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

Abstract

The present application provides an energy-saving control method and device for a fully automatic car washing machine, which relates to the field of automation control technology. The method and device determine the basic information of the equipment; supervise and train the dual-drive control model based on the dual-drive collaboration of the motion control dimension and the execution control dimension; sense the target vehicle and perform a cyclic scan to determine the multi-source sensor data and identify the stain distribution point cloud of the cleaning target; traverse the stain distribution point cloud, with low consumption and energy saving as the orientation, combine the dual-drive control model to perform dimensional parallel decision-making and fitting optimization, and determine the energy-saving control strategy; by calibrating the frequency modulation loss and execution loss, identify the necessary frequency modulation points and configure the digital frequency modulator, respond to the energy-saving control strategy, and perform washing brake control. The present application solves the technical problem that the existing automatic control technology of car washing machines lacks targeted and refined energy management measures when facing different vehicle conditions, resulting in energy waste in the car washing process, improves the energy utilization efficiency of the car washing process, and saves energy.

Description

Technical Field

[0001] The present application relates to the field of automation control technology, and in particular to an energy-saving control method and device for a fully automatic car washing machine. Background Art

[0002] As an efficient and convenient car washing tool, the fully automatic car washing machine plays an important role in the modern automotive service industry. With the advancement of technology, the automatic control technology of car washing machines is also constantly developing, aiming to improve car washing efficiency, safety and user-friendliness.

[0003] Existing automatic control technologies for car wash machines mainly rely on programmable logic controllers (PLCs), sensor technology, and electric control valves. These systems control various aspects of the car wash process, such as water spraying, brush movement, detergent spraying, etc., through preset programs. Although these technologies have improved the efficiency and safety of car washing to a certain extent, they still have some shortcomings. First, existing automatic control methods often lack targeted and refined energy management. This means that whether it is a large SUV or a small sedan, the car wash machine usually uses the same washing mode and consumes similar energy and water resources. This approach is prone to energy and water waste, especially when washing smaller or cleaner vehicles. Secondly, existing automatic control technology cannot accurately identify the specific stain distribution of the vehicle. This may result in unnecessary washing areas being processed, further increasing energy consumption. Summary of the invention

[0004] The present application provides an energy-saving control method and device for a fully automatic car washing machine, which solves the technical problem that the existing automatic control technology of car washing machines fails to fully consider the real-time vehicle status and specific cleaning needs, lacks targeted and refined energy management measures, and leads to energy waste in the car washing process, thereby achieving the technical effect of improving the energy utilization efficiency of the car washing process and saving energy.

[0005] In view of the above problems, on the one hand, the present application provides an energy-saving control method for a fully automatic car washing machine, the method comprising: obtaining configuration information and a closed-loop control circuit of the car washing machine, determining basic equipment information, wherein the closed-loop control circuit includes a closed loop based on a sensor module-control module-execution module, and a full loop intervention based on a communication module; combining the basic equipment information with the dual-drive collaboration based on the motion control dimension and the execution control dimension, supervising the training of a dual-drive control model, the dual-drive control model being built into the control module; sensing the target vehicle and performing a cyclic scan to determine the multi-source The sensing data is used to identify the stain distribution point cloud of the cleaning target, wherein each point cloud is marked with a stain characteristic; the stain distribution point cloud is traversed, and with low consumption and energy saving as the orientation, dimensional parallel decision-making and fitting optimization are combined with the dual-drive control model to determine the energy-saving control strategy; the energy-saving control strategy is traversed, and the necessary frequency modulation points are identified by calibrating the frequency modulation loss and the execution loss, and the necessary frequency modulation points are identified with frequency modulation parameters; a digital frequency modulator is introduced, and the digital frequency modulator is configured based on the necessary frequency modulation points, and the execution module responds to the energy-saving control strategy to perform cleaning braking control on the target vehicle.

[0006] On the other hand, the present application also provides an energy-saving control device for a fully automatic car washing machine, the device comprising: an equipment basic information acquisition unit, the equipment basic information acquisition unit is used to acquire the configuration information and closed-loop control circuit of the car washing machine, and determine the equipment basic information, wherein the closed-loop control circuit includes a closed loop based on a sensor module-control module-execution module, and a full loop intervention based on a communication module; a dual-drive control model training unit, the dual-drive control model training unit is used to combine the equipment basic information, to supervise the training of the dual-drive control model based on the dual-drive collaboration of the motion control dimension and the execution control dimension, and the dual-drive control model is built into the control module; a cleaning target recognition unit, the cleaning target recognition unit is used to sense the target vehicle and perform cyclic scanning to determine the multi-source The sensing data is used to identify the stain distribution point cloud of the cleaning target, wherein each point cloud is marked with a stain characteristic; an energy-saving control strategy generation unit, which is used to traverse the stain distribution point cloud, and is guided by low consumption and energy saving, and is combined with the dual-drive control model to perform dimensional parallel decision-making and fitting optimization to determine the energy-saving control strategy; a necessary frequency modulation point identification unit, which is used to traverse the energy-saving control strategy, and identify the necessary frequency modulation points by calibrating the frequency modulation loss and the execution loss, and the necessary frequency modulation points are marked with frequency modulation parameters; an energy-saving control unit, which is used to introduce a digital frequency modulator, and configure the digital frequency modulator based on the necessary frequency modulation points, and the execution module responds to the energy-saving control strategy to perform 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:

