Current and voltage self-calibration method

Abstract

The invention discloses a current and voltage self-calibration method, and relates to the technical field of motor control, and the method comprises the steps: carrying out the compensation and correction of an acquired signal through a software algorithm, and setting a zero drift compensation value. The problem of false current signals caused by environmental interference is effectively solved, an expensive high-precision current sensor is not needed, the equipment cost and the installation and calibration cost are greatly reduced, sensor drift caused by factors such as temperature change, aging and mechanical stress can be compensated by correcting the collected signals in real time, and the accuracy of the sensor is improved. According to the real-time compensation method, the accuracy of measured data is ensured, the compensation model can accurately predict compensation parameter values according to the actual running situation of the motor and environment changes through learning and training of historical compensation data and environment parameter data, and therefore more accurate zero drift compensation can be conducted on collected current signals in the real-time compensation step.

Description

Technical Field

[0001] The invention relates to the technical field of motor control, and in particular to a current and voltage self-calibration method. Background Art

[0002] During the motor control process, environmental interference may cause the current sampling device to collect false current signals, even if there is no actual current flowing in the loop. The common method of solving this problem in the existing technology is to use high-precision current sensors, that is, to use sensors with higher resolution and better anti-interference ability to reduce the impact of environmental interference on the measurement results. Existing high-precision sensors are usually more expensive than ordinary sensors, including the cost of the equipment itself as well as the cost of installation and calibration, which may not be economical for projects with limited budgets. If the equipment is not processed during initialization, the current waveform will be distorted when the motor is controlled at a high speed, causing unstable control. In addition, high-precision sensors are more sensitive to environmental changes such as temperature and humidity, and additional environmental control measures are required to ensure the accuracy of the measurement, which may not be possible in some working environments. To this end, we propose a current and voltage self-calibration method. Summary of the invention

[0003] The purpose of the present invention is to provide a current and voltage self-calibration method.

[0004] In order to solve the problems raised in the above background technology, the present invention provides the following technical solutions: a current and voltage self-calibration method, comprising a software algorithm to compensate and correct the collected signal and set the zero drift compensation value, and the specific operation steps of the current and voltage self-calibration method are as follows:

[0005] Step 1: System initialization: ensure that the current sensor and sampling circuit are correctly installed and connected, initialize the controller hardware, start the control algorithm, set the initial parameters, and prepare for zero drift detection and compensation. Before system initialization, add a comprehensive electrical performance test of the current sensor and sampling circuit.

[0006] Step 2: Compensation value detection: when the motor is running without load, the acquisition time is extended, and the output signal of the current sensor is sampled at a fixed frequency, and the average value is processed to calculate the drift compensation value. At the same time, the compensation value detection is performed on the motor running without load at different speeds, and the average value is taken as the final zero drift compensation value as the basic data;

[0007] Step 3: Establish a compensation model, introduce a neural network algorithm, learn and train historical compensation data and environmental parameter data, establish a compensation model, and predict the compensation parameter value according to the input parameters of motor running time, ambient temperature and humidity;

[0008] Step 4: Real-time compensation: In actual operation, historical compensation data and environmental parameter data are introduced for learning and training to establish a more accurate compensation model, perform zero drift compensation on the current signal collected once, calculate the compensated current value, and use the compensated current value for control;

[0009] Step 5: Adaptive adjustment: Adopt an adaptive algorithm and dynamically adjust compensation parameters according to environmental changes to ensure the accuracy and real-time performance of zero drift compensation. Regularly evaluate and verify the performance of the adaptive adjustment algorithm.

[0010] Step 6: Monitor the motor status and monitor the motor's operating status parameters in real time. When the parameters change, adjust the zero drift compensation value accordingly according to pre-set rules.

[0011] As a further solution of the present invention: in the step one, the connection endpoints between the current sensor and the sampling circuit are checked to ensure that the connection is firm and complies with electrical specifications, and at the same time, a comprehensive pre-power-on inspection of the controller hardware is performed, including checking chip installation risks and circuit board installation risks.

[0012] As a further solution of the present invention: in the step 1, the sampling frequency in the initial parameters is set to 1000 times per second, and the initial threshold value of zero drift detection and compensation is set to 0.01A.

