Pediatric nephropathy patient peritoneal dialysis anti-clogging high-circulation nursing device

Abstract

The invention discloses a peritoneal dialysis anti-blocking high-circulation nursing device for pediatric nephropathy patients, and belongs to the technical field of medical nursing. Comprising the following modules: a multi-mode sensing module for collecting pressure, flow velocity, turbidity, vibration spectrum and temperature data in a catheter in real time; the signal optimization processing module highlights weak features of potential blockage and improves data quality; the feature fusion analysis module is used for comprehensively evaluating the state of the catheter; the AI prediction analysis module is used for predicting the occurrence probability, the position and the possible time window of blockage; the risk early warning and intervention module is used for sending out graded early warning signals according to the prediction result and generating personalized anti-blocking intervention suggestions; the micro-fluidic execution module inhibits formation of initial sediments through a micro-pulse technology and dynamic fluid mechanics adjustment, and effectively reduces the risk of blockage; and the remote medical support module provides visual data visualization and intervention suggestions, supports remote monitoring and emergency intervention, and is seamlessly integrated with a hospital system while ensuring data security.

Description

Technical Field

[0001] This application relates to the technical field of medical care, and more specifically, to a high-flow nursing device for preventing blockage in peritoneal dialysis of pediatric nephropathy patients. Background Art

[0002] Peritoneal dialysis (PD) is an important renal replacement therapy, especially suitable for pediatric nephropathy patients. Due to its relatively mild treatment method and high quality of life, peritoneal dialysis is increasingly widely used in pediatric patients with chronic kidney disease (CKD). However, the problem of catheter blockage during dialysis has always been one of the key challenges affecting the treatment effect and patient safety.

[0003] Currently, the main causes of peritoneal dialysis catheter blockage include: protein deposition, biofilm formation, fibrinoid substance blockage, peritoneal infection, and catheter displacement, etc. Due to the special physiological characteristics of children, such as small abdominal cavity volume, imperfect immune function, different metabolic characteristics, etc., the risk of peritoneal dialysis catheter blockage is higher and the treatment difficulty is greater. Once blockage occurs, it will not only affect the dialysis efficiency, resulting in insufficient clearance of metabolic wastes and toxins, but may also cause serious complications such as infection and peritonitis, and even require emergency catheter replacement, increasing medical costs and patient pain.

[0004] Currently, the countermeasures for peritoneal dialysis catheter blockage mainly include: flushing the catheter, using anticoagulants or solvents to dissolve the blockage, and even surgical intervention in severe cases. However, these methods mostly belong to ex post facto remedies, with lag, unstable effects, and certain medical risks. Some studies have tried to reduce the blockage risk by optimizing the composition of dialysis fluid, adjusting catheter design, etc., but it is still difficult to achieve real-time monitoring and intelligent prevention fundamentally. In addition, the existing peritoneal dialysis monitoring technologies mainly rely on single sensing parameters, such as flow or pressure monitoring, and it is difficult to comprehensively and accurately predict the blockage risk, resulting in limited early warning effects.

[0005] With the development of artificial intelligence, big data analysis, and microfluidic technology, the application of intelligent monitoring and intervention systems in the medical field has gradually attracted attention. Innovative solutions integrating multi-modal sensing technology, intelligent prediction analysis, and remote medical support are expected to improve the patency of peritoneal dialysis catheters, reduce the occurrence of blockages, and improve the safety and stability of treatment.

[0006] In summary, how to develop a nursing device that can real-time monitor the status of peritoneal dialysis catheters, accurately predict the blockage risk, and provide effective intervention has become a technical problem that urgently needs to be solved. Summary of the Invention

[0007] To overcome a series of defects existing in the prior art, the purpose of this application is to provide a high-flow nursing device for preventing blockage in peritoneal dialysis for pediatric nephropathy patients in view of the above problems, including the following modules:

[0008] The multi-modal sensing module integrates a variety of micro sensors to collect data on catheter pressure, flow rate, turbidity, vibration spectrum and temperature in real time, and constructs a comprehensive data basis for blockage prediction;

[0009] The signal optimization and processing module performs noise reduction, calibration and enhancement processing on the collected data, effectively filters out interference signals, highlights the weak features of potential blockages, and improves the data quality;

[0010] The feature fusion and analysis module extracts key features from the optimized data and performs intelligent fusion to form a comprehensive assessment of the catheter state;

[0011] The AI prediction and analysis module integrates the comprehensive assessment data of the individual characteristics of the child and the catheter state to predict the probability, location and possible time window of blockage;

[0012] The risk warning and intervention module issues graded warning signals according to the prediction results, generates personalized anti-blockage intervention suggestions, and dynamically adjusts the warning threshold to balance sensitivity and specificity;

[0013] The microfluidic execution module inhibits the formation of initial sediments through micro-pulse technology and dynamic fluid mechanics regulation, effectively reducing the risk of blockage;

[0014] The remote medical support module provides intuitive data visualization and intervention suggestions, supports remote monitoring and emergency intervention, and ensures data security while being seamlessly integrated with the hospital system.

[0015] Furthermore, the multi-modal sensing module includes the following components:

[0016] The pressure sensing unit accurately captures the real-time pressure changes in the catheter through a high-precision micro pressure sensor, providing basic data for early blockage detection;

[0017] The hydrodynamic unit dynamically monitors the flow characteristics of peritoneal dialysis fluid using a micro flow meter and a flow rate sensor, and real-time tracks the minute changes in the liquid channel in the catheter;

[0018] The optical detection unit uses micro spectroscopy and image sensing technology to quickly analyze the turbidity, particle distribution and sediment characteristics of the liquid in the catheter, and identify potential blockage signals;

[0019] The vibration spectrum unit integrates a high-sensitivity micro vibration sensor to capture the microscopic vibration modes of the catheter wall and the fluid, revealing abnormalities in the internal structure of the catheter and fluid dynamics;

[0020] A temperature mapping unit deploys a precision temperature sensing array to monitor the temperature gradient and local thermodynamic changes in different regions of the catheter in real time, reflecting potential biofilm formation;

[0021] A data integration unit constructs a high-dimensional and multi-scale catheter status perception matrix, providing a comprehensive and accurate data basis for other modules.

[0022] Furthermore, the signal optimization and processing module includes the following components:

[0023] A multi-dimensional noise reduction unit applies wavelet transform and adaptive filtering algorithms to effectively suppress random noise from physiology, electronics, and the environment, retaining the key features of the signal;

[0024] A signal correction unit eliminates individual sensor differences and measurement biases through polynomial fitting and reference benchmark calibration techniques, ensuring the consistency and accuracy of cross-sensor data;

[0025] A spectrum enhancement unit uses frequency domain enhancement algorithms and feature extraction techniques to amplify weak spectrum signals related to potential blockages, improving the identifiability of hidden features;

[0026] An abnormal feature extraction unit uses intelligent signal processing algorithms to identify and isolate key feature signals related to catheter blockages, highlighting significant abnormal dynamic changes;

[0027] A data normalization unit performs standardized transformation on sensor data of different types, dimensions, and scales, establishing a unified data representation space;

[0028] A real-time filtering unit uses a high-speed digital signal processor to achieve millisecond-level dynamic filtering and feature reconstruction, ensuring the real-time performance and low latency of signal processing;

[0029] A quality assessment unit dynamically evaluates the quality and reliability of the processed data through signal entropy, signal-to-noise ratio, and feature stability indicators.