[0008] The configuration information and closed-loop control loop of the car washing machine are obtained, and the basic information of the equipment is determined, wherein the closed-loop control loop includes a closed loop based on a sensor 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 washing machine, and the use of the closed-loop control loop improves the stability and response speed of the control. Combined with the basic information of the equipment, the dual-drive coordination based on the motion control dimension and the execution control dimension is used to supervise the training of the dual-drive control model, 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 washing process and improve the cleaning efficiency and effect. Sense the target vehicle and perform a cyclic scan to determine the multi-source sensor data and identify the stain distribution point cloud of the cleaning target, wherein each point cloud is marked with a stain characteristic. Using the multi-source sensor data, the device can accurately identify the stain distribution of the vehicle, provide accurate guidance for subsequent cleaning, ensure the pertinence and efficiency of cleaning, and avoid energy waste. Traverse the stain distribution point cloud, with low consumption and energy saving as the guide, combine the dual-drive control model to perform dimensional parallel decision-making and fitting optimization, determine the energy-saving control strategy, and optimize the energy utilization of the car washing process. Traverse the energy-saving control strategy, identify the necessary frequency modulation points by calibrating the frequency modulation loss and execution loss, and the necessary frequency modulation points are marked with frequency modulation parameters. This step further optimizes the control strategy and reduces energy waste by identifying and adjusting the necessary frequency modulation points. Introduce a digital frequency modulator, configure the digital frequency modulator based on the necessary frequency modulation points, and the execution module responds to the energy-saving control strategy to perform cleaning brake control on the target vehicle, ensuring the accuracy and efficiency of the cleaning process.

[0009] To summarize, this application not only reduces the energy and water consumption during the operation of the car washing machine, but also improves the cleaning accuracy and efficiency while ensuring the quality of car washing through precise stain identification, dual-drive collaborative control, energy-saving optimization decision-making, loss proofreading and refined execution control, thereby promoting the car washing industry to develop in a more environmentally friendly and efficient direction.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flow chart of an energy-saving control method for a fully automatic car washing machine provided in an embodiment of the present application.

[0012] Figure 2A schematic diagram of a flow chart for determining a point cloud execution strategy in an energy-saving control method for a fully automatic car washing machine provided in an embodiment of the present application.

[0013] Figure 3 A flow chart of identifying necessary frequency modulation points in an energy-saving control method for a fully automatic car washing machine provided in an embodiment of the present application.

[0014] Figure 4 A schematic structural diagram of an energy-saving control device for a fully automatic car washing machine provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: equipment basic information acquisition unit 10, dual-drive control model training unit 20, cleaning target recognition unit 30, energy-saving control strategy generation unit 40, necessary frequency modulation point identification unit 50, energy-saving control unit 60. DETAILED DESCRIPTION

[0016] The embodiment of the present application provides an energy-saving control method and device for a fully automatic car washing machine, thereby solving the technical problem that the existing automatic control technology of car washing machines fails to fully consider the real-time vehicle status and specific cleaning needs, lacks targeted and refined energy management measures, and leads to energy waste in the car washing process, thereby achieving the technical effect of improving the energy utilization efficiency of the car washing process and saving energy.

[0017] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides an energy-saving control method for a fully automatic car washing machine, the method comprising:

[0018] Step S1: Obtain the configuration information and closed-loop control circuit of the car washing machine, and determine the basic information of the equipment, wherein the closed-loop control circuit includes a closed loop based on a sensor module-control module-execution module, and a full-loop intervention based on a communication module.

[0019] Specifically, the configuration information of the car washing machine refers to the hardware configuration information of the car washing machine, including but not limited to the type and quantity of sensors, the type of actuators, the model and parameters of the motor, and the type of communication module. The closed-loop control loop is the basic component of the automatic control system of the car washing machine, which includes three main modules: the sensor module, the control module and the execution module. The closed-loop system adjusts the operation of the car washing machine through a continuous feedback loop to achieve the predetermined goal. Among them, the sensor module is responsible for collecting real-time data during the car washing process, such as vehicle position, speed, cleaning effect, etc.; the control module receives the data from the sensor module, makes decisions according to the preset procedures and algorithms, and directs the operation of the execution module. The execution module actually controls the various components of the car washing machine, such as the spray arm, brush, etc., according to the instructions of the control module. The communication module is responsible for transmitting data in the closed-loop control loop to ensure the information flow between the modules, such as the Wi-Fi module and the 4G / 5G module.

[0020] First, the device automatically obtains the configuration information of the car washer when it starts, including detailed information about the hardware and software, and summarizes this information into basic equipment information to help the device understand the functions and limitations of the car washer. Next, the device initializes a closed-loop control loop, which is based on the close cooperation of the sensing module, the control module, and the execution module. For example, the sensing module may include a radar sensor for detecting the position of the vehicle and a pressure sensor for monitoring the water pressure. The control module may be a computer system with a built-in PLC to make decisions based on the received sensor data. The execution module may include electric valves and motors to operate the mechanical parts of the car washer according to the instructions of the control module. In addition, the communication module plays a key role in the whole process, ensuring that the data collected by the sensing module can be transmitted to the control module in real time, and the instructions of the control module can be quickly passed to the execution module. For example, the communication module may use wireless or wired networks to transmit data.

[0021] Through step S1, the device can fully grasp the hardware configuration and control logic of the car washing machine, and provide accurate data support and control basis for subsequent intelligent upgrades.

[0022] Step S2: combining the basic information of the device, supervising the training of a dual-drive control model based on dual-drive collaboration in the motion control dimension and the execution control dimension, wherein the dual-drive control model is built into the control module.

[0023] Specifically, the motion control dimension refers to the parameters and variables related to controlling the motion of the car wash machine, such as the speed of the brush, the direction and strength of the water spray, etc. The execution control dimension involves the control strategy of the hardware part that actually performs the car wash operation, such as the water spray head, brush, etc. The dual-drive control model is an algorithmic model that can automatically adjust the motion and execution control parameters based on the configuration information and real-time data of the car wash machine to optimize the car wash process.

