Control method, system, device and medium for pool cleaning robot

Abstract

The present disclosure provides a control method, system, device and medium of a pool cleaning robot, relating to the technical field of robots, the method comprising: collecting pool cleaning robot turbidity data through a turbidity sensor arranged at a water outlet position of the pool cleaning robot; wherein the turbidity data comprises a current turbidity value; calculating a relative turbidity based on the current turbidity value and a preset baseline turbidity value; calculating a turbidity change rate based on the current turbidity value and a turbidity value collected at a previous time; determining a cleaning condition of a current area according to the turbidity change rate and the relative turbidity, or determining the cleaning condition of the current area only according to the turbidity change rate; determining a cleaning strategy of the pool cleaning robot based on the cleaning condition; and cleaning the current area according to the cleaning strategy. The method can effectively ensure the cleaning effect and improve the cleaning efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of robotics technology, and in particular to a control method, system, device and medium for a pool cleaning robot. Background Technology

[0002] In related technologies, pool cleaning robots typically employ fixed cleaning patterns and paths to clean pools. While this method can achieve a certain level of pool cleaning, it is prone to issues such as the path covering areas where dirt remains, over-cleaning, repeated scrubbing of already clean areas, wasted time and electricity when cleaning lightly soiled pools, and incomplete cleaning of heavily soiled pools. These problems result in inconsistent cleaning performance and low cleaning efficiency for pool cleaning robots. Summary of the Invention

[0003] This disclosure provides a control method, system, device, and medium for a swimming pool cleaning robot.

[0004] According to a first aspect of this disclosure, a control method for a pool cleaning robot is provided, comprising: Turbidity data of the pool cleaning robot is collected by a turbidity sensor installed at the water outlet of the pool cleaning robot; wherein, the turbidity data includes the current turbidity value; Calculate the relative turbidity based on the current turbidity value and the preset baseline turbidity value; Based on the current turbidity value and the turbidity value collected at the previous moment, calculate the turbidity change rate; The cleanliness of the current area is determined based on the turbidity change rate and the relative turbidity; or, the cleanliness of the current area is determined based solely on the turbidity change rate. The cleaning strategy of the pool cleaning robot is determined based on the cleaning situation; Clean the current area according to the cleaning strategy described.

[0005] According to a second aspect of this disclosure, a control system for a pool cleaning robot is provided, comprising: The main body of the pool cleaning robot; A turbidity sensor is installed at the water outlet of the pool cleaning robot body to collect turbidity data of the pool cleaning robot; wherein, the turbidity data includes the current turbidity value; the water outlet includes the system water outlet or drain; the turbidity sensor is an infrared scattering turbidity sensor; The control unit, connected to the turbidity sensor, is used to collect turbidity data collected by the turbidity sensor; calculate relative turbidity based on the current turbidity value and a preset baseline turbidity value; calculate the turbidity change rate based on the current turbidity value and the turbidity value collected at the previous moment; determine the cleanliness of the current area based on the turbidity change rate and the relative turbidity; determine the cleaning strategy of the pool cleaning robot based on the cleanliness; and clean the current area according to the cleaning strategy.

[0006] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the control method for the pool cleaning robot described in the first aspect above.

[0007] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to perform the control method for the pool cleaning robot described in the first aspect above.

[0008] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the control method for a pool cleaning robot as described in the first aspect above.

[0009] In this embodiment, a turbidity sensor installed at the water outlet of the pool cleaning robot collects turbidity data. This turbidity data includes the current turbidity value. Relative turbidity is calculated based on the current turbidity value and a preset baseline turbidity value. A turbidity change rate is calculated based on the current turbidity value and the turbidity value collected at the previous moment. The cleaning status of the current area is determined based on the turbidity change rate and the relative turbidity, or solely based on the turbidity change rate. A cleaning strategy for the pool cleaning robot is determined based on the cleaning status. The current area is then cleaned according to the cleaning strategy. This allows for real-time quantitative evaluation of the cleaning status using a turbidity sensor, and dynamic adjustment of the cleaning strategy based on the cleaning status. This makes the cleaning effect transparent, enabling automatic adjustment of the cleaning strategy to adapt to different pools and cleaning conditions. It eliminates the need for manual adjustment by the user, achieving one-click intelligent cleaning, effectively ensuring cleaning results and improving cleaning efficiency.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A flowchart illustrating a control method for a pool cleaning robot provided in an embodiment of this disclosure; Figure 2 A schematic diagram of the control system of a pool cleaning robot provided in an embodiment of this disclosure; Figure 3 This is a schematic diagram of the optical path design of a turbidity sensor provided in an embodiment of the present disclosure; Figure 4 This is a schematic diagram of a circuit connection provided in an embodiment of the present disclosure. Detailed Implementation

[0012] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0013] The control method, system, device, and medium of the pool cleaning robot according to embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0014] Figure 1 This is a flowchart illustrating a control method for a pool cleaning robot provided in an embodiment of this disclosure. Figure 1 As shown, the method includes the following steps: Step 101: Collect turbidity data of the pool cleaning robot by using a turbidity sensor located at the water outlet of the pool cleaning robot.

[0015] The turbidity data includes the current turbidity value.

[0016] In this embodiment, a turbidity sensor, such as an infrared scattering turbidity sensor, can be installed on the pool cleaning robot. This sensor can be installed at the water outlet of the robot, such as the outlet or drain of a filtration system. For example, the turbidity data can be the current turbidity value. As an example, the turbidity sensor can emit 860nm infrared light, calculate the turbidity value by detecting the intensity of the 90° scattered light, and continuously collect turbidity data at a frequency of 1Hz. The turbidity sensor body and the water flow channel can be integrally injection molded, using double O-ring seals, and have an IP68 waterproof rating. The optical path window can be made of sapphire glass and designed with a self-cleaning structure to prevent dirt adhesion by water flow.

[0017] Step 102: Calculate the relative turbidity based on the current turbidity value and the preset baseline turbidity value.

[0018] In this embodiment of the disclosure, the instantaneous turbidity value T can be used as the basis. i (i.e., the current turbidity value) and the preset baseline turbidity value T0 are used to calculate the relative turbidity ΔT: ΔT = T i T0. The preset baseline turbidity value T0 can be pre-set. For example, the baseline turbidity of a high-quality swimming pool is usually in the range of 0.1-0.5 NTU, while that of an ordinary swimming pool is in the range of 0.5-1.0 NTU.

[0019] Step 103: Calculate the turbidity change rate based on the current turbidity value and the turbidity value collected at the previous moment.

