Power drive method and device for robot micro-motor gearbox module

Abstract

The present invention provides a power driving method and apparatus for a robot micro-motor gearbox module, comprising: starting the robot micro-motor gearbox module and outputting initial power in a basic magnetic field state; acquiring load feedback signals and speed feedback signals during the operation of the gearbox module in real time to form dual-dimensional feedback data; inputting the dual-dimensional feedback data into a magnetic field-torque dynamic coupling model to output a magnetic field correction amount; adjusting the stator magnetic field distribution of the micro-motor based on the correction amount to achieve adaptive matching between the output torque of the gearbox module and the real-time load; and continuously issuing driving commands to the gearbox module based on the corrected magnetic field state to achieve closed-loop adaptive adjustment of power output. This invention overcomes the shortcomings of current robot micro-motor gearbox modules, such as easy jamming and energy waste during power driving.

Description

Technical Field

[0001] This invention relates to the field of motor technology, and in particular to a power drive method and device for a robot micro-motor gearbox module. Background Technology

[0002] As humanoid robots, micro-joint modules, and other equipment upgrade towards lightweight, high precision, and long endurance, micro-motor gearbox modules, as core power units, need to achieve high torque output in confined spaces while adapting to complex working conditions with frequent load changes and dynamic speed adjustments, placing stringent requirements on the accuracy and adaptability of the drive method.

[0003] Current mainstream driver solutions suffer from significant technical bottlenecks: Firstly, the load adaptive capability is insufficient, relying mostly on a single feedback signal or fixed parameter control, which cannot correlate the dynamic coupling relationship between load and speed, resulting in heavy load jamming and light load energy waste. Moreover, the improvement method of increasing motor power will sacrifice the miniaturization advantage. Secondly, the feedback signal is susceptible to gear vibration and electromagnetic interference. Existing simple filtering methods lack correlation verification mechanisms, resulting in severe distortion of control accuracy and dynamic response error exceeding 10%.

[0004] Third, the control algorithm has weak robustness. The traditional architecture relies on offline calibration, which results in lag and oscillation when faced with complex operating conditions such as sudden load changes and speed fluctuations, thus aggravating component wear. Summary of the Invention

[0005] The main objective of this invention is to provide a power drive method and device for a robot micro-motor gearbox module, aiming to overcome the defects of current robot micro-motor gearbox modules that are prone to jamming and energy waste during power drive.

[0006] To achieve the above objectives, the present invention provides a power drive method for a robot micro-motor gearbox module, comprising the following steps: Start the robot's micro-motor gearbox module to output initial power in the basic magnetic field state; The load feedback signal and speed feedback signal of the gearbox module are acquired in real time during operation to form two-dimensional feedback data; The dual-dimensional feedback data is input into the magnetic field-torque dynamic coupling model, and the magnetic field correction amount is output. Based on the correction amount, the magnetic field distribution of the micro motor stator is adjusted so that the output torque of the gearbox module forms an adaptive match with the real-time load. Based on the corrected magnetic field state, drive commands are continuously sent to the gearbox module to achieve closed-loop adaptive adjustment of power output.

[0007] Furthermore, the output torque of the gearbox module is adaptively matched with the real-time load, including: synchronously strengthening the magnetic field coupling strength when the load increases, and dynamically weakening the magnetic field coupling strength when the load decreases.

[0008] Furthermore, the load feedback signal includes the output shaft torque signal of the gearbox module and the motor operating current signal, and the speed feedback signal includes the real-time angular velocity signal and angular acceleration signal of the output shaft of the gearbox module.

[0009] Furthermore, the robot's micro-motor gearbox module is activated to output initial power in the basic magnetic field state, including: Based on the rated load and rated speed of the gearbox module, the basic magnetic field strength and magnetic field distribution of the motor stator are preset; A fixed-amplitude initial drive current is input to the micro motor to stably output initial power in the basic magnetic field state, and the torque value of the initial power is 30%-60% of the rated torque of the gearbox module.

[0010] Furthermore, the dual-dimensional feedback data is input into the magnetic field-torque dynamic coupling model, which outputs a magnetic field correction value. Based on this correction value, the stator magnetic field distribution of the micromotor is adjusted to achieve an adaptive match between the output torque of the gearbox module and the real-time load. This includes: The real-time load amplitude of the load feedback signal and the speed change rate of the speed feedback signal are extracted as input features and input into the magnetic field-torque dynamic coupling model. The magnetic field-torque dynamic coupling model is based on a preset load-speed-magnetic field mapping relationship. By comparing the deviation between the input feature quantities and the target operating parameters, it generates magnetic field strength correction and magnetic field distribution angle correction. The coupling strength of the stator magnetic field is adjusted according to the magnetic field strength correction amount, and the current phase of the stator winding is adjusted according to the magnetic field distribution angle correction amount, so as to change the stator magnetic field distribution together. The matching degree between the output torque of the gearbox module and the real-time load is verified in real time. If the matching deviation exceeds the preset threshold, the model calculation and magnetic field adjustment process are repeated until the output torque and the real-time load are dynamically matched.

[0011] Furthermore, the dual-dimensional feedback data is input into the magnetic field-torque dynamic coupling model, which outputs a magnetic field correction value. Based on this correction value, the stator magnetic field distribution of the micromotor is adjusted to achieve an adaptive match between the output torque of the gearbox module and the real-time load. This includes: The load feedback signal and the speed feedback signal are synchronized in the time domain, and the real-time peak load, load change rate, steady-state speed value, and speed fluctuation amplitude are extracted as the model input feature vector. The magnetic field-torque dynamic coupling model pre-stores a three-dimensional mapping matrix of load-speed-magnetic field based on the rated parameters of the gearbox module. The input feature vector is substituted into the three-dimensional mapping matrix for interpolation to obtain the matching target magnetic field parameters. The magnetic field strength correction and magnetic field spatial phase correction are calculated and generated through the deviation compensation algorithm. The amplitude of the drive current of the stator winding of the micro-motor is adjusted based on the magnetic field strength correction amount, and the phase difference of the three-phase current of the stator winding is adjusted based on the magnetic field spatial phase correction amount. Through the coordinated adjustment of the current amplitude and the phase difference of the three-phase current, the coupling strength and spatial distribution of the stator magnetic field are changed. The measured output torque of the gearbox module after adjustment is collected and the difference between it and the required torque value corresponding to the real-time load signal is calculated. If the absolute value of the difference reaches the threshold, the difference is fed back to the magnetic field-torque dynamic coupling model as a compensation signal to recalculate the magnetic field correction amount.

