Control method, device, medium, equipment and program product of servo driver

Abstract

The application provides a control method, device, medium, equipment and program product of a servo driver, and the method comprises the following steps: when a device to which the servo driver belongs is in a standby state, historical operation data of a load of the device is acquired; according to the acquired historical operation data, a next start time of the load is predicted; and according to a time interval between a current time and the predicted next start time of the load, a standby mode of the servo driver is controlled. The scheme provided by the application can realize dynamic power consumption switching, so that standby power consumption of the servo driver is stably reduced.

Description

Technical Field

[0001] This invention relates to the field of control, and more particularly to a control method, apparatus, medium, device, and program product for a servo drive. Background Technology

[0002] In related technologies, industrial equipment servo drives generally have high power consumption in standby mode, and the standby time of industrial equipment accounts for more than 40%.

[0003] The solutions in related technologies focus on hardware-level power consumption management, but fail to address the core pain point of dynamically matching device operating patterns, resulting in insufficient energy efficiency optimization. Summary of the Invention

[0004] The main objective of this invention is to overcome the deficiencies of the aforementioned related technologies and provide a control method, device, medium, equipment, and program product for a servo driver, so as to solve the problem that the standby scheme of the servo driver in the related technologies focuses on hardware-level power consumption management, but does not solve the problem of dynamically matching the operating rules of the device.

[0005] The present invention provides a control method for a servo drive, comprising: when the device to which the servo drive belongs is in a standby state, acquiring historical operating data of the load of the device; predicting the next start time of the load based on the acquired historical operating data; and controlling the standby mode of the servo drive based on the time interval between the current time and the predicted next start time of the load.

[0006] Optionally, the historical running data is stored in a circular buffer, which stores the most recent N historical running data entries and / or the most recent M days of historical running data.

[0007] Optionally, the historical operating data includes: historical equipment startup time, historical load torque, and historical ambient temperature; predicting the next startup time of the load based on the acquired historical operating data includes: calculating the startup interval time between two adjacent startups sequentially based on the historical equipment startup time in the historical operating data to generate a historical startup interval time sequence; averaging the historical load torque in the historical operating data to obtain the historical average load torque, and averaging the historical ambient temperature in the historical operating data to obtain the historical average ambient temperature; and predicting the next startup time of the load based on the generated historical startup interval time sequence and the obtained historical average load torque and historical average ambient temperature.

[0008] Optionally, predicting the next startup time of the load based on the acquired historical operating data includes: determining the historical startup interval data of the load based on the acquired historical operating data; the historical startup interval data includes: the most recent N startup interval times; calculating the average historical startup interval times of the load based on the acquired historical startup interval data; and predicting the next startup time of the load based on the calculated average historical startup interval times.

[0009] Optionally, predicting the next startup time of the load based on the calculated average of the historical startup interval times includes: predicting the next startup time of the load based on the average of the historical startup interval times and the historical standard deviation of fluctuations.

[0010] Optionally, the standby mode of the servo drive is controlled according to the time interval between the current time and the predicted next start time of the load, including: if the time interval is greater than a first preset time, the main power supply is turned off to switch to the first standby mode; if the time interval is greater than or equal to a second preset time and less than or equal to the first preset time, the main power supply voltage is reduced to a preset voltage to switch to the second standby mode; if the time interval is less than the first preset time, the servo drive is woken up a third preset time in advance.

[0011] Another aspect of the present invention provides a control device for a servo drive, comprising: an acquisition unit, configured to acquire historical operating data of the load of the device when the device to which the servo drive belongs is in a standby state; a prediction unit, configured to predict the next start time of the load based on the historical operating data acquired by the acquisition unit; and a control unit, configured to control the standby mode of the servo drive based on the time interval between the current time and the predicted next start time of the load.

[0012] Optionally, the historical running data is stored in a circular buffer, which stores the most recent N historical running data entries and / or the most recent M days of historical running data.

