A smart toilet control method applied to a smart home system

Abstract

The application discloses a kind of intelligent toilet control methods applied to smart home system, the present application includes the following steps, S1, establishes user excretion behavior portrait;S2, identify easy adhesion constitution user;S3, execute anti-adhesion pretreatment;S4, execute initial cleaning operation;S5, obtain and judge cleaning feedback information;S6, self-adapting adjustment cleaning factor and supplement cleaning, the present application is recognized easy adhesion constitution user by constructing user portrait, executes anti-adhesion pretreatment in advance, and is combined with cleaning feedback self-adapting adjustment cleaning factor, the problem that after cleaning, general cleaning scheme poor adaptability, easy adhesion user is not washed and the problem of large water consumption in prior art is solved, realizes before prediction, targeted adaptation cleaning control, improves the use convenience and cleaning effect of intelligent toilet.

Description

Technical Field

[0001] This invention relates to the field of smart home device technology, and in particular to a smart toilet control method applied to a smart home system. Background Technology

[0002] Existing smart toilets typically integrate multiple sensors and automatic control functions. For example, they can automatically detect the user's defecation behavior through infrared sensors, ultrasonic sensors, or image recognition modules, and dynamically adjust the flushing water volume according to the detected stool or urine type. Some high-end smart toilets can use optical sensors to determine whether the stool is sticky and automatically add detergent during the next flush to enhance the cleaning effect.

[0003] However, in actual use, it has been found that due to individual differences such as dietary habits and bowel movement patterns, different users exhibit long-term stable differences in the viscosity, adhesion, and residual properties of their excrement. In other words, some users are prone to sticking. Existing technologies mainly respond to existing adhesions, which is a reactive solution and cannot provide users with a proactive cleaning solution. In addition, existing cleaning measures for feces are designed to be universal, with fixed water volume and detergent dosage. However, since each user is unique, it cannot be guaranteed that universal cleaning measures can clean the feces completely in one go. Users need to rinse twice or even multiple times to clean the feces thoroughly, which has limitations.

[0004] Because it cannot recognize a user's tendency to stick to surfaces, smart toilets continue to use the regular cleaning mode when no obvious stickiness is detected. This causes users with a tendency to stick to surfaces to face the problem of not being able to flush clean in a single flush and having to flush repeatedly. This not only increases water consumption but also reduces the user experience, thus presenting limitations. Summary of the Invention

[0005] The purpose of this invention is to provide a smart toilet control method for use in smart home systems, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a smart toilet control method applied to a smart home system, comprising the following steps:

[0007] S1. Establish a user excretion behavior profile, collect historical excretion data of the same user within a preset time period, and construct an excretion behavior profile to represent the user's stickiness tendency characteristics.

[0008] S2. Identify users with a tendency to stick together. Based on the excretion behavior profile, identify users who meet the preset adhesion tendency conditions as users with a tendency to stick together.

[0009] S3. Perform anti-adhesion pretreatment. If a user is identified as having a tendency to stick during the use of the smart toilet, an anti-adhesion pretreatment will be performed on the inner wall of the toilet before or during excretion.

[0010] S4. Perform the initial cleaning operation. After the user finishes defecating, perform the first cleaning operation according to the current cleaning configuration.

[0011] S5. Obtain and judge cleaning feedback information. After the initial cleaning is completed, obtain the cleaning status detection data of the toilet inner wall and / or the user's subsequent operation behavior, and judge whether there is insufficient cleaning.

[0012] S6. Adaptively adjust cleaning factors and supplement cleaning. When it is determined that there is insufficient cleaning, automatically increase at least one cleaning factor and simultaneously update the subsequent anti-adhesion pretreatment strategy for the user. Finally, perform supplementary cleaning operation based on the adjusted cleaning factors.

[0013] Preferably, in step S1, a multi-dimensional sensor is installed in the toilet. The multi-dimensional sensor specifically includes a weight sensor, a timing module, and an infrared sensor. The user's weight characteristics are collected by the weight sensor and combined with the toilet usage time to establish a user identity profile.

[0014] The collected historical excretion data includes excretion type, excretion duration, and corresponding inner wall cleanliness parameters; among which, the excretion type is collected by the timing module and is divided into event A and event B, namely feces and urine.