[0012] Furthermore, the dual-dimensional feedback data is input into the magnetic field-torque dynamic coupling model, which outputs a magnetic field correction value. Based on this correction value, the stator magnetic field distribution of the micromotor is adjusted to achieve an adaptive match between the output torque of the gearbox module and the real-time load. This includes: The real-time load value, characterized by historical load feedback signals, is the dependent variable, and the real-time speed value, characterized by historical speed feedback signals, is the independent variable. A nonlinear correlation equation between the two is constructed. The real-time acquired load feedback signal and speed feedback signal are substituted into the nonlinear correlation equation for verification and screening. If the corresponding load value is within the reasonable fluctuation threshold range of the corresponding speed range, the load feedback signal is used as the model input. If the load value exceeds the reasonable fluctuation threshold range, it is determined to be an abnormal working condition, the load feedback signal is corrected, and the original value of the speed feedback signal is retained. The magnetic field-torque dynamic coupling model is based on the verified and screened two-dimensional feedback signal. Combined with the magnetic field coupling efficiency characteristics of the gearbox module, the magnetic field correction amount is generated by the proportional-integral-derivative and fuzzy control fusion algorithm. The magnetic field correction amount includes the magnetic field strength adjustment coefficient and the magnetic field phase offset. The magnetic field strength adjustment coefficient increases linearly with the increase of the load value, and the magnetic field phase offset decreases linearly with the increase of the speed value. The stator winding's drive current duty cycle is changed based on the magnetic field strength adjustment coefficient, and the stator winding's energizing phase angle is adjusted based on the magnetic field phase offset. During the adjustment process, the output results of the load-speed correlation constraint model are used as a reference to ensure that the magnetic field adjustment direction is consistent with the operating conditions.

[0013] The present invention also provides a power drive device for a robot micro-motor gearbox module, comprising: The starting unit is used to start the robot's micro-motor gearbox module and output initial power in the basic magnetic field state. The acquisition unit is used to acquire load feedback signals and speed feedback signals during the operation of the gearbox module in real time, forming two-dimensional feedback data; The adjustment unit is used to input the dual-dimensional feedback data into the magnetic field-torque dynamic coupling model, output the magnetic field correction amount, and adjust the stator magnetic field distribution of the micro motor based on the correction amount, so that the output torque of the gearbox module forms an adaptive match with the real-time load. The drive unit is used to continuously send drive commands to the gearbox module based on the corrected magnetic field state, so as to realize closed-loop adaptive adjustment of power output.

[0014] The present invention provides a power driving method and apparatus for a robot micro-motor gearbox module, comprising: starting the robot micro-motor gearbox module and outputting initial power in a basic magnetic field state; acquiring load feedback signals and speed feedback signals during the operation of the gearbox module in real time to form dual-dimensional feedback data; inputting the dual-dimensional feedback data into a magnetic field-torque dynamic coupling model to output a magnetic field correction amount; adjusting the stator magnetic field distribution of the micro-motor based on the correction amount to achieve adaptive matching between the output torque of the gearbox module and the real-time load; and continuously issuing driving commands to the gearbox module based on the corrected magnetic field state to achieve closed-loop adaptive adjustment of power output. In this invention, the load feedback signals and speed feedback signals during the operation of the gearbox module are acquired in real time, and the output torque of the gearbox module is adaptively matched with the real-time load through a magnetic field-torque dynamic coupling model, overcoming the defects of current robot micro-motor gearbox modules that are prone to jamming and energy waste during power driving. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the power drive method steps for a robot micro-motor gearbox module in one embodiment of the present invention; Figure 2 This is a structural block diagram of the power drive device of the robot micro-motor gearbox module in one embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0016] The implementation, functional features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] It is particularly important to note that all technical steps, algorithm applications, and parameter settings in the technical solution of this application have clear technical objectives and application value. They do not utilize complex steps and algorithmic formulas to achieve simple functions. To provide detailed explanations of each step and avoid ambiguity, some conventional algorithms are used for illustration. However, this does not mean that the algorithms and technical features listed herein are the only way to implement the technical solution of this application, nor is it intended to limit the scope of protection of this application. This application is not a combination or stacking of the listed algorithms and technical features; its essence is to exemplify the implementation methods of this application to fully explain it. It does not pursue formal complexity by adding meaningless technical steps, nor does it involve the accumulation of technologies divorced from practical needs; it conforms to the conventional logic of technical improvement and design.

[0019] Reference Figure 1 One embodiment of the present invention provides a power driving method for a robot micro-motor gearbox module, comprising the following steps: Step S1: Start the robot's micro-motor gearbox module to output initial power in the basic magnetic field state; Step S2: Real-time acquisition of load feedback signal and speed feedback signal during the operation of gearbox module to form two-dimensional feedback data; Step S3: Input the dual-dimensional feedback data into the magnetic field-torque dynamic coupling model, output the magnetic field correction amount, and adjust the stator magnetic field distribution of the micro motor based on the correction amount so that the output torque of the gearbox module forms an adaptive match with the real-time load. Step S4: Based on the corrected magnetic field state, drive commands are continuously sent to the gearbox module to achieve closed-loop adaptive adjustment of power output.

[0020] In this embodiment, as described in step S1 above, the core objective is to establish a stable initial operating foundation for subsequent adaptive adjustment. After the start command is issued, the micro-motor constructs a stator base magnetic field based on preset base magnetic field parameters. This base magnetic field state is pre-calibrated based on core parameters such as the module's rated load and rated speed, ensuring that the magnetic field strength and distribution can adapt to the load requirements of the module's start-up phase, avoiding start-up overload or insufficient power. Under the action of the base magnetic field, the motor rotor drives the gearbox to operate synchronously, outputting preset initial power, enabling the module to smoothly transition from a stationary state to an operating state. This provides a stable and controllable initial operating condition for the subsequent acquisition of dual-dimensional feedback data, while avoiding damage to the module's mechanical structure and electrical components caused by start-up shock.

[0021] As described in step S2 above, this is the core data acquisition stage for achieving adaptive drive, aiming to comprehensively capture the real-time operating status of the module and provide accurate input basis for subsequent magnetic field adjustment. Through the signal acquisition units equipped in the module (such as torque sensors and speed encoders), two key signals are simultaneously captured in real time: one is the load feedback signal, which directly reflects the real-time load magnitude at the gearbox output end, including static load components and dynamic load fluctuation information, accurately characterizing the current stress condition of the module; the other is the speed feedback signal, which records the real-time speed and speed change trend of the gearbox output shaft, including instantaneous speed values, speed fluctuation amplitudes, and other key information, intuitively reflecting the module's operating speed status. After synchronous preprocessing of the two types of signals (such as time domain alignment and interference removal), they are integrated to form dual-dimensional feedback data covering load and speed, breaking through the limitations of traditional single signal acquisition and achieving comprehensive and three-dimensional monitoring of the module's operating status.

[0022] As described in step S3 above, precise matching of torque and load is achieved through dynamic modeling and magnetic field optimization. First, the acquired dual-dimensional feedback data is input into a pre-defined magnetic field-torque dynamic coupling model. This model is pre-calibrated based on the module's magnetic field coupling characteristics and torque output patterns, establishing a dynamic correlation between load, speed, and magnetic field parameters. After receiving the dual-dimensional feedback data, the model analyzes the current load and speed matching status through algorithmic calculations, determines whether magnetic field parameters need adjustment, and then outputs corresponding magnetic field corrections (including magnetic field strength correction coefficients and magnetic field distribution phase corrections). Subsequently, based on these magnetic field corrections, the stator magnetic field distribution of the micro-motor is dynamically adjusted: the magnetic field strength is changed by adjusting the current amplitude of the stator windings, and the spatial distribution of the magnetic field is changed by adjusting the current phase, so that the coupling efficiency of the stator magnetic field and the rotor magnetic field adapts to the current load requirements in real time. Ultimately, the output torque of the gearbox module dynamically changes with the real-time load, increasing torque output to avoid jamming under heavy loads and optimizing torque output to reduce energy consumption under light loads, achieving adaptive and precise matching of load and torque.

[0023] As described in step S4 above, the aim is to ensure the stability and continuity of power output. Based on the stator magnetic field state adjusted in step S3, corresponding drive control commands are generated and continuously sent to the micro-motor via the control bus to ensure that the motor operates stably according to the optimized magnetic field parameters, thereby ensuring that the power output of the gearbox module is always adapted to the real-time load. Simultaneously, this step, together with steps S2 and S3, forms a complete closed-loop adjustment logic: during the continuous issuance of drive commands, step S2 synchronously collects two-dimensional feedback data of the module's operating status in real time; step S3 recalculates the magnetic field correction amount and adjusts the magnetic field distribution based on the new feedback data. This iterative cycle achieves continuous closed-loop optimization of acquisition-modeling-adjustment-output-reacquisition. This closed-loop mechanism can respond in real time to dynamic changes in load and speed, and can quickly adjust the power output even under complex operating conditions such as sudden load changes and speed fluctuations, ensuring the stability, accuracy, and low energy consumption characteristics of the module operation.

[0024] In one embodiment, the output torque of the gearbox module is adaptively matched with the real-time load, including: synchronously strengthening the magnetic field coupling strength when the load increases, and dynamically weakening the magnetic field coupling strength when the load decreases.

[0025] In this embodiment, when the load feedback signal indicates a real-time increase in load (such as increased external force on robot joints or heavier load on actuators), the magnetic field-torque dynamic coupling model determines, based on dual-dimensional feedback data, that the current magnetic field coupling strength is insufficient to meet load requirements. Consequently, it strengthens the magnetic field strength adjustment command in the output magnetic field correction. Specifically, by increasing the driving current amplitude of the micro-motor stator winding, the magnetic induction intensity of the stator magnetic field is enhanced, making the coupling between the stator and rotor magnetic fields closer and significantly improving magnetic field coupling efficiency. During this process, the motor's electromagnetic torque increases synchronously with the magnetic field coupling strength, and after transmission through the gearbox, it is converted into a larger output torque, precisely offsetting the increased load pressure and preventing problems such as insufficient power, operational stalls, or overload shutdowns in the module, ensuring stable operation under heavy load conditions.

[0026] When the load feedback signal indicates a real-time load decrease (such as reduced force on robot joints or light-load operation of actuators), the magnetic field-torque dynamic coupling model determines that the current magnetic field coupling strength is excessive. At this point, the output magnetic field correction will include a control command to weaken the magnetic field strength. By appropriately reducing the drive current amplitude of the stator windings, the magnetic induction intensity of the stator magnetic field is reduced, thus mitigating the magnetic field coupling between the stator and rotor, and adapting the magnetic field coupling efficiency to light-load requirements. Simultaneously, the motor's electromagnetic torque decreases synchronously with the magnetic field coupling strength, and the gearbox output torque decreases accordingly. This avoids energy waste caused by excessive power output under light-load conditions and reduces electromagnetic losses and component wear caused by excessively strong magnetic fields, balancing operating efficiency and module lifespan.

[0027] The entire adaptation process requires no manual intervention. The magnetic field-torque dynamic coupling model determines the load change trend in real time based on dual-dimensional feedback data. By adjusting the magnetic field coupling strength in real time, it achieves a dynamic balance between torque output and load demand. This not only overcomes the adaptation lag problem caused by traditional fixed magnetic field parameter driving, but also significantly improves the module's adaptability and energy utilization efficiency under complex working conditions.

[0028] In one embodiment, the load feedback signal includes the output shaft torque signal of the gearbox module and the motor operating current signal, and the speed feedback signal includes the real-time angular velocity signal and angular acceleration signal of the output shaft of the gearbox module.

[0029] In one embodiment, activating the robot's micro-motor gearbox module to output initial power in the basic magnetic field state includes: Based on the rated load and rated speed of the gearbox module, the basic magnetic field strength and magnetic field distribution of the motor stator are preset; A fixed-amplitude initial drive current is input to the micro motor to stably output initial power in the basic magnetic field state, and the torque value of the initial power is 30%-60% of the rated torque of the gearbox module.

[0030] In this embodiment, firstly, the rated load and rated speed of the gearbox module are core parameters characterizing its operating capability, directly determining the module's force limit and speed upper limit under stable operating conditions. However, the load characteristics during the startup phase (such as static frictional resistance and startup inertial load) differ from those during stable operation, requiring targeted design of magnetic field parameters. Based on these rated parameters, the basic magnetic field strength and magnetic field distribution of the motor stator are preset through offline calibration and simulation optimization. The setting of the basic magnetic field strength must balance startup power requirements and safety, providing sufficient electromagnetic torque to overcome startup resistance while avoiding excessive magnetic field strength that could cause startup shock. The magnetic field distribution is predefined through the stator winding method and initial energization phase, ensuring uniform spatial distribution and stable coupling efficiency, reserving a reasonable adjustment range for subsequent dynamic magnetic field adjustments. This preset process ensures that the basic magnetic field is not blindly set but rather a personalized match based on the module's inherent performance parameters, guaranteeing the stability and adaptability of the initial power output from the source.

