Method and device for performing health detection by applying intelligent closestool remote controller

Abstract

The invention discloses a method and a device for performing health detection by applying an intelligent closestool remote controller, and relates to the technical field of data processing. The method comprises the steps of obtaining fingerprint sensing information according to a fingerprint sensing module, and performing user authentication to obtain a user authentication result; when the user authentication result is successful, receiving a user operation instruction, and calling user basic information; obtaining user monitoring data and environment sensing data; health prediction is carried out based on the health association feature registration constraint conditions, and health state prediction data is obtained; performing health deviation identification on the user monitoring data to obtain a user health deviation identification result; health detection is carried out, a health detection report is generated, and health early warning is carried out in combination with an intelligent closestool remote controller. The technical problem that in the prior art, an intelligent closestool is lack of user health detection, so that interactivity is poor is solved, user health detection is achieved through the intelligent closestool remote controller, and the technical effect of improving interactivity between a user and the intelligent closestool is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and a device for health detection using an intelligent toilet remote controller. Background Art

[0002] With the rapid development of science and technology and the improvement of people's living standards, smart home products have gradually been integrated into daily life, bringing unprecedented convenience and comfort to residents. Among them, smart toilets, as representatives of bathroom intelligence, not only optimize the user experience of traditional toilets, but also incorporate a number of innovative technologies to enhance users' health awareness and quality of life. However, existing smart toilets mostly focus on the automated operation of basic functions, such as automatic flushing and heating, and lack the function of monitoring and analyzing the health status of users, resulting in deficiencies in user interactivity and personalized services. In particular, the existing technology cannot combine the user's daily usage habits and physiological characteristics to perform accurate health detection and real-time warning. Therefore, how to use smart toilet equipment and its supporting devices to achieve user health monitoring and enhance the user's interactive experience has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The present application provides a method and device for health detection using a smart toilet remote control, which solves the technical problem in the prior art that the smart toilet lacks user health detection, resulting in poor interactivity.

[0004] In view of the above problems, the present application provides a method and device for health detection using a smart toilet remote control.

[0005] In a first aspect of the present application, a method for health detection using a smart toilet remote controller is provided, the method comprising: According to the fingerprint sensing module of the smart toilet remote control, fingerprint sensing information is obtained, and user authentication is performed according to the fingerprint sensing information to obtain a user authentication result; when the user authentication result is passed, a user operation instruction is received, and the user basic information is retrieved according to the smart toilet remote control; based on the user operation instruction, the smart toilet is controlled according to the smart toilet remote control, and user monitoring data and environmental perception data are obtained according to the smart toilet and the smart toilet remote control; based on the health-related feature registration constraint, health prediction is performed according to the user basic information and the environmental perception data to obtain health status prediction data; based on the health status prediction data, health deviation is identified on the user monitoring data to obtain a user health deviation identification result; based on the health risk detection dual channel, health detection is performed according to the user health deviation identification result, a health detection report is generated, and health warning is performed in combination with the smart toilet remote control.

[0006] The second aspect of the present application provides a device for health detection using a smart toilet remote controller, the device comprising: An authentication component, the authentication component is used to obtain fingerprint sensing information according to the fingerprint sensing module of the smart toilet remote controller, and perform user authentication according to the fingerprint sensing information to obtain a user authentication result; an information retrieval component, the information retrieval component is used to receive a user operation instruction when the user authentication result is passed, and retrieve the user basic information according to the smart toilet remote controller; a control component, the control component is used to control the smart toilet according to the smart toilet remote controller based on the user operation instruction, and obtain user monitoring data and environmental perception data according to the smart toilet and the smart toilet remote controller; a health prediction component, the health prediction component is used to perform health prediction according to the user basic information and the environmental perception data based on the health-related feature registration constraint condition to obtain health status prediction data; a deviation identification component, the deviation identification component is used to perform health deviation identification on the user monitoring data based on the health status prediction data to obtain a user health deviation identification result; a health detection component, the health detection component is used to perform health detection according to the user health deviation identification result based on the health risk detection dual channel, generate a health detection report, and perform health warning in combination with the smart toilet remote controller.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, according to the fingerprint sensing module of the smart toilet remote controller, fingerprint sensing information is obtained, and user authentication is performed according to the fingerprint sensing information to obtain the user authentication result. When the user authentication result is passed, the user operation instruction is received, and the user basic information is retrieved according to the smart toilet remote controller. Then, based on the user operation instruction, the smart toilet is controlled according to the smart toilet remote controller, and user monitoring data and environmental perception data are obtained according to the smart toilet and the smart toilet remote controller. Further, based on the health-related feature registration constraint condition, health prediction is performed according to the user basic information and environmental perception data to obtain health status prediction data. Then, health deviation identification is performed on the user monitoring data based on the health status prediction data to obtain the user health deviation identification result. Finally, based on the health risk detection dual channel, health detection is performed according to the user health deviation identification result, a health detection report is generated, and a health warning is performed in combination with the smart toilet remote controller. The technical problem of poor interactivity caused by the lack of user health detection in the prior art of smart toilets is solved, and user health detection is realized through the smart toilet remote controller, achieving the technical effect of improving the interactivity between users and smart toilets. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A flowchart of a method for performing health detection using a smart toilet remote controller provided in an embodiment of the present application; Figure 2 A schematic diagram of the device structure for performing health checks using a smart toilet remote control provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: authentication component 11, information retrieval component 12, control component 13, health prediction component 14, deviation identification component 15, health detection component 16. DETAILED DESCRIPTION