[0020] In this embodiment of the disclosure, after collecting turbidity data from the pool cleaning robot, the current turbidity value T collected at the current moment can be used as a basis for... i The turbidity value T collected at the previous moment i 1. Calculate the rate of change of turbidity: dT / dt = (T i T i 1) / Δt. Where dT / dt is the rate of change of turbidity, T i Let T be the instantaneous turbidity value of the i-th sample. i-1 Let t be the instantaneous turbidity value of the (i-1)th sample, and Δt be the time interval between the i-th and (i-1)th samples.

[0021] Step 104: Determine the cleanliness of the current area based on the turbidity change rate and relative turbidity.

[0022] In this embodiment, the cleanliness of the current area can be assessed based on the turbidity change rate and relative turbidity. For example, the cleanliness status can be considered as cleaning completion or contamination level. It is understood that different turbidity change rates and relative turbidities correspond to different cleaning completion and contamination levels. A mapping table between different turbidity change rates and relative turbidities and cleaning completion and contamination levels can be pre-established and invoked during the execution of the pool cleaning robot's control method. As an example, the cleaning status can be assessed by combining the two parameters, turbidity change rate dT / dt and relative turbidity ΔT, according to a preset threshold logic. For example, this could be used to assess cleaning completion or whether cleaning is complete. Thus, by simultaneously using both turbidity change rate and relative turbidity to assess the cleaning status, the cleaning effect can be reflected more accurately. The turbidity change rate reflects the dynamic trend of pollutants being stirred up during the cleaning process, while relative turbidity reflects the degree of similarity between the discharged water and the original water quality. Combining the two can avoid misjudgments caused by a single indicator and improve the accuracy of the assessment.

[0023] Step 105: Determine the cleaning strategy for the pool cleaning robot based on the cleanliness status.

[0024] In this embodiment of the disclosure, the cleaning strategy of the pool cleaning robot can be dynamically adjusted based on the assessed cleanliness of the current area. For example, different cleanliness levels can correspond to different cleaning strategies. For instance, if the cleaning completion rate is below 60%, the dwell time in the current area can be extended by 50%, and an enhanced cleaning mode can be activated (the water pump motor speed is increased to 3500 rpm); if the cleaning completion rate is between 60% and 90%, cleaning can continue, and a 30-second countdown will be followed by a reassessment; if the cleaning completion rate reaches 90%-100%, the robot proceeds normally to the next area.

[0025] Step 106: Clean the current area according to the cleaning strategy.

[0026] In this embodiment of the disclosure, after determining the cleaning strategy of the pool cleaning robot, the current area can be cleaned according to the cleaning strategy. As an example, the operating states of the walking motor and water pump motor of the pool cleaning robot can be adjusted by outputting a PWM (Pulse Width Modulation) signal to control the drive motor and execute the determined cleaning strategy: the dwell time can be adjusted by controlling the start and stop of the walking motor, and the cleaning intensity can be changed by adjusting the speed of the water pump motor (e.g., 3000 rpm in light mode and 3500 rpm in strong mode).

[0027] In this embodiment, a turbidity sensor installed at the water outlet of the pool cleaning robot collects turbidity data. This turbidity data includes the current turbidity value. Relative turbidity is calculated based on the current turbidity value and a preset baseline turbidity value. The turbidity change rate is calculated based on the current turbidity value and the turbidity value collected at the previous moment. The cleaning status of the current area is determined based on the turbidity change rate and the relative turbidity. A cleaning strategy for the pool cleaning robot is determined based on the cleaning status. The current area is then cleaned according to the cleaning strategy. This allows for real-time quantitative evaluation of the cleaning status through the turbidity sensor, and dynamic adjustment of the cleaning strategy based on the cleaning status. This makes the cleaning effect transparent, enabling automatic adjustment of the cleaning strategy to adapt to different pools and different cleaning conditions. It not only eliminates the need for manual adjustment by the user, achieving one-click intelligent cleaning, but also effectively ensures cleaning results and improves cleaning efficiency.

[0028] In some possible implementations, the cleanliness of the current area is determined based on the rate of change of turbidity and relative turbidity, including: If, after a preset duration, the turbidity change rate is below the first threshold and the relative turbidity is less than the second threshold, the cleaning of the current area is determined to be complete; otherwise, the cleaning of the current area is determined to be incomplete. The cleaning strategy for the pool cleaning robot is determined based on the cleanliness level, including: Once the current area has been cleaned, control the pool cleaning robot to move to the next area; If it is determined that the cleaning of the current area is not complete, control the pool cleaning robot to continue cleaning the current area until it is determined that the cleaning of the current area is complete or the cleaning time reaches the set maximum time.

[0029] In this embodiment, two parameters, turbidity change rate and relative turbidity, can be continuously monitored. When the turbidity change rate is consistently below a first threshold (e.g., 0.2 NTU / min) and the relative turbidity is consistently below a second threshold (e.g., 0.3 NTU), and the duration reaches a preset time, the cleaning of the current area can be determined to be complete. It is understood that a low turbidity change rate indicates that no new contaminants are stirred up during the cleaning process, and relative turbidity close to the baseline (ΔT < 0.3 NTU) indicates that the turbidity of the discharged water is close to the level of the original water. If both conditions are met simultaneously and for a certain duration, it indicates that the area is basically clean. If these conditions are not met—that is, the turbidity change rate is higher than or equal to the first threshold, or the relative turbidity is greater than or equal to the second threshold—then the cleaning of the current area is determined to be incomplete. If the cleaning of the current area is determined to be complete, a control signal can be output to the drive motor to start the walking motor, allowing the pool cleaning robot to advance normally to the next area to continue cleaning. It is understood that at this time, the water pump motor can maintain a standard speed (e.g., 3000 rpm in light mode), without the need for additional cleaning enhancement. Conversely, if it is determined that the cleaning of the current area is not complete, the cleaning strategy can be adjusted by controlling the drive motor through the control signal. For example, the dwell time in the current area can be extended (e.g., by 50%), and the water pump motor speed can be increased to the enhanced mode (3500 rpm) to enhance the cleaning effect. The robot continues to clean the current area, while the turbidity sensor continuously collects data at a frequency of 1 Hz, and re-evaluates the turbidity change rate and relative turbidity in real time. The above process is repeated until the cleaning completion conditions are met (e.g., the turbidity change rate is continuously lower than the first threshold and the relative turbidity is less than the second threshold), or the cleaning time of the current area reaches the set maximum dwell time.

[0030] Understandably, if the cleaning is not completed within the maximum time limit, the coordinates of the area will be added to the "list to be cleaned a second time" and a secondary cleaning path will be generated for supplementary cleaning after the first full-coverage cleaning is completed.

[0031] In some possible implementations, the cleanliness of the current area is determined based on the rate of change of turbidity and relative turbidity, including: If the turbidity change rate is less than the first preset change rate and the relative turbidity is less than the first preset turbidity, then the cleaning completion rate of the current area is determined to be the first completion rate. If the turbidity change rate is greater than or equal to the first preset change rate but less than the second preset change rate, and the relative turbidity is greater than or equal to the first preset turbidity but less than the second preset turbidity, then the cleanliness completion rate of the current area is determined to be the second completion rate; the second completion rate is less than the first completion rate. If the turbidity change rate is greater than or equal to the second preset change rate, or the relative turbidity is greater than or equal to the second preset turbidity, then the cleanliness completion level of the current area is determined to be the third completion level; the third completion level is less than the second completion level. The cleaning strategy for the pool cleaning robot is determined based on the cleanliness level, including: Proceed to the next area when the cleaning completion degree of the current area reaches the first completion degree; Continue cleaning the current area when the cleaning completion degree of the current area reaches the second completion degree, and re-evaluate the cleaning completion degree of the current area after a set duration; Extend the staying duration of the pool cleaning robot in the current area and switch to the enhanced cleaning mode when the cleaning completion degree of the current area reaches the third completion degree.

[0032] In the embodiments of the present disclosure, when determining the cleaning condition of the current area according to the turbidity change rate and the relative turbidity, the turbidity change rate dT / dt calculated in real time may be compared with a first preset change rate (e.g., 0.2 NTU / min), and the relative turbidity ΔT may be compared with a first preset turbidity (0.3 NTU). When both dT / dt < 0.2 NTU / min and ΔT < 0.3 NTU are satisfied, it indicates that the turbidity of discharged water has approached the baseline level of raw water, no new pollutants are stirred up during the cleaning process, the area is basically clean, and the cleaning completion degree of the current area can be determined as the first completion degree (e.g., 90%-100%). When the turbidity change rate satisfies 0.2 NTU / min ≤ dT / dt < 0.5 NTU / min and the relative turbidity satisfies 0.3 NTU ≤ ΔT < 0.8 NTU, it indicates that there are still many pollutants remaining uncleared in the area, but the cleaning process is taking effect and continuous cleaning is required, so the cleaning completion degree of the current area can be determined as the second completion degree (e.g., 60%-90%). When the turbidity change rate dT / dt ≥ 0.5 NTU / min, or the relative turbidity ΔT ≥ 0.8 NTU (satisfying either condition is sufficient), it indicates that the area is seriously polluted, the cleaning brush stirs up a large amount of suspended matters, and the cleaning intensity needs to be significantly enhanced, so the cleaning completion degree of the current area is determined as the third completion degree (e.g., less than 60%). It can be understood that if the first preset change rate < dT / dt < the second preset change rate and ΔT < the first preset turbidity, it indicates that the turbidity is already very low but still changes rapidly, that is, the cleaning process is coming to an end but has not been completely stabilized, so the cleaning completion degree of the current area can be determined as the second completion degree. If dT / dt < the first preset change rate and the first preset turbidity < ΔT < the second preset turbidity, it indicates that the turbidity change has slowed down but the relative turbidity is relatively high, that is, the cleaning is in progress, the trend is positive but not completed, so the cleaning completion degree of the current area can be determined as the second completion degree.

[0033] Accordingly, when the cleaning completion level of the current area is at the first completion level, a control signal can be output to the drive motor to start the walking motor, allowing the pool cleaning robot to move normally to the next area to continue cleaning. At this time, the water pump motor maintains the standard speed of the standard cleaning mode (e.g., 3000 rpm in light mode), without any additional stop. When the cleaning completion level of the current area is at the second completion level, cleaning of the current area continues, and the cleaning completion level of the current area is reassessed after a set time. For example, the current cleaning state can be maintained through a PWM signal, the water pump motor continues to run at the standard speed, and a countdown is started (e.g., a set time of 30 seconds). After the countdown ends, turbidity data can be collected again, the turbidity change rate and relative turbidity can be calculated, and the cleaning completion level can be reassessed to determine whether to upgrade or downgrade the cleaning strategy. When the cleaning completion level of the current area is at the third completion level, the dwell time of the pool cleaning robot in the current area is extended, and the robot switches to an enhanced cleaning mode. For example, the drive motor can be adjusted via a PWM signal to extend the dwell time in the current area by 50%, while simultaneously increasing the water pump motor speed from 3000 rpm in mild mode to 3500 rpm in strong mode, thereby enhancing the water flow flushing and filtration effect. The coordinates of this area are added to the "List of Areas to be Cleaned Again". After the initial full-coverage cleaning is completed, the system will generate a secondary cleaning path to prioritize returning to this area for supplementary cleaning.

[0034] In some possible implementations, the cleanliness of the current area is determined based on the rate of change in turbidity, including: If the turbidity change rate is less than the first change rate threshold, the cleanliness of the current area is determined to be slightly polluted; If the turbidity change rate is greater than or equal to the first change rate threshold and less than the second change rate threshold, the cleanliness of the current area is determined to be moderately polluted. If the turbidity change rate is greater than the second change rate threshold, the cleanliness of the current area is determined to be heavily polluted; The cleaning strategy for the pool cleaning robot is determined based on the cleanliness level, including: Given that the current area is only slightly contaminated, the cleaning strategy for the pool cleaning robot is set to the standard cleaning mode. Given that the current area is moderately polluted, the cleaning strategy for the pool cleaning robot is determined to be an enhanced cleaning mode; in this enhanced cleaning mode, the water pump speed is higher than that in the standard cleaning mode. Given that the current area is heavily polluted, the cleaning strategy for the pool cleaning robot is determined to be a deep cleaning mode; wherein, the dwell time in the deep cleaning mode is longer than that in the standard cleaning mode, or the number of cleaning cycles in the deep cleaning mode is greater than that in the standard cleaning mode.

[0035] In this embodiment of the disclosure, the cleanliness status can also refer to the degree of contamination. For example, if the turbidity change rate is less than a first change rate threshold, the cleanliness status of the current area can be determined to be slightly contaminated. For example, the real-time calculated turbidity change rate dT / dt can be compared with the first change rate threshold (e.g., 0.3 NTU / min). When dT / dt < 0.3 NTU / min, it indicates that less suspended matter was stirred up during the cleaning process, and the degree of contamination in the area is relatively light, thus the cleanliness status of the current area can be determined to be slightly contaminated. If the turbidity change rate is greater than or equal to the first change rate threshold and less than a second change rate threshold, the cleanliness status of the current area can be determined to be moderately contaminated. For example, when the turbidity change rate satisfies 0.3 NTU / min ≤ dT / dt < 1.0 NTU / min, it indicates that a certain amount of suspended matter was continuously stirred up during the cleaning process, and the area has a moderate degree of contamination, requiring increased cleaning efforts, thus the cleanliness status of the current area can be determined to be moderately contaminated. If the turbidity change rate is greater than the second change rate threshold, the cleanliness status of the current area can be determined to be heavily contaminated. For example, when the turbidity change rate dT / dt ≥ 1.0 NTU / min, it indicates that the cleaning brush agitation has generated a large amount of suspended matter, the area is seriously polluted, and the cleaning time needs to be significantly extended and the number of cleanings increased. The current cleaning status of the area can be determined as heavily polluted.