[0031] After the basic magnetic field strength and distribution pattern are preset, a fixed-amplitude initial drive current is input to the stator winding of the micro-motor via the drive circuit. This current amplitude corresponds one-to-one with the preset basic magnetic field strength. The electromagnetic induction effect generated after the current enters the winding will accurately reproduce the preset basic magnetic field state, ensuring that the magnetic field parameters are implemented without deviation. Under the action of a stable basic magnetic field, the motor rotor and stator magnetic fields generate electromagnetic torque. After being reduced and amplified by the gearbox, the initial power is output, and the torque value of this initial power is strictly controlled within the range of 30%-60% of the module's rated torque. The lower limit of 30% ensures that the torque is sufficient to overcome the static friction and inertial load during the starting stage, avoiding problems such as weak starting or inability to start. The upper limit of 60% avoids mechanical shock caused by excessive starting torque, prevents damage to the gearbox meshing parts, motor shaft system, and other structures due to instantaneous overload, and reduces energy redundancy during the starting stage. The fixed-amplitude current input ensures the stability of the basic magnetic field, thereby achieving a smooth output of initial power and laying a stable initial operating condition foundation for subsequent dual-dimensional feedback data acquisition and adaptive adjustment.

[0032] In one embodiment, the dual-dimensional feedback data is input into the magnetic field-torque dynamic coupling model, and a magnetic field correction is output. Based on the correction, the stator magnetic field distribution of the micromotor is adjusted to achieve an adaptive match between the output torque of the gearbox module and the real-time load, including: The real-time load amplitude of the load feedback signal and the speed change rate of the speed feedback signal are extracted as input features and input into the magnetic field-torque dynamic coupling model. The magnetic field-torque dynamic coupling model is based on a preset load-speed-magnetic field mapping relationship. By comparing the deviation between the input feature quantities and the target operating parameters, it generates magnetic field strength correction and magnetic field distribution angle correction. The coupling strength of the stator magnetic field is adjusted according to the magnetic field strength correction amount, and the current phase of the stator winding is adjusted according to the magnetic field distribution angle correction amount, so as to change the stator magnetic field distribution together. The matching degree between the output torque of the gearbox module and the real-time load is verified in real time. If the matching deviation exceeds the preset threshold, the model calculation and magnetic field adjustment process are repeated until the output torque and the real-time load are dynamically matched.

[0033] In this embodiment, firstly, the real-time load amplitude of the load feedback signal and the speed change rate of the speed feedback signal are extracted as input features and input into the magnetic field-torque dynamic coupling model. The core purpose is to filter out key features from the dual-dimensional feedback data that can directly characterize the module's operating status and load requirements, providing efficient and accurate input basis for model calculation. The load feedback signal contains multiple information such as static load and dynamic fluctuations. The real-time load amplitude can intuitively reflect the actual load currently borne by the module and is a core indicator for determining torque requirements. The speed change rate (the amount of change in speed per unit time) in the speed feedback signal can accurately capture the dynamic trend of speed and help determine whether load changes are accompanied by speed fluctuations, avoiding misjudgments caused by a single load signal. The two types of core features are separated and extracted from the original feedback data by signal processing algorithms (such as filtering and noise reduction, time domain analysis, etc.), and irrelevant interference components such as electromagnetic interference and mechanical vibration are eliminated to ensure the accuracy and effectiveness of the features. Then, the extracted real-time load amplitude and speed change rate are synchronously input into the magnetic field-torque dynamic coupling model as input features, laying the data foundation for the accurate calculation of the subsequent magnetic field correction, and overcoming the problem of insufficient control accuracy caused by redundant input parameters or missing key information in traditional control.

[0034] Next, the magnetic field-torque dynamic coupling model, based on a pre-defined load-speed-magnetic field mapping relationship, generates magnetic field strength correction and magnetic field distribution angle correction by comparing the deviations between the input characteristic quantities and the target operating parameters. The magnetic field-torque dynamic coupling model, through offline calibration, simulation testing, and verification under actual operating conditions, establishes a three-dimensional load-speed-magnetic field mapping relationship covering the entire operating range of the module. This mapping relationship clarifies the optimal magnetic field parameters (including magnetic field strength and magnetic field distribution angle) that enable accurate torque output under different real-time load amplitudes and speed change rates. After receiving the input feature values, the model first calls the preset three-dimensional mapping relationship to determine the target magnetic field parameters corresponding to the current working condition. Then, it compares and analyzes the input feature values ​​with the module's preset target operating parameters (such as the target load adaptation range, target speed stability threshold, etc.), calculates the deviation value between the two, and determines the degree of difference between the current magnetic field state and the optimal magnetic field parameters. Based on this deviation value, it generates targeted magnetic field correction values ​​through algorithms such as proportional compensation and phase correction. Specifically, it includes magnetic field strength correction values ​​(used to adjust the magnetic field coupling strength) and magnetic field distribution angle correction values ​​(used to adjust the spatial distribution shape of the magnetic field), ensuring that the corrected magnetic field parameters can accurately offset the deviation and achieve torque and load adaptation.

[0035] Furthermore, the coupling strength of the stator magnetic field is adjusted according to the magnetic field strength correction amount, and the current phase of the stator winding is adjusted according to the magnetic field distribution angle correction amount, thus jointly changing the stator magnetic field distribution. This step is the specific execution link of the magnetic field correction amount, achieving dynamic optimization of the magnetic field distribution through the coordinated control of current parameters. Regarding the magnetic field strength correction amount, the drive current amplitude of the micro-motor stator winding is adjusted through the drive circuit: if the correction amount is positive, it indicates that the current magnetic field coupling strength is insufficient, and the current amplitude needs to be increased to enhance the magnetic induction intensity of the stator magnetic field and strengthen the magnetic field coupling between the stator and rotor; if the correction amount is negative, the current amplitude is decreased to reduce the magnetic field coupling strength, adapting to light load requirements. Regarding the magnetic field distribution angle correction amount, the phase difference of the three-phase current of the stator winding is adjusted through a vector control algorithm: by changing the current conduction sequence, the spatial distribution angle of the stator magnetic field is shifted, optimizing the coupling angle between the magnetic field and the rotor, further improving the magnetic field coupling efficiency, and ensuring the smoothness of torque output. The adjustment of magnetic field strength and magnetic field distribution angle is performed synchronously, which together change the overall distribution state of the stator magnetic field. This makes the magnetic field parameters not only adapt to the load requirements in terms of strength, but also achieve the optimal coupling effect in terms of spatial distribution, thus overcoming the problem of limited adaptation accuracy caused by traditional single adjustment of magnetic field strength.

