Temperature monitoring method and system for motor controller

Abstract

The invention relates to the technical field of data processing, and discloses a temperature monitoring method and system for a motor controller. The method comprises the steps of obtaining a three-dimensional temperature data flow in real time through a temperature sensor array, performing temperature analysis and hot spot detection, constructing an adaptive protection strategy, dynamically adjusting PWM parameters, and optimizing the operation safety and performance of a motor. According to the invention, the technical problems that the coverage of temperature monitoring points is not comprehensive, and the protection strategy lacks pertinence and adaptability are solved, through multi-point temperature collection and intelligent analysis processing, the accuracy of temperature monitoring of the motor controller and the flexibility of the protection strategy are improved, and it is ensured that the motor is safe and reliable and keeps the optimal performance under different working conditions.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and in particular, to a temperature monitoring method and system for a motor controller. Background Art

[0002] In the application field of motor control systems, temperature monitoring and overheat protection have always been key technologies to ensure the safe and reliable operation of the system. Traditional motor controller temperature protection schemes usually adopt single-point temperature detection technology, that is, one or a few temperature sensors are set in the key areas of the controller, and the overheat protection action is triggered based on a fixed threshold. Such schemes are widely used in the field of vehicle motor controllers, such as water pump controllers and fan controllers in automotive thermal management systems. These systems usually set up a simple two-level protection mechanism: when the detected temperature exceeds the warning threshold, the output power is reduced, and when it exceeds the danger threshold, a safety shutdown is executed. With the rapid development of electric vehicles and hybrid vehicles, motor controllers face more complex and changeable working environments and load conditions, and temperature monitoring technology has gradually developed into a complex system based on multi-point temperature detection and dynamic threshold adjustment.

[0003] However, the existing technical solutions still have obvious deficiencies. First, single-point or a small number of multi-point temperature detections cannot comprehensively reflect the temperature distribution inside the motor controller, and it is easy to miss hidden hot spots; second, the protection strategy with a fixed threshold lacks pertinence to the temperature characteristics under different working conditions, often resulting in problems of overprotection or underprotection; third, most of the existing solutions do not consider the temperature characteristic differences in different operating stages of the motor (such as starting, accelerating, stable operation, and braking), and the protection strategy lacks refinement and differentiation; finally, the existing technology fails to make full use of historical temperature data for self-learning optimization, the protection strategy lacks self-adaptability, and cannot dynamically adjust protection parameters according to factors such as seasonal changes and environmental conditions. These deficiencies lead to the motor controller being either overly conservative and affecting performance or overly radical and threatening safety in actual applications. Summary of the Invention

[0004] This application provides a temperature monitoring method and system for a motor controller, which is used to solve the technical problems of incomplete coverage of temperature monitoring points, lack of pertinence and self-adaptability of the protection strategy. By collecting multi-point temperature and intelligent analysis and processing, it improves the accuracy of motor controller temperature monitoring and the flexibility of the protection strategy, and ensures that the motor is both safe and reliable and maintains the best performance under different working conditions.

[0005] In a first aspect, the present application provides a temperature monitoring method for a motor controller. The temperature monitoring method for the motor controller includes: performing multi-point temperature acquisition on the power MOSFET area, the drive IC area, and the PWM control module area of the motor controller through a temperature sensor array to obtain a three-dimensional temperature data stream including a timestamp, a spatial position, and a temperature value; filtering and denoising the data and performing outlier marking processing on the data according to the three-dimensional temperature data stream to obtain a standardized temperature matrix and a reliability mark; analyzing the temperature distribution during the motor startup, acceleration, steady operation, and braking processes based on the standardized temperature matrix and the reliability mark to obtain a two-dimensional temperature distribution field matrix associated with the motor operation state; detecting and performing correlation analysis on the temperature peak points under different load conditions according to the two-dimensional temperature distribution field matrix to obtain a set of hot spot feature vectors including a position, a motor speed correlation degree, and a load correlation; determining an adaptive protection threshold corresponding to different operation stages based on the set of hot spot feature vectors, in combination with the temperature specifications of the power devices and the current operation parameters of the motor, and generating a protection execution instruction set including hierarchical trigger conditions and corresponding control strategies; and performing real-time regulation on the PWM duty cycle and the switching frequency of the motor according to the protection execution instruction set, and optimizing and adjusting the protection parameters through historical data on the relationship between the operation conditions and the temperature rise.

[0006] In a second aspect, the present application provides a temperature monitoring system for a motor controller. The temperature monitoring system for the motor controller includes: An acquisition module, configured to perform multi-point temperature acquisition on the power MOSFET area, the drive IC area, and the PWM control module area of the motor controller through a temperature sensor array to obtain a three-dimensional temperature data stream including a timestamp, a spatial position, and a temperature value; A processing module, configured to filter and denoise the data and perform outlier marking processing on the data according to the three-dimensional temperature data stream to obtain a standardized temperature matrix and a reliability mark; An analysis module, configured to analyze the temperature distribution during the motor startup, acceleration, steady operation, and braking processes based on the standardized temperature matrix and the reliability mark to obtain a two-dimensional temperature distribution field matrix associated with the motor operation state; A correlation module, configured to detect and perform correlation analysis on the temperature peak points under different load conditions according to the two-dimensional temperature distribution field matrix to obtain a set of hot spot feature vectors including a position, a motor speed correlation degree, and a load correlation; A generation module, configured to determine an adaptive protection threshold corresponding to different operation stages based on the set of hot spot feature vectors, in combination with the temperature specifications of the power devices and the current operation parameters of the motor, and generate a protection execution instruction set including hierarchical trigger conditions and corresponding control strategies; A regulation module, configured to perform real-time regulation on the PWM duty cycle and switching frequency of the motor according to the protection execution instruction set, and optimize and adjust the protection parameters based on the historical data of the relationship between the operating condition and the temperature rise.

[0007] The third aspect of the present invention provides a computer device, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory to cause the computer device to execute the above-mentioned temperature monitoring method for a motor controller.

[0008] The fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the above-mentioned temperature monitoring method for a motor controller.

[0009] In the technical solution provided by this application, a temperature sensor array is arranged in the power MOSFET area, the drive IC area, and the PWM control module area for multi-point temperature acquisition, and a three-dimensional temperature data stream including time stamps, spatial positions, and temperature values is constructed, effectively solving the problem of missed detection of hot spots caused by traditional single-point temperature measurement and making temperature monitoring more comprehensive and accurate; based on the three-dimensional temperature data stream, filtering, noise reduction, and outlier marking processing are performed to obtain a standardized temperature matrix and reliability marks, significantly improving the accuracy and reliability of temperature data, effectively identifying and marking sensor failures and temperature anomalies; targeted analysis is carried out on the temperature distributions in different operating stages such as motor startup, acceleration, stable operation, and braking to generate a two-dimensional temperature distribution field matrix associated with the motor operating state, realizing a refined characterization of the temperature characteristics in different operating stages; the temperature peak points under different load conditions are detected and correlation analyzed to form a set of hot spot feature vectors including positions, motor speed correlation degrees, and load correlations, providing rich data support for the subsequent formulation of protection strategies; combined with the temperature specifications of power devices and the current operating parameters of the motor, adaptive protection thresholds are determined for different operating stages, and a protection execution instruction set with hierarchical trigger conditions and corresponding control strategies is generated, realizing the precision and differentiation of protection strategies; by adjusting the motor PWM duty cycle and switching frequency in real time and optimizing the protection parameters based on the historical data of the relationship between operating conditions and temperature rise, a closed-loop adaptive temperature protection mechanism is constructed. This invention applies a variety of artificial intelligence algorithms in specific functional or application fields, such as the Z-score normalization method for outlier identification, the heat diffusion model for temperature field reconstruction, the correlation calculation method for hot spot correlation analysis, etc. These algorithm features make important contributions to the solution, realizing a technical leap from simple threshold triggering to intelligent analysis and decision-making; the application of artificial intelligence algorithms enables the motor controller to implement differentiated protection strategies according to the temperature characteristics in different working conditions and different operating stages like an experienced operator, ensuring system safety while maximizing the motor performance. This intelligent feature is incomparable to traditional temperature monitoring methods; by combining artificial intelligence analysis and real-time control technology, this invention effectively solves key technical problems such as the easy overheating of the motor controller, inaccurate protection strategies, and incomplete temperature monitoring, and is particularly suitable for application scenarios with high requirements for control accuracy and reliability, such as the thermal management system of new energy vehicles; compared with the prior art, this invention realizes the upgrade from passive temperature protection to active intelligent temperature management, greatly improving the thermal safety, reliability, and performance of the motor controller. Description of the Drawings