[0015] Preferably, in step S2, after a cleaning operation is completed, optical reflection data at multiple points are collected by an infrared sensor set on the periphery of the inner wall of the toilet. The residue index at each point is calculated based on the optical reflection data, and the adhesion index corresponding to the excretion is obtained by combining the data.

[0016] When a user's average adhesion index exceeds a preset threshold within a preset time period, the user is determined to be a user with a body prone to adhesion.

[0017] Preferably, step S2 further includes performing time decay weighting processing on the adhesion index detected at different times, so that the detection data corresponding to the recent event A has a higher weight, and updating the user's adhesion index when the weighted adhesion tendency index exceeds a preset threshold.

[0018] Preferably, in step S3, when a user is identified as having a tendency to stick to the toilet and the current excretion type is determined to be event A, the inner wall of the toilet is sprayed with an anti-sticking medium through a spraying device to form a low surface energy isolation layer before the excrement comes into contact with the inner wall.

[0019] Preferably, step S3 further includes dynamically adjusting the spray angle and coverage density of the anti-adhesion medium based on the high residue areas reported by the infrared sensor in historical detection.

[0020] Preferably, in step S5, after the initial cleaning operation is completed, the cleanliness detection data of the toilet inner wall is obtained by an infrared sensor. At the same time, the user's behavior of getting up and returning to the seat after the initial cleaning is monitored by a weight sensor, or the interval time of the flushing trigger signal is monitored to determine whether there is a need for repeated cleaning.

[0021] Preferably, the condition for determining insufficient cleaning in step S5 is that when the cleanliness parameter detected by the infrared sensor deviates from the preset benchmark range, or when the user triggers the flushing operation again within a preset time, it is determined to be insufficient cleaning.

[0022] Preferably, in step S6, when it is determined that the cleaning is insufficient, at least one cleaning factor is increased according to a preset gradient. The cleaning factor includes the amount of water used for rinsing, the amount of cleaning agent added, and the intensity of the rinsing water flow.

[0023] Perform supplementary cleaning based on the adjusted cleaning factor, and return to S5 to reacquire cleanliness detection data after the supplementary cleaning is completed;

[0024] Perform supplementary cleaning based on the adjusted cleaning factor, and return to S5 to reacquire cleanliness detection data after the supplementary cleaning is completed;

[0025] The updated anti-adhesion pretreatment strategy specifically includes:

[0026] When cleaning is deemed insufficient, increase the spray volume of the anti-adhesion medium, extend the spray time, or expand the spray coverage area by at least one of the following:

[0027] This ensures that the anti-adhesion preprocessing strength is higher when the user executes event A again.

[0028] Preferably, in step S6, when the cumulative number of supplementary cleaning operations exceeds the preset maximum number and the cleanliness parameter still fails to return to the preset reference range, automatic adjustment is stopped and the abnormal event is recorded, while maintenance prompt information is sent to the user terminal.

[0029] The technical effects and advantages of this invention are as follows:

[0030] 1. This application identifies users with a tendency to stick to surfaces by constructing user profiles, performs anti-sticking pretreatment in advance, and adaptively adjusts cleaning factors based on cleaning feedback. This solves the problems of post-cleaning, poor adaptability of general cleaning solutions, incomplete flushing of users with a tendency to stick to surfaces, and high water consumption in the background technology. It achieves pre-judgment and targeted adaptation of cleaning control, improving the convenience of use and cleaning effect of smart toilets.

[0031] 2. This application reduces the adhesion of excrement from the source by spraying an anti-adhesion medium before or during excretion to form an isolation layer, thereby reducing the difficulty of subsequent cleaning, avoiding the trouble of repeated flushing for users with easily sticky excrement, and reducing the amount of water and cleaning agents consumed during the cleaning process, thus meeting the needs of users.

[0032] 3. The combination of cleaning feedback and adaptive adjustment mechanism enables the cleaning solution to be dynamically optimized according to the actual cleaning effect and individual user differences. Compared with traditional solutions, this application can flexibly adjust cleaning factors such as water volume and detergent dosage according to the adhesion characteristics and different cleaning needs of different users, ensuring that the cleaning standard is met in a single cleaning and satisfying the user's needs. Attached Figure Description

[0033] Figure 1 This is the main flowchart of the control method of the present invention;

[0034] Figure 2 A flowchart for creating a user excretion behavior profile for this invention;

[0035] Figure 3 This is a schematic diagram illustrating the identification of users with a tendency to adhere to surfaces, as per the present invention.