[0013] Optionally, the historical operating data includes: historical equipment startup time, historical load torque, and historical ambient temperature; the prediction unit predicts the next startup time of the load based on the historical operating data acquired by the acquisition unit, including: calculating the startup interval time between two adjacent startups sequentially based on the historical equipment startup time in the historical operating data to generate a historical startup interval time sequence; averaging the historical load torque in the historical operating data to obtain the historical average load torque, and averaging the historical ambient temperature in the historical operating data to obtain the historical average ambient temperature; and predicting the next startup time of the load based on the generated historical startup interval time sequence and the obtained historical average load torque and historical average ambient temperature.

[0014] Optionally, the prediction unit predicts the next startup time of the load based on the historical operating data acquired by the acquisition unit, including: determining the historical startup interval data of the load based on the acquired historical operating data; the historical startup interval data including: the most recent N startup interval times; calculating the average of the historical startup interval times of the load based on the acquired historical startup interval data; and predicting the next startup time of the load based on the calculated average of the historical startup interval times.

[0015] Optionally, the prediction unit predicts the next startup time of the load based on the calculated average of the historical startup interval times, including: predicting the next startup time of the load based on the average of the historical startup interval times and the historical standard deviation of fluctuations.

[0016] Optionally, the control unit controls the standby mode of the servo drive based on the time interval between the current time and the predicted next start time of the load, including: if the time interval is greater than a first preset time, turning off the main power supply to switch to the first standby mode; if the time interval is greater than or equal to a second preset time and less than or equal to the first preset time, reducing the main power supply voltage to a preset voltage to switch to the second standby mode; if the time interval is less than the first preset time, waking up the servo drive a third preset time in advance.

[0017] In another aspect, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0018] In another aspect, the present invention provides an apparatus including a processor, a memory, and a computer program stored in the memory that can run on the processor, wherein the processor executes the program to implement the steps of any of the methods described above.

[0019] In another aspect, the present invention provides an apparatus comprising any of the control devices described above.

[0020] In another aspect, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the methods described above.

[0021] According to the technical solution of the present invention, without the need for additional hardware, dynamic power consumption management based on the device's operating status can stably reduce the standby power consumption of the servo driver.

[0022] According to the technical solution of the present invention, by analyzing the historical motion data of the device (last start time, load size, ambient temperature), a prediction model for the next load start time is generated. The standby power consumption is switched according to the predicted next load start time, realizing dynamic power consumption switching. No additional hardware is required, there is no hardware dependency, and it can be adapted to different servo drives. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0024] Figure 1 This is a schematic diagram of an embodiment of the servo driver control method provided by the present invention;

[0025] Figure 2 This is a schematic diagram of a specific embodiment of the servo driver control method provided by the present invention;

[0026] Figure 3 This is a structural block diagram of an embodiment of the control device for the servo driver provided by the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Solutions for reducing standby power consumption of servo drives in related technologies mainly include:

[0030] 1. Fixed low power mode: The servo drive continuously stands by with a fixed high power consumption, which cannot match the actual load demand, resulting in an average annual energy waste.

[0031] 2. External sensor solution: Relies on vibration or temperature sensors (such as TI's INA226 module) to trigger standby switching, but increases hardware cost (15%) and has response delay (>100ms).

[0032] This invention provides a control method for a servo driver.

[0033] Figure 1 This is a schematic diagram of an embodiment of the servo driver control method provided by the present invention.

[0034] like Figure 1 As shown, according to an embodiment of the present invention, the control method of the servo driver includes at least steps S110, S120 and S130.

[0035] Step S110: When the device to which the servo driver belongs is in standby mode, acquire the historical operating data of the device's load.

[0036] The device to which the servo drive belongs can be industrial equipment. Specifically, the servo drive records and stores the load's operating data. This operating data may include: device startup time, load torque, and ambient temperature. When the device to which the servo drive belongs is in standby mode, it retrieves the stored historical operating data of the load. This historical operating data may include: historical device startup time, historical load torque, and historical ambient temperature. The historical operating data is stored in a circular buffer, which stores the most recent N historical operating data entries and / or the most recent M days' historical operating data. For example, the load's operating data can be stored in Flash memory (occupying ≤2KB), managed using a circular buffer, retaining the most recent 50 records, covering the device's operating patterns over the past 3 days.

[0037] Step S120: Based on the acquired historical operating data, predict the next startup time of the load.