[0013] As a further solution of the present invention: in the step 2, when the motor is running without load, the output signal of the current sensor is continuously collected for 1 minute. Within 1 minute, one data point is collected every 10 ms, and a total of 6000 data points are collected. The 6000 collected data points are averaged, and the drift compensation value is calculated according to the following formula:

[0014]

[0015] Among them, C offset Indicates the zero drift compensation value, C i represents the i-th collected current signal value, and N represents the total number of collected data points.

[0016] As a further solution of the present invention: in the step three, a neural network algorithm is introduced to construct a compensation model, a large amount of historical compensation data and the corresponding motor operating time, ambient temperature and humidity data are collected as a training sample set, and the motor is classified and encoded according to three different working stages: starting stage, running stage and speed change stage. The three different working stages are represented by 1, 2 and 3 respectively, and the ambient temperature and humidity data are normalized and controlled to be between 0 and 1.

[0017] As a further solution of the present invention: in the step three, a neural network structure with multiple layers of neurons is constructed, and a neural network including an input layer, two groups of hidden layers and an output layer is used. The input layer has 3 neurons, which correspond to the motor running time encoding value, the normalized ambient temperature value and the humidity value respectively. The first group of hidden layers contains 10 neurons, and the activation function uses the ReLU function, whose mathematical expression is f(x)=max(0,x). The second hidden layer contains 5 neurons, and the ReLU activation function is also used. The output layer is 1 neuron, and the compensation parameter value of the output prediction is obtained. During the training process, the back propagation algorithm is used to continuously adjust the weights and biases of the neural network, and the loss function is defined as the mean square error between the predicted compensation value and the actual compensation parameter value, which is expressed as follows:

[0018]

[0019] Where L represents the damage function, C predicted,j represents the predicted compensation parameter value of the jth sample, C actual,j represents the actual compensation parameter value of the jth sample, and M represents the total number of samples.

[0020] As a further solution of the present invention: in step 4, when the motor is running, the new current signal value collected is marked as C raw , use the established compensation model to predict the current compensation parameter value and mark it as C compensation , perform zero drift compensation and calculate the compensated current value according to the following formula:

[0021] C final =C raw -C offset +C compensation

[0022] Among them, C final Indicates the current value after compensation, C offset Indicates the zero drift compensation value calculated in step 2.

[0023] As a further solution of the present invention: in step five, the compensation value is adjusted according to a preset temperature compensation coefficient, and the temperature compensation coefficient is set according to actual test experience so that the compensation value changes by 0.01A for every increase of 1°C, and the change of the compensation value includes increase and decrease.

[0024] As a further solution of the present invention: in the step five, an adaptive algorithm is used to monitor the changes in environmental parameters in real time, and the monitoring conditions are set to collect ambient temperature and humidity data every 1 second. The ambient temperature change is set to exceed 1%, and the humidity change is set to exceed 5%. When the temperature and humidity changes exceed the set values, the adaptive adjustment mechanism is triggered. According to the new environmental parameters and the current motor operating status, the compensation model is used to predict the new compensation parameter values, and the compensation parameters being used are dynamically adjusted.

[0025] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0026] 1. The present invention effectively solves the problem of false current signals caused by environmental interference through the combination of software algorithms and ordinary current sensors. It does not need to rely on expensive high-precision current sensors, greatly reducing equipment costs as well as installation and calibration costs. By correcting the collected signals in real time, it can compensate for sensor drift caused by factors such as temperature changes, aging, and mechanical stress, ensuring the accuracy of the measurement data. The compensation model can accurately predict the compensation parameter value according to the actual situation of the motor operation and environmental changes through learning and training of historical compensation data and environmental parameter data. This makes it possible to perform more accurate zero drift compensation on the collected current signal in the real-time compensation step. During the operation of the motor, its working state and environmental conditions are constantly changing. The compensation model can adapt to these changes in real time and dynamically adjust the compensation parameters.