[0030] Furthermore, the feature fusion and analysis module includes the following components:

[0031] A hydrodynamic characteristics unit accurately captures the velocity field, pressure gradient, and shear stress changes of peritoneal dialysis fluid in the catheter, constructing a mathematical model of fluid behavior;

[0032] A liquid physical parameter unit precisely measures the viscosity, density, ion concentration, and particle distribution of the dialysis fluid, depicting the subtle changes in the microscopic physical state of the liquid;

[0033] A catheter vibration mode unit deeply analyzes the microscopic vibration characteristics, resonance frequency, and structural response of the catheter wall, revealing potential mechanical deformation signals;

[0034] The multi-dimensional correlation analysis unit establishes a non-linear mapping relationship among hydrodynamics, physical parameters, and vibration modes;

[0035] The intelligent reasoning unit applies probability graph models and Bayesian networks to perform probability inference and weight fusion on multi-dimensional features, generating a comprehensive evaluation probability distribution of the catheter state;

[0036] The state evaluation mapping unit maps the multi-dimensional feature fusion result to a standardized catheter health evaluation space, forming intuitive and quantitative catheter integrity and functional state indicators.

[0037] Furthermore, the AI prediction analysis module includes the following components:

[0038] The patient feature modeling unit constructs an accurate individual health feature vector by establishing a physiological feature data model of the child, including age, weight, renal function, and immune status indicators;

[0039] The deep learning prediction unit learns and captures the complex non-linear patterns of catheter blockage based on a multi-layer neural network and a long short-term memory model, realizing dynamic prediction of the blockage probability;

[0040] The spatial positioning reasoning unit uses high-dimensional space mapping and probability inference techniques to accurately locate the specific location and influence range of potential catheter blockage, forming a visual risk heat map;

[0041] The time window prediction unit applies survival analysis and time series prediction algorithms to estimate the time interval and risk evolution trend of possible catheter blockage based on historical data and real-time features;

[0042] The risk assessment unit comprehensively fuses clinical data, individual characteristics, and real-time monitoring signals to construct a multi-dimensional blockage risk assessment model, generating a dynamic and personalized risk index;

[0043] The prediction uncertainty analysis unit quantifies the uncertainty and reliability of the prediction results through Bayesian probability inference and confidence interval estimation, providing a confidence level assessment of the prediction.

[0044] Furthermore, the risk warning and intervention module includes the following components:

[0045] The risk grading unit constructs a multi-level risk assessment system based on the AI prediction results, accurately classifying the catheter blockage risk into low, medium, and high warning levels to achieve precise risk stratification;

[0046] The dynamic threshold adjustment unit uses adaptive algorithms and machine learning techniques to dynamically adjust the warning threshold in real time, balancing the sensitivity and specificity of the warning and reducing the probability of false alarms and missed alarms;

[0047] The personalized intervention advice unit combines the individual characteristics of the child and the knowledge of clinical experts to generate customized prevention and intervention strategies for different risk levels and provide precise clinical guidance;

[0048] The real-time warning generation unit converts the risk level and intervention advice into immediate and clear warning signals, supports multi-terminal push and multi-modal reminders, and ensures the timeliness and effectiveness of information transmission;

[0049] The intervention effect tracking unit records and analyzes the actual intervention process and clinical effect after each warning, and constructs a feedback closed-loop mechanism for continuous learning and optimization;

[0050] The abnormal response coordination unit designs a multi-level emergency response mechanism, automatically triggers the medical team collaboration process during high-risk warnings, and supports remote consultation and rapid intervention.

[0051] Furthermore, the microfluidic execution module includes the following components:

[0052] The micro-pulse dynamic regulation unit uses precision solenoid valves and high-frequency pulse technology to precisely regulate the liquid flow characteristics in the catheter at the microscale, and reduces sediment attachment through periodic micro-perturbations;

[0053] The fluid mechanics optimization unit, based on the computational fluid dynamics algorithm, adjusts the liquid flow rate and pressure gradient in the catheter in real time, actively changes the local flow pattern, and inhibits the formation of initial sediments;

[0054] The biological cleaning mechanism unit integrates electrochemical and ultrasonic synergistic technologies to periodically activate the self-cleaning function of the catheter inner wall, and effectively removes the tiny sediments and biofilms formed in the early stage;

[0055] The liquid composition regulation unit dynamically adjusts the ion concentration, pH value and surface activity of the peritoneal dialysis solution through precision chemical sensing and real-time ratio technology, and inhibits adverse deposition reactions.

[0056] Furthermore, the remote medical support module includes the following components:

[0057] The data visualization unit converts complex clinical data and prediction results into intuitive and easy-to-understand visualization charts and trend analyses through a multi-dimensional and interactive graphical interface, helping medical staff quickly grasp the catheter status of the child;

[0058] The remote monitoring unit constructs a safe and low-latency remote real-time transmission channel, supports continuous monitoring and rapid response of the medical team across regions and terminals, and realizes all-weather dynamic tracking of the peritoneal dialysis catheter of the child;

[0059] Intelligent early warning push unit, which develops multi-channel and personalized early warning push mechanisms, and accurately pushes emergency intervention information and detailed clinical suggestions according to the risk level and medical staff permissions;

[0060] Hospital system integration unit, which designs standardized data interfaces and security authentication protocols, realizes seamless docking with existing medical information systems, and supports electronic medical record synchronization and cross-system data sharing;

[0061] Security encryption unit, which adopts multi-level encryption technologies and secure communication protocols to provide end-to-end dynamic encryption protection for sensitive medical data of children, ensuring the absolute security of data transmission and storage;

[0062] Multi-terminal adaptation unit, which develops a cross-platform adaptive interface system, supports multiple terminals, and provides a consistent and efficient operation experience for medical staff in different usage scenarios.

[0063] Compared with the prior art, the present application has the following beneficial effects:

[0064] The present application realizes high-throughput care to prevent blockage during peritoneal dialysis for pediatric nephropathy patients through the collaborative work of multiple modules such as multi-modal perception, AI prediction analysis, and microfluidic execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic structural diagram of a high-throughput care device for preventing blockage during peritoneal dialysis for pediatric nephropathy patients disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] To make the objectives, technical solutions, and advantages of the implementation of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The described embodiments are some, but not all, of the embodiments of the present invention.

[0067] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0068] The embodiments described below with reference to the accompanying drawings and directional terms are exemplary only and are intended to explain the present invention and should not be construed as limiting the present invention.

[0069] As Figure 1 shown, a high-throughput care device for preventing blockage during peritoneal dialysis for pediatric nephropathy patients includes the following modules:

[0070] The multimodal perception module integrates a variety of micro sensors to collect data on catheter pressure, flow rate, turbidity, vibration spectrum, and temperature in real time, constructing a comprehensive data basis for blockage prediction;

[0071] The signal optimization and processing module performs noise reduction, calibration, and enhancement processing on the collected data, effectively filtering out interference signals, highlighting the weak features of potential blockages, and improving data quality;

[0072] The feature fusion and analysis module extracts key features from the optimized data and performs intelligent fusion to form a comprehensive assessment of the catheter state;

[0073] The AI prediction and analysis module integrates the comprehensive assessment data of the child's individual characteristics and catheter state to predict the probability, location, and possible time window of blockage occurrence;

[0074] The risk warning and intervention module issues graded warning signals according to the prediction results, generates personalized anti-blockage intervention suggestions, and dynamically adjusts the warning threshold to balance sensitivity and specificity;

[0075] The microfluidic execution module inhibits the formation of initial deposits through micro-pulse technology and dynamic hydrodynamics regulation, effectively reducing the blockage risk;

[0076] The remote medical support module provides intuitive data visualization and intervention suggestions, supports remote monitoring and emergency intervention, and ensures seamless integration with the hospital system while ensuring data security.

