Cloud computing-based servo motor controller intelligent management system

Abstract

The application discloses a cloud-computing-based intelligent management system for servo motor controllers and relates to the technical field of servo motor management. For each device, the data response time of the operation data of the servo motor is obtained to obtain a time coefficient; the specific data value of the operation data is obtained to calculate a data deviation coefficient; the task end time is used to calculate a 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 operation data of the servo motor of the corresponding device is uploaded to a cloud platform in the order from large to small according to the priority coefficient, so that the operation data of the servo motors of multiple devices is uploaded to the cloud platform in the order from large to small according to the priority coefficient to manage the servo motor controllers of the devices, the occupation of network bandwidth is good, data transmission delay or packet loss and other problems are reduced, the data received by the cloud platform is consistent with the actual data, and the control accuracy of the servo motors of the partial or multiple devices is ensured.

Description

Technical Field

[0001] This invention relates to the field of servo motor management technology, and more specifically to a cloud-based intelligent management system for servo motor controllers. Background Technology

[0002] A cloud-based servo motor controller management system refers to a system architecture that utilizes cloud computing technology to remotely manage and optimize servo motor controllers. This system collects real-time operating data of the servo motors, such as current, voltage, speed, position, and temperature, through a cloud platform. Combined with big data analytics and artificial intelligence algorithms, it enables monitoring of the servo motor's operating status, anomaly detection, parameter optimization, and fault prediction. Furthermore, the system supports centralized management of multiple devices. Users can remotely access the control interface via the internet to configure controller parameters, update firmware, and query status, thereby improving equipment management efficiency, reducing maintenance costs, and enhancing 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 result in excessive network bandwidth consumption, causing problems such as data transmission delays or packet loss. This may lead to inconsistencies between the data received by the cloud platform and the actual data, resulting in errors in the control of the servo motors of some or more devices. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned above and provide a cloud-based intelligent management system for servo motor controllers.

[0005] This invention relates to a cloud-based intelligent management system for servo motor controllers, the system comprising:

[0006] Time coefficient module: For each device, acquire 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 values ​​of the servo motor operation data of each device, and calculates the data deviation coefficient by combining it with the preset standard data values;

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

[0009] Upload Management Module: Calculates the priority coefficient of the corresponding device based on the time coefficient, data deviation coefficient, and task progress coefficient, and uploads the running data of the servo motor of the corresponding device to the cloud platform in descending order of priority coefficient to manage the servo motor controller of the device.

[0010] Optionally, calculating the time coefficient based on the data response time includes:

[0011] Obtain the data type of the servo motor and the response timestamp for each data type to obtain a time-ordered sequence of data responses.

[0012] Calculate the time interval between every two adjacent timestamps in the timestamp sequence and serialize it to obtain the response interval sequence;

[0013] The response intervals in the response interval sequence are labeled as... , This indicates the sequence number of the response intervals in the response interval sequence. =1, 2, 3, 4, ... , Let be the total number of response intervals in the response interval sequence, and It is a positive integer;

[0014] The mean of the response interval sequence is calculated using the following formula:

[0015] The formula for calculating the response time fluctuation coefficient is as follows:

[0016]

[0017] in, For response time fluctuation coefficient, Number the data type for the servo motor's operation. This represents the total number of data types that the servo motor can operate on, and It is a positive integer;

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

[0019] Optionally, calculating the time coefficient based on the response time fluctuation coefficient includes:

[0020] The response intervals in the response interval sequence and within the preset response interval range Re-mark the time outside of time, and mark it as , express Within the preset data response interval range The sequential numbering of the external response intervals, =0, 1, 2, 3, 4, ... , It is a positive integer;

[0021] Calculate the response interval anomaly coefficient The calculation formula is: ;

[0022] The time coefficient is calculated using the following formula:

[0023]

[0024] In the formula, For time coefficient, and These are the response time fluctuation coefficient and the response interval anomaly coefficient, respectively. They are respectively and The preset proportional coefficient, and The average score is greater than 0.

