Charging type brushless electric grass trimmer control method based on intelligent speed regulation control

Abstract

The invention provides a charging type brushless electric grass trimmer control method based on intelligent speed regulation control, and relates to the technical field of motor control. According to the method, operation parameters such as vibration, torque, rotating speed and temperature are collected in real time through a multi-dimensional sensor, and then a dynamic operation intensity index DOII and an energy distribution efficiency coefficient EDEC are calculated. Based on the two indexes, a basic speed regulation instruction is generated by using a fuzzy logic controller, and accurate control is realized through a double-closed-loop speed regulation system, so that efficient and stable operation of the motor is ensured. When the electric quantity of the battery is insufficient, a gradient speed reduction protection mode is adopted, the operation time is effectively prolonged, and the battery health is protected. In addition, the method also has a self-learning capability, and can continuously optimize a control strategy according to historical operation data. The operation efficiency and the energy utilization rate of the electric grass trimmer are improved, the intelligent level and the user friendliness of the electric grass trimmer are remarkably improved, and an innovative technical solution is brought to the field of gardening tools.

Description

Technical Field

[0001] The invention relates to the technical field of motor control, and in particular to a control method of a rechargeable brushless electric lawn mower based on intelligent speed control. Background Art

[0002] The current control technology of rechargeable brushless electric lawn mowers has many deficiencies in practical applications and is difficult to meet the needs of complex operating scenarios. First, the existing speed control method relies too much on a single sensor (such as speed or current monitoring) and lacks the fusion analysis of multi-dimensional parameters such as vibration, torque, and temperature. When faced with sudden changes in vegetation density, weeds entangled in the blade, or hard objects on the ground blocking it, the system cannot accurately identify the dynamic changes of the mechanical load, resulting in a lag in the speed control response. For example, when the blade suddenly gets stuck in a hard object and causes a sudden increase in torque, the traditional method may misjudge it as a change in vegetation hardness and continue to maintain high speed operation due to the lack of joint analysis of triaxial vibration data, causing the motor to overload or even burn out. On the other hand, the extensiveness of battery energy management limits the efficiency of the equipment. The existing technology lacks dynamic modeling of the energy conversion process, especially under conditions such as battery temperature rise and internal resistance changes. A real-time evaluation mechanism for energy distribution efficiency has not been established, resulting in a large amount of electric energy being ineffectively dissipated under high temperature or low power conditions, significantly shortening the battery cycle life, and the endurance fluctuates severely with changes in the use environment.

[0003] At the control algorithm level, the mainstream single closed-loop PID speed control scheme has principle defects. When the load fluctuates frequently or the operation intensity changes rapidly (such as switching from soft lawns to bushes), the cumulative effect of the integral term will cause output saturation, causing violent speed fluctuations or overshoot, affecting cutting uniformity. In practice, when the vegetation density changes by more than 30%, the time for the speed to recover to the target value often exceeds 2 seconds, which not only reduces the operation efficiency, but also easily leads to vegetation residue or blade idling wear due to inconsistent speed. In addition, the traditional PWM duty cycle adjustment relies on a fixed rule base and does not dynamically associate the remaining battery capacity (SOC) with the current operation mode. For example, in the low-battery stage, the existing technology mostly adopts a step-by-step forced speed reduction strategy. When the SOC is lower than 20%, the speed is directly limited to 50% of the baseline value, ignoring the remaining operation time and the impact of vegetation hardness differences on the energy consumption rate, which may cause the battery to run out before the key task is completed, or cause the operation to be interrupted due to insufficient power in high-hardness vegetation scenarios, forcing users to charge frequently.

[0004] The lack of adaptive capabilities in long-term use scenarios further exposes the shortcomings of the system. Most of the existing control parameters are factory-fixed configurations, lacking adaptive adjustments to dynamic factors such as blade wear, bearing aging, and seasonal temperature differences. As the equipment is used for longer, the decrease in blade sharpness will lead to an increase in torque demand under the same operating intensity, and the system fails to dynamically correct the load characteristic parameters through vibration spectrum analysis. Continuing to use the original control logic will accelerate motor loss. At the same time, the cutting resistance of different vegetation types (such as hard shrubs and soft weeds) varies by several times. Traditional methods have not established a self-learning mechanism for the operating mode and rely on manual switching of preset gears. The operation is cumbersome and it is easy to cause inefficiency or equipment overload due to misselection of gears.

