Servo motor controller intelligent management system based on cloud computing

Abstract

The invention discloses a servo motor controller intelligent management system based on cloud computing, and relates to the technical field of servo motor management, for each device, data response time of operation data of a servo motor is obtained to obtain a time coefficient; acquiring a specific data value of the operation data and calculating a data deviation coefficient; calculating a task progress coefficient at the task ending time; calculating priority coefficients of the corresponding devices according to the time coefficients, the data deviation coefficients and the task progress coefficients, and uploading the operation data of the servo motors of the corresponding devices to a cloud platform according to a descending order of the priority coefficients, the operation data of the servo motors of the multiple devices can be uploaded to the cloud platform according to the descending order of the priority coefficients to manage the servo motor controllers of the devices, so that the occupation of the network bandwidth is good, the problems of data transmission delay or packet loss and the like are reduced, the data received by the cloud platform is consistent with the actual data, and the user experience is improved. And accurate control of servo motors of part of or multiple pieces of equipment is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of servo motor management, and in particular to a servo motor controller intelligent management system based on cloud computing. Background Art

[0002] The cloud computing-based servo motor controller management system refers to a system architecture that uses cloud computing technology to remotely manage and optimize servo motor controllers. The system collects real-time operating data of servo motors, such as current, voltage, speed, position, and temperature, through the cloud platform, and combines big data analysis and artificial intelligence algorithms to monitor the operating status of servo motors, detect anomalies, optimize parameters, and predict faults. In addition, the system supports centralized management of multiple devices. Users can remotely access the control interface through the Internet to configure parameters, update firmware, and query status of the controller, thereby improving equipment management efficiency, reducing maintenance costs, and improving the level of intelligence in industrial production.

[0003] However, uploading the operating data of the servo motors of multiple devices to the cloud platform together may cause excessive network bandwidth usage, thereby causing problems such as data transmission delays or packet loss. It may also cause the data received by the cloud platform to be inconsistent with the actual data, resulting in errors in the control of the servo motors of some or multiple devices. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned problems and provide a servo motor controller intelligent management system based on cloud computing.

[0005] The present invention provides a servo motor controller intelligent management system based on cloud computing, the system comprising:

[0006] Time coefficient module: for each device, obtain the data response time of the servo motor's operating data, and calculate the time coefficient based on the data response time;

[0007] Data deviation module: obtains the specific data value of the servo motor operation data of each device, and calculates the data deviation coefficient in combination with the preset standard data value;

[0008] Task progress module: obtain the task end time of the servo motor of each device, and calculate the task progress coefficient based on the task end time;

[0009] Upload management module: Calculate the priority coefficient of the corresponding device according to the time coefficient, data deviation coefficient and task progress coefficient, and upload the operating data of the servo motor of the corresponding device to the cloud platform in the order of priority coefficient from large to small to manage the servo motor controller of the device.

[0010] Optionally, calculating the time coefficient according to the data response time and the task end time includes:

[0011] Obtain the running data type of the servo motor, and obtain the response timestamp of each type of data, to obtain a timestamp sequence of data response based on time sequence;

[0012] Calculate the time interval between every two adjacent time stamps in the time stamp sequence, and perform sequencing to obtain a response interval sequence;

[0013] The response interval in the response interval sequence is marked as Q w , w represents the order number of the response interval in the response interval sequence, w=1, 2, 3, 4, ..., u, u is the total number of response intervals in the response interval sequence, and u is a positive integer;

[0014] Calculate the mean of the response interval series. The calculation formula is:

[0015] Calculate the response time fluctuation coefficient using the following formula:

[0016]

[0017] Wherein, Dza is the response time fluctuation coefficient, d is the operation data type number of the servo motor, n is the total number of the operation data types of the servo motor, and n is a positive integer;

[0018] The time coefficient is calculated based on the response time fluctuation coefficient.

