A State-Aware Self-Cleaning Process Control Method for Vacuum Cleaners

Abstract

This invention relates to the field of vacuum cleaner self-cleaning technology and discloses a state-aware self-cleaning process control method for vacuum cleaners, applied to a vacuum cleaner. The vacuum cleaner includes a dust collection box, a sensor assembly disposed on the side wall of the dust collection box, and a controller. The state-aware self-cleaning process control method includes acquiring data collected by the sensor assembly within a predetermined time period. This invention constructs a multi-dimensional dust obstruction quantification model that integrates signal attenuation, signal fluctuation, and signal-to-noise ratio changes. This model can accurately distinguish between dust adhering to the sensor surface and the actual dust-filled state of the dust collection box, avoiding misjudgment of dust fullness due to side wall dust obstruction, which could lead to the vacuum motor stopping. Simultaneously, it adaptively triggers the sensor's self-cleaning action based on the obstruction index, fundamentally solving the technical problem of signal distortion caused by sensor contamination in traditional infrared dust-filled detection schemes.

Description

Technical Field

[0001] This invention relates to the field of vacuum cleaner self-cleaning technology, and in particular to a method for controlling the self-cleaning process of a vacuum cleaner based on state perception. Background Technology

[0002] With the increasing popularity of smart homes, cleaning equipment such as vacuum cleaners and floor scrubbers have widely adopted dust full detection functions to remind users to empty the dustbin or perform self-cleaning. Currently, one of the mainstream dust full detection solutions involves installing an infrared sensor on the side wall of the dustbin. This sensor detects whether the light beam between the transmitter and receiver is blocked by dust to determine the dust full status. However, in actual long-term use, this technical solution has significant drawbacks: First, fine dust particles easily adhere to the sensor's light-transmitting window on the side wall of the dustbin. Even if the dustbin isn't truly overflowing, the dust layer on the side wall can partially or completely block the infrared beam, causing the sensor to output a false "dust full" signal. Upon receiving this false alarm, the device will frequently stop vacuuming or issue an alarm, severely interfering with normal cleaning tasks. While some devices have a "continue cleaning even when overflowing" forced operating mode, the sensor signal is already distorted, making it impossible for the device to accurately distinguish between "side wall dust obstruction" and "true dust fullness." This mode doesn't fundamentally solve the problem, often forcing users to manually clean the sensor surface, resulting in a poor user experience.

[0003] Secondly, the higher the cleaning frequency, the more fine dust the vacuum cleaner sucks in, and the faster the dust accumulates on the side walls of the dustbin, leading to a higher frequency of sensor false alarms. Existing technologies lack quantitative assessment methods for the degree of contamination of the sensors themselves, and cannot adaptively perform sensor self-cleaning or shield false alarm signals based on the sensor's real-time status. For example, while patent application CN119214537A uses a color sensor to detect the color temperature of the medium inside the suction pipe to determine the level of dirt, it detects the sewage or dust flowing through the pipe, not the contamination level of the sensor window itself, and does not address compensation or self-cleaning control for sensor signal failure caused by surface dust accumulation. Similarly, while patent application CN117398032A involves roller brush self-cleaning, it cleans the roller brush itself and does not sense or process the dust obstruction status of the sensors on the side walls of the dustbin. Summary of the Invention

[0004] This invention provides a state-aware method for controlling the self-cleaning process of a vacuum cleaner, which solves the problem of signal distortion caused by sensor contamination in traditional infrared dust detection schemes.

[0005] To address the aforementioned technical problems, according to one aspect of the present invention, more specifically, a state-aware self-cleaning process control method for a vacuum cleaner is provided, applied to a vacuum cleaner comprising a dust collection box, a sensor assembly disposed on the side wall of the dust collection box, and a controller. The state-aware self-cleaning process control method for the vacuum cleaner includes: Step S1: Acquire the data collected by the sensor component within a predetermined time period; Step S2: Extract at least one feature index from the collected data to characterize the sensor state; Step S3: Input the feature index into the preset dust blocking quantification model for calculation to obtain the current sensor blocking index; Step S4: Determine whether the sensor assembly is in a dust-blocked state based on the sensor blocking index. Step S5: If it is determined that the sensor is blocked by dust, a first self-cleaning command is generated and executed. The first self-cleaning command is used to initiate the cleaning action of the sensor component.

