Energy consumption optimization control method and device for agricultural plant protection motor

Abstract

The application provides an energy consumption optimization control method and device of an agricultural plant protection motor, comprising: collecting agricultural plant protection motor operation parameters, plant protection operation scene parameters and load characteristic data in real time; identifying a dynamic load rate and equivalent wind resistance loss in real time through an extended Kalman filtering algorithm; adaptively adjusting equivalent magnetic resistance of a rotor magnetic circuit according to an identification result, changing air gap magnetic flux distribution through gap change of a deformable magnetic barrier structure of the motor; correcting a maximum torque current ratio control strategy based on the dynamic load rate and equivalent wind resistance loss, and dynamically distributing motor direct axis demagnetizing current and quadrature axis driving current, so that the sum of copper loss and iron loss of the motor under current working conditions is minimum. In the application, the coupling relationship between the dynamic load rate and the equivalent wind resistance loss is accurately identified, the energy efficiency of the plant protection motor is improved, and operation endurance is prolonged.

Description

Technical Field

[0001] This invention relates to the technical field of agricultural plant protection motors, and in particular to a method and device for optimizing and controlling the energy consumption of agricultural plant protection motors. Background Technology

[0002] With the development of smart agriculture, equipment such as plant protection drones are widely used. As a core power source, the energy consumption of agricultural plant protection motors directly affects the operating range, efficiency, and operating costs. Agricultural plant protection operations have significant unique characteristics: the motor load fluctuates in a stepped and pulsed manner, with the load rate reaching over 90% when the tank is fully loaded and dropping sharply to below 30% when unloaded. In addition, the operating environment is subject to interference such as gusts of wind and aircraft tilt, resulting in drastic fluctuations in equivalent wind resistance loss, leading to complex and variable motor operating conditions.

[0003] Current energy consumption control solutions for plant protection motors have significant technical bottlenecks: Traditional methods rely solely on parameters such as motor current and speed to determine the load, without considering scenario and load characteristics data such as medicine tank volume and ambient wind speed. This makes it impossible to accurately identify the coupling relationship between dynamic load rate and equivalent wind resistance loss, and can easily lead to ineffective energy consumption due to control adjustment lag or deviation.

[0004] Traditional maximum torque-to-current ratio (MTPA) strategies have fixed parameters, which only apply to rated operating conditions. They do not incorporate dynamic load and wind resistance losses for real-time correction, and therefore cannot minimize the sum of copper and iron losses, resulting in energy redundancy or insufficient power. Summary of the Invention

[0005] The main objective of this invention is to provide an energy consumption optimization control method and device for agricultural plant protection motors, which aims to accurately identify the coupling relationship between dynamic load rate and equivalent wind resistance loss, thereby improving the energy efficiency of plant protection motors and extending their operating range.

[0006] To achieve the above objectives, the present invention provides an energy consumption optimization control method for agricultural plant protection motors, comprising the following steps: Real-time acquisition of agricultural plant protection motor operating parameters, plant protection operation scenario parameters, and load characteristic data; real-time identification of dynamic load rate and equivalent wind resistance loss through extended Kalman filter algorithm; The equivalent magnetic reluctance of the rotor magnetic circuit is adaptively adjusted based on the identification results, and the air gap magnetic flux distribution is changed by the gap change of the deformable magnetic barrier structure of the motor. Based on the dynamic load rate and equivalent wind resistance loss, the maximum torque-current ratio control strategy is modified, and the direct-axis demagnetizing current and quadrature-axis drive current of the motor are dynamically allocated to minimize the sum of copper loss and iron loss of the motor under the current operating conditions. Furthermore, the operating parameters include stator three-phase current, rotor mechanical angular velocity, and winding temperature; The parameters for the operational scenario include ambient wind speed and spray flow rate; The load characteristic data includes the remaining amount of medicine in the medicine box and the tilt angle of the machine body.

[0007] Furthermore, the air gap magnetic flux distribution is altered by changing the gap of the deformable magnetic barrier structure of the motor, including: When the dynamic load rate is ≥50%, the magnetic barrier gap of the deformable magnetic barrier structure is reduced to improve the magnetic coupling strength. When the dynamic load rate is <50%, the magnetic barrier gap of the deformable magnetic barrier structure is increased to reduce the no-load loss.

[0008] Furthermore, the method also includes: The motor's heat dissipation efficiency is regulated by coordinating winding temperature and equivalent wind resistance loss. This includes: using a linked structure of semiconductor cooling chip and passive heat dissipation fins. When the winding temperature is ≥75℃ and the wind resistance loss is ≥60% of the rated loss, the semiconductor cooling chip starts and maximizes the cooling power; when the winding temperature is <60℃ and the wind resistance loss is <30% of the rated loss, the semiconductor cooling chip stops working and only the fins retain natural heat dissipation.

[0009] Furthermore, the dynamic load rate and equivalent wind resistance loss are identified in real time using an extended Kalman filter algorithm, including: The state equations of the extended Kalman filter are established based on the electromagnetic torque equation and the mechanical motion equation of the motor. The observation equations are established based on the inherent mapping relationship between the stator current, rotor mechanical angular velocity and state variables. Among them, the dynamic load rate and equivalent wind resistance loss are used as state variables, and the real-time acquisition values ​​of the stator three-phase current and rotor mechanical angular velocity are used as observation variables. Initialize the state variables and covariance matrix. The initial value of the dynamic load rate is set to 50% of the rated load rate of the motor, the initial value of the equivalent wind resistance loss is set to 30% of the rated loss of the motor, and the initial value of the covariance matrix is ​​set to a diagonal matrix. State prediction and update are performed every 5-10ms: the prior estimate of the state variable is predicted based on the electromagnetic torque equation of the motor and the mechanical motion equation, the Kalman gain is calculated by combining the actual collected value of the observed variable, the prior estimate is corrected by the Kalman gain to obtain the posterior estimate, and the covariance matrix is ​​updated synchronously. Set constraint thresholds for the identification results. The identification results of dynamic load rate are limited to the range of 0-120%, and the identification results of equivalent wind resistance loss are limited to the range of 0-150% of rated loss. If the threshold is exceeded, the nearest threshold is used for replacement.

[0010] Furthermore, an electromagnetic adjustment component is configured in the rotor magnetic circuit. The electromagnetic adjustment component includes an excitation coil and a magnetic reluctance detection unit. The equivalent magnetic reluctance of the rotor magnetic circuit is adjusted by changing the magnitude of the current flowing through the excitation coil. The equivalent magnetic reluctance of the rotor magnetic circuit is adaptively adjusted based on the identification results, including: Adjustment is aided by dynamic load rate combined with equivalent wind resistance loss: When the dynamic load rate is ≥50%, increase the excitation coil current, reduce the equivalent magnetic reluctance of the rotor magnetic circuit, and increase the air gap magnetic flux intensity to ensure torque output; when the equivalent wind resistance loss is ≥40% of the rated loss, further increase the excitation coil current to compensate for the power attenuation caused by wind resistance loss. When the dynamic load rate is less than 50%, reduce the excitation coil current, increase the equivalent magnetic reluctance of the rotor magnetic circuit, and weaken the redundant magnetic flux to reduce iron loss. The actual equivalent magnetic reluctance value of the rotor magnetic circuit is collected in real time by the magnetic reluctance detection unit and compared with the target magnetic reluctance range corresponding to the current operating condition. The excitation coil current is then finely adjusted to ensure that the magnetic reluctance is accurately matched with the operating condition.

