Anti-shake control method for household energy-saving electric fan

Abstract

The invention relates to the technical field of household appliance control, and discloses an anti-shake control method for a household energy-saving electric fan, which comprises the following steps of: operating the fan under a plurality of preset load characterization states, and acquiring and storing a reference data set used for associating the load characterization states, the operation rotating speed, vibration and power consumption; when the fan operates normally, vibration and power consumption data are monitored in real time, and the current load characterization state is identified by inquiring the reference data set; an operation target set by a user is obtained, and an optimization function integrating vibration suppression, energy efficiency and output performance indexes is constructed based on the identified load characterization state and the operation target; and determining an optimal driving instruction of the motor under the current condition by solving the optimization function, and executing the instruction to realize anti-shake control. According to the method, the anti-shake strategy can be adaptive to the individual difference and real-time state of the fan, effective vibration suppression is guaranteed, and the user experience and comprehensive energy efficiency of a product are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of household appliance control technology, and in particular to a method for anti-shake control of a household energy-saving electric fan. Background Technology

[0002] In existing technologies, solutions to the problem of vibration during operation of household energy-saving electric fans are mainly divided into two categories.

[0003] The first category is mechanical improvement solutions. These include reducing vibration transmission by improving the dynamic balance accuracy of the fan blades or adding vibration damping pads between the motor and the housing. This type of solution is a passive physical vibration isolation method; while it can alleviate the problem, it cannot actively suppress vibration excitation at its source. More importantly, its anti-vibration effectiveness is fixed and cannot adapt to dynamic changes in load conditions caused by dust accumulation on the fan blades or replacement of different fan guards during use. It also cannot compensate for individual differences caused by manufacturing tolerances, resulting in limited long-term effectiveness and high costs.

[0004] The second category is simple control and protection schemes. For example, when the vibration amplitude is detected to exceed a certain fixed threshold, the fan is stopped or forced to slow down to a safe speed. While this type of scheme can avoid dangerous vibrations, it comes at the cost of interrupting user use or sacrificing the set airflow, severely impacting the user experience. Its essence is "overprotection" rather than "optimized control." More importantly, whether it's a mechanical scheme or this simple control scheme, its control objective is a single "shake prevention" or "vibration avoidance," failing to simultaneously suppress vibration while optimizing other key performance indicators such as energy efficiency, noise, and airflow maintenance, which are contradictory to these. The differentiated needs of users in different scenarios cannot be met.

[0005] Therefore, existing technologies lack a control method that can actively adapt to changes in the individual fan and load, and intelligently balance and coordinate optimization between anti-vibration and other performance indicators. To address this, this invention proposes an anti-vibration control method for household energy-saving electric fans. Summary of the Invention

[0006] The purpose of this invention is to solve the problems of poor adaptive capability and inability to take into account multi-objective collaborative optimization in the existing anti-shake control technology, and to propose an anti-shake control method for household energy-saving electric fans.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for anti-shake control of a household energy-saving electric fan, the electric fan comprising a motor and fan blades, comprising the following steps: Step S1: Run the fan under multiple preset load characterization states, and acquire and store a benchmark dataset for correlating load characterization states, operating speed, vibration and power consumption. The preset load characterization states include a first state with clean fan blades, a second state simulating uniform dust adhesion on the fan blades, and a third state with a specific type of protective mesh installed, as detailed below: First condition: Select brand new, unused three-blade ABS engineering plastic fan blades, each weighing 320g. Wipe the surface and edges of the fan blades with a lint-free cloth to ensure there is no dust, oil, or other contaminants. Install the blades onto the motor output shaft and tighten them.

[0008] Second state: Based on the fan blade in the first state, common household dust (mainly composed of a mixture of dust and fibers) was selected and evenly attached to the surface and edges of the fan blade using a spray bonding method. The mass of the attached dust was controlled at 25.6g (8% of the mass of the fan blade itself) to simulate the dust accumulation after long-term use.

[0009] Third state: Select a metal protective mesh cover with a hole diameter of 5mm and a wire diameter of 0.8mm, install it on the front shell of the fan, and ensure that the distance between the mesh cover and the fan blades is uniform (minimum distance 15mm), does not interfere with the fan blades, and the mesh cover is intact, undamaged and undeformed.

[0010] In step S1, acquiring and storing the benchmark dataset used to correlate load characterization status, operating speed, vibration, and power consumption includes the following sub-steps: S1-1: Control the fan to scan within a preset speed range (500r / min to 1500r / min) at equal steps (50r / min) to ensure coverage of the fan's commonly used operating speed range. Each speed point is used as a scanning speed point.

[0011] S1-2: Data is collected synchronously at each scanning speed point for 3 seconds to ensure data stability. Vibration data is collected by a three-dimensional vibration sensor (sampling frequency 1kHz) installed at the 12 o'clock position of the mesh cover, power consumption data is collected by a power detection circuit (measurement accuracy ±1%) connected in series with the motor power supply circuit, and current speed data is collected by a motor encoder (resolution 2500 lines).

[0012] S1-3: Perform spectrum analysis on the collected vibration data to extract vibration characteristic parameters, including the fundamental frequency amplitude, second harmonic amplitude, third harmonic amplitude, and vibration energy integral value in the 15 to 50 Hz resonance band corresponding to the motor frequency. This frequency band is the main resonance band of the fan grille and the head of the whole machine.

[0013] S1-4: Process the collected power consumption data, calculate the normalized power coefficient (the ratio of current power to rated power at this speed) and the local slope of the power-speed curve at the current speed, and characterize the energy efficiency level at different speeds.

[0014] S1-5: Using the rotational speed as an index, the corresponding vibration characteristic parameters and power consumption characteristic parameters are associated and stored to form a subset of baseline data representing the current load state. Specifically, under clean fan blade conditions, the vibration baseline V_base=0.08g, optimal power consumption P_opt=35W, and airflow calibration coefficient K_q=0.06m at 1000r / min are defined. 3 / (min·r); V_base=0.12g, P_opt=42W, K_q=0.055m corresponding to 1000r / min under dust accumulation conditions on the fan blades. 3 / (min·r); With protective mesh, 1000r / min corresponds to V_base=0.10g, P_opt=38W, K_q=0.058m 3 / (min·r).

[0015] S1-6: Repeat S1-1 to S1-5 to complete the acquisition and storage of the reference data for the above three preset load characterization states, form a complete reference dataset, and store it in the controller's Flash memory.

[0016] Step S2: While the fan is running normally, monitor vibration and power consumption data in real time, and identify the current load characterization state by querying the benchmark dataset. Step S2 further includes the following sub-steps: S2-1: Real-time acquisition of vibration data at the current rotational speed. Using the same spectrum analysis method as in step S1-3, a real-time vibration characteristic vector is obtained. This vector includes the fundamental frequency vibration amplitude, the second harmonic amplitude, the third harmonic amplitude, and the vibration energy integral value in the 15 to 50 Hz frequency band.

[0017] S2-2 measures the current power consumption data in real time through the power detection circuit and calculates the real-time power consumption characteristic parameters (normalized power coefficient, local slope of the power-speed curve).

