An intelligent brushless DC motor drive system

Abstract

The present invention provides an intelligent brushless DC motor drive system, which relates to the field of data processing and comprises: a power inverter, which is used to convert DC power into AC power to drive the brushless DC motor to work; a rotor position sensor, which is used to detect the position information of the rotor; a current sensor, which is used to detect the magnitude and direction of the current in the motor winding; a temperature monitoring module, which comprises a plurality of temperature monitoring units arranged at a plurality of different positions; a temperature control module, which comprises a plurality of temperature control units; and a controller, which is used to determine a temperature control strategy based on the temperature information collected by the plurality of temperature monitoring units at a plurality of consecutive time points, control the operation of the plurality of temperature control units, and control the operation of the power inverter based on the temperature information collected by the plurality of temperature monitoring units at a plurality of consecutive time points, the position information of the rotor of the brushless DC motor, and the magnitude and direction of the current in the motor winding of the brushless DC motor, thereby having the advantage of improving the reliability of the brushless DC motor system.

Description

Technical Field

[0001] The invention relates to the field of motor driving, and in particular to an intelligent brushless DC motor driving system. Background Art

[0002] In the field of motors, brushless DC motors are developing rapidly and are widely used. They are widely used in daily life appliances, automotive industry, aviation, consumer electronics, medical electronics, industrial automation and other devices and instruments. Brushless DC motors do not use a mechanical commutator and have many advantages over brushed motors in use, such as better torque-speed characteristics, dynamic response, high efficiency, long life, small size and high speed. In addition, the better torque and size ratio of brushless DC motors makes them more suitable for occasions that are sensitive to the weight and size of the motor itself.

[0003] When the motor runs at high speed or the ambient temperature is abnormal, it is easy to cause the power device to burn out due to excessive temperature. In order to solve the above problems, a temperature control device is mostly configured in the DC brushless motor drive system. The existing temperature control device includes a temperature detection module and a motor start / stop control module. When the temperature detection module detects that the temperature of the power device is lower than the protection temperature, the motor start / stop control module controls the motor to work; when the temperature detection module detects that the temperature of the power device is higher than the protection temperature, the motor start / stop control module controls the motor to stop working. In this way, the problem of power devices burning out due to excessive temperature is solved. However, frequent starting / stopping of the motor will shorten the service life of the motor, thereby reducing the reliability of the brushless DC motor system.

[0004] Therefore, it is necessary to provide an intelligent brushless DC motor drive system for optimizing the drive of the brushless DC motor and improving the reliability of the brushless DC motor system. Summary of the invention

[0005] The present invention provides an intelligent DC brushless motor drive system, comprising: a power inverter, used for converting DC power into AC power to drive the DC brushless motor to work; a rotor position sensor, used for detecting the position information of the rotor of the DC brushless motor; a current sensor, used for the magnitude and direction of the current in the motor winding of the DC brushless motor; a temperature monitoring module, comprising a plurality of temperature monitoring units arranged at a plurality of different positions; a temperature control module, comprising a plurality of temperature control units arranged at a plurality of different positions; a controller, used for determining a temperature control strategy based on the temperature information collected by the plurality of temperature monitoring units at a plurality of consecutive time points, and controlling the operation of the plurality of temperature control units based on the temperature control strategy, and also generating a drive control strategy based on the temperature information collected by the plurality of temperature monitoring units at a plurality of consecutive time points, the position information of the rotor of the DC brushless motor and the magnitude and direction of the current in the motor winding of the DC brushless motor, and controlling the operation of the power inverter based on the drive control strategy.

[0006] Furthermore, the temperature monitoring module sets up multiple temperature monitoring units, including: obtaining test data of sample DC brushless motors and sample power inverters under multiple test conditions; determining multiple candidate monitoring positions based on the test data of the sample DC brushless motors and sample power inverters under multiple test conditions; determining temperature correlation parameters between any two of the candidate monitoring positions based on the test data of the sample DC brushless motors and sample power inverters under multiple test conditions; determining multiple target monitoring positions from the multiple candidate monitoring positions based on the temperature correlation parameters between any two of the candidate monitoring positions; and setting multiple temperature monitoring units according to the multiple target monitoring positions.