[0019] Optionally, calculating the time coefficient according to the response time fluctuation coefficient includes:

[0020] The response interval Q in the response interval sequence w and within the preset response interval range T 快 , T 慢 ) are re-marked as R f , f represents Q w Within the preset data response interval range (T 快 , T 慢 ), f=0, 1, 2, 3, 4, ..., s, s is a positive integer;

[0021] Calculate the response interval anomaly coefficient Dax, the calculation formula is:

[0022] Calculate the time coefficient using the following formula:

[0023]

[0024] Wherein, AZ time coefficient, Dza and Dax are response time fluctuation coefficient and response interval abnormality coefficient respectively, α and β are preset proportional coefficients of Dza and Dax respectively, and α and β are both greater than 0.

[0025] Optionally, obtaining specific data values ​​of the operation data of the servo motor of each device, and calculating the data deviation coefficient in combination with the preset standard data value includes:

[0026] Acquire the running data type of the servo motor, and acquire the actual data value of each type of data, to obtain an actual data value sequence based on time sequence;

[0027] Each actual data value and the corresponding preset standard data value in the actual data value sequence are marked as G k and GN, k represents the order number of the actual data value in the actual data value sequence, k=1, 2, 3, 4, ..., l, l is a positive integer;

[0028] According to G k The data deviation coefficient DF is obtained by adding GN. The calculation formula is: d is the operation data type number of the servo motor, n is the total number of the operation data types of the servo motor, and n is a positive integer.

[0029] Optionally, obtaining the task end time of the servo motor of each device and calculating the task progress coefficient according to the task end time includes:

[0030] For each device, obtain the task start time TA, task current time TB, and task preset end time TC of the servo motor;

[0031] Calculate the time difference TD from the start of the task to the current time, TD = TB-TA;

[0032] Calculate the total time TE from the start to the preset end of the task, TE = TC-TA;

[0033] Calculate the percentage of the current task completed VB,

[0034] According to the closeness between the current task time TB and the preset end time TC, the task urgency coefficient VM is calculated.

[0035] Calculate the task progress coefficient Ygf, the calculation formula is: Ygf = VB × VM.

[0036] Optionally, calculating the priority coefficient of the corresponding device according to the time coefficient, the data deviation coefficient and the task progress coefficient includes:

[0037]

[0038] Where XGF is the priority coefficient, AZ, DF, and Ygf are the time coefficient, data deviation coefficient, and task progress coefficient, respectively; a1, a2, and a3 are the preset proportional coefficients of AZ, DF, and Ygf, respectively, and a1, a2, and a3 are all greater than 0.

[0039] Beneficial effects of the present invention:

[0040] The present invention proposes a servo motor controller intelligent management system based on cloud computing. For each device, the data response time of the servo motor's operating data is obtained to obtain the time coefficient; the specific data value of the servo motor's operating data of each device is obtained to calculate the data deviation coefficient; the task end time of the servo motor of each device is obtained to calculate the task progress coefficient; the corresponding device priority coefficient is calculated according to the time coefficient, the data deviation coefficient and the task progress coefficient, and the operating data of the servo motor of the corresponding device is uploaded to the cloud platform in the order of the priority coefficient from large to small to manage the servo motor controller of the device. In this way, the operating data of the servo motors of multiple devices can be uploaded to the cloud platform in the order of the priority coefficient from large to small to manage the servo motor controllers of the devices, so that the network bandwidth is well occupied, and the problems such as data transmission delay or packet loss are reduced, so that the data received by the cloud platform is consistent with the actual data, ensuring the accurate control of the servo motors of some or multiple devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] Figure 1 This is a framework diagram of the servo motor controller intelligent management system based on cloud computing. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0045] The embodiment of the present invention provides a servo motor controller intelligent management system based on cloud computing. Figure 1 , Figure 1A framework diagram of a servo motor controller intelligent management system based on cloud computing provided by an embodiment of the present invention, the system comprising:

[0046] Time coefficient module: for each device, obtain the data response time of the servo motor's operating data, and calculate the time coefficient based on the data response time;

[0047] Data deviation module: obtains the specific data value of the servo motor operation data of each device, and calculates the data deviation coefficient in combination with the preset standard data value;

[0048] Task progress module: obtain the task end time of the servo motor of each device, and calculate the task progress coefficient based on the task end time;

[0049] Upload management module: Calculate the priority coefficient of the corresponding device according to the time coefficient, data deviation coefficient and task progress coefficient, and upload the operating data of the servo motor of the corresponding device to the cloud platform in the order of priority coefficient from large to small to manage the servo motor controller of the device.