[0006] Furthermore, the sensor assembly is an infrared light sensor, including an infrared transmitter and an infrared receiver; the collected data includes the infrared signal intensity value received by the infrared receiver.

[0007] Furthermore, the characteristic indicators include at least one of signal attenuation rate, signal fluctuation amplitude, and signal-to-noise ratio change value.

[0008] Furthermore, the dust blocking quantification model calculates the sensor blocking index using the following formula: ; In the above formula, Indicates the sensor obstruction index; This represents the infrared signal strength value at the current moment. This indicates the initial infrared signal intensity value of the sensor assembly in a clean state; Indicates the current signal attenuation ratio; This indicates its weighting coefficient; This represents the average absolute fluctuation of the signal value within a predetermined time period; This indicates its weighting coefficient; This represents the real-time signal-to-noise ratio at the current moment. This represents the baseline signal-to-noise ratio under clean conditions; Indicates the rate of change of signal-to-noise ratio; This indicates its weighting coefficient.

[0009] Furthermore, when the sensor obstruction index is greater than a preset first threshold, it is determined to be in a dust obstruction state.

[0010] Furthermore, the temperature and humidity data of the vacuum cleaner are acquired, wherein the dust blocking quantification model includes a temperature compensation coefficient and a humidity compensation coefficient, and the specific calculation formula for the sensor blocking index is as follows: ; In the above formula, Indicates the temperature compensation coefficient; This represents the humidity compensation coefficient.

[0011] Furthermore, the historical cleaning frequency of the sensor component and the duration of recovery to a clean state after each cleaning are recorded. When the sensor blockage index is between the second threshold and the first threshold, if it is determined that the duration of recovery to a clean state is less than a preset time threshold and the historical cleaning frequency exceeds a preset frequency threshold, then the sensor component is determined to be in a high-frequency dust accumulation state, and a second self-cleaning command is generated and executed. The second self-cleaning command corresponds to a cleaning mode that is stronger than the first self-cleaning command.

[0012] Furthermore, when the sensor determines that the device is in a dust-blocked state based on the sensor's blocking index, the sensor component's signal output for triggering a dust full alarm is temporarily disabled, and the vacuum cleaner's motor is controlled to continue operating.

[0013] Furthermore, step 5 generates and executes the first self-cleaning command to initiate the cleaning action on the sensor components, specifically including: Step 1: Control the miniature air pump or fan located near the sensor assembly to spray airflow onto the photosensitive surface of the sensor assembly; Step 2: Control the airflow direction inside the dust collection box to create a local negative pressure on the surface of the sensor assembly to adsorb dust; Step 3: Control the vibration element set on the sensor assembly to generate high-frequency vibration.

[0014] This invention provides a state-aware self-cleaning process control method for vacuum cleaners, which, compared to existing technologies, achieves the following advantages: 1. This invention constructs a multi-dimensional dust obstruction quantification model that integrates signal attenuation, signal fluctuation, and signal-to-noise ratio changes. This model can accurately distinguish between dust adhering to the sensor surface and the actual dust-filled state of the dust collection box, avoiding misjudgment of dust fullness due to dust blocking the side walls, which would cause the vacuum motor to stop. At the same time, based on the obstruction index, it adaptively triggers the sensor's self-cleaning action, fundamentally solving the technical problem of signal distortion caused by sensor contamination in traditional infrared dust fullness detection schemes.

[0015] 2. This invention introduces temperature compensation coefficient and humidity compensation coefficient to correct the sensor blocking index for environmental conditions, eliminating the interference of infrared signal drift caused by temperature and humidity changes on dust blocking judgment, so that the blocking index always maintains comparability and stability in different seasons and different regional environments, thereby significantly improving the robustness and environmental adaptability of self-cleaning trigger conditions.

