A real-time monitoring and early warning method and system for variable frequency energy-saving motors

Abstract

The present invention discloses a real-time monitoring and early warning method and system for a variable frequency energy-saving motor, comprising: collecting motor operating parameters in real time through a multi-source sensor group configured on the variable frequency energy-saving motor; determining the current motor operating condition based on the motor operating parameters, and determining the motor real-time load rate based on a preset load operating condition association model; obtaining historical operating data of the variable frequency energy-saving motor, and generating a dynamic threshold curve based on the historical operating data; generating a dynamic threshold based on the motor operating parameters and the dynamic threshold curve, calculating the health of the variable frequency energy-saving motor based on the dynamic threshold, and determining a life prediction value of the variable frequency energy-saving motor based on the health; when the life prediction value of the variable frequency energy-saving motor is lower than a preset value, automatically triggering a frequency reduction instruction and switching to a magnetic field oriented vector control mode, and synchronously generating an alarm and equipment health file for the variable frequency energy-saving motor.

Description

Technical Field

[0001] The present invention relates to the technical field of motor monitoring and early warning, and in particular to a real-time monitoring and early warning method and system for a variable frequency energy-saving motor. Background Art

[0002] A variable frequency energy-saving motor is a motor system that controls the motor speed and power by adjusting the power frequency of the input motor through a frequency converter. It usually combines efficient motor design and advanced variable frequency control technology to achieve energy-saving operation under different load conditions.

[0003] However, some current variable-frequency energy-saving motor systems rely on periodic data collection (such as mean value calculation per unit time), which cannot achieve millisecond-level response and may miss transient faults. At the same time, most systems use static thresholds to judge abnormalities, which are difficult to adapt to complex working conditions and are prone to false alarms or missed alarms. There are defects in real-time performance, compatibility, algorithm accuracy and closed-loop control, and the real-time and accuracy of monitoring and early warning of variable-frequency energy-saving motors cannot be guaranteed. Summary of the Invention

[0004] The present invention provides a real-time monitoring and early warning method and system for variable frequency energy-saving motors to solve the technical problem that variable frequency energy-saving motors in the prior art have defects in real-time performance, compatibility, algorithm accuracy and closed-loop control, and cannot ensure the real-time and accuracy of monitoring and early warning of variable frequency energy-saving motors.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a real-time monitoring and early warning method for a variable frequency energy-saving motor, comprising:

[0006] The multi-source sensor group configured on the variable frequency energy-saving motor collects the motor operating parameters in real time;

[0007] Determining the current motor operating condition based on the motor operating parameters, and determining the motor real-time load rate based on a preset load condition association model;

[0008] Obtain historical operating data of variable frequency energy-saving motors and generate dynamic threshold curves based on the historical operating data;

[0009] generating a dynamic threshold value according to the motor operating parameters and the dynamic threshold curve, calculating the health of the variable frequency energy-saving motor based on the dynamic threshold value, and determining a life prediction value of the variable frequency energy-saving motor according to the health value;

[0010] When the life prediction value of the variable frequency energy-saving motor is lower than the preset value, the frequency reduction instruction is automatically triggered and the control mode is switched to the magnetic field oriented vector control mode, and the alarm and equipment health file of the variable frequency energy-saving motor are generated synchronously.

[0011] As a preferred solution, the multi-source sensor group configured on the variable frequency energy-saving motor collects the motor operating parameters in real time, specifically including:

[0012] The stator winding temperature is collected by a first temperature sensor provided inside the stator winding of the variable frequency energy-saving motor;

[0013] The three-phase current of the variable frequency energy-saving motor is collected by a current sensor provided on the variable frequency energy-saving motor, and the harmonic distortion rate is calculated based on the three-phase current;

[0014] The vibration acceleration of the bearing of the variable frequency energy-saving motor is collected by a vibration acceleration sensor provided on the bearing of the variable frequency energy-saving motor;

[0015] The magnetic flux density inside the variable frequency energy-saving motor is collected by a magnetic flux sensor provided on the variable frequency energy-saving motor;

[0016] The temperature of the cooling medium is measured by a second temperature sensor provided at the inlet and outlet of the cooling system in the variable frequency energy-saving motor, and the temperature difference of the cooling medium when passing through the inlet and outlet is calculated;

[0017] Among them, the multi-source sensor group includes: a first temperature sensor, a second temperature sensor, a current sensor, a vibration acceleration sensor and a magnetic flux sensor, and the motor operating parameters include: stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value and cooling system inlet and outlet temperature difference.

[0018] As a preferred solution, the current motor operating condition is determined according to the motor operating parameters, and the real-time motor load rate is determined based on a preset load condition association model, specifically including:

[0019] Comprehensively analyzing the stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value, and cooling system inlet and outlet temperature difference, and determining the current motor operating condition based on a preset motor operating state model; wherein the current motor operating condition includes: normal operating condition, abnormal operating condition, special operating condition, fault operating condition, and protection operating condition, and the motor operating state model is trained using historical motor operating parameters combined with historical motor operating conditions;

[0020] The current motor operating condition, whether in normal operating condition, abnormal operating condition, special operating condition, fault operating condition or protection operating condition, is input into a preset load operating condition association model to determine the real-time load rate of the motor.

[0021] As a preferred solution, the method for constructing the preset load condition association model includes:

[0022] Collect historical load-related time series data and its corresponding operating parameters;

[0023] Calculate the rolling mean and standard deviation of the historical load-related time series data within a sliding window, and determine the operating condition correlation characteristics of the historical load-related time series data and its corresponding operating condition parameters in combination with the lag term of the historical load;

[0024] An initial LSTM model is constructed, and an adaptive learning rate and regularization are determined. The initial LSTM model is trained using the working condition association features until a preset number of iterations and a preset termination condition are reached, thereby generating a preset load working condition association model.