[0050] Based on the cloud computing-based servo motor controller intelligent management system provided by the embodiment of the present invention, through the above-mentioned method, the operating data of the servo motors of multiple devices can be uploaded to the cloud platform in the order of priority coefficients from large to small to manage the servo motor controllers of the devices, so that the network bandwidth is well occupied, and problems such as data transmission delay or packet loss are reduced, so that the data received by the cloud platform is consistent with the actual data, ensuring the accurate control of the servo motors of some or multiple devices.

[0051] In one embodiment, obtaining the data response time and the task end time of the operation data of the servo motor, and calculating the time coefficient according to the data response time and the task end time includes:

[0052] Obtain the running data type of the servo motor, and obtain the response timestamp of each type of data, to obtain a timestamp sequence of data response based on time sequence;

[0053] Calculate the time interval between every two adjacent time stamps in the time stamp sequence, and perform sequencing to obtain a response interval sequence;

[0054] The response interval in the response interval sequence is marked as Q w , w represents the order number of the response interval in the response interval sequence, w=1, 2, 3, 4, ..., u, u is the total number of response intervals in the response interval sequence, and u is a positive integer;

[0055] Calculate the mean of the response interval series. The calculation formula is:

[0056] Calculate the response time fluctuation coefficient using the following formula:

[0057]

[0058] Wherein, Dza is the response time fluctuation coefficient, d is the operation data type number of the servo motor, n is the total number of the operation data types of the servo motor, and n is a positive integer;

[0059] The response interval Q in the response interval sequence w and within the preset response interval range T 快 , T 慢 ) are re-marked as R f , f represents Q w Within the preset data response interval range (T 快 , T 慢 ), f=0, 1, 2, 3, 4, ..., s, s is a positive integer;

[0060] Calculate the response interval anomaly coefficient Dax, the calculation formula is:

[0061] Calculate the time coefficient, the calculation formula is:

[0062]

[0063] Wherein, AZ time coefficient, Dza and Dax are response time fluctuation coefficient and response interval abnormality coefficient respectively, α and β are preset proportional coefficients of Dza and Dax respectively, and α and β are both greater than 0.

[0064] It should be noted that α and β are set by professionals according to actual conditions. Generally, the sum of α and β is 1, and no specific limitation or elaboration is given.

[0065] It should be noted that the preset response interval range is set by professionals based on the servo motor and application scenarios of the actual equipment according to experience and data analysis, and is not limited or elaborated on in detail;

[0066] It should be noted that the operating data types of servo motors may include but are not limited to key parameters such as current, voltage, speed, position, temperature, load, vibration and power. These data types reflect the electrical characteristics, mechanical characteristics and environmental conditions of the servo motor during operation. For example, current and voltage data can be used to evaluate the input power and energy consumption of the servo motor; speed and position data can be used to monitor the dynamic performance and positioning accuracy of the motor; temperature data can be used to determine whether the motor has an overheating risk; load data can reflect the workload of the servo motor and help determine whether it is overloaded; vibration data helps identify potential mechanical failures, such as bearing wear or imbalance problems. These data types work together to provide a comprehensive and detailed basis for performance monitoring, status evaluation and fault diagnosis of servo motors.

