Method for improving temperature sampling precision of motor at low temperature

Abstract

The invention discloses a method for improving the temperature sampling precision of a motor at a low temperature, and relates to the technical field of motor temperature measurement, and the method comprises the following steps: 1, preparing an experiment environment: recording the temperature data of a controller and the motor under different working loads and environment conditions in a normal working environment of the controller and the motor, the method has the advantages that temperature measurement of the motor is approximately replaced by data of the temperature sensor of the controller, and data fusion, algorithm adjustment and monitoring calibration operation are carried out, so that the temperature sampling precision of the motor at low temperature is improved; the method solves the problems that the precision of a calibration curve in a low-temperature area is reduced and the accuracy of a thermal model depends on the stability of an external environment, can reflect the actual temperature of the motor more accurately, ensures that a relation model of the controller and the motor temperature is kept accurate, greatly improves the reliability of a system, reduces faults caused by wrong temperature detection, and improves the reliability of the system. And a powerful guarantee is provided for stable operation of the motor.

Description

Technical Field

[0001] The present invention relates to the technical field of motor temperature measurement, and in particular to a method for improving the temperature sampling accuracy of a motor at low temperature. Background Art

[0002] In today's industrial production and technological development, motors, as an important power equipment, are widely used in various fields. Whether in manufacturing, transportation, or energy, the stable operation of motors plays a vital role in the performance and reliability of the entire system. The temperature of the motor is one of the key factors affecting its performance and life. Therefore, accurately measuring the temperature of the motor is of great significance to ensure the safe operation of the motor and optimize its performance.

[0003] The existing technology has certain technical defects. First, it is difficult to ensure the accuracy of traditional temperature sensors such as PT100 and NTC in high and low temperature zones at the same time. Either the high temperature zone is quasi-low temperature zone but inaccurate, or the low temperature zone is quasi-high temperature zone but inaccurate. Although the combination of multiple sensors can solve the problem, it is costly and the model is complex, which makes it difficult to meet actual needs. Secondly, the existing technology predicts the motor temperature through calibration curves or thermal models, which has defects such as complex models, heavy calculation burden, large initial workload and high requirements for environmental stability, which makes it difficult to meet actual needs. To this end, we propose a method to improve the temperature sampling accuracy of motors at low temperatures. Summary of the invention

[0004] The object of the present invention is to provide a method for improving the temperature sampling accuracy of a motor at low temperatures.

[0005] In order to solve the problems raised in the above background technology, the present invention provides the following technical solutions: a method for improving the temperature sampling accuracy of a motor at low temperature, the improving method specifically comprising the following steps:

[0006] Step 1. Experimental environment preparation: Under normal working environment of controller and motor, record the temperature data of controller and motor under different workload and environmental conditions. The temperature acquisition range is [-40℃, 0℃], and the acquisition frequency is once every 10 minutes. At the same time, record the environmental humidity data. The humidity range is [20%, 80%]. Record the reading of controller temperature sensor and the actual temperature of motor at the same time point to provide comprehensive data support for subsequent analysis.

[0007] Step 2: Data analysis and modeling: Use linear regression to analyze the relationship between controller temperature and motor temperature. Select 50 sets of data for modeling. Set the model error threshold to ±0.5°C. Use data not involved in modeling to verify the accuracy and robustness of the model to ensure that the model can accurately reflect the relationship between the two.

[0008] Step 3: Data collection and real-time calculation: Read the data of the controller temperature sensor in real time, once a minute. When the data of the controller temperature sensor fluctuates abnormally by more than ±5°C, start the early warning mechanism, conduct further inspection and calibration, and use the established relationship model to calculate the estimated temperature of the motor to improve the real-time and accuracy of temperature calculation.

[0009] Step 4: System integration: Integrate the above algorithm into the software of the motor control system. The software has automatic backup and recovery functions to prevent data loss and system failure, ensure real-time acquisition of controller temperature data and calculation of motor temperature, and the software response time does not exceed 1s, thereby improving the stability and reliability of the system.

[0010] Step 5. Calibration and maintenance: Regularly verify and calibrate the temperature relationship model, and perform calibration once a week. During the calibration and maintenance steps, when updating the model parameters, an adaptive algorithm is used to automatically adjust the parameters according to real-time data to improve calibration efficiency and ensure the accuracy of the model under different environmental conditions and operating conditions. If the error is found to exceed the error threshold, the model parameters are updated immediately.