[0005] The handling mechanism for abnormal working conditions also needs to be optimized. Existing technologies for detecting problems such as blade jamming and motor overheating mainly rely on single-parameter threshold triggering. For example, jamming is determined only by the current peak value, but the correlation characteristics between torque and vibration are ignored, and the detection delay often exceeds 200 milliseconds. During this period, the blade may have caused local high temperature due to continuous stalling, and even caused deformation of plastic parts. What's more serious is that when the vibration amplitude exceeds the safe range, most systems only trigger a warning sound but fail to link the speed reduction protection. In an emergency, the operator may cause equipment damage or safety hazards due to untimely response. In addition, the monitoring method of the battery health status is single, and the degree of aging is only judged by the voltage drop. It fails to combine multi-dimensional parameters such as temperature rise rate and internal resistance change to predict the remaining battery life, resulting in users being unable to predict the risk of battery failure in advance.

[0006] The combined effect of the above-mentioned defects has resulted in significant shortcomings in the existing electric lawn mowers in terms of energy efficiency, adaptability to working conditions and long-term reliability. There is an urgent need to achieve a systematic upgrade through multi-source data fusion, dynamic performance optimization and intelligent decision-making algorithms, so as to improve the operating quality and equipment durability in complex environments. Summary of the invention

[0007] In order to solve the technical problems in the prior art of mechanical load identification lag caused by insufficient multi-dimensional parameter fusion capability, poor endurance stability caused by lack of dynamic evaluation of energy conversion efficiency, speed regulation oscillation caused by integral saturation of single closed-loop control algorithm, mismatch between fixed speed reduction mode in low power stage and operating requirements, lack of adaptive capability caused by long-term operation parameter solidification and imperfect linkage handling mechanism for abnormal working conditions, the present invention provides a control method for a rechargeable brushless electric lawn mower based on intelligent speed control.

[0008] The technical solution provided by the present invention is as follows: The present invention provides a control method for a rechargeable brushless electric lawn mower based on intelligent speed control, comprising: S1. Real-time acquisition of operating parameters through a multi-dimensional sensor array, wherein the sensor array includes a triaxial vibration sensor, a torque sensor, a rotation speed sensor, a battery temperature sensor, and a motor winding temperature sensor; S2. Calculate the dynamic operation intensity index DOII and the energy distribution efficiency coefficient EDEC based on the sensor data, where DOII represents the mechanical load fluctuation characteristics of the current cutting operation, and EDEC represents the effectiveness of converting battery energy into mechanical energy; S3, generating a basic speed control command according to DOII and EDEC, and converting the speed control command into a PWM duty cycle adjustment signal through a fuzzy logic controller; S4, adopts dual closed-loop speed control, the outer loop adjusts the target current based on the speed deviation, and the inner loop adjusts the PWM output based on the current deviation; S5: Real-time monitoring of the remaining battery capacity SOC during the speed regulation process, and starting the gradient speed reduction protection mode when the SOC is lower than the preset threshold; S6. Establish an operation mode database through a self-learning algorithm and optimize the calculation weights of DOII and EDEC based on historical operation data; The gradient speed reduction protection mode includes a three-level speed reduction strategy, and the speed reduction amplitude of each level is nonlinearly related to the current speed and the remaining operation time.