[0027] 2. The present invention lays a foundation for the stable operation of the motor through comprehensive system initialization steps, including strict inspection of current sensors and circuits and reasonable setting of initial parameters. When the motor runs at high speed, it can effectively prevent current waveform distortion. By using compensation models and adaptive adjustment mechanisms, the compensation parameters are dynamically adjusted according to motor running time, environmental parameters, etc., thereby ensuring the accuracy of current measurement in real time. When the motor running state parameters change, the zero drift compensation value can be adjusted in time, thereby ensuring the stable operation of the entire motor control system, reducing control instability caused by current measurement errors, improving the working efficiency and reliability of the motor, and extending the service life of the motor. It is of great significance for efficient and stable operation in various motor control scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Schematic diagram of the current and voltage calibration method in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The specific embodiments of the present invention are further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0030] The present invention provides a current and voltage self-calibration method, which includes a software algorithm to compensate and correct the collected signal and set a zero drift compensation value. The specific operation steps of the current and voltage self-calibration method are as follows:

[0031] Step 1: System initialization: ensure that the current sensor and sampling circuit are correctly installed and connected, initialize the controller hardware, start the control algorithm, set the initial parameters, and prepare for zero drift detection and compensation. Before system initialization, add a comprehensive electrical performance test of the current sensor and sampling circuit.

[0032] Step 2: Compensation value detection: when the motor is running without load, the acquisition time is extended, and the output signal of the current sensor is sampled at a fixed frequency, and the average value is processed to calculate the drift compensation value. At the same time, the compensation value detection is performed on the motor running without load at different speeds, and the average value is taken as the final zero drift compensation value as the basic data;

[0033] Step 3: Establish a compensation model, introduce a neural network algorithm, learn and train historical compensation data and environmental parameter data, establish a compensation model, and predict the compensation parameter value according to the input parameters of motor running time, ambient temperature and humidity;

[0034] Step 4: Real-time compensation: In actual operation, historical compensation data and environmental parameter data are introduced for learning and training to establish a more accurate compensation model, perform zero drift compensation on the current signal collected once, calculate the compensated current value, and use the compensated current value for control;

[0035] Step 5: Adaptive adjustment: Adopt an adaptive algorithm and dynamically adjust compensation parameters according to environmental changes to ensure the accuracy and real-time performance of zero drift compensation. Regularly evaluate and verify the performance of the adaptive adjustment algorithm.

[0036] Step 6: Monitor the motor status and monitor the motor's operating status parameters in real time. When the parameters change, adjust the zero drift compensation value accordingly according to pre-set rules.

[0037] In one embodiment of the present invention: in step one, the connection endpoints between the current sensor and the sampling circuit are checked to ensure that the connection is firm and complies with electrical specifications, and a comprehensive pre-power-on check is performed on the controller hardware, including checking chip installation risks and circuit board installation risks.

[0038] In one embodiment of the present invention: in step 1, the sampling frequency in the initial parameters is set to 1000 times per second, and the initial threshold value of zero drift detection and compensation is set to 0.01A.

[0039] In one embodiment of the present invention: in step 2, when the motor is running without load, the output signal of the current sensor is continuously collected for 1 minute. Within 1 minute, one data point is collected every 10 ms, and a total of 6000 data points are collected. The 6000 collected data points are averaged, and the drift compensation value is calculated according to the following formula:

[0040]

[0041] Among them, C offset Indicates the zero drift compensation value, C i represents the i-th collected current signal value, and N represents the total number of collected data points.

[0042] In one embodiment of the present invention: in step three, a neural network algorithm is introduced to construct a compensation model, a large amount of historical compensation data and the corresponding motor operating time, ambient temperature and humidity data are collected as a training sample set, and the motor is classified and encoded according to three different working stages: starting stage, running stage and speed change stage. The three different working stages are represented by 1, 2 and 3 respectively, and the ambient temperature and humidity data are normalized and controlled to be between 0 and 1.

[0043] In one embodiment of the present invention: in step three, a neural network structure with multiple layers of neurons is constructed, and a neural network including an input layer, two groups of hidden layers and an output layer is used. The input layer has 3 neurons, which correspond to the motor running time encoding value, the normalized ambient temperature value and the humidity value respectively. The first group of hidden layers contains 10 neurons, and the activation function uses the ReLU function, whose mathematical expression is f(x)=max(0,x). The second hidden layer contains 5 neurons, and the ReLU activation function is also used. The output layer is 1 neuron, and the output predicted compensation parameter value is obtained. During the training process, the back propagation algorithm is used to continuously adjust the weights and biases of the neural network, and the loss function is defined as the mean square error between the predicted compensation value and the actual compensation parameter value, which is expressed as follows:

[0044]

[0045] Where L represents the damage function, C predicted,j represents the predicted compensation parameter value of the jth sample, C actual,j represents the actual compensation parameter value of the jth sample, and M represents the total number of samples.