[0067] It should be noted that the time coefficient refers to the response time fluctuation degree and the response interval abnormality degree of the operation data of the servo motor of the equipment. If the response time fluctuation degree and the response interval abnormality degree of the operation data of the servo motor of the equipment are greater, the priority of uploading the operation data of the servo motor of the corresponding equipment to the cloud platform is greater. This is because the response time fluctuation degree and the response interval abnormality degree reflect the stability and consistency of the operation data of the servo motor of the equipment. If the response time of the equipment fluctuates greatly, it means that the operation of the equipment may be affected by external interference, internal performance fluctuations or other abnormal conditions, which may cause the servo motor control accuracy to decrease or the operation efficiency to decrease. At the same time, the abnormal response interval reflects the abnormality of data transmission between the equipment and the cloud, such as data loss or delay caused by network congestion, equipment hardware failure or abnormal operation status. When such anomalies occur frequently, the cloud management system needs to give priority to obtaining the operation data of the equipment to quickly locate the source of the problem and take necessary control adjustments or maintenance measures to reduce the risk of system failure; in addition, as a key component in industrial production, the operation data of the servo motor usually has high real-time requirements. Once the response fluctuation or abnormality of the operation data is not uploaded to the cloud in time for analysis and processing, it may cause further deterioration of the operation status of the equipment and even affect the stability of the entire production chain. Prioritizing uploading data with large fluctuations and anomalies can ensure that these potential risks are captured and handled in a timely manner, avoiding the accumulation of problems and causing larger failures. At the same time, this priority mechanism can also provide key real-time data input for system optimization algorithms, enabling the cloud system to dynamically adjust the operating parameters of other devices to maintain the smooth operation of the overall system. Therefore, the higher the time coefficient of the device, the higher the priority of its data upload, in order to better maintain the safety, efficiency and reliability of the equipment and system.

[0068] In one implementation, the benefits of analyzing the time coefficient for determining the running data of the servo motor of the device and uploading it to the cloud platform for managing the servo motor controller of the device are:

[0069] Analyzing the time coefficient can significantly improve the management efficiency and accuracy of the cloud platform for the servo motor controller of the equipment by judging the upload priority of the equipment servo motor operation data. First, by comprehensively evaluating the response time fluctuation degree and abnormal conditions of the data through the time coefficient, it is possible to dynamically determine which equipment's operating status requires higher attention, so as to prioritize uploading key data and provide timely support for abnormality detection and problem diagnosis. Secondly, this priority mechanism based on the time coefficient avoids the disorder of data upload, effectively alleviates bandwidth pressure, and improves the utilization rate and processing efficiency of cloud resources. At the same time, it can also help the system optimize the control strategy in real time in the scenario of multi-device collaborative operation, reduce decision-making errors caused by data delays or anomalies, and ensure the stability of equipment operation. In addition, by accumulating upload records based on the time coefficient for a long time, the system can form an operation data characteristic analysis model for different equipment and scenarios, and provide data support for predictive maintenance and operation optimization of equipment, thereby extending equipment life and improving overall production efficiency.

[0070] In one embodiment, obtaining specific data values ​​of the operation data of the servo motor of each device and calculating the data deviation coefficient in combination with the preset standard data value includes:

[0071] Acquire the running data type of the servo motor, and acquire the actual data value of each type of data, to obtain an actual data value sequence based on time sequence;

[0072] Each actual data value and the corresponding preset standard data value in the actual data value sequence are marked as G k and GN, k represents the order number of the actual data value in the actual data value sequence, k=1, 2, 3, 4, ..., l, l is a positive integer;

[0073] According to G k The data deviation coefficient DF is obtained by adding GN. The calculation formula is: d is the operation data type number of the servo motor, n is the total number of the operation data types of the servo motor, and n is a positive integer.

[0074] It should be noted that the preset standard data value refers to the reference value or target value pre-set by professionals based on the design parameters, operating specifications and actual application scenarios of the servo motor. These data are usually derived from the technical manuals provided by the equipment manufacturer, laboratory test results or historical operating data settings of the equipment under normal working conditions, and are not limited or elaborated on in detail;

[0075] It should be noted that all operating data of the servo motor can be collected in real time through sensors and control modules installed on the equipment. These sensors include special sensors for measuring current, voltage, speed, position, temperature, vibration and load. The control module will preliminarily process and store the collected data, and then transmit it to the data acquisition system through a communication interface (such as CAN bus, Ethernet or wireless communication), thereby realizing comprehensive monitoring and recording of the operating status of the servo motor.

