Brushless motor adaptive driving system and driving method for chef machine

Abstract

The invention relates to the technical field of motor driving control, in particular to a brushless motor self-adaptive driving system and method for a chef machine. The method comprises the following steps: acquiring data of a working module of the chef machine; performing circuit topology analysis on the data of the working module of the chef machine to generate circuit topology data of the working module of the chef machine; performing brushless motor deployment on the circuit topology data of the working module of the chef machine to generate brushless motor deployment data of the chef machine; performing deployment position adjustment on the chef machine brushless motor deployment data by using the chef machine working module circuit topology data to generate chef machine brushless motor deployment optimization data; and performing motor operation data acquisition on the chef machine brushless motor deployment optimization data to obtain brushless motor operation data. By optimizing motor deployment, real-time mode adjustment and self-adaptive driving parameter optimization, intelligence and self-adaptability of brushless motor driving of the chef machine are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor drive control, and in particular to a brushless motor adaptive drive system and a drive method for a food processor. Background Art

[0002] As an important tool in modern kitchens, the performance of kitchen machines is closely related to motor drive technology. Traditional kitchen machines mostly use brushed motors, which are simple to drive and low in cost, but due to mechanical friction, they have low efficiency and limited life. With the increasing requirements of users for performance and reliability, brushless motors have gradually become the mainstream choice for kitchen machines. Compared with brushed motors, brushless motors have the significant advantages of high efficiency, high torque density, low noise and maintenance-free. However, the drive system of brushless motors is more complex, especially in scenarios where large load changes in kitchen machines need to be handled, such as mixing dough and chopping nuts. The traditional fixed parameter drive method is difficult to balance motor efficiency and load adaptability, resulting in problems such as low operating efficiency or motor overheating. In recent years, adaptive drive technology has made rapid progress in brushless motor applications. By introducing real-time load detection and dynamic control algorithms, the adaptive drive method can automatically adjust the drive parameters according to load changes to achieve optimized performance. However, the current deployment positions of motors are mostly designed to be static, resulting in low working efficiency or increased energy consumption. At the same time, parameter adjustments rely on manual debugging or simple preset logic, which makes it difficult to achieve real-time optimization, resulting in low intelligence and adaptability of the brushless motor drive of the chef machine. Summary of the invention

[0003] Based on this, it is necessary to provide a brushless motor adaptive drive system and drive method for a chef machine to solve at least one of the above technical problems.

[0004] To achieve the above object, a brushless motor adaptive driving method for a chef machine is provided, the method comprising the following steps: Step S1: Acquire the working module data of the chef machine; perform circuit topology analysis on the working module data of the chef machine to generate the circuit topology data of the working module of the chef machine; perform brushless motor deployment on the circuit topology data of the working module of the chef machine to generate the brushless motor deployment data of the chef machine; use the circuit topology data of the working module of the chef machine to adjust the deployment position of the brushless motor deployment data of the chef machine to generate the brushless motor deployment optimization data of the chef machine; Step S2: collecting motor operation data of the brushless motor deployment optimization data of the chef machine to obtain brushless motor operation data; dividing the brushless motor operation data into motor working modes to generate a brushless motor single working mode and a brushless motor multiple working modes; performing real-time commutation analysis on the brushless motor single working mode and the brushless motor multiple working modes according to the brushless motor deployment optimization data of the chef machine to generate brushless motor working mode commutation data; Step S3: Adaptively optimize the brushless motor operation data according to the commutation data of the brushless motor operation mode to generate brushless motor drive optimization data; perform modulation signal feedback control on the brushless motor drive optimization data to generate brushless motor adaptive drive control feedback data; Step S4: Evaluate the driving performance of the brushless motor adaptive drive control feedback data and generate an adaptive drive performance evaluation report; transmit the adaptive drive performance evaluation report to the cloud platform for cloud storage to execute the brushless motor adaptive drive operation for the food processor.

[0005] The present invention can accurately understand the system structure through the analysis of the circuit of the working module of the chef machine, which helps to optimize the motor deployment position and ensure the efficient compatibility of the system circuit and the motor operation. Reasonably optimize the motor deployment, improve the operating efficiency of the chef machine, reduce energy waste, avoid conflicts or inefficient work between the motor and the working module, and ensure that the motor plays the best performance in the right position. Circuit topology optimization improves the stability of the entire system, reduces the risk of failure, and extends the service life of the system. Through real-time monitoring and analysis of the motor operation data, it can identify whether the motor is in a single working mode or a multi-working mode, ensuring that the motor operates in the optimal mode under different load conditions. Through commutation data analysis, the system can adjust the working direction and state of the motor in real time, reduce the unstable operation during the switching process, and improve the stability and efficiency of the system under complex operations. The working mode and commutation analysis enable the motor to dynamically adapt to load changes, improve the adaptive ability of the system, reduce problems such as overload and stagnation, and ensure efficient and smooth operation. Adjust the drive parameters in real time according to the motor working mode and load changes to ensure that the motor is always in the best working state, improve motor efficiency, reduce energy consumption, and avoid overheating, overload and other phenomena. By modulating the signal feedback control system, the running state of the motor can be monitored in real time, and the control parameters can be adjusted in time to make the response of the motor more accurate during the working process, ensuring that the motor is always in the best performance state. By optimizing the drive parameters, the motor performance can be maximized, the motor loss can be reduced, the energy utilization rate can be improved, and the service life of the motor can be extended. By evaluating the motor drive performance, the performance of the motor under different working conditions can be fully understood, potential problems can be discovered in time, and measures can be taken to optimize it to ensure that the motor always operates in an efficient and low-energy manner. The evaluation report is uploaded to the cloud platform to facilitate remote monitoring and data analysis, providing strong support for subsequent optimization. Cloud data storage ensures the security and reliability of the data, and is also convenient for later system upgrades and fault analysis. Therefore, the present invention improves the intelligence and adaptability of the brushless motor drive of the chef machine by optimizing motor deployment, real-time mode adjustment, and adaptive drive parameter optimization.

[0006] Preferably, step S1 comprises the following steps: Step S11: Obtaining chef machine working module data; Step S12: performing circuit simulation on the chef machine working module data to generate the chef machine working module circuit simulation data; performing circuit topology analysis on the chef machine working module data according to the chef machine working module circuit simulation data to generate the chef machine working module circuit topology data; Step S13: confirming the circuit connection point of the circuit topology data of the working module of the chef machine to obtain the circuit connection point data of the working module; deploying the brushless motor of the circuit topology data of the working module of the chef machine based on the circuit connection point data of the working module to generate the brushless motor deployment data of the chef machine; Step S14: Calculate the module circuit distance of the chef machine brushless motor deployment data and the chef machine working module circuit topology data to obtain the motor-working module circuit distance; use the motor-working module circuit distance to adjust the deployment position of the chef machine brushless motor deployment data to generate the chef machine brushless motor deployment optimization data.

[0007] The present invention can accurately simulate the behavior of the working module by performing circuit simulation and topological analysis on the working module of the chef machine, help designers predict the response of the circuit under different working conditions, and avoid design defects or problems in actual production. The deployment of the brushless motor can ensure the efficient connection between the motor and the working module through the circuit connection point confirmation. The reasonable deployment of the brushless motor not only improves the work efficiency, but also effectively reduces energy consumption and system noise, and improves the overall performance of the equipment. After confirming the circuit connection point and optimizing the motor deployment, the connectivity stability of each part of the circuit can be ensured, and electrical failures or performance degradation caused by poor circuit connection can be reduced. Through the module circuit distance calculation and position adjustment, the position of the motor deployment can be optimized in space, and the design problems caused by space limitations or component conflicts can be reduced, thereby improving the operating efficiency and reliability of the overall system. The whole process can reduce the trial and error process in actual production through circuit simulation, topological analysis, distance calculation and other links, improve the manufacturing accuracy and product quality of the product, and thus extend the service life and stability of the product.

[0008] Preferably, confirming the circuit connection point of the circuit topology data of the chef machine working module includes: Performing circuit network table analysis on the circuit topology data of the working module of the chef machine to generate circuit connection information data, wherein the circuit network table analysis includes node analysis, component analysis and connection analysis; performing node reduction on the circuit topology data of the working module of the chef machine according to the circuit connection information data to generate circuit connection reduction data; Marking the circuit connection reduction data with key connection elements to obtain key connection element data; traversing the circuit connection reduction data with the key connection element data to generate a preliminary circuit connection point list, wherein the preliminary circuit connection point list includes a number of circuit connection points; Perform characteristic analysis on each interconnection point in the preliminary interconnection point list to generate interconnection point characteristic data; calculate the optimal circuit path for the preliminary circuit interconnection point list based on the interconnection point characteristic data to obtain the optimal circuit path data; screen the circuit interconnection points in the preliminary circuit interconnection point list based on the optimal circuit path data to obtain the working module circuit interconnection point data.

[0009] The present invention decomposes the circuit into three basic parts: nodes, components and connections through circuit network table analysis, thereby providing detailed circuit connection information for subsequent analysis. This detailed analysis can help to understand the overall structure of the circuit more accurately and ensure that there is a solid foundation for the optimization and adjustment of the circuit in the subsequent steps. The node reduction step can effectively reduce the redundant connections in the circuit and improve the simplicity and efficiency of the circuit design. By streamlining the circuit connection, the complexity of the system can be reduced and the stability and response speed of the circuit can be improved. The marking of key connection components helps to identify the parts of the circuit that have a greater impact on performance, which is convenient for subsequent troubleshooting and maintenance. By marking key components, the problem area can be quickly located, reducing maintenance time and cost. The process of connecting node traversal and optimal circuit path calculation can ensure that the current flows through the most efficient path through accurate path optimization, reduce unnecessary resistance and loss, and improve the overall performance and efficiency of the working module. By analyzing the characteristics of the circuit connection points, it can be ensured that all connection points can be stably connected during the working process, avoiding circuit failures caused by unstable connection points, thereby improving the safety and reliability of the system. The final screened working module circuit connection point data is based on the optimal path and the positioning of key components to ensure the stability and efficiency of the circuit connection, thereby improving the operating efficiency and long-term stability of the entire chef machine working module.