[0007] Furthermore, the temperature monitoring module determines the temperature correlation parameters between any two of the candidate monitoring positions based on the test data of the sample brushless DC motor and the sample power inverter under multiple test conditions, including: for each test condition, based on the temperature mean values ​​and time period temperature fluctuation parameters of any two of the candidate monitoring positions in multiple test time periods, determining the single-point temperature correlation parameters of any two of the candidate monitoring positions corresponding to the test condition; based on the single-point temperature correlation parameters of any two of the candidate monitoring positions corresponding to each of the test conditions, determining the temperature correlation parameters between any two of the candidate monitoring positions.

[0008] Furthermore, the temperature control unit includes a plurality of Peltier elements, the positions of the plurality of temperature control units are determined based on the plurality of target monitoring positions, and the number of Peltier elements included in the temperature control unit is determined based on test data of the sample DC brushless motor and the sample power inverter under a variety of test conditions; the temperature control module also includes a fan arranged at a plurality of different positions, and the position of the fan is determined based on test data of the sample DC brushless motor and the sample power inverter under a variety of test conditions.

[0009] Furthermore, the temperature control module determines the number of Peltier elements included in the temperature control unit based on the test data of the sample brushless DC motor and the sample power inverter under multiple test conditions, including: for each temperature control unit, based on the temperature mean of multiple test time periods and the time period temperature fluctuation parameters of the target monitoring position where the temperature control unit is located under each test condition, the number of Peltier elements included in the temperature control unit is determined.

[0010] Furthermore, the temperature control module determines the position of the fan based on the test data of the sample brushless DC motor and the sample power inverter under multiple test conditions, including: clustering the multiple target monitoring positions through a clustering algorithm to determine multiple candidate ventilation areas; for each of the candidate ventilation areas, determining the heat dissipation requirement parameters of the candidate ventilation area; and determining the position of the fan based on the heat dissipation requirement parameters of each of the candidate ventilation areas.

[0011] Furthermore, the temperature control module determines the heat dissipation requirement parameters of the candidate ventilation area, including: determining the heat dissipation requirement parameters of the candidate ventilation area based on the temperature mean and time period temperature fluctuation parameters of multiple test time periods under each test condition for each target monitoring position included in the candidate ventilation area.

[0012] Further, the controller determines a temperature control strategy based on the temperature information collected by the multiple temperature monitoring units at multiple consecutive time points, including: for each of the temperature monitoring units, determining an information reliability parameter based on the temperature correlation parameter between any two of the target monitoring positions and the temperature information collected by the temperature monitoring unit at multiple consecutive time points; taking a temperature monitoring unit whose corresponding information reliability parameter is greater than a preset reliability parameter threshold as a target temperature monitoring unit; based on the temperature correlation parameter between any two of the target monitoring positions, determining a data correlation parameter between any two of the target temperature monitoring units; for each of the target temperature monitoring units, determining a related temperature monitoring unit of the target temperature monitoring unit based on the data correlation parameter between any two of the target temperature monitoring units; based on the temperature information collected by the related temperature monitoring unit at multiple consecutive time points, correcting the temperature information collected by the target temperature monitoring unit at multiple consecutive time points; and determining the temperature control strategy based on the corrected temperature information collected by the multiple target temperature monitoring units at multiple consecutive time points.

[0013] Further, the controller determines the information reliability parameter based on the temperature correlation parameter between any two of the target monitoring positions and the temperature information collected by the temperature monitoring unit at multiple consecutive time points, including: for each of the temperature monitoring units, determining the associated temperature monitoring unit of the temperature monitoring unit based on the temperature correlation parameter between any two of the target monitoring positions; for each associated temperature monitoring unit of the temperature monitoring unit, calculating the information association parameter between the temperature monitoring unit and the associated temperature monitoring unit based on the temperature information collected by the temperature monitoring unit at multiple consecutive time points and the temperature information collected by the associated temperature monitoring unit at multiple consecutive time points; determining the information reliability parameter based on the temperature correlation parameter between any two of the target monitoring positions and the information association parameter between the temperature monitoring unit and each of the associated temperature monitoring units.