[0036] Accordingly, when the current area is lightly polluted, the cleaning strategy of the pool cleaning robot can be determined to be the standard cleaning mode. For example, a PWM signal can be output to the drive motor to control the water pump motor to operate at a standard speed (3000 rpm), and the walking motor to advance along the normal planned path without extending the dwell time, thus controlling energy consumption while ensuring cleaning effectiveness. When the current area is moderately polluted, the cleaning strategy of the pool cleaning robot is determined to be the enhanced cleaning mode, where the water pump speed is higher than in the standard cleaning mode. For example, the water pump motor speed can be increased from 3000 rpm in the standard cleaning mode to 3500 rpm in the enhanced cleaning mode via a PWM signal to enhance the water flow rinsing and filtration effect. The walking motor maintains normal advancement, without extending the dwell time. Thus, by increasing the water pump speed, the rinsing force of the water flow on the pool bottom and walls can be increased, improving the cleaning effect on moderately polluted areas. If the current area is heavily contaminated, the cleaning strategy for the pool cleaning robot can be determined to be a deep cleaning mode. In this deep cleaning mode, at least one of the dwell time and number of cleaning cycles is greater than that of the standard cleaning mode. For example, the drive motor can be adjusted via a PWM signal to execute the deep cleaning mode, such as extending the dwell time in the current area by 50% (dwell time greater than the standard cleaning mode), or adding the area's coordinates to the "list to be cleaned a second time" so that it can return for repeated cleaning after the initial full-coverage cleaning (number of cleaning cycles greater than one coverage in the standard cleaning mode). The water pump motor can maintain an enhanced mode speed (3500 rpm) or a standard speed, depending on the specific strategy. This mode targets heavily contaminated areas, ensuring the required cleaning effect by increasing the dwell time or the number of repeated cleaning cycles.

[0037] In some possible implementations, at least one of the following is also included: After cleaning each area of ​​the pool, record the turbidity change characteristics of each area; based on the turbidity change characteristics of each area, establish a mapping table between the area and the degree of pollution. After completing full-coverage cleaning of all areas of the pool, the cleaning completion rate of each area is determined based on the turbidity change rate, turbidity value, and the turbidity difference between the baseline turbidity value when the pool cleaning robot is turned on. Select areas where the cleaning completion rate is lower than the set completion rate to generate a list of secondary cleaning tasks; Control the pool cleaning robot to clean the areas in the secondary cleaning task list.

[0038] In this embodiment of the disclosure, after cleaning each area of ​​the pool is completed, the turbidity change characteristics of each area can be recorded; based on the turbidity change characteristics of each area, a mapping table between area and pollution level can be established. For example, after cleaning each area of ​​the pool is completed, the turbidity change characteristics of each area can be recorded, including the turbidity change rate dT / dt, the peak value and duration of the relative turbidity ΔT, and the time required to reach each completion level. Based on this historical data, a 'area-pollution level' mapping table can be established to associate and store different areas of the pool with their historical pollution levels. During the next cleaning, time can be prioritized for historically high-pollution areas, realizing an area learning mechanism and making the cleaning strategy more accurate and efficient. After full-coverage cleaning of each area of ​​the pool is completed, the cleaning completion level of each area is determined based on the turbidity change rate, turbidity value, and the turbidity difference between the baseline turbidity value when the pool cleaning robot starts. For example, after the pool cleaning robot completes its first full-coverage cleaning, the cleaning data of each area can be reviewed, based on the turbidity change rate dT / dt and instantaneous turbidity value T during the cleaning process of each area. i The turbidity difference (i.e., relative turbidity ΔT=T) between the turbidity value and the baseline turbidity value T0 (preset baseline turbidity value) calibrated at startup i (T0) comprehensively evaluates the final cleaning completion rate of each area. Understandably, the evaluation logic for cleaning completion rate is the same as the method described above for real-time evaluation. As an example, if dT / dt < 0.2 NTU / min and ΔT < 0.3 NTU at the end of cleaning for a certain area, then the cleaning completion rate for that area is high (90%-100%); if dT / dt < 0.5 NTU / min and ΔT < 0.8 NTU, then the completion rate is medium (60%-90%); if dT / dt ≥ 0.5 NTU / min or ΔT ≥ 0.8 NTU, then the completion rate is low (< 60%).

[0039] Then, the cleaning completion rate of each area can be compared with the set completion rate (e.g., 70%), and areas with a cleaning completion rate lower than the set completion rate can be filtered out. Understandably, these areas failed to meet the expected standards in the initial cleaning and require supplementary cleaning. The coordinate information of these areas can be compiled into a secondary cleaning task list, recording the reasons for the low completion rate of each area (e.g., excessively high turbidity change rate or excessive relative turbidity), providing a reference for secondary cleaning. Afterwards, the pool cleaning robot can be controlled to clean the areas in the secondary cleaning task list. For example, a secondary cleaning path can be generated based on the secondary cleaning task list, prioritizing the return of the pool cleaning robot to the low-completion areas for supplementary cleaning. During the secondary cleaning process, an enhanced cleaning mode (increasing the water pump speed to 3500 rpm) or a deep cleaning mode (extending the dwell time) can be automatically adopted, and turbidity data can be continuously monitored until the standard is met. Understandably, after the secondary cleaning is completed, a cleaning report can be pushed to the user, including a heatmap of the completion rate of each area, allowing the user to intuitively understand the cleaning effect.

[0040] In some possible implementations, the turbidity change rate is calculated based on the current turbidity value and the turbidity value collected at the previous time, including: The turbidity data is subjected to multi-factor compensation processing to obtain compensated turbidity data; wherein, the factor compensation processing includes at least one of temperature compensation, bubble interference suppression compensation and chemical interference filtration compensation. The turbidity change rate is calculated based on the compensated turbidity data.

[0041] In this embodiment, turbidity data (including at least one of the current turbidity value and the turbidity value collected at the previous moment) can be processed using multi-factor compensation to eliminate the interference of environmental factors on the measurement results and improve data accuracy. For example, turbidity data can be processed using multi-factor compensation based on real-time water temperature measured by a temperature sensor, bubble interference, chemical interference, etc., to obtain compensated turbidity data. Then, the turbidity change rate can be calculated based on the turbidity data processed using multi-factor compensation. In this way, based on the corrected and accurate turbidity data, misjudgments caused by temperature drift, bubble jumps, and chemical interference can be avoided, ensuring that the turbidity change rate can truly reflect the actual change trend of pollutants during the cleaning process, providing a reliable basis for subsequent cleaning completion assessment and cleaning strategy adjustment.