[0010] Preferably, step S2 comprises the following steps: Step S21: using sensors to collect motor operation data of the brushless motor deployment optimization data of the chef machine to obtain brushless motor operation data; Step S22: dividing the brushless motor operation data into motor operation modes to generate a brushless motor single operation mode and a brushless motor multiple operation modes; Step S23: performing brushless motor structure analysis on the brushless motor deployment optimization data of the chef machine to generate brushless motor structure data, wherein the brushless motor structure data includes a stator, a rotor, and an electronic controller; Step S24: performing real-time commutation analysis on the brushless motor structure data based on the brushless motor single working mode and the brushless motor multiple working modes to generate brushless motor working mode commutation data.

[0011] The present invention collects the operating data of the brushless motor through a sensor, can monitor the working state of the motor in real time, collect key operating data, and provide a basis for the subsequent division and optimization of working modes, which is helpful to accurately evaluate and adjust the motor performance and ensure its optimal performance under different working conditions. The working mode division of the brushless motor operating data can identify the behavioral characteristics of the motor in single and multiple working modes. This process helps designers understand the performance of the motor and ensure that the motor can achieve efficient operation under various working conditions, thereby improving the overall performance of the system. Through the structural analysis of the brushless motor, the components of the motor (such as the stator, rotor and electronic controller) are understood in detail. This analysis provides a structural basis for the optimization of the motor, which helps to further improve the motor design, reduce energy consumption, and improve the stability and life of the motor. Based on the working mode of the brushless motor, real-time commutation analysis is performed to effectively manage the switching process of the motor in different working modes. By adjusting the motor commutation in real time, it is ensured that the motor can run smoothly when the speed and load change, reduce the mechanical impact and energy loss during the commutation process, and improve the efficiency and reliability of the motor. The entire process ensures that the brushless motor can maintain efficient and stable operation in various working environments through precise motor working mode division, structural analysis and commutation control, which improves system performance, reduces the probability of failure, and extends the service life of the motor. Through real-time commutation analysis and working mode optimization, the entire system can make intelligent adjustments based on the actual operation of the motor, so that the performance of the kitchen machine is optimized, further improving user experience and energy efficiency.

[0012] Preferably, step S22 includes the following steps: Step S221: performing time series decomposition on the brushless motor operation data to generate brushless motor operation timing decomposition data; performing key feature extraction on the brushless motor operation data based on the brushless motor operation timing decomposition data to generate motor operation feature data; Step S222: performing cluster analysis on the motor operation characteristic data to generate motor operation cluster grouping data; assigning mode labels to the motor operation cluster grouping data to obtain preliminary working mode label data; performing mode statistical characteristic calculation on the preliminary working mode label data to obtain preliminary working mode statistical characteristic data; Step S223: performing spectrum analysis on the preliminary working mode label data to generate preliminary working mode frequency characteristic data; constructing a mode characteristic description vector according to the preliminary working mode statistical characteristic data and the preliminary working mode frequency characteristic data to generate mode characteristic description data, wherein the mode characteristic description data includes dynamic characteristic description data and steady-state characteristic data; Step S224: dividing the brushless motor operation data into a first working mode of the brushless motor based on the dynamic characteristic description data to generate a brushless motor multi-working mode; dividing the brushless motor operation data into a second working mode of the brushless motor based on the steady-state characteristic data to generate a brushless motor single working mode.

[0013] The present invention can extract features that are crucial to the motor performance and working state from the running data of the brushless motor through time series decomposition and key feature extraction, which enables the running state of the motor to be understood and characterized more carefully, and provides a more accurate basis for running data analysis. Through cluster analysis and mode label assignment, the running data of the brushless motor can be automatically grouped and different working modes can be identified. This process effectively eliminates the interference of human factors, makes the working mode recognition of the motor more intelligent and automated, and improves the accuracy and reliability of working mode recognition. Combined with mode statistical characteristics and spectrum analysis, the dynamic characteristics and steady-state characteristics of each working mode of the motor can be fully described. Through the construction of the mode characteristic description vector, the accuracy of the motor working mode is further improved, which helps to fully understand the behavior of the motor under different working conditions. Through the division based on dynamic and steady-state characteristics, the working mode of the brushless motor can be refined into multiple working modes and single working modes, so that the motor can show the best performance under different working environments and load conditions. Accurate working mode division helps to improve the efficiency and adaptability of the motor and reduce unnecessary energy loss. Through precise division and analysis of each motor working mode, the system can make adaptive adjustments according to the different working states of the motor to ensure that the motor can maintain efficient and stable operation under load changes, environmental changes, etc. It effectively reduces the ineffective operation of the motor under overload or unsuitable conditions and optimizes energy consumption. Through comprehensive analysis of the motor working mode, it is possible to identify potential problems and abnormal performance of the motor in advance, such as abnormal vibration or frequency changes during operation, which provides data support for fault prediction and advance maintenance, helps to extend the service life of the motor and reduce maintenance costs.

[0014] Preferably, step S24 includes the following steps: Step S241: extracting the stator winding distribution in the brushless motor from the brushless motor structure data to obtain the stator winding distribution data; performing finite element simulation on the stator winding distribution data to generate a stator magnetic flux density distribution diagram; Step S242: performing steady-state magnetic flux characteristic analysis on the stator magnetic flux density distribution diagram based on the single working mode of the brushless motor to generate steady-state magnetic flux characteristic data; performing dynamic magnetic flux change characteristic analysis on the stator magnetic flux density distribution diagram based on the multiple working modes of the brushless motor to generate dynamic magnetic flux change characteristic data; integrating the steady-state magnetic flux characteristic data and the dynamic magnetic flux change characteristic data to generate stator electromagnetic characteristic data; Step S243: extracting rotor motion data in the brushless motor from the brushless motor structure data to obtain rotor motion data, wherein the rotor motion data includes moment of inertia, dynamic balance data and air gap width; performing air gap flux coupling characteristic analysis on the moment of inertia and the air gap width based on the single working mode of the brushless motor to generate air gap flux coupling characteristic data; performing centrifugal effect analysis on the dynamic balance data and the moment of inertia based on the multiple working modes of the brushless motor to generate centrifugal effect characteristic data; integrating the air gap flux coupling characteristic data and the centrifugal effect characteristic data to generate rotor dynamic characteristic data; Step S244: Calculate the magnetic field coupling coefficient for the stator electromagnetic characteristic data and the rotor dynamic characteristic data to obtain stator-rotor coupling characteristic data, wherein the formula for calculating the magnetic field coupling coefficient is as follows: In the formula, Expressed as the magnetic field coupling coefficient, Expressed as the stator magnetic field strength, Expressed as the rotor magnetic field strength, It is expressed as the volume of the coupling region; Step S245: Based on the stator-rotor coupling characteristic data, the electronic controller performs real-time commutation analysis on the single working mode of the brushless motor and the multiple working modes of the brushless motor to generate commutation data of the brushless motor working mode.

[0015] The present invention can accurately describe the stator magnetic field characteristics of the brushless motor by extracting the stator winding distribution data and performing finite element simulation to generate a stator flux density distribution diagram, which provides basic data for further analyzing the magnetic field distribution of the motor and its influence, helps optimize the motor design, and improves the magnetic field utilization efficiency and stability of the motor. By analyzing the steady-state flux characteristics and dynamic flux change characteristics of the single working mode and multi-working mode of the brushless motor respectively, the flux performance of the motor under different working conditions can be fully understood, which helps to adjust the performance of the motor according to different operating conditions, ensure that it can work efficiently under various working conditions, and reduce magnetic loss and energy waste. By extracting the rotor motion data, especially analyzing the moment of inertia, air gap width, and dynamic balance data, the motion characteristics of the rotor and its influence on the motor performance can be accurately captured. Combined with the centrifugal effect and the analysis of the air gap flux coupling characteristics, the rotor design of the motor can be better optimized, and the vibration and noise caused by the unbalanced rotor motion can be reduced, thereby improving the stability and comfort of the motor. By calculating the magnetic field coupling coefficient, the degree of magnetic field coupling between the stator and the rotor can be quantified. This analysis provides an important basis for optimizing the motor magnetic circuit design, improving motor efficiency, and reducing electromagnetic interference. The calculation formula accurately reflects the magnetic field coupling strength between the stator and the rotor, which helps to predict the motor performance in the design stage and perform targeted optimization. Combined with the stator-rotor coupling characteristic data, real-time commutation analysis based on single working mode and multiple working modes ensures that the commutation process of the motor under different working conditions is smoother, which can reduce the current shock and heat generated during motor commutation and improve the operating efficiency and life of the motor. The entire step involves the joint optimization of the stator and the rotor to ensure that the motor can achieve the best electromagnetic efficiency and mechanical balance during operation. Through accurate analysis of magnetic flux and rotor dynamic characteristics, not only can energy loss be reduced, but also the output power and working stability of the motor can be improved, thereby improving the overall performance of the motor.

[0016] Preferably, performing real-time commutation analysis of an electronic controller on a single working mode of a brushless motor and a multiple working mode of a brushless motor based on stator-rotor coupling characteristic data includes: Based on the stator-rotor coupling characteristic data, the electronic controller speed analysis is performed on the single working mode and the multi-working mode of the brushless motor to generate the electronic controller speed data; the electronic controller speed data is converted into a curve to obtain the electronic controller speed curve; the electronic controller speed curve is segmented into a fluctuation curve to generate the electronic controller fluctuation curve and the electronic controller flat curve; The optimal commutation time point under the condition of single working mode of brushless motor is calculated by using the gentle curve of electronic controller, and the static commutation timing data is obtained; the real-time commutation point time of brushless motor in multiple working modes is calculated by using the fluctuation curve of electronic controller, and the dynamic commutation timing data is obtained; The static commutation timing data and the dynamic commutation timing data are boundary smoothed to generate the commutation data of the brushless motor working mode.

