Self-cleaning control method and system for intelligent squat toilet

Abstract

The present application relates to the technical field of squatting toilet self-cleaning control, and provides a self-cleaning control method and system for an intelligent squatting toilet, the method comprising: detecting the gas and human body in the squatting toilet area by using a sensor, identifying an existing odor event, and executing a corresponding self-cleaning scheme, realizing a fundamental change of drying control from "preset program driving" to "visual perception driving", solving the extensive control problem caused by the dependence of the prior art on fixed timing or simple human body sensing, significantly improving the energy utilization efficiency and user experience comfort, saving energy consumption and improving user experience and comfort.

Description

Technical Field

[0001] This invention relates to the field of self-cleaning control technology for squat toilets, and in particular to a self-cleaning control method and system for intelligent squat toilets. Background Technology

[0002] Traditional squat toilets are limited in function, lacking automated cleaning and comfort features. While some existing smart toilets offer functions like washing and drying, their design is based on seated toilets and cannot be directly applied to squat toilets. Furthermore, they still fall short in terms of cleaning efficiency, energy conservation, and user experience. In particular, squat toilets suffer from problems such as a large cleaning area, easy splattering of waste, and low drying efficiency due to varying user postures. The level of automation in public and residential restrooms is an important indicator of their level of civilization and hygiene. Currently, most mainstream automatic sensor squat toilets on the market use passive infrared (PIR) sensor technology. This technology can only detect the movement and presence of a person. Its working logic is: triggering a preparatory state when a person approaches, and triggering flushing when a person leaves. This mode has technical bottlenecks. First, it cannot distinguish between different scenarios such as "urinating," "defecation," "washing hands," "tidying clothes," or "briefly passing by," causing all "leaving" events to trigger the same large volume of water flushing, resulting in water waste and indiscriminate behavior recognition. Moreover, a single presence sensor cannot provide any data support for more advanced intelligent functions (such as personalized posterior wash, feminine wash, precise drying, stain and splash prevention, health monitoring, etc.), severely limiting product upgrade paths. PIR sensors are susceptible to interference from ambient temperature, non-human heat sources (such as hot water pipes), and rapidly passing objects, resulting in high false triggering and missed triggering rates, poor user experience, and poor environmental adaptability.

[0003] Therefore, there is an urgent need for an integrated solution specifically designed for squat toilets that can efficiently and intelligently perform self-cleaning, deodorization, and drying. Summary of the Invention

[0004] This invention provides a self-cleaning control method for intelligent squat toilets, which solves the technical problem of difficulty in self-cleaning squat toilets in the prior art.

[0005] The first aspect of this invention provides a self-cleaning control method for an intelligent squat toilet, comprising: Acquire real-time sensor data, including gas concentration data and infrared detection data; trigger an odor event when the gas concentration data exceeds a preset threshold, and monitor the gas concentration data for a preset time to determine the type of odor event; When the odor event is a continuous odor event, the presence of the user is determined based on infrared detection data. If the user has not yet left the smart squat toilet, an instant self-cleaning program is executed. After the user leaves the smart squat toilet, a deep self-cleaning program is executed.

[0006] Optionally, before performing the deep self-cleaning solution, the method further includes: The user activity area is identified based on infrared detection data, and the gas concentration distribution in the user activity area is detected based on distributed gas sensors to identify the location of pollution source areas, and a deep self-cleaning solution is implemented for the pollution source areas.

[0007] Optionally, the real-time sensor data may also include image data; The deep self-cleaning scheme further includes: detecting damp areas based on image data, identifying and locating damp areas in the image using image algorithms, performing quantitative analysis of the dampness of the damp areas, adjusting the drying strategy based on the dampness, and performing drying control in the deep self-cleaning scheme.

[0008] Optionally, after implementing the deep self-cleaning solution, the method further includes: After the deep self-cleaning process is completed, sensor data is acquired again, and the self-cleaning effect is determined based on the sensor data for feedback control.

