Dirt regulation control method and control device for surface cleaning equipment
By dynamically calculating and updating the dirt detection threshold in the surface cleaning equipment, the problem of detection inaccuracy caused by the aging and contamination of infrared sensors is solved, and the accuracy of dirt detection and the reliability of control are achieved throughout the equipment's life cycle.
Patent Information
- Application Number
- CN202511816001.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-04
- Publication Date
- 2026-02-06
AI Technical Summary
The infrared sensors in existing surface cleaning equipment are susceptible to aging and contamination, leading to a decline in the accuracy of dirt detection and the reliability of control.
By collecting dirt sensing values within multiple monitoring time windows, dynamically calculating and updating the dirt judgment threshold, and using methods such as moving average, quantile, or moving average plus offset superposition, the sensor reading drift is automatically compensated, thereby achieving accuracy in dirt detection and reliability in control.
It effectively overcomes the problem of sensor reading drift, maintains the long-term accuracy of dirt judgment and the reliability of equipment control, and enhances the adaptability and intelligent adjustment capabilities of cleaning equipment.
Smart Images
Figure CN121465447A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of cleaning equipment technology, and in particular to a method and system for dirt control in surface cleaning equipment. Background Technology
[0002] Surface cleaning equipment, a common household cleaning tool, is widely used for cleaning various surfaces such as floors, tiles, and carpets. To improve the adaptability of the cleaning process, existing technologies typically equip surface cleaning equipment with infrared sensors to monitor the degree of dirt on the surface to be cleaned. This device emits infrared light into the surface and receives the reflected light. Based on the effect of the degree of dirt on the reflectivity, it converts this into different electrical signal measurements (such as voltage or digital readings). The device then determines the level of dirt on the surface based on these measurements and dynamically adjusts operating parameters such as equipment power and water output.
[0003] However, during long-term use, infrared sensors are susceptible to internal and external factors, leading to abnormal sensitivity and becoming a source of instability in the system. Specifically: firstly, the devices themselves gradually age over time, causing attenuation of the infrared emitter's light intensity and a decrease in the photoelectric conversion efficiency of the receiver; secondly, dust or water stains easily accumulate on the sensor's optical window, causing attenuation of incident light and obstruction of received light. Both of these situations can cause systematic drift in the sensor's reference readings, thus affecting the accuracy of dirt detection and the reliability of control, and consequently impacting the power consumption adjustment of surface cleaning equipment equipped with the infrared sensor. Summary of the Invention
[0004] To address the aforementioned problems, the present invention aims to provide a method and device for controlling dirt regulation in surface cleaning equipment, thereby achieving accurate dirt detection and reliable control throughout the entire equipment lifecycle.
[0005] Therefore, in a first aspect, the present invention provides a method for dirt adjustment and control of a surface cleaning device, comprising:
[0006] S1: Obtain multiple preset monitoring time windows, collect the dirt sensing values of the surface cleaning device within the first monitoring time window and store them to obtain the first dirt dataset;
[0007] S2: Compare the data in the first dirt dataset with the obtained initial dirt judgment threshold to obtain the dirt level of the target area; adjust the cleaning behavior according to the dirt level of the target area;
[0008] S3: Based on the first dirty dataset, a first dirty judgment threshold is calculated using a preset statistical algorithm, and the first dirty judgment threshold is used to replace the initial dirty judgment threshold.
[0009] S4: Collect and store the dirt sensing values within the second monitoring time window to obtain a second dirt dataset; compare the data in the second dirt dataset with the first dirt judgment threshold to obtain the dirt level of the second target area; adjust the cleaning behavior according to the dirt level of the second target area; calculate the second dirt judgment threshold based on the second dirt dataset using the preset statistical algorithm, and use it for comparison in the next monitoring time window.
[0010] In some embodiments, starting from the second monitoring time window, the dirt judgment threshold for each monitoring time window is the dirt judgment threshold calculated from the previous monitoring time window.
[0011] In some embodiments, the second monitoring time window is continuous with or partially overlaps with the first monitoring time window, and parallel monitoring is performed.
[0012] In some embodiments, steps S2 and S3 can be performed simultaneously.
[0013] In some embodiments, the process of obtaining the degree of dirtiness of the target area includes: counting the data in the first dirty dataset that exceed the initial dirtiness judgment threshold, calculating the proportion of the data in the first dirty dataset, and obtaining the degree of dirtiness of the target area by judgment based on the proportion.