[0024] In step S2, a large amount of data from the cleaning process is first collected, including sensor data such as vehicle position, water spray pressure, feedback from the execution module such as motor current, brush speed, and cleaning results such as the degree of stain removal. Then, supervised training is performed using these data sets to train a dual-drive collaborative control model. The model input is sensor data and execution module status, and the output is an optimized control signal (such as motor speed, water spray pressure). Then, supervised training is performed using these data sets to train a dual-drive collaborative control model. The model input is sensor data and execution module status, and the output is an optimized control signal such as motor speed, water spray pressure, etc. The training process can use deep neural networks, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other technologies to capture complex input-output relationships. For example, a convolutional neural network (CNN) is used to process image data to identify stain types; a recurrent neural network (RNN) is 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 that when a specific type of stain is detected, the water head pressure should be adjusted to a certain value, and the brush motor speed should be adjusted to efficiently remove the stain. During the training process, model parameters such as learning rate and network structure need to be continuously adjusted to improve model performance. For example, through cross-validation, the optimal learning rate is found to ensure that the model is neither overfitting nor underfitting.

[0025] Finally, the trained dual-drive control model is deployed in the control module of the car washing machine to achieve intelligent and refined control of the car washing process. For example, the vehicle sensor data is analyzed in real time, and the water spray pressure and brush speed are dynamically adjusted to ensure high efficiency and energy saving under different washing conditions.

[0026] Through the above steps, the device can automatically generate control strategies and adjust its operations according to different vehicles and cleaning requirements, thereby achieving an efficient and precise car washing process.

[0027] Step S3: Sense the target vehicle and perform a cyclic scan to determine the multi-source sensing data and identify the stain distribution point cloud of the cleaning target, wherein each point cloud is marked with a stain characteristic.

[0028] Specifically, cyclic scanning refers to the continuous and periodic scanning of the target vehicle by the sensor installed on the car washing machine to capture the real-time status of the vehicle surface, including the distribution and changes of stains. Multi-source sensing data is comprehensive information collected by various types of sensors, which is used to fully understand the vehicle condition. The stain distribution point cloud is a three-dimensional point cloud model that reflects the distribution of stains on the vehicle surface after processing of data collected by sensors. Each point on the point cloud corresponds to a position on the vehicle surface, and the attributes of the point contain the stain characteristics at that position. Among them, the stain characteristics include the type, size, shape, density, etc. of the stain.

[0029] In step S3, first, multiple types of sensors on the car washing machine work together to perform a cyclic scan of the vehicles entering. For example, infrared sensors are used to detect the contour and position of the vehicle to ensure a safe distance; laser radars are used to measure the physical properties of the vehicle surface, such as the degree of concavity, to identify potential stain hiding points; and high-resolution cameras are responsible for capturing images of the vehicle surface for stain type identification. Then, these multi-source sensor data are fused and processed to construct a three-dimensional point cloud model of the vehicle surface. This process can be completed using image processing technology, point cloud construction algorithms, and data fusion technology. For example, through image processing algorithms, stains in the image captured by the camera are identified, and their position information is matched with the three-dimensional data of the laser radar to generate point cloud data with stain characteristics. Each point on the point cloud not only contains position information, but also has additional characteristics such as stain type and size. The determination of stain characteristics can use deep learning technology, such as convolutional neural network (CNN) for stain type identification to improve identification accuracy. For example, a convolutional neural network (CNN) model is trained to learn the characteristics of stains through a large amount of vehicle image data before and after washing to accurately classify the stain type. Ultimately, the generated point cloud data will serve as an important input for subsequent steps such as the formulation of cleaning strategies, so that the device can develop the most optimized cleaning plan based on the distribution and characteristics of stains on the vehicle surface, achieving efficient, energy-saving and personalized cleaning effects.

[0030] Step S4: traverse the stain distribution point cloud, take low consumption and energy saving as the guide, combine the dual-drive control model to perform dimensional parallel decision-making and fitting optimization, and determine the energy-saving control strategy.

[0031] Specifically, after obtaining the point cloud of stain distribution on the vehicle, the device will traverse these point clouds and analyze the characteristics of the stains on each point cloud one by one. Then, this information is input into the dual-drive control model for decision-making. With low consumption and energy saving as the goal, the dual-drive control model trained previously is combined to make decisions and optimization. For example, the device may use a genetic algorithm or a particle swarm optimization algorithm to find the best cleaning strategy. In this process, the device will consider multiple dimensions, such as the pressure of the water spray, the speed of the brush, the amount of detergent used, etc., and find the best combination. For example, for small stains on the vehicle, the device may decide to use lower water pressure and slower brush speed to reduce energy consumption. Through this process of parallel decision-making and fitting optimization in this dimension, the device finally determines an energy-saving control strategy. This strategy not only takes into account the cleaning efficiency, but also the energy consumption, thereby achieving an efficient and energy-saving cleaning process.

[0032] Step S5: traverse the energy-saving control strategy, identify necessary frequency modulation points by checking frequency modulation loss and execution loss, and the necessary frequency modulation points are identified with frequency modulation parameters.

[0033] Specifically, frequency modulation loss refers to the energy loss caused by the adjustment of the actuator frequency during the car washing process. Execution loss refers to the energy loss generated when executing the control strategy, that is, the difference in energy consumption before and after frequency modulation. The necessary frequency modulation point is the frequency modulation operation point that has a greater impact on energy consumption in the energy-saving control strategy, and is the focus of optimization. Frequency modulation parameters refer to parameters used to adjust the inverter settings, such as frequency, voltage, etc. In step S5, the device first analyzes the energy-saving control strategies generated by S4 one by one, focusing on evaluating the actual energy consumption of each strategy during execution. For example, for a strategy, its energy consumption when controlling the motor frequency change is analyzed, including energy conversion loss during frequency conversion, mechanical loss during execution, etc. Then, through comparative analysis, the necessary frequency modulation points that have a greater impact on the 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 modulation point that needs to be optimized. For these necessary frequency modulation points, the device further optimizes the frequency modulation parameters.

