Control method and system for improving motor efficiency and chef machine

Abstract

The invention relates to the technical field of motor control, in particular to a control method and system for improving motor efficiency and a chef machine. The method comprises the following steps: acquiring working state data of a motor of a chef machine; the chef motor working state data is transmitted to the controller component for motor load sensing, and motor load sensing data is generated; generating an initial motor control instruction for a controller component according to the motor load sensing data to obtain chef motor initial control instruction data; and performing time sequence state monitoring on the chef motor working state data based on the chef motor initial control instruction data to generate motor working time sequence state data. According to the invention, through real-time load sensing, load balancing control, energy recovery optimization and dynamic feedback adjustment, the efficiency of the motor during long-term operation is improved.

Description

Technical Field

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

[0002] Early motor control methods mainly relied on simple switch control, which had relatively low efficiency and large energy loss. With the development of power electronics technology and computer control technology, variable frequency control technology was gradually introduced to improve efficiency by adjusting the running speed and torque of the motor. The emergence of digital signal processing (DSP) technology has enabled more accurate real-time monitoring and adjustment of motor control, further improving motor efficiency. At this time, technologies based on vector control and direct torque control (DTC) have become a research hotspot in the field of motor control. These methods significantly improve the dynamic response and efficiency of the motor by optimizing the current and voltage waveforms of the motor. With the rise of intelligent algorithms and artificial intelligence technology, advanced control methods such as model predictive control (MPC) and fuzzy control have begun to be applied to motor control. These methods can predict the optimal control strategy of the motor based on the real-time working status, achieving more precise adjustment and higher efficiency. However, traditional control methods usually use fixed control parameters when the motor is in a low-load or high-load state, and cannot be dynamically adjusted according to the actual load conditions of the motor. At the same time, energy recovery and optimization are not considered under high load conditions, resulting in energy waste and increased energy consumption of the motor, which in turn leads to reduced efficiency of the motor when it is running for a long time. Summary of the invention

[0003] Based on this, it is necessary to provide a control method, system and food processor for improving motor efficiency to solve at least one of the above technical problems.

[0004] To achieve the above object, a control method for improving motor efficiency is provided, the method comprising the following steps: Step S1: Acquire the working state data of the chef machine motor; transmit the working state data of the chef machine motor to the controller component to sense the motor load and generate motor load sensing data; generate initial motor control instructions for the controller component according to the motor load sensing data to obtain initial control instruction data of the chef machine motor; Step S2: Based on the initial control instruction data of the chef machine motor, the timing state monitoring of the working state data of the chef machine motor is performed to generate the motor working timing state data; the actual load of the motor is compared with the motor load sensing data through the motor working timing state data to generate high-load motor state marking data and motor load balancing distribution data; the motor low-load control adjustment of the initial control instruction data of the chef machine motor is performed using the motor load balancing distribution data to generate a motor low-load control adjustment instruction; Step S3: collecting motor current data based on the high-load motor state mark data to obtain motor current data; optimizing the motor energy recovery of the power supply unit of the motor component of the chef machine based on the motor current data to generate motor working parameter adjustment data; using the motor working parameter adjustment data to perform motor high-load control adjustment on the initial control instruction data of the chef machine motor to generate a motor high-load control adjustment instruction; Step S4: performing motor working state control feedback on the chef machine motor working state data according to the motor low load control adjustment instruction and the motor high load control adjustment instruction to generate chef machine motor working control feedback data; performing real-time optimization on the chef machine motor working control feedback data to execute control operations to improve motor efficiency.

[0005] The present invention can accurately understand the current load of the motor by acquiring the working state data of the chef machine motor and performing load sensing. The initial control instruction is generated according to the load sensing data to ensure that the motor starts and runs under the appropriate working state, thereby optimizing energy efficiency and avoiding motor overload or inefficient operation. By monitoring the timing of the motor working state, the motor load change information can be obtained in real time. By comparing with the motor load sensing data, it is identified that the motor is in a high load or low load state, and high load motor state marking data and load balancing distribution data are generated. These data are used to adjust the control instructions, especially when the load is low, the motor performance is optimized by the motor low load control adjustment instruction to reduce energy waste. By collecting current data for the high load motor state, the current change of the motor can be monitored in real time and the working efficiency of the motor can be evaluated. The power supply unit is optimized for energy recovery using these current data, so that the motor can recover more energy under high load conditions, reduce energy waste and improve energy efficiency. In addition, the generation of the motor high load control adjustment instruction helps to control the motor work more efficiently under high load. Through the control feedback of the motor working state, the control instruction can be adjusted and optimized in real time according to the actual operation situation. This real-time feedback mechanism ensures that the motor always operates in the optimal working state, thereby further improving the motor efficiency, avoiding the decline of energy efficiency during operation, and achieving continuous optimization, thereby improving the overall energy efficiency and performance of the equipment. Therefore, the present invention improves the efficiency of the motor during long-term operation through real-time load perception, load balancing control, energy recovery optimization and dynamic feedback adjustment.

[0006] Preferably, step S1 comprises the following steps: Step S11: using a sensor to obtain working status data of the chef machine motor; Step S12: performing data preprocessing on the working state data of the chef machine motor to generate standard working state data of the chef machine motor, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: transmitting the working state data of the standard chef machine motor to the controller component for motor load sensing, thereby generating motor load sensing data; Step S14: generating initial motor control instructions for the controller component according to the motor load sensing data, and obtaining initial control instruction data for the chef machine motor.

[0007] The present invention can more accurately understand the operating status of the motor by acquiring and processing the motor working status data in real time, so as to timely discover potential abnormalities or faults and avoid overloading and damage to the equipment. Effective motor control instructions can ensure that the motor operates within the optimal load range, avoid energy waste and excessive consumption, and improve overall operating efficiency. Through preprocessing steps such as data cleaning, denoising, missing value filling and standardization, the data quality can be significantly improved to ensure the accuracy of subsequent control instructions. This process can reduce the control error caused by data quality problems and make motor control more accurate. The motor load perception data obtained by the sensor can automatically generate the initial motor control instructions according to the actual load conditions to achieve intelligent load adjustment. The controller component optimizes the working state of the motor according to these instructions, which helps to extend the life of the motor and reduce the probability of failure.

[0008] Preferably, step S14 comprises the following steps: Step S141: extracting key motor load features from the motor load sensing data to obtain key motor load feature data, wherein the motor key load feature extraction includes motor speed, motor power and motor frequency; Step S142: performing motor control logic design on the key load characteristic data of the motor based on the fuzzy logic control method to generate preliminary motor control logic data; Step S143: performing instruction encoding on the preliminary motor control logic data to generate motor control logic encoding instructions; Step S144: Verify the motor control logic coding instruction to generate initial control instruction data for the chef machine motor.

[0009] The present invention can more accurately capture the core working state of the motor (such as speed, power, frequency, etc.) by extracting key load features from the motor load sensing data, and provide an accurate basis for the subsequent control logic design. This helps to generate more sensitive and fast-responding motor control instructions, and improve the motor's adjustment ability and stability. The use of fuzzy logic control method for motor control logic design can effectively deal with the uncertainty and nonlinear problems existing in motor operation. Fuzzy control can generate reasonable control strategies based on different input data (such as speed, power, etc.), without relying on accurate mathematical models, making the control more flexible and adaptable to changing working environments. Through instruction encoding and verification steps, it can be ensured that the final generated motor control instructions will not be wrong during transmission and execution. Instruction encoding enables control instructions to be efficiently and safely transmitted to the motor control system, while instruction verification can ensure the validity of the instructions and avoid motor failure or damage caused by instruction errors. By extracting key load features and designing control logic based on these features, more sophisticated motor load adjustment can be achieved to ensure that the motor works within a suitable load range. This can not only effectively reduce energy consumption and extend the life of the motor, but also avoid overload and performance degradation. The fuzzy control method can automatically adjust the control strategy according to a variety of input conditions, enhancing the flexibility of the system. In addition, the encoded and verified control instructions can be reused in different chef machine models or configurations, providing better support for future expansion and upgrades of the system. This step combines fuzzy logic and instruction verification to enable the system to intelligently adjust the control strategy according to the real-time operating status of the motor to achieve adaptive control. This not only improves the operating efficiency of the equipment, but also enables the system to automatically adjust in different working environments to adapt to complex and changing operating requirements.

[0010] Preferably, step S2 comprises the following steps: Step S21: performing time sequence state monitoring on the working state data of the motor of the chef machine based on the initial control instruction data of the motor of the chef machine to generate the working time sequence state data of the motor; Step S22: analyzing the influence of transmission friction load on transmission components through the motor working timing state data to generate transmission friction load influence data; analyzing the influence of motor heat loss on heat dissipation components through the motor working timing state data to generate heat loss load influence data; Step S23: Calculate the actual load fluctuation of the motor load sensing data according to the transmission friction load influence data and the heat loss load influence data to obtain the actual load data of the motor operation; compare the actual load data of the motor operation with the preset standard load threshold value, and when the actual load data of the motor operation is greater than the preset standard load threshold value, mark the actual load data of the motor operation as a high load state, and generate high load motor state mark data; Step S24: when the actual load data of the motor operation is less than or equal to the preset standard load threshold, the actual load data of the motor operation is marked as a motor low load state, and low load motor state marking data is generated; based on the low load motor state marking data, load balancing distribution is performed on the motor components of the chef machine, and motor load balancing distribution data is generated; Step S25: Using the motor load balancing distribution data, the initial control instruction data of the chef machine motor is adjusted for motor low load control, and a motor low load control adjustment instruction is generated.