[0009] A second aspect of this application provides a self-cleaning control system for an intelligent squat toilet, comprising: An odor recognition module is used to acquire real-time data from sensors, including gas concentration data and infrared detection data. When the gas concentration data exceeds a preset threshold, an odor event is triggered, and the gas concentration data for a preset time is monitored to determine the type of odor event. The self-cleaning program adjustment module is used to determine the presence of the user based on infrared detection data when the odor event is a continuous odor event. If the user has not yet left the smart squat toilet, an instant self-cleaning program is executed. After the user leaves the smart squat toilet, a deep self-cleaning program is executed.

[0010] Optionally, before executing the deep self-cleaning scheme adjustment module, it further includes: The user activity area is identified based on infrared detection data, and the gas concentration distribution in the user activity area is detected based on distributed gas sensors to identify the location of pollution source areas, and a deep self-cleaning solution is implemented for the pollution source areas.

[0011] Optionally, in the odor recognition module, the real-time sensor data also includes image data; The self-cleaning scheme adjustment module further includes the following steps for implementing the deep self-cleaning scheme: detecting damp areas based on image data, identifying and locating damp areas in the image using image algorithms, performing quantitative analysis of the dampness of the damp areas, adjusting the drying strategy based on the dampness, and performing drying control in the deep self-cleaning scheme.

[0012] Optionally, after executing the deep self-cleaning scheme, the self-cleaning scheme adjustment module further includes: After the deep self-cleaning process is completed, sensor data is acquired again, and the self-cleaning effect is determined based on the sensor data for feedback control.

[0013] A third aspect of this application provides a self-cleaning control method device for an intelligent squat toilet, the device comprising a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute, according to the instructions in the program code, a self-cleaning control method for a smart squat toilet as described in any of the first aspects of the present invention.

[0014] The fourth aspect of this application provides a computer-readable storage medium for storing program code for executing a self-cleaning control method for an intelligent squat toilet as described in any of the first aspects of the present invention.

[0015] As can be seen from the above technical solutions, the present invention has the following advantages: by using sensors to detect the gas and human body in the squat toilet area, identifying the odor events and executing the corresponding self-cleaning scheme, the drying control has achieved a fundamental transformation from "preset program driven" to "visual perception driven", solving the problem of rough control caused by existing technologies relying on fixed timing or simple human body sensing, significantly improving energy utilization efficiency and user experience comfort, saving energy consumption and improving user experience and comfort. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart of a self-cleaning control method for a smart squat toilet; Figure 2 This is a structural diagram of a self-cleaning control system for an intelligent squat toilet. Detailed Implementation

[0018] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0019] This invention provides a self-cleaning control method for intelligent squat toilets, which solves the technical problem of difficulty in self-cleaning squat toilets in the prior art.

[0020] Please see Figure 1 , Figure 1 This is the first flowchart of a self-cleaning control method for an intelligent squat toilet, provided in an embodiment of the present invention.

[0021] S100, acquire real-time sensor data, including gas concentration data and infrared detection data; when the gas concentration data exceeds a preset threshold, an odor event is triggered, and the gas concentration data for a preset time is monitored to determine the type of odor event; It should be noted that the smart squat toilet is equipped with a sensor module that continuously collects data at a frequency of several times per second and sends it to the main control unit. The sensor module includes various types of semiconductor gas sensors, which can detect and collect key gas data including: gas concentration: total VOC (semiconductor sensor), ammonia (NH3), and hydrogen sulfide (H2S) concentration values ​​(unit: ppm or ppb); as well as acquire environmental temperature and humidity parameters, and use infrared sensors to detect the spatial state and determine the presence of human sensor signals; When any gas concentration exceeds its corresponding preset threshold, such as a 50% increase in VOCs, the system determines it as an "odor event triggered." An instantaneous trigger indicates an odor event, meaning the smart toilet smells bad at that moment and needs cleaning. However, this instantaneous odor might be a temporary smell, such as a malfunction caused by flushing. For deodorization in squat toilets, a more intelligent approach is needed, focusing on cleaning long-term, persistent odors. Therefore, in-depth analysis and continuous pollution assessment are required. To avoid short-lived odors, the system initiates an analysis window period. Within this preset window period, the system continuously monitors the trend of gas concentration changes. If the gas concentration rises rapidly and then drops quickly within the preset time, returning to below the preset threshold, it is determined to be an instantaneous odor, and an instantaneous odor self-cleaning mode is activated. If the gas concentration remains high or fluctuates around the preset threshold and fails to drop effectively within the window period, it is determined to be "persistent pollution," requiring a deep self-cleaning solution.