[0034] 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 washing process.

[0035] Step S6: introducing a digital frequency modulator, configuring the digital frequency modulator based on the necessary frequency modulation point, and the execution module performing washing brake control on the target vehicle in response to the energy-saving control strategy.

[0036] Specifically, the digital frequency modulator is an electronic device used to control the operating frequency of actuators such as motors to achieve precise speed control. First, according to the necessary frequency modulation points determined in step S5, the control parameters of the digital frequency modulator are configured to adjust the operating frequency of the execution module. For example, if it is identified that 45Hz is the optimal frequency when washing the side of the vehicle, the digital frequency modulator will be set to automatically adjust the motor frequency to 45Hz when a side washing instruction is detected. Next, the digital frequency modulator is connected to the execution module to ensure that the execution module accurately responds to the energy-saving control strategy. For example, when the cleaning strategy indicates that side washing is required, the digital frequency modulator will automatically adjust the motor frequency to a preset value to ensure that the brush works at the optimal speed. Through this precise control method, the control parameters of the car washing machine are automatically adjusted according to real-time vehicle information and cleaning needs, thereby achieving an efficient and energy-saving cleaning process.

[0037] Furthermore, step S3 of the embodiment of the present application also includes:

[0038] Step S31: establishing a three-dimensional framework of the target vehicle, wherein the three-dimensional framework is located in a spatial coordinate system.

[0039] Step S32: traverse the multi-source sensor data, perform multi-source fitting of the data based on the same structural position, and determine the fitting sensor data.

[0040] Step S33: 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.

[0041] Specifically, first, the device uses sensors such as cameras and laser scanners to capture the external shape and size of the vehicle, and integrates this information into a 3D model to build a 3D structure of the target vehicle. For example, the device may use a laser scanner to create an accurate 3D point cloud of the vehicle, which represents high-precision geometric information of the vehicle surface.

[0042] Next, the data from different sensors of the car washing machine are integrated into a unified coordinate system to ensure the accuracy and consistency of the data. For example, the location of the stain identified in the camera image is matched with the location information in the lidar point cloud data to ensure the consistency and accuracy of the data. The sensor data is traversed and fitted, and the stain features are extracted from the fitted sensor data. The extracted stain features are distributedly identified to construct a stain distribution point cloud. For example, through image processing algorithms such as edge detection and image segmentation, the boundaries and shapes of the stains are identified, and their precise positions in the three-dimensional architecture are determined in combination with the point cloud data. At the same time, machine learning models such as convolutional neural networks (CNNs) are used to classify the types of stains. For each identified stain, not only its type and size are recorded, but also this information is associated with the corresponding position in the three-dimensional architecture to generate point cloud data with stain characteristics. In this way, each stain not only has a precise position in three-dimensional space, but also a detailed characteristic description, providing accurate data support for the subsequent cleaning process.

[0043] Furthermore, step S4 of the embodiment of the present application also includes:

[0044] Step S41: introducing a time phase coordinate axis, performing an execution control decision and a motion control decision on the stain distribution point cloud, and determining an execution control strategy and a motion control strategy.

[0045] Step S42: Fitting the execution control strategy and the motion control strategy, and performing dimensionally coordinated timestamp constraints to generate the energy-saving control strategy.

[0046] Specifically, the time-phase coordinate axis is a reference axis established for the cleaning process in the time dimension, which is used to accurately control the time sequence and duration of the cleaning action. First, the time-phase coordinate axis is introduced to establish a reference in the time dimension for the cleaning process. For example, the time-phase coordinate axis is set from the start to the end of cleaning, divided in seconds, and an accurate timestamp is assigned to each cleaning action. Then, according to the stain distribution point cloud, execution control decisions and motion control decisions are made. According to the stain distribution point cloud, the time, duration and intensity of cleaning actions such as water spraying and brushing are determined, and the movement path and speed of cleaning equipment such as nozzles and brushes are determined to ensure cleaning efficiency and energy saving. For example, for heavy stains on the front of the vehicle, the device may decide to start at the 10th second and last for 30 seconds, using high-pressure water spray combined with rapid brushing; at the same time, the device movement path and speed are determined to ensure that the equipment reaches the stain location accurately to remove the stain efficiently.

[0047] The device further fits the execution control strategy and the motion control strategy, and synchronizes the execution and motion control strategies on the time-phase coordinate axis to ensure collaborative work and generate energy-saving control strategies. For example, for the above-mentioned cleaning task, the device accurately matches the timestamps of the water spraying and brushing actions with the timestamps of the device movement to ensure that the device has accurately reached the position when the cleaning action starts. The generated strategy is: at the 10th second, the device reaches the heavy stain position at the front of the vehicle, and starts high-pressure water spraying and fast brushing for 30 seconds; at the 40th second, the device reaches the light stain position on the side, and starts medium-pressure water spraying and medium-speed brushing for 20 seconds.

[0048] Through coordinated control on the time coordinate axis, Zhuangzhai can ensure that each cleaning action is performed at the most appropriate time and location, avoiding unnecessary waiting and energy consumption, and achieving a precise and energy-saving cleaning process.

[0049] Furthermore, step S41 of the embodiment of the present application further includes:

[0050] Step S411: determine the full execution steps, wherein the full execution steps have a sequence identifier and an execution component identifier.

[0051] Step S412: traverse the entire execution steps, 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 low-consumption and energy-saving requirements.

[0052] Step S413: based on the preset execution standard, traverse the stain distribution point cloud and determine the point cloud execution strategy, wherein the point cloud execution strategy corresponds to the stain distribution point cloud one by one.