[0025] As a preferred solution, the method of obtaining historical operating data of the variable frequency energy-saving motor and generating a dynamic threshold curve based on the historical operating data specifically includes:

[0026] Acquire historical operating data of the variable frequency energy-saving motor; wherein the historical operating data includes historical load rate, temperature parameter time series data, vibration parameter time series data, current parameter time series data and flux parameter time series data;

[0027] dividing the temperature parameter time series data, the vibration parameter time series data, the current parameter time series data, and the flux parameter time series data according to the interval of the historical load rate;

[0028] Construct a historical load rate sequence based on the historical load rate, and obtain the historical temperature threshold, historical vibration threshold, historical harmonic threshold, and historical flux threshold;

[0029] A dynamic threshold curve is generated based on the historical temperature threshold, historical vibration threshold, historical harmonic threshold and historical flux threshold, combined with the historical load rate sequence, and the divided temperature parameter time series data, vibration parameter time series data, current parameter time series data and flux parameter time series data.

[0030] As a preferred solution, the method of generating a dynamic threshold value according to the motor operating parameters and the dynamic threshold curve, calculating the health of the variable frequency energy-saving motor based on the dynamic threshold value, and determining a life prediction value of the variable frequency energy-saving motor according to the health value specifically includes:

[0031] Inputting the motor operating parameters into the dynamic threshold curve to generate current real-time dynamic thresholds corresponding to stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value, and cooling system inlet and outlet temperature difference; wherein the dynamic thresholds include dynamic temperature threshold, dynamic vibration threshold, dynamic harmonic threshold, and dynamic magnetic flux threshold;

[0032] Comparing the stator winding temperature and the cooling system inlet and outlet temperature difference with the dynamic temperature threshold, and calculating the first health level according to the difference between the stator winding temperature and the cooling system inlet and outlet temperature difference and the dynamic temperature threshold;

[0033] Comparing the three-phase current harmonic distortion rate with the dynamic harmonic threshold, and calculating the second health level according to the difference between the three-phase current harmonic distortion rate and the dynamic harmonic threshold;

[0034] comparing the bearing vibration acceleration with the dynamic vibration threshold, and calculating a third health degree based on the difference between the bearing vibration acceleration and the dynamic vibration threshold;

[0035] Comparing the magnetic flux density value with the dynamic vibration magnetic flux, and calculating a fourth health level based on a difference between the magnetic flux density value and the dynamic magnetic flux threshold;

[0036] The life prediction value of the variable frequency energy-saving motor is determined based on the first health degree, the second health degree, the third health degree and the fourth health degree in combination with a preset health weight coefficient.

[0037] As a preferred solution, it also includes:

[0038] When the life prediction value of the variable frequency energy-saving motor is not lower than a preset value, the current motor operating parameters of the variable frequency energy-saving motor are recorded in real time, and the corresponding life prediction value is recorded in the equipment health file.

[0039] Accordingly, the present invention also provides a real-time monitoring and early warning system for a variable frequency energy-saving motor, comprising:

[0040] The acquisition module is used to collect motor operating parameters in real time through a multi-source sensor group configured on the variable frequency energy-saving motor;

[0041] A load module, configured to determine the current motor operating condition based on the motor operating parameters, and determine the motor real-time load rate based on a preset load operating condition association model;

[0042] The threshold module is used to obtain the historical operating data of the variable frequency energy-saving motor and generate a dynamic threshold curve based on the motor's real-time load rate and historical operating data;

[0043] a prediction module, configured to calculate the health of the variable frequency energy-saving motor according to the motor operating parameters and the dynamic threshold curve, and determine a life prediction value of the variable frequency energy-saving motor according to the health;

[0044] The control module is used to automatically trigger a frequency reduction instruction and switch to a magnetic field oriented vector control mode when the life prediction value of the variable frequency energy-saving motor is lower than a preset value, and simultaneously generate an alarm and equipment health file for the variable frequency energy-saving motor.

[0045] Correspondingly, the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the real-time monitoring and early warning method of the variable frequency energy-saving motor as described in any one of the above.

[0046] Accordingly, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the real-time monitoring and early warning method of the variable frequency energy-saving motor as described in any one of the above.

[0047] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0048] The technical solution of the present invention collects the motor operating parameters in real time through a multi-source sensor group configured on the variable frequency energy-saving motor, and then determines the real-time load rate of the motor, and generates a dynamic threshold curve through the historical operating data of the variable frequency energy-saving motor, so as to determine the current dynamic threshold of the variable frequency energy-saving motor, so as to ensure that the variable frequency energy-saving motor can evaluate and predict the current variable frequency energy-saving motor in real time, thereby improving the accuracy and efficiency of real-time monitoring and early warning. Finally, by determining the life prediction value of the variable frequency energy-saving motor, and when the life prediction value of the variable frequency energy-saving motor is lower than the preset value, the frequency reduction instruction is automatically triggered and switched to the magnetic field oriented vector control mode to ensure that the variable frequency energy-saving motor can be controlled after the early warning, ensuring the closed-loop control of the overall process of the motor, and synchronously generating the alarm and equipment health file of the variable frequency energy-saving motor to ensure its compatibility with alarms and queries on other platforms, thereby improving the overall reliability of the variable frequency energy-saving motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 : A flow chart of a real-time monitoring and early warning method for a variable frequency energy-saving motor provided by an embodiment of the present invention;

[0050] Figure 2 : A structural diagram of a real-time monitoring and early warning system for a variable frequency energy-saving motor provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0052] Example 1

[0053] Please refer to Figure 1 , a real-time monitoring and early warning method for a variable frequency energy-saving motor provided by an embodiment of the present invention, comprising the following steps S101-S105:

[0054] S101: The motor operating parameters are collected in real time by configuring a multi-source sensor group on the variable frequency energy-saving motor.