[0036] Finally, the matching degree between the output torque of the gearbox module and the real-time load is verified in real time. If the matching deviation exceeds the preset threshold, the model calculation and magnetic field adjustment process are repeated until the output torque and the real-time load achieve dynamic adaptation. This step is a closed-loop verification link to ensure the accuracy of adaptive matching. Iterative optimization ensures accurate adaptation between torque and load. After the magnetic field adjustment is completed, the measured value of the output torque of the gearbox module is collected in real time by the torque sensor and compared with the required torque value corresponding to the current real-time load. The matching deviation between the two is calculated (such as relative error, absolute error, etc.). The preset deviation threshold is a pre-set allowable error range based on the application scenario requirements of the module (such as the accuracy requirements of robot joint movement). If the measured matching deviation is within the threshold range, it indicates that the current magnetic field parameters have met the load adaptation requirements, and the current magnetic field state is maintained and operation continues. If the matching deviation exceeds the preset threshold, it is determined that the current magnetic field adjustment has not achieved the optimal effect. The deviation value is used as a feedback signal to be re-input into the magnetic field-torque dynamic coupling model, triggering a new round of model calculation, generating an updated magnetic field correction value, and repeating the magnetic field adjustment operation. The above verification and iteration process is repeated until the matching deviation is reduced to within the preset threshold, so as to achieve dynamic and accurate matching between the output torque and the real-time load. This effectively solves the matching deviation problem that is easy to occur when a single adjustment is made under complex working conditions, and significantly improves the load adaptive capability and control accuracy of the module.

[0037] In one embodiment, the dual-dimensional feedback data is input into the magnetic field-torque dynamic coupling model, and a magnetic field correction is output. Based on the correction, the stator magnetic field distribution of the micromotor is adjusted to achieve an adaptive match between the output torque of the gearbox module and the real-time load, including: The load feedback signal and the speed feedback signal are synchronized in the time domain, and the real-time peak load, load change rate, steady-state speed value, and speed fluctuation amplitude are extracted as the model input feature vector. The magnetic field-torque dynamic coupling model pre-stores a three-dimensional mapping matrix of load-speed-magnetic field based on the rated parameters of the gearbox module. The input feature vector is substituted into the three-dimensional mapping matrix for interpolation to obtain the matching target magnetic field parameters. The magnetic field strength correction and magnetic field spatial phase correction are calculated and generated through the deviation compensation algorithm. The amplitude of the drive current of the stator winding of the micro-motor is adjusted based on the magnetic field strength correction amount, and the phase difference of the three-phase current of the stator winding is adjusted based on the magnetic field spatial phase correction amount. Through the coordinated adjustment of the current amplitude and the phase difference of the three-phase current, the coupling strength and spatial distribution of the stator magnetic field are changed. The measured output torque of the gearbox module after adjustment is collected and the difference between it and the required torque value corresponding to the real-time load signal is calculated. If the absolute value of the difference reaches the threshold, the difference is fed back to the magnetic field-torque dynamic coupling model as a compensation signal to recalculate the magnetic field correction amount.

[0038] In this embodiment, since the load feedback signal and the speed feedback signal are acquired by different acquisition units (such as torque sensors and speed encoders), there is a slight time difference in acquisition. Directly inputting these signals into the model would lead to distortion in the judgment of the operating conditions. Therefore, the two types of signals are first aligned in the time domain using a timestamp synchronization algorithm to ensure that the load and speed data at the same moment correspond one-to-one, accurately reflecting the coupling relationship between the two under the current operating conditions. Subsequently, a signal feature extraction algorithm is used to select core parameters from the aligned signals: the real-time peak load can represent the maximum demand of the current load, the load change rate can capture the dynamic change trend of the load (such as sudden changes or gradual changes), the steady-state speed value reflects the current basic operating speed of the module, and the speed fluctuation amplitude reflects the stability of the speed. These four types of parameters are integrated into the model input feature vector, which not only comprehensively covers the static values ​​and dynamic trends of the load and speed, but also eliminates redundant interference information, providing accurate and comprehensive operating condition basis for subsequent model calculations.

[0039] Next, the magnetic field-torque dynamic coupling model pre-stores a three-dimensional mapping matrix of load-speed-magnetic field based on the rated parameters of the gearbox module. The input feature vector is substituted into the three-dimensional mapping matrix for interpolation to obtain the matching target magnetic field parameters. A deviation compensation algorithm is then used to calculate the magnetic field strength correction and magnetic field spatial phase correction. This step is the core computational step for achieving precise magnetic field control, relying on the pre-calibrated mapping matrix and interpolation algorithm to achieve accurate matching between operating conditions and magnetic field parameters. The load-speed-magnetic field three-dimensional mapping matrix built into the magnetic field-torque dynamic coupling model is constructed based on the gearbox module's rated load, rated speed, rated power, and other core parameters through extensive offline calibration experiments, simulation tests, and actual operating condition verification. The matrix contains the optimal magnetic field parameters (magnetic field strength, spatial phase) corresponding to different load and speed combinations across the entire operating range. After receiving the input feature vector, the model substitutes the real-time peak load, load change rate, steady-state speed value, and speed fluctuation amplitude into the three-dimensional mapping matrix. Since the actual operating conditions may not completely cover the calibration nodes, a bilinear interpolation algorithm is used to interpolate the discrete data in the mapping matrix to obtain the target magnetic field parameters that accurately match the current real-time operating conditions. Subsequently, the difference between the current actual magnetic field parameters and the target magnetic field parameters is calculated through a deviation compensation algorithm, and targeted magnetic field strength correction (used to adjust the magnetic field coupling capability) and magnetic field spatial phase correction (used to optimize the magnetic field spatial distribution) are generated to ensure that the corrected magnetic field parameters can directly adapt to the real-time load requirements.

[0040] Then, the drive current amplitude of the micromotor stator winding is adjusted based on the magnetic field strength correction, and the phase difference of the three-phase current in the stator winding is adjusted based on the magnetic field spatial phase correction. Through the coordinated adjustment of the current amplitude and the phase difference of the three-phase current, the coupling strength and spatial distribution of the stator magnetic field are changed. This step is the specific execution link of the magnetic field correction, and the dynamic optimization of the stator magnetic field is achieved through the coordinated control of current parameters. The coupling strength and spatial distribution of the stator magnetic field are jointly determined by the drive current amplitude of the stator winding and the phase difference of the three-phase current: For the magnetic field strength correction, the drive current amplitude of the stator winding is adjusted by a PWM (Pulse Width Modulation) controller. When the current amplitude increases, the magnetic induction intensity of the stator magnetic field increases synchronously, and the coupling strength between the magnetic field and the rotor magnetic field is enhanced; when the current amplitude decreases, the magnetic induction intensity decreases, and the coupling strength weakens. For the magnetic field spatial phase correction, the phase difference of the three-phase current in the stator winding is adjusted by a vector control algorithm, changing the current conduction sequence, so that the spatial distribution angle of the stator magnetic field is precisely offset, optimizing the coupling angle between the magnetic field and the rotor, and improving the magnetic field energy conversion efficiency. The two adjustments mentioned above are executed simultaneously, forming a coordinated control mechanism of intensity and phase. This ensures that the magnetic field coupling strength is adapted to the load size, and also guarantees the smoothness of torque output through spatial distribution optimization, thus overcoming the problem of insufficient adaptation accuracy caused by traditional single adjustment of current amplitude.

