A method and system for monitoring and alarming of excretion of an AI intelligent diaper

Abstract

The application relates to the technical field of paper diapers, and particularly discloses a method and system for monitoring and alarming excretion of AI intelligent paper diapers. A detection signal is sent to an inductive paper diaper through a belt sensor to obtain excretion data of a person under care; when the excretion data is received, a monitoring terminal calls a data analysis model to analyze the excretion data, obtains a first analysis result, and sends an alarm to the person under care based on the first analysis result. Through the cooperative work of the belt sensor and the inductive paper diaper, the application can realize real-time and automatic sensing of an excretion event, and overcome the randomness and hysteresis of traditional manual timing inspection; the data analysis model called by the monitoring terminal can accurately distinguish the types (urine and feces) of excretion and estimate the order of magnitude, so that a precise alarm is triggered based on the first analysis result, intelligent monitoring and precise alarming are realized, and the intelligent degree of excretion monitoring and alarming is improved.

Description

Technical Field

[0001] This application relates to the field of diaper technology, and in particular to an AI smart diaper's excretion monitoring and alarm method and system. Background Technology

[0002] With the accelerating aging of the global population and the increasing demand for care for the disabled and those with dementia, incontinence care has become a major challenge for home care and professional medical institutions. Traditional care methods mainly rely on the use of disposable absorbent pads or adult diapers, requiring caregivers to rely on experience to check regularly and manually touch the skin to determine when a change is needed. However, manual checks have significant delays and randomness, failing to provide immediate responses. Prolonged contact between excrement and skin can easily lead to serious complications such as incontinence dermatitis, maceration, and pressure sores. Therefore, how to implement diaper excretion monitoring and alarm systems has become an urgent problem to be solved. Summary of the Invention

[0003] This application provides a method and system for monitoring and alarming the excretion of AI smart diapers, so as to realize the monitoring and alarm of diaper excretion.

[0004] In a first aspect, this application provides a method for monitoring and alarming the excretion of an AI smart diaper. This method is applied to an AI smart diaper excretion monitoring and alarm system, which includes a clip-on sensor, a sensor-operated diaper, and a monitoring terminal. The method includes: The clip-on sensor sends a detection signal to the sensor-equipped diaper to obtain the excretion data of the monitored individual; When the monitoring terminal receives the discharge data, it calls the data analysis model to analyze the discharge data and obtains a first analysis result. The monitoring terminal sends an alarm to the guardian based on the first analysis result.

[0005] Secondly, this application also provides an AI smart diaper excretion monitoring and alarm system, the system comprising: The sensor-operated diaper contains a sensor element. The clip-on sensor is connected to the sensing element via a snap or a strong magnetic contact, and is used to collect the excretion data of the monitored person. The monitoring terminal is wirelessly connected to the clip-on sensor to analyze the excretion data and vital sign data, obtain analysis results, and issue an alarm to the guardian based on the analysis results.

[0006] This application discloses an excretion monitoring and alarm method and system for AI smart diapers. The clip-on sensor sends a detection signal to the sensor-activated diaper to obtain the excretion data of the monitored individual. Upon receiving the excretion data, the monitoring terminal calls a data analysis model to analyze the data and obtain a first analysis result. Based on the first analysis result, the monitoring terminal issues an alarm to the guardian. This application, through the collaborative work of the clip-on sensor and the sensor-activated diaper, can detect excretion events in real time and automatically, overcoming the randomness and lag of traditional manual periodic checks. The data analysis model called by the monitoring terminal can accurately distinguish the type of excrement (urine and feces) and estimate the magnitude, thereby triggering a precise alarm based on the first analysis result. This achieves intelligent monitoring and precise alarm, improving the intelligence level of excretion monitoring and alarm. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 is a first schematic flowchart of an AI smart diaper excretion monitoring and alarm method provided by an embodiment of this application; Figure 2 is a second schematic flowchart of an AI smart diaper excretion monitoring and alarm method provided by an embodiment of this application; Figure 3 is a third schematic flowchart of an AI smart diaper excretion monitoring and alarm method provided by an embodiment of this application; Figure 4 is a schematic block diagram of an AI smart diaper excretion monitoring and alarm system provided in an embodiment of this application; Figure 5 is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0009] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0010] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0011] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0012] It should also be understood that the term "and / or" as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0013] This application provides an AI-powered smart diaper excretion monitoring and alarm method and system. The excretion monitoring and alarm method can be applied to a server. Through the collaborative work of a clip-on sensor and a sensor-operated diaper, excretion events can be detected in real time and automatically, overcoming the randomness and lag of traditional manual periodic checks. The data analysis model called by the monitoring terminal can accurately distinguish the type of excrement (urine and feces) and estimate the magnitude, thereby triggering a precise alarm based on the first analysis result. This achieves intelligent monitoring and precise alarm, improving the intelligence level of excretion monitoring and alarm. The server can be a standalone server or a server cluster.