[0018] S2-3 combines the real-time vibration feature vector and power consumption feature parameters at the current rotational speed into a query feature group to ensure that the feature dimension is consistent with the feature dimension in the benchmark dataset.

[0019] S2-4 uses a weighted distance metric to calculate the similarity between the query feature group and the baseline feature vectors in the corresponding speed range (current speed ± 100 r / min) of the baseline dataset. Dynamically adjustable weight coefficients are assigned to the vibration feature components and power consumption feature components, with the sum of the weight coefficients always being 1. The initial value for the vibration feature weight coefficient is set to 0.3, and the initial value for the power consumption feature weight coefficient is set to 0.7. A speed correlation factor k is defined (k = 0.1 at 500 r / min, k = 0.9 at 1500 r / min). The vibration feature weight coefficient = initial value + (k - 0.5) × 0.4, and the power consumption feature weight coefficient = 1 - vibration feature weight coefficient. For example, at 500 r / min, the vibration feature weight = 0.14, and the power consumption feature weight = 0.86; at 1000 r / min, the vibration feature weight = 0.3, and the power consumption feature weight = 0.7; at 1500 r / min, the vibration feature weight = 0.46, and the power consumption feature weight = 0.54.

[0020] S2-5: Based on the similarity comparison results, the load representation state with the highest matching degree is taken as the identification result. The preset confidence threshold is 0.8. When the similarity value corresponding to the load representation state with the highest matching degree is lower than this threshold, the identification result is marked as an unknown load state, and a simple anti-shake mode is triggered and executed. The preset speed reduction strategy is a speed reduction step of 50 r / min, a speed reduction interval of 2 seconds, and a 1-second holding period after each speed reduction before vibration detection. The lower limit of speed reduction is 500 r / min. The preset safety threshold is -12dB. The motor is controlled to reduce its speed according to this speed reduction strategy until the real-time vibration amplitude drops below -12dB, and then maintains operation at this speed.

[0021] Step S3: Obtain the user-defined operating target, and based on the identified load characterization state and operating target, construct an optimization function that integrates vibration suppression, energy efficiency and output performance indicators. Step S3 further includes the following sub-steps: The S3-1 provides a touch-screen user interface for users to select operating modes, including silent mode (prioritizing vibration suppression), energy-saving mode (prioritizing energy efficiency optimization), and standard mode (balanced optimization).

[0022] S3-2 automatically recommends an operating mode based on environmental sensor data and historical usage records when the user does not explicitly select one. The preset light intensity threshold is 50 lux, and the preset nighttime time range is 22:00 to 6:00. If the light intensity detected by the ambient light intensity sensor is below 50 lux and the system clock indicates the current time is within this range, the silent mode is recommended. The preset temperature threshold is 30℃, and the preset duration threshold is 1 hour. If the temperature detected by the ambient temperature sensor is above 30℃ and the fan's continuous operation time exceeds 1 hour, the energy-saving mode is recommended. The preset statistical time window is 1 hour, and the preset adjustment count threshold is 3 times. If the user adjusts the fan speed more than 3 times within this time window, the standard mode is recommended.

[0023] S3-3 converts the user-selected or system-recommended operating mode into the corresponding optimization target priority settings, clarifying the weight allocation principles for the three major objectives of vibration suppression, energy efficiency optimization, and output performance maintenance.

[0024] S3-4. Based on the identified load characterization state, obtain the vibration baseline value V_base, the optimal efficiency power reference value P_opt, and the air volume calibration coefficient K_q under the current state from the corresponding benchmark dataset.

[0025] S3-5, based on the obtained operational objectives, assign weighting coefficients a, b, and c to the three optimization objectives of vibration suppression, energy efficiency optimization, and output performance maintenance, respectively, satisfying a+b+c=1. In silent mode, configure a=0.5, b=0.2, and c=0.3; in energy-saving mode, configure a=0.2, b=0.5, and c=0.3; in standard mode, configure a=0.3, b=0.3, and c=0.4.

[0026] S3-6, based on the acquired V_base, P_opt, K_q, and the real-time measured vibration value V_act, power value P_act, and rotational speed value n, an optimization function is constructed: J = a × f(V_act, V_base) + b × g(P_act, P_opt) + c × h(n, K_q, Q_tar). Where J is the objective function value (the smaller the value, the better the overall performance); f(·) is the vibration suppression evaluation function, using f(V_act, V_base) = |V_act - V_base| / V_base; g(·) is the energy efficiency evaluation function, using g(P_act, P_opt) = |P_act - P_opt| / P_opt; h(·) is the output performance evaluation function, using h(n, K_q, Q_tar) = |K_q × n - Q_tar| / Q_tar; Q_tar is the standard airflow value corresponding to the user-set speed, defaulting to 60m³ / h. 3 / h. Assuming the current identification is that the fan blades are in a dust accumulation state, the real-time vibration V_act=0.15g, the real-time power P_act=45W, the current speed n=1000r / min, and if it is in silent mode, then J=0.164.

[0027] Step S4: By solving the optimization function, the optimal drive command for the motor under the current conditions is determined, and the command is executed to achieve anti-shake control.

[0028] Step S4 further includes the following sub-steps: S4-1 generates multiple candidate speed points within a preset range (current speed ± 150 r / min) centered on the current speed, with an interval of 20 r / min, ensuring coverage of the range where the optimal speed may exist. For example, when the current speed is 1000 r / min, the candidate speed points include 850 r / min, 870 r / min, ..., 1150 r / min.

[0029] S4-2, for each candidate rotational speed point, the expected vibration value, power value, and output air volume value are estimated using interpolation based on the benchmark dataset. These estimated values ​​are then substituted into the optimization function constructed in step S3-6 to calculate the objective function value J corresponding to each candidate rotational speed point. For example, at 980 r / min, the estimated values ​​are V_est = 0.115g, P_est = 41W, and Q_est = 0.055 × 980 = 53.9m. 3 Substituting / h into the silent mode optimization function, we get J≈0.057.

[0030] S4-3. Select the candidate rotational speed that minimizes the objective function value J as the optimal target rotational speed. This rotational speed is the balance point of vibration, energy efficiency and air volume under the current conditions. For example, 980 r / min is the optimal target rotational speed in the example above.

[0031] S4-4: Based on the optimal target speed and the corresponding optimal efficiency operating point, query the benchmark dataset for the optimal PWM duty cycle and commutation timing parameters at that speed to generate motor drive commands. For example, the optimal PWM duty cycle for 980 r / min is 48%, and the commutation timing parameter is 120 microseconds.

[0032] S4-5 sends the drive command to the motor driver, controls the motor to run according to the command, and continuously monitors the actual vibration value, power value and air volume value (calculated by speed and air volume calibration coefficient) during the execution process, and compares them with the expected value in real time.

[0033] S4-6, preset allowable error thresholds: vibration deviation ≤10%, power deviation ≤15%, airflow deviation ≤10%. When the deviation between the actual value and the expected value continues to exceed the allowable error threshold (duration ≥3 seconds), control parameter fine-tuning is triggered: if the deviation is caused by a slight change in load, the PWM duty cycle is fine-tuned by ±1%; if the deviation is large, steps S4-1 to S4-4 are re-executed to re-solve the optimal drive command to ensure stable control effect.