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic diagram of an embodiment of the temperature monitoring method for a motor controller in an embodiment of the present application; Figure 2 It is a schematic diagram of an embodiment of the temperature monitoring system for a motor controller in an embodiment of the present application; Figure 3 It is a schematic block diagram of the structure of a computer device in an embodiment of the present invention. Specific embodiments

[0012] The embodiments of the present application provide a temperature monitoring method and system for a motor controller. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0013] For ease of understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 , an embodiment of the temperature monitoring method for a motor controller in an embodiment of the present application includes: Step S101: Perform multi-point temperature acquisition on the power MOSFET area, drive IC area, and PWM control module area of the motor controller through a temperature sensor array to obtain a three-dimensional temperature data stream including a timestamp, a spatial position, and a temperature value; Step S102: According to the three-dimensional temperature data stream, perform filtering, noise reduction, and outlier marking processing on the data to obtain a standardized temperature matrix and a reliability mark; Step S103: Based on the standardized temperature matrix and the reliability mark, analyze the temperature distribution during the motor startup, acceleration, stable operation, and braking processes to obtain a two-dimensional temperature distribution field matrix associated with the motor operating state; Step S104: Detect and perform correlation analysis on the temperature peak points under different load conditions based on the two-dimensional temperature distribution field matrix to obtain a set of hot spot feature vectors including position, motor speed correlation, and load correlation; Step S105: Based on the set of hot spot feature vectors, combine the temperature specifications of the power devices and the current operating parameters of the motor to determine the adaptive protection thresholds corresponding to different operating stages, and generate a set of protection execution instructions including hierarchical trigger conditions and corresponding control strategies; Step S106: According to the set of protection execution instructions, perform real-time regulation on the PWM duty cycle and switching frequency of the motor, and optimize and adjust the protection parameters based on the historical data of the relationship between the operating conditions and the temperature rise.

[0014] It can be understood that the execution entity of this application can be a temperature monitoring system for a motor controller, or a terminal or a server. Specifically, it is not limited here. This application embodiment is described by taking the server as the execution entity as an example.

[0015] Specifically, starting from multi-point temperature acquisition, a high-precision NTC thermistor sensor is arranged in the power MOSFET area of the motor controller, a digital temperature sensor is arranged in the drive IC area, and temperature monitoring points are arranged in the PWM control module area to form a temperature sensor array. These sensors dynamically adjust the sampling frequency according to the motor operating state. The 1Hz frequency is used during normal operation, and it is increased to 10Hz during high load or startup. The data is aggregated through a single bus or I²C bus and timestamp information is added, and a spatial position identifier is attached to each temperature data point. Finally, a three-dimensional temperature data stream including timestamp, spatial position, and temperature value is constructed. After obtaining the three-dimensional temperature data stream, filtering, noise reduction, and outlier marking processing are performed. First, high-frequency noise is eliminated through median filtering in a sliding window, and a 7-point window is used to process the sampling sequence to effectively remove sudden interference. Subsequently, exponential weighted moving average processing is applied to the preliminarily filtered temperature data, and the weight coefficient is set to 0.2, giving higher weight to new data while retaining the historical trend. The temperature sequence is reorganized into a two-dimensional temperature data table according to the spatial distribution of the sensors, and the Z-score value of each point is calculated. When the Z-score value exceeds 3.0, it is marked as a potential outlier. By analyzing the correlation between the potential outlier and the data of adjacent sensors in space, sensor faults can be distinguished from real temperature anomalies. For example, when the temperature of a certain point suddenly rises by 50°C while the surrounding sensors only rise by 2°C, it is determined as a sensor fault. Finally, the stabilized temperature sequence is standardized and combined with the sensor reliability weight to form a standardized temperature matrix and a reliability mark.

[0016] For the temperature distribution analysis of different operating states, the system obtains the operating state signals from the motor controller, divides the motor operation process into a starting stage, an acceleration stage, a steady operation stage, and a braking stage, and groups the standardized temperature matrix according to the operating stages in time series. Assigns trust weights to each data point according to the reliability mark, maps the weighted stage temperature data onto the physical layout of the motor controller, fills in the non-measured point positions by thermal conduction interpolation, and finally generates a two-dimensional temperature distribution field matrix associated with the motor operating state.

[0017] For the hot spot analysis under different load conditions, the system extracts the temperature distribution sub-matrices of low load, medium load, and high load conditions from the two-dimensional temperature distribution field matrix. Performs local extreme value detection on the temperature distribution of the low load condition, identifies the temperature peak points for the medium load condition, and screens the hot spots for the high load condition, and calculates the low load hot spot temperature gradient index, the medium load hot spot spatial distribution characteristics, and the high load thermal mass index respectively. Calculates the correlation between these hot spot positions and the motor speed data. For example, when the motor speed rises from 1000 rpm to 2000 rpm, the temperature of a certain hot spot rises by 15 °C, then the correlation between this hot spot and the speed is relatively high. Finally, integrates various indicators to form a hot spot feature vector set.

[0018] In the stage of determining the protection threshold, obtains the maximum junction temperature specification value of the power MOSFET (usually 175 °C) and the maximum operating temperature specification value of the drive IC (usually 150 °C) as the basic protection upper limit, adjusts the weight according to the hot spot position information, constructs a speed-temperature rise relationship matrix, and generates a five-level progressive protection threshold in combination with the load correlation, corresponding to setting the temperature warning trigger condition, the PWM frequency reduction trigger condition, the power limit trigger condition, the forced frequency reduction trigger condition, and the safety shutdown trigger condition.

[0019] Finally, when implementing the protection control, the system regulates the motor PWM according to the trigger signals in the protection execution instruction set. When the temperature warning is triggered, the sampling rate is increased to the high-speed state; when the PWM frequency reduction is triggered, the switching frequency is reduced to 80% of the original frequency according to the slope limit function (the maximum change within 100 ms is 10%); when the power limit is triggered, the upper limit of the duty cycle is lowered to 85%; when the forced frequency reduction is triggered, the power is further limited to 70%. The system records the data of each temperature protection action into the temperature event log, constructs a working condition-temperature rise characteristic map, and periodically optimizes the protection threshold.

[0020] Taking a brushless fan motor controller for a vehicle as an example, the temperature measured in the power MOSFET area was 125°C and rising rapidly. The system immediately and smoothly reduced the PWM switching frequency from 20 kHz to 16 kHz, and the rate of temperature rise slowed down. Subsequently, when the temperature reached 140°C, the system restricted the PWM duty cycle from 90% to 76.5%, and the temperature began to drop. By analyzing historical data, it was found that the controller was more prone to overheating when the ambient temperature was higher than 35°C in summer. The system automatically lowered the summer protection threshold and intervened in protection in advance, effectively preventing the motor controller from being damaged due to overheating.

[0021] In the embodiment of the present application, a temperature sensor array is arranged in the power MOSFET area, the drive IC area and the PWM control module area for multi-point temperature acquisition, and a three-dimensional temperature data stream including time stamps, spatial positions and temperature values is constructed, effectively solving the problem of missed detection of hot spots caused by traditional single-point temperature measurement and making temperature monitoring more comprehensive and accurate; based on the three-dimensional temperature data stream, filtering, noise reduction and outlier marking processing are carried out to obtain a standardized temperature matrix and reliability marks, significantly improving the accuracy and reliability of temperature data, effectively identifying and marking sensor failures and temperature anomalies; targeted analysis is carried out on the temperature distributions in different operating stages such as motor startup, acceleration, stable operation and braking to generate a two-dimensional temperature distribution field matrix associated with the motor operating state, realizing a refined characterization of the temperature characteristics in different operating stages; the temperature peak points under different load conditions are detected and correlation analysis is carried out to form a set of hot spot feature vectors including positions, motor speed correlation degrees and load correlations, providing rich data support for the formulation of subsequent protection strategies; in combination with the temperature specifications of power devices and the current operating parameters of the motor, adaptive protection thresholds are determined for different operating stages, and a protection execution instruction set with hierarchical trigger conditions and corresponding control strategies is generated, realizing the precision and differentiation of protection strategies; by adjusting the PWM duty cycle and switching frequency of the motor in real time and optimizing the protection parameters based on the historical data of the relationship between operating conditions and temperature rise, a closed-loop adaptive temperature protection mechanism is constructed. The present invention applies a variety of artificial intelligence algorithms in specific functions or application fields, such as the Z-score normalization method for outlier identification, the heat diffusion model for temperature field reconstruction, the correlation calculation method for hot spot correlation analysis, etc. These algorithm features make important contributions to the solution, realizing a technical leap from simple threshold triggering to intelligent analysis and decision-making; the application of artificial intelligence algorithms enables the motor controller to implement differentiated protection strategies according to the temperature characteristics of different operating conditions and different operating stages like an experienced operator, ensuring system safety while maximizing motor performance to the greatest extent. This intelligent feature is incomparable to traditional temperature monitoring methods; by combining artificial intelligence analysis and real-time control technology, the present invention effectively solves key technical problems such as easy overheating of the motor controller, inaccurate protection strategies and incomplete temperature monitoring, and is particularly suitable for application scenarios with high requirements for control accuracy and reliability such as the thermal management system of new energy vehicles; compared with the prior art, the present invention realizes the upgrade from passive temperature protection to active intelligent temperature management, greatly improving the thermal safety, reliability and performance of the motor controller.