[0014] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0015] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating an AI-powered smart diaper excretion monitoring and alarm method provided in an embodiment of this application. This AI-powered smart diaper excretion monitoring and alarm method can be applied to a server, enabling real-time and automatic sensing of excretion events through the collaborative work of a clip-on sensor and an inductive diaper. This overcomes the randomness and lag of traditional manual periodic checks. The data analysis model invoked by the monitoring terminal can accurately distinguish the type of excrement (urine and feces) and estimate its magnitude, thereby triggering a precise alarm based on the first analysis result. This achieves intelligent monitoring and precise alarm, improving the intelligence level of excretion monitoring and alarm.

[0016] like Figure 1As shown, the excretion monitoring and alarm method of the AI ​​smart diaper specifically includes steps S101 to S103.

[0017] S101. The clip-on sensor sends a detection signal to the sensor-type diaper to obtain the excretion data of the monitored person. In one embodiment, the detection signal is an excitation signal generated by a clip-on sensor to detect changes in physical state. It is typically a low-voltage, low-current, specific-frequency alternating current signal or a constant weak electric field, with the aim of safely detecting the electrical properties of the diaper's sensing layer.

[0018] Clip-on sensors apply a weak detection signal to the sensing layer inside the diaper via a metal spring or capacitive coupling plate at its bottom. The sensing area of ​​the diaper is made of a special material (such as a resistive network printed with carbon-based conductive ink, or a polymer material with a specific dielectric constant). In a dry state, it has a reference resistance or capacitance value. When excrement (urine / feces) wets the sensing layer, it causes changes in the physicochemical properties of the material: the water in urine, containing electrolytes, significantly reduces the resistance of the sensing layer and increases its capacitance due to its high dielectric constant; feces alter the dielectric constant and ionic environment of the sensing layer, leading to characteristic changes in its capacitance and impedance.

[0019] Changes in the electrical properties of the sensing layer directly modulate the transmitted detection signal. For example, a decrease in resistance leads to an increase in signal current; a change in capacitance causes a shift in signal phase.

[0020] In one embodiment, the analog front-end circuitry inside the clip-on sensor continuously measures the modulated detection signal returned from the diaper. These analog signals (such as voltage, current, and phase difference) are converted into digital readings, and the resulting electrical parameters (resistance, capacitance, and signal strength distribution) characterize changes in the diaper's physical state. For example: resistance value: a sharp drop from megaohms to kiloohms indicates liquid wetting; capacitance value: specific patterns of change can help distinguish the type of excrement; signal strength distribution: a humidity distribution map can be plotted using multiple sensing points.

[0021] In one embodiment, the obtained electrical parameters are analyzed to obtain the final discharge data.

[0022] Furthermore, the clip-on sensor sends a detection signal to the sensor-equipped diaper to obtain the excretion data of the monitored individual, including: the clip-on sensor sending a detection signal to the sensor-equipped diaper, obtaining the characteristic change of the sensing element corresponding to the sensor-equipped diaper based on the detection signal, and analyzing the characteristic change of the sensing element to obtain the excretion data.

[0023] In one embodiment, the clip-on sensor applies a known, stable, and weak electrical excitation signal to a sensing element within the diaper. This could be a constant small current, an AC voltage of a specific frequency, or an AC electric field used to measure capacitance.

[0024] The sensing elements inside sensor-activated diapers are usually made of special functional materials (such as carbon-based conductive ink, humidity-sensitive polymers, dielectric materials, etc.), and their electrical properties change regularly as they are soaked in excrement.

[0025] In one embodiment, the microprocessor and analog-to-digital conversion circuitry within the clip-on sensor analyze the modulated signal to obtain structured, digitized discharge data. Specifically, the clip-on sensor measures changes in current flowing through the sensing element (reflecting changes in resistance) or the phase difference between the signal voltage and current (reflecting changes in capacitance). The measured analog quantities are then converted into high-precision digital readings (e.g., resistance R: 1250 Ω, capacitance: 2.3 nF).

[0026] Analyze the curves showing the change in resistance and capacitance over time. A steep dip in the curve (on the order of milliseconds to seconds) strongly indicates a discharge event; a slow drift may indicate a humid environment.

[0027] If the sensing element is in the form of an array, then analyze which points change first, the diffusion path and range of the change, and thus determine the initial location and diffusion area of ​​the discharge.

[0028] In one embodiment, key features are extracted from the aforementioned quantitative data and curves, such as: rate of change (dR / dt), steady-state value, spatial diffusion area, and synergistic changes in multiple parameters (e.g., whether resistance and capacitance change drastically and synchronously). These extracted key features are input into a pre-trained lightweight classification model or decision rule to make a urination judgment and obtain urination data. Specifically, if the rate of change is greater than a preset rate of change threshold and the capacitance change is greater than a preset change threshold, then a urination event is determined to have occurred. If the resistance decreases at a rate greater than a preset rate threshold and the temperature change is less than a preset threshold, then the urination type is determined to be urination; if the capacitance change pattern is a preset pattern and accompanied by a slight temperature increase, then the urination type is determined to be defecation.