[0014] Furthermore, the controller corrects the temperature information collected by the target temperature monitoring unit at multiple continuous time points based on the temperature information collected by the relevant temperature monitoring units at multiple continuous time points, including: performing empirical mode decomposition on the temperature information collected by the target temperature monitoring unit at multiple continuous time points to generate multiple first temperature connotation modal components and first temperature residuals; performing empirical mode decomposition on the temperature information collected by each of the relevant temperature monitoring units at multiple continuous time points to generate multiple second temperature connotation modal components and second temperature residuals; and correcting the multiple first temperature connotation modal components and first temperature residuals corresponding to the target temperature monitoring unit based on the multiple second temperature connotation modal components and second temperature residuals corresponding to each of the relevant temperature monitoring units through a multimodal joint denoising model.

[0015] Compared with the prior art, the intelligent brushless DC motor drive system provided by the present invention has at least the following beneficial effects:

[0016] By setting up a temperature monitoring module and a temperature control module, the temperature monitoring and automatic control of the brushless DC motor and the power inverter are realized, and then based on the temperature information collected by multiple temperature monitoring units at multiple consecutive time points, the position information of the rotor of the brushless DC motor and the current size and direction in the motor winding of the brushless DC motor, a drive control strategy is generated, which realizes the optimization of the drive of the brushless DC motor in combination with the temperature information and improves the reliability of the brushless DC motor system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0018] Figure 1 is a module schematic diagram of an intelligent brushless DC motor drive system according to some embodiments of this specification;

[0019] Figure 2 It is a schematic diagram of a process for determining a temperature control strategy according to some embodiments of this specification. DETAILED DESCRIPTION

[0020] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0021] Figure 1 is a module schematic diagram of an intelligent brushless DC motor drive system according to some embodiments of this specification, such as Figure 1 As shown, an intelligent brushless DC motor drive system includes a power inverter, a rotor position sensor, a current sensor, a temperature monitoring module, a temperature control module and a controller.

[0022] A power inverter can be used to convert DC power into AC power to drive a brushless DC motor.

[0023] Specifically, the power inverter adopts a bridge circuit structure, such as a three-phase bridge or a three-phase half-bridge. The switching element (such as MOSFET or IGBT) in the bridge circuit switches the current path according to the control signal to realize the power on and off of the motor winding of the brushless DC motor.

[0024] The rotor position sensor can be used to detect the position information of the rotor of the brushless DC motor.

[0025] As an example only, the rotor position sensor may be a Hall sensor, a photoelectric encoder, a magnetic encoder, or the like.

[0026] Current sensors can be used to measure the magnitude and direction of the current in the motor windings of a brushless DC motor.

[0027] An intelligent brushless DC motor drive system may also include a driving power supply for providing a stable DC power supply for the entire system. The driving power supply may be a battery pack, a power adapter, or a generator.

[0028] The temperature monitoring module may include a plurality of temperature monitoring units disposed at a plurality of different locations.

[0029] In some embodiments, the temperature monitoring module is provided with a plurality of temperature monitoring units, including:

[0030] Obtain test data of a sample brushless DC motor and a sample power inverter under various test conditions, wherein the test conditions may include test environment temperature, test environment humidity, test environment air pressure and load, etc.;

[0031] determining a plurality of candidate monitoring locations based on test data of a sample brushless DC motor and a sample power inverter under a plurality of test conditions;

[0032] Determine the temperature correlation parameter between any two candidate monitoring positions based on the test data of the sample brushless DC motor and the sample power inverter under various test conditions;

[0033] Determine a plurality of target monitoring positions from a plurality of candidate monitoring positions based on a temperature correlation parameter between any two candidate monitoring positions;

[0034] Multiple temperature monitoring units are set according to multiple target monitoring locations.

[0035] Specifically, the candidate monitoring positions may be positions where the sample brushless DC motor and the sample power inverter are prone to overheating under various test conditions.