[0036] Figure 4 This is a flowchart of the anti-adhesion pretreatment process of the present invention;

[0037] Figure 5 This is a flowchart of the initial cleaning and cleaning feedback judgment process of the present invention;

[0038] Figure 6 This invention adaptively adjusts cleaning factors and supplements the cleaning process diagram;

[0039] Figure 7 The flowchart for the updated anti-adhesion pretreatment strategy of this invention is shown below. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] This invention provides, for example Figure 1 The method for controlling a smart toilet in a smart home system, as shown, includes the following steps:

[0042] S1. Establish a user excretion behavior profile, collect historical excretion data of the same user within a preset time period, and construct an excretion behavior profile to represent the user's stickiness tendency characteristics.

[0043] S2. Identify users with a tendency to stick together. Based on the excretion behavior profile, identify users who meet the preset adhesion tendency conditions as users with a tendency to stick together.

[0044] S3. Perform anti-adhesion pretreatment. If a user is identified as having a tendency to stick during the use of the smart toilet, an anti-adhesion pretreatment will be performed on the inner wall of the toilet before or during excretion.

[0045] S4. Perform the initial cleaning operation. After the user finishes defecating, perform the first cleaning operation according to the current cleaning configuration.

[0046] S5. Obtain and judge cleaning feedback information. After the initial cleaning is completed, obtain the cleaning status detection data of the toilet inner wall and / or the user's subsequent operation behavior, and judge whether there is insufficient cleaning.

[0047] S6. Adaptively adjust cleaning factors and supplement cleaning. When it is determined that there is insufficient cleaning, automatically increase at least one cleaning factor and simultaneously update the subsequent anti-adhesion pretreatment strategy for the user. Finally, perform supplementary cleaning operation based on the adjusted cleaning factors.

[0048] Example 1: In this example, the overall operation process is as follows: The smart toilet pre-sets two different basic cleaning schemes for excretion types A and B, namely defecation and urination. Event A corresponds to a high-volume flushing scheme, with a high basic flush volume, appropriate detergent dosage, and medium water flow intensity. Event B corresponds to a low-volume flushing scheme, with a low basic flush volume, no detergent dosage, and low water flow intensity. The smart toilet's built-in multi-dimensional sensors, namely a weight sensor, a timing module, and an infrared sensor, first collect the weight characteristics of each user through the weight sensor, and distinguish different user identities by combining information such as the time and frequency of each toilet use. Then, the timing module and infrared sensor continuously collect historical excretion data of each user within a preset time period, including excretion type, excretion duration, and corresponding toilet inner wall cleanliness parameters. Based on these data, the user's adhesion tendency characteristics are extracted to generate a personalized user excretion behavior profile. Subsequently, based on this behavior profile, users with a tendency to adhere are identified. For users determined to have a tendency to adhere and whose current excretion type is event A, before excretion... During the excretion process, the spray device is activated to spray anti-adhesion medium, performing anti-adhesion pretreatment in advance. After the user finishes excretion, the smart toilet performs the corresponding initial cleaning operation according to the current excretion type. After the initial cleaning is completed, the cleanliness detection data of the toilet inner wall is obtained through infrared sensors, and the user's sitting behavior or flushing trigger interval is monitored through weight sensors to determine whether there is insufficient cleaning. If insufficient cleaning is determined, at least one of the cleaning factors, such as flush volume, detergent dosage, and flushing water flow intensity, is automatically increased according to the preset gradient. At the same time, the subsequent anti-adhesion pretreatment strategy for this user is updated. Specifically, at least one of the following can be increased: spray volume of anti-adhesion medium, extended spray time, or expanded spray coverage area, to ensure that the anti-adhesion pretreatment intensity is higher when the user performs event A next time. Finally, a supplementary cleaning operation is performed based on the adjusted cleaning factors. After supplementary cleaning, the cleaning effect is checked again, and the cycle is repeated until the cleaning standard is met. If the cumulative number of supplementary cleanings exceeds the preset maximum value and the standard is still not met, the automatic adjustment stops, the abnormal event is recorded, and a maintenance prompt is sent to the user terminal.