[0022] S200: When the odor event is a continuous odor event, the system determines the presence of the user based on infrared detection data. If the user has not yet left the smart squat toilet, the system executes an instant self-cleaning program. After the user leaves the smart squat toilet, the system executes a deep self-cleaning program.

[0023] It should be noted that the system continuously monitors the trend of gas concentration changes. When the gas concentration rises rapidly and then drops quickly within a preset time and returns to below the preset threshold, this type of instantaneous odor is judged as an instantaneous odor event, and the corresponding instantaneous self-cleaning plan is executed; for example, adjusting the exhaust fan to the high speed to increase the air exchange rate. When the gas concentration remains high or fluctuates near a preset threshold and fails to effectively decrease within the window period, it is determined to be "persistent pollution," and a deep self-cleaning program is executed. At this point, the system first checks for human presence. If infrared detection data indicates the user's presence but they haven't left the smart toilet, executing the deep self-cleaning program would affect the user experience. In this case, an instant self-cleaning program should be executed first to temporarily suppress odors without affecting the user. The deep self-cleaning device enters a silent standby state, not activating any execution devices that might disturb the user, such as spray disinfection. Once infrared detection data indicates the user has left the smart toilet, the deep self-cleaning program is executed. The main control unit sends a command to the inverter to increase the exhaust fan to its maximum power for strong ventilation. Simultaneously, the fresh air system valve can be opened to introduce fresh outdoor air, creating air convection. After a 10-15 second delay following the start of strong ventilation to ensure airflow, the smart spray device is triggered to spray chemical fragrances, such as deodorizers composed of biological enzymes or nanomaterials, directly decomposing odor molecules. Furthermore, it can perform deep disinfection. At the end of the cleaning cycle, the ultraviolet disinfection lamp can be turned on automatically. This deep disinfection stage needs to be connected to the human body sensor. When a user enters to use the smart squat toilet, the deep self-cleaning program will be stopped immediately to ensure that no one is present. Furthermore, if the gas concentration detected by the gas sensor remains excessively high, or if it is frequently triggered during non-use periods, the system infers that there may be a blockage, leak, or equipment malfunction, and will then issue an alarm signal to notify maintenance personnel to carry out repairs.

[0024] In this embodiment, by using sensors to detect the gas and human body in the squat toilet area, identifying odor events and executing corresponding self-cleaning schemes, a fundamental shift in drying control from "preset program driven" to "visual perception driven" is achieved. This solves the problem of coarse control caused by existing technologies relying on fixed timing or simple human body sensing, significantly improving energy utilization efficiency and user experience comfort, saving energy consumption and enhancing user experience and comfort.

[0025] The above is a detailed description of the first embodiment of the self-cleaning control method for a smart squat toilet provided by this application. The following is a detailed description of the second embodiment of the self-cleaning control method for a smart squat toilet provided by this application.

[0026] In this embodiment, a self-cleaning control method for a smart squat toilet is further provided. In the aforementioned step S200, before executing the deep self-cleaning scheme, the method further includes: identifying the user activity area based on infrared detection data, and identifying the location of the pollution source area based on the gas concentration distribution of the user activity area detected by a distributed gas sensor, and executing the deep self-cleaning scheme for the pollution source area.