[0076] It should be noted that the data deviation coefficient refers to the degree to which the actual data value of the operating data of the equipment servo motor deviates from the preset standard data value. If the degree of deviation from the preset standard data value is greater, it means that the corresponding equipment has a greater possibility of failure, and the corresponding equipment servo motor operation data upload data to the cloud platform has a greater priority, because the data deviation coefficient directly reflects the degree of difference between the operating state of the equipment servo motor and the normal state. The greater the degree of deviation from the preset standard data value, it may indicate that there are more serious problems in the operation of the servo motor, such as overload operation, mechanical failure, electrical anomaly or environmental interference. If these problems are not detected and processed in time, they may cause equipment performance degradation, or even cause more serious safety hazards or equipment damage. Therefore, when the data deviation coefficient is large, uploading the operating data of the equipment to the cloud platform first can enable the management system to quickly capture these anomalies and diagnose them, thereby providing a basis for equipment fault warning and timely maintenance, and reducing the risk of fault expansion; in addition, uploading data with a larger degree of deviation first can also help optimize cloud resource allocation and improve data processing efficiency. In the scenario where multiple devices are running in parallel, the cloud platform may face bandwidth or computing power limitations, so it is necessary to use the deviation coefficient to screen out the devices that need the most attention for priority processing. By analyzing these abnormal data, the system can quickly make adjustments, such as dynamically optimizing the control parameters of the servo motor or generating troubleshooting guidelines, so as to avoid greater impact on the stability of the entire production line or system due to equipment failure. This strategy can not only maximize the efficiency of equipment use, but also reduce downtime and maintenance costs caused by failures, thereby providing higher reliability and economic benefits for the entire management system.

[0077] In one implementation, the benefits of analyzing the data deviation coefficient for determining the operation data of the servo motor of the device and uploading it to the cloud platform for managing the servo motor controller of the device are:

[0078] Analyzing the data deviation coefficient is of great significance for determining the priority of uploading the equipment servo motor operation data. It can significantly improve the accuracy and response speed of the cloud platform's management of the equipment controller. By calculating the data deviation coefficient, the system can quantify the degree of abnormality of each operating data, thereby quickly screening out devices with potential faults or abnormal status, and prioritize uploading these key data to ensure that the problem is handled in a timely manner. This method can not only optimize the allocation of cloud resources and avoid low-priority data occupying limited upload bandwidth, but also improve the efficiency of anomaly detection and provide more targeted diagnosis and decision support for the cloud platform. In addition, the data deviation coefficient can also play a role in multi-device collaborative management scenarios, helping the management system to grasp the differences in operating status in real time, formulate equipment optimization strategies from a global perspective, reduce production losses and maintenance costs caused by faults, and improve the reliability and management efficiency of equipment operation.

[0079] In one embodiment, obtaining the task end time of the servo motor of each device and calculating the task progress coefficient according to the task end time includes:

[0080] For each device, obtain the task start time TA, task current time TB, and task preset end time TC of the servo motor;

[0081] Calculate the time difference TD from the start of the task to the current time, TD = TB-TA;

[0082] Calculate the total time TE from the start to the preset end of the task, TE = TC-TA;

[0083] Calculate the percentage of the current task completed VB,

[0084] According to the closeness between the current task time TB and the preset end time TC, the task urgency coefficient VM is calculated.

[0085] Calculate the task progress coefficient Ygf, the calculation formula is: Ygf = VB × VM.

[0086] It should be noted that the task start time, current task time and preset task end time of the servo motor can be obtained through the task scheduling module built into the device or the task plan data recorded by the controller; the current task status and time can be collected in real time through the device sensor and control system, and uploaded to the cloud platform for synchronization; in addition, the preset end time is usually derived from the task parameters set by the device, and is pre-configured by the control system in combination with the task objectives and operation plan. These data are uploaded to the cloud through the Internet of Things communication protocol to ensure the timeliness and accuracy of the data, thereby supporting the efficient calculation and priority judgment of the task progress coefficient.

