Dynamic monitoring and management method for cerebrospinal fluid

Abstract

The invention relates to a cerebrospinal fluid dynamic monitoring management method. Comprising the following steps: performing mode identification by using dynamic characteristics of cerebrospinal fluid pressure and flow velocity; by monitoring the flow velocity and pressure fluctuation of cerebrospinal fluid in real time, a comparison library of a normal mode and an abnormal mode is established; deep learning is carried out by using a fluid mechanics model and a neural network, and abnormal points of non-linear change are identified; through combination of a pressure sensor array and a flow velocity sensor, dynamic flow changes are captured in different areas of a cerebrospinal fluid system including a ventricle and a subarachnoid space, and abnormal fluctuations are identified through continuous analysis of time sequence data; by analyzing the interaction between the intracranial flow mode of cerebrospinal fluid and the compression expansion effect of brain tissues, an intelligent adjustment mechanism based on the flow pressure coupling effect is introduced; under the influence of cerebrospinal fluid pressure changes, deformation of brain tissue including ventricular expansion influences blood flow and tissue pressure in other intracranial areas.

Description

Technical Field

[0001] The present invention relates to a method for dynamically monitoring and managing cerebrospinal fluid. Background Art

[0002] Currently, although the methods for dynamically monitoring and managing cerebrospinal fluid have made some progress in clinical applications, there are still many deficiencies and drawbacks, which limit their wide application and effectiveness improvement in clinical practice. First of all, most of the existing cerebrospinal fluid monitoring methods rely on traditional pressure sensors and flow sