module, and a full-loop intervention based on a communication module. This step ensures that the device can customize the control strategy according to the specific configuration and function of the car wash machine. The use of the closed-loop control circuit improves the stability and response speed of the control. Combining the basic equipment information, a dual-drive control model is trained using a dual-drive collaboration based on motion control and execution control dimensions. The dual-drive control model is built into the control module. By combining motion control and execution control, this model can more accurately adjust the car wash process, improving cleaning efficiency and effectiveness. It senses the target vehicle and performs a cyclic scan, determining multi-source sensor data and identifying the stain distribution point cloud of the cleaning target. Each point cloud is labeled with stain characteristics. Using multi-source sensor data, the device can accurately identify the stain distribution of the vehicle, ensuring that only the parts that need cleaning are operated on, avoiding energy waste. The system iterates through the stain distribution point cloud, prioritizing low energy consumption, and performs dimensional parallel decision-making and fitting optimization using the dual-drive control model to determine an energy-saving control strategy and optimize energy utilization during the car wash process. It iterates through the energy-saving control strategy, calibrating frequency modulation losses and execution losses to identify necessary frequency modulation points, each marked with a frequency modulation parameter. This step, by identifying and adjusting necessary frequency modulation points, further optimizes the control strategy and reduces energy waste. A digital frequency modulator is introduced and configured based on the necessary frequency modulation points. The execution module responds to the energy-saving control strategy, performing cleaning braking control on the target vehicle to ensure the accuracy and efficiency of the cleaning process.

[0071] Overall, the embodiments of this application, through accurate stain recognition, dual-drive collaborative control, energy-saving optimization decision-making, loss correction, and refined execution control, not only reduce the energy and water consumption during the operation of the car wash machine, but also improve the accuracy and efficiency of cleaning, while ensuring the quality of car washing, thereby promoting the development of the car wash industry towards a more environmentally friendly and efficient direction.

[0072] Example 2, as shown in Figure 4, provides an energy-saving control device for a fully automatic car wash machine. The device includes: a basic equipment information acquisition unit 10, which is used to acquire the configuration information and closed-loop control circuit of the car wash machine and determine the basic equipment information. The closed-loop control circuit includes a closed loop based on the sensing module-control module-execution module and a full-loop intervention based on the communication module.

[0073] The dual-drive control model training unit 20 is used to combine the basic information of the equipment to supervise the training of the dual-drive control model based on the dual-drive collaboration of motion control dimension and execution control dimension. The dual-drive control model is built into the control module.

[0074] The cleaning target identification unit 30 is used to sense the target vehicle and perform cyclic scanning to determine multi-source sensor data and identify the stain distribution point cloud of the cleaning target, wherein each point cloud is marked with stain characteristics.

[0075] The energy-saving control strategy generation unit 40 is used to traverse the stain distribution point cloud, and with low energy consumption as the guide, combine the dual-drive control model to perform dimensional parallel decision-making and fitting optimization to determine the energy-saving control strategy.

[0076] The necessary frequency tuning point identification unit 50 is used to traverse the energy-saving control strategy, identify the necessary frequency tuning point by calibrating the frequency tuning loss and the execution loss, and the necessary frequency tuning point is identified by the frequency tuning parameter.

[0077] An energy-saving control unit 60 is used to introduce a digital frequency tuner, configure the digital frequency tuner based on the necessary frequency tuning point, and the execution module responds to the energy-saving control strategy to perform cleaning braking control on the target vehicle.

[0078] Furthermore, in this embodiment of the application, the cleaning target identification unit 30 is also used to perform the following steps: establishing a three-dimensional architecture of the target vehicle, wherein the three-dimensional architecture is located in a spatial coordinate system; traversing the multi-source sensor data, performing multi-source data fitting based on the same structural position, and determining the fitted sensor data; traversing the fitted sensor data, performing stain feature extraction and distributed identification based on the three-dimensional architecture, and determining the stain distribution point cloud.

[0079] Furthermore, in this embodiment of the application, the energy-saving control strategy generation unit 40 is also used to perform the following steps: introducing a temporal coordinate axis, performing execution control decisions and motion control decisions on the stain distribution point cloud, and determining the execution control strategy and motion control strategy; fitting the execution control strategy and the motion control strategy, and performing dimensional coordination time stamp constraints to generate the energy-saving control strategy.