[0011] As a further solution of the present invention: In step 2, the linear regression model used is expressed as follows:

[0012] T motor =a×T ctrl +b

[0013] Where: T motor Indicates the motor temperature, T ctrl represents the controller temperature, a and b represent the coefficients determined by modeling;

[0014] By selecting 50 sets of data for modeling, the diversity and complexity of the data can be fully considered. During the modeling process, the least squares optimization algorithm is used to determine the coefficients a and b, so that the model accurately fits the relationship between the controller temperature and the motor temperature, thereby improving the temperature sampling accuracy.

[0015] As a further solution of the present invention: in the step 2, an early warning mechanism is introduced. When the data of the controller temperature sensor fluctuates abnormally by more than ±5°C, the early warning mechanism is activated to perform further inspection and calibration. At the same time, the motor estimated temperature is calculated in real time. The specific motor estimated temperature calculation formula is as follows:

[0016] T motor·est =α×T ctrl +(1-α)×T last +β×(T ctrl -T last ) 2

[0017] Where: Tmotor·est Indicates the estimated motor temperature, T last represents the estimated motor temperature calculated last time, α and β represent weight coefficients determined according to experimental data, 50 sets of data are selected for verification and adjustment, the influence of the current controller temperature and the historical estimated temperature on the estimated motor temperature is balanced, and a high-speed digital signal processor (DSP) is used to improve the calculation efficiency and the accuracy of the results, quickly process the processor data, and calculate the estimated motor temperature in real time.

[0018] As a further solution of the present invention: in the step five, an adaptive algorithm is used to update the model parameters, and the parameters are automatically adjusted according to real-time data. The adaptive algorithm can dynamically adjust the model parameters according to the motor's operating status data and environmental condition data, so that the model always maintains a high accuracy. The Kalman filter algorithm is used to estimate and update the model parameters to improve the calibration efficiency and accuracy.

[0019] As a further solution of the present invention: in the step one, the humidity range is [20%, 80%], and the humidity data is included in the data analysis and modeling to further optimize the accuracy of the model. The humidity has a certain influence on the temperature characteristics of the motor and the controller. By recording the humidity data, the influence of environmental factors on temperature sampling is fully understood. In the process of data analysis and modeling, the multivariate regression analysis method is adopted, and the humidity data is used as an independent variable to model together with the controller temperature and the motor temperature to improve the accuracy and adaptability of the model.

[0020] As a further solution of the present invention: in the step four, the software has data encryption and permission management functions to ensure the security and confidentiality of the data. The data encryption adopts a symmetric encryption algorithm to encrypt the sensitive information of the sensor data and model parameters to prevent the data from being illegally stolen and tampered with. The permission management function sets different permission levels according to the user's role and responsibilities to ensure that only authorized users can access and operate related data and functions, thereby improving the security and reliability of the system.

[0021] As a further solution of the present invention: in the step five, a remote monitoring and diagnostic system is established, which can monitor the operating status of the temperature relationship model in real time and perform remote calibration and maintenance. The remote monitoring and diagnostic systems are connected through a network, and the temperature data of the motor and controller and the information of the model parameters are transmitted to the remote server in real time, which is convenient for the management personnel to perform real-time monitoring and analysis. When model parameter deviations and system failures are found, the management personnel can perform remote calibration and maintenance through the remote server, solve the problems in time, and improve the reliability and stability of the system.

[0022] As a further solution of the present invention: in the step 2, a machine learning algorithm is used to analyze and model the data to improve the accuracy and adaptability of the model. The machine learning algorithm can automatically learn the complex relationship between the controller temperature and the motor temperature from the data without manually designing the model. A neural network learning algorithm can be used to train and optimize a large amount of data to establish a more accurate and adaptable model.

[0023] As a further solution of the present invention: In the step three, a high-precision sensor is used, and the accuracy of the sensor reaches ±0.1°C to improve the accuracy of temperature acquisition. The high-precision sensor can accurately measure the temperature of the controller and the motor and reduce the temperature measurement error. At the same time, in order to ensure the accuracy and stability of the sensor, the sensor is regularly calibrated and maintained to promptly detect and resolve sensor failures.