[0017] The present invention can accurately identify the optimal commutation timing in static and dynamic working modes by performing curve conversion and fluctuation curve segmentation on the speed data of the electronic controller, which helps to ensure the smoothness and efficiency of the commutation process, reduce motor losses, and improve the overall working performance of the motor. The analysis of the fluctuation curve of the electronic controller can adjust the commutation time in the multiple working modes of the brushless motor in real time to adapt to the changes in different working conditions, which can effectively avoid premature or late commutation and improve the running stability and response speed of the motor. By performing boundary smoothing on the static and dynamic commutation timing data, the impact in the commutation process is reduced, the vibration and noise of the motor are reduced, which is conducive to extending the service life of the motor and its related components. Accurate commutation timing calculation can significantly reduce the error in the commutation process, enhance the real-time control capability of the electronic controller, and thus improve the stability and reliability of the entire system. By optimizing the commutation strategy, it is possible to ensure that the energy efficiency of the brushless motor in each working mode is maximized, avoid unnecessary energy loss, and improve the overall efficiency of the motor.

[0018] Preferably, step S3 comprises the following steps: Step S31: Adaptively optimize the brushless motor operation data according to the commutation data of the brushless motor operation mode to generate brushless motor drive optimization data, wherein the formula for adaptive drive parameter optimization is as follows: In the formula, Expressed as time The optimized drive parameters, The optimized drive parameters, Expressed as time The real-time current, Expressed as the phase weight coefficient, Expressed as the phase change rate, Expressed as the angular velocity weight coefficient, Expressed as time The rotor angular velocity; Step S32: performing signal conversion on the brushless motor drive optimization data to generate a brushless motor adaptive drive signal, and transmitting the brushless motor adaptive drive signal to the electronic controller for PWM signal modulation to generate a brushless motor drive modulation signal; Step S33: performing drive signal feedback control on the brushless motor drive modulation signal to generate brushless motor adaptive drive control feedback data.

[0019] The present invention can accurately adjust the drive parameters (U(t)) according to the dynamic changes of real-time current, phase change rate and rotor angular velocity through adaptive drive parameter optimization in the formula. This dynamic adjustment can effectively improve the response speed and stability of the motor and ensure that the motor always maintains the best drive efficiency in different working modes. By considering multiple factors such as current, phase change rate and angular velocity, the motor drive parameters can be fully optimized, unnecessary fluctuations in motor operation can be reduced, energy loss can be reduced, and overall performance can be improved, which also helps to improve the starting, acceleration and load adaptability of the motor and meet the needs under different working conditions. Through signal conversion and PWM signal modulation, the motor drive can be accurately controlled to reduce the error in the motor control system. This precise control can effectively improve the stability of the motor system, reduce system errors, and ensure stable operation of the motor. By feedback control of the drive signal, the motor operation state can be monitored in real time, and the drive parameters can be adaptively adjusted. This type of feedback control can effectively avoid overload, overheating or other potential faults, and ensure that the motor operates in an efficient and safe state. Adaptive drive optimization can adjust the drive parameters according to the real-time operating state of the motor, reduce the motor load, improve its working efficiency, and reduce excessive wear, which helps to extend the service life of the motor and its related components.

[0020] Preferably, step S4 comprises the following steps: Step S41: performing a driving performance evaluation on the brushless motor adaptive driving control feedback data to generate driving performance evaluation data; performing a multi-dimensional performance index quantification on the driving performance evaluation data to generate a driving performance quantification index; Step S42: Visualize the driving performance evaluation data according to the driving performance quantification index to generate an adaptive driving performance evaluation report; transmit the adaptive driving performance evaluation report to the cloud platform for cloud storage to perform the adaptive driving operation of the brushless motor for the food processor.

[0021] The present invention can fully understand the operation performance of the motor drive system by evaluating the drive performance of the brushless motor adaptive drive control feedback data. The multi-dimensional performance index quantification of the evaluation data can accurately reflect the key performance of the motor under different working conditions, such as working efficiency, stability, and response speed, which is helpful to identify potential problems and bottlenecks of the system. The generated drive performance quantification index provides data support for decision-making, which is convenient for system optimization and tuning. These indicators can help engineers identify areas that need to be improved, thereby further improving the motor performance by adjusting the control strategy or hardware settings. By visualizing the drive performance quantification index and generating an adaptive drive performance evaluation report, the working status of the motor drive can be clearly and intuitively displayed, which is convenient for users or maintenance personnel to quickly diagnose problems and take action. The visualized report improves the convenience and transparency of system management, making operation and maintenance more efficient. The adaptive drive performance evaluation report is transmitted to the cloud platform for cloud storage, which not only ensures the security and long-term accessibility of the data, but also provides support for subsequent data analysis, model optimization and system upgrades. The centralized storage of the cloud platform also provides a convenient access method for multi-device and multi-user collaboration. Through data stored in the cloud, the system can perform remote monitoring and real-time tuning, and promptly capture various data changes generated by the brushless motor during operation. This remote tuning capability can help users make online adjustments and optimizations, thereby further improving the overall performance of the motor and user experience.

[0022] In this specification, a brushless motor adaptive driving system for a chef machine is provided, which is used to execute the above-mentioned brushless motor adaptive driving method for a chef machine. The brushless motor adaptive driving system for a chef machine includes: The circuit integration module is used to obtain the working module data of the chef machine; perform circuit topology analysis on the working module data of the chef machine to generate the circuit topology data of the working module of the chef machine; perform brushless motor deployment on the circuit topology data of the working module of the chef machine to generate the brushless motor deployment data of the chef machine; use the circuit topology data of the working module of the chef machine to adjust the deployment position of the brushless motor deployment data of the chef machine to generate the brushless motor deployment optimization data of the chef machine; The motor drive analysis module is used to collect motor operation data for the brushless motor deployment optimization data of the chef machine to obtain brushless motor operation data; divide the brushless motor operation data into motor working modes to generate brushless motor single working mode and brushless motor multiple working modes; perform real-time commutation analysis on the brushless motor single working mode and brushless motor multiple working modes according to the brushless motor deployment optimization data of the chef machine to generate brushless motor working mode commutation data; A drive optimization module is used to adaptively optimize the drive parameters of the brushless motor operation data according to the commutation data of the brushless motor operation mode, and generate brushless motor drive optimization data; perform modulation signal feedback control on the brushless motor drive optimization data, and generate brushless motor adaptive drive control feedback data; The performance feedback module is used to evaluate the driving performance of the brushless motor adaptive drive control feedback data and generate an adaptive drive performance evaluation report; the adaptive drive performance evaluation report is transmitted to the cloud platform for cloud storage to execute the brushless motor adaptive drive operation for the chef machine.

[0023] The beneficial effect of the present invention is that the working efficiency and performance of the chef machine can be significantly improved through the synergy of the circuit integration module, the brushless motor deployment optimization and the motor drive analysis module. The system optimizes the deployment position and drive parameters of the brushless motor to ensure that the motor operates more efficiently and stably under different working conditions, thereby improving the working efficiency and durability of the entire chef machine. Through circuit topology analysis and motor deployment optimization, the system can intelligently adjust the deployment position of the motor to make it more adaptable to the needs of different working modes, which not only improves the operating efficiency of the motor, but also reduces the problem of uneven motor load or loss, and extends the service life of the motor. The drive optimization module adaptively adjusts the motor drive parameters according to the working mode commutation data. This optimization enables the motor to achieve the best driving performance in various working modes, thereby achieving more precise control and less energy consumption. The motor drive analysis module performs real-time commutation analysis on the motor operation data, optimizes the commutation timing of the motor, and avoids instability and impact during commutation, which helps to improve the response speed and running stability of the motor, thereby improving the overall operation experience of the chef machine. The performance feedback module not only evaluates the performance of the motor drive, but also guides subsequent optimization operations by generating a detailed evaluation report. By transmitting the evaluation report to the cloud platform, remote management and adjustment can be performed, thereby improving the intelligence level of the system. Through cloud storage, the system can collect and store a large amount of drive performance data, which provides a rich basis for further analysis and optimization. The advantages of cloud platform storage also lie in the long-term traceability of data and big data analysis capabilities, which can continuously improve system performance. Therefore, the present invention improves the intelligence and adaptability of the brushless motor drive of the chef machine by optimizing motor deployment, real-time mode adjustment, and adaptive drive parameter optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of a process flow of a brushless motor adaptive driving method for a kitchen machine; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, and the term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a brushless motor adaptive driving method for a kitchen machine, the method comprising the following steps: Step S1: Acquire the working module data of the chef machine; perform circuit topology analysis on the working module data of the chef machine to generate the circuit topology data of the working module of the chef machine; perform brushless motor deployment on the circuit topology data of the working module of the chef machine to generate the brushless motor deployment data of the chef machine; use the circuit topology data of the working module of the chef machine to adjust the deployment position of the brushless motor deployment data of the chef machine to generate the brushless motor deployment optimization data of the chef machine; Step S2: collecting motor operation data of the brushless motor deployment optimization data of the chef machine to obtain brushless motor operation data; dividing the brushless motor operation data into motor working modes to generate a brushless motor single working mode and a brushless motor multiple working modes; performing real-time commutation analysis on the brushless motor single working mode and the brushless motor multiple working modes according to the brushless motor deployment optimization data of the chef machine to generate brushless motor working mode commutation data; Step S3: Adaptively optimize the brushless motor operation data according to the commutation data of the brushless motor operation mode to generate brushless motor drive optimization data; perform modulation signal feedback control on the brushless motor drive optimization data to generate brushless motor adaptive drive control feedback data; Step S4: Evaluate the driving performance of the brushless motor adaptive drive control feedback data and generate an adaptive drive performance evaluation report; transmit the adaptive drive performance evaluation report to the cloud platform for cloud storage to execute the brushless motor adaptive drive operation for the food processor.

