Brushless motor control circuit for high-temperature double-control water distributor

Abstract

The invention relates to the field of intelligent control, and particularly discloses a brushless motor control circuit for a high-temperature double-control water distributor, which comprises an MCU (Microprogrammed Control Unit) control circuit, a gate drive circuit, a current detection circuit, a three-phase H-bridge circuit and a high-temperature direct-current brushless motor, wherein the current detection circuit is electrically connected to the three-phase H-bridge circuit to collect real-time current and input the real-time current to the MCU control circuit, and the MCU control circuit judges the position of a rotor based on the real-time current and outputs a PWM control signal based on the position of the rotor; wherein the PWM control signal output by the MCU control circuit drives the high-temperature direct-current brushless motor through the gate drive circuit and the three-phase H-bridge circuit.

Description

Technical Field

[0001] This application relates to the field of intelligent control, and more specifically, to a brushless motor control circuit for a high-temperature dual-control water distributor. Background Art

[0002] In the field of oil extraction, intelligent stratified water injection technology plays a crucial role in improving the crude oil recovery rate. As a key core device of this technology, the performance of the high-temperature dual-control water distributor directly determines the effect of stratified water injection. Such water distributors usually operate in harsh environments such as high temperature and high pressure underground. It is necessary to precisely control the opening degree of the internal water nozzles to adjust the water injection volume of each oil layer, which poses extremely high requirements for the reliability and accuracy of its drive motor and its control circuit. Brushless DC motors have become an ideal choice for such applications due to their advantages such as high efficiency, long life, and easy precise control. Therefore, researching and developing a brushless motor control circuit for a high-temperature dual-control water distributor that can adapt to high-temperature environments, achieve precise control, and ensure long-term stable operation is of great significance for improving the overall performance of intelligent water injection equipment and ensuring efficient oilfield exploitation.

[0003] Traditional brushless motor control schemes usually rely on Hall effect sensors to detect the rotor position. However, the reliability of Hall sensors will significantly decrease in high-temperature environments (such as underground at 150 °C or even higher), prone to failure, and increase the system complexity and cost. Therefore, sensorless control technology, that is, without relying on physical position sensors, but estimating the rotor position by detecting electrical signals (such as back electromotive force, phase current, etc.) during motor operation, has become a research hotspot in high-temperature application scenarios. However, existing sensorless control methods based on back electromotive force often lead to inaccurate rotor position estimation during motor startup, low-speed operation, or load mutation due to weak back electromotive force signals or difficult to accurately detect, affecting the control performance and even resulting in startup failure. Especially in the application of high-temperature dual-control water distributors that require precise control of startup, stop, and operation processes, how to accurately obtain rotor position information under high dynamic range and harsh working conditions is the core challenge faced by sensorless control technology.

[0004] Therefore, an optimized brushless motor control circuit for a high-temperature dual-control water distributor is desired. Summary of the Invention

[0005] To solve the above technical problems, the present application is proposed. An embodiment of the present application provides a brushless motor control circuit for a high-temperature dual-control water distributor, which does not rely on traditional physical position sensors, but instead collects motor current signals (including A and B phase currents) and DC bus voltage signals in real time through a current detection circuit, extracts key features from these signals, and further performs complex response reasoning based on the implicit analysis of the signal features for the extracted signal features to estimate the electrical angle of the motor rotor. Compared with traditional sensorless control methods that rely on physical sensors or back electromotive force, this technical solution can help improve the rotor position estimation accuracy and the overall robustness of the system in harsh high-temperature environments, especially showing more superior performance under challenging working conditions such as motor startup and low-speed operation, thereby ensuring the precise control and long-term stable operation of the high-temperature dual-control water distributor.

[0006] According to one aspect of the present application, there is provided a brushless motor control circuit for a high-temperature dual-control water distributor, which includes: an MCU control circuit, a gate drive circuit, a current detection circuit, a three-phase H-bridge circuit, and a high-temperature DC brushless motor; wherein, the current detection circuit is electrically connected to the three-phase H-bridge circuit to collect real-time current and input the real-time current into the MCU control circuit, and the MCU control circuit determines the rotor position based on the real-time current and outputs a PWM control signal based on the rotor position; wherein, the PWM control signal output by the MCU control circuit drives the high-temperature DC brushless motor through the gate drive circuit and the three-phase H-bridge circuit.

[0007] In the above brushless motor control circuit for a high-temperature dual-control water distributor, during the rotor alignment stage, the MCU control circuit controls the gate drive circuit and the three-phase H-bridge circuit to apply a fixed DC current to the A phase and B phase of the high-temperature DC brushless motor.

[0008] In the above brushless motor control circuit for a high-temperature dual-control water distributor, during the open-loop acceleration stage, the MCU control circuit applies a PWM signal to the gate drive circuit and the three-phase H-bridge circuit to generate a continuously accelerating rotating magnetic field in the stator of the high-temperature DC brushless motor. Wherein, under the action of the continuously accelerating rotating magnetic field, the speed of the rotor of the high-temperature DC brushless motor gradually increases from zero to the minimum speed.

[0009] In the above brushless motor control circuit for a high-temperature dual-control water distributor, during the closed-loop control stage, the MCU control circuit collects three-phase currents in real time through the current detection circuit and estimates the rotor electrical angle based on the three-phase currents, and then outputs a PWM signal based on the rotor electrical angle.

[0010] In the above brushless motor control circuit for the high-temperature dual-control water distributor, the MCU control circuit collects three-phase currents in real time through the current detection circuit and estimates the rotor electrical angle based on the three-phase currents, including the steps of: obtaining an A-phase current analog signal and a B-phase current analog signal; obtaining a DC bus voltage signal; extracting current signal features from the A-phase current analog signal and the B-phase current analog signal to obtain an A-phase current analog signal feature coding vector and a B-phase current analog signal feature coding vector; extracting voltage signal features from the DC bus voltage signal to obtain a DC bus voltage signal feature coding vector; performing response inference based on signal feature implicit analysis on the basis of the current analog signal joint coding feature between the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector and the DC bus voltage signal feature coding vector to obtain a voltage-current response inference coding vector; and performing feature decoding on the voltage-current response inference coding vector to obtain an estimated value of the rotor electrical angle.

[0011] Compared with the prior art, a brushless motor control circuit for a high-temperature dual-control water distributor provided by the present application does not rely on a traditional physical position sensor, but instead collects motor current signals (including A and B phase currents) and a DC bus voltage signal in real time through a current detection circuit, extracts key features from these signals, and further performs complex response inference based on signal feature implicit analysis on the extracted signal features to estimate the electrical angle of the motor rotor. Compared with traditional sensorless control methods that rely on physical sensors or back electromotive force, the technical solution of the present application can help improve the accuracy of rotor position estimation and the overall robustness of the system in a harsh high-temperature environment, especially showing more excellent performance under challenging working conditions such as motor startup and low-speed operation, thereby ensuring the precise control and long-term stable operation of the high-temperature dual-control water distributor.

