Intelligent excrement detection method for paper diaper and related device

Abstract

The invention provides an intelligent excrement detection method for a paper diaper and a related device, the intelligent excrement detection method is applied to a detection host of an intelligent nursing system, the intelligent nursing system further comprises the paper diaper, a cloud server and user equipment, and the detection host comprises a signal transmission module, a signal processing module and an alarm prompt module. The detection host is in communication connection with the paper diaper and the cloud server. The method comprises the following steps: acquiring a first electric signal of the paper diaper in a preset time period through the signal transmission module; processing the first electric signal through a signal processing module to determine m voltage K lines, determining m pieces of excrement attribute information and m pieces of abnormal information according to the m voltage K lines, executing corresponding early warning prompt operation parameters according to the m pieces of abnormal information through an alarm prompt module, and sending the parameters to a cloud server for storage, therefore, the user equipment can access the early warning prompt operation parameters in the cloud server. In this way, the user experience of a user of the paper diaper is improved, and the nursing cost of the user is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent nursing equipment, and in particular to an intelligent detection method and related device for excrement in diapers. Background Art

[0002] Currently, traditional nursing diapers only offer physical absorption capabilities, relying on absorbent materials (such as superabsorbent resins and non-woven fabrics) to contain excreta. They are widely used by infants, the elderly, and those with mobility impairments. Their core function is passive absorption, and they cannot actively monitor or indicate the presence or type of excreta. However, in clinical practice, users of nursing diapers also have other requirements, such as real-time detection of urination and defecation, and differentiation of excreta type (urine or feces). These requirements restrict the cost of care and the user experience.

[0003] Therefore, how to improve the user experience of diaper users and reduce their care costs is an urgent issue to be solved. Summary of the Invention

[0004] The embodiments of the present application provide an intelligent excrement detection method and related devices for diapers, which improve the user experience of diaper users and reduce the user's care costs.

[0005] In a first aspect, an embodiment of the present application provides an intelligent detection method for excrement in diapers, which is applied to a detection host of an intelligent care system, wherein the intelligent care system also includes diapers, a cloud server, and a user device. The detection host includes: a signal transmission module, a signal processing module, and an alarm prompt module. The detection host is communicatively connected to the diaper, and the cloud server is communicatively connected to the detection host. The method includes: Acquiring a first electrical signal of the diaper within a preset time period through the signal transmission module; Processing the first electrical signal by the signal processing module to obtain n second electrical signals, where n is an integer greater than 1; Determining m voltage K lines according to the n second electrical signals; m is an integer greater than 1 and less than n; determining m pieces of excrement attribute information according to the m voltage K lines; Determining m pieces of abnormal information based on the m pieces of excrement attribute information; Executing corresponding early warning prompt operation parameters according to the m abnormal information through the alarm prompt module; The early warning prompt operation parameters are sent to the cloud server and stored so that the user device can access the early warning prompt operation parameters in the cloud server.

[0006] In a second aspect, an embodiment of the present application provides an intelligent excrement detection device for diapers, which is applied to a detection host of an intelligent care system. The intelligent care system also includes diapers, a cloud server, and a user device. The detection host includes: a signal transmission module, a signal processing module, and an alarm prompt module. The detection host is communicatively connected to the diaper, and the cloud server is communicatively connected to the detection host. The device includes: an acquiring unit, configured to acquire a first electrical signal of the diaper within a preset time period through the signal transmission module; a control unit, configured to process the first electrical signal through the signal processing module to obtain n second electrical signals; n is an integer greater than 1; a determining unit, configured to determine m voltage K lines based on the n second electrical signals; m is an integer greater than 1 and less than n; determine m pieces of feces attribute information based on the m voltage K lines; and determine m pieces of abnormality information based on the m pieces of feces attribute information; An early warning unit is used to execute corresponding early warning prompt operation parameters according to the m abnormal information through the alarm prompt module; send the early warning prompt operation parameters to the cloud server and store them so that the user device can access the early warning prompt operation parameters in the cloud server.

[0007] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program comprises instructions for executing the steps of any method of the first aspect of the embodiment of the present application.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute part or all of the steps described in any method of the first aspect of the embodiment of the present application.

[0009] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to execute some or all of the steps described in any method of the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0010] By implementing the embodiments of the present application, the following beneficial effects are achieved: An embodiment of the present application describes an intelligent excrement detection method and related device for diapers, which are applied to a detection host of an intelligent care system. The intelligent care system also includes diapers, a cloud server and a user device. The detection host includes: a signal transmission module, a signal processing module, and an alarm prompt module. The detection host is communicatively connected to the diaper, and the cloud server is communicatively connected to the detection host. The method includes: obtaining a first electrical signal of the diaper in a preset time period through the signal transmission module, processing the first electrical signal through the signal processing module to obtain n second electrical signals, determining m voltage K lines based on the n second electrical signals, determining m excrement attribute information based on the m voltage K lines, determining m abnormal information based on the m excrement attribute information, executing corresponding early warning prompt operation parameters according to the m abnormal information through the alarm prompt module, sending the early warning prompt operation parameters to the cloud server and storing them so that the user device can access the early warning prompt operation parameters in the cloud server. In this way, on the one hand, by actively sensing the excretion of urine and feces in real time, caregivers or users can be notified to change the diapers in time to avoid diaper rash, skin inflammation or skin infection caused by long-term contact of excrement with the skin, thereby improving the user experience; on the other hand, by detecting whether the excrement is feces or urine, it is further identified whether the urination and defecation are normal, which can assist in health testing and provide dietary advice to users, thereby reducing the user's care costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0012] Figure 1 This is an architecture diagram of an intelligent excrement detection system for diapers provided in an embodiment of the present application; Figure 2 This is a structural diagram of a diaper provided in an embodiment of the present application; Figure 3 This is a schematic structural diagram of another diaper provided in an embodiment of the present application; Figure 4 This is a layered schematic diagram of a diaper provided in an embodiment of the present application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application; Figure 6 This is a flow chart of an intelligent detection method for excrement in diapers provided in an embodiment of the present application; Figure 7 This is a flow chart of another intelligent detection method for excrement in diapers provided in an embodiment of the present application; Figure 8 This is a schematic diagram of the workflow of an intelligent excrement detection method for diapers provided in an embodiment of the present application; Figure 9 This is a schematic diagram of the functional interaction relationship of an intelligent detection method for excrement in diapers provided in an embodiment of the present application; Figure 10 This is a block diagram of the functional modules of an intelligent excrement detection device for diapers provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0015] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document indicates that the associated objects are in an "or" relationship. The "plurality" appearing in the embodiments of this application refers to two or more.

[0016] In the embodiments of the present application, "at least one item" or similar expressions refers to any combination of these items, including any combination of single items or plural items, and refers to one or more, and multiple refers to two or more. For example, at least one item (item) of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0017] The "connection" appearing in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0018] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0019] The following are the explanations of the relevant terms involved in this application: Excreta: Excreta refers to waste products that are excreted from the body through specific organs or systems after metabolic processes that are unusable or excessive. In humans, excreta primarily include feces, urine, carbon dioxide, and sweat. In this application, excreta primarily refers to feces and urine.

[0020] Conductivity: Conductivity can also be called electrical conductivity. Conductivity is the reciprocal of resistivity. It indicates the ability of a material to conduct electric current and is an inherent property of the material.

[0021] Currently, traditional nursing diapers only offer physical absorption capabilities, relying on absorbent materials (such as superabsorbent resins and non-woven fabrics) to contain excreta. They are widely used by infants, the elderly, and those with mobility impairments. Their core function is passive absorption, and they cannot actively monitor or indicate the presence or type of excreta. However, in clinical practice, users of nursing diapers also have other requirements, such as real-time detection of urination and defecation, and differentiation of excreta type (urine or feces). These requirements restrict the cost of care and the user experience.

[0022] To solve the above problems, an embodiment of the present application provides an intelligent detection method and related device for excrement in diapers, which is applied to a detection host of an intelligent care system. The intelligent care system also includes diapers, a cloud server and a user device. The detection host includes a signal transmission module, a signal processing module and an alarm prompt module. The detection host is communicatively connected to the diaper, and the cloud server is connected to the detection host through communication. Among them, an intelligent detection method for excrement in diapers includes: obtaining a first electrical signal of the diaper in a preset time period through the signal transmission module, processing the first electrical signal through the signal processing module to obtain n second electrical signals, determining m voltage K lines according to the n second electrical signals, determining m excrement attribute information according to the m voltage K lines, determining m abnormal information according to the m excrement attribute information, executing corresponding early warning prompt operation parameters according to the m abnormal information through the alarm prompt module, sending the early warning prompt operation parameters to the cloud server and storing them so that the user device can access the early warning prompt operation parameters in the cloud server. In this way, on the one hand, by actively sensing the excretion of urine and feces in real time, caregivers or users can be notified to change the diapers in time to avoid diaper rash, skin inflammation or skin infection caused by long-term contact of excrement with the skin, thereby improving the user experience; on the other hand, by detecting whether the excrement is feces or urine, it is further identified whether the urination and defecation are normal, which can assist in health testing and provide dietary advice to users, thereby reducing the user's care costs.

[0023] The following combination Figure 1 The system architecture of an intelligent detection method for excrement in diapers according to an embodiment of the present application is described. Figure 1 This is an architecture diagram of an intelligent excrement detection system for diapers provided in an embodiment of the present application. The intelligent excrement detection system 100 for diapers includes: a diaper 110, a detection host 120, a cloud server 130, and a mobile communication device 140.

[0024] Diaper 110 serves as the front-end sensing unit for intelligent excrement detection, providing the physical foundation for detecting excrement and transmitting electrical signals. Embedded within the diaper 110 are electrodes (transmitting and receiving electrodes). When stool or urine is produced, the excrement fills the space between the electrodes, causing them to conduct, facilitating subsequent electrical signal generation and detection. Diaper 110 effectively covers the potential excrement distribution area while also minimizing any impact on the diaper's performance, such as comfort and breathability.