[0041] Finally, the measured output torque value of the adjusted gearbox module is collected and compared with the required torque value corresponding to the real-time load signal. The difference is calculated, and if the absolute value of the difference reaches a threshold, it is fed back as a compensation signal to the magnetic field-torque dynamic coupling model to recalculate the magnetic field correction. This step is a closed-loop compensation process to ensure adaptive matching accuracy, achieving iterative optimization of the magnetic field parameters through deviation feedback. After the magnetic field adjustment is completed, the measured output torque value of the gearbox module is collected in real time using a high-precision torque sensor. Simultaneously, the corresponding required torque value (i.e., the adaptive torque required by the load) is analyzed based on the current load feedback signal, and the absolute difference between the two is calculated to quantify the torque matching deviation. The preset difference threshold is a reasonable upper limit of error pre-set based on the accuracy requirements of the module's application scenario (such as the allowable range of robot joint motion error). If the absolute value of the difference does not reach the threshold, it indicates that the current magnetic field parameters meet the load adaptation requirements, and the current state is maintained. If the absolute value of the difference reaches or exceeds the threshold, it is determined that the current magnetic field adjustment has not achieved the optimal effect. The difference is fed back as a compensation signal to the magnetic field-torque dynamic coupling model, triggering the model to re-execute the interpolation calculation and deviation compensation algorithm to generate an updated magnetic field correction amount, and then repeating the magnetic field adjustment steps. This closed-loop mechanism can correct the magnetic field parameter deviation in real time, ensuring that even under complex working conditions such as sudden load changes and speed fluctuations, the output torque can maintain accurate adaptation with the real-time load, significantly improving the robustness and control accuracy of the drive method.

[0042] In one embodiment, the dual-dimensional feedback data is input into the magnetic field-torque dynamic coupling model, and a magnetic field correction is output. Based on the correction, the stator magnetic field distribution of the micromotor is adjusted to achieve an adaptive match between the output torque of the gearbox module and the real-time load, including: The real-time load value, characterized by historical load feedback signals, is the dependent variable, and the real-time speed value, characterized by historical speed feedback signals, is the independent variable. A nonlinear correlation equation between the two is constructed. The real-time acquired load feedback signal and speed feedback signal are substituted into the nonlinear correlation equation for verification and screening. If the corresponding load value is within the reasonable fluctuation threshold range of the corresponding speed range, the load feedback signal is used as the model input. If the load value exceeds the reasonable fluctuation threshold range, it is determined to be an abnormal working condition, the load feedback signal is corrected, and the original value of the speed feedback signal is retained. The magnetic field-torque dynamic coupling model is based on the verified and screened two-dimensional feedback signal. Combined with the magnetic field coupling efficiency characteristics of the gearbox module, the magnetic field correction amount is generated by the proportional-integral-derivative and fuzzy control fusion algorithm. The magnetic field correction amount includes the magnetic field strength adjustment coefficient and the magnetic field phase offset. The magnetic field strength adjustment coefficient increases linearly with the increase of the load value, and the magnetic field phase offset decreases linearly with the increase of the speed value. The stator winding's drive current duty cycle is changed based on the magnetic field strength adjustment coefficient, and the stator winding's energizing phase angle is adjusted based on the magnetic field phase offset. During the adjustment process, the output results of the load-speed correlation constraint model are used as a reference to ensure that the magnetic field adjustment direction is consistent with the operating conditions.

[0043] In this embodiment, firstly, a nonlinear correlation equation is constructed between the real-time load value represented by historical load feedback signals and the real-time speed value represented by historical speed feedback signals. This step is a core preliminary step for verifying the validity of signals, aiming to establish the inherent coupling relationship between load and speed, providing a benchmark for subsequent operating condition judgment. Based on the historical operating data accumulated by the gearbox module across the entire operating condition range, the real-time load value (dependent variable) converted from the load feedback signal and the real-time speed value (independent variable) converted from the speed feedback signal are extracted. These two types of data cover various typical operating conditions such as light load, heavy load, constant speed, and variable speed. Through data fitting and nonlinear modeling algorithms (such as polynomial regression and neural network fitting), the inherent coupling law between load and speed is explored, and a nonlinear correlation equation is constructed. This equation can accurately describe the reasonable range of load variation under different speed ranges, clarify the dynamic adaptation relationship between speed and load, avoid misjudgment of operating conditions caused by single parameter judgment, and provide a scientific and reliable reference standard for subsequent real-time signal verification and screening.

[0044] Then, the real-time acquired load feedback signals and speed feedback signals are substituted into the nonlinear correlation equation for verification and filtering. If the corresponding load value is within the reasonable fluctuation threshold range of the corresponding speed range, the load feedback signal is used as the model input. If the load value exceeds the reasonable fluctuation threshold range, it is determined to be an abnormal operating condition, the load feedback signal is corrected, and the original value of the speed feedback signal is retained. This step is a key verification step to ensure the accuracy of the model input data, and the correlation equation is used to achieve the effective filtering and correction of real-time signals. The real-time acquired load feedback signals and speed feedback signals are synchronously substituted into the preset nonlinear correlation equation, and the equation outputs the corresponding reasonable load fluctuation threshold range based on the current speed value. Subsequently, it is determined whether the real-time load value is within the threshold range: if it is within the range, it indicates that the coupling relationship between the current load and speed conforms to the inherent law, and the signal is not severely interfered with. The load feedback signal and speed feedback signal are directly used as valid data input into the magnetic field-torque dynamic coupling model; if it exceeds the threshold range, it is determined to be an abnormal operating condition (which may be caused by electromagnetic interference, sensor error, or instantaneous impact leading to signal distortion). At this time, the distorted load feedback signal is corrected by data correction algorithms (such as prediction value compensation based on correlation equations, median filtering, etc.) to ensure that the load data input to the model matches the actual operating condition, while retaining the original value of the speed feedback signal (because it does not show abnormal characteristics). This avoids the impact of invalid data on control accuracy and retains the true information of the operating condition to the greatest extent.