[0038] In one specific implementation, based on the historical device startup time in the historical operating data, the startup interval time between two adjacent startups is calculated sequentially to generate a historical startup interval time sequence; the historical load torque in the historical operating data is averaged to obtain the historical average load torque, and the historical ambient temperature in the historical operating data is averaged to obtain the historical average ambient temperature; based on the generated historical startup interval time sequence and the obtained historical average load torque and historical average ambient temperature, the next startup time of the load is predicted.

[0039] First, based on the device startup time in the historical operation data, the startup interval between two adjacent startups is calculated sequentially to generate a historical startup interval time sequence. For example, the acquired historical operation data includes N records, each containing the historical device startup time, historical load torque, and historical ambient temperature of one device startup. The time difference between the startup time in each record and the startup time in the previous record is calculated. , This refers to the startup interval between two consecutive startups, where i = 1, 2, ..., N, and N is the number of historical running data points. This allows us to generate a historical startup interval time series { }

[0040] Then, the historical load torque in the historical operating data is averaged to obtain the historical average load torque, and the historical ambient temperature in the historical operating data is averaged to obtain the historical average ambient temperature.

[0041] Historical load torque from N records Summation is performed, and the historical average load torque is calculated. ,Right now:

[0042] ;

[0043] Historical ambient temperature from N records Summation is performed, and the historical average ambient temperature is calculated. ,Right now:

[0044] ;

[0045] High load torque (e.g., >80% of rated torque) typically leads to more frequent device starts (shorter intervals), requiring increased model sensitivity. High temperatures (e.g., >35°C) reduce device heat dissipation efficiency, potentially extending standby time (longer intervals), requiring decreased model sensitivity.

[0046] After obtaining the historical start-up interval time series, the historical average load torque, and the historical average ambient temperature, the next start-up time of the load is predicted based on the historical start-up interval time series, the historical average load torque, and the historical average ambient temperature.

[0047] Specifically, the exponentially smoothed attenuation factor λ is first adjusted based on the current load torque and current ambient temperature, and then the next start time of the load is predicted based on the historical start interval time series, the historical average load torque, the historical average ambient temperature, and the attenuation factor.

[0048] The model for predicting the next startup time of the aforementioned load is as follows:

[0049]

[0050] in, This indicates the predicted time of the next startup under the load. The historical startup interval is i = 1, 2, ..., N, where N is the number of historical running data. This is the attenuation factor.

[0051] Based on the current load torque, current ambient temperature, historical start-up interval, historical load torque, and historical average ambient temperature, adjust the exponentially smoothed attenuation factor λ to adapt the model to the real-time state of the equipment. For example, the attenuation factor λ can be adjusted using the following formula:

[0052]

[0053] Let the initial attenuation factor be, for example, take... =0.1, This represents the deviation between the current load torque and the historical average load torque. This represents the deviation between the current ambient temperature and the historical average ambient temperature. , The slope of the linear regression model can be obtained in advance by fitting historical data.

[0054]

[0055]

[0056] in, This refers to the historical startup interval. Historical load torque Let Cov represent the historical ambient temperature, Var represent the variance, i = 1, 2, ..., N, and N be the number of historical data points. Then, the predicted next startup time of the load is... for:

[0057]

[0058] Simple time series (such as moving averages) fail when device patterns change abruptly (such as weekend downtime), while exponentially weighted systems can quickly respond to recent changes.

[0059] To ensure long-term robustness, an adaptive prediction error mechanism is adopted, which dynamically adjusts the prediction when the prediction deviation is large. To prevent model offset. Actual startup time. Once achieved, calculate the error. :

[0060]

[0061] like :

[0062] ,For example,

[0063] like : Leave it unchanged; no adjustment is needed. This is the error range, which can be adjusted according to the actual situation, for example, 120 seconds.

[0064] In another specific implementation, the historical startup interval data of the load is determined based on the acquired historical operating data; the average historical startup interval time of the load is calculated based on the acquired historical startup interval data; and the next startup time of the load is predicted based on the calculated average historical startup interval time.