[0025] For easier understanding, see Figure 2 , Figure 2This is a structural diagram of a diaper provided in an embodiment of the application. It can be seen that the electrode structure of the diaper mainly includes a connector, a transmitting electrode and a receiving electrode. The connector serves as a bridge for signal interaction between the entire electrode structure and the detection host. It is mainly used for the input and output functions of electrical signals. It can stably connect the signal transmission line of the detection host through a specific interface design (it can be a Type-C interface or a Micro-USB interface, which is not limited here), and ensure that the electrical signal is transmitted efficiently and error-free between the diaper electrode and the detection host. The transmitting electrode and the receiving electrode are arranged in parallel in the absorption area of ​​the diaper to form an electrical signal detection path. The transmitting electrode is responsible for transmitting a preset electrical signal (such as a low-frequency excitation signal) to the absorption area of ​​the diaper. The amplitude, frequency and other parameters of the signal need to be set according to the requirements of excrement detection. When excrement (feces or urine) appears, the excrement will change the electrical properties such as conductivity and dielectric constant between the electrodes, thereby affecting the transmission of electrical signals and causing corresponding changes in the electrical signals received by the receiving electrodes; the receiving electrodes are mainly used to collect electrical signals after passing through the absorption area of ​​the diaper. The receiving electrodes have the characteristics of high sensitivity and low noise, and can accurately capture subtle changes in electrical signals, providing reliable original signals for the subsequent detection host to distinguish the type of excrement and judge the status of excrement by analyzing characteristic parameters such as voltage K-line (such as peak time and attenuation amplitude).

[0026] For easier understanding, see Figure 3 , Figure 3This is a schematic diagram of the structure of another diaper provided by an embodiment of the present application. It can be seen that the diaper is mainly composed of a skin-friendly surface non-woven fabric, a permeable cutout, a surface non-woven fabric, ultrasonic bonding points, and a conductive fiber line. These components work together to ensure excrement detection and comfortable wearing functions. Among them, the skin-friendly surface non-woven fabric is the outermost structure that contacts the human body. It is made of soft, breathable and skin-friendly materials. Its main function is to provide a comfortable contact experience for the user, reducing skin friction and discomfort. At the same time, this layer of non-woven fabric must have a certain liquid penetration and guiding ability. When excrement is generated, it can help the liquid quickly penetrate into the lower layer, preventing the excrement from staying in the surface layer for a long time and causing skin problems, thereby avoiding causing skin inflammation and other diseases to the user. The permeable cutouts are regularly distributed between the skin-friendly surface non-woven fabric and the surface non-woven fabric. Its function is to further accelerate the penetration and diffusion of excrement. By providing a cutout structure in the non-woven fabric layer, the flow path of the liquid in the diaper can be changed, the contact area between the liquid and the lower absorbent structure can be increased, and the absorption efficiency and speed can be improved. Furthermore, the permeable cutouts allow excreta to more evenly and quickly reach the conductive fiber strands beneath, reducing detection delays or errors caused by uneven liquid distribution and ensuring timely and accurate electrical signal detection. The surface nonwoven fabric acts as a supporting and auxiliary permeable layer. It combines with the skin-friendly surface nonwoven fabric to form the diaper's surface structure, sharing the functions of liquid guidance and skin contact. It also provides a support for the arrangement of the conductive fiber strands. Ultrasonic bonding points are used to connect and secure multi-layer structures, such as the skin-friendly surface nonwoven fabric and the surface nonwoven fabric. Compared to traditional gluing or sewing methods, ultrasonic bonding offers advantages such as chemical-free operation, high bond strength, and excellent sealing. Within the diaper structure, ultrasonic bonding points ensure a tight bond between the nonwoven fabric layers, preventing interlayer shifting and avoiding potential skin irritation from chemicals such as glue. The conductive fiber strands are the core component for electrical excreta detection and are distributed in a double layer within the diaper structure. Its function is to build an electrical signal transmission path. When excrement penetrates into this layer, the electrical properties of the excrement (such as conductivity) will change the electrical signal transmission parameters between the conductive fiber lines (such as voltage attenuation). The detection host can distinguish the type of excrement (feces or urine) and judge the state of the excrement (such as consistency and concentration) by collecting the changes in electrical signals on the conductive fiber lines (such as analyzing the peak time and attenuation amplitude through the voltage K-line).

[0027] For easier understanding, see Figure 4 , Figure 4This is a schematic diagram of the layers of a diaper provided in an embodiment of the present application. As can be seen, the diaper's structure features a multi-layered, coordinated design to integrate excrement absorption, electrical detection, and comfortable wearing. The diaper primarily comprises a skin-friendly surface layer, conductive fiber yarns, a non-woven fabric, a water-locking layer, a polymer water-absorbing layer, and a leak-proof layer. Each layer is arranged in an orderly manner and complements its functions. The skin-friendly surface layer, the outermost layer that comes into direct contact with the human body, features a double-layer design made of soft, breathable, and skin-friendly materials. This provides a comfortable contact experience for the wearer, reduces skin friction and irritation, and also serves to initially guide excrement through the surface. When excrement (such as urine) is produced, the skin-friendly surface layer quickly directs the liquid to the layers below, preventing it from remaining on the surface for extended periods and causing skin discomfort. The conductive fiber yarns, sandwiched between the two skin-friendly surface layers, sense changes in electrical properties caused by excrement. When excrement penetrates this area, it alters parameters such as the conductivity and dielectric constant between the conductive fiber yarns, causing the transmitted electrical signal to fluctuate in a specific pattern. The detection host can distinguish the type of excrement and determine the state of the excrement by collecting changes in the electrical signals of the conductive fiber threads. The conductive fiber threads have excellent conductivity and chemical stability to maintain stable electrical performance and ensure the reliability of the test results. The non-woven fabric layer plays multiple roles in the diaper structure. On the one hand, it serves as an intermediate transition layer, connecting the skin-friendly surface layer with the water-locking layer and the polymer water-absorbing layer. Its fiber structure assists liquid penetration and diffusion, improving absorption efficiency. On the other hand, it provides physical support for the conductive fiber threads and the water-locking layer, ensuring the stability of each layer structure and preventing displacement and deformation. The water-locking layer and the polymer water-absorbing layer are key to the diaper's efficient absorption function. The water-locking layer can quickly capture the infiltrated liquid, prevent back-seepage, and keep the surface dry. The polymer water-absorbing layer, by leveraging the high water absorption and water retention properties of the polymer material, fully absorbs and fixes the liquid, significantly improving the diaper's absorption capacity and water-locking ability. The leak-proof layer is located at the bottom of the diaper structure and is made of waterproof and breathable material. Its main function is to prevent excrement leakage and protect external clothing or bedding.

[0028] The detection host 120 is the core processing unit of the intelligent excrement detection system 100 for diapers, responsible for key functions such as electrical signal processing, analytical calculations, and command interaction. The electrical signal receiving and rectifying module receives the raw electrical signals from the electrodes of the diaper 110. Because the electrical signals transmitted by the diaper electrodes often have unstable amplitudes and contain noise interference, this module first performs voltage doubling on the signal and then rectifies it, converting the AC signal into a DC signal. High-frequency noise and low-frequency drift components are also removed through filtering circuits. For example, a bridge rectifier circuit is used to achieve full-wave rectification of the signal, and an RC filter circuit or active filter circuit is used to filter the rectified signal, ensuring that the electrical signal input to subsequent modules has good stability and signal-to-noise ratio. The multi-channel signal transmission module is mainly used to interact with the signals between the electrodes of the diaper 110, sending excitation signals to different electrode paths according to a preset timing and frequency, and simultaneously receiving feedback signals. It uses multiplexing technology, which can detect multiple electrode pathways in the same time period, greatly improving the efficiency and comprehensiveness of detection. For example, through time-division multiplexing, specific time slices are allocated to different electrode pathways in turn, low-frequency excitation signals are sent, and feedback signals such as voltage attenuation are synchronously collected to achieve rapid scanning and detection of excretion conditions in different areas of the diaper; ADC (analog-to-digital conversion module) is used to convert rectified and filtered analog electrical signals into digital signals. It can use high-resolution (such as 12 bits and above) and high-speed ADC chips to ensure that it can accurately capture subtle changes in electrical signals. The converted digital signals are transmitted to the microcontroller module, and the microcontroller module performs a series of analysis and calculation operations based on the received digital signals. On the one hand, it performs time-series analysis on the signal, plotting a voltage-over-time curve (i.e., a voltage K-line). By identifying characteristic parameters such as the peak time, decay amplitude, and decay rate of the curve, it can distinguish the type of excrement (feces or urine) and determine the state of the excrement (such as urine transparency and consistency). The PWM module is primarily used to output controllable pulse signals, collaborating with other modules to implement various functions. For example, in the electrical signal excitation stage, PWM technology can be used to generate a low-frequency excitation signal with a specific duty cycle and frequency, which is sent to the electrodes of the diaper 110 to meet the signal requirements of different detection scenarios. It can also control the output intensity of the alarm prompt module. By adjusting the pulse width, the PWM module can achieve precise control of the buzzer volume and LED brightness. The alarm and indication module is the direct medium for information exchange between the detection host 120 and the external environment (such as caregivers and wearers). It is used to issue intuitive alarm and indication signals when abnormal excrement or specific states are detected. The module integrates peripherals such as a buzzer and LED indicator lights. When the microcontroller module determines that there is an abnormality in excretion (such as abnormal urine transparency, abnormal stool consistency, etc.), it will trigger the corresponding alarm action.

[0029] In one possible embodiment, upon detecting electrical signals from the diaper 110, the electrical signal receiving and rectifying module performs preliminary signal screening based on a preset threshold range. If the signal amplitude exceeds the fluctuation range of electrical signals generated by normal physiological excretion, it is determined to be an interference signal and directly filtered out, preventing invalid signals from entering subsequent processing, thereby improving the system's anti-interference capability and detection efficiency. The multi-channel signal transmission module dynamically adjusts the multi-channel signal transmission strategy based on the size and electrode layout of the diaper 110. For infant diapers, given the relatively small amount of excrement and limited distribution range, the frequency of multi-channel scanning can be reduced to reduce system power consumption. For adult diapers, due to the more complex excrement situation in the usage scenario, the scanning frequency and signal collection points can be increased to improve detection accuracy. The specific scanning frequency is not limited here. The microcontroller determines that urination occurs when the voltage K-line reaches a peak at approximately 2.5 seconds and the decay amplitude is between 25% and 35% within 2 minutes. If the peak time is approximately 8 seconds and the decay amplitude is less than or equal to 8%, it is determined to be defecation. In addition, the MCU stores a database of electrical signal characteristics of feces from people of different age groups and health statuses. When receiving the current digital signal, it automatically compares and matches it with the data in the database, enabling more accurate attribute judgment of the feces and auxiliary analysis of health status. For example, in the detection of infant diapers, if an electrical signal pattern that matches the characteristics of diarrhea is matched, it will be promptly marked as abnormal and the alarm will be triggered first. The PWM module works in conjunction with the MCU module to dynamically adjust the PWM signal parameters based on the abnormality level analyzed by the MCU. For example, the abnormality levels are divided into mild, moderate, and severe, corresponding to different PWM duty cycle and frequency combinations, to achieve differentiated and intelligent control of alarm prompts.

[0030] The cloud server 130 receives various data uploaded from the detection host 120, including raw electrical signal data, analyzed and processed fecal attribute information, anomaly information, and warning prompt operation parameters. It then securely stores these data for a long period of time according to standardized data formats and storage strategies. It utilizes a distributed storage architecture, categorizing and storing data based on data type, generation time, and associated devices. It also includes data backup and disaster recovery mechanisms to ensure data integrity and reliability. Furthermore, the cloud server 130 possesses data processing and analysis capabilities, utilizing big data analysis and machine learning techniques to conduct in-depth mining of massive amounts of fecal detection data. By analyzing historical data from the same device or user group, it can identify trends in fecal attribute changes and assist in determining changes in the wearer's health status. Furthermore, the cloud server 130 can aggregate and analyze user data from different regions, age groups, and health conditions to construct fecal characteristic models and health assessment systems, providing data reference for healthcare, product development, and other fields.

[0031] The mobile communication device 140 is the primary means for caregivers and users to access fecal test information and conduct interactive operations, enabling visualization of test data and remote control. A dedicated application is installed on the mobile communication device 140. This application serves as the user interface for interacting with the system and features a rich set of functional modules. This application includes a data display module for receiving and displaying fecal test data from the cloud server 130 in real time. This module displays information such as fecal type, attribute parameters (such as urine transparency, stool consistency, density), and test time, presented in intuitive charts and text lists, allowing users to quickly understand diaper usage and fecal status. The application also includes personalized settings for alarm reminders, allowing users to enable or disable specific reminder modes and adjust parameters such as reminder volume and vibration intensity. Furthermore, the mobile communication device 140 app supports remote management and control of the system. Users can access the cloud server 130 through the APP to set and adjust the working parameters of the detection host 120, such as modifying the frequency of electrical signal detection, adjusting the threshold for abnormal judgment, configuring alarm prompts, etc. The APP can also realize the query, export and analysis functions of historical detection data. Users can filter data according to time range, device number and other conditions, generate data analysis reports, and assist in health management and care decision-making.

[0032] In a possible embodiment, when the diaper 110 absorbs excrement (feces or urine), the internal electrodes are turned on to generate electrical signals. After the electrical signals are processed by the electrical signal receiving and rectifying module of the detection host 120, the multi-channel signal sending module cooperates with the ADC and the single-chip computer module to collect, convert and analyze the signals. The single-chip computer module determines the type and properties of the excrement by identifying characteristics such as the voltage K line, and determines whether there is an abnormality. If there is an abnormality, the corresponding early warning prompt operation parameters are generated, and local prompts are given through the alarm and indication module. At the same time, the parameters are uploaded to the cloud server 130 for storage. The mobile communication device 140 accesses the cloud server 130 through the APP to obtain detection data and early warning information, thereby realizing remote monitoring and management.

[0033] It can be seen that the intelligent excrement detection system 100 for diapers achieves accurate, intelligent and convenient excrement detection through the collaborative work of diapers 110, detection host 120, cloud server 130 and mobile communication device 140, providing innovative technical solutions and application models for the field of smart nursing, and is expected to play an important role in medical care and scenarios.

[0034] The following combination Figure 5 The electronic device in the embodiment of the present application is described. Figure 5is a structural diagram of an electronic device provided in an embodiment of the present application, such as Figure 5 As shown, the electronic device 500 includes one or more processors 510, a memory 520, a communication interface 530 and one or more programs 521. The processor 510 is communicatively connected to the memory 520 and the communication interface 530 via an internal communication bus.

[0035] The processor 510 may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, units, and circuits described in conjunction with the disclosure of this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and so on. The communication unit may be a communication interface, a transceiver, a transceiver circuit, etc., and the storage unit may be a memory.

[0036] The memory 520 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0037] The one or more programs 521 are stored in the memory 520 and are configured to be executed by the processor 510. The one or more programs 521 include instructions for executing any step in an embodiment of the following intelligent detection method for excrement of diapers.

[0038] It is understood that the electronic device 500 may include more or fewer structural elements than those in the above structural block diagram, for example, including a power module, physical buttons, Wi-Fi module, speaker, Bluetooth module, sensor, display module, etc., which are not limited here. It is understood that the electronic device may be equipped with Figure 1 The architecture of the intelligent excrement detection system for diapers.

[0039] After understanding the software and hardware architecture of this application, Figure 6 An intelligent detection method for excrement in diapers according to an embodiment of the present application is described. Figure 6This is a flow chart of an intelligent excrement detection method for diapers provided in an embodiment of the present application. The method is applied to a detection host of an intelligent care system. The intelligent care system also includes diapers, a cloud server, and a user device. The detection host includes: a signal transmission module, a signal processing module, and an alarm prompt module. The detection host is communicatively connected to the diaper, and the cloud server is communicatively connected to the detection host. The method specifically includes the following steps: Step S610: acquiring a first electrical signal of the diaper in a preset time period through the signal transmission module.

[0040] The first electrical signal is the electrical signal generated between the transmitting and receiving electrodes in the diaper due to contact with excreta. This signal is actively transmitted by the frequency-converting electrical signal transmission module in the signal transmission module and conducted to the receiving electrodes via the conductive fiber cotton in the diaper. It serves as the raw data carrier for subsequent signal processing and excreta property identification. Due to the differences in the composition and form of excreta (urine, feces), their transmission characteristics (attenuation amplitude, propagation speed) for electrical signals in different frequency bands vary. Therefore, the first electrical signal can objectively reflect the type and state characteristics of the excreta.

[0041] The preset time period must be set based on the penetration and diffusion patterns of excrement, typically 1-2 minutes. On the one hand, the entire process of urine in a diaper, from contact with the surface layer to absorption by the absorbent layer, takes approximately 2 minutes. Due to the high viscosity of feces, its penetration and signal stabilization process also fall within this timeframe, ensuring that the complete timing characteristics of the signal from initial contact to stable attenuation are captured. On the other hand, if the time period is too short, the decay phase after the signal peak may be missed, making it impossible to accurately extract the attenuation amplitude parameter. If the time period is too long, the signal may stabilize after the absorbent layer completely locks in moisture, resulting in data redundancy and increased system processing load.

[0042] Specifically, the signal transmission module consists of a variable-frequency electrical signal sending module and a signal receiving unit. It obtains a first electrical signal and transmits a detection signal through the variable-frequency electrical signal sending module in a "low-frequency-high-frequency alternating" mode. That is, it first transmits a set of low-frequency signals (frequency range 1KHz~1MHz), and then transmits a set of high-frequency signals (frequency range 1.1MHz~12MHz) after the signal returns to zero. This cycle continues until the end of the preset time period. Alternatively, the variable-frequency electrical signal sending module can transmit low-frequency or high-frequency signals separately, or it can alternately transmit low-frequency and high-frequency electrical signals once or multiple times without any restrictions. The low-frequency signal is used to reflect the overall penetration rate of excrement (because the low-frequency signal has a large penetration depth, the attenuation is mainly determined by conductivity), and the high-frequency signal is used to reflect the surface composition characteristics of the excrement (because the high-frequency signal has a small penetration depth, the attenuation is more significantly affected by dielectric loss and particle scattering). The receiving electrode transmits the conducted electrical signal to the receiving unit of the signal transmission module, which performs preliminary amplification and filtering on the signal to remove environmental electromagnetic interference (such as electrostatic signals generated by clothing friction), and finally forms a first electrical signal containing low-frequency and high-frequency components, which is temporarily stored in the cache unit of the signal transmission module, awaiting subsequent processing.

[0043] It should be noted that in the process of obtaining the first electrical signal, there may be the following special circumstances and processing methods: if the diaper does not contact the excrement, there is no conductive path between the transmitting electrode and the receiving electrode. At this time, the first electrical signal is a zero value or a noise signal close to zero. The signal transmission module will record this state and continue to monitor it until a non-zero signal is detected and the timing is started; if the excrement only partially contacts the electrode (such as a small amount of urine does not completely cover the conductive fiber cotton thread), it may cause the first electrical signal to fluctuate. At this time, the signal transmission module will reduce the impact of signal fluctuations on subsequent processing by transmitting the same frequency band signal multiple times and taking the average; the contact state of the conductive fiber cotton thread (such as electrode displacement caused by diaper deformation) may affect the signal conduction efficiency, so the signal transmission module will monitor the initial impedance between the electrodes in real time (the impedance is greater than 10MΩ in the dry state). If the impedance is abnormal (such as less than 5MΩ), the signal is marked as "suspicious data" and verified in combination with historical data in subsequent processing to ensure the reliability of the first electrical signal.

[0044] Step S620: Process the first electrical signal through the signal processing module to obtain n second electrical signals; n is an integer greater than 1.

[0045] The second electrical signal is obtained by processing the first electrical signal and can be used as a standardized electrical signal for subsequent voltage K-line analysis. Because the first electrical signal contains multi-band components, such as low-frequency (1kHz to 1MHz or 0.1kHz) and high-frequency (1.1MHz or 12MHz), and the original signal is weak and susceptible to interference, it requires targeted processing by the signal processing module to separate the effective signals in different frequency bands and eliminate noise. The value of n is related to the number of signal frequency bands and the sampling accuracy. For example, after processing the low-frequency and high-frequency signals separately, at least two second electrical signals can be obtained, ensuring that the properties of feces can be analyzed based on the characteristics of different frequency bands.

[0046] The goal of signal processing is to convert the original electrical signal into a stable, quantifiable DC voltage signal. The first electrical signal is an AC signal, which is converted to a DC voltage by a voltage-doubling rectifier module (composed of capacitors, resistors, and diodes). Simultaneously, filtering removes environmental electromagnetic interference and noise during signal transmission, making the electrical signal characteristics of different frequency bands clearer.

[0047] With the above Figure 6 For details on the embodiment shown, please refer to Figure 7 , Figure 7 : This is a flow chart of another intelligent detection method for excrement in diapers provided in an embodiment of the present application. The signal processing module processes the first electrical signal to obtain n second electrical signals, specifically comprising the following steps: S710, obtaining a reference electrical signal of the diaper; the reference electrical signal is an electrical signal of the diaper in a dry environment detected by the detection host; S720: Perform a differential operation on the reference electrical signal and the first electrical signal to obtain a first differential electrical signal; S730: Perform voltage doubling and rectification processing on the first differential electrical signal to obtain a first rectified electrical signal; S740: Process the first rectified electrical signal according to a preset filtering method to obtain a first rectified filtered signal; S750: Sample the first rectified and filtered signal according to a preset sampling rate to obtain the n second electrical signals.