[0077] In this embodiment, the multi-modal perception module is the core basic part of the peritoneal dialysis anti-blocking nursing device for pediatric nephropathy patients. By integrating a variety of micro-sensors, it can collect a variety of key data in the catheter in real time, including pressure, flow rate, turbidity, vibration spectrum, and temperature, etc. The integration of these sensors enables the device to comprehensively perceive the operating state of the catheter from multiple dimensions, providing a rich and accurate data basis for blockage prediction. The pressure sensor can accurately capture the pressure changes generated when the liquid flows in the catheter. Under normal circumstances, the pressure value should fluctuate within a certain range, while when there are signs of catheter blockage, the pressure will increase or decrease significantly. The flow rate sensor monitors the speed of the liquid passing through the catheter in real time. When blockage occurs, the flow rate will decrease significantly or even stop. By continuously monitoring the flow rate changes, the device can timely capture potential blockage risks. The turbidity sensor is used to detect the purity of the liquid in the catheter. The peritoneal dialysis solution is relatively clear under normal circumstances, while when impurities such as proteins and cell debris are deposited, the turbidity will increase, which is often a precursor to blockage formation. The vibration spectrum sensor can capture the vibration signals generated by the liquid flow in the catheter. The vibration spectrum characteristics are different under different states, and the vibration mode will change significantly when blockage occurs. By analyzing the vibration spectrum, the abnormal conditions in the catheter can be perceived more subtly. The temperature sensor monitors the temperature of the liquid in the catheter. Although the direct correlation between temperature changes and blockage is not as strong as other parameters, in some cases, such as the temperature increase caused by local inflammation, it may also indirectly affect the patency of the catheter. The advantage of the multi-modal perception module lies in its comprehensiveness and real-time nature, which can capture catheter state information from multiple perspectives, providing rich and accurate data support for subsequent analysis and prediction, greatly improving the reliability of blockage prediction, and providing a solid data guarantee for the safe peritoneal dialysis of pediatric nephropathy patients.

[0078] In this embodiment, the signal optimization processing module plays a crucial role in the nursing device. It processes the raw data collected by the multi-modal perception module to improve the data quality. In an actual medical environment, the data collected by sensors is often affected by various interference factors, such as electromagnetic interference, environmental noise, and patient body movement. These interference signals can mask the weak characteristics of potential blockages, resulting in data distortion and affecting the accuracy of subsequent analysis and prediction. The signal optimization processing module first performs noise reduction processing on the collected data. By using advanced digital signal processing algorithms, such as wavelet transform and Fourier transform, these interference signals are effectively filtered out. Wavelet transform can perform adaptive noise reduction according to the frequency and time characteristics of the signal, and has unique advantages in signal processing in complex environments. It can decompose the signal into components of different frequency bands, and then specifically remove high-frequency noise while retaining the effective low-frequency signals. Fourier transform, on the other hand, identifies and filters out the noise components that do not belong to the normal signal frequency range through frequency domain analysis of the signal. After the noise reduction processing, the signal-to-noise ratio of the data is significantly improved, and the identifiability of potential blockage characteristics is enhanced. Next, calibration processing is carried out. Since the sensor may have problems such as drift and sensitivity change during long-term use, resulting in deviation of the collected data. The signal optimization processing module calibrates the data collected by the sensor by establishing a calibration model and combining known standard signals or historical data to restore it to an accurate measurement level. Finally, enhancement processing is carried out. For those weak characteristic signals related to blockages, techniques such as signal amplification and feature extraction are used for enhancement to make them more prominent, facilitating further analysis by subsequent modules. After being processed by the signal optimization processing module, the data quality is greatly improved, providing high-quality and highly reliable data input for the feature fusion analysis module, thus ensuring that the blockage prediction function of the entire device can operate efficiently and accurately, effectively reducing the risk of misjudgment caused by data quality problems, and ensuring the safety and effectiveness of the peritoneal dialysis process for pediatric nephropathy patients.

[0079] In this embodiment, the feature fusion analysis module is one of the key components of the nursing device. It extracts key features from the high-quality data output by the signal optimization processing module and performs intelligent fusion to form a comprehensive assessment of the catheter status. During peritoneal dialysis, the change in catheter status is a complex multi-factor process, and a single feature often fails to comprehensively reflect the blockage risk of the catheter. For example, just looking at the pressure change, although an increase in pressure may be a sign of blockage, it may also be caused by non-blockage factors such as a change in the patient's body position; similarly, a decrease in flow rate may be due to changes in the composition of the dialysate or changes in the patient's physiological state. Therefore, a comprehensive analysis of multiple features is required. The feature fusion analysis module first performs feature extraction on the optimized data, using machine learning algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), etc., to screen out the features with the strongest correlation with blockage from a large amount of data. PCA can, through dimensionality reduction techniques, combine multiple related features into a few principal components. These principal components contain most of the information of the original data, while removing redundant features and improving the computational efficiency. LDA, while performing dimensionality reduction, can maximize the separation of data categories in different states, such as the normal state and the blocked state, making the features more discriminative. The extracted key features include, but are not limited to, the pressure change rate, the flow rate fluctuation frequency, the turbidity growth trend, specific patterns in the vibration spectrum, etc. These features respectively reflect the physical state and potential changes inside the catheter from different perspectives. Then, the feature fusion analysis module uses intelligent fusion algorithms such as Bayesian fusion and neural network fusion to organically integrate these key features. The Bayesian fusion method is based on Bayes' theorem. By calculating the conditional probabilities of each feature in different states and comprehensively considering the weights of each feature, it obtains the comprehensive probability distribution of the catheter status. Neural network fusion utilizes the self-learning and adaptive capabilities of neural networks. Through a large amount of training data, the network automatically learns the complex relationships between features, thereby achieving a precise assessment of the catheter status. After being processed by the feature fusion analysis module, the device can form a comprehensive and accurate comprehensive assessment result of the catheter status, which can not only determine whether the catheter is in a normal state, but also quantitatively evaluate the potential blockage risk, providing reliable data support for the subsequent AI prediction analysis module. This comprehensive assessment method overcomes the limitations of single-feature analysis, greatly improves the ability to identify the catheter blockage risk, enables the device to detect blockage signs earlier and more accurately, and ensures the safety of peritoneal dialysis for pediatric nephropathy patients.

[0080] In this embodiment, the AI prediction and analysis module is the core intelligent part of the nursing device. It integrates the comprehensive evaluation data of the individual characteristics of the child and the catheter status, and through advanced machine learning and artificial intelligence algorithms, accurately predicts the occurrence probability, location, and possible time window of blockage. During the peritoneal dialysis process of pediatric nephropathy patients, the physiological characteristics, disease conditions, dialysis history, etc. of each child are different, and these individual characteristics have an important impact on the risk of catheter blockage. For example, the age, weight, renal function level of the child, the composition and usage frequency of the dialysate, etc. will all affect the fluid flow characteristics in the catheter and the formation of deposits. The AI prediction and analysis module first records and analyzes the individual characteristics of the child in detail, and establishes an individual characteristic database of the child, including the child's basic physiological information, disease diagnosis information, dialysis treatment plan, etc. Then, it combines these individual characteristics with the comprehensive evaluation data of the catheter status output by the feature fusion analysis module and inputs them into a pre-trained machine learning model. This model can adopt deep learning algorithms, such as convolutional neural network (CNN), recurrent neural network (RNN) and its variant long short-term memory network (LSTM), etc. CNN can automatically extract the spatial features in the data and has good adaptability to the multi-dimensional features of the catheter status; RNN and LSTM can process time series data and fully consider the changing law of the catheter status over time, which is particularly important for predicting the occurrence time window of blockage. Through training with a large amount of historical data, the model can learn the complex relationship between the individual characteristics of the child and catheter blockage, as well as the association pattern between the change of catheter status and blockage events. In actual operation, the AI prediction and analysis module can receive the latest data input in real time, quickly calculate the occurrence probability of blockage, and intuitively display it to medical staff in the form of a percentage. At the same time, it can also locate the possible catheter position where blockage may occur according to the feature analysis results, such as the catheter inlet, bend, or outlet, etc., which is of great significance for medical staff to take targeted intervention measures in a timely manner. In addition, this module can also predict the approximate time window when blockage may occur, issue a warning several hours or even days in advance, and provide sufficient time for medical staff to prepare for intervention. The introduction of the AI prediction and analysis module makes the nursing device highly intelligent and personalized, can accurately predict the catheter blockage risk according to the specific situation of each child, greatly improves the safety and effectiveness of peritoneal dialysis for pediatric nephropathy patients, reduces the work burden of medical staff, and improves the quality of medical services.