[0049] The core operating logic of this method can be further explained as follows: Taking individual user differences as the core, multi-dimensional sensors are used to distinguish user identities and accurately collect excretion data, constructing user profiles that include adhesion tendencies. Based on this, accurate identification of those prone to adhesion and pre-treatment to prevent adhesion are achieved. Then, through a mechanism of initial cleaning - cleaning feedback - adaptive adjustment of cleaning factors + updating of anti-adhesion strategies - supplementary cleaning, it not only dynamically adapts to the cleaning needs of different users and different excretion scenarios, but also gradually reduces the probability of adhesion from the source by iteratively optimizing the anti-adhesion pre-treatment strategy, avoiding the problem of repeated flushing for users prone to adhesion, while reducing the ineffective consumption of water resources and cleaning agents, improving the intelligence of smart toilets, and meeting user needs.

[0050] This invention provides, for example Figures 2 to 7 The method for controlling a smart toilet in a smart home system, as shown, includes the following steps:

[0051] S1. Establish a user excretion behavior profile, collect historical excretion data of the same user within a preset time period, and construct an excretion behavior profile to represent the user's stickiness tendency characteristics.

[0052] S2. Identify users with a tendency to stick together. Based on the excretion behavior profile, identify users who meet the preset adhesion tendency conditions as users with a tendency to stick together.

[0053] S3. Perform anti-adhesion pretreatment. If a user is identified as having a tendency to stick during the use of the smart toilet, an anti-adhesion pretreatment will be performed on the inner wall of the toilet before or during excretion.

[0054] S4. Perform the initial cleaning operation. After the user finishes defecating, perform the first cleaning operation according to the current cleaning configuration.

[0055] S5. Obtain and judge cleaning feedback information. After the initial cleaning is completed, obtain the cleaning status detection data of the toilet inner wall and / or the user's subsequent operation behavior, and judge whether there is insufficient cleaning.

[0056] S6. Adaptively adjust cleaning factors and supplement cleaning. When it is determined that there is insufficient cleaning, automatically increase at least one cleaning factor and simultaneously update the subsequent anti-adhesion pretreatment strategy for the user. Finally, perform supplementary cleaning operation based on the adjusted cleaning factors.

[0057] In step S1, a multi-dimensional sensor is installed in the toilet. The multi-dimensional sensor specifically includes a weight sensor, a timing module, and an infrared sensor. The user's weight characteristics are collected by the weight sensor and combined with the toilet usage time to establish a user identity profile.

[0058] The collected historical excretion data includes excretion type, excretion duration, and corresponding inner wall cleanliness parameters; among which, the excretion type is collected by the timing module and is divided into event A and event B, namely feces and urine.

[0059] In step S2, after a cleaning operation is completed, optical reflection data at multiple points are collected by an infrared sensor set on the periphery of the inner wall of the toilet. The residue index at each point is calculated based on the optical reflection data, and the adhesion index corresponding to the excretion is obtained by combining the data.

[0060] When a user's average adhesion index exceeds a preset threshold within a preset time period, the user is determined to be a user with a body prone to adhesion.

[0061] Step S2 also includes performing time decay weighting on the adhesion index detected at different times, so that the detection data corresponding to the recent event A has a higher weight. When the weighted adhesion tendency index exceeds a preset threshold, the user's adhesion index is updated.

[0062] In step S3, when a user is identified as having a tendency to stick to the toilet and the current excretion type is determined to be event A, the toilet bowl is sprayed with an anti-sticking medium through a spraying device to form a low surface energy isolation layer before the excrement comes into contact with the inner wall.

[0063] Step S3 also includes dynamically adjusting the spray angle and coverage density of the anti-adhesion medium based on the high residue areas reported by the infrared sensor in historical detection.

[0064] In step S5, after the initial cleaning operation is completed, the cleanliness detection data of the toilet inner wall is obtained by an infrared sensor. At the same time, the user's behavior of getting up and returning to the seat after the initial cleaning is monitored by a weight sensor, or the interval of the flushing trigger signal is monitored to determine whether there is a need for repeated cleaning.

[0065] The condition for determining insufficient cleaning in step S5 is that when the cleanliness parameter detected by the infrared sensor deviates from the preset benchmark range, or when the user triggers the flushing operation again within a preset time, it is determined to be insufficient cleaning.