[0011] Furthermore, based on the dynamic load rate and equivalent wind resistance loss, the maximum torque-to-current ratio control strategy is modified, and the direct-axis demagnetizing current and quadrature-axis drive current of the motor are dynamically allocated to minimize the sum of copper losses and iron losses of the motor under the current operating conditions, including: Establish a basic maximum torque-current ratio control model, preset the initial distribution ratio of direct-axis demagnetizing current and quadrature-axis drive current, and take the maximum torque output per unit current as the basic objective; A two-parameter correction model is constructed using dynamic load factor and equivalent wind resistance loss as correction factors: When the dynamic load rate is ≥50%, the equivalent wind resistance loss is <40% of the rated loss. Increase the proportion of quadrature axis drive current and maintain a low direct axis demagnetizing current. When the equivalent wind resistance loss is ≥40% of the rated loss, further increase the proportion of quadrature axis drive current and reduce the direct axis demagnetizing current to compensate for wind resistance loss. When the dynamic load rate is less than 50%, reduce the proportion of quadrature axis drive current, increase direct axis demagnetizing current, and weaken air gap flux to reduce redundant losses. With the goal of minimizing the sum of copper loss and iron loss, the current loss is calculated by combining the real-time collected stator three-phase current and winding temperature. The amplitude of the direct-axis demagnetizing current and quadrature-axis driving current is finely adjusted by the gradient descent method until the loss reaches the optimal value under the current operating conditions. The direct-axis demagnetizing current is limited to 0-30% of the rated current, and the quadrature-axis drive current is limited to 0-100% of the rated current. The current command is converted into a drive signal through space vector modulation technology.

[0012] The present invention also provides an energy consumption optimization control device for an agricultural plant protection motor, comprising: The data acquisition unit is used to collect real-time operating parameters of agricultural plant protection motors, parameters of plant protection operation scenarios, and load characteristic data; and to identify dynamic load rate and equivalent wind resistance loss in real time through an extended Kalman filter algorithm. The adjustment unit is used to adaptively adjust the equivalent magnetic reluctance of the rotor magnetic circuit according to the identification results, and change the air gap magnetic flux distribution by changing the gap of the deformable magnetic barrier structure of the motor. The control unit is used to modify the maximum torque-current ratio control strategy based on the dynamic load rate and equivalent wind resistance loss, and dynamically allocate the direct-axis demagnetizing current and quadrature-axis drive current of the motor to minimize the sum of copper loss and iron loss of the motor under the current operating conditions. The present invention provides an energy consumption optimization control method and device for agricultural plant protection motors, comprising: real-time acquisition of operating parameters of the agricultural plant protection motor, parameters of plant protection operation scenarios, and load characteristic data; real-time identification of dynamic load rate and equivalent wind resistance loss through an extended Kalman filter algorithm; adaptive adjustment of the equivalent magnetic reluctance of the rotor magnetic circuit according to the identification results, and changing the air gap magnetic flux distribution by changing the gap of the deformable magnetic barrier structure of the motor; based on the dynamic load rate and equivalent wind resistance loss, modifying the maximum torque-to-current ratio control strategy, and dynamically allocating the direct-axis demagnetizing current and quadrature-axis drive current of the motor to minimize the sum of copper loss and iron loss of the motor under the current operating conditions. In this invention, the real-time acquisition of operating parameters of the agricultural plant protection motor, parameters of plant protection operation scenarios, and load characteristic data; the real-time identification of dynamic load rate and equivalent wind resistance loss through an extended Kalman filter algorithm, accurately identifying the coupling relationship between dynamic load rate and equivalent wind resistance loss; and the modification of the maximum torque-to-current ratio control strategy based on the dynamic load rate and equivalent wind resistance loss, and the dynamic allocation of the direct-axis demagnetizing current and quadrature-axis drive current of the motor, thereby improving the energy efficiency of the plant protection motor and extending its operating range. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the steps of an energy consumption optimization control method for an agricultural plant protection motor in one embodiment of the present invention; Figure 2 This is a structural block diagram of an energy consumption optimization control device for an agricultural plant protection motor according to an embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0014] The implementation, functional features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] It is particularly important to note that all technical steps, algorithm applications, and parameter settings in the technical solution of this application have clear technical objectives and application value. They do not utilize complex steps and algorithmic formulas to achieve simple functions. To provide detailed explanations of each step and avoid ambiguity, some conventional algorithms are used for illustration. However, this does not mean that the algorithms and technical features listed herein are the only way to implement the technical solution of this application, nor is it intended to limit the scope of protection of this application. This application is not a combination or stacking of the listed algorithms and technical features; its essence is to exemplify the implementation methods of this application to fully explain it. It does not pursue formal complexity by adding meaningless technical steps, nor does it involve the accumulation of technologies divorced from practical needs; it conforms to the conventional logic of technical improvement and design.

[0017] Reference Figure 1 One embodiment of the present invention provides an energy consumption optimization control method for agricultural plant protection motors, comprising the following steps: Step S1: Real-time collection of agricultural plant protection motor operating parameters, plant protection operation scenario parameters, and load characteristic data; real-time identification of dynamic load rate and equivalent wind resistance loss through extended Kalman filter algorithm; Step S2: Adaptively adjust the equivalent magnetic reluctance of the rotor magnetic circuit according to the identification results, and change the air gap magnetic flux distribution by changing the gap of the deformable magnetic barrier structure of the motor. Step S3: Based on the dynamic load rate and equivalent wind resistance loss, modify the maximum torque current ratio control strategy and dynamically allocate the direct-axis demagnetizing current and quadrature-axis drive current of the motor to minimize the sum of copper loss and iron loss of the motor under the current operating conditions. In this embodiment, as described in step S1 above, the core objective is to overcome the limitations of traditional single-parameter monitoring and achieve comprehensive perception and precise quantification of the operating status, working environment, and load characteristics of agricultural plant protection motors. First, multi-dimensional data is collected in real time using the motor's built-in current sensor, speed sensor, temperature sensor, and external sensors such as pesticide dosage sensor, attitude sensor, wind speed sensor, and flow sensor. Motor operating parameters include stator three-phase current, rotor mechanical angular velocity, and winding temperature, directly reflecting the motor's electromagnetic and thermal operating status. Plant protection operation scenario parameters include ambient wind speed and spray flow rate, accurately capturing dynamic interference factors in outdoor operations. Load characteristic data includes the remaining pesticide level in the tank and the motor's tilt angle, intuitively reflecting the motor's load-bearing foundation and stress state.

[0018] After data acquisition, an extended Kalman filter algorithm is introduced to fuse and identify the operating conditions of the multi-source data. This algorithm possesses powerful nonlinear system state estimation capabilities, effectively suppressing the impact of sensor noise and environmental interference on data accuracy. By constructing a state model that includes dynamic load rate and equivalent wind resistance loss, and combining the motor's electromagnetic torque equation and mechanical motion equation, it calculates in real time the dynamic load rate characterizing the motor's current load intensity, and the equivalent wind resistance loss quantifying the impact of environmental wind resistance on motor energy consumption. Compared to the traditional single load judgment method relying solely on current and speed, this step achieves dual operating condition quantification of load intensity and environmental interference through multi-parameter fusion and advanced algorithm identification. This provides a reliable decision-making basis for the accurate adaptation of subsequent magnetic circuit adjustment and current control strategies, avoiding control deviations and energy waste caused by misjudgment of operating conditions.

