Intelligent urine volume monitoring closestool system and monitoring method

Abstract

The invention discloses an intelligent urine volume monitoring toilet system and a monitoring method, the system comprises a toilet seat and a cavity arranged in the toilet seat, the toilet seat is also provided with a toilet cushion structure, the toilet cushion structure comprises a cushion, pressure sensors arranged in an array are symmetrically arranged on two sides in the cushion, and the pressure sensors are electrically connected with the toilet seat. The pressure sensor is connected with the main control module and the wireless transmission module, the wireless transmission module is connected with a cloud server or a mobile terminal APP, the inner wall of the cavity is further provided with an asymmetric spiral flow guide groove, the spiral flow guide groove is provided with multiple sets of flow sensors, and a temperature sensor is further arranged in the spiral flow guide groove. The output end of the flow sensor and the output end of the temperature sensor are both connected with the input end of the main control module and the input end of the wireless transmission module. The application limitation of a traditional flow measurement technology in an unstructured scene is broken through, and a novel urine volume calculation model based on fluid dynamic characteristics is established; and early screening and curative effect evaluation of urinary system diseases are assisted.

Description

Technical Field

[0001] The present invention belongs to the technical field of urine volume monitoring, and in particular relates to an intelligent urine volume monitoring toilet system and a monitoring method. Background Art

[0002] With the acceleration of the global aging process and the increasing burden of chronic diseases, the importance of accurate monitoring of urine volume in clinical diagnosis and health management is becoming increasingly prominent as a key physiological indicator for assessing renal function, cardiovascular health and metabolic status. Currently, urine volume monitoring mainly relies on manual records or medical urine collection devices, which have inherent defects such as inconvenient operation, discrete data, and poor privacy, making it difficult to meet the needs of long-term home monitoring.

[0003] For example, the invention disclosed in the authorization announcement number CN113374042B discloses a health monitoring toilet that automatically collects urine based on radar positioning. The toilet includes a toilet body, a urine collection device, a drive device, a urine detection system, a radar positioning system, and a control system. The radar positioning system is used to scan in the direction of urine, calculate the position coordinates of the urine on the radar scanning plane, and determine the rotation angle compensation value of the urine collection device. The control system is used to calculate the rotation angle of the urine collection device based on the position coordinates of the urine on the radar scanning plane and the rotation angle compensation value, and control the drive device to rotate the urine collection device to the corresponding angle based on the calculated rotation angle to collect urine. This solution uses two miniature radars to locate urine and comprehensively calculates the rotation angle of the urine collection rod through coordinate compensation, thereby achieving automatic urine collection and having a simple structure.

[0004] However, the above solution cannot combine multimodal sensors and adaptive algorithms to establish a urine volume calculation model, which reduces the accuracy of urine volume calculation. At the same time, it cannot analyze urine volume based on dynamic urine volume data, and cannot assist in early screening and efficacy evaluation of urinary system diseases. For this reason, we propose an intelligent urine volume monitoring toilet system and monitoring method. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent urine volume monitoring toilet system and a monitoring method to solve the problem that the existing technology proposed in the above background technology cannot combine multimodal sensors and adaptive algorithms to establish a urine volume calculation model, which reduces the accuracy of urine volume calculation. At the same time, it cannot analyze the urine volume based on the dynamic urine volume data, and cannot assist in the early screening and efficacy evaluation of urinary system diseases.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: an intelligent urine volume monitoring toilet system, comprising a toilet seat and a cavity arranged in the toilet seat, a toilet pad structure further provided on the toilet seat, the toilet pad structure comprising a seat cushion, pressure sensors arranged in an array symmetrically provided on both sides of the seat cushion, the pressure sensors being connected to a main control module and a wireless transmission module, the main control module and the wireless transmission module being both provided in the seat cushion, the wireless transmission module being connected to a cloud server or a mobile terminal APP, an asymmetric spiral guide groove being further provided on the inner wall of the cavity, a plurality of flow sensors being provided in the spiral guide groove, and a temperature sensor being further provided in the spiral guide groove, the output ends of the flow sensor and the temperature sensor being connected to the input ends of the main control module and the wireless transmission module;

[0007] A multimodal sensor array is constructed by combining multiple groups of flow sensors with array-arranged pressure sensors and temperature sensors, and a novel urine volume calculation model based on fluid dynamics characteristics is established through the multimodal sensor array.