[0024] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0025] 1. The present invention uses controller temperature sensor data to approximately replace motor temperature measurement, and performs data fusion, algorithm adjustment and monitoring calibration operations, thereby improving the accuracy of temperature sampling of the motor at low temperatures, solving the problem that the accuracy of the calibration curve decreases in the low temperature area and the accuracy of the thermal model depends on the stability of the external environment, and can more accurately reflect the actual temperature of the motor. At the same time, the number of sensors is reduced, which reduces the potential failure points in the system, and by regularly monitoring and calibrating the temperature sensor, it is ensured that the relationship model between the controller and the motor temperature remains accurate, which greatly improves the reliability of the system, reduces the occurrence of failures caused by temperature misdetection, and provides a strong guarantee for the stable operation of the motor;

[0026] 2. The present invention estimates the motor temperature by adopting linear regression analysis and using a specific calculation formula, while introducing a weight coefficient and making adjustments. By selecting 50 groups of data for verification and adjusting the weight coefficient, the present invention can better balance the influence of the current controller temperature and the historical estimated temperature on the motor estimated temperature. In addition, a high-speed digital signal processor (DSP) is used to improve the calculation efficiency and the accuracy of the results, quickly process the processor data, and calculate the motor estimated temperature in real time, which can accurately reflect the relationship between the controller temperature and the motor temperature, reduce the error of temperature estimation, and improve the system's responsiveness to environmental changes, thereby greatly improving the accuracy of temperature estimation; BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the method steps in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The specific embodiments of the present invention will be 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.

[0029] 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] Embodiment 1

[0031] In today's industrial production and technological development, motors, as an important power equipment, are widely used in various fields. Whether in manufacturing, transportation, or energy, the stable operation of motors plays a vital role in the performance and reliability of the entire system. The temperature of the motor is one of the key factors affecting its performance and life. Therefore, accurately measuring the temperature of the motor is of great significance to ensure the safe operation of the motor and optimize its performance.

[0032] The existing technology has certain technical defects. First, it is difficult to ensure the accuracy of traditional temperature sensors such as PT100 and NTC in high and low temperature zones at the same time. Either the high temperature zone is inaccurate in the low temperature zone, or the low temperature zone is inaccurate in the high temperature zone. Although the combination of multiple sensors can solve the problem, the cost is high and the model is complex, which is difficult to meet actual needs. Secondly, the existing technology predicts the motor temperature through calibration curves or thermal models, which has defects such as complex models, heavy calculation burden, large initial workload, and high requirements for environmental stability, which is difficult to meet actual needs. Therefore, we propose a method for improving the temperature sampling accuracy of motors at low temperatures;

[0033] Therefore, in order to effectively solve the above problems, the present application proposes a method for improving the temperature sampling accuracy of a motor at low temperature, as shown in the accompanying drawings of the specification. Figure 1 As shown, the following steps are included:

[0034] Step 1. Experimental environment preparation: Under normal working environment of controller and motor, record the temperature data of controller and motor under different workload and environmental conditions. The temperature acquisition range is [-40℃, 0℃], and the acquisition frequency is once every 10 minutes. At the same time, record the environmental humidity data. The humidity range is [20%, 80%]. Record the reading of controller temperature sensor and the actual temperature of motor at the same time point to provide comprehensive data support for subsequent analysis.

[0035] Step 2: Data analysis and modeling: Use linear regression to analyze the relationship between controller temperature and motor temperature. Select 50 sets of data for modeling. Set the model error threshold to ±0.5°C. Use data not involved in modeling to verify the accuracy and robustness of the model to ensure that the model can accurately reflect the relationship between the two.

[0036] Step 3: Data collection and real-time calculation: Read the data of the controller temperature sensor in real time, once a minute. When the data of the controller temperature sensor fluctuates abnormally by more than ±5°C, start the early warning mechanism, conduct further inspection and calibration, and use the established relationship model to calculate the estimated temperature of the motor to improve the real-time and accuracy of temperature calculation.

[0037] Step 4: System integration: Integrate the above algorithm into the software of the motor control system. The software has automatic backup and recovery functions to prevent data loss and system failure, ensure real-time acquisition of controller temperature data and calculation of motor temperature, and the software response time does not exceed 1s, thereby improving the stability and reliability of the system.