[0048] The signal processing module processes the first electrical signal to eliminate environmental noise, separate multi-band valid signals, and complete the conversion from analog to digital signals, providing standardized data for the subsequent extraction of the voltage K-line. The first electrical signal, as the original detection signal, contains multi-band components such as low-frequency (1KHz~1MHz) and high-frequency (1.1MHz~12MHz), and is affected by factors such as diaper material and environmental electromagnetic interference, resulting in baseline drift and uneven signal attenuation. Through differential operations, rectification, filtering, and sampling, invalid noise can be removed, retaining the unique electrical signal characteristics of excrement (such as the penetration velocity characteristics of the low-frequency band and the particle interface characteristics of the high-frequency band), making the second electrical signal quantifiable and comparable. The value of n is related to the number of frequency bands, sampling accuracy and processing dimension of the first electrical signal. Since the first electrical signal contains two core frequency band signals, low-frequency and high-frequency, and each frequency band needs to be processed independently to retain its unique characteristics (such as low-frequency signals reflect penetration speed, and high-frequency signals reflect particle size), n is at least 2 (corresponding to the low-frequency processed signal and the high-frequency processed signal, respectively).

[0049] Specifically, the acquisition of the reference electrical signal needs to be completed when the diaper is in a dry state without contact with excrement. Usually, the calibration process is automatically triggered by the detection host during the initial startup phase. The sending electrode transmits a detection signal in a preset frequency band, and the receiving electrode collects the signal strength when there is no excrement (at this time, the signal should be close to zero or stable at an extremely low noise level, such as less than 0.01V), and the signal is stored as the reference value. The differential operation is implemented through the hardware circuit or algorithm of the signal processing module. The calculation formula is "first differential electrical signal = first electrical signal - reference electrical signal". The differential operation can effectively remove background noise (such as the inherent resistance noise of conductive fibers and environmental electromagnetic interference), retaining only the change in electrical signals caused by contact with excrement, highlighting the dynamic characteristics of the effective signal (such as the sudden rise in signal during urine penetration and the slow change in signal caused by differences in fecal viscosity); the voltage doubling and rectification processing is completed by the voltage doubling and rectification module (composed of diodes, capacitors and resistors). The differential AC signal is superimposed on the voltage through the unidirectional conductive characteristics of the diode, converting the weak alternating signal into a DC signal with amplified amplitude, and the rectification accuracy is controlled within 0.001V, ensuring that subsequent ADC sampling can capture tiny voltage changes (such as the subtle difference in attenuation amplitude between feces and urine). The filtering process uses a preset RC filter circuit (e.g., a low-pass filter composed of resistors and capacitors). Corresponding cutoff frequencies are set for signals in different frequency bands. Low-frequency signals use a lower cutoff frequency to filter out high-frequency noise, while high-frequency signals use a higher cutoff frequency to preserve the high-frequency characteristics of the particle interface. The result is a smooth and stable first rectified filtered signal. Sampling is performed by the ADC pins of the microcontroller module. The preset sampling rate must meet the Nyquist sampling theorem to avoid signal aliasing. The n secondary electrical signals obtained after sampling are digitized voltage sequences. Each sequence corresponds to the voltage variation of a specific frequency band within a preset time period, providing raw data support for the subsequent extraction of m voltage K-lines (e.g., low-frequency K-line and high-frequency K-line).

[0050] Step S630, determining m voltage K lines according to the n second electrical signals; m is an integer greater than 1 and less than n.

[0051] The voltage K-line refers to the voltage change curve formed by the second electrical signal in the time dimension, which reflects the characteristics of excrement. Its essence is to present the dynamic changes of the electrical signal in the form of a curve by quantifying the voltage values ​​in different time periods. It contains key parameters such as peak time, attenuation amplitude, and stable value. These parameters are directly related to the type of excrement (urine or feces) and the state (normal or abnormal). For example, the voltage K-line corresponding to urine usually reaches its peak at around 2.5 seconds, while the voltage K-line corresponding to feces reaches its peak at around 8 seconds, and the attenuation amplitude of the two is significantly different (25-35% for urine and less than or equal to 6% or 8% for feces).

[0052] The value of m is determined based on the frequency band characteristics and analysis of the second electrical signal. Since the second electrical signal contains two core signals, low-frequency (1 kHz to 1 MHz) and high-frequency (1.1 MHz to 12 MHz), and the low-frequency signal mainly reflects the penetration rate of excrement (used to distinguish the type of urine and feces), and the high-frequency signal mainly reflects the surface composition and particle characteristics of excrement (used to identify urine transparency and stool consistency), m is at least 2 (corresponding to the low-frequency voltage K-line and the high-frequency voltage K-line, respectively).

[0053] In a possible embodiment, determining m voltage K lines according to the n second electrical signals specifically includes the following steps: 631. Segmentally process the target second electrical signal according to a preset time interval to obtain p target signal sequences; the target second electrical signal is any one of the n second electrical signals; and p is a positive integer greater than 1. 632. Extract the average voltage of each target signal sequence from the p target signal sequences to obtain p average voltages; 633. Perform a curve fitting operation on the p average voltages and the p target signal sequences with voltage as the vertical axis and time as the horizontal axis to obtain a first voltage K-line; 634. Determine the slope corresponding to each signal sequence in the p target signal sequences to obtain p target voltage slopes; 635. Perform calculations on each of the p target signal sequences using Fourier transform to obtain p amplitude spectra corresponding to the p target signal sequences; 636. Perform time-frequency feature fusion on the p amplitude spectra and the p target voltage slopes according to a preset first formula to obtain p first fusion parameters; 637. Determine a voltage K-line of the target second electrical signal according to the p first fusion parameters and the first voltage K-line.

[0054] The conversion of the second electrical signal into a voltage K-line is designed to preserve the temporal characteristics (e.g., peak onset time, decay rate) and frequency-band specificity (e.g., penetration dynamics in the low-frequency band, particle interface characteristics in the high-frequency band) of the fecal electrical signal. The voltage K-line is essentially a quantized curve of voltage per unit time. Its key parameters (e.g., peak time and decay amplitude) are essential for distinguishing fecal and urination types and abnormal conditions. First, segmented processing discretizes the continuous signal into analyzable time windows, capturing the dynamic changes during the initial contact phase (signal surge), mid-penetration phase (signal peak), and stable phase (signal decay). Next, average voltage calculation and curve fitting eliminate signal fluctuation noise, generating a smooth voltage curve. Furthermore, by extracting the slope and amplitude spectrum (using Fourier transform to obtain the energy distribution of different frequency components, corresponding to the particle scattering characteristics of high-frequency signals), and preserving multi-dimensional features through time-frequency fusion, the voltage K-line ensures that it reflects both the decay patterns in the temporal dimension and the component differences in the frequency band dimension.

[0055] Specifically, the preset first formula realizes the weighted fusion of the target voltage slope and the amplitude spectrum through the weight coefficients (α and β). For the second electrical signal in the low-frequency band (reflecting the penetration rate), the target voltage slope (such as the rising rate of the signal from the initial value to the peak value) is used to distinguish between urine and feces (the slope of urine is large and the slope of feces is small); for the second electrical signal in the high-frequency band (reflecting the interface characteristics of the particles), the amplitude spectrum (especially the energy attenuation of the high-frequency component) can better reflect the size of the feces particles (the larger the particles, the faster the high-frequency attenuation) or the transparency of urine (the lower the transparency, the stronger the high-frequency scattering and the lower the amplitude spectrum energy). By dynamically adjusting the weights, the first fusion parameters can retain the core features of signals in different frequency bands in a targeted manner, so that the final voltage K-line not only contains the temporal variation rules (such as peaking at 8 seconds and attenuation amplitude at 2 minutes), but also integrates frequency band-specific information. Among them, the preset first formula can be in the form of:

[0056] in, For the The first fusion parameter, is the first weight coefficient, is the second weight coefficient, and , For the A target voltage slope, For the Amplitude spectrum, Indicates the characteristic frequencies, is the number of amplitude spectra.

[0057] Finally, the voltage value of each segment is calculated using the first formula and the K-line is reconstructed for subsequent abnormality detection.

[0058] Step S640: determining m pieces of excrement attribute information according to the m voltage K lines.

[0059] Excreta attributes are derived from voltage K-line analysis, primarily including excreta type (urine or feces) and fecal status (e.g., urine transparency, presence of sediment, stool consistency). Voltage K-lines, including timing characteristics (e.g., peak time and decay amplitude) and frequency band characteristics (low-frequency signals reflect permeation velocity, high-frequency signals reflect particle interface properties), are key to determining these attributes. For example, if a voltage K-line corresponding to a low-frequency signal exhibits a peak around 2.5 seconds and a decay of 25-35% within 2 minutes, it can be identified as urine; if it exhibits a peak around 8 seconds and a decay of less than or equal to 8% within 2 minutes, it can be identified as feces. High-frequency signals, on the other hand, can further identify urine transparency (slower decay indicates higher transparency) or stool consistency (faster decay indicates larger particles and thicker stool) based on their decay rate.

[0060] In a possible embodiment, determining m pieces of feces attribute information based on the m voltage K lines specifically includes the following steps: 641. Determine m voltage attenuation parameters according to the m voltage K-lines; 642. Determine m excrement densities based on the m voltage attenuation parameters; 643. Perform feature extraction on the m voltage K-lines based on a preset feature extraction rule to obtain m feature vectors; 644. Input the m feature vectors into a preset excrement attribute recognition model to obtain m first excrement attribute information; 645. Determine the m pieces of excrement attribute information based on the m pieces of first excrement attribute information and the m pieces of excrement densities.

[0061] Accurate identification of excrement properties is achieved through the multi-dimensional fusion of voltage decay characteristics and eigenvectors. The voltage decay parameter is an indicator that reflects the conductivity and permeability of excrement, and is directly related to the type and state of the excrement. Excrement density is positively correlated with the concentration (density) of solid particles in the excrement based on conductivity (e.g., feces have a higher density than urine, have lower conductivity, and decay more slowly). Therefore, density can be reverse-mapped using the decay parameter to distinguish excrement with similar characteristics (e.g., loose stools and large amounts of urine). The eigenvector integrates the temporal characteristics (peak time, decay rate) and frequency band characteristics (low-frequency permeability, high-frequency particle scattering characteristics) of the voltage K-line, providing comprehensive input for the recognition model. The preset model is trained using a neural network model based on large-scale labeled data (e.g., voltage curves of excrement of different types and states), achieving automated mapping from features to attributes.