[0087] It should be noted that the task progress coefficient refers to the task progress of the servo motor of the equipment. The greater the task progress, the higher the priority of uploading the operation data of the servo motor of the corresponding equipment to the cloud platform, which means that the task of the equipment is close to completion or is in the critical task stage. At this time, the operation data of the servo motor is crucial to the performance of the equipment and the completion of the task. If the equipment is in the later stage of the task or close to the preset end time, any sudden abnormality or data deviation may directly affect the final effect of the task, so more accurate and timely data upload is required. At this time, uploading the operation data to the cloud platform first can not only help monitor the equipment status in time, but also provide early warning of potential faults to ensure that the task is completed on time and efficiently; in addition, when the task progress coefficient is large, it usually means that the importance and urgency of the task are high. The servo motor of the equipment may be in a high-load operation state or undergoing important adjustments. Therefore, any delay or wrong operation may lead to task failure or production interruption. By uploading these key data first, control parameters can be adjusted in real time, fault diagnosis can be performed, or data support can be provided for further decision-making to ensure that the equipment can maintain stable operation at the critical moment of the task and avoid delays in task progress or unexpected downtime of the equipment.

[0088] In one implementation method, the benefit of analyzing the task progress coefficient for determining the priority of uploading the operation data of the servo motor of the device to the cloud platform for managing the servo motor controller of the device is as follows: the task progress coefficient has significant management benefits for determining the priority of uploading the operation data of the servo motor of the device to the cloud platform. First, when the task progress coefficient is large, it means that the servo motor of the device is in the critical stage of the task, and any abnormal operation may directly affect the completion quality and efficiency of the task. Therefore, uploading these data in priority can ensure that the control system obtains real-time operation status at critical moments, discovers potential problems in time and makes adjustments, and avoids the task being affected by equipment failure or performance degradation. In addition, by monitoring the progress of the task, the cloud platform can effectively dynamically adjust the equipment management strategy according to the importance and urgency of the task to achieve refined management. For example, a device that is close to the end of the task will receive a higher upload priority, ensuring that the data is analyzed and processed in time at the most critical moment, thereby improving production efficiency, reducing downtime, and ultimately optimizing the overall equipment operation and maintenance costs.

[0089] In one embodiment, calculating the priority coefficient of the corresponding device according to the time coefficient, the data deviation coefficient and the task progress coefficient includes:

[0090]

[0091] Where XGF is the priority coefficient, AZ, DF, and Ygf are the time coefficient, data deviation coefficient, and task progress coefficient, respectively; a1, a2, and a3 are the preset proportional coefficients of AZ, DF, and Ygf, respectively, and a1, a2, and a3 are all greater than 0.

[0092] It should be noted that a1, a2, and a3 are set by professionals according to actual conditions. Generally, the sum of a1, a2, and a3 is 1. For example, a1, a2, and a3 can be 0.4, 0.3, and 0.3, respectively, or other numbers, without specific limitation. In addition, before calculating the priority coefficient, the time coefficient, data deviation coefficient, and task progress coefficient need to be normalized. Commonly used normalization methods include Min-Max normalization, Z-Score normalization, etc. The specific method is selected by professionals according to actual conditions, and no specific limitation or elaboration is made.

[0093] In one implementation, the operation data of the servo motor of the corresponding device is uploaded to the cloud platform in the order of priority coefficient from large to small to manage the servo motor controller of the device, which has significant management benefits. First, the priority upload of device data with higher priority coefficients can ensure that the operation status of the most critical devices is monitored and analyzed in time. These devices are usually in the critical stage of the task or have a large load, which may have a great impact on the production process or task completion. By obtaining the data of these devices in priority, a quick response can be made before the failure occurs, and the control strategy can be adjusted in time to avoid production interruption or task failure; in addition, this priority upload method helps to reasonably allocate the computing resources and bandwidth of the cloud platform to avoid network congestion and data loss. When multiple devices upload data at the same time, sorting according to the priority coefficient can ensure that the order of data upload is consistent with the urgency of the device and the progress of the task, thereby ensuring the real-time and accuracy of the data. Finally, this intelligent data upload management method not only optimizes the operation management of the equipment and improves the efficiency of equipment maintenance, but also significantly reduces potential risks, ensures that the equipment performs at its best in the production process, and maximizes the smooth completion of production tasks.