[0029] In another embodiment, the discharge level can also be determined based on the final steady-state value of the resistance drop and the number of triggered sensing points.

[0030] It is understandable that excretion data includes, but is not limited to, excretion type, excretion volume, and excretion level.

[0031] S102. When the monitoring terminal receives the discharge data, it calls the data analysis model to analyze the discharge data and obtains a first analysis result. In one embodiment, the monitoring terminal (such as a mobile phone, tablet, computer, or nursing center server) receives the excretion data packets sent by the clip-on sensor via wireless communication methods such as Bluetooth.

[0032] In one embodiment, the discharge data in the discharge data packet is preprocessed, including but not limited to data cleaning (filtering out abnormal fluctuation noise) and normalization (mapping the readings of different sensor components to a uniform dimension).

[0033] In one embodiment, the data analysis model is embedded in the microserver corresponding to the monitoring terminal. It is a lightweight artificial intelligence algorithm module, including a time-series pattern recognition sub-model for analyzing abrupt change curves of signals such as humidity and resistance. For example, a steep dip may represent a urination event. A feature classifier is used to extract multidimensional features of the preprocessed data (such as change rate, peak value, time to reach steady state, and spatial diffusion pattern), which are input into a classifier (such as support vector machine, random forest, or micro neural network) to determine the event type (urination / defecation / false alarm). A quantization regression sub-model estimates the volume of excrement or humidity saturation level (small / medium / large) based on the area of ​​humidity diffusion, the proportion of sensing points triggered, and the magnitude of resistance decrease. After the data analysis model is run, it outputs structured information with clear nursing significance, which is the first analysis result.

[0034] S103, The monitoring terminal sends an alarm to the guardian based on the first analysis result.

[0035] In one embodiment, the monitoring terminal has a pre-set alarm rule engine. The received first analysis result is compared with the rule base to determine whether an alarm is needed and the alarm level.

[0036] For example, when the event type is urination or defecation and the excretion volume level is large, a high-priority alarm for excessive excretion is triggered. When the continuously monitored humidity is greater than a preset temperature threshold and there are no excretion events, a medium-priority alert for abnormal humidity and possible causes (such as sweating) is triggered. When the time interval between the last replacement time and the current time is greater than a preset duration, a medium-priority reminder for wearing the garment overtime is triggered.

[0037] In one embodiment, when an alarm rule is triggered, the information is ensured to reach the caregiver through multiple channels and modalities, including but not limited to: APP push: a strong reminder notification pops up on the caregiver's mobile phone or dedicated device, including the specific bed, alarm type, and suggested actions; smart hardware linkage: triggering the vibration of the smart bracelet or name tag worn by the caregiver; central dashboard display: the corresponding bed icon turns red or flashes on the central screen of the nurse station and is added to the alarm list; family member notification: in a home setting, a gentle reminder can be sent to the family member's APP.

[0038] In one embodiment, after an alarm is issued, a timer and escalation mechanism is activated. If the guardian does not confirm the alarm on the app within the specified time, the alarm will automatically escalate (e.g., by notifying other guardians or administrators).

[0039] After the caregiver arrives and handles the situation, they can mark it as "handled" on the APP. The system records the person who handled the situation and the time, forming a traceable care log and completing a closed loop from "perception-analysis-decision-action-recording".

[0040] Furthermore, the excretion monitoring and alarm method of the AI ​​smart diaper also includes: the clip-on sensor collects the vital signs data of the monitored person and sends a detection signal to the sensor-type diaper to obtain the excretion data of the monitored person; the clip-on sensor calls a data analysis model to analyze the excretion data and the vital signs data to obtain a second analysis result, and transmits the analysis result to the monitoring terminal; the monitoring terminal issues an alarm to the guardian based on the second analysis result.

[0041] In one embodiment, the clip-on sensor integrates a miniature biosensor module (such as a PPG photoelectric sensor or a high-precision thermistor) on its inner side. When clipped to the front waist of the diaper, it fits directly and tightly against the lower abdominal skin of the diaper wearer (the person being monitored).

[0042] In one embodiment, heart rate and blood oxygenation data are calculated in real time by emitting green and infrared light and detecting changes in the reflected light signal from subcutaneous capillaries (photoplethysmography); body surface temperature is directly measured using a contact temperature sensor. This yields a continuous, digitized time-series data set of vital signs.

[0043] In one embodiment, as described above, the clip-on sensor sends a weak detection signal (electrical signal) to the sensing element inside the diaper and measures the changes in the resistance, capacitance, impedance, and other characteristics of the sensing element caused by the wetting of excrement to obtain excretion data.

[0044] In one embodiment, the data analysis module can be directly embedded in the microprocessor of the clip-on sensor. The model first quickly analyzes the excretion data stream to identify excretion events and their basic attributes (e.g., event type = urination, confidence level = 0.9, excretion volume level = moderate). It then performs deep correlation analysis with vital sign data from the same period and preceding / following periods to uncover deeper health status information. The combined analysis results generate a second analysis result.

[0045] For example, if a significant increase in heart rate and an increase in blood pressure (if measurable) are detected during a defecation event, the analysis results can include "This defecation may be accompanied by defecation pressure or discomfort".

[0046] If the frequency of excretion is abnormally high (derived from historical patterns of excretion data) and the body temperature shows a low-grade fever trend, then the analysis results can be marked as "increased risk of urinary tract infection".

[0047] If there is no excretion for a long period of time (such as 6 hours) or the amount of excretion is very small, and at the same time the heart rate is fast and the body temperature is slightly elevated, the analysis results may indicate "potential dehydration risk".

[0048] In one embodiment, the clip-on sensor transmits the second analysis result to the monitoring terminal via wireless transmission (such as Bluetooth Low Energy). The monitoring terminal has a pre-set alarm rule engine. The received first analysis result is compared with the rule base to determine whether an alarm is needed and the alarm level.

[0049] In another embodiment, since a guardian may monitor multiple wards simultaneously, the monitoring terminal will receive multiple second analysis results at the same time. In this case, a nursing needs analysis model is invoked to analyze each second analysis result, determining the nursing needs and urgency level of each ward. Based on these needs and urgency levels, at least one pending event (such as changing diapers, turning over, etc.) and the processing order of the pending events are generated for the guardian, and the pending events and their order are sent to the guardian. The nursing needs analysis model is trained using historical data.

[0050] In the above embodiments, the lightweight data analysis model is directly embedded in the microserver of the clip-on sensor, realizing distributed and edge data analysis, reducing the data analysis pressure on the central server, and improving data analysis efficiency.

[0051] Furthermore, after the clip-on sensor collects the vital signs data of the monitored individual and sends a detection signal to the sensor-activated diaper to obtain the excretion data of the monitored individual, the process further includes: the clip-on sensor calling a risk prediction model to identify anomalies in the excretion data and vital signs data, obtaining abnormal features, and identifying risk factors for the abnormal features to obtain predicted risk events and risk scores; when the monitoring terminal receives the predicted risk time and the risk score, it generates risk alarm information and sends an alarm to the guardian.

[0052] In one embodiment, the risk prediction model is a highly optimized and tailored lightweight AI model (such as the TinyML model) that is directly embedded in the microcontroller of the clip-on sensor.

[0053] In a specific embodiment, the risk prediction model first identifies "abnormal features" that deviate from the normal or baseline pattern in the multidimensional data stream composed of excretion data and vital sign data, including: temporal abnormalities: heart rate rises abnormally sharply after the start of the excretion event and recovers slowly (normally it should rise slightly and then recover quickly); pattern deviations: multiple small excretions occur consecutively at night, accompanied by a gradual increase in the average heart rate baseline (possibly related to infection or heart failure); correlation abnormalities: persistently high pressure in the sacral and coccygeal region (a pressure ulcer risk area) is detected, and at the same time, the temperature and humidity in this area increase synchronously due to excretion (deteriorating microenvironment); composite events: during a straining defecation event (judged according to pressure pattern), a transient and significant decrease in blood oxygen saturation occurs simultaneously.

[0054] In one embodiment, the risk prediction model transmits the identified anomalies to a sub-model to calculate risk factors and comprehensive scores for different risks.

[0055] In one embodiment, after receiving the output of the risk prediction model, the monitoring terminal performs data parsing, verifies the data source and integrity, and after verification, matches the preset alarm strategy based on the risk event and risk score.

[0056] For example, a high-risk condition (e.g., risk score ≥ 75) triggers an immediate, strong intervention alarm. This is pushed through multiple channels, including strong notifications via the app, high-frequency vibrations on caregiver smart badges, and red flashing on the nurses' station screen. The information emphasizes urgency, such as "High Risk: Suspected defecation triggering cardiac overload!" Medium risk (e.g., risk score 50 ≤ risk score < 75): Triggers a priority alert. This is indicated by a prominent notification in the app and a yellow alert on the main screen. The message includes specific recommendations, such as "Medium risk: Increased risk of pressure sores on the sacrococcygeal region; it is recommended to adjust body position and examine the skin within 2 hours." Low risk / trend alert (e.g., risk score <50): Only one nursing alert may be generated in the APP history or daily report for nursing staff to review, such as "Alert: Nighttime excretion frequency increased by 20% compared to usual". In one embodiment, the monitoring terminal translates structured risk events and scores into natural language descriptions and clear recommendations that are easy for caregivers to understand, and sends alerts to the monitors according to the alert strategy. For example, it may push notifications through multiple channels such as strong reminders via the APP, high-frequency vibration of the caregiver's smart badge, and red flashing on the nurse station's large screen: "Alert (High Risk): During a defecation event detected at 14:30, the monitored person's heart rate increased sharply, accompanied by a decrease in blood oxygen. The cardiac load has increased significantly. Please immediately assess the user's condition and consider contacting medical staff." In the above embodiments, the lightweight risk prediction model is directly embedded in the microserver of the clip-on sensor, realizing distributed and edge-based risk prediction, reducing the data analysis pressure on the central server, improving data analysis efficiency, and achieving fast and accurate risk prediction, thereby improving monitoring efficiency.

[0057] Please see Figure 2 , Figure 2 This is a schematic flowchart illustrating an AI-powered smart diaper's excretion monitoring and alarm method, as provided in an embodiment of this application. This AI-powered smart diaper's excretion monitoring and alarm method can be applied to a server to dynamically adjust the data acquisition scheme by analyzing relevant information about the monitored individual, making the monitoring strategy more targeted while reducing the power consumption of the clip-on sensor.

[0058] like Figure 2 As shown, the excretion monitoring and alarm method of the AI ​​smart diaper specifically includes steps S201 to S203.

[0059] S201. The monitoring terminal receives the binding operation from the guardian and determines the person being monitored corresponding to the clip-on sensor based on the binding operation. S202. The control module collects relevant information corresponding to the ward from the ward information database, analyzes the relevant information, and determines the data acquisition scheme of the clip-on sensor. S203, The control module controls the clip-on sensor to collect data based on the data acquisition scheme.

[0060] In one embodiment, the binding operation includes, but is not limited to: scanning a QR code or NFC touch: the guardian uses the monitoring terminal APP to scan the unique identification QR code printed on the clip-on sensor or its exclusive packaging, or to sense the NFC chip in close proximity; manual input and selection: the guardian enters the sensor's serial number in the monitoring terminal APP, or drags the sensor from the "Unassigned Devices" list to the name of the designated ward in the institution's back-end system; Bluetooth discovery and pairing: near the device, the monitoring terminal APP discovers the unbound sensor via Bluetooth and clicks to complete the pairing.

[0061] In one embodiment, the monitoring terminal associates the clip-on sensor ID with the information of the monitored person (name, bed number, medical record number, etc.) retrieved from the backend database, and establishes a binding record in both the cloud and local storage. After binding, all data from the clip-on sensor is automatically attributed to the monitored person it is bound to, such as Zhang San (bed 3). All subsequent alarms, recordings, and analyses will be based on this association.

[0062] In one embodiment, the ward information database includes, but is not limited to, static records: age, gender, weight, incontinence type (complete / stress), skin sensitivity, major diseases (such as diabetes, heart failure, kidney disease); dynamic nursing records: past bowel movement frequency patterns, common turning intervals, areas prone to pressure sores, and responses to certain nursing products; and medical instructions: special requirements set by doctors or nurses, such as "focus on monitoring heart rate" or "keep extremely dry due to skin conditions".

[0063] In one embodiment, because the clip-on sensor applies detection signals to the sensor-equipped diaper in real time to collect data, power consumption increases significantly. To reduce the power consumption of the clip-on sensor, the sleep and working times of the clip-on sensor are intelligently adjusted, and a data acquisition scheme is dynamically generated by the control module. The control module (located in the cloud or on an institutional server) runs a strategy engine, collects relevant information about the monitored individuals corresponding to each clip-on sensor from the monitored individual information database, analyzes the relevant information, and obtains a set of executable sensor parameter instructions.

[0064] For example, for a high-risk patient with a history of pressure ulcers, whose database shows a history of pressure ulcers on the sacrum and coccyx, requiring close monitoring of pressure, the data acquisition plan would be to increase the sampling frequency of the pressure sensor array (e.g., from 1 time / minute to 1 time / 30 seconds); and shorten the judgment threshold for turning reminders (e.g., from 2 hours to 1.5 hours). For a patient with heart failure, requiring close monitoring of fluid retention and cardiac load, the data acquisition plan would be to improve the accuracy of conductivity monitoring in excrement to indirectly reflect urine ion concentration (indicating fluid status); set heart rate monitoring to continuous mode (rather than event-triggered mode); and set a more sensitive alarm threshold for abnormal heart rate.

[0065] In one embodiment, the control module distributes a pre-defined data acquisition scheme configuration file to a designated clip-on sensor via a wireless network (such as through a Bluetooth gateway). The sensor receives and parses the scheme, adjusts the operating parameters of its internal microcontroller, including adjusting the sampling rate and duty cycle of each sensor; setting local judgment thresholds for event detection; and adjusting the sleep and wake-up strategies of the wireless communication module.

[0066] In the above embodiments, by analyzing the relevant information of the monitored person, the data acquisition scheme is dynamically adjusted, making the monitoring strategy more targeted and reducing the power consumption of the clip-on sensor.

[0067] Please see Figure 3 , Figure 3 This is a schematic flowchart illustrating an AI-powered smart diaper excretion monitoring and alarm method provided in an embodiment of this application. This AI-powered smart diaper excretion monitoring and alarm method can be applied to a server, using intelligent scheduling of a data distribution module and distributed processing of edge servers to construct an industrial-grade nursing IoT solution capable of handling massive device connections while ensuring real-time, reliable, and secure services, thus improving the efficiency and accuracy of excretion event monitoring.