[0080] Furthermore, in this embodiment of the energy-saving control strategy generation unit 40 is also used to perform the following steps: determining all execution steps, wherein the all execution steps are marked with a sequence identifier and an execution component identifier; traversing the all execution steps and setting a preset execution standard, wherein the preset execution standard corresponds to a preset stain characteristic state, the preset execution standard is a reference standard, and meets the low-consumption and energy-saving requirements; based on the preset execution standard, traversing the stain distribution point cloud and determining a point cloud execution strategy, wherein the point cloud execution strategy corresponds one-to-one with the stain distribution point cloud.

[0081] Furthermore, in this embodiment of the application, the energy-saving control strategy generation unit 40 is also used to perform the following steps: traversing the stain distribution point cloud and identifying the stain features of the point cloud; traversing the stain features of the point cloud and performing differential calibration with the preset stain feature state to determine a preset difference value, wherein the preset difference value has a positive or negative sign, and the preset difference value corresponds one-to-one with the stain features of the point cloud; traversing the preset difference value to determine the parameter control frequency modulation ratio; traversing the parameter control frequency modulation ratio, initializing the preset execution standard, and determining the point cloud execution strategy.

[0082] Furthermore, the necessary frequency modulation point identification unit 50 in this embodiment is also used to perform the following steps: traversing the energy-saving control strategy and identifying frequency modulation strategy nodes; determining the necessity of frequency modulation based on the frequency modulation loss of the frequency modulation strategy node and the execution loss of the frequency modulation control section, wherein the execution loss is the energy consumption difference before and after frequency modulation; wherein, if the frequency modulation loss is greater than or equal to the execution loss, the frequency modulation strategy node is set as a non-necessary frequency modulation point; if the frequency modulation loss is less than the execution loss, the frequency modulation strategy node is set as a necessary frequency modulation point.

[0083] Furthermore, in this embodiment of the application, the energy-saving control strategy generation unit 40 is also used to perform the following steps: obtaining the fault-tolerant configuration based on the motion control dimension and the execution control dimension, and setting the fault-tolerant interval; performing inward processing on the fault-tolerant interval, and traversing the energy-saving control strategy to perform strategy fault-tolerant node matching, performing strategy fault-tolerant processing, and performing strong limiting processing on the energy-saving control strategy using risk characteristics, and determining the calibration energy-saving control strategy.

[0084] Through the foregoing detailed description of an energy-saving control method for a fully automatic car wash machine, those skilled in the art can clearly understand that the energy-saving control device for a fully automatic car wash machine in this embodiment corresponds to the device disclosed in Embodiment 2. Since it is in line with the method disclosed in Embodiment 1, it has corresponding functional units and beneficial effects. For relevant details, please refer to the method section.

[0085] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An energy-saving control method for a fully automatic car wash machine, characterized in that, The method includes: The configuration information and closed-loop control circuit of the car wash machine are obtained, and the basic information of the equipment is determined. The closed-loop control circuit includes a closed loop based on the sensing module-control module-execution module and a full-loop intervention based on the communication module. Based on the aforementioned basic equipment information, a dual-drive control model is trained under supervision using a dual-drive collaboration based on motion control and execution control dimensions. The dual-drive control model is built into the control module. The system senses the target vehicle and performs a cyclic scan to determine multi-source sensor data and identify the point cloud of stain distribution on the target to be cleaned. Each point cloud is labeled with stain characteristics. By traversing the point cloud of the stain distribution, and guided by low energy consumption, the dual-drive control model is combined with dimensional parallel decision-making and fitting optimization to determine the energy-saving control strategy. By traversing the energy-saving control strategy and calibrating the frequency modulation loss and execution loss, the necessary frequency modulation points are identified, and the necessary frequency modulation points are identified with frequency modulation parameters. A digital frequency tuner is introduced and configured based on the necessary frequency tuning point. The execution module responds to the energy-saving control strategy and performs cleaning braking control on the target vehicle.

2. The energy-saving control method for a fully automatic car wash machine as described in claim 1, characterized in that, The process of determining multi-source sensor data and identifying the stain distribution point cloud of the cleaning target includes: Establish a three-dimensional architecture of the target vehicle, wherein the three-dimensional architecture is located in a spatial coordinate system; Traverse the multi-source sensing data, perform multi-source data fitting based on the same structural position, and determine the fitted sensing data; By traversing the fitted sensor data, stain features are extracted and distributed identification based on the three-dimensional architecture is performed to determine the stain distribution point cloud.