[0066] In step S6, when it is determined that the cleaning is insufficient, at least one cleaning factor is increased according to a preset gradient. The cleaning factor includes the amount of water used for rinsing, the amount of cleaning agent added, and the intensity of the rinsing water flow.

[0067] Perform supplementary cleaning based on the adjusted cleaning factor, and return to S5 to reacquire cleanliness detection data after the supplementary cleaning is completed;

[0068] Perform supplementary cleaning based on the adjusted cleaning factor, and return to S5 to reacquire cleanliness detection data after the supplementary cleaning is completed;

[0069] The updated anti-adhesion pretreatment strategy specifically includes:

[0070] When cleaning is deemed insufficient, increase the spray volume of the anti-adhesion medium, extend the spray time, or expand the spray coverage area by at least one of the following:

[0071] This ensures that the anti-adhesion preprocessing strength is higher when the user executes event A again.

[0072] In step S6, when the cumulative number of supplementary cleaning operations exceeds the preset maximum number and the cleanliness parameter still fails to return to the preset reference range, automatic adjustment is stopped and abnormal events are recorded. At the same time, maintenance prompt information is sent to the user terminal.

[0073] Example 2: In this example, the detailed steps are as follows:

[0074] S1. Establish a user excretion behavior profile;

[0075] Multi-dimensional sensors are installed in the smart toilet. These sensors include a weight sensor, a timing module, and an infrared sensor. The weight sensor is used to collect the user's weight characteristics and combine them with information such as the time and frequency of each toilet use to build a basic user profile, enabling the differentiation and identification of different users and avoiding confusion of excretion data from different users.

[0076] Historical excretion data of the same user within a preset time period is collected. The preset time period can be set according to actual usage needs. In this embodiment, it is set to 30 days to ensure that the collected data can reflect the user's long-term stable excretion characteristics. It should be noted that each person's dietary habits and living environment will not change significantly. Therefore, recording historical data can help infer the user's physical condition. The collected historical excretion data specifically includes excretion type, excretion duration, and corresponding internal wall cleanliness parameters. Among them, excretion type is collected by a timing module. Based on the length of excretion duration, excretion type is divided into event A and event B, namely defecation and urination. Generally, excretion duration ≥ 30 seconds is defined as event A, and excretion duration < 30 seconds is defined as event B.

[0077] Based on the collected historical excretion data, the user's adhesion tendency characteristics are extracted. These characteristics mainly include the residue on the toilet bowl after each event A, the frequency of adhesion, etc. An excretion behavior profile representing the user's adhesion tendency characteristics is constructed, providing theoretical data for the subsequent identification of users with a tendency to adhere.

[0078] S2. Identify users with a tendency to stick to surfaces;

[0079] After each cleaning operation, infrared sensors installed around the inner wall of the toilet collect optical reflection data from multiple points on the inner wall. The infrared sensors are evenly distributed in the front, back, left and right directions of the inner wall of the toilet, with at least one sensor in each direction to ensure that the collected data can fully cover the inner wall of the toilet.

[0080] The residual index of each point is calculated based on the collected optical reflection data. The residual index is calculated by comparing the optical reflection data with the preset cleanliness benchmark value and assigning a corresponding score according to the degree of deviation. The greater the deviation, the higher the residual index. The adhesion index corresponding to this excretion is obtained by combining the residual indices of all points. The higher the adhesion index, the more serious the adhesion of this excretion.

[0081] The adhesion index corresponding to all events A for the user within a preset time period, i.e., 30 days, is statistically analyzed, and the average adhesion index is calculated. When the average adhesion index exceeds a preset threshold, the user is determined to be a user with a tendency to stick. The preset threshold can be calibrated according to factors such as the cleaning ability of the toilet and the user's usage needs, and is preferably 0.6. The residue index ranges from 0 to 1, where 0 indicates no residue and 1 indicates complete adhesion.

[0082] Simultaneously, the adhesion index detected at different times is subjected to time decay weighting processing, and a time decay coefficient is set. The detection data corresponding to event A within the past 7 days is given a higher weight, such as 0.8, while the detection data corresponding to event A within the past 8-30 days is given a lower weight, such as 0.2. This makes the recent excretion adhesion situation have a greater impact on the judgment of the user's adhesion tendency. When the weighted adhesion tendency index exceeds the preset threshold, the user's adhesion index is updated in a timely manner to ensure the accuracy and timeliness of the identification of the easy-to-adhere constitution, so that the final profile can adapt to the short-term changes in the user's diet and constitution.

