Self-adaptive wearing and monitoring method of intelligent drainage monitoring system

Abstract

The invention discloses a self-adaptive wearing and monitoring method of an intelligent drainage monitoring system, and relates to the technical field of medical intelligent wearing, and the method comprises the steps: collecting the drainage part data of a patient through multi-modal biological characteristics, optimizing the wearing parameters through a particle swarm algorithm, and driving a shape memory polymer to adjust the equipment fitting degree, biochemical indexes, pressure and flow of drainage liquid are detected in real time through a micro-fluidic chip and various sensors, intelligent early warning of abnormity is achieved in combination with a self-adaptive neural network, parameters such as drainage negative pressure are automatically adjusted according to physiological signals of a patient, risks are predicted through a digital twinborn model, and a maintenance scheme is generated. According to the intelligent drainage monitoring system, self-adaptive wearing is achieved, and the comfort level of a patient is improved; real-time accurate monitoring and intelligent early warning are achieved, and medical safety is guaranteed; drainage parameters are automatically adjusted according to patient states, and treatment efficiency is improved; the intelligent dredging and anti-infection functions are achieved, the medical risk and cost are reduced, and the clinical application value is high.

Description

Technical Field

[0001] This invention relates to the field of medical smart wearable technology, and in particular to an adaptive wearable and monitoring method for a smart drainage monitoring system. Background Technology

[0002] In clinical medicine, drainage is a common and important treatment method, widely used to treat various conditions such as postoperative effusion, abscess, and pleural effusion. Traditional drainage devices rely heavily on the experience of medical staff for operation and monitoring, which has several shortcomings. For example, in terms of wearability, existing drainage devices typically employ a fixed structure design, making it difficult to adapt to individual patient differences and physiological conditions. This can lead to patient discomfort and even complications such as skin damage. Furthermore, wearing a drainage device in the same position for extended periods can further exacerbate patient discomfort due to concentrated local pressure.

[0003] In monitoring the drainage process, traditional methods mainly rely on periodic manual inspection and analysis, which cannot achieve real-time and continuous monitoring. This makes it difficult for medical staff to detect abnormalities in drainage, such as blockage or infection, in a timely manner, potentially delaying treatment and affecting the patient's recovery process. At the same time, traditional monitoring methods also suffer from problems such as detection lag and low accuracy in detecting changes in the biochemical indicators of drainage fluid, failing to provide timely and accurate information for clinical treatment.

[0004] Furthermore, existing drainage systems lack intelligent adjustment capabilities when dealing with complex conditions and individual patient differences. Different patients have varying drainage needs and physiological responses, and traditional systems cannot automatically adjust drainage parameters, such as negative pressure and flushing frequency, according to the patient's specific situation. This limits treatment effectiveness and increases patient suffering and medical risks. Therefore, developing a wearable, real-time monitoring, and intelligently adjustable drainage system has significant clinical implications and practical application value. Summary of the Invention

[0005] This invention proposes an adaptive wearable and monitoring method for an intelligent traffic diversion monitoring system to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] An adaptive wearable and monitoring method for an intelligent lead generation monitoring system includes the following steps:

[0008] The adaptive wearable adjustment steps for multimodal biometric fusion are as follows: Three-dimensional point cloud data of the patient's body surface are acquired using binocular structured light scanning technology. The wearable parameter combination is solved using a particle swarm optimization algorithm, as shown in the following formula: F represents the overall comfort score, n represents the number of pressure sensors, and P represents the overall comfort score.i S represents the pressure sensor measurement. i For skin deformation rate, A vent For breathable area, A total The total contact area is represented by α, β, and γ, which are weighting coefficients.

[0009] Real-time biochemical analysis steps integrated with microfluidic chip: A PDMS microfluidic chip with 8 independent detection channels is integrated into the tip of the drainage tube to detect biochemical indicators. Automatic sample introduction and mixing are achieved through a thermoresponsive hydrogel. A multiple linear regression model is constructed to predict the risk of tube blockage. Where R is the pipe blockage risk index, The rate of change of pressure, and These represent the rate of change in protein and lactate dehydrogenase concentrations, respectively.

[0010] The steps of intelligent early warning driven by adaptive neural networks are as follows: Construct a bidirectional LSTM network based on spatiotemporal attention mechanism, adopt attention weight allocation mechanism to automatically focus on abnormal change features, design dynamic threshold update algorithm, calculate standard deviation σ based on the patient's 24-hour baseline data, and set the early warning threshold to mean ± 3σ.

[0011] The steps for optimizing drainage parameters in biofeedback regulation are as follows: Collect patient heart rate variability, skin conductance, and respiratory rate; construct a psychological stress assessment model; and employ a nonlinear decreasing strategy: P(t) = P0·e -λ·t Where P(t) is the negative pressure value, P0 is the initial negative pressure, and λ is the attenuation coefficient;

[0012] Digital twin-driven closed-loop management system: Establish a digital twin model of the traffic diversion system based on finite element analysis. Compare the actual monitoring data with the model predictions every 15 minutes. When the deviation exceeds 5%, trigger the model parameter calibration. Use a genetic algorithm to optimize the model parameters. Each iteration contains 20 candidate solutions. Find the parameter combination through 10 generations of evolution.

[0013] Furthermore, it also includes adaptive acoustic resonance unblocking technology: a piezoelectric ceramic ultrasonic transducer is deployed on the outer wall of the drainage tube, and the resonant frequency point of the pipeline system is found through a frequency sweeping algorithm. When an increase in drainage resistance is detected, ultrasonic waves with a frequency fluctuating within ±5% of the resonant point are automatically generated, and the echo signal is analyzed in real time using short-time Fourier transform.

