Intelligent cluster car washing robot system and intelligent cluster car washing robot control method

Abstract

The invention provides an intelligent cluster car washing robot system and an intelligent cluster car washing robot control method, and relates to the technical field of intelligent car washing control. The intelligent cluster car washing robot system comprises a user interaction terminal, a cloud management and control platform and a plurality of robot nodes; the cloud management and control platform is in communication connection with the user interaction terminal and the robot node; the user interaction terminal sends a control instruction to the cloud management and control platform; the cloud management and control platform generates a cleaning operation strategy according to the control instruction and state information from at least one robot node, and issues an operation instruction to one or more robot nodes; and each robot node is used for performing controlled displacement in the space around the vehicle according to the operation instruction and performing physical cleaning operation on the surface of the vehicle. The intelligent car washing system has the functions of portable deployment, lossless cleaning, autonomous decision making, energy self-supply and the like, and integrated intelligent car washing is achieved.

Description

Technical Field

[0001] This application relates to the field of intelligent car wash control technology, and in particular to an intelligent cluster car wash robot system and an intelligent cluster car wash robot control method. Background Technology

[0002] Existing car wash equipment mainly falls into two categories: one is large, fixed tunnel car wash machines or gantry robots, which require vehicles to drive into specific workstations and rely on preset paths for operation. These cannot cover complex curved surfaces such as the roof and the base of rearview mirrors, and a single point of failure can lead to service interruption. The other category is small, portable cleaning robots, which mostly use wired power or built-in batteries. These have short battery life, require frequent manual intervention for recharging, and each robot operates independently, lacking task coordination and status sharing mechanisms. Such equipment cannot dynamically adjust cleaning strategies based on the real-time dirt level of the vehicle during operation, nor can multiple units be remotely coordinated to complete the full vehicle cleaning task. Summary of the Invention

[0003] The purpose of this application is to provide an intelligent cluster car wash robot system and an intelligent cluster car wash robot control method to alleviate the aforementioned technical problems existing in the prior art.

[0004] In a first aspect, the present invention provides an intelligent cluster car wash robot system, including a user interaction terminal, a cloud management platform and multiple robot nodes, wherein the cloud management platform is communicatively connected to the user interaction terminal and the robot nodes respectively. The user interaction terminal is used to send control commands to the cloud management platform; The cloud management platform is used to generate cleaning operation strategies based on the received control instructions and status information from at least one robot node, and to issue corresponding operation instructions to one or more robot nodes. Each robot node is used to perform controlled displacement within the space surrounding the vehicle and to perform physical cleaning operations on the vehicle surface according to the received work instructions.

[0005] In an optional implementation, the multiple robot nodes include a reconnaissance node, a cleaning node, and a resupply node; The reconnaissance nodes are used to collect status information of the vehicle surface and upload it to the cloud management platform; The cleaning node is used to perform physical cleaning operations on the vehicle surface according to the work instructions issued by the cloud management platform; Supply nodes are used to provide power and cleaning agents to reconnaissance nodes and / or cleaning nodes.

[0006] In an optional implementation, the reconnaissance node is equipped with a sensing module, which includes a high-definition imaging unit and a multispectral sensor, for acquiring optical reflectance characteristics data and spectral response data of the vehicle surface. The cloud management platform is used to identify the type and distribution area of ​​stains based on optical reflection characteristics data and spectral response data, and to generate cleaning operation strategies that include area allocation and action parameters.

[0007] In an optional implementation, the supply node is equipped with a wireless charging coil and a cleaning agent delivery interface; The reconnaissance nodes and / or cleaning nodes are equipped with power monitoring modules and cleaning agent balance monitoring modules; When the power or cleaning agent balance of any node falls below a preset threshold, the cloud management platform issues an energy replenishment command to the replenishment node and a task redistribution command to other normally operating nodes.

[0008] In an optional implementation, the reconnaissance node, the cleaning node, and the supply node are all equipped with a moving module, and the moving module adopts at least one of the following: negative pressure adsorption structure, biomimetic microstructure adsorption structure, or magnetic adsorption structure. The negative pressure adsorption structure includes a miniature turbofan fan and an elastic sealing ring; The biomimetic microstructure adsorption structure uses micro- and nano-scale van der Waals force array materials; Magnetic adsorption structures include permanent magnets or electromagnets.

[0009] In an optional implementation, the cleaning node is equipped with a cleaning execution module, which includes a micro water pump, a liquid storage container, an atomizing nozzle, and a flexible rotating brush. Miniature water pumps are used to draw liquid from a storage container and spray it out through an atomizing nozzle; The flexible rotating brush is driven to rotate by a motor.

[0010] In an optional implementation, the reconnaissance node, cleaning node, and supply node are all equipped with energy modules, which include solar energy harvesting units and electrochemical energy storage units. The solar energy harvesting unit can be a flexible solar thin film or a rigid solar panel; The electrochemical energy storage unit is a lithium polymer battery or a lithium iron phosphate battery.