[0027] It should be noted that, under the premise of ensuring absolute privacy and security, this embodiment can use visual / 3D sensing assistance, non-identifying 3D depth cameras or millimeter-wave radar to detect the location and range of liquid splashes or solid residues, but does not record any images that can identify faces or features. Combined with infrared detection data, the user's activity area is identified, narrowing the scope that needs to be monitored and reducing the number of sensors that need to be called. Distributed sensors are set up in the smart squat toilet area to form a sensor distribution network. This embodiment no longer relies on a single central sensor, but deploys low-cost gas sensor nodes in key areas of the toilet, such as behind each toilet, above the urinal, and near the floor drain, to form a sensor array. Based on identifying user activity areas, the system invokes distributed gas sensors in the corresponding areas to detect differences in gas concentration and determine pollution source areas. It can combine this with a core AI processing unit to process multi-source sensor data and run pollution localization algorithms. When the reading of a local sensor node spikes abnormally and exceeds a threshold, the system immediately marks it as a "suspected pollution source area" for pollution event detection and initial localization. Simultaneously, the system checks data from other nodes. When only a few sensor nodes at a certain interval show extremely high readings, while sensor nodes in adjacent areas show slight increases and distant nodes remain unchanged, the system can initially determine that the pollution source is located within that compartment. The increased readings of sensor nodes in adjacent areas are due to the diffusion of odorous gases, thus enabling precise location of the pollution source. When multiple nodes show simultaneous increases, it may indicate widespread pollution or extremely poor ventilation, and the system will activate a global cleaning mode, which may indicate multiple pollution sources. After locking down the approximate area, the system initiates a more refined identification process, including detailed identification of the pollution nature and extent, and gas composition analysis: analyzing the ratio of NH3 (urine) to H2S (feces) measured at the node to preliminarily determine the main components of the pollutants. By monitoring the speed and direction of odor diffusion through the sensor array and combining it with a preset toilet airflow model, AI can reverse-engineer the most likely location of the pollution source (such as inside the toilet, on the floor, or in the wastebasket) and perform spatial diffusion pattern analysis. If equipped with 3D sensors, this can be activated to quickly scan the target area, detecting abnormal surface humidity distribution or residue outlines, thereby confirming the precise two-dimensional coordinates and area of ​​the polluted area for visual assistance. Initiating precise cleaning, the system controls actuators that can move in a specific direction or be controlled in a specific zone to perform the operation. The fixed multi-nozzle zoned system is pre-installed with independent micro nozzles and control valves above each potential contamination point (such as above the toilet, above the urinal, or on the floor). The main control unit only opens the deodorizing agent spray valve above the corresponding toilet and the enhanced exhaust valve for that area. The nozzles spray a special cleaning and deodorizing agent (such as foam containing bio-enzymes) to cover the preset toilet area. The directional strong suction device in that area is activated to quickly remove the decomposed odor and prevent it from spreading to other areas. The nozzles and fans in other areas remain silent, achieving energy saving and precise intervention.

[0028] Furthermore, the real-time sensor data mentioned in step S100 above also includes image data; The aforementioned step S200 further includes: detecting humid areas based on image data, identifying and locating humid areas in the image using image algorithms, performing quantitative analysis of the humidity level of the humid areas, adjusting the drying strategy based on the humidity level, and performing drying control in the deep self-cleaning scheme.

[0029] It should be noted that when the system detects that the user has left the toilet area and finished using it, the drying process can be automatically or manually triggered, and the control system enters the damp area monitoring mode. Before drying starts, the camera immediately captures the first frame of the real-time image to be analyzed. During the drying process, subsequent image sequences are continuously captured at fixed time intervals for image acquisition and preprocessing, including damp feature extraction and region segmentation. This includes static feature analysis, threshold segmentation, and morphological operations on the image to initially extract areas where pixel values ​​change significantly due to moisture, as candidate damp areas. For areas such as water droplets and wet stains that may produce high light reflectance under specific lighting conditions, high-light detection is performed on the original image. For example, in the HSV color space, areas with high saturation (S) and high brightness (V) are extracted and fused with candidate damp areas to enhance the ability to identify water stains, performing dynamic feature enhancement. The finally identified damp pixel clusters are divided into different connected regions, each region corresponding to an independent damp patch for region segmentation.