[0009] The beneficial effects brought about by the technical solution provided by the present invention include at least: (1) In the present invention, by integrating multi-dimensional sensors to collect operating parameters in real time and calculating the dynamic operating intensity index DOII and the energy distribution efficiency coefficient EDEC, the problem that traditional electric lawn mowers cannot accurately control the motor speed in complex operating environments is solved. This technical means enables the lawn mower to intelligently adjust the output according to the real-time load and energy conversion efficiency, effectively avoiding overload and energy waste, and significantly improving operating efficiency and battery life; (2) In the present invention, a dual closed-loop speed control system is adopted, combined with an improved PID algorithm, to achieve precise control of the motor speed and current. The outer loop adjusts the target current based on the speed deviation, and the inner loop adjusts the PWM output based on the current deviation. This dual guarantee effectively improves the dynamic response speed and stability of the system. By suppressing the integral saturation phenomenon, the robustness of the system is further enhanced, solving the oscillation and loss of control problems that are prone to occur in traditional speed control systems under complex working conditions; (3) In the present invention, a self-learning algorithm and a gradient speed reduction protection mode are introduced. The control strategy is continuously optimized through the deep Q network DQN architecture, and the speed is intelligently adjusted according to the remaining battery capacity SOC, which effectively extends the battery life and operation time. At the same time, the gradient speed reduction protection mode is automatically activated when the battery power is low, and the speed is smoothly reduced through a three-level speed reduction strategy, avoiding operation interruption and equipment damage caused by sudden power failure, and significantly improving the reliability and user satisfaction of the electric lawn mower. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic flow chart of a control method for a rechargeable brushless electric lawn mower based on intelligent speed control provided by an embodiment of the present invention; Figure 2 A schematic diagram of a fuzzy logic controller for a control method of a rechargeable brushless electric lawn mower based on intelligent speed control provided by an embodiment of the present invention; Figure 3 A schematic diagram of a deep Q network architecture of a rechargeable brushless electric lawn mower control method based on intelligent speed control provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0013] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0014] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0015] In the embodiments of the present invention, sometimes the subscripts are as follows It may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0016] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0017] Reference Manual Attached Figure 1 , showing a flow chart of a control method for a rechargeable brushless electric lawn mower based on intelligent speed control provided by an embodiment of the present invention.

[0018] The embodiment of the present invention provides a control method for a rechargeable brushless electric lawn mower based on intelligent speed control, and the processing flow may include the following steps: S1. Real-time acquisition of operating parameters through a multi-dimensional sensor array, wherein the sensor array includes a triaxial vibration sensor, a torque sensor, a rotation speed sensor, a battery temperature sensor, and a motor winding temperature sensor; S2. Calculate the Dynamic Operating Intensity Index (DOII) and Energy Distribution Efficiency Coefficient (EDEC) based on sensor data, where DOII represents the mechanical load fluctuation characteristics of the current cutting operation, and EDEC represents the effectiveness of converting battery energy into mechanical energy; S3, generating a basic speed control command according to DOII and EDEC, and converting the speed control command into a PWM duty cycle adjustment signal through a fuzzy logic controller; S4, adopts dual closed-loop speed control, the outer loop adjusts the target current based on the speed deviation, and the inner loop adjusts the PWM output based on the current deviation; S5: Real-time monitoring of the remaining battery capacity (State of Charge, SOC) during the speed regulation process, and starting the gradient speed reduction protection mode when the SOC is lower than the preset threshold; S6. Establish an operation mode database through a self-learning algorithm and optimize the calculation weights of DOII and EDEC based on historical operation data; The gradient speed reduction protection mode includes a three-level speed reduction strategy, and the speed reduction amplitude of each level is nonlinearly related to the current speed and the remaining operation time.

[0019] In a possible implementation manner, the dual closed-loop speed control in step S4 includes: The outer loop adopts the improved PID (Proportional-Integral-Derivative) control algorithm, and its control law is: ; in, To control the output, is the proportionality coefficient, ranging from 0.8 to 1.2. is the integral coefficient, ranging from 0.05 to 0.15. is the differential coefficient, ranging from 0.3 to 0.7, is the anti-saturation factor, with a value of 0.1-0.3, The improved PID algorithm improves the dynamic response speed by suppressing the integral saturation phenomenon.

[0020] In a possible implementation, the calculation formula of the dynamic work intensity index DOII in step S2 is: ; in, , , is the weighting coefficient, satisfying ' is the real-time torque measurement value, is the rated maximum torque, is the real-time speed, is the rated maximum speed, is the motor winding temperature, is the ambient temperature, To allow the maximum temperature rise, the weighting factor , , Dynamically adjust according to the operation mode, when the vegetation density is >50% Increase by 0.2, when blade wear > 30% Increase by 0.15.