[0011] In an optional implementation, the cloud management platform is also used to control the reconnaissance nodes to re-inspect the vehicle surface after receiving the task completion signals uploaded by all cleaning nodes, and to generate a cleanliness assessment report based on the re-inspection results. The cleanliness assessment report includes at least one of the following: the cleanliness improvement value, the removal status of major stains, and the health status of the vehicle paint.

[0012] In an optional implementation, the robot nodes are connected to each other to exchange information needed for collaboration with other robot nodes.

[0013] Secondly, this invention provides a method for controlling intelligent cluster car wash robots, applied to a cloud management platform; the method includes: Receive control commands sent by the user interaction terminal; Obtain status information from at least one robot node; Based on control commands and status information, a cleaning operation strategy is generated; Based on the cleaning operation strategy, corresponding operation instructions are issued to one or more robot nodes; Each robot node performs controlled displacement within the space surrounding the vehicle according to the received work instructions, and performs physical cleaning operations on the vehicle surface.

[0014] The intelligent cluster car wash robot system and control method provided in this application enable remote initiation and on-demand response to car wash operations by sending control commands through a user interaction terminal. This breaks the time and space limitations of traditional car washes that require manual intervention, improving operational portability and user experience. The cloud management platform generates cleaning operation strategies based on control commands and robot status information, enabling the system to make dynamic decisions based on real-time vehicle conditions (such as vehicle cleanliness, stain type, and energy status), avoiding the problems of rough operation and insufficient intelligence caused by relying on preset paths. Multiple robot nodes achieve controlled displacement in the space around the vehicle through adsorption or flying movement modules. Through heterogeneous functional division of labor such as reconnaissance, cleaning, and refueling, they perform physical cleaning operations on areas such as the roof, curved surfaces, and recesses, achieving full-area coverage without blind spots. This overcomes the limitations of large equipment in precise operation and the poor robustness of single machines. Overall, the system adopts a cloud-edge collaboration, multi-body collaboration, and perception-driven closed-loop technical architecture, integrating functions such as portable deployment, non-destructive cleaning, autonomous decision-making, and self-sufficient energy, providing an integrated intelligent car wash solution. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This application provides a schematic diagram of the system architecture of an intelligent cluster car wash robot system. Figure 2 A schematic diagram of a data storage and analysis center provided in an embodiment of this application; Figure 3 This application provides a schematic diagram illustrating the specific modular composition of each robot node in an embodiment of the present application. Figure 4 A schematic diagram of a user terminal provided in an embodiment of this application; Figure 5 A flowchart illustrating a control method for an intelligent cluster car wash robot provided in this application embodiment; Figure 6 A specific car wash control flowchart is provided for an embodiment of this application; Figure 7 A car wash control flowchart based on remote control and intelligent reminders is provided for embodiments of this application; Figure 8 A complete energy flow diagram of a solar power replenishment system provided in this application embodiment. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0018] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0020] This application provides an intelligent cluster car wash robot system, see [link]. Figure 1 As shown, the system includes a user interaction terminal, a cloud management platform, and multiple robot nodes. The cloud management platform is communicatively connected to both the user interaction terminal and the robot nodes.

[0021] The user interaction terminal is a smart wearable device, including smartphones, smartwatches, and other portable smart devices commonly found in the current market. It runs a dedicated user terminal app, which provides a graphical user interface and supports vehicle diagnostics, cleaning mode selection, status queries, information reminders, and personalized settings. Users send control commands to the cloud management platform through this app, including but not limited to: vehicle diagnostic commands, immediate cleaning commands, smart cleaning commands, cleaning intensity adjustment commands (such as "powerful cleaning" or "regular cleaning"), recharge reminder settings, and personalized parameter settings such as preset cleanliness thresholds and cleaning intervals. Commands are sent to the cloud management platform via the internet in standardized data packet format, enabling remote expression and reception of user intentions.

[0022] The cloud management platform is the central decision-making and scheduling core of the system, consisting of a task decision module, a data storage and analysis center, and a collaborative scheduling algorithm library. Based on received control commands and status information from at least one robot node, the cloud management platform generates cleaning operation strategies and issues corresponding operation commands to one or more robot nodes.

[0023] The aforementioned status information refers to the vehicle body cleanliness images and spectral data collected and uploaded by the reconnaissance nodes. This data includes information such as stain type, spatial distribution location, coverage area, and degree of paint oxidation; it also includes real-time power, cleaning agent balance, operating temperature, and current task execution progress reported by the cleaning nodes; and it also includes operational status parameters such as energy reserves, wireless charging output capacity, and cleaning agent inventory fed back by the supply nodes.