[0025] Optionally, the specific operating data values ​​of the servo motors of each device are obtained, and the data deviation coefficient is calculated by combining it with preset standard data values, including:

[0026] Obtain the operating data type of the servo motor, and obtain the actual data value of each data type to obtain a sequence of actual data values ​​based on time order;

[0027] Each actual data value in the actual data value sequence and its corresponding preset standard data value are respectively labeled as follows: and , This indicates the sequence number of the actual data values ​​in the actual data value sequence. =1, 2, 3, 4, ... , It is a positive integer;

[0028] according to and Obtain the data deviation coefficient The calculation formula is: , Number the data type for the servo motor's operation. This represents the total number of data types that the servo motor can operate on, and It is a positive integer.

[0029] Optionally, the task completion time of the servo motor for each device is obtained, and the task progress coefficient is calculated based on the task completion time, including:

[0030] For each device, obtain the task start time of the servo motor. Current time of the task Preset task end time ;

[0031] Calculate the time difference from the start of the task to the current time. , ;

[0032] Calculate the total time from the start of the task to the preset end time. , ;

[0033] Calculate the percentage of progress of the current task. , ;

[0034] Based on the current time of the task With preset end time The degree of proximity is used to calculate the task urgency coefficient. , ;

[0035] Calculate the task progress coefficient The calculation formula is: .

[0036] Optionally, the priority coefficient for the corresponding device can be calculated based on the time coefficient, data deviation coefficient, and task progress coefficient, including:

[0037]

[0038] In the formula, As a priority coefficient, , , These are the time coefficient, data deviation coefficient, and task progress coefficient, respectively. , , They are respectively , , The preset proportional coefficient, and , , All are greater than 0.

[0039] The beneficial effects of this invention are:

[0040] This invention proposes a cloud-based intelligent management system for servo motor controllers. For each device, the system obtains a time coefficient from the data response time of the servo motor's operating data; calculates a data deviation coefficient from the specific data values ​​of the servo motor's operating data for each device; calculates a task progress coefficient from the task completion time of the servo motor for each device; and calculates a priority coefficient for the corresponding device based on the time coefficient, data deviation coefficient, and task progress coefficient. The system then uploads the operating data of the servo motors of the corresponding devices to the cloud platform in descending order of priority coefficient to manage the servo motor controllers. This allows the operating data of servo motors from multiple devices to be uploaded to the cloud platform in descending order of priority coefficient for management of the servo motor controllers. This ensures efficient network bandwidth utilization, reduces data transmission delays or packet loss, and ensures that the data received by the cloud platform matches the actual data, thereby guaranteeing precise control of the servo motors of some or more devices. Attached Figure Description

[0041] The present invention will now be further described with reference to the accompanying drawings.

[0042] Figure 1 This is a framework diagram of a cloud-based intelligent management system for servo motor controllers. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] This invention provides a cloud-based intelligent management system for servo motor controllers. See also... Figure 1 , Figure 1 A framework diagram of a cloud-based intelligent management system for servo motor controllers provided in this embodiment of the invention. The system includes:

[0046] Time coefficient module: For each device, acquire 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 values ​​of the servo motor operation data of each device, and calculates the data deviation coefficient by combining it with the preset standard data values;

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

[0049] Upload Management Module: Calculates the priority coefficient of the corresponding device based on the time coefficient, data deviation coefficient, and task progress coefficient, and uploads the running data of the servo motor of the corresponding device to the cloud platform in descending order of priority coefficient to manage the servo motor controller of the device.

[0050] Based on the cloud-based intelligent management system for servo motor controllers provided in this invention, the above-mentioned method enables the operation data of servo motors of multiple devices to be uploaded to the cloud platform in descending order of priority to manage the servo motor controllers of the devices. This ensures good network bandwidth utilization, reduces data transmission delays or packet loss, and ensures that the data received by the cloud platform is consistent with the actual data, thereby ensuring precise control of the servo motors of some or more devices.

[0051] In one embodiment, the data response time and task completion time for acquiring the servo motor's operating data, and the calculation of the time coefficient based on the data response time, include:

[0052] Obtain the data type of the servo motor and the response timestamp for each data type to obtain a time-ordered sequence of data responses.

