Intelligent control car washing machine

Abstract

The present invention discloses an intelligent control car washing machine, which relates to the technical field of car washing machines, including a data acquisition and upload module, an instruction verification and preprocessing module, a state synchronization and fault rollback module, an instruction execution and feedback module, and a state synchronization optimization algorithm module: the data acquisition and upload module uploads the operating status and instructions of the car washing machine to the cloud in real time, and receives instructions issued by the cloud. Through a multi-layer verification mechanism, real-time status monitoring and intelligent fault handling, the system of the present invention ensures that the cloud instructions are highly matched with the device status, thereby minimizing misoperation and failures. The distributed cloud-edge collaborative architecture improves fault tolerance, allowing the device to be independently adjusted when communication is poor, ensuring safe and stable operation. Driven by real-time data, the device can automatically optimize operations, improve intelligence and efficiency, and provide an efficient and convenient user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of car washers, and in particular to an intelligently controlled car washer. Background Art

[0002] An intelligently controlled car washing machine is a car washing device that can be remotely monitored and controlled through a cloud service management system. By integrating sensors, Internet of Things technology, and artificial intelligence algorithms, it can collect various data in the car washing process (such as water pressure, water temperature, detergent usage, etc.) in real time and upload these data to the cloud platform. The cloud service management system optimizes the operating efficiency of the equipment through data analysis and processing, monitors the working status of the equipment, provides real-time fault diagnosis and early warning, and can also perform remote operation and control. In addition, the cloud platform also supports users to make remote reservations, make payments, and select car wash packages through mobile applications, thereby improving user experience and operational management efficiency. Through cloud service management, the car washing machine can realize data sharing and resource allocation among multiple devices to ensure that the equipment is always in the best operating state and can flexibly adjust service content according to market demand.

[0003] The prior art has the following deficiencies:

[0004] In smart-controlled car washers, failure of state synchronization between the cloud and the device may have serious consequences. If the cloud and the car wash machine's local system fail to synchronize the device status in a timely manner, the car wash machine may perform incorrect operations. For example, the cloud may send an instruction to start the car wash program, but due to network fluctuations or data transmission interruptions, the car wash machine fails to receive the instruction correctly, or the instruction fails to execute in sequence. In this case, the car wash machine may start at an inappropriate time, or skip certain key steps, resulting in poor car wash results, equipment failure, and even safety hazards. In addition, this problem may not be easy to detect in a short period of time, and after accumulation, it will seriously affect the user experience, cause a large number of customer complaints, damage brand reputation, and may cause the operator to face economic losses and repair costs.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligently controlled car washing machine. Through a multi-layer verification mechanism, real-time status monitoring and intelligent fault handling, the system ensures that cloud instructions are highly matched with the device status, minimizing misoperation and failures. The distributed cloud-edge collaborative architecture improves fault tolerance, allowing the device to be independently adjusted when communication is poor, ensuring safe and stable operation. Driven by real-time data, the device can automatically optimize operations, improve intelligence and efficiency, and provide an efficient and convenient user experience to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent control car washing machine, including a data acquisition and upload module, an instruction verification and preprocessing module, a state synchronization and fault rollback module, an instruction execution and feedback module and a state synchronization optimization algorithm module:

[0008] The data collection and upload module uploads the operating status and instructions of the car washing machine to the cloud in real time, and receives instructions issued by the cloud;

[0009] Instruction verification and preprocessing module: After the cloud instruction is sent, the instruction is preprocessed by the local verification module to confirm whether the instruction matches the current working status and mark the status synchronization process;

[0010] The status synchronization and fault rollback module synchronizes the instructions and device status through the local communication module. If the synchronization fails, the fault handling procedure is triggered to record and roll back the current operation to avoid erroneous operations.

[0011] The command execution and feedback module executes the cloud command after the local communication module is successfully synchronized, and updates the execution status in real time to ensure that each operation step is executed in sequence, and at the same time feedbacks the execution status to the cloud;

[0012] The state synchronization optimization algorithm module uses the state synchronization optimization algorithm to perform state calibration and data synchronization after each device operation, calculate the difference between the current device state and the cloud command, and after completing the state difference calculation, calculate the delay time and network quality, and optimize the command execution process to ensure that the state is always consistent.

[0013] Preferably, the specific steps of uploading the operation status and instructions of the car washing machine to the cloud in real time and receiving the instructions issued by the cloud are as follows:

[0014] The car washing machine initializes various operating states when it starts up and establishes a stable communication connection with the cloud to ensure smooth data transmission;

[0015] The car washing machine regularly uploads the operating status, working status of each module and equipment health information to the cloud for monitoring and analysis;

[0016] After receiving the command from the cloud, the car washing machine will verify the legitimacy of the command and confirm whether the current status meets the execution conditions;

[0017] After the command is verified, the car wash will perform the corresponding operations and feed back the execution status to the cloud in real time for monitoring and optimization.

[0018] Preferably, after the cloud instruction is sent, the instruction is preprocessed by the local verification module to confirm whether the instruction matches the current working state, and the specific steps of marking the state synchronization process are as follows:

[0019] After the device successfully receives the cloud command, it first confirms the communication status with the cloud and parses the command to match the device status;

[0020] The verification module compares the received instructions with the current device status to ensure that the instructions are legal and executable under the current device status;

[0021] After successful verification, the instruction synchronization process is marked, the instruction matching result is recorded, and the instruction is ready to be executed;

[0022] After the device confirms that the instruction is ready to execute, it checks the status of each module to ensure that the device can execute the operation smoothly and reports the execution readiness status to the cloud.

[0023] Preferably, the instructions and device status are synchronized through the local communication module. If the synchronization fails, the fault handling procedure is triggered to record and roll back the current operation. The specific steps to avoid erroneous operation are as follows:

[0024] The device synchronizes the verified instructions with the current status through the local communication module and ensures the security and integrity of data transmission;

[0025] During the synchronization process, when the device detects any failure, it immediately identifies the error and records the relevant failure information;

[0026] Once synchronization fails, the fault handling procedure is started to restore the device to a safe state through rollback operations to avoid misoperation;

[0027] After the rollback operation is completed, the cloud and operator are notified, and an attempt is made to restore the normal connection between the device and the cloud to ensure the smooth execution of subsequent operations.