[0068] like Figure 3 As shown, the excretion monitoring and alarm method of the AI ​​smart diaper specifically includes steps S301 to S304.

[0069] S301, The clamp-on sensor transmits the excretion data to the data distribution module; S302, the data distribution module obtains the current working status of each edge server, determines the target server among the edge servers based on the current working status, and distributes the discharge data to the target server; S303. The target server calls the data analysis model to analyze the discharge data, obtains the first analysis result, and sends the analysis result to the monitoring terminal. S304. The monitoring terminal sends an alarm to the guardian based on the first analysis result.

[0070] In one embodiment, the clip-on sensor sends discharge data, including timestamps and device IDs, to the data distribution module via a wireless network (such as via a Bluetooth gateway within the nursing home).

[0071] The data distribution module is typically a standalone software service deployed on the core server or high-performance gateway of the elderly care facility. It does not handle specific business logic; it is solely responsible for receiving all incoming data and acts as the system's "main entry point" and "router."

[0072] In one embodiment, the data allocation module collects health and load metrics of each edge server in real time through a heartbeat mechanism or monitoring interface, mainly including: computing resources: CPU utilization, memory usage; service load: length of the data queue currently being processed, number of waiting tasks; network status: network latency with the data allocation module and the database; service health: whether it is online, whether there are any recent error logs.

[0073] The data allocation module incorporates a load balancing algorithm that dynamically makes decisions based on the aforementioned status. Common strategies include: Least Connections: allocating new data to the edge server with the fewest current processing tasks; Weighted Round Robin: assigning different weights based on server performance (number of CPU cores, memory size), allowing high-performance servers to receive more tasks; and Lowest Resource Utilization: selecting the server with the lowest CPU or memory utilization to achieve the most balanced resource utilization. The algorithm outputs a target server address.

[0074] The data distribution module forwards the received discharge data directly to the selected target edge server via the internal high-speed network (such as a local area network) according to the target server address.

[0075] In one embodiment, upon receiving the data, the target server immediately invokes its locally deployed data analysis model. The model runs on an edge server, analyzing the excretion event, type, and magnitude, generating a structured first analysis result (e.g., {Event: urination, Magnitude: copious, Confidence: 95%}). The target server then directly sends the first analysis result back to the corresponding monitoring terminal (such as the monitoring device of the nurse in charge of the bed), and can also synchronize the result to a central database for persistent storage.

[0076] In one embodiment, after receiving the analysis results from the edge server, the monitoring terminal triggers its local alarm rule engine. Based on the information in the results (such as "magnitude: large amount"), it generates specific alarm instructions and notifies the monitor through UI pop-ups, sound, vibration, and other means.

[0077] In the above embodiments, through the intelligent scheduling of the data allocation module and the distributed processing of the edge server, an industrial-grade nursing IoT solution is constructed that can handle massive device connections and ensure real-time, reliable, and secure services, thereby improving the efficiency and accuracy of excretion event monitoring.

[0078] Please see Figure 4 , Figure 4This application provides a schematic block diagram of an AI smart diaper's excretion monitoring and alarm system, which is used to execute the aforementioned AI smart diaper excretion monitoring and alarm method. The AI ​​smart diaper's excretion monitoring and alarm system can be configured on a server.

[0079] like Figure 4 As shown, the excretion monitoring and alarm system of this AI smart diaper includes a clip-on sensor, an inductive diaper, and a monitoring terminal. The sensor-operated diaper contains a sensor element. The clip-on sensor is connected to the sensing element via a snap or a strong magnetic contact, and is used to collect the excretion data of the monitored person. The monitoring terminal is wirelessly connected to the clip-on sensor to analyze the excretion data and vital sign data, obtain analysis results, and issue an alarm to the guardian based on the analysis results.

[0080] Furthermore, the clip-on sensor also includes at least one of a pressure sensor assembly, a temperature sensor assembly, and a photoelectric sensor assembly, for collecting vital sign data of the monitored person.

[0081] In one embodiment, the clip-on sensor is a reusable device that integrates an excretion data acquisition interface and a control and communication module. The excretion data acquisition interface establishes a dual physical and electrical connection with the diaper's sensing element via a snap-on clip or strong magnetic contact, enabling quick assembly and disassembly of the sensor, reuse, and ensuring reliable signal transmission. The control and communication module contains a built-in microprocessor responsible for controlling all sensors, reading data from the sensing element, performing preliminary processing, and transmitting the data to the monitoring terminal wirelessly via Bluetooth / Wi-Fi or other methods.

[0082] The clip-on sensor may also include a vital signs data acquisition module, which includes at least one of a pressure sensor assembly, a temperature sensor assembly, and a photoelectric sensor assembly.