[0036] In some embodiments, the temperature monitoring module determines the temperature correlation parameter between any two candidate monitoring positions based on the test data of the sample brushless DC motor and the sample power inverter under various test conditions, including:

[0037] For each test condition, based on the time period temperature mean and time period temperature fluctuation parameters of any two candidate monitoring locations in multiple test time periods, determine the single point temperature correlation parameters of any two candidate monitoring locations corresponding to the test condition;

[0038] Based on the single-point temperature correlation parameters of any two candidate monitoring positions corresponding to each test condition, the temperature correlation parameters between any two candidate monitoring positions are determined.

[0039] For example, the time period temperature fluctuation parameter can be calculated based on the following formula:

[0040]

[0041]

[0042] Among them, V (i,k,h) is the time period temperature fluctuation parameter of the kth test period at the ith candidate monitoring location under the hth test condition, T (i,k,h) is the mean temperature of the kth test period at the ith candidate monitoring location under the hth test condition, T (i,k,h,g) is the temperature of the i-th candidate monitoring position at the g-th time point in the k-th test period under the h-th test condition, and G is the total number of time points sampled at the i-th candidate monitoring position in the k-th test period under the h-th test condition.

[0043] The single-point temperature dependency parameters can be calculated based on the following formula:

[0044]

[0045] Among them, C (i,j,h) is the single-point temperature correlation parameter of the i-th candidate monitoring position and the j-th candidate monitoring position under the h-th test condition, T (j,k,h) V is the mean temperature of the jth candidate monitoring location in the kth test period under the hth test condition, (j,k,h)is the time period temperature fluctuation parameter of the kth test time period of the jth candidate monitoring location under the hth test condition, M1 is a preset parameter, and M1 is greater than 0, a1 and a2 are both preset weights, both greater than 0, and a1+a2=1, and K is the total number of test time periods under the hth test condition.

[0046] The temperature correlation parameter between two candidate monitoring locations can be calculated based on the following formula:

[0047]

[0048] Among them, C (i,j) is the temperature correlation parameter between the ith candidate monitoring position and the jth candidate monitoring position, and H is the total number of test conditions.

[0049] For each candidate monitoring location, the candidate monitoring locations whose temperature correlation parameters are greater than the preset temperature correlation parameter threshold are taken as their associated candidate monitoring locations, the candidate monitoring locations whose number of associated candidate monitoring locations is greater than the preset number threshold are taken as target monitoring locations, and a temperature monitoring unit is set at each target monitoring location.

[0050] The temperature control module may include a plurality of temperature control units arranged at a plurality of different positions.

[0051] In some embodiments, the temperature control unit includes a plurality of Peltier elements, the positions of the plurality of temperature control units are determined based on a plurality of target monitoring positions, and the number of Peltier elements included in the temperature control unit is determined based on test data of a sample brushless DC motor and a sample power inverter under a plurality of test conditions. The temperature control module also includes fans arranged at a plurality of different positions, and the positions of the fans are determined based on test data of a sample brushless DC motor and a sample power inverter under a plurality of test conditions.

[0052] In some embodiments, the temperature control module determines the number of Peltier elements included in the temperature control unit based on test data of a sample brushless DC motor and a sample power inverter under multiple test conditions, including:

[0053] For each temperature control unit, the number of Peltier elements included in the temperature control unit is determined based on the time period temperature mean and time period temperature fluctuation parameters of multiple test time periods under each test condition at the target monitoring position where the temperature control unit is located.

[0054] For example, the number of Peltier elements included in the temperature control unit may be determined based on the following formula:

[0055]

[0056] Among them, N iis the number of Peltier elements included in the temperature control unit at the i-th candidate monitoring position, max() is the maximum value operation, [] is the rounding operation, T preset is the preset temperature average, V preset is the temperature fluctuation parameter for the preset time period, N preset The number of Peltier elements included in a preset single temperature control unit is k=1, 2, ..., K, and h=1, 2, ..., H.