[0030] In the feature quantification analysis of humid areas, for each identified humid area, the following feature vectors are calculated: the calculated pixel area, and through a pre-defined mapping relationship, its actual physical area, location, and shape features are estimated. Then, the centroid of the area is calculated and mapped to the actual spatial planar coordinates, the bounding rectangle and direction are determined, the minimum bounding rectangle is calculated, and its length, width, aspect ratio, and principal orientation angle are obtained to determine whether it is a dotted water droplet, a linear wet streak, or a planar water stain. By analyzing the degree of pixel value variation within the area, texture features (water stain areas are usually smooth), or combining surface temperature data provided by an infrared thermal imager (the temperature is slightly lower due to heat absorption during water evaporation), the humidity level of each area is divided into discrete levels, achieving humidity level estimation. Feature-based dynamic generation and control of drying strategies: The control system generates and executes dynamic drying strategies based on the feature set of all humid areas: Drying start / stop decision: If the total area of ​​all areas is less than a very small threshold, it is determined that drying is not required, and the drying process is skipped directly or only a very short "courtesy" air supply is performed. Otherwise, drying control is initiated. Dynamic control of drying parameters: Air direction and focus control: The system controls a multi-degree-of-freedom air direction adjustment device (such as a two-dimensional gimbal air guide plate) to sequentially align the main focus of the warm airflow with high-weight areas. For large areas, the air direction is controlled to perform scanning air supply along its main direction.

[0031] Furthermore, after performing the deep self-cleaning solution as described in step S200 above, the process also includes: After the deep self-cleaning process is completed, sensor data is acquired again, and the self-cleaning effect is determined based on the sensor data for feedback control. It should be noted that while executing the above cleaning instructions, the system continuously monitors gas sensor data and sets a "freshness target threshold" slightly higher than the baseline value. When the concentration of all gases decreases and stabilizes below the freshness target threshold for more than a preset time, the environment is considered to have returned to freshness. Then, a reset operation is performed, gradually reducing the exhaust fan power until it returns to standby low speed or is turned off. The fresh air valve and spray system are shut off, and the system event log records the duration of this odor event, the maximum pollution value, and the measures taken.

[0032] The above is a detailed description of a self-cleaning control method for an intelligent squat toilet, which is the first aspect of this application. The following is a detailed description of an embodiment of a self-cleaning control system for an intelligent squat toilet, which is the second aspect of this application.

[0033] Please see Figure 2 , Figure 2 This is a structural diagram of a self-cleaning control system for a smart squat toilet. This embodiment provides a self-cleaning control system for a smart squat toilet, including: Odor recognition module 10 is used to acquire real-time data from sensors, including gas concentration data and infrared detection data; when the gas concentration data exceeds a preset threshold, an odor event is triggered, and the gas concentration data for a preset time is monitored to determine the type of odor event; The self-cleaning scheme adjustment module 20 is used to determine the presence of the user based on infrared detection data when the odor event is a continuous odor event. If the user has not yet left the smart squat toilet, an instant self-cleaning scheme is executed. After the user leaves the smart squat toilet, a deep self-cleaning scheme is executed.

[0034] Furthermore, before executing the deep self-cleaning scheme adjustment module 20, it also includes: The user activity area is identified based on infrared detection data, and the gas concentration distribution in the user activity area is detected based on distributed gas sensors to identify the location of pollution source areas, and a deep self-cleaning solution is implemented for the pollution source areas.

[0035] Furthermore, in the odor recognition module 10, the real-time sensor data also includes image data; The self-cleaning scheme adjustment module further includes the following steps for implementing the deep self-cleaning scheme: detecting damp areas based on image data, identifying and locating damp areas in the image using image algorithms, performing quantitative analysis of the dampness of the damp areas, adjusting the drying strategy based on the dampness, and performing drying control in the deep self-cleaning scheme.

[0036] Furthermore, after executing the deep self-cleaning scheme, the self-cleaning scheme adjustment module 20 also includes: After the deep self-cleaning process is completed, sensor data is acquired again, and the self-cleaning effect is determined based on the sensor data for feedback control.