[0014] Furthermore, it also includes a biodegradable smart coating system: a nanocomposite coating is applied to the inner wall of the drainage tube. The bottom layer is a polylactic acid-glycolic acid copolymer sustained-release layer loaded with antimicrobial peptides, which continues to act for 7 days; the middle layer is a pH-responsive hydrogel; and the outer layer is a super-slippery coating. The surface friction coefficient is reduced to 0.003 by using zwitterionic polymer grafting technology. When the coating detects the formation of bacterial biofilm, it triggers the release of silver ions from the electroactive layer.

[0015] Furthermore, it also includes deep diagnostics through cross-modal data fusion: time series data are transformed into two-dimensional time-frequency graphs, and two-dimensional CNNs are used for feature extraction; at the same time, multi-dimensional scalar analysis is performed on biochemical indicators, and the dimensionality is reduced to 32-dimensional feature vectors through an autoencoder. A feature fusion network is designed, which includes attention mechanisms and residual connections, so that the weight ratio of time series features and scalar features is dynamically adjusted. When signs of infection are detected, the system automatically retrieves the patient's full data for the past 3 days and analyzes the causal relationship between various indicators through graph neural networks.

[0016] Furthermore, it also includes an energy harvesting and wireless power supply system: integrating flexible solar cells onto the surface of wearable devices, combined with triboelectric nanogenerators, to generate 5mW / cm² under conditions of light intensity >200 lux and motion frequency >0.5 Hz. 2 To achieve continuous power output, a dual-frequency rectifier circuit is designed, which simultaneously collects ambient radio frequency energy in the 2.4GHz and 5GHz bands. A three-coil magnetically coupled resonant structure is used to realize wireless power transmission. The system energy management unit adopts an adaptive sleep strategy to achieve autonomous operation for 15 consecutive days.

[0017] Furthermore, it also includes an intelligent pressure gradient control system: five distributed pressure sensors are installed in the drainage tube to monitor the pressure gradient changes in the tube in real time. When the pressure difference between adjacent sensors exceeds 0.5 kPa, the speed of the drainage pump is automatically adjusted to maintain the pressure gradient within the range of 0.1-0.3 kPa / cm. A fluid dynamics model is constructed to predict the flow velocity distribution under different pressure gradients. When the Reynolds number exceeds 2000, the pulse drainage mode is activated.

[0018] Furthermore, it also includes personalized adjustment based on reinforcement learning: a personalized adjustment model is constructed using a deep deterministic policy gradient algorithm. The agent's state space contains 15 dimensions and the action space is 4 dimensions. A hierarchical reward function is designed, with short-term rewards focusing on immediate traffic generation effects and long-term rewards considering complication prevention. Through transfer learning, the model is pre-trained using historical patient data and adjusted for individual patients.

[0019] Furthermore, it also includes an adaptive optics monitoring system: a miniature spectrometer is integrated at the tip of the drainage tube to analyze the components of the drainage fluid through diffuse reflectance spectroscopy, and the characteristic spectra of the substances are analyzed independently to calculate their concentration changes. When an increase in hemoglobin concentration >2g / L is detected, the fluorescence imaging module is triggered to detect the activity of matrix metalloproteinases and construct a parameter evaluation model. When MMP-9 activity >10ng / mL and hemoglobin concentration >5g / L, a warning of bleeding risk is issued.

[0020] Furthermore, it also includes wearable adjustment guided by haptic feedback: a 32×32 array of piezoelectric haptic sensors is deployed inside the wearable device to monitor the contact pressure distribution in real time. When a local pressure exceeding 2 kPa for a duration of >5 minutes is detected, haptic feedback is provided through a vibration motor. A pressure distribution optimization algorithm is designed, taking the area ratio of pressure hotspots as the optimization target. Pressure uniformity is achieved by adjusting the tension of the straps, and a Monte Carlo tree search algorithm is used to find the adjustment scheme.

[0021] Furthermore, it includes a blockchain-driven medical collaboration network: building a consortium blockchain network to connect hospitals, equipment manufacturers, and regulatory nodes; using the PBFT consensus algorithm to achieve a processing capacity of 1,000 transactions per second; protecting patient data privacy through zero-knowledge proof technology; allowing authorized parties to verify data authenticity without disclosing the original data; automatically triggering smart contracts when equipment malfunctions to notify manufacturers for remote diagnosis and maintenance; establishing a medical quality traceability mechanism; and storing key parameters of each diagnosis and treatment operation on the blockchain to form an immutable medical record.

[0022] Compared with existing technologies, the beneficial effects of this invention are:

[0023] The system uses binocular structured light scanning and near-infrared spectroscopy to accurately acquire patient surface features and subcutaneous tissue information, and dynamically adjusts wearing parameters using a particle swarm optimization algorithm. This significantly improves the fit between the wearable device and the patient's body, effectively dispersing contact pressure and reducing the risk of pressure sores and tissue damage. Simultaneously, optimized breathability design and material selection enhance patient comfort during long-term wear, reducing the probability of treatment interruptions due to discomfort.

[0024] This invention possesses real-time biochemical analysis and intelligent early warning capabilities, providing timely and accurate decision-making basis for clinical treatment. Microfluidic chip integration technology enables rapid detection of multiple biochemical indicators in drainage fluid, and cross-validation mechanisms further improve detection accuracy. The adaptive neural network-driven early warning system can analyze monitoring data in real time, promptly detect abnormalities, and issue warnings, saving valuable treatment time for medical staff. This not only improves patient safety but also reduces the workload of medical staff and the rate of misdiagnosis.