[0016] 3. This invention records the historical cleaning frequency of the sensor components and the duration of the cleanliness state after each cleaning. When the blockage index is in the middle threshold range, it intelligently identifies the high-frequency dust accumulation state and automatically switches to a stronger second cleaning mode. This achieves adaptive upgrade processing for repeated and rapid dust accumulation scenarios of the sensor, effectively avoiding energy waste and repeated alarm problems caused by inefficient cleaning.

[0017] 4. When the present invention determines that the sensor is blocked by dust, it temporarily blocks the dust full alarm signal output and keeps the vacuum motor working. At the same time, it combines three cleaning actions, namely air jet, local negative pressure adsorption and high frequency vibration, to clean the sensor components. This ensures that the vacuum cleaner can continue to perform cleaning tasks during the short-term failure of the sensor, which significantly improves the continuous working capability of the device and the user experience. Attached Figure Description

[0018] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of a sample in Embodiment 1 of the present invention; Figure 3 This is a flowchart of Embodiment 2 of the present invention. Detailed Implementation

[0019] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Example 1 like Figure 1 As shown, according to one aspect of the present invention, a state-aware self-cleaning process control method for a vacuum cleaner is provided, applied to a vacuum cleaner, the vacuum cleaner including a dust collection box, a sensor assembly disposed on the side wall of the dust collection box, and a controller, the state-aware self-cleaning process control method for the vacuum cleaner including: Step S1: Acquire the data collected by the sensor components within a predetermined time period; Step S2: Extract at least one feature index from the collected data to characterize the sensor state; Step S3: Input the feature index into the preset dust blocking quantification model for calculation to obtain the current sensor blocking index; the dust blocking quantification model calculates the sensor blocking index using the following formula: ; In the above formula, This indicates the sensor's obstruction index; a higher value indicates more severe dust obstruction. Indicates the current time The infrared receiver signal strength (raw measurement value); This indicates the initial infrared signal intensity of the sensor assembly in a clean state (reference value, calibrated at the factory or immediately after cleaning). This represents the average signal strength over a predetermined time period. Indicates the current signal attenuation ratio; This indicates its weighting coefficient; This represents the average absolute fluctuation of the signal value within a predetermined time period; This indicates its weighting coefficient; This represents the real-time signal-to-noise ratio at the current moment. This represents the baseline signal-to-noise ratio under clean conditions; Indicates the rate of change of signal-to-noise ratio; This indicates its weighting coefficient.

[0021] When the sensor's blocking index is greater than a preset first threshold, it is determined to be in a dust blocking state.

[0022] Step S4: Determine whether the sensor component is in a dust-blocking state based on the sensor obstruction index; if it is determined to be in a dust-blocking state based on the sensor obstruction index, temporarily disable the signal output of the sensor component used to trigger the dust full alarm, and control the vacuum cleaner's vacuum motor to continue working.

[0023] Step S5: If it is determined that the sensor is blocked by dust, a first self-cleaning command is generated and executed. The first self-cleaning command is used to initiate the cleaning action of the sensor component.

[0024] In this embodiment, the sensor component is an infrared light sensor, including an infrared transmitter and an infrared receiver; the collected data includes the infrared signal intensity value received by the infrared receiver. Characteristic indicators include at least one of signal attenuation rate, signal fluctuation amplitude, and signal-to-noise ratio change.

[0025] The infrared sensor installed on the side wall of the vacuum cleaner's dustbin works on the principle that the infrared emitter emits a fixed-intensity infrared beam, and the infrared receiver receives the signal intensity after reflection or direct illumination from dust inside the dustbin. As the sensor surface gradually becomes obscured by dust, the received signal intensity attenuates, fluctuates more, and the signal-to-noise ratio decreases. To quantify this "degree of dust obstruction," rather than simply determining "full / not full," a multi-dimensional, comprehensive mathematical model is needed. Therefore, the specific steps for constructing this mathematical model are as follows: I. Signal Attenuation Factor ; Among them, ideal clean time ,but When dust completely blocks it (extreme case) If it approaches 0, then Approaching 1. This indicates the relative attenuation of the current signal compared to the reference. The thicker the dust layer, the greater the loss of transmitted or reflected light intensity.