[0053] Specifically, the full execution steps refer to all the operation steps that the car washing machine needs to perform during the cleaning process, including water spraying, brushing, detergent spraying, etc. The preset execution standards refer to the standards set to achieve specific stain cleaning effects, which take energy-saving requirements into consideration.

[0054] First, the device determines the full execution steps, which include all necessary cleaning operations. Each operation step is accompanied by a sequence identifier and an execution component identifier to ensure the order and accuracy of the cleaning process. Among them, the sequence identifier is the execution order of each execution step in the cleaning process. The execution component identifier is the specific equipment or component that executes each step, such as a nozzle or a brush. For example, the first step: the nozzle sprays water at medium pressure on the front of the vehicle, which is identified as 1-Sprayer; the second step: the brush quickly brushes the front of the vehicle, which is identified as 2-Brush.

[0055] Next, the device will traverse all the execution steps and set the preset execution standards. These standards are set according to the characteristic state of the stains on the vehicle and are used to guide the operation of each step. For example, for medium-pressure water spraying, the set standard is a water spray pressure of 3 Bar and a duration of 10 seconds; the standard for rapid brushing is a brush speed of 100 rpm and a duration of 15 seconds. These preset execution standards correspond to preset characteristic states of stains, such as light, moderate, and heavy stains, while meeting the requirements of low consumption and energy saving. For dirtier areas, the device may set higher water pressure and brush speed; for cleaner areas, lower water pressure and brush speed may be set.

[0056] Finally, based on the preset execution standards, the device will traverse the stain distribution point cloud, and decide how to adjust the cleaning operation and determine the point cloud execution strategy according to the location and characteristics of each stain point cloud. For example, for the identified medium stain points, the device refers to the preset execution standards and decides to use a combination of medium-pressure water spraying and fast brushing, that is, applying a combination strategy of 1-Sprayer and 2-Brush, with specific parameters of water spray pressure 3Bar, duration 10 seconds; brush speed 100 rpm, duration 15 seconds.

[0057] By executing the point cloud strategy based on the stain distribution point cloud, the device can adopt the most appropriate cleaning strategy for stains of different locations and degrees, avoiding excessive cleaning and energy waste, and achieving a precise and energy-saving cleaning process.

[0058] Further, such as Figure 2 As shown, step S413 of the embodiment of the present application also includes:

[0059] Step S413 - 1 : traverse the stain distribution point cloud and identify point cloud stain features.

[0060] Step S413-2: traverse the point cloud stain feature, perform differential calibration with the preset stain feature state, and determine a preset difference value, wherein the preset difference value is marked with a positive and negative sign, and the preset difference value corresponds to the point cloud stain feature one by one.

[0061] Step S413-3: traverse the preset difference value to determine the parameter control frequency modulation ratio.

[0062] Step S413-4: traverse the parameter control frequency modulation ratio, initialize the preset execution standard, and determine the point cloud execution strategy.

[0063] Specifically, the stain distribution point cloud is first traversed to identify the stain feature of each point. Then, the device traverses the stain feature of the point cloud, performs a differential calibration with the preset stain feature state, and determines the preset difference. The preset difference is the difference between the stain feature of the point cloud and the preset stain feature state, with a positive and negative sign, reflecting the degree of deviation of the stain feature.

[0064] Then, the preset difference is traversed to determine the parameter control frequency modulation ratio. The parameter control frequency modulation ratio is determined based on the preset difference and is used to adjust the frequency control ratio of the execution standard. A positive parameter control frequency modulation ratio means increasing the execution intensity, such as increasing the water spray pressure, increasing the scrubbing speed, etc.; a negative parameter control frequency modulation ratio means reducing the execution intensity. This process first requires the establishment of a set of mapping rules between the preset difference and the parameter control frequency modulation ratio. For example, for the stain size, the preset standard is 20cm². If the actual stain size is 22cm², the difference is +2cm², and the corresponding parameter control frequency modulation ratio is +3%; if the stain size is 18cm², the difference is -2cm², and 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 stain feature of each point cloud is analyzed and the difference between it and the preset state is calculated. According to the difference and the preset mapping rule, the system determines the parameter control frequency modulation ratio, and according to the determined parameter control frequency modulation ratio, the system adjusts the parameters in the preset execution standard. Finally, the device traverses the parameter control frequency modulation ratio, adjusts each execution standard according to the corresponding parameter control frequency modulation ratio, and generates a point cloud execution strategy for a specific stain point.

[0065] Exemplarily, for the stain point on the front of the vehicle, the device identifies its characteristics as: medium size, oil-based type, and located on the left side of the hood. For medium-sized oil-based stains, the preset state is: size 20cm², oil-based type; the device calculates the difference between the stain characteristics of this point and the preset state as: size deviation 2cm² (positive deviation), oil-based type matching. For example, for oil-based stains with a size deviation of 2cm², the device may set the parameter control frequency modulation ratio to: +5% (positive deviation, increase water spray pressure and brushing speed). For the above-mentioned oil-based stain point, the device adjusts the preset medium-pressure water spray and fast brushing strategy based on the +5% parameter control frequency modulation ratio, and the generated point cloud execution strategy is: water spray pressure 3.15Bar (3Bar+5%), duration 10.5 seconds; brush speed 105 rpm (100 rpm+5%), duration 15 seconds.

[0066] Further, such as Figure 3 As shown, step S5 of the embodiment of the present application also includes:

[0067] Step S51: traverse the energy-saving control strategy and identify the frequency modulation strategy node.

[0068] Step S52: Determine the frequency modulation loss based on the frequency modulation strategy node and the execution loss of the frequency modulation control section to determine the necessity of frequency modulation, wherein the execution loss is the energy consumption difference before and after the frequency modulation.