[0065] The historical operating data may include the historical startup time of the load. Based on the historical startup time, the historical startup interval data of the load can be obtained. Based on the obtained historical startup interval data, the average historical startup interval time of the load can be calculated. Specifically, the historical startup interval data can be the startup interval time of the most recent N times. For example, the startup interval time of the most recent 50 records can be extracted, and the average value can be calculated to obtain the average historical startup interval time.

[0066] The next startup time of the load can be predicted based on the calculated average of the historical startup intervals. Specifically, the next startup time of the load is predicted based on the average of the historical startup intervals and the historical standard deviation of fluctuation. The historical standard deviation of fluctuation is an empirical value for the fluctuation of startup intervals. Calculations are performed according to different usage scenarios. For example, in factory operation, the startup and shutdown times of this production line may vary each time. This empirical value is obtained through statistical calculation, such as averaging. The predicted next startup time of the load is equal to the previous startup time of the load plus the predicted startup interval.

[0067] More specifically, a dynamic prediction algorithm is used to analyze time series data using a sliding window. For example, the startup time interval of the most recent 50 records is extracted, a moving average interval is calculated, and combined with the historical standard deviation of fluctuations, the next startup time of the load is predicted. For example, a prediction is triggered every 5 minutes, the moving average of historical startup time intervals is calculated (e.g., an average startup time interval of 58 minutes is obtained), and combined with the historical standard deviation of fluctuations (e.g., ±4 minutes), a prediction time window (58 ± 4 minutes) is generated. The next startup time is predicted through the generated prediction time window. The algorithm requires a short computation time (only 8ms in experimental tests) and does not affect the operation of the main control unit.

[0068] Step S130: Control the standby mode of the servo drive according to the time interval between the current time and the predicted next start time of the load.

[0069] Specifically, if the time interval is greater than a first preset time, the main power supply (high-voltage power supply) of the servo driver is turned off, while the low-voltage power supply (main chip power supply) is retained to switch to the first standby mode; if the time interval is greater than or equal to a second preset time and less than or equal to the first preset time, the main power supply voltage is reduced to a preset voltage to switch to the second standby mode; if the time interval is less than the first preset time, the servo driver is woken up before the third preset time (by controlling a relay to connect to the main power supply). The power consumption of the first standby mode is less than that of the second standby mode. For example, the first standby mode is an ultra-low power standby mode, and the second standby mode is a reduced power standby mode. The second preset time can be equal to the predicted interval time. That is, when the time interval from the predicted next start time is less than the predicted interval time, the servo starter is woken up before the third preset time.

[0070] For example, if the first preset time is 10 minutes and the second preset time is 5 minutes, when the time interval from the predicted next startup time is greater than 10 minutes, the servo driver's main power supply is turned off, switching to ultra-low power standby mode, with only the chip power supply remaining. If the time interval from the predicted next startup time is between 5 and 10 minutes (greater than or equal to 5 minutes and less than or equal to 10 minutes), the servo driver's main power supply voltage is reduced to 80%, maintaining basic communication. When the time interval from the predicted next startup time is less than 5 minutes, it is woken up 5 seconds in advance (GPIO triggers the power IC, which is achieved by controlling a relay switch).

[0071] To clearly illustrate the technical solution of the present invention, the execution flow of the servo driver control method provided by the present invention will be described below with reference to a specific embodiment.

[0072] Figure 2 This is a schematic diagram of a specific embodiment of the servo driver control method provided by the present invention. For example... Figure 2 As shown, the servo driver automatically records historical load operation data and stores it in Flash memory (occupying ≤2KB). It performs standby state detection. When in standby mode, it triggers load start-up time prediction logic to predict the next load start-up time, determines the start-up time range, and triggers prediction every 5 minutes. If the time interval from the predicted start-up time is greater than 10 minutes, it switches to ultra-low power standby mode, shutting down the main power module and retaining only the RTC wake-up circuit. If the time interval from the predicted start-up time is between 5 and 10 minutes, it reduces the main power supply voltage to 80% to maintain basic communication. If the time interval from the predicted start-up time is less than 5 minutes, it wakes up 5 seconds in advance (GPIO triggers the power IC).

[0073] The present invention also provides a control device for a servo driver.

[0074] Figure 3 This is a structural block diagram of an embodiment of the control device for the servo driver provided by the present invention. Figure 3 As shown, the control device 100 of the servo driver includes: an acquisition unit 110, a prediction unit 120, and a control unit 130.