[0011] The present invention can track the working state of the motor in real time and generate accurate motor working time state data by performing time state monitoring on the initial control instruction data of the chef machine motor. This data provides an accurate basis for subsequent load analysis and control, and helps to timely discover problems (such as overload, low load, etc.) in the operation of the motor and make effective adjustments. By analyzing the friction load influence of the transmission components and the heat loss influence of the heat dissipation components through the motor working time state data, the negative impact of these factors on the operation of the motor can be accurately evaluated. Friction load and heat loss are important factors affecting the efficiency of the motor. Regular analysis can help design more efficient transmission systems and heat dissipation systems, reduce mechanical friction and heat loss, and thus improve overall performance and energy saving effects. Combined with the transmission friction load influence data and the heat loss load influence data, the actual load data of the motor operation is obtained through actual load fluctuation calculation. This data is crucial to evaluating the working state of the motor, and helps to accurately determine whether the motor is in a high load or low load state, and ensure that the motor always operates within a reasonable load range. According to the comparison between the actual load data of the motor and the preset standard load threshold, the system can intelligently label the motor state as "high load" or "low load". This intelligent marking helps the system to automatically adjust the operating state of the motor, prevent the motor from overloading when the load is high, and reduce the probability of failure; it also avoids the inefficient operation of the motor under low load conditions. When the motor is in a low-load state, the system can balance the load distribution of various components of the motor through the motor load balancing distribution data generated in step S24. This process can ensure that the motor does not operate ineffectively at low loads, while keeping the system at a high energy efficiency. In addition, the motor control instructions are adjusted at low loads to avoid the motor being in an inefficient operating mode, further improving energy efficiency. Through the intelligent marking of high and low loads of the motor, the system can take timely measures, such as adjusting the control instructions or optimizing the motor operating parameters, to avoid overloading the motor and reduce damage or failures caused by overload. The load balancing strategy helps to reduce the wear of motor components and improve the long-term stability and service life of the equipment.

[0012] Preferably, step S22 includes the following steps: Step S221: screening the vibration data of the transmission component from the vibration sensor in the transmission component through the motor working timing state data to obtain the vibration data of the transmission component; extracting the vibration time domain features of the transmission component vibration data to obtain the vibration time domain data of the transmission component; Step S222: Perform fast Fourier transform on the transmission component vibration data to generate a transmission component vibration spectrum diagram; perform instantaneous frequency fluctuation analysis on the transmission component vibration spectrum diagram to generate transmission component instantaneous frequency fluctuation data; perform transmission friction load influence calculation based on the transmission component vibration time domain data and the transmission component instantaneous frequency fluctuation data to obtain transmission friction load influence data; wherein the formula for transmission friction load influence calculation is as follows: ; In the formula, Expressed as the friction load influence index, It is expressed as the contribution coefficient of adjusting the time domain characteristics to the friction load, Expressed as the root mean square value of the vibration signal, Expressed as the contribution coefficient of adjusting the frequency domain characteristics, Expressed as the maximum frequency, Expressed as the minimum frequency, Expressed as frequency The spectral density at It is expressed as the contribution coefficient of adjusting the effect of instantaneous frequency fluctuation on friction load, Expressed as frequency The instantaneous frequency at Step S223: screening the heat dissipation component temperature data of the temperature sensor in the transmission component through the motor working timing state data to obtain the heat dissipation component temperature data; performing temperature gradient conversion on the heat dissipation component temperature data to generate a heat dissipation component temperature gradient diagram; Step S224: Segment the temperature gradient map of the heat dissipation component into temperature difference regions to generate heat source hot spot region data; perform local heat flow calculation on the temperature gradient map of the heat dissipation component based on the heat source hot spot region data to obtain heat flow data of the heat dissipation component; quantify the heat loss of the temperature gradient map of the heat dissipation component through the heat flow data of the heat dissipation component to generate heat loss load impact data; wherein the formula for local heat flow calculation is as follows: ; In the formula, Expressed as heat flow, Expressed as thermal conductivity, Expressed as the cross-sectional area of ​​heat flow conduction, Expressed as temperature gradient.

[0013] The present invention can accurately capture the load fluctuation caused by transmission friction through the dual analysis of vibration time domain characteristics and spectrum diagram, and avoid equipment failure caused by failure to identify vibration anomalies. Real-time detection of the vibration and friction load of the transmission components can give early warning of potential mechanical failures and reduce losses and equipment damage caused by excessive friction. Through temperature gradient diagrams and heat flow calculations, the system can optimize the hot spot areas of heat sources, enhance the working efficiency of heat dissipation components, and ensure that the motor operates stably within a suitable temperature range. Effective management of the heat dissipation system can significantly reduce the heat loss of the motor, reduce the energy efficiency reduction caused by overheating, and extend the service life of the motor. Through comprehensive analysis of the impact of friction and heat loss, the motor can be optimized and controlled under different loads and thermal conditions, thereby improving the overall operating efficiency. Accurate load analysis and thermal management can reduce unnecessary energy consumption and component wear while ensuring motor efficiency, thereby saving energy and maintenance costs. Accurate quantification of heat loss can help design a more efficient heat dissipation system and reduce performance degradation caused by overheating. Temperature gradient diagrams and heat flow data help identify heat dissipation problems and correct them before the problems become more serious, reducing failures caused by overheating. By comprehensively monitoring vibration, heat and load conditions, the system can intelligently adjust the working conditions of the motor and cooling system to achieve adaptive control. By timely detecting and dealing with vibration and heat loss problems, the reliability and long-term stability of the motor can be greatly improved.

[0014] Preferably, load balancing distribution of the chef machine motor components based on the low-load motor status mark data includes: Based on the low-load motor state marking data, the motor work priority of the chef machine motor component is evaluated to generate motor work priority evaluation data; based on the motor work priority evaluation data, the motor load ratio of the chef machine motor component is calculated to obtain motor load ratio data; The motor load ratio data is used to coordinately distribute the multi-motor loads of the chef machine motor components to generate motor load coordination distribution data; the motor operation energy efficiency of the chef machine motor components is balanced according to the motor load coordination distribution data to generate motor load balance distribution data.

[0015] The present invention can ensure that the motor reasonably distributes work tasks according to its actual load state through accurate work priority evaluation, avoiding unnecessary excessive or too small load. Reduce the energy waste caused by excessive or insufficient load, and improve the resource utilization of the motor and the system. Through accurate load ratio calculation, it is possible to avoid uneven load distribution and ensure that each motor component operates within its optimal load range. The reasonable distribution of load balance can ensure the efficient coordination of each component at work, thereby improving the operating efficiency and stability of the overall system. Through the coordinated distribution of multi-motor loads, different motor components can cooperate with each other to maintain the smooth operation of the overall system and prevent the excessive load of a certain component from causing system instability. Even in the case of large load fluctuations, the coordinated distribution of multiple motors can help the system flexibly respond to load changes and improve the adaptability and stability of the system. Through load balancing, the motor can operate under the condition of optimal energy efficiency, reduce energy waste, and improve the overall energy efficiency of the motor. The balanced operating state helps to reduce excessive wear of the motor, extend the service life of the motor, and reduce maintenance and replacement costs. Load balancing can effectively avoid frequent overloading of the motor, reduce damage caused by overload, and reduce the failure rate of the equipment. The long-term stable operation of the equipment reduces unnecessary repair and replacement costs, thereby reducing the overall maintenance cost. The load balancing distribution system can automatically adjust according to the real-time status, enhance the intelligent operation of the system, and make the motor operation more accurate and efficient. Intelligent load balancing can help the system quickly adapt to different working environments and load changes, ensuring the long-term stable operation of the system.

[0016] Preferably, step S3 comprises the following steps: Step S31: Based on the high-load motor status mark data, the motor current data of the motor component of the chef machine is collected through the power supply unit to obtain the motor current data; based on the motor current data, the motor current trend analysis of the motor component of the chef machine is performed to generate the motor current trend data; Step S32: performing motor energy recovery on a power supply unit of a motor component of the chef machine according to the motor current trend data to generate motor energy recovery data; performing motor operating parameter adjustment on the working state data of the chef machine motor based on the motor energy recovery data to generate motor operating parameter adjustment data; Step S33: Using the motor working parameter adjustment data, the initial control instruction data of the chef machine motor is adjusted for motor high load control, and a motor high load control adjustment instruction is generated.

[0017] The present invention can timely detect the change of motor load through current trend analysis, ensure that the motor runs within the optimal load range, and avoid overload or inefficient operation. Current data and trend analysis can help detect potential motor failures or abnormalities, such as abnormal current increase, and then provide early warning for the system to reduce the probability of failure. Energy recovery not only effectively saves power consumption and reduces energy waste, but also reduces the carbon footprint of equipment, with environmental benefits. Through motor energy recovery, wasted energy can be converted into useful electrical energy, improve the efficiency of the overall system, and reduce operating costs. Adjustment of working parameters based on motor energy recovery data makes the motor work more flexible and accurate, and can still maintain the best operating state under high load conditions, thereby optimizing the energy efficiency of the motor. High-load control adjustment can effectively prevent motor overload, ensure that the motor can still run smoothly under large load fluctuations, and improve the overall stability of the system. Through appropriate load control and parameter adjustment, damage to the motor due to long-term high-load operation can be avoided, thereby extending the service life of the motor. Optimizing the high-load working state of the motor can effectively reduce energy loss, especially when the motor is running at high load, avoiding the problem of low energy efficiency caused by overload operation. Real-time monitoring and fault warning can accurately monitor the motor load and current changes and warn of faults in advance. Through precise control command adjustment, it ensures that the motor can still operate stably and efficiently under high load conditions, thereby extending the service life of the equipment.