[0081] In this embodiment, the risk warning and intervention module is an important part of the nursing device to achieve the active defense function. It issues graded warning signals according to the prediction results of the AI prediction and analysis module, generates personalized anti-blockage intervention suggestions, and dynamically adjusts the warning threshold to balance sensitivity and specificity. During the peritoneal dialysis process of pediatric nephropathy patients, timely and accurate warning is crucial for preventing catheter blockage. The risk warning and intervention module classifies the warning signals into multiple levels according to the prediction information such as the occurrence probability, location, and time window of blockage, such as low-risk warning, medium-risk warning, and high-risk warning. A low-risk warning indicates a relatively low possibility of blockage, but still requires attention. At this time, the module will remind medical staff to regularly check the catheter status; a medium-risk warning means that the blockage risk is gradually increasing, and the module will issue a more obvious warning signal and suggest that medical staff take some preventive measures, such as adjusting the composition or flow rate of the dialysate, changing the patient's body position, etc.; a high-risk warning indicates that the blockage is about to occur or has already occurred. At this time, the module will issue an emergency alarm to prompt medical staff to immediately take intervention measures, such as flushing the catheter or replacing the catheter. In addition to the graded warning function, the module can also generate personalized anti-blockage intervention suggestions according to the individual characteristics of the child and the catheter status. These suggestions include, but are not limited to, adjusting the dialysis plan, using specific anti-blockage drugs, performing physical interventions, etc., aiming to reduce the blockage risk from multiple aspects. At the same time, to ensure the accuracy and effectiveness of the warning, the risk warning and intervention module also has the function of dynamically adjusting the warning threshold. It automatically adjusts the warning threshold according to the real-time catheter status data and historical warning results to balance the sensitivity and specificity of the warning. Too high sensitivity may lead to frequent false alarms, increasing the workload of medical staff; too low sensitivity may miss the real blockage risk. By dynamically adjusting the threshold, the module can minimize false alarms and missed alarms on the premise of ensuring warning accuracy, improving the practicability and reliability of the device. The introduction of the risk warning and intervention module enables the nursing device not only to predict the blockage risk, but also to actively provide intervention measures to help medical staff respond to the blockage problem in a timely and effective manner, thus significantly improving the safety and efficiency of peritoneal dialysis for pediatric nephropathy patients.

[0082] In this embodiment, the microfluidic execution module is a key part of the nursing device for actively intervening in catheter blockage. Through micro-pulse technology and dynamic fluid mechanics regulation, it effectively inhibits the formation of initial deposits, thereby reducing the risk of blockage. During peritoneal dialysis, the flow characteristics of the liquid in the catheter play an important role in the formation of blockage. The microfluidic execution module uses micro-pulse technology to apply tiny pulse pressures or flow rate changes in the catheter, breaking the static equilibrium of the liquid and preventing impurities such as proteins and cell debris from depositing on the catheter wall. This micro-pulse technology can continuously fine-tune the liquid flow in the catheter without affecting the normal dialysis process, making it difficult for deposits to adhere to the catheter wall. At the same time, this module also combines dynamic fluid mechanics regulation technology to dynamically adjust the liquid flow pattern according to real-time flow rate, pressure and other data in the catheter. For example, when it detects that the flow rate is too low or local eddies are formed, the microfluidic execution module can automatically adjust the flow rate and optimize the liquid flow path to avoid the accumulation of impurities in the low-flow rate area. This dynamic regulation ability enables the device to flexibly respond to potential blockage risks according to different catheter states and dialysis conditions. The advantages of the microfluidic execution module lie in its active intervention and precision. It can not only prevent the occurrence of blockage, but also take timely measures at the initial stage of blockage to avoid the further deterioration of blockage. Through the combination of micro-pulse technology and dynamic fluid mechanics regulation, this module can optimize the hydrodynamic environment in the catheter at the micro level, reduce the formation of deposits, and thus significantly reduce the probability of catheter blockage. In addition, the operation process of the microfluidic execution module is automated and does not require frequent intervention by medical staff, greatly reducing the workload of medical staff and improving the continuity and stability of the dialysis process. In the peritoneal dialysis of pediatric nephropathy patients, the introduction of the microfluidic execution module provides an efficient and reliable solution for preventing catheter blockage, effectively ensuring the smooth progress of the dialysis process and improving the treatment effect and quality of life of patients.

[0083] In this embodiment, the remote medical support module is an important component of the nursing device. It supports remote monitoring and emergency intervention by providing intuitive data visualization and intervention suggestions, while ensuring data security and seamless integration with the hospital system. In the modern medical environment, the application of remote medical technology is becoming increasingly widespread. It can break through the limitations of time and space, enabling medical staff to monitor and manage the treatment conditions of patients anytime and anywhere. This module visualizes the catheter status data collected by the device and presents it to medical staff in the form of intuitive charts, curves, etc., enabling them to quickly understand the real-time operating status and potential risks of the catheter. At the same time, the module will generate corresponding intervention suggestions based on the output results of the AI prediction analysis module and transmit this information to medical staff in real time through remote communication technology. This remote monitoring and intervention capability enables medical staff to take timely measures when the patient is at risk of blockage, even if they are not beside the patient. The remote medical support module also has a powerful data security guarantee function. During data transmission and storage, encryption technology is used to ensure the privacy and security of patient information, preventing data leakage or tampering. In addition, this module can be seamlessly integrated with the hospital's medical information system, synchronizing the patient's dialysis data and warning information to the hospital's electronic medical record system in real time, facilitating centralized management and analysis by medical staff within the hospital. This integration ability not only improves the utilization efficiency of medical resources but also provides comprehensive data support for the long-term treatment of patients. The introduction of the remote medical support module enables this nursing device to make full use of the advantages of modern information technology to achieve efficient management and remote intervention in the peritoneal dialysis process of pediatric nephropathy patients. It not only improves the work efficiency of medical staff but also provides safer and more convenient medical services for patients, further enhancing the overall treatment level of peritoneal dialysis for pediatric nephropathy patients.

[0084] In summary, through the collaborative work of the multi-modal perception module, signal optimization processing module, feature fusion analysis module, AI prediction analysis module, risk warning and intervention module, microfluidic execution module, and remote medical support module, this peritoneal dialysis anti-blockage and high-flow nursing device for pediatric nephropathy patients realizes all-round monitoring, prediction, and intervention of the catheter blockage risk. The multi-modal perception module provides a rich data foundation for the device, the signal optimization processing module ensures the high quality of the data, and the feature fusion analysis module and AI prediction analysis module achieve accurate prediction of the blockage risk through intelligent algorithms. The risk warning and intervention module and the microfluidic execution module provide active defense capabilities from the software and hardware levels respectively, while the remote medical support module further enhances the intelligence level of the device and the utilization efficiency of medical resources. Overall, through the organic combination of each module, this device significantly improves the safety and efficiency of peritoneal dialysis for pediatric nephropathy patients, reduces the workload of medical staff, and provides strong guarantee for the treatment of pediatric nephropathy patients.

[0085] Furthermore, the multimodal perception module includes the following components:

[0086] A pressure sensing unit that precisely captures the real-time pressure changes inside the catheter through a high-precision micro pressure sensor, providing basic data for early blockage detection;

[0087] A hydrodynamic unit that dynamically monitors the flow characteristics of peritoneal dialysis fluid using a micro flowmeter and a flow rate sensor, and tracks the minute changes in the liquid channel inside the catheter in real time;

[0088] An optical detection unit that uses micro spectroscopy and image sensing technology to quickly analyze the turbidity, particle distribution, and sediment characteristics of the liquid inside the catheter, and identify potential blockage signals;

[0089] A vibration spectrum unit that integrates a high-sensitivity micro vibration sensor to capture the microscopic vibration modes of the catheter wall and the fluid, revealing abnormalities in the internal structure and fluid dynamics of the catheter;

[0090] A temperature mapping unit that deploys a precision temperature sensing array to monitor the temperature gradient and local thermodynamic changes in different regions of the catheter in real time, reflecting potential biofilm formation;

[0091] A data integration unit that constructs a high-dimensional and multi-scale catheter state perception matrix, providing a comprehensive and accurate data basis for other modules.