[0055] As a preferred solution of this embodiment, the multi-source sensor group configured on the variable frequency energy-saving motor collects the motor operating parameters in real time, specifically including:

[0056] The stator winding temperature is collected by a first temperature sensor provided inside the stator winding of the variable frequency energy-saving motor;

[0057] The three-phase current of the variable frequency energy-saving motor is collected by a current sensor provided on the variable frequency energy-saving motor, and the harmonic distortion rate is calculated based on the three-phase current;

[0058] The vibration acceleration of the bearing of the variable frequency energy-saving motor is collected by a vibration acceleration sensor provided on the bearing of the variable frequency energy-saving motor;

[0059] The magnetic flux density inside the variable frequency energy-saving motor is collected by a magnetic flux sensor provided on the variable frequency energy-saving motor;

[0060] The temperature of the cooling medium is measured by a second temperature sensor provided at the inlet and outlet of the cooling system in the variable frequency energy-saving motor, and the temperature difference of the cooling medium when passing through the inlet and outlet is calculated;

[0061] Among them, the multi-source sensor group includes: a first temperature sensor, a second temperature sensor, a current sensor, a vibration acceleration sensor and a magnetic flux sensor, and the motor operating parameters include: stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value and cooling system inlet and outlet temperature difference.

[0062] In this embodiment, a multi-source sensor group comprehensively collects multiple operating parameters of the motor, including stator winding temperature, three-phase current harmonic distortion, bearing vibration acceleration, magnetic flux density, and the cooling system inlet and outlet temperature difference. Specifically, a first temperature sensor, such as a PT100 platinum resistance temperature sensor, can collect stator winding temperature data in real time, convert the collected temperature signal into an electrical signal, and transmit it to the monitoring system. Current sensors, including but not limited to Hall effect current sensors, are installed on the three-phase power lines of the variable frequency energy-saving motor to collect three-phase current data in real time, convert the collected three-phase current signals into digital signals, and calculate the harmonic distortion of the three-phase current using algorithms such as Fourier transform. A vibration acceleration sensor, such as a piezoelectric acceleration sensor, is installed on the bearing seat of the variable frequency energy-saving motor to collect bearing vibration acceleration data in real time, convert the vibration acceleration signal into a digital signal, and perform spectrum analysis. A magnetic flux sensor, such as a Hall effect sensor, is installed near the stator core or air gap of the variable frequency energy-saving motor to collect the magnetic flux density inside the motor in real time, convert the magnetic flux density signal into a digital signal, and transmit it to the monitoring system. The second temperature sensor, which can be a thermal resistance temperature sensor, is installed at the inlet and outlet of the cooling system respectively to measure the inlet and outlet temperatures of the cooling medium in real time and calculate the temperature difference between the inlet and outlet of the cooling medium.

[0063] In this embodiment, through the multi-source sensor monitoring solution, the operating status of the variable frequency energy-saving motor can be comprehensively and real-time monitored and evaluated, providing a strong guarantee for the safe and efficient operation of the motor. In addition, the real-time monitoring data can promptly detect abnormal changes in the operation of the motor, such as abnormal temperature increase, increased vibration acceleration, increased harmonic distortion rate, etc., thereby realizing early warning of faults.

[0064] S102: Determine the current motor operating condition based on the motor operating parameters, and determine the real-time motor load rate based on a preset load condition association model.

[0065] As a preferred solution, the current motor operating condition is determined according to the motor operating parameters, and the real-time motor load rate is determined based on a preset load condition association model, specifically including:

[0066] Comprehensively analyzing the stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value, and cooling system inlet and outlet temperature difference, and determining the current motor operating condition based on a preset motor operating state model; wherein the current motor operating condition includes: normal operating condition, abnormal operating condition, special operating condition, fault operating condition, and protection operating condition, and the motor operating state model is trained using historical motor operating parameters combined with historical motor operating conditions;

[0067] The current motor operating condition, whether in normal operating condition, abnormal operating condition, special operating condition, fault operating condition or protection operating condition, is input into a preset load operating condition association model to determine the real-time load rate of the motor.

[0068] In this embodiment, a comprehensive analysis of collected multi-source data, including stator winding temperature, three-phase current harmonic distortion, bearing vibration acceleration, magnetic flux density, and the cooling system inlet and outlet temperature difference, is performed. These data are then input into a motor operating state model to determine the current motor operating condition, including normal, abnormal, special, fault, and protection conditions. The determined current motor operating condition is then simultaneously input into a pre-set load condition association model. Through model calculation, the real-time motor load factor is determined, providing a basis for optimizing motor operation. Furthermore, a motor operating state model is constructed through training using historical motor operating parameters (such as current, temperature, and vibration) combined with historical motor operating conditions, such as normal, abnormal, special, fault, and protection conditions.

[0069] In this embodiment, the motor operating state model can be constructed by fitting historical motor operating parameters in combination with historical motor operating conditions, and the load condition association model is also obtained by fitting corresponding load data under each condition.

[0070] In this embodiment, the calculation formula of the stator winding temperature is:

[0071] ;

[0072] in, is the stator winding temperature, is the ambient temperature, is the effective value of the stator current, is the stator resistance, and are core loss and mechanical loss respectively, is the product of the heat dissipation coefficient and the surface area. When the load increases, the current The temperature rise of the winding will be significant. If the efficiency of the cooling system remains unchanged, the temperature rise is approximately in positive phase with the square of the load current.

[0073] The calculation formula for the three-phase current harmonic distortion (THDi) is:

[0074] ;

[0075] in, THDi is the effective value of the nth harmonic current. Under high load, the internal magnetic field distortion of the motor increases, resulting in increased harmonic current (such as the 5th and 7th harmonics). A THDi exceeding 5% may indicate abnormal load or nonlinear effects of the inverter.

[0076] The calculation formula for bearing vibration acceleration is:

[0077] ;

[0078] in, is the load torque, is the rotation speed, Bearing wear. When the load increases, the radial and axial forces on the bearing increase, and the vibration amplitude increases. Spectrum analysis is also necessary. Low-frequency vibration (<1kHz) may be caused by load fluctuations or mechanical imbalance, while high-frequency vibration (>10kHz) may be caused by local bearing damage.