[0014] In some embodiments, the process of obtaining the degree of dirtiness of the target area includes: calculating the average value of the central data of the first dirtiness dataset, and obtaining the degree of dirtiness of the target area by comparing the average value with the initial dirtiness judgment threshold.
[0015] In some embodiments, the preset statistical algorithm is a moving average method, specifically including: calculating the arithmetic mean or weighted average of the first dirty dataset as the first dirty judgment threshold.
[0016] In some embodiments, the preset statistical algorithm is the quantile method, which specifically includes: sorting the data in the first dirty dataset according to size, and selecting a higher quantile as the first dirty judgment threshold.
[0017] In some embodiments, the preset statistical algorithm is a moving average and offset superposition method, which obtains the first dirt judgment threshold by linear calculation based on the average value of the first dirt dataset.
[0018] Secondly, this application provides a control device for a surface cleaning device, the control device being configured to execute the dirt adjustment control method described in the first aspect above, the control device being signal-connected to a dirt detection device of the surface cleaning device, and the control device comprising:
[0019] The data collection module is configured to collect and store dirt sensing values from the dirt detection device according to a preset monitoring time window, forming a dirt dataset;
[0020] The dirt detection module is configured to compare the dirt detection values in the dirt dataset stored by the data collection module with the currently effective dirt detection threshold, and output the dirt level detection result of the target area.
[0021] The threshold calculation module is configured to calculate the dirt judgment threshold for the next monitoring time window based on the data stored in the data collection module and through a preset statistical algorithm.
[0022] The threshold update module is configured to replace the currently used dirt judgment threshold with the dirt judgment threshold for the next monitoring time window calculated by the threshold calculation module.
[0023] The control module, which is communicatively connected to the dirt judgment module, threshold calculation module and threshold update module, is configured to execute the dirt adjustment control method and adjust the cleaning behavior of the surface cleaning equipment according to the dirt degree judgment result of the target area output by the dirt judgment module.
[0024] Compared with the prior art, the beneficial effects of the present invention are as follows: by dynamically calculating and updating the dirt judgment threshold used in the next monitoring time window based on the dirt sensing value dataset collected within the current monitoring time window, the judgment benchmark can automatically adapt to the overall changes in sensor readings, effectively overcoming the benchmark drift problem caused by changes in the sensor's own state, thereby maintaining the long-term accuracy of dirt judgment. Attached Figure Description
[0025] Figure 1 This is a schematic flowchart illustrating a dirt control method for surface cleaning equipment provided in an embodiment of this application.
[0026] Figure 2 This is a schematic diagram showing the correspondence between multiple monitoring time windows and dirt judgment thresholds provided in the embodiments of this application.
[0027] Figure 3 This is a schematic diagram of the framework of a control device provided in an embodiment of this application. Detailed Implementation
[0028] To illustrate the technical content, structural features, achieved objectives, and effects of the invention in detail, the technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. In the following description, for illustrative purposes, numerous specific details are set forth to provide a detailed description of various exemplary embodiments or implementations of the invention. However, various exemplary embodiments may also be implemented without these specific details or in one or more equivalent arrangements. Furthermore, the various exemplary embodiments may differ, but are not necessarily exclusive. For example, the specific shape, construction, and characteristics of the exemplary embodiments may be used or implemented in another exemplary embodiment without departing from the inventive concept.
[0029] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.
[0030] In this embodiment, the surface cleaning equipment includes sensors and a control device. The sensors include dirt detection devices (such as infrared sensors) for real-time detection of environmental dirt conditions and output of sensing values. They can be installed on the cleaning head or the dirt fluid flow channel of the surface cleaning equipment. The control device is located inside the surface cleaning equipment and includes a memory and a processor. The memory is responsible for storing preset programs and dirt data collected by the sensors according to monitoring time windows. The processor is connected to the sensors and the memory respectively and is used to execute control logic and process sensor data.
[0031] In some embodiments, the dirt detection device is an infrared sensor, with an infrared transmitter emitting infrared light toward the target area and an infrared receiver receiving the infrared light from the infrared transmitter and generating a dirt sensing value based on the received infrared light.