[0046] In one embodiment of the present invention: in step 4, when the motor is running, the new current signal value collected is marked as C raw , use the established compensation model to predict the current compensation parameter value and mark it as C compensation , perform zero drift compensation and calculate the compensated current value according to the following formula:

[0047] C final =C raw -C offset +C compensation

[0048] Among them, C final Indicates the current value after compensation, C offset Indicates the zero drift compensation value calculated in step 2.

[0049] In one embodiment of the present invention: in step five, the compensation value is adjusted according to a preset temperature compensation coefficient, and the temperature compensation coefficient is set according to actual test experience so that the compensation value changes by 0.01A for every 1°C increase, and the compensation value change includes increase and decrease.

[0050] In one embodiment of the present invention: in step five, an adaptive algorithm is used to monitor changes in environmental parameters in real time, and the monitoring conditions are set to collect ambient temperature and humidity data every 1 second. The ambient temperature change is set to exceed 1%, and the humidity change is set to exceed 5%. When the temperature and humidity changes exceed the set values, the adaptive adjustment mechanism is triggered. According to the new environmental parameters and the current motor operating status, the compensation model is used to predict new compensation parameter values, and the compensation parameters currently being used are dynamically adjusted.

[0051] Embodiment 1

[0052] Experimental scenario: Conduct a motor current and voltage calibration experiment in a test environment of a small motor production workshop.

[0053] Experimental equipment: ordinary current sensor, independently developed controller with self-calibration function, several connecting wires and environmental control equipment for simulating different ambient temperatures and humidity.

[0054] Experimental steps:

[0055] Initialize the system according to step 1, carefully check the connection between the current sensor and the sampling circuit, make sure the connection is firm, perform a comprehensive check on the controller hardware, then start the control algorithm and set the sampling frequency to 1000 times per second, and the initial threshold of zero drift detection and compensation to 0.01A;

[0056] Let the motor run without load at different speeds. According to the method in step 2, collect the output signal of the current sensor at speeds of 1000rpm, 2000rpm and 3000rpm respectively. Within 1 minute of the motor running without load, collect a data point every 10ms and perform average processing, calculate the zero drift compensation value at different speeds, and take the average value as the basic data;

[0057] According to step 3, a compensation model is constructed, and the historical compensation data of the motor operation in the workshop and the corresponding motor operation time, ambient temperature and humidity data are collected as a training sample set. The motor working stage is encoded, and after the environmental data is normalized, a neural network including an input layer, two hidden layers and an output layer is constructed for training, and the weights and biases are adjusted to minimize the loss function.

[0058] In the actual operation of the motor, real-time compensation is performed according to step 4, current signals are collected, compensation parameter values ​​are predicted using the compensation model, and then the compensated current value is calculated according to the formula and used for motor control;

[0059] According to steps five and six, the environmental parameters and motor operating status parameters are monitored in real time. When the ambient temperature changes by more than 1% or the humidity changes by more than 5%, the adaptive adjustment mechanism is triggered to adjust the compensation parameters according to the new environmental parameters and motor operating status. At the same time, when the motor speed changes, the zero drift compensation value is also adjusted according to the pre-set rules.

[0060] Experimental results: By comparing the current measurement error before and after using the self-calibration method, it was found that the error was significantly reduced and the operating stability of the motor was significantly improved.

[0061] Embodiment 2

[0062] Experimental scenario: In a research and development laboratory, a current and voltage self-calibration experiment is performed on a small motor used to drive a precision instrument.

[0063] Experimental equipment: high-precision data acquisition equipment, customized current sensors, dedicated controllers with self-calibration algorithms, and experimental boxes with adjustable temperature and humidity.

[0064] Experimental steps:

[0065] First, the system is initialized. The connection between the current sensor and the sampling circuit is checked in strict accordance with the electrical installation specifications. After ensuring that everything is correct, the controller is started and the initial parameters are set, including setting the sampling frequency to 1000 times per second and the initial threshold of zero drift to 0.01A. At the same time, a comprehensive electrical performance test is performed on the current sensor and the sampling circuit.