[0026] The formula uses "1 - ratio" instead of the ratio itself because it makes the attenuation level positively correlated with the blocking index D, which facilitates subsequent threshold determination. However, relying solely on amplitude attenuation cannot distinguish between "uniform dust coverage" and "localized, accidental occlusion by large particles." The latter has a large instantaneous attenuation but may detach quickly, thus requiring coordination with other terms.

[0027] II. Signal Fluctuation Factor ; This represents the average absolute deviation of the signal within the time window. When the sensor surface is unevenly dusted or disturbed by airflow, the received signal will fluctuate rapidly. Therefore, the instability of the quantified signal is considered. In a clean state, the signal is stable. Furthermore, as dust accumulates, the dust particles fly in the airflow, causing intermittent blockage of the optical path, resulting in fluctuating signal strength.

[0028] Furthermore, the mean absolute deviation is less sensitive to outliers, better reflects the fluctuation characteristics caused by dust "shaking off," and its dimensions are consistent with the original signal, making it easy to combine with the first term. Therefore, even if the average signal strength decreases only slightly (e.g., only a small amount of dust drifts in front of the lens), the fluctuation term B can rise rapidly, triggering early obstruction detection.

[0029] III. Signal-to-noise ratio attenuation factor ; Among them, during clean time At this time there is As dust obstructs the signal, the effective signal weakens while the noise level (due to scattering and stray light) may increase or remain the same, leading to... Therefore Therefore, the signal-to-noise ratio (SNR) directly reflects the measurement quality of the sensor. Even if the first two parameters do not change significantly (e.g., uniform semi-transparent dust causes the signal to decay slowly and fluctuate little), a decrease in the SNR can keenly detect the process of the signal becoming "dirty".

[0030] IV. Typical value ranges for weighting coefficients (based on test experience for infrared light sensors): (Amplitude decay is the most important indicator); (Wave-assisted judgment of airflow disturbance); (Signal-to-noise ratio as a fine-grained correction).

[0031] Furthermore, in a clean state, ,in In the event of slight obstruction (which can trigger sensor self-cleaning), ,in In cases of severe obstruction (where dust full detection must be stopped and cleaning must be forcibly performed), among which... .

[0032] like Figure 2 As shown, the data includes the following different sensor status data, covering scenarios ranging from completely clean to severely dust-blocked, as well as high fluctuations and signal-to-noise ratio degradation caused by dust movement:

[0033] A multi-dimensional dust blocking quantification model was constructed. This model does not rely on a single sensor signal (such as infrared intensity value) for simple threshold judgment, but instead simultaneously extracts three feature indicators: signal attenuation rate, signal fluctuation amplitude, and signal-to-noise ratio change. It calculates the attenuation ratio of the current signal relative to a cleanliness baseline, the average absolute fluctuation within a time window, and the signal-to-noise ratio change rate, and assigns weight coefficients to each (typically set to...). , , The model can comprehensively reflect the thickness and unevenness of dust coverage, as well as the degree of signal quality degradation. When the calculated sensor obstruction index exceeds the preset first threshold, the system determines that the dust is obstructed and immediately generates the first self-cleaning command. At the same time, it temporarily disables the dust full alarm output to avoid the vacuum motor stopping due to the sensor being blocked and the dust box being mistakenly judged to be full.

[0034] Compared to existing technologies that rely solely on signal strength attenuation or simple timing-triggered cleaning, this method significantly improves the accuracy and robustness of dust obstruction recognition. By introducing fluctuation factors and signal-to-noise ratio factors, it can effectively distinguish between different scenarios such as "uniform dust coverage," "occasional obstruction by large local particles," and "dust drift caused by airflow disturbances," avoiding frequent false cleaning due to instantaneous interference or missed cleaning due to slow attenuation. Experimental data shows that when the obstruction index increases from 0.083 (slight adhesion) to 0.438 (heavy dust), the system can adaptively trigger cleaning actions, and by temporarily delaying the dust full alarm, it ensures that the vacuum cleaner can continue to work during the brief sensor failure period, improving user experience and cleaning efficiency.