[0083] S3. Perform anti-adhesion pretreatment;

[0084] During the use of the smart toilet, the system identifies the user's current excretion type through weight sensors and a timing module. If the user is determined to be prone to sticking and the current excretion type is event A, the spray device on the inner wall of the toilet is activated to spray an anti-sticking medium. This medium is food-grade silicone oil or a special anti-sticking agent, which is non-toxic and harmless. It forms a low surface energy barrier layer on the inner wall of the toilet, preventing excrement from directly contacting the inner wall and reducing sticking at the source. Furthermore, based on high-residue areas detected by the infrared sensor in historical data, the spray angle and coverage density of the anti-sticking medium are dynamically adjusted. For areas with high historical residue indices, such as the back of the inner wall of the toilet, the spray angle and medium density are increased to ensure sufficient coverage and further enhance the anti-sticking effect. For areas with low historical residue indices, the spray density is appropriately reduced to conserve the anti-sticking medium.

[0085] If the user is not prone to adhesion, or if the current excretion type is event B, then the anti-adhesion pretreatment will not be performed, and the normal usage mode will be adopted to avoid wasting resources.

[0086] S4. Perform initial cleaning procedures;

[0087] After the user finishes urinating, the smart toilet performs the first cleaning operation according to the current cleaning configuration. The cleaning configuration includes the basic flush volume, detergent dosage, and flushing water flow intensity. The basic cleaning configuration can be calibrated according to the toilet model and the user's default settings. For users with a tendency to stick to grime, the flush volume and detergent dosage of the basic cleaning configuration can be appropriately higher than those of users without a tendency to stick to grime, to ensure the basic cleaning effect.

[0088] S5. Obtain and determine cleaning feedback information;

[0089] After the initial cleaning is completed, an infrared sensor collects cleanliness data of the toilet bowl's inner wall again to calculate the current residue index, which serves as the core feedback on the cleaning effect. At the same time, a weight sensor monitors the user's behavior of getting up and returning to the seat after the initial cleaning, or monitors the interval between flushing trigger signals to help determine if there is any insufficient cleaning.

[0090] The specific conditions for determining insufficient cleaning are as follows: when the cleanliness parameter detected by the infrared sensor, which is actually the residue index, deviates from the preset benchmark range, the preset benchmark range in this case is 0-0.2, that is, when the residue index is >0.2, it is determined to be insufficient cleaning; or when it is detected that the user triggers the flushing operation again within a preset time, preferably 5 minutes, it is determined to be insufficient cleaning, indicating that the initial cleaning did not meet the user's expectations or actual cleaning needs.

[0091] S6. Adaptively adjusts cleaning agents and replenishes cleaning;

[0092] When insufficient cleaning is detected, at least one cleaning factor is automatically increased according to the preset gradient. The cleaning factors include water volume, detergent dosage, and rinsing water flow intensity. The preset gradient can be set according to actual cleaning needs, such as increasing water volume by 20%, detergent dosage by 15%, and rinsing water flow intensity by 30%. Depending on the degree of residue after the initial cleaning, a single factor can be increased or multiple factors can be increased simultaneously. If the degree of residue is serious, multiple cleaning factors will be increased first.

[0093] Based on the adjusted cleaning factor, perform supplementary cleaning. After supplementary cleaning is completed, return to step S5 to reacquire the cleanliness detection data of the toilet inner wall and the user's operation behavior, and determine again whether there is insufficient cleaning. If there is still insufficient cleaning, continue to increase the cleaning factor and perform supplementary cleaning until the cleaning standard is met.

[0094] At the same time, a maximum number of times to perform supplementary cleaning is set, specifically 3 times in this case. When the cumulative number of supplementary cleaning operations exceeds the preset maximum number and the cleanliness parameters still fail to return to the preset benchmark range, automatic adjustment stops and abnormal events are recorded. At the same time, maintenance prompts are sent to the user terminal, prompting the user to check if there is a malfunction in the toilet, such as a clogged spray device or insufficient detergent, which requires manual cleaning to avoid resource waste and equipment damage caused by endless supplementary cleaning.