[0042] In a further possible implementation, the turbidity data is subjected to multi-factor compensation processing to obtain compensated turbidity data, including at least one of the following: The turbidity data was corrected based on the pool water temperature to unify the turbidity data to the standard temperature. If a sudden jump in turbidity data is detected and the jump characteristic matches the bubble interference characteristic, the current turbidity data is replaced by the interpolated normal reading from the time closest to the current time. The sudden jump characteristic includes a preset jump amplitude and duration. If the moment of sudden change in turbidity data is identified to coincide with the moment the user adds the drug, the moment of change is marked as a chemical interference period, and the turbidity data during the chemical interference period is not used to determine the cleanliness status.

[0043] In this embodiment of the disclosure, when performing temperature compensation, the pool water temperature Twater can be measured in real time using a temperature sensor, and the temperature compensation formula Tcorrected = Traw × (1 + k × (Twater)) can be applied. 25) Correct the raw turbidity data (Traw) collected by the turbidity sensor. Here, Tcorrected represents the compensated turbidity data, Traw represents the uncompensated turbidity data, k is the temperature coefficient determined through calibration experiments (e.g., a typical value of 0.02 / ℃), and 25 is the standard reference temperature (in ℃). Temperature compensation can uniformly correct turbidity data measured under different water temperature conditions to turbidity data at a standard temperature (e.g., 25℃), eliminating the influence of water temperature changes on the intensity of infrared scattered light and ensuring the comparability of turbidity data under different temperature environments.

[0044] If a sudden jump in turbidity data is detected, and the characteristics of this jump (such as preset jump amplitude and duration) match the characteristics of bubble interference, the current turbidity data can be replaced by an interpolated normal reading from the closest available time. For example, turbidity data can be monitored in real time to identify sudden jump phenomena. When the turbidity jump amplitude between adjacent samples is |T i T i If 1 | > 2 NTU (preset transition amplitude), and the duration of this transition is less than 0.5 seconds (preset duration), then the instantaneous transition can be determined to conform to the bubble interference characteristics. At this time, the control unit calls the time closest to the current time (i.e., T). i 1 and T i+1 The normal reading of ) is used to calculate and replace the turbidity data T at the current moment through interpolation. i Understandably, in a pool with a baseline of 0.5 NTU, a jump of 2 NTU is equivalent to 4 times the baseline, which is significantly beyond the turbidity change range of a normal cleaning process (usually less than 1 NTU). Therefore, it can be determined to be bubble interference rather than a real change in contamination.

[0045] If a sudden jump in turbidity data is detected at a time that coincides with the user's dosing of chemicals, this time is marked as a chemical interference period, and turbidity data within this period is not used to determine the cleaning status. For example, turbidity data can be monitored for sudden jumps, and the time of the jump can be recorded. Then, the time of the jump can be compared with the user's preset "dosing time." If they match, the jump is determined to be caused by chemical addition, and the corresponding time period is marked as a chemical interference period. Turbidity data within the chemical interference period (such as periods where microbubbles or colloids are generated in the water after adding disinfectants like sodium hypochlorite, causing abnormal increases in turbidity) is excluded from the cleaning completion assessment and is not used to determine the cleaning status of the current area, thus avoiding interference from chemical factors in determining the cleaning effect.

[0046] To make the control method and system of the pool cleaning robot provided in this disclosure clearer, the following description is provided in conjunction with the examples below.

[0047] The control method and system for a pool cleaning robot provided in this disclosure can monitor water quality changes in real time during the cleaning process using a turbidity sensor, dynamically evaluate the cleaning effect, and intelligently adjust the cleaning strategy. This includes: Turbidity sensor integrated design: Turbidity sensors can be integrated into the drain outlet or filtration system outlet of the pool cleaning robot to monitor changes in turbidity of the discharged water in real time, quantifying the cleaning effect into measurable turbidity data; Turbidity change rate-cleaning completion mapping algorithm: A dynamic mapping relationship between turbidity change rate and cleaning completion can be established. By analyzing the trend of turbidity change over time (rather than a single threshold judgment), the cleaning completion level of the current area can be intelligently assessed; Adaptive cleaning strategy adjustment: Based on the turbidity assessment results, the robot's dwell time or number of cleaning repetitions in the current area can be automatically adjusted to achieve intelligent decision-making of "leaving when the cleaning effect meets the standard and strengthening the cleaning when the cleaning is insufficient"; Underwater environment-specific turbidity assessment model: Dedicated turbidity signal processing and calibration algorithms are designed for the optical characteristics of the underwater environment (water absorption and scattering effects) and the pool cleaning scenario (complex components such as silt, algae, and chemical agents).

[0048] Examples of the control method and system for the pool cleaning robot provided in this disclosure can be as follows: 1. Hardware integration solution: The installation position, optical path design, and waterproof sealing structure of the turbidity sensor in the pool cleaning robot's drainage / filtration system ensure that the sensor (including the turbidity sensor) can accurately detect water quality without affecting the normal drainage and filtration functions of the pool cleaning robot.

[0049] 2. Turbidity-Cleanliness Mapping Algorithm, including: Method for calculating the turbidity change rate (dT / dt); The correspondence between the rate of change threshold and the cleaning completion level (e.g., rate of change < 0.2 NTU / min and lasts for 30 seconds → cleaning completed; rate of change > 1.0 NTU / min → heavily contaminated and requires enhanced cleaning). Multi-region historical data learning and optimization (automatic calibration of baseline turbidity in different areas of the same swimming pool); Understandably, pool water itself has low turbidity (e.g., 0.1-0.5 NTU for high-quality water and <2.0 NTU for qualified water). Therefore, the threshold setting should be based on the relative change rather than the absolute value. The increment of turbidity change relative to the baseline (T0) during the cleaning process is the key criterion.

[0050] 3. Adaptive cleaning control logic, including: Cleaning completion assessment → Adjustment of dwell time (e.g., 60% completion → Increase dwell time by 50%; 90% completion → Proceed as normal). Cleaning completion assessment → Repeat cleaning decision (e.g.: completion <50% → mark as area to be cleaned a second time); Linkage with the path planning system (prioritize returning to areas with low completion rates).

[0051] In this way, pool cleaning can shift from "time / path driven" to "effect driven", reducing ineffective cleaning time while ensuring cleaning quality. This can not only improve overall cleaning efficiency by 20%-40%, but also reduce energy consumption.

[0052] The following specific examples will illustrate this point.