[0022] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Arrange NTC thermistor sensors in the power MOSFET area of the motor controller to collect the temperature around the power device with high precision; (2) Place a digital temperature sensor in the driver IC area of ​​the motor controller to accurately measure the temperature of the control circuit; (3) Arrange temperature monitoring points in the PWM control module area to track the temperature of the signal processing area in real time; (4) Dynamically adjust the temperature sampling frequency according to the motor operating status, sampling at 1 Hz when the motor is working normally and at 10 Hz during high load or startup phase; (5) Summarize the collected temperature data and add timestamp information through a single bus or I²C bus; (6) A spatial location identifier is attached to each temperature data point to construct a three-dimensional temperature data stream containing timestamp, spatial location, and temperature value.

[0023] Specifically, when arranging NTC thermistor sensors in the power MOSFET area, select NTC thermistor elements with an accuracy of up to ±0.5°C and attach them directly to the back of the MOSFET device heat sink or the PCB copper foil area next to the power tube, and ensure good heat conduction through thermal conductive silicone. The resistance value of the NTC thermistor decreases with increasing temperature, showing nonlinear characteristics, and thus constructs the resistance-temperature correspondence formula:

[0024] in, is the resistance value at temperature T, is the nominal resistance value at 25°C, is the material constant, is the temperature value. By measuring the resistance value and substituting it into the formula, the accurate temperature value can be calculated.

[0025] When arranging digital temperature sensors in the driver IC area, integrated temperature sensors such as DS18B20 or TMP102 are used with an accuracy of ±0.25°C. These digital temperature sensors have built-in temperature-digital conversion circuits and directly output digital temperature values ​​to avoid interference problems during analog signal transmission. Driver ICs usually have a narrow operating temperature range and require precise monitoring. Digital temperature sensors are arranged on the surface of the driver IC or on the PCB at a position no more than 2mm away from the copper foil at the bottom of the IC to ensure measurement accuracy.

[0026] The temperature monitoring points in the PWM control module area are arranged in the periphery of the signal processing chip and key signal routing by thermocouples or thermistors combined with operational amplifiers to track these temperature-sensitive areas in real time. The temperature changes in this area are usually slow, but they have a significant impact on system stability. A low-noise preamplifier circuit is used to pre-process the temperature signal to improve the signal-to-noise ratio.

[0027] The dynamic sampling frequency adjustment of the motor operating state is realized based on the motor state signals. By monitoring the motor phase current, rotational speed signal, and control commands, the current motor operating state is judged. When it is detected that the motor is in a normal and stable operating state, the temperature change rate is relatively low, and the sampling frequency is set to 1 Hz, that is, all sensor data is collected once per second; when it is detected that the motor is in a high-load state (the phase current exceeds 80% of the rated value) or the starting stage (from the starting moment to the process of reaching the stable rotational speed), the temperature change rate increases significantly. At this time, the sampling frequency is automatically increased to 10 Hz to capture the rapidly changing temperature information. The sampling frequency control is achieved through a hardware timer and a software counter, and the main processor of the motor controller dynamically adjusts the timer overflow time according to the operating state parameters.

[0028] The aggregation of temperature data is realized through a single-wire or I²C bus. For analog sensors such as NTC thermistors, they are first converted into digital signals through an ADC; digital temperature sensors directly output digital quantities. The single-wire technology allows multiple devices to share a data line and identifies different devices through timing, which is suitable for sensors arranged dispersedly; the I²C bus communicates through the SCL clock line and the SDA data line, supports multi-master and multi-slave devices, and is suitable for scenarios with high system integration. The aggregated temperature data is added with millisecond-level timestamp information to accurately record the sampling moment. The timestamp is generated by the internal real-time clock of the controller and is in the format of a 32-bit unsigned integer, representing the number of milliseconds since the system startup.

[0029] When attaching a spatial position identifier to each temperature data point, a two-dimensional coordinate system of the motor controller PCB board surface is established, with the lower left corner as the origin (0, 0), the X-axis to the right, and the Y-axis upward, with the unit being millimeters. The physical position of each sensor is uniquely identified by the (x, y) coordinates. For example, the power MOSFET is located at (15.3, 42.8), and the driver IC is located at (28.6, 35.2). Combining the timestamp t, the spatial coordinates (x, y), and the temperature value T, a quadruple in the form of (t, x, y, T) is constructed to form a three-dimensional temperature data stream. The three dimensions mean that the data contains information in three dimensions: the time dimension, the spatial X dimension, and the spatial Y dimension.

[0030] Taking a brushless motor controller for an automotive thermal management system as an example, its power MOSFET area consists of 6 power transistors, with an NTC sensor arranged beside each power transistor, 2 digital temperature sensors arranged in the drive IC area, and 3 temperature monitoring points arranged in the PWM control module area. When the motor starts from a stationary state, the sampling frequency immediately rises from 1 Hz to 10 Hz to record the rapid rise in the temperature of the power area during the startup process. A fragment of the temperature data stream at a certain moment is: (5328, 15.3, 42.8, 78.6), (5328, 22.4, 43.1, 76.2), (5328, 28.6, 35.2, 62.4), indicating that at 5328 milliseconds after the system starts, the temperature of the power MOSFET at (15.3, 42.8) reaches 78.6 °C, the temperature of another power transistor at (22.4, 43.1) is 76.2 °C, and the temperature of the drive IC at (28.6, 35.2) is 62.4 °C.

[0031] In a specific embodiment, the process of performing step S102 may specifically include the following steps: (1) Eliminate high-frequency noise from the temperature data of each sampling point in the three-dimensional temperature data stream through sliding window median filtering to generate preliminary filtered temperature data; (2) Apply exponential weighted moving average processing to the preliminary filtered temperature data to form a stabilized temperature sequence; (3) Recombine the stabilized temperature sequence into a two-dimensional temperature data table according to the sensor spatial distribution and calculate the Z-score value of each point based on the historical temperature mean; (4) Screen the temperature mutation points according to the Z-score value, and mark them as potential abnormal points when the Z-score value exceeds the preset threshold; (5) Distinguish sensor faults from real temperature anomalies by analyzing the correlation between potential abnormal points and the data of spatially adjacent sensors to generate sensor reliability weights; (6) Standardize the stabilized temperature sequence and combine it with the sensor reliability weights to form a standardized temperature matrix and reliability marks.

[0032] Specifically, when processing the three-dimensional temperature data stream, the sliding window median filtering algorithm is first used to eliminate high-frequency noise from the original temperature data. Sliding window median filtering is a non-linear signal processing technique. For each temperature sampling point, a total of z points before and after it (z is usually an odd number, such as 5, 7, or 9) are taken to form a window. After sorting these data by size, the median value is taken as the filtered output of the current point. For example, when the window size is 7, when processing each temperature point, 7 consecutive points in the time series are taken, and the 4th value after sorting is taken as the temperature value after preliminary filtering. Median filtering has a good inhibitory effect on pulse interference and random noise, and at the same time can retain the edge information of the temperature data, avoiding excessive smoothing like mean filtering.

[0033] When further processing the preliminarily filtered temperature data, the exponentially weighted moving average (EWMA) algorithm is used to form a stabilized temperature sequence. EWMA processing gives higher weights to recent data while retaining the influence of historical data. In the temperature monitoring of the motor controller, the smoothing coefficient is usually set to 0.2 - 0.3, which can not only quickly respond to real temperature changes but also suppress residual noise. For the first data point, its preliminary filtering value is directly used as the starting value of EWMA. This processing method is particularly suitable for the temperature monitoring of the motor controller because the motor temperature has both a slow-changing basic trend and rapid fluctuations caused by load changes.

[0034] The stabilized temperature sequence needs to be reorganized into a two-dimensional temperature data table according to the sensor spatial distribution for subsequent spatial correlation analysis. During the reorganization process, each sensor position is used as a row of the table, the time point is used as a column, and the value is the corresponding EWMA temperature value. This two-dimensional table intuitively shows the temperature change trend of each spatial point over time. When calculating the Z-score value based on the historical temperature mean, for each sensor position, the temperature mean and standard deviation within a past time window (such as 1 minute) are taken, and then the Z-score of the current temperature is calculated. The Z-score represents the multiple of the standard deviation by which the current temperature deviates from the historical mean and is a quantitative indicator of the degree of temperature anomaly.