[0092] In summary, the multimodal perception module realizes multi-dimensional and high-precision real-time monitoring inside the peritoneal dialysis catheter by integrating a pressure sensing unit, a hydrodynamic unit, an optical detection unit, a vibration spectrum unit, a temperature mapping unit, and a data integration unit. The pressure sensing unit precisely captures the pressure changes inside the catheter, providing key data for early blockage detection; the hydrodynamic unit dynamically monitors the liquid flow characteristics and tracks minute changes in real time; the optical detection unit quickly analyzes the turbidity and sediment characteristics of the liquid to identify potential blockage signals; the vibration spectrum unit captures the microscopic vibration modes to reveal internal structure and fluid dynamics abnormalities; the temperature mapping unit monitors the temperature gradient and thermodynamic changes to reflect the risk of biofilm formation. The data integration unit fuses these multi-source information into a high-dimensional and multi-scale perception matrix, providing a comprehensive and accurate data basis for other modules, thus significantly improving the accuracy and timeliness of blockage prediction, and providing a strong guarantee for the safety and effectiveness of peritoneal dialysis for pediatric nephropathy patients.

[0093] Furthermore, the signal optimization and processing module includes the following components:

[0094] A multi-dimensional noise reduction unit that applies wavelet transform and adaptive filtering algorithms to effectively suppress random noise from physiology, electronics, and the environment, and retains the key features of the signal;

[0095] The signal correction unit eliminates the individual differences of sensors and measurement biases through polynomial fitting and reference benchmark calibration techniques, ensuring the consistency and accuracy of cross-sensor data;

[0096] The spectrum enhancement unit uses frequency-domain enhancement algorithms and feature extraction techniques to amplify weak spectrum signals related to potential blockages and improve the identifiability of hidden features;

[0097] The abnormal feature extraction unit uses intelligent signal processing algorithms to identify and separate key feature signals related to catheter blockages, highlighting significant abnormal dynamic changes;

[0098] The data normalization unit performs standard transformation on sensor data of different types, dimensions, and scales to establish a unified data representation space;

[0099] The real-time filtering unit uses a high-speed digital signal processor to achieve millisecond-level dynamic filtering and feature reconstruction, ensuring the real-time performance and low latency of signal processing;

[0100] The quality assessment unit dynamically assesses the quality and reliability of the processed data through signal entropy, signal-to-noise ratio, and feature stability indicators.

[0101] In summary, the signal optimization processing module realizes the comprehensive optimization processing of the acquired signals by integrating the multi-dimensional noise reduction unit, signal correction unit, spectrum enhancement unit, abnormal feature extraction unit, data normalization unit, real-time filtering unit, and quality assessment unit. The multi-dimensional noise reduction unit effectively suppresses physiological, electronic, and environmental noises and retains the key features of the signals by using wavelet transform and adaptive filtering algorithms; the signal correction unit eliminates the individual differences of sensors and measurement biases through polynomial fitting and reference benchmark calibration techniques, ensuring the consistency and accuracy of cross-sensor data; the spectrum enhancement unit amplifies weak spectrum signals related to potential blockages and improves the identifiability of hidden features by using frequency-domain enhancement algorithms and feature extraction techniques; the abnormal feature extraction unit uses intelligent signal processing algorithms to identify and separate key feature signals related to catheter blockages, highlighting significant abnormal dynamic changes; the data normalization unit performs standard transformation on sensor data of different types, dimensions, and scales to establish a unified data representation space; the real-time filtering unit uses a high-speed digital signal processor to achieve millisecond-level dynamic filtering and feature reconstruction, ensuring the real-time performance and low latency of signal processing; the quality assessment unit dynamically assesses the quality and reliability of the processed data through signal entropy, signal-to-noise ratio, and feature stability indicators. The coordinated action of these components significantly improves the quality and usability of the signals, provides a high-quality data basis for subsequent feature fusion analysis and blockage prediction, thereby improving the detection accuracy and response speed of the device to the risk of catheter blockage and enhancing the safety and reliability of the peritoneal dialysis process for pediatric nephropathy patients.

[0102] Further, identify and isolate the key feature signals related to catheter blockage, highlighting significant abnormal dynamic changes, including the following steps:

[0103] Use the following formula to calculate the energy distribution of the signal within the time window: When E(t) shows abnormal fluctuations within a specific time window, it can be determined as a blockage feature signal. Among them, E(t) is the short-time energy at time t, used to measure the instantaneous change amplitude of the signal; S filtered (t) is the signal after noise reduction at time t, that is, the signal value after noise has been removed;

[0104] Obtain the spectral feature F(ω) through Fourier transform and calculate the spectral centroid ω c , if ω c shows a significant shift within a short time, it indicates that there are frequency features related to blockage in the signal, which is expressed by the formula: Among them, ω is the frequency variable, representing the frequency component of the signal in the frequency domain;

[0105] Based on the mean value μ S and standard deviation σ S of the signal under normal conditions, calculate the anomaly score A(t). When A(t) exceeds the preset threshold τ, it can be determined that the signal is abnormal, indicating a blockage risk, which is expressed by the formula: Among them,

[0106] Decompose the signal into multiple components C k (t), and screen out the part set K related to blockage, and reconstruct the key feature signal, which is expressed by the formula: S clog (t) = ∑ k∈K C k (t), where S clog (t) is the feature signal related to catheter blockage separated at time t, that is, the feature part extracted from the original signal, used to identify the blockage state;

[0107] Perform weighted fusion on the time-domain energy, frequency-domain centroid, and the separated key feature signals to construct a comprehensive evaluation index.

[0108] In summary, through the above series of steps, the key feature signals related to catheter blockage can be accurately identified and separated, and their significant abnormal dynamic changes can be highlighted. First, the short-time energy formula is used to calculate the energy distribution of the signal within the time window. When abnormal fluctuations occur, it is determined as the blockage feature signal, thereby capturing the instantaneous change amplitude of the signal. Second, the spectral features are obtained through Fourier transform and the spectral centroid is calculated. If it significantly deviates within a short time, it indicates the existence of frequency features related to blockage. Further, the anomaly score is calculated based on the signal mean and standard deviation in the normal state. When the score exceeds the preset threshold, it indicates the blockage risk. In addition, the signal is decomposed into multiple components and the parts related to blockage are screened out to reconstruct the key feature signal, thereby extracting the feature part directly related to blockage from the original signal. Finally, the time-domain energy, frequency-domain centroid, and the separated key feature signals are weighted and fused to construct a comprehensive evaluation index, realizing a comprehensive and quantitative evaluation of the catheter blockage risk. This technical process effectively improves the recognition ability and accuracy of catheter blockage feature signals, provides a reliable basis for subsequent blockage warning and intervention, and significantly enhances the safety and reliability of the peritoneal dialysis process for pediatric nephropathy patients.

[0109] Furthermore, the feature fusion analysis module includes the following components:

[0110] The hydrodynamic feature unit accurately captures the changes in the velocity field, pressure gradient, and shear stress of peritoneal dialysis fluid in the catheter, and constructs a mathematical model of fluid behavior;

[0111] The liquid physical parameter unit precisely measures the viscosity, density, ion concentration, and particle distribution of the dialysis fluid, and depicts the subtle changes in the microscopic physical state of the liquid;

[0112] The catheter vibration mode unit deeply analyzes the microscopic vibration characteristics, resonance frequency, and structural response of the catheter wall, and reveals potential mechanical deformation signals;

[0113] The multi-dimensional correlation analysis unit establishes a non-linear mapping relationship between hydrodynamic, physical parameters, and vibration modes;

[0114] The intelligent inference unit applies probabilistic graphical models and Bayesian networks to perform probabilistic inference and weight fusion on multi-dimensional features, and generates a comprehensive evaluation probability distribution of the catheter state;

[0115] The state evaluation mapping unit maps the multi-dimensional feature fusion result to a standardized catheter health evaluation space, and forms intuitive and quantitative catheter integrity and functional state indicators.