[0034] The beneficial effects of the technical solution provided by this invention include at least the following: This invention establishes a multi-state benchmark dataset, which can pre-learn and store the "fingerprint" features of a specific fan under different load conditions. Then, through real-time query and identification, it can identify the current operating status of the fan online. This enables the anti-shake control strategy to be dynamically adjusted based on the individual characteristics and real-time status of each fan, solving the problem of reduced anti-shake performance caused by manufacturing differences, wear and dust accumulation, and realizing the leap from "general parameters" to "individual adaptation".

[0035] This invention creatively constructs an optimization function based on state and objective, taking both the "user-defined operating objective" and the "identified load state" as inputs to build a multi-objective optimization function that integrates vibration suppression, energy efficiency, and output performance. This transforms the system from a simple anti-shake controller into an intelligent decision-making system that prioritizes the user's most pressing performance needs while effectively suppressing vibration, thus resolving the conflict between anti-shake and user experience in traditional methods.

[0036] This invention, by solving and executing an optimization function, can automatically find and stably operate at the optimal operating point that meets the current overall objectives. This method proactively avoids high-vibration, low-efficiency speed ranges, fundamentally reducing energy loss caused by ineffective vibration. Simultaneously, through optimization and potential compensation mechanisms, it can maintain the user's desired airflow level to the maximum extent while adjusting the speed to suppress vibration, thus achieving both vibration reduction and energy saving while preserving performance. Attached Figure Description

[0037] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the method flow provided in an embodiment of the present invention; Figure 2This is a schematic diagram of the execution flow provided for an embodiment of the present invention. Detailed Implementation

[0039] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a vibration control method for a household energy-saving electric fan proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0041] The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0042] The specific solution of the anti-shake control method for a household energy-saving electric fan provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0043] Please see Figure 1 and Figure 2 It illustrates a method flow diagram and execution flow diagram of an anti-shake control method for a household energy-saving electric fan according to an embodiment of the present invention, including the following steps: Step S1: Run the fan under multiple preset load characterization states, and acquire and store a benchmark dataset for correlating load characterization states, operating speed, vibration and power consumption. Step S2: While the fan is running normally, monitor vibration and power consumption data in real time, and identify the current load characterization state by querying the benchmark dataset. Step S3: Obtain the user-defined operating target, and based on the identified load characterization state and operating target, construct an optimization function that integrates vibration suppression, energy efficiency and output performance indicators. Step S4: By solving the optimization function, the optimal drive command for the motor under the current conditions is determined, and the command is executed to achieve anti-shake control.

[0044] It should be noted that the multiple preset load characterization states refer to simulated typical load scenarios that may occur in actual use of the fan, including situations such as clean fan blades without any attached substances, fan blades uniformly covered with dust, and different types of protective covers being installed. These are mainly to provide a comprehensive reference benchmark for subsequent state identification.

[0045] Acquiring and storing a benchmark dataset refers to controlling the fan to operate systematically under various preset load conditions, covering commonly used speed ranges, and simultaneously collecting vibration data, power consumption data, and speed information corresponding to different speeds. After feature extraction and correlation processing, a structured dataset is formed and stored. Its main purpose is to provide data support for load condition identification and optimized control.

[0046] Real-time monitoring of vibration and power consumption data refers to continuously collecting vibration amplitude, frequency characteristics, and real-time power consumption values ​​during operation by deploying vibration detection elements at key locations of the fan and power consumption detection devices in the power supply circuit. Its main purpose is to obtain the core parameters of the current operating status.

[0047] Identifying the load characterization state by querying a benchmark dataset involves extracting features from real-time collected vibration and power consumption data, comparing the similarity with the feature data of the corresponding speed range in the benchmark dataset, and determining the current load characterization state based on the matching results. This primarily provides accurate state information for subsequent control strategy adjustments.

[0048] Obtaining the user-defined operating target means receiving the operating mode selected by the user through the fan's operating interface, or automatically recommending modes based on environmental perception data and historical usage records, including types such as silent, energy-saving, and standard. Its main purpose is to clarify the priority guidance of control.

[0049] Constructing a comprehensive optimization function refers to extracting reference values ​​such as vibration baseline and optimal power consumption from the benchmark data based on the load characterization state, configuring weight coefficients that match the operating objectives for the three indicators of vibration suppression, energy efficiency, and output performance, and establishing a quantified objective function. Its main purpose is to transform multi-dimensional control requirements into a solvable mathematical model.

[0050] Determining the optimal drive command by solving the optimization function involves generating candidate speed points near the current speed, estimating the expected index values ​​corresponding to each point, and substituting them into the optimization function to select the speed with the best overall performance and the corresponding motor control parameters. Its main purpose is to obtain a precise control scheme that is adapted to the current state.

[0051] The execution command to achieve anti-shake control refers to sending the optimal drive command to the motor driver, while continuously monitoring the operating data and dynamically fine-tuning the parameters according to the deviation between the actual and expected values. Its main purpose is to ensure stable vibration suppression while taking into account energy efficiency and output performance.

[0052] This application's solution overcomes the limitations of traditional fixed-parameter control, such as poor adaptability and the disconnect between anti-vibration and energy saving, through load adaptive identification and multi-objective collaborative optimization. Specifically, it first establishes a benchmark dataset by pre-setting typical load scenarios, providing precise reference for control under different usage conditions and solving the problem of anti-vibration failure caused by load changes. Next, it automatically identifies the load state through real-time data acquisition and comparison with benchmark data, allowing for targeted adjustments to the control strategy and avoiding blind control. Then, it determines the operating objectives based on user needs or scenario recommendations, constructing an optimization function that integrates multiple indicators. This quantifies the requirements for vibration suppression, energy efficiency improvement, and performance maintenance into a solvable model, breaking the limitations of single-objective control. Finally, through optimization and dynamic adjustment, it outputs the optimal drive command and continuously calibrates, ensuring a balance among these three aspects under different load conditions. The entire process forms a complete closed loop of "data support - state identification - objective guidance - precise control," enabling the fan to adapt to complex usage scenarios, achieving stable anti-vibration while also considering energy saving and user experience.

[0053] In one specific implementation, the household energy-saving electric fan is equipped with a brushless DC motor, a three-dimensional vibration sensor, a power detection circuit, an environmental sensor, and a microcontroller. Multiple preset load characterization states include a clean fan blade state, a simulated state with dust adhering to the fan blades equivalent to 8% of its own mass, and a state with a 5mm diameter protective mesh cover installed.

[0054] In step S1, the fan is controlled to operate within a speed range of 500 to 1500 revolutions per minute, sweeping the frequency in 50-revolutions per minute increments. Data is continuously collected for 3 seconds at each speed point. The vibration sensor is installed at the 12 o'clock position on the grille, with a sampling frequency of 1000 Hz. The power detection circuit is connected in series with the power supply circuit. Features such as the fundamental frequency amplitude, third harmonic energy, and normalized power coefficient are extracted and stored according to the speed index to form a baseline dataset. Specifically, under clean fan blade conditions, the vibration baseline V_base = 0.08g, the optimal power consumption P_opt = 35W, and the airflow calibration coefficient K_q = 0.06m³ / min corresponds to 1000 revolutions per minute. 3 / (min·r); Under dust accumulation conditions on the fan blades, 1000 revolutions per minute corresponds to V_base=0.12g, P_opt=42W, K_q=0.055m 3 / (min·r); With the protective mesh cover, 1000 revolutions per minute corresponds to V_base=0.10g, P_opt=38W, K_q=0.058m 3 / (min·r).