[0008] Preferably, the main control module is responsible for signal processing, algorithm operation and communication control of the pressure sensor, flow sensor and temperature sensor.

[0009] Preferably, a power module is also provided in the seat cushion, and the power module is used to supply power to the components of the system.

[0010] A urine monitoring method for an intelligent urine volume monitoring toilet comprises the following steps:

[0011] S1. When the user touches the seat cushion, the built-in array pressure sensor first detects the change in pressure distribution, automatically activates the system and determines the effective measurement area;

[0012] S2, the spiral guide groove guides the urine, and the flow sensor monitors the urine flow rate and flow characteristics in real time;

[0013] S3, combining the environmental data collected by the temperature sensor, and using the embedded temperature compensation algorithm in the main control module to eliminate the measurement error caused by the change in liquid viscosity;

[0014] S4, the main control module dynamically calculates the cumulative urine volume based on the fluid dynamics model and calibration parameters, and identifies the start and end time of urination through a sliding window algorithm;

[0015] S5. The processed data is encrypted and transmitted to the cloud server or mobile terminal APP by the wireless transmission module;

[0016] S6, and present it in the form of visual charts (such as urine volume curve, daily average trend); support abnormal value warning and health data synchronization to the medical platform.

[0017] Preferably, the temperature compensation algorithm in S3 is specifically to establish a compensation model based on heat transfer: ΔV=k(αΔT+βΔT2), where α=0.03% / °C, β=0.0015% / °C2.

[0018] In the formula, ΔV: represents the change in the original physical quantity V that needs to be compensated for due to temperature changes, that is, due to temperature changes, the original physical quantity V needs to be added (or subtracted, depending on the positive or negative calculation result) ΔV to obtain a more accurate value; k: is a proportional coefficient, and its specific value needs to be determined according to the actual application scenario and the characteristics of the measurement system. This coefficient plays the role of scaling the compensation amount, which comprehensively reflects factors such as the sensitivity of the entire measurement system to temperature changes; α and β: are temperature-related coefficients, which determine the contribution of the linear and quadratic temperature terms to the compensation amount respectively; ΔT: represents the change in temperature, and the unit is degrees Celsius (℃). The temperature change is expressed by the linear term αΔT and the quadratic term βΔT 2 Together they affect the compensation amount ΔV. The linear term reflects the linear relationship between the compensation amount and temperature change, while the quadratic term reflects the nonlinear relationship, indicating that the effect of temperature change on the compensation amount is not simply linear. Comprehensive consideration of these two relationships can more accurately perform temperature compensation.

[0019] Preferably, the fluid dynamics model in S4 is specifically: by measuring the Doppler frequency shift expression when urine flows:

[0020] Δf=2f·v·cosθ / c

[0021] Where Δf is the frequency difference between the received and transmitted ultrasound waves, in Hz; f is the ultrasound emission frequency, in Hz; v is the urine flow velocity, in m / s; c is the propagation velocity of ultrasound waves in urine, in m / s; and θ is the angle between the ultrasound beam and the flow direction, in radians / degrees.

[0022] Preferably, the dynamic calculation of the cumulative urine volume in S4 is specifically as follows:

[0023] A dynamic calibration equation was established based on the urine volume calculation model;

[0024] The urine volume calculation model is:

[0025]

[0026] Where: v i represents the flow rate of channel i, A i represents the effective cross-sectional area, α is the temperature compensation coefficient;

[0027] The dynamic calibration equation is:

[0028]