[0116] In summary, the feature fusion analysis module realizes a comprehensive and accurate assessment of the peritoneal dialysis catheter state by integrating a hydrodynamic feature unit, a liquid physical parameter unit, a catheter vibration mode unit, a multi-dimensional correlation analysis unit, an intelligent reasoning unit, and a state evaluation mapping unit. The hydrodynamic feature unit accurately captures the changes in the velocity field, pressure gradient, and shear stress of the dialysate in the catheter and constructs a mathematical model of the fluid behavior; the liquid physical parameter unit precisely measures the viscosity, density, ion concentration, and particle distribution of the dialysate to depict the subtle changes in the microscopic physical state of the liquid; the catheter vibration mode unit deeply analyzes the microscopic vibration characteristics, resonance frequency, and structural response of the catheter wall to reveal potential mechanical degeneration signals. The multi-dimensional correlation analysis unit establishes a non-linear mapping relationship between the hydrodynamic, physical parameters, and vibration modes to achieve the correlation analysis of multi-dimensional features. The intelligent reasoning unit applies a probabilistic graphical model and a Bayesian network to perform probabilistic inference and weight fusion on multi-dimensional features and generate a comprehensive evaluation probability distribution of the catheter state. The state evaluation mapping unit maps the multi-dimensional feature fusion result to a standardized catheter health evaluation space to form intuitive and quantitative catheter integrity and functional state indicators. Through the comprehensive analysis and intelligent reasoning of multi-dimensional features, this module provides a scientific basis for the accurate assessment of the catheter state, significantly improves the ability to identify the catheter blockage risk and the early warning accuracy, and provides a strong guarantee for the safety and effectiveness of peritoneal dialysis for pediatric nephropathy patients.

[0117] Furthermore, applying a probabilistic graphical model and a Bayesian network to perform probabilistic inference and weight fusion on multi-dimensional features and generate a comprehensive evaluation probability distribution of the catheter state includes the following steps:

[0118] Construct a Bayesian network according to the causal relationship between multi-dimensional features and the catheter state to determine the joint probability distribution, expressed as: where P(X1,X2,…,X n ,C) is the joint probability of all features and the catheter state C; P(C) is the prior probability of the catheter state; n is the number of features; P(X i ∣Pa(X i ),C) is the conditional probability of feature X i given its parent node Pa(X i ) and the catheter state C;

[0119] Based on the feature values obtained in real time and expert knowledge, use maximum likelihood estimation or Bayesian estimation to calculate the conditional probability P(X i ∣Pa(X i ),C);

[0120] According to the importance of each feature, introduce a weight factor to correct the probability calculation to enhance the influence of key features on the prediction, expressed as: where P(X1,X2,…,Xn P(C) is the joint conditional probability of all features for a given catheter state C;

[0121] The posterior probability P(C|X1, X2, …, X n ) is calculated through Bayesian inference to form a comprehensive evaluation probability distribution of the catheter state, and the formula is: where, ∑ C is the summation over all possible catheter states.

[0122] In summary, by applying the probabilistic graphical model and Bayesian network, deep probabilistic inference and weight fusion of multi-dimensional features are realized, and a comprehensive evaluation probability distribution of the catheter state is generated. First, a Bayesian network is constructed based on the causal relationship between multi-dimensional features and the catheter state to determine the joint probability distribution, where the joint probability comprehensively reflects the probability association between features and the catheter state. On this basis, based on the real-time obtained feature values and expert knowledge, the maximum likelihood estimation or Bayesian estimation is used to dynamically calculate the conditional probability to ensure the accuracy and adaptability of the probability estimation. Further, a weight factor is introduced to correct the probability calculation, and the influence of key features on the prediction is enhanced according to the importance of each feature, so as to optimize the prediction accuracy of the probability model. Finally, the posterior probability is calculated through Bayesian inference to form a comprehensive evaluation probability distribution of the catheter state, providing a comprehensive and quantitative evaluation basis for the catheter health state. This technical process effectively integrates multi-dimensional feature information, improves the accuracy and reliability of catheter state evaluation, and provides strong support for timely detection of potential blockage risks.

[0123] Furthermore, the AI prediction analysis module includes the following components:

[0124] Patient feature modeling unit, by establishing a physiological feature data model of the child, including age, weight, renal function and immune status indicators, to construct an accurate individual health feature vector;

[0125] Deep learning prediction unit, based on a multi-layer neural network and a long short-term memory model, to learn and capture the complex non-linear patterns of catheter blockage and realize the dynamic prediction of the blockage probability;

[0126] Spatial location inference unit, using high-dimensional space mapping and probability inference technology to accurately locate the specific location and influence range of potential catheter blockage and form a visualized risk heat map;

[0127] Time window prediction unit, applying survival analysis and time series prediction algorithms, based on historical data and real-time features, to estimate the time interval and risk evolution trend of possible catheter blockage;

[0128] The risk assessment unit comprehensively integrates clinical data, individual characteristics, and real-time monitoring signals to construct a multi-dimensional blockage risk assessment model and generate dynamic and personalized risk indices.

[0129] The prediction uncertainty analysis unit quantifies the uncertainty and reliability of the prediction results through Bayesian probability inference and confidence interval estimation, and provides a confidence assessment of the prediction.

[0130] In summary, the AI prediction analysis module realizes the accurate and dynamic prediction of the peritoneal dialysis catheter blockage risk in pediatric nephropathy patients by integrating the patient feature modeling unit, deep learning prediction unit, spatial location reasoning unit, time window prediction unit, risk assessment unit, and prediction uncertainty analysis unit. The patient feature modeling unit constructs an individual health feature vector containing key information such as age, weight, renal function, and immune status, providing a basis for personalized prediction. The deep learning prediction unit uses multi-layer neural networks and long short-term memory models to learn the complex non-linear patterns of catheter blockage and realizes the dynamic prediction of the blockage probability. The spatial location reasoning unit accurately locates potential blockage positions and generates a visual risk heat map through high-dimensional space mapping and probability inference techniques. The time window prediction unit estimates the time interval of blockage occurrence and the risk evolution trend based on survival analysis and time series algorithms. The risk assessment unit integrates clinical data, individual characteristics, and real-time monitoring signals to construct a multi-dimensional risk assessment model and generate dynamic and personalized risk indices. The prediction uncertainty analysis unit quantifies the uncertainty and reliability of the prediction results through Bayesian inference and confidence interval estimation, providing a confidence assessment for clinical decision-making. The comprehensive technical application of this module significantly improves the accuracy, timeliness, and reliability of catheter blockage prediction, provides intelligent and personalized safety guarantees for the peritoneal dialysis treatment of pediatric nephropathy patients, optimizes the allocation of medical resources, and reduces the workload of medical staff.

[0131] Furthermore, the risk warning and intervention module includes the following components:

[0132] The risk grading unit constructs a multi-level risk assessment system based on the AI prediction results, accurately classifies the catheter blockage risk into low, medium, and high warning levels, and realizes the precise stratification of risks.

[0133] The dynamic threshold adjustment unit uses adaptive algorithms and machine learning techniques to dynamically adjust the warning threshold in real time, balance the sensitivity and specificity of the warning, and reduce the probability of false alarms and missed alarms.

[0134] The personalized intervention recommendation unit combines the individual characteristics of the child and clinical expert knowledge to generate customized prevention and intervention strategies for different risk levels and provide precise clinical guidance.

[0135] The real-time warning generation unit converts the risk level and intervention suggestions into immediate and clear warning signals, supports multi-terminal push and multi-modal reminders, and ensures the timeliness and effectiveness of information transmission;

[0136] The intervention effect tracking unit records and analyzes the actual intervention process and clinical effects after each warning, and constructs a feedback closed-loop mechanism for continuous learning and optimization;

[0137] The abnormal response coordination unit designs a multi-level emergency response mechanism, automatically triggers the medical team collaboration process during high-risk warnings, and supports remote consultation and rapid intervention.