[0055] During step S2, as the fan operates normally, the microcontroller collects vibration and power consumption data every 10 milliseconds, extracts real-time vibration feature vectors and power consumption feature parameters, combines them into a query feature group, and calculates the weighted distance similarity between this feature group and the benchmark feature vector within the corresponding speed range of ±100 rpm in the benchmark dataset. The vibration feature weight is positively correlated with the current speed, and the power consumption feature weight is negatively correlated with the current speed. The load characterization state is determined based on the similarity result. If the similarity is less than 0.8, it is marked as an unknown state, triggering the speed reduction and anti-shake mode.

[0056] When performing step S3, the user can select silent, energy-saving, or standard mode via the touch panel. If no specific selection is made, silent mode is recommended if the ambient light intensity is below 50 lux and the time is between 22:00 and 06:00; energy-saving mode is recommended if the ambient temperature is above 30℃ and the system has been running continuously for more than 1 hour; and standard mode is recommended if the fan speed is adjusted more than 3 times within 1 hour. When the user sets or the system recommends silent mode, the weighting coefficients a=0.5, b=0.2, and c=0.3, and the target airflow Q_tar=60m³ / h for the user-set speed is used. 3 / h; When energy-saving mode is recommended, the weighting coefficients are a=0.2, b=0.5, c=0.3, and Q_tar=60m. 3 / h; When the standard mode is recommended, the weighting coefficients are a=0.3, b=0.3, c=0.4, and Q_tar=60m. 3 / h. Combining the corresponding parameters in the baseline data, construct the optimization function: J=a×(V_act-V_base) / V_base+b×(P_act-P_opt) / P_opt+c×(K_q×n-Q_tar) / Q_tar. Assuming the current identification is a fan blade dust accumulation state, the real-time vibration V_act=0.15g, the real-time power P_act=45W, and the current speed n=1000 revolutions per minute, if it is in silent mode, then J=0.5×(0.15-0.12) / 0.12+0.2×(45-42) / 42+0.3×(0.055×1000-60) / 60=0.164.

[0057] During step S4, candidate speed points (850, 870, ..., 1150 rpm) are generated at intervals of 20 rpm within a range of ±150 rpm, centered at the current speed of 1000 rpm. The index values ​​for each candidate point are estimated using interpolation based on the benchmark dataset. For example, at 980 rpm, the estimated values ​​are V_est = 0.115g, P_est = 41W, and Q_est = 0.055 × 980 = 53.9m. 3Substituting / h into the silent mode optimization function yields J=0.057. After calculating the J value for all candidate points, the minimum J value of 980 rpm is selected as the optimal target speed. The optimal PWM duty cycle at this speed is found to be 48%, and the commutation timing parameter is 120 microseconds. A drive command is then generated and sent to the motor driver. During operation, actual data is continuously monitored. If the vibration deviation exceeds 10%, the power deviation exceeds 15%, or the airflow deviation exceeds 10% and lasts for more than 3 seconds, a slight deviation will result in a fine adjustment of the PWM duty cycle by ±1%, while a larger deviation will require recalculating the optimal command to ensure stable anti-vibration effect and overall performance.

[0058] In step S1, multiple preset load characterization states include a first state where the fan blades are clean, a second state where dust is uniformly attached to the fan blades, and a third state where a specific type of protective mesh is installed.

[0059] Further, in step S1, acquiring and storing the benchmark dataset used to correlate load characterization status, operating speed, vibration, and power consumption includes the following sub-steps: S1-1 controls the fan to scan within a preset speed range in equal steps; S1-2, at each scanning rotation speed point, synchronously collect vibration data from the vibration sensor, power consumption data from the power detection circuit, and current rotation speed data; S1-3, Perform spectrum analysis on the collected vibration data to extract vibration characteristic parameters including fundamental frequency amplitude, specific harmonic component amplitude, and specific frequency band energy; S1-4, Process the collected power consumption data to calculate the normalized power coefficient and the local slope of the power-speed curve at the current speed; S1-5, using the rotational speed as an index, associate and store the corresponding vibration characteristic parameters and power consumption characteristic parameters to form a subset of benchmark data for the current load characterization state; S1-6, repeat S1-1 to S1-5 to complete the acquisition and storage of benchmark data for all preset load characterization states, forming a benchmark dataset.

[0060] It should be noted that the preset speed range refers to the typical speed range that covers the daily use of the fan. It is a set of speeds defined based on the fan's rated speed and the user's commonly used speed settings, which provides a clear range boundary for scanning operation.

[0061] Equal step size means that the speed difference between two adjacent speed points remains consistent during scanning, ensuring uniformity of speed coverage and systematic data acquisition.

[0062] The scanning speed point refers to the specific speed value divided into equal steps within a preset speed range. Each speed point corresponds to a complete data acquisition process, providing a specific carrier for obtaining characteristic data at different speeds.

[0063] Synchronous acquisition refers to acquiring vibration data, power consumption data, and rotational speed data simultaneously at the same time point or within the same acquisition cycle, ensuring the temporal correlation and matching degree of the three types of data.

[0064] Vibration data refers to the digital data generated by vibration sensors sensing the vibration signals of a fan during operation, which reflects the intensity and frequency characteristics of the fan vibration.

[0065] Power consumption data refers to the data related to the electrical energy consumption of the fan during operation, which is measured by the power detection circuit. It directly represents the energy consumption level of the fan.

[0066] Current speed data refers to the actual operating speed of the fan at the time of data collection, which provides the core index basis for data association.

[0067] Spectrum analysis refers to the method of frequency domain transformation processing of vibration data. By decomposing the frequency components of the vibration signal, it can uncover the vibration characteristics at different frequencies.

[0068] The fundamental frequency amplitude refers to the vibration intensity value corresponding to the fundamental frequency of the motor in the vibration signal. It is a key parameter that reflects the dominant component of fan vibration.

[0069] The amplitude of a specific harmonic component refers to the vibration intensity value corresponding to an integer multiple of the fundamental frequency in the vibration signal, which supplements the harmonic characteristics of the vibration.

[0070] Specific frequency band energy refers to the cumulative energy value of the vibration signal within a preset frequency range, which is the key frequency band vibration characteristic that makes the focusing fan prone to resonance.

[0071] The normalized power factor is the ratio of the actual power consumption at the current speed to the rated power consumption at that speed. It standardizes the energy consumption level at different speeds, making it convenient for comparisons across speeds.

[0072] The local slope of the power-speed curve refers to the rate at which power changes with speed, with the current speed as the base point. It reflects the degree to which speed changes affect power consumption.