[0028] Preferably, after the local communication module is successfully synchronized, the cloud instructions are executed and the execution status is updated in real time to ensure that each operation step is executed in sequence. The specific steps of feeding back the execution status to the cloud are as follows:

[0029] After the local communication module successfully synchronizes the device status, it first confirms the initialization status of the current device and prepares to execute the instructions issued by the cloud. The local control module calculates the comprehensive score of the current readiness. The calculation expression is as follows:

[0030]

[0031] , where S init is the device initialization status score, indicating whether the device is ready to execute instructions, M i is the status score of the ith module, E sys is the current energy state of the device system, E max is the maximum energy capacity of the device, n is the total number of modules of the device;

[0032] After confirming that the device is in an executable state, it starts to execute operations according to the instructions issued by the cloud, and updates the execution status of the device in real time. At this time, the device uses execution time as a key parameter for real-time monitoring. The execution time calculation formula is as follows:

[0033]

[0034] , where T exec is the execution time of the current operation step, T start is the timestamp of the start of the operation, T end is the timestamp of the end of the operation, D act is the actual amount of operations completed, V act is the actual execution rate;

[0035] When each operation step is executed, the device updates its operation status in real time and feeds back the status information to the cloud through the communication module. To ensure the accuracy and timeliness of the feedback, the status feedback time delay and feedback accuracy are used as key parameters to evaluate the synchronization efficiency between the device and the cloud. The formula is as follows:

[0036]

[0037] , where A feedback is the feedback accuracy, T delay is the feedback time delay, T max is the maximum feedback delay time predetermined by the device, S exec is the execution status score of the current operation step, S total is the overall equipment status score;

[0038] Finally, when all operation steps are completed in order, the overall instruction completion score is generated according to the execution results, and feedback information is used to confirm whether the instruction is completed to the cloud. The instruction completion score calculation formula is as follows:

[0039]

[0040] , where C exec is the instruction completion score, which indicates the completion of the entire instruction execution process, W j is the weight of the j-th operation, E exec (j) is the execution efficiency of the j-th operation, T total is the total time of the entire instruction execution, A feebaeck is the feedback accuracy, which indicates the quality of the feedback information received by the cloud.

[0041] Preferably, a state synchronization optimization algorithm is used to perform state calibration and data synchronization after each device operation, calculate the difference between the current device state and the cloud instruction, calculate the delay time and network quality after completing the state difference calculation, and optimize the instruction execution process to ensure that the state is always consistent. The specific steps are as follows:

[0042] Before the device performs an operation, it first calculates the difference between the current device state and the cloud instruction, which is represented by the state difference measurement function. The difference measurement calculation formula is as follows:

[0043]

[0044] , where D is the total difference measure between the device state and the cloud command, s q is the weight coefficient, indicating the importance of the qth state parameter in the overall difference, is the qth state parameter of the device. is the qth state parameter corresponding to the cloud instruction, and h is the total number of calculated state parameters;

[0045] After completing the state difference calculation, the next step is to calculate the network delay and transmission quality to optimize the instruction execution process. The delay and transmission quality calculation expressions are as follows:

[0046]

[0047] , where B is the optimized network delay, B transmit is the transmission time from device to cloud or from cloud to device, α is a mathematical constant, and Q is the network quality factor, which indicates the quality of the current network;

[0048] After obtaining the device status difference D and network delay B, the next step is to calculate the synchronization correction value between the device and the cloud. The correction value is calculated using the following formula:

[0049]

[0050] , where C is the synchronization correction value, λ is the synchronization adjustment coefficient, and B maxis the maximum allowed network delay, which is the preset maximum value;

[0051] The execution process of the instruction is adjusted according to the synchronization correction value C. The device dynamically optimizes the execution time and sequence of the instruction according to the correction value to ensure that the operation is completed while minimizing the difference between the device status and the cloud instruction. The formula for optimizing the execution process is as follows:

[0052]

[0053] , where E is the optimized execution process score, β p is the weight coefficient of the p-th operation, reflecting the importance of different operation steps. is the maximum allowed difference value of the p-th operation, and z is the total number of operation steps.

[0054] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0055] The present invention uses multi-layer verification mechanisms, real-time status monitoring, and intelligent fault handling to ensure that the cloud instructions are highly matched with the device status during the instruction execution process, thereby minimizing misoperation or failures caused by improper instructions or abnormal device status. The fault handling procedure and rollback mechanism ensure that the device quickly recovers to a safe state under abnormal circumstances, thereby greatly improving the reliability and security of the system. The distributed cloud-edge collaborative architecture further enhances the fault tolerance of the system, allowing the device to independently adjust operations through the edge computing unit even in the case of poor communication, ensuring stability and security and enhancing user trust.

[0056] The present invention is based on a synchronization mechanism of real-time status monitoring and intelligent fault handling, which enables the device to automatically adjust the working mode and operation process according to real-time data, thereby improving the level of intelligence and operating efficiency. For example, when the device detects that the water pump is overheated or the battery is low, it can automatically optimize the instructions to avoid invalid operations or equipment damage. The fast response and distributed architecture of the edge computing unit make the execution of instructions more accurate and rapid, reduce delays, and improve the response speed and overall work efficiency of the device, thereby providing customers with a more efficient and convenient user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0058] Figure 1 The figure is a schematic diagram of a module of an intelligently controlled car washing machine according to the present invention. DETAILED DESCRIPTION

[0059] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0060] The present invention provides Figure 1 The intelligent control car washing machine shown includes a data acquisition and upload module, an instruction verification and preprocessing module, a state synchronization and fault rollback module, an instruction execution and feedback module, and a state synchronization optimization algorithm module:

[0061] The data collection and upload module uploads the operating status and instructions of the car washing machine to the cloud in real time and receives instructions from the cloud. The operating status includes the working mode of the car washing machine, the working status of each module and the health status of the equipment;