[0032] In some embodiments, the various dirt sensing values, including the first dirt sensing value, can be voltage values, so that the magnitude of the voltage value represents different degrees of dirt. In other embodiments, the various dirt sensing values, including the first dirt sensing value, can also be digital values, etc., and the magnitude of the digital value represents different degrees of dirt, etc. This disclosure does not specifically limit the form in which the dirt sensing values are represented.
[0033] Although surface cleaning equipment can adaptively adjust its cleaning behavior based on the readings of the dirt sensor, its control reliability is highly dependent on the long-term stability of the sensor itself. In actual use, the sensor may experience signal distortion due to natural aging (such as attenuation of infrared emitting tube light intensity and decreased efficiency of receiving tube) or contamination of the optical window (dust or water stains obstructing the light path), which severely restricts the accuracy of dirt detection and the reliability of system control.
[0034] Based on this, please refer to Figure 1 This application illustrates a method for controlling dirt levels in a surface cleaning device, comprising:
[0035] S1: Obtain multiple preset monitoring time windows, collect the dirt sensing values of the surface cleaning equipment within the first monitoring time window and store them to obtain the first dirt dataset;
[0036] S2: Compare the data in the first dirty dataset with the obtained initial dirty judgment threshold to obtain the degree of dirtiness in the target area; adjust the cleaning behavior according to the degree of dirtiness in the target area;
[0037] S3: Calculate the first dirt judgment threshold based on the first dirt dataset using a preset statistical algorithm, and replace the initial dirt judgment threshold with the first dirt judgment threshold;
[0038] S4: Collect and store the dirt sensing values within the second monitoring time window to obtain the second dirt dataset; compare the data in the second dirt dataset with the first dirt judgment threshold to obtain the dirt level of the second target area; adjust the cleaning behavior according to the dirt level of the second target area; calculate the second dirt judgment threshold based on the second dirt dataset using a preset statistical algorithm, and use it for comparison in the next monitoring time window.
[0039] In this embodiment, when the surface cleaning equipment is started, an initial dirt judgment threshold can be set based on experimental data of the new equipment in a standard clean environment or the threshold saved before the last shutdown. Then, a preset total monitoring time window is divided into multiple consecutive monitoring time windows. The monitoring time windows can overlap (for example, the first monitoring time window is the first 30 seconds after the equipment is started or the initial stage of entering a certain cleaning area, and the second monitoring time window is a very short calculation cycle that follows the first monitoring time window, or partially overlaps with the first time window (starting from the 27th second, parallel monitoring)).
[0040] In this embodiment, within the first monitoring time window, the dirt sensors (such as infrared dust sensors, image sensors, etc.) on the surface cleaning equipment continuously or periodically collect dirt sensing values (such as voltage values, ADC values, grayscale values, etc.) of the environment. These dirt sensing values are collected and stored in the memory to form a first dirt dataset. The dirt judgment module compares each data point in the first dirt dataset with an initial dirt judgment threshold.
[0041] As one implementation method, the number of data points exceeding the initial dirt judgment threshold in the first dirt dataset is counted, and the proportion of these data points to the total amount of data in the first dirt dataset is calculated. If the proportion is lower than a preset first dirt level value (e.g., 10%), the target area is judged to be "clean," and the control device controls the surface cleaning equipment to perform a regular cleaning mode. If the proportion is between the first dirt level value and a preset second dirt level value, the target area is judged to be "lightly dirty," and the control device controls the surface cleaning equipment to increase suction power or increase the amount of water used for mopping. If the proportion is higher than the second dirt level value, the target area is judged to be "heavily dirty," and the control device controls the surface cleaning equipment to perform a cleaning mode with the highest power, which may trigger a deep cleaning action in place.
[0042] In another implementation, the average value of the first dirt dataset is calculated to obtain the average dirt data. This average value is then compared with an initial dirt assessment threshold. If the average dirt data is less than or equal to the initial threshold, the target area is judged to be "clean," and the control device controls the surface cleaning equipment to perform a regular cleaning mode. If the initial threshold is greater than the average dirt data but less than or equal to a preset second average dirt data value, the target area is judged to be "lightly dirty," and the control device controls the surface cleaning equipment to increase suction power or water volume. If the initial threshold is greater than the second average dirt data value, the target area is judged to be "heavily dirty," and the control device controls the surface cleaning equipment to perform the highest power cleaning mode, potentially triggering a deep cleaning action. The second average dirt data value is obtained by multiplying the average dirt data value by an empirical coefficient greater than 1.