[0053] Example 1, as follows: System components: 1. Turbidity sensor module: Sensor type: Infrared scattering turbidity sensor; Installation location: At the water outlet or drain of the filtration system; Detection principle: Emit infrared light (860nm), detect the intensity of the scattered light at 90°, and convert it into turbidity value (NTU). Waterproof design: IP68 (Ingress Protection 68) level seal, integrated design with water flow channel.

[0054] 2. Control unit, as follows: The microprocessor acquires turbidity sensor data in real time; Calculate the rate of change of turbidity (dT / dt); The cleaning completion rate is determined based on a preset threshold.

[0055] 3. Cleaning strategy adjustment module, as follows: If the cleaning completion rate is less than 60%, the time spent in the current area can be extended by 50%. If the cleaning completion rate is 60% or less and less than 90%, normal cleaning is possible. When the cleaning completion rate is ≥90%, the dwell time can be shortened or the process can proceed normally.

[0056] The workflow can be as follows: 1. The robot (pool cleaning robot) enters a certain area and begins cleaning; 2. Turbidity sensors monitor the turbidity of the discharged water in real time; 3. The control unit calculates the turbidity change rate; 4. If the rate of change remains below the threshold (e.g., 0.2 NTU / min) and the relative turbidity ΔT is close to the baseline (<0.3 NTU), cleaning is considered complete, and the robot moves to the next area; 5. If the rate of change is higher than the threshold or the relative turbidity is significantly higher, continue cleaning until the standard is met or the maximum residence time is reached.

[0057] II. Furthermore, this disclosure also provides another embodiment, including: An enhanced turbidity detection algorithm is as follows: Regional learning mechanism: Record the turbidity changes in each area during each cleaning cycle; Establish a "region-pollution level" mapping table; Prioritize allocating time to historically high-contamination areas during the next cleaning cycle.

[0058] Intelligent cleaning strategies include: 1. Tiered cleaning mode: Slight contamination (turbidity change rate <0.3 NTU / min): Standard cleaning mode; Moderate contamination (0.3-1.0 NTU / min): Enhanced cleaning mode (increase pump speed); Heavy pollution (>1.0 NTU / min): Deep cleaning mode (extended dwell time + repeated cleaning).

[0059] Understandably, given that baseline turbidity in pool water is typically in the range of 0.1-1.0 NTU, the above thresholds can be set based on the rate of change of turbidity relative to the baseline. For example, for a pool with a baseline of 0.3 NTU, a rate of change of 0.5 NTU / min means that there is significant agitation of contaminants in that area.

[0060] 2. Secondary cleaning scheduling: After the initial full-coverage cleaning, areas with a completion rate of less than 70% are identified. Generate a list of secondary cleaning tasks; Prioritize returning to areas with low completion rates for additional cleaning.

[0061] 3. Energy consumption-performance balance strategy: Users can set either "Energy Saving Mode" (allowing 80% completion) or "Deep Mode" (requiring 95% completion), with the threshold dynamically adjusted according to the settings.

[0062] III. Furthermore, a schematic diagram of the system structure of the control system for the pool cleaning robot provided in this embodiment can be found in [reference needed]. Figure 2 The control system of the pool cleaning robot may include: cleaning brush 101 (roller brush / disc brush), filtration system 102, drain outlet 103, turbidity sensor 201 (infrared scattering type, installed at the outlet of the filtration system), control unit 301 (MCU / processor), drive motor 401 (walking motor, water pump motor), path planning module 501, and communication module 601 (WiFi / Bluetooth, used for data upload and user notification).

[0063] The turbidity sensor 201 is installed at the outlet or drain 103 of the filtration system 102. At this location, the water flow still contains small suspended solids after filtration, and the water flow is stable with few bubbles, making it suitable for turbidity detection. The optical path design of the turbidity sensor 201 can be found in [reference needed]. Figure 3 The light source and detector are arranged at a 90° angle. The optical path window is made of sapphire glass, which is wear-resistant and scratch-resistant. The window surface is designed with a self-cleaning structure (using water flow to clean it). The turbidity sensor 201 body and water flow channel are integrally injection molded and sealed with double O-rings to ensure an IP68 waterproof rating. Figure 4 This is a schematic diagram of the circuit connection relationship provided in the embodiments of this disclosure. Figure 4 In this configuration, the turbidity sensor 201 can output an analog voltage to the MCU ADC1 channel (Microcontroller Unit Analog-to-Digital Converter Channel 1); the temperature sensor 202 can output a digital signal to the MCU GPIO (Microcontroller Unit General-Purpose Input / Output); the drive motor 401 can output a PWM signal to the walking motor and the water pump motor; and the WiFi module 402 can communicate with the UART (Universal Asynchronous Receiver / Transmitter) to achieve data interaction with the user application.

[0064] Based on the control system of the swimming pool cleaning robot described above, the working principle and operation process of this embodiment can be as follows: Phase 1: Baseline calibration, as follows: The robot (pool cleaning robot) is set with a preset baseline turbidity value (that is, the turbidity value that is ultimately desired after cleaning). For example, the preset baseline turbidity value for a high-quality pool can be set in the range of 0.1-0.5 NTU, while the preset baseline turbidity value for an everyday pool can be set in the range of 0.5-1.0 NTU.

[0065] Phase Two: Real-time monitoring of the cleaning process, as follows: 1. The robot enters a certain area, the cleaning brush starts, and the water pump runs at the standard flow rate; 2. Turbidity sensor collects turbidity data T i ; 3. Real-time calculation by the control unit: Turbidity change rate: dT / dt = (T i - T i-1 ) / Δt Relative turbidity: ΔT = T i - T0 Phase 3: Cleaning completion assessment, as follows: The control unit can assess the cleaning completion rate based on the following logic: If dT / dt < 0.2 NTU / min and ΔT < 0.3 NTU, the cleaning completion rate is between 90% and 100%; cleaning strategy: proceed normally to the next area; If dT / dt < 0.5 NTU / min and ΔT < 0.8 NTU, the cleaning completion rate is between 60% and 90%; Cleaning strategy: Continue cleaning, and evaluate after 30 seconds of countdown. If dT / dt≥0.5NTU / min or ΔT≥0.8NTU, the cleaning completion rate is less than 60%; cleaning strategy: extend the dwell time by 50% and strengthen the cleaning mode.

[0066] The baseline turbidity T0 can be: 0.1-0.5 NTU for high-quality swimming pools and 0.5-1.0 NTU for ordinary swimming pools. ΔT = Ti - T0 (relative turbidity); the threshold setting is based on the following: when ΔT < 0.3 NTU, the turbidity of the discharged water is close to the level of the raw water, indicating that the area is basically clean.