[0019] Step S2, as described above, is a core improvement addressing the poor adaptability of existing fixed magnetic circuit structures. It achieves dynamic matching between the air gap magnetic flux distribution and real-time operating conditions by actively adjusting the equivalent magnetic reluctance of the rotor magnetic circuit. This step uses the dynamic load rate and equivalent wind resistance loss identified in step S1 as control inputs, and relies on the deformable magnetic barrier structure built into the motor to perform magnetic reluctance adjustment. The deformable magnetic barrier structure consists of elastic magnetic barrier plates, an electromagnetic drive assembly, and a displacement detection unit. The elastic magnetic barrier plates are uniformly embedded in the rotor core along the rotor circumference, with one end fixed to the core and the other end connected to the electromagnetic drive assembly. Under the action of electromagnetic driving force, they can extend and retract, thereby changing the size of the magnetic barrier gap.

[0020] In the specific adjustment process, a differentiated reluctance adjustment strategy is formulated based on the operating condition identification results: When the dynamic load rate is high (e.g., ≥50%), the motor is in a medium-heavy load condition. At this time, it is necessary to increase the air gap magnetic flux density to ensure torque output capability, control the electromagnetic drive component to output positive driving force, compress the elastic magnetic barrier to reduce the magnetic barrier gap, reduce the equivalent magnetic reluctance of the rotor magnetic circuit, enhance the air gap magnetic coupling strength, and ensure that the motor can still output torque efficiently under heavy load or high wind resistance interference, avoiding excessive copper loss; When the dynamic load rate is low (e.g., <50%), the motor is in a light load / no-load condition. Redundant magnetic flux will cause serious iron loss. At this time, the electromagnetic drive component is de-energized, and the elastic magnetic barrier extends under its own elastic restoring force, increasing the magnetic barrier gap, increasing the equivalent magnetic reluctance of the rotor magnetic circuit, weakening the air gap magnetic flux density, and suppressing the generation of iron loss from the source of the magnetic circuit. Meanwhile, the displacement detection unit collects the actual value of the magnetic barrier gap in real time and compares it with the target gap value. If there is a deviation, it is finely adjusted and calibrated through the electromagnetic drive component to ensure the accuracy and stability of the magnetoresistive adjustment and realize the closed-loop adaptation of working condition, magnetic circuit and magnetic flux.

[0021] As described in step S3 above, by modifying the traditional maximum torque-to-current ratio (MTPA) control strategy, dynamic distribution of direct-axis and quadrature-axis currents is achieved, ultimately minimizing the sum of copper and iron losses. This step uses the dynamic load rate and equivalent wind resistance loss identified in step S1 as dual correction factors, breaking through the limitations of the traditional MTPA strategy with fixed parameters that only adapt to rated operating conditions, and constructing an operating condition-adaptive current distribution model.

[0022] First, a basic MTPA control model is established, with an initial distribution ratio of the direct-axis (d-axis) demagnetizing current and the quadrature-axis (q-axis) drive current preset to ensure the basic performance of maximum torque output per unit current. Then, the initial distribution ratio is dynamically adjusted based on real-time operating conditions: when the dynamic load rate is ≥50% (medium-heavy load condition), if the equivalent wind resistance loss is small (e.g., <40% of rated loss), the proportion of the quadrature-axis drive current is increased while maintaining a low level of the direct-axis demagnetizing current, reducing copper losses while ensuring torque output; if the equivalent wind resistance loss is large (e.g., ≥40% of rated loss), the proportion of the quadrature-axis drive current is further increased, and the direct-axis demagnetizing current is appropriately reduced to compensate for torque attenuation caused by wind resistance loss and avoid additional energy consumption due to insufficient power; when the dynamic load rate is <50% (light load / no-load condition), the proportion of the quadrature-axis drive current is reduced, and the direct-axis demagnetizing current is appropriately increased to reduce iron loss redundancy by weakening the air gap magnetic flux density.

[0023] After completing the proportional correction, the copper loss (based on the relationship between winding resistance and the square of current) and iron loss (based on the relationship between magnetic flux density and frequency) under the current operating conditions are calculated in real time, using the stator three-phase current and winding temperature data collected in step S1 as the target. The specific amplitudes of the direct-axis and quadrature-axis currents are iteratively fine-tuned with the goal of minimizing the sum of copper and iron losses. Simultaneously, to ensure safe motor operation, current constraint thresholds are set: the direct-axis demagnetizing current is limited to 0-30% of the rated current, and the quadrature-axis drive current is limited to 0-100% of the rated current. Finally, space vector modulation technology is used to convert the optimized current command into a motor drive signal, achieving dual protection of energy consumption optimization and operational safety. This step, through a multi-layered logic of basic strategy, dual-parameter correction, loss optimization, and safety constraints, ensures precise matching of current distribution with real-time operating conditions and magnetic circuit status, maximizing the electromagnetic efficiency of the motor.

[0024] In this embodiment, the operating parameters of the agricultural plant protection motor, the parameters of the plant protection operation scenario, and the load characteristic data are collected in real time. The dynamic load rate and equivalent wind resistance loss are identified in real time through the extended Kalman filter algorithm, and the coupling relationship between the dynamic load rate and equivalent wind resistance loss is accurately identified. Based on the dynamic load rate and equivalent wind resistance loss, the maximum torque-current ratio control strategy is modified, and the direct-axis demagnetizing current and quadrature-axis drive current of the motor are dynamically allocated to improve the energy efficiency of the plant protection motor and extend the operating range.

[0025] In one embodiment, the operating parameters include stator three-phase current, rotor mechanical angular velocity, and winding temperature; The parameters for the operational scenario include ambient wind speed and spray flow rate; The load characteristic data includes the remaining amount of medicine in the medicine box and the tilt angle of the machine body.

[0026] In one embodiment, the air gap magnetic flux distribution is altered by changing the gap of the deformable magnetic barrier structure of the motor, including: When the dynamic load rate is ≥50%, the magnetic barrier gap of the deformable magnetic barrier structure is reduced to improve the magnetic coupling strength. When the dynamic load rate is <50%, the magnetic barrier gap of the deformable magnetic barrier structure is increased to reduce the no-load loss.

[0027] In this embodiment, the deformable magnetic barrier structure achieves precise adaptation of the air gap magnetic flux distribution by dynamically adjusting the magnetic barrier gap, with the core focusing on a differentiated adjustment strategy based on dynamic load rate. It is known that this structure consists of elastic magnetic barrier plates, an electromagnetic drive assembly, and a displacement detection unit. The elastic magnetic barrier plates are uniformly embedded in the iron core along the rotor circumference, with one end fixed and the other end connected to the drive assembly. The gap size can be changed through extension and retraction, forming a closed-loop adjustment in conjunction with the detection unit.