[0012] Meanwhile, through response inference based on implicit analysis of signal characteristics, the complex interaction between current and voltage characteristics can be understood and quantified, thus overcoming the limitations of traditional methods under complex working conditions and signal interference. Specifically, by arranging the input feature vectors (i.e., the joint encoding vector of the A-B phase current analog signal characteristics and the encoding vector of the DC bus voltage signal characteristics) in an orderly manner to focus on the intrinsic intensity of the features, and then performing equal-grained feature segmentation to achieve a detailed analysis of local features. Subsequently, the core transfer response inference unit conducts in-depth interaction modeling on the local feature segments of each pair of A-B phase current analog signals and DC bus voltage signals, captures the mutual influence, response pattern, or information transfer mechanism between them, and retains rich local interaction details of the voltage-current signals in matrix form. Finally, through the sequence transfer and aggregation module, this local interaction information of the voltage-current signals is integrated to capture the dependency relationships and overall trends across different feature intensity intervals. The ultimate goal of all this is to generate a concise and information-rich voltage-current response inference encoding vector. This encoding vector can represent the dynamic response relationship between the applied voltage and the generated current in a highly generalized and strongly discriminative manner for the rotor position. By deeply mining and understanding the complex coupling relationship between voltage and current, the control system can more accurately identify the subtle electrical signal characteristics caused by rotor position changes, and maintain a high estimation reliability even under harsh working conditions such as high temperature, high noise, motor parameter drift, or severe load fluctuations. Brief Description of the Drawings

[0013] The above and other objects, features, and advantages of the present application will become more apparent by describing the embodiments of the present application in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application, and do not constitute a limitation to the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0014] Figure 1 It is a schematic diagram of the principle of a brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application; Figure 2 It is a schematic diagram of the MCU control circuit of a brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application; Figure 3 It is a schematic diagram of the gate drive circuit of a brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application; Figure 4 It is a schematic diagram of the current detection circuit of a brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application; Figure 5Schematic diagram of a three-phase H-bridge circuit and a high-temperature DC brushless motor for a high-temperature dual-control water distributor according to an embodiment of the present application; Figure 6 Flowchart of the MCU control circuit of the brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application, which collects three-phase currents in real time through the current detection circuit and estimates the rotor electrical angle based on the three-phase currents; Figure 7 Schematic diagram of data flow of the MCU control circuit of the brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application, which collects three-phase currents in real time through the current detection circuit and estimates the rotor electrical angle based on the three-phase currents; Figure 8 Flowchart of the brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application, which performs response inference based on signal feature implicit analysis on the current analog signal joint coding feature between the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector and the DC bus voltage signal feature coding vector to obtain a voltage-current response inference coding vector; Figure 9 Flowchart of the brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application, which performs response inference based on signal feature implicit analysis on the A-B phase current analog signal feature joint coding vector and the DC bus voltage signal feature coding vector to obtain the voltage-current response inference coding vector. Detailed implementation

[0015] Next, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein.

[0016] As shown in the present application and the claims, unless the context clearly indicates otherwise, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0017] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0018] In this application, flowcharts are used to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the operations before or below do not necessarily need to be executed precisely in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0019] Next, example embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited by the example embodiments described herein.

[0020] In the technical solution of this application, a brushless motor control circuit for a high-temperature dual-control water distributor is proposed. Figure 1 It is a schematic diagram of the principle of the brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of this application. As Figure 1 shown, the brushless motor control circuit for a high-temperature dual-control water distributor includes: an MCU control circuit, a gate drive circuit, a current detection circuit, a three-phase H-bridge circuit, and a high-temperature DC brushless motor; wherein, the current detection circuit is electrically connected to the three-phase H-bridge circuit to collect real-time current and input the real-time current into the MCU control circuit, and the MCU control circuit determines the rotor position based on the real-time current and outputs a PWM control signal based on the rotor position; wherein, the PWM control signal output by the MCU control circuit drives the high-temperature DC brushless motor through the gate drive circuit and the three-phase H-bridge circuit.

[0021] Figure 2 It is a schematic diagram of the principle of the MCU control circuit of the brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of this application. As Figure 2 shown, the MCU control circuit includes: an external crystal oscillator circuit, an external power supply filtering circuit, a program download and debugging interface circuit, and a chip dsPIC33CK256MP306T-H / PT; the chip dsPIC33CK256MP306T-H / PT also includes an ADC analog-to-digital acquisition unit, a PWM pulse width modulation unit, an SPI communication unit, a UART communication unit, and a GPIO port configuration unit inside.

[0022] Figure 2For the MCU chip dsPIC33CK256MP306T-H / PT, pins 36, 37, 39 and 38 are the pins of the SPI communication module, which are the clock signal SCK, data input SDI, data output SDO and chip select signal SPI_CS respectively. This communication interface is connected to the main control circuit of the high-temperature dual-control water distributor in this solution to complete data communication with the main control board of the instrument, and realize the adjustment of the water nozzle opening and the transmission of data such as the opening and motor current. Figure 2 Capacitor C12, C17 and crystal oscillator X1 in the circuit together form a crystal oscillation circuit and are connected to pins 28 and 30 of the MCU chip dsPIC33CK256MP306T-H / PT. Figure 2 Capacitors C3, C4 and C5 in the circuit together form a filter circuit for the ADC power supply.

[0023] Figure 3 It is the schematic diagram of the gate drive circuit of the brushless motor control circuit for the high-temperature dual-control water distributor according to the embodiment of the present application. As Figure 2 and Figure 3 shown, Figure 2 For the MCU chip dsPIC33CK256MP306T-H / PT, pins 1, 2, 63, 64, 61 and 62 are the PWM signals output by the MCU chip. Among them, pins 1 and 2 respectively output the high-end and low-end PWM control signals of phase A, pins 63 and 64 respectively output the high-end and low-end PWM control signals of phase B, and pins 61 and 62 respectively output the high-end and low-end PWM control signals of phase C; these 3-way PWM signals are respectively connected to Figure 3 pins 6, 7, 4, 5, 3 and 2 of the gate control chip in the circuit.

[0024] As Figure 2 and Figure 3 shown, Figure 2 Pin 5 of the MCU chip dsPIC33CK256MP306T-H / PT in the circuit is used as the enable output signal and is connected to Figure 3 pin 8, the enable input pin of MCP8022T-3315H / NHXVAO in the circuit.

[0025] As Figure 2 and Figure 3 shown, Figure 2 Pin 3 of the MCU chip dsPIC33CK256MP306T-H / PT in the circuit is used as the wake-up output signal and is connected to Figure 3 pin 20, the wake-up input pin of MCP8022T-3315H / NHXVAO in the circuit.

[0026] As Figure 2 and Figure 3 shown, Figure 3 The fault output pin 9 of MCP8022T-3315H / NHXVAO in the circuit is connected toFigure 2 It is connected to the fault input pin 4 of the MCU chip dsPIC33CK256MP306T-H / PT.

[0027] As Figure 2 and Figure 3 shown, Figure 2 Pin 34 of the MCU chip dsPIC33CK256MP306T-H / PT is used as the transmission pin of the UART communication module and is connected to the left-end pin of R0. The right-end pin of R0 is then connected to pin 35 (the receiving pin of the UART communication module) of the MCU chip dsPIC33CK256MP306T-H / PT. Pin 35 of the MCU chip dsPIC33CK256MP306T-H / PT is connected to Figure 3 pin 1 of the communication pin of the MCP8022T-3315H / NHXVAO in

[0028] As Figure 2 and Figure 3 shown, Figure 3 Pins 17, 14, and 11 of the MCP8022T-3315H / NHXVAO in Figure 2 are the output pins of the built-in operational amplifier of the chip, and are respectively

[0029] Figure 4 is the schematic diagram of the current detection circuit of the brushless motor control circuit for the high-temperature dual-control water distributor according to the embodiment of the present application. As Figure 3 and Figure 4 shown, Figure 3 Pins 17, 14, and 11 of the MCP8022T-3315H / NHXVAO in Figure 4 are the signal output pins of the built-in operational amplifier of the chip. Pin 17 is connected to Figure 4 the right-end pins of R21 and C25 in Figure 4 and are connected together. Pin 14 is connected to

[0030] As Figure 3 and Figure 4 shown, Figure 3Pins 19 and 18 of the MCP8022T-3315H / NHXVAO are the signal input pins of the built-in operational amplifier of this chip. Among them, pin 19 is connected to Figure 4 the right-end pin of resistor R14 in Figure 4 and pin 18 is connected to

[0031] As Figure 3 and Figure 4 shown, Figure 3 Pins 16 and 15 of the MCP8022T-3315H / NHXVAO are the signal input pins of the built-in operational amplifier of this chip. Among them, pin 16 is connected to Figure 4 the right-end pin of resistor R16 in Figure 4 and pin 15 is connected to

[0032] As Figure 3 and Figure 4 shown, Figure 3 Pins 13 and 12 of the MCP8022T-3315H / NHXVAO are the signal input pins of the built-in operational amplifier of this chip. Among them, pin 13 is connected to Figure 4 the right-end pin of resistor R25 in Figure 4 and pin 12 is connected to

[0033] As Figure 3 shown, diodes D1, D2, D3, D4, capacitors C1, C2, C9, C10, C11, and resistors R5, R6, R7 and the bootstrap module inside the gate control chip MCP8022T-3315H / NHXVAO together form a bootstrap circuit so that the chip MCP8022T-3315H / NHXVAO can drive a high-side N-channel MOSFET.

[0034] Figure 5 is the schematic diagram of the 3-phase H-bridge circuit and the high-temperature DC brushless motor for the high-temperature dual-control water distributor according to the embodiment of the present application. As Figure 3 and Figure 5 shown, Figure 3 Pins 29, 26, and 23 of the gate control chip MCP8022T-3315H / NHXVAO in Figure 5 are the gate drive output signals of the high-side MOSFETs. Among them, pin 29 is connected to Figure 5 pin 1 (gate) of MOSFET Q11 in Figure 5 through resistor R2, pin 26 is connected to Figure 5 pin 1 (gate) of MOSFET Q21 in

[0035] As Figure 3 andFigure 5 As shown Figure 3 In the figure, pins 33, 32, and 31 of the gate control chip MCP8022T-3315H / NHXVAO are Figure 5 the gate drive output signals of the low and medium-end MOS transistors. Among them, pin 33 is connected to Figure 5 pin 1 (gate) of MOS transistor Q12 in the figure through resistor R8, and pin 32 is connected to Figure 5 pin 1 (gate) of MOS transistor Q22 in the figure through resistor R9, and pin 31 is connected to Figure 5 pin 1 (gate) of MOS transistor Q32 in the figure.

[0036] As Figure 3 and Figure 5 shown Figure 3 In the figure, pins 28, 25, and 22 of the gate control chip MCP8022T-3315H / NHXVAO are respectively Figure 5 the midpoint bias signals of the three-phase bridge arms A, B, and C of the three-phase H-bridge circuit in the figure. Among them, pin 28 is connected to Figure 5 pin 3 (source) of MOS transistor Q11 and pin 2 (drain) of Q12 in the figure through resistor R5, and pin 25 is connected to Figure 5 pin 3 (source) of MOS transistor Q21 and pin 2 (drain) of Q22 in the figure through resistor R6, and pin 22 is connected to Figure 5 pin 3 (source) of MOS transistor Q31 and pin 2 (drain) of Q32 in the figure through resistor R7.

[0037] As Figure 4 and Figure 5 shown Figure 4 In the figure, resistors R11, R13, R14, R17, R18, R21, capacitors C21, C22, C25, and Figure 5 R26 in the figure form the motor A-phase current acquisition and amplification circuit; Figure 4 In the figure, resistors R12, R15, R16, R19, R20, R27, capacitors C23, C24, C26, and Figure 5 R27 in the figure form the motor B-phase current acquisition and amplification circuit; Figure 4 In the figure, resistors R23, R24, R25, R29, R30, R31, capacitors C31, C32, C33, and Figure 5 R26 in the figure form the motor C-phase current acquisition and amplification circuit.

[0038] As Figure 5As shown, MOS transistors Q11, Q12 and R26 form the A-phase bridge arm circuit in the three-phase H-bridge circuit; MOS transistors Q21, Q22 and R27 form the B-phase bridge arm circuit in the three-phase H-bridge circuit; MOS transistors Q31, Q32 and R28 form the C-phase bridge arm circuit in the three-phase H-bridge circuit; M1 is a high-temperature DC brushless motor, and its three-phase windings are respectively connected to the midpoints of the three-phase H-bridge circuit.

[0039] Specifically, Figure 1 A brushless motor control circuit for a high-temperature dual-control water distributor as shown, the working process of its hardware circuit is described as follows: The MCU chip dsPIC33CK256MP306T-H / PT serves as the core part of this control circuit, and according to a fixed software algorithm, it outputs 6-channel PWM control signals with corresponding frequencies according to the timing. This PWM signal is then output to the gate driver chip MCP8022T-3315H / NHXVAO, and finally, after being optimized by the internal logic circuit of this chip, it is respectively output to the gates of each MOS transistor in the three-phase H-bridge circuit to drive and control the motor operation. At the same time, the three-phase current sampling circuit collects the real-time current of each bridge arm and finally inputs it to the built-in ADC analog-to-digital conversion unit of the MCU chip dsPIC33CK256MP306T-H / PT. Furthermore, based on the current of each phase, the rotor position is determined through a software algorithm, and then the PWM signal of the corresponding bridge arm is output according to the rotor position to complete the commutation of the motor rotation.

[0040] As Figure 1As shown, the brushless motor control circuit for the high-temperature dual-control water distributor adopts the FOC (Field Oriented Control) method to achieve the drive control of the DC brushless motor through the hardware circuit and software algorithm. The field oriented control method requires accurate rotor position information. However, the sensorless brushless motor drive mode is adopted in this scheme, so there is no rotor position sensor, which requires other methods to judge the rotor position. In this scheme, the mathematical model algorithm control is used to judge the rotor position, that is, the phase currents of the three phases of the H-bridge circuit are collected in real time, and a series of mathematical operations are performed, such as Clarke transformation, Park transformation and their inverse transformations. For the sensorless control of the DC brushless motor, the main difficulty lies in the judgment of the rotor position at startup. The bottom layer software of this scheme is mainly divided into three stages when the motor starts: In the rotor alignment stage, the MCU control circuit controls the gate drive circuit and the three-phase H-bridge circuit to apply a fixed DC current to phase A and phase B of the high-temperature DC brushless motor. In the open-loop acceleration stage, the MCU control circuit applies a PWM signal to the gate drive circuit and the three-phase H-bridge circuit to generate a continuously accelerating rotating magnetic field in the stator of the high-temperature DC brushless motor. Among them, under the action of the continuously accelerating rotating magnetic field, the speed of the rotor of the high-temperature DC brushless motor gradually increases from zero to the minimum speed. In the closed-loop control stage, the MCU control circuit collects the three-phase current in real time through the current detection circuit and estimates the rotor electrical angle based on the three-phase current. Furthermore, based on the rotor electrical angle, a PWM signal is output.

[0041] Accordingly, to overcome the limitations of traditional control methods and meet the stringent requirements of high-temperature dual-control water distributors for motor control, this technical solution proposes to collect three-phase currents in real time through a current detection circuit and estimate the rotor electrical angle based on the three-phase currents. Using current signals for rotor position estimation is because current signals exist throughout the entire process of motor operation. Even at low speeds or during startup, current signals are still present and measurable, with a wider effective working range compared to the back-electromotive force method. Additionally, components such as current sampling resistors are more stable and reliable than Hall sensors at high temperatures. Further, to improve the accuracy and robustness of rotor position estimation based solely on current signals, especially under signal noise interference, motor parameter changes, or complex operating conditions, this solution introduces a more complex signal processing and intelligent reasoning mechanism. Specifically, by extracting the characteristics of the A and B phase current analog signals and the DC bus voltage signal, and performing response reasoning on the encoded characteristics of these signals based on signal feature implicit analysis, the complex non-linear mapping relationship between voltage and current signals can be more deeply explored, thereby more accurately inferring the rotor electrical angle. This advanced signal processing and reasoning method can better adapt to the dynamic changes and noise interference of signals compared to traditional filtering and simple mathematical model calculations, extract deeper features more strongly related to the rotor position, and thus significantly improve the rotor position estimation accuracy and the overall control performance and reliability of the system for sensorless control of brushless motors in harsh environments such as high temperatures.

[0042] Figure 6 FIG. is a flowchart of the MCU control circuit of the brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application, which collects three-phase currents in real time through the current detection circuit and estimates the rotor electrical angle based on the three-phase currents. Figure 7 FIG. is a schematic diagram of data flow of the MCU control circuit of the brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application, which collects three-phase currents in real time through the current detection circuit and estimates the rotor electrical angle based on the three-phase currents. As Figure 6 and Figure 7As shown, the MCU control circuit of the brushless motor control circuit for the high-temperature dual-control water distributor according to the embodiments of the present application collects three-phase currents in real time through the current detection circuit and estimates the rotor electrical angle based on the three-phase currents, including the steps of: S100, obtaining the A-phase current analog signal and the B-phase current analog signal; S200, obtaining the DC bus voltage signal; S300, extracting current signal features from the A-phase current analog signal and the B-phase current analog signal to obtain the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector; S400, extracting voltage signal features from the DC bus voltage signal to obtain the DC bus voltage signal feature coding vector; S500, based on the current analog signal joint coding feature between the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector and the DC bus voltage signal feature coding vector, performing response inference based on signal feature implicit analysis to obtain the voltage-current response inference coding vector; S600, performing feature decoding on the voltage-current response inference coding vector to obtain the estimated value of the rotor electrical angle.

[0043] Specifically, in steps S100 and S200, the A-phase current analog signal and the B-phase current analog signal are obtained, and the DC bus voltage signal is obtained. It should be understood that without using a physical position sensor (such as a Hall sensor, especially with poor reliability in high-temperature environments), the real-time operating state of the motor, including its rotor position and speed, must be obtained by analyzing and inferring the electrical signals of the motor itself. The three-phase currents (the third phase can be deduced by measuring any two phases, usually measuring the A-phase and B-phase currents) directly reflect the electromagnetic effect generated by the applied voltage in the motor windings and the modulation effect of the back electromotive force generated by the motor rotation on the current, and are one of the most direct signals containing rotor position information that can be measured throughout the process. Especially at startup and low speeds, they are easier to obtain and process compared to the back electromotive force signal. At the same time, the DC bus voltage is the energy source for driving the three-phase H-bridge, and its numerical change directly affects the voltage level actually applied to the motor windings through PWM control. Therefore, obtaining the DC bus voltage signal can provide necessary reference information for accurately interpreting the current response and compensating for the influence of power supply fluctuations on the control effect. Comprehensive analysis of the dynamic characteristics and mutual relationships of these real-time voltage and current signals is the fundamental basis for high-precision rotor electrical angle estimation, especially in applications that need to overcome high-temperature environment interference and achieve stable control across the entire speed range.

[0044] Specifically, in a specific example of this application, the steps of obtaining the A-phase current analog signal, the B-phase current analog signal, and the DC bus voltage signal are as follows: First, current detection elements are arranged at appropriate positions in the three-phase H-bridge circuit to collect the currents flowing through the A-phase and B-phase windings. Commonly used current detection elements include low-value sampling resistors (shunt resistors) or Hall effect-based current sensors. For example, a sampling resistor can be connected in series in the circuit path connected to the lower tube of the bridge arm (usually the ground terminal). When current flows through the resistor, a voltage drop proportional to the current will be generated across its two ends. Then, since the voltage drop across the current sampling resistor is often very small and may be affected by common-mode voltage or noise, the collected analog current signal needs to be signal-conditioned. This usually includes using a differential amplifier or a dedicated current sampling amplifier to amplify the weak voltage signal and performing filtering (such as low-pass filtering) to suppress high-frequency switching noise and external interference, and adjusting the signal to the input voltage range and signal quality requirements suitable for the ADC (analog-to-digital converter) inside the MCU. At the same time, in order to obtain the DC bus voltage signal, a resistive voltage divider circuit is usually used to attenuate the higher DC bus voltage proportionally to within the input range of the MCU's ADC. Subsequently, appropriate filtering may also be required to eliminate the influence of power supply ripple or transient fluctuations on voltage measurement.

[0045] Then, the conditioned and filtered A-phase current analog signal, B-phase current analog signal, and DC bus voltage analog signal are input into the ADC channels of the MCU control circuit. The MCU performs timed sampling on these analog signals according to a preset sampling strategy, usually synchronized with the PWM switching signal, and converts the analog quantity into a digital quantity. The selection of the sampling moment is crucial and should avoid switching transients and select a moment when the current waveform is stable and representative for acquisition. Finally, the MCU obtains the digital values of the A-phase current, B-phase current, and DC bus voltage after digitization. These values are the original digital inputs for subsequent signal feature extraction and response inference based on implicit analysis of signal features, thereby starting the estimation process of the rotor electrical angle. Through the above series of standardized acquisition and processing procedures, the control system can reliably and real-time obtain the key electrical signal information required to drive the brushless motor.

[0046] Specifically, in step S300, current signal features are extracted from the phase A current analog signal and the phase B current analog signal to obtain a phase A current analog signal feature encoding vector and a phase B current analog signal feature encoding vector. It should be understood that in the brushless motor control circuit for the high-temperature dual-control water distributor, although the original current analog signal contains information about the motor operating state, this information is often mixed and not very efficient to directly utilize. Especially under harsh working conditions such as high temperature, high dynamics, and noise, the signal quality will further deteriorate. Therefore, in the technical solution of this application, current signal features are further extracted from the phase A current analog signal and the phase B current analog signal to obtain a phase A current analog signal feature encoding vector and a phase B current analog signal feature encoding vector. Specifically, in a specific example of this application, a signal feature extractor based on a dilated convolutional neural network model can be used to extract current signal features, so as to express the key information strongly related to the rotor position and insensitive to interference in the original current analog signal in a more compact and robust form, and eliminate redundant and noise components to provide high-quality input for subsequent processing. That is to say, the feature extraction of the current signal aims to transform the original analog signal that may carry noise and redundant information into a structured, higher information density, and more easily understandable and processable digital representation by machine learning models.

[0047] Specifically, in step S400, voltage signal features are extracted from the DC bus voltage signal to obtain a DC bus voltage signal feature encoding vector. It should be understood that since the DC bus voltage signal is not only the direct source of energy for driving the motor, its own fluctuations and characteristics also indirectly reflect the motor load conditions, power supply quality, and the interaction relationship with the current. The original bus voltage signal may contain power supply noise, ripple, and dynamic fluctuations caused by load changes. Directly using these unprocessed signals is difficult to accurately reveal its deep relationship with the rotor position and may even introduce interference. Therefore, voltage signal features are further extracted from the DC bus voltage signal to obtain a DC bus voltage signal feature encoding vector. Specifically, in a specific example of this application, a signal feature extractor based on a dilated convolutional neural network model can be used to extract voltage signal features, so as to extract the key information valuable for rotor position estimation from the original bus voltage signal, filter out irrelevant noise and redundant components, and form a more stable and representative signal representation. The purpose of this step is to transform the original bus voltage signal into a structured feature encoding vector for effective fusion and processing with the current feature encoding vectors extracted from the phase A and phase B currents. Then, by combining the voltage features with the current features, the complex non-linear dynamic relationship between voltage application and current response under different power supply and load conditions can be more comprehensively learned and understood, so as to more accurately invert the real-time electrical angle of the motor rotor.

[0048] Specifically, in step S500, based on the joint coding feature of the current analog signal between the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector, and the DC bus voltage signal feature coding vector, response inference based on implicit analysis of signal features is performed to obtain a voltage-current response inference coding vector. Figure 8 It is a flowchart for performing response inference based on implicit analysis of signal features on the joint coding feature of the current analog signal between the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector, and the DC bus voltage signal feature coding vector, to obtain a voltage-current response inference coding vector, for the brushless motor control circuit of a high-temperature dual-control water distributor according to an embodiment of the present application. As Figure 8 shown, for the brushless motor control circuit of a high-temperature dual-control water distributor according to an embodiment of the present application, step S500 includes: S510, combining the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector to obtain an A-B phase current analog signal feature joint coding vector as the joint coding feature of the current analog signal; S520, performing response inference based on implicit analysis of signal features on the A-B phase current analog signal feature joint coding vector and the DC bus voltage signal feature coding vector to obtain the voltage-current response inference coding vector.

[0049] Specifically, in step S510, the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector are combined to obtain an A-B phase current analog signal feature joint coding vector as the joint coding feature of the current analog signal. It should be understood that although analyzing the single-phase current feature alone can extract certain motor operation information, due to the inherent mutual correlation and coupling relationship among the three-phase currents of the brushless motor (usually the third phase is deduced by measuring two phases), the single-phase feature cannot comprehensively reflect the electromagnetic state of the motor and the relative position of the rotor in the stator magnetic field. Therefore, in the technical solution of the present application, the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector are combined to obtain an A-B phase current analog signal feature joint coding vector. In particular, the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector can be cascaded to combine the feature coding vectors of the A-phase and B-phase currents, which can fully capture the dynamic linkage features, amplitude relationships, and phase relationships between the two-phase currents. These comprehensive information is crucial for accurately judging the real-time electromagnetic position and motion state of the rotor, especially in sensorless control, where the current signal is the main basis for indirectly inferring the rotor position.

[0050] Specifically, in step S520, response inference based on signal feature implicit analysis is performed on the combined coding vector of the A-B phase current analog signal features and the coding vector of the DC bus voltage signal features to obtain the voltage-current response inference coding vector. It should be understood that in the actual operation of the motor, the relationship between the combined coding representation of the A-B phase current analog signal features and the coding representation of the DC bus voltage signal features is not a simple linear correspondence, but is comprehensively affected by the motor's own parameters (such as inductance, resistance), load changes, power supply fluctuations, and environmental factors such as high temperature, showing highly complex and non-linear dynamic characteristics. Traditional analysis methods often have difficulty capturing this deep-seated and implicit feature interaction and response pattern. Therefore, in order to deeply extract the implicit information strongly related to the rotor position from the combined coding representation of the A-B phase current analog signal features and the coding representation of the DC bus voltage signal features, and to overcome the limitations of traditional methods under complex working conditions and signal interference, in the technical solution of this application, response inference based on signal feature implicit analysis is performed on the combined coding vector of the A-B phase current analog signal features and the coding vector of the DC bus voltage signal features to obtain the voltage-current response inference coding vector. Through response inference based on signal feature implicit analysis, the complex interaction between current and voltage features can be understood and quantified. Specifically, by arranging the input feature vectors (i.e., the combined coding vector of the A-B phase current analog signal features and the coding vector of the DC bus voltage signal features) in an orderly manner to focus on the intrinsic strength of the features, and then performing equal-grained feature segmentation to achieve a detailed analysis of local features. Subsequently, the core transfer response inference unit deeply interacts and models the local feature segments of each pair of A-B phase current analog signals and DC bus voltage signals, captures their mutual influence, response pattern or information transfer mechanism, and retains rich local interaction details of the voltage-current signals in matrix form. Finally, these local interaction information of the voltage-current signals are integrated through the sequence transfer and aggregation module to capture the dependence relationship and overall trend across different feature strength intervals. The ultimate goal of all this is to generate a concise and information-rich voltage-current response inference coding vector. This coding vector can characterize the dynamic response relationship between the applied voltage and the generated current in a highly generalized and strongly discriminative manner for the rotor position. By deeply mining and understanding the complex coupling relationship between voltage and current, the control system can more accurately identify the subtle electrical signal features caused by rotor position changes, and can maintain a high estimation reliability even under harsh working conditions such as high temperature, high noise, motor parameter drift or severe load fluctuations.

[0051] Figure 9A flowchart for performing response inference based on signal feature implicit analysis on the joint encoding vector of the A-B phase current analog signal features and the encoding vector of the DC bus voltage signal features of the brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application to obtain the voltage-current response inference encoding vector. As Figure 9 shown, the brushless motor control circuit for a high-temperature dual-control water distributor according to an embodiment of the present application, step S520, includes: S521, performing an ordered arrangement and equal-granularity segmentation on the joint encoding vector of the A-B phase current analog signal features and the encoding vector of the DC bus voltage signal features to obtain a sequence of A-B phase current analog signal local feature ordered encoding vectors and a sequence of DC bus voltage signal local feature ordered encoding vectors; S522, inputting each group of corresponding A-B phase current analog signal local feature ordered encoding vectors and DC bus voltage signal local feature ordered encoding vectors in the sequence of A-B phase current analog signal local feature ordered encoding vectors and the sequence of DC bus voltage signal local feature ordered encoding vectors into a transfer response inference unit to obtain a sequence of voltage-current local transfer response encoding matrices; S523, performing a transfer response inference sequence transfer on the sequence of voltage-current local transfer response encoding matrices to obtain the voltage-current response inference encoding vector.

[0052] Correspondingly, according to an embodiment of the present application, step S521 includes: performing an ordered arrangement based on the eigenvalue magnitudes on the joint encoding vector of the A-B phase current analog signal features and the encoding vector of the DC bus voltage signal features to obtain an A-B phase current analog signal feature ordered arrangement encoding vector and a DC bus voltage signal feature ordered arrangement encoding vector; performing equal-granularity feature segmentation on the A-B phase current analog signal feature ordered arrangement encoding vector and the DC bus voltage signal feature ordered arrangement encoding vector to obtain the sequence of A-B phase current analog signal local feature ordered encoding vectors and the sequence of DC bus voltage signal local feature ordered encoding vectors.

[0053] More specifically, performing an ordered arrangement based on the eigenvalue magnitudes on the joint encoding vector of the A-B phase current analog signal features and the encoding vector of the DC bus voltage signal features to obtain an A-B phase current analog signal feature ordered arrangement encoding vector and a DC bus voltage signal feature ordered arrangement encoding vector, which is expressed by the formula: ; where and respectively represent the joint encoding vector of the A-B phase current analog signal features and the encoding vector of the DC bus voltage signal features, represents sorting the vector elements, and respectively represent the orderly arranged coding vectors of the A-B phase current analog signal features and the orderly arranged coding vectors of the DC bus voltage signal features.

[0054] It should be understood that the features extracted from the original current and voltage signals, although containing the operating information of the motor, the arrangement order of these features in the vector may be arbitrary, depending on the implementation of the feature extraction algorithm or the subjective setting of the designer, and has no necessary connection with the inherent importance or strength of the features themselves. However, in the response inference process based on the implicit analysis of signal features, the complex relationship between the motor voltage application and the current response is often more profoundly reflected in the relative strength and distribution of different electrical features, rather than their original positions in the input vector. Especially when it is necessary to capture the complex non-linear interaction patterns between features, a normalized representation based on numerical magnitude sorting can help the network learn and generalize more effectively. Therefore, by sorting the vector elements according to the numerical magnitude of the feature values, the arbitrariness brought by the original arrangement order can be eliminated, enabling subsequent processing to focus on the inherent strength and relative magnitude of the features, thereby enhancing the understanding of the essential attributes of the signal. The purpose of performing this step is to provide a standardized input representation that has a certain degree of invariance or equivariance to the input arrangement order for subsequent processing. By arranging the combined coding vector of the A-B phase current analog signal features and the coding vector of the DC bus voltage signal features in an orderly manner according to their numerical magnitudes, even if there are slight differences in the sampling or feature extraction process of the input signal resulting in different original feature orders, as long as the numerical values of the features remain the same, the orderly vectors obtained after sorting will be the same or similar. This normalization process enables subsequent feature segmentation and local interaction modeling to always be carried out on feature intervals with similar strength or similar importance, thereby simplifying the learning task of the network and improving the generalization ability and robustness of the model.

[0055] More specifically, perform equal-granularity feature segmentation on the orderly arranged coding vector of the A-B phase current analog signal features and the orderly arranged coding vector of the DC bus voltage signal features to obtain a sequence of orderly coded vectors of the A-B phase current analog signal local features and a sequence of orderly coded vectors of the DC bus voltage signal local features, which can be expressed by the formula: ; where represents the feature segmentation function, respectively represent the 1st, 2nd, th, and th orderly coded vectors of the A-B phase current analog signal local features in the sequence of orderly coded vectors of the A-B phase current analog signal local features, respectively represent the 1st, 2nd, th, and An ordered coding vector of local features of the DC bus voltage signal, is the voltage-current local transfer response coding matrix between and

[0056] It should be understood that even after feature extraction and ordered arrangement, the ordered coding vector of the A-B phase current analog signal features and the ordered coding vector of the DC bus voltage signal features may still contain a large number of dimensions. Directly performing global interaction modeling on the entire high-dimensional vector involves a huge computational amount and is difficult to capture the fine correlations between features. The electrical behavior of the motor often exhibits local coupling relationships. For example, a specific current feature intensity may be closely related to a specific range of voltage change patterns, and this correlation is easily diluted or masked in the high-dimensional global space. Therefore, by performing an equal-granularity feature segmentation operation, the ordered feature vector can be decomposed into a series of consecutive sub-vector segments (local features) of the same size (i.e., equal-granularity), thereby transforming the complex global interaction problem into a local interaction analysis between the A-B phase current analog signal features and the DC bus voltage signal features, which is simpler and easier to model. The purpose of this step is to provide input data for the subsequent transfer response inference unit so that it can capture the local interaction patterns between the A-B phase current features and the DC bus voltage features at a finer granularity. By splitting the ordered vector into equal-granularity local feature segments, the subsequent transfer response inference unit can focus on the mutual influence and response relationship between the A-B phase current and the DC bus voltage features within a similar feature intensity range. The segmentation granularity, as a key hyperparameter, determines the fineness of the analysis. A smaller granularity can capture more subtle local correlations, while a larger granularity focuses on more macroscopic local trends. This localized analysis strategy enables the model to more effectively learn the specific response laws of the current and voltage signals in different feature intensity intervals, such as the correlation between weak current features and low voltage changes, or the correlation between strong current features and high voltage transients.

[0057] More specifically, in step S522, each corresponding A-B phase current analog signal local feature ordered coding vector and DC bus voltage signal local feature ordered coding vector in the sequence of the A-B phase current analog signal local feature ordered coding vectors and the sequence of the DC bus voltage signal local feature ordered coding vectors are input into the transfer response inference unit to obtain a sequence of voltage-current local transfer response coding matrices, which can be expressed by the formula: ; where is the voltage-current local transfer response coding matrix between and For the activation function, is a trainable modulation weight matrix.

[0058] It should be understood that simply segmenting the ordered arrangement encoding vectors of the A - B phase current analog signal features and the ordered arrangement encoding vectors of the DC bus voltage signal features into local segments is not sufficient to reveal their complex interactions. During the operation of a brushless motor, there may be a highly non - linear and dynamically changing coupling relationship between its current response and the applied voltage in different characteristic intensity intervals and local detail levels, and this relationship cannot be fully captured by simple linear combinations or global comparisons. Therefore, a dedicated mechanism, namely the "transfer response inference unit", is required to perform in - depth interaction modeling on each pair of local feature segments representing specific intensity intervals extracted from the current and voltage feature sequences, so as to finely capture their mutual influence, response patterns, or information transfer mechanisms. By deeply modeling and quantifying the complex, non - linear interaction relationship between each pair of corresponding local current feature vectors and local voltage feature vectors. The "transfer response inference unit" is a carefully designed neural network module, and its core function is to utilize the powerful fitting ability of deep learning to quantify and encode various possible interaction patterns between the ordered encoding vectors of the local features of the A - B phase current analog signal and the ordered encoding vectors of the local features of the DC bus voltage signal within a specific eigenvalue interval, such as their local similarity, difference, alignment relationship, conditional dependence, co - activation or inhibition, etc. In this way, a voltage - current local transfer response encoding matrix is generated for each pair of input local feature vectors. This matrix is not a simple scalar score, but is designed to encapsulate rich and structured interaction information within the corresponding local region, such as capturing the local association strength at the element level of voltage and current, so as to retain more interaction details and provide high - quality local insights for subsequent forming a comprehensive understanding of the overall interaction. This fine - grained interaction modeling enables the model to more accurately identify the true mapping relationship between voltage and current under different operating conditions (such as startup, low speed, load change, high - temperature influence), thereby improving the accuracy and robustness of the rotor electrical angle estimation.