[0011] The present application solves the technical problem in the prior art that the smart toilet lacks user health detection, resulting in poor interactivity, by providing a method and device for health detection using a smart toilet remote control.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a method for health detection using a smart toilet remote controller, wherein the method includes: According to the fingerprint sensing module of the smart toilet remote controller, fingerprint sensing information is obtained, and user authentication is performed according to the fingerprint sensing information to obtain a user authentication result.

[0015] In the wave of smart home innovation, smart toilets and their matching smart toilet remote controls are gradually becoming an indispensable part of modern bathrooms. Smart toilets are integrated with multiple health detection sensors, including urine analysis sensors, stool analysis sensors, weight sensors, heart rate sensors and blood pressure sensors; urine analysis sensors and stool analysis sensors can detect the user's urine and stool samples and analyze the components therein, such as glucose, protein, blood or other health-related indicators, to help users with early health warnings; weight sensors can measure the user's weight in real time when the user is sitting on the toilet, helping users monitor weight changes, especially for users who are losing weight or need weight management; heart rate sensors and blood pressure sensors can detect the user's heart rate and blood pressure by contacting the user's skin or fingerprint area, and track the user's cardiovascular health status in real time.

[0016] The smart toilet remote control is not only used to control the operation of the smart toilet, such as washing, heating, drying and other routine functions, but also has the function of displaying health data; the smart toilet remote control is wirelessly connected to the smart toilet body to ensure the stability and convenience of data transmission. Users can view real-time health monitoring data through the remote control screen, such as urine analysis results, weight changes, heart rate and blood pressure data.

[0017] The smart toilet remote controller not only has traditional remote control functions, but also incorporates cutting-edge biometric technology, especially the fingerprint sensing module, which brings users unprecedented personalized experience and security. When the user touches the smart toilet remote controller, its built-in fingerprint sensing module will quickly capture and analyze the user's fingerprint features to form unique fingerprint sensing information; the fingerprint sensing information will be compared with the pre-stored fingerprint data to authenticate the user's identity and return the user authentication result, which can be divided into pass or fail. Only after the authentication is passed, the smart remote controller can unlock all personalized functions, allowing users to freely adjust the settings of the smart toilet according to their preferences and needs.

[0018] Furthermore, performing user authentication according to the fingerprint sensing information to obtain a user authentication result includes: Perform accuracy evaluation based on the fingerprint sensing information to obtain fingerprint accuracy; determine whether the fingerprint accuracy is greater than or equal to a fingerprint accuracy threshold; if the fingerprint accuracy is greater than or equal to the fingerprint accuracy threshold, activate the pre-stored fingerprint library of the smart toilet remote control; based on the fingerprint sensing information, perform consistency comparison on each pre-stored fingerprint information in the pre-stored fingerprint library to obtain multiple fingerprint comparison results; based on the multiple fingerprint comparison results, generate the user authentication result.