[0021] In a possible implementation, step S2 includes: The calculation formula of energy distribution efficiency coefficient EDEC is: ; in, is the real-time voltage of the battery, is the real-time battery current, , is the internal resistance of the battery, is the battery temperature, , is the heat dissipation coefficient, with a value of 0.05-0.15 , , It is the rated parameter of the battery. When EDEC<0.7, the battery health warning is triggered.

[0022] In a possible implementation manner, the fuzzy logic controller in step S3 includes: like Figure 2 As shown, the fuzzy logic controller adopts a five-layer structure: First layer: Input fuzzification layer, mapping DOII and EDEC to 7 language variables The second layer: rule antecedent layer, including 49 IF-THEN rules The third layer: rule strength calculation layer, using algebraic product reasoning method The fourth layer: rule consequence layer, using the centroid method to defuzzify The fifth layer: output adjustment layer, which performs secondary correction on PWM duty cycle according to SOC value The rule base is automatically updated every 10 hours of work, and the best 20% of the rules are retained during the update.

[0023] In a possible implementation manner, the gradient speed reduction protection mode in step S5 includes: When SOC∈(20%,30%], the speed drops linearly to 80% of the base speed; When SOC∈(10%,20%], the speed drops to 60% of the base speed according to the quadratic curve; When SOC≤10%, the speed immediately drops to 40% of the base speed and remains constant; The reference speed is determined according to the current operating mode, and is increased by 15% in the hard vegetation mode and decreased by 10% in the soft vegetation mode.

[0024] In a possible implementation, the self-learning algorithm in step S6 includes: like Figure 3 As shown, the Deep Q-Network (DQN) architecture is adopted, including: Input layer: 14 neurons, corresponding to 7 sensor data and their first-order derivatives; Hidden layer: 3 fully connected layers, with 64, 32, and 16 neurons respectively; Output layer: 3 action nodes, corresponding to the weight adjustment direction and amplitude of DOII and EDEC; The experience replay buffer stores the most recent 1000 state-action-reward tuples; The reward function is designed as: R = 0.5·(1-speed deviation rate) + 0.3·energy efficiency ratio + 0.2·temperature safety factor.

[0025] In a possible implementation, the weighting coefficient , , The dynamic adjustment strategy is: ; in, is the percentage of vegetation density obtained through image recognition, is the blade wear degree, which is obtained through vibration spectrum analysis and takes values ​​from 0 to 1, where 0 represents a new blade and 1 represents complete wear. The above parameters are updated every 30 seconds and the adjustment step does not exceed 0.05.

[0026] In a possible implementation manner, the heat dissipation coefficient k includes: The calculation method is: ; in, is the battery surface area, is the convective heat transfer coefficient, ranging from 8 to 12. is the battery volume, is the battery material density kg / m³, is the specific heat capacity, is the radiation heat transfer correction term, , is the Stefan-Boltzmann constant, , is the surface emissivity, with a value of 0.8-0.9.

[0027] In a possible implementation, an abnormal condition handling strategy is also included: When the DOII mutation rate is detected to be >50% / s and lasts for 0.5s, it is determined that the blade is stuck and the power output is immediately cut off; When the EDEC decrease rate is >5% / s for 5 consecutive sampling cycles, the forced cooling mode is started; When the vibration amplitude exceeds 120% of the ISO 5349-1 standard limit, the speed is automatically reduced to a safe range; The sampling period is 10ms, and manual reset is required to resume operation after the abnormality is resolved.

[0028] In the embodiment provided by the present invention, the lawn mower body integrates a multi-dimensional sensor array: The three-axis vibration sensor (installed on the motor housing, sampling frequency 1kHz) monitors the X / Y / Z axial vibration acceleration in real time (range ±16g); The torque sensor (embedded in the transmission shaft, measuring range 0-10N·m, accuracy ±0.5%) collects real-time load torque; The speed sensor (Hall effect type, resolution 0.1rad / s) detects the angular velocity of the motor rotor; The battery temperature sensor (PT100 type, attached to the battery surface) and the motor winding temperature sensor (K-type thermocouple, embedded in the stator slot) monitor the temperature rise status respectively.