[0018] Preferably, performing motor energy recovery on a power supply unit of a motor component of a chef machine according to the motor current trend data comprises: According to the motor current trend data, the controller component in the motor component of the chef machine is operated in reverse, and energy conversion data is collected through the power supply unit to generate motor energy conversion data; feedback energy efficiency monitoring is performed on the motor energy conversion data to generate feedback energy monitoring data; The motor feedback energy ratio is adjusted for the motor current trend data according to the feedback energy monitoring data to generate motor energy recovery optimization data; the energy recovery efficiency of the motor current data is calculated using the motor energy recovery optimization data to obtain the motor energy recovery data.

[0019] The present invention converts the kinetic energy generated by the motor into electrical energy through the reverse operation of the motor, realizes energy recovery and reuse, and reduces energy waste. By effectively recovering excess energy, the dependence on external power supplies is reduced, thereby reducing the overall energy consumption and operating costs of the system. By optimizing the energy conversion process, the secondary utilization of electricity is realized, the efficiency of energy utilization is improved, and unnecessary energy consumption is reduced. Through feedback energy efficiency monitoring, the system can monitor the effect of energy recovery in real time, and promptly discover and correct energy loss or reduced efficiency. According to the feedback energy monitoring data, the system can be dynamically adjusted to ensure that the energy recovery process is continuously optimized, and further improve the overall energy efficiency of the system. Timely acquisition of energy feedback information can help make more accurate adjustments, maximize the efficiency in the energy recovery process, and reduce energy loss. By adjusting the motor feedback energy ratio, it is ensured that the energy recovery efficiency of the motor is maximized under different loads and operating conditions. Reasonable adjustment of the energy feedback ratio helps the motor to achieve efficient energy recovery under both high load and low load conditions, avoiding excessive or insufficient energy. The optimized energy recovery mechanism helps the system to operate stably and maintain efficient energy management under changing load and current conditions. By calculating the energy recovery efficiency, the system can accurately understand the energy recovery effect of the motor under different operating conditions, identify the links with low energy efficiency and optimize them. Through accurate energy recovery data, the overall efficiency of the system can be further improved, and the system energy consumption and operating costs can be significantly reduced in long-term operation. Real-time calculation of energy recovery efficiency can help the system flexibly adjust the operation mode according to the current load and current changes to adapt to different working environments.

[0020] In this specification, a control system for improving motor efficiency is provided, which is used to execute the above-mentioned control method for improving motor efficiency. The control system for improving motor efficiency includes: The motor control instruction generation module is used to obtain the working state data of the chef machine motor; transmit the working state data of the chef machine motor to the controller component for motor load perception, and generate motor load perception data; generate initial motor control instructions for the controller component according to the motor load perception data, and obtain initial control instruction data for the chef machine motor; The low-load instruction control module is used to perform time-series state monitoring on the working state data of the chef machine motor based on the initial control instruction data of the chef machine motor, and generate the motor working time-series state data; compare the actual motor load with the motor load sensing data through the motor working time-series state data, and generate high-load motor state marking data and motor load balancing distribution data; use the motor load balancing distribution data to perform low-load control adjustment on the initial control instruction data of the chef machine motor, and generate a motor low-load control adjustment instruction; The high-load instruction control module collects motor current data based on the high-load motor state tag data to obtain motor current data; optimizes the motor recovery energy of the power supply unit of the chef machine motor component based on the motor current data to generate motor working parameter adjustment data; uses the motor working parameter adjustment data to adjust the motor high-load control of the chef machine motor initial control instruction data to generate a motor high-load control adjustment instruction; The real-time optimization module is used to perform motor working state control feedback on the chef machine motor working state data according to the motor low-load control adjustment instruction and the motor high-load control adjustment instruction, generate the chef machine motor working control feedback data; and perform real-time optimization on the chef machine motor working control feedback data to execute control operations to improve motor efficiency.

[0021] A food processor includes a food processor motor component, a controller component, a transmission component, a heat sink component and a power supply unit. The power supply unit is electrically connected to the motor component, the controller component, the transmission component and the heat sink component. The controller component is installed in the food processor motor component. The transmission component is equipped with a vibration sensor. The heat sink component is equipped with a temperature sensor. The controller component is used to execute the control method for improving motor efficiency as described above.

[0022] The beneficial effect of the present invention is that through real-time perception and monitoring of the working state of the chef machine motor, the load condition of the motor can be grasped in time, so as to make appropriate control adjustments according to different workloads, avoid motor overload or low load operation, and improve the working efficiency of the motor. The method automatically realizes load balance distribution under high load and low load conditions by analyzing the motor load perception data. Control adjustment is performed at low load to avoid waste of motor resources; at high load, energy recovery optimization is performed to reduce energy consumption and improve motor working stability to prevent overheating or loss. By collecting and analyzing the motor current data, the energy recovery of the power supply unit is optimized, so that the system can effectively use the braking energy of the motor for recovery, improve the overall energy efficiency, and reduce unnecessary energy loss. By generating motor working control feedback data, the system can continuously adjust and optimize the working state of the motor to ensure that the motor is always maintained in the optimal working state under various use conditions, thereby maximizing the working efficiency and service life of the motor. Through intelligent control instruction adjustment and feedback optimization, the method can effectively reduce the loss of the motor under long-term high-load working conditions, slow down the wear of the equipment, and thus extend the service life of the chef machine and reduce the failure rate. Therefore, the present invention improves the efficiency of the motor during long-term operation through real-time load perception, load balancing control, energy recovery optimization and dynamic feedback adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic flow chart of the steps of a control method for improving motor efficiency; 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

[0024] 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.

[0025] 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.

[0026] 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 unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0027] To achieve this, please refer to Figures 1 to 3 , a control method for improving motor efficiency, the method comprising the following steps: Step S1: Acquire the working state data of the chef machine motor; transmit the working state data of the chef machine motor to the controller component to sense the motor load and generate motor load sensing data; generate initial motor control instructions for the controller component according to the motor load sensing data to obtain initial control instruction data of the chef machine motor; Step S2: Based on the initial control instruction data of the chef machine motor, the timing state monitoring of the working state data of the chef machine motor is performed to generate the motor working timing state data; the actual load of the motor is compared with the motor load sensing data through the motor working timing state data to generate high-load motor state marking data and motor load balancing distribution data; the motor low-load control adjustment of the initial control instruction data of the chef machine motor is performed using the motor load balancing distribution data to generate a motor low-load control adjustment instruction; Step S3: collecting motor current data based on the high-load motor state mark data to obtain motor current data; optimizing the motor energy recovery of the power supply unit of the motor component of the chef machine based on the motor current data to generate motor working parameter adjustment data; using the motor working parameter adjustment data to perform motor high-load control adjustment on the initial control instruction data of the chef machine motor to generate a motor high-load control adjustment instruction; Step S4: performing motor working state control feedback on the chef machine motor working state data according to the motor low load control adjustment instruction and the motor high load control adjustment instruction to generate chef machine motor working control feedback data; performing real-time optimization on the chef machine motor working control feedback data to execute control operations to improve motor efficiency.

[0028] The present invention can accurately understand the current load of the motor by acquiring the working state data of the chef machine motor and performing load sensing. The initial control instruction is generated according to the load sensing data to ensure that the motor starts and runs under the appropriate working state, thereby optimizing energy efficiency and avoiding motor overload or inefficient operation. By monitoring the timing of the motor working state, the motor load change information can be obtained in real time. By comparing with the motor load sensing data, it is identified that the motor is in a high load or low load state, and high load motor state marking data and load balancing distribution data are generated. These data are used to adjust the control instructions, especially when the load is low, the motor performance is optimized by the motor low load control adjustment instruction to reduce energy waste. By collecting current data for the high load motor state, the current change of the motor can be monitored in real time and the working efficiency of the motor can be evaluated. The power supply unit is optimized for energy recovery using these current data, so that the motor can recover more energy under high load conditions, reduce energy waste and improve energy efficiency. In addition, the generation of the motor high load control adjustment instruction helps to control the motor work more efficiently under high load. Through the control feedback of the motor working state, the control instruction can be adjusted and optimized in real time according to the actual operation situation. This real-time feedback mechanism ensures that the motor always operates in the optimal working state, thereby further improving the motor efficiency, avoiding the decline of energy efficiency during operation, and achieving continuous optimization, thereby improving the overall energy efficiency and performance of the equipment. Therefore, the present invention improves the efficiency of the motor during long-term operation through real-time load perception, load balancing control, energy recovery optimization and dynamic feedback adjustment.