[0062] Among them, the dimension of the feature vector must include the "peak time difference" in the low-frequency band (2.5s for urination and 8s for defecation) and the amplitude spectrum attenuation rate in the high-frequency band (for example, the high-frequency 12MHz signal decays faster when the stool particles are larger); the training data of the excrement attribute recognition model must cover excrement samples of different age groups (infants / elderly people) and different health conditions (normal / abnormal) to improve the model's generalization ability.

[0063] Specifically, the voltage attenuation parameter is calculated as: (peak voltage - steady-state voltage) / peak voltage × 100%. The peak voltage is the maximum value in the voltage K-line (e.g., 2.5V after 2.5 seconds of urination, 1.66V after 8 seconds of defecation), and the steady-state voltage is the voltage at the end of the preset time period (e.g., the value after 2 minutes). The attenuation parameter of the low-frequency K-line primarily reflects differences in penetration rate (faster attenuation for urine, larger parameter; slower attenuation for feces, smaller parameter), while the attenuation parameter of the high-frequency K-line reflects particle scattering intensity (e.g., larger fecal particles, more pronounced attenuation of the high-frequency signal, and larger parameter). Fecal density is determined based on a preset "voltage attenuation parameter - conductivity - density" mapping table. The corresponding conductivity is first retrieved using the attenuation parameter (e.g., a 25% low-frequency attenuation corresponds to a urine conductivity of 10-20 mS / cm, while a 5% attenuation corresponds to a fecal conductivity of 2-5 mS / cm). Density is then calculated based on the linear relationship between conductivity and density. This mapping table can be derived through extensive experimentation, machine learning based on historical experimental data, or a statistical model derived from a large sample size. The machine learning method can be based on a convolutional neural network, a recurrent neural network, or a long-short-term memory neural network, though this is not a limitation. The feature extraction rules must include three types of features: time series features (peak time, decay amplitude within 2 minutes, and rising slope), frequency band features (signal duration at 1 kHz in the low-frequency band, and the main peak frequency of the amplitude spectrum at 12 MHz in the high-frequency band), and stability features (voltage fluctuation standard deviation, e.g., fluctuations of less than or equal to 0.05V for feces and less than or equal to 0.1V for urine). These three features are normalized to form a feature vector, which is then input into the fecal attribute recognition model. The fecal attribute recognition model uses a machine learning model based on support vector machines, random forests, or a neural network model (e.g., a convolutional neural network, LSTM, etc.). The training data consists of a large sample size (including normal and abnormal feces from infants, children, and the elderly), with labels such as "urine / feces" and "normal / abnormal." The model input is a feature vector and outputs the first excrement attribute information. If the first attribute is urine and the density is greater than 1.025g / cm 3 , it is corrected to high-concentration urine; if the first attribute is feces and the density is less than 1.015g / cm 3, it is corrected to loose stool (abnormal). The final output of fecal attribute information includes type, state, key parameters (such as attenuation amplitude 28%, density 1.02g / cm 3 ), providing a basis for subsequent abnormal judgment.

[0064] In a possible embodiment, determining m voltage attenuation parameters according to the m voltage K lines specifically includes the following steps: 6411. Extract target characteristic voltage parameters from a target voltage K-line; the target characteristic voltage parameters include: a target peak voltage and a target steady-state voltage; the target voltage K-line is any one of the m voltage K-lines; 6412. Get the reference voltage parameters of the reference voltage K line; 6413. Determine a reference steady-state voltage in the reference voltage parameter; 6414. Determine a voltage difference between the target steady-state voltage and the reference steady-state voltage; 6415. Determine a first voltage weight corresponding to the voltage difference based on a preset mapping relationship between the voltage difference and the steady-state voltage weight; 6416. Determine a second steady-state voltage according to the first voltage weight and the target steady-state voltage. 3417. Determine a first time length from the target peak voltage to the target steady-state voltage according to the target voltage K-line; 6418. If the first time length is less than or equal to a preset first time threshold, determine a voltage decay parameter of the target voltage K-line according to the target peak voltage, the second steady-state voltage, and the first time length; 6419. If the first time length is greater than the first time threshold, use the preset first parameter as the voltage attenuation parameter of the target voltage K line.

[0065] Among them, the target peak voltage corresponds to the maximum value of the signal when the excrement just contacts the electrode, and its occurrence time (e.g., 2.5 seconds for urination and 8 seconds for defecation) is a sign to distinguish the type; the target steady-state voltage is the stable value after the preset time period to reflect the signal state after the excrement is absorbed by the diaper or stably attached. The difference between the two is the basis for calculating the attenuation amplitude. Due to individual specificity, a reference voltage K-line is introduced to eliminate individual differences (such as the difference in conductivity between infants and the elderly) and environmental interference (such as the effect of temperature on the conductive fiber signal), and the calculation accuracy is improved by calibrating the steady-state voltage. The mapping relationship between the voltage difference and the weight is used to correct the steady-state voltage deviation caused by differences in diaper batches (such as fluctuations in the resistance of the conductive fiber).

[0066] Among them, the first time threshold is set to a signal stabilization period of 1 to 2 minutes: when the first time length (time from peak to steady state) is ≤2 minutes, it means that the signal has completed the complete penetration-attenuation process, and the attenuation parameter calculated at this time can effectively distinguish the type of excrement; if the time length exceeds 2 minutes, the signal has stabilized (the attenuation amplitude is extremely small, such as ≤1%), and it is meaningless to continue calculating, so the preset first parameter is used to simplify the processing. In addition, the purpose of introducing weights is to correct the steady-state voltage. Because high-frequency, low-voltage signals have high attenuation, the steady-state voltage is adjusted by calculating the steady-state voltage and the weight to improve the measurement accuracy. The mapping relationship between the voltage difference and the steady-state voltage weight can be obtained through a large number of experimental statistics and is not limited here. For example, according to statistics based on multiple measurements, feces reaches the highest voltage value in about 8 seconds, and the voltage attenuation amplitude is within 8% within 2 minutes. The raw data of feces detection are shown in Table 1 below: Table 1 Raw data of stool sample testing

[0067] For example, based on statistics from multiple measurements, the voltage reaches its highest value in 2.5 seconds after urination, and decreases by 25% to 30% within 2 minutes. After urine saturation, the voltage reaches its highest value in 2.5 seconds, and decreases by 10% to 15% within 2 minutes. The raw data of urine testing is shown in Table 2 below: Table 2 Raw data of urine sample testing

[0068] Specifically, the target characteristic voltage parameters are extracted using the host's ADC module. The digitized sampled data of the target voltage K-line (sampling rate ≥ 24MHz, meeting high-frequency signal requirements) is traversed, and the maximum value is determined as the target peak voltage (accurate to 0.001V), such as 2.5V at 2.5 seconds in a urine K-line. The voltage value at the end of a preset time period is intercepted as the target steady-state voltage, such as 1.55V at 60 seconds in urination. The reference voltage K-line is derived from a standard sample library pre-stored in the host. This library contains average voltage curves for different types of excreta (normal urine, normal stool, abnormal urine, and abnormal stool). The reference steady-state voltage is the average voltage of the standard sample over a 2-minute period. The preset mapping relationship is a piecewise function: when the voltage difference is ≤ 0.03V, the first voltage weight is 0.1; when 0.03V < difference ≤ 0.07V, the weight is 0.3; and when the difference is > 0.07V, the weight is 0.5. The second steady-state voltage = target steady-state voltage × (1 - first voltage weight) + reference steady-state voltage × first voltage weight. For example, a difference of 0.05V corresponds to a second steady-state voltage of 1.55 × 0.7 + 1.5 × 0.3 = 1.535V, which is closer to the true steady-state value after correction. For a clearer explanation, the following example illustrates this: when the duration is ≤ 2 minutes, the attenuation parameter is (target peak voltage - second steady-state voltage) / target peak voltage × 100%. The attenuation parameter for urination is (2.5 - 1.535) / 2.5 × 100% ≈ 38.6%. For defecation, with a peak value of 1.66V, a second steady-state voltage of 1.62V, and a duration of 8 seconds (≤ 120 seconds), the attenuation parameter is (1.66 - 1.62) / 1.66 × 100% ≈ 2.4%. If the duration is greater than 120 seconds, the preset first parameter (0.01%) is used as the attenuation parameter, corresponding to the stable signal state.

[0069] In a possible embodiment, determining the m excrement densities according to the m voltage decay parameters specifically includes the following steps: 6421. Determine the conductivity of the excrement corresponding to each of the m voltage decay parameters based on a preset mapping relationship between the voltage decay parameter and the excrement conductivity, and obtain m first excrement conductivities; 6422. Get historical fecal conductivity; 6423. Determine an error correction parameter for the electrical conductivity of the excreta based on the historical electrical conductivity of the excreta; 6424. Determine m second feces conductivities based on the error correction parameter and the m first feces conductivities; 6425. Determine the feces density corresponding to each of the m second feces conductivities based on a preset mapping relationship between feces conductivity and feces density, to obtain the m feces densities.

[0070] The voltage decay parameter is negatively correlated with conductivity, which in turn is positively correlated with fecal density. A preset mapping relationship converts the decay parameter into conductivity. This is then combined with historical data to correct for errors caused by individual differences (such as baseline conductivity differences between infants and elderly people). Ultimately, the density value is derived based on the inherent relationship between conductivity and density. This mapping relationship between the voltage decay parameter and conductivity is established based on extensive sample experiments. Historical fecal conductivity should be derived from historical data stored on a cloud server for the same user (e.g., the same elderly person or infant) to eliminate the impact of individual metabolic differences (such as changes in urine concentration due to diet) on the current measurement. The error correction parameter reflects the fluctuation range of the historical data (such as standard deviation or mean deviation), ensuring that the corrected conductivity is closer to the true value, thereby improving detection accuracy.