[0067] Phase 4: Adaptive strategy execution, as follows: Based on the cleaning completion assessment results, the control unit can adjust the dwell time by controlling the start and stop of the walking motor; adjust the cleaning intensity by adjusting the water pump motor speed (3000 rpm for light mode, 3500 rpm for strong mode); and mark areas with a cleaning completion rate of <60% as areas to be cleaned a second time.

[0068] Phase 5: Secondary cleaning schedule, as follows: 1. After the initial full-coverage cleaning is completed, the control unit can check the list of items to be cleaned a second time; 2. If the list of items to be cleaned a second time is not empty, a path for the second cleaning can be generated; 3. The robot returns to the low-completion area along the path to perform supplementary cleaning; After the second cleaning is completed, a cleaning report (including a heat map of the completion status of each area) will be sent to the user.

[0069] IV. Further, the multi-factor compensation algorithm is as follows: Temperature compensation: Tcorrected =Traw×(1+k×(Twater-25)) Where: Tcorrected is the turbidity value after compensation, Traw is the original sensor reading; k is the temperature coefficient (determined through calibration experiments, typical value 0.02 / ℃); Twater is the water temperature (provided by the temperature sensor).

[0070] Bubble interference suppression: When air bubbles pass through the detection area, they cause a momentary jump in turbidity readings (lasting <0.5 seconds, amplitude >2 NTU). Considering the low baseline turbidity of the pool water (less than 2 NTU), even small jumps can significantly affect the judgment. The control unit identifies and eliminates air bubble interference using the following algorithm: If |T i -T i-1 |>2NTU and|T i+1 -T i If |>2NTU, it is determined to be bubble interference, and T can be used. i-1 and T i+1 The interpolation substitution for Ti.

[0071] Understandably, in a pool with a baseline of 0.5 NTU, a jump of 2 NTU is equivalent to 4 times the baseline, which is significantly beyond the turbidity change range of a normal cleaning process (usually <1 NTU). Therefore, it can be reliably identified as bubble interference.

[0072] Chemical interference filtering: When disinfectants (such as sodium hypochlorite) are added to a swimming pool, tiny bubbles or colloids may be generated in the water, causing an abnormal increase in turbidity. The system identifies this by monitoring the time points of turbidity abrupt changes; if the abrupt change matches a preset "dosing time" (input by the user through the application), the data for that period is marked as a chemical interference period; turbidity data during the chemical interference period is not used for cleaning completion assessment.

[0073] As a specific example, taking a swimming pool cleaned by a robot (pool cleaning robot) as an everyday swimming pool, and setting a preset baseline turbidity T0 of 0.7 NTU, the determination of cleaning completion and cleaning strategy in the control method of the swimming pool cleaning robot provided in this embodiment can be implemented as follows: 1. The robot enters a certain area of ​​the pool, the cleaning brush starts, and the water pump runs at the standard flow rate; 2. Turbidity sensor collects turbidity data T i Assume that the sampled T2 is 1.8 NTU, T1 is 2.5 NTU, and the time interval between sampling T1 and T2 is 1 min; 3. Real-time calculation by the control unit: Turbidity change rate: dT / dt = (T i - T i-1 ) / Δt=(2.5-1.8) / 1=0.7NTU / min Relative turbidity: ΔT = Ti - T0 = 1.8 - 0.7 = 1.1 NTU We can calculate that dT / dt = 0.7 NTU / min and ΔT = 1.1 NTU.

[0074] 4. Cleaning completion assessment, as follows: The control unit can assess the cleaning completion rate based on the following logic: ΔT At this point, dT / dt = 0.7 NTU / min ≥ 0.5 NTU / min and ΔT = 1.1 NTU ≥ 0.8 NTU, then the cleaning completion rate is less than 60%; Cleaning strategy: Extend the dwell time by 50% and strengthen the cleaning mode.

[0075] Assuming that the subsequent T6 data collection is 1.4 NTU and T5 is 1.8 NTU, and the time interval between T5 and T6 is 1 minute, then dT / dt = 0.4 NTU / min < 0.5 NTU / min and ΔT = 0.7 NTU < 0.8 NTU, the cleaning completion rate is between 60% and 90%; Cleaning strategy: Continue cleaning, evaluate after 30 seconds of countdown. Assuming that T is collected later 10 The T9 has a bandwidth of 0.8 NTU, and the T9 has a bandwidth of 0.9 NTU. Data is collected from T9 and T9. 10 If the time interval is 1 minute, then dT / dt = 0.1 NTU / min < 0.2 NTU / min and ΔT = 0.1 NTU < 0.3 NTU, indicating that the cleaning completion rate is between 90% and 100%. Cleaning strategy: Proceed normally to the next area. 5. Adaptive policy execution, as follows: Based on the cleaning completion assessment results, the control unit can adjust the dwell time by controlling the start and stop of the walking motor; adjust the cleaning intensity by adjusting the water pump motor speed (3000 rpm for light mode, 3500 rpm for strong mode); and mark areas with a cleaning completion rate of <60% as areas to be cleaned a second time.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0077] According to embodiments of this disclosure, a control system for a pool cleaning robot is also provided. For example, the control system for the pool cleaning robot includes: The main body of the pool cleaning robot; A turbidity sensor is installed at the water outlet of the pool cleaning robot to collect turbidity data. The turbidity data includes the current turbidity value. The water outlet includes the system's water outlet or drain. The turbidity sensor is an infrared scattering type turbidity sensor. The control unit, connected to the turbidity sensor, is used to collect turbidity data collected by the turbidity sensor; calculate relative turbidity based on the current turbidity value and a preset baseline turbidity value; calculate the turbidity change rate based on the current turbidity value and the turbidity value collected at the previous moment; determine the cleanliness of the current area based on the turbidity change rate and the relative turbidity; determine the cleaning strategy of the pool cleaning robot based on the cleanliness; and clean the current area according to the cleaning strategy.

[0078] It should be noted that the description of the features of the control system of the pool cleaning robot in the corresponding embodiment can be found in the relevant description of the control method of the pool cleaning robot in the corresponding embodiment, and will not be repeated here.

[0079] Embodiments of this disclosure also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above-described embodiments of the control method for a pool cleaning robot.

[0080] Embodiments of this disclosure also provide a computer-readable storage medium storing a computer program configured to execute the steps in any of the above-described control method embodiments for a pool cleaning robot.

[0081] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0082] The embodiments of this disclosure also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above-described control method embodiments for a pool cleaning robot.

[0083] Embodiments of this disclosure also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above-described control method embodiments for a pool cleaning robot.

[0084] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0085] The control method for a swimming pool cleaning robot provided in this disclosure has been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this disclosure. The descriptions of the embodiments above are only for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make several improvements and modifications to this disclosure without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this disclosure.