[0029] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a control method for improving motor efficiency according to the present invention. In this example, the control method for improving motor efficiency includes the following steps: Step S1: Acquire the working state data of the chef machine motor; transmit the working state data of the chef machine motor to the controller component to sense the motor load and generate motor load sensing data; generate initial motor control instructions for the controller component according to the motor load sensing data to obtain initial control instruction data of the chef machine motor; In an embodiment of the present invention, sensors are installed at key positions of the motor of the chef machine (such as the motor shaft, stator, rotor, etc.) to collect real-time working status data of the motor. The speed sensor is used to monitor the speed of the motor in real time and generate speed data. The current sensor is used to monitor the current change of the motor and generate current data. The temperature sensor is used to monitor the temperature of the motor and its surrounding environment and obtain temperature data. The power sensor is used to measure the input power and output power of the motor and calculate the energy efficiency of the motor. The data of each sensor is transmitted to the central control system through the signal acquisition system. The signal acquisition system should have a multi-channel input interface that can process analog signals from different sensors and convert them into digital signals. The motor working status data is transmitted to the motor controller component through a data bus (such as CAN bus, MODBUS, Ethernet, etc.). The controller analyzes the received motor working status data in real time to determine the motor load. Common load sensing methods include: judging the load of the motor by the current size of the motor. When the load is large, the current increases; when the load is small or unloaded, the current decreases. The motor load is judged by the ratio of the motor input and output power (such as efficiency). The greater the power, the higher the load. The controller generates motor load sensing data based on the load sensing algorithm and transmits it to the subsequent control module for initial control command generation. Based on the load sensing data, the controller designs the corresponding motor control logic. The control logic usually includes threshold judgment and adjustment strategy based on load data. If the load is low, the controller will reduce energy consumption by reducing the power output of the motor or adjusting the speed. If the load is high, the controller will increase the power output of the motor to ensure stable operation of the motor and prevent overload. The motor speed, power, and current are adjusted based on the load data. Write logic in the controller: when the load exceeds a certain threshold, adjust the motor speed, increase the current, or change the motor's operating mode (for example, change the operating frequency, switch to high-efficiency mode). The controller transmits the generated initial motor control command data to the motor driver or actuator through a signal interface (such as PWM signal, CAN bus, etc.).

[0030] Step S2: Based on the initial control instruction data of the chef machine motor, the timing state monitoring of the working state data of the chef machine motor is performed to generate the motor working timing state data; the actual load of the motor is compared with the motor load sensing data through the motor working timing state data to generate high-load motor state marking data and motor load balancing distribution data; the motor low-load control adjustment of the initial control instruction data of the chef machine motor is performed using the motor load balancing distribution data to generate a motor low-load control adjustment instruction; In an embodiment of the present invention, the working state data of the motor is collected regularly by using sensors (such as current sensors, speed sensors, temperature sensors, etc.) installed in the motor system. These data include the speed, input current, temperature, power, etc. of the motor. The speed (RPM) and current value (A) of the motor, as well as the power consumption, are continuously monitored. The motor and ambient temperature, as well as the changes in the motor load, are monitored. The collected motor state data are recorded and stored in chronological order through a data acquisition system (for example, using a PLC, an embedded controller, etc.). These data will become the motor working timing state data. The motor working timing state data is time series data that can reflect the changes in the working state of the motor at different time points. According to the power, current, speed and other parameters in the motor working timing state data, the motor load perception data (such as the load judged by the motor current and power) are compared. It is mainly judged by the following method: when the power, speed, current and other parameters of the motor exceed the set threshold, the motor is considered to be in a high load state. When the load of the motor is lower than a certain threshold, it is judged to be in a low load state. When the motor load exceeds the preset threshold, "high load" marking data is generated. When the motor load is lower than the threshold, "low load" tag data is generated. Based on the load comparison results, if the motor is in a low load state, load balancing is required. At this time, the motor load is optimized through the load balancing algorithm to improve the working efficiency of the system. When the motor load is in a low load state, the motor is ensured to operate efficiently and reduce energy consumption by adjusting the motor speed, current and power parameters. For example, the input power of the motor is reduced, the speed is reduced, or the operating frequency is adjusted to reduce unnecessary energy consumption. According to the load balancing distribution data, the motor speed is reduced to reduce the power consumption of the motor. The input power of the motor is adjusted to ensure that the motor can still maintain normal operation under low load conditions. By reducing the current consumption, the heat loss and energy loss of the motor are reduced. The control command data of the motor is generated according to the low load control algorithm. These command data will be transmitted to the motor driver to ensure the efficient operation of the motor under low load conditions. The generated low load control adjustment command is sent to the motor driver through the controller to adjust the running state of the motor to ensure that the motor works stably under low load conditions.

[0031] Step S3: collecting motor current data based on the high-load motor state mark data to obtain motor current data; optimizing the motor energy recovery of the power supply unit of the motor component of the chef machine based on the motor current data to generate motor working parameter adjustment data; using the motor working parameter adjustment data to perform motor high-load control adjustment on the initial control instruction data of the chef machine motor to generate a motor high-load control adjustment instruction; In an embodiment of the present invention, a current sensor (such as a Hall sensor, a current transformer, etc.) is deployed in a motor control system to monitor the working current of the motor in real time. According to the working cycle and load changes of the motor, an appropriate data acquisition frequency is set, usually once per second, to capture current changes in real time. The current data of the motor includes instantaneous current value, average current value, current fluctuation, etc. The collected data will be used to evaluate the actual working load of the motor and the potential for energy recovery. The collected current data is analyzed to evaluate the load of the motor. For example, the magnitude of the current is used to determine whether the motor is in a high-load state. When the current value is high, it indicates that the motor is in a high-load working state. When the motor is in a high-load state, the control system adjusts the direction of the motor to enter a reverse operation mode. In this mode, the mechanical energy of the motor can be converted into electrical energy and fed back through a power supply unit. The mechanical energy of the motor is converted into electrical energy through a power supply unit (such as a power inverter, a DC-DC converter, etc.), and it is fed back to the power grid or stored in a battery for subsequent use. According to the current data of the motor and the data of the power supply unit, the efficiency of the energy recovery process is monitored in real time. Through feedback control, the energy recovery efficiency is optimized and the loss is reduced. Adjust the proportion of energy recovery based on the motor current data and feedback energy monitoring data. Usually, the current feedback path can be optimized by controlling the working mode of the inverter to achieve more efficient energy recovery. According to the results of energy recovery optimization, adjust the speed of the motor. For example, when the motor's recovery energy is high, it is necessary to reduce the speed to avoid excessive energy feedback and maintain system balance. If the energy feedback is sufficient, the input current of the motor can be reduced to avoid unnecessary energy consumption. According to the situation of recovered energy, the working mode of the motor can be adjusted at high load, such as low speed, high torque mode, or enter a more efficient working state. Generate new control instructions based on the motor operating parameter adjustment data. These instructions include adjustment parameters such as the motor's speed, current, and working mode. For example, if the motor is in a high load state and the energy recovery efficiency is high, the operation can be optimized by reducing the speed and current while increasing the proportion of energy recovery. The generated high load control adjustment instructions are passed to the motor's controller and driver. The controller will adjust the motor's working state in real time according to these instructions to ensure that the motor's load can run efficiently and smoothly under high load conditions.

[0032] Step S4: performing motor working state control feedback on the chef machine motor working state data according to the motor low load control adjustment instruction and the motor high load control adjustment instruction to generate chef machine motor working control feedback data; performing real-time optimization on the chef machine motor working control feedback data to execute control operations to improve motor efficiency.

[0033] In an embodiment of the present invention, during the operation of the motor, the real-time working state data of the motor is collected by a sensor. Including but not limited to the speed, load, current, temperature, vibration, etc. of the motor. The collected data can be transmitted to a central control system (such as a microprocessor or a PLC controller) through wireless communication or a wired data bus. According to the real-time working state data of the motor and the control adjustment instructions (low-load control instructions and high-load control instructions) generated previously, control feedback calculation is performed to adjust the working state of the motor. For example, when the motor is under high load, the motor controller needs to reduce the speed or adjust the current output. When the load is low, the speed needs to be increased or the working mode of the motor needs to be adjusted to improve energy efficiency. After obtaining the working state feedback data of the motor, the control system optimizes these data. The purpose of optimization is to adjust the control strategy of the motor according to the current load condition, energy efficiency requirements and operating state of the motor to improve the working efficiency of the motor. The optimization algorithm can be based on technologies such as PID control, fuzzy control or neural network control, and continuously adjust the control instructions according to the real-time feedback data of the motor. The control system can adjust the working state of the motor through a PID control algorithm (proportional-integral-differential control). The algorithm adjusts the control instructions in real time according to the error between the actual working state of the motor and the expected state (such as speed error, current error, etc.) to optimize the motor operation efficiency. For complex motor control systems, fuzzy control algorithms can be used, which can optimize control strategies under uncertainty and nonlinear conditions. Based on the real-time working data of the motor, the fuzzy control system adjusts the working state of the motor by setting fuzzy rules (such as reducing the speed at high load and improving efficiency at low load). Deep learning and neural network algorithms can be used to optimize motor control in real time. After collecting a large amount of motor operation data, a neural network model can be trained, which can predict the best control instructions based on the real-time state of the motor and continuously improve the optimization accuracy through learning. The real-time working state data of the motor is transmitted to the central controller (such as PLC, microprocessor, etc.) through the data transmission system. The central controller calculates and adjusts the control instructions in real time based on the real-time data and optimization algorithms (such as PID, fuzzy control or neural network). Based on the optimization calculation results, adjustment instructions are generated and fed back to the motor through the control system. The working state of the motor will be adjusted in real time according to the feedback data to improve energy efficiency and ensure that the motor is in the best operating state. The final optimized control instructions are executed by the control system. These instructions can control the motor's speed, current output, working mode, etc., so that the motor is always in an efficient working state under different load conditions. After executing the optimization instructions, the control system will continue to monitor the motor's operating status. If the motor's working state deviates from expectations, the system will automatically adjust the control instructions to ensure that the motor is always in the optimal working state. After the optimization control instructions are executed, the motor's energy efficiency, load conditions, temperature and other key indicators are continuously monitored to evaluate the effectiveness of the control strategy.If the control strategy does not achieve the expected effect, it can be improved by adjusting the control parameters, optimizing the algorithm, or further increasing the complexity of the control strategy. Based on the long-term operation data of the motor, the control algorithm is continuously optimized and the control strategy is adjusted to maximize the motor energy efficiency.