[0019] Specifically, the fingerprint accuracy is obtained by performing precision analysis and evaluation based on the collected fingerprint sensing information. The fingerprint accuracy reflects the clarity and completeness of the fingerprint information and is an important basis for the accuracy of fingerprint recognition. The obtained fingerprint accuracy is compared with the preset fingerprint accuracy threshold. If the fingerprint accuracy is greater than or equal to the preset accuracy threshold, it is considered that the fingerprint information is clear enough and suitable for the next comparison operation. Otherwise, the user is prompted to re-scan the fingerprint. When the fingerprint accuracy meets the threshold condition, the pre-stored fingerprint library in the smart toilet remote control is automatically activated. The fingerprint library contains the registered fingerprint information of multiple users, and the database is searched for a record matching the current fingerprint. Based on the currently collected fingerprint sensing information, each pre-stored fingerprint information in the pre-stored fingerprint library is compared for consistency one by one. The comparison process calculates the matching degree through the matching algorithm of the fingerprint feature points to generate multiple fingerprint comparison results. According to the matching degree of multiple fingerprint comparison results, it is determined whether there is a fingerprint record matching the current fingerprint sensing information. If the matching degree reaches a certain standard, a passed user authentication result is generated, allowing the user to continue to operate the smart toilet; otherwise, an authentication failure result is generated, and access is denied.

[0020] When the user authentication result is passed, the user operation instruction is received, and the user basic information is retrieved according to the smart toilet remote control.

[0021] After the user authentication process is successfully completed, that is, when the user authentication result is passed, the smart toilet remote control will officially enter the next stage of interaction with the user. At this time, the remote control will be in a highly sensitive and responsive state, ready to receive and execute various user operation instructions. At the same time, the smart toilet remote control will automatically retrieve the basic information associated with the user based on the user authentication result. The user basic information usually includes the user's age, gender, health data history, personalized usage preferences, etc.

[0022] Based on the user operation instruction, the smart toilet is controlled according to the smart toilet remote controller, and user monitoring data and environmental perception data are obtained according to the smart toilet and the smart toilet remote controller.

[0023] Based on the operation instructions input by the user through the smart toilet remote control, the smart toilet can be controlled accordingly. Specifically, the smart toilet remote control will adjust various functions of the smart toilet according to the user's instructions, such as seat heating, flushing water temperature, water flow intensity, drying wind speed, etc. While controlling the functions of the smart toilet, the user's health monitoring data and environmental perception data will be collected through the built-in sensors of the smart toilet and the perception module of the remote control. The user monitoring data may include health indicators such as heart rate, blood pressure, body temperature, and weight, and the environmental perception data may include the temperature, humidity, and air quality of the current environment.

[0024] Based on the health-related feature registration constraint condition, health prediction is performed according to the user basic information and the environmental perception data to obtain health status prediction data.

[0025] The health-related feature registration constraint provides a solid theoretical basis for predicting the user's health status. This constraint is based on a large amount of medical research and health data analysis, and aims to reveal the intrinsic connection between user basic information, environmental perception data and health status.

[0026] When the user successfully authenticates through the smart toilet remote control and starts using the toilet, the user's monitoring data (such as heart rate, blood pressure, etc.) and environmental perception data (such as room temperature, humidity, air quality, etc.) will be collected in real time. Combined with the health-related feature registration constraints, the preset health prediction algorithm (such as a health prediction model based on machine learning) is used to conduct in-depth analysis and processing of these data to predict the health risks or potential problems that the user may face, and generate the user's health status prediction data. The health status prediction data includes an assessment of the user's current health status and a prediction of possible health problems that may arise in the future.

[0027] Furthermore, based on the health-related feature registration constraint condition, health prediction is performed according to the user basic information and the environmental perception data to obtain health status prediction data, including: Based on the user basic information and the environmental perception data, a health-related feature registration evaluation is performed on the user health status record library to determine multiple health-related feature registration coefficients; based on the multiple health-related feature registration coefficients, the user health status record library is selected according to the health-related feature registration constraint conditions to establish a registered health status space; based on the registered health status space, a centralized value calculation is performed to generate the health status prediction data.

[0028] Specifically, the user's basic information (such as age, gender, health history, etc.) and real-time environmental perception data (such as temperature, humidity, air quality, etc.) are used to analyze the user's health status record library; through health-related feature registration evaluation, the features related to the user's current health status can be identified, and multiple health-related feature registration coefficients can be calculated. The health-related feature registration coefficients represent the degree of association between different health features and environmental factors. For example, a user may be prone to respiratory problems in a high-humidity environment, and this association will be assigned a higher registration coefficient; based on multiple health-related feature registration coefficients, according to the preset health-related feature registration constraints, the health-related features related to the current health status are screened out from the user's health status record library. The historical health data that is most relevant to the user's current condition is used, and the health status historical data corresponding to the health-related feature registration coefficients that meet the health-related feature registration constraints are added to the registered health status space. The registered health status space is a multidimensional data set that contains the user's current and potential health status. After the registered health status space is constructed, the historical data in the space is analyzed and the concentrated value calculation method (such as weighted average or median calculation) is used to generate health status prediction data. These concentrated values ​​are based on a comprehensive evaluation of the user's individual health characteristics and environmental perception data, and can provide a prediction of the user's current health status. For example, it can predict the fluctuation trend of the user's blood pressure or potential health risks in the current environment.