[0057] In some embodiments, the temperature control module determines the position of the fan based on test data of a sample brushless DC motor and a sample power inverter under a variety of test conditions, including:

[0058] Clustering multiple target monitoring positions by a clustering algorithm (for example, a K-means clustering algorithm, etc.) to determine multiple candidate ventilation areas. Specifically, clustering multiple target monitoring positions with shorter distances into the same candidate ventilation area;

[0059] For each candidate ventilation area, determining a heat dissipation demand parameter of the candidate ventilation area;

[0060] Based on the heat dissipation requirement parameter of each candidate ventilation area, the position of the fan is determined. For example, the candidate ventilation area whose heat dissipation requirement parameter is greater than a preset heat dissipation requirement parameter threshold is taken as the target ventilation area, and the fan is installed in the target ventilation area.

[0061] In some embodiments, the temperature control module determines the heat dissipation requirement parameters of the candidate ventilation area, including: determining the heat dissipation requirement parameters of the candidate ventilation area based on the temperature mean and time period temperature fluctuation parameters of each target monitoring location included in the candidate ventilation area under multiple test time periods under each test condition.

[0062] Specifically, the heat dissipation requirement parameters of the candidate ventilation area can be determined based on the following formula:

[0063]

[0064] Among them, R f is the heat dissipation requirement parameter of the fth candidate ventilation area, R preset is the heat dissipation requirement parameter of the preset ventilation area, e=1,2,…,E.

[0065] The controller can be used to determine a temperature control strategy based on temperature information collected by multiple temperature monitoring units at multiple consecutive time points, and control the operation of multiple temperature control units based on the temperature control strategy.

[0066] Figure 2 is a schematic diagram of a process for determining a temperature control strategy according to some embodiments of this specification, such as Figure 2As shown, in some embodiments, the controller determines a temperature control strategy based on temperature information collected by multiple temperature monitoring units at multiple consecutive time points, including:

[0067] For each temperature monitoring unit, determining an information reliability parameter based on a temperature correlation parameter between any two target monitoring locations and temperature information collected by the temperature monitoring unit at multiple consecutive time points;

[0068] The temperature monitoring unit corresponding to which the information reliability parameter is greater than the preset reliability parameter threshold is used as the target temperature monitoring unit;

[0069] Determining a data correlation parameter between any two target temperature monitoring units based on a temperature correlation parameter between any two target monitoring positions;

[0070] For each target temperature monitoring unit, determining a related temperature monitoring unit of the target temperature monitoring unit based on a data correlation parameter between any two target temperature monitoring units;

[0071] Based on the temperature information collected by the relevant temperature monitoring unit at multiple consecutive time points, the temperature information collected by the target temperature monitoring unit at multiple consecutive time points is corrected;

[0072] A temperature control strategy is determined based on the corrected temperature information collected by the multiple target temperature monitoring units at multiple consecutive time points.

[0073] Specifically, the temperature control strategy can be determined by the first strategy generation model based on the temperature information collected by the corrected multiple target temperature monitoring units at multiple consecutive time points, wherein the temperature control strategy may include the operating voltage and fan speed of each temperature control unit at multiple consecutive time points in the future, and the first strategy generation model can be a deep reinforcement learning model.

[0074] In some embodiments, the controller determines the information reliability parameter based on the temperature correlation parameter between any two target monitoring positions and the temperature information collected by the temperature monitoring unit at multiple consecutive time points, including:

[0075] For each temperature monitoring unit, determining an associated temperature monitoring unit of the temperature monitoring unit based on a temperature correlation parameter between any two target monitoring positions;

[0076] For each associated temperature monitoring unit of the temperature monitoring unit, based on the temperature information collected by the temperature monitoring unit at multiple consecutive time points and the temperature information collected by the associated temperature monitoring unit at multiple consecutive time points, calculating an information association parameter between the temperature monitoring unit and the associated temperature monitoring unit;

[0077] An information reliability parameter is determined based on a temperature correlation parameter between any two target monitoring locations and an information correlation parameter between the temperature monitoring unit and each associated temperature monitoring unit.