[0029] The present invention can accurately understand the system structure through the analysis of the circuit of the working module of the chef machine, which helps to optimize the motor deployment position and ensure the efficient compatibility of the system circuit and the motor operation. Reasonably optimize the motor deployment, improve the operating efficiency of the chef machine, reduce energy waste, avoid conflicts or inefficient work between the motor and the working module, and ensure that the motor plays the best performance in the right position. Circuit topology optimization improves the stability of the entire system, reduces the risk of failure, and extends the service life of the system. Through real-time monitoring and analysis of the motor operation data, it can identify whether the motor is in a single working mode or a multi-working mode, ensuring that the motor operates in the optimal mode under different load conditions. Through commutation data analysis, the system can adjust the working direction and state of the motor in real time, reduce the unstable operation during the switching process, and improve the stability and efficiency of the system under complex operations. The working mode and commutation analysis enable the motor to dynamically adapt to load changes, improve the adaptive ability of the system, reduce problems such as overload and stagnation, and ensure efficient and smooth operation. Adjust the drive parameters in real time according to the motor working mode and load changes to ensure that the motor is always in the best working state, improve motor efficiency, reduce energy consumption, and avoid overheating, overload and other phenomena. By modulating the signal feedback control system, the running state of the motor can be monitored in real time, and the control parameters can be adjusted in time to make the response of the motor more accurate during the working process, ensuring that the motor is always in the best performance state. By optimizing the drive parameters, the motor performance can be maximized, the motor loss can be reduced, the energy utilization rate can be improved, and the service life of the motor can be extended. By evaluating the motor drive performance, the performance of the motor under different working conditions can be fully understood, potential problems can be discovered in time, and measures can be taken to optimize it to ensure that the motor always operates in an efficient and low-energy manner. The evaluation report is uploaded to the cloud platform to facilitate remote monitoring and data analysis, providing strong support for subsequent optimization. Cloud data storage ensures the security and reliability of the data, and is also convenient for later system upgrades and fault analysis. Therefore, the present invention improves the intelligence and adaptability of the brushless motor drive of the chef machine by optimizing motor deployment, real-time mode adjustment, and adaptive drive parameter optimization.

[0030] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a step flow chart of a brushless motor adaptive driving method for a chef machine according to the present invention. In this example, the brushless motor adaptive driving method for a chef machine includes the following steps: Step S1: Acquire the working module data of the chef machine; perform circuit topology analysis on the working module data of the chef machine to generate the circuit topology data of the working module of the chef machine; perform brushless motor deployment on the circuit topology data of the working module of the chef machine to generate the brushless motor deployment data of the chef machine; use the circuit topology data of the working module of the chef machine to adjust the deployment position of the brushless motor deployment data of the chef machine to generate the brushless motor deployment optimization data of the chef machine; In an embodiment of the present invention, data of various working modules of the chef machine, including motor performance parameters, heating element status, transmission system working conditions, etc., are obtained through sensors or data acquisition systems. Data can be collected in real time through device connection interfaces (such as serial ports, USB or wireless communications, etc.). By analyzing the data of the working modules of the chef machine, the connection structure of its electrical system can be understood. Circuit simulation software (such as SPICE or other circuit simulation tools) can be used to extract the topological structure of the circuit, determine the connection method of each component (such as power supply, drive circuit, motor interface, etc.), and generate circuit topology data of the working module of the chef machine. Based on the circuit topology data, the brushless motor design is deployed to a suitable position. This process can optimize the motor position and connection method through simulation tools to ensure the optimal electrical connection between the motor and other working modules (such as controllers, power modules, sensors, etc.), and generate the brushless motor deployment data of the chef machine, including information such as motor deployment position, connection method, and electrical control path. Use optimization algorithms (such as genetic algorithms or particle swarm algorithms) to further adjust the deployment position of the brushless motor to improve overall work efficiency. The goals of the adjustment include minimizing current loss, optimizing mechanical structure, and improving heat dissipation efficiency. Based on the adjusted data, optimized data for the brushless motor deployment of the chef machine is generated, which will help achieve more efficient and stable operation.

[0031] Step S2: collecting motor operation data of the brushless motor deployment optimization data of the chef machine to obtain brushless motor operation data; dividing the brushless motor operation data into motor working modes to generate a brushless motor single working mode and a brushless motor multiple working modes; performing real-time commutation analysis on the brushless motor single working mode and the brushless motor multiple working modes according to the brushless motor deployment optimization data of the chef machine to generate brushless motor working mode commutation data; In an embodiment of the present invention, a brushless motor is monitored in real time by using a sensor or an embedded control system to collect the operating data of the motor, including key parameters such as current, voltage, speed, temperature, power, torque, etc. The collected operating data can be obtained by an embedded system (such as a microcontroller or a real-time data acquisition system) and stored in a local database for subsequent analysis. Based on the collected brushless motor operating data, data mining or machine learning technology is used to classify the working state of the motor. For example, a clustering algorithm (such as K-means or DBSCAN) can be used to perform cluster analysis on the working data of the motor, and the motor operating state can be divided into different working modes. Generally, these working modes can be divided into a single working mode (such as constant speed operation, starting, stopping) and multiple working modes (such as variable speed, load change, etc.), and brushless motor single working mode and brushless motor multiple working mode data are generated. Using deployment optimization data as input, the commutation process of the brushless motor in single working mode and multiple working modes is analyzed in real time. The specific analysis includes current changes, speed fluctuations, load response, etc. during commutation. By establishing a real-time monitoring system, the dynamic behavior of the motor during commutation is tracked, and relevant data is collected and analyzed. Commutation analysis can optimize the matching of commutation time with key parameters such as current and voltage by using mathematical modeling or rule-based algorithms with the help of real-time control systems. Based on the results of real-time commutation analysis, commutation data of brushless motor operating modes are generated. This data includes the time points when the motor commutates in different operating modes, key parameters of the commutation process (such as current waveform, speed fluctuation, temperature change, etc.), and commutation efficiency. The behavior of the commutation process can be displayed through data visualization tools (such as waveforms or heat maps) to help evaluate motor performance and provide a basis for optimizing motor control strategies.

[0032] Step S3: Adaptively optimize the brushless motor operation data according to the commutation data of the brushless motor operation mode to generate brushless motor drive optimization data; perform modulation signal feedback control on the brushless motor drive optimization data to generate brushless motor adaptive drive control feedback data; In an embodiment of the present invention, by using the commutation data of the brushless motor working mode as input, the performance of the motor under different commutation conditions (such as current fluctuation, speed change, temperature rise, etc.) is analyzed. The motor drive parameters (such as PWM frequency, duty cycle, current limit, torque control, etc.) are automatically adjusted by an algorithm (such as the least squares method or an adaptive control algorithm based on optimization) to optimize the motor performance. The goal of adaptive optimization is to improve the efficiency, stability and response speed of the motor under different working modes, thereby reducing energy loss and overheating, and ensuring the best performance of the motor under various working modes. After adaptive drive parameter optimization, brushless motor drive optimization data is generated. The data contains optimized drive parameters, such as speed regulation range, optimal current waveform, torque control strategy, power factor, etc. These data will serve as the basis for subsequent control systems to guide the operation of the motor drive system and ensure the efficiency, stability and long life of the brushless motor in actual operation. The brushless motor drive optimization data is feedback controlled using a modulation signal (such as a PWM control signal). Specifically, a PWM signal is generated by a controller, and the frequency, duty cycle and other parameters of the modulation signal are adjusted according to the optimization data to ensure that the motor operates in the optimal state. Through the real-time feedback mechanism, the system will automatically adjust the PWM signal according to the actual operating status of the motor (such as speed, load changes, temperature, etc.) to optimize the motor performance. Based on the modulation signal feedback control, the working status of the brushless motor is monitored in real time, including current, voltage, speed, temperature and other parameters. These real-time data are compared and analyzed with the drive optimization data, and the drive control strategy of the motor is adjusted according to the feedback information to generate the brushless motor adaptive drive control feedback data, which includes the adjustment record of the modulation signal, the change of motor performance, the response time, the temperature control effect and other information.

[0033] Step S4: Evaluate the driving performance of the brushless motor adaptive drive control feedback data and generate an adaptive drive performance evaluation report; transmit the adaptive drive performance evaluation report to the cloud platform for cloud storage to execute the brushless motor adaptive drive operation for the food processor.

[0034] In an embodiment of the present invention, the adaptive drive control feedback data of the brushless motor (such as the adjustment record of the modulation signal, the motor working state data, the temperature change, the current fluctuation, etc.) is collected. Then, the driving performance of the motor is evaluated by using a performance evaluation algorithm (such as a regression model based on PID control, fuzzy control, or machine learning). The evaluation content includes the start-up response time, load adaptability, energy efficiency, temperature rise control effect, vibration, noise level, etc. of the motor. Based on these evaluation results, an adaptive drive performance evaluation report of the brushless motor is generated. The report should include the following main parts: such as the actual changes of current, voltage, speed, temperature, etc. and the expected values ​​are compared. According to the performance test results under different working conditions, the working efficiency, stability, thermal management ability and other aspects of the motor are evaluated. Based on the evaluation results, further optimization schemes are proposed, such as drive parameter adjustment, system configuration optimization, etc. The generated adaptive drive performance evaluation report is transmitted to the cloud platform through an encrypted transmission channel. Standard cloud storage services (such as AWS, Google Cloud, Azure, etc.) can be used for storage. The specific transmission method may include: uploading the evaluation report to the cloud platform through a standard RESTful API interface to ensure the security and integrity of the data. Use encryption protocols such as SSL / TLS to ensure that the evaluation report is not tampered with or leaked during transmission. The evaluation report can be stored in standard JSON, XML or PDF formats for analysis and subsequent access in the cloud. In the cloud platform, the stored evaluation report can be used for further analysis and processing. Based on the data in the report, the cloud system can execute optimization algorithms to further adjust the drive control strategy of the brushless motor. For example: use the computing resources on the cloud platform to train deep learning or other optimization algorithms to produce more accurate drive parameters. Connect the cloud platform to the chef machine system to obtain motor operation data in real time and automatically adjust the motor drive strategy according to the optimization plan in the cloud. The adaptive drive control strategy stored in the cloud can be executed through intelligent management and directly affect the operation of the chef machine through the remote control system to perform tasks scheduling, performance optimization and other operations.