[0038] Step 5, Calibration and maintenance: Regularly verify and calibrate the temperature relationship model, and calibrate it once a week. In the calibration and maintenance steps, when updating the model parameters, an adaptive algorithm is used to automatically adjust the parameters according to real-time data to improve the calibration efficiency and ensure the accuracy of the model under different environmental conditions and operating conditions. If the error is found to exceed the error threshold, the model parameters are updated immediately;

[0039] Specific workflow: In the experimental environment preparation, record the temperature and humidity data of the controller and motor under different workloads and environmental conditions. The temperature acquisition range is [-40℃, 0℃], the acquisition frequency is once every 10 minutes, and the humidity range is [20%, 80%]. At the same time, record the reading of the controller temperature sensor and the actual temperature of the motor. During data analysis and modeling, use linear regression to analyze the relationship between the controller temperature and the motor temperature. Select 50 sets of data for modeling. The error threshold is ±0.5℃, and use data not involved in modeling to verify the accuracy and robustness of the model. During data acquisition and real-time calculation, read the data of the controller temperature sensor in real time once a minute. When the abnormal fluctuation exceeds ±5℃, start the early warning mechanism, and use the model to calculate the estimated temperature of the motor. During system integration, integrate the algorithm into the motor control system software. The software has automatic backup and recovery functions, and the response time does not exceed 1s. During calibration and maintenance, regularly verify and calibrate the temperature relationship model once a week. Use an adaptive algorithm to automatically adjust the parameters according to real-time data to ensure the accuracy of the model. Update the parameters when the error exceeds the threshold.

[0040] Furthermore, by using the controller temperature sensor data to approximately replace the motor temperature measurement, and performing data fusion, algorithm adjustment and monitoring calibration operations, the accuracy of the motor temperature sampling at low temperatures is improved, and the problems of the calibration curve accuracy decreasing in the low temperature area and the thermal model accuracy relying on the stability of the external environment are solved. It can more accurately reflect the actual temperature of the motor and, at the same time, reduce the number of sensors, which reduces the potential failure points in the system. By regularly monitoring and calibrating the temperature sensor, the relationship model between the controller and the motor temperature is ensured to remain accurate, which greatly improves the reliability of the system, reduces the occurrence of failures caused by temperature misdetection, and provides a strong guarantee for the stable operation of the motor.

[0041] Embodiment 2:

[0042] Based on the first embodiment, as shown in the accompanying drawings of the specification Figure 1 As shown, in step 2, the linear regression model used is expressed as follows:

[0043] T motor =a×T ctrl +b

[0044] Where: T motor Indicates the motor temperature, T ctrl represents the controller temperature, a and b represent the coefficients determined by modeling;

[0045] By selecting 50 sets of data for modeling, the diversity and complexity of the data can be fully considered. In the modeling process, the least squares optimization algorithm is used to determine the coefficients a and b, so that the model accurately fits the relationship between the controller temperature and the motor temperature, and improves the temperature sampling accuracy. In step 2, an early warning mechanism is introduced. When the data of the controller temperature sensor fluctuates abnormally by more than ±5°C, the early warning mechanism is activated for further inspection and calibration. At the same time, the estimated motor temperature is calculated in real time. The specific motor estimated temperature calculation formula is as follows:

[0046] T motor·est =α×T ctrl +(1-α)×T last +β×(T ctrl -T last ) 2

[0047] Where: T motor·est Indicates the estimated motor temperature, T lastrepresents the estimated motor temperature calculated last time, α and β represent weight coefficients determined according to experimental data, 50 groups of data are selected for verification and adjustment, the influence of the current controller temperature and the historical estimated temperature on the estimated motor temperature is balanced, and a high-speed digital signal processor (DSP) is used to improve the calculation efficiency and the accuracy of the results, the processor data is quickly processed, and the estimated motor temperature is calculated in real time. In step five, an adaptive algorithm is used to update the model parameters, and the parameters are automatically adjusted according to real-time data. The adaptive algorithm can dynamically adjust the model parameters according to the motor's operating status data and environmental condition data, so that the model always maintains a high accuracy. The Kalman filter algorithm is used to estimate and update the model parameters to improve the calibration efficiency and accuracy. In step one, the humidity range is [20%, 80%], and the humidity data is included in the data analysis and modeling to further optimize the accuracy of the model. Humidity has a certain influence on the temperature characteristics of the motor and the controller. By recording the humidity data, the influence of environmental factors on temperature sampling is fully understood. In the process of data analysis and modeling, a multivariate regression analysis method is used to take the humidity data as an independent variable, and the controller temperature and the motor temperature are modeled together to improve the accuracy and adaptability of the model.