[0069] 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 a necessary frequency modulation point.

[0070] Specifically, the device first traverses the energy-saving control strategy and identifies the frequency modulation strategy node. The frequency modulation strategy node is a key operation point involving variable frequency control in the energy-saving control strategy, which has a significant impact on energy consumption. For example, in the strategy of washing the side of the vehicle, the operation point involving adjusting the motor frequency to optimize the washing effect is the frequency modulation strategy node. Then, the device evaluates the frequency modulation loss and execution loss of each frequency modulation strategy node. The frequency modulation loss is the additional energy consumption caused by frequency adjustment during the execution of the frequency modulation strategy node. The execution loss is the difference in energy consumption before and after the frequency modulation when executing the same cleaning task, reflecting the impact of frequency modulation on actual energy consumption. According to 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 0.5kWh and the execution loss is only 0.2kWh, the frequency modulation strategy node will be determined as a non-essential frequency modulation point because the benefits brought by the frequency modulation are not obvious. On the contrary, if the frequency modulation loss is 0.3kWh and the execution loss is 0.5kWh, the node will be set as a 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 really need to be adjusted, thereby optimizing the energy use of the car washing machine and achieving energy saving effects.

[0071] 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 adapts to the cleaning needs of different stains and vehicle surfaces.

[0072] Furthermore, after determining the energy-saving control strategy in step S4 of the embodiment of the present application, the following steps are further included:

[0073] Obtain the fault-tolerant configuration based on the motion control dimension and the execution control dimension, and set the fault-tolerant interval; perform inward processing on the fault-tolerant interval, and traverse the energy-saving control strategy to match the strategy fault-tolerant nodes, perform strategy fault-tolerant processing, and execute risk characteristics to perform strong limit processing on the energy-saving control strategy to determine and calibrate the energy-saving control strategy.

[0074] Specifically, the fault-tolerant configuration is a pre-set allowable error configuration based on the motion control and execution control dimensions, which is used to deal with uncertainties in actual execution. The fault-tolerant interval refers to the specific numerical range of the fault-tolerant configuration, such as the allowable fluctuation range of the water spray pressure and the brushing speed. Risk characteristics refer to factors that may cause the car washing machine to operate unstably or fail. Strong limit processing refers to limiting strategies that may exceed the fault-tolerant interval to avoid failures of the car washing machine.

[0075] First, the device retrieves the pre-set fault tolerance parameters from the device configuration file or database. These parameters define under what circumstances the device can continue to operate without being affected by the fault. For example, the device may set a fault tolerance interval for water pressure to ensure that the water pressure remains within an acceptable range in the event of certain sensor failures or actuator failures. Based on the obtained fault tolerance configuration, a range is set, namely the fault tolerance interval, which defines the acceptable performance difference between the normal operation of the device and the failure.

[0076] Next, the device will shrink the fault tolerance interval, that is, reduce the fault tolerance interval to improve the stability and reliability of the device. For example, the device may reduce the fault tolerance interval of water pressure from 50-100 Pascal to 45-95 Pascal. Then, the device will traverse the energy-saving control strategy, identify the fault-tolerant nodes therein, match the strategy fault-tolerant nodes, and check whether each control node is within the fault tolerance interval to ensure the normal operation of the device. Among them, the fault-tolerant node refers to the specific node in the energy-saving control strategy that needs to be fault-tolerant. If a control node is found to be beyond the fault tolerance interval, the device will perform strategy fault tolerance processing and take measures, including adjusting control parameters or switching to backup control mode to deal with possible abnormal situations. For example, if the device detects that an actuator fails, it may automatically switch to the backup actuator and adjust the control parameters to adapt to the new execution conditions. Finally, the device will perform strong limit processing on the energy-saving control strategy based on potential risk factors, that is, risk characteristics, that is, limit the strategy that may exceed the fault tolerance interval to avoid device failure. According to the adjusted fault tolerance interval and strategy, the energy-saving control strategy is re-determined, that is, the energy-saving control strategy is calibrated. This means that the device may, for example, limit the maximum water pressure to avoid damage to the vehicle.

[0077] Through the above steps, the device can ensure stable operation under various conditions while achieving energy saving effects.

[0078] In summary, the energy-saving control method of a fully automatic car washing machine provided in the embodiment of the present application has the following technical effects:

[0079] The configuration information and closed-loop control loop of the car washing machine are obtained, and the basic information of the equipment is determined, wherein the closed-loop control loop includes a closed loop based on a sensor 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 washing machine, and the use of the closed-loop control loop improves the stability and response speed of the control. Combined with the basic information of the equipment, the dual-drive coordination based on the motion control dimension and the execution control dimension is used to supervise the training of the dual-drive control model, 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 washing process and improve the cleaning efficiency and effect. Sense the target vehicle and perform a cyclic scan to determine the multi-source sensor data and identify the stain distribution point cloud of the cleaning target, wherein each point cloud is marked with a stain characteristic. Using the multi-source sensor data, the device can accurately identify the stain distribution of the vehicle, ensure that only the part that needs to be cleaned is operated, and avoid energy waste. Traverse the stain distribution point cloud, with low consumption and energy saving as the guide, combine the dual-drive control model to perform dimensional parallel decision-making and fitting optimization, determine the energy-saving control strategy, and optimize the energy utilization of the car washing process. Traverse the energy-saving control strategy, identify the necessary frequency modulation points by calibrating the frequency modulation loss and execution loss, and the necessary frequency modulation points are marked with frequency modulation parameters. This step further optimizes the control strategy and reduces energy waste by identifying and adjusting the necessary frequency modulation points. Introduce a digital frequency modulator, configure the digital frequency modulator based on the necessary frequency modulation points, and the execution module responds to the energy-saving control strategy to perform cleaning brake control on the target vehicle, ensuring the accuracy and efficiency of the cleaning process.