3. The energy-saving control method for a fully automatic car wash machine as described in claim 1, characterized in that, The determination of the energy-saving control strategy includes: By introducing a temporal coordinate axis, execution control decisions and motion control decisions are made on the stain distribution point cloud to determine the execution control strategy and motion control strategy. The execution control strategy and the motion control strategy are fitted together, and the time stamp constraints of dimensional coordination are applied to generate the energy-saving control strategy.

4. The energy-saving control method for a fully automatic car wash machine as described in claim 3, characterized in that, Perform control decisions on the stain distribution point cloud, including: Determine the full execution steps, wherein the full execution steps are marked with a sequence identifier and an execution component identifier; Traverse all execution steps and set preset execution standards. The preset execution standards correspond to preset stain characteristic states. The preset execution standards are reference standards and meet the requirements of low power consumption and energy saving. Based on the preset execution criteria, the stain distribution point cloud is traversed to determine the point cloud execution strategy, wherein the point cloud execution strategy corresponds one-to-one with the stain distribution point cloud.

5. The energy-saving control method for a fully automatic car wash machine as described in claim 4, characterized in that, Based on the preset execution criteria, the point cloud of stain distribution is traversed to determine the point cloud execution strategy, including: Traverse the point cloud of stain distribution and identify the stain features in the point cloud; Traverse the point cloud stain features and perform differential calibration with the preset stain feature state to determine the preset difference value, wherein the preset difference value has a positive or negative sign, and the preset difference value corresponds one-to-one with the point cloud stain feature; By iterating through the preset differences, the frequency modulation ratio of the parameter control is determined; The parameter control frequency ratio is traversed, the preset execution standard is initialized, and the point cloud execution strategy is determined.

6. The energy-saving control method for a fully automatic car wash machine as described in claim 1, characterized in that, The step of identifying the necessary frequency modulation point by calibrating the frequency modulation loss and the execution loss includes: Traverse the energy-saving control strategies and identify the frequency modulation strategy nodes; The frequency modulation loss based on the frequency modulation strategy node and the execution loss of the frequency modulation control section are determined to determine the necessity of frequency modulation. The execution loss is the energy consumption difference before and after frequency modulation. If the frequency modulation loss is greater than or equal to the execution loss, the frequency modulation strategy node is set as a non-essential frequency modulation point; if the frequency modulation loss is less than the execution loss, the frequency modulation strategy node is set as an essential frequency modulation point.

7. The energy-saving control method for a fully automatic car wash machine as described in claim 1, characterized in that, After determining the energy-saving control strategy, the following should be included: Obtain the fault tolerance configuration based on the motion control dimension and the execution control dimension, and set the fault tolerance range; The fault tolerance range is shrunk, and the energy-saving control strategy is traversed to match the fault tolerance nodes. The strategy is then processed for fault tolerance, and the risk characteristics are used to apply strong limiting to the energy-saving control strategy to determine and calibrate the energy-saving control strategy.

8. An energy-saving control device for a fully automatic car wash machine, characterized in that, The device is used to execute the energy-saving control method for a fully automatic car wash machine according to any one of claims 1-7, the device comprising: The equipment basic information acquisition unit is used to acquire the configuration information and closed-loop control loop of the car wash machine and determine the equipment basic information. The closed-loop control loop includes a closed loop based on the sensing module-control module-execution module and a full-loop intervention based on the communication module. A dual-drive control model training unit is used to combine the basic information of the equipment to supervise the training of a dual-drive control model based on the dual-drive collaboration of motion control dimension and execution control dimension. The dual-drive control model is built into the control module. A cleaning target identification unit is used to sense the target vehicle and perform cyclic scanning to determine multi-source sensor data and identify the stain distribution point cloud of the cleaning target, wherein each point cloud is identified with stain characteristics. An energy-saving control strategy generation unit is used to traverse the stain distribution point cloud, and, guided by low energy consumption, combine the dual-drive control model to perform dimensional parallel decision-making and fitting optimization to determine the energy-saving control strategy. A necessary frequency tuning point identification unit is used to traverse the energy-saving control strategy, identify necessary frequency tuning points by calibrating frequency tuning loss and execution loss, and the necessary frequency tuning point identification unit has frequency tuning parameters. An energy-saving control unit is provided, which is used to introduce a digital frequency tuner and configure the digital frequency tuner based on the necessary frequency tuning point. The execution module responds to the energy-saving control strategy and performs cleaning braking control on the target vehicle.

Images