[0029] Where: γ: viscosity-temperature coefficient (0.015 / ℃); α i : is a coefficient related to the i-th item, which is related to the measurement position, sensor characteristics, etc. and is used to weight the calculation results of this item; v i (t) is the flow rate of urine at the i-th position at time t; dA i is the area element corresponding to the i-th position. This integral term calculates the flow accumulation of urine through a certain area in a certain time at the i-th position. T is the current temperature, and T0 is the reference temperature. Temperature affects the viscosity and other properties of urine, which in turn affects the flow rate and flow calculation. This correction term is used to compensate the flow calculation result for temperature. β is a coefficient, dA i / dt represents the rate of change of the area of the ith position over time.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] (1) This application constructs a multimodal sensor array by using multiple groups of flow sensors (5) and array-arranged pressure sensors (34) and temperature sensors (6). By combining the multimodal sensor array with an adaptive algorithm, the application limitations of traditional flow measurement technology in unstructured scenarios are broken through, and a new urine volume calculation model based on fluid dynamic characteristics is established.

[0032] (2) At the clinical application level, this application provides continuous urine volume dynamic data for patients with chronic kidney disease, heart failure, etc., realizes the automated analysis of urination curves (peak urine flow rate, residual urine volume, etc.), and assists in the early screening and efficacy evaluation of urinary system diseases.

[0033] (3) At the health management level, this application improves user experience through non-invasive monitoring, builds a personal health baseline database, and provides data support for telemedicine and precision health intervention.

[0034] (4) In terms of social benefits, this application can not only alleviate the pressure on medical resources, but also reduce the frequency of medical treatment for special groups through home monitoring, while protecting the privacy and dignity of users.

[0035] (5) The present application diverts urine by setting up a spiral guide groove, and the flow sensor monitors the urine flow rate and flow characteristics in real time, which can accurately collect the urine flow rate and flow characteristics.

[0036] The breakthrough of this application will promote the in-depth application of the Internet of Medical Things (IoMT) technology in the field of home health monitoring. Its technical paradigm can also be extended to other body fluid monitoring scenarios, which is of great strategic significance for achieving the medical transformation of "active health". BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a structural diagram of the intelligent urine volume monitoring toilet system;

[0038] Figure 2 Schematic diagram of the top cross-sectional structure of the toilet seat structure of the present invention;

[0039] Figure 3 This is a schematic diagram of a half-section structure of a toilet seat in the present invention;

[0040] Figure 4 for Figure 3 Schematic diagram of the enlarged structure at A in the middle;

[0041] Figure 5 This is a flow chart of the overall algorithm in the urine monitoring method of the intelligent urine volume monitoring toilet;

[0042] In the figure: 1. Toilet seat; 2. Cavity; 3. Toilet pad structure; 4. Spiral guide groove; 5. Flow sensor; 6. Temperature sensor; 31. Cushion; 32. Main control module; 33. Wireless transmission module; 34. Pressure sensor; 35. Power module. DETAILED DESCRIPTION

[0043] 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 making creative efforts are within the scope of protection of the present invention.

[0044] See also Figures 1-4 The present invention provides a technical solution: an intelligent urine volume monitoring toilet system, comprising a toilet seat 1 and a cavity 2 arranged in the toilet seat 1, a toilet pad structure 3 is also provided on the toilet seat 1, the toilet pad structure 3 includes a seat cushion 31, and pressure sensors 34 arranged in an array are symmetrically arranged on both sides of the seat cushion 31, the pressure sensor 34 is connected to a main control module 32 and a wireless transmission module 33, the main control module 32 and the wireless transmission module 33 are both arranged in the seat cushion 31, the wireless transmission module 33 is connected to a cloud server or a mobile APP, an asymmetric spiral guide groove 4 is also provided on the inner wall of the cavity 2, multiple groups of flow sensors 5 are provided in the spiral guide groove 4, and a temperature sensor 6 is also provided in the spiral guide groove 4, the output ends of the flow sensor 5 and the temperature sensor 6 are both connected to the input ends of the main control module 32 and the wireless transmission module 33, a power supply module 35 is also provided in the seat cushion 31, and the power supply module 35 is used to power the components of this system.

[0045] Flow sensor 5: Ultrasonic Doppler probe (such as SFM3000), measuring flow velocity.