[0053] Calculate the time interval between every two adjacent timestamps in the timestamp sequence and serialize it to obtain the response interval sequence;

[0054] The response intervals in the response interval sequence are labeled as... , This indicates the sequence number of the response intervals in the response interval sequence. =1, 2, 3, 4, ... , Let be the total number of response intervals in the response interval sequence, and It is a positive integer;

[0055] The mean of the response interval sequence is calculated using the following formula:

[0056] The formula for calculating the response time fluctuation coefficient is as follows:

[0057]

[0058] in, For response time fluctuation coefficient, Number the data type for the servo motor's operation. This represents the total number of data types that the servo motor can operate on, and It is a positive integer;

[0059] The response intervals in the response interval sequence and within the preset response interval range Re-mark the time outside of time, and mark it as , express Within the preset data response interval range The sequential numbering of the external response intervals, =0, 1, 2, 3, 4, ... , It is a positive integer;

[0060] Calculate the response interval anomaly coefficient The calculation formula is: ;

[0061] The time coefficient is calculated using the following formula:

[0062]

[0063] In the formula, For time coefficient, and These are the response time fluctuation coefficient and the response interval anomaly coefficient, respectively. They are respectively and The preset proportional coefficient, and The average score is greater than 0.

[0064] It should be noted that, It is set up by professionals based on the actual situation. Generally speaking, The sum of is 1, without further limitations or elaboration;

[0065] It should be noted that the preset response interval range is set by professionals based on experience and data analysis, according to the actual servo motor of the device and the application scenario. No specific limitations or details are provided.

[0066] It should be noted that the data types for servo motor operation can include, but are not limited to, key parameters such as current, voltage, speed, position, temperature, load, vibration, and power. These data types respectively 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 assess the input power and energy consumption of the servo motor; speed and position data can be used to monitor the motor's dynamic performance and positioning accuracy; temperature data can be used to determine if the motor is at risk of overheating; load data reflects the servo motor's workload and helps determine if it is operating under overload; vibration data helps identify potential mechanical faults, such as bearing wear or imbalance problems. These data types work together to provide a comprehensive and detailed basis for servo motor performance monitoring, condition assessment, and fault diagnosis.

[0067] It's important to note that the time coefficient refers to the degree of fluctuation in the response time and the degree of abnormality in the response interval of the device's servo motor operating data. The greater the fluctuation in the response time and the degree of abnormality in the response interval, the higher the priority for uploading the servo motor's operating data to the cloud platform. This is because the fluctuation in response time and the degree of abnormality in the response interval reflect the stability and consistency of the servo motor's operating data. Large fluctuations in the device's response time indicate that the device's operation may be affected by external interference, internal performance fluctuations, or other abnormalities, potentially leading to decreased servo motor control precision or reduced operating efficiency. Simultaneously, abnormal response intervals reflect anomalies in data transmission between the device and the cloud, such as data loss or delays due to network congestion, device hardware failure, or abnormal operating status. When such anomalies occur frequently, the cloud management system needs to prioritize acquiring the device's operating data to quickly locate the source of the problem and take necessary control adjustments or maintenance measures, thereby reducing the risk of system failure. Furthermore, as a critical component in industrial production, the servo motor's operating data typically requires high real-time performance. If fluctuations or anomalies in the operating data are not uploaded to the cloud for analysis and processing in a timely manner, it may lead to further deterioration of the device's operating status and even affect the stability of the entire production chain. Prioritizing the uploading of data with significant fluctuations and anomalies ensures that these potential risks are promptly identified and addressed, preventing the accumulation of problems and the development of larger failures. Simultaneously, this prioritization mechanism provides crucial real-time data input to the system's optimization algorithms, enabling the cloud system to dynamically adjust the operating parameters of other devices to maintain the overall system's stable operation. Therefore, devices with higher time coefficients receive higher data upload priority, in order to better maintain the security, efficiency, and reliability of devices and the system.

[0068] In one implementation, the advantages of analyzing the time coefficient for determining the operation data of the device's servo motor and uploading it to the cloud platform for managing the device's servo motor controller are as follows:

[0069] Analyzing time coefficients to prioritize the upload of servo motor operating data significantly improves the efficiency and accuracy of cloud platforms in managing servo motor controllers. First, by comprehensively evaluating data response time fluctuations and anomalies using time coefficients, it dynamically determines which devices require higher attention, prioritizing the upload of critical data and providing timely support for anomaly detection and problem diagnosis. Second, this time-coefficient-based prioritization mechanism avoids disordered data uploads, effectively alleviating bandwidth pressure and improving cloud resource utilization and processing efficiency. Simultaneously, it helps the system optimize control strategies in real-time in scenarios with multiple devices operating collaboratively, reducing decision-making errors caused by data delays or anomalies and ensuring equipment stability. Furthermore, by accumulating time-coefficient-based upload records over time, the system can develop operational data characteristic analysis models for different devices and scenarios, providing data support for predictive maintenance and operational optimization, thereby extending equipment lifespan and improving overall production efficiency.