[0095] This invention provides, for example Figure 1 The method for controlling a smart toilet in a smart home system, as shown, includes the following steps:

[0096] S1. Establish a user excretion behavior profile, collect historical excretion data of the same user within a preset time period, and construct an excretion behavior profile to represent the user's stickiness tendency characteristics.

[0097] S2. Identify users with a tendency to stick together. Based on the excretion behavior profile, identify users who meet the preset adhesion tendency conditions as users with a tendency to stick together.

[0098] S3. Perform anti-adhesion pretreatment. If a user is identified as having a tendency to stick during the use of the smart toilet, an anti-adhesion pretreatment will be performed on the inner wall of the toilet before or during excretion.

[0099] S4. Perform the initial cleaning operation. After the user finishes defecating, perform the first cleaning operation according to the current cleaning configuration.

[0100] S5. Obtain and judge cleaning feedback information. After the initial cleaning is completed, obtain the cleaning status detection data of the toilet inner wall and / or the user's subsequent operation behavior, and judge whether there is insufficient cleaning.

[0101] S6. Adaptively adjust cleaning factors and supplement cleaning. When it is determined that there is insufficient cleaning, automatically increase at least one cleaning factor and simultaneously update the subsequent anti-adhesion pretreatment strategy for the user. Finally, perform supplementary cleaning operation based on the adjusted cleaning factors.

[0102] Example 3: In this example, the specific formulas involved in the above steps are as follows;

[0103] S1: Create a user excretion behavior profile;

[0104]

[0105] For users The excretion behavior profile is used to characterize the user's long-term excretion characteristics; The user is uniquely identified by a combination of weight sensor readings and usage time. The timestamp of the i-th excretion behavior; The type of excretion is represented by either A or B. The cleanliness parameter of the inner wall after the i-th excretion is obtained by an array-type infrared sensor; This represents the total number of valid discharge records within the statistical period.

[0106] S2: Identify users with a tendency to stick to surfaces;

[0107]

[0108]

[0109] The average stickiness index of user u during the statistical period; This represents the total number of times event A occurred for user u within the statistical period. is the adhesion index corresponding to the i-th event A, calculated based on the degree of residue on the inner wall; A preset threshold is used to determine whether a user has a body type prone to adhesion.

[0110] S3: Perform anti-adhesion pretreatment;

[0111]

[0112]

[0113] Pre-treatment to prevent adhesion; It is a monotonically increasing mapping function used to convert adhesion tendency into pretreatment intensity; To prevent the media from sticking together, the spray volume should be adjusted. The baseline spray volume; This represents the increase in the spray volume; To prevent sticking, the spraying time should be extended; The baseline spraying time; This is the increment of the spraying time; To prevent sticking, the coverage area of ​​the spray; As the baseline coverage area; This represents the increment in coverage.

[0114] S4: Perform initial cleaning.

[0115]

[0116] This is the initial cleaning operation; This is the amount of water used for the initial cleaning. This refers to the amount of cleaning agent used for the initial cleaning. The intensity of the initial rinsing water flow.

[0117] S5: Obtain and interpret cleaning feedback information;

[0118]

[0119] For the cleanliness assessment results, This indicates that the cleaning is up to standard. This indicates insufficient cleaning; These are the cleanliness parameters of the inner wall detected by the array-type infrared sensor after the initial cleaning. This is a preset threshold for cleanliness.

[0120] S6: Adaptively adjusts the cleaning strategy and replenishes cleaning;

[0121]

[0122]

[0123]

[0124] This is the adjusted flush volume; This represents an increase in the amount of water used for flushing; This is the adjusted cleaning agent dosage; This represents the increase in the amount of cleaning agent used; The adjusted flushing water flow intensity; This represents the increment in the intensity of the flushing water flow; The adjusted anti-adhesion pretreatment strength; To prevent adhesion, the pretreatment strength is increased; To supplement the cleaning operation.