[0025] This invention achieves personalized and precise treatment through biofeedback regulation and closed-loop management driven by digital twins. The system automatically adjusts drainage parameters, such as negative pressure and flushing protocols, based on the patient's physiological state, improving drainage efficiency while alleviating patient stress. The digital twin model simulates the drainage process in real time, predicting potential risks and generating pre-maintenance plans, effectively reducing the occurrence of malfunctions such as tube blockage, thus lowering medical costs and patient suffering. Furthermore, the application of adaptive acoustic resonance unblocking technology and a biodegradable intelligent coating system further enhances the reliability and safety of the drainage system, extends equipment lifespan, and reduces infection risks, demonstrating significant clinical application value and social benefits. Attached Figure Description

[0026] Figure 1 This is a schematic block diagram of an adaptive wearable and monitoring method for an intelligent traffic diversion monitoring system proposed in this invention;

[0027] Figure 2 A comparison chart of the accuracy rates of biochemical indicators in SERS detection and cross-validation;

[0028] Figure 3 A pie chart comparing the effectiveness of preventative maintenance using traditional methods and digital twin systems (clinical cases n=100);

[0029] Figure 4 This graph shows the relationship between drainage efficiency and cortisol levels under stress. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0032] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0033] Reference Figures 1 to 4 An adaptive wearable and monitoring method for an intelligent lead generation monitoring system includes the following steps:

[0034] The adaptive wearable adjustment steps involving multimodal biometric fusion are as follows: A binocular structured light scanning module, composed of a Baslerac A1300-30gm industrial camera, is used in conjunction with a self-developed 850nm infrared structured light projector to scan the patient's drainage area. Three-dimensional point cloud data acquisition is completed within one second, achieving a point cloud density of 1000 points per square centimeter and an accuracy of 0.1mm. Simultaneously, a near-infrared spectral analysis module, composed of an OceanInsight NIRQuest512 spectrometer, is used to establish a subcutaneous fat thickness inversion model based on the Lambert-Beer law using five characteristic wavelengths, including 940nm and 1100nm, to obtain subcutaneous fat thickness information.

[0035] Contact pressure was acquired using a FlexiForce A401 thin-film pressure sensor array. This sensor measures 10mm × 10mm, has a range of 0-50kPa, an accuracy of 0.1kPa, and an array density of 4 sensors per square centimeter. Skin deformation rate was measured using PDMS strain gauges attached to the skin. The strain gauge grating length was 2mm, the sensitivity coefficient was 2.0, and it could detect deformation ranging from 0.1% to 50%. Air permeability was measured using a differential pressure method. A 5mm diameter air vent was placed on the surface of the wearable device, and the pressure difference between the two sides was measured using a pressure sensor with a range of 0-10kPa and an accuracy of 0.01kPa. The air permeability was calculated based on the area of ​​the air vent. The optimal combination of wearable parameters was solved using a particle swarm optimization algorithm. Initially, 100 particles were used, each representing a set of wearable parameters, and the algorithm was continuously optimized over 300 iterations. The calculation formula is as follows: Where F is the overall comfort score, n represents the number of pressure sensors, and P iS represents the measurement value of the i-th pressure sensor, reflecting the contact pressure condition; i Skin deformation rate, reflecting the degree of skin deformation; A vent For breathable area, A total The ratio of the total contact area to the total surface area measures breathability. α, β, and γ are weighting coefficients (α+β+γ=1), and adjusting these coefficients can emphasize different comfort dimensions. The purpose of this formula is to comprehensively consider three key factors affecting wearing comfort: contact pressure, skin deformation rate, and breathability. Through mathematical calculation, a quantitative comprehensive comfort score is derived to evaluate the comfort under different combinations of wearing parameters, thereby finding the optimal combination. Shape memory polymer (SMP) is used as the adjustment structure, with a glass transition temperature set at 37℃ and a built-in 1W nickel-chromium alloy heating wire. When heating is triggered, the radius of curvature can be adjusted from 50mm to 15mm within 2 minutes, achieving adaptive wear adjustment.

[0036] The real-time biochemical analysis steps integrated with the microfluidic chip are as follows: A PDMS microfluidic chip with dimensions of 10mm × 10mm × 1mm is fabricated using soft photolithography. The chip contains eight independent detection channels, each 100μm wide and 50μm deep. After fabrication, different biological probes are modified onto the surface of each channel. For example, glucose oxidase is immobilized in a specific channel for glucose detection, while antibody-antigen pairs are used to modify other channels to detect proteins and lactate dehydrogenase. A silicon substrate modified with gold nanoparticles is selected as the SERS detection platform, and a 532nm solid-state laser with a power of 10mW is used as the excitation source. The integration time is set to 100ms, which enables a detection sensitivity of 10mol / L. The microvalve control system is made of thermally responsive hydrogel with a lower critical dissolution temperature of 32℃. The temperature is controlled by a 0.5W heating resistor. When the temperature reaches or exceeds 32℃, the hydrogel undergoes a phase transition, realizing the opening and closing of the valve, thereby precisely controlling the sample and reagent injection volume. The minimum injection volume can reach 5nL.