[0024] When generating the cleaning operation strategy, the task decision engine of the cloud management platform first calls an image recognition model based on a convolutional neural network to analyze the multispectral sensor data uploaded by the reconnaissance nodes, identifying different categories and distribution characteristics of bird droppings on the roof, dust on the hood, stains on the windshield, and mud stains on the lower part of the doors. See also... Figure 2As shown, the system retrieves the vehicle's historical cleaning records from the data storage and analysis center, for example, if it has been 5 days since the last cleaning, and combines this with real-time external environmental information, such as no rain forecast for the next two days. Based on this information, it determines whether a cleaning task needs to be initiated (a cleaning decision is triggered when the cleanliness level is below a preset threshold, or when the time since the last cleaning exceeds the user-defined number of days and environmental conditions are suitable). Subsequently, the collaborative scheduling algorithm library generates differentiated and zoned cleaning operation strategies based on the current load, energy consumption status, spatial location, and obstacle avoidance constraints of each node. For example, one cleaning node is assigned to focus on the bird droppings area on the roof, performing high-pressure pre-spraying plus medium-speed gentle brushing; another cleaning node is assigned to handle the sides and doors of the vehicle, performing medium-pressure rinsing plus low-speed brushing; simultaneously, the supply node is instructed to enter standby mode, continuously monitoring the battery level and cleaning agent remaining of the cleaning nodes, and automatically initiating the wireless charging support process when the battery level of any node falls below 25%.

[0025] Each robot node includes a reconnaissance node, a cleaning node, and a supply node, which together constitute the robot swarm execution layer. Each node is equipped with a mobility module, a communication module, an energy module, and corresponding functional modules: the reconnaissance node is equipped with a sensor module to collect images and spectral data of the vehicle surface; the cleaning node is equipped with a cleaning execution module to perform cleaning actions; and the supply node is equipped with an energy module and a cleaning supply module to provide power and cleaning agent replenishment to other nodes.

[0026] Each robot node performs controlled displacement within the space surrounding the vehicle based on received work instructions, and performs physical cleaning operations on the vehicle surface. That is, each node achieves active, controllable movement relative to the vehicle body through a mobile module. This mobile module employs negative pressure adsorption and / or biomimetic microstructure adsorption mechanisms, or a multi-axis ducted multi-rotor flight structure. The negative pressure adsorption mechanism uses a micro-turbo fan to create a negative pressure cavity between the bottom of the robot shell and the vehicle body, combined with a wear-resistant silicone sealing ring, adapting to curved and slightly uneven surfaces. The biomimetic microstructure adsorption mechanism uses micro-nano array materials mimicking a gecko's foot, relying on van der Waals forces to achieve silent, low-energy adsorption, suitable for glass and smooth painted surfaces. The multi-axis ducted multi-rotor flight structure supports non-contact aerial displacement, used to traverse obstacles such as rearview mirrors and antennas, or to perform large-scale rapid inspections. All displacement paths are precisely planned by work instructions issued by the cloud management platform, and the movement trajectories are dynamically adjusted through real-time communication and negotiation between nodes via a self-organizing network, avoiding collisions and repetitive operations.

[0027] When performing physical cleaning operations on vehicle surfaces, cleaning nodes can execute specific cleaning actions in designated areas. The cleaning execution module includes a miniature water pump, a reservoir, atomizing nozzles, and a motor-driven flexible cleaning brush. During operation, the miniature water pump draws clean water or premixed cleaning solution from the reservoir and sprays it evenly onto the target surface through the atomizing nozzles, achieving wetting and softening of stains. Subsequently, the flexible cleaning brush rotates and scrubs at a preset speed and pressure, with the scrubbing force and stroke dynamically matched to the type of stain (e.g., high-pressure pre-spraying plus medium-speed scrubbing for bird droppings, normal-pressure spraying plus low-speed scrubbing for dusty areas, and medium-pressure rinsing plus low-speed scrubbing for muddy areas). The entire cleaning process is controlled by closed-loop feedback: the cleaning node continuously monitors its own battery level and cleaning agent remaining. When the battery level falls below a preset threshold, it immediately transmits the status information back to the cloud management platform. The platform then activates a task redistribution mechanism, instructing a replenishment node to move to the vicinity of the low-battery node and perform short-distance non-contact charging via a wireless charging coil. After charging is complete, the node resumes its original cleaning task. This physical cleaning operation is refined, on-demand, and non-destructive throughout the entire process, ensuring cleaning effectiveness while significantly saving water resources and cleaning agent usage.

[0028] In summary, this system initiates cleaning intentions through user interaction terminals, and the cloud management platform integrates multi-source status information to generate intelligent cleaning operation strategies. Multiple heterogeneous robot nodes collaboratively execute controlled displacement and differentiated physical cleaning operations, constructing a fully closed-loop intelligent car wash system that integrates detection, diagnosis, decision-making, execution, and verification. This effectively solves the fundamental defects of existing technologies, such as poor portability, low cleaning accuracy, high single-point failure rate, and strong energy dependence.