[0073] Using rotational speed as an index means using rotational speed values ​​as the key field for data retrieval, establishing a mapping relationship between rotational speed and corresponding feature parameters, and enabling fast data querying and matching.

[0074] A reference data subset refers to a structured data set formed by associating characteristic parameters of all speed points under a single load characterization state, which provides data support for the analysis of a single load state.

[0075] The benchmark dataset refers to a complete dataset that integrates a subset of benchmark data representing all preset load characteristics. It covers comprehensive characteristic data under different load conditions, providing a unified data foundation for subsequent load identification and optimization control.

[0076] In one specific implementation, taking a household energy-saving electric fan with a rated power of 750W and a blade diameter of 400mm as an example, the process of determining the load characterization state is explained in detail: 1. First state (clean fan blades) Select brand new, unused three-blade ABS engineering plastic fan blades, wipe the surface and edges of the fan blades with a lint-free cloth to ensure that there is no dust, oil or other contaminants attached. The fan blades weigh 320g. Install them onto the motor output shaft and tighten them to achieve the first clean state of the fan blades.

[0077] 2. Second state (simulating the state where dust is evenly attached to the fan blades) Based on the fan blades in the first state, common household dust (mainly a mixture of dust and fibers) was selected and evenly adhered to the surface and edges of the fan blades using a spray bonding method. The mass of the adhered dust was controlled at 25.6g, which is exactly 8% of the mass of the fan blades themselves, simulating the dust accumulation after long-term use, thus forming the second state.

[0078] 3. Third state (state with specific type of protective mesh installed) Select a metal protective mesh cover with a 5mm aperture and a 0.8mm wire diameter. This type of mesh cover is commonly used in household fans. Install it on the front shell of the fan, ensuring that the distance between the mesh cover and the fan blades is uniform (minimum distance 15mm) and does not interfere with the fan blades. At the same time, keep the mesh cover intact, without damage or deformation, forming the third state.

[0079] 4. Verification Results We confirmed that the mass deviation of the fan blades in each state was ≤1% by weighing. We also confirmed that there were no abnormalities such as eccentricity or interference by taking pictures of the fan blades' rotation state with a high-speed camera. This ensured that the three load characterization states could realistically simulate typical load scenarios in actual use of the fan, providing a reliable load basis for the establishment of the benchmark dataset.

[0080] Step S2 further includes the following sub-steps: S2-1, Real-time acquisition of vibration data at the current rotational speed, and spectral analysis to obtain real-time vibration feature vector. The real-time vibration feature vector includes the fundamental frequency vibration amplitude corresponding to the current motor frequency, its second harmonic amplitude and third harmonic amplitude, and the vibration energy integral value within the preset frequency band. S2-2, measure the current power consumption data in real time and calculate the real-time power consumption characteristic parameters; S2-3, combine the real-time vibration feature vector and power consumption feature parameters at the current rotation speed into a query feature group; S2-4, calculate the similarity between the query feature group and the benchmark feature vector of the corresponding speed range in the benchmark dataset; S2-5, Based on the similarity comparison results, determine the load representation state with the highest matching degree with the current operating state as the identification result.

[0081] Furthermore, in sub-step S2-4, the similarity calculation adopts a weighted distance metric method, assigning dynamically adjustable weight coefficients to the vibration characteristic component and the power consumption characteristic component respectively. The adjustment of the vibration characteristic weight is positively correlated with the current operating speed, while the adjustment of the power consumption characteristic weight is negatively correlated with the current operating speed.

[0082] Furthermore, sub-step S2-5 also includes setting a confidence threshold, and when the similarity value corresponding to the load characterization state with the highest matching degree is lower than the confidence threshold, the identification result is marked as an unknown load state, and a simple anti-shake mode is triggered and executed. The simple anti-shake mode includes controlling the motor to reduce the speed according to a preset speed reduction strategy until the real-time vibration amplitude drops back to the preset safety threshold, and maintaining operation at that speed.

[0083] It should be noted that the real-time vibration feature vector refers to the vector data formed by extracting key features after performing spectral analysis on the vibration data at the current rotational speed. It centrally reflects the core characteristics of the current vibration.

[0084] The electric frequency of a motor refers to the electrical operating frequency corresponding to the motor speed, and it is the fundamental frequency parameter for motor operation.

[0085] The fundamental frequency vibration amplitude refers to the vibration intensity value in the vibration signal corresponding to the electrical frequency of the motor, and it is the dominant component index of vibration.

[0086] The second harmonic amplitude refers to the vibration intensity value corresponding to twice the electrical frequency of the motor in the vibration signal, while the third harmonic amplitude refers to the vibration intensity value corresponding to three times the electrical frequency of the motor. Together, they reflect the harmonic characteristics of the vibration.

[0087] The vibration energy integral value within a preset frequency band refers to the cumulative energy value of the vibration signal within a predefined specific frequency range, focusing on the vibration performance of key frequency bands.

[0088] Real-time power consumption characteristic parameters refer to parameters that characterize energy consumption characteristics obtained after calculating and processing real-time measured power consumption data, reflecting the current energy consumption level.

[0089] The query feature group refers to a dataset formed by combining real-time vibration feature vectors and real-time power consumption feature parameters at the same rotational speed, which fully presents the core features of the current operating state.

[0090] The corresponding speed range refers to a specific speed range in the benchmark dataset that is close to the current speed, providing a targeted reference range for feature matching.

[0091] The benchmark feature vector refers to the combination vector of vibration and power consumption features stored in the benchmark dataset within the corresponding speed range, and serves as a reference benchmark for similarity calculation.

[0092] Similarity refers to the quantitative value of the degree of similarity between the query feature group and the baseline feature vector, which reflects the degree of fit between the current running state and the baseline state.

[0093] The weighted distance metric method refers to a method that assigns different weights to different feature components when calculating similarity, and then performs distance calculations, highlighting the influence of key features on similarity determination.

[0094] The weighting coefficient refers to the importance coefficient assigned to the vibration feature component and the power consumption feature component. Its value determines the degree of contribution of the corresponding feature in the similarity calculation.

[0095] The vibration characteristic weight is positively correlated with the current operating speed, meaning that the higher the speed, the larger the weight coefficient of the vibration characteristic component; the power consumption characteristic weight is negatively correlated with the current operating speed, meaning that the higher the speed, the smaller the weight coefficient of the power consumption characteristic component.

[0096] The confidence threshold is a pre-set critical value used to determine the reliability of load status identification, and it provides a criterion for judging the validity of the identification results.

[0097] Unknown load status refers to the state classification when the matching degree between the features of the current running state and all load representation states in the benchmark dataset does not reach the confidence threshold, indicating that the current load exceeds the preset typical scenario.

[0098] The simplified anti-shake mode refers to the emergency anti-shake control logic triggered by unknown load conditions, and its core objective is to quickly suppress vibration.

[0099] The preset speed reduction strategy refers to the pre-set rules for gradually reducing the motor speed to ensure a smooth and controllable speed reduction process.

[0100] The preset safety threshold refers to the pre-defined safe upper limit of vibration amplitude, which provides a clear target benchmark for anti-shake control.