[0070] In one embodiment, obtaining the specific operating data values ​​of the servo motors of each device and calculating the data deviation coefficient by combining them with preset standard data values ​​includes:

[0071] Obtain the operating data type of the servo motor, and obtain the actual data value of each data type to obtain a sequence of actual data values ​​based on time order;

[0072] Each actual data value in the actual data value sequence and its corresponding preset standard data value are respectively labeled as follows: and , This indicates the sequence number of the actual data values ​​in the actual data value sequence. =1, 2, 3, 4, ... , It is a positive integer;

[0073] according to and Obtain the data deviation coefficient The calculation formula is: , Number the data type for the servo motor's operation. This represents the total number of data types that the servo motor can operate on, and It is a positive integer.

[0074] It should be noted that preset standard data values ​​refer to reference or target values ​​pre-set by professionals based on the servo motor's design parameters, operating specifications, and actual application scenarios. These data typically originate from technical manuals provided by equipment manufacturers, laboratory test results, or historical operating data settings of the equipment under normal operating conditions; specific details are not limited or elaborated upon.

[0075] It should be noted that all operating data of the servo motor can be collected in real time by sensors and control modules installed on the equipment. These sensors include dedicated sensors that measure current, voltage, speed, position, temperature, vibration and load. The control module performs preliminary processing and storage on the collected data, and then transmits 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 servo motor's operating status.

[0076] It's important to note that the data deviation coefficient refers to the degree to which the actual data value of a device's servo motor deviates from a preset standard data value. A larger deviation indicates a higher likelihood of device malfunction, and consequently, a higher priority for uploading the servo motor's operating data to the cloud platform. This is because the data deviation coefficient directly reflects the difference between the servo motor's operating state and its normal state. A larger deviation from the preset standard data value may indicate more serious problems in the servo motor's operation, such as overload, mechanical failure, electrical abnormalities, or environmental interference. If these problems are not detected and addressed in a timely manner, they may lead to decreased device performance or even more serious safety hazards or device damage. Therefore, prioritizing the uploading of operating data from devices with large deviation coefficients to the cloud platform allows the management system to quickly capture and diagnose these anomalies, providing a basis for fault warnings and timely maintenance, and reducing the risk of escalating faults. Furthermore, prioritizing the uploading of data with larger deviations also helps optimize cloud resource allocation and improve data processing efficiency. In scenarios where multiple devices operate in parallel, the cloud platform may face bandwidth or computing power limitations; therefore, the deviation coefficient is used to filter out the devices requiring the most attention for priority processing. By analyzing this abnormal data, the system can quickly make adjustments, such as dynamically optimizing the control parameters of servo motors or generating troubleshooting guidelines, thereby preventing equipment failures from having a greater impact on the stability of the entire production line or system. This strategy not only maximizes equipment utilization efficiency but also reduces downtime and maintenance costs caused by failures, thus providing higher reliability and economic benefits to the entire management system.

[0077] In one implementation, the advantages of analyzing the data deviation coefficient for determining the servo motor's operating data and uploading it to the cloud platform for managing the device's servo motor controller are as follows:

[0078] Analyzing data deviation coefficients is crucial for prioritizing the uploading of servo motor operation data, significantly improving the accuracy and responsiveness of cloud platforms in managing device controllers. By calculating the data deviation coefficient, the system can quantify the degree of anomaly in each type of operational data, quickly identifying devices with potential faults or abnormal states, and prioritizing the uploading of this critical data to ensure timely problem resolution. This method not only optimizes the allocation of cloud resources, preventing low-priority data from consuming limited upload bandwidth, but also improves the efficiency of anomaly detection, providing the cloud platform with more targeted diagnostic and decision support. Furthermore, data deviation coefficients can also play a role in multi-device collaborative management scenarios, helping the management system to monitor operational status differences in real time, formulate equipment optimization strategies from a global perspective, reduce production losses and maintenance costs caused by faults, and simultaneously improve equipment reliability and management efficiency.