[0029] All sensor data are transmitted to the main control MCU (model STM32F407, built-in 12-bit ADC) via the CAN bus and synchronously sampled and preprocessed with a period of 10ms.

[0030] Vegetation density :The camera module (OV5640, 30fps) is used to collect images of the work area and the improved HSV color space segmentation algorithm is used to calculate the percentage of vegetation pixels. When >50%, α increases by 0.2 to strengthen the torque weighting.

[0031] Blade wear Perform FFT analysis on the vibration signal (window function Hanning, number of points 1024) to extract the energy proportion of the 2-4kHz frequency band. When the energy attenuation is >30%, it is judged =0.3, and increases linearly until complete wear.

[0032] Temperature rise compensation item: Set to 60℃, when >0.8 triggers overheat protection.

[0033] Battery internal resistance :Online calibration by pulse discharge method, the calculation formula is , updated every 5 minutes.

[0034] Thermal dissipation coefficient k Combined with the real-time calculation of battery physical parameters, the battery surface area A=0.012m², and the convection coefficient h=12W / m²·K when forced air cooling is used.

[0035] Fuzzy logic controller design, five-layer fuzzy controller implementation rules: Input fuzzification: DOII and EDEC are divided into seven fuzzy sets {NB, NM, NS, ZO, PS, PM, PB}, and the membership function adopts Gaussian type (σ=0.15).

[0036] Rule base construction: The example rule is “IF DOII=PB AND EDEC=NM THEN PWM_Δ=NS”. The initial rule base is generated based on expert experience.

[0037] Dynamic update mechanism: every 10 hours of operation, the worst 80% of rules are eliminated based on historical data (stored in FLASH), the best rules are retained and random mutations are introduced (mutation rate 5%).

[0038] Dual closed-loop speed regulation The outer loop adopts improved PID algorithm: Anti-saturation processing: When the integral term accumulates and exceeds the limit value, β=0.2 increases the weight of the differential term to prevent overshoot.

[0039] Parameter tuning: Preliminary setting by Ziegler-Nichols method =1.0, =0.1, =0.5, and then combined with the particle swarm optimization algorithm (PSO) for online fine-tuning.

[0040] Battery management and protection strategy, gradient speed reduction strategy: When the SOC drops to 28%, the first level of deceleration is activated; when the SOC is ≤ 10%, it switches to emergency mode: immediately decelerate to 40%. , and activate the buzzer alarm.

[0041] Reference speed : Dynamically adjusted according to the operation mode, in hard vegetation mode =1.15× , soft mode =0.9× ( =3000rpm).

[0042] Deep Q Network (DQN) Architecture: State space: Contains 14-dimensional feature vectors such as DOII, EDEC, SOC, and temperature gradient.

[0043] Action space: six weight adjustment actions {α+, α-, γ+, γ-, η+, η-}, with a step size of 0.05.

[0044] Reward function: The ε-greedy strategy is adopted during training (ε is initially 0.5 and decays by 1% per cycle), and the experience replay buffer prioritizes high-reward samples.

[0045] Blade stuck detection: When the three-axis vibration sensor detects Z-axis acceleration > 8g and DOII mutation rate > 50% / s within 0.5s, the MOSFET drive signal is immediately cut off and the fault indicator is lit. Forced cooling mode: If EDEC continues to drop, the backup cooling fan is started (speed increased to 12000rpm) and the maximum current is limited to 70% of the rated value.

[0046] Safety speed control: Referring to ISO 5349-1 standard, when the total hand vibration value is >2.5m / s², the speed will be automatically limited to below 2500rpm.

[0047] To cut high density shrubs ( =65%) as an example: The system detects >50%, automatically Adjusted from 0.4 to 0.6 to strengthen the torque influence factor.

[0048] Blade wear =0.4, Adjust to 0.34 to increase the speed change rate weight.

[0049] When the SOC drops to 25%, the secondary speed reduction strategy is triggered, and the speed drops to 60% of the baseline value according to the quadratic curve. At the same time, the DQN network updates the weight coefficient of EDEC.

[0050] When the blade gets stuck suddenly, the system cuts off the power within 0.1s to avoid motor stalling and damage.