[0079] The calculation formula for magnetic flux density is:

[0080] ;

[0081] in, is the number of winding turns, is the magnetic path length, = is the magnetic permeability correction factor. When the load increases, the stator current increases, causing the magnetic flux density to rise. If the magnetic flux density deviates from the rated value (for example, ±0.5mT), it may indicate an overload or a short circuit between winding turns.

[0082] The calculation formula for the inlet and outlet temperature difference of the cooling system is:

[0083] ;

[0084] in, is the total power loss, is the specific heat capacity of the cooling medium, is the mass flow rate of the cooling medium. When the load increases, the motor power loss Increase, if cooling flow Constant, temperature difference A significant increase in temperature. Under typical operating conditions, a temperature difference of >5°C may indicate overload.

[0085] As a preferred solution, the method for constructing the preset load condition association model includes:

[0086] Collect historical load-related time series data and its corresponding operating parameters;

[0087] Calculate the rolling mean and standard deviation of the historical load-related time series data within a sliding window, and determine the operating condition correlation characteristics of the historical load-related time series data and its corresponding operating condition parameters in combination with the lag term of the historical load;

[0088] An initial LSTM model is constructed, and an adaptive learning rate and regularization are determined. The initial LSTM model is trained using the working condition association features until a preset number of iterations and a preset termination condition are reached, thereby generating a preset load working condition association model.

[0089] In this embodiment, multi-source heterogeneous data is first integrated to collect load-related time series data (such as current, vibration, and temperature) and operating parameters (parameters corresponding to normal operating conditions, abnormal operating conditions, special operating conditions, fault conditions, and protection operating conditions, respectively). The exponential moving average (EMA) is used to eliminate noise interference, and variational mode decomposition (VMD) is used to separate high-frequency noise and low-frequency trend components in the signal. Then, the time series features are calculated, the mean and variance within the sliding window are determined, and the lag term of the historical load is introduced to capture the time series dependency. The cross term between the environmental parameters and the load is constructed, and the operating condition correlation characteristics of the historical load-related time series data and its corresponding operating condition parameters are determined, thereby quantifying the dynamic impact of external conditions on the load.

[0090] In this embodiment, a bidirectional LSTM layer (BiLSTM) is used to simultaneously capture forward and backward time series features. It is combined with a convolutional block attention module (CBAM) to highlight key operating parameters. A shared LSTM layer is set up to extract common time series features. A fully connected layer outputs load values (regression tasks) and operating state labels, respectively. Task weights are automatically assigned based on training loss to train and optimize the model, determining adaptive learning rates and regularization. The AdamW optimizer (with weight decay) and L2 regularization combined with a Dropout layer can be used to prevent overfitting. Furthermore, the initial LSTM model is trained using operating condition-related features.

[0091] Furthermore, the initial LSTM model is trained using the working condition association features until a preset number of iterations and a preset termination condition are reached, and a preset load working condition association model is generated; wherein the preset termination condition includes: the training is terminated if the validation set loss does not decrease for n consecutive rounds, and n can be set to 10.

[0092] S103: Obtain historical operating data of the variable frequency energy-saving motor, and generate a dynamic threshold curve based on the historical operating data.

[0093] As a preferred solution, the method of obtaining historical operating data of the variable frequency energy-saving motor and generating a dynamic threshold curve based on the historical operating data specifically includes:

[0094] Acquire historical operating data of the variable frequency energy-saving motor; wherein the historical operating data includes historical load rate, temperature parameter time series data, vibration parameter time series data, current parameter time series data and flux parameter time series data;

[0095] dividing the temperature parameter time series data, the vibration parameter time series data, the current parameter time series data, and the flux parameter time series data according to the interval of the historical load rate;

[0096] Construct a historical load rate sequence based on the historical load rate, and obtain the historical temperature threshold, historical vibration threshold, historical harmonic threshold, and historical flux threshold;

[0097] A dynamic threshold curve is generated based on the historical temperature threshold, historical vibration threshold, historical harmonic threshold and historical flux threshold, combined with the historical load rate sequence, and the divided temperature parameter time series data, vibration parameter time series data, current parameter time series data and flux parameter time series data.

[0098] In this embodiment, the historical operating data of the variable frequency energy-saving motor is collected, including the historical load rate, temperature parameter time series data, vibration parameter time series data, current parameter time series data and flux parameter time series data, and the temperature parameter time series data, vibration parameter time series data, current parameter time series data and flux parameter time series data are divided according to the interval of the historical load rate. Thus, a historical load rate sequence is constructed based on the historical load rate data, and the historical temperature threshold, historical vibration threshold, historical harmonic threshold and historical flux threshold are obtained. Combined with the historical load rate sequence and the divided parameter time series data, a dynamic threshold curve is generated using the historical threshold. The dynamic threshold curve can dynamically adjust the threshold according to different load rate intervals, thereby more accurately monitoring the operating status of the motor.

[0099] In this embodiment, the dynamic threshold curve can dynamically adjust the threshold according to the actual operating load rate of the motor, avoiding misjudgment or missed judgment due to a fixed threshold, thereby more accurately monitoring the operating status of the motor, and being able to promptly detect abnormal changes in the operation of the motor, and provide early warning of potential faults, thereby reducing equipment damage and downtime, and realizing accurate monitoring and optimized control of the operating status of the variable frequency energy-saving motor, which has significant economic benefits and application value.

[0100] S104: Generate a dynamic threshold value according to the motor operating parameters and the dynamic threshold curve, calculate the health of the variable frequency energy-saving motor based on the dynamic threshold value, and determine the life prediction value of the variable frequency energy-saving motor according to the health.