[0035] Screening for temperature mutation points based on the Z-score value is a key step in anomaly detection. When the absolute value of the Z-score of a certain point is greater than 3.0 (that is, deviating from the mean by more than 3 standard deviations), this point is marked as a potential anomaly point. The threshold of 3.0 is based on the normal distribution theory. Under this threshold, the probability of normal data appearing is less than 0.3%. Therefore, points exceeding this threshold have a high probability of being anomaly points. The marking process generates a binary anomaly flag matrix, where the value of 1 indicates potential anomaly and the value of 0 indicates normal.

[0036] For potential abnormal points marked, it is necessary to further distinguish between sensor failures and real temperature anomalies. This distinction is achieved by analyzing the correlation between potential abnormal points and the data of spatially adjacent sensors. Calculate the Pearson correlation coefficient between the abnormal point and its spatially adjacent points (for example, points with a distance less than 5 mm). The correlation coefficient calculation is based on the recent temperature change trends of these points. If the correlation coefficient is greater than 0.7, it indicates that the temperature change trends are consistent, and the abnormal point is a real temperature anomaly; if the correlation coefficient is less than 0.3, it indicates that the temperature change trends are inconsistent, and the abnormal point is likely to be a sensor failure. According to the analysis results, assign a reliability weight to each sensor position. When the sensor is normal, the weight is 1.0, and when a fault is suspected, the weight is a value between 0 and 0.5, and the weight value changes linearly with the correlation coefficient.

[0037] Standardize the stabilized temperature sequence and convert it into standardized temperature values. The standardization process maps the temperature values to the range of 0 - 1, which is convenient for comparison of different temperature scales and subsequent processing. Combine the standardized temperature values with the corresponding sensor reliability weights to form a standardized temperature matrix and reliability markers, which serve as the basic data for subsequent temperature field reconstruction and hot spot analysis.

[0038] Take an actual situation in an automotive motor controller as an example: During the operation of the motor controller, sudden interference occurs in the temperature data of a certain point in the power MOSFET area, and the original values are [70.2, 71.5, 69.8, 85.4, 72.3, 70.1, 71.8] °C. After median filtering with a 7 - point sliding window, the obtained median is 71.5 °C, effectively filtering out the abnormal 85.4 °C. Process the filtered values of this point at multiple consecutive moments through the EWMA algorithm to obtain a stabilized temperature value of 72.3 °C. Calculate the historical mean of 68.9 °C and the standard deviation of 1.2 °C for this point, and obtain a Z - score of 2.83, which is close to but does not exceed the abnormal threshold of 3.0. At the same time, the temperature correlation coefficient between this point and the surrounding sensors is 0.92, indicating a highly consistent temperature change trend, and the sensor reliability weight is 1.0. After standardization, the temperature value of this point is 0.76 (based on the system's lowest temperature of 25 °C and highest temperature of 100 °C).

[0039] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Obtain the motor operation state signal from the motor controller, and divide the motor operation process into a starting stage, an acceleration stage, a stable operation stage, and a braking stage; (2) Group the standardized temperature matrix by motor operation stage in time series to form a stage temperature data set; (3) Assign trust weights to each data point in the stage temperature data set based on the reliability markers to generate weighted stage temperature data; (4)Map the weighted stage temperature data to the physical layout of the motor controller according to the spatial position to construct a temperature spatial distribution map; (5)Perform heat conduction interpolation on the positions of non-measured points in the temperature spatial distribution map to fill the temperature blank areas; (6)Synchronize the temperature spatial distribution map with the motor operating state signal in time to generate a two-dimensional temperature distribution field matrix associated with the motor operating state.

[0040] Specifically, obtain the operating state signal from the motor controller. The motor controller outputs operating parameters to the temperature monitoring system through the CAN bus or a dedicated interface, including information such as phase current value, motor speed, control instructions, and load feedback. According to these parameters, the motor operation process is divided into four typical stages: The starting stage is characterized by a sudden increase in phase current and a rapid rise in speed from zero; the acceleration stage shows a relatively high current value but lower than the starting peak, and the speed continues to rise; the stable operation stage is reflected by small fluctuations in current and speed within a set range; the braking stage is characterized by a rapid decrease in speed, possibly accompanied by energy feedback or reverse current. The specific basis for dividing these four stages is the motor speed change rate and the phase current value. For example, when the speed starts from zero and the current exceeds 150% of the rated value, it is determined as the starting stage; when the speed change rate exceeds 200 rpm / s and the current is greater than 120% of the rated value, it is determined as the acceleration stage; when the speed change rate is less than 50 rpm / s and the current is within the range of 80%-110% of the rated value, it is determined as the stable operation stage; when the speed decrease rate exceeds 300 rpm / s, it is determined as the braking stage.

[0041] When grouping the standardized temperature matrix according to the motor operating stage in time sequence, associate the temperature data with the operating stage identifier through the time stamp. The specific operation is to establish a two-level index structure. The first level is the operating stage type, and the second level is the time stamp. The corresponding value is the standardized temperature matrix at that moment. This grouping method is convenient for analyzing the temperature distribution characteristics and evolution laws of different operating stages. After grouping, temperature data sets for four typical stages are formed: the starting stage temperature data set, the acceleration stage temperature data set, the stable operation stage temperature data set, and the braking stage temperature data set. Each data set contains the temperature distribution data of all sampling moments within that stage, retaining the time stamp information, enabling subsequent analysis to focus on both spatial distribution characteristics and track time evolution trends.

[0042] When assigning trust weights to each data point in the stage temperature data set based on the reliability mark, directly apply the sensor reliability weight generated in the previous step to the corresponding temperature data point. Through this weighting process, the influence of unreliable sensor data is reduced, and the accuracy of temperature analysis is improved. The temperature data generated after weighting retains the original time and space index structure, but the values are more reliable and can better reflect the true temperature state of the motor controller.

[0043] Mapping the weighted-stage temperature data to the physical layout of the motor controller according to spatial positions requires establishing a digital two-dimensional plane model of the motor controller. This model includes information such as the shape outline of the PCB board, component distribution, copper foil area, electrical connection relationships, etc. For each sampling moment, place the temperature values of each sensor at their corresponding physical coordinate positions to form a discrete temperature point distribution. This mapping process associates the abstract temperature data with the specific physical structure, making the temperature analysis more practically significant. Due to the limited number of sensors, the initial mapping result is a sparsely distributed set of temperature points, with temperature values only at the sensor positions.

[0044] Performing heat conduction interpolation on the positions of non-measured points in the temperature spatial distribution mapping is to fill the temperature blank areas and obtain the temperature field distribution. The interpolation method is based on the physical model of heat conduction, considering the PCB material characteristics, copper foil distribution, and device heating characteristics. Specifically, the discrete solution method of the two-dimensional Laplace equation is used. For any non-measured point, its temperature value is calculated by the weighted average of the surrounding known temperature points. The weight is inversely proportional to the distance and the heat conduction path characteristics are considered. For areas with thermal resistance barriers (such as vias, device boundaries), adjust the weight calculation method to reduce the influence of heat conduction between partitioned areas. The interpolation calculation uses the iterative method, starting from the boundary known temperature points and gradually advancing inward until all blank areas are filled. This interpolation method based on the physical model is more in line with the actual heat conduction law than simple mathematical interpolation, and the generated temperature field is more realistic.

[0045] Performing time synchronization association between the temperature spatial distribution mapping and the motor operating state signal is to analyze the temperature distribution characteristics under different operating states. The complete temperature field distribution at each moment is marked with the corresponding timestamp and operating state identifier, forming a temperature data structure including three dimensions: space, time, and state. The finally generated two-dimensional temperature distribution field matrix is a multi-dimensional data set, and each cell contains position coordinates, temperature value, timestamp, and operating state identifier. This data structure is convenient for targeted analysis, such as extracting the temperature rise rate during the startup stage, the temperature distribution pattern during the stable operation stage, and the change of hot spot positions during different operating stages.