[0046] Pressure sensor 34: Matrix FlexiForce thin film sensor, used to locate the user's sitting posture.

[0047] Temperature sensor 6: waterproof digital probe (DS18B20), real-time environmental monitoring.

[0048] Wireless transmission module 33: supports low-power Bluetooth (BLE 5.0) or Wi-Fi to enable real-time data upload to the cloud or mobile APP.

[0049] Power module 35: uses a rechargeable lithium battery + energy recovery circuit, combined with a dynamic sleep mechanism, to extend the battery life to more than 3 months.

[0050] A waterproof isolation layer is also provided on the seat cushion 31 , and an antibacterial contact layer is also provided on the waterproof isolation layer. The antibacterial contact layer has a medical-grade silicone surface that inhibits bacterial growth and is easy to clean.

[0051] Waterproof isolation layer: Nano hydrophobic membrane (PTFE coating) to prevent liquid penetration and damage to the circuit.

[0052] The active module is a low-power MCU (such as the STM32F4 series), which is responsible for signal processing, algorithm calculation and communication control.

[0053] The working principle and usage process of the present invention are as follows: When the user touches the seat cushion 31, the built-in flexible pressure sensor 34 array first detects the change in pressure distribution, automatically activates the system and determines the effective measurement area; then, the multi-channel flow sensor 5 (such as an ultrasonic or capacitive sensor) monitors the urine flow rate and flow characteristics in real time, and combines the environmental data collected by the temperature sensor 6 to eliminate the measurement error caused by the change in liquid viscosity through the embedded temperature compensation algorithm; the main control MCU (such as the STM32F4 series) dynamically calculates the cumulative urine volume based on the fluid dynamics model and calibration parameters, and uses a sliding window algorithm to identify the start and end time of urination to improve accuracy; the processed data is encrypted by AES-128 and transmitted to the cloud server or mobile terminal APP by a low-power Bluetooth (BLE) or Wi-Fi module, and finally presented in the form of visual charts (such as urine volume curve, daily average trend), while supporting abnormal value warnings (such as oliguria / polyuria) and health data synchronization to the medical platform. The entire process completes a single measurement within 1 second, and the system then enters a low-power sleep state to achieve energy efficiency optimization. This workflow achieves non-invasive and high-precision home urine volume monitoring through the three-level collaboration of "sensing-computing-communication".

[0054] This solution uses a three-level sensing architecture to achieve high-precision urine volume monitoring:

[0055] The urine volume calculation model in this application is:

[0056]

[0057] (1) Main detection layer:

[0058] Using a Melexis MLX90808 Doppler ultrasonic sensor array (4×4 layout), operating at a frequency of 2.4MHz, the Doppler frequency shift expression when measuring urine flow is:

[0059] Δf=2f·v·cosθ / c (1-2)

[0060] Where Δf is the frequency difference between the received and transmitted ultrasound waves, in Hz; f is the ultrasound emission frequency, in Hz; v is the urine flow velocity, in m / s; c is the propagation velocity of ultrasound waves in urine, in m / s; and θ is the angle between the ultrasound beam and the flow direction, in radians / degrees.

[0061] Achieve ±1.2% flow rate measurement accuracy. The sensor integrates temperature compensation function and maintains ±0.5% stability in the ambient temperature range of 10-40℃.

[0062] (2) Auxiliary perception layer:

[0063] It is equipped with 16-channel FlexiForceA201 thin film pressure sensors (sensitivity 0.1N, response time 10ms), arranged at a spacing of 5×5cm, to detect the user's sitting pressure distribution.

[0064] The pressure-position mapping model is established using the cubic spline interpolation algorithm to achieve a positioning accuracy of ±2mm.

[0065] Spend

[0066] (3) Environmental compensation layer:

[0067] Arrange three DS18B20 waterproof temperature sensors (±0.5℃ accuracy);

[0068] Based on heat transfer theory, a compensation model is established: ΔV = k(αΔT + βΔT2), where α = 0.03% / °C.