[0080] Overall, the embodiments of the present application not only reduce the energy and water consumption during the operation of the car washing machine, but also improve the accuracy and efficiency of cleaning, while ensuring the quality of car washing, through precise stain identification, dual-drive collaborative control, energy-saving optimization decision-making, loss proofreading and refined execution control, thereby promoting the car washing industry to develop in a more environmentally friendly and efficient direction.

[0081] Embodiment 2, as Figure 4 As shown, the embodiment of the present application provides an energy-saving control device for a fully automatic car washing machine, the device comprising:

[0082] The basic equipment information acquisition unit 10 is used to obtain the configuration information and closed-loop control circuit of the car washing machine, and determine the basic equipment information, wherein the closed-loop control circuit includes a closed loop based on a sensor module-control module-execution module, and a full-loop intervention based on a communication module.

[0083] The dual-drive control model training unit 20 is used to combine the basic information of the device to supervise the training of the dual-drive control model based on the dual-drive collaboration of the motion control dimension and the execution control dimension. The dual-drive control model is built into the control module.

[0084] The cleaning target identification unit 30 is used to sense the target vehicle and perform cyclic scanning, determine multi-source sensing data and identify the stain distribution point cloud of the cleaning target, wherein each point cloud is marked with a stain characteristic.

[0085] The energy-saving control strategy generation unit 40 is used to traverse the stain distribution point cloud, take low consumption and energy saving as the guide, combine the dual-drive control model to perform dimensional parallel decision-making and fitting optimization, and determine the energy-saving control strategy.

[0086] The necessary frequency modulation point identification unit 50 is used to traverse the energy-saving control strategy, identify the necessary frequency modulation points by calibrating the frequency modulation loss and the execution loss, and the necessary frequency modulation point identification has a frequency modulation parameter.

[0087] The energy-saving control unit 60 is used to introduce a digital frequency modulator, configure the digital frequency modulator based on the necessary frequency modulation point, and the execution module performs washing brake control on the target vehicle in response to the energy-saving control strategy.

[0088] Furthermore, the cleaning target identification unit 30 in the embodiment of the present application is also used to perform the following steps:

[0089] A three-dimensional structure of the target vehicle is established, wherein the three-dimensional structure is located in a spatial coordinate system; the multi-source sensor data is traversed, and multi-source fitting of the data is performed based on the same structural position to determine the fitted sensor data; the fitted sensor data is traversed, and stain feature extraction and distributed identification based on the three-dimensional structure are performed to determine the stain distribution point cloud.

[0090] Furthermore, the energy-saving control strategy generation unit 40 in the embodiment of the present application is also used to perform the following steps:

[0091] The time phase coordinate axis is introduced to perform execution control decision and motion control decision on the stain distribution point cloud, and the execution control strategy and the motion control strategy are determined; the execution control strategy and the motion control strategy are fitted, and the timestamp constraints of dimension coordination are performed to generate the energy-saving control strategy.

[0092] Furthermore, the energy-saving control strategy generation unit 40 in the embodiment of the present application is also used to perform the following steps:

[0093] Determine a full execution step, wherein the full execution step carries a sequence identifier and an execution component identifier; traverse the full execution step, set a preset execution standard, the preset execution standard corresponds to a preset stain feature state, the preset execution standard is a reference standard, and meets low consumption and energy saving requirements; based on the preset execution standard, traverse the stain distribution point cloud, and determine a point cloud execution strategy, wherein the point cloud execution strategy corresponds one-to-one to the stain distribution point cloud.

[0094] Furthermore, the energy-saving control strategy generation unit 40 in the embodiment of the present application is further configured to perform the following steps:

[0095] Traverse the stain distribution point cloud and identify the point cloud stain features; traverse the point cloud stain features, perform differential calibration with the preset stain feature state, and determine a preset difference, wherein the preset difference is marked with a positive and negative sign, and the preset difference corresponds to the point cloud stain feature one by one; traverse the preset difference to determine the parameter control frequency modulation ratio; traverse the parameter control frequency modulation ratio, initialize the preset execution standard, and determine the point cloud execution strategy.

[0096] Furthermore, the necessary frequency modulation point identification unit 50 in the embodiment of the present application is further configured to perform the following steps:

[0097] Traverse the energy-saving control strategy and identify the frequency modulation strategy node; 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 judgment, wherein the execution loss is the energy consumption difference before and after the frequency modulation; wherein, if the frequency modulation loss is greater than or equal to the execution loss, set the frequency modulation strategy node as a non-essential frequency modulation point; if the frequency modulation loss is less than the execution loss, set the frequency modulation strategy node as a necessary frequency modulation point.

[0098] Furthermore, the energy-saving control strategy generation unit 40 in the embodiment of the present application is further configured to perform the following steps:

[0099] Obtain the fault-tolerant configuration based on the motion control dimension and the execution control dimension, and set the fault-tolerant interval; perform inward processing on the fault-tolerant interval, and traverse the energy-saving control strategy to match the strategy fault-tolerant nodes, perform strategy fault-tolerant processing, and execute risk characteristics to perform strong limit processing on the energy-saving control strategy to determine and calibrate the energy-saving control strategy.

[0100] Through the above-mentioned detailed description of the energy-saving control method of a fully automatic car washing machine in this specification, those skilled in the art can clearly understand the energy-saving control device of a fully automatic car washing machine in this embodiment. For the device disclosed in Example 2, since it corresponds to the method disclosed in Example 1 and has corresponding functional units and beneficial effects, the relevant parts can be referred to the description of the method part.