[0138] In summary, an intelligent and precise catheter occlusion risk warning and intervention system is constructed through the risk warning and intervention module. The risk grading unit divides the catheter occlusion risk into low, medium, and high levels based on the AI prediction results, achieving precise stratification of risks; the dynamic threshold adjustment unit adjusts the warning threshold in real time through adaptive algorithms and machine learning techniques, balances sensitivity and specificity, and reduces the probability of false alarms and missed alarms. The personalized intervention suggestion unit combines the individual characteristics of the child and clinical expert knowledge to generate customized intervention strategies for different risk levels and provide precise clinical guidance. The real-time warning generation unit converts the risk level and intervention suggestions into immediate and clear warning signals, supports multi-terminal push and multi-modal reminders, and ensures the timeliness and effectiveness of information transmission. The intervention effect tracking unit records and analyzes the intervention process and clinical effects, and constructs a feedback closed-loop mechanism to continuously optimize performance. The abnormal response coordination unit designs a multi-level emergency response mechanism, automatically triggers the medical team collaboration process during high-risk warnings, and supports remote consultation and rapid intervention. The comprehensive technical application of this module significantly improves the warning accuracy and intervention efficiency of catheter occlusion risks, provides comprehensive and dynamic safety guarantees for the peritoneal dialysis treatment of pediatric nephropathy patients, optimizes the allocation of medical resources and reduces the workload of medical staff, while improving the treatment effect and quality of life of patients.

[0139] Furthermore, the microfluidic execution module includes the following components:

[0140] The micro-pulse dynamic regulation unit uses precision solenoid valves and high-frequency pulse technology to precisely regulate the liquid flow characteristics in the catheter at the microscale, and reduces sediment attachment through periodic micro-perturbations;

[0141] The fluid mechanics optimization unit, based on computational fluid dynamics algorithms, adjusts the liquid flow rate and pressure gradient in the catheter in real time, actively changes the local flow pattern, and inhibits the formation of initial sediments;

[0142] The biological cleaning mechanism unit integrates electrochemistry and ultrasonic synergy technologies to periodically activate the self-cleaning function of the catheter inner wall, and effectively removes the tiny sediments and biofilms formed in the early stage;

[0143] The liquid composition adjustment unit dynamically adjusts the ion concentration, pH value, and surface activity of peritoneal dialysis fluid through precise chemical sensing and real-time proportioning technology, inhibiting adverse deposition reactions.

[0144] In summary, through the microfluidic execution module, all-round active intervention in the internal environment of the peritoneal dialysis catheter is achieved, significantly reducing the risk of catheter blockage. The micro-pulse dynamic regulation unit uses precise solenoid valves and high-frequency pulse technology to precisely regulate the liquid flow characteristics inside the catheter at the microscale, reducing sediment attachment through periodic micro-perturbations; the hydrodynamic optimization unit, based on computational fluid dynamics algorithms, adjusts the liquid flow rate and pressure gradient inside the catheter in real time, actively changing the local flow pattern and inhibiting the formation of initial sediments. The biological cleaning mechanism unit integrates electrochemistry and ultrasonic synergy technology to periodically activate the self-cleaning function of the catheter inner wall, effectively removing tiny sediments and biofilms formed in the early stage; the liquid composition adjustment unit dynamically adjusts the ion concentration, pH value, and surface activity of peritoneal dialysis fluid through precise chemical sensing and real-time proportioning technology, inhibiting adverse deposition reactions. The synergistic effect of these components not only optimizes the hydrodynamic environment and liquid characteristics inside the catheter from the physical and chemical levels but also prevents sediment accumulation through the active cleaning function, thus providing an efficient and reliable anti-blockage solution for peritoneal dialysis of pediatric nephropathy patients, ensuring the smooth progress of the dialysis process, and improving the treatment effect and quality of life of patients.

[0145] Furthermore, the telemedicine support module includes the following components:

[0146] The data visualization unit converts complex clinical data and prediction results into intuitive and easy-to-understand visual charts and trend analyses through a multi-dimensional, interactive graphical interface, helping medical staff quickly grasp the catheter status of the child;

[0147] The remote monitoring unit constructs a safe and low-latency remote real-time transmission channel, supporting continuous monitoring and rapid response of the medical team across regions and terminals, and realizing all-weather dynamic tracking of the peritoneal dialysis catheter of the child;

[0148] The intelligent early warning push unit develops a multi-channel, personalized early warning push mechanism, and accurately pushes emergency intervention information and detailed clinical suggestions according to the risk level and medical staff permissions;

[0149] The hospital system integration unit designs standardized data interfaces and security authentication protocols to achieve seamless docking with existing medical information systems, supporting electronic medical record synchronization and cross-system data sharing;

[0150] The security encryption unit uses multi-level encryption technology and secure communication protocols to provide end-to-end dynamic encryption protection for sensitive medical data of children, ensuring the absolute security of data transmission and storage;

[0151] Multi-terminal adaptation unit, which develops a cross-platform adaptive interface system, supports multiple terminals, and provides a consistent and efficient operation experience for medical staff in different usage scenarios.

[0152] In summary, through the remote medical support module, efficient remote management of the peritoneal dialysis catheter status of pediatric nephropathy patients and secure data sharing are achieved. The data visualization unit converts complex clinical data and prediction results into intuitive visual charts to help medical staff quickly understand the catheter status of children; the remote monitoring unit constructs a low-latency real-time transmission channel to support continuous monitoring across regions and terminals, realizing all-weather dynamic tracking; the intelligent early warning push unit develops a multi-channel and personalized early warning mechanism to accurately push intervention information and clinical suggestions; the hospital system integration unit realizes seamless docking with the medical information system through standardized interfaces and security protocols, supporting electronic medical record synchronization and data sharing; the security encryption unit adopts multi-level encryption technology to ensure the security of the transmission and storage of children's data; the multi-terminal adaptation unit develops a cross-platform interface to provide a consistent operation experience for different terminals. The synergistic effect of these components improves the efficiency and security of remote medical treatment, optimizes the allocation of medical resources, and provides comprehensive remote support and guarantee for the peritoneal dialysis treatment of children.

[0153] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high-flow nursing device for peritoneal dialysis of pediatric renal patients with anti-blockage, characterized in that: Includes the following modules: The multimodal sensing module integrates a variety of micro sensors to collect real-time data on pressure, flow rate, turbidity, vibration spectrum and temperature in the catheter, building a comprehensive blockage prediction data foundation; The signal optimization processing module performs noise reduction, correction and enhancement processing on the collected data, effectively filters out interference signals, highlights the weak features of potential blockages, and improves data quality; The feature fusion analysis module extracts key features from the optimized data and performs intelligent fusion to form a comprehensive assessment of the catheter status; The AI ​​prediction and analysis module integrates comprehensive assessment data of individual characteristics of children and catheter status to predict the probability, location and possible time window of blockage; The risk warning and intervention module issues graded warning signals based on the prediction results, generates personalized anti-blockage intervention suggestions, and dynamically adjusts the warning threshold to balance sensitivity and specificity; Microfluidic execution module, through micro-pulse technology and dynamic fluid mechanics regulation, inhibits the formation of initial sediments and effectively reduces the risk of clogging; The telemedicine support module provides intuitive data visualization and intervention suggestions, supports remote monitoring and emergency intervention, ensures data security and seamlessly integrates with hospital systems.