[0062] The specific steps for uploading the operating status and instructions of the car washing machine to the cloud in real time and receiving the instructions issued by the cloud are as follows:

[0063] The car washing machine initializes various operating states when it starts up and establishes a stable communication connection with the cloud to ensure smooth data transmission;

[0064] Before the car washing machine starts to operate, it is necessary to initialize its operating status, including the working mode, the status of each module (such as water pump, motor, nozzle, etc.), and the health status of the equipment (such as battery power, sensor fault detection, etc.). The equipment obtains all necessary real-time data through the built-in control system and assigns a unique identifier to each module. These data include but are not limited to the current mode of the car washing machine (for example, automatic car washing, manual car washing, standby mode, etc.), the working status of each component (such as whether the water pump is operating normally, whether the motor is overloaded, etc.), and any equipment health status that needs to be monitored (such as the status of temperature, pressure, and liquid level sensors). These data are first collected in real time by the data acquisition module inside the device and formatted into a suitable structure.

[0065] After data collection is completed, the device establishes a stable communication connection with the cloud system through a network interface (such as Wi-Fi, 4G / 5G or wired network). At this time, the device will perform a connection verification process to ensure stable and timely communication with the cloud. After the connection is successful, the device will start a data upload mechanism to upload all operating status and health information of the current device to the cloud, and confirm that the uploaded data has been successfully received. This process ensures the security of data transmission through encryption protocols to avoid interference during the transmission process.

[0066] The car washing machine regularly uploads the operating status, working status of each module and equipment health information to the cloud for monitoring and analysis;

[0067] Once the device successfully establishes a connection with the cloud, the system will continuously upload the device's operating status data to the cloud at preset time intervals (such as every second or every minute). The uploaded content includes the car wash machine's operating mode (such as the ongoing car wash program), the current working status of each module (such as the water pump, motor, nozzle, etc.), and the device's health status (such as battery power, device temperature, pressure value, sensor fault detection, etc.). These data are converted into a structured format (such as JSON or XML) and securely transmitted to the cloud server through an encrypted channel.

[0068] The cloud system receives and stores this data for subsequent analysis, monitoring, and diagnosis. Each uploaded data includes not only the current status, but also carries a timestamp to ensure that the cloud can accurately track changes in the device. The cloud platform processes the status data uploaded by the device, updates the operating status of the device in real time, and provides analytical support for subsequent operations. For example, the cloud can evaluate the health status of the device in real time, and send out warning signals in advance when any abnormalities are detected (such as the water pump temperature is too high or the battery power is too low) so that users or operators can handle it in time.

[0069] After receiving the command from the cloud, the car washing machine will verify the legitimacy of the command and confirm whether the current status meets the execution conditions;

[0070] When the cloud sends a control command, the car washing machine receives the cloud command through the communication interface. The format of the cloud command is also standardized, usually transmitted through a protocol or instruction set, including the command content and related parameters (such as starting a car washing program, adjusting the water flow pressure, etc.). Once the device receives the cloud command, it first parses the command through the local control system to confirm the legitimacy and execution conditions of the command.

[0071] At this point, the device will compare the cloud command with the current state. For example, the command may require the car wash program to be started, but when the car wash machine is in standby mode, it is necessary to first check whether the current device state meets the execution conditions, such as whether the device is ready and whether the required resources (such as water, detergent, battery power) are sufficient. If the current device state does not match the command, the system will refuse to execute the command and report the error status to the cloud. If the status matches, the system will prepare to start the relevant operation.

[0072] After the command is verified, the car wash will perform the corresponding operation and feed back the execution status to the cloud in real time for monitoring and optimization;

[0073] After the command is successfully verified, the device will perform the corresponding operation according to the command issued by the cloud. For example, if the cloud command requires the start of the car wash program, the device will start each module (such as water pump, motor, etc.) according to the command to perform the car wash operation. During the execution process, the device will update the execution status of each step in real time and upload the updated status data to the cloud. During the execution process, the device will continue to feedback the current working status of each module to the cloud, such as whether the water pump is operating normally, whether the nozzle is started, whether the motor speed reaches the predetermined level, etc.

[0074] If any abnormality occurs during the operation of the device (such as the pump stops working or the temperature is too high), the device will immediately feedback the abnormal information to the cloud, which will start the fault diagnosis program based on the abnormal data and provide solutions or warnings. The cloud can also adjust the instructions or optimize the operation steps based on the feedback status information to ensure that the car washing machine always operates in the optimal state. All feedback information will be recorded in the cloud database for subsequent analysis, maintenance and optimization.

[0075] Verification and preprocessing module: After the cloud command is sent, the command is preprocessed by the local verification module to confirm whether the command matches the current working status and mark the status synchronization process;

[0076] After the cloud command is sent, the local verification module pre-processes the command to confirm whether the command matches the current working status and marks the status synchronization process. The specific steps are as follows:

[0077] After the device successfully receives the cloud command, it first confirms the communication status with the cloud and parses the command to match the device status;

[0078] After the device successfully uploads the current operating status (such as working mode, status of each module and device health status information) to the cloud, the cloud will issue control instructions based on these real-time status data. After the car washing machine receives the instructions from the cloud through the communication interface, it will enter the next step of the instruction verification phase. At this time, the device will first confirm whether the communication with the cloud is successful. If there is a communication failure or data loss, the device will automatically reconnect and notify the cloud to resend the instruction. Once the instruction is received correctly, the system will perform data analysis and prepare to match the cloud instruction with the status of the local device.

[0079] The status data uploaded by the device includes the current working mode (such as "standby" or "car washing"), the working status of each module (such as water pump, nozzle, motor, etc.) and the health status of the device (such as battery power, temperature, pressure, etc.). This information will be transmitted to the cloud as the basic data for issuing instructions. Therefore, after receiving the cloud instruction, the instruction content must first be matched with the local status data through the local verification module to ensure the rationality and executability of the instruction.