[0029] The aforementioned multiple robot nodes include reconnaissance nodes, cleaning nodes, and supply nodes. The reconnaissance nodes are used to collect the status information of the vehicle surface and upload it to the cloud management platform. The cleaning nodes are used to perform physical cleaning operations on the vehicle surface according to the operation instructions issued by the cloud management platform. The supply nodes are used to provide power and cleaning agents to the reconnaissance nodes and / or cleaning nodes.

[0030] Specifically, the reconnaissance node is used for environmental perception. Its sensing module continuously collects images and spectral data of the vehicle surface and uploads them to the cloud management platform in real time through the communication module. The cleaning node is used to perform cleaning operations. It receives zoned operation instructions issued by the cloud management platform and performs physical cleaning actions such as spraying and scrubbing in the designated area. The supply node is used for supply support. It has a built-in energy storage unit and cleaning agent tank, and is equipped with a wireless charging transmitting coil and cleaning agent delivery interface. It can provide power and liquid replenishment to the reconnaissance node or cleaning node with insufficient power or running out of cleaning agent according to the scheduling instructions, so as to ensure the continuous operation capability of the robot cluster.

[0031] Figure 3The specific module composition of each robot node is shown. The reconnaissance node is equipped with a sensing module, which includes a high-definition imaging unit and a multispectral sensor, used to collect optical reflectance characteristics data and spectral response data of the vehicle surface. During car wash control, the cloud management platform identifies the type and distribution area of ​​stains based on this optical reflectance characteristics data and spectral response data, and generates a cleaning operation strategy that includes area allocation and action parameters.

[0032] In one implementation, the sensing module of the reconnaissance node integrates a high-definition imaging unit and a multispectral sensor, enabling it to simultaneously acquire reflection images and spectral response curves of the vehicle surface in the visible, near-infrared, and short-wave infrared bands during movement. Different stains exhibit characteristic absorption peaks or reflectivity anomalies in specific bands; for example, bird droppings have strong absorption characteristics in the near-infrared band, while oxidized paint surfaces show significantly reduced reflectivity in the blue light band. The cloud management platform, based on a pre-trained convolutional neural network model, jointly analyzes the aforementioned optical reflection characteristic data and spectral response data to accurately identify the type of stain (dust, mud, bird droppings, tree sap, oxide layer), its spatial coordinates, and coverage area. Based on this, it generates a cleaning operation strategy: dividing the entire vehicle into several sub-regions and matching differentiated action parameters to each region, including spray pressure level, brushing speed, action duration, and cleaning fluid ratio.

[0033] The aforementioned reconnaissance, cleaning, and resupply nodes are all equipped with mobile modules. These modules can be used in any combination of three adsorption mechanisms or switched as needed: The negative pressure adsorption structure consists of a micro-turbo fan and a wear-resistant silicone elastic sealing ring. The high-speed rotation of the fan creates a stable negative pressure chamber between the robot's bottom and the body, suitable for various substrates such as metal, plastic, and painted surfaces; The biomimetic microstructure adsorption structure uses micro-nano-level columnar array materials, whose surface structure mimics the bristles of a gecko's toes. It achieves silent, zero-power adsorption through controllable van der Waals forces, making it particularly suitable for glass, chrome-plated parts, and high-gloss painted surfaces; The magnetic adsorption structure incorporates neodymium iron boron permanent magnets or low-voltage DC electromagnets to provide strong adsorption force. It is specifically designed for steel body parts, and its activation status is remotely set by the user after confirming the body material on the APP. The system automatically activates the corresponding adsorption mode.

[0034] Furthermore, the aforementioned cleaning node is equipped with a cleaning execution module, which is an integrated electromechanical unit. The cleaning execution module includes a micro water pump, a liquid storage container, an atomizing nozzle, and a flexible rotating brush. In one example, the liquid storage container has a dual-chamber design for both clean water and concentrated detergent, allowing for on-demand mixing. The micro-pump is a brushless DC diaphragm pump with wide voltage input and constant current / voltage regulation, used to draw liquid from the storage container and spray it through the atomizing nozzle, enabling precise flow control. The atomizing nozzle uses a piezoelectric ceramic oscillator structure to break the liquid into uniform droplets of 10–50 micrometers, achieving full-coverage wetting. The flexible rotating brush disc is driven by a motor and can be constructed with food-grade silicone bristles and a shape memory alloy frame. The bristle density and hardness are gradient-distributed, and the soft edges can conform to complex curved surfaces such as door handle recesses and license plate frames, while the central rigid support ensures scrubbing force. The drive motor is a high-torque stepper motor that supports stepless speed regulation from 0–300 rpm and starts and stops synchronously with the spraying action, ensuring a standard cleaning rhythm of "wetting first, then scrubbing, and then rinsing."