[0034] Preferably, step S1 comprises the following steps: Step S11: using a sensor to obtain working status data of the chef machine motor; Step S12: performing data preprocessing on the working state data of the chef machine motor to generate standard working state data of the chef machine motor, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: transmitting the working state data of the standard chef machine motor to the controller component for motor load sensing, thereby generating motor load sensing data; Step S14: generating initial motor control instructions for the controller component according to the motor load sensing data, and obtaining initial control instruction data for the chef machine motor.

[0035] In an embodiment of the present invention, the working state of the chef machine motor is monitored in real time by integrating sensors (such as current sensors, temperature sensors, speed sensors, etc.). The sensor should be able to capture key parameters during the operation of the motor, such as current, voltage, speed, temperature, etc., to ensure the accuracy and real-time performance of data acquisition. The data acquisition system should be equipped with a high-speed data acquisition card, support high-frequency sampling, and be compatible with the control system of the chef machine. Remove noise and outliers in sensor data. Usually, statistical-based methods (such as median filtering and mean filtering) can be used to remove spikes or extreme values. Use filtering techniques, such as low-pass filters or wavelet transforms, to remove high-frequency noise and ensure that the motor state data is smooth and in line with the actual working environment. There are missing values ​​in the sensor or data acquisition process, which are filled by interpolation methods (such as linear interpolation, Lagrange interpolation, or time series-based filling methods) to ensure data integrity. Standardize all types of sensor data (such as current, speed, etc.) to the same dimension, usually using zero mean standardization or maximum and minimum standardization methods to unify the data range for subsequent processing. The standardized motor working status data is transmitted to the controller component through a high-speed communication interface (such as CAN bus, Modbus or Ethernet). The data should be transmitted in real time to ensure that the controller can respond to the changes of the motor in a timely manner. After receiving the data, the controller component performs a perception analysis of the motor load through an algorithm (such as fuzzy control, neural network, etc.). According to the change trend of the motor working status data, the current motor load level is calculated and the motor load perception data is generated. These data may include the current load value of the motor, the load change rate, etc. Based on the motor load perception data, the controller generates the initial control instructions of the motor through a preset control algorithm (such as PID control, fuzzy control or adaptive control). These instructions include operations such as adjusting the motor speed and changing the current output to ensure that the motor can still run smoothly when the load changes. The controller generates the initial control instructions of the motor based on the motor load perception data. The instruction data includes parameters such as target speed and target current, and is sent to the motor drive module through the control bus to realize the control of the motor.

[0036] Preferably, step S14 includes the following steps: Step S141: extracting key motor load features from the motor load sensing data to obtain key motor load feature data, wherein the motor key load feature extraction includes motor speed, motor power and motor frequency; Step S142: performing motor control logic design on the key load characteristic data of the motor based on the fuzzy logic control method to generate preliminary motor control logic data; Step S143: performing instruction encoding on the preliminary motor control logic data to generate motor control logic encoding instructions; Step S144: Verify the motor control logic coding instruction to generate initial control instruction data for the chef machine motor.

[0037] In an embodiment of the present invention, the speed information of the motor is extracted from the motor working state data, usually using a speed sensor or by frequency analysis of the current signal. The motor speed is an important indicator for judging the motor load. When the load is high, the speed is often low. The motor power is calculated by the current and voltage data of the motor. It can be calculated by the following formula: P=V×I×cos(θ); where P is power, V is voltage, I is current, and θ is the phase angle between voltage and current. The motor power reflects the energy consumption of the motor during operation, and the power usually increases when the load is large. The motor operating frequency is extracted according to the periodic change of the input signal frequency or the output signal of the motor. Frequency analysis can be achieved by methods such as Fourier transform. The frequency change reflects the change of the motor load. When the load increases, the motor frequency changes. Based on fuzzy logic control theory, the control logic of the motor is designed by the key load characteristic data of the motor (such as speed, power, frequency, etc.). The fuzzy logic controller processes the input data by setting a fuzzy rule base and outputs a fuzzy control quantity. Specifically, the following process can be used: the key load characteristics of the motor (such as speed, power, frequency) are converted into fuzzy sets. For example, the speed can be classified into three fuzzy sets: "low speed", "medium speed" and "high speed". According to the preset fuzzy rule base (such as "if the speed is low and the power is high, the motor control amount increases"), fuzzy reasoning is performed to obtain control decisions. The fuzzy output is converted into specific control instructions to generate preliminary motor control logic data. These data may include adjustment instructions for motor power, speed, frequency, etc. The data includes motor control parameters obtained based on the fuzzy logic control results, such as the adjustment amplitude of motor speed, target power value, frequency adjustment range, etc. The preliminary motor control logic data (such as target speed, power and other control instructions) are converted into executable coding instructions. This can be achieved through common programming languages ​​(such as C language, Python) or hardware description languages ​​(such as VHDL). The purpose of instruction encoding is to convert high-level control logic into a format that can be understood and executed by a specific computer, ensuring that the controller can accurately perform tasks. Map each control parameter (such as speed, power, etc.) in the preliminary control logic data with the corresponding machine instructions. Convert the parameters into a standard instruction encoding format to ensure that they can be correctly parsed and executed by the hardware control unit (such as microcontroller, PLC, etc.). These coded instructions include control commands for the motor (such as start, stop, speed regulation, etc.) and are sent to the motor drive module in an appropriate format. Verify the generated motor control logic coded instructions to ensure the validity and accuracy of the instructions. Verification can be performed in the following ways: Check whether the syntax of the coded instructions meets the specifications to avoid control failure caused by incorrect instruction formats. Check the rationality of the control logic to ensure that there is no conflict between the execution order of the instructions and the control conditions to avoid incorrect control decisions. Ensure that the control instructions will not cause dangerous conditions such as motor overload and overheating, and verify whether the safe operation conditions are met.After verification, the final initial control instruction data of the chef machine motor is generated. These data contain the control instructions required by the motor drive module, such as motor start, stop, speed adjustment, etc. The instructions will be sent to the control system in real time for precise control of the motor.

[0038] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: performing time sequence state monitoring on the working state data of the motor of the chef machine based on the initial control instruction data of the motor of the chef machine to generate the working time sequence state data of the motor; Step S22: analyzing the influence of transmission friction load on transmission components through the motor working timing state data to generate transmission friction load influence data; analyzing the influence of motor heat loss on heat dissipation components through the motor working timing state data to generate heat loss load influence data; Step S23: Calculate the actual load fluctuation of the motor load sensing data according to the transmission friction load influence data and the heat loss load influence data to obtain the actual load data of the motor operation; compare the actual load data of the motor operation with the preset standard load threshold value, and when the actual load data of the motor operation is greater than the preset standard load threshold value, mark the actual load data of the motor operation as a high load state, and generate high load motor state mark data; Step S24: when the actual load data of the motor operation is less than or equal to the preset standard load threshold, the actual load data of the motor operation is marked as a motor low load state, and low load motor state marking data is generated; based on the low load motor state marking data, load balancing distribution is performed on the motor components of the chef machine, and motor load balancing distribution data is generated; Step S25: Using the motor load balancing distribution data, the initial control instruction data of the chef machine motor is adjusted for motor low load control, and a motor low load control adjustment instruction is generated.