[0071] Specifically, the preset mapping relationship between voltage decay parameters and fecal conductivity is derived from simultaneous measurements of different types of fecal matter. For urine (normal / abnormal) and feces (thin / normal / thick), the voltage decay parameter (decay amplitude over 1-2 minutes) and conductivity (measured using a portable conductivity meter with an accuracy of 0.1 mS / cm) are simultaneously recorded under a controlled temperature (25°C ± 1°C). Statistical analysis establishes a corresponding relationship. For example, a 28% decay in urine corresponds to a conductivity of 15 mS / cm, and a 4% decay in feces corresponds to a conductivity of 3 mS / cm. These values ​​are stored in the local database of the testing host. When the voltage decay parameter is entered, the initial fecal conductivity is calculated through table lookup or interpolation. Historical fecal conductivity is derived from historical data for the same user stored on the cloud server. For new users, average conductivity data for the same group (e.g., infants and toddlers of the same age or elderly people in the same health status) is retrieved. The data spans the last 30 days to ensure coverage of diverse metabolic states. For example, in the historical data of an elderly individual, normal urine conductivity ranges from 12-18 mS / cm, while normal stool ranges from 2-4 mS / cm. The error correction parameter is calculated based on the statistical characteristics of the historical conductivity. If the standard deviation of the historical data is σ (e.g., σ = 2 mS / cm for urine), the error correction parameter k = σ / mean (e.g., a mean of 15 mS / cm corresponds to k = 0.13), reflecting the degree of historical data fluctuation. Greater fluctuations result in stronger corrections, mitigating the impact of random factors (e.g., sudden changes in conductivity caused by a single dietary abnormality) on the current measurement. Secondary conductivity = primary conductivity × (1 - k) + historical mean × k. For example, if the first conductivity is 18mS / cm (higher than the historical average of 15mS / cm) and k=0.13, then the second conductivity = 18×0.87 + 15×0.13 = 17.46mS / cm. This not only preserves the characteristics of the current measurement value but also calibrates to the historical baseline to improve stability. The mapping relationship between fecal conductivity and density is based on an experimentally established linear model: density = 0.005×conductivity + 1.000 (unit: g / cm 3 ), the model is obtained by fitting the density measurements of samples with different conductivity. For example, the second conductivity of 15mS / cm corresponds to a density of 0.005×15+1.000=1.075g / cm 3 (Urine), 3mS / cm2 corresponds to a density of 0.005×3+1.000=1.015g / cm2. 3 (defecation).

[0072] Step S650: determining m pieces of abnormal information based on the m pieces of excrement attribute information.

[0073] Excreta attribute information includes type and status parameters (such as urine clarity, stool consistency, and density). Abnormal information specifically indicates when these parameters exceed normal thresholds. The type information (urine / stool) determined by the low-frequency signal provides the applicable normal range (e.g., normal urine clarity differs from the normal stool consistency). Surface characteristics analyzed by the high-frequency signal (e.g., abnormal high-frequency attenuation due to urine sedimentation, and excessively large / small stool particles) provide specific criteria for determining abnormalities. Combined, these two factors enable precise identification of abnormalities. The thresholds for the normal range are determined based on extensive sample testing. For example, "normal urine clarity corresponds to a high-frequency signal voltage attenuation parameter of 10-15%, while abnormality indicates an attenuation parameter of >20%," and "normal stool consistency corresponds to a high-frequency signal attenuation rate of 5-8% / min, while excessively thin stools indicate an attenuation rate of >10% / min." Furthermore, abnormal information includes specific parameter values ​​(e.g., "stool consistency deviates by 30% from the reference value"), providing a quantitative basis for subsequent alarm alerts and ensuring that caregivers can quickly identify the problem.

[0074] In a possible embodiment, determining m pieces of abnormal information based on the m pieces of excrement attribute information specifically includes the following steps: 651. Determine a target excrement type identifier in the target excrement attribute information; the target excrement type identifier includes feces or urine; the target excrement attribute information is any one of the m excrement attribute information; the target excrement attribute information includes target transparency, target concentration, and target consistency; 652. Determine target abnormality information according to the target excrement type identifier; 653. Determining target abnormality information based on the target excrement type identifier includes: 654. If the target excrement type is identified as urine, obtaining a reference transparency and a reference concentration of normal urine; if the target transparency is greater than the reference transparency and the target concentration is less than the reference concentration, generating target abnormality information for characterizing abnormal urine; 655. If the target excrement type is identified as the stool, obtain a reference consistency of normal stool; when the target consistency is less than or equal to the reference consistency, generate the target abnormality information for characterizing abnormal stool.

[0075] The determination of the target excreta type (stool / urine) relies on the temporal characteristics of low-frequency signal analysis. This classification is used to determine the relationship between abnormalities (urine abnormalities are related to transparency and concentration; stool abnormalities are related to consistency). Normal reference values ​​(reference transparency, concentration, and consistency) are set based on experimental data and high-frequency signal characteristics. Reference transparency corresponds to the voltage attenuation parameter of a high-frequency signal (e.g., 12MHz) (normal urine has high transparency, slow high-frequency attenuation, and a voltage attenuation parameter of 10-15%; abnormality indicates low transparency, with a voltage attenuation parameter >20%). Reference concentration is associated with the conductivity of the low-frequency signal (normal urine concentration corresponds to a conductivity of 10-20mS / cm; abnormality indicates <8mS / cm or >25mS / cm). Reference consistency corresponds to the particle scattering intensity of the high-frequency signal (normal stool particles are uniform, with a high-frequency attenuation rate of 5-8% / min; excessively dilute stool particles are small, with an attenuation rate >10% / min).

[0076] Specifically, if the voltage K-line peaks at approximately 2.5 seconds and decays by 25-35% within 2 minutes, it is considered "urine." If it peaks at approximately 8 seconds and decays by ≤8%, it is considered "feces." When the type is identified as "urine," the reference transparency and reference concentration are retrieved from a pre-stored standard library (based on the high-frequency signal characteristics of urine from healthy individuals. For example, the average voltage decay parameter for a 12MHz signal in normal urine is 12%, and the corresponding average conductivity is 15mS / cm). The target transparency is calculated based on the attenuation amplitude of the high-frequency signal (lower attenuation indicates higher transparency), and the target concentration is calculated based on the conductivity of the low-frequency signal (higher conductivity indicates higher concentration). If the voltage attenuation parameter corresponding to the target transparency is greater than 20% (i.e., transparency is lower than the reference value) and the conductivity corresponding to the target concentration is less than 8 mS / cm (i.e., concentration is lower than the reference value), a target abnormality message of "abnormal urine (suspected to contain sediment, low concentration)" is generated. When the type is identified as "stool," the reference consistency is determined based on the high-frequency signal attenuation rate of normal stool (5-8% / min), and the target consistency is determined by particle scattering analysis of the high-frequency signal (faster attenuation indicates smaller particles, looser stool). If the attenuation rate corresponding to the target consistency is greater than 10% / min (i.e., consistency ≤ reference consistency), a target abnormality message of "abnormal stool (suspected loose stool)" is generated. The generated abnormality message must include specific parameter values ​​and be associated with characteristic segments of the voltage K-line (such as the voltage fluctuation curve during the abnormal period), providing a traceable basis for subsequent alarm notification modules.

[0077] Step S660: executing corresponding early warning prompt operation parameters according to the m pieces of abnormal information through the alarm prompt module.

[0078] Among them, the early warning prompt operation parameters must match the type and severity of the abnormal information. For example, the prompt method and intensity corresponding to abnormal urination (such as reduced transparency) and abnormal stool (such as too watery) are different to ensure that nursing staff can quickly judge the degree of urgency. The generation of parameters needs to be combined with the hardware capabilities of the detection host (such as buzzers, LED lights) and communication functions (such as pushing messages to mobile devices), which can meet both local real-time prompts and remote notifications through cloud servers. The early warning prompt operation parameters can be local sound and light alarms, remote message pushes (such as APP notifications, text messages), or a combination of the two, which are not limited here. Among them, the prompt intensity is positively correlated with the severity of the abnormality; the prompt content must contain key information (such as "abnormal urination: low transparency" and "abnormal stool: watery stool") to facilitate quick understanding.

[0079] Step S670: Send the early warning prompt operation parameters to the cloud server and store them so that the user device can access the early warning prompt operation parameters in the cloud server.

[0080] Among them, early warning prompt operation parameters (such as alarm type, time, and degree of abnormality) are key to nursing decision-making. Storing data on a cloud server allows for long-term data retention and multi-device access, solving the problem of limited local storage capacity and easy data loss. Furthermore, as a data hub, the cloud server can integrate historical abnormality data for the same wearer (such as the number of daily abnormalities and changes in type), providing a foundation for health trend analysis.

[0081] As can be seen, the above-mentioned intelligent excrement detection method for diapers is applied to the detection host of the intelligent nursing system, which also includes diapers, a cloud server, and a user device. The detection host includes a signal transmission module, a signal processing module, and an alarm prompt module. The detection host is in communication with the diaper and the cloud server. The method includes: obtaining a first electrical signal from the diaper in a preset time period through the signal transmission module; processing the first electrical signal through the signal processing module to determine m voltage K lines, determining m excrement attribute information and m abnormal information based on the m voltage K lines; executing corresponding early warning prompt operation parameters based on the m abnormal information through the alarm prompt module, and sending and storing the corresponding early warning prompt operation parameters to the cloud server so that the user device can access the early warning prompt operation parameters in the cloud server. In this way, the user experience of diaper users can be improved and the user's care costs can be reduced.

[0082] For easier understanding, see Figure 8 , Figure 8This is a workflow diagram of an intelligent excrement detection method for diapers provided by the present application. It can be seen that the intelligent excrement detection method for diapers is based on a finite state machine architecture. From the state transition, signal feature analysis, event type determination and human-computer interaction architecture, the modular design realizes the automatic detection and intelligent response of diaper excrement signals. This method can achieve low-power management, and can also accurately determine the type and state of excrement. Specifically, the initial state of the method includes two modes: sleep and start. The state switching is achieved by key operation. Pressing the power button can make it enter the start state from the sleep state, and pressing the power button can make it return to the sleep state from the start state. In this way, energy consumption can be reduced during non-working hours and the battery life of the device can be extended. When the intelligent detection is started, it first enters the idle state, at which time it is in a ready state waiting for the signal to appear until the user starts the detection process by pressing the button and triggers the signal acquisition mechanism. The signal detection phase follows the timing logic of "rise-peak-fall" (i.e., voltage K-line), periodically sampling the target signal voltage. When the signal rises to the threshold, the system enters the "wait for signal peak" phase, continuously monitoring for 6 seconds. The system then determines the signal has occurred and enters the "peak detected and stabilized" phase. During this continuous sampling process, if the current voltage value continuously exceeds the historical peak value and the difference remains within a small threshold (e.g., 50mV), the peak value is considered to have occurred and stabilized, and the "fall time to signal drop" phase is then entered. Signal feature analysis uses the percentage drop between the peak value and the current voltage as a criterion, categorizing the degree of signal drop into three ranges. A "fall" of less than 3% (percentage) is considered Event A (e.g., defecation), triggering a voice prompt and a 30-second waiting period. A "fall" of greater than 70% (percentage) is considered Event B (e.g., urination), also followed by a 30-second waiting period. If the "fall" is less than 3% (percentage) and less than 70% (percentage), the system continues observing and maintains a 30-second waiting period. After the 30-second wait period, the device will determine the next action based on its current state, regardless of whether it is identified as event A, event B, or continued observation. If the voice playback is complete and no new signal triggers, the device will return to the idle state to await the next detection request, or automatically enter the sleep state after a long period of inactivity.