[0043] In this embodiment, steps S2 and S3 can be performed simultaneously. When calculating the dirt judgment threshold (such as the first dirt judgment threshold), at least one data point is extracted from the stored first dirt dataset for calculation. Preferably, all or a statistically representative subset is extracted (e.g., the first 80% of the data or the data after removing the maximum and minimum values). Then, a new dirt judgment threshold (the first dirt judgment threshold) is calculated by processing it through a preset statistical algorithm, so that it takes effect in the next monitoring time window.
[0044] As a basic implementation method, the default statistical algorithm is the moving average method, which calculates the arithmetic mean of all data in the dirty dataset (such as the first dirty dataset) and uses this arithmetic mean as the new dirtyness judgment threshold (such as the first dirtyness judgment threshold). As another basic implementation method, the default statistical algorithm is the quantile method, which sorts all data in the dirty dataset (such as the first dirty dataset) in ascending order of numerical value. Then, a higher quantile (such as the 75th, 80th, or 90th quantile) is selected as the new dirtyness judgment threshold (such as the first dirtyness judgment threshold). This method can effectively filter out instantaneous extreme high values and more stably reflect the sensor's basic offset.
[0045] As a basic implementation method, the default statistical algorithm is the moving average and offset superposition method. Specifically, the average value of the dirty dataset (such as the first dirty dataset) is first calculated, and then this average value is multiplied by a coefficient and added to a fixed offset to obtain a new dirty judgment threshold (such as the first dirty judgment threshold).
[0046] The formula can be expressed as: Dirt judgment threshold for the next monitoring time window = α * average value of the dataset + β. Here, α is a coefficient slightly greater than 1 (e.g., 1.05-1.2), and β is a fixed offset to provide a safety margin and prevent the new threshold from being set too low. This method ensures that the threshold is always slightly higher than the average distribution level of the data, improving the sensitivity of the judgment and avoiding missed detections.
[0047] In this implementation, upon entering the second monitoring time window, the system repeats the following process:
[0048] Collect the sensor values within the window to form a second dirt dataset; compare the data in the second dirt dataset with the first dirt judgment threshold to obtain the current dirt level of the target area, and adjust the cleaning behavior accordingly; calculate the second dirt judgment threshold based on the second dirt dataset using the same preset statistical algorithm; replace the first dirt judgment threshold with the second dirt judgment threshold for comparison in the third monitoring time window.
[0049] This process is repeated until the cleaning task is completed. Figure 2 As shown, each monitoring time window (T1, T2, T3, T4, T5...) corresponds to a dirt judgment threshold (n1, n2, n3, n4, n5...) for the next monitoring time window. In this way, the dirt judgment threshold can be dynamically adjusted according to the data distribution center collected by the sensor, automatically compensating for the reading baseline drift caused by dust on the sensor mirror, device aging, etc., and realizing adaptive intelligent adjustment.
[0050] This embodiment provides a control device for a surface cleaning equipment, which is capable of executing the aforementioned intelligent dirt adjustment control method. The control device is disposed within the surface cleaning equipment and is signal-connected to a dirt detection device. Figure 3 As shown, the control device 200 includes:
[0051] The data collection module 201 is configured to collect and store dirt sensing values from the dirt detection device according to a preset monitoring time window, forming a dirt dataset;
[0052] Dirt judgment module 202: is configured to read the currently effective dirt judgment threshold and the dirt sensing value in the dirt dataset stored by data collection module 201, and output the dirt degree judgment result of the target area by comparison (such as calculating the excess ratio or directly comparing the average value).
[0053] Threshold calculation module 203: It is configured to use a preset statistical algorithm (such as moving average method, quantile method, etc.) to calculate the dirt judgment threshold for the next monitoring time window based on the complete dataset saved by the data collection module 201.
[0054] Threshold update module 204: is configured to replace the currently used dirt judgment threshold with the dirt judgment threshold for the next monitoring time window calculated by threshold calculation module 202.
[0055] Control module 205: It is communicatively connected to data collection module 201, dirt judgment module 202, threshold calculation module 203 and threshold update module 204, and is configured to execute the dirt intelligent adjustment method and adjust the cleaning behavior of the surface cleaning equipment according to the dirt degree judgment result of the target area output by dirt judgment module 202.