[0066] The motor is placed in a test box and operated without load under different ambient temperatures (10°C, 25°C, and 40°C) and humidity (30%RH, 60%RH, and 80%RH). The current sensor output signal is collected and the zero drift compensation value is calculated according to step 2.

[0067] According to step 3, a compensation model is established, a large amount of historical compensation data under different environmental conditions and corresponding parameters such as motor running time are collected, and a neural network is constructed and trained;

[0068] During the actual operation of the motor, the current signal is collected in real time, and the compensation model is used to perform zero drift compensation and calculate the compensated current value to control the speed and torque of the motor;

[0069] Continuously monitor the environmental parameters in the experimental box and the operating status parameters of the motor. When the environmental parameters change, adaptively adjust the compensation parameters according to step five; when the speed or torque of the motor changes, adjust the zero drift compensation value according to pre-set rules.

[0070] Experimental results: Under different environmental conditions, the current control accuracy of the motor has been effectively improved, meeting the high-precision requirements of precision instruments for motor drive.

[0071] Embodiment 3

[0072] Experimental scenario: Conduct a current and voltage self-calibration experiment in the motor control link of an industrial automation production line.

[0073] Experimental equipment: industrial-grade current sensor, production line-specific controller, data transmission line and environmental monitoring equipment.

[0074] Experimental steps:

[0075] According to the electrical installation standards of the production line, the current sensor and sampling circuit are installed and connected. Then the controller hardware is initialized, the control algorithm is started, and the sampling frequency is set to 1000 times per second and the initial threshold of zero drift is set to 0.01A. At the same time, a comprehensive performance test is performed on the entire electrical system before the system is initialized.

[0076] Allow the motor to run without load at different working stages of the production line (starting, constant speed operation, acceleration stage), collect the output signals of the current sensor at different stages and process them according to step 2, and calculate the zero drift compensation value at each stage;

[0077] According to the historical operation data of the production line and the environmental parameter data, the compensation model is constructed according to step 3, the motor operation time is encoded according to the workflow of the production line, and the environmental temperature and humidity data are normalized before training the neural network;

[0078] In the actual operation of the production line, the current signal of the motor is collected in real time, and the compensation model is used to perform zero drift compensation and calculate the compensated current value, which is used to control the running state of the motor to meet the needs of the production line;

[0079] Environmental parameters are monitored in real time through environmental monitoring equipment. When the ambient temperature changes by more than 1% or the humidity changes by more than 5%, the adaptive adjustment mechanism is triggered to adjust the compensation parameters according to the new environmental parameters and the current operating status of the motor. At the same time, the zero drift compensation value is adjusted in time according to the change in motor load caused by product switching on the production line.

[0080] Experimental results: The stability and reliability of the motor in the industrial production environment were successfully improved, the failure rate of the production line caused by current measurement errors was reduced, and production efficiency was improved.

[0081] Through the three groups of examples, it can be seen that the current and voltage self-calibration method can significantly reduce the current measurement error in different scenarios, enhance the stability of motor operation, adapt to various environmental conditions, motor working stages and operating conditions, and at the same time use ordinary current sensors to reduce costs, and can also improve production efficiency and product quality, fully demonstrating its effectiveness, adaptability and economic value in various fields.

[0082] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions and variations of these embodiments are made without departing from the principles and spirit of the present invention, and still fall within the scope of protection of the present invention.

[0083] In the description of the specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0084] The above contents are merely examples and explanations of the present invention. Various modifications or additions to the specific embodiments described or replacements in similar ways by technicians in the technical field shall fall within the protection scope of the present invention as long as they do not deviate from the invention or exceed the scope defined by the claims.

[0085] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A current and voltage self-calibration method, comprising a software algorithm to compensate and correct the collected signal and set a zero drift compensation value, characterized in that: The specific operation steps of the current and voltage self-calibration method are as follows: Step 1: System initialization: ensure that the current sensor and sampling circuit are correctly installed and connected, initialize the controller hardware, start the control algorithm, set the initial parameters, and prepare for zero drift detection and compensation; Step 2: Compensation value detection: when the motor is running without load, the output signal of the current sensor is collected, averaged, and the drift compensation value is calculated; Step 3: Establish a compensation model, introduce a neural network algorithm, learn and train historical compensation data and environmental parameter data, establish a compensation model, and predict the compensation parameter value according to the input parameters of motor running time, ambient temperature and humidity; Step 4: real-time compensation. In actual operation, the current signal collected once is compensated for zero drift, and the compensated current value is calculated, and the compensated current value is used for control; Step 5: Adaptive adjustment: Adopt adaptive algorithm and dynamically adjust compensation parameters according to environmental changes to ensure the accuracy and real-time performance of zero drift compensation. Step 6: Monitor the motor status and the motor's operating status parameters in real time. When the parameters change, adjust the zero drift compensation value accordingly.