[0037] Samples and reagents enter the microfluidic chip channel via a microvalve control system, and the SERS detection module detects biochemical indicators such as glucose concentration in the samples. When the SERS detects a glucose concentration decrease rate exceeding 0.5 mm mmol / L / h, the system automatically triggers an interdigitated electrode array located at the chip edge with a spacing of 50 μm. Cyclic voltammetry is then used for electrochemical detection to obtain redox signals of relevant substances, cross-validating the biochemical indicator detection results. This mechanism significantly improves the accuracy of biochemical indicator detection.

[0038] A miniature pressure sensor was selected to monitor the pressure inside the drainage tube in real time, and an ultrasonic Doppler flow meter was used to measure the flow rate. Based on the pressure and flow data, a formula for calculating the risk of tube blockage was applied. The calculation is performed, where R is the pipe blockage risk index. The pressure change rate reflects the change in pressure inside the drainage tube over time. and These represent the rate of change in protein and lactate dehydrogenase concentrations, respectively, indicating the degree to which the concentrations of these two substances in the drainage fluid change over time. This formula comprehensively considers changes in pressure, protein concentration, and lactate dehydrogenase concentration to quantify the risk of tubing blockage, allowing for proactive preventative measures to be taken.

[0039] The intelligent early warning process driven by an adaptive neural network employs an NVIDIA Jetson AGXXavier as the edge computing unit to provide computational support for model operation. This module receives data from 12 sensors, including pressure, flow, and biochemical indicators, with a sampling frequency of 10Hz and a data sequence length of 120 time steps, corresponding to 12 seconds of data, ensuring the ability to capture changes within a short period.

[0040] A bidirectional LSTM network based on a spatiotemporal attention mechanism is implemented. After receiving the sensor data, the input layer of this network is divided into three parallel branches, each processing different types of data. Each branch contains four LSTM hidden layers with 128 neurons per layer, using ReLU as the activation function. The spatiotemporal attention mechanism plays a crucial role here, automatically focusing on abnormal changes by calculating attention weights at different time steps and feature dimensions. For example, in pressure signals, when a 0.3Hz low-frequency fluctuation is detected with an amplitude exceeding the baseline value by 25%, the attention weights for the corresponding time step and pressure feature dimension increase from the initial 0.1 to 0.8, making the network pay more attention to this abnormal feature and improving its ability to detect anomalies. A dynamic threshold update algorithm calculates the mean μ and standard deviation σ every hour based on data from the past 24 hours, setting the warning threshold to μ ± 3σ. This dynamic adjustment adapts to data trends and is more flexible than fixed threshold methods.

[0041] The optimization steps for drainage parameters in biofeedback regulation are as follows: Heart rate variability (HRV) is measured using a heart rate sensor at a sampling frequency of 1000 Hz. Intervals between adjacent normal heartbeats are extracted to calculate indicators such as SDNN (standard deviation, reflecting overall HRV variability) and RMSSD (root mean square of the difference between adjacent RR intervals, primarily reflecting vagal nerve activity). A capacitive sensor is used to measure skin conductance, with a resolution of 0.1 μS, to reflect changes in skin electrical properties. A piezoelectric film respiratory sensor is used to measure respiratory rate, with an accuracy of 0.1 breaths / minute, accurately acquiring the patient's respiratory status. When the HRV low-frequency / high-frequency ratio is greater than 3 and the GSR fluctuation amplitude exceeds 5 μS, the patient is considered to be in a stress state. The low-frequency / high-frequency ratio reflects the balance between the sympathetic and parasympathetic nervous systems of the autonomic nervous system; an increased ratio often indicates enhanced sympathetic nerve activity. Increased GSR fluctuation amplitude suggests drastic changes in skin electrical activity; the combination of these two factors effectively determines the stress state. Once a stress state is detected, drainage parameter adjustment is initiated. The drainage pump automatically adjusts the negative pressure value, based on the formula P(t)=P0·e -λ·t A nonlinear decreasing adjustment is performed. Here, P(t) represents the negative pressure value at time t; P0 is the initial negative pressure value, initially set to -15 kPa; λ is the adjustment coefficient, with a value of 0.05 / min; and t is time. This formula uses an exponential decay method to gradually reduce the negative pressure value over time, decreasing it from -15 kPa to -5 kPa within 15 minutes, thus avoiding excessive negative pressure that could cause discomfort to the patient.

[0042] Simultaneously, a 2 / 100Hz sparse-dense wave is applied to the patient through a 5mm diameter auricular acupoint electrode, with the current intensity automatically adjusted from 1-3mA according to the patient's tolerance. The sparse-dense wave has the effects of promoting blood circulation and regulating nerve function, and through auricular acupoint stimulation, it helps to relieve the patient's stress state.

[0043] A digital twin-driven closed-loop management system: A digital twin model was built using COMSOL Multiphysics software, encompassing three sub-models. The fluid dynamics model adopted the k-ε turbulence model, setting the viscosity of the guiding fluid to 1-10 mPa·s and the density to 1000-1050 kg / m³. 3 The model is used to simulate the flow state of the drainage fluid; the tissue mechanics model uses a hyperelastic material model to simulate the deformation of tissue during drainage; the material transport model considers the convection-diffusion equation and sets corresponding diffusion coefficients for different substances, such as 0.6 × 10⁻⁶ for glucose. -9 m 2 / s is used to describe the transport and diffusion of substances during the drainage process.