[0075] The acquisition unit 110 is used to acquire historical operating data of the load of the device when the device to which the servo driver belongs is in a standby state.

[0076] The device to which the servo drive belongs can be industrial equipment. Specifically, the servo drive records and stores the load's operating data. This operating data may include: device startup time, load torque, and ambient temperature. When the device to which the servo drive belongs is in standby mode, it retrieves the stored historical operating data of the load. This historical operating data may include: historical device startup time, historical load torque, and historical ambient temperature. The historical operating data is stored in a circular buffer, which stores the most recent N historical operating data entries and / or the most recent M days' historical operating data. For example, the load's operating data can be stored in Flash memory (occupying ≤2KB), managed using a circular buffer, retaining the most recent 50 records, covering the device's operating patterns over the past 3 days.

[0077] The prediction unit 120 is used to predict the next startup time of the load based on the historical operating data obtained by the acquisition unit.

[0078] In one specific implementation, based on the historical device startup time in the historical operating data, the startup interval time between two adjacent startups is calculated sequentially to generate a historical startup interval time sequence; the historical load torque in the historical operating data is averaged to obtain the historical average load torque, and the historical ambient temperature in the historical operating data is averaged to obtain the historical average ambient temperature; based on the generated historical startup interval time sequence and the obtained historical average load torque and historical average ambient temperature, the next startup time of the load is predicted.

[0079] First, based on the device startup time in the historical operation data, the startup interval between two adjacent startups is calculated sequentially to generate a historical startup interval time sequence. For example, if the acquired historical operation data includes N records, each record contains the startup time of one device startup, historical load torque, and historical ambient temperature, the time difference between the historical device startup time in each record and the startup time in the previous record is calculated. , This refers to the startup interval between two consecutive startups, where i = 1, 2, ..., N-1, and N is the number of historical running data points. This allows the generation of a historical startup interval time series { }

[0080] Then, the historical load torque in the historical operating data is averaged to obtain the historical average load torque, and the historical ambient temperature in the historical operating data is averaged to obtain the historical average ambient temperature.

[0081] Historical load torque from N records Summation is performed, and the historical average load torque is calculated. ,Right now:

[0082] ;

[0083] Historical ambient temperature from N records Summation is performed, and the historical average ambient temperature is calculated. ,Right now:

[0084] ;

[0085] High load torque (e.g., >80% of rated torque) typically leads to more frequent device starts (shorter intervals), requiring increased model sensitivity. High temperatures (e.g., >35°C) reduce device heat dissipation efficiency, potentially extending standby time (longer intervals), requiring decreased model sensitivity.

[0086] After obtaining the historical start-up interval time series, the historical average load torque, and the historical average ambient temperature, the next start-up time of the load is predicted based on the historical start-up interval time series, the historical average load torque, and the historical average ambient temperature.

[0087] Specifically, the exponentially smoothed attenuation factor λ is first adjusted based on the current load torque and current ambient temperature, and then the next start time of the load is predicted based on the historical start interval time series, the historical average load torque, the historical average ambient temperature, and the attenuation factor.

[0088] The model for predicting the next startup time of the aforementioned load is as follows:

[0089]

[0090] in, This indicates the predicted time of the next startup under the load. The historical startup interval is i = 1, 2, ..., N, where N is the number of historical running data. This is the attenuation factor.

[0091] Based on the current load torque, current ambient temperature, historical start-up interval, historical load torque, and historical average ambient temperature, adjust the exponentially smoothed attenuation factor λ to adapt the model to the real-time state of the equipment. For example, the attenuation factor λ can be adjusted using the following formula:

[0092]

[0093] Let the initial attenuation factor be, for example, take... =0.1, This represents the deviation between the current load torque and the historical average load torque. This represents the deviation between the current ambient temperature and the historical average ambient temperature. , The slope of the linear regression model can be obtained in advance by fitting historical data.

[0094]

[0095]

[0096] in, This refers to the historical startup interval. Historical load torque Let Cov represent the historical ambient temperature, Var represent the variance, i = 1, 2, ..., N, and N be the number of historical data points. Then, the predicted next startup time of the load is... for:

[0097]

[0098] Simple time series (such as moving averages) fail when device patterns change abruptly (such as weekend downtime), while exponentially weighted systems can quickly respond to recent changes.