[0035] Example 2 like Figure 3As shown, according to one aspect of the present invention, a state-aware self-cleaning process control method for a vacuum cleaner is provided, applied to a vacuum cleaner, the vacuum cleaner including a dust collection box, a sensor assembly disposed on the side wall of the dust collection box, and a controller, the state-aware self-cleaning process control method for the vacuum cleaner including: Step S1: Acquire the data collected by the sensor components within a predetermined time period; Step S2: Extract at least one feature index from the collected data to characterize the sensor state; Step S3: Input the feature index into the preset dust blocking quantification model for calculation to obtain the current sensor blocking index; This involves acquiring temperature and humidity data from the vacuum cleaner. The dust blocking quantification model includes temperature and humidity compensation coefficients. The specific formula for calculating the sensor blocking index is as follows: ; In the above formula, Indicates the temperature compensation coefficient; This represents the humidity compensation coefficient.

[0036] Record the historical cleaning frequency of the sensor component and the duration of the clean state after each cleaning; when the sensor blockage index is between the second threshold and the first threshold, if it is determined that the duration of the clean state is less than the preset time threshold and the historical cleaning frequency exceeds the preset frequency threshold, then it is determined that the sensor component is in a high-frequency dust accumulation state, and a second self-cleaning command is generated and executed. The second self-cleaning command corresponds to a cleaning mode that is stronger than the first self-cleaning command.

[0037] Step S4: Determine whether the sensor assembly is blocked by dust based on the sensor blocking index. Step S5: If it is determined that the sensor is blocked by dust, a first self-cleaning command is generated and executed. The first self-cleaning command is used to initiate the cleaning action of the sensor assembly. This step of generating and executing the first self-cleaning command to initiate the cleaning action of the sensor assembly specifically includes: Step 1: Control the miniature air pump or fan located near the sensor assembly to spray airflow onto the photosensitive surface of the sensor assembly; Step 2: Control the airflow direction inside the dust collection box to create a local negative pressure on the surface of the sensor assembly to adsorb dust; Step 3: Control the vibration element set on the sensor assembly to generate high-frequency vibration.

[0038] Among them, temperature compensation coefficient The acquisition process is as follows: 1. Assuming there is no dust or temperature influence, the received signal is: When only temperature changes (no dust), the received signal is: ; in, This is the temperature transfer function, which can be determined experimentally. Experimental steps: 1. Measure the received signal strength on a clean sensor surface at different ambient temperatures (e.g., 5℃-45℃, in 5℃ increments).

[0039] 2. Calculate the normalized signal based on 25℃: ; 3. Fitting yields empirical formulas, typically using linear or quadratic functions: ; in, This represents the average temperature coefficient (e.g., 0.005 / ℃).