2. The anti-blockage high-flow nursing device for peritoneal dialysis of pediatric renal patients according to claim 1, characterized in that: The multimodal perception module includes the following components: The pressure sensing unit uses a high-precision micro pressure sensor to accurately capture real-time pressure changes in the catheter, providing basic data for early detection of blockage; The fluid dynamics unit uses a micro flow meter and flow rate sensor to dynamically monitor the flow characteristics of the peritoneal dialysis fluid and track the slight changes in the fluid channel in the catheter in real time; Optical detection unit, which uses micro-spectroscopy and image sensing technology to quickly analyze the turbidity, particle distribution and sediment characteristics of the fluid in the catheter to identify potential blockage signals; The vibration spectrum unit integrates a highly sensitive micro-vibration sensor to capture the microscopic vibration patterns of the catheter wall and fluid, revealing abnormalities in the catheter's internal structure and fluid dynamics; The temperature mapping unit deploys a precision temperature sensing array to monitor temperature gradients and local thermodynamic changes in different areas of the catheter in real time, reflecting potential biofilm formation; The data integration unit constructs a high-dimensional, multi-scale catheter status perception matrix to provide a comprehensive and accurate data foundation for other modules.

3. The anti-blockage high-flow nursing device for peritoneal dialysis of pediatric renal patients according to claim 1, characterized in that: The signal optimization processing module includes the following components: Multi-dimensional noise reduction unit, using wavelet transform and adaptive filtering algorithm, effectively suppresses random noise from physiological, electronic and environmental sources, and retains the key features of the signal; The signal correction unit eliminates individual differences and measurement deviations of sensors through polynomial fitting and reference benchmark calibration technology, ensuring consistency and accuracy of data across sensors; The spectrum enhancement unit uses frequency domain enhancement algorithms and feature extraction techniques to amplify weak spectrum signals related to potential jams and improve the identifiability of hidden features; The abnormal feature extraction unit uses intelligent signal processing algorithms to identify and separate key characteristic signals related to catheter blockage and highlight significant abnormal dynamic changes; The data normalization unit performs standardized transformation on sensor data of different types, dimensions and scales to establish a unified data representation space; The real-time filtering unit uses a high-speed digital signal processor to achieve millisecond-level dynamic filtering and feature reconstruction, ensuring real-time and low-latency signal processing; The quality assessment unit dynamically evaluates the quality and reliability of the processed data through signal entropy, signal-to-noise ratio and feature stability indicators.

4. The anti-blockage high-flow nursing device for peritoneal dialysis of pediatric renal patients according to claim 1, characterized in that: The feature fusion analysis module includes the following components: The fluid dynamics characteristic unit accurately captures the velocity field, pressure gradient and shear stress changes of peritoneal dialysis fluid in the catheter and constructs a mathematical model of fluid behavior; Liquid physical parameter unit, which accurately measures the viscosity, density, ion concentration and particle distribution of dialysate, and describes the subtle changes in the microscopic physical state of the liquid; Catheter vibration mode unit, which deeply analyzes the microscopic vibration characteristics, resonant frequency and structural response of the catheter wall, revealing potential mechanical degeneration signals; Multidimensional correlation analysis unit, which establishes nonlinear mapping relationships between fluid dynamics, physical parameters and vibration modes; The intelligent reasoning unit uses probabilistic graphical models and Bayesian networks to perform probability inference and weight fusion on multi-dimensional features to generate a comprehensive evaluation probability distribution of the catheter status; The status assessment mapping unit maps the multi-dimensional feature fusion results to the standardized catheter health assessment space to form intuitive and quantitative indicators of catheter integrity and functional status.

5. The anti-blockage high-flow nursing device for peritoneal dialysis of pediatric renal patients according to claim 1, characterized in that: The AI ​​predictive analysis module includes the following components: The patient characteristic modeling unit builds a precise individual health characteristic vector by establishing a data model of the physiological characteristics of the child, including age, weight, renal function and immune status indicators; Deep learning prediction unit, based on multi-layer neural network and long short-term memory model, learns and captures complex nonlinear patterns of catheter blockage and realizes dynamic prediction of blockage probability; The spatial positioning reasoning unit uses high-dimensional spatial mapping and probability inference technology to accurately locate the specific location and impact range of potential catheter blockage and form a visual risk heat map; The time window prediction unit uses survival analysis and time series prediction algorithms to estimate the time interval when the catheter may be blocked and the risk evolution trend based on historical data and real-time features; The risk assessment unit integrates clinical data, individual characteristics and real-time monitoring signals to build a multi-dimensional blockage risk assessment model and generate a dynamic and personalized risk index; The prediction uncertainty analysis unit quantifies the uncertainty and reliability of the prediction results through Bayesian probability inference and confidence interval estimation, and provides a confidence assessment of the prediction.

6. The anti-blockage high-flow nursing device for peritoneal dialysis of pediatric renal patients according to claim 1, characterized in that: The risk warning and intervention module includes the following components: The risk grading unit builds a multi-level risk assessment system based on AI prediction results, accurately classifies the risk of catheter blockage into three warning levels: low, medium, and high, to achieve accurate stratification of risks; Dynamic threshold adjustment unit, using adaptive algorithms and machine learning technology to dynamically adjust the warning threshold in real time, balance the sensitivity and specificity of the warning, and reduce the probability of false alarms and missed alarms; The personalized intervention recommendation unit combines the individual characteristics of children with the knowledge of clinical experts to generate customized prevention and intervention strategies for different risk levels and provide precise clinical guidance; Real-time warning generation unit, which converts risk levels and intervention suggestions into immediate and clear warning signals, supports multi-terminal push and multi-modal reminders, and ensures the timeliness and effectiveness of information transmission; The intervention effect tracking unit records and analyzes the actual intervention process and clinical effect after each warning, and builds a feedback closed-loop mechanism for continuous learning and optimization; The abnormal response coordination unit is designed with a multi-level emergency response mechanism to automatically trigger the medical team collaboration process when a high-risk warning is issued, supporting remote consultation and rapid intervention.

7. The anti-blockage high-flow nursing device for peritoneal dialysis of pediatric renal patients according to claim 1, characterized in that: The microfluidic execution module includes the following components: The micro-pulse dynamic control unit uses precision solenoid valves and high-frequency pulse technology to accurately adjust the flow characteristics of the liquid in the catheter on a microscopic scale and reduce sediment adhesion through periodic micro-disturbance; The fluid dynamics optimization unit, based on computational fluid dynamics algorithms, adjusts the flow rate and pressure gradient of the liquid in the catheter in real time, actively changes the local flow pattern, and inhibits the formation of initial deposits; The biological cleaning mechanism unit integrates electrochemical and ultrasonic synergistic technologies to periodically activate the self-cleaning function of the inner wall of the catheter, effectively removing micro-deposits and biofilms formed in the early stage; The liquid composition adjustment unit dynamically adjusts the ion concentration, pH value and surface activity of the peritoneal dialysis fluid through precise chemical sensing and real-time proportioning technology to inhibit adverse deposition reactions.

8. The anti-blockage high-flow nursing device for peritoneal dialysis of pediatric renal patients according to claim 1, characterized in that: The Telemedicine Support module includes the following components: The data visualization unit converts complex clinical data and prediction results into intuitive and easy-to-understand visualization charts and trend analysis through a multi-dimensional, interactive graphical interface, helping medical staff quickly understand the catheter status of children; The remote monitoring unit builds a secure, low-latency remote real-time transmission channel to support the medical team's continuous monitoring and rapid response across regions and terminals, and achieves all-weather dynamic tracking of children's peritoneal dialysis catheters; Intelligent early warning push unit, develops a multi-channel, personalized early warning push mechanism, and accurately pushes emergency intervention information and detailed clinical recommendations based on risk levels and medical personnel authority; Hospital system integration unit, designing standardized data interfaces and security authentication protocols to achieve seamless integration with existing medical information systems, supporting electronic medical record synchronization and cross-system data sharing; The security encryption unit uses multi-level encryption technology and secure communication protocols to provide end-to-end dynamic encryption protection for children's sensitive medical data, ensuring absolute security of data transmission and storage; The multi-terminal adaptation unit develops a cross-platform adaptive interface system that supports multiple terminals and provides a consistent and efficient operating experience for medical personnel in different usage scenarios.

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