[0099] To ensure long-term robustness, an adaptive prediction error mechanism is adopted, which dynamically adjusts the prediction when the prediction deviation is large. To prevent model offset. Actual startup time. Once achieved, calculate the error. :

[0100]

[0101] like : ,For example,

[0102] like : Leave it unchanged; no adjustment is needed. This is the error range, which can be adjusted according to the actual situation, for example, 120 seconds.

[0103] In another specific implementation, the historical startup interval data of the load is determined based on the acquired historical operating data; the average historical startup interval time of the load is calculated based on the acquired historical startup interval data; and the next startup time of the load is predicted based on the calculated average historical startup interval time.

[0104] The historical operating data may include the historical startup time of the load. Based on the historical startup time, the historical startup interval data of the load can be obtained. Based on the obtained historical startup interval data, the average historical startup interval time of the load can be calculated. Specifically, the historical startup interval data can be the startup interval time of the most recent N times. For example, the startup interval time of the most recent 50 records can be extracted, and the average value can be calculated to obtain the average historical startup interval time.

[0105] The next startup time of the load can be predicted based on the calculated average of the historical startup intervals. Specifically, the next startup time of the load is predicted based on the average of the historical startup intervals and the historical standard deviation of fluctuation. The historical standard deviation of fluctuation is an empirical value for the fluctuation of startup intervals. Calculations are performed according to different usage scenarios. For example, in factory operation, the startup and shutdown times of this production line may vary each time. This empirical value is obtained through statistical calculation, such as averaging. The predicted next startup time of the load is equal to the previous startup time of the load plus the predicted startup interval.

[0106] More specifically, a dynamic prediction algorithm is used to analyze time series data using a sliding window. For example, the startup time interval of the most recent 50 records is extracted, a moving average interval is calculated, and combined with the historical standard deviation of fluctuations, the next startup time of the load is predicted. For example, a prediction is triggered every 5 minutes, the moving average of historical startup time intervals is calculated (e.g., an average startup time interval of 58 minutes is obtained), and combined with the historical standard deviation of fluctuations (e.g., ±4 minutes), a prediction time window (58 ± 4 minutes) is generated. The next startup time is predicted through the generated prediction time window. The algorithm requires a short computation time (only 8ms in experimental tests) and does not affect the operation of the main control unit.

[0107] Control unit 130 is used to control the standby mode of the servo drive based on the time interval between the predicted next start time of the load from the current time distance.

[0108] Specifically, if the time interval is greater than a first preset time, the main power supply (high-voltage power supply) of the servo driver is turned off, while the low-voltage power supply (main chip power supply) is retained to switch to the first standby mode; if the time interval is greater than or equal to a second preset time and less than or equal to the first preset time, the main power supply voltage is reduced to a preset voltage to switch to the second standby mode; if the time interval is less than the first preset time, the servo driver is woken up before the third preset time. (The main power supply is connected via a control relay.) The power consumption of the first standby mode is less than that of the second standby mode. For example, the first standby mode is an ultra-low power standby mode, and the second standby mode is a reduced power standby mode. The second preset time can be equal to the predicted interval time. That is, when the time interval from the predicted next start time is less than the predicted interval time, the servo starter is woken up before the third preset time.

[0109] For example, if the first preset time is 10 minutes and the second preset time is 5 minutes, when the time interval from the predicted next startup time is greater than 10 minutes, the servo driver's main power supply is turned off, switching to ultra-low power standby mode, with only the chip power supply remaining. If the time interval from the predicted next startup time is between 5 and 10 minutes (greater than or equal to 5 minutes and less than or equal to 10 minutes), the servo driver's main power supply voltage is reduced to 80%, maintaining basic communication. When the time interval from the predicted next startup time is less than 5 minutes, it is woken up 5 seconds in advance (GPIO triggers the power IC, which is achieved by controlling a relay switch).

[0110] The present invention also provides a storage medium corresponding to the control method of the servo driver, wherein a computer program is stored thereon, which, when executed by a processor, implements the steps of any of the aforementioned methods.