[0101] As a preferred solution, the method of generating a dynamic threshold value according to the motor operating parameters and the dynamic threshold curve, calculating the health of the variable frequency energy-saving motor based on the dynamic threshold value, and determining a life prediction value of the variable frequency energy-saving motor according to the health value specifically includes:

[0102] Inputting the motor operating parameters into the dynamic threshold curve to generate current real-time dynamic thresholds corresponding to stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value, and cooling system inlet and outlet temperature difference; wherein the dynamic thresholds include dynamic temperature threshold, dynamic vibration threshold, dynamic harmonic threshold, and dynamic magnetic flux threshold;

[0103] Comparing the stator winding temperature and the cooling system inlet and outlet temperature difference with the dynamic temperature threshold, and calculating the first health level according to the difference between the stator winding temperature and the cooling system inlet and outlet temperature difference and the dynamic temperature threshold;

[0104] Comparing the three-phase current harmonic distortion rate with the dynamic harmonic threshold, and calculating the second health level according to the difference between the three-phase current harmonic distortion rate and the dynamic harmonic threshold;

[0105] comparing the bearing vibration acceleration with the dynamic vibration threshold, and calculating a third health degree based on the difference between the bearing vibration acceleration and the dynamic vibration threshold;

[0106] Comparing the magnetic flux density value with the dynamic vibration magnetic flux, and calculating a fourth health level based on a difference between the magnetic flux density value and the dynamic magnetic flux threshold;

[0107] The life prediction value of the variable frequency energy-saving motor is determined based on the first health degree, the second health degree, the third health degree and the fourth health degree in combination with a preset health weight coefficient.

[0108] In this embodiment, collected motor operating parameters (stator winding temperature, three-phase current harmonic distortion, bearing vibration acceleration, magnetic flux density, and cooling system inlet and outlet temperature difference) are input into a dynamic threshold curve. This dynamic threshold curve can be generated based on historical operating data and dynamically adjusts thresholds based on load factors. These thresholds include dynamic temperature thresholds, dynamic vibration thresholds, dynamic harmonic thresholds, and dynamic magnetic flux thresholds. The stator winding temperature and the cooling system inlet and outlet temperature difference are compared with the dynamic temperature thresholds, and a first health level is calculated based on the difference. The smaller the difference, the higher the health level. Similarly, the current harmonic health level (second health level), vibration health level (third health level), and magnetic flux health level (fourth health level) follow the same logic. Furthermore, based on the first, second, third, and fourth health levels, combined with a preset health weighting coefficient, a comprehensive lifespan prediction for the variable-frequency energy-saving motor is calculated. The health weighting coefficient can be adjusted based on the impact of different parameters on the motor's lifespan to more accurately reflect the motor's actual health.

[0109] In this embodiment, the calculation formula for the first health level is:

[0110] ;

[0111] The calculation formula for the second health is:

[0112] ;

[0113] The calculation formula for the third health is:

[0114] ;

[0115] The calculation formula for the fourth health is:

[0116] ;

[0117] Based on the first health degree, the second health degree, the third health degree, and the fourth health degree, combined with the preset health weight coefficient, the life prediction value of the variable frequency energy-saving motor is determined:

[0118] ;

[0119] in, 、 、 and It is a preset health weight coefficient, which is allocated according to the degree of influence of different parameters on the motor life. The health weight coefficient can be adjusted according to the actual application scenario and experience to more accurately reflect the influence of different parameters on the motor life. 、 、 and They are the first health, the second health, the third health and the fourth health.

[0120] S105: When the life prediction value of the variable frequency energy-saving motor is lower than the preset value, the frequency reduction instruction is automatically triggered and the control mode is switched to the magnetic field oriented vector control mode, and the alarm and equipment health file of the variable frequency energy-saving motor are synchronously generated.

[0121] In this embodiment, when the monitored parameters (such as temperature, current harmonic distortion rate, vibration acceleration, etc.) exceed the preset dynamic threshold, the control unit automatically triggers the frequency reduction instruction. The triggering logic of the frequency reduction instruction can be customized according to different application scenarios and motor characteristics. After the frequency reduction instruction is triggered, the control unit automatically switches to the magnetic field oriented vector control mode to optimize the operating efficiency and stability of the motor. The FOC mode (magnetic field oriented vector control) ensures that the motor can still maintain high efficiency and stability when operating at low frequency by precisely controlling the size and direction of the magnetic field. When the frequency reduction instruction is triggered or an abnormality is detected, the alarm system immediately issues an alarm and records the relevant data in the health record. The health record system regularly updates the operating status and historical data of the equipment to provide support for equipment maintenance and management.

[0122] As can be seen, real-time monitoring, intelligent control, and health management enable efficient and stable operation of variable-frequency energy-saving motors. Automatically triggering frequency reduction commands and switching to field-oriented vector control ensure optimal motor operation under varying load conditions. Furthermore, the simultaneous generation of alarms and equipment health records provides strong support for equipment maintenance and management. This solution has broad application prospects in various fields, including industry, commerce, and transportation.

[0123] As a preferred solution, it also includes:

[0124] When the life prediction value of the variable frequency energy-saving motor is not lower than a preset value, the current motor operating parameters of the variable frequency energy-saving motor are recorded in real time, and the corresponding life prediction value is recorded in the equipment health file.

[0125] In this embodiment, when the life prediction value of the variable frequency energy-saving motor is not lower than the preset value, it means that the current life prediction value of the variable frequency energy-saving motor is relatively high. It is only necessary to keep the variable frequency energy-saving motor running according to normal conditions and record the current life prediction value in the equipment health file. There is no need to issue a corresponding alarm. At the same time, the equipment health file can be recorded in the cloud server, and users can query and read it on the corresponding different device platforms, which improves the query compatibility of the variable frequency energy-saving motor and improves the overall reliability of the variable frequency energy-saving motor.