[0069] β=0.0015% / ℃2;

[0070] In the formula: ΔV: represents the change in the original physical quantity V that needs to be compensated for due to temperature changes, that is, due to temperature changes, the original physical quantity V needs to be added (or subtracted, depending on the positive or negative calculation result) ΔV to obtain a more accurate value; k: is a proportional coefficient, and its specific value needs to be determined according to the actual application scenario and the characteristics of the measurement system. This coefficient plays the role of scaling the compensation amount, which comprehensively reflects factors such as the sensitivity of the entire measurement system to temperature changes; α and β: are temperature-related coefficients, which respectively determine the contribution of the linear and quadratic temperature terms to the compensation amount; ΔT: represents the change in temperature, and the unit is degrees Celsius (℃). The temperature change is expressed by the linear term αΔT and the quadratic term βΔT 2 Together they affect the compensation amount ΔV. The linear term reflects the linear relationship between the compensation amount and temperature change, while the quadratic term reflects the nonlinear relationship, indicating that the effect of temperature change on the compensation amount is not simply linear. Comprehensive consideration of these two relationships can more accurately perform temperature compensation.

[0071] 3.3 Mechanical structure design

[0072] (1) Fluid guide system:

[0073] Adopt asymmetric spiral guide groove (groove depth 3mm, inclination angle 15°).

[0074] Calculate the Reynolds number Re = ρvd / μ ≈ 1200 (laminar flow region) to ensure measurement stability.

[0075] 3.4 Electronic System Design

[0076] (1) Signal processing chain:

[0077] Preamplifier: AD8237 instrumentation amplifier (gain 1000 times).

[0078] ADC conversion: ADS131M08 (24-bit delta-sigma, sampling rate 64kSPS).

[0079] Main control: STM32F4 (Cortex-M4).

[0080] (2) Power consumption optimization:

[0081] Dynamic power management: operating current 6.5mA@3.3V, sleep current 1.8μA.

[0082] A time-triggered architecture is used to optimize the sampling interval to 50ms.

[0083] 3.5 Algorithm Design

[0084] (1) Urine volume calculation model:

[0085] Calculate according to expression (1-1).

[0086] (2) Anomaly detection algorithm:

[0087] A uroflow rate baseline curve was established based on LSTM (prediction error < 3%).

[0088] Set dynamic threshold: Q max =25ml / s, Q avg =12ml / s±15%.

[0089] The implementation method of this application is now described as follows:

[0090] (1) 2-time frequency multiplication mechanism:

[0091] The coefficient 2 in formula (2-2) comes from the fact that ultrasonic waves undergo a dual-path propagation of "transmission → reflection → reception", which makes them more sensitive than single-reflection systems such as radar.

[0092] (2) Angle optimization:

[0093] When θ = 0°, cosθ = 1 (maximum sensitivity), but the toilet scene requires a compromise of 45°:

[0094] Avoid mirror reflection interference caused by vertical installation.

[0095] Balance signal strength with spatial layout constraints.

[0096] (3) Necessity of temperature compensation:

[0097] The relationship between the speed of sound c(T) and the urine temperature T is expressed as:

[0098] c(T)=1402.5+5.03T-0.058T 2 (T∈[20,40]℃) (1-3)

[0099] Where T is the urine temperature, °C; c(T) is the speed of sound, ms -1 Analyzing the expression (1-3), we know that every 1°C temperature difference will introduce a flow rate error of about 0.3%, so it must be compensated in real time.

[0100] (4) Actual processing method of this application:

[0101] Improved Δf detection accuracy (±0.1Hz) through cross-correlation algorithm

[0102] The sensor array is installed at a 45° angle to reduce the impact of turbulence through spatial averaging.

[0103] Implementing a real-time frequency shift calculation pipeline within an FPGA (latency < 1ms)

[0104] Expressions (1-3) are the theoretical basis of ultrasonic flow measurement. This application adapts traditional industrial flow meter technology to civil sanitary ware scenarios by optimizing the θ angle design and innovative temperature / angle composite compensation algorithm.