[0035] Preferably, step S1 comprises the following steps: Step S11: Obtaining chef machine working module data; Step S12: performing circuit simulation on the chef machine working module data to generate the chef machine working module circuit simulation data; performing circuit topology analysis on the chef machine working module data according to the chef machine working module circuit simulation data to generate the chef machine working module circuit topology data; Step S13: confirming the circuit connection point of the circuit topology data of the working module of the chef machine to obtain the circuit connection point data of the working module; deploying the brushless motor of the circuit topology data of the working module of the chef machine based on the circuit connection point data of the working module to generate the brushless motor deployment data of the chef machine; Step S14: Calculate the module circuit distance of the chef machine brushless motor deployment data and the chef machine working module circuit topology data to obtain the motor-working module circuit distance; use the motor-working module circuit distance to adjust the deployment position of the chef machine brushless motor deployment data to generate the chef machine brushless motor deployment optimization data.

[0036] In an embodiment of the present invention, the operating state of the chef machine working module is monitored by using built-in sensors (such as temperature sensors, current sensors, voltage sensors, etc.). For example, in the process of stirring, heating, cutting, etc., data such as current, voltage, temperature and speed are obtained in real time. The collected data is transmitted to the central processing unit (CPU) or microcontroller through a data interface (such as I2C, SPI or CAN bus) and stored in a database. Data storage can be processed using local storage (such as SD card) or cloud storage. The data is preliminarily cleaned, such as removing abnormal data, filling missing values, etc. The electrical system of the chef machine working module is simulated using circuit simulation software (such as SPICE, PSpice, MATLAB / Simulink, etc.). According to the load and current requirements of the actual working module, the circuit elements (resistors, capacitors, diodes, etc.) and their connection methods are set. The circuit performance is analyzed by simulation tools, and the simulation results (such as current, voltage waveforms, power consumption, etc.) are output. For example, the current and power requirements of the working module in different operating modes are simulated to predict the circuit load. Based on the simulation data, the circuit elements and their connection relationships are extracted to establish a circuit topology model. Use graph theory algorithms (such as depth-first search or breadth-first search) to analyze the connections between components in the circuit. By analyzing the circuit diagram, determine the interconnection points of the circuit modules and the current transmission path. According to the circuit topology diagram, use automated tools (such as the NetworkX library in Python) to identify the interconnection points in the circuit. These interconnection points are usually the connection points between different circuit modules, such as the interface between the power supply and the drive circuit. The algorithm automatically extracts these interconnection points and records their coordinate positions in the circuit diagram. The generated data includes the voltage, current, and connection method of the interconnection points with other modules. According to the circuit interconnection point data, select a suitable location for deploying the brushless motor. The brushless motor needs to be connected to the drive part of the circuit, so the location selection should consider the layout of the power line, drive line, and signal line. Use a space planning algorithm to ensure that the distance between the motor and other circuit modules is minimized while avoiding electrical interference. Use a multi-objective optimization algorithm (such as particle swarm optimization and genetic algorithm) to determine the motor position to optimize the working efficiency of the motor and the stability of the electrical connection. In three-dimensional space, use the coordinate system to calculate the electrical connection distance between the brushless motor and the working module. This step usually uses geometric calculations or distance matrices to calculate the distance between each working module and the motor. For example, assuming that the coordinates of the motor are (0, 0, 0) and the coordinates of the working module are (3, 4, 0), the electrical connection distance between the motor and the working module is √(3² + 4²) = 5 meters. Calculate the distance between all working modules and the motor and output a motor-working module circuit distance matrix. According to the motor-working module circuit distance data, use the optimization algorithm to adjust the deployment position of the brushless motor.For example, optimization algorithms such as genetic algorithms and simulated annealing algorithms are used to minimize the electrical loss between the motor and the working module while ensuring the stability of the electrical connection. If some working modules are far away from the motor, the deployment position of the motor needs to be rearranged to minimize the connection distance between the motor and the module, thereby improving electrical efficiency and reducing energy loss. Ultimately, the optimized deployment data of the brushless motor of the chef machine is generated, including the new position of the motor, the corresponding electrical connection distance, and the optimal connection method for each working module.

[0037] Preferably, confirming the circuit connection point of the circuit topology data of the chef machine working module includes: Performing circuit network table analysis on the circuit topology data of the working module of the chef machine to generate circuit connection information data, wherein the circuit network table analysis includes node analysis, component analysis and connection analysis; performing node reduction on the circuit topology data of the working module of the chef machine according to the circuit connection information data to generate circuit connection reduction data; Marking the circuit connection reduction data with key connection elements to obtain key connection element data; traversing the circuit connection reduction data with the key connection element data to generate a preliminary circuit connection point list, wherein the preliminary circuit connection point list includes a number of circuit connection points; Perform characteristic analysis on each interconnection point in the preliminary interconnection point list to generate interconnection point characteristic data; calculate the optimal circuit path for the preliminary circuit interconnection point list based on the interconnection point characteristic data to obtain the optimal circuit path data; screen the circuit interconnection points in the preliminary circuit interconnection point list based on the optimal circuit path data to obtain the working module circuit interconnection point data.

[0038] In an embodiment of the present invention, all nodes are first extracted from the circuit topology data of the chef machine working module. Each node can represent a connection point of a circuit component, such as a power supply end, a load end, a ground end, etc. Nodes can be identified by number or ID. Use a graph database or a network analysis tool (such as Graphviz or NetworkX) to convert the circuit topology data into a graph structure, and extract all node information from it. Assuming that there is a circuit consisting of a power supply, a load, and a ground, node parsing will identify the power supply node, the load node, and the ground node. Extract the components used in the circuit (such as resistors, capacitors, diodes, brushless motors, etc.) from the circuit topology data. Each component has a unique identifier indicating their type, parameters (such as resistance, voltage rating, etc.) and their connection location. Each component is connected to other components or nodes through a connection node. For example, a resistor connects the power supply and load nodes, a capacitor connects the power supply and ground nodes, etc. For a circuit containing a power supply, a resistor, and a load, component parsing will identify the power supply node, the resistor element, the load node, and their connection relationship. Parse the connection lines between the components in the circuit and clarify the nodes connected by each connection line. Each connection will be marked with the starting point and end point according to the circuit diagram to confirm the current flow direction and function of its connection. These connection relationships are converted into edges in the graph and connected to nodes, which provides graph structure data for subsequent topological analysis. For the connection lines between the power node and the load node, the connection analysis will identify its current flow direction and connectivity. Use graph algorithms to reduce nodes, merge redundant or functional nodes in the topology, and reduce the complexity of the circuit system. During the reduction process, keep the circuit function and connectivity unchanged. By analyzing the connection relationship of the circuit, reduce the minimum set of nodes and simplify the topological structure of the circuit. For example, if there are repeated connection relationships between multiple nodes, they can be reduced to one node. If multiple resistance nodes are connected in series in the topology, these nodes can be reduced to one node, reducing the number of nodes that need to be processed in the circuit. According to the functional requirements and electrical connection relationships of the circuit, identify the critical connection elements in the circuit (such as power supply, main load, control element, etc.), which usually play a key role in the operation of the circuit and are the basis for maintaining the normal operation of the circuit. Mark these key elements through algorithms or manual settings and mark them specially in the circuit diagram. Common methods include using specific identifiers, colors, or shapes to distinguish these key components. In a control circuit, the power module, motor drive module, and switching elements are key components that affect the main functions of the circuit. Use a graph traversal algorithm (such as depth-first search DFS or breadth-first search BFS) to traverse the connected nodes of the circuit. The goal is to find all nodes that are interconnected by electrical connections by traversing the topological structure of the circuit to form connected nodes. Whenever a node is traversed, record whether the node is connected to other nodes through circuit elements.If connected, these nodes are considered to form a connection point. In a circuit consisting of a power supply, a load, and a motor, the connection point between the power supply, the load, and the motor is the connection point. Based on the results of the traversal, a preliminary connection point list is generated. This list contains all the circuit connection points found, and each connection point is a group of nodes connected by an electrical path. The connection point list is used as a data storage structure for further analysis. The connection point list includes "power node and load node connection", "motor and load node connection", etc. For each connection point, analyze its electrical characteristics, such as voltage, current, impedance, etc. The characteristics of each connection point can be obtained through circuit simulation tools (such as SPICE). According to the electrical characteristics of the connection point, evaluate its importance in the circuit. For example, some connection points are specifically bottlenecks for current flow, affecting the efficiency of the circuit. If the current of a certain connection point is much higher than that of other points, it means that the point is a key point in the circuit and the optimization path needs to be given priority. Use the shortest path algorithm (such as the Dijkstra algorithm) to calculate the optimal circuit path between each connection point, and select the path with the best electrical performance to reduce power loss and interference in the circuit. When calculating the path, the circuit's current, impedance, and voltage constraints are considered to ensure that the selected path is not only the shortest, but also the best in terms of electrical performance. Among multiple possible electrical connection paths, a path with the least current loss is selected as the optimal path. Based on the optimal circuit path data, select those interconnection points that are optimal in electrical performance and critical to the circuit function. The interconnection point data finally selected constitutes the "working module circuit interconnection point data", which will be used as key data input in subsequent circuit layout and optimization.

[0039] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: using sensors to collect motor operation data of the brushless motor deployment optimization data of the chef machine to obtain brushless motor operation data; Step S22: dividing the brushless motor operation data into motor operation modes to generate a brushless motor single operation mode and a brushless motor multiple operation modes; Step S23: performing brushless motor structure analysis on the brushless motor deployment optimization data of the chef machine to generate brushless motor structure data, wherein the brushless motor structure data includes a stator, a rotor, and an electronic controller; Step S24: performing real-time commutation analysis on the brushless motor structure data based on the brushless motor single working mode and the brushless motor multiple working modes to generate brushless motor working mode commutation data.