[0126] The implementation of the above embodiment has the following effects:

[0127] The technical solution of the present invention collects the motor operating parameters in real time through a multi-source sensor group configured on the variable frequency energy-saving motor, and then determines the real-time load rate of the motor, and generates a dynamic threshold curve through the historical operating data of the variable frequency energy-saving motor, so as to determine the current dynamic threshold of the variable frequency energy-saving motor, so as to ensure that the variable frequency energy-saving motor can evaluate and predict the current variable frequency energy-saving motor in real time, thereby improving the accuracy and efficiency of real-time monitoring and early warning. Finally, by determining the life prediction value of the variable frequency energy-saving motor, and when the life prediction value of the variable frequency energy-saving motor is lower than the preset value, the frequency reduction instruction is automatically triggered and switched to the magnetic field oriented vector control mode to ensure that the variable frequency energy-saving motor can be controlled after the early warning, ensuring the closed-loop control of the overall process of the motor, and synchronously generating the alarm and equipment health file of the variable frequency energy-saving motor to ensure its compatibility with alarms and queries on other platforms, thereby improving the overall reliability of the variable frequency energy-saving motor.

[0128] Example 2

[0129] See also Figure 2 , which is a real-time monitoring and early warning system for a variable frequency energy-saving motor provided by the present invention, comprising:

[0130] The acquisition module 201 is used to collect motor operating parameters in real time through a multi-source sensor group configured on the variable frequency energy-saving motor;

[0131] The load module 202 is used to determine the current motor operating condition according to the motor operating parameters and determine the real-time load rate of the motor based on a preset load operating condition association model;

[0132] The threshold module 203 is used to obtain the historical operating data of the variable frequency energy-saving motor and generate a dynamic threshold curve according to the real-time load rate of the motor and the historical operating data;

[0133] A prediction module 204 is configured to calculate the health of the variable frequency energy-saving motor based on the motor operating parameters and the dynamic threshold curve, and determine a life prediction value of the variable frequency energy-saving motor based on the health;

[0134] The control module 205 is used to automatically trigger a frequency reduction instruction and switch to a magnetic field oriented vector control mode when the life prediction value of the variable frequency energy-saving motor is lower than a preset value, and simultaneously generate an alarm and equipment health file for the variable frequency energy-saving motor.

[0135] As a preferred solution, the multi-source sensor group configured on the variable frequency energy-saving motor collects the motor operating parameters in real time, specifically including:

[0136] The stator winding temperature is collected by a first temperature sensor provided inside the stator winding of the variable frequency energy-saving motor;

[0137] The three-phase current of the variable frequency energy-saving motor is collected by a current sensor provided on the variable frequency energy-saving motor, and the harmonic distortion rate is calculated based on the three-phase current;

[0138] The vibration acceleration of the bearing of the variable frequency energy-saving motor is collected by a vibration acceleration sensor provided on the bearing of the variable frequency energy-saving motor;

[0139] The magnetic flux density inside the variable frequency energy-saving motor is collected by a magnetic flux sensor provided on the variable frequency energy-saving motor;

[0140] The temperature of the cooling medium is measured by a second temperature sensor provided at the inlet and outlet of the cooling system in the variable frequency energy-saving motor, and the temperature difference of the cooling medium when passing through the inlet and outlet is calculated;

[0141] Among them, the multi-source sensor group includes: a first temperature sensor, a second temperature sensor, a current sensor, a vibration acceleration sensor and a magnetic flux sensor, and the motor operating parameters include: stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value and cooling system inlet and outlet temperature difference.

[0142] As a preferred solution, the current motor operating condition is determined according to the motor operating parameters, and the real-time motor load rate is determined based on a preset load condition association model, specifically including:

[0143] Comprehensively analyzing the stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value, and cooling system inlet and outlet temperature difference, and determining the current motor operating condition based on a preset motor operating state model; wherein the current motor operating condition includes: normal operating condition, abnormal operating condition, special operating condition, fault operating condition, and protection operating condition, and the motor operating state model is trained using historical motor operating parameters combined with historical motor operating conditions;

[0144] The current motor operating condition, whether in normal operating condition, abnormal operating condition, special operating condition, fault operating condition or protection operating condition, is input into a preset load operating condition association model to determine the real-time load rate of the motor.

[0145] As a preferred solution, the method for constructing the preset load condition association model includes:

[0146] Collect historical load-related time series data and its corresponding operating parameters;

[0147] Calculate the rolling mean and standard deviation of the historical load-related time series data within a sliding window, and determine the operating condition correlation characteristics of the historical load-related time series data and its corresponding operating condition parameters in combination with the lag term of the historical load;

[0148] An initial LSTM model is constructed, and an adaptive learning rate and regularization are determined. The initial LSTM model is trained using the working condition association features until a preset number of iterations and a preset termination condition are reached, thereby generating a preset load working condition association model.

[0149] As a preferred solution, the method of obtaining historical operating data of the variable frequency energy-saving motor and generating a dynamic threshold curve based on the historical operating data specifically includes:

[0150] Acquire historical operating data of the variable frequency energy-saving motor; wherein the historical operating data includes historical load rate, temperature parameter time series data, vibration parameter time series data, current parameter time series data and flux parameter time series data;

[0151] dividing the temperature parameter time series data, the vibration parameter time series data, the current parameter time series data, and the flux parameter time series data according to the interval of the historical load rate;

[0152] Construct a historical load rate sequence based on the historical load rate, and obtain the historical temperature threshold, historical vibration threshold, historical harmonic threshold, and historical flux threshold;

[0153] A dynamic threshold curve is generated based on the historical temperature threshold, historical vibration threshold, historical harmonic threshold and historical flux threshold, combined with the historical load rate sequence, and the divided temperature parameter time series data, vibration parameter time series data, current parameter time series data and flux parameter time series data.