[0105] The following is a detailed explanation of the development process and core technologies of the optimized smart urine volume monitoring toilet, presented in a modular structure:

[0106] 1) Fluid dynamics modeling

[0107] Simulation parameters: Reynolds number Re=800-1500 (laminar flow-transitional flow).

[0108] Boundary conditions: inlet velocity 0.5-1.2 m / s, outlet pressure 0 Pa.

[0109] Optimization target: vortex intensity of guide groove <0.1m 2 / s.

[0110] 2) Signal processing chain

[0111] Table 1 shows the processing stages of the signal processing chain and their schemes.

[0112] Table 1

[0113] Processing stage Technical Solution Performance indicators Front-end filtering 4th-order Butterworth (20-200Hz) SNR>45dB Denoising Db4 wavelet transform (5-layer decomposition) RMSE<0.8% Data fusion Kalman gain adaptive adjustment Convergence time <50ms

[0114] 3) Deepening of the urine volume calculation model

[0115] The dynamic calibration equation of the urine volume calculation model is:

[0116]

[0117] The equation used to calculate urine volume, V, is obtained by summing up the calculations for multiple components (i, which ranges from 1 to 64). The overall idea is to accurately calculate urine volume by taking into account factors such as urine flow rate, temperature, and pressure changes. Where: γ: viscosity-temperature coefficient (0.015 / °C); α: i : is a coefficient related to the i-th item, which is related to the measurement position, sensor characteristics, etc. and is used to weight the calculation results of this item; v i (t) is the flow rate of urine at the i-th position at time t; dA i is the area element corresponding to the i-th position. This integral term calculates the flow accumulation of urine through a certain area in a certain time at the i-th position. T is the current temperature, and T0 is the reference temperature. Temperature affects the viscosity and other properties of urine, which in turn affects the flow rate and flow calculation. This correction term is used to compensate the flow calculation result for temperature. β is a coefficient, dA i / dt represents the rate of change of the area of the ith position over time. This term takes into account the impact of changes in the measurement area over time on urine volume calculation, which may be caused by changes in the container shape, etc.

[0118] 4) Composite sensing technology:

[0119] Ultrasonic Doppler (flow velocity) + impedance detection (liquid level)

[0120] Dynamic weighted fusion algorithm:

[0121]

[0122] Where: w i represents the fusion weight of the i-th sensor; σ i Represents the measurement standard deviation of the i-th type sensor; σ j 2 represents the variance of all sensors involved in the fusion.

[0123] 4.2 How the Algorithm Works

[0124] The working principle of the multimodal sensor data fusion algorithm of this application is now explained as follows:

[0125] (1) Precision-oriented weighting:

[0126] Variance σ i 2 The smaller (higher precision) sensor, the higher its weight w i The larger the value, the larger the value. For example:

[0127] Ultrasonic Doppler flow velocity measurement: σ=1.0ml→weight w i ≈0.64;

[0128] Impedance measurement of liquid level: σ=1.8ml→weight w i ≈0.36.

[0129] (2) Dynamic adjustment mechanism:

[0130] The σ value is updated every 5 seconds, and the variance of the latest 30 measurements is counted through a sliding window;

[0131] When a sensor fails (σ>3ml), it will automatically downgrade to w i =0.1 or less.

[0132] (3) Multimodal fusion example:

[0133] V final =w us ·V ultrasound +w imp ·V impedance (1-6)

[0134] Where: V ultrasound Indicates ultrasound-related measurement values or parameters; w us Indicates V ultrasound The corresponding weight coefficient indicates the proportion of the ultrasonic measurement value in the final result; V impedance Represents impedance-related measurement values or parameters; w imp It is V impedance The corresponding weight coefficient reflects the contribution of the impedance measurement value to the final result, and w us +w imp =1.