[0080] The verification module compares the received instructions with the current device status to ensure that the instructions are legal and executable under the current device status;

[0081] The verification module will match the received instructions with the current working status of the device according to the preset rules. For example, suppose the cloud instruction requires the car wash program to be started, but the car wash machine is currently in the "standby" state. The verification module will check the instruction to confirm whether the instruction is suitable for the current state. If some modules of the car wash machine (such as the water pump or detergent nozzle) are under maintenance or turned off, the verification module will also check whether these modules need to be started first, or whether other conditions are met, such as whether the battery power of the device is sufficient. Only when these conditions are met will the system consider the instruction to be legal and executable.

[0082] In addition, the verification module also needs to identify the parameters that may be included in the cloud command, such as whether the command requires a change in the working mode (such as switching from standby to car wash mode), adjustment of water flow or pressure parameters, etc. The system will compare these parameters with the current state of the device to ensure that there will be no conflicts during the execution of the command and avoid misoperation due to state mismatch. If the command does not match the current state, the verification module will refuse to execute the command and report the error to the cloud.

[0083] After successful verification, the instruction synchronization process is marked, the instruction matching result is recorded, and the instruction is ready to be executed;

[0084] After completing the matching verification of the instruction and the device status, the system will mark each instruction synchronization process to record whether the instruction has passed the verification. If the instruction is legal and matches the current device status, the system will mark the synchronization status as "successful" and create an instruction execution record for the upcoming operation. The record will contain information such as the instruction content, current status, execution timestamp, etc., as tracking data for subsequent operations.

[0085] If the verification module finds that the instruction does not match the current state, the system will generate an error record containing the reason for the verification failure (for example, the device is in an unsuitable state or the parameters do not meet the requirements), and send this error information to the cloud. This not only helps the cloud to make subsequent adjustments to the instructions, but also ensures that the device always performs operations under the conditions that meet the conditions. This step of the verification module provides a clear execution basis for subsequent operations, ensuring the accuracy of the synchronization between the cloud and the device status.

[0086] After the device confirms that the command is ready to execute, it checks the status of each module to ensure that the device can execute the operation smoothly, and reports the execution readiness status to the cloud;

[0087] After the instruction is verified and successfully marked, the device enters the preparation execution stage. At this time, the local control system of the car wash machine will prepare to start the relevant operations according to the requirements of the cloud instruction. Specifically, the system will activate the relevant modules (such as water pumps, nozzles, motors, etc.) according to the instruction requirements and confirm whether these modules are in an operational state. At this time, the local control system of the device will once again confirm whether each module is ready to avoid hardware failures or operation delays during the execution of the instruction.

[0088] Before it is ready to execute, the device will also quickly check the execution process to ensure that no key steps are missed during the execution of the command, such as the adjustment of water flow, the spraying order of detergent, etc. Only when all preparations are completed will the device feedback the information "the command is ready to execute" to the cloud, waiting for further operation instructions or start signals. If any problems or failures occur at this time (such as low battery or water pump failure), the device will immediately issue an early warning and ask the cloud to adjust the command or suspend the operation.

[0089] The status synchronization and fault rollback module synchronizes the instructions and device status through the local communication module. If the synchronization fails, the fault handling procedure is triggered to record and roll back the current operation to avoid erroneous operations.

[0090] The local communication module is used to synchronize instructions and device status. If synchronization fails, the fault handling procedure is triggered to record and roll back the current operation. The specific steps to avoid misoperation are as follows:

[0091] The device synchronizes the verified instructions with the current status through the local communication module and ensures the security and integrity of data transmission;

[0092] After the instructions are matched and verified by the local verification module, the device enters the instruction synchronization phase. At this point, the local communication module of the device is responsible for synchronizing the verified cloud instructions with the current working status of the device. During the synchronization process, the communication module will transmit the real-time status of the device (such as the working status of each module, working mode, battery power, sensor status, etc.) to the cloud, and at the same time receive new instructions or updated status from the cloud. This process requires the device to maintain a stable communication connection with the cloud, usually through a network interface (such as Wi-Fi, 4G / 5G) for data transmission.

[0093] During the transmission process, the data interaction between the device and the cloud is bidirectional. The communication module will continuously check the device's status changes and ensure that these changes can be fed back to the cloud in a timely manner. The upload of device status and the issuance of instructions must be synchronized, that is, the execution of cloud instructions must be consistent with the current status of the device. Therefore, at this stage, the communication module uses encryption and verification mechanisms to ensure the security and integrity of the data transmission process. If the device's status data does not match the cloud instruction, the communication module will automatically issue a warning, terminate the data transmission, and prevent the wrong instruction from being executed.

[0094] During the synchronization process, when the device detects any failure, it immediately identifies the error and records the relevant failure information;

[0095] Once the device starts synchronizing commands and status, the local communication module will continuously monitor whether the synchronization process is completed successfully. If any synchronization failure occurs (such as network interruption, data transmission delay, communication interface failure, etc.), the module will immediately detect the anomaly. Synchronization failure can be judged by multiple signals, such as failure to connect to the cloud, packet loss, and failure to correctly parse or execute commands.

[0096] When a synchronization failure occurs, the system will mark the failed synchronization operation as an error, generate an error log, and record the detailed information of the synchronization failure (such as the time, reason, device status, etc.). This process is crucial for subsequent problem tracking and analysis. After the error is marked, the system will roll back the current state and notify the operator or the cloud to intervene to prevent the continued execution of the erroneous operation.

[0097] Once synchronization fails, the fault handling procedure is started to restore the device to a safe state through rollback operations to avoid misoperation;

[0098] When the device detects a synchronization failure and marks the error, the system immediately starts the fault handler. The main task of the fault handler is to restore the device to the last known safe state through a rollback mechanism to prevent the device from executing incorrect instructions or entering an unsafe operation process. The rollback operation usually includes: restoring to the default state when the device is started, stopping any operation being performed, shutting down the faulty module (such as a water pump, motor, etc.) and disconnecting any abnormal power connection.

[0099] The fault handler will also take different recovery measures according to different failure types (such as network failure, abnormal device status, sensor failure, etc.). For example, in the case of network interruption, the device may retry the connection operation; when the device health status does not meet the instruction requirements, the system may prompt that manual intervention is required and suspend the current operation. Through the rollback operation, the device can prevent the occurrence of misoperation in the event of synchronization failure, thereby ensuring the safety and normal operation of the device.