[0029] Furthermore, based on the user basic information and the environmental perception data, a health-related feature registration evaluation is performed on the user health status record library to determine a plurality of health-related feature registration coefficients, including: Traverse the user health status record library and extract the first user health status record group, wherein the first user health status record group includes first historical user basic information, first historical environmental perception data and first health status historical data; based on the user basic information, perform a registration evaluation on the first historical user basic information to determine the first user feature registration coefficient; based on the environmental perception data, perform a registration evaluation on the first historical environmental perception data to determine the first environmental feature registration coefficient; based on the health-related feature registration evaluation weight condition, perform a weighted calculation on the first user feature registration coefficient and the first environmental feature registration coefficient to generate a first health-related feature registration coefficient, and add the first health-related feature registration coefficient to the multiple health-related feature registration coefficients.

[0030] Specifically, the user health status record library is traversed to extract the first user health status record group, which includes the user's historical basic information (such as age, gender, height, weight, etc., recorded as the first historical user basic information), historical environmental perception data (such as room temperature, humidity, air quality, etc., recorded as the first historical environmental perception data) and health status historical data (such as historical blood pressure, blood sugar, heart rate and other physiological indicators, as well as disease diagnosis records, etc.); based on the basic information of the current user (such as age, gender, health records, etc.), the extracted first historical user basic information is aligned and evaluated, that is, the current user's features are compared with the user basic information in the historical data, and the Euclidean distance can be used to calculate the similarity, so as to generate the first user feature alignment coefficient, which represents the degree of matching between the current user and the user in the historical record in terms of health status features; based on the current environmental perception data (such as temperature, humidity, air quality, etc.), the first historical environmental perception data is aligned and evaluated, By comparing the similarity between the current environment and the historical environment data, a first environment feature registration coefficient is generated, and the first environment feature registration coefficient is used to quantify the matching degree of the impact of the current environment and the historical environment on the health status; after obtaining the first user feature registration coefficient and the first environment feature registration coefficient, the two coefficients are weighted calculated according to the preset health-related feature registration evaluation weight condition, wherein the health-related feature registration evaluation weight condition is obtained based on medical research and health data analysis, and aims to reflect the relative importance of user basic information and environmental factors in health prediction; through weighted calculation, a first health-related feature registration coefficient can be generated, and the first health-related feature registration coefficient comprehensively reflects the common impact of user basic information and environmental factors on user health status; the generated first health-related feature registration coefficient is added to multiple health-related feature registration coefficient sets; the above process is repeated until all record groups in the user health status record library are traversed, thereby generating a data set containing multiple health-related feature registration coefficients.

[0031] Based on the health status prediction data, health deviation identification is performed on the user monitoring data to obtain a user health deviation identification result.

[0032] The health status prediction data reflects the user's expected health status under current environmental conditions. By identifying health deviations between the health status prediction data and the user's monitoring data, that is, calculating the difference between the real-time monitoring data and the predicted data, it is determined whether the user's current health status is abnormal. For example, if certain indicators in the user's urine composition significantly deviate from the predicted range, the abnormality will be identified and marked as a health deviation. Based on the degree and type of deviation identification, a health deviation identification result is generated. The health deviation identification result can be classified into different risk levels, such as slight deviation, moderate deviation or severe deviation. According to the severity of the deviation, the user is provided with corresponding health advice or warning information.

[0033] Based on the dual channels of health risk detection, health detection is performed according to the user's health deviation identification results, a health detection report is generated, and a health warning is performed in combination with the smart toilet remote control.

[0034] The user's health deviation identification results are input into the health risk detection dual channel for health detection, and a health detection report is generated. This report not only lists the user's health status in detail, but also provides targeted health suggestions. More importantly, the smart toilet remote control will issue health warnings in time to remind users to pay attention to their own health, thereby effectively preventing potential health risks.

[0035] Furthermore, based on the dual channels of health risk detection, health detection is performed according to the user health deviation identification result, and a health detection report is generated, including: Based on the user health deviation identification result, a health risk detection result is obtained according to the health risk detection dual channel; based on the health risk detection result, a proportion calculation is performed to obtain a risk detection improvement weight condition; based on the risk detection initial weight condition and the risk detection improvement weight condition, a centralized value calculation is performed to generate a health risk detection weight condition; based on the health risk detection weight condition, a weighted calculation is performed on the health risk detection result to generate a health risk detection coefficient; the user monitoring data, the user health deviation identification result, the health risk detection result and the health risk detection coefficient are sorted to obtain the health detection report.