[0083] The pressure sensor assembly is used to monitor the user's body position, posture, and pressure distribution; the temperature sensor assembly is used to monitor body surface temperature to help determine the type of excretion (feces may be accompanied by slight temperature changes), environmental comfort, and infection and fever warnings; the photoelectric sensor assembly collects heart rate and blood oxygen saturation data through photoplethysmography for basic vital sign monitoring.

[0084] Sensor-activated diapers are disposable products with built-in sensors. These sensors are typically circuits or films made of flexible materials (such as conductive ink or moisture-sensitive polymers) and are used only once. When excrement (urine or feces) soaks into the sensor, the electrical properties (such as resistance and capacitance) of the sensor undergo predictable and significant changes.

[0085] Furthermore, the excretion monitoring and alarm system of the AI ​​smart diaper also includes a database of the monitored person's information and a control module corresponding to the clip-on sensor.

[0086] In one embodiment, the ward information database includes, but is not limited to, static records: age, gender, weight, incontinence type (complete / stress), skin sensitivity, major diseases (such as diabetes, heart failure, kidney disease); dynamic nursing records: past bowel movement frequency patterns, common turning intervals, areas prone to pressure sores, and responses to certain nursing products; and medical instructions: special requirements set by doctors or nurses, such as "focus on monitoring heart rate" or "keep extremely dry due to skin conditions".

[0087] The control module is used to collect relevant information about the monitored persons from the monitored persons information database, analyze the relevant information, and determine the data acquisition scheme of the clip-on sensor.

[0088] Furthermore, the excretion monitoring and alarm system of the AI ​​smart diaper also includes a data distribution module and at least one edge server.

[0089] In one embodiment, the data allocation module is used to obtain the current working status of each edge server, determine the target server among the edge servers based on the current working status, and distribute the discharge data to the target server.

[0090] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the system and each module described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0091] The above-described system can be implemented as a computer program, which can be used in, for example... Figure 5 It runs on the computer device shown.

[0092] Please see Figure 5 , Figure 5 This is a schematic block diagram illustrating the structure of a computer device according to an embodiment of this application. The computer device may be a server.

[0093] See Figure 5The computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0094] The non-volatile storage medium can store an operating system and a computer program. This computer program includes program instructions that, when executed, cause the processor to perform any method for monitoring and alarming the excretion of the AI-powered smart diaper.

[0095] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0096] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any method of monitoring and alarming the excretion of AI smart diapers.

[0097] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0098] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0099] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps: The clip-on sensor sends a detection signal to the sensor-equipped diaper to obtain the excretion data of the monitored individual; When the monitoring terminal receives the discharge data, it calls the data analysis model to analyze the discharge data and obtains a first analysis result. The monitoring terminal sends an alarm to the guardian based on the first analysis result.

[0100] In one embodiment, the processor, in implementing the excretion monitoring and alarm system of the AI ​​smart diaper, further includes a subject information database and a control module corresponding to the clip-on sensor. Before the clip-on sensor sends a detection signal to the sensory diaper to obtain the subject's excretion data, it also includes: The monitoring terminal receives the binding operation from the guardian and determines the person being monitored corresponding to the clip-on sensor based on the binding operation. The control module collects relevant information corresponding to the ward from the ward information database, analyzes the relevant information, and determines the data acquisition scheme of the clip-on sensor. The control module controls the clip-on sensor to collect data based on the data acquisition scheme.

[0101] In one embodiment, the processor, in implementing the excretion monitoring and alarm system of the AI ​​smart diaper, further includes a data distribution module and at least one edge server. After the clip-on sensor sends a detection signal to the sensory diaper to obtain the excretion data of the monitored individual, it is also used to: The clip-on sensor transmits the excretion data to the data distribution module; The data allocation module obtains the current working status of each edge server, determines the target server among the edge servers based on the current working status, and distributes the discharge data to the target server. The target server calls a data analysis model to analyze the excretion data, obtains the first analysis result, and sends the analysis result to the monitoring terminal; The monitoring terminal sends an alarm to the guardian based on the first analysis result.

[0102] In one embodiment, when the processor sends a detection signal from the clip-on sensor to the sensory diaper to obtain the subject's excretion data, it is configured to: The clip-on sensor sends a detection signal to the sensor-type diaper. Based on the detection signal, the sensor element characteristic change corresponding to the sensor-type diaper is obtained, and the characteristic change of the sensor element is analyzed to obtain the excretion data.

[0103] In one embodiment, the processor is further configured to implement: The clip-on sensor collects the vital signs data of the monitored person and sends a detection signal to the sensor-activated diaper to obtain the excretion data of the monitored person. The clip-on sensor calls a data analysis model to analyze the excretion data and the vital signs data, obtains a second analysis result, and transmits the analysis result to the monitoring terminal; The monitoring terminal sends an alarm to the guardian based on the second analysis result.