[0039] In an embodiment of the present invention, the working state of the motor is monitored in real time by using the initial control instruction data of the motor. These instructions include working parameters such as motor speed and current. By collecting the working state data of the motor (such as speed, temperature, power, etc.) in real time and tracking the running time of the motor according to the change of the control instruction, complete timing state data can be generated. The timing state data not only includes the timing changes of various parameters of the motor, but also records the response behavior of the motor under different control instructions. High-frequency data acquisition technology is used to record the working state of the motor at each moment. Sensors (such as current sensors, temperature sensors, speed sensors) and data acquisition cards (DAQ) can be used for real-time monitoring to generate a motor working state data set containing a timestamp, and the data includes the speed, power, load current, temperature, etc. of the motor. Based on the working timing state data of the motor (such as motor speed, load current, etc.), combined with the friction mechanics model between the motor and the transmission components (such as gears, belts, etc.), the influence of the friction load of the transmission system on the motor load is analyzed. By dynamically analyzing the load, speed and other data of the motor, combined with the friction torque, friction coefficient and other parameters of the transmission components, the load change caused by the transmission friction is calculated. Friction will increase the actual load of the motor, thus affecting the energy efficiency of the motor. Finally, the transmission friction load impact data is generated to record the impact of the friction load on the motor performance, and the friction characteristics of the transmission system are modeled through numerical simulation or actual measurement data. The influence of the heat loss generated by the motor during operation on the motor load is analyzed by combining the motor's thermodynamic model with the motor's working time sequence state data (such as motor temperature, current, power, etc.). When the motor is under heavy load, the increase in motor power and speed will cause more current to pass through the motor coil, generating heat loss. Based on the heat loss analysis model (for example, calculating the Joule heat loss and iron loss of the motor), the heat loss load impact data is generated. Based on the data fed back by the temperature sensor and the current sensor, the impact data of the motor heat loss on the motor load is generated to help evaluate the heat loss of the motor under high load. Based on the transmission friction load impact data and the heat loss load impact data, the actual load fluctuation of the motor is calculated. Due to the influence of friction load and heat loss, the motor load will fluctuate periodically. Therefore, the actual load data of the motor can be calculated by weighted average method or filtering technology combined with the above two load impact data. After combining these data, the actual load change trend of the motor during operation is obtained. Define the preset standard load threshold of the motor (for example, set the threshold based on 80% of the rated load of the motor). When the actual load data of the motor is greater than the threshold, the motor is considered to be in a high load state. Compare the actual load data of the motor with the standard load threshold. If it exceeds the threshold, mark the motor as a high load state and generate high load motor state marking data. When the actual load data of the motor is less than or equal to the preset standard load threshold, mark the motor state as low load. At this time, generate low load motor state marking data.Based on the low-load motor state tag data, the load of each component of the chef machine motor is balanced. For example, when the motor load is low, the load can be balanced and the efficiency can be improved by adjusting the motor speed, power distribution or transmission system configuration. Optimization algorithms (such as genetic algorithms and particle swarm optimization) can be used to design load balancing strategies. By adjusting the working parameters of the motor, it can maintain high efficiency and extend the life of the equipment under low load conditions, and generate motor load balancing distribution data, including motor load adjustment strategies, detailed load distribution plans, etc. According to the motor load balancing distribution data, the initial control instructions of the motor (such as speed, power, etc.) are adjusted. For example, when the motor is under low load, the motor speed can be reduced or the power output can be adjusted to reduce energy consumption and avoid excessive wear. The motor low load control adjustment instruction is generated according to the balanced distribution strategy and sent to the motor drive module to achieve low load control of the motor.

[0040] Preferably, step S22 includes the following steps: Step S221: screening the vibration data of the transmission component from the vibration sensor in the transmission component through the motor working timing state data to obtain the vibration data of the transmission component; extracting the vibration time domain features of the transmission component vibration data to obtain the vibration time domain data of the transmission component; Step S222: Perform fast Fourier transform on the transmission component vibration data to generate a transmission component vibration spectrum diagram; perform instantaneous frequency fluctuation analysis on the transmission component vibration spectrum diagram to generate transmission component instantaneous frequency fluctuation data; perform transmission friction load influence calculation based on the transmission component vibration time domain data and the transmission component instantaneous frequency fluctuation data to obtain transmission friction load influence data; wherein the formula for transmission friction load influence calculation is as follows: ; In the formula, Expressed as the friction load influence index, It is expressed as the contribution coefficient of adjusting the time domain characteristics to the friction load, Expressed as the root mean square value of the vibration signal, Expressed as the contribution coefficient of adjusting the frequency domain characteristics, Expressed as the maximum frequency, Expressed as the minimum frequency, Expressed as frequency The spectral density at It is expressed as the contribution coefficient of adjusting the effect of instantaneous frequency fluctuation on friction load, Expressed as frequency The instantaneous frequency at Step S223: screening the heat dissipation component temperature data of the temperature sensor in the transmission component through the motor working timing state data to obtain the heat dissipation component temperature data; performing temperature gradient conversion on the heat dissipation component temperature data to generate a heat dissipation component temperature gradient diagram; Step S224: Segment the temperature gradient map of the heat dissipation component into temperature difference regions to generate heat source hot spot region data; perform local heat flow calculation on the temperature gradient map of the heat dissipation component based on the heat source hot spot region data to obtain heat flow data of the heat dissipation component; quantify the heat loss of the temperature gradient map of the heat dissipation component through the heat flow data of the heat dissipation component to generate heat loss load impact data; wherein the formula for local heat flow calculation is as follows: ; In the formula, Expressed as heat flow, Expressed as thermal conductivity, It is expressed as the cross-sectional area of ​​heat flow conduction, Expressed as temperature gradient.

[0041] In an embodiment of the present invention, the vibration data of the transmission component is screened out through the output of the vibration sensor in the motor working timing state data. These data can come from the acceleration sensor, displacement sensor, etc. in the transmission system, and the vibration signal of the transmission component is collected in real time through the sensor. Time domain features such as root mean square value (RMS), peak value, pulse width, amplitude, standard deviation, etc. are extracted from the filtered vibration data. These time domain features help to reveal the intensity and change trend of the vibration signal. The extracted time domain feature data is saved as the vibration time domain data of the transmission component, which reflects the dynamic characteristics of the transmission system. Fast Fourier transform (FFT) is applied to the vibration time domain data of the transmission component to convert the time domain signal into a frequency domain signal to generate a vibration spectrum diagram of the transmission component. The spectrum diagram can display the amplitude of each frequency component, thereby revealing the frequency characteristics of the vibration source. The spectrum diagram shows the energy distribution of the vibration signal at different frequencies. Common analysis targets include finding vibration characteristics caused by specific frequencies (such as the resonant frequency of the transmission system). By performing a joint analysis of the spectrum diagram in the time domain and frequency domain, the instantaneous frequency fluctuation data of the vibration signal is extracted. These fluctuations are usually dynamic effects caused by factors such as load changes, friction, and wear. According to the changes in the spectrum graph, the instantaneous frequency fluctuation at each moment is calculated to generate the instantaneous frequency fluctuation data of the transmission component, which reflects the frequency changes and instability of the system during operation. Using the above-mentioned transmission component vibration time domain data, vibration spectrum graph and instantaneous frequency fluctuation data, according to the friction load influence calculation formula, the friction load influence data is obtained, which is used to evaluate the friction load in the transmission system. The temperature data of the heat sink component is obtained through the temperature sensor (such as thermocouple or RTD) installed in the transmission component. This data reflects the temperature change of the heat sink component and is usually related to the working load of the motor. The temperature data is spatially gradient calculated to obtain a temperature gradient map. The temperature gradient map can reveal the heat distribution of the heat sink component at different positions and help analyze the heat conduction path and heat source area. Using image processing technology (such as threshold segmentation or edge detection algorithm), the heat sink component temperature gradient map is segmented into multiple areas to identify the heat source hot spot areas with large temperature differences. These areas are usually where heat is concentrated, resulting in local overheating. Based on the temperature gradient map and the heat source hot spot area data, the following heat flow formula is applied to perform local heat flow calculation, calculate the heat flow of each area in the heat sink component, and obtain the heat flow data of the heat sink component. Quantify the heat loss of the heat dissipation components based on the heat flow data. The heat loss is mainly related to the motor load, current and temperature gradient. By calculating the heat flow distribution of the heat dissipation components, the heat loss can be estimated.

[0042] Preferably, load balancing distribution of the chef machine motor components based on the low-load motor status mark data includes: Based on the low-load motor state marking data, the motor work priority of the chef machine motor component is evaluated to generate motor work priority evaluation data; based on the motor work priority evaluation data, the motor load ratio of the chef machine motor component is calculated to obtain the motor load ratio data; The motor load ratio data is used to coordinately distribute the multi-motor loads of the chef machine motor components to generate motor load coordination distribution data; the motor operation energy efficiency of the chef machine motor components is balanced according to the motor load coordination distribution data to generate motor load balance distribution data.

[0043] In an embodiment of the present invention, the priority of each motor component is evaluated by using the low-load motor state marking data. The following factors can be considered during the evaluation: the longer the motor is in a low-load state, the lower the priority, because it is in an unloaded or lightly loaded state. The health status (such as vibration, temperature, etc.) of the motor components is evaluated to affect their working priority, and motors in good health can be given a higher working priority. Frequent load fluctuations cause the motor to be overloaded or damaged. The frequency of load fluctuations is evaluated to determine its priority. The working priority of each motor component is evaluated, and the corresponding motor working priority evaluation data is generated to determine which components should be put into use first and which components can wait. Based on the motor working priority evaluation data, the load ratio of each motor component is calculated by weight. The load ratio reflects the distribution ratio of each component under the overall load of the motor. The following factors are considered when calculating the load ratio: motor components with higher priority can bear more loads, and components with lower priority are allocated less loads. Different types of motor components (such as DC motors, AC motors, etc.) have different load capacities and characteristics when distributing loads. In a multi-motor system, each motor component needs to work together to ensure that the load is reasonably distributed among the motors to avoid a single motor from bearing too much load. The motor load ratio data is used to coordinate the load distribution of multiple motors to ensure that each motor component works in coordination with other motors during the load distribution process. After the motor load is coordinated, the energy efficiency status of each motor is considered and the load distribution of each component is adjusted to optimize the overall energy efficiency of the motor system. Different motors have different efficiencies, and motors with heavier loads have low efficiency, resulting in uneven energy efficiency. By optimizing the motor load distribution, the working state of each motor component is ensured to be in the optimal load range, thereby improving the energy efficiency of the overall system. According to the calculation results of energy efficiency balance, the motor load distribution strategy is adjusted to optimize the energy efficiency of each motor, thereby generating the final motor load balance distribution data.