[0036] Specifically, based on the user's health deviation identification results, a comprehensive health risk assessment is performed through the dual channels of health risk detection. The detection results of each channel will generate corresponding health risk detection results, reflecting the user's current health status and potential risks; based on the health risk detection results, a risk ratio calculation is performed, that is, the impact ratio of each health risk factor is evaluated, and the key indicators of risk detection are determined. According to the ratio calculation results, a risk detection weight enhancement condition is generated, which is used to dynamically adjust the weights of various risks in the health detection process; a centralized value calculation is performed based on the risk detection initial weight condition (the preset risk weight values) and the risk detection weight enhancement condition to generate a health risk detection weight condition; based on the health risk detection weight condition, a weighted calculation is performed on the health risk detection results to generate a health risk detection coefficient. The health risk detection coefficient reflects the overall level of health risk, and provides a comprehensive assessment of the user's current health status by combining the influence of physiological health and environmental related factors; the user's real-time monitoring data, health deviation identification results, health risk detection results and health risk detection coefficient are sorted out, and a health detection report is generated based on these data.

[0037] Furthermore, based on the user health deviation identification result, a health risk detection result is obtained according to the health risk detection dual channel, including: The health risk detection dual channels include a health risk breadth detection channel and a health risk depth detection channel; based on the user health deviation identification result, according to the health risk breadth detection channel, a health risk breadth detection coefficient is output; based on the user health deviation identification result, according to the health risk depth detection channel, a health risk depth detection coefficient is obtained; the health risk breadth detection coefficient and the health risk depth detection coefficient are added to the health risk detection result.

[0038] The dual channels of health risk detection include a health risk breadth detection channel and a health risk depth detection channel. The health risk breadth detection channel is used to assess the overall risk level and range of the user's current health status, and the health risk depth detection channel is used to conduct in-depth analysis of specific health risks and assess the depth and severity of the impact of a specific health risk on the user's health status. Specifically, multi-dimensional data such as user physiological parameters, environmental factors, and living habits are collected, including but not limited to physiological indicators such as heart rate, blood pressure, and blood sugar, as well as environmental factors such as ambient temperature, humidity, and air quality, as well as information on the user's daily habits such as diet, exercise, and sleep; after preprocessing, the collected data is input into a machine learning or deep learning model, such as a random forest, support vector machine, or neural network, for training; during the training process, the model will learn how to identify and assess the various health risks that the user may face based on the input data; after the training is completed, the model becomes a health risk breadth detection channel, which can output a detection coefficient that reflects the breadth of the user's health risk. The construction of the deep health risk detection model is based on the results of the broad detection model, that is, the specific health risk points that have been identified. For these risk points, the model will conduct more in-depth detection and analysis, including collecting more detailed data, such as continuous monitoring data of specific physiological indicators, real-time monitoring data of environmental factors, etc., as well as using professional medical knowledge and technology to conduct risk assessment; similarly, these data will also be input into the machine learning or deep learning model for training; after the training is completed, the model becomes a deep health risk detection channel, which can output detection coefficients that reflect the severity and potential impact of the user's specific health risks.

[0039] The process of building a dual-channel health risk detection system is to collect multi-dimensional data from users and train them using machine learning or deep learning models, ultimately obtaining two channels that can identify and assess the breadth and severity of users' health risks. Together, these two channels form a dual-channel system for user health risk detection, providing users with more comprehensive and in-depth health risk assessment services.

[0040] Based on the results of the user's health deviation identification, the health risk breadth detection channel is used for evaluation. The health risk breadth detection channel conducts a comprehensive analysis of the user's multiple health indicators (such as weight, heart rate, urine composition, etc.) and environmental perception data, and outputs a health risk breadth detection coefficient. The health risk breadth detection coefficient reflects the breadth and impact range of the user's deviation from the normal state in multiple health dimensions. Based on the results of the user's health deviation identification, the health risk depth detection channel is used for analysis. The health risk depth detection channel pays more attention to the severity and depth of impact of a specific health risk of the user. According to the in-depth analysis results of the health risk depth detection channel, the health risk depth detection coefficient is generated. The health risk depth detection coefficient represents the potential degree of harm of a specific health risk. The health risk breadth detection coefficient and the health risk depth detection coefficient are added to the final health risk detection results.