[0078] Specifically, the method of determining the information correlation parameter between the temperature monitoring unit and the associated temperature monitoring unit is similar to the method of determining the single-point temperature correlation parameter, which will not be repeated here. The information reliability parameter can be determined based on the temperature correlation parameter between the two target monitoring positions and the difference between the information correlation parameter between the temperature monitoring unit and each associated temperature monitoring unit. The larger the difference between the temperature correlation parameter between the two target monitoring positions and the information correlation parameter between the temperature monitoring unit and each associated temperature monitoring unit, the smaller the information reliability parameter.

[0079] In some embodiments, the controller corrects the temperature information collected by the target temperature monitoring unit at multiple consecutive time points based on the temperature information collected by the relevant temperature monitoring unit at multiple consecutive time points, including:

[0080] Performing empirical mode decomposition on the temperature information collected by the target temperature monitoring unit at multiple consecutive time points to generate multiple first temperature connotation modal components and first temperature residuals;

[0081] Performing empirical mode decomposition on the temperature information collected by each relevant temperature monitoring unit at multiple consecutive time points to generate multiple second temperature connotation modal components and second temperature residuals;

[0082] The multiple first temperature connotation modal components and first temperature residuals corresponding to the target temperature monitoring unit are corrected based on the multiple second temperature connotation modal components and second temperature residuals corresponding to each relevant temperature monitoring unit through a multimodal joint denoising model, wherein the multimodal joint denoising model can be a convolutional neural network (CNN) model.

[0083] Specifically, the empirical mode decomposition may include the following steps:

[0084] S31, identifying extreme value points: first, scan the input signal to find all local maximum points and local minimum points, and execute S32;

[0085] S32, constructing upper and lower envelopes: using cubic spline interpolation to interpolate the extreme points, constructing the upper envelope and lower envelope of the signal, and executing S33;

[0086] S33, calculating the mean envelope: calculating the average value of the upper and lower envelopes to obtain the mean envelope, and executing S34;

[0087] S34, obtaining an intermediate signal: subtracting the mean envelope from the original signal to obtain a new signal, namely, the intermediate signal, and executing S35;

[0088] S35, judging whether the intermediate signal satisfies the intrinsic modal component condition: checking whether the intermediate signal satisfies the intrinsic modal component condition: in the entire data segment, the number of extreme value points and the number of zero-crossing points must be equal or the difference cannot exceed one at most; at any time, the average value of the upper envelope formed by the local maximum value points and the lower envelope formed by the local minimum value points is zero, that is, the upper and lower envelopes are locally symmetrical with respect to the time axis, and executing S36;

[0089] S36, iterative screening process: if the intermediate signal does not meet the intrinsic modal component condition, it is regarded as a new input signal, and steps S32 to S35 are repeated until a signal that meets the intrinsic modal component condition is obtained, and the process stops when the remaining signal contains no more than two extreme values, and S37 is executed;

[0090] S37, decomposing to obtain the intrinsic modal component: after the above iterative screening process, the signal that finally meets the intrinsic modal component condition is the first intrinsic modal component, and S38 is executed;

[0091] S38, extracting the remaining connotation modal components: subtracting the first connotation modal component from the original signal to obtain the remaining signal. Then, taking the remaining signal as the new input signal, repeating steps S31 to S37, extracting other connotation modal components in turn, and executing S39;

[0092] S39, decomposition completed: When the residual signal can no longer extract components that meet the conditions of the intrinsic modal components, the decomposition process ends. At this time, the original signal is decomposed into a series of intrinsic modal components and a residual.

[0093] The control can also generate a drive control strategy based on temperature information collected by multiple temperature monitoring units at multiple consecutive time points, position information of the rotor of the brushless DC motor, and current magnitude and direction in the motor winding of the brushless DC motor, and control the operation of the power inverter based on the drive control strategy.

[0094] Specifically, the drive control strategy can be determined through the second strategy generation model based on the temperature information collected by the corrected multiple target temperature monitoring units at multiple consecutive time points, the position information of the rotor of the brushless DC motor, and the current magnitude and direction in the motor winding of the brushless DC motor. The drive control strategy may include parameters such as the speed, direction, and torque of the brushless DC motor at multiple consecutive time points in the future. The second strategy generation model can be a deep reinforcement learning model.