[0101] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be 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 the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An energy-saving control method for a fully automatic car washing machine, characterized in that: include: The configuration information and closed-loop control circuit of the car washing machine are obtained to determine the basic information of the equipment. The closed-loop control circuit includes a closed loop based on the sensor module-control module-execution module, and a full loop intervention based on the communication module; combined with the basic information of the equipment, the dual-drive coordination based on the motion control dimension and the execution control dimension is used to supervise the training of the dual-drive control model, and the dual-drive control model is built into the control module; the target vehicle is sensed and cyclically scanned to determine the multi-source sensor data and identify the stain distribution point cloud of the cleaning target, where each point cloud is marked with stain characteristics; the stain distribution point cloud is traversed, and with low consumption and energy saving as the guide, dimensional parallel decision-making and fitting optimization are combined with the dual-drive control model to determine the energy-saving control strategy; the energy-saving control strategy is traversed, and the necessary frequency modulation points are identified by calibrating the frequency modulation loss and the execution loss. The necessary frequency modulation points are marked with frequency modulation parameters; a digital frequency modulator is introduced, and the digital frequency modulator is configured based on the necessary frequency modulation points. The execution module responds to the energy-saving control strategy and performs washing braking control on the target vehicle; Identifying the necessary frequency modulation point includes: traversing the energy-saving control strategy and identifying the frequency modulation strategy node; determining the frequency modulation loss based on the frequency modulation strategy node and the execution loss of the frequency modulation control section, and determining the necessity of frequency modulation, wherein the execution loss is the energy difference before and after the 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 it is less than the execution loss, the frequency modulation strategy node is set as a necessary frequency modulation point; Obtain the fault-tolerant configuration based on the motion control dimension and the execution control dimension, and set the fault-tolerant interval; shrink the fault-tolerant interval, traverse the energy-saving control strategy to match the strategy fault-tolerant nodes, perform strategy fault-tolerant processing, and execute risk characteristics to perform strong limit processing on the energy-saving control strategy to determine and calibrate the energy-saving control strategy.

2. The energy-saving control method of a fully automatic car washing machine according to claim 1, characterized in that: The method of determining multi-source sensing data and identifying a stain distribution point cloud of a cleaning target includes: Establishing a three-dimensional framework of the target vehicle, wherein the three-dimensional framework is located in a spatial coordinate system; Traversing the multi-source sensor data, performing multi-source fitting of the data based on the same structural position, and determining fitting sensor data; The fitting sensor data is traversed to perform stain feature extraction and distributed identification based on the three-dimensional architecture to determine the stain distribution point cloud.

3. The energy-saving control method of a fully automatic car washing machine according to claim 1, characterized in that: The determining of the energy-saving control strategy includes: Introducing a time phase coordinate axis, performing execution control decision and motion control decision on the stain distribution point cloud, and determining the execution control strategy and the motion control strategy; The execution control strategy and the motion control strategy are fitted, and timestamp constraints of dimension coordination are performed to generate the energy-saving control strategy.

4. The energy-saving control method of a fully automatic car washing machine as claimed in claim 3, characterized in that: Performing control decision on the stain distribution point cloud includes: Determine a full execution step, wherein the full execution step has a sequence identifier and an execution component identifier; Traversing the entire execution steps, setting 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 low-consumption and energy-saving requirements; Based on the preset execution standard, the stain distribution point cloud is traversed to determine a point cloud execution strategy, wherein the point cloud execution strategy corresponds to the stain distribution point cloud one by one.

5. The energy-saving control method of a fully automatic car washing machine as claimed in claim 4, characterized in that: Based on the preset execution standard, traversing the stain distribution point cloud and determining the point cloud execution strategy include: Traversing the stain distribution point cloud and identifying point cloud stain features; Traversing the point cloud stain feature, performing a difference check with the preset stain feature state, and determining a preset difference value, wherein the preset difference value is marked with a positive and negative sign, and the preset difference value corresponds to the point cloud stain feature one by one; Traversing the preset difference values ​​to determine the parameter control frequency modulation ratio; The parameter control frequency modulation ratio is traversed, the preset execution standard is initialized, and the point cloud execution strategy is determined.

6. An energy-saving control device for a fully automatic car washing machine, characterized in that: The device is used to execute the energy-saving control method of a fully automatic car washing machine according to any one of claims 1 to 5, and the device comprises: A basic equipment information acquisition unit, which is used to acquire the configuration information and closed-loop control circuit of the car washing machine and determine the basic equipment information, wherein the closed-loop control circuit includes a closed loop based on a sensor module-control module-execution module and a full loop intervention based on a communication module; A dual-drive control model training unit, the dual-drive control model training unit is used to supervise the training of a dual-drive control model based on dual-drive collaboration in a motion control dimension and an execution control dimension in combination with the basic information of the device, and the dual-drive control model is built into the control module; A cleaning target identification unit, the cleaning target identification unit is used to sense the target vehicle and perform cyclic scanning, determine multi-source sensing data and identify the stain distribution point cloud of the cleaning target, wherein each point cloud is marked with a stain characteristic; An energy-saving control strategy generation unit, the energy-saving control strategy generation unit is used to traverse the stain distribution point cloud, guide low consumption and energy saving, combine the dual-drive control model to perform dimensional parallel decision-making and fitting optimization, and determine the energy-saving control strategy; A necessary frequency modulation point identification unit, the necessary frequency modulation point identification unit is used to traverse the energy-saving control strategy, identify the necessary frequency modulation point by calibrating the frequency modulation loss and the execution loss, and the necessary frequency modulation point identification has a frequency modulation parameter; An energy-saving control unit is used to introduce a digital frequency modulator, configure the digital frequency modulator based on the necessary frequency modulation point, and the execution module performs washing brake control on the target vehicle in response to the energy-saving control strategy.

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