[0041] Furthermore, collating the user monitoring data, the user health deviation identification result, the health risk detection result and the health risk detection coefficient to obtain the health detection report includes: Based on the user basic information and according to the smart toilet remote control, the user's historical health risk detection records are retrieved; based on the user's historical health risk detection records, a change trend analysis is performed on the health risk detection results to generate a first health trend analysis result; based on the user's historical health risk detection records, a change trend analysis is performed on the health risk detection coefficient to generate a second health trend analysis result; and the health detection report is optimized according to the first health trend analysis result and the second health trend analysis result.

[0042] Based on the user's basic information, the user's historical health risk detection records are accessed and retrieved through the smart toilet remote control. These records include the health risk detection results, deviation identification results and detection coefficients of the user in the past; based on the user's historical health risk detection records, the system performs a change trend analysis on the current health risk detection results. By comparing past health risk detection data with current detection results, it can identify the changing trend of the user's health status and generate a first health trend analysis result. For example, if a user's urine composition shows a trend of gradual deterioration in multiple tests, the trend will be marked as an increase in health risk; based on historical records, a change trend analysis of the health risk detection coefficient is performed to assess whether the severity of the user's health risk is increasing or easing, and a second health trend analysis result is generated; based on the first health trend analysis result and the second health trend analysis result, the health detection report is optimized to ensure that the report not only reflects the user's current health status, but also provides a long-term analysis of the user's health changes. For example, the report will describe in detail the long-term changing trends of the user in specific health indicators to help users and doctors better understand the evolution of health risks and early signals of potential health problems.

[0043] In summary, the embodiments of the present application have at least the following technical effects: First, according to the fingerprint sensing module of the smart toilet remote controller, fingerprint sensing information is obtained, and user authentication is performed according to the fingerprint sensing information to obtain the user authentication result. When the user authentication result is passed, the user operation instruction is received, and the user basic information is retrieved according to the smart toilet remote controller. Then, based on the user operation instruction, the smart toilet is controlled according to the smart toilet remote controller, and user monitoring data and environmental perception data are obtained according to the smart toilet and the smart toilet remote controller. Further, based on the health-related feature registration constraint condition, health prediction is performed according to the user basic information and environmental perception data to obtain health status prediction data. Then, health deviation identification is performed on the user monitoring data based on the health status prediction data to obtain the user health deviation identification result. Finally, based on the health risk detection dual channel, health detection is performed according to the user health deviation identification result, a health detection report is generated, and a health warning is performed in combination with the smart toilet remote controller. The technical problem of poor interactivity caused by the lack of user health detection in the prior art of smart toilets is solved, and user health detection is realized through the smart toilet remote controller, achieving the technical effect of improving the interactivity between users and smart toilets.

[0044] Embodiment 2 is based on the same inventive concept as the method for health detection using a smart toilet remote controller in the aforementioned embodiment. Figure 2 As shown, the present application provides a device for health detection using a smart toilet remote controller, wherein the device includes: An authentication component 11, the authentication component 11 is used to obtain fingerprint sensing information according to the fingerprint sensing module of the smart toilet remote controller, and perform user authentication according to the fingerprint sensing information to obtain a user authentication result; an information retrieval component 12, the information retrieval component 12 is used to receive user operation instructions when the user authentication result is passed, and retrieve user basic information according to the smart toilet remote controller; a control component 13, the control component 13 is used to control the smart toilet according to the smart toilet remote controller based on the user operation instructions, and obtain user monitoring data and environmental perception data according to the smart toilet and the smart toilet remote controller ; A health prediction component 14, wherein the health prediction component 14 is used to perform health prediction based on the health-related feature registration constraint conditions, according to the user basic information and the environmental perception data, and obtain health status prediction data; a deviation identification component 15, wherein the deviation identification component 15 is used to perform health deviation identification on the user monitoring data based on the health status prediction data, and obtain a user health deviation identification result; a health detection component 16, wherein the health detection component 16 is used to perform health detection based on the health risk detection dual channel, according to the user health deviation identification result, generate a health detection report, and perform health warnings in combination with the smart toilet remote control.

[0045] Furthermore, the authentication component 11 is used to execute the following method: Perform accuracy evaluation based on the fingerprint sensing information to obtain fingerprint accuracy; determine whether the fingerprint accuracy is greater than or equal to a fingerprint accuracy threshold; if the fingerprint accuracy is greater than or equal to the fingerprint accuracy threshold, activate the pre-stored fingerprint library of the smart toilet remote control; based on the fingerprint sensing information, perform consistency comparison on each pre-stored fingerprint information in the pre-stored fingerprint library to obtain multiple fingerprint comparison results; based on the multiple fingerprint comparison results, generate the user authentication result.