[0035] Furthermore, the aforementioned reconnaissance nodes, cleaning nodes, and resupply nodes are all equipped with energy modules, which include solar energy harvesting units and electrochemical energy storage units. The solar energy harvesting units cover the top and side arc-shaped areas of the robot's shell, and are adapted to the shape using flexible solar thin films (which can bend and fit the streamlined shell to maximize the light-receiving area) or rigid solar panels (used for structures with a high planar ratio, such as resupply nodes). The electrochemical energy storage units use high-energy-density lithium polymer batteries (balancing lightweight and battery life) or lithium iron phosphate batteries with better thermal stability (used for high-power load scenarios such as cleaning nodes). The power management integrated circuit monitors the solar direct power voltage, battery SOC (state of charge), and load current in real time, and intelligently switches the power supply path—prioritizing the use of solar direct power to drive low-power standby and sensing operations, while the battery only intervenes to provide power at night, during rainy weather, or during high-load cleaning phases, thereby maximizing energy utilization efficiency.

[0036] The aforementioned robot nodes are interconnected to exchange information needed for collaboration with other robot nodes. Specifically, a low-power self-organizing communication network is constructed between the reconnaissance, cleaning, and resupply nodes, which can utilize protocols such as Bluetooth Mesh to achieve multi-hop routing and network-wide broadcasting. The information exchanged between nodes in real time can include: their own real-time location coordinates (via IMU + visual odometry fusion positioning), current task status (idle / in progress / faulty), remaining battery power and cleaning agent supply, movement direction and velocity vector, and neighboring node avoidance requests. This local communication network is independent of the cloud platform's wide-area communication link, ensuring the normal operation of task negotiation, path planning, and collision avoidance functions within the cluster, even in a closed garage environment without internet access. This significantly improves the system's robustness and autonomy in weak network or offline scenarios.

[0037] Furthermore, the aforementioned supply node is equipped with a wireless charging coil and a cleaning agent delivery interface. For example, a high-power wireless charging transmitting coil and a cleaning agent delivery interface with a pressure regulating valve can be built into the housing of the supply node.

[0038] Both the reconnaissance and cleaning nodes are equipped with high-precision power monitoring modules (based on multi-dimensional sampling of voltage, internal resistance, and temperature) and cleaning agent level monitoring modules (using ultrasonic level sensing or resistive level detection). When any node detects that its power level is below 25% or its cleaning agent level is below 10%, it immediately sends an alarm signal to the cloud management platform via the communication module. Upon receiving the signal, the cloud management platform immediately generates two collaborative instructions: the first is an energy replenishment instruction, which dispatches a replenishment node to move near the low-power node and initiates wireless charging to achieve contact-based or non-contact energy transfer within a distance of 3-5 cm; the second is a task redistribution instruction, which temporarily adjusts the working range of other normal nodes, allowing cleaning node B to take over the unfinished areas originally handled by cleaning node A, ensuring that the cleaning task is not interrupted or downgraded.

[0039] In an optional implementation, the cloud management platform is also used to control the reconnaissance nodes to re-inspect the vehicle surface after receiving the task completion signal uploaded by all cleaning nodes, and generate a cleanliness assessment report based on the re-inspection results; the cleanliness assessment report includes at least one of the following: cleanliness improvement value, main stain removal status, and vehicle paint health status information.

[0040] Specifically, once the cloud management platform confirms that all cleaning nodes have uploaded task completion signals, it immediately issues a re-inspection command to the reconnaissance nodes. The reconnaissance nodes then quickly scan the key areas of the entire vehicle along the optimized path, collecting re-inspection images and spectral data. The cloud management platform performs pixel-level comparison between the re-inspection data and the initial inspection data, calculates the reflectance improvement rate and spectral characteristic peak attenuation rate of each area, and then quantifies the improvement in cleanliness (e.g., cleanliness improved from 65% to 97%). At the same time, based on the re-inspection spectral analysis results, it identifies the type and location of residual stains and generates a description of the main stain removal status (e.g., bird droppings on the roof are 100% removed, oxidation spots still exist on the left front door, and subsequent maintenance is recommended). In addition, the system also combines historical data trends to output information on the health status of the vehicle paint, including oxidation degree classification (mild / moderate / severe), micro-scratch density change rate, and paint gloss recovery value. Finally, these are integrated into a visualized cleanliness assessment report and pushed to the user's dedicated APP.

[0041] Furthermore, the user terminal-specific APP's interface supports vehicle condition diagnostics (cleanliness diagnostics, paint diagnostics, etc.), cleaning modes (powerful cleaning, regular cleaning, intelligent cleaning, etc.), queries (real-time location, working status, stain distribution, cleaning reports, etc. of each robot node), information reminders (node ​​recharge reminders, cleaning interval reminders, paint maintenance reminders, etc.), and personalized settings (preset cleanliness thresholds and / or cleaning interval days and / or preset recharge thresholds, etc.). See [link to relevant documentation]. Figure 4 As shown.