[0028] When the dynamic load rate identified by the extended Kalman filter algorithm is ≥50%, the motor is under medium to heavy load conditions. In this case, it is necessary to prioritize ensuring torque output capability. Controlling the electromagnetic drive component to output positive driving force compresses the elastic magnetic barrier plates, reducing the magnetic barrier gap, thereby reducing the equivalent magnetic reluctance of the rotor magnetic circuit and significantly improving the air gap magnetic coupling strength. This ensures that the motor can still efficiently transmit electromagnetic torque under heavy load or high wind resistance interference, avoiding a surge in copper losses due to insufficient magnetic flux.

[0029] When the dynamic load rate is less than 50%, the motor is under light load or no-load conditions, and redundant magnetic flux will cause serious iron loss. At this time, the control electromagnetic drive component is de-energized, and the elastic magnetic barrier plate naturally extends under its own elastic restoring force, which increases the magnetic barrier gap. The equivalent magnetic reluctance of the rotor magnetic circuit is increased accordingly, and the air gap magnetic flux density is precisely weakened, suppressing the waste of iron loss under no-load conditions from the source of the magnetic circuit.

[0030] During the adjustment process, the displacement detection unit collects the actual value of the magnetic barrier gap in real time and compares it with the target gap value for the corresponding working condition. If the deviation exceeds the allowable range, the electromagnetic drive component is used for fine-tuning and calibration to ensure that the magnetic flux distribution is accurately matched with the current load state, which not only meets the power requirements under different working conditions, but also minimizes energy loss.

[0031] In one embodiment, the method further includes: The motor's heat dissipation efficiency is regulated by coordinating winding temperature and equivalent wind resistance loss. This includes: using a linked structure of semiconductor cooling chip and passive heat dissipation fins. When the winding temperature is ≥75℃ and the wind resistance loss is ≥60% of the rated loss, the semiconductor cooling chip starts and maximizes the cooling power; when the winding temperature is <60℃ and the wind resistance loss is <30% of the rated loss, the semiconductor cooling chip stops working and only the fins retain natural heat dissipation.

[0032] In this embodiment, addressing the shortcomings of traditional cooling systems—namely, "fixed power operation and significant energy waste"—dynamic adaptation of cooling efficiency is achieved through the coordinated determination of winding temperature and equivalent wind resistance loss. This ensures motor thermal safety while avoiding ineffective energy consumption by the cooling system. This step relies on a linked cooling structure of a thermoelectric cooler and passive heat sinks. The passive heat sinks, as the basic cooling unit, are always in contact with the motor housing, achieving basic heat dissipation through heat conduction and natural convection. The thermoelectric cooler, as the active cooling unit, is attached to the outside of the heat sinks and can be started, stopped, and its cooling power adjusted according to operating conditions, forming a highly efficient cooling mode with passive cooling as the foundation and active cooling intervening on demand. The heat dissipation control logic uses the winding temperature collected in step S1 and the identified equivalent wind resistance loss as dual criteria to construct a differentiated heat dissipation strategy: When the winding temperature is ≥75℃ and the equivalent wind resistance loss is ≥60% of the rated loss, the motor is determined to be in a high load + strong interference condition. At this time, the motor's copper loss, iron loss and wind resistance loss are superimposed, and the heat generation rate is fast. If only passive heat dissipation is relied upon, it will easily lead to overheating and aging of the winding, and even affect the insulation performance. Therefore, the semiconductor cooling chip is immediately activated and switched to the maximum cooling power. Through the forced heat exchange effect of the semiconductor cooling chip, the heat of the motor shell and winding is quickly transferred to the heat dissipation fins. Then, the heat dissipation area is expanded by the fins to accelerate the heat dissipation and ensure that the winding temperature drops to the safe range quickly. When the winding temperature is <60℃ and the equivalent wind resistance loss is <30% of the rated loss, the motor is determined to be in a low load + weak interference condition. At this point, the motor loss is low and the heat generation is minimal. The natural heat dissipation capacity of the passive heat sink fins is sufficient to meet the heat dissipation requirements. If the thermoelectric cooler continues to operate, it will cause additional energy consumption. Therefore, the thermoelectric cooler is stopped, and only the passive heat sink fins are retained for natural heat dissipation to minimize heat dissipation energy consumption. Furthermore, in the transition range between the two extreme operating conditions mentioned above (such as winding temperature of 60-75℃ or equivalent wind resistance loss of 30%-60%), the thermoelectric cooler will dynamically adjust the cooling power based on the specific values ​​of winding temperature and equivalent wind resistance loss using pulse width modulation technology. This ensures a precise match between heat dissipation efficiency and heat generation rate, avoiding overheating risks while preventing energy waste. This step, through a logic of dual-parameter collaborative judgment, active / passive heat dissipation linkage, and dynamic power adjustment, forms a closed-loop control of loss, temperature, and heat dissipation. Combined with the previously mentioned magnetic circuit adjustment and current optimization steps, this constructs a comprehensive energy consumption optimization system.

[0033] In one embodiment, the dynamic load rate and equivalent wind resistance loss are identified in real time using an extended Kalman filter algorithm, including: The state equations of the extended Kalman filter are established based on the electromagnetic torque equation and the mechanical motion equation of the motor. The observation equations are established based on the inherent mapping relationship between the stator current, rotor mechanical angular velocity and state variables. Among them, the dynamic load rate and equivalent wind resistance loss are used as state variables, and the real-time acquisition values ​​of the stator three-phase current and rotor mechanical angular velocity are used as observation variables. Initialize the state variables and covariance matrix. The initial value of the dynamic load rate is set to 50% of the rated load rate of the motor, the initial value of the equivalent wind resistance loss is set to 30% of the rated loss of the motor, and the initial value of the covariance matrix is ​​set to a diagonal matrix. State prediction and update are performed every 5-10ms: the prior estimate of the state variable is predicted based on the electromagnetic torque equation of the motor and the mechanical motion equation, the Kalman gain is calculated by combining the actual collected value of the observed variable, the prior estimate is corrected by the Kalman gain to obtain the posterior estimate, and the covariance matrix is ​​updated synchronously. Set constraint thresholds for the identification results. The identification results of dynamic load rate are limited to the range of 0-120%, and the identification results of equivalent wind resistance loss are limited to the range of 0-150% of rated loss. If the threshold is exceeded, the nearest threshold is used for replacement.

[0034] In this embodiment, the core variables of the equation are defined first: the dynamic load rate (characterizing the current load intensity of the motor) and the equivalent wind resistance loss (quantifying the interference of environmental wind resistance on the motor energy consumption) are used as state variables, which directly determine the operating condition of the motor; the real-time collected values ​​of the stator three-phase current and the rotor mechanical angular velocity are used as observation variables. These parameters can be directly obtained by the motor's built-in sensors and can intuitively reflect the electromagnetic and mechanical operating status of the motor.

[0035] The state equations are constructed based on the electromagnetic torque equation and the mechanical motion equation of the motor. The electromagnetic torque equation describes the relationship between the motor's output torque and current components, while the mechanical motion equation reveals the laws governing torque balance and speed changes. Combining the influence of dynamic load rate and equivalent wind resistance loss on torque balance, a dynamic model of state variables changing over time is established to ensure that the state equations accurately characterize the dynamic evolution of load and wind resistance loss. The observation equations are constructed based on the inherent mapping relationship between the motor's stator current, rotor mechanical angular velocity, and state variables. Changes in dynamic load rate directly lead to fluctuations in stator current and speed, and increases or decreases in equivalent wind resistance loss are indirectly reflected through speed changes. This mapping relationship establishes a mathematical connection between the observed variables and state variables, providing data support for subsequent state estimation.