[0135] (4) Overall algorithm architecture

[0136] See also Figure 5

[0137] Step 1: Input Layer

[0138] Data Source:

[0139] Ultrasonic Doppler probe: provides flow velocity information (time resolution 20ms);

[0140] Temperature sensor: environmental compensation parameters (accuracy ±0.5℃);

[0141] Pressure sensor: user posture calibration signal;

[0142] Step 2: Preprocessing Layer

[0143] Data synchronization: using hardware timestamp (STM32 RTC synchronization, error <1ms)

[0144] Normalization processing:

[0145]

[0146] Where: μ calib is the calibration mean, σ calib is the calibration standard deviation.

[0147] Step 3: Feature-level fusion (weighted Kalman filtering)

[0148] Equation of state:

[0149]

[0150] The state equation is used to describe the change of system state over time. is the estimated value of the system state at time k; A is the state transfer matrix, which reflects the impact of the state at the previous moment on the current state; is the estimated value of the system state at time k-1; B is the control input matrix; uk is the control input at time k; w k is the process noise at time k.

[0151] Observation equation:

[0152] Z k =HX k +v k (1-9)

[0153] The observation equation is used to establish the relationship between the system state and the observed value. k is the observation value at time k; H is the observation matrix, which maps the system state to the observation space; X k is the actual state of the system at time k (often unknown in practice and needs to be estimated); v k is the observation noise at time k.

[0154] Adaptive weight matrix:

[0155]

[0156] Where: R i represents the noise covariance matrix of the i-th sensor; real-time update strategy: adjust the weights by maximum likelihood estimation every 100ms. us 、w imp 、w temp They are the weights corresponding to data fusion of different sensors (such as ultrasound, impedance, and temperature-related sensors).

[0157] Step 4: Decision-level fusion (DS evidence theory)

[0158] The basic probability distribution function is shown in Table 2.

[0159] Table 2

[0160]

[0161] Dempster Combination Rules:

[0162]

[0163] Where: m 12 (A) represents the basic probability distribution function value of proposition A after fusion; m1 and m2 are the basic probability distribution functions of two different evidence sources respectively; B and C are elements in the proposition set corresponding to the evidence. The numerator is obtained by summing m1(B)m2(C) that satisfies B∩C=A; the denominator 1-K is the normalization factor; K is the conflict factor, and when K>0.8, the sensor self-test is triggered.

[0164] Step 5: Conflict Detection Mechanism:

[0165] Define the confidence interval: μ±2σ

[0166] When the difference between ultrasound and impedance results is >3σ, the following process is initiated:

[0167] 1) Check whether the temperature sensor is abnormal;

[0168] 2) Enable backup pressure sensor data;

[0169] 3) Record fault codes (for later maintenance).

[0170] Table 3 shows the sensor test data collection.

[0171] Table 3

[0172]

[0173] In summary:

[0174] (1) By combining a multimodal sensor array with an adaptive algorithm, we can break through the application limitations of traditional flow measurement technology in unstructured scenarios and establish a new urine volume calculation model based on fluid dynamics characteristics.

[0175] (2) At the clinical application level, this application provides continuous urine volume dynamic data for patients with chronic kidney disease, heart failure, etc., realizes the automated analysis of urination curves (peak urine flow rate, residual urine volume, etc.), and assists in the early screening and efficacy evaluation of urinary system diseases.

[0176] (3) At the health management level, this application improves user experience through non-invasive monitoring, builds a personal health baseline database, and provides data support for telemedicine and precision health intervention.

[0177] (4) In terms of social benefits, this application can not only alleviate the pressure on medical resources, but also reduce the frequency of medical treatment for special groups through home monitoring, while protecting the privacy and dignity of users.