[0037] A third aspect of this application also provides a self-cleaning control method device for an intelligent squat toilet, including a processor and a memory: wherein the memory is used to store program code and transmit the program code to the processor; the processor is used to execute the above-mentioned self-cleaning control method for an intelligent squat toilet according to the instructions in the program code.

[0038] A fourth aspect of this application provides a computer-readable storage medium, characterized in that the computer-readable storage medium is used to store program code for executing the above-described self-cleaning control method for an intelligent squat toilet.

[0039] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and equipment can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0040] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0041] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0042] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0043] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0044] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A self-cleaning control method for an intelligent squat toilet, characterized in that... include: Acquire real-time sensor data, which includes gas concentration data and infrared detection data; When the gas concentration data exceeds the preset threshold, an odor event is triggered, and the gas concentration data for the preset time is monitored to determine the type of odor event. When the odor event is a continuous odor event, the presence of the user is determined based on infrared detection data. If the user has not yet left the smart squat toilet, an instant self-cleaning program is executed. After the user leaves the smart squat toilet, a deep self-cleaning program is executed.

2. The self-cleaning control method for an intelligent squat toilet according to claim 1, characterized in that, Before implementing the deep self-cleaning solution, the following steps are also included: The user activity area is identified based on infrared detection data, and the gas concentration distribution in the user activity area is detected based on distributed gas sensors to identify the location of pollution source areas, and a deep self-cleaning solution is implemented for the pollution source areas.

3. The self-cleaning control method for an intelligent squat toilet according to claim 1, characterized in that, The real-time data from the sensor also includes image data; The deep self-cleaning scheme further includes: detecting damp areas based on image data, identifying and locating damp areas in the image using image algorithms, performing quantitative analysis of the dampness of the damp areas, adjusting the drying strategy based on the dampness, and performing drying control in the deep self-cleaning scheme.

4. The self-cleaning control method for an intelligent squat toilet according to claim 1, characterized in that, After implementing the deep self-cleaning solution, the following is also included: After the deep self-cleaning process is completed, sensor data is acquired again, and the self-cleaning effect is determined based on the sensor data for feedback control.

5. A self-cleaning control system for an intelligent squat toilet, characterized in that, include: An odor recognition module is used to acquire real-time data from sensors, including gas concentration data and infrared detection data. When the gas concentration data exceeds a preset threshold, an odor event is triggered, and the gas concentration data for a preset time is monitored to determine the type of odor event. The self-cleaning program adjustment module is used to determine the presence of the user based on infrared detection data when the odor event is a continuous odor event. If the user has not yet left the smart squat toilet, an instant self-cleaning program is executed. After the user leaves the smart squat toilet, a deep self-cleaning program is executed.

6. A self-cleaning control system for an intelligent squat toilet according to claim 5, characterized in that, Before executing the deep self-cleaning scheme, the self-cleaning scheme adjustment module further includes: The user activity area is identified based on infrared detection data, and the gas concentration distribution in the user activity area is detected based on distributed gas sensors to identify the location of pollution source areas, and a deep self-cleaning solution is implemented for the pollution source areas.

7. A self-cleaning control system for an intelligent squat toilet according to claim 5, characterized in that, In the odor recognition module, the real-time sensor data also includes image data; The self-cleaning scheme adjustment module further includes the following steps for implementing the deep self-cleaning scheme: detecting damp areas based on image data, identifying and locating damp areas in the image using image algorithms, performing quantitative analysis of the dampness of the damp areas, adjusting the drying strategy based on the dampness, and performing drying control in the deep self-cleaning scheme.

8. A self-cleaning control system for an intelligent squat toilet according to claim 5, characterized in that, The self-cleaning scheme adjustment module, after executing the deep self-cleaning scheme, also includes: After the deep self-cleaning process is completed, sensor data is acquired again, and the self-cleaning effect is determined based on the sensor data for feedback control.

9. A self-cleaning control device for an intelligent squat toilet, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute, according to the instructions in the program code, a self-cleaning control method for a smart squat toilet as described in any one of claims 1-4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing a self-cleaning control method for an intelligent squat toilet as described in any one of claims 1-4.

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