[0040] In the embodiment of the present invention, multiple types of sensors including current sensors, speed sensors and temperature sensors are deployed on the brushless motor of the chef machine to collect key parameters in real time during the operation of the motor. Current sensor: detects the current fluctuation of the brushless motor coil. Speed ​​sensor: captures the speed data of the brushless motor. Temperature sensor: monitors the temperature rise during the operation of the motor. The embedded data acquisition module is used to process and store the real-time data to generate the brushless motor operation data, including: speed change curve; current waveform; temperature change trend. The collected brushless motor operation data is filtered and denoised to remove external interference signals and generate a purified motor operation data set. Based on the operation data, the working state of the brushless motor under different loads and speeds is analyzed and divided according to the following rules: single working mode: the brushless motor is in a stable operating state under constant speed and load conditions; multi-working mode: the brushless motor is in a dynamic operating state of variable speed and variable load. The chef machine brushless motor deployment optimization data is deeply analyzed to extract its structural information, including: stator: analyze the coil layout and material properties; rotor: evaluate the magnetic material distribution and rotational inertia; electronic controller: analyze the control algorithm and circuit design. The analysis results are organized into brushless motor structural data, including stator winding distribution diagram, rotor pole layout diagram and electronic controller module function data. Based on the motor working mode (single working mode or multiple working modes), formulate appropriate commutation rules: single working mode: maintain commutation stability; multiple working mode: optimize dynamic commutation efficiency. Combine operation data with structural data to analyze the commutation performance of brushless motors in different modes. Use dynamic commutation algorithm to calculate commutation angle and time in real time, and output commutation data of brushless motor working mode.

[0041] Preferably, step S22 includes the following steps: Step S221: performing time series decomposition on the brushless motor operation data to generate brushless motor operation timing decomposition data; performing key feature extraction on the brushless motor operation data based on the brushless motor operation timing decomposition data to generate motor operation feature data; Step S222: performing cluster analysis on the motor operation characteristic data to generate motor operation cluster grouping data; assigning mode labels to the motor operation cluster grouping data to obtain preliminary working mode label data; performing mode statistical characteristic calculation on the preliminary working mode label data to obtain preliminary working mode statistical characteristic data; Step S223: performing spectrum analysis on the preliminary working mode label data to generate preliminary working mode frequency characteristic data; constructing a mode characteristic description vector according to the preliminary working mode statistical characteristic data and the preliminary working mode frequency characteristic data to generate mode characteristic description data, wherein the mode characteristic description data includes dynamic characteristic description data and steady-state characteristic data; Step S224: dividing the brushless motor operation data into a first working mode of the brushless motor based on the dynamic characteristic description data to generate a brushless motor multi-working mode; dividing the brushless motor operation data into a second working mode of the brushless motor based on the steady-state characteristic data to generate a brushless motor single working mode.

[0042] In an embodiment of the present invention, the operation data of the brushless motor is decomposed, and the specific steps are as follows: Use the moving average method or the Loess smoothing method to separate the long-term trend of the operation data. For example, extract the long-term current growth trend from the operating current to ensure that the baseline data of the motor performance changes over a long period of time is captured. Use the fast Fourier transform (FFT) or the empirical mode decomposition (EMD) to extract the periodic signal. For example, determine the main cycle and sub-cycle components of the motor operation. The remaining part is regarded as noise, and the random component is further separated by wavelet transform or filtering method. Calculate the average, maximum and minimum values ​​of the long-term trend component, and extract key performance indicators during stable operation, such as average voltage, constant speed, etc. Use the periodic component and the random component to calculate the frequency distribution, amplitude change and other characteristics, such as the dynamic characteristics such as the starting acceleration and the frequency fluctuation range, and generate the motor operation characteristic data, including steady-state characteristics and dynamic characteristics. The motor operation characteristic data is divided into several state clusters, such as "low-speed operation cluster", "medium-speed operation cluster" and "high-speed operation cluster". Use the DBSCAN algorithm: detect abnormal working conditions during the operation of the motor, such as sudden speed changes or current abnormalities. Assign labels to clustered grouped data: label names such as "constant speed mode" and "dynamic acceleration mode", and each label is combined with a feature range (such as speed range, current fluctuation range). Count the average duration and number of switching times of each mode; construct a mode switching probability matrix to describe the transition probability between modes. Apply Fourier transform (FFT) to the preliminary working mode label data: extract the main frequencies and their corresponding amplitudes, such as low-frequency stable operation and high-frequency dynamic switching characteristics. Based on spectrum analysis and statistical characteristics, construct feature descriptions: transient characteristics such as acceleration, frequency fluctuation range, switching speed, and static characteristics such as constant speed and current stability. Analyze the switching behavior of the motor from start to stop, and extract dynamic operating characteristics, such as dynamic acceleration, load switching, etc. Identify the working state of the motor at a constant speed, such as "low-speed constant mode" and "high-speed constant mode".

[0043] Preferably, step S24 includes the following steps: Step S241: extracting the stator winding distribution in the brushless motor from the brushless motor structure data to obtain the stator winding distribution data; performing finite element simulation on the stator winding distribution data to generate a stator magnetic flux density distribution diagram; Step S242: performing steady-state magnetic flux characteristic analysis on the stator magnetic flux density distribution diagram based on the single working mode of the brushless motor to generate steady-state magnetic flux characteristic data; performing dynamic magnetic flux change characteristic analysis on the stator magnetic flux density distribution diagram based on the multiple working modes of the brushless motor to generate dynamic magnetic flux change characteristic data; integrating the steady-state magnetic flux characteristic data and the dynamic magnetic flux change characteristic data to generate stator electromagnetic characteristic data; Step S243: extracting rotor motion data in the brushless motor from the brushless motor structure data to obtain rotor motion data, wherein the rotor motion data includes moment of inertia, dynamic balance data and air gap width; performing air gap flux coupling characteristic analysis on the moment of inertia and the air gap width based on the single working mode of the brushless motor to generate air gap flux coupling characteristic data; performing centrifugal effect analysis on the dynamic balance data and the moment of inertia based on the multiple working modes of the brushless motor to generate centrifugal effect characteristic data; integrating the air gap flux coupling characteristic data and the centrifugal effect characteristic data to generate rotor dynamic characteristic data; Step S244: Calculate the magnetic field coupling coefficient for the stator electromagnetic characteristic data and the rotor dynamic characteristic data to obtain stator-rotor coupling characteristic data, wherein the formula for calculating the magnetic field coupling coefficient is as follows: In the formula, Expressed as the magnetic field coupling coefficient, Expressed as the stator magnetic field strength, Expressed as the rotor magnetic field strength, It is expressed as the volume of the coupling region; Step S245: Based on the stator-rotor coupling characteristic data, the electronic controller performs real-time commutation analysis on the single working mode of the brushless motor and the multiple working modes of the brushless motor to generate commutation data of the brushless motor working mode.

[0044] In an embodiment of the present invention, the geometric distribution parameters of the stator winding, including the number of winding turns, winding angle, winding phase, etc., are extracted by analyzing the brushless motor structure data. The winding distribution is visualized and modeled using analytical methods or software tools (such as AnsysMaxwell). Based on the extracted winding distribution data, a finite element model is constructed, and simulation parameters (such as material magnetic permeability, current density, etc.) are set. The simulation is run to generate a stator magnetic flux density distribution diagram to show the distribution state of magnetic flux over time and position. Based on the single working mode of the brushless motor, the stator magnetic flux density distribution diagram is analyzed under steady-state conditions: the steady-state value of the magnetic flux, the average value and fluctuation range of the magnetic flux waveform are extracted; the hot spot area and the degree of magnetic flux saturation in the magnetic flux distribution are calculated. Based on the multiple working modes of the brushless motor, the dynamic behavior of the magnetic flux changing over time is analyzed: the frequency of the magnetic flux waveform, the amplitude change rate, and the influence of the dynamic working mode switching on the magnetic flux distribution are calculated. Combine the steady-state magnetic flux characteristic data with the dynamic magnetic flux change characteristic data: use data fusion technology to generate complete stator electromagnetic characteristic data, including static and dynamic characteristic descriptions. Extract the key parameters of the rotor from the brushless motor structural data: moment of inertia: calculate the effect of rotor mass distribution on rotational motion; dynamic balancing data: evaluate the vibration characteristics of the rotor during rotation; air gap width: measure the gap between the stator and the rotor. Use the air gap width and moment of inertia parameters to evaluate the distribution of magnetic flux in the air gap and its coupling with the rotor motion: calculate the flux coupling efficiency and the effect of rotor position on the flux intensity. Based on the dynamic balancing data and moment of inertia, analyze the effect of centrifugal force on rotor performance when the rotor rotates at high speed: evaluate the disturbance of rotor eccentricity on the air gap flux distribution. Integrate the air gap flux coupling characteristic data with the centrifugal effect characteristic data to generate a comprehensive dynamic characteristic description of the rotor. Calculate the magnetic field coupling coefficient using the following formula: In the formula, Expressed as the magnetic field coupling coefficient, Expressed as the stator magnetic field strength, Expressed as the rotor magnetic field strength, It is expressed as the volume of the coupling area. The strength of the magnetic field coupling between the stator and rotor is calculated using the numerical integration method, and the stator-rotor coupling characteristic data is output. Based on the stator-rotor coupling characteristic data, the commutation control analysis of the single working mode and multi-working mode of the brushless motor is performed: Single working mode: Maintain the steady state of the magnetic field to achieve efficient operation of the brushless motor; Multi-working mode: According to the dynamic characteristics of the magnetic field, dynamically adjust the commutation timing of the electronic controller to avoid the magnetic field from being out of synchronization. Use the electronic controller to adjust the commutation logic in real time to ensure that the magnetic flux coupling between the stator and rotor remains in the best state.

[0045] Preferably, performing real-time commutation analysis of an electronic controller on a single working mode of a brushless motor and a multiple working mode of a brushless motor based on stator-rotor coupling characteristic data includes: Based on the stator-rotor coupling characteristic data, the electronic controller speed analysis is performed on the single working mode and the multi-working mode of the brushless motor to generate the electronic controller speed data; the electronic controller speed data is converted into a curve to obtain the electronic controller speed curve; the electronic controller speed curve is segmented into a fluctuation curve to generate the electronic controller fluctuation curve and the electronic controller flat curve; The optimal commutation time point under the condition of single working mode of brushless motor is calculated by using the gentle curve of electronic controller, and the static commutation timing data is obtained; the real-time commutation point time of brushless motor in multiple working modes is calculated by using the fluctuation curve of electronic controller, and the dynamic commutation timing data is obtained; The static commutation timing data and the dynamic commutation timing data are boundary smoothed to generate the commutation data of the brushless motor working mode.