[0083] Furthermore, this method combines user interaction, signal acquisition and processing, intelligent identification, voice interaction, and low-power management. Key input (the on / off button) processes power on / off and detects startup commands. Signal acquisition (implemented through the host's electrical signal receiving and rectifier module) regularly samples voltage and records key time points. Intelligent identification (implemented through the microcontroller module) classifies events by calculating percentage decreases. Voice interaction (implemented through the alarm and indication module) plays prompts based on the identification results. The low-power management module optimizes energy consumption through sleep / wake-up control. These modules work together to ensure accurate detection of excrement properties in portable health monitoring scenarios while enhancing the user experience through intelligent interaction. This not only improves the user experience but also reduces care costs through low power consumption.

[0084] For easier understanding, see Figure 9 , Figure 9This is a schematic diagram of the functional interaction relationship of an intelligent excrement detection method for diapers provided in an embodiment of the present application. It can be seen that this intelligent excrement detection method for diapers includes: user interaction function, low-power management function, signal acquisition and processing function, intelligent identification function, and voice interaction function. These five functions, through orderly operation calls and data flow, realize the complete function from user command input to detection result output and interactive feedback, and build an intelligent excrement detection method adapted to the needs of portable health monitoring. First, the user interaction function. User interaction serves as the entry point for interaction with the user and is used for command input functions, including two operations: "Key 2 power on / off (set on the diaper)" and "Key 1 start detection (set on the detection host)". Among them, "Key 2 power on / off" provides basic power management instructions. Users can use this button to switch from sleep state to start state, or from running to sleep state. It is the basic interaction point for controlling device energy consumption and working status; "Key 1 start detection" triggers the entry into the excrement detection process. When the user needs to monitor the status of the diaper, pressing this button will start the subsequent signal acquisition and analysis tasks. Second, the low-power management function. When the user turns off the device using the key KEY2 or the system has been inactive for a long time, this module responds to instructions or is automatically triggered, causing it to enter sleep state, reducing power consumption during non-working hours and extending battery life; and when the key KEY2 is turned on or a detection requirement is triggered, it can wake it up from sleep state and resume normal working mode. Through the module's precise sleep and wake-up control, system performance and energy consumption are balanced to ensure that the device has both continuous working capabilities and effective energy saving in portable usage scenarios. Third, the signal acquisition and processing function is mainly used to detect the electrical signals sent by diapers received by the host. The electrical signals are collected and preliminarily processed by the single-chip microcomputer module of the detection host, which includes the operations of timing signal voltage acquisition, signal rise / peak / fall detection, and recording key time points and voltage. Among them, "timing signal voltage acquisition" samples the voltage signal of the corresponding detection part of the diaper at a preset time interval, providing the original data source for subsequent analysis, ensuring that the dynamic changes of the signal during the excrement production process can be captured; "signal rise / peak / fall detection" performs feature recognition on the collected voltage signal to determine whether the signal shows an upward trend, whether it reaches a peak, and whether it enters a falling phase. By tracking these key change nodes of the signal, characteristic information related to excrement events can be mined; "recording key time points and voltage" stores important moments in the signal change process (such as the start time of signal rise, the time when the peak occurs, the start time of fall, etc.) and the corresponding voltage values, providing a data basis containing time series and voltage characteristics for the intelligent discrimination module. Fourth, the intelligent discrimination function performs event discrimination based on the data provided by signal acquisition and processing, mainly including: calculating the percentage of decrease and discriminating the event type.Among them, "calculating the percentage of drop" calculates the voltage drop ratio by calculating the signal peak value and the current voltage value. This ratio is a key indicator for distinguishing different excretion events (such as defecation, urination, etc.). According to the design of signal feature analysis with the peak value and the current voltage drop percentage as the core judgment basis, the drop ratio is accurately calculated to provide a quantitative reference for distinguishing event types; "distinguishing event type" classifies and identifies excretion events occurring in diapers based on the calculated drop percentage and the preset threshold (such as a drop ratio of less than 3% is judged as a defecation event, and greater than 70% is judged as a urination event, etc.), and clearly determines whether it is urination, defecation or other states, realizing the conversion from signal data to event type, and providing judgment results for the subsequent voice interaction module. The fifth is the voice interaction function, which is implemented through the alarm and indication module. It is mainly used for the detection result feedback function, including playing the corresponding voice prompt and controlling the playback interval and number of times. Among them, "play corresponding voice prompts" calls the pre-set corresponding voice content according to the event type output by the intelligent discrimination module (such as playing urination prompt voice when urination is detected, and playing defecation prompt voice when defecation is detected), feedbacks the current status of the diaper to the user, realizes intelligent interaction, and improves user experience; "control playback interval and number of times" manages the frequency and duration of voice playback to avoid excessive voice interference to users due to frequent detection of the same event or misjudgment. By reasonably controlling the playback interval and number of times, while ensuring the effective transmission of information, the user experience is taken into account, so that in the health monitoring scenario, it can not only accurately feedback the test results, but also interact with the user in a friendly manner.

[0085] The above mainly introduces the solution of the embodiment of the present application from the perspective of the execution process of the method side. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0086] The embodiment of the present application can divide the functional units of the electronic device according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0087] In the case of dividing each functional module into corresponding functional modules, Figure 10 This is a functional module block diagram of an intelligent excrement detection device for diapers provided in an embodiment of the present application. The intelligent excrement detection device for diapers is applied to a detection host of an intelligent nursing system. The intelligent nursing system also includes diapers, a cloud server, and a user device. The detection host includes: a signal transmission module, a signal processing module, and an alarm prompt module. The detection host is communicatively connected to the diaper, and the cloud server is communicatively connected to the detection host. The intelligent excrement detection device 1000 for diapers includes: An acquiring unit 1010 is configured to acquire a first electrical signal of the diaper within a preset time period through the signal transmission module; The control unit 1020 is configured to process the first electrical signal through the signal processing module to obtain n second electrical signals, where n is an integer greater than 1; The determining unit 1030 is configured to determine m voltage K lines based on the n second electrical signals, where m is an integer greater than 1 and less than n; determine m pieces of feces attribute information based on the m voltage K lines; and determine m pieces of abnormality information based on the m pieces of feces attribute information. The early warning unit 1040 is used to execute corresponding early warning prompt operation parameters according to the m abnormal information through the alarm prompt module; send the early warning prompt operation parameters to the cloud server and store them so that the user device can access the early warning prompt operation parameters in the cloud server.

[0088] In a possible embodiment, the control unit, in processing the first electrical signal by the signal processing module to obtain n second electrical signals, is specifically configured to: Acquire a reference electrical signal of the diaper; the reference electrical signal is an electrical signal of the diaper in a dry environment detected by the detection host; performing a differential operation on the reference electrical signal and the first electrical signal to obtain a first differential electrical signal; performing voltage-doubling rectification processing on the first differential electrical signal to obtain a first rectified electrical signal; Processing the first rectified electrical signal according to a preset filtering method to obtain a first rectified filtered signal; The first rectified and filtered signal is sampled according to a preset sampling rate to obtain the n second electrical signals.

[0089] In a possible embodiment, in determining the m voltage K lines according to the n second electrical signals, the determining unit 1030 is specifically configured to: The target second electrical signal is segmented and processed according to a preset time interval to obtain p target signal sequences; the target second electrical signal is any one of the n second electrical signals; p is a positive integer greater than 1; extracting an average voltage of each target signal sequence from the p target signal sequences to obtain p average voltages; With voltage as the vertical axis and time as the horizontal axis, a curve fitting operation is performed on the p average voltages and the p target signal sequences to obtain a first voltage K-line; Determining a slope corresponding to each signal sequence in the p target signal sequences to obtain p target voltage slopes; Using Fourier transform to calculate each signal sequence in the p target signal sequences to obtain p amplitude spectra corresponding to the p target signal sequences; Performing time-frequency feature fusion on the p amplitude spectra and the p target voltage slopes according to a preset first formula to obtain p first fusion parameters; A voltage K line of the target second electrical signal is determined according to the p first fusion parameters and the first voltage K line.

[0090] In a possible embodiment, the determining unit 1030, in determining the m pieces of feces attribute information based on the m voltage K lines, is specifically configured to: Determining m voltage attenuation parameters according to the m voltage K lines; determining m excrement densities according to the m voltage decay parameters; the greater the voltage decay parameter, the greater the excrement density; Perform feature extraction on the m voltage K lines based on a preset feature extraction rule to obtain m feature vectors; Inputting the m feature vectors into a preset excrement attribute recognition model to obtain m first excrement attribute information; The m pieces of excrement attribute information are determined according to the m first pieces of excrement attribute information and the m excrement densities.

[0091] In a possible embodiment, the determining unit 1030, in determining the m voltage attenuation parameters according to the m voltage K lines, is specifically configured to: Extracting target characteristic voltage parameters from the target voltage K line; the target characteristic voltage parameters include: target peak voltage, target steady-state voltage; the target voltage K line is any one of the m voltage K lines; Get the reference voltage parameters of the reference voltage K line; Determining a reference steady-state voltage among the reference voltage parameters; determining a voltage difference between the target steady-state voltage and the reference steady-state voltage; Determining a first voltage weight corresponding to the voltage difference based on a preset mapping relationship between the voltage difference and the steady-state voltage weight; determining a second steady-state voltage according to the first voltage weight and the target steady-state voltage; Determining a first time length from the target peak voltage to the target steady-state voltage according to the target voltage K-line; If the first time length is less than or equal to a preset first time threshold, determining a voltage attenuation parameter of the target voltage K line according to the target peak voltage, the second steady-state voltage, and the first time length; If the first time length is greater than the first time threshold, the preset first parameter is used as the voltage attenuation parameter of the target voltage K line.

[0092] In a possible embodiment, the determining unit 1030, in determining the m excrement densities according to the m voltage attenuation parameters, is specifically configured to: Determining the conductivity of the excrement corresponding to each of the m voltage decay parameters based on a preset mapping relationship between the voltage decay parameter and the conductivity of the excrement, to obtain m first excrement conductivities; Obtain historical fecal conductivity; determining an error correction parameter for the fecal conductivity based on the historical fecal conductivity; determining m second feces conductivities based on the error correction parameter and the m first feces conductivities; The feces density corresponding to each of the m second feces conductivities is determined based on a preset mapping relationship between feces conductivity and feces density to obtain the m feces densities.