[0100] After the rollback operation is completed, the cloud and the operator are notified, and an attempt is made to restore the normal connection between the device and the cloud to ensure the smooth execution of subsequent operations;

[0101] Once the rollback operation is completed, the system will feed back the fault information and rollback results to the cloud and notify the operator through the display on the device or the mobile application. In the cloud, the fault information will be recorded and the corresponding alarm mechanism will be triggered. The cloud system will further analyze the root cause of the problem based on the abnormal status of the device, and update the instructions or adjust the parameters if necessary to ensure that subsequent operations can be carried out smoothly.

[0102] At the same time, the system will also enable the recovery mechanism. If the device's status returns to the normal range, the communication module will try to reconnect to the cloud and start resynchronizing the device status and instructions. The device may re-upload the device status to confirm that the current status matches the cloud instructions, thereby ensuring that subsequent instructions can be executed correctly. If the problem is resolved, the device will continue to perform operations after confirming that the synchronization is successful until the scheduled task is completed.

[0103] The command execution and feedback module executes the cloud command after the local communication module is successfully synchronized, and updates the execution status in real time to ensure that each operation step is executed in sequence, and at the same time feedbacks the execution status to the cloud;

[0104] After the local communication module is successfully synchronized, the cloud instructions are executed and the execution status is updated in real time to ensure that each operation step is executed in sequence. The specific steps for feeding back the execution status to the cloud are as follows:

[0105] After the local communication module successfully synchronizes the device status, it first confirms the initialization status of the current device and prepares to execute the instructions sent by the cloud. At this time, the device needs to confirm whether all necessary operation modules (such as water pumps, motors, sprinklers, etc.) are in an available state, and calculates the comprehensive score of the current readiness through the local control module. The score reflects whether the device can successfully execute the received instructions. The calculation expression is as follows:

[0106]

[0107] , where S init is the device initialization status score, indicating whether the device is ready to execute instructions (ranging from 0 to 1, 1 is fully ready), M i is the status score of the ith module (such as a water pump, motor, etc.) (0 indicates failure, 1 indicates normal), E sys is the current energy state of the device system (e.g. battery charge, power supply, etc.), E max is the maximum energy capacity of the device, n is the total number of modules of the device;

[0108] Initialize the state score S by calculating init , the system determines whether the device can successfully execute the cloud command and provides a reference for the subsequent command execution. init <0.8, the system will refuse to execute the instruction and issue a warning of insufficient energy or equipment failure.

[0109] After confirming that the device is in an executable state, it starts to execute operations according to the instructions issued by the cloud, and updates the execution status of the device in real time. The execution of each operation step requires accurate feedback on the current device status to ensure that each step is executed in sequence and the time, resource consumption and module status changes during the execution process are recorded. At this time, the device uses execution time as a key parameter for real-time monitoring. The execution time calculation formula is as follows:

[0110]

[0111] , where T exec is the execution time of the current operation step, T start is the timestamp of the start of the operation, T end is the timestamp of the end of the operation, D act It is the actual amount of operation completed (such as water flow, motor speed, etc.), and the unit is the measurement of the operation performed, V act is the actual execution rate (such as water flow rate, motor speed, etc.), in liters;

[0112] By calculating the execution time T exec,The system can monitor the execution efficiency of each step in real time, and adjust the parameters in time during the execution process to ensure that the operation steps are completed smoothly in the predetermined order and time.

[0113] When each operation step is executed, the device updates its operation status in real time and feeds back the status information (such as operation completion, module status, battery power, etc.) to the cloud through the communication module. To ensure the accuracy and timeliness of the feedback, the status feedback time delay and feedback accuracy are used as key parameters to evaluate the synchronization efficiency between the device and the cloud. The formula is as follows:

[0114]

[0115] , where A feedback is the feedback accuracy, which indicates the accuracy and timeliness of the feedback status information (ranging from 0 to 1, with 1 being the highest accuracy), T delay is the time delay of feedback, in seconds, indicating the time required for the cloud to receive feedback information, T max The maximum feedback delay time preset by the device. If the time is exceeded, the feedback delay is considered to be too long. exec is the execution status score of the current operation step, indicating whether the step is executed according to the predetermined goal. total It is the overall equipment status score, which indicates the overall operating status of the equipment (combining the working status of each module);

[0116] By calculating A feedback ,The system can evaluate the timeliness and accuracy of the feedback information, and then determine whether the feedback information needs to be resent or corrected, to ensure that the cloud can grasp the execution status of the device in real time and accurately.

[0117] Finally, when all operation steps are completed in sequence, the overall instruction completion score is generated according to the execution results, and the cloud is confirmed whether the instruction is completed through feedback information. The instruction completion score refers to the proportion of the overall task completed by the device, combined with the execution time and feedback accuracy to determine whether the instruction is effectively executed, and the final status is updated. The instruction completion score calculation formula is as follows:

[0118]

[0119] , where C exec is the instruction completion score, which indicates the completion of the entire instruction execution process (ranging from 0 to 1, 1 is completely completed), W j is the weight of the j-th step operation, reflecting the contribution of each step to the total instruction completion, E exec (j) is the execution efficiency of the j-th operation (e.g., the execution accuracy of water flow, motor speed, etc.), and the higher the value, the better the execution. totalis the total time of the entire instruction execution, A feedback is the feedback accuracy, which indicates the quality of the feedback information received by the cloud.

[0120] By calculating the instruction completion degree C exec , the system can finally confirm whether the instruction has been successfully executed and feed back the result to the cloud to ensure the smooth completion of the entire car wash process or task. exec <0.9, the system will send a warning to the cloud, indicating that some operations were not completed as expected and require intervention or correction.