[0051] Through the above-mentioned implementation mode, the present invention realizes adaptive speed regulation of the lawn mower under different working conditions, improves energy efficiency by 23% compared with the traditional method, extends battery life by 40%, and significantly reduces operator fatigue.

[0052] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: (1) In the present invention, by integrating multi-dimensional sensors to collect operating parameters in real time and calculating the dynamic operating intensity index DOII and the energy distribution efficiency coefficient EDEC, the problem that traditional electric lawn mowers cannot accurately control the motor speed in complex operating environments is solved. This technical means enables the lawn mower to intelligently adjust the output according to the real-time load and energy conversion efficiency, effectively avoiding overload and energy waste, and significantly improving operating efficiency and battery life; (2) In the present invention, a dual closed-loop speed control system is adopted, combined with an improved PID algorithm, to achieve precise control of the motor speed and current. The outer loop adjusts the target current based on the speed deviation, and the inner loop adjusts the PWM output based on the current deviation. This dual guarantee effectively improves the dynamic response speed and stability of the system. By suppressing the integral saturation phenomenon, the robustness of the system is further enhanced, solving the oscillation and loss of control problems that are prone to occur in traditional speed control systems under complex working conditions; (3) In the present invention, a self-learning algorithm and a gradient speed reduction protection mode are introduced. The control strategy is continuously optimized through the deep Q network DQN architecture, and the speed is intelligently adjusted according to the remaining battery capacity SOC, which effectively extends the battery life and operation time. At the same time, the gradient speed reduction protection mode is automatically activated when the battery power is low, and the speed is smoothly reduced through a three-level speed reduction strategy, avoiding operation interruption and equipment damage caused by sudden power failure, and significantly improving the reliability and user satisfaction of the electric lawn mower.

[0053] The above contents are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0054] There are a few points to note: (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0055] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0056] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0057] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A control method for a rechargeable brushless electric lawn mower based on intelligent speed control, characterized in that: include: S1. Real-time acquisition of operating parameters through a multi-dimensional sensor array, wherein the sensor array includes a triaxial vibration sensor, a torque sensor, a rotation speed sensor, a battery temperature sensor, and a motor winding temperature sensor; S2. Calculate the dynamic operation intensity index DOII and the energy distribution efficiency coefficient EDEC based on the sensor data, where DOII represents the mechanical load fluctuation characteristics of the current cutting operation, and EDEC represents the effectiveness of converting battery energy into mechanical energy; S3, generating a basic speed control command according to DOII and EDEC, and converting the speed control command into a PWM duty cycle adjustment signal through a fuzzy logic controller; S4, adopts dual closed-loop speed control, the outer loop adjusts the target current based on the speed deviation, and the inner loop adjusts the PWM output based on the current deviation; S5: Real-time monitoring of the remaining battery capacity SOC during the speed regulation process, and starting the gradient speed reduction protection mode when the SOC is lower than the preset threshold; S6. Establish an operation mode database through a self-learning algorithm and optimize the calculation weights of DOII and EDEC based on historical operation data; The gradient speed reduction protection mode includes a three-level speed reduction strategy, and the speed reduction amplitude of each level is nonlinearly related to the current speed and the remaining operation time.

2. A control method for a rechargeable brushless electric lawn mower based on intelligent speed control according to claim 1, characterized in that: The dual closed-loop speed control in step S4 includes: The outer loop adopts an improved PID control algorithm, and its control law is: ； in, To control the output, is the proportionality coefficient, ranging from 0.8 to 1.

2. is the integral coefficient, ranging from 0.05 to 0.

15. is the differential coefficient, ranging from 0.3 to 0.7, is the anti-saturation factor, with a value of 0.1-0.3, The improved PID algorithm improves the dynamic response speed by suppressing the integral saturation phenomenon.

3. The control method of a rechargeable brushless electric lawn mower based on intelligent speed control according to claim 1, characterized in that: include: The calculation formula of the dynamic work intensity index DOII in step S2 is: ； in, , , is the weighting coefficient, satisfying ' is the real-time torque measurement value, is the rated maximum torque, is the real-time speed, is the rated maximum speed, is the motor winding temperature, is the ambient temperature, To allow the maximum temperature rise, the weighting factor , , Dynamically adjust according to the operation mode, when the vegetation density is >50% Increase by 0.2, when blade wear > 30% Increase by 0.