[0042] This invention also provides a control method for intelligent cluster car wash robots, applied to a cloud management platform, see [link to relevant documentation]. Figure 5 As shown, the method includes the following steps: S510 receives control commands sent by the user interaction terminal; S520, acquire status information from at least one robot node; S530 generates cleaning operation strategies based on control commands and status information; S540 sends corresponding operation instructions to one or more robot nodes based on the cleaning operation strategy; In the S550, each robot node performs controlled displacement within the space surrounding the vehicle according to the received work instructions, and performs physical cleaning operations on the vehicle surface.

[0043] The following combination Figure 6 A detailed description of one embodiment of the present invention is provided. This embodiment describes a complete car wash service process based on swarm intelligence, which is collaboratively completed by a cloud management platform and a robot cluster execution layer. Figure 6 As shown, the process includes the following steps: Step 1: User initiates a service request. The car owner clicks the smart cleaning button through a dedicated app on their user terminal, and the service request is sent to the cloud management platform via the internet.

[0044] Step 2: Request Type Determination. After receiving the request, the task decision engine of the cloud management platform identifies the request type and confirms it as an intelligent cleaning task.

[0045] Step 3: Dispatch reconnaissance nodes for full vehicle scanning. The mission decision engine issues a start command to the execution layer of the robot swarm parked in a specific area of ​​the vehicle (such as the roof) via the communication module. The reconnaissance nodes in the swarm respond first. Their movement modules use a combination of negative pressure fans and microstructure suction cups to stably attach the nodes to the vehicle surface and scan the entire vehicle along a preset path. During the scanning process, the reconnaissance nodes' high-definition cameras and multispectral sensors continuously collect vehicle images and spectral data, which are uploaded in real time to the data storage and analysis center of the cloud management platform.

[0046] Step 4: The cloud platform generates an immediate execution command. After receiving the data uploaded by the reconnaissance node, the data storage and analysis center automatically generates an immediate execution command to trigger the subsequent analysis process.

[0047] Step 5: Cloud platform AI engine analyzes data. The cloud management platform's AI algorithms (such as image recognition models based on convolutional neural networks) analyze the uploaded data and extract vehicle body surface features.

[0048] Step 6: Identify stain types and distribution. The AI ​​engine analysis results identified a large amount of dust and pollen on the roof and hood, a small amount of bird droppings on the windshield, and mud stains on the lower part of the doors, and generated a stain distribution map.

[0049] Step 7: Assess the health of the paint. Simultaneously, the AI ​​engine assesses the health of the paint based on spectral data, ruling out the risk of paint damage.

[0050] Step 8: Generate an optimized collaborative cleaning strategy in the cloud. The data storage and analysis center retrieves the vehicle's historical cleaning records (5 days since the last cleaning) and combines them with the obtained weather forecast information (no rain for the next two days). The task decision engine integrates the above information to generate an optimized collaborative cleaning strategy, which includes: dispatching cleaning node A to be responsible for the roof, front and rear windshields, and hood area, and performing an enhanced cleaning sub-process of "high-pressure pre-spray + medium-speed brushing" on bird droppings; dispatching cleaning node B to be responsible for the four doors and sides of the vehicle, and performing a regular cleaning sub-process of "medium-pressure rinsing + low-speed brushing" on mud stains on the lower part of the doors; the supply node stands by at the initial position and monitors the battery level and cleaning agent level of the two cleaning nodes in real time; the reconnaissance node performs a quick re-inspection of the entire vehicle after the cleaning task is completed.

[0051] Step 9: The cloud platform issues task instructions to the cluster. The above strategy is transformed into specific control instructions by the collaborative scheduling algorithm library and then sent to the robot cluster execution layer through the communication module.

[0052] Step 10: Autonomous Negotiation of the Robot Cluster. Each node in the cluster communicates and negotiates through a self-organizing network to confirm its respective task scope and movement path, in order to avoid collisions and duplicate operations.

[0053] Step 11: Task Assignment. After negotiation, cleaning nodes A and B are assigned to designated areas, and supply and reconnaissance nodes clarify their respective responsibilities.

[0054] Step 12: Each node executes its subtask in parallel. Cleaning nodes A and B move to their designated areas and activate the cleaning module: a miniature water pump draws clean water or premixed cleaning solution from the storage tank and sprays it evenly onto the vehicle surface through atomizing nozzles; the motor-driven flexible cleaning brush then rotates and scrubs. During execution, each node operates in parallel.

[0055] Step 13: Process Monitoring. When the energy module of Clean Node A detects that its power level has dropped to 25%, its communication module immediately reports this status to the cloud management platform. Simultaneously, each node continuously monitors its own status and reports it.