[0111] The present invention also provides an air conditioner corresponding to the control method of the servo drive, comprising a processor, a memory, and a computer program stored in the memory that can run on the processor, wherein the processor executes the program to implement the steps of any of the aforementioned methods.

[0112] The present invention also provides an air conditioner corresponding to the control device of the servo driver, including any of the control devices described above.

[0113] The present invention also provides a computer program product corresponding to the control method of the servo driver, comprising a computer program that, when executed by a processor, implements the steps of any of the aforementioned methods.

[0114] Accordingly, the solution provided by this invention generates a prediction model for the next load start time by analyzing the historical motion data of the device (last start time, load size, ambient temperature); and switches the standby power consumption according to the predicted next load start time, thus realizing dynamic power consumption switching; it does not require additional hardware, has no hardware dependency, and can be adapted to different servo drivers.

[0115] The technical solution of this invention significantly saves energy and reduces standby power consumption. It only requires integrating prediction logic into the existing driver firmware to avoid startup delays caused by false wake-ups.

[0116] The technical solution of this invention eliminates the need for additional hardware and reduces standby power consumption through firmware-level dynamic power management.

[0117] The functions described herein can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions can be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this invention and the appended claims. For example, due to the nature of software, the functions described above can be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units can be integrated into a single processing unit, or each unit can exist physically separately, or two or more units can be integrated into a single unit.

[0118] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0119] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0120] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0121] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A control method for a servo driver, characterized in that, include: When the device to which the servo driver belongs is in standby mode, acquire the historical operating data of the device's load; Based on the acquired historical operating data, predict the next startup time of the load; The standby mode of the servo drive is controlled based on the time interval between the current time and the predicted next start time of the load.

2. The method according to claim 1, characterized in that, The historical running data is stored in a circular buffer, which stores the most recent N historical running data entries and / or the most recent M days of historical running data.

3. The method according to claim 1, characterized in that, The historical operating data includes: historical equipment startup time, historical load torque, and historical ambient temperature; based on the acquired historical operating data, the next startup time of the load is predicted, including: Based on the historical device startup time in the historical operation data, the startup interval time between two adjacent startups is calculated sequentially to generate a historical startup interval time sequence; The historical average load torque is obtained by averaging the historical load torque in the historical operating data, and the historical average ambient temperature is obtained by averaging the historical ambient temperature in the historical operating data. Based on the generated historical start-up interval time series and the obtained historical average load torque and historical average ambient temperature, the predicted start-up time of the load is determined.

4. The method according to claim 1, characterized in that, Based on the acquired historical operational data, predict the next startup time of the load, including: Based on the acquired historical operating data, the historical startup interval data of the load is determined; the historical startup interval data includes: the time interval between the most recent N startups; Based on the acquired historical startup interval data, calculate the average historical startup interval time of the load; The next startup time of the load is predicted based on the calculated average of the historical startup intervals.

5. The method according to claim 4, characterized in that, Based on the calculated average of the historical startup intervals, predict the next startup time of the load, including: The next startup time of the load is predicted based on the average historical startup interval and the historical standard deviation of fluctuation.

6. The method according to any one of claims 1-5, characterized in that, Based on the time interval between the predicted next startup time of the load from the current time distance, control the standby mode of the servo drive, including: If the time interval is greater than the first preset time, the main power is turned off to switch to the first standby mode; If the time interval is greater than or equal to the second preset time and less than or equal to the first preset time, the main power supply voltage is reduced to the preset voltage to switch to the second standby mode. If the time interval is less than the first preset time, the servo driver is woken up before the third preset time.

7. A control device for a servo driver, characterized in that, include: The acquisition unit is used to acquire historical operating data of the load of the device when the device to which the servo driver belongs is in a standby state. The prediction unit is used to predict the next startup time of the load based on the historical operating data obtained by the acquisition unit. The control unit is used to control the standby mode of the servo drive based on the time interval of the predicted next start time of the load from the current time distance.

8. A storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

9. A device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory that can run on the processor, wherein the processor executes the program to implement the steps of any of the methods of claims 1-6, or includes the control device as described in claim 7.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

Images