[0040] II. In practical applications, the purpose of the temperature compensation coefficient is to eliminate the disturbance of temperature on the calculation of the blocking index. Because the original... All terms in the formula (the formula in Example 1) are calculated based on signal amplitude and noise. Temperature changes will cause... , This deviates from the true value under pure dust obstruction. Therefore, a temperature compensation coefficient is defined. for: (Under clean conditions); Measured signal when dust is present ,in Let's assume the signal is generated by pure dust without any temperature effect. Then the corrected true signal should be: ; Similarly, signal-to-noise ratio Affected by temperature: ; in, The effect of temperature on signal-to-noise ratio (usually) (And it decreases with increasing temperature). Therefore, the signal-to-noise ratio compensation coefficient should be... Similar or independent calibration. To simplify the model, a unified calibration method is used. Corrections can be made to the signal and signal-to-noise ratio terms, or the compensation can be applied directly to the entire signal. (The formula in Example 1 is denoted as) The product factor of ), that is: ; in, This is the raw exponent calculated directly from uncompensated measurements. Since signal attenuation due to temperature and attenuation due to dust are mathematically multiplicative, [the following is a separate, unrelated statement:] ... As a leading factor in the entire expression, it can negate the effect of temperature. A more rigorous derivation is as follows: Assuming the signal is dust-free and at the reference temperature, it is... When there is dust but no temperature effect, the signal attenuation ratio is: ; The signal obtained from the actual measurement is: ; Calculating the first term directly will yield: ; It seems that the effect of temperature has been eliminated, but this depends on This is a cleanliness baseline measured at the current temperature. If the system only stores baselines at a fixed temperature... If so, compensation is necessary. Therefore, dynamic calibration can be performed at each startup or periodically in a clean state. and To adapt to ambient temperature, temperature drift to the reference has been dynamically updated and absorbed. However, the nonlinear effects of temperature on the signal-to-sound ratio and fluctuation terms, as well as the need for timely reference updates, still require correction. Therefore, a new approach is introduced... As an empirical correction factor, its functional relationship with temperature was determined experimentally. or ; in, The temperature effect coefficient calibrated for the experiment (e.g., 0.002-0.01 / ℃).

[0041] So, humidity compensation coefficient The acquisition process is as follows: Similar to temperature, a humidity transfer function is defined. ,in Relative humidity (%). Signal strength was measured at different humidity levels under clean sensor conditions and constant temperature. Normalization: ; Specifically, the signal attenuation can be 5%-15% when the humidity increases from 50% to 90%. An empirical formula is obtained through fitting: (Linear) or ; The humidity compensation coefficient is defined as: ; Humidity also affects the fluctuation term and signal-to-noise ratio. At high humidity levels, water vapor condensation and dust absorption cause slow signal drift and increased fluctuations. Similar to temperature, [the following text is incomplete and requires further context to translate accurately]. As a multiplicative factor, placed at the outermost layer of the formula, it can simultaneously compensate for signal amplitude and fluctuation characteristics. In common indoor environments (temperature 10-35℃, humidity 30%-80%), the effects of temperature and humidity are approximately independent and multiplicatively separable.

[0042] By introducing environmental adaptation and intelligent decision-making mechanisms, on the one hand, the system collects temperature and humidity data from the vacuum cleaner, constructs temperature compensation coefficients and humidity compensation coefficients respectively, and applies them as multiplicative factors to the original blocking index calculation formula. This eliminates the nonlinear disturbances of environmental temperature and humidity changes on the amplitude, signal-to-noise ratio, and fluctuation characteristics of the infrared signal, ensuring the consistency of the blocking index in different seasons and geographical environments. On the other hand, the system records the historical cleaning frequency of the sensor components and the duration of recovery to a clean state after each cleaning. When the current blocking index is between the second threshold (lower threshold) and the first threshold (higher threshold), it combines historical data to determine whether it is in a "high-frequency dust accumulation state"—that is, the recovery time to clean is too short and the cleaning frequency is too high. At this time, a second self-cleaning command is generated and executed, corresponding to a stronger cleaning mode than the first self-cleaning command (such as increasing the airflow jet pressure, extending the vibration time, or combining multiple cleaning actions).

[0043] This embodiment addresses the technical problem of existing self-cleaning methods being unable to adapt to environmental changes and differences in user habits. Through temperature and humidity compensation, it avoids false or missed triggers caused by natural signal drift in high-temperature or high-humidity environments, ensuring the comparability and stability of the sensor blockage index under different operating conditions. Simultaneously, by introducing intelligent recognition of high-frequency dust accumulation, the system can distinguish between "occasional dust obstruction" and "repeated rapid dust accumulation caused by sensor installation location or vacuum cleaner structural defects," and automatically upgrades cleaning intensity, avoiding energy waste from inefficient cleaning and repeated alarms due to insufficient cleaning effect. This adaptive adjustment strategy based on historical data significantly extends the effective working cycle of the sensor components, reduces the frequency of manual intervention, and improves the vacuum cleaner's intelligence level and long-term operational reliability.