[0056] Step 14: Supply Node Intervention: Charging / Replenishment. Upon receiving a low battery alarm, the cloud management platform immediately activates the collaborative scheduling algorithm, generating a new instruction: instructing the supply node to move to the vicinity of cleaning node A; simultaneously, ordering cleaning node A to suspend its current task, and having cleaning node B expand its cleaning range to temporarily take over a portion of the area. Upon receiving the instruction, the supply node uses its mobility module to move to cleaning node A and performs contact charging via its wireless charging coil, increasing the battery level to 50% in approximately 3 minutes. After charging is complete, each node resumes its original task and continues execution.

[0057] Step 15: The reconnaissance node conducts a final review of the results. After all cleaning tasks are completed, the reconnaissance node is deployed again to quickly scan key areas and confirm that the cleanliness level meets the preset standard (e.g., above 95%).

[0058] Step 16: Generate a service report. The cloud management platform summarizes all data from this task, including task duration, total water consumption, total power consumption, cleanliness improvement curve, and identified main stain types, and automatically generates a visual service report.

[0059] Step 17: The report is uploaded to the cloud and pushed to the user. The report is pushed to the car owner's mobile app, showing that the vehicle's cleanliness improved from 65% to 97% and that the cleaning focused on removing bird droppings and mud stains.

[0060] Step 18: Service complete, cluster in standby mode. Finally, all nodes in the robot cluster execution layer return to the standby area, and the entire system enters a low-power sleep state.

[0061] This embodiment thus fully describes the car wash service process based on swarm intelligence, reflecting the entire process from the user initiating a request to the end of the service.

[0062] Figure 7 Another specific embodiment is shown to illustrate the overall logic of remote control and intelligent reminders in this invention, intuitively presenting the complex yet orderly two-way interactive relationship between the user, the cloud, and the robot. The entire process covers three typical scenarios: active control, passive query, and intelligent reminders. In specific implementation, the following process is adopted: Once a user initiates an action on the app, the remote interaction officially begins. For example, a user in another location might discover their vehicle has been splashed by a water truck through the dedicated app and click the "Clean Now" command. Or, a user might adjust the "Cleaning Sensitivity" to "High" and set the "Recommended Cleaning Interval" to 5 days within the app. These commands are then sent to the cloud via the app.

[0063] After receiving the instruction, the cloud server parses it, while the robot continuously monitors its own status. For example, the reconnaissance node checks the cleanliness of the vehicle body, and the replenishment node monitors the remaining cleaning agent. The cloud server queries the robot's status based on the instruction to obtain current operational data. Even in scenarios where cleaning is forced by the user, the system will first confirm whether the robot is online and malfunction-free to ensure safe execution.

[0064] Next, the cloud will generate an executable task based on the instructions and the retrieved status. For example, in a mandatory cleaning scenario, the cloud will generate a standard full-vehicle cleaning task and directly send it to the robot cluster for execution. In a personalized settings scenario, the cloud will update the user's sensitivity parameters or prepare to send back the queried data.

[0065] After a task is generated, the cloud platform performs rule checks to see if preset conditions have been triggered. If a rule is triggered, such as a replenishment node detecting that only 10% of the cleaning agent remains, the cloud platform generates an alert message and pushes it to the user's app, along with a one-click purchase link, completing the loop from alert to service. Another example is when a reconnaissance node detects a cleanliness level of 80%, which, while higher than the standard threshold of 70%, is lower than the user-defined high-sensitivity threshold of 85%, and the system will also push a cleaning recommendation message. If no rule is triggered, the cloud platform assigns the task to a robot for execution.

[0066] The robot reports its status in real time while performing tasks, such as cleaning progress and estimated completion time. The cloud aggregates this data and sends it to the user's app, where the user can see the progress bar updating in real time, or check information such as the last cleaning time and the current cleanliness of the vehicle.

[0067] Throughout the process, the cloud continuously monitors and aggregates data. Once a new situation triggers a rule, it will re-enter the alert branch. For example, if a user sets a high sensitivity level, the system will continuously monitor changes in cleanliness until the task is completed or the user exits. The remote interaction ends when the cleaning task is finished, the user closes the query interface, or the alert is processed.

[0068] The entire process demonstrates efficient collaboration between users, the cloud, and robots, forming a complete closed loop from command initiation to execution feedback. At the same time, intelligent warnings and personalized settings make the user experience more considerate.

[0069] also, Figure 8 The complete energy flow logic of the solar power replenishment system of the present invention is also shown. After the solar cell film senses ambient light, the generated electricity is preferentially used to drive loads such as motors, sensors, and computing units. The power management integrated module determines in real time whether there is a surplus of energy based on load consumption and light intensity. If there is a surplus, it switches to the secondary path to charge the lithium battery, increasing the battery capacity; if there is no surplus or the capacity is insufficient, the lithium battery discharges to replenish the energy, decreasing the battery capacity.