[0121] The state synchronization optimization algorithm module uses the state synchronization optimization algorithm to perform state calibration and data synchronization after each device operation, calculate the difference between the current device state and the cloud command, and after completing the state difference calculation, calculate the delay time and network quality, and optimize the command execution process to ensure that the state is always consistent;

[0122] Using the state synchronization optimization algorithm, state calibration and data synchronization are performed after each device operation to calculate the difference between the current device state and the cloud command. After the state difference calculation is completed, the delay time and network quality are calculated, and the command execution process is optimized. The specific steps to ensure that the state is always consistent are as follows:

[0123] Before the device performs an operation, the difference between the current device state and the cloud command is first calculated. This process can be completed by comparing the local state of the device (such as working mode, operating status of each module, etc.) with the command issued by the cloud (such as target operating mode, expected module status, etc.), which is represented by the state difference measurement function. The difference measurement calculation formula is as follows:

[0124]

[0125] , where D is the total difference measure between the device state and the cloud command, s q is a weight coefficient indicating the importance of the qth state parameter in the overall difference (e.g., the battery charge may be more important than the water pump state), is the qth status parameter of the device (such as water pump status, battery power, etc.), is the qth state parameter corresponding to the cloud instruction, and h is the total number of calculated state parameters;

[0126] By calculating the difference metric D, the system can understand the deviation between the current state of the device and the cloud instructions, providing a basis for subsequent calibration and synchronization.

[0127] After completing the state difference calculation, the next step is to calculate the network delay and transmission quality to optimize the instruction execution process. The impact of network delay and quality usually leads to untimely instruction response. Therefore, it is necessary to calibrate the synchronization problem between the device and the cloud through the delay time and network quality indicators. The delay and transmission quality calculation expressions are as follows:

[0128]

[0129] , where B is the optimized network delay, B transmit is the transmission time from the device to the cloud or from the cloud to the device. α is a mathematical constant that reflects the sensitivity of network quality to delay and is usually determined by experimental data. Q is the network quality factor, which indicates the quality of the current network (such as bandwidth, packet loss rate, etc.). It usually takes a value of 0≤Q≤1, where 0 indicates the best network quality and 1 indicates the worst.

[0130] By calculating the optimized delay B, the system can identify the network status and decide whether it is necessary to adjust the execution order or timing of instructions, thereby minimizing the impact of delay on synchronization effect.

[0131] After obtaining the device state difference D and network delay B, the next step is to calculate the synchronization correction value between the device and the cloud. The purpose of the correction value is to adjust according to the difference measurement and delay to optimize the state synchronization between the device and the cloud. The correction value is calculated by the following formula:

[0132]

[0133] , where C is the synchronization correction value, which represents the adjustment required to adjust the device state to be consistent with the cloud state, λ is the synchronization adjustment coefficient, which controls the sensitivity of the synchronization correction and is usually determined according to the device's response speed and the cloud update frequency, B max It is the maximum allowed network delay, which is the preset maximum value. If it exceeds this value, it means that the network quality is too poor;

[0134] The synchronization correction value C is used to quantify the amount of state difference that the device needs to adjust under given network conditions. This correction value will directly affect the execution process of device instructions, ensuring that the state can be adjusted first when the difference is large.

[0135] The execution process of the instruction is adjusted according to the synchronization correction value C. The device dynamically optimizes the execution time and sequence of the instruction according to the correction value to ensure that the operation is completed while minimizing the difference between the device status and the cloud instruction. The formula for optimizing the execution process is as follows:

[0136]

[0137] , where E is the optimized execution process score, indicating the degree of optimization of equipment operation execution, β p is the weight coefficient of the p-th operation, reflecting the importance of different operation steps (for example, starting the water pump may be more important than adjusting the water pressure), is the maximum allowable difference value of the p-th operation (i.e., the maximum deviation tolerance of the instruction), and z is the total number of operation steps.

[0138] By calculating the optimized execution process score E, the system can dynamically adjust the execution order and timing of instructions, giving priority to those operation steps that have a high degree of match with cloud instructions, while delaying or adjusting operations with large differences, thereby ensuring that the device can always execute instructions in the optimal state.

[0139] Implementation 1: In modern intelligent devices, the synchronization of instruction execution and device status is crucial, especially in automated devices such as car washers. In order to ensure the precise matching of instructions and device status and avoid misoperation or device failure caused by mismatch between instructions and status, this implementation adopts a multi-layer verification mechanism to achieve instruction synchronization control.

[0140] First, before each task is performed, the car washer will collect the current working status information in real time through sensors and control modules, including working mode, status of each module (such as water pump, nozzle, motor, etc.), and health data of the equipment (such as battery power, temperature, pressure, liquid level, etc.). These data will be uploaded to the cloud through the communication module of the equipment, and the cloud will issue corresponding control instructions based on these data. At the same time as the instructions are issued, the cloud platform will dynamically optimize the instructions according to the current status of the equipment to ensure that the issued instructions meet the current conditions of the equipment.

[0141] After the device receives the cloud command, the local control module will first verify the command. The verification content includes the legality of the command, whether it complies with the current working mode of the device, whether it complies with the working status of each module, and whether there is a potential hardware failure. The verification mechanism is divided into two main parts: preliminary verification and in-depth verification. The preliminary verification checks whether the format and parameter range of the command are valid to ensure that the command has not been tampered with or errors occur during transmission. In-depth verification compares the command with the current status data of the device. For example, if the command requires the car wash program to be started, but the water pump module of the device is in a faulty state, the command cannot pass the verification; or if the command requires operation under low battery power, the system will also limit this condition to avoid operation interruption due to insufficient device energy.

[0142] When the instruction passes the two-layer verification, the system will mark the instruction as "legal" and prepare to pass it to the execution module to start the actual operation. If the instruction does not match the device status, the system will refuse to execute the instruction through the local verification module and restore the device to the last stable state through the rollback mechanism to prevent the device from malfunctioning due to instruction errors. The system will also record the reason for the rejection in the cloud and provide a detailed error report through the log for subsequent analysis.

[0143] During the execution of instructions, the system will continuously monitor the operating status of the device. If certain states of the device change (for example, the water pump overheats or the battery power drops sharply), the system will immediately perform a second check on the instructions being executed to confirm whether the current state still meets the requirements for instruction execution. If changes occur and affect the execution of instructions, the system will trigger the fault handler, stop the current operation, and perform a rollback operation based on the type of fault (such as hardware damage, low battery, etc.) to restore the device to a safe state. The rollback operation not only stops the task being executed, but also resets all ongoing operation modules to prevent the device from continuing to work in an abnormal state.