[0036] The core objective of the initialization phase is to provide a reasonable initial input for the filtering algorithm, ensuring the convergence and initial accuracy of the identification process. Targeted initial parameters are set based on the operational characteristics of agricultural plant protection motors: the initial value of the dynamic load rate is set to 50% of the motor's rated load rate. This value closely matches the common load conditions at the beginning of plant protection operations (starting with the pesticide tank at half or full load), avoiding identification lag caused by excessive deviation between the initial value and the actual operating conditions; the initial value of the equivalent wind resistance loss is set to 30% of the motor's rated loss. Considering that wind resistance interference is usually at a moderate level in the initial stage of outdoor operations, this initial value can balance the estimation deviation of wind resistance loss.

[0037] The initial value of the covariance matrix is ​​set as a diagonal matrix. The setting of the diagonal matrix means that the initial uncertainties of the two state variables are independent by default, which is consistent with the actual situation that the dynamic load rate and the equivalent wind resistance loss have no obvious coupling interference in the initial stage. At the same time, the value of the diagonal element needs to take into account the reasonable quantification of the initial uncertainty. It should neither exaggerate the initial error, which would lead to slow algorithm convergence, nor reduce the error, which would lead to insufficient initial estimation accuracy. This provides a reliable uncertainty quantification basis for subsequent iterations and updates.

[0038] Accurate estimation of state variables is achieved through periodic iteration, with the iteration period set at 5-10ms. This range satisfies the dynamic response requirements of agricultural plant protection motor load and wind resistance loss (avoiding identification lag due to excessively long periods) while preventing excessively short periods from increasing the hardware computational burden. The iteration process consists of two key stages: prediction and update. The prediction phase is based on the electromagnetic torque equation and mechanical motion equation of the motor, combined with the state estimation results obtained from the previous iteration cycle, to calculate the prior estimates of the state variables for the current cycle through the state equations. Essentially, this process predicts the changing trends of dynamic load rate and equivalent wind resistance loss based on the physical laws of motor operation, providing an initial reference for subsequent corrections.

[0039] The update phase is crucial for improving estimation accuracy. First, the Kalman gain is calculated by combining the actual collected values ​​of the observed variables with the predicted observed values ​​corresponding to the prior estimates. The core function of the Kalman gain is to balance the prediction accuracy of the state equation with the measurement accuracy of the observed variables. When the measurement accuracy of the observed variables is high, the Kalman gain is larger, relying more on the observation data for correction; when the prediction accuracy of the state equation is high, the Kalman gain is smaller, preserving more of the prediction results. Subsequently, the prior estimates are corrected using the Kalman gain to obtain a more accurate posterior estimate. This value integrates the predicted information of the motor's physical laws with the measured information from the sensors, effectively suppressing the influence of sensor noise and environmental interference. Simultaneously, the covariance matrix is ​​updated, and the uncertainty quantification parameter is adjusted based on the estimation error of the current iteration, providing an accurate error reference for the prediction and update in the next cycle, forming a closed-loop iterative logic of prediction-update-repreneurial.

[0040] Next, based on the actual operating characteristics of agricultural plant protection motors, reasonable constraint thresholds were set: the identification result of dynamic load rate was limited to the range of 0-120%, with 0% corresponding to the motor's no-load state and 120% covering the extreme heavy-load condition of the pesticide tank being fully loaded and strong wind resistance superimposed. This not only covers all normal operating scenarios but also filters out abnormal values ​​exceeding the range caused by sensor failure or sudden interference. The identification result of equivalent wind resistance loss was limited to the range of 0-150% of the rated loss, with 0% corresponding to a windless environment and 150% covering the extreme scenario of strong gust interference, which is consistent with the wind resistance variation range of outdoor plant protection operations.

[0041] When the identification result exceeds the above constraint thresholds, a nearest threshold replacement strategy is adopted. Specifically, when the dynamic load rate identification value is less than 0%, 0% is taken as the valid output; when it is greater than 120%, 120% is taken as the valid output. Similarly, when the equivalent wind resistance loss identification value is less than 0%, 0% is taken as the valid output; when it is greater than 150% of the rated loss, 150% of the rated loss is taken as the valid output. This processing method ensures that the output identification result always conforms to the actual operating conditions of the motor, preventing abnormal values ​​from entering subsequent magnetic circuit adjustment, current control, and heat dissipation regulation stages, thus guaranteeing the stability and reliability of the entire energy consumption optimization control method.

[0042] In one embodiment, an electromagnetic adjustment component is configured in the rotor magnetic circuit. The electromagnetic adjustment component includes an excitation coil and a magnetic reluctance detection unit. The equivalent magnetic reluctance of the rotor magnetic circuit is adjusted by changing the magnitude of the current flowing through the excitation coil. The equivalent magnetic reluctance of the rotor magnetic circuit is adaptively adjusted based on the identification results, including: Adjustment is aided by dynamic load rate combined with equivalent wind resistance loss: When the dynamic load rate is ≥50%, increase the excitation coil current, reduce the equivalent magnetic reluctance of the rotor magnetic circuit, and increase the air gap magnetic flux intensity to ensure torque output; when the equivalent wind resistance loss is ≥40% of the rated loss, further increase the excitation coil current to compensate for the power attenuation caused by wind resistance loss. When the dynamic load rate is less than 50%, reduce the excitation coil current, increase the equivalent magnetic reluctance of the rotor magnetic circuit, and weaken the redundant magnetic flux to reduce iron loss. The actual equivalent magnetic reluctance value of the rotor magnetic circuit is collected in real time by the magnetic reluctance detection unit and compared with the target magnetic reluctance range corresponding to the current operating condition. The excitation coil current is then finely adjusted to ensure that the magnetic reluctance is accurately matched with the operating condition.

[0043] In this embodiment, an electromagnetic adjustment component is integrated on the critical magnetic flux path of the rotor magnetic circuit. This component consists of an excitation coil and a magnetoresistive detection unit, which work together to achieve a closed-loop function of magnetoresistive adjustment and state feedback. The excitation coil is wound with high thermal conductivity and low loss electromagnetic wire and is uniformly arranged along the circumference of the rotor core. Its installation position corresponds to the dense magnetic flux area of ​​the rotor magnetic circuit, ensuring that the magnetic field generated after current is applied can efficiently act on the rotor magnetic circuit, achieving rapid adjustment of the magnetoresistive force. The magnetoresistive detection unit uses a high-precision magnetoresistive sensor, installed in close contact with the non-magnetic area of ​​the rotor core surface, to collect the actual equivalent magnetoresistive value of the rotor magnetic circuit in real time. This provides data feedback for the execution and correction of the adjustment strategy, avoiding magnetic flux adaptation failure caused by magnetoresistive adjustment deviations.

[0044] Next, based on the operating condition identification results, the dynamic adaptation of magnetic reluctance to operating conditions is achieved through differentiated current control. The core idea is to take dynamic load rate as the main factor and equivalent wind resistance loss as the auxiliary factor to ensure that the magnetic circuit characteristics are accurately matched with the motor operating requirements.

[0045] When the dynamic load rate identified by the extended Kalman filter algorithm is ≥50%, the motor is determined to be under medium-heavy load conditions. In this case, the motor needs to output sufficient torque to meet the power requirements of plant protection operations (such as spraying pesticides with a full tank or flying against the wind). For this condition, the core control strategy is to reduce the equivalent magnetic reluctance of the rotor magnetic circuit and increase the air gap magnetic flux density. First, the current flowing through the excitation coil is increased. The strong magnetic field generated by the current compresses the magnetic reluctance path of the rotor magnetic circuit, reducing losses during flux transmission and increasing the air gap magnetic coupling strength. This ensures the motor can output torque with high efficiency and avoids a surge in copper losses due to insufficient magnetic flux. If the identified equivalent wind resistance loss is ≥40% of the rated loss, the motor is determined to be under heavy load + strong wind resistance conditions. Wind resistance loss will cause a decrease in effective torque output. In this case, the current in the excitation coil needs to be further increased. A stronger magnetic field further reduces the magnetic reluctance and increases the air gap magnetic flux density, compensating for the power loss caused by wind resistance loss. This ensures the motor torque output meets the operational requirements and avoids decreased operational efficiency or additional energy consumption due to insufficient power.

[0046] When the dynamic load rate is less than 50%, the motor is considered to be under light load or no-load conditions (such as a medicine tank returning to base empty or low-speed flight in a light wind). In this condition, the motor torque demand is low, and excessive air gap flux will create redundant flux, leading to severe iron losses (including hysteresis and eddy current losses). For this condition, the core control strategy is to increase the equivalent reluctance of the rotor magnetic circuit and suppress redundant flux: by reducing the current flowing through the excitation coil, the effect of the magnetic field on the rotor magnetic circuit is weakened, increasing the equivalent reluctance of the magnetic circuit. The air gap flux density is precisely reduced, decreasing iron losses caused by redundant flux at the source of the magnetic circuit, thus optimizing energy consumption under light load conditions and avoiding the energy waste of traditional fixed magnetic circuit structures under light load.

[0047] Furthermore, through real-time feedback and dynamic fine-tuning, the equivalent magnetic reluctance of the rotor magnetic circuit is ensured to accurately match the target magnetic reluctance range under the current operating conditions, avoiding adjustment deviations caused by factors such as electromagnetic interference and component aging. The magnetic reluctance detection unit collects the actual equivalent magnetic reluctance value of the rotor magnetic circuit in real time according to a preset acquisition cycle (e.g., 2-3ms), and compares the collected actual value with the target magnetic reluctance range corresponding to the current operating conditions. The target magnetic reluctance range is preset based on the identification results of dynamic load rate and equivalent wind resistance loss (e.g., the target magnetic reluctance range is 500-800mH under medium-heavy load and low wind resistance, and 1200-1500mH under light load).

[0048] If the actual equivalent reluctance value deviates from the target range (e.g., the deviation exceeds ±5%), a current fine-tuning mechanism is activated: the current flowing through the excitation coil is dynamically adjusted using pulse width modulation (PWM) technology. If the actual reluctance is greater than the target range, it indicates that the current magnetic field strength is insufficient, and the current needs to be slightly increased; if the actual reluctance is less than the target range, it indicates that the current magnetic field strength is excessive, and the current needs to be slightly decreased. Through continuous acquisition-comparison-fine-tuning closed-loop control, the equivalent reluctance of the rotor magnetic circuit is ensured to remain stable within the target range, achieving precise adaptation of reluctance to operating conditions. This not only ensures the motor's operating performance under different operating conditions but also maximizes the energy consumption optimization effect of magnetic circuit regulation.

[0049] In one embodiment, based on the dynamic load rate and equivalent wind resistance loss, the maximum torque-to-current ratio control strategy is modified, and the direct-axis demagnetizing current and quadrature-axis drive current of the motor are dynamically allocated to minimize the sum of copper losses and iron losses of the motor under the current operating conditions, including: Establish a basic maximum torque-current ratio control model, preset the initial distribution ratio of direct-axis demagnetizing current and quadrature-axis drive current, and take the maximum torque output per unit current as the basic objective; A two-parameter correction model is constructed using dynamic load factor and equivalent wind resistance loss as correction factors: When the dynamic load rate is ≥50%, the equivalent wind resistance loss is <40% of the rated loss. Increase the proportion of quadrature axis drive current and maintain a low direct axis demagnetizing current. When the equivalent wind resistance loss is ≥40% of the rated loss, further increase the proportion of quadrature axis drive current and reduce the direct axis demagnetizing current to compensate for wind resistance loss. When the dynamic load rate is less than 50%, reduce the proportion of quadrature axis drive current, increase direct axis demagnetizing current, and weaken air gap flux to reduce redundant losses. With the goal of minimizing the sum of copper loss and iron loss, the current loss is calculated by combining the real-time collected stator three-phase current and winding temperature. The amplitude of the direct-axis demagnetizing current and quadrature-axis driving current is finely adjusted by the gradient descent method until the loss reaches the optimal value under the current operating conditions. The direct-axis demagnetizing current is limited to 0-30% of the rated current, and the quadrature-axis drive current is limited to 0-100% of the rated current. The current command is converted into a drive signal through space vector modulation technology.

[0050] In this embodiment, a basic MTPA control model is first constructed. Its core design goal is to output maximum torque per unit current. That is, by reasonably presetting the initial distribution ratio of the direct axis (d-axis) demagnetizing current and the quadrature axis (q-axis) driving current, it is ensured that the motor can output maximum torque with the lowest current consumption under rated operating conditions, providing stable basic power support for plant protection operations.

[0051] The initial allocation ratio is set based on the motor's electromagnetic characteristic parameters (such as permanent magnet flux linkage and stator inductance) and is derived through the motor's electromagnetic torque equation. Typically, under rated operating conditions, the direct-axis demagnetizing current is set to a low level (e.g., 5-10% of the rated current), while the quadrature-axis drive current is set to the main power output component (e.g., 80-90% of the rated current). This avoids the risk of permanent magnet demagnetization due to excessive direct-axis demagnetizing current, while also efficiently converting electromagnetic torque through the quadrature-axis drive current, ensuring the motor's basic operating efficiency and providing a reliable benchmark for subsequent dynamic adjustments based on operating conditions.

[0052] Next, based on the operating condition identification results, the current distribution ratio is accurately adapted to the real-time operating conditions through the coordinated correction of dynamic load rate and equivalent wind resistance loss. A two-parameter correction model is constructed, with dynamic load rate as the primary parameter and equivalent wind resistance loss as the secondary parameter, to formulate differentiated current distribution strategies for different operating conditions. When the dynamic load rate identified by the extended Kalman filter algorithm is ≥50%, the motor is determined to be in a medium-to-heavy load condition (such as a fully loaded medicine tank spraying or flying against the wind). In this case, priority should be given to ensuring torque output to meet operational requirements. If the equivalent wind resistance loss identified is <40% of the rated loss, it indicates that the operating condition is mainly a pure load with relatively small wind resistance interference. The control strategy focuses on efficient torque output by increasing the proportion of quadrature shaft drive current (e.g., increasing it to 90-95% of the rated current) and maintaining a low level of direct shaft demagnetizing current (e.g., keeping it at 5-10%), while ensuring torque output. While ensuring sufficient torque, reduce copper losses. If the equivalent wind resistance loss is ≥ 40% of the rated loss, it indicates that the working condition is heavy load + strong wind resistance superposition. Wind resistance loss leads to the reduction of effective torque output. At this time, it is necessary to further increase the proportion of quadrature shaft drive current (such as increasing it to 95-100% of the rated current) and appropriately reduce the direct shaft demagnetizing current (such as reducing it to 3-8%). By enhancing the torque contribution of quadrature shaft drive current and weakening the suppression effect of direct shaft demagnetizing current on magnetic flux, the power reduction caused by wind resistance loss can be compensated, and the motor overload operation or reduced operating efficiency due to insufficient torque can be avoided.

[0053] When the dynamic load rate is less than 50%, the motor is considered to be under light load or no-load conditions (such as a medicine tank returning to base without load or low-speed flight in a light wind). At this time, the motor torque demand is low, and excessive air gap flux will cause serious redundant losses (mainly iron losses). The control strategy focuses on energy consumption optimization. By reducing the proportion of quadrature axis drive current (e.g., reducing it to 30-50% of the rated current) and increasing the direct axis demagnetizing current (e.g., increasing it to 20-30% of the rated current), the demagnetizing effect of the direct axis demagnetizing current is used to weaken the air gap flux density, suppressing hysteresis losses and eddy current losses from the source. At the same time, it reduces the copper loss redundancy caused by the quadrature axis drive current, thereby minimizing the total loss under light load conditions.

[0054] Next, based on the current ratio corrected by the two parameters, the current amplitude is further refined to ensure that the sum of copper loss and iron loss reaches the optimal value under the current operating conditions. First, the basis for loss calculation is clarified: combining real-time collected stator three-phase current and winding temperature, the current copper loss and iron loss are calculated using a motor loss model. Copper loss is derived based on the proportional relationship between winding resistance and the square of the current (winding resistance is corrected in real-time with temperature), while iron loss is calculated based on the correlation between air gap magnetic flux density, motor speed, and core material properties, ensuring the real-time nature and accuracy of loss calculation.

[0055] With the goal of minimizing the sum of copper loss and iron loss, a gradient descent method is introduced to iteratively fine-tune the amplitudes of the direct-axis demagnetizing current and the quadrature-axis driving current. By calculating the gradient direction of the loss function with respect to each current component, the adjustment trend of the current amplitude is determined (if the gradient is positive, it means that the current increase leads to an increase in loss, and the current needs to be reduced; if the gradient is negative, it means that the current increase leads to a decrease in loss, and the current can be appropriately increased). The current amplitude is gradually adjusted according to a preset step size (such as 0.5-1% of the rated current). After each adjustment, the loss is recalculated until the sum of losses drops to the minimum value under the current operating conditions. This forms a closed-loop optimization logic of proportional correction - amplitude fine-tuning - loss feedback, avoiding the problem of incomplete loss optimization caused by relying solely on proportional correction.

[0056] Finally, through current threshold constraints and signal conversion, the optimized current command is translated into a drive signal that the motor can execute. First, strict current safety constraints are set: the amplitude range of the direct-axis demagnetizing current is limited to 0-30% of the motor's rated current, which satisfies the demagnetizing requirements under light load conditions while avoiding excessive current that could lead to irreversible demagnetization of the permanent magnets; the amplitude range of the quadrature-axis drive current is limited to 0-100% of the motor's rated current to ensure that the current does not exceed the carrying capacity of the motor windings and to avoid overheating, insulation aging, or burnout of the windings due to overcurrent.

[0057] After optimizing and constraining the current amplitude, the direct-axis and quadrature-axis current commands are converted into motor drive signals using Space Vector Pulse Width Modulation (SVPWM) technology. The optimized d / q-axis current components are transformed into current commands in a three-phase stationary coordinate system through coordinate transformation. Then, a pulse width modulation signal is generated using the SVPWM algorithm to drive the power switching devices of the motor inverter, achieving precise current output. This technology effectively reduces current harmonic distortion and harmonic losses, while improving the dynamic response speed of current control. It ensures that the optimized current command can be applied to the motor quickly and accurately, enabling the motor to achieve both optimal power performance and energy consumption under different operating conditions.

[0058] In the above embodiments, this application incorporates some existing algorithms and technical features for explanation and description to make the specification more detailed, clear, and complete, thus complying with the provisions of the Patent Law. However, this is not achieved by using a series of complex steps and algorithmic formulas, nor by complicating the technical solution, nor by combining or stacking conventional or simple features. The existing algorithms and technical features listed are for the purpose of disclosing the specific implementation methods of each step of this application (not to limit this application) and to avoid situations where this application cannot be implemented.

[0059] Reference Figure 2 In another embodiment of the present invention, an energy consumption optimization and control device for an agricultural plant protection motor is also provided, comprising: The data acquisition unit is used to collect real-time operating parameters of agricultural plant protection motors, parameters of plant protection operation scenarios, and load characteristic data; and to identify dynamic load rate and equivalent wind resistance loss in real time through an extended Kalman filter algorithm. The adjustment unit is used to adaptively adjust the equivalent magnetic reluctance of the rotor magnetic circuit according to the identification results, and change the air gap magnetic flux distribution by changing the gap of the deformable magnetic barrier structure of the motor. The control unit is used to modify the maximum torque-current ratio control strategy based on the dynamic load rate and equivalent wind resistance loss, and dynamically allocate the direct-axis demagnetizing current and quadrature-axis drive current of the motor to minimize the sum of copper loss and iron loss of the motor under the current operating conditions. In this embodiment, the specific implementation of each unit in the above device embodiment is described in the above method embodiment, and will not be repeated here.

[0060] Reference Figure 3 This invention also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0061] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0062] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0063] In summary, the energy consumption optimization control method and device for agricultural plant protection motors provided in this embodiment of the invention includes: real-time acquisition of operating parameters of the agricultural plant protection motor, parameters of plant protection operation scenarios, and load characteristic data; real-time identification of dynamic load rate and equivalent wind resistance loss through an extended Kalman filter algorithm; adaptive adjustment of the equivalent magnetic reluctance of the rotor magnetic circuit based on the identification results, and changing the air gap magnetic flux distribution through the gap change of the deformable magnetic barrier structure of the motor; based on the dynamic load rate and equivalent wind resistance loss, correction of the maximum torque-current ratio control strategy, and dynamic allocation of the direct-axis demagnetizing current and quadrature-axis driving current of the motor, so as to minimize the sum of copper loss and iron loss of the motor under the current operating conditions. In this invention, the operating parameters of agricultural plant protection motors, parameters of plant protection operation scenarios, and load characteristic data are collected in real time; the dynamic load rate and equivalent wind resistance loss are identified in real time through an extended Kalman filter algorithm, and the coupling relationship between the dynamic load rate and equivalent wind resistance loss is accurately identified; based on the dynamic load rate and equivalent wind resistance loss, the maximum torque-current ratio control strategy is modified, and the direct-axis demagnetizing current and quadrature-axis drive current of the motor are dynamically allocated to improve the energy efficiency of the plant protection motor and extend the operating range.

[0064] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0065] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0066] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for optimizing and controlling the energy consumption of an agricultural plant protection motor, characterized in that, Includes the following steps: Real-time collection of agricultural plant protection motor operating parameters, plant protection operation scenario parameters, and load characteristic data; Dynamic load rate and equivalent wind resistance loss are identified in real time using the extended Kalman filter algorithm. The equivalent magnetic reluctance of the rotor magnetic circuit is adaptively adjusted based on the identification results, and the air gap magnetic flux distribution is changed by the gap change of the deformable magnetic barrier structure of the motor. Based on the dynamic load rate and equivalent wind resistance loss, the maximum torque-current ratio control strategy is modified, and the direct-axis demagnetizing current and quadrature-axis drive current of the motor are dynamically allocated to minimize the sum of copper loss and iron loss of the motor under the current operating conditions.

2. The energy consumption optimization and control method for agricultural plant protection motors according to claim 1, characterized in that, The operating parameters include stator three-phase current, rotor mechanical angular velocity, and winding temperature; The parameters for the operational scenario include ambient wind speed and spray flow rate; The load characteristic data includes the remaining amount of medicine in the medicine box and the tilt angle of the machine body.

3. The energy consumption optimization and control method for agricultural plant protection motors according to claim 1, characterized in that, The air gap magnetic flux distribution is altered by changing the gap of the deformable magnetic barrier structure of the motor, including: When the dynamic load rate is ≥50%, the magnetic barrier gap of the deformable magnetic barrier structure is reduced to improve the magnetic coupling strength. When the dynamic load rate is <50%, the magnetic barrier gap of the deformable magnetic barrier structure is increased to reduce the no-load loss.

4. The energy consumption optimization and control method for agricultural plant protection motors according to claim 2, characterized in that, The method further includes: The motor's heat dissipation efficiency is regulated by coordinating winding temperature and equivalent wind resistance loss. This includes: using a linked structure of semiconductor cooling chip and passive heat dissipation fins. When the winding temperature is ≥75℃ and the wind resistance loss is ≥60% of the rated loss, the semiconductor cooling chip starts and maximizes the cooling power; when the winding temperature is <60℃ and the wind resistance loss is <30% of the rated loss, the semiconductor cooling chip stops working and only the fins retain natural heat dissipation.

5. The energy consumption optimization and control method for agricultural plant protection motors according to claim 2, characterized in that, The extended Kalman filter algorithm is used to identify dynamic load factor and equivalent wind resistance loss in real time, including: The state equations of the extended Kalman filter are established based on the electromagnetic torque equation and the mechanical motion equation of the motor. The observation equations are established based on the inherent mapping relationship between the stator current, rotor mechanical angular velocity and state variables. Among them, the dynamic load rate and equivalent wind resistance loss are used as state variables, and the real-time acquisition values ​​of the stator three-phase current and rotor mechanical angular velocity are used as observation variables. Initialize the state variables and covariance matrix. The initial value of the dynamic load rate is set to 50% of the rated load rate of the motor, the initial value of the equivalent wind resistance loss is set to 30% of the rated loss of the motor, and the initial value of the covariance matrix is ​​set to a diagonal matrix. State prediction and update are performed every 5-10ms: the prior estimate of the state variable is predicted based on the electromagnetic torque equation of the motor and the mechanical motion equation, the Kalman gain is calculated by combining the actual collected value of the observed variable, the prior estimate is corrected by the Kalman gain to obtain the posterior estimate, and the covariance matrix is ​​updated synchronously. Set constraint thresholds for the identification results. The identification results of dynamic load rate are limited to the range of 0-120%, and the identification results of equivalent wind resistance loss are limited to the range of 0-150% of rated loss. If the threshold is exceeded, the nearest threshold is used for replacement.

6. The energy consumption optimization and control method for agricultural plant protection motors according to claim 1, characterized in that, An electromagnetic adjustment component is configured in the rotor magnetic circuit. The electromagnetic adjustment component includes an excitation coil and a magnetic reluctance detection unit. The equivalent magnetic reluctance of the rotor magnetic circuit is adjusted by changing the magnitude of the current flowing through the excitation coil. The equivalent magnetic reluctance of the rotor magnetic circuit is adaptively adjusted based on the identification results, including: Adjustment is aided by dynamic load rate combined with equivalent wind resistance loss: When the dynamic load rate is ≥50%, increase the excitation coil current, reduce the equivalent magnetic reluctance of the rotor magnetic circuit, and increase the air gap magnetic flux intensity to ensure torque output; when the equivalent wind resistance loss is ≥40% of the rated loss, further increase the excitation coil current to compensate for the power attenuation caused by wind resistance loss. When the dynamic load rate is less than 50%, reduce the excitation coil current, increase the equivalent magnetic reluctance of the rotor magnetic circuit, and weaken the redundant magnetic flux to reduce iron loss. The actual equivalent magnetic reluctance value of the rotor magnetic circuit is collected in real time by the magnetic reluctance detection unit and compared with the target magnetic reluctance range corresponding to the current operating condition. The excitation coil current is then finely adjusted to ensure that the magnetic reluctance is accurately matched with the operating condition.

7. The energy consumption optimization and control method for agricultural plant protection motors according to claim 2, characterized in that, Based on the dynamic load rate and equivalent wind resistance loss, the maximum torque-to-current ratio control strategy is modified, and the direct-axis demagnetizing current and quadrature-axis drive current of the motor are dynamically allocated to minimize the sum of copper losses and iron losses of the motor under the current operating conditions, including: Establish a basic maximum torque-current ratio control model, preset the initial distribution ratio of direct-axis demagnetizing current and quadrature-axis drive current, and take the maximum torque output per unit current as the basic objective; A two-parameter correction model is constructed using dynamic load factor and equivalent wind resistance loss as correction factors: When the dynamic load rate is ≥50%, the equivalent wind resistance loss is <40% of the rated loss. Increase the proportion of quadrature axis drive current and maintain a low direct axis demagnetizing current. When the equivalent wind resistance loss is ≥40% of the rated loss, further increase the proportion of quadrature axis drive current and reduce the direct axis demagnetizing current to compensate for wind resistance loss. When the dynamic load rate is less than 50%, reduce the proportion of quadrature axis drive current, increase direct axis demagnetizing current, and weaken air gap flux to reduce redundant losses. With the goal of minimizing the sum of copper loss and iron loss, the current loss is calculated by combining the real-time collected stator three-phase current and winding temperature. The amplitude of the direct-axis demagnetizing current and quadrature-axis driving current is finely adjusted by the gradient descent method until the loss reaches the optimal value under the current operating conditions. The direct-axis demagnetizing current is limited to 0-30% of the rated current, and the quadrature-axis drive current is limited to 0-100% of the rated current. The current command is converted into a drive signal through space vector modulation technology.

8. An energy consumption optimization and control device for an agricultural plant protection motor, characterized in that, include: The data acquisition unit is used to collect real-time operating parameters of agricultural plant protection motors, parameters of plant protection operation scenarios, and load characteristic data. Dynamic load rate and equivalent wind resistance loss are identified in real time using the extended Kalman filter algorithm. The adjustment unit is used to adaptively adjust the equivalent magnetic reluctance of the rotor magnetic circuit according to the identification results, and change the air gap magnetic flux distribution by changing the gap of the deformable magnetic barrier structure of the motor. The control unit is used to modify the maximum torque-current ratio control strategy based on the dynamic load rate and equivalent wind resistance loss, and dynamically allocate the direct-axis demagnetizing current and quadrature-axis drive current of the motor to minimize the sum of copper loss and iron loss of the motor under the current operating conditions.

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