[0044] Every 15 minutes, the system compares the actual monitored data, such as pressure, flow rate, and biochemical indicators, with the predicted values ​​from the digital twin model. When the deviation exceeds 5%, a genetic algorithm is triggered to calibrate the model parameters. The initial population of the genetic algorithm is set to 20 individuals, each containing 10 model parameters such as pipe wall roughness and tissue elastic modulus. After 10 generations of evolution, using operations such as roulette wheel selection, single-point crossover, and mutation, the optimal combination of parameters is found in the parameter space, making the model's predicted values ​​more closely match the actual monitored data.

[0045] When the digital twin model predicts a blockage probability exceeding 80%, the system automatically generates a pre-maintenance plan. The flushing fluid selection depends on the drainage fluid composition. If the protein concentration in the drainage fluid exceeds 10 g / L, urokinase solution is used, leveraging its thrombolytic properties to prevent blockage. If the protein concentration is within the acceptable range, physiological saline is used. The flushing pressure is adjusted according to the pipe diameter, ranging from 3-8 kPa. Smaller diameter pipes require lower pressure, while larger diameter pipes require relatively higher pressure. The flushing time is set according to the degree of blockage, ranging from 5-15 minutes. The more severe the blockage, the longer the flushing time, effectively preventing blockage and ensuring the normal operation of the drainage system.

[0046] This invention also includes an adaptive acoustic resonance unblocking technology: a piezoelectric ceramic ultrasonic transducer, model V303-SD10, is deployed on the outer wall of the drainage tube. The transducer measures 10mm × 5mm, operates in the frequency range of 20-40kHz, and has a maximum output sound intensity of 2W / cm². 2 Using a frequency sweep algorithm, starting from 20kHz, a sinusoidal signal lasting 100ms was emitted into the drainage tube in increments of 0.1kHz, while simultaneously acquiring the echo signal. Short-time Fourier transform was used to perform spectral analysis on the echo signal. When the reflection coefficient at a certain frequency point was found to be more than 3dB higher than that of adjacent frequencies, that frequency point was determined as the resonant frequency point. This is because at the resonant frequency, the interaction between the sound wave and the blockage or drainage tube produces special reflection characteristics; these characteristics are detected to determine the resonant frequency.

[0047] The system continuously monitors the pressure difference before and after the drainage tube. When the increased drainage resistance exceeds the baseline value by more than 20%, it determines that the drainage tube is blocked. At this time, the system automatically generates ultrasonic waves with a frequency fluctuating within ±5% of the resonance point, and the sound intensity ranges from 0.5-2 W / cm² depending on the degree of blockage. 2Dynamic adjustment. The more severe the blockage, the greater the required sound intensity to enhance its effectiveness against the obstruction. During the unblocking process, the echo spectrum characteristics are continuously monitored. When the amplitude attenuation at 28kHz exceeds 3dB, it indicates that the blockage has been effectively removed, and the blockage is considered cleared. This is because the echo spectrum changes accordingly after the blockage is broken or loosened; by monitoring the amplitude attenuation at specific frequencies, the clearance of the blockage can be determined.

[0048] This invention also includes a biodegradable smart coating system: a drainage tube made of polyurethane is used, and the coating is prepared by dip-coating. PLGA with a molecular weight of 50 kDa and an antimicrobial peptide are dissolved in dichloromethane at a mass ratio of 10:1 to prepare a 10% solution. The drainage tube is immersed in this solution, fully wetted, and then removed and dried at room temperature to form a 500 nm thick underlayer coating. The antimicrobial peptide loading in this coating is 10 mg / cm³. 2 Under physiological conditions, it can reach 0.5 μg / cm 2 It is released continuously at a rate of / h for 7 days, inhibiting bacterial growth by continuously releasing antimicrobial peptides.

[0049] A middle-layer hydrogel was prepared by free radical polymerization using acrylic acid and N-isopropylacrylamide in a molar ratio of 3:7, with 0.5% N,N-methylenebisacrylamide added as a crosslinking agent. The resulting hydrogel exhibited a swelling rate of up to 200% in an alkaline environment with pH > 7.4, effectively capturing particles larger than 5 μm in diameter and preventing particle accumulation and blockage within the drainage tube.

[0050] An outer super-lubricating coating was prepared by grafting zwitterionic polymers onto the surface of the middle hydrogel, with the grafting density controlled at 1.5 mmol / m². 2 This process ensures the contact angle of the coating surface is less than 10°, reducing the coefficient of friction to 0.003 and decreasing the resistance to fluid flow within the pipe. Simultaneously, an ion-selective electrode is installed within the coating to monitor the surface potential in real time. When the potential change exceeds 15mV, bacterial biofilm formation is detected. At this point, the electroactive layer in the coating is triggered to oxidize and release silver ions. The silver ion concentration is automatically adjusted between 10-50 ppm according to the degree of contamination, utilizing the antibacterial properties of silver ions to inhibit biofilm growth.

[0051] This invention also includes deep diagnostics based on cross-modal data fusion: for time-series data such as pressure and flow, it is transformed into a two-dimensional time-frequency graph, and relevant features are extracted using the powerful image feature extraction capabilities of a two-dimensional convolutional neural network. The two-dimensional CNN captures the changing patterns of data at different times and frequencies by sliding convolutional kernels on the time-frequency graph. Simultaneously, for multi-dimensional scalar data such as biochemical indicators, an autoencoder is used for dimensionality reduction. The autoencoder learns the latent feature representation of the data by encoding and decoding high-dimensional data, reducing the biochemical indicators to 32-dimensional feature vectors, removing redundant information while retaining key features.