[0046] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Extract the low-load condition temperature distribution sub-matrix, medium-load condition temperature distribution sub-matrix, and high-load condition temperature distribution sub-matrix from the two-dimensional temperature distribution field matrix; (2) Perform local extreme value detection on the low-load condition temperature distribution sub-matrix to identify the positions and temperature values of the temperature peak points under the low-load state, and calculate the low-load hot spot temperature gradient index; (3) Identify the temperature peak points of the temperature distribution sub - matrix under the medium - load condition, extract the coordinates and temperature values of the high - temperature points in the medium - load state, and analyze the spatial distribution characteristics of the medium - load hot spots; (4) Screen the hot spots of the high - load condition temperature distribution sub - matrix, locate the boundary and center point of the high - load hot - spot area, and calculate the high - load thermal mass index; (5) Calculate the correlation between the positions of the low - load hot spots, medium - load hot spots and high - load hot spots and the motor speed data to generate the motor speed correlation index for each hot spot; (6) Integrate the low - load hot - spot temperature gradient index, medium - load hot - spot spatial distribution characteristics, high - load thermal mass index, the positions of each hot spot and the motor speed correlation index to form a hot - spot feature vector set including position, motor speed correlation and load correlation.

[0047] Specifically, classify the data according to the motor load state. Extracting the temperature distribution sub - matrices of different load conditions from the two - dimensional temperature distribution field matrix is realized based on the motor load parameters. The motor load state is usually defined by the ratio of phase current to rated current or the ratio of output power to rated power. The low - load condition is defined as the load ratio in the range of 0 - 40%, the medium - load condition is the load ratio in the range of 40 - 70%, and the high - load condition is the load ratio in the range of 70 - 100%. For each load condition, extract the temperature data of the corresponding time period from the original two - dimensional temperature distribution field matrix to form three sub - matrices: the low - load condition temperature distribution sub - matrix (TL), the medium - load condition temperature distribution sub - matrix (TM), and the high - load condition temperature distribution sub - matrix (TH). Each sub - matrix retains the spatial distribution information of the original matrix but only contains the temperature data under a specific load state.

[0048] When performing local extreme detection on the low - load condition temperature distribution sub - matrix, use the sliding window search algorithm. Set a search window (usually a 5×5 or 7×7 grid) in the two - dimensional space. The window slides over the entire temperature distribution sub - matrix. Each time it moves one step, calculate the temperature difference between the center point of the window and the surrounding points. When the center point temperature is higher than all other points in the window and the temperature difference exceeds a preset threshold (usually 2℃), mark this point as the temperature peak point under the low - load state. Record the position coordinates and temperature values of each peak point to form the low - load hot - spot set. For each low - load hot spot, calculate its temperature gradient index, expressed as:

[0049] Among them, represents the low - load hot - spot temperature gradient index, is the number of neighbor points around the hot spot, is the hot - spot temperature value, is the temperature of the k - th neighbor point, is the distance from the hot spot to the k-th neighbor point. The larger the value, the steeper the temperature change around the hot spot and the higher the possibility of heat diffusion being blocked.

[0050] A similar method is used to identify the temperature peak points in the temperature distribution sub-matrix under medium load conditions. However, the window size is usually expanded to a 9×9 or 11×11 grid, and the threshold is increased to 3 - 4°C to adapt to the more dispersed temperature distribution under medium load conditions. The recorded position coordinates and temperature values of the medium load hot spots form a medium load hot spot set. The analysis of the spatial distribution characteristics of medium load hot spots includes calculating the hot spot density, hot spot spacing, and hot spot clustering degree. The hot spot density represents the number of hot spots per unit area; the hot spot spacing calculates the minimum distance between any two hot spots; the hot spot clustering degree is calculated by the spatial autocorrelation method, reflecting the degree of aggregation or dispersion of the hot spot distribution. These characteristics help to understand the overall pattern of the temperature distribution under medium load conditions.

[0051] For the hot spot screening of the temperature distribution sub-matrix under high load conditions, more stringent screening criteria need to be adopted. First, an absolute temperature threshold is set (usually 80% of the maximum allowable operating temperature of the device), and any point exceeding this threshold is considered a potential high load hot spot. Then, the region growing algorithm is used to determine the hot spot region. Starting from each potential hot spot, adjacent points with similar temperatures are gradually included in the same region until the temperature at the region boundary is lower than a given value (usually the center point temperature minus 10°C). What is obtained in this way is a hot spot region with a continuous boundary, rather than discrete points. Record the set of boundary contour coordinates and the center point position of each hot spot region. For each high load hot spot region, calculate its thermal mass index, which is defined as:

[0052] where, represents the high load thermal mass index, is the area of the hot spot region, is the temperature at the center point of the region, is the average temperature of the entire motor controller, is the time change rate of the hot spot temperature. Comprehensively considering the size, temperature exceeding degree, and temperature rise rate of the hot spot, it is an important indicator for evaluating the severity of the hot spot.

[0053] Calculating the correlation between the positions of low load hot spots, medium load hot spots, and high load hot spots and the motor speed data is to quantify the degree of association between hot spot formation and motor operating parameters. For any hot spot (x, y), its motor speed correlation index is calculated as:

[0054] where, Indicates the rotational speed correlation index of the hot spot at position (x, y). Is the temperature at this position at the i-th sampling moment. Is the average temperature at this position. Is the motor speed at the i-th sampling moment. Is the average speed. Is the number of samples. This formula essentially calculates the absolute value of the Pearson correlation coefficient between temperature and speed, and the result range is 0 - 1. The closer the value is to 1, the stronger the correlation between the hot spot temperature and the motor speed.

[0055] Integrate various indicators to construct a feature vector for each hot spot. The feature vector contains the following elements: the hot spot position coordinates (x, y), the load operating condition category it belongs to (low / medium / high), the temperature value, the load-specific indicator (the temperature gradient indicator for low-load hot spots, the spatial distribution characteristics for medium-load hot spots, the heat mass indicator HMI for high-load hot spots), and the motor speed correlation index SCI. These sets of feature vectors completely describe the key characteristics of the temperature distribution of the motor controller and provide a data basis for formulating subsequent protection strategies.

[0056] Taking a brushless motor controller used in an electric vehicle air conditioning system as an example, the temperature analysis process under different load states is as follows: When the motor operates in a low-load state (phase current is 30% of the rated value), 3 temperature peak points are identified through local extreme value detection, which are located in the power MOSFET area, the drive IC area, and the PWM control module area respectively, and the temperature values are 56°C, 42°C, and 38°C respectively. The temperature gradient indicator of the hot spot in the power MOSFET area is calculated to be 1.5°C / mm, indicating that the heat diffusion is relatively gentle. When the motor switches to a medium-load state (phase current is 60% of the rated value), the temperature in the power MOSFET area rises to 78°C, the hot spot area expands, the hot spot density increases, and the hot spot spacing decreases. When the motor operates in a high-load state (phase current is 90% of the rated value), the temperature in the power MOSFET area reaches 112°C, forming a continuous hot spot area with an area of approximately 300 mm², and the calculated value of the heat mass indicator HMI is 8400°C·mm²·°C / s, indicating that this is a serious hot spot with a large area and rapid temperature rise. Through the rotational speed correlation calculation, it is found that the SCI value of this hot spot is 0.87, indicating that its temperature is highly correlated with the motor speed. Integrate all these features into a set of hot spot feature vectors.

[0057] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Obtain the maximum junction temperature specification value of the power MOSFET area and the maximum operating temperature specification value of the drive IC area, and set them as the basic protection upper limit temperature. (2)Adjust the upper limit of the basic protection temperature according to the position information of each hot spot in the hot spot feature vector set to form a position-weighted protection temperature value; (3)Based on the motor speed correlation index in the hot spot feature vector set, construct a speed-temperature rise relationship matrix and calculate the temperature correction coefficients for different speed ranges; (4)Based on the load correlation in the hot spot feature vector set and combined with the temperature correction coefficients, generate five-level progressive protection thresholds for the starting stage, acceleration stage, stable operation stage, and braking stage respectively; (5)For the five-level progressive protection thresholds, set the temperature warning trigger condition, PWM frequency reduction trigger condition, power limit trigger condition, forced frequency reduction trigger condition, and safety shutdown trigger condition respectively; (6)Combine the five-level progressive protection thresholds with the corresponding trigger conditions to generate a protection execution instruction set including hierarchical trigger conditions and corresponding control strategies.

[0058] Specifically, extract the maximum junction temperature specification value of the power MOSFET area and the maximum operating temperature specification value of the driver IC area from the device specification of the motor controller. These values are the maximum allowable operating temperatures specified by the device manufacturer. The typical maximum junction temperature specification value of the power MOSFET is 150 - 175 °C, while the maximum operating temperature specification value of the driver IC is usually 125 - 150 °C. Considering the reliability of long-term operation, these values are set as the upper limit of the basic protection temperature and used as the reference value for subsequent protection threshold calculation.