[0045] Then, based on the verified and filtered two-dimensional feedback signal, and combined with the magnetic field coupling efficiency characteristics of the gearbox module, the magnetic field correction quantity is generated through a proportional-integral-derivative (PID) and fuzzy control fusion algorithm. This magnetic field correction quantity includes a magnetic field strength adjustment coefficient and a magnetic field phase offset. The magnetic field strength adjustment coefficient increases linearly with increasing load, while the magnetic field phase offset decreases linearly with increasing rotational speed. This step is the core computational stage, achieving accurate generation of the magnetic field correction quantity through the fusion control algorithm, balancing control precision and adaptability to operating conditions. The magnetic field-torque dynamic coupling model first calls the magnetic field coupling efficiency characteristic parameters of the gearbox module (pre-calibrated based on the module structure and operating characteristics), which clarifies the correspondence between magnetic field parameters and torque output efficiency. Subsequently, it receives the verified and filtered two-dimensional feedback signal, using it as the input variable for the fusion algorithm. A fusion algorithm combining proportional-integral-derivative (PID) and fuzzy control is employed for computation. The PID control component rapidly responds to linear changes in load and speed, ensuring steady-state accuracy of torque regulation. The fuzzy control component, on the other hand, flexibly adapts to complex nonlinear conditions such as sudden load changes and speed fluctuations through a pre-defined fuzzy rule base, compensating for the insufficient robustness of single PID control. This fusion algorithm generates two types of magnetic field corrections: first, a magnetic field strength adjustment coefficient, which increases linearly with the load value—the larger the load, the larger the adjustment coefficient—ensuring a synchronous increase in magnetic field strength to provide sufficient torque; and second, a magnetic field phase offset, which decreases linearly with the speed value—the higher the speed, the smaller the phase offset—avoiding energy loss caused by magnetic field coupling angle deviations and achieving precise matching of load, speed, and magnetic field.

[0046] Finally, the stator winding drive current duty cycle is changed based on the magnetic field strength adjustment coefficient, and the stator winding energization phase angle is adjusted based on the magnetic field phase offset. During the adjustment process, the output results of the load-speed correlation constraint model are used as a reference to ensure that the magnetic field adjustment direction is consistent with the operating conditions. This step is the execution stage of the magnetic field correction, achieving magnetic field optimization through precise control of current parameters, while ensuring the effectiveness of the adjustment through the correlation constraint model. Regarding the magnetic field strength adjustment coefficient, the stator winding drive current duty cycle is changed through a PWM (Pulse Width Modulation) controller: when the adjustment coefficient increases, the current duty cycle increases synchronously, enhancing the magnetic induction intensity of the stator magnetic field and improving torque output capability; when the adjustment coefficient decreases, the current duty cycle decreases, weakening the magnetic field strength to meet light-load energy-saving requirements. Regarding the magnetic field phase offset, the stator winding energization phase angle is adjusted through a vector control algorithm: the phase offset is precisely adjusted according to speed changes, ensuring that the spatial distribution of the stator magnetic field maintains the optimal coupling angle with the rotor magnetic field, improving energy conversion efficiency. Throughout the adjustment process, the output results of the load-speed correlation constraint model are continuously used as a reference to verify in real time whether the coupling relationship between load and speed after magnetic field adjustment conforms to the preset law. If deviation occurs, the adjustment direction is corrected in time to ensure that the adjustment of magnetic field parameters is always consistent with the working condition requirements and to avoid ineffective or reverse adjustment problems.

[0047] In the above embodiments, this application incorporates some existing algorithms and technical features for explanation and description to make the specification more detailed, clear, and complete, thus complying with the provisions of the Patent Law. However, this is not achieved by using a series of complex steps and algorithmic formulas, nor by complicating the technical solution, nor by combining or stacking conventional or simple features. The existing algorithms and technical features listed are for the purpose of disclosing the specific implementation methods of each step of this application (not to limit this application) and to avoid situations where this application cannot be implemented.

[0048] Reference Figure 2 In another embodiment of the present invention, a power drive device for a robot micro-motor gearbox module is also provided, comprising: The starting unit is used to start the robot's micro-motor gearbox module and output initial power in the basic magnetic field state. The acquisition unit is used to acquire load feedback signals and speed feedback signals during the operation of the gearbox module in real time, forming two-dimensional feedback data; The adjustment unit is used to input the dual-dimensional feedback data into the magnetic field-torque dynamic coupling model, output the magnetic field correction amount, and adjust the stator magnetic field distribution of the micro motor based on the correction amount, so that the output torque of the gearbox module forms an adaptive match with the real-time load. The drive unit is used to continuously send drive commands to the gearbox module based on the corrected magnetic field state, so as to realize closed-loop adaptive adjustment of power output.

[0049] In this embodiment, the specific implementation of each unit in the above device embodiment is described in the above method embodiment, and will not be repeated here.

[0050] Reference Figure 3 This invention also provides a computer device, the internal structure of which can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0051] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0052] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0053] In summary, the power driving method and apparatus for a robot micro-motor gearbox module provided in this embodiment of the invention includes: starting the robot micro-motor gearbox module and outputting initial power in a basic magnetic field state; acquiring load feedback signals and speed feedback signals during the operation of the gearbox module in real time to form dual-dimensional feedback data; inputting the dual-dimensional feedback data into a magnetic field-torque dynamic coupling model to output a magnetic field correction amount; adjusting the stator magnetic field distribution of the micro-motor based on the correction amount to achieve adaptive matching between the output torque of the gearbox module and the real-time load; and continuously issuing driving commands to the gearbox module based on the corrected magnetic field state to achieve closed-loop adaptive adjustment of power output. In this invention, by acquiring load feedback signals and speed feedback signals during the operation of the gearbox module in real time and using a magnetic field-torque dynamic coupling model in real time to achieve adaptive matching between the output torque of the gearbox module and the real-time load, the defects of current robot micro-motor gearbox modules, such as easy jamming and energy waste, are overcome.

[0054] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. 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), dual-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.

[0055] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0056] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A power drive method for a robot micro-motor gearbox module, characterized in that, Includes the following steps: Start the robot's micro-motor gearbox module to output initial power in the basic magnetic field state; The load feedback signal and speed feedback signal of the gearbox module are acquired in real time during operation to form two-dimensional feedback data; The dual-dimensional feedback data is input into the magnetic field-torque dynamic coupling model, and the magnetic field correction amount is output. Based on the correction amount, the magnetic field distribution of the micro motor stator is adjusted so that the output torque of the gearbox module forms an adaptive match with the real-time load. Based on the corrected magnetic field state, drive commands are continuously sent to the gearbox module to achieve closed-loop adaptive adjustment of power output.