[0052] We design a feature fusion network that integrates an attention mechanism and residual connections. The attention mechanism dynamically adjusts the weight ratio of temporal and scalar features according to different task requirements, allowing the network to focus more on important features. For example, in some cases, temporal features such as stress and traffic flow may be more critical for diagnosis, and the attention mechanism will increase their weight; while in other cases, scalar features of biochemical indicators are more diagnostically valuable, and their weights will be adjusted accordingly. Residual connections help alleviate the vanishing gradient problem in deep network training, ensuring that the network can learn more complex feature mapping relationships, making the fused features more discriminative.

[0053] When the system detects potential signs of infection, it automatically retrieves the patient's full data from the past three days. This data is then analyzed using a graph neural network, which treats different indicators as nodes in a graph and the relationships between indicators as edges. This method helps to uncover the causal relationships between the indicators.

[0054] This invention also includes an energy harvesting and wireless power supply system: In the energy harvesting stage, a flexible solar cell is integrated on the surface of the wearable device, which functions when the light intensity is >200 lux. Simultaneously, a triboelectric nanogenerator is used; when the motion frequency is >0.5 Hz, the two can work together to generate 5 mW / cm². 2 Continuous power output. Furthermore, a dual-frequency rectifier circuit is designed to collect ambient radio frequency energy in the 2.4GHz and 5GHz bands. For power transmission, a three-coil magnetically coupled resonant structure is employed to achieve wireless power transfer. Carefully designed coil parameters enable simultaneous power supply to three implanted sensor nodes.

[0055] In terms of energy management, the system's energy management unit adopts an adaptive sleep strategy. It monitors the system status in real time, reducing the power consumption of non-critical modules or even putting them into sleep mode when the equipment is under low load or experiencing no data changes. Based on the equipment's operational needs and energy reserves, it intelligently adjusts the operating mode and power consumption level of each module, reducing overall power consumption and ultimately achieving 15 days of continuous autonomous operation.

[0056] This invention also includes an intelligent pressure gradient control system: the system first evenly distributes five pressure sensors at 2cm intervals inside the drainage tube to monitor changes in the pressure gradient within the tube in real time and accurately. When the pressure difference between adjacent sensors exceeds 0.5kPa, the system automatically adjusts the speed of the drainage pump. Through a closed-loop control algorithm, based on the difference between the pressure difference and the target pressure gradient range, a suitable speed adjustment value is calculated to ensure that the pressure gradient is maintained within a reasonable range.

[0057] Simultaneously, a fluid dynamics model based on the Navier-Stokes equations was constructed, incorporating drainage tube geometry and fluid properties to predict velocity distribution under different pressure gradients. When the model calculated a Reynolds number exceeding 2000, indicating high turbulence, the system activated a pulsed drainage mode. By controlling the start and stop of the drainage pump, setting the frequency between 1-3 Hz and the duty cycle between 30%-70%, the fluid flow state was altered, reducing damage to blood cells caused by turbulence. Furthermore, the system can measure the drainage fluid viscosity in real time and automatically adjust operating parameters such as the drainage pump speed and pulse mode parameters based on viscosity changes.

[0058] This invention also includes personalized adjustment based on reinforcement learning: building the DDPG algorithm framework. The agent's state space is set with 15 dimensions, comprehensively incorporating physiological and biochemical indicators such as drainage pressure, flow rate, glucose concentration, and white blood cell count. The action space is set with 4 dimensions, covering negative pressure adjustment, flushing frequency adjustment, drug dosage control, and temperature regulation. Both the Actor network and the Critic network adopt a 3-layer fully connected structure, with 256, 128, and 64 neurons in each layer, respectively, and the ReLU function is selected as the activation function.

[0059] The short-term reward function focuses on the immediate traffic generation effect, and calculates the reward based on the deviation between the actual traffic generation and the target traffic generation, and the deviation between the current pressure and the ideal pressure. The smaller the deviation, the higher the reward, prompting the system to quickly optimize the traffic generation status. The long-term reward function evaluates factors such as changes in inflammatory markers such as CRP and IL-6, and the degree of blood cell damage, and gives negative rewards to behaviors that may cause complications, incentivizing the system to maintain the patient's long-term health status.

[0060] First, the model is pre-trained using a large amount of historical patient data to enable it to master general drainage regulation patterns. When targeting new patients, the pre-trained model is fine-tuned based on their individual physiological characteristics, disease conditions, and other data to quickly generate personalized regulation strategies that are suitable for the patient, thereby achieving precise and efficient adjustment of drainage parameters.

[0061] This invention also includes an adaptive optics monitoring system: a miniature spectrometer is integrated at the tip of the drainage tube, with a wavelength detection range of 400-900 nm and a resolution of 2 nm, capable of acquiring diffuse reflectance spectral data of the drainage fluid in real time. After acquiring the spectral data, an independent component analysis algorithm is used to separate the characteristic spectra of each substance based on the absorption characteristics of substances such as hemoglobin and bilirubin at different wavelengths. A concentration calculation model is then established using Lambert-Beer's law to accurately calculate the concentration changes of these substances.

[0062] When spectral analysis detects a sudden increase in hemoglobin concentration exceeding 2 g / L, the system immediately triggers the fluorescence imaging module. This module uses 488 nm as the excitation wavelength to detect the activity of matrix metalloproteinases (MMPs). By analyzing the correlation between fluorescence signal intensity and MMP activity, real-time activity data of MMPs are obtained.