[0046] In the embodiment of the present invention, the real-time speed of the electronic controller is calculated according to the stator-rotor coupling characteristic data, and the speed data of the electronic controller is generated in combination with the operating parameters of the single working mode and the multi-working mode of the brushless motor, mainly including: single working mode: speed under constant load conditions; multi-working mode: dynamic response of speed under load changes, starting and braking conditions. The speed data of the electronic controller is curve-fitted and converted to generate a speed curve, which includes the following processing: eliminating the influence of high-frequency noise on the speed data; segmenting the speed data into a single-mode curve and a multi-mode curve. The speed curve is subjected to fluctuation analysis to extract the fluctuation part with significant changes and the relatively stable part in the curve: the fluctuation curve represents the rapid change of speed in the dynamic mode, and the gentle curve represents the stable speed change under steady-state operation. Using the electronic controller gentle curve data, the optimal commutation time point in the single working mode of the brushless motor is calculated: the phase relationship between the magnetic field waveform and the speed is analyzed; and the optimal time point of the stator-rotor magnetic flux synchronization is determined. Using the electronic controller fluctuation curve data, the real-time commutation point time in the multi-working mode of the brushless motor is calculated: the speed change trend under dynamic conditions is analyzed; and the real-time control time point of commutation is determined in combination with the dynamic characteristics of the stator-rotor magnetic flux. Perform boundary smoothing on static commutation timing data and dynamic commutation timing data to avoid mutations caused by mode switching: use boundary condition continuity algorithm to smooth the transition between static and dynamic commutation time points; ensure that the timing data of commutation control remains stable under multi-mode switching.

[0047] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: Adaptively optimize the brushless motor operation data according to the commutation data of the brushless motor operation mode to generate brushless motor drive optimization data, wherein the formula for adaptive drive parameter optimization is as follows: In the formula, Expressed as time The optimized drive parameters, Expressed as the current weight coefficient, Expressed as time The real-time current, Expressed as the phase weight coefficient, Expressed as the phase change rate, Expressed as the angular velocity weight coefficient, Expressed as time The rotor angular velocity; Step S32: performing signal conversion on the brushless motor drive optimization data to generate a brushless motor adaptive drive signal, and transmitting the brushless motor adaptive drive signal to the electronic controller for PWM signal modulation to generate a brushless motor drive modulation signal; Step S33: performing drive signal feedback control on the brushless motor drive modulation signal to generate brushless motor adaptive drive control feedback data.

[0048] In the embodiment of the present invention, the real-time current during operation is obtained by commutation data according to the working mode of the brushless motor. , Phase change rate , rotor angular velocity The following adaptive drive parameter optimization formula is used to calculate the drive parameters: In the formula, Expressed as time The optimized drive parameters, Expressed as the current weight coefficient, Expressed as time The real-time current, Expressed as the phase weight coefficient, Expressed as the phase change rate, Expressed as the angular velocity weight coefficient, Expressed as time The rotor angular velocity is adjusted by multiple iterations. , , The value of makes the optimization formula meet the driving requirements under different working modes. Based on the brushless motor drive optimization data, signal conversion is performed to convert the optimization data into an adaptive drive signal suitable for the electronic controller. The conversion process includes: digital signal encoding; filtering processing to reduce signal noise; time synchronization to ensure real-time control. The adaptive drive signal is input to the electronic controller for pulse width modulation (PWM). According to the optimized drive parameters , generate a PWM signal with the corresponding duty cycle; ensure the high accuracy and real-time performance of the drive signal. Collect feedback signals in real time from the running state of the brushless motor, including: actual current, actual phase change rate and actual rotor angular velocity. Compare and analyze the feedback signal with the drive modulation signal to identify the deviation: the error between the optimization target and the actual feedback = target feedback value - actual feedback value. According to the error , dynamically adjust the weight coefficient , , value, optimize the control strategy, update the adaptive drive signal and retransmit it to the electronic controller.

[0049] Preferably, step S4 comprises the following steps: Step S41: performing a driving performance evaluation on the brushless motor adaptive driving control feedback data to generate driving performance evaluation data; performing a multi-dimensional performance index quantification on the driving performance evaluation data to generate a driving performance quantification index; Step S42: Visualize the driving performance evaluation data according to the driving performance quantification index to generate an adaptive driving performance evaluation report; transmit the adaptive driving performance evaluation report to the cloud platform for cloud storage to perform the adaptive driving operation of the brushless motor for the food processor.

[0050] In an embodiment of the present invention, key parameters such as speed, torque, current waveform, etc. are extracted from the adaptive drive control feedback data of the brushless motor. These parameters are analyzed using a multidimensional drive performance evaluation model to evaluate the stability, response speed, efficiency, etc. of the drive performance, and generate drive performance evaluation data. The drive performance evaluation data is input into a performance index quantification algorithm. The algorithm includes the following sub-steps: classifying the evaluation data into speed stability index, energy efficiency index, noise control index, etc. Allocating weights according to the importance of different indicators, calculating the comprehensive performance quantification score, and outputting the drive performance quantification index data, and the data format includes the specific value of each indicator and the comprehensive performance score. According to the drive performance quantification index, the data is converted into a visual form, such as a chart, a dashboard, or a heat map. An adaptive drive performance evaluation report is generated through an adaptive data visualization tool, and the report content includes trend analysis and optimization suggestions for key performance indicators. The adaptive drive performance evaluation report is uploaded to the cloud platform through a secure transmission protocol (such as HTTPS). The report is classified and stored on the cloud platform, and a tag index is created for subsequent retrieval. Ensure that the report can be used for the adaptive drive operation of the brushless motor of the chef machine, including real-time query and remote diagnosis functions for performance optimization.

[0051] In this specification, a brushless motor adaptive driving system for a chef machine is provided, which is used to execute the above-mentioned brushless motor adaptive driving method for a chef machine. The brushless motor adaptive driving system for a chef machine includes: The circuit integration module is used to obtain the working module data of the chef machine; perform circuit topology analysis on the working module data of the chef machine to generate the circuit topology data of the working module of the chef machine; perform brushless motor deployment on the circuit topology data of the working module of the chef machine to generate the brushless motor deployment data of the chef machine; use the circuit topology data of the working module of the chef machine to adjust the deployment position of the brushless motor deployment data of the chef machine to generate the brushless motor deployment optimization data of the chef machine; The motor drive analysis module is used to collect motor operation data for the brushless motor deployment optimization data of the chef machine to obtain brushless motor operation data; divide the brushless motor operation data into motor working modes to generate brushless motor single working mode and brushless motor multiple working modes; perform real-time commutation analysis on the brushless motor single working mode and brushless motor multiple working modes according to the brushless motor deployment optimization data of the chef machine to generate brushless motor working mode commutation data; A drive optimization module is used to adaptively optimize the drive parameters of the brushless motor operation data according to the commutation data of the brushless motor operation mode, and generate brushless motor drive optimization data; perform modulation signal feedback control on the brushless motor drive optimization data, and generate brushless motor adaptive drive control feedback data; The performance feedback module is used to evaluate the driving performance of the brushless motor adaptive drive control feedback data and generate an adaptive drive performance evaluation report; the adaptive drive performance evaluation report is transmitted to the cloud platform for cloud storage to execute the brushless motor adaptive drive operation for the chef machine.

[0052] The beneficial effect of the present invention is that the working efficiency and performance of the chef machine can be significantly improved through the synergy of the circuit integration module, the brushless motor deployment optimization and the motor drive analysis module. The system optimizes the deployment position and drive parameters of the brushless motor to ensure that the motor operates more efficiently and stably under different working conditions, thereby improving the working efficiency and durability of the entire chef machine. Through circuit topology analysis and motor deployment optimization, the system can intelligently adjust the deployment position of the motor to make it more adaptable to the needs of different working modes, which not only improves the operating efficiency of the motor, but also reduces the problem of uneven motor load or loss, and extends the service life of the motor. The drive optimization module adaptively adjusts the motor drive parameters according to the working mode commutation data. This optimization enables the motor to achieve the best driving performance in various working modes, thereby achieving more precise control and less energy consumption. The motor drive analysis module performs real-time commutation analysis on the motor operation data, optimizes the commutation timing of the motor, and avoids instability and impact during commutation, which helps to improve the response speed and running stability of the motor, thereby improving the overall operation experience of the chef machine. The performance feedback module not only evaluates the performance of the motor drive, but also guides subsequent optimization operations by generating a detailed evaluation report. By transmitting the evaluation report to the cloud platform, remote management and adjustment can be performed, thereby improving the intelligence level of the system. Through cloud storage, the system can collect and store a large amount of drive performance data, which provides a rich basis for further analysis and optimization. The advantages of cloud platform storage also lie in the long-term traceability of data and big data analysis capabilities, which can continuously improve system performance. Therefore, the present invention improves the intelligence and adaptability of the brushless motor drive of the chef machine by optimizing motor deployment, real-time mode adjustment, and adaptive drive parameter optimization.

[0053] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0054] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A brushless motor adaptive driving method for a chef machine, characterized in that: The following steps are involved: Step S1: Acquire the working module data of the chef machine; perform circuit topology analysis on the working module data of the chef machine to generate the circuit topology data of the working module of the chef machine; perform brushless motor deployment on the circuit topology data of the working module of the chef machine to generate the brushless motor deployment data of the chef machine; use the circuit topology data of the working module of the chef machine to adjust the deployment position of the brushless motor deployment data of the chef machine to generate the brushless motor deployment optimization data of the chef machine; Step S2: collecting motor operation data of the brushless motor deployment optimization data of the chef machine to obtain brushless motor operation data; dividing the brushless motor operation data into motor working modes to generate a brushless motor single working mode and a brushless motor multiple working modes; performing real-time commutation analysis on the brushless motor single working mode and the brushless motor multiple working modes according to the brushless motor deployment optimization data of the chef machine to generate brushless motor working mode commutation data; Step S3: Adaptively optimize the drive parameters of the brushless motor operation data according to the commutation data of the brushless motor operation mode to generate brushless motor drive optimization data; Performing modulation signal feedback control on brushless motor drive optimization data to generate brushless motor adaptive drive control feedback data; Step S4: performing a driving performance evaluation on the brushless motor adaptive driving control feedback data and generating an adaptive driving performance evaluation report; The adaptive drive performance evaluation report is transmitted to the cloud platform for cloud storage to perform the adaptive drive operation of the brushless motor for the kitchen machine.