[0154] As a preferred solution, the method of generating a dynamic threshold value according to the motor operating parameters and the dynamic threshold curve, calculating the health of the variable frequency energy-saving motor based on the dynamic threshold value, and determining a life prediction value of the variable frequency energy-saving motor according to the health value specifically includes:

[0155] Inputting the motor operating parameters into the dynamic threshold curve to generate current real-time dynamic thresholds corresponding to stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value, and cooling system inlet and outlet temperature difference; wherein the dynamic thresholds include dynamic temperature threshold, dynamic vibration threshold, dynamic harmonic threshold, and dynamic magnetic flux threshold;

[0156] Comparing the stator winding temperature and the cooling system inlet and outlet temperature difference with the dynamic temperature threshold, and calculating the first health level according to the difference between the stator winding temperature and the cooling system inlet and outlet temperature difference and the dynamic temperature threshold;

[0157] Comparing the three-phase current harmonic distortion rate with the dynamic harmonic threshold, and calculating the second health level according to the difference between the three-phase current harmonic distortion rate and the dynamic harmonic threshold;

[0158] comparing the bearing vibration acceleration with the dynamic vibration threshold, and calculating a third health degree based on the difference between the bearing vibration acceleration and the dynamic vibration threshold;

[0159] Comparing the magnetic flux density value with the dynamic vibration magnetic flux, and calculating a fourth health level based on a difference between the magnetic flux density value and the dynamic magnetic flux threshold;

[0160] The life prediction value of the variable frequency energy-saving motor is determined based on the first health degree, the second health degree, the third health degree and the fourth health degree in combination with a preset health weight coefficient.

[0161] As a preferred solution, it also includes:

[0162] The recording module is used to record the current motor operating parameters of the variable frequency energy-saving motor in real time when the life prediction value of the variable frequency energy-saving motor is not lower than a preset value, and record the corresponding life prediction value in the equipment health file.

[0163] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0164] The implementation of the above embodiment has the following effects:

[0165] The technical solution of the present invention collects the motor operating parameters in real time through a multi-source sensor group configured on the variable frequency energy-saving motor, and then determines the real-time load rate of the motor, and generates a dynamic threshold curve through the historical operating data of the variable frequency energy-saving motor, so as to determine the current dynamic threshold of the variable frequency energy-saving motor, so as to ensure that the variable frequency energy-saving motor can evaluate and predict the current variable frequency energy-saving motor in real time, thereby improving the accuracy and efficiency of real-time monitoring and early warning. Finally, by determining the life prediction value of the variable frequency energy-saving motor, and when the life prediction value of the variable frequency energy-saving motor is lower than the preset value, the frequency reduction instruction is automatically triggered and switched to the magnetic field oriented vector control mode to ensure that the variable frequency energy-saving motor can be controlled after the early warning, ensuring the closed-loop control of the overall process of the motor, and synchronously generating the alarm and equipment health file of the variable frequency energy-saving motor to ensure its compatibility with alarms and queries on other platforms, thereby improving the overall reliability of the variable frequency energy-saving motor.

[0166] Example 3

[0167] Correspondingly, the present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the real-time monitoring and early warning method of the variable frequency energy-saving motor as described in any one of the above embodiments.

[0168] The terminal device of this embodiment includes: a processor, a memory, and a computer program and computer instructions stored in the memory and capable of running on the processor. When the processor executes the computer program, each step in the above embodiment 1 is implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiment, such as the prediction module 204, are implemented.

[0169] Exemplarily, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device. For example, the prediction module 204 is used to calculate the health of the variable frequency energy-saving motor based on the motor operating parameters and the dynamic threshold curve, and determine the life prediction value of the variable frequency energy-saving motor based on the health.

[0170] The terminal device may be a computing device such as a desktop computer, laptop, PDA, or cloud server. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not limit the terminal device. The terminal device may include more or fewer components than shown, or a combination of certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, and the like.

[0171] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the terminal device and connects various parts of the entire terminal device using various interfaces and lines.

[0172] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store an operating system, at least one application required for a function, etc.; the data storage area may store data created based on the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0173] If the module / unit integrated into the terminal device 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 present invention can implement all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0174] Example 4

[0175] Accordingly, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the real-time monitoring and early warning method of the variable frequency energy-saving motor as described in any one of the above embodiments.

[0176] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A real-time monitoring and early warning method for variable frequency energy-saving motors, characterized in that: include: The multi-source sensor group configured on the variable frequency energy-saving motor collects the motor operating parameters in real time; Determine the current motor operating condition based on the motor operating parameters, and determine the real-time motor load rate based on a preset load condition association model; Obtain historical operating data of variable frequency energy-saving motors and generate dynamic threshold curves based on the motor's real-time load rate and historical operating data; generating a dynamic threshold value according to the motor operating parameters and the dynamic threshold curve, calculating the health of the variable frequency energy-saving motor based on the dynamic threshold value, and determining a life prediction value of the variable frequency energy-saving motor according to the health value; When the life prediction value of the variable frequency energy-saving motor is lower than the preset value, the frequency reduction instruction is automatically triggered and the control mode is switched to the magnetic field oriented vector control mode, and the alarm and equipment health file of the variable frequency energy-saving motor are generated synchronously.

2. The real-time monitoring and early warning method for a variable frequency energy-saving motor according to claim 1, characterized in that: The multi-source sensor group configured on the variable frequency energy-saving motor collects the motor operating parameters in real time, specifically including: The stator winding temperature is collected by a first temperature sensor provided inside the stator winding of the variable frequency energy-saving motor; The three-phase current of the variable frequency energy-saving motor is collected by a current sensor provided on the variable frequency energy-saving motor, and the harmonic distortion rate is calculated based on the three-phase current; The vibration acceleration of the bearing of the variable frequency energy-saving motor is collected by a vibration acceleration sensor provided on the bearing of the variable frequency energy-saving motor; The magnetic flux density inside the variable frequency energy-saving motor is collected by a magnetic flux sensor provided on the variable frequency energy-saving motor; The temperature of the cooling medium is measured by a second temperature sensor provided at the inlet and outlet of the cooling system in the variable frequency energy-saving motor, and the temperature difference of the cooling medium when passing through the inlet and outlet is calculated; Among them, the multi-source sensor group includes: a first temperature sensor, a second temperature sensor, a current sensor, a vibration acceleration sensor and a magnetic flux sensor, and the motor operating parameters include: stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value and cooling system inlet and outlet temperature difference.