[0044] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: Based on the high-load motor status mark data, the motor current data of the motor component of the chef machine is collected through the power supply unit to obtain the motor current data; based on the motor current data, the motor current trend analysis of the motor component of the chef machine is performed to generate the motor current trend data; Step S32: performing motor energy recovery on a power supply unit of a motor component of the chef machine according to the motor current trend data to generate motor energy recovery data; performing motor operating parameter adjustment on the working state data of the chef machine motor based on the motor energy recovery data to generate motor operating parameter adjustment data; Step S33: Using the motor working parameter adjustment data, the initial control instruction data of the chef machine motor is adjusted for motor high load control, and a motor high load control adjustment instruction is generated.

[0045] In an embodiment of the present invention, the motor current is collected in real time by a current sensor in a power supply unit. The collected data includes current value, timestamp, instantaneous current, etc. A high-precision current sensor is used, which is installed at the current input end of the power supply unit to monitor the working current of the motor in real time. The current sensor converts the current signal through an analog-to-digital converter (ADC) and transmits the collected current data to a controller or a data storage system. The current data is analyzed by time series, the trend of current change over time is plotted, and whether there is abnormal current fluctuation is analyzed. The fast Fourier transform (FFT) is applied to perform spectrum analysis on the current signal to identify the frequency components of the current fluctuation, especially to identify the frequency anomaly under high load. According to the above analysis results, the motor current trend data is generated to present the long-term and short-term fluctuations of the current change. Based on the motor current trend analysis, the braking or deceleration stage when the motor load is too heavy is judged. When the motor is in the deceleration state, the reverse current feedback system is used for energy recovery. Using an electronic braking system or a feedback braking system, the current is fed back to the power grid or energy storage device. According to the recovered energy data, the working parameters of the motor, such as voltage, current or frequency, are adjusted to optimize the performance of the motor and reduce energy consumption. Based on the motor energy recovery data, the working mode of the power supply unit is adjusted. For example, when the energy recovery rate is high, adjust the motor's operating voltage or frequency to reduce energy consumption. Dynamically adjust the motor's operating parameters (such as current, voltage, and power) based on the motor current trend and energy recovery data to ensure that the motor always operates in the best working state. Fine-tune the motor through the control system to ensure the stability, efficiency, and safety of the motor when it is running under high load. According to the motor's operating parameter adjustment data, adjust the motor's voltage, current and other control parameters to optimize the motor's control strategy under high load. The control system automatically adjusts the control strategy according to changes in the motor load, such as reducing the current or voltage, or increasing the heat dissipation of the motor to avoid overload.

[0046] Preferably, performing motor energy recovery on a power supply unit of a motor component of a chef machine according to the motor current trend data comprises: According to the motor current trend data, the controller component in the motor component of the chef machine is operated in reverse, and energy conversion data is collected through the power supply unit to generate motor energy conversion data; feedback energy efficiency monitoring is performed on the motor energy conversion data to generate feedback energy monitoring data; The motor feedback energy ratio is adjusted for the motor current trend data according to the feedback energy monitoring data to generate motor energy recovery optimization data; the energy recovery efficiency of the motor current data is calculated using the motor energy recovery optimization data to obtain the motor energy recovery data.

[0047] In an embodiment of the present invention, the load state and operation mode of the motor are determined by analyzing the motor current trend data. In particular, when the motor load is reduced or in a deceleration stage, the reverse operation mode can be started. When the motor is running in reverse, the mechanical energy of the motor is converted into electrical energy and fed back to the power grid or energy storage device through the power supply unit. The control mode of reverse operation is achieved by adjusting the direction of the motor (for example, by controlling the inverter). The reverse motor operation mode is set in the controller, and a reverse drive instruction is sent through the motor controller. The controller determines whether the motor is in a state suitable for reverse operation (for example: when the current drops and the load is light) based on the current trend data. The power supply unit collects the electrical energy generated during the reverse operation. The electrical energy output by the motor is monitored in real time through current and voltage sensors, and this information is fed back to the control system. In the power supply unit, current and voltage sensors are installed to monitor the electrical energy output by the motor during the reverse operation. The collected current, voltage and power data are transmitted to the control system for processing through analog-to-digital conversion (ADC). The energy feedback efficiency is calculated by analyzing the energy conversion data of the motor (including voltage, current, power, etc.). Through the monitoring system, the loss or loss in the energy conversion process is evaluated. Compare the actual collected feedback power with the expected theoretical value to evaluate the actual feedback efficiency. Use the feedback control mechanism to adjust the operating parameters of the energy feedback system according to the real-time current and voltage data (for example: control the working mode of the inverter, adjust the feedback current). Adjust the feedback energy ratio of the motor according to the real-time monitored energy recovery efficiency. By adjusting the output of the inverter, optimize the energy feedback amount of the motor, thereby improving the energy recovery efficiency of the overall system. Set a dynamic adjustment mechanism in the controller to adjust the feedback energy ratio of the motor based on real-time efficiency monitoring data. Dynamically adjust the feedback energy ratio according to different load conditions (for example, the motor is in a high-speed rotation, deceleration, etc.). Based on the adjusted feedback energy ratio, calculate the energy recovery efficiency of the entire motor system in the reverse operation mode. Use the feedback energy adjustment data obtained in the previous steps to calculate the final energy recovery data. The calculation of the motor feedback energy efficiency needs to comprehensively consider energy loss, feedback power, and all influencing factors in the energy conversion process.

[0048] In this specification, a control system for improving motor efficiency is provided, which is used to execute the above-mentioned control method for improving motor efficiency. The control system for improving motor efficiency includes: The motor control instruction generation module is used to obtain the working state data of the chef machine motor; transmit the working state data of the chef machine motor to the controller component for motor load perception, and generate motor load perception data; generate initial motor control instructions for the controller component according to the motor load perception data, and obtain initial control instruction data for the chef machine motor; The low-load instruction control module is used to perform time-series state monitoring on the working state data of the chef machine motor based on the initial control instruction data of the chef machine motor, and generate the motor working time-series state data; compare the actual motor load with the motor load sensing data through the motor working time-series state data, and generate high-load motor state marking data and motor load balancing distribution data; use the motor load balancing distribution data to perform low-load control adjustment on the initial control instruction data of the chef machine motor, and generate a motor low-load control adjustment instruction; The high-load instruction control module collects motor current data based on the high-load motor state tag data to obtain motor current data; optimizes the motor recovery energy of the power supply unit of the chef machine motor component based on the motor current data to generate motor working parameter adjustment data; uses the motor working parameter adjustment data to adjust the motor high-load control of the chef machine motor initial control instruction data to generate a motor high-load control adjustment instruction; The real-time optimization module is used to perform motor working state control feedback on the chef machine motor working state data according to the motor low-load control adjustment instruction and the motor high-load control adjustment instruction, generate the chef machine motor working control feedback data; and perform real-time optimization on the chef machine motor working control feedback data to execute control operations to improve motor efficiency.

[0049] A food processor includes a food processor motor component, a controller component, a transmission component, a heat sink component and a power supply unit. The power supply unit is electrically connected to the motor component, the controller component, the transmission component and the heat sink component. The controller component is installed in the food processor motor component. The transmission component is equipped with a vibration sensor. The heat sink component is equipped with a temperature sensor. The controller component is used to execute the control method for improving motor efficiency as described above.

[0050] The beneficial effect of the present invention is that through real-time perception and monitoring of the working state of the chef machine motor, the load condition of the motor can be grasped in time, so as to make appropriate control adjustments according to different workloads, avoid motor overload or low load operation, and improve the working efficiency of the motor. The method automatically realizes load balance distribution under high load and low load conditions by analyzing the motor load perception data. Control adjustment is performed at low load to avoid waste of motor resources; at high load, energy recovery optimization is performed to reduce energy consumption and improve motor working stability to prevent overheating or loss. By collecting and analyzing the motor current data, the energy recovery of the power supply unit is optimized, so that the system can effectively use the braking energy of the motor for recovery, improve the overall energy efficiency, and reduce unnecessary energy loss. By generating motor working control feedback data, the system can continuously adjust and optimize the working state of the motor to ensure that the motor is always maintained in the optimal working state under various use conditions, thereby maximizing the working efficiency and service life of the motor. Through intelligent control instruction adjustment and feedback optimization, the method can effectively reduce the loss of the motor under long-term high-load working conditions, slow down the wear of the equipment, and thus extend the service life of the chef machine and reduce the failure rate. Therefore, the present invention improves the efficiency of the motor during long-term operation through real-time load perception, load balancing control, energy recovery optimization and dynamic feedback adjustment.

[0051] 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.