[0101] In one specific implementation, taking a fan speed range of 500 to 1500 revolutions per minute as an example, the process of determining the weighting coefficient is explained in detail: 1. Setting association rules for weighted coefficients The initial value of the vibration characteristic weighting coefficient is set to 0.3, the initial value of the power consumption characteristic weighting coefficient is set to 0.7, and the sum of the weighting coefficients is always 1. A rotational speed correlation factor k is defined, which has a linear relationship with the rotational speed: k = 0.1 at 500 rpm and k = 0.9 at 1500 rpm.

[0102] Vibration characteristic weight coefficient = initial value + (k-0.5)×0.4, power consumption characteristic weight coefficient = 1-vibration characteristic weight coefficient, ensuring that the vibration characteristic weight is positively correlated with the rotational speed and the power consumption characteristic weight is negatively correlated with the rotational speed.

[0103] 2. Determination of weighting coefficients at different speeds When the rotational speed is 500 revolutions per minute, k=0.1, vibration characteristic weight=0.3+(0.1-0.5)×0.4=0.14, power consumption characteristic weight=1-0.14=0.86; When the rotational speed is 1000 revolutions per minute, k=0.5, vibration characteristic weight=0.3+(0.5-0.5)×0.4=0.3, power consumption characteristic weight=1-0.3=0.7; When the rotational speed is 1500 rpm, k=0.9, vibration characteristic weight=0.3+(0.9-0.5)×0.4=0.46, power consumption characteristic weight=1-0.46=0.54.

[0104] 3. Verification Results Tests were conducted at different speeds when the fan blades were dusty, and the similarity between the query feature group and the baseline feature vector was calculated. The results show that at high speeds, the weight of vibration features is increased, which can more accurately identify load changes dominated by vibration; at low speeds, the weight of power consumption features is increased, which can more accurately identify load differences dominated by energy consumption. The similarity matching accuracy is 23% higher than that of fixed weights, verifying the rationality of dynamic weight adjustment.

[0105] In one specific implementation, taking a fan vibration safety range of ≤-12dB as an example, the process of determining the relevant parameters is explained in detail: 1. Determining the confidence threshold Benchmark datasets under three load characterization states were selected, and feature vectors for each speed range were extracted. 100 sets of real-time feature data with different load fluctuations were simulated, and their similarity to the benchmark feature vectors was calculated. Statistics show that when the matching degree is ≥0.8, the load state identification accuracy reaches over 95%; when the matching degree is <0.8, the identification accuracy drops below 60%. Therefore, the confidence threshold was set to 0.8.

[0106] 2. Determining the preset deceleration strategy Set the deceleration step size to 50 RPM, with a deceleration interval of 2 seconds. After each deceleration, maintain operation for 1 second before performing vibration detection to avoid vibration shock caused by excessively rapid deceleration. Set the lower limit of deceleration to 500 RPM (the minimum effective fan speed) to ensure a smooth and controllable deceleration process.

[0107] 3. Determining the preset safety threshold A full-speed-range vibration test of the fan revealed that when the vibration amplitude was ≤-12dB, the user had no noticeable vibration, and the fan operating noise was ≤45dB (meeting the household quiet standard). Therefore, the preset safety threshold was set to -12dB.

[0108] 4. Verification Results Simulating an unknown load condition (foreign objects attached to the fan blades, similarity 0.72 < 0.8), a simple anti-vibration mode was triggered. The fan started at 1000 RPM and decreased in 50 RPM increments. After 6 reductions, the speed dropped to 700 RPM, and the vibration amplitude decreased from -7.8 dB to -12.5 dB, stabilizing below the safety threshold, thus verifying the effectiveness of the relevant parameter settings.

[0109] Step S3 further includes the following sub-steps: S3-1 provides a user interface for users to select operating modes, including silent mode, energy-saving mode and standard mode; S3-2, when the user does not make a specific selection, automatically recommends the operating mode based on environmental sensor data and historical usage records; S3-3 converts the user-selected or system-recommended operating mode into the corresponding optimization target priority setting; S3-4, Based on the identified load characterization state, obtain the vibration baseline value V_base, the optimal efficiency power reference value P_opt, and the air volume calibration coefficient K_q under the current state from the corresponding benchmark dataset; S3-5, Based on the obtained user-defined operational goals, assign weight coefficients a, b, and c to the three optimization goals of vibration suppression, energy efficiency optimization, and output performance maintenance, respectively; S3-6. Based on the acquired V_base, P_opt, K_q, and the real-time measured vibration value V_act, power value P_act, and rotational speed value n, an optimization function of the following form is constructed: J = a × f (V_act, V_base) + b × g (P_act, P_opt) + c × h (n, K_q, Q_tar), where J is the objective function value, f (·) is the vibration suppression evaluation function, g (·) is the energy efficiency evaluation function, h (·) is the output performance evaluation function, and Q_tar is the target airflow of the user-set speed.

[0110] Furthermore, in sub-step S3-2, the logic for automatically recommending operating modes based on environmental sensor data and historical usage records includes: If the ambient light intensity sensor detects a light intensity value lower than the preset light intensity threshold, and the system clock indicates that the current time is within the preset nighttime time range, silent mode is recommended. If the ambient temperature sensor detects a temperature value higher than the preset temperature threshold, and the fan runs continuously for a longer period than the preset duration threshold, the energy-saving mode is recommended. If, within the preset statistical time window, the number of times a user adjusts the wind speed exceeds the preset adjustment threshold, the standard mode is recommended.

[0111] It should be noted that the user interface refers to the operating platform through which the user interacts with the fan control system, and is used to receive the user's mode selection commands.

[0112] Silent mode refers to an operating mode that prioritizes vibration suppression and focuses on reducing operating noise.

[0113] Energy-saving mode refers to an operating mode that prioritizes energy efficiency optimization and focuses on reducing electricity consumption.

[0114] The standard mode refers to the operating mode that balances vibration suppression, energy efficiency optimization, and output performance, and is suitable for common usage scenarios.

[0115] Environmental sensor data refers to environmental condition data collected by detection elements such as ambient light intensity sensors and ambient temperature sensors, which provides environmental basis for model recommendation.

[0116] Historical usage records refer to data such as user operating habits and mode selection preferences stored during the fan's past operation, providing user behavior references for mode recommendations.

[0117] Automatic recommended operating mode refers to the function of the system to actively match and adapt the mode based on the environmental conditions and user habits when no explicit selection is received from the user.

[0118] Optimizing target priority settings refers to transforming the operating mode into a clear control target ranking rule, defining the importance levels of vibration suppression, energy efficiency optimization, and output performance maintenance.

[0119] The vibration baseline value V_base refers to the minimum vibration amplitude corresponding to each rotational speed under the current load characterization state, and is a reference benchmark for vibration suppression.

[0120] The optimal efficiency power reference value P_opt refers to the lowest power consumption value corresponding to each speed under the current load characterization state, and is a reference benchmark for energy efficiency optimization.

[0121] The air volume calibration coefficient K_q refers to the coefficient between the rotational speed and the air volume under the current load characterization state, which is used to quantify the impact of rotational speed on the output air volume.

[0122] The weight coefficients a, b, and c refer to the importance quantification values ​​assigned to the three optimization objectives, and their magnitudes determine the contribution weight of the corresponding objective in the optimization function.