[0059] More specifically, in step S523, a transfer response inference sequence transfer is performed on the sequence of the voltage - current local transfer response encoding matrix to obtain the voltage - current response inference encoding vector, which is expressed by the formula: ; where represents flattening the matrix into a vector for processing, is the voltage - current local transfer response encoding vector, is of norm, is the value of the natural exponential function with the natural constant e as the base, For the transfer response inference weighting operation, For the encoder, is the voltage-current response inference coding vector.

[0060] It should be understood that although in the previous step, the local feature segments of each pair of A-B phase current analog signals and DC bus voltage signals were deeply interactively modeled by the transfer response inference unit and a sequence of local transfer response coding matrices was generated, which detailedly characterized the interaction between voltage and current features in different local regions, the overall voltage-current response relationship of the motor is not a simple set of isolated local interactions. There are interdependencies and influences between the local interactions in different feature intensity regions, and its evolution process (along with the sorting of feature intensity levels) contains global and deep response patterns. Therefore, a mechanism is needed to integrate the local interaction information distributed in different feature intensity intervals and critically consider the sequential relationship between them, so as to form a comprehensive understanding of the overall interaction. A discrete sequence of local matrices alone is not sufficient to fully represent this overall dynamics. By performing the transfer response inference sequence transfer, it is possible to learn and capture how the local interaction patterns between current and voltage signals evolve with the change of feature intensity, and discover the dependencies and global context across different feature intensity intervals. This process aims to meaningfully integrate and refine the rich current-voltage local interaction information, and finally output a condensed and information-rich voltage-current response inference coding vector. This vector is a highly abstract and compact representation of the overall and deep interaction response characteristics between the original two input feature vectors (joint current feature and voltage feature), aiming to capture the overall pattern of current response caused by voltage application and how this pattern is affected by various factors (including rotor position). Compared with relying only on the original features or simple aggregation methods, this vector obtained through fine-grained local interaction modeling and global sequence aggregation can more accurately and stably reflect the electrical state information strongly related to the electrical angle of the motor rotor.

[0061] Preferably, it can be seen that when the segmentation granularity of the feature segmentation function affects the local partition range of the voltage-current local transfer response coding matrix, it will also directly affect the expression of the coupling relationship between the corresponding local feature ordered coding vector of the A-B phase current analog signal and the local feature ordered coding vector of the DC bus voltage signal.

[0062] Since the voltage-current local transfer response coding matrix, as the transfer response between two local regions, essentially expresses the spatial measure of the transfer response space based on its row vectors, and the dimension of the row vector is also the representation of the above local region size. If the local region size, that is, the row vector length If introduced as a local size-intensity constraint, then the low-dimensional characterization metric of the voltage-current local transfer response coding matrix, i.e., the F-norm should follow a relationship similar to the Poisson distribution: ; that is, the length of the row vector acts on the low-rank space measure of the local area transfer response space as the local size-intensity constraint for times, represents the F-norm of the matrix, represents the factorial of. Thus, the parameter can be solved.

[0063] In this way, when the transfer response interaction is generated by a class-Poisson process with an introduced intensity of and a mean expectation of , with and describing the edge connection representation of the space measure, the transfer response correlation probability between two local areas is further determined as: ; where represents the two-norm of the vector, is the transfer response correlation probability.

[0064] Then, using the transfer response correlation probability to iteratively adjust the local area size : ; that is, under the condition of strictly ensuring an expected degree of , the regularization of the overall space measure is determined through the regularization constraint of the mean expectation connection probability fluctuation of each row, so that the structured coupling information within the voltage-current local space measure can avoid local overfitting and enhance the global expression ability of the sequence of the voltage-current local transfer response coding matrix.

[0065] Specifically, in step S600, the voltage-current response inference coding vector is subjected to feature decoding to obtain an estimated value of the rotor electrical angle. That is to say, although the voltage-current response inference coding vector is highly condensed and information-rich, it is itself an abstract high-dimensional feature expression, which contains complex electrical characteristics and implicit associations of the rotor position, but has not been intuitively mapped into actual angle information available for control. Therefore, in the technical solution of the present application, the voltage-current response inference coding vector is further subjected to feature decoding to obtain an estimated value of the rotor electrical angle to meet the requirement of the control system for accurately grasping the rotor position in real time. Through effective feature decoding, the system can accurately restore the real-time position of the rotor in the stator magnetic field of the motor, providing an accurate basis for the generation of subsequent PWM control signals, thereby realizing the efficient closed-loop control of the brushless DC motor.

[0066] Specifically, in the embodiments of the present application, feature decoding is performed on the voltage-current response inference coding vector to obtain an estimated value of the rotor electrical angle, including: passing the voltage-current response inference coding vector through a rotor electrical angle controller based on a decoder to obtain an estimated decoded value of the rotor electrical angle.

[0067] Further, after obtaining the estimated decoded value of the rotor electrical angle, first, the estimated decoded value of the rotor electrical angle obtained through signal feature implicit analysis and feature decoding is used as a core feedback signal. Then, the control system enters a hierarchical control loop, including a position or speed controller in the outer loop and a current controller in the inner loop. The outer loop controller (such as a PID controller) receives the target position or speed command from the upper computer or a preset motion curve, and compares it with the rotor speed estimated through integration (for example, obtained by differentiating the angle estimate with respect to time or through a specific observer) or the direct position estimate, generating a torque command for the motor. This torque command is then converted into a reference current command in the d-q coordinate system, especially generating the q-axis current command for controlling torque, while the d-axis current command for controlling flux linkage is usually set to zero to achieve the highest efficiency (below the base speed).

[0068] Then, the current controller in the inner loop is responsible for accurately tracking these reference current commands. The current controller first compares the actual d-axis and q-axis currents collected in real time and transformed through the Park transformation in the previous steps with the reference current commands to obtain the current errors. These two current error signals are input into independent current regulators (usually PI controllers), and the regulators output the reference voltage commands in the d-q coordinate system. Subsequently, in order to convert the voltage commands in the d-q coordinate system into the actual voltage that can be applied to the motor phase windings, an inverse Park transformation is required. Using the estimated decoded value of the rotor electrical angle obtained previously, the reference voltage commands are converted into voltage commands in the stationary coordinate system through the inverse Park transformation. Finally, the voltage commands in the stationary coordinate system are input into the space vector pulse width modulation (SVPWM) module. The SVPWM algorithm calculates the duty cycle and timing of the PWM pulse signals for each power switch tube in the three-phase H-bridge circuit according to the input voltage commands. These PWM signals are amplified and isolated through the gate drive circuit and then applied to the power switch tubes of the three-phase H-bridge, thereby generating a synthesized vector voltage, which accurately drives the three-phase windings of the high-temperature DC brushless motor, enabling the actual phase current to quickly track the output commands of the current controller, and thus achieving precise control of the motor torque, speed, or position. The entire closed-loop control process is executed in real-time and cyclically, continuously adjusting the drive voltage according to the latest estimated value of the rotor electrical angle to ensure that the motor can operate stably and efficiently under different working conditions.