[0048] Specific workflow: A linear regression model is used to represent the relationship between the motor temperature and the controller temperature. By selecting 50 sets of data to build a model and determine the coefficients, the model is accurately fitted. At the same time, an early warning mechanism is introduced. When the abnormal fluctuation of the controller temperature sensor data exceeds ±5°C, an early warning is activated. At the same time, the motor estimated temperature is calculated through a specific formula, and a high-speed digital signal processor is used to improve the calculation efficiency and accuracy. An adaptive algorithm is used to automatically adjust the model parameters according to real-time data, such as using the Kalman filter algorithm to improve calibration efficiency and accuracy. In practical applications, the formula for calculating the estimated motor temperature can quickly and accurately calculate the estimated temperature of the motor based on the real-time collected controller temperature and the historical estimated temperature. For example, during the operation of the motor, when the controller temperature changes, the formula can adjust the estimated motor temperature in time to make it closer to the actual temperature, providing a more reliable temperature reference for the motor control system, thereby improving the operation efficiency and stability of the motor;

[0049] Furthermore, by adopting linear regression analysis and using a specific calculation formula to estimate the motor temperature, introducing weight coefficients and making adjustments, and selecting 50 sets of data for verification and adjusting the weight coefficients, it is possible to better balance the impact of the current controller temperature and the historical estimated temperature on the estimated motor temperature. In addition, a high-speed digital signal processor (DSP) is used to improve calculation efficiency and result accuracy, quickly process processor data, and calculate the estimated motor temperature in real time, which can accurately reflect the relationship between the controller temperature and the motor temperature, reduce the error of temperature estimation, and improve the system's responsiveness to environmental changes, thereby greatly improving the accuracy of temperature estimation.

[0050] Embodiment three:

[0051] Based on the second embodiment, as shown in the accompanying drawings of the specification Figure 1 As shown, in step 4, the software has data encryption and permission management functions to ensure the security and confidentiality of data. Data encryption uses a symmetric encryption algorithm to encrypt sensitive information of sensor data and model parameters to prevent data from being illegally stolen and tampered with. The permission management function sets different permission levels according to the user's role and responsibilities to ensure that only authorized users can access and operate related data and functions, thereby improving the security and reliability of the system. In step 5, a remote monitoring and diagnosis system is established to monitor the operating status of the temperature relationship model in real time, and to perform remote calibration and maintenance. The remote monitoring and diagnosis systems are connected through a network to transmit the temperature data of the motor and controller and the information of the model parameters to the remote server in real time, which is convenient for management personnel to monitor and analyze in real time. When model parameter deviations and system failures are found, the manager The staff can perform remote calibration and maintenance through the remote server, solve problems in time, and improve the reliability and stability of the system. In step 2, the machine learning algorithm is used to analyze and model the data to improve the accuracy and adaptability of the model. The machine learning algorithm can automatically learn the complex relationship between the controller temperature and the motor temperature from the data. There is no need to manually design the model. The neural network learning algorithm can be used to train and optimize a large amount of data to establish a more accurate and adaptable model. In step 3, a high-precision sensor is used with an accuracy of ±0.1°C to improve the accuracy of temperature acquisition. The high-precision sensor can accurately measure the temperature of the controller and motor and reduce temperature measurement errors. At the same time, in order to ensure the accuracy and stability of the sensor, the sensor is regularly calibrated and maintained to detect and solve sensor failures in time.

[0052] Specific workflow: The software has data encryption and permission management functions to ensure data security and confidentiality, prevent data from being illegally stolen or tampered with, establish a remote monitoring and diagnostic system, monitor the operating status of the temperature relationship model in real time, perform remote calibration and maintenance, and improve system reliability and stability. Finally, use high-precision sensors and calibrate and maintain them regularly to improve temperature acquisition accuracy and reduce measurement errors. The established remote monitoring and diagnostic system plays an important role in actual application. The system transmits the temperature data, model parameters and other information of the motor and controller to the remote server in real time through network connection. For example, in industrial production, multiple motors are distributed in different areas. Managers can use the remote monitoring system to monitor the temperature of these motors in real time in the central control room. When the temperature of a motor is found to be abnormal, the system will automatically issue an alarm and provide detailed fault information. Managers can use this information to remotely calibrate and maintain the motor through the remote server without having to visit the site in person, which greatly improves maintenance efficiency, reduces production downtime, reduces maintenance costs, and ensures the continuity and stability of production.