[0123] An optimization function is a mathematical model that integrates three optimization objectives, and achieves multi-objective collaborative optimization through quantitative calculation.

[0124] The objective function value J refers to the calculated result of the optimization function, and its magnitude reflects the degree of overall performance.

[0125] The vibration suppression evaluation function f(·) is a function that quantifies the degree of deviation of the actual vibration value from the vibration baseline value. The smaller the deviation, the smaller the function value.

[0126] The energy efficiency evaluation function g(·) is a function that quantifies the degree of deviation between the actual power value and the optimal efficiency power reference value. The smaller the deviation, the smaller the function value.

[0127] The output performance evaluation function h(·) is a function that quantifies the degree of deviation between the actual air volume and the target air volume. The smaller the deviation, the smaller the function value.

[0128] The target airflow Q_tar for the user-defined gear level refers to the standard airflow value corresponding to the user-selected gear level, which serves as a reference benchmark for output performance.

[0129] The preset light intensity threshold refers to a predefined critical value for light intensity, used to determine the brightness of the environment.

[0130] The preset nighttime time range refers to the pre-defined range of nighttime hours, which meets the user's need for low-noise use.

[0131] The preset temperature threshold refers to a predefined critical temperature value used to determine the degree of environmental heat.

[0132] The preset duration threshold refers to the pre-set critical value for the continuous operation time of the fan, which is used to determine whether energy saving needs to be prioritized.

[0133] The preset statistical time window refers to the pre-defined time range for statistical analysis of user operations, and the preset adjustment threshold refers to the pre-set critical number of wind speed adjustment operations. The two are combined to determine the degree of fluctuation in the user's demand for wind speed.

[0134] In a specific implementation, taking a home indoor usage scenario as an example, the process of determining each threshold is explained in detail: 1. Determining the preset light intensity threshold and nighttime time interval. Indoor light intensity data was collected by an ambient light sensor at different times. Statistical analysis showed that indoor light intensity was generally below 50 lux from 10:00 PM to 6:00 AM the next day. During this period, users are mostly resting and sensitive to noise. Therefore, the preset light intensity threshold was set to 50 lux, and the preset nighttime time range was set to 10:00 PM to 6:00 AM.

[0135] 2. Determination of preset temperature threshold and preset duration threshold Data collected on indoor temperatures during summer revealed that users tend to run fans for extended periods when temperatures exceed 30℃, highlighting a strong need for energy conservation. Analysis of fan usage habits indicates that continuous operation for over one hour constitutes a long-term usage scenario, necessitating a focus on energy efficiency. Therefore, the preset temperature threshold was set to 30℃, and the preset duration threshold was set to one hour.

[0136] 3. Determination of preset statistical time window and preset adjustment number threshold Statistical analysis of user fan speed adjustment behavior revealed that adjusting the fan speed ≥ 3 times per hour indicates significant fluctuations in user airflow demand, necessitating a balance across various performance metrics. Therefore, the preset statistical time window was set to 1 hour, and the preset adjustment threshold was set to 3 times.

[0137] 4. Verification Results The automatic recommendation logic was tested in different scenarios: at 11 PM and a light intensity of 40 lux, the silent mode was recommended; at 2 PM, a temperature of 32℃, and after 1.5 hours of continuous operation, the energy-saving mode was recommended; and when the user adjusted the fan speed 4 times within 1 hour, the standard mode was recommended. The recommendation results matched the user's actual needs with 92% accuracy, validating the rationality of the threshold settings.

[0138] Step S4 further includes the following sub-steps: S4-1, Generate multiple candidate speed points within a preset range, centered on the current speed; S4-2, for each candidate speed point, estimate its expected vibration value, power value and output air volume value based on the benchmark dataset, and substitute the estimated values ​​into the optimization function to calculate the objective function value corresponding to each candidate speed point; S4-3, Select the candidate rotational speed that minimizes the objective function value as the optimal target rotational speed; S4-4: Based on the optimal target speed and the corresponding optimal efficiency operating point, determine the PWM control parameters and commutation timing parameters of the motor to form drive commands; S4-5 sends the drive command to the motor driver, controls the motor to run according to the command, and continuously monitors the actual vibration and power consumption data during the execution process and compares them with the expected values; S4-6: When at least one of the actual vibration value, power value, or output air volume value continuously deviates from its corresponding expected value by more than the allowable error, the control parameter fine-tuning or optimization calculation is triggered.

[0139] It should be noted that the preset range refers to the speed fluctuation range defined based on the current speed. It provides a clear boundary for the generation of candidate speed points and ensures that the range of possible optimal speeds is covered.

[0140] Candidate speed points refer to specific speed values ​​within a preset range divided at set intervals. Each point corresponds to an optimization function calculation, providing sufficient samples for selecting the optimal speed.

[0141] The expected vibration value refers to the vibration intensity value corresponding to the candidate rotation speed point estimated by interpolation or fitting methods based on the benchmark dataset.

[0142] The expected power value refers to the energy consumption value corresponding to the candidate speed point estimated by the same method.

[0143] The expected output air volume value refers to the air volume value corresponding to the candidate speed point estimated by combining the air volume calibration coefficient. The three together constitute the comprehensive performance prediction data of the candidate speed point.

[0144] The objective function value refers to the quantitative result obtained by substituting the expected vibration value, power value, and output air volume value into the optimization function. Its value directly reflects the overall performance of the candidate speed point.

[0145] The optimal target speed is the speed at which the objective function value is minimized among all candidate speed points. It represents the best balance between vibration suppression, energy efficiency optimization, and output performance under the current conditions.

[0146] The optimal operating point refers to the set of operating state parameters at which the motor achieves the lowest power consumption under the optimal target speed, providing a reference for determining control parameters.

[0147] PWM control parameters are parameters used to adjust the duty cycle of the motor power supply, which determine the motor's input power and operating speed.

[0148] Commutation timing parameters are parameters that control the timing of current switching in motor windings, and they affect the smoothness and efficiency of motor operation.

[0149] The drive command refers to the motor control signal formed by integrating PWM control parameters and commutation timing parameters, which provides explicit operating instructions for the motor driver.

[0150] A motor driver is an actuator that receives drive commands and drives the motor to operate. It is responsible for converting control signals into the actual operating actions of the motor.

[0151] Continuous monitoring refers to the uninterrupted collection of actual vibration data, actual power consumption data, and actual output air volume data during motor operation to ensure real-time monitoring of the operating status.

[0152] The expected values ​​refer to the estimated vibration values, power values, and output air volume values ​​corresponding to the optimal target speed, which provide a benchmark for comparison with actual operating data.

[0153] The allowable error refers to the maximum permissible deviation range between the pre-set actual value and the expected value, which provides a quantitative standard for determining whether the operating status is normal.

[0154] Control parameter fine-tuning refers to making small adjustments to the PWM control parameters or commutation timing parameters when the deviation exceeds the allowable error, in order to quickly correct the deviation.

[0155] Re-execution of optimization calculations refers to re-executing steps such as candidate speed point generation and objective function calculation when the deviation is large or fine-tuning is ineffective, in order to redetermine the optimal driving scheme and ensure stable system operation.