[0046] Furthermore, the health prediction component 14 is used to perform the following method: Based on the user basic information and the environmental perception data, a health-related feature registration evaluation is performed on the user health status record library to determine multiple health-related feature registration coefficients; based on the multiple health-related feature registration coefficients, the user health status record library is selected according to the health-related feature registration constraint conditions to establish a registered health status space; based on the registered health status space, a centralized value calculation is performed to generate the health status prediction data.

[0047] Furthermore, the health prediction component 14 is used to perform the following method: Traverse the user health status record library and extract the first user health status record group, wherein the first user health status record group includes first historical user basic information, first historical environmental perception data and first health status historical data; based on the user basic information, perform a registration evaluation on the first historical user basic information to determine the first user feature registration coefficient; based on the environmental perception data, perform a registration evaluation on the first historical environmental perception data to determine the first environmental feature registration coefficient; based on the health-related feature registration evaluation weight condition, perform a weighted calculation on the first user feature registration coefficient and the first environmental feature registration coefficient to generate a first health-related feature registration coefficient, and add the first health-related feature registration coefficient to the multiple health-related feature registration coefficients.

[0048] Furthermore, the health detection component 16 is used to perform the following method: Based on the user health deviation identification result, a health risk detection result is obtained according to the health risk detection dual channel; based on the health risk detection result, a proportion calculation is performed to obtain a risk detection improvement weight condition; based on the risk detection initial weight condition and the risk detection improvement weight condition, a centralized value calculation is performed to generate a health risk detection weight condition; based on the health risk detection weight condition, a weighted calculation is performed on the health risk detection result to generate a health risk detection coefficient; the user monitoring data, the user health deviation identification result, the health risk detection result and the health risk detection coefficient are sorted to obtain the health detection report.

[0049] Furthermore, the health detection component 16 is used to perform the following method: The health risk detection dual channels include a health risk breadth detection channel and a health risk depth detection channel; based on the user health deviation identification result, according to the health risk breadth detection channel, a health risk breadth detection coefficient is output; based on the user health deviation identification result, according to the health risk depth detection channel, a health risk depth detection coefficient is obtained; the health risk breadth detection coefficient and the health risk depth detection coefficient are added to the health risk detection result.

[0050] Furthermore, the health detection component 16 is used to perform the following method: Based on the user basic information and according to the smart toilet remote control, the user's historical health risk detection records are retrieved; based on the user's historical health risk detection records, a change trend analysis is performed on the health risk detection results to generate a first health trend analysis result; based on the user's historical health risk detection records, a change trend analysis is performed on the health risk detection coefficient to generate a second health trend analysis result; and the health detection report is optimized according to the first health trend analysis result and the second health trend analysis result.

[0051] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0053] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for health detection using a smart toilet remote controller, characterized in that: The method comprises: According to the fingerprint sensing module of the smart toilet remote controller, fingerprint sensing information is obtained, and user authentication is performed according to the fingerprint sensing information to obtain a user authentication result; When the user authentication result is passed, receiving the user operation instruction, and retrieving the user basic information according to the smart toilet remote controller; Based on the user operation instruction, the smart toilet is controlled according to the smart toilet remote controller, and user monitoring data and environmental perception data are obtained according to the smart toilet and the smart toilet remote controller; Based on the health-related feature registration constraint condition, health prediction is performed according to the user basic information and the environmental perception data to obtain health status prediction data; Based on the health status prediction data, the user monitoring data is subjected to health deviation identification to obtain a user health deviation identification result; Based on the dual channels of health risk detection, health detection is performed according to the user's health deviation identification results, a health detection report is generated, and a health warning is performed in combination with the smart toilet remote control.

2. The method according to claim 1, characterized in that Performing user authentication according to the fingerprint sensing information to obtain a user authentication result includes: Performing accuracy evaluation according to the fingerprint sensing information to obtain fingerprint accuracy; Determining whether the fingerprint accuracy is greater than or equal to a fingerprint accuracy threshold; If the fingerprint accuracy is greater than or equal to the fingerprint accuracy threshold, activating the pre-stored fingerprint library of the smart toilet remote controller; Based on the fingerprint sensing information, performing consistency comparison on each pre-stored fingerprint information in the pre-stored fingerprint library to obtain multiple fingerprint comparison results; The user authentication result is generated based on the multiple fingerprint comparison results.