Claims

1. A control method for a swimming pool cleaning robot, characterized in that, include: Turbidity data of the pool cleaning robot is collected by a turbidity sensor installed at the water outlet of the pool cleaning robot; wherein, the turbidity data includes the current turbidity value; Calculate the relative turbidity based on the current turbidity value and the preset baseline turbidity value; Based on the current turbidity value and the turbidity value collected at the previous moment, calculate the turbidity change rate; The cleanliness of the current area is determined based on the turbidity change rate and the relative turbidity; or, the cleanliness of the current area is determined based solely on the turbidity change rate. The cleaning strategy of the pool cleaning robot is determined based on the cleaning situation; Clean the current area according to the cleaning strategy described above; The step of determining the cleanliness of the current area solely based on the turbidity change rate includes: If the turbidity change rate is less than the first change rate threshold, the cleanliness of the current area is determined to be slightly polluted; If the turbidity change rate is greater than or equal to the first change rate threshold and less than the second change rate threshold, the cleanliness of the current area is determined to be moderately polluted. If the turbidity change rate is greater than the second change rate threshold, the cleanliness of the current area is determined to be heavily polluted; The step of determining the cleaning strategy for the pool cleaning robot based on the cleanliness status includes: Given that the current area is only slightly polluted, the cleaning strategy of the pool cleaning robot is determined to be the standard cleaning mode. Given that the current area is moderately polluted, the cleaning strategy of the pool cleaning robot is determined to be an enhanced cleaning mode; wherein, the water pump speed in the enhanced cleaning mode is higher than that in the standard cleaning mode; When the current area is heavily polluted, the cleaning strategy of the pool cleaning robot is determined to be a deep cleaning mode; wherein, the dwell time in the deep cleaning mode is longer than that in the standard cleaning mode, or the number of cleaning cycles in the deep cleaning mode is greater than that in the standard cleaning mode.

2. The control method for the pool cleaning robot according to claim 1, characterized in that, Determining the cleanliness of the current area based on the turbidity change rate and the relative turbidity includes: If, for a continuously preset duration, the turbidity change rate is lower than a first threshold and the relative turbidity is less than a second threshold, the cleaning of the current area is determined to be complete; otherwise, the cleaning of the current area is determined to be incomplete. The step of determining the cleaning strategy for the pool cleaning robot based on the cleanliness status includes: Once the current area is determined to be cleaned, the pool cleaning robot is controlled to move to the next area; If it is determined that the cleaning of the current area is not completed, the pool cleaning robot is controlled to continue cleaning the current area until it is determined that the cleaning of the current area is completed or the cleaning time reaches the set maximum time.

3. The control method for the pool cleaning robot according to claim 1, characterized in that, Determining the cleanliness of the current area based on the turbidity change rate and the relative turbidity includes: If the turbidity change rate is less than the first preset change rate and the relative turbidity is less than the first preset turbidity, then the cleaning completion degree of the current area is determined to be the first completion degree. If the turbidity change rate is greater than or equal to the first preset change rate and less than the second preset change rate, and the relative turbidity is greater than or equal to the first preset turbidity and less than the second preset turbidity, then the cleaning completion degree of the current area is determined to be the second completion degree; the second completion degree is less than the first completion degree. If the turbidity change rate is greater than or equal to the second preset change rate, or the relative turbidity is greater than or equal to the second preset turbidity, then the cleaning completion degree of the current area is determined to be the third completion degree; the third completion degree is less than the second completion degree. The step of determining the cleaning strategy for the pool cleaning robot based on the cleanliness status includes: If the cleaning completion rate of the current area is at the first completion rate, proceed to the next area; If the cleaning completion rate of the current area is the second completion rate, continue cleaning the current area and reassess the cleaning completion rate of the current area after a set time. If the cleaning completion rate of the current area is at level three, the duration of the pool cleaning robot's stay in the current area is extended, and the cleaning mode is switched to enhanced cleaning mode.

4. The control method for the pool cleaning robot according to claim 1, characterized in that, The method further includes: After cleaning each area of ​​the pool, the turbidity change characteristics of each area of ​​the pool are recorded; based on the turbidity change characteristics of each area, a mapping table between area and pollution level is established. After completing full-coverage cleaning of all areas of the pool, the cleaning completion rate of each area is determined based on the turbidity change rate, turbidity value and the turbidity difference between the baseline turbidity value when the pool cleaning robot is turned on. Select areas where the cleaning completion rate is lower than the set completion rate to generate a list of secondary cleaning tasks; Control the pool cleaning robot to clean the areas in the secondary cleaning task list.

5. The control method for the pool cleaning robot according to claim 1, characterized in that, The calculation of the turbidity change rate based on the current turbidity value and the turbidity value collected at the previous moment includes: The turbidity data is subjected to multi-factor compensation processing to obtain the compensated turbidity data; wherein the factor compensation processing includes at least one of temperature compensation, bubble interference suppression compensation, and chemical interference filtration compensation. The turbidity change rate is calculated based on the compensated turbidity data.

6. The control method for the pool cleaning robot according to claim 5, characterized in that, The multi-factor compensation processing of the turbidity data to obtain the compensated turbidity data includes at least one of the following: The temperature compensation refers to correcting the turbidity data according to the pool water temperature, and unifying the turbidity data to the turbidity data at the standard temperature. If a sudden jump in the turbidity data is detected and the jump characteristic matches the bubble interference characteristic, then the normal reading at the time closest to the current time is used as the interpolation value to replace the current turbidity data; wherein, the sudden jump characteristic includes a preset jump amplitude and duration; If the moment when the turbidity data changes abruptly coincides with the moment when the user adds the drug, the moment of the change is marked as a chemical interference period, and the turbidity data during the chemical interference period is not used to determine the cleanliness status.

7. A control system for a swimming pool cleaning robot, characterized in that, include: The main body of the pool cleaning robot; A turbidity sensor is installed at the water outlet of the pool cleaning robot body to collect turbidity data of the pool cleaning robot; wherein, the turbidity data includes the current turbidity value; the water outlet includes the water outlet of the filtration system or the drain outlet; the turbidity sensor is an infrared scattering turbidity sensor; The control unit, connected to the turbidity sensor, is used to collect turbidity data collected by the turbidity sensor; calculate relative turbidity based on the current turbidity value and a preset baseline turbidity value; calculate the turbidity change rate based on the current turbidity value and the turbidity value collected at the previous moment; determine the cleanliness of the current area based on the turbidity change rate and the relative turbidity; determine the cleaning strategy of the pool cleaning robot based on the cleanliness; and clean the current area according to the cleaning strategy.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the control method of the pool cleaning robot according to any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the control method for the pool cleaning robot according to any one of claims 1-6.

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