[0156] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for anti-vibration control of a household energy-saving electric fan, the electric fan comprising a motor and fan blades, characterized in that, Includes the following steps: Step S1: Run the fan under multiple preset load characterization states, and acquire and store a benchmark dataset for correlating load characterization states, operating speed, vibration and power consumption. Step S2: While the fan is running normally, monitor vibration and power consumption data in real time, and identify the current load characterization state by querying the benchmark dataset. Step S3: Obtain the user-defined operating target, and based on the identified load characterization state and operating target, construct an optimization function that integrates vibration suppression, energy efficiency and output performance indicators. Step S4: By solving the optimization function, the optimal drive command for the motor under the current conditions is determined, and the command is executed to achieve anti-shake control.

2. The anti-shake control method for a household energy-saving electric fan according to claim 1, characterized in that, In step S1, the multiple preset load characterization states include a first state where the fan blades are clean, a second state where dust is uniformly attached to the fan blades, and a third state where a specific type of protective mesh is installed.

3. The anti-shake control method for a household energy-saving electric fan according to claim 1, characterized in that: In step S1, acquiring and storing the benchmark dataset used to correlate load characterization status, operating speed, vibration, and power consumption includes the following sub-steps: S1-1 controls the fan to scan within a preset speed range in equal steps; S1-2, at each scanning rotation speed point, synchronously collect vibration data from the vibration sensor, power consumption data from the power detection circuit, and current rotation speed data; S1-3, Perform spectrum analysis on the collected vibration data to extract vibration characteristic parameters including fundamental frequency amplitude, specific harmonic component amplitude, and specific frequency band energy; S1-4, Process the collected power consumption data to calculate the normalized power coefficient and the local slope of the power-speed curve at the current speed; S1-5, using the rotational speed as an index, associate and store the corresponding vibration characteristic parameters and power consumption characteristic parameters to form a subset of benchmark data for the current load characterization state; S1-6, repeat S1-1 to S1-5 to complete the acquisition and storage of benchmark data for all preset load characterization states, forming a benchmark dataset.

4. The anti-vibration control method for a household energy-saving electric fan according to claim 1, characterized in that: Step S2 further includes the following sub-steps: S2-1, Real-time acquisition of vibration data at the current rotational speed, and spectral analysis to obtain a real-time vibration feature vector. The real-time vibration feature vector includes the fundamental frequency vibration amplitude corresponding to the current motor frequency, its second harmonic amplitude and third harmonic amplitude, and the vibration energy integral value within a preset frequency band. S2-2, measure the current power consumption data in real time and calculate the real-time power consumption characteristic parameters; S2-3, combine the real-time vibration feature vector and power consumption feature parameters at the current rotation speed into a query feature group; S2-4, calculate the similarity between the query feature group and the benchmark feature vector of the corresponding speed range in the benchmark dataset; S2-5. Based on the similarity comparison results, determine the load representation state with the highest matching degree with the current running state as the identification result.

5. The anti-shake control method for a household energy-saving electric fan according to claim 4, characterized in that, In sub-steps S2-4, the similarity is calculated using a weighted distance metric method, which assigns dynamically adjustable weight coefficients to the vibration feature component and the power consumption feature component, respectively. The adjustment of the vibration feature weight is positively correlated with the current operating speed, while the adjustment of the power consumption feature weight is negatively correlated with the current operating speed.

6. The anti-shake control method for a household energy-saving electric fan according to claim 4, characterized in that: Sub-step S2-5 further includes setting a confidence threshold, and when the similarity value corresponding to the load characterization state with the highest matching degree is lower than the confidence threshold, the identification result is marked as an unknown load state, and a simple anti-shake mode is triggered and executed. The simple anti-shake mode includes controlling the motor to reduce the speed according to a preset speed reduction strategy until the real-time vibration amplitude drops back to the preset safety threshold, and maintaining operation at that speed.

7. The anti-vibration control method for a household energy-saving electric fan according to claim 1, characterized in that: In step S3, obtaining the user-defined running target includes the following sub-steps: S3-1 provides a user interface for users to select operating modes, including silent mode, energy-saving mode and standard mode; S3-2, when the user does not make a specific selection, automatically recommends the operating mode based on environmental sensor data and historical usage records; S3-3 converts the user-selected or system-recommended operating mode into the corresponding optimization target priority setting.

8. The anti-vibration control method for a household energy-saving electric fan according to claim 7, characterized in that, In sub-step S3-2, the logic for automatically recommending operating modes based on environmental sensor data and historical usage records includes: If the ambient light intensity sensor detects a light intensity value lower than the preset light intensity threshold, and the system clock indicates that the current time is within the preset nighttime time range, silent mode is recommended. If the ambient temperature sensor detects a temperature value higher than the preset temperature threshold, and the fan runs continuously for a longer period than the preset duration threshold, the energy-saving mode is recommended. If, within the preset statistical time window, the number of times a user adjusts the wind speed exceeds the preset adjustment threshold, the standard mode is recommended.

9. The anti-vibration control method for a household energy-saving electric fan according to claim 1, characterized in that: In step S3, constructing an optimization function that integrates vibration suppression, energy efficiency, and output performance indicators includes the following sub-steps: S3-4, Based on the identified load characterization state, obtain the vibration baseline value V_base, the optimal efficiency power reference value P_opt, and the air volume calibration coefficient K_q under the current state from the corresponding benchmark dataset; S3-5, Based on the obtained user-defined operational goals, assign weight coefficients a, b, and c to the three optimization goals of vibration suppression, energy efficiency optimization, and output performance maintenance, respectively; S3-6. Based on the acquired V_base, P_opt, K_q, and the real-time measured vibration value V_act, power value P_act, and rotational speed value n, an optimization function of the following form is constructed: J = a × f (V_act, V_base) + b × g (P_act, P_opt) + c × h (n, K_q, Q_tar), where J is the objective function value, f (·) is the vibration suppression evaluation function, g (·) is the energy efficiency evaluation function, h (·) is the output performance evaluation function, and Q_tar is the target airflow of the user-set speed.

10. The anti-vibration control method for a household energy-saving electric fan according to claim 1, characterized in that: Step S4 further includes the following sub-steps: S4-1, Generate multiple candidate speed points within a preset range, centered on the current speed; S4-2, for each candidate speed point, estimate its expected vibration value, power value and output air volume value based on the benchmark dataset, and substitute the estimated values ​​into the optimization function to calculate the objective function value corresponding to each candidate speed point; S4-3, Select the candidate rotational speed that minimizes the objective function value as the optimal target rotational speed; S4-4: Based on the optimal target speed and the corresponding optimal efficiency operating point, determine the PWM control parameters and commutation timing parameters of the motor to form drive commands; S4-5 sends the drive command to the motor driver, controls the motor to run according to the command, and continuously monitors the actual vibration and power consumption data during the execution process and compares them with the expected values; S4-6: When at least one of the actual vibration value, power value, or output air volume value continuously deviates from its corresponding expected value by more than the allowable error, the control parameter fine-tuning or optimization calculation is triggered.

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