[0070] The status detection unit continuously monitors the battery level and classifies it according to thresholds. When the battery level is above 90% or between 20% and 90%, the system maintains normal operation; when the battery level drops below 20%, a low battery warning logic is triggered.

[0071] Upon triggering the warning, the robot activates its active light-seeking mode, moving to a well-lit area to recharge using solar power. Simultaneously, it sends a recharge reminder to the cloud, which then pushes a notification to the user's app. If the robot cannot find a light source due to environmental limitations, the user can place it in sunlight as prompted by the app. Once the robot detects effective light, it automatically switches to high-efficiency charging. After the battery level recovers to a safe range, the robot exits the light-seeking mode and resumes normal operation, completing a closed-loop management process from energy collection, distribution, warning, to human assistance.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. An intelligent cluster car wash robot system, characterized in that, It includes a user interaction terminal, a cloud management platform, and multiple robot nodes, wherein the cloud management platform is communicatively connected to the user interaction terminal and the robot nodes respectively; The user interaction terminal is used to send control commands to the cloud management platform; The cloud management platform is used to generate a cleaning operation strategy based on the received control instructions and status information from at least one robot node, and to issue corresponding operation instructions to one or more robot nodes. Each of the robot nodes is used to perform controlled displacement within the space surrounding the vehicle and to perform physical cleaning operations on the vehicle surface according to the received work instructions.

2. The system according to claim 1, characterized in that, The multiple robot nodes include reconnaissance nodes, cleaning nodes, and supply nodes; The reconnaissance node is used to collect status information of the vehicle surface and upload it to the cloud management platform; The cleaning node is used to perform physical cleaning operations on the vehicle surface according to the operation instructions issued by the cloud management platform; The supply node is used to provide electrical energy and cleaning agents to the reconnaissance node and / or cleaning node.

3. The system according to claim 2, characterized in that, The reconnaissance node is equipped with a sensing module, which includes a high-definition imaging unit and a multispectral sensor, for collecting optical reflectance characteristics data and spectral response data of the vehicle surface; The cloud management platform is used to identify the type and distribution area of ​​stains based on the optical reflection characteristic data and spectral response data, and to generate a cleaning operation strategy that includes area allocation and action parameters.

4. The system according to claim 2, characterized in that, The supply node is equipped with a wireless charging coil and a cleaning agent delivery interface; The reconnaissance node and / or cleaning node are equipped with a power monitoring module and a cleaning agent remaining monitoring module; When the power or cleaning agent balance of any node falls below a preset threshold, the cloud management platform issues an energy replenishment command to the replenishment node and a task reallocation command to other normally operating nodes.

5. The system according to claim 2, characterized in that, The reconnaissance node, cleaning node and supply node are all equipped with a mobile module, and the mobile module adopts at least one of the following: negative pressure adsorption structure, biomimetic microstructure adsorption structure or magnetic adsorption structure. The negative pressure adsorption structure includes a micro turbofan fan and an elastic sealing ring; The biomimetic microstructure adsorption structure adopts a micro-nano-scale van der Waals force array material. The magnetic adsorption structure includes a permanent magnet or an electromagnet.

6. The system according to claim 2, characterized in that, The cleaning node is equipped with a cleaning execution module, which includes a micro water pump, a liquid storage container, an atomizing nozzle, and a flexible rotating brush. The micro water pump is used to draw liquid from the liquid storage container and spray it out through the atomizing nozzle; The flexible rotating brush is driven to rotate by a motor.

7. The system according to claim 2, characterized in that, The reconnaissance node, cleaning node and supply node are all equipped with energy modules, which include solar energy harvesting units and electrochemical energy storage units. The solar energy harvesting unit is a flexible solar thin film or a rigid solar panel; The electrochemical energy storage unit is a lithium polymer battery or a lithium iron phosphate battery.

8. The system according to claim 2, characterized in that, The cloud management platform is also used to control the reconnaissance node to re-inspect the vehicle surface after receiving the task completion signal uploaded by all cleaning nodes, and to generate a cleanliness assessment report based on the re-inspection results. The cleanliness assessment report includes at least one of the following: cleanliness improvement value, removal status of major stains, and information on the health status of the vehicle paint.

9. The system according to claim 2, characterized in that, The robot nodes are connected to each other to exchange information needed for collaboration with other robot nodes.

10. A control method for an intelligent cluster car wash robot, characterized in that, Applications in cloud management platforms include: Receive control commands sent by the user interaction terminal; Obtain status information from at least one robot node; A cleaning operation strategy is generated based on the control commands and the status information; Based on the cleaning operation strategy, corresponding operation instructions are issued to one or more robot nodes to trigger each robot node to perform controlled displacement in the space around the vehicle according to the received operation instructions and to perform physical cleaning operations on the vehicle surface.

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