2. The power drive method for the robot micro-motor gearbox module according to claim 1, characterized in that, The output torque of the gearbox module is adaptively matched with the real-time load, including: synchronously strengthening the magnetic field coupling strength when the load increases and dynamically weakening the magnetic field coupling strength when the load decreases.

3. The power drive method for the robot micro-motor gearbox module according to claim 1, characterized in that, The load feedback signal includes the output shaft torque signal of the gearbox module and the motor operating current signal, and the speed feedback signal includes the real-time angular velocity signal and angular acceleration signal of the output shaft of the gearbox module.

4. The power drive method for the robot micro-motor gearbox module according to claim 1, characterized in that, Start the robot's micro-motor gearbox module to output initial power in the basic magnetic field state, including: Based on the rated load and rated speed of the gearbox module, the basic magnetic field strength and magnetic field distribution of the motor stator are preset; A fixed-amplitude initial drive current is input to the micro motor to stably output initial power in the basic magnetic field state, and the torque value of the initial power is 30%-60% of the rated torque of the gearbox module.

5. The power drive method for the robot micro-motor gearbox module according to claim 1, characterized in that, The dual-dimensional feedback data is input into the magnetic field-torque dynamic coupling model, which outputs a magnetic field correction value. Based on this correction value, the stator magnetic field distribution of the micromotor is adjusted to achieve an adaptive match between the gearbox module's output torque and the real-time load. This includes: The real-time load amplitude of the load feedback signal and the speed change rate of the speed feedback signal are extracted as input features and input into the magnetic field-torque dynamic coupling model. The magnetic field-torque dynamic coupling model is based on a preset load-speed-magnetic field mapping relationship. By comparing the deviation between the input feature quantities and the target operating parameters, it generates magnetic field strength correction and magnetic field distribution angle correction. The coupling strength of the stator magnetic field is adjusted according to the magnetic field strength correction amount, and the current phase of the stator winding is adjusted according to the magnetic field distribution angle correction amount, so as to change the stator magnetic field distribution together. The matching degree between the output torque of the gearbox module and the real-time load is verified in real time. If the matching deviation exceeds the preset threshold, the model calculation and magnetic field adjustment process are repeated until the output torque and the real-time load are dynamically matched.

6. The power drive method for the robot micro-motor gearbox module according to claim 1, characterized in that, The dual-dimensional feedback data is input into the magnetic field-torque dynamic coupling model, which outputs a magnetic field correction value. Based on this correction value, the stator magnetic field distribution of the micromotor is adjusted to achieve an adaptive match between the gearbox module's output torque and the real-time load. This includes: The load feedback signal and the speed feedback signal are synchronized in the time domain, and the real-time peak load, load change rate, steady-state speed value, and speed fluctuation amplitude are extracted as the model input feature vector. The magnetic field-torque dynamic coupling model pre-stores a three-dimensional mapping matrix of load-speed-magnetic field based on the rated parameters of the gearbox module. The input feature vector is substituted into the three-dimensional mapping matrix for interpolation to obtain the matching target magnetic field parameters. The magnetic field strength correction and magnetic field spatial phase correction are calculated and generated through the deviation compensation algorithm. The amplitude of the drive current of the stator winding of the micro-motor is adjusted based on the magnetic field strength correction amount, and the phase difference of the three-phase current of the stator winding is adjusted based on the magnetic field spatial phase correction amount. Through the coordinated adjustment of the current amplitude and the phase difference of the three-phase current, the coupling strength and spatial distribution of the stator magnetic field are changed. The measured output torque of the gearbox module after adjustment is collected and the difference between it and the required torque value corresponding to the real-time load signal is calculated. If the absolute value of the difference reaches the threshold, the difference is fed back to the magnetic field-torque dynamic coupling model as a compensation signal to recalculate the magnetic field correction amount.

7. The power drive method for the robot micro-motor gearbox module according to claim 1, characterized in that, The dual-dimensional feedback data is input into the magnetic field-torque dynamic coupling model, which outputs a magnetic field correction value. Based on this correction value, the stator magnetic field distribution of the micromotor is adjusted to achieve an adaptive match between the gearbox module's output torque and the real-time load. This includes: The real-time load value, characterized by historical load feedback signals, is the dependent variable, and the real-time speed value, characterized by historical speed feedback signals, is the independent variable. A nonlinear correlation equation between the two is constructed. The real-time acquired load feedback signal and speed feedback signal are substituted into the nonlinear correlation equation for verification and screening. If the corresponding load value is within the reasonable fluctuation threshold range of the corresponding speed range, the load feedback signal is used as the model input. If the load value exceeds the reasonable fluctuation threshold range, it is determined to be an abnormal working condition, the load feedback signal is corrected, and the original value of the speed feedback signal is retained. The magnetic field-torque dynamic coupling model is based on the verified and screened two-dimensional feedback signal. Combined with the magnetic field coupling efficiency characteristics of the gearbox module, the magnetic field correction amount is generated by the proportional-integral-derivative and fuzzy control fusion algorithm. The magnetic field correction amount includes the magnetic field strength adjustment coefficient and the magnetic field phase offset. The magnetic field strength adjustment coefficient increases linearly with the increase of the load value, and the magnetic field phase offset decreases linearly with the increase of the speed value. The stator winding's drive current duty cycle is changed based on the magnetic field strength adjustment coefficient, and the stator winding's energizing phase angle is adjusted based on the magnetic field phase offset. During the adjustment process, the output results of the load-speed correlation constraint model are used as a reference to ensure that the magnetic field adjustment direction is consistent with the operating conditions.

8. A power drive device for a robot micro-motor gearbox module, characterized in that, include: The starting unit is used to start the robot's micro-motor gearbox module and output initial power in the basic magnetic field state. The acquisition unit is used to acquire load feedback signals and speed feedback signals during the operation of the gearbox module in real time, forming two-dimensional feedback data; The adjustment unit is used to input the dual-dimensional feedback data into the magnetic field-torque dynamic coupling model, output the magnetic field correction amount, and adjust the stator magnetic field distribution of the micro motor based on the correction amount, so that the output torque of the gearbox module forms an adaptive match with the real-time load. The drive unit is used to continuously send drive commands to the gearbox module based on the corrected magnetic field state, so as to realize closed-loop adaptive adjustment of power output.

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