[0178] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent urine volume monitoring toilet system, comprising a toilet seat (1) and a cavity (2) arranged in the toilet seat (1), characterized in that: A toilet pad structure (3) is also provided on the toilet seat (1), and the toilet pad structure (3) includes a seat cushion (31). Pressure sensors (34) arranged in an array are symmetrically provided on both sides of the seat cushion (31). The pressure sensors (34) are connected to a main control module (32) and a wireless transmission module (33). The main control module (32) and the wireless transmission module (33) are both provided in the seat cushion (31). The wireless transmission module (33) is connected to a cloud server or a mobile terminal APP. An asymmetric spiral guide groove (4) is also provided on the inner wall of the cavity (2). The spiral guide groove (4) is provided with multiple groups of flow sensors (5), and a temperature sensor (6) is also provided in the spiral guide groove (4). The output ends of the flow sensor (5) and the temperature sensor (6) are both connected to the input ends of the main control module (32) and the wireless transmission module (33). A plurality of groups of flow sensors (5) and array-arranged pressure sensors (34) and temperature sensors (6) form a multimodal sensor array, and a novel urine volume calculation model based on fluid dynamics characteristics is established through the multimodal sensor array.

2. The intelligent urine volume monitoring toilet system according to claim 1, characterized in that: The main control module (32) is responsible for signal processing, algorithm operation and communication control of the pressure sensor (34), the flow sensor (5) and the temperature sensor (6).

3. The intelligent urine volume monitoring toilet system and monitoring method according to claim 1, characterized in that: A power module (35) is also provided in the seat cushion (31), and the power module (35) is used to supply power to the components of the system.

4. A method for monitoring a smart urine volume monitoring toilet according to any one of claims 1 to 3, characterized in that: The steps include: S1. When the user touches the seat cushion (31), the built-in array pressure sensor (34) first detects the pressure distribution change, automatically activates the system and determines the effective measurement area; S2, the spiral guide groove (4) guides the urine, and the flow sensor (5) monitors the urine flow rate and flow characteristics in real time; S3, combining the environmental data collected by the temperature sensor (6), and eliminating the measurement error caused by the change of liquid viscosity through the embedded temperature compensation algorithm by the main control module (32); S4, the main control module (32) dynamically calculates the cumulative urine volume based on the fluid dynamics model and calibration parameters, and identifies the start and end time of urination through a sliding window algorithm; S5, the processed data is encrypted and transmitted to the cloud server or mobile terminal APP by the wireless transmission module (33); S6, and present it in the form of visual charts (such as urine volume curve, daily average trend); support abnormal value warning and health data synchronization to the medical platform.

5. The method for monitoring a smart urine volume monitoring toilet according to claim 4, characterized in that: The temperature compensation algorithm in S3 is specifically to establish a compensation model based on heat transfer: ΔV = k(αΔT + βΔT2), where α = 0.03% / °C, β = 0.0015% / °C2; Where, ΔV: represents the change in the original physical quantity V that needs to be compensated for due to temperature changes; k: is a proportional coefficient; α and β: are coefficients related to temperature, and ΔT: represents the change in temperature.

6. The method for monitoring a smart urine volume monitoring toilet according to claim 4, characterized in that: The fluid dynamics model in S4 is specifically: by measuring the Doppler frequency shift expression when urine flows: Δf=2f·v·cosθ / c Where Δf is the frequency difference between the received and transmitted ultrasound waves, in Hz; f is the ultrasound emission frequency, in Hz; v is the urine flow velocity, in m / s; c is the propagation velocity of ultrasound waves in urine, in m / s; and θ is the angle between the ultrasound beam and the flow direction, in radians / degrees.

7. The method for monitoring a smart urine volume monitoring toilet according to claim 4, characterized in that: The dynamic calculation of the cumulative urine volume in S4 is specifically as follows: A dynamic calibration equation was established based on the urine volume calculation model; The urine volume calculation model is: Where: v i represents the flow rate of the i-th channel; k i : is the coefficient corresponding to the i-th channel measurement unit or time period; A i represents the effective cross-sectional area of the i-th channel; α is the temperature compensation coefficient; T is the current temperature, T0 is the reference temperature; Δt: the measurement time interval, the unit is time. The dynamic calibration equation is: Where: γ: viscosity-temperature coefficient; α i : is a coefficient related to the i-th item; v i (t) is the flow rate of urine at the i-th position at time t; dA i is the area element corresponding to the i-th position; T is the current temperature, T0 is the reference temperature; β is a coefficient, dA i / dt represents the rate of change of the area of the ith position over time.

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