[0052] 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 control method for improving motor efficiency, characterized in that: The following steps are involved: Step S1: Acquire the working state data of the chef machine motor; transmit the working state data of the chef machine motor to the controller component to sense the motor load and generate motor load sensing data; generate initial motor control instructions for the controller component according to the motor load sensing data to obtain initial control instruction data of the chef machine motor; Step S2: Based on the initial control instruction data of the chef machine motor, the working state data of the chef machine motor is monitored in a time sequence state to generate the motor working time sequence state data; Compare the actual motor load with the motor load sensing data through the motor working timing state data to generate high-load motor state marking data and motor load balancing distribution data; Using the motor load balancing distribution data, the initial control instruction data of the chef machine motor is adjusted for motor low load control, and a motor low load control adjustment instruction is generated; Step S3: collecting motor current data based on the high-load motor state mark data to obtain motor current data; Based on the motor current data, the power supply unit of the motor component of the chef machine is optimized for motor energy recovery, and motor working parameter adjustment data is generated; the motor working parameter adjustment data is used to adjust the initial control instruction data of the chef machine motor for motor high load control, and a motor high load control adjustment instruction is generated; Step S4: performing motor working state control feedback on the chef machine motor working state data according to the motor low load control adjustment instruction and the motor high load control adjustment instruction to generate chef machine motor working control feedback data; performing real-time optimization on the chef machine motor working control feedback data to execute control operations to improve motor efficiency.

2. The control method for improving motor efficiency according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: using a sensor to obtain working status data of the chef machine motor; Step S12: performing data preprocessing on the working state data of the chef machine motor to generate standard working state data of the chef machine motor, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: transmitting the working state data of the standard chef machine motor to the controller component for motor load sensing, thereby generating motor load sensing data; Step S14: generating initial motor control instructions for the controller component according to the motor load sensing data, and obtaining initial control instruction data for the chef machine motor.

3. The control method for improving motor efficiency according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: extracting key motor load features from the motor load sensing data to obtain key motor load feature data, wherein the motor key load feature extraction includes motor speed, motor power and motor frequency; Step S142: performing motor control logic design on the motor key load characteristic data based on the fuzzy logic control method to generate preliminary motor control logic data; Step S143: performing instruction encoding on the preliminary motor control logic data to generate motor control logic encoding instructions; Step S144: Verify the motor control logic coding instruction to generate initial control instruction data for the chef machine motor.

4. The control method for improving motor efficiency according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing time sequence state monitoring on the working state data of the motor of the chef machine based on the initial control instruction data of the motor of the chef machine to generate the working time sequence state data of the motor; Step S22: analyzing the influence of transmission friction load on transmission components through the motor working timing state data to generate transmission friction load influence data; analyzing the influence of motor heat loss on heat dissipation components through the motor working timing state data to generate heat loss load influence data; Step S23: Calculate the actual load fluctuation of the motor load sensing data according to the transmission friction load influence data and the heat loss load influence data to obtain the actual load data of the motor operation; compare the actual load data of the motor operation with the preset standard load threshold value, and when the actual load data of the motor operation is greater than the preset standard load threshold value, mark the actual load data of the motor operation as a high load state, and generate high load motor state mark data; Step S24: when the actual load data of the motor operation is less than or equal to the preset standard load threshold, the actual load data of the motor operation is marked as a motor low load state, and low load motor state marking data is generated; based on the low load motor state marking data, load balancing distribution is performed on the motor components of the chef machine, and motor load balancing distribution data is generated; Step S25: Using the motor load balancing distribution data, the initial control instruction data of the chef machine motor is adjusted for motor low load control, and a motor low load control adjustment instruction is generated.

5. The control method for improving motor efficiency according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: screening the vibration data of the transmission component from the vibration sensor in the transmission component through the motor working timing state data to obtain the vibration data of the transmission component; extracting the vibration time domain features of the transmission component vibration data to obtain the vibration time domain data of the transmission component; Step S222: Perform fast Fourier transform on the transmission component vibration data to generate a transmission component vibration spectrum diagram; perform instantaneous frequency fluctuation analysis on the transmission component vibration spectrum diagram to generate transmission component instantaneous frequency fluctuation data; perform transmission friction load influence calculation based on the transmission component vibration time domain data and the transmission component instantaneous frequency fluctuation data to obtain transmission friction load influence data; wherein the formula for transmission friction load influence calculation is as follows: ； In the formula, Expressed as the friction load influence index, It is expressed as the contribution coefficient of adjusting the time domain characteristics to the friction load, Expressed as the root mean square value of the vibration signal, Expressed as the contribution coefficient of adjusting the frequency domain characteristics, Expressed as the maximum frequency, Expressed as the minimum frequency, Expressed as frequency The spectral density at It is expressed as the contribution coefficient of adjusting the effect of instantaneous frequency fluctuation on friction load, Expressed as frequency The instantaneous frequency at Step S223: screening the heat dissipation component temperature data of the temperature sensor in the transmission component through the motor working timing state data to obtain the heat dissipation component temperature data; performing temperature gradient conversion on the heat dissipation component temperature data to generate a heat dissipation component temperature gradient diagram; Step S224: Segment the temperature gradient map of the heat dissipation component into temperature difference regions to generate heat source hot spot region data; perform local heat flow calculation on the temperature gradient map of the heat dissipation component based on the heat source hot spot region data to obtain heat flow data of the heat dissipation component; quantify the heat loss of the temperature gradient map of the heat dissipation component through the heat flow data of the heat dissipation component to generate heat loss load impact data; wherein the formula for local heat flow calculation is as follows: ； In the formula, Expressed as heat flow, Expressed as thermal conductivity, Expressed as the cross-sectional area of ​​heat flow conduction, Expressed as temperature gradient.

6. The control method for improving motor efficiency according to claim 4, characterized in that: Load balancing of the chef machine motor components based on low-load motor status tag data includes: Based on the low-load motor state marking data, the motor work priority of the chef machine motor component is evaluated to generate motor work priority evaluation data; based on the motor work priority evaluation data, the motor load ratio of the chef machine motor component is calculated to obtain motor load ratio data; The motor load ratio data is used to coordinately distribute the multi-motor loads of the chef machine motor components to generate motor load coordination distribution data; the motor operation energy efficiency of the chef machine motor components is balanced according to the motor load coordination distribution data to generate motor load balance distribution data.

7. The control method for improving motor efficiency according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Based on the high-load motor status mark data, the motor current data of the motor component of the chef machine is collected through the power supply unit to obtain the motor current data; based on the motor current data, the motor current trend analysis of the motor component of the chef machine is performed to generate the motor current trend data; Step S32: performing motor energy recovery on a power supply unit of a motor component of the chef machine according to the motor current trend data to generate motor energy recovery data; performing motor operating parameter adjustment on the working state data of the chef machine motor based on the motor energy recovery data to generate motor operating parameter adjustment data; Step S33: Using the motor working parameter adjustment data, the initial control instruction data of the chef machine motor is adjusted for motor high load control, and a motor high load control adjustment instruction is generated.

8. The control method for improving motor efficiency according to claim 7, characterized in that: Motor energy recovery for the power supply unit of the chef machine motor component based on the motor current trend data includes: According to the motor current trend data, the controller component in the motor component of the chef machine is operated in reverse, and energy conversion data is collected through the power supply unit to generate motor energy conversion data; feedback energy efficiency monitoring is performed on the motor energy conversion data to generate feedback energy monitoring data; The motor feedback energy ratio is adjusted for the motor current trend data according to the feedback energy monitoring data to generate motor energy recovery optimization data; the energy recovery efficiency of the motor current data is calculated using the motor energy recovery optimization data to obtain the motor energy recovery data.

9. A control system for improving motor efficiency, characterized in that: The control system for improving the efficiency of a motor is used to execute the control method for improving the efficiency of a motor as claimed in claim 1, wherein the control system for improving the efficiency of a motor comprises: The motor control instruction generation module is used to obtain the working state data of the chef machine motor; transmit the working state data of the chef machine motor to the controller component for motor load perception, and generate motor load perception data; generate initial motor control instructions for the controller component according to the motor load perception data, and obtain initial control instruction data for the chef machine motor; The low-load instruction control module is used to perform time-series state monitoring on the working state data of the chef machine motor based on the initial control instruction data of the chef machine motor, and generate the motor working time-series state data; compare the actual motor load with the motor load sensing data through the motor working time-series state data, and generate high-load motor state marking data and motor load balancing distribution data; use the motor load balancing distribution data to perform low-load control adjustment on the initial control instruction data of the chef machine motor, and generate a motor low-load control adjustment instruction; The high-load instruction control module collects motor current data based on the high-load motor state tag data to obtain motor current data; optimizes the motor recovery energy of the power supply unit of the chef machine motor component based on the motor current data to generate motor working parameter adjustment data; uses the motor working parameter adjustment data to adjust the motor high-load control of the chef machine motor initial control instruction data to generate a motor high-load control adjustment instruction; The real-time optimization module is used to perform motor working state control feedback on the chef machine motor working state data according to the motor low-load control adjustment instruction and the motor high-load control adjustment instruction, generate the chef machine motor working control feedback data; and perform real-time optimization on the chef machine motor working control feedback data to execute control operations to improve motor efficiency.

10. A chef machine, characterized in that: The chef machine comprises a motor component, a controller component, a transmission component, a heat dissipation component and a power supply unit. The power supply unit is electrically connected to the motor component, the controller component, the transmission component and the heat dissipation component. The controller component is installed in the chef machine motor component. The transmission component has a built-in vibration sensor, and the heat dissipation component has a built-in temperature sensor. The controller component is used to execute the control method for improving motor efficiency as described in any one of claims 1 to 8.

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