3. The method according to claim 1, characterized in that Based on the health-related feature registration constraint condition, health prediction is performed according to the user basic information and the environmental perception data to obtain health status prediction data, including: Based on the user basic information and the environmental perception data, a health-related feature registration evaluation is performed on the user health status record library to determine a plurality of health-related feature registration coefficients; Based on the multiple health-related feature registration coefficients, the user health status record library is selected according to the health-related feature registration constraint conditions to establish a registered health status space; Central value calculation is performed based on the registered health state space to generate the health state prediction data.

4. The method according to claim 3, characterized in that Based on the user basic information and the environmental perception data, a health-related feature registration evaluation is performed on the user health status record library to determine a plurality of health-related feature registration coefficients, including: Traversing the user health status record library, extracting a first user health status record group, wherein the first user health status record group includes first historical user basic information, first historical environment perception data, and first health status historical data; Based on the user basic information, performing a registration evaluation on the first historical user basic information to determine a first user feature registration coefficient; Based on the environmental perception data, performing a registration evaluation on the first historical environmental perception data to determine a first environmental feature registration coefficient; The first user feature registration coefficient and the first environment feature registration coefficient are weightedly calculated based on the health-related feature registration evaluation weight condition to generate a first health-related feature registration coefficient, and the first health-related feature registration coefficient is added to the multiple health-related feature registration coefficients.

5. The method according to claim 1, characterized in that Based on the dual channels of health risk detection, health detection is performed according to the user health deviation identification result, and a health detection report is generated, including: Based on the user health deviation identification result, obtaining a health risk detection result according to the health risk detection dual channel; Calculate the proportion based on the health risk detection results to obtain the risk detection weight improvement conditions; Performing concentrated value calculation based on the risk detection initial weight condition and the risk detection enhanced weight condition to generate a health risk detection weight condition; Performing weighted calculation on the health risk detection result based on the health risk detection weight condition to generate a health risk detection coefficient; The user monitoring data, the user health deviation identification result, the health risk detection result and the health risk detection coefficient are sorted to obtain the health detection report.

6. The method according to claim 5, characterized in that Based on the user health deviation identification result, a health risk detection result is obtained according to the health risk detection dual channel, including: The health risk detection dual channels include a health risk breadth detection channel and a health risk depth detection channel; Based on the user health deviation identification result, outputting a health risk breadth detection coefficient according to the health risk breadth detection channel; Based on the user health deviation identification result, and according to the health risk depth detection channel, a health risk depth detection coefficient is obtained; The health risk breadth detection coefficient and the health risk depth detection coefficient are added to the health risk detection result.

7. The method according to claim 5, characterized in that Arranging the user monitoring data, the user health deviation identification result, the health risk detection result and the health risk detection coefficient to obtain the health detection report includes: Based on the basic information of the user, and according to the remote controller of the smart toilet, retrieve the historical health risk detection record of the user; Based on the historical health risk detection record of the user, performing a change trend analysis on the health risk detection result to generate a first health trend analysis result; Based on the historical health risk detection records of the user, performing a change trend analysis on the health risk detection coefficient to generate a second health trend analysis result; The health check report is optimized according to the first health trend analysis result and the second health trend analysis result.

8. A device for health detection using a smart toilet remote controller, characterized in that: The device is used to implement the method for health detection using a smart toilet remote controller as described in any one of claims 1 to 7, comprising: An authentication component, the authentication component is used to obtain fingerprint sensing information according to the fingerprint sensing module of the smart toilet remote controller, and perform user authentication according to the fingerprint sensing information to obtain a user authentication result; An information retrieval component, which is used to receive user operation instructions when the user authentication result is passed, and retrieve user basic information according to the smart toilet remote controller; A control component, the control component is used to control the smart toilet according to the smart toilet remote controller based on the user operation instruction, and obtain user monitoring data and environmental perception data according to the smart toilet and the smart toilet remote controller; A health prediction component, the health prediction component is used to perform health prediction based on the health-related feature registration constraint condition, according to the user basic information and the environmental perception data, to obtain health status prediction data; A deviation identification component, the deviation identification component is used to identify health deviations of the user monitoring data based on the health status prediction data to obtain a user health deviation identification result; A health detection component is used to perform health detection based on a dual-channel health risk detection, generate a health detection report according to the user's health deviation identification result, and perform health warning in combination with the smart toilet remote control.

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