[0044] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A state-aware self-cleaning process control method for a vacuum cleaner, applied to a vacuum cleaner, the vacuum cleaner including a dust collection box, a sensor assembly disposed on the side wall of the dust collection box, and a controller, characterized in that, The state-aware-based method for controlling the self-cleaning process of a vacuum cleaner includes: Step S1: Acquire the data collected by the sensor component within a predetermined time period; Step S2: Extract at least one feature index from the collected data to characterize the sensor state; Step S3: Input the feature index into the preset dust blocking quantification model for calculation to obtain the current sensor blocking index; Step S4: Determine whether the sensor assembly is in a dust-blocked state based on the sensor blocking index. Step S5: If it is determined that the sensor is blocked by dust, a first self-cleaning command is generated and executed. The first self-cleaning command is used to initiate the cleaning action of the sensor component.

2. The vacuum cleaner self-cleaning process control method based on state perception according to claim 1, characterized in that: The sensor assembly is an infrared light sensor, including an infrared transmitter and an infrared receiver; the collected data includes the infrared signal intensity value received by the infrared receiver.

3. The vacuum cleaner self-cleaning process control method based on state perception according to claim 1, characterized in that: The characteristic indicators include at least one of signal attenuation rate, signal fluctuation amplitude, and signal-to-noise ratio change.

4. The vacuum cleaner self-cleaning process control method based on state perception according to claim 1, characterized in that: The dust blocking quantification model calculates the sensor blocking index using the following formula: ； In the above formula, Indicates the sensor obstruction index; This represents the infrared signal strength value at the current moment; This indicates the initial infrared signal intensity value of the sensor assembly in a clean state; This indicates its weighting coefficient; This indicates its weighting coefficient; This represents the real-time signal-to-noise ratio at the current moment. This represents the baseline signal-to-noise ratio under clean conditions; This indicates its weighting coefficient.

5. The vacuum cleaner self-cleaning process control method based on state perception according to claim 4, characterized in that: When the sensor's blocking index is greater than a preset first threshold, it is determined to be in a dust blocking state.

6. The vacuum cleaner self-cleaning process control method based on state perception according to claim 4, characterized in that: The temperature and humidity data of the vacuum cleaner are acquired, wherein the dust blocking quantification model includes a temperature compensation coefficient and a humidity compensation coefficient, and the specific calculation formula for the sensor blocking index is as follows: ； In the above formula, Indicates the temperature compensation coefficient; This represents the humidity compensation coefficient.

7. The vacuum cleaner self-cleaning process control method based on state perception according to claim 4, characterized in that: Record the historical cleaning frequency of the sensor assembly and the duration of the cleanliness recovery after each cleaning; When the sensor blockage index is between the second threshold and the first threshold, if the duration of restoring the clean state is less than the preset time threshold and the historical cleaning frequency exceeds the preset frequency threshold, the sensor component is determined to be in a high-frequency dust accumulation state, and a second self-cleaning command is generated and executed. The second self-cleaning command corresponds to a cleaning mode that is stronger than the first self-cleaning command.

8. The vacuum cleaner self-cleaning process control method based on state perception according to claim 1, characterized in that: When the sensor determines that the device is in a dust-blocked state based on the sensor's blocking index, the sensor component's signal output for triggering the dust full alarm is temporarily disabled, and the vacuum cleaner's motor is controlled to continue working.

9. The method for controlling the self-cleaning process of a vacuum cleaner based on state perception according to claim 1, characterized in that: Step 5 generates and executes the first self-cleaning command to initiate the cleaning process for the sensor components, specifically including: Step 1: Control the miniature air pump or fan located near the sensor assembly to spray airflow onto the photosensitive surface of the sensor assembly; Step 2: Control the airflow direction inside the dust collection box to create a local negative pressure on the surface of the sensor assembly to adsorb dust; Step 3: Control the vibration element set on the sensor assembly to generate high-frequency vibration.

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