[0095] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. An intelligent brushless DC motor drive system, characterized in that: include: Power inverter, used to convert DC power into AC power to drive the brushless DC motor; A rotor position sensor, used to detect the position information of the rotor of the brushless DC motor; A current sensor for the magnitude and direction of the current in the motor winding of the brushless DC motor; A temperature monitoring module, comprising a plurality of temperature monitoring units arranged at a plurality of different positions; A temperature control module, comprising a plurality of temperature control units arranged at a plurality of different positions; A controller, configured to determine a temperature control strategy based on the temperature information collected by the multiple temperature monitoring units at multiple consecutive time points, and control the operation of the multiple temperature control units based on the temperature control strategy, and to generate a drive control strategy based on the temperature information collected by the multiple temperature monitoring units at multiple consecutive time points, the position information of the rotor of the brushless DC motor, and the magnitude and direction of the current in the motor winding of the brushless DC motor, and control the operation of the power inverter based on the drive control strategy; The temperature monitoring module is provided with a plurality of temperature monitoring units, including: Obtain test data of sample brushless DC motors and sample power inverters under various test conditions; Determining a plurality of candidate monitoring locations based on test data of the sample brushless DC motor and the sample power inverter under a plurality of test conditions; Determining a temperature correlation parameter between any two of the candidate monitoring positions based on test data of the sample brushless DC motor and the sample power inverter under a variety of test conditions; Determining a plurality of target monitoring positions from the plurality of candidate monitoring positions based on a temperature correlation parameter between any two of the candidate monitoring positions; Setting a plurality of temperature monitoring units according to the plurality of target monitoring locations; The temperature monitoring module determines the temperature correlation parameters between any two candidate monitoring positions based on the test data of the sample brushless DC motor and the sample power inverter under various test conditions, including: For each test condition, based on the time period temperature averages and time period temperature fluctuation parameters of any two of the candidate monitoring locations in multiple test time periods, determine the single-point temperature correlation parameters of any two of the candidate monitoring locations corresponding to the test condition; Determine the temperature correlation parameter between any two of the candidate monitoring positions based on the single-point temperature correlation parameter of any two of the candidate monitoring positions corresponding to each of the test conditions; The single-point temperature dependence parameters are calculated based on the following formula: Among them, C (i,j,h) is the single-point temperature correlation parameter of the i-th candidate monitoring position and the j-th candidate monitoring position under the h-th test condition, T (i,k,h) is the mean temperature of the kth test period at the ith candidate monitoring location under the hth test condition, T (j,k,h) V is the mean temperature of the jth candidate monitoring location in the kth test period under the hth test condition, (j,k,h) is the time period temperature fluctuation parameter of the jth candidate monitoring location in the kth test period under the hth test condition, V (i,k,h) is the time period temperature fluctuation parameter of the kth test period of the ith candidate monitoring position under the hth test condition, M1 is a preset parameter, and M1 is greater than 0, a1 and a2 are preset weights, both greater than 0, and a1+a2=1, K is the total number of test periods under the hth test condition; The temperature correlation parameter between two candidate monitoring locations is calculated based on the following formula: Among them, C (i,j) is the temperature correlation parameter between the ith candidate monitoring position and the jth candidate monitoring position, and H is the total number of test conditions.

2. The intelligent brushless DC motor drive system according to claim 1, characterized in that: The temperature control unit includes a plurality of Peltier elements, the positions of the plurality of temperature control units are determined based on the plurality of target monitoring positions, and the number of the Peltier elements included in the temperature control unit is determined based on test data of the sample brushless DC motor and the sample power inverter under a plurality of test conditions; The temperature control module further includes fans arranged at a plurality of different positions, and the positions of the fans are determined based on test data of the sample brushless DC motor and the sample power inverter under a plurality of test conditions.