2. A current and voltage self-calibration method according to claim 1, characterized in that: In the step 1, the connection terminals between the current sensor and the sampling circuit are checked to ensure that the connection is firm and complies with electrical specifications. At the same time, a comprehensive pre-power-on check is performed on the controller hardware, including checking chip installation risks and circuit board installation risks.

3. A current and voltage self-calibration method according to claim 1, characterized in that: In the step 1, the sampling frequency in the initial parameters is set to 1000 times per second, and the initial threshold of zero drift detection and compensation is set to 0.01A.

4. A current and voltage self-calibration method according to claim 1, characterized in that: In the step 2, when the motor is running without load, the output signal of the current sensor is continuously collected for 1 minute. Within 1 minute, one data point is collected every 10 ms, and a total of 6000 data points are collected. The 6000 collected data points are averaged and the drift compensation value is calculated according to the following formula: Among them, C offset Indicates the zero drift compensation value, C i represents the i-th collected current signal value, and N represents the total number of collected data points.

5. The current and voltage self-calibration method according to claim 1, characterized in that: In the step three, a neural network algorithm is introduced to construct a compensation model, a large amount of historical compensation data and the corresponding motor operating time, ambient temperature and humidity data are collected as a training sample set, and the motor is classified and encoded according to three different working stages: the startup stage, the operation stage and the speed change stage. The three different working stages are represented by 1, 2 and 3 respectively, and the ambient temperature and humidity data are normalized to control their range between 0 and 1.

6. A current and voltage self-calibration method according to claim 1, characterized in that: In the step three, a neural network structure with multiple layers of neurons is constructed, and a neural network including an input layer, two groups of hidden layers and an output layer is used. The input layer has 3 neurons, which correspond to the motor running time encoding value, the normalized ambient temperature value and the humidity value respectively. The first group of hidden layers contains 10 neurons, and the activation function uses the ReLU function, whose mathematical expression is f(x)=max(0,x). The second hidden layer contains 5 neurons, and the ReLU activation function is also used. The output layer is 1 neuron, and the compensation parameter value of the output prediction is obtained. During the training process, the back propagation algorithm is used to continuously adjust the weights and biases of the neural network, and the loss function is defined as the mean square error between the predicted compensation value and the actual compensation parameter value, and the expression formula is as follows: Where L represents the damage function, C predicted,j represents the predicted compensation parameter value of the jth sample, C actual,j represents the actual compensation parameter value of the jth sample, and M represents the total number of samples.

7. A current and voltage self-calibration method according to claim 1, characterized in that: In step 4, when the motor is running, the new current signal value collected is marked as C raw , use the established compensation model to predict the current compensation parameter value and mark it as C compensation , perform zero drift compensation and calculate the compensated current value according to the following formula: C final =C raw -C offset +C compensation Among them, C final Indicates the current value after compensation, C offset Indicates the zero drift compensation value calculated in step 2.

8. The current and voltage self-calibration method according to claim 1, characterized in that: In step five, the compensation value is adjusted according to a preset temperature compensation coefficient. The temperature compensation coefficient is set according to actual test experience so that the compensation value changes by 0.01A for every 1°C increase. The change in the compensation value includes increase and decrease.

9. A current and voltage self-calibration method according to claim 1, characterized in that: In the step five, an adaptive algorithm is used to monitor the changes in environmental parameters in real time. The monitoring conditions are set to collect ambient temperature and humidity data every 1 second. The ambient temperature change is set to exceed 1%, and the humidity change is set to exceed 5%. When the temperature and humidity changes exceed the set values, the adaptive adjustment mechanism is triggered. According to the new environmental parameters and the current motor operating status, the compensation model is used to predict the new compensation parameter values, and the compensation parameters currently being used are dynamically adjusted.

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