[0125] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling a smart toilet in a smart home system, characterized in that, Includes the following steps: S1. Establish a user excretion behavior profile, collect historical excretion data of the same user within a preset time period, and construct an excretion behavior profile to represent the user's stickiness tendency characteristics. S2. Identify users with a tendency to stick together. Based on the excretion behavior profile, identify users who meet the preset adhesion tendency conditions as users with a tendency to stick together. S3. Perform anti-adhesion pretreatment. If a user is identified as having a tendency to stick during the use of the smart toilet, an anti-adhesion pretreatment will be performed on the inner wall of the toilet before or during excretion. S4. Perform the initial cleaning operation. After the user finishes defecating, perform the first cleaning operation according to the current cleaning configuration. S5. Obtain and judge cleaning feedback information. After the initial cleaning is completed, obtain the cleaning status detection data of the toilet inner wall and / or the user's subsequent operation behavior, and judge whether there is insufficient cleaning. S6. Adaptively adjust cleaning factors and supplement cleaning. When it is determined that there is insufficient cleaning, automatically increase at least one cleaning factor and simultaneously update the subsequent anti-adhesion pretreatment strategy for the user. Finally, perform supplementary cleaning operation based on the adjusted cleaning factors.

2. The smart toilet control method applied to a smart home system according to claim 1, characterized in that, In step S1, a multi-dimensional sensor is installed in the toilet. The multi-dimensional sensor specifically includes a weight sensor, a timing module, and an infrared sensor. The user's weight characteristics are collected by the weight sensor and combined with the toilet usage time to establish a user identity profile. The collected historical excretion data includes excretion type, excretion duration, and corresponding inner wall cleanliness parameters; among which, the excretion type is collected by the timing module and is divided into event A and event B, namely feces and urine.

3. The smart toilet control method applied to a smart home system according to claim 1, characterized in that, In step S2, after a cleaning operation is completed, optical reflection data at multiple points are collected by an infrared sensor set on the periphery of the inner wall of the toilet. The residue index at each point is calculated based on the optical reflection data, and the adhesion index corresponding to the excretion is obtained by combining the data. When a user's average adhesion index exceeds a preset threshold within a preset time period, the user is determined to be a user with a body prone to adhesion.

4. The smart toilet control method applied to a smart home system according to claim 3, characterized in that, Step S2 also includes performing time decay weighting on the adhesion index detected at different times, so that the detection data corresponding to the recent event A has a higher weight. When the weighted adhesion tendency index exceeds a preset threshold, the user's adhesion index is updated.

5. The smart toilet control method applied to a smart home system according to claim 1, characterized in that, In step S3, when a user is identified as having a tendency to stick to the toilet and the current excretion type is determined to be event A, the toilet bowl is sprayed with an anti-sticking medium through a spraying device to form a low surface energy isolation layer before the excrement comes into contact with the inner wall.

6. A smart toilet control method for a smart home system according to claim 5, characterized in that, Step S3 also includes dynamically adjusting the spray angle and coverage density of the anti-adhesion medium based on the high residue areas reported by the infrared sensor in historical detection.

7. A smart toilet control method for a smart home system according to claim 1, characterized in that, In step S5, after the initial cleaning operation is completed, the cleanliness detection data of the toilet inner wall is obtained by an infrared sensor. At the same time, the user's behavior of getting up and returning to the seat after the initial cleaning is monitored by a weight sensor, or the interval of the flushing trigger signal is monitored to determine whether there is a need for repeated cleaning.

8. A smart toilet control method for a smart home system according to claim 1, characterized in that, The condition for determining insufficient cleaning in step S5 is that when the cleanliness parameter detected by the infrared sensor deviates from the preset benchmark range, or when the user triggers the flushing operation again within a preset time, it is determined to be insufficient cleaning.

9. A smart toilet control method for a smart home system according to claim 1, characterized in that, In step S6, when it is determined that the cleaning is insufficient, at least one cleaning factor is increased according to a preset gradient. The cleaning factor includes the amount of water used for rinsing, the amount of cleaning agent added, and the intensity of the rinsing water flow. Perform supplementary cleaning based on the adjusted cleaning factor, and return to S5 to reacquire cleanliness detection data after the supplementary cleaning is completed; The updated anti-adhesion pretreatment strategy specifically includes: When cleaning is deemed insufficient, increase the spray volume of the anti-adhesion medium, extend the spray time, or expand the spray coverage area by at least one of the following: This ensures that the anti-adhesion preprocessing strength is higher when the user executes event A again.

10. A smart toilet control method for a smart home system according to claim 9, characterized in that, In step S6, when the cumulative number of supplementary cleaning operations exceeds the preset maximum number and the cleanliness parameter still fails to return to the preset reference range, automatic adjustment is stopped and abnormal events are recorded. At the same time, maintenance prompt information is sent to the user terminal.

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