[0069] In summary, the brushless motor control circuit for the high-temperature dual-control water distributor according to the embodiments of the present application is elucidated. It does not rely on traditional physical position sensors, but instead real-time collects motor current signals (including A-phase and B-phase currents) and DC bus voltage signals through a current detection circuit, extracts key features from these signals, and further performs complex response reasoning based on the implicit analysis of the signal features for the extracted signal features to estimate the electrical angle of the motor rotor. Compared with traditional sensorless control methods that rely on physical sensors or back electromotive force, this technical solution can help improve the rotor position estimation accuracy in harsh high-temperature environments and the overall robustness of the system, especially showing more excellent performance under challenging working conditions such as motor startup and low-speed operation, thereby ensuring the precise control and long-term stable operation of the high-temperature dual-control water distributor.

[0070] The various embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to technologies in the market, or to enable other ordinary skill in the art in the technical field to understand the embodiments disclosed herein.

Claims

1. A brushless motor control circuit for a high-temperature dual-control water distributor, characterized in that, Including: MCU control circuit, gate drive circuit, current detection circuit, three-phase H-bridge circuit and high-temperature DC brushless motor; Wherein, the current detection circuit is electrically connected to the three-phase H-bridge circuit to collect real-time current, and input the real-time current into the MCU control circuit. The MCU control circuit judges the rotor position based on the real-time current and outputs a PWM control signal based on the rotor position; Wherein, the PWM control signal output by the MCU control circuit drives the high-temperature DC brushless motor through the gate drive circuit and the three-phase H-bridge circuit; Wherein, the MCU control circuit collects three-phase current in real time through the current detection circuit and estimates the rotor electrical angle based on the three-phase current, including: obtaining the A-phase current analog signal and the B-phase current analog signal; obtaining the DC bus voltage signal; extracting current signal features from the A-phase current analog signal and the B-phase current analog signal to obtain the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector; extracting voltage signal features from the DC bus voltage signal to obtain the DC bus voltage signal feature coding vector; based on the current analog signal joint coding feature between the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector and the DC bus voltage signal feature coding vector, performing response inference based on signal feature implicit analysis to obtain a voltage-current response inference coding vector; decoding the features of the voltage-current response inference coding vector to obtain an estimated value of the rotor electrical angle.

2. The brushless motor control circuit for the high-temperature dual-control water distributor according to claim 1, characterized in that In the rotor alignment stage, the MCU control circuit controls the gate drive circuit and the three-phase H-bridge circuit to apply a fixed DC current to the A-phase and B-phase of the high-temperature DC brushless motor.

3. The brushless motor control circuit for a high-temperature dual-control water distributor according to claim 2, characterized in that, In the open-loop acceleration stage, the MCU control circuit applies a PWM signal to the gate drive circuit and the three-phase H-bridge circuit to generate a continuously accelerating rotating magnetic field in the stator of the high-temperature DC brushless motor. Wherein, under the action of the continuously accelerating rotating magnetic field, the speed of the rotor of the high-temperature DC brushless motor gradually increases from zero to the minimum speed.

4. The brushless motor control circuit for a high-temperature dual-control water distributor according to claim 3, characterized in that In the closed-loop control stage, the MCU control circuit collects three-phase current in real time through the current detection circuit and estimates the rotor electrical angle based on the three-phase current, and then outputs a PWM signal based on the rotor electrical angle.

5. The brushless motor control circuit for a high-temperature dual-control water distributor according to claim 4, characterized in that, Based on the current analog signal joint coding feature between the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector and the DC bus voltage signal feature coding vector, performing response inference based on signal feature implicit analysis to obtain a voltage-current response inference coding vector, including: Combining the A-phase current analog signal feature coding vector and the B-phase current analog signal feature coding vector to obtain an A-B phase current analog signal feature joint coding vector as the current analog signal joint coding feature; Perform response inference based on signal feature implicit analysis on the combined encoding vector of the A-B phase current analog signal features and the encoding vector of the DC bus voltage signal features to obtain the voltage-current response inference encoding vector.

6. The brushless motor control circuit for a high-temperature dual-control water distributor according to claim 5, characterized in that, Performing response inference based on signal feature implicit analysis on the combined encoding vector of the A-B phase current analog signal features and the encoding vector of the DC bus voltage signal features to obtain the voltage-current response inference encoding vector includes: Performing ordered arrangement and equal-granularity segmentation on the combined encoding vector of the A-B phase current analog signal features and the encoding vector of the DC bus voltage signal features to obtain a sequence of ordered encoding vectors of local features of the A-B phase current analog signal and a sequence of ordered encoding vectors of local features of the DC bus voltage signal; Inputting each group of corresponding ordered encoding vectors of local features of the A-B phase current analog signal and ordered encoding vectors of local features of the DC bus voltage signal in the sequence of ordered encoding vectors of local features of the A-B phase current analog signal and the sequence of ordered encoding vectors of local features of the DC bus voltage signal into a transfer response inference unit to obtain a sequence of voltage-current local transfer response encoding matrices; Performing transfer response inference sequence transfer on the sequence of voltage-current local transfer response encoding matrices to obtain the voltage-current response inference encoding vector.

7. The brushless motor control circuit for the high-temperature dual-control water distributor according to claim 6, wherein Performing ordered arrangement and equal-granularity segmentation on the combined encoding vector of the A-B phase current analog signal features and the encoding vector of the DC bus voltage signal features to obtain a sequence of ordered encoding vectors of local features of the A-B phase current analog signal and a sequence of ordered encoding vectors of local features of the DC bus voltage signal includes: Performing ordered arrangement based on the eigenvalue magnitude on the combined encoding vector of the A-B phase current analog signal features and the encoding vector of the DC bus voltage signal features to obtain an ordered encoding vector of the A-B phase current analog signal features and an ordered encoding vector of the DC bus voltage signal features; Performing equal-granularity feature segmentation on the ordered encoding vector of the A-B phase current analog signal features and the ordered encoding vector of the DC bus voltage signal features to obtain a sequence of ordered encoding vectors of local features of the A-B phase current analog signal and a sequence of ordered encoding vectors of local features of the DC bus voltage signal.

8. The brushless motor control circuit for the high-temperature dual-control water distributor according to claim 7, wherein, Performing feature decoding on the voltage-current response inference encoding vector to obtain an estimated value of the rotor electrical angle, including: passing the voltage-current response inference encoding vector through a rotor electrical angle controller based on a decoder to obtain an estimated decoded value of the rotor electrical angle.

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