[0059] Adjusting the upper limit of the basic protection temperature according to the position information of each hot spot in the hot spot feature vector set is a key step in considering the importance of the area where the hot spot is located. Hot spots in different positions have different degrees of impact on system safety and need to be assigned different weights. The position weight adjustment uses a regional importance mapping table to divide the PCB board surface of the motor controller into several key areas and assign an importance weight to each area. The power input area and the key signal processing area have higher weights (usually 0.8 - 0.9), while the auxiliary circuit area has a lower weight (usually 0.95 - 1.0). The position-weighted protection temperature value is calculated as follows:

[0060] where, is the position-weighted protection temperature value at position (x, y), is the upper limit of the basic protection temperature of the corresponding device at this position, is the importance weight of this position. The weight value is less than 1, resulting in a lower protection temperature than the basic upper limit temperature, thus providing a greater safety margin for key areas.

[0061] First, divide the motor speed range into several intervals (such as low speed, medium speed, and high speed). Then, based on historical operation data, calculate the average temperature rise rate of each hot spot in each speed interval. The speed-temperature rise relationship matrix is a two-dimensional matrix, where the rows represent different hot spots, the columns represent different speed intervals, and the matrix elements are the corresponding temperature rise rates. Based on this matrix, for each speed interval s and each hot spot (x, y), calculate the temperature correction coefficient :

[0062] Among them, is the temperature rise rate of the hot spot (x, y) at the reference speed, is the temperature rise rate of this hot spot in the speed interval s, is the motor speed correlation index of this hot spot, is the adjustment coefficient (usually taking values between 0.2 and 0.5). When the hot spot temperature is highly correlated with the speed (SCI is close to 1) and the current speed results in a high temperature rise rate, the value is small, and the protection threshold is correspondingly reduced, triggering protection in advance.

[0063] When formulating protection thresholds for different operating stages based on the load correlation in the hot spot feature vector set and combining the temperature correction coefficient, the characteristics of each operating stage of the motor need to be considered. The instantaneous value of the current is large but the duration is short during the start-up stage, the power remains relatively high during the acceleration stage, the temperature tends to be stable during the stable operation stage, and reverse energy feedback may occur during the braking stage. For each operating stage p and each hot spot (x, y), design a five-level progressive protection threshold:

[0064] Among them, is the k-th level protection threshold (k = 1, 2, 3, 4, 5), is the basic temperature margin for the k-th level protection (such as 20℃, 10℃, 5℃, 2℃, 0℃), is the temperature correction coefficient corresponding to the speed in the operating stage p, is the load correlation correction coefficient, which is determined according to the sensitivity of the hot spot to the load. What is obtained in this way is a set of five-level progressive protection thresholds for different positions and different operating stages, which can dynamically adjust the protection response according to the actual operating conditions.

[0065] For the five - level progressive protection threshold, corresponding trigger conditions and control strategies are set. The first level (temperature warning trigger condition) is triggered when the temperature reaches the first temperature threshold, increasing the temperature sampling frequency to prepare for subsequent possible protection actions; the second level (PWM frequency reduction trigger condition) is triggered when the temperature reaches the second temperature threshold, reducing the switching loss by reducing the PWM switching frequency (usually by 20%); the third level (power limit trigger condition) is triggered when the temperature reaches the third temperature threshold, limiting the maximum power output of the motor to 85% of the rated power; the fourth level (forced frequency reduction trigger condition) is triggered when the temperature reaches the fourth temperature threshold, further limiting the power to 70% and increasing the cooling fan speed; the fifth level (safety shutdown trigger condition) is triggered when the temperature reaches the fifth temperature threshold, executing the safety shutdown procedure to prevent device damage. Each trigger condition not only includes the temperature threshold but also the temperature duration requirement to avoid unnecessary protection actions caused by transient temperature fluctuations. Combining the five - level progressive protection threshold with the corresponding trigger conditions and control strategies generates a complete protection execution instruction set. Each instruction includes a trigger condition (temperature threshold, duration), an execution action (such as adjusting PWM parameters), and a recovery condition (the temperature drops to a certain value and lasts for a certain time). These instructions are sorted by priority, and higher - level protection is executed prior to lower - level protection. The protection execution instruction set is written into the control program of the motor controller, and during actual operation, the temperature data is monitored in real - time. Once the trigger condition is met, the corresponding control strategy is immediately executed.

[0066] Taking a controller for the cooling system of a new energy vehicle drive motor as an example, this controller adopts a multi-phase bridge power drive circuit. The highest junction temperature specification in the power MOSFET area is 175°C, and the highest operating temperature of the drive IC is 150°C. Considering long-term reliability, these two values are respectively set as the basic protection upper limit temperatures. After position weight adjustment, the protection temperature of the power MOSFET near the power input terminal is reduced to 157.5°C (weight 0.9), and the protection temperature of the drive IC in the signal control area is reduced to 135°C (weight 0.9). By analyzing the temperature rise characteristics of this controller at different speeds, it is found that the temperature rise rate of a certain power MOSFET hot spot at high speed is 2.5 times that at low speed, and its speed correlation index SCI is 0.85. According to the formula calculation, the temperature correction coefficient of this hot spot in the high-speed range is 0.37, which means that the protection threshold will be significantly reduced during high-speed operation. The finally generated five-level protection thresholds are: warning trigger temperature 137.5°C, PWM frequency reduction trigger temperature 147.5°C, power limit trigger temperature 152.5°C, forced frequency reduction trigger temperature 155.5°C, and safety shutdown trigger temperature 157.5°C. When the temperature reaches 137.5°C during the operation of the motor, the controller immediately increases the temperature sampling frequency from 1Hz to 20Hz; when the temperature continues to rise to 147.5°C, the PWM frequency automatically decreases from 20kHz to 16kHz, effectively reducing the heat generated by switching losses; if the temperature still rises to 152.5°C, the controller will limit the maximum output power to ensure the safe operation of the system. This multi-level protection strategy can not only cope with overheating situations of different severities, but also dynamically adjust the protection parameters according to the operating state of the motor, ensuring the safe and reliable operation of the motor controller while minimizing unnecessary performance losses.

[0067] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Receive the temperature warning trigger signal in the protection execution instruction set, and increase the sampling rate of the PWM controller from the standard state to the high-speed sampling state; (2) According to the PWM frequency reduction trigger signal in the protection execution instruction set, reduce the motor PWM switching frequency according to the slope limit function, and gradually reduce the switching frequency from the original frequency value to 80% of the original frequency, thereby reducing the heat generated by switching losses; (3) Based on the power limit trigger signal in the protection execution instruction set, controllably lower the upper limit of the motor PWM duty cycle, and limit the maximum power output of the motor to 85% of the normal value; (4) Based on the forced frequency reduction trigger signal in the protection execution instruction set, jointly regulate the PWM duty cycle and the switching frequency at the same time, further limit the motor power to 70% of the normal value, and record the temperature change rate during the regulation process; (5) Record the trigger temperature, motor operating parameters and temperature response data of each temperature protection action into the temperature event log database to construct an operating condition-temperature rise characteristic map; (6) By analyzing the temperature variation patterns under different seasons and environmental conditions in the operating condition-temperature rise characteristic diagram, the hierarchical trigger condition thresholds in the protection execution instruction set are periodically optimized and adjusted.

[0068] Specifically, when the temperature warning trigger signal in the protection execution instruction set is received, the signal is captured by the interrupt service program and the temperature warning processing flow is entered. At this time, the controller immediately modifies the configuration parameters of the internal timer and increases the sampling rate of the PWM controller from the standard state (usually 1Hz) to the high-speed sampling state (usually 20Hz). The sampling rate adjustment is achieved by reconfiguring the timer division coefficient and count value. The division coefficient is reduced from the original value to 1 / 20 of the original value, so that the temperature sampling interrupt trigger frequency is increased accordingly. The high-speed sampling state can capture the temperature change trend more finely and provide more timely data support for subsequent possible protection actions. At the same time, the controller sets the temperature warning state flag to allow other modules to query the current protection state of the system. When the temperature continues to rise and triggers the PWM frequency to reduce the protection level, the controller receives the PWM frequency reduction trigger signal in the protection execution instruction set and immediately starts the smooth frequency modulation program. In order to avoid unstable motor operation or current shock caused by frequency mutation, the controller uses the slope limit function to gradually reduce the motor PWM switching frequency. The slope limit function defines the maximum frequency change allowed for each control cycle, which is usually set to 4% of the maximum change of the original frequency every 100 milliseconds. Assuming that the original PWM frequency is 20kHz, the target is reduced to 80% of the original frequency, that is, 16kHz, and the total frequency reduction is 4kHz, then according to the slope limit, at least 500 milliseconds are required to complete the smooth transition. The specific implementation process is that the controller calculates the current PWM frequency value to be set in each control cycle (such as 10 milliseconds) and updates the frequency register of the PWM generator. During the frequency reduction process, the controller continuously monitors temperature changes and records the relationship data between frequency and temperature. Reducing the PWM frequency can effectively reduce the switching loss of the power tube. The switching loss is approximately proportional to the switching frequency. Therefore, reducing the frequency by 20% can reduce the switching heat generation by nearly 20%.