2. The brushless motor adaptive driving method for a chef machine according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining chef machine working module data; Step S12: performing circuit simulation on the chef machine working module data to generate the chef machine working module circuit simulation data; performing circuit topology analysis on the chef machine working module data according to the chef machine working module circuit simulation data to generate the chef machine working module circuit topology data; Step S13: confirming the circuit connection point of the circuit topology data of the working module of the chef machine to obtain the circuit connection point data of the working module; deploying the brushless motor of the circuit topology data of the working module of the chef machine based on the circuit connection point data of the working module to generate the brushless motor deployment data of the chef machine; Step S14: Calculate the module circuit distance of the chef machine brushless motor deployment data and the chef machine working module circuit topology data to obtain the motor-working module circuit distance; use the motor-working module circuit distance to adjust the deployment position of the chef machine brushless motor deployment data to generate the chef machine brushless motor deployment optimization data.

3. The brushless motor adaptive driving method for a chef machine according to claim 2, characterized in that: Confirmation of the circuit connection points of the circuit topology data of the chef machine working module includes: Performing circuit network table analysis on the circuit topology data of the working module of the chef machine to generate circuit connection information data, wherein the circuit network table analysis includes node analysis, component analysis and connection analysis; performing node reduction on the circuit topology data of the working module of the chef machine according to the circuit connection information data to generate circuit connection reduction data; Marking the circuit connection reduction data with key connection elements to obtain key connection element data; traversing the circuit connection reduction data with the key connection element data to generate a preliminary circuit connection point list, wherein the preliminary circuit connection point list includes a number of circuit connection points; Perform characteristic analysis on each interconnection point in the preliminary interconnection point list to generate interconnection point characteristic data; calculate the optimal circuit path for the preliminary circuit interconnection point list based on the interconnection point characteristic data to obtain the optimal circuit path data; screen the circuit interconnection points in the preliminary circuit interconnection point list based on the optimal circuit path data to obtain the working module circuit interconnection point data.

4. The brushless motor adaptive driving method for a chef machine according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: using sensors to collect motor operation data of the brushless motor deployment optimization data of the chef machine to obtain brushless motor operation data; Step S22: dividing the brushless motor operation data into motor operation modes to generate a brushless motor single operation mode and a brushless motor multiple operation modes; Step S23: performing brushless motor structure analysis on the brushless motor deployment optimization data of the chef machine to generate brushless motor structure data, wherein the brushless motor structure data includes a stator, a rotor, and an electronic controller; Step S24: performing real-time commutation analysis on the brushless motor structure data based on the brushless motor single working mode and the brushless motor multiple working modes to generate brushless motor working mode commutation data.

5. The brushless motor adaptive driving method for a chef machine according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: performing time series decomposition on the brushless motor operation data to generate brushless motor operation timing decomposition data; performing key feature extraction on the brushless motor operation data based on the brushless motor operation timing decomposition data to generate motor operation feature data; Step S222: performing cluster analysis on the motor operation characteristic data to generate motor operation cluster grouping data; assigning mode labels to the motor operation cluster grouping data to obtain preliminary working mode label data; performing mode statistical characteristic calculation on the preliminary working mode label data to obtain preliminary working mode statistical characteristic data; Step S223: performing spectrum analysis on the preliminary working mode label data to generate preliminary working mode frequency characteristic data; constructing a mode characteristic description vector according to the preliminary working mode statistical characteristic data and the preliminary working mode frequency characteristic data to generate mode characteristic description data, wherein the mode characteristic description data includes dynamic characteristic description data and steady-state characteristic data; Step S224: dividing the brushless motor operation data into a first working mode of the brushless motor based on the dynamic characteristic description data to generate a brushless motor multi-working mode; dividing the brushless motor operation data into a second working mode of the brushless motor based on the steady-state characteristic data to generate a brushless motor single working mode.

6. The brushless motor adaptive driving method for a chef machine according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: extracting the stator winding distribution in the brushless motor from the brushless motor structure data to obtain the stator winding distribution data; performing finite element simulation on the stator winding distribution data to generate a stator magnetic flux density distribution diagram; Step S242: performing steady-state magnetic flux characteristic analysis on the stator magnetic flux density distribution diagram based on the single working mode of the brushless motor to generate steady-state magnetic flux characteristic data; performing dynamic magnetic flux change characteristic analysis on the stator magnetic flux density distribution diagram based on the multiple working modes of the brushless motor to generate dynamic magnetic flux change characteristic data; integrating the steady-state magnetic flux characteristic data and the dynamic magnetic flux change characteristic data to generate stator electromagnetic characteristic data; Step S243: extracting rotor motion data in the brushless motor from the brushless motor structure data to obtain rotor motion data, wherein the rotor motion data includes moment of inertia, dynamic balance data and air gap width; performing air gap flux coupling characteristic analysis on the moment of inertia and the air gap width based on the single working mode of the brushless motor to generate air gap flux coupling characteristic data; performing centrifugal effect analysis on the dynamic balance data and the moment of inertia based on the multiple working modes of the brushless motor to generate centrifugal effect characteristic data; integrating the air gap flux coupling characteristic data and the centrifugal effect characteristic data to generate rotor dynamic characteristic data; Step S244: Calculate the magnetic field coupling coefficient for the stator electromagnetic characteristic data and the rotor dynamic characteristic data to obtain stator-rotor coupling characteristic data, wherein the formula for calculating the magnetic field coupling coefficient is as follows: In the formula, Expressed as the magnetic field coupling coefficient, Expressed as the stator magnetic field strength, Expressed as the rotor magnetic field strength, It is expressed as the volume of the coupling region; Step S245: Based on the stator-rotor coupling characteristic data, the electronic controller performs real-time commutation analysis on the single working mode of the brushless motor and the multiple working modes of the brushless motor to generate commutation data of the brushless motor working mode.

7. The brushless motor adaptive driving method for a chef machine according to claim 6, characterized in that: Real-time commutation analysis of electronic controllers for single-mode and multi-mode brushless motors based on stator-rotor coupling characteristic data includes: Based on the stator-rotor coupling characteristic data, the electronic controller speed analysis is performed on the single working mode and the multi-working mode of the brushless motor to generate the electronic controller speed data; the electronic controller speed data is converted into a curve to obtain the electronic controller speed curve; the electronic controller speed curve is segmented into a fluctuation curve to generate the electronic controller fluctuation curve and the electronic controller flat curve; The optimal commutation time point under the condition of single working mode of brushless motor is calculated by using the gentle curve of electronic controller, and the static commutation timing data is obtained; the real-time commutation point time of brushless motor in multiple working modes is calculated by using the fluctuation curve of electronic controller, and the dynamic commutation timing data is obtained; The static commutation timing data and the dynamic commutation timing data are boundary smoothed to generate the commutation data of the brushless motor working mode.

8. The brushless motor adaptive driving method for a chef machine according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Adaptively optimize the brushless motor operation data according to the commutation data of the brushless motor operation mode to generate brushless motor drive optimization data, wherein the formula for adaptive drive parameter optimization is as follows: In the formula, Expressed as time The optimized drive parameters, Expressed as the current weight coefficient, Expressed as time The real-time current, Expressed as the phase weight coefficient, Expressed as the phase change rate, Expressed as the angular velocity weight coefficient, Expressed as time The rotor angular velocity; Step S32: performing signal conversion on the brushless motor drive optimization data to generate a brushless motor adaptive drive signal, and transmitting the brushless motor adaptive drive signal to the electronic controller for PWM signal modulation to generate a brushless motor drive modulation signal; Step S33: performing drive signal feedback control on the brushless motor drive modulation signal to generate brushless motor adaptive drive control feedback data.

9. The brushless motor adaptive driving method for a chef machine according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing a driving performance evaluation on the brushless motor adaptive driving control feedback data to generate driving performance evaluation data; performing a multi-dimensional performance index quantification on the driving performance evaluation data to generate a driving performance quantification index; Step S42: Visualize the driving performance evaluation data according to the driving performance quantification index to generate an adaptive driving performance evaluation report; transmit the adaptive driving performance evaluation report to the cloud platform for cloud storage to perform the adaptive driving operation of the brushless motor for the food processor.

10. A brushless motor adaptive drive system for a food processor, characterized in that: For executing the brushless motor adaptive driving method for a chef machine as claimed in claim 1, the brushless motor adaptive driving system for a chef machine comprises: The circuit integration module is used to obtain the working module data of the chef machine; perform circuit topology analysis on the working module data of the chef machine to generate the circuit topology data of the working module of the chef machine; perform brushless motor deployment on the circuit topology data of the working module of the chef machine to generate the brushless motor deployment data of the chef machine; use the circuit topology data of the working module of the chef machine to adjust the deployment position of the brushless motor deployment data of the chef machine to generate the brushless motor deployment optimization data of the chef machine; The motor drive analysis module is used to collect motor operation data for the brushless motor deployment optimization data of the chef machine to obtain brushless motor operation data; divide the brushless motor operation data into motor working modes to generate brushless motor single working mode and brushless motor multiple working modes; perform real-time commutation analysis on the brushless motor single working mode and brushless motor multiple working modes according to the brushless motor deployment optimization data of the chef machine to generate brushless motor working mode commutation data; A drive optimization module is used to adaptively optimize the drive parameters of the brushless motor operation data according to the commutation data of the brushless motor operation mode, and generate brushless motor drive optimization data; perform modulation signal feedback control on the brushless motor drive optimization data, and generate brushless motor adaptive drive control feedback data; The performance feedback module is used to evaluate the driving performance of the brushless motor adaptive drive control feedback data and generate an adaptive drive performance evaluation report; the adaptive drive performance evaluation report is transmitted to the cloud platform for cloud storage to execute the brushless motor adaptive drive operation for the chef machine.

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