[0063] Finally, a multi-parameter assessment model was constructed. Hemoglobin concentration and MMP-9 activity were used as key assessment indicators. When MMP-9 activity exceeded 10 ng / mL and hemoglobin concentration was greater than 5 g / L, the system determined a bleeding risk and issued an early warning. This model, by integrating multiple parameters, effectively improved the accuracy and timeliness of bleeding risk warnings.

[0064] This invention also includes tactile feedback-guided wearable adjustment: a 32×32 array of piezoelectric tactile sensors is uniformly deployed inside the wearable device, with a pressure resolution of 0.05 kPa, capable of accurately capturing the pressure distribution details in the area of ​​contact between the device and the human body, and collecting data in real time at a frequency of 10 times per second. When the sensor detects local pressure exceeding 2 kPa for a duration greater than 5 minutes, the system immediately activates a vibration motor located at the edge of the wearable device. The vibration frequency of the motor can be adjusted within the range of 100-300 Hz, providing tactile feedback to the user through vibrations of different frequencies, alerting them to abnormal pressure.

[0065] Subsequently, the system initiates a pressure distribution optimization algorithm. This algorithm uses the area ratio of pressure hotspots as the core optimization objective and achieves pressure uniformity by adjusting the strap tension. Specifically, a Monte Carlo tree search algorithm is used to find the optimal adjustment scheme. This algorithm first constructs a search tree containing different strap tension adjustment strategies, with each node representing an adjustment state. The merits of each node are evaluated through a large number of simulated random trials, with each decision time controlled within 0.5 seconds.

[0066] This invention also includes a blockchain-driven medical collaboration network: a consortium blockchain is built using the Hyperledger Fabric framework, connecting nodes such as hospital information systems, equipment manufacturer IoT platforms, and drug regulatory department systems, with 31 consensus nodes configured in the network. The consensus mechanism uses the PBFT algorithm, setting a message latency limit of 500ms and a block generation time of 2 seconds, achieving a processing capacity of 1000 transactions per second.

[0067] Sensitive information such as patient physiological data and medical records are encrypted using AES-256 and stored in the distributed file system IPFS. The blockchain only stores the data hash values. Zero-knowledge proof technology is used for data verification. The verifier generates a proof circuit to verify the authenticity of the data without revealing the original data. For example, when verifying the compliance of equipment parameters, the proof circuit can verify whether the parameters are within a preset threshold range without exposing the specific parameter values.

[0068] Develop a smart contract based on the Solidity language, including modules for equipment status monitoring, anomaly alarms, and maintenance process management. When the equipment's pressure sensor detects values ​​exceeding a threshold three consecutive times, the smart contract automatically triggers an alarm process, sending a notification to the equipment manufacturer containing the equipment ID, anomaly timestamp, and historical operational data hashes. Upon receiving the notification, the manufacturer's node can decrypt it using its private key to view detailed fault information and respond within two hours. Design a data on-chain protocol, formatting key parameters for each medical procedure according to the ISO / IEEE 11073 standard, adding timestamps, and generating a root hash value using a Merkle tree structure for on-chain storage. Regulatory authorities can query medical records within a specific time period through the smart contract, using Merkle proofs to quickly verify data integrity and create an immutable audit trail for medical procedures.

[0069] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An adaptive wearable and monitoring method for an intelligent traffic diversion monitoring system, characterized in that, Includes the following steps: The adaptive wearable adjustment steps for multimodal biometric fusion are as follows: Three-dimensional point cloud data of the patient's body surface are acquired using binocular structured light scanning technology. The wearable parameter combination is solved using a particle swarm optimization algorithm, as shown in the following formula: F represents the overall comfort score, n represents the number of pressure sensors, and P represents the overall comfort score. i S represents the pressure sensor measurement. i For skin deformation rate, A vent For breathable area, A total The total contact area is represented by α, β, and γ, which are weighting coefficients. Real-time biochemical analysis steps integrated with microfluidic chip: A PDMS microfluidic chip with 8 independent detection channels is integrated into the tip of the drainage tube to detect biochemical indicators. Automatic sample introduction and mixing are achieved through a thermoresponsive hydrogel. A multiple linear regression model is constructed to predict the risk of tube blockage. Where R is the pipe blockage risk index, The rate of change of pressure, and These represent the rate of change in protein and lactate dehydrogenase concentrations, respectively. The steps of intelligent early warning driven by adaptive neural networks are as follows: Construct a bidirectional LSTM network based on spatiotemporal attention mechanism, adopt attention weight allocation mechanism to automatically focus on abnormal change features, design dynamic threshold update algorithm, calculate standard deviation σ based on the patient's 24-hour baseline data, and set the early warning threshold to mean ± 3σ. The steps for optimizing drainage parameters in biofeedback regulation are as follows: Collect patient heart rate variability, skin conductance, and respiratory rate; construct a psychological stress assessment model; and employ a nonlinear decreasing strategy: P(t) = P0·e -λ·t Where P(t) is the negative pressure value, P0 is the initial negative pressure, and λ is the attenuation coefficient; Digital twin-driven closed-loop management system: Establish a digital twin model of the traffic diversion system based on finite element analysis. Compare the actual monitoring data with the model predictions every 15 minutes. When the deviation exceeds 5%, trigger the model parameter calibration. Use a genetic algorithm to optimize the model parameters. Each iteration contains 20 candidate solutions. Find the parameter combination through 10 generations of evolution.