[0069] When the temperature protection is upgraded to the power limit level, the controller starts the PWM duty cycle limit program based on the power limit trigger signal in the protection execution instruction set. The upper limit of the duty cycle is controlled to decrease by modifying the comparator threshold of the PWM generator. The upper limit of the duty cycle decreases from the original maximum value (such as 95%) to the limit value (such as 80.75%, which is 85% of the normal value). Considering that the motor output power is proportional to the square of the duty cycle, the power is limited to about 72.25% of the original value when the duty cycle is limited to 85% of the original value. The duty cycle adjustment also adopts a slope limit method to avoid mechanical shocks caused by sudden changes. At the same time, the controller dynamically fine-tunes the upper limit of the duty cycle according to the actual temperature change to keep the motor power within a safe range.

[0070] When the forced frequency reduction protection level is triggered, the controller starts the combined regulation program of the PWM duty cycle and the switching frequency based on the forced frequency reduction trigger signal in the protection execution instruction set. At this time, more stringent power limitation is carried out, and the motor power is further limited to 70% of the normal value. The implementation method is to simultaneously reduce the PWM switching frequency and the upper limit of the duty cycle. The switching frequency may be further reduced to 70% of the original frequency, and the upper limit of the duty cycle is further reduced to about 84% of the original value (the calculation basis is 0.84²≈0.7). During the combined regulation process, the controller also calculates and records the temperature change rate in real time. The method is to record the current temperature in each control cycle and calculate the average change rate together with the temperature values of the previous several cycles. The temperature change rate, as an important indicator of the regulation effect, guides the controller to judge whether the current protection measures are effective enough.

[0071] Temperature event log recording is an important means to track and analyze the protection process. The controller records the complete information of each temperature protection action into the temperature event log database in the non-volatile memory (such as EEPROM or Flash). The recorded content includes: the trigger temperature, the trigger time, the motor operating condition parameters at that time (such as speed, load, phase current, bus voltage, etc.), the control measures after triggering, and the temperature response curve (temperature change data within a period of time after the protection action). Each record is marked with a timestamp and environmental parameters (such as ambient temperature, humidity, etc.) to facilitate the subsequent analysis of the temperature behavior under different conditions. These records are organized in chronological order to form a structured temperature event data set. Based on these data, the controller constructs a working condition-temperature rise characteristic map, that is, the mapping relationship between different working states of the motor and the temperature change trend.

[0072] The periodic analysis of the operating condition - temperature rise characteristic graph is the key to realizing the adaptive optimization of the protection strategy. The controller sets a fixed analysis period (such as every 100 hours of operating time or once a month), and batch - processes the accumulated temperature event logs. The analysis content includes: comparison of temperature rise characteristics in different seasons, differences in temperature responses under similar operating conditions, evaluation of the effectiveness of protection actions, etc. The analysis process uses pattern recognition methods to identify the influence rules of environmental factors on temperature behavior. For example, in a high - temperature environment in summer, the temperature rise rate under the same operating conditions is about 30% faster than in winter; or it is found that under certain specific operating conditions, the protection threshold is too conservative or too aggressive. Based on the analysis results, the controller optimizes and adjusts the hierarchical trigger condition thresholds in the protection execution instruction set. For example, in summer, it automatically reduces the temperature thresholds of each level of protection by 5 - 10°C, or adjusts the intensity of protection actions for specific operating conditions. The adjusted protection parameters take effect in the next operating cycle, forming a closed - loop self - optimizing protection mechanism.

[0073] Taking a fan motor controller for an electric vehicle thermal management system as an example, when the controller operates under the condition of an ambient temperature of 15°C in spring, the temperature in the power MOSFET area reaches 125°C, triggering a temperature warning, and the sampling rate quickly increases from 1Hz to 20Hz. Subsequently, the temperature continues to rise to 135°C, triggering the PWM frequency reduction protection. The controller smoothly reduces the PWM frequency from the original 25kHz to 20kHz within 600 milliseconds. The effect is not obvious, and the temperature still rises to 142°C, triggering the power limit protection. At this time, the upper limit of the duty cycle is reduced from 95% to 80.75%, and the motor power drops to about 72% of the normal value. This measure effectively controls the temperature rise, and the temperature stops at 145°C and begins to slowly decline. The data of the entire protection process is detailedly recorded, including the trigger - point temperature, ambient temperature, motor speed (4500rpm), current (8.2A), and the temperature response curve after frequency and power reduction. As the seasons change, the controller accumulates a large amount of temperature event data under different environmental conditions. After analysis, it is found that when the ambient temperature in summer exceeds 30°C, the temperature rise rate under the same operating conditions is about 25% faster than in spring. Therefore, the controller automatically reduces all protection thresholds in summer by 7°C, intervenes in advance, and effectively avoids performance fluctuations caused by frequent triggering of high - level protection in the high - temperature environment in summer.

[0074] The temperature monitoring method for a motor controller in the embodiments of the present application has been described above. Next, the temperature monitoring system for a motor controller in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the temperature monitoring system for a motor controller in the embodiments of the present application includes: An acquisition module, configured to perform multi - point temperature acquisition on the power MOSFET area, drive IC area, and PWM control module area of the motor controller through a temperature sensor array, and obtain a three - dimensional temperature data stream including a timestamp, a spatial position, and a temperature value; A processing module, configured to filter out noise and mark outliers from the three-dimensional temperature data stream, so as to obtain a standardized temperature matrix and reliability marks; An analysis module, configured to analyze the temperature distribution during the motor starting, accelerating, steady running, and braking processes based on the standardized temperature matrix and reliability marks, so as to obtain a two-dimensional temperature distribution field matrix associated with the motor operating state; An association module, configured to detect and perform association analysis on the temperature peak points under different load conditions according to the two-dimensional temperature distribution field matrix, so as to obtain a set of hot spot feature vectors including position, motor speed correlation degree, and load correlation; A generation module, configured to determine adaptive protection thresholds corresponding to different operating stages according to the set of hot spot feature vectors, in combination with the temperature specifications of power devices and the current operating parameters of the motor, and generate a protection execution instruction set including hierarchical trigger conditions and corresponding control strategies; A regulation module, configured to perform real-time regulation on the PWM duty cycle and switching frequency of the motor according to the protection execution instruction set, and optimize and adjust the protection parameters through historical data on the relationship between the operating conditions and temperature rise.

[0075] Refer to Figure 3 , in an embodiment of the present invention, a computer device is further provided. The computer device may be a server, and its internal structure may be as Figure 3 shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0076] Those skilled in the art can understand that Figure 3 the structure shown in

[0077] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0078] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0079] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0080] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc., which can store program codes.

Claims

1. A temperature monitoring method for a motor controller, characterized in that, The temperature monitoring method for the motor controller includes: Performing multi-point temperature acquisition on the power MOSFET area, drive IC area, and PWM control module area of the motor controller through a temperature sensor array to obtain a three-dimensional temperature data stream including time stamps, spatial positions, and temperature values; According to the three-dimensional temperature data stream, performing filtering, noise reduction, and outlier marking processing on the data to obtain a standardized temperature matrix and reliability markings; Based on the standardized temperature matrix and reliability markings, analyzing the temperature distribution during motor startup, acceleration, stable operation, and braking to obtain a two-dimensional temperature distribution field matrix associated with the motor operating state; According to the two-dimensional temperature distribution field matrix, detecting and performing correlation analysis on the temperature peak points under different load conditions to obtain a set of hot spot feature vectors including positions, motor speed correlation degrees, and load correlations; Based on the set of hot spot feature vectors, combining the temperature specifications of power devices and the current operating parameters of the motor to determine adaptive protection thresholds corresponding to different operating stages, and generating a protection execution instruction set including hierarchical trigger conditions and corresponding control strategies; According to the protection execution instruction set, performing real-time regulation on the PWM duty cycle and switching frequency of the motor, and optimizing and adjusting the protection parameters through historical data on the relationship between operating conditions and temperature rise.