[0056] In summary, this invention can adjust the suction power of the cleaning equipment based on the dirt detection value monitored by the dirt detection device in the current usage state, which is real-time; it can also update the dirt judgment threshold of the current time window based on the data collected in the previous monitoring time window, thereby achieving the accuracy of dirt detection and the reliability of control throughout the entire equipment life cycle.
[0057] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope. The scope of protection of the present invention is defined by the appended claims, specification, and their equivalents.
Claims
1. A dirty adjustment control method for a surface cleaning apparatus, characterized by, The method comprises: S1: obtaining a plurality of preset monitoring time windows, collecting and storing the dirt sensing values of the surface cleaning device in the first monitoring time window to obtain a first dirt data set; S2: comparing the data in the first dirt data set with the obtained initial dirt judgment threshold to obtain the target area dirt degree; adjusting the cleaning behavior according to the target area dirt degree; S3: calculating the first dirt judgment threshold based on the first dirt data set through a preset statistical algorithm, and replacing the initial dirt judgment threshold with the first dirt judgment threshold; S4: collecting and storing the dirt sensing values in the second monitoring time window to obtain a second dirt data set; comparing the data in the second dirt data set with the first dirt judgment threshold to obtain a second target area dirt degree; adjusting the cleaning behavior according to the second target area dirt degree; calculating the second dirt judgment threshold based on the second dirt data set through the preset statistical algorithm, and using it for comparison in the next monitoring time window.
2. The soiling adjustment control method according to claim 1, characterized by, Starting from the second monitoring time window, the dirt judgment threshold of each monitoring time window is the dirt judgment threshold calculated in the previous monitoring time window.
3. The soiling adjustment control method according to claim 1, characterized by, The second monitoring time window is continuous with the first monitoring time window or partially overlaps with the first monitoring time window, and parallel monitoring is performed.
4. The soiling adjustment control method according to claim 1, characterized by, The step S2 and the step S3 can be performed synchronously.
5. The soiling adjustment control method according to claim 1, characterized by, The target area dirt degree obtaining process comprises: counting the data in the first dirt data set that exceeds the initial dirt judgment threshold, calculating the proportion of the data in the first dirt data set, and obtaining the target area dirt degree by judging according to the proportion.
6. The soiling adjustment control method according to claim 1, characterized by The target area dirt degree obtaining process comprises: calculating the average value of the data in the first dirt data set, and obtaining the target area dirt degree by comparing the average value with the initial dirt judgment threshold.
7. The soiling adjustment control method according to claim 1, characterized by, The preset statistical algorithm is a moving average method, which specifically comprises: calculating the arithmetic mean or weighted mean of the first dirt data set as the first dirt judgment threshold.
8. The soiling adjustment control method according to claim 7, characterized by, The preset statistical algorithm is a quantile method, which specifically comprises: sorting the data in the first dirt data set according to size, and selecting a higher quantile as the first dirt judgment threshold.
9. The soiling adjustment control method according to claim 7, characterized by, The preset statistical algorithm is a moving average and offset superposition method, which obtains the first dirt judgment threshold by linear calculation based on the average value of the first dirt data set.
10. A control device for a surface cleaning apparatus, the control device configured to perform the dirty adjustment control method of any one of claims 1-9, the control device in signal communication with a dirty detection device of the surface cleaning apparatus, characterized in that, The control device comprises: a data collection module configured to collect and store dirt sensing values from a dirt detection device according to a preset monitoring time window to form a dirt data set; a dirt judgment module configured to compare the dirt sensing values in the dirt data set stored by the data collection module with a currently effective dirt judgment threshold, and output a target area dirt degree judgment result; a threshold calculation module configured to calculate a next monitoring time window contamination judgment threshold value based on the data stored in the data collection module through a preset statistical algorithm; a threshold updating module configured to replace the currently used contamination judgment threshold value with the next monitoring time window contamination judgment threshold value calculated by the threshold calculation module; a control module in communication connection with the contamination judgment module, the threshold calculation module and the threshold updating module, configured to execute the contamination adjustment control method and adjust the cleaning behavior of the surface cleaning device according to the target area contamination degree judgment result output by the contamination judgment module.