[0079] In one embodiment, obtaining the task completion time of the servo motor for each device and calculating the task progress coefficient based on the task completion time includes:

[0080] For each device, obtain the task start time of the servo motor. Current time of the task Preset task end time ;

[0081] Calculate the time difference from the start of the task to the current time. , ;

[0082] Calculate the total time from the start of the task to the preset end time. , ;

[0083] Calculate the percentage of progress of the current task. , ;

[0084] Based on the current time of the task With preset end time The degree of proximity is used to calculate the task urgency coefficient. , ;

[0085] Calculate the task progress coefficient The calculation formula is: .

[0086] It should be noted that the servo motor's task start time, current task time, and preset task end time can be obtained from the task plan data recorded by the device's built-in task scheduling module or controller. The current task status and time can be collected in real time by the device's sensors 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 conjunction with the task objectives and operation plan. This data is uploaded to the cloud via IoT communication protocols to ensure the timeliness and accuracy of the data, thereby supporting the efficient calculation of task progress coefficients and priority determination.

[0087] It's important to note that the task progress coefficient refers to the progress of the equipment's servo motors. A higher task progress coefficient means higher priority for uploading the servo motor's operating data to the cloud platform, indicating the task is nearing completion or is in a critical phase. At this stage, the servo motor's operating data is crucial for equipment performance and task completion. If the equipment is in the later stages of the task or nearing the preset end time, any sudden anomalies or data deviations can directly affect the final result, requiring more accurate and timely data uploads. Prioritizing the upload of operating data to the cloud platform not only helps monitor equipment status in a timely manner but also provides early warnings of potential faults, ensuring timely and efficient task completion. Furthermore, a higher task progress coefficient usually indicates a higher level of importance and urgency for the task. The equipment's servo motors may be operating under high load or undergoing significant adjustments; therefore, any delays or errors could lead to task failure or production interruption. By prioritizing the upload of this critical data, control parameters can be adjusted in real time, fault diagnosis can be performed, or data support can be provided for further decision-making, ensuring stable operation of the equipment at critical moments and avoiding delays or unexpected equipment downtime.

[0088] In one implementation, analyzing the task progress coefficient offers several advantages for managing the servo motor controller of a device by prioritizing the uploading of servo motor operation data to the cloud platform. Firstly, a high task progress coefficient indicates that the device's servo motor is at a critical stage of the task, and any operational anomalies could directly impact the task's completion quality and efficiency. Therefore, prioritizing the uploading of this data ensures the control system receives real-time operational status updates at critical moments, allowing for timely detection and adjustments to prevent task disruptions due to equipment malfunctions or performance degradation. Secondly, by monitoring task progress, the cloud platform can effectively and dynamically adjust equipment management strategies based on task importance and urgency, achieving refined management. For example, devices nearing task completion receive higher upload priority, ensuring timely data analysis and processing at the most critical moments, thereby improving production efficiency, reducing downtime, and ultimately optimizing overall equipment maintenance costs.

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

[0090]

[0091] In the formula, As a priority coefficient, 、 、 These are the time coefficient, data deviation coefficient, and task progress coefficient, respectively. 、 、 They are respectively 、 、 The preset proportional coefficient, and 、 、 All are greater than 0.

[0092] It should be noted that, 、 、 It is set up by professionals based on the actual situation. Generally speaking, 、 、 The sum of is 1, for example 、 、 These can be 0.4, 0.3, 0.3, or other numbers, without any 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 standardization, etc. The specific method is selected by professionals according to the actual situation, without any limitation or elaboration.

[0093] In one implementation, the operating data of the servo motors of corresponding devices are uploaded to the cloud platform in descending order of priority to manage the servo motor controllers. This approach offers significant management benefits. First, prioritizing the upload of data from devices with higher priority ensures that the operating status of the most critical devices is monitored and analyzed in a timely manner. These devices are typically in critical stages of a task or under heavy load, potentially significantly impacting the production process or task completion. By prioritizing the acquisition of data from these devices, a rapid response can be initiated before a failure occurs, allowing for timely adjustments to control strategies and preventing production interruptions or task failures. Furthermore, this priority-based upload method helps to rationally allocate computing resources and bandwidth on the cloud platform, avoiding network congestion and data loss. When multiple devices upload data simultaneously, prioritizing them ensures that the upload order aligns with the urgency of the devices and the task schedule, thereby guaranteeing data real-time performance and accuracy. Ultimately, this intelligent data upload management method not only optimizes equipment operation management and improves equipment maintenance efficiency but also significantly reduces potential risks, ensuring that equipment performs at its best during production and maximizing the successful completion of production tasks.