3. The intelligent brushless DC motor drive system according to claim 2, characterized in that: The temperature control module determines the number of Peltier elements included in the temperature control unit based on test data of the sample brushless DC motor and the sample power inverter under multiple test conditions, including: For each of the temperature control units, the number of Peltier elements included in the temperature control unit is determined based on the temperature mean of multiple test time periods and the time period temperature fluctuation parameters at the target monitoring position where the temperature control unit is located under each test condition.

4. The intelligent brushless DC motor drive system according to claim 2, characterized in that: The temperature control module determines the position of the fan based on the test data of the sample brushless DC motor and the sample power inverter under various test conditions, including: Clustering the multiple target monitoring locations by using a clustering algorithm to determine multiple candidate ventilation areas; For each of the candidate ventilation areas, determining a heat dissipation requirement parameter of the candidate ventilation area; The location of the fan is determined based on the heat dissipation requirement parameter of each candidate ventilation area.

5. The intelligent brushless DC motor drive system according to claim 4, characterized in that: The temperature control module determines the heat dissipation requirement parameters of the candidate ventilation area, including: Based on the time period temperature average and time period temperature fluctuation parameter of multiple test time periods under each test condition at each target monitoring position included in the candidate ventilation area, the heat dissipation requirement parameter of the candidate ventilation area is determined.

6. An intelligent brushless DC motor drive system according to any one of claims 1 to 5, characterized in that: The controller determines a temperature control strategy based on the temperature information collected by the multiple temperature monitoring units at multiple consecutive time points, including: For each of the temperature monitoring units, determining an information reliability parameter based on a temperature correlation parameter between any two of the target monitoring locations and temperature information collected by the temperature monitoring unit at a plurality of consecutive time points; The temperature monitoring unit corresponding to which the information reliability parameter is greater than the preset reliability parameter threshold is used as the target temperature monitoring unit; Determining a data correlation parameter between any two of the target temperature monitoring units based on a temperature correlation parameter between any two of the target monitoring positions; For each of the target temperature monitoring units, determining a related temperature monitoring unit of the target temperature monitoring unit based on a data correlation parameter between any two of the target temperature monitoring units; Based on the temperature information collected by the relevant temperature monitoring unit at multiple consecutive time points, the temperature information collected by the target temperature monitoring unit at multiple consecutive time points is corrected; The temperature control strategy is determined based on the corrected temperature information collected by the multiple target temperature monitoring units at multiple consecutive time points.

7. The intelligent brushless DC motor drive system according to claim 6, characterized in that: The controller determines the information reliability parameter based on the temperature correlation parameter between any two of the target monitoring positions and the temperature information collected by the temperature monitoring unit at multiple consecutive time points, including: For each of the temperature monitoring units, determining an associated temperature monitoring unit of the temperature monitoring unit based on a temperature correlation parameter between any two of the target monitoring locations; For each associated temperature monitoring unit of the temperature monitoring unit, calculating an information association parameter between the temperature monitoring unit and the associated temperature monitoring unit based on the temperature information collected by the temperature monitoring unit at multiple consecutive time points and the temperature information collected by the associated temperature monitoring unit at multiple consecutive time points; The information reliability parameter is determined based on a temperature correlation parameter between any two of the target monitoring positions and an information association parameter between the temperature monitoring unit and each of the associated temperature monitoring units.

8. The intelligent brushless DC motor drive system according to claim 6, characterized in that: The controller corrects the temperature information collected by the target temperature monitoring unit at multiple consecutive time points based on the temperature information collected by the relevant temperature monitoring unit at multiple consecutive time points, including: Performing empirical mode decomposition on the temperature information collected by the target temperature monitoring unit at multiple consecutive time points to generate multiple first temperature connotation modal components and first temperature residuals; Performing empirical mode decomposition on the temperature information collected by each of the relevant temperature monitoring units at multiple consecutive time points to generate multiple second temperature connotation modal components and second temperature residuals; The multiple first temperature connotation modal components and the first temperature residual corresponding to the target temperature monitoring unit are corrected based on the multiple second temperature connotation modal components and the second temperature residual corresponding to each of the related temperature monitoring units through a multi-modal joint denoising model.

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