2. The adaptive wearable and monitoring method of the intelligent diversion monitoring system according to claim 1, characterized in that, It also includes adaptive acoustic resonance unblocking technology: a piezoelectric ceramic ultrasonic transducer is deployed on the outer wall of the drainage tube, and the resonant frequency point of the pipeline system is found by a frequency sweeping algorithm. When an increase in drainage resistance is detected, ultrasonic waves with a frequency fluctuating within ±5% of the resonant point are automatically generated, and the echo signal is analyzed in real time using short-time Fourier transform.

3. The adaptive wearable and monitoring method of the intelligent diversion monitoring system according to claim 1, characterized in that, It also includes a biodegradable smart coating system: a nanocomposite coating is applied to the inner wall of the drainage tube. The bottom layer is a polylactic acid-glycolic acid copolymer sustained-release layer loaded with antimicrobial peptides, which continues to act for 7 days; the middle layer is a pH-responsive hydrogel; and the outer layer is a super-slippery coating. The surface friction coefficient is reduced to 0.003 by using zwitterionic polymer grafting technology. When the coating detects the formation of bacterial biofilm, it triggers the release of silver ions from the electroactive layer.

4. The adaptive wearable and monitoring method of the intelligent diversion monitoring system according to claim 1, characterized in that, It also includes deep diagnostics through cross-modal data fusion: time series data are transformed into two-dimensional time-frequency graphs, and two-dimensional CNNs are used for feature extraction; at the same time, multi-dimensional scalar analysis is performed on biochemical indicators, and the dimensionality is reduced to 32-dimensional feature vectors through an autoencoder. A feature fusion network is designed, which includes attention mechanisms and residual connections, so that the weight ratio of time series features and scalar features is dynamically adjusted. When signs of infection are detected, the system automatically retrieves the patient's full data for the past 3 days and analyzes the causal relationship between various indicators through graph neural networks.

5. The adaptive wearable and monitoring method of the intelligent diversion monitoring system according to claim 1, characterized in that, It also includes an energy harvesting and wireless power supply system: integrating flexible solar cells onto the surface of wearable devices, combined with triboelectric nanogenerators, to generate 5mW / cm² under conditions of light intensity >200 lux and motion frequency >0.5 Hz. 2 To achieve continuous power output, a dual-frequency rectifier circuit is designed, which simultaneously collects ambient radio frequency energy in the 2.4GHz and 5GHz bands. A three-coil magnetically coupled resonant structure is used to realize wireless power transmission. The system energy management unit adopts an adaptive sleep strategy to achieve autonomous operation for 15 consecutive days.

6. The adaptive wearable and monitoring method of the intelligent diversion monitoring system according to claim 1, characterized in that, It also includes an intelligent pressure gradient control system: five distributed pressure sensors are installed in the drainage tube to monitor the pressure gradient changes in the tube in real time. When the pressure difference between adjacent sensors exceeds 0.5 kPa, the speed of the drainage pump is automatically adjusted to maintain the pressure gradient within the range of 0.1-0.3 kPa / cm. A fluid dynamics model is constructed to predict the flow velocity distribution under different pressure gradients. When the Reynolds number exceeds 2000, the pulse drainage mode is activated.

7. The adaptive wearable and monitoring method of the intelligent diversion monitoring system according to claim 1, characterized in that, It also includes personalized adjustment based on reinforcement learning: a personalized adjustment model is constructed using a deep deterministic policy gradient algorithm. The agent's state space contains 15 dimensions and the action space is 4 dimensions. A hierarchical reward function is designed, with short-term rewards focusing on immediate traffic generation effects and long-term rewards considering complication prevention. Through transfer learning, the model is pre-trained using historical patient data and adjusted for individual patients.

8. The adaptive wearable and monitoring method of the intelligent diversion monitoring system according to claim 1, characterized in that, It also includes an adaptive optics monitoring system: a miniature spectrometer is integrated at the tip of the drainage tube to analyze the components of the drainage fluid through diffuse reflectance spectroscopy, and the characteristic spectra of the substances are analyzed independently to calculate their concentration changes. When an increase in hemoglobin concentration >2g / L is detected, the fluorescence imaging module is triggered to detect the activity of matrix metalloproteinases and construct a parameter evaluation model. When MMP-9 activity >10ng / mL and hemoglobin concentration >5g / L, a warning of bleeding risk is issued.

9. The adaptive wearable and monitoring method of the intelligent diversion monitoring system according to claim 1, characterized in that, It also includes wearable adjustment guided by haptic feedback: a 32×32 array of piezoelectric haptic sensors is deployed inside the wearable device to monitor the contact pressure distribution in real time. When the local pressure exceeds 2 kPa for more than 5 minutes, haptic feedback is provided through a vibration motor. A pressure distribution optimization algorithm is designed, taking the area ratio of pressure hot spots as the optimization target. Pressure uniformity is achieved by adjusting the tension of the straps, and the Monte Carlo tree search algorithm is used to find the adjustment scheme.

10. The adaptive wearable and monitoring method of the intelligent diversion monitoring system according to claim 1, characterized in that, It also includes a blockchain-driven medical collaboration network: building a consortium blockchain network to connect hospitals, equipment manufacturers, and regulatory nodes, using the PBFT consensus algorithm to achieve a processing capacity of 1,000 transactions per second, protecting patient data privacy through zero-knowledge proof technology, allowing authorized parties to verify data authenticity without disclosing the original data, automatically triggering smart contracts when equipment malfunctions to notify manufacturers for remote diagnosis and maintenance when equipment malfunctions, establishing a medical quality traceability mechanism, storing key parameters of each diagnosis and treatment operation on the blockchain to form an immutable medical record.

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