[0053] Furthermore, by using the controller temperature sensor data to approximately replace the motor temperature measurement, data fusion, algorithm adjustment and monitoring calibration operations are performed, which improves the temperature sampling accuracy of the motor at low temperatures and solves the problems of decreased accuracy of the calibration curve in the low temperature zone and dependence of the thermal model on the stability of the external environment in the prior art. At the same time, linear regression analysis and specific calculation formulas are used to estimate the motor temperature, and weight coefficients are introduced and 50 groups of data are selected for verification and adjustment. Combined with a high-speed digital signal processor, the calculation efficiency and accuracy are improved, the temperature estimation error is reduced, and a strong guarantee is provided for the stable operation of the motor.

[0054] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0055] Some aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". The processor may be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. In addition, various aspects of the present application may be expressed as computer products located in one or more computer-readable media, which include computer-readable program codes. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, tapes ...), optical disks (e.g., compact disks CDs, digital versatile disks DVDs ...), smart cards, and flash memory devices (e.g., cards, sticks, key drives ...).

[0056] A computer-readable medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may have a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination. A computer-readable medium may be any computer-readable medium other than a computer-readable storage medium, which may be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit a program for use. The program code on the computer-readable medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, radio frequency signal, or similar medium, or a combination of any of the above mediums.

[0057] Similarly, it should be noted that in order to simplify the description disclosed in this application, so as to help understand one or more embodiments of the invention, in the above description of the embodiments of the present application, sometimes multiple features are merged into one embodiment, drawings or descriptions thereof. However, this disclosure method does not mean that the features required by the object of the present application are more than the features mentioned in the claims. In fact, the features of the embodiment are less than all the features of the single embodiment disclosed above. In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "approximately", "approximately" or "substantially" in some examples. Unless otherwise specified, "approximately", "approximately" or "substantially" indicate that the numbers allow a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, such numerical settings are as accurate as possible within the feasible range.

[0058] Although the present invention is disclosed as above in terms of preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications without departing from the spirit and scope of the present invention. Therefore, any modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for improving the temperature sampling accuracy of a motor at low temperature, characterized in that: The lifting method specifically comprises the following steps: Step 1. Experimental environment preparation: Under normal working environment of controller and motor, record the temperature data of controller and motor under different workload and environmental conditions. The temperature acquisition range is [-40℃, 0℃], and the acquisition frequency is once every 10 minutes. At the same time, record the environmental humidity data. The humidity range is [20%, 80%]. Record the reading of controller temperature sensor and the actual temperature of motor at the same time point to provide comprehensive data support for subsequent analysis. Step 2: Data analysis and modeling: Use linear regression to analyze the relationship between controller temperature and motor temperature. Select 50 sets of data for modeling. Set the model error threshold to ±0.5°C. Use data not involved in modeling to verify the accuracy and robustness of the model to ensure that the model can accurately reflect the relationship between the two. Step 3: Data collection and real-time calculation: Read the data of the controller temperature sensor in real time, once a minute. When the data of the controller temperature sensor fluctuates abnormally by more than ±5°C, start the early warning mechanism, conduct further inspection and calibration, and use the established relationship model to calculate the estimated temperature of the motor to improve the real-time and accuracy of temperature calculation. Step 4: System integration: Integrate the above algorithm into the software of the motor control system. The software has automatic backup and recovery functions to prevent data loss and system failure, ensure real-time acquisition of controller temperature data and calculation of motor temperature, and the software response time does not exceed 1s, thereby improving the stability and reliability of the system. Step 5. Calibration and maintenance: Regularly verify and calibrate the temperature relationship model, and perform calibration once a week. During the calibration and maintenance steps, when updating the model parameters, an adaptive algorithm is used to automatically adjust the parameters according to real-time data to improve calibration efficiency and ensure the accuracy of the model under different environmental conditions and operating conditions. If the error is found to exceed the error threshold, the model parameters are updated immediately.