3. A real-time monitoring and early warning method for a variable frequency energy-saving motor according to claim 2, characterized in that: The method of determining the current motor operating condition according to the motor operating parameters and determining the real-time motor load rate based on a preset load operating condition association model specifically includes: Comprehensively analyzing the stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value, and cooling system inlet and outlet temperature difference, and determining the current motor operating condition based on a preset motor operating state model; wherein the current motor operating condition includes: normal operating condition, abnormal operating condition, special operating condition, fault operating condition, and protection operating condition, and the motor operating state model is trained using historical motor operating parameters combined with historical motor operating conditions; The current motor operating condition, whether in normal operating condition, abnormal operating condition, special operating condition, fault operating condition or protection operating condition, is input into a preset load operating condition association model to determine the real-time load rate of the motor.

4. A real-time monitoring and early warning method for a variable frequency energy-saving motor according to any one of claims 1 to 3, characterized in that: The method for constructing the preset load condition association model includes: Collect historical load-related time series data and its corresponding operating parameters; Calculate the rolling mean and standard deviation of the historical load-related time series data within a sliding window, and determine the operating condition correlation characteristics of the historical load-related time series data and its corresponding operating condition parameters in combination with the lag term of the historical load; An initial LSTM model is constructed, and an adaptive learning rate and regularization are determined. The initial LSTM model is trained using the working condition association features until a preset number of iterations and a preset termination condition are reached, thereby generating a preset load working condition association model.

5. The real-time monitoring and early warning method for a variable frequency energy-saving motor according to claim 1, characterized in that: The process of obtaining historical operating data of the variable frequency energy-saving motor and generating a dynamic threshold curve based on the real-time load rate of the motor and the historical operating data specifically includes: Acquire historical operating data of the variable frequency energy-saving motor; wherein the historical operating data includes historical load rate, temperature parameter time series data, vibration parameter time series data, current parameter time series data and flux parameter time series data; dividing the temperature parameter time series data, the vibration parameter time series data, the current parameter time series data, and the flux parameter time series data according to the interval of the historical load rate; Construct a historical load rate sequence based on the historical load rate, and obtain the historical temperature threshold, historical vibration threshold, historical harmonic threshold, and historical flux threshold; A dynamic threshold curve is generated based on the historical temperature threshold, historical vibration threshold, historical harmonic threshold and historical flux threshold, combined with the historical load rate sequence, and the divided temperature parameter time series data, vibration parameter time series data, current parameter time series data and flux parameter time series data.

6. A real-time monitoring and early warning method for a variable frequency energy-saving motor according to claim 5, characterized in that: Generating a dynamic threshold value according to the motor operating parameters and the dynamic threshold curve, calculating the health of the variable frequency energy-saving motor based on the dynamic threshold value, and determining a life prediction value of the variable frequency energy-saving motor according to the health value specifically includes: Inputting the motor operating parameters into the dynamic threshold curve to generate current real-time dynamic thresholds corresponding to stator winding temperature, three-phase current harmonic distortion rate, bearing vibration acceleration, magnetic flux density value, and cooling system inlet and outlet temperature difference; wherein the dynamic thresholds include dynamic temperature threshold, dynamic vibration threshold, dynamic harmonic threshold, and dynamic magnetic flux threshold; Comparing the stator winding temperature and the cooling system inlet and outlet temperature difference with the dynamic temperature threshold, and calculating the first health level according to the difference between the stator winding temperature and the cooling system inlet and outlet temperature difference and the dynamic temperature threshold; Comparing the three-phase current harmonic distortion rate with the dynamic harmonic threshold, and calculating the second health level according to the difference between the three-phase current harmonic distortion rate and the dynamic harmonic threshold; comparing the bearing vibration acceleration with the dynamic vibration threshold, and calculating a third health degree based on the difference between the bearing vibration acceleration and the dynamic vibration threshold; Comparing the magnetic flux density value with the dynamic vibration magnetic flux, and calculating a fourth health level based on a difference between the magnetic flux density value and the dynamic magnetic flux threshold; The life prediction value of the variable frequency energy-saving motor is determined based on the first health degree, the second health degree, the third health degree and the fourth health degree in combination with a preset health weight coefficient.

7. The real-time monitoring and early warning method for a variable frequency energy-saving motor according to claim 1, characterized in that: Also includes: When the life prediction value of the variable frequency energy-saving motor is not lower than a preset value, the current motor operating parameters of the variable frequency energy-saving motor are recorded in real time, and the corresponding life prediction value is recorded in the equipment health file.

8. A real-time monitoring and early warning system for variable frequency energy-saving motors, characterized in that: include: The acquisition module is used to collect motor operating parameters in real time through a multi-source sensor group configured on the variable frequency energy-saving motor; A load module, configured to determine the current motor operating condition based on the motor operating parameters, and determine the motor real-time load rate based on a preset load operating condition association model; The threshold module is used to obtain the historical operating data of the variable frequency energy-saving motor and generate a dynamic threshold curve based on the motor's real-time load rate and historical operating data; a prediction module, configured to calculate the health of the variable frequency energy-saving motor according to the motor operating parameters and the dynamic threshold curve, and determine a life prediction value of the variable frequency energy-saving motor according to the health; The control module is used to automatically trigger a frequency reduction instruction and switch to a magnetic field oriented vector control mode when the life prediction value of the variable frequency energy-saving motor is lower than a preset value, and simultaneously generate an alarm and equipment health file for the variable frequency energy-saving motor.

9. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the real-time monitoring and early warning method of the variable frequency energy-saving motor as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the real-time monitoring and early warning method for the variable frequency energy-saving motor according to any one of claims 1 to 7.

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