[0094] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be used to artificially limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. The intelligent management system of servo motor controller based on cloud computing is characterized by: The system comprises: Time coefficient module: for each device, obtain the data response time of the servo motor's operating data, and calculate the time coefficient based on the data response time; Data deviation module: obtains the specific data value of the servo motor operation data of each device, and calculates the data deviation coefficient in combination with the preset standard data value; Task progress module: obtain the task end time of the servo motor of each device, and calculate the task progress coefficient based on the task end time; Upload management module: Calculate the priority coefficient of the corresponding device according to the time coefficient, data deviation coefficient and task progress coefficient, and upload the operating data of the servo motor of the corresponding device to the cloud platform in the order of priority coefficient from large to small to manage the servo motor controller of the device.

2. The servo motor controller intelligent management system based on cloud computing according to claim 1 is characterized in that: The time coefficients calculated based on the data response time and task end time include: Obtain the running data type of the servo motor, and obtain the response timestamp of each type of data, to obtain a timestamp sequence of data response based on time sequence; Calculate the time interval between every two adjacent time stamps in the time stamp sequence, and perform sequencing to obtain a response interval sequence; The response interval in the response interval sequence is marked as Q w , w represents the order number of the response interval in the response interval sequence, w=1, 2, 3, 4, ..., u, u is the total number of response intervals in the response interval sequence, and u is a positive integer; Calculate the mean of the response interval series. The calculation formula is: Calculate the response time fluctuation coefficient using the following formula: Wherein, Dza is the response time fluctuation coefficient, d is the operation data type number of the servo motor, n is the total number of the operation data types of the servo motor, and n is a positive integer; The time coefficient is calculated based on the response time fluctuation coefficient.

3. The servo motor controller intelligent management system based on cloud computing according to claim 2 is characterized in that: The time coefficient calculated based on the response time fluctuation coefficient includes: The response interval Q in the response interval sequence w and within the preset response interval range (T 快 , T 慢 ) are re-marked as R f , f represents Q w Within the preset data response interval range (T 快 , T 慢 ), f=0, 1, 2, 3, 4, ..., s, s is a positive integer; Calculate the response interval anomaly coefficient Dax, the calculation formula is: Calculate the time coefficient, the calculation formula is: Wherein, AZ time coefficient, Dza and Dax are response time fluctuation coefficient and response interval abnormality coefficient respectively, α and β are preset proportional coefficients of Dza and Dax respectively, and α and β are both greater than 0.

4. The servo motor controller intelligent management system based on cloud computing according to claim 1 is characterized in that: Obtain the specific data values ​​of the servo motor operation data of each device, and calculate the data deviation coefficient in combination with the preset standard data values, including: Acquire the running data type of the servo motor, and acquire the actual data value of each type of data, to obtain an actual data value sequence based on time sequence; Each actual data value and the corresponding preset standard data value in the actual data value sequence are marked as G k and GN, k represents the order number of the actual data value in the actual data value sequence, k=1, 2, 3, 4, ..., l, l is a positive integer; According to G k The data deviation coefficient DF is obtained by adding GN. The calculation formula is: d is the operation data type number of the servo motor, n is the total number of the operation data types of the servo motor, and n is a positive integer.

5. The servo motor controller intelligent management system based on cloud computing according to claim 1 is characterized in that: Obtain the task end time of the servo motor of each device, and calculate the task progress coefficient based on the task end time, including: For each device, obtain the task start time TA, task current time TB, and task preset end time TC of the servo motor; Calculate the time difference TD from the start of the task to the current time, TD = TB-TA; Calculate the total time TE from the start to the preset end of the task, TE = TC-TA; Calculate the percentage of the current task completed VB, According to the closeness between the current task time TB and the preset end time TC, the task urgency coefficient VM is calculated. Calculate the task progress coefficient Ygf, the calculation formula is: Ygf = VB × VM.

6. The cloud computing-based servo motor controller intelligent management system according to claim 1, characterized in that: The priority coefficients of the corresponding equipment are calculated based on the time coefficient, data deviation coefficient and task progress coefficient, including: Where XGF is the priority coefficient, AZ, DF, and Ygf are the time coefficient, data deviation coefficient, and task progress coefficient, respectively; a1, a2, and a3 are the preset proportional coefficients of AZ, DF, and Ygf, respectively, and a1, a2, and a3 are all greater than 0.

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