2. A method for improving the temperature sampling accuracy of a motor at low temperature according to claim 1, characterized in that: In step 2, the linear regression model used is expressed as follows: T motor =a×T ctrl +b Where: T motor Indicates the motor temperature, T ctrl represents the controller temperature, a and b represent the coefficients determined by modeling; By selecting 50 sets of data for modeling, the diversity and complexity of the data can be fully considered. During the modeling process, the least squares optimization algorithm is used to determine the coefficients a and b, so that the model accurately fits the relationship between the controller temperature and the motor temperature, thereby improving the temperature sampling accuracy.

3. The method for improving the temperature sampling accuracy of a motor at low temperature according to claim 1, characterized in that: In step 2, an early warning mechanism is introduced. When the data of the controller temperature sensor fluctuates abnormally by more than ±5°C, the early warning mechanism is activated for further inspection and calibration. At the same time, the motor estimated temperature is calculated in real time. The specific motor estimated temperature calculation formula is as follows: T motor·est =α×T ctrl +(1-α)×T last +β×(T ctrl -T last ) 2 Where: T motor·est Indicates the estimated motor temperature, T last represents the estimated motor temperature calculated last time, α and β represent weight coefficients determined according to experimental data, 50 sets of data are selected for verification and adjustment, the influence of the current controller temperature and the historical estimated temperature on the estimated motor temperature is balanced, and a high-speed digital signal processor (DSP) is used to improve the calculation efficiency and the accuracy of the results, quickly process the processor data, and calculate the estimated motor temperature in real time.

4. The method for improving the temperature sampling accuracy of a motor at low temperature according to claim 1, characterized in that: In step five, an adaptive algorithm is used to update the model parameters, and the parameters are automatically adjusted according to real-time data. The adaptive algorithm can dynamically adjust the model parameters according to the motor's operating status data and environmental condition data, so that the model always maintains a high accuracy. The Kalman filter algorithm is used to estimate and update the model parameters to improve calibration efficiency and accuracy.

5. The method for improving the temperature sampling accuracy of a motor at low temperature according to claim 1, characterized in that: In the step 1, the humidity range is [20%, 80%], and the humidity data is included in the data analysis and modeling to further optimize the accuracy of the model. Humidity has a certain influence on the temperature characteristics of the motor and the controller. By recording the humidity data, the influence of environmental factors on temperature sampling is fully understood. In the process of data analysis and modeling, the multivariate regression analysis method is adopted, and the humidity data is used as an independent variable to model together with the controller temperature and the motor temperature to improve the accuracy and adaptability of the model.

6. The method for improving the temperature sampling accuracy of a motor at low temperature according to claim 1, characterized in that: In step 4, the software has data encryption and permission management functions to ensure the security and confidentiality of the data. The data encryption adopts a symmetric encryption algorithm to encrypt the sensitive information of sensor data and model parameters to prevent the data from being illegally stolen and tampered with. The permission management function sets different permission levels according to the user's role and responsibilities to ensure that only authorized users can access and operate related data and functions, thereby improving the security and reliability of the system.

7. The method for improving the temperature sampling accuracy of a motor at low temperature according to claim 1, characterized in that: In the step five, a remote monitoring and diagnosis system is established, which can monitor the operating status of the temperature relationship model in real time and perform remote calibration and maintenance. The remote monitoring and diagnosis system is connected through a network, and the temperature data of the motor and controller and the information of the model parameters are transmitted to the remote server in real time, which is convenient for the management personnel to perform real-time monitoring and analysis. When model parameter deviations and system failures are found, the management personnel can perform remote calibration and maintenance through the remote server, solve the problems in time, and improve the reliability and stability of the system.

8. The method for improving the temperature sampling accuracy of a motor at low temperature according to claim 1, characterized in that: In the step 2, a machine learning algorithm is used to analyze and model the data to improve the accuracy and adaptability of the model. The machine learning algorithm can automatically learn the complex relationship between the controller temperature and the motor temperature from the data without manually designing the model. A neural network learning algorithm can be used to train and optimize a large amount of data to establish a more accurate and adaptable model.

9. The method for improving the temperature sampling accuracy of a motor at low temperature according to claim 1, characterized in that: In the step three, a high-precision sensor is used, and the accuracy of the sensor reaches ±0.1°C to improve the accuracy of temperature acquisition. The high-precision sensor can accurately measure the temperature of the controller and the motor and reduce the temperature measurement error. At the same time, in order to ensure the accuracy and stability of the sensor, the sensor is regularly calibrated and maintained to promptly detect and resolve sensor failures.

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