[0094] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the patent coverage of the present invention.

Claims

1. A cloud-based intelligent management system for servo motor controllers, characterized in that, The system includes: Time coefficient module: For each device, acquire 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 values ​​of the servo motor operation data of each device, and calculates the data deviation coefficient by combining it with the preset standard data values; Task progress module: Obtain the task completion time of the servo motor of each device, and calculate the task progress coefficient based on the task completion time; Upload Management Module: Calculates the priority coefficient of the corresponding device based on the time coefficient, data deviation coefficient, and task progress coefficient, and uploads the running data of the servo motor of the corresponding device to the cloud platform in descending order of priority coefficient to manage the servo motor controller of the device; The time coefficient is calculated based on the data response time, including: Obtain the data type of the servo motor and the response timestamp for each data type to obtain a time-ordered sequence of data responses. Calculate the time interval between every two adjacent timestamps in the timestamp sequence and serialize it to obtain the response interval sequence; The response intervals in the response interval sequence are labeled as... , This indicates the sequence number of the response intervals in the response interval sequence. =1, 2, 3, 4, ... , Let be the total number of response intervals in the response interval sequence, and It is a positive integer; The mean of the response interval sequence is calculated using the following formula: The formula for calculating the response time fluctuation coefficient is as follows: in, For response time fluctuation coefficient, Number the data type for the servo motor's operation. This represents the total number of data types that the servo motor can operate on, and It is a positive integer; Calculate the time coefficient based on the response time fluctuation coefficient; The time coefficient is calculated based on the response time fluctuation coefficient, including: The response intervals in the response interval sequence and within the preset response interval range Re-mark the time outside of time, and mark it as , express Within the preset data response interval range The sequential numbering of the external response intervals, =0, 1, 2, 3, 4, ... , It is a positive integer; Calculate the response interval anomaly coefficient The calculation formula is: ; The time coefficient is calculated using the following formula: In the formula, For time coefficient, and These are the response time fluctuation coefficient and the response interval anomaly coefficient, respectively. They are respectively and The preset proportional coefficient, and The average score is greater than 0; The priority coefficient for the corresponding equipment is calculated based on the time coefficient, data deviation coefficient, and task progress coefficient, including: In the formula, As a priority coefficient, 、 、 These are the time coefficient, data deviation coefficient, and task progress coefficient, respectively. 、 、 They are respectively 、 、 The preset proportional coefficient, and 、 、 All are greater than 0.

2. The cloud-based intelligent management system for servo motor controllers according to claim 1, characterized in that, Obtain the specific operating data values ​​of the servo motors for each device, and calculate the data deviation coefficient by combining it with preset standard data values, including: Obtain the operating data type of the servo motor, and obtain the actual data value of each data type to obtain a sequence of actual data values ​​based on time order; Each actual data value in the actual data value sequence and its corresponding preset standard data value are respectively labeled as follows: and , This indicates the sequence number of the actual data values ​​in the actual data value sequence. =1, 2, 3, 4, ... , It is a positive integer; according to and Obtain the data deviation coefficient The calculation formula is: , Number the data type of the servo motor's operation. This represents the total number of data types that the servo motor can operate on, and It is a positive integer.

3. The intelligent management system for servo motor controllers based on cloud computing according to claim 1, characterized in that, Obtain the task completion time of the servo motor for each device, and calculate the task progress coefficient based on the task completion time, including: For each device, obtain the task start time of the servo motor. Current time of the task Preset task end time ; Calculate the time difference from the start of the task to the current time. , ; Calculate the total time from the start of the task to the preset end time. , ; Calculate the percentage of progress of the current task. , ; Based on the current time of the task With preset end time The degree of proximity is used to calculate the task urgency coefficient. , ; Calculate the task progress coefficient The calculation formula is: .

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