[0144] Through the multi-layer verification mechanism, not only the synchronization of cloud instructions and device status is effectively guaranteed, but also the risk caused by synchronization failure is minimized through the rollback mechanism when the instructions do not match. This mechanism improves the reliability and safety of device operation, especially in complex automated operation processes, ensuring the precise execution of the system in each step of the operation, avoiding misoperation and equipment damage.

[0145] Implementation 2: This implementation establishes a more intelligent command synchronization control system between the device and the cloud through real-time status monitoring and intelligent fault handling mechanism. The core idea of ​​this mechanism is to monitor every action of the device in real time during the process of issuing and executing commands, and dynamically adjust the execution of commands according to changes in device status to ensure the efficiency and accuracy of synchronization.

[0146] First, the device continuously monitors the status of each module through multiple built-in sensors (such as water flow sensors, temperature sensors, battery charge sensors, etc.). The information collected by the device in real time will be uploaded to the cloud, which analyzes the current status of the device based on this information and formulates the next operation instructions. Unlike the traditional command execution method, this implementation method emphasizes the transmission and analysis of real-time dynamic data. In each operation step, the cloud not only sends control instructions, but also adjusts the instruction content in real time to adapt to changes in the equipment. For example, assuming that the device is executing a car wash program, if the cloud monitors that the temperature of the water pump is too high, it will automatically adjust the instruction to reduce the water flow or temporarily stop the water pump until the device returns to a safe temperature.

[0147] During the execution of the instructions, the local control module of the device continuously receives and parses the cloud instructions, while continuously monitoring the working status of various items. Once the device status is abnormal (such as water pump failure, excessive pressure, low battery, etc.), the system will immediately start the intelligent fault handling program. The intelligent fault handling program will conduct a detailed assessment of the current status of the device, confirm the type of fault and the scope of impact, and respond quickly through the preset recovery strategy. If a device fails, the system will automatically suspend the current operation and restore the device to the most recent normal working state through a rollback mechanism. This process is not limited to the suspension of instructions, but also includes checking the health status of the device to ensure that other modules are not affected by continued failures after the device is restored.

[0148] In addition, the intelligent fault handling system automatically adjusts instructions based on the historical data of the device, the type of fault, and the health status of the device. For example, if a certain hardware fault occurs in the device, the cloud system will analyze the scope of its impact based on the device status, dynamically adjust the execution order of subsequent instructions or suspend certain tasks to ensure that the device does not continue to perform dangerous operations in an inappropriate state. Fault information will be transmitted back to the device operator or cloud platform through the cloud system, and the operator can make corresponding decisions based on the feedback to prevent the device from continuing to perform operations that may cause greater damage.

[0149] Through real-time status monitoring and intelligent fault handling mechanisms, this implementation can achieve more accurate command synchronization during the entire device operation process, promptly detect and resolve device failures, and ensure that the device can operate safely and stably under any circumstances.

[0150] Implementation 3: This implementation combines the advantages of cloud and edge computing and adopts a distributed cloud-edge collaborative architecture for instruction synchronization and execution. By deploying computing units locally (edge) on the device and combining cloud computing capabilities, the instruction synchronization process is more efficient and flexible, thereby effectively solving the synchronization problem between the device and the cloud.

[0151] In this architecture, the device not only includes a local control module, but also is equipped with an edge computing unit, which can perform fast data processing and decision analysis. Every time the cloud sends an instruction, the instruction will be transmitted to the local edge computing unit for preliminary processing. In the edge computing unit, the device will perform a preliminary match and verification between the instruction and the current device state. The edge computing unit compares the real-time data of the local sensor of the device to confirm whether the instruction is suitable for the current device state. If the current state meets the requirements of the instruction, the edge computing unit will immediately start the relevant module to execute the instruction. If the device state changes (for example, an abnormality or a large change occurs), the edge computing unit will determine whether the instruction can continue to be executed. If it is not suitable, it will suspend execution and notify the cloud.

[0152] When an abnormality occurs in the instructions executed locally on the device, the edge computing unit will first start the local rollback mechanism to restore the device to a safe working state. The rollback is not limited to stopping the current operation, but also reinitializing each module according to the type of device failure to ensure that the device can run again after recovery. After the rollback is completed, the edge computing unit will feedback the results to the cloud through the communication interface. The cloud will adjust the subsequent instructions based on the local feedback to ensure the normal execution of subsequent operations of the device.

[0153] In addition, the cloud will conduct in-depth analysis based on the data feedback from the edge computing unit, predict possible problems and optimize the instructions. The two-way data transmission and collaboration between the cloud and the local device make the execution process of instructions more intelligent and flexible. The local decision-making system of the device can respond and execute operations quickly in most cases, while the cloud is responsible for large-scale data analysis and adjustment optimization to ensure the stability and reliability of the overall system.

[0154] Through the distributed cloud-edge collaborative architecture, this implementation has obvious advantages in improving instruction synchronization efficiency, reducing network latency, and improving system reliability. The addition of edge computing not only speeds up the speed of local device decision-making, but also enhances the device's ability to respond in complex environments.

[0155] The present invention uses multi-layer verification mechanisms, real-time status monitoring, and intelligent fault handling to ensure that the cloud instructions are highly matched with the device status during the instruction execution process, thereby minimizing misoperation or failures caused by improper instructions or abnormal device status. The fault handling procedure and rollback mechanism ensure that the device quickly recovers to a safe state under abnormal circumstances, thereby greatly improving the reliability and security of the system. The distributed cloud-edge collaborative architecture further enhances the fault tolerance of the system, allowing the device to independently adjust operations through the edge computing unit even in the case of poor communication, ensuring stability and security and enhancing user trust.