[0104] In one embodiment, after the processor implements the clip-on sensor to collect the vital signs data of the monitored individual and sends a detection signal to the sensor-activated diaper to obtain the excretion data of the monitored individual, it is further configured to implement: The clip-on sensor calls the risk prediction model to identify anomalies in the excretion data and vital sign data, obtain abnormal features, and identify risk factors for the abnormal features to obtain predicted risk events and risk scores. Upon receiving the predicted risk time and the risk score, the monitoring terminal generates a risk alarm message and sends an alert to the guardian.

[0105] The embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, and the processor executing the program instructions to implement any of the excretion monitoring and alarm methods for AI smart diapers provided in the embodiments of this application.

[0106] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0107] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring and alarming the excretion of an AI-powered smart diaper, characterized in that, The excretion monitoring and alarm method for the AI ​​smart diaper is applied to the excretion monitoring and alarm system of the AI ​​smart diaper. The excretion monitoring and alarm system of the AI ​​smart diaper includes a clip-on sensor, an inductive diaper, and a monitoring terminal. The excretion monitoring and alarm method for the AI ​​smart diaper includes: The clip-on sensor sends a detection signal to the sensor-equipped diaper to obtain the excretion data of the monitored individual; When the monitoring terminal receives the discharge data, it calls the data analysis model to analyze the discharge data and obtains a first analysis result. The monitoring terminal sends an alarm to the guardian based on the first analysis result.

2. The method for monitoring and alarming the excretion of an AI smart diaper according to claim 1, characterized in that, The excretion monitoring and alarm system of the AI ​​smart diaper also includes a patient information database and a control module corresponding to the clip-on sensor. Before the clip-on sensor sends a detection signal to the sensory diaper to obtain the patient's excretion data, it also includes: The monitoring terminal receives the binding operation from the guardian and determines the person being monitored corresponding to the clip-on sensor based on the binding operation. The control module collects relevant information corresponding to the ward from the ward information database, analyzes the relevant information, and determines the data acquisition scheme of the clip-on sensor. The control module controls the clip-on sensor to collect data based on the data acquisition scheme.

3. The method for monitoring and alarming the excretion of an AI smart diaper according to claim 1, characterized in that, The excretion monitoring and alarm system of the AI ​​smart diaper also includes a data distribution module and at least one edge server. After the clip-on sensor sends a detection signal to the sensory diaper to obtain the excretion data of the monitored person, it also includes: The clip-on sensor transmits the excretion data to the data distribution module; The data allocation module obtains the current working status of each edge server, determines the target server among the edge servers based on the current working status, and distributes the discharge data to the target server. The target server calls a data analysis model to analyze the excretion data, obtains the first analysis result, and sends the analysis result to the monitoring terminal; The monitoring terminal sends an alarm to the guardian based on the first analysis result.

4. The method for monitoring and alarming the excretion of AI smart diapers according to any one of claims 1-3, characterized in that, The clip-on sensor sends a detection signal to the sensor-activated diaper to obtain the subject's excretion data, including: The clip-on sensor sends a detection signal to the sensor-type diaper. Based on the detection signal, the sensor element characteristic change corresponding to the sensor-type diaper is obtained, and the characteristic change of the sensor element is analyzed to obtain the excretion data.

5. The method for monitoring and alarming the excretion of an AI smart diaper according to claim 1, characterized in that, The excretion monitoring and alarm method of the AI ​​smart diaper also includes: The clip-on sensor collects the vital signs data of the monitored person and sends a detection signal to the sensor-activated diaper to obtain the excretion data of the monitored person. The clip-on sensor calls a data analysis model to analyze the excretion data and the vital signs data, obtains a second analysis result, and transmits the analysis result to the monitoring terminal; The monitoring terminal sends an alarm to the guardian based on the second analysis result.

6. The method for monitoring and alarming the excretion of an AI smart diaper according to claim 5, characterized in that, After the clip-on sensor collects the vital signs data of the monitored individual and sends a detection signal to the sensor-activated diaper to obtain the excretion data of the monitored individual, it also includes: The clip-on sensor calls the risk prediction model to identify anomalies in the excretion data and vital sign data, obtain abnormal features, and identify risk factors for the abnormal features to obtain predicted risk events and risk scores. Upon receiving the predicted risk time and the risk score, the monitoring terminal generates a risk alarm message and sends an alert to the guardian.

7. An AI-powered smart diaper excretion monitoring and alarm system, characterized in that, This includes clip-on sensors, sensor-activated diapers, and monitoring terminals; The sensor-operated diaper contains a sensor element; The clip-on sensor is connected to the sensing element via a snap or a strong magnetic contact, and is used to collect the excretion data of the monitored person. The monitoring terminal is wirelessly connected to the clip-on sensor to analyze the excretion data and vital sign data, obtain analysis results, and issue an alarm to the guardian based on the analysis results.

8. The excretion monitoring and alarm system for AI smart diapers according to claim 7, characterized in that, The clip-on sensor includes at least one of a pressure sensor assembly, a temperature sensor assembly, and a photoelectric sensor assembly, and is used to collect vital sign data of the monitored person.

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