[0093] In a possible embodiment, the determining unit 1030, in determining the m pieces of abnormal information based on the m pieces of feces attribute information, is specifically configured to: Determining a target excrement type identifier in the target excrement attribute information; the target excrement type identifier includes feces or urine; the target excrement attribute information is any one of the m excrement attribute information; the target excrement attribute information includes target transparency, target concentration, and target consistency; determining target abnormality information according to the target excrement type identifier; The step of determining target abnormality information according to the target excrement type identifier includes: If the target excrement type is identified as urine, a reference transparency and a reference concentration of normal urine are obtained; when the target transparency is greater than the reference transparency and the target concentration is less than the reference concentration, target abnormality information for characterizing abnormal urine is generated; If the target excrement type is identified as the stool, a reference consistency of normal stool is obtained; when the target consistency is less than or equal to the reference consistency, the target abnormality information for characterizing abnormal stool is generated.

[0094] It should be noted that the specific functional implementation of the intelligent excrement detection device 1000 for diapers can be found in the above Figure 6 The description of a method for intelligent detection of excrement for diapers is shown. For example, the acquisition unit 1010 is used to implement the relevant content of executing S610, and the early warning unit 1040 is used to implement the relevant content of executing S660 and S670, which will not be described in detail. The various units or modules in the intelligent detection device 1000 for excrement for diapers can be combined into one or several other units or modules, or one (or some) of the units or modules can be further divided into multiple functionally smaller units or modules to form a structure, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units or modules are divided based on logical functions. In actual applications, the functions of one unit (or module) are implemented by multiple units (or modules), or the functions of multiple units (or modules) are implemented by one unit (or module).

[0095] It can be seen that the embodiment of the present application describes an intelligent excrement detection device for diapers, which is applied to the detection host of the intelligent care system. The intelligent care system also includes diapers, a cloud server and a user device. The detection host includes: a signal transmission module, a signal processing module, and an alarm prompt module, wherein the detection host is communicatively connected to the diaper, and the cloud server is communicatively connected to the detection host. The device obtains a first electrical signal of the diaper in a preset time period through the signal transmission module in the acquisition unit; the first electrical signal is processed by the signal processing module in the control unit to obtain n second electrical signals; n is an integer greater than 1; m voltage K lines are determined according to the n second electrical signals by the determination unit; m is an integer greater than 1 and less than n; m excrement attribute information is determined according to the m voltage K lines; m abnormal information is determined according to the m excrement attribute information; the alarm prompt module in the early warning unit executes corresponding early warning prompt operation parameters according to the m abnormal information, and sends the early warning prompt operation parameters to the cloud server and stores them so that the user device can access the early warning prompt operation parameters in the cloud server, thereby improving the user experience of diaper users and reducing the user's care costs.

[0096] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0097] The present application also provides a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may comprise an electronic device.

[0098] It should be noted that, for the above-mentioned various embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. Those skilled in the art should know that this application is not limited by the order of the actions described, because some steps in the embodiments of the present application can be performed in other orders or simultaneously. In addition, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions, steps, modules or units involved are not necessarily required by the embodiments of the present application.

[0099] In the above embodiments, the embodiments of the present application have different focuses on the description of each embodiment. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0100] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0101] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, removable hard disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and storage medium can also exist as discrete components in the terminal device or the management device.

[0102] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part via software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. This computer program product comprises one or more computer instructions. When these computer program instructions are loaded and executed on a computer, they fully or partially produce the processes or functions described in the embodiments of the present application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0103] The modules / units included in the various devices and products described in the above embodiments may be software modules / units, hardware modules / units, or partly software modules / units and partly hardware modules / units. For example, for various devices and products applied to or integrated into a chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least part of the modules / units may be implemented in the form of software programs, which run on a processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated into a chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as a chip, circuit module, etc.) or different components of the chip module, or at least part of the modules / units may be It is implemented in the form of a software program, which runs on the processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal equipment, or at least some modules / units can be implemented in the form of a software program, which runs on the processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits.

[0104] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only a specific implementation method of the embodiments of the present application and is not intended to limit the scope of protection of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of protection of the embodiments of the present application.

Claims

1. An intelligent detection method for excrement in diapers, characterized in that: A detection host applied to an intelligent nursing system, the intelligent nursing system also including diapers, a cloud server and a user device, the detection host including: a signal transmission module, a signal processing module, an alarm prompt module, the detection host is communicatively connected to the diaper, the cloud server is communicatively connected to the detection host, and the method includes: Acquiring a first electrical signal of the diaper within a preset time period through the signal transmission module; Processing the first electrical signal by the signal processing module to obtain n second electrical signals, where n is an integer greater than 1; Determining m voltage K lines according to the n second electrical signals; m is an integer greater than 1 and less than n; determining m pieces of excrement attribute information according to the m voltage K lines; Determining m pieces of abnormal information based on the m pieces of excrement attribute information; Executing corresponding early warning prompt operation parameters according to the m abnormal information through the alarm prompt module; The early warning prompt operation parameters are sent to the cloud server and stored so that the user device can access the early warning prompt operation parameters in the cloud server.

2. The method according to claim 1, wherein The processing of the first electrical signal by the signal processing module to obtain n second electrical signals includes: Acquire a reference electrical signal of the diaper; the reference electrical signal is an electrical signal of the diaper in a dry environment detected by the detection host; performing a differential operation on the reference electrical signal and the first electrical signal to obtain a first differential electrical signal; performing voltage-doubling rectification processing on the first differential electrical signal to obtain a first rectified electrical signal; Processing the first rectified electrical signal according to a preset filtering method to obtain a first rectified filtered signal; The first rectified and filtered signal is sampled according to a preset sampling rate to obtain the n second electrical signals.

3. The method according to claim 1, wherein The determining m voltage K lines according to the n second electrical signals includes: The target second electrical signal is segmented and processed according to a preset time interval to obtain p target signal sequences; the target second electrical signal is any one of the n second electrical signals; p is a positive integer greater than 1; extracting an average voltage of each target signal sequence from the p target signal sequences to obtain p average voltages; With voltage as the vertical axis and time as the horizontal axis, a curve fitting operation is performed on the p average voltages and the p target signal sequences to obtain a first voltage K-line; Determining a slope corresponding to each signal sequence in the p target signal sequences to obtain p target voltage slopes; Using Fourier transform to calculate each signal sequence in the p target signal sequences to obtain p amplitude spectra corresponding to the p target signal sequences; Performing time-frequency feature fusion on the p amplitude spectra and the p target voltage slopes according to a preset first formula to obtain p first fusion parameters; A voltage K line of the target second electrical signal is determined according to the p first fusion parameters and the first voltage K line.

4. The method according to any one of claims 1 to 3, wherein The determining m pieces of excrement attribute information according to the m voltage K lines includes: Determining m voltage attenuation parameters according to the m voltage K lines; determining m excrement densities according to the m voltage decay parameters; Perform feature extraction on the m voltage K lines based on a preset feature extraction rule to obtain m feature vectors; Inputting the m feature vectors into a preset excrement attribute recognition model to obtain m first excrement attribute information; The m pieces of excrement attribute information are determined according to the m first pieces of excrement attribute information and the m excrement densities.

5. The method according to claim 4, wherein The determining m voltage attenuation parameters according to the m voltage K lines includes: Extracting target characteristic voltage parameters from the target voltage K line; the target characteristic voltage parameters include: target peak voltage, target steady-state voltage; the target voltage K line is any one of the m voltage K lines; Get the reference voltage parameters of the reference voltage K line; Determining a reference steady-state voltage among the reference voltage parameters; determining a voltage difference between the target steady-state voltage and the reference steady-state voltage; Determining a first voltage weight corresponding to the voltage difference based on a preset mapping relationship between the voltage difference and the steady-state voltage weight; determining a second steady-state voltage according to the first voltage weight and the target steady-state voltage; Determining a first time length from the target peak voltage to the target steady-state voltage according to the target voltage K-line; If the first time length is less than or equal to a preset first time threshold, determining a voltage attenuation parameter of the target voltage K line according to the target peak voltage, the second steady-state voltage, and the first time length; If the first time length is greater than the first time threshold, the preset first parameter is used as the voltage attenuation parameter of the target voltage K line.

6. The method according to claim 4, wherein The determining m excrement densities according to the m voltage attenuation parameters includes: Determining the conductivity of the excrement corresponding to each of the m voltage decay parameters based on a preset mapping relationship between the voltage decay parameter and the conductivity of the excrement, to obtain m first excrement conductivities; Obtain historical fecal conductivity; determining an error correction parameter for the fecal conductivity based on the historical fecal conductivity; determining m second feces conductivities based on the error correction parameter and the m first feces conductivities; The feces density corresponding to each of the m second feces conductivities is determined based on a preset mapping relationship between feces conductivity and feces density to obtain the m feces densities.

7. The method according to any one of claims 1 to 3, wherein: The determining m pieces of abnormal information based on the m pieces of excrement attribute information includes: Determining a target excrement type identifier in the target excrement attribute information; the target excrement type identifier includes feces or urine; the target excrement attribute information is any one of the m excrement attribute information; the target excrement attribute information includes target transparency, target concentration, and target consistency; determining target abnormality information according to the target excrement type identifier; The step of determining target abnormality information according to the target excrement type identifier includes: If the target excrement type is identified as urine, a reference transparency and a reference concentration of normal urine are obtained; when the target transparency is greater than the reference transparency and the target concentration is less than the reference concentration, target abnormality information for characterizing abnormal urine is generated; If the target excrement type is identified as the stool, a reference consistency of normal stool is obtained; when the target consistency is less than or equal to the reference consistency, the target abnormality information for characterizing abnormal stool is generated.

8. An intelligent excrement detection device for diapers, characterized in that: A detection host applied to an intelligent nursing system, the intelligent nursing system also including diapers, a cloud server and a user device, the detection host including: a signal transmission module, a signal processing module, an alarm prompt module, the detection host is communicatively connected to the diaper, the cloud server is communicatively connected to the detection host, and the device includes: an acquiring unit, configured to acquire a first electrical signal of the diaper within a preset time period through the signal transmission module; a control unit, configured to process the first electrical signal through the signal processing module to obtain n second electrical signals; n is an integer greater than 1; a determining unit, configured to determine m voltage K lines based on the n second electrical signals; m is an integer greater than 1 and less than n; determine m pieces of feces attribute information based on the m voltage K lines; and determine m pieces of abnormality information based on the m pieces of feces attribute information; An early warning unit is used to execute corresponding early warning prompt operation parameters according to the m abnormal information through the alarm prompt module; send the early warning prompt operation parameters to the cloud server and store them so that the user device can access the early warning prompt operation parameters in the cloud server.

9. An electronic device, characterized in that: include: a processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, wherein the programs include instructions for executing the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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