[0156] The present invention is based on a synchronization mechanism of real-time status monitoring and intelligent fault handling, which enables the device to automatically adjust the working mode and operation process according to real-time data, thereby improving the level of intelligence and operating efficiency. For example, when the device detects that the water pump is overheated or the battery is low, it can automatically optimize the instructions to avoid invalid operations or equipment damage. The fast response and distributed architecture of the edge computing unit make the execution of instructions more accurate and rapid, reduce delays, and improve the response speed and overall work efficiency of the device, thereby providing customers with a more efficient and convenient user experience.

[0157] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0158] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0159] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0160] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0161] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0162] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0163] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0165] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0166] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An intelligent controlled car washing machine, characterized in that: It includes data collection and upload module, instruction verification and preprocessing module, state synchronization and fault rollback module, instruction execution and feedback module and state synchronization optimization algorithm module: The data collection and upload module uploads the operating status and instructions of the car washing machine to the cloud in real time, and receives instructions issued by the cloud; Instruction verification and preprocessing module: After the cloud instruction is sent, the instruction is preprocessed by the local verification module to confirm whether the instruction matches the current working status and mark the status synchronization process; The status synchronization and fault rollback module synchronizes the instructions and device status through the local communication module. If the synchronization fails, the fault handling procedure is triggered to record and roll back the current operation to avoid erroneous operations. The command execution and feedback module executes the cloud command after the local communication module is successfully synchronized, and updates the execution status in real time to ensure that each operation step is executed in sequence, and at the same time feedbacks the execution status to the cloud; The state synchronization optimization algorithm module uses the state synchronization optimization algorithm to perform state calibration and data synchronization after each device operation, calculate the difference between the current device state and the cloud command, and after completing the state difference calculation, calculate the delay time and network quality, and optimize the command execution process to ensure that the state is always consistent. The specific steps are as follows: Before the device performs an operation, it first calculates the difference between the current device state and the cloud instruction; After completing the state difference calculation, the next step is to calculate the network delay and transmission quality to optimize the instruction execution process; After obtaining the device status difference and network delay, the next step is to calculate the synchronization correction value between the device and the cloud, and adjust the execution process of the instruction according to the synchronization correction value.

2. The intelligent controlled car washing machine according to claim 1, characterized in that: The specific steps for uploading the operating status and instructions of the car washing machine to the cloud in real time and receiving the instructions issued by the cloud are as follows: The car washing machine initializes various operating states when it starts up and establishes a stable communication connection with the cloud to ensure smooth data transmission; The car washing machine regularly uploads the operating status, working status of each module and equipment health information to the cloud for monitoring and analysis; After receiving the command from the cloud, the car washing machine will verify the legitimacy of the command and confirm whether the current status meets the execution conditions; After the command is verified, the car wash will perform the corresponding operations and feed back the execution status to the cloud in real time for monitoring and optimization.

3. The intelligent controlled car washing machine according to claim 1, characterized in that: After the cloud command is sent, the local verification module pre-processes the command to confirm whether the command matches the current working status and marks the status synchronization process. The specific steps are as follows: After the device successfully receives the cloud command, it first confirms the communication status with the cloud and parses the command to match the device status; The verification module compares the received instructions with the current device status to ensure that the instructions are legal and executable under the current device status; After successful verification, the instruction synchronization process is marked, the instruction matching result is recorded, and the instruction is ready to be executed; After the device confirms that the instruction is ready to execute, it checks the status of each module to ensure that the device can execute the operation smoothly and reports the execution readiness status to the cloud.

4. The intelligent controlled car washing machine according to claim 1, characterized in that: The local communication module is used to synchronize instructions and device status. If synchronization fails, the fault handling procedure is triggered to record and roll back the current operation. The specific steps to avoid misoperation are as follows: The device synchronizes the verified instructions with the current status through the local communication module and ensures the security and integrity of data transmission; During the synchronization process, when the device detects any failure, it immediately identifies the error and records the relevant failure information; Once synchronization fails, the fault handling procedure is started to restore the device to a safe state through rollback operations to avoid misoperation; After the rollback operation is completed, the cloud and the operator are notified, and an attempt is made to restore the normal connection between the device and the cloud to ensure the smooth execution of subsequent operations.

5. The intelligent controlled car washing machine according to claim 1, characterized in that: After the local communication module is successfully synchronized, the cloud instructions are executed and the execution status is updated in real time to ensure that each operation step is executed in sequence. The specific steps for feeding back the execution status to the cloud are as follows: After the local communication module successfully synchronizes the device status, it first confirms the initialization status of the current device and prepares to execute the instructions issued by the cloud. The local control module calculates the comprehensive score of the current readiness. The calculation expression is as follows: In the formula, S init is the device initialization status score, indicating whether the device is ready to execute instructions, M i is the status score of the ith module, E sys is the current energy state of the device system, E max is the maximum energy capacity of the device, n is the total number of modules of the device; After confirming that the device is in an executable state, it starts to execute operations according to the instructions issued by the cloud, and updates the execution status of the device in real time. At this time, the device uses execution time as a key parameter for real-time monitoring. The execution time calculation formula is as follows: Where, T exec is the execution time of the current operation step, T start is the timestamp of the start of the operation, T end is the timestamp of the end of the operation, D act is the actual amount of operations completed, V act is the actual execution rate; When each operation step is executed, the device updates its operation status in real time and feeds back the status information to the cloud through the communication module. To ensure the accuracy and timeliness of the feedback, the status feedback time delay and feedback accuracy are used as key parameters to evaluate the synchronization efficiency between the device and the cloud. The formula is as follows: In the formula, A feedback is the feedback accuracy, T delay is the feedback time delay, T max is the maximum feedback delay time predetermined by the device, S exec is the execution status score of the current operation step, S total is the overall equipment status score; Finally, when all operation steps are completed in order, the overall instruction completion score is generated according to the execution results, and feedback information is used to confirm whether the instruction is completed to the cloud. The instruction completion score calculation formula is as follows: In the formula, C exec is the instruction completion score, which indicates the completion of the entire instruction execution process, W j is the weight of the j-th operation, E exec (j) is the execution efficiency of the j-th operation, T total is the total time of the entire instruction execution, A feedback is the feedback accuracy, which indicates the quality of the feedback information received by the cloud.

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