2. The temperature monitoring method for a motor controller according to claim 1, characterized in that, The performing multi-point temperature acquisition on the power MOSFET area, drive IC area, and PWM control module area of the motor controller through a temperature sensor array to obtain a three-dimensional temperature data stream including time stamps, spatial positions, and temperature values includes: Arranging NTC thermistor sensors in the power MOSFET area of the motor controller to perform high-precision acquisition of the temperature around the power devices; Arranging digital temperature sensors in the drive IC area of the motor controller to accurately measure the temperature of the control circuit; Arranging temperature monitoring points in the PWM control module area to perform real-time tracking of the temperature of the signal processing area; Dynamically adjusting the temperature sampling frequency according to the motor operating state, sampling at a frequency of 1 Hz during normal motor operation and sampling at a frequency of 10 Hz during high-load or startup stages; Summarizing the collected temperature data through a single-wire or I²C bus and adding time stamp information; Attaching spatial position identifiers to each temperature data point to construct a three-dimensional temperature data stream including time stamps, spatial positions, and temperature values.

3. The temperature monitoring method for a motor controller according to claim 1, characterized in that, The according to the three-dimensional temperature data stream, performing filtering, noise reduction, and outlier marking processing on the data to obtain a standardized temperature matrix and reliability markings includes: Eliminating high-frequency noise from the temperature data of each sampling point in the three-dimensional temperature data stream through sliding window median filtering to generate preliminary filtered temperature data; Applying exponential weighted moving average processing to the preliminary filtered temperature data to form a stabilized temperature sequence; Recombining the stabilized temperature sequence into a two-dimensional temperature data table according to the spatial distribution of the sensors, and calculating the Z-score value of each point based on the historical temperature mean; Screening temperature mutation points according to the Z-score value, and marking them as potential outlier points when the Z-score value exceeds a preset threshold; By analyzing the correlation between the potential abnormal points and the data of spatially adjacent sensors, distinguish sensor failures from real temperature anomalies, and generate sensor reliability weights; Perform normalization processing on the stabilized temperature sequence and combine it with the sensor reliability weights to form a normalized temperature matrix and reliability marks.

4. The temperature monitoring method for a motor controller according to claim 1, characterized in that Based on the normalized temperature matrix and reliability marks, analyze the temperature distribution during the motor startup, acceleration, steady operation, and braking processes to obtain a two-dimensional temperature distribution field matrix associated with the motor operating state, including: Obtain the motor operating state signal from the motor controller, and divide the motor operating process into a startup stage, an acceleration stage, a steady operation stage, and a braking stage; Perform time-series grouping on the normalized temperature matrix according to the motor operating stage to form a stage temperature data set; Assign trust weights to each data point in the stage temperature data set based on the reliability marks to generate weighted stage temperature data; Map the weighted stage temperature data to the physical layout of the motor controller according to the spatial position to construct a temperature spatial distribution mapping; Perform heat conduction interpolation on the non-measured point positions in the temperature spatial distribution mapping to fill the temperature blank areas; Synchronize the temperature spatial distribution mapping with the motor operating state signal in time to generate a two-dimensional temperature distribution field matrix associated with the motor operating state.

5. The temperature monitoring method for a motor controller according to claim 1, characterized in that, According to the two-dimensional temperature distribution field matrix, detect and perform correlation analysis on the temperature peak points under different load conditions to obtain a set of hot spot feature vectors including position, motor speed correlation, and load correlation, including: Extract the low-load condition temperature distribution sub-matrix, medium-load condition temperature distribution sub-matrix, and high-load condition temperature distribution sub-matrix from the two-dimensional temperature distribution field matrix; Perform local extreme value detection on the low-load condition temperature distribution sub-matrix to identify the position and temperature value of the temperature peak point under the low-load state, and calculate the low-load hot spot temperature gradient index; Identify the temperature peak points of the medium-load condition temperature distribution sub-matrix, extract the temperature high point coordinates and temperature values of the medium-load state, and analyze the spatial distribution characteristics of the medium-load hot spots; Perform hot spot screening on the high-load condition temperature distribution sub-matrix to locate the boundary and center point of the high-load hot spot area, and calculate the high-load heat mass index; Perform correlation calculation on the positions of the low-load hot spots, medium-load hot spots, and high-load hot spots and the motor speed data to generate the motor speed correlation index for each hot spot; Integrate the low-load hot spot temperature gradient index, medium-load hot spot spatial distribution characteristics, high-load heat mass index, the positions of each hot spot, and the motor speed correlation index to form a set of hot spot feature vectors including position, motor speed correlation, and load correlation.

6. The temperature monitoring method for a motor controller according to claim 1, characterized in that, Based on the set of hot spot feature vectors, combine the temperature specifications of the power devices and the current operating parameters of the motor to determine the adaptive protection threshold for different operating stages, and generate a protection execution instruction set including hierarchical trigger conditions and corresponding control strategies, including: Obtain the highest junction temperature specification value of the power MOSFET area and the highest operating temperature specification value of the driver IC area, and set them as the basic protection upper limit temperature; According to the position information of each hot spot in the hot spot feature vector set, adjust the weight of the basic protection upper limit temperature for the hot spot position to form a position-weighted protection temperature value; Based on the motor speed correlation index in the hot spot feature vector set, construct a speed-temperature rise relationship matrix and calculate the temperature correction coefficient for different speed intervals; According to the load correlation in the hot spot feature vector set and in combination with the temperature correction coefficient, generate five-level progressive protection thresholds for the starting stage, acceleration stage, stable operation stage, and braking stage respectively; For the five-level progressive protection thresholds, set temperature warning trigger conditions, PWM frequency reduction trigger conditions, power limit trigger conditions, forced frequency reduction trigger conditions, and safety shutdown trigger conditions respectively; Combine the five-level progressive protection thresholds with the corresponding trigger conditions to generate a protection execution instruction set including hierarchical trigger conditions and corresponding control strategies.

7. The temperature monitoring method for a motor controller according to claim 1, characterized in that, According to the protection execution instruction set, perform real-time regulation on the PWM duty cycle and switching frequency of the motor, and optimize and adjust the protection parameters through historical data of the relationship between the operating conditions and temperature rise, including: Receive the temperature warning trigger signal in the protection execution instruction set and increase the sampling rate of the PWM controller from the standard state to the high-speed sampling state; According to the PWM frequency reduction trigger signal in the protection execution instruction set, reduce the motor PWM switching frequency according to the slope limit function, and gradually reduce the switching frequency from the original frequency value to 80% of the original frequency, so as to reduce the heat generated by switching losses; According to the power limit trigger signal in the protection execution instruction set, controllably lower the upper limit of the motor PWM duty cycle and limit the maximum power output of the motor to 85% of the normal value; Based on the forced frequency reduction trigger signal in the protection execution instruction set, jointly regulate the PWM duty cycle and switching frequency at the same time, further limit the motor power to 70% of the normal value, and record the temperature change rate during the regulation process; Record the trigger temperature, motor operating condition parameters, and temperature response data of each temperature protection action into the temperature event log database to construct a working condition-temperature rise characteristic map; By analyzing the temperature change rules under different seasons and environmental conditions in the working condition-temperature rise characteristic map, perform periodic optimization and adjustment on the hierarchical trigger condition thresholds in the protection execution instruction set.

8. A temperature monitoring system for a motor controller, which is used to implement the temperature monitoring method for a motor controller according to any one of claims 1-7, characterized in that, The temperature monitoring system used by the motor controller includes: An acquisition module for performing multi-point temperature acquisition on the power MOSFET area, drive IC area, and PWM control module area of the motor controller through a temperature sensor array to obtain a three-dimensional temperature data stream including time stamps, spatial positions, and temperature values; A processing module for performing filtering, noise reduction, and outlier marking processing on the data according to the three-dimensional temperature data stream to obtain a standardized temperature matrix and a reliability mark; An analysis module for analyzing the temperature distribution during the motor starting, accelerating, stable operation, and braking processes based on the standardized temperature matrix and the reliability mark to obtain a two-dimensional temperature distribution field matrix associated with the motor operating state; An association module, configured to detect and perform association analysis on temperature peak points under different load conditions according to the two-dimensional temperature distribution field matrix, so as to obtain a set of hot spot feature vectors including position, motor speed correlation degree, and load correlation; A generation module, configured to determine adaptive protection thresholds corresponding to different operation stages based on the set of hot spot feature vectors, in combination with the temperature specifications of power devices and the current operation parameters of the motor, and generate a set of protection execution instructions including hierarchical trigger conditions and corresponding control strategies; A regulation module, configured to perform real-time regulation on the PWM duty ratio and switching frequency of the motor according to the set of protection execution instructions, and optimize and adjust the protection parameters through historical data on the relationship between the operation conditions and temperature rise.

9. A computer device, characterized in that, It includes a memory and a processor, and the memory stores a computer program that can run on the processor. It is characterized in that when the processor executes the computer program, the temperature monitoring method for a motor controller described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the processor is caused to execute the temperature monitoring method for a motor controller described in any one of claims 1 to 7.

Images