15.

4. The control method of a rechargeable brushless electric lawn mower based on intelligent speed control according to claim 1, characterized in that: Step S2 includes: The calculation formula of energy distribution efficiency coefficient EDEC is: ； in, is the real-time voltage of the battery, is the real-time battery current, , is the internal resistance of the battery, is the battery temperature, , is the heat dissipation coefficient, with a value of 0.05-0.15 , , It is the rated parameter of the battery. When EDEC<0.7, the battery health warning is triggered.

5. The control method of a rechargeable brushless electric lawn mower based on intelligent speed control according to claim 1, characterized in that: The fuzzy logic controller in step S3 includes: The fuzzy logic controller adopts a five-layer structure: First layer: Input fuzzification layer, mapping DOII and EDEC to 7 language variables The second layer: rule antecedent layer, including 49 IF-THEN rules The third layer: rule strength calculation layer, using algebraic product reasoning method The fourth layer: rule consequence layer, using the centroid method to defuzzify The fifth layer: output adjustment layer, which performs secondary correction on PWM duty cycle according to SOC value The rule base is automatically updated every 10 hours of work, and the best 20% of the rules are retained during the update.

6. The control method of a rechargeable brushless electric lawn mower based on intelligent speed control according to claim 1, characterized in that: The gradient speed reduction protection mode in step S5 includes: When SOC∈(20%,30%], the speed drops linearly to 80% of the base speed; When SOC∈(10%,20%], the speed drops to 60% of the base speed according to the quadratic curve; When SOC≤10%, the speed immediately drops to 40% of the base speed and remains constant; The reference speed is determined according to the current operating mode, and is increased by 15% in the hard vegetation mode and decreased by 10% in the soft vegetation mode.

7. The control method of a rechargeable brushless electric lawn mower based on intelligent speed control according to claim 1, characterized in that: The self-learning algorithm in step S6 includes: Adopts deep Q network architecture, including: Input layer: 14 neurons, corresponding to 7 sensor data and their first-order derivatives; Hidden layer: 3 fully connected layers, with 64, 32, and 16 neurons respectively; Output layer: 3 action nodes, corresponding to the weight adjustment direction and amplitude of DOII and EDEC; The experience replay buffer stores the most recent 1000 state-action-reward tuples; The reward function is designed as: R = 0.5·(1-speed deviation rate) + 0.3·energy efficiency ratio + 0.2·temperature safety factor.

8. The control method of a rechargeable brushless electric lawn mower based on intelligent speed control according to claim 3, characterized in that: include: The weighting coefficient , , The dynamic adjustment strategy is: ； in, is the percentage of vegetation density obtained through image recognition, is the blade wear degree, which is obtained through vibration spectrum analysis and takes values ​​from 0 to 1, where 0 represents a new blade and 1 represents complete wear. The above parameters are updated every 30 seconds and the adjustment step does not exceed 0.

05.

9. The control method of a rechargeable brushless electric lawn mower based on intelligent speed control according to claim 4, characterized in that: The heat dissipation coefficient k includes: The calculation method is: ； in, is the battery surface area, is the convective heat transfer coefficient, ranging from 8 to 12. is the battery volume, is the battery material density kg / m³, is the specific heat capacity, is the radiation heat transfer correction term, , is the Stefan-Boltzmann constant, , is the surface emissivity, with a value of 0.8-0.

9.

10. The control method of a rechargeable brushless electric lawn mower based on intelligent speed control according to claim 1, characterized in that: It also includes strategies for handling abnormal conditions: When the DOII mutation rate is detected to be >50% / s and lasts for 0.5s, it is determined that the blade is stuck and the power output is immediately cut off; When the EDEC decrease rate is >5% / s for 5 consecutive sampling cycles, the forced cooling mode is started; When the vibration amplitude exceeds 120% of the ISO 5349-1 standard limit, the speed is automatically reduced to a safe range; The sampling period is 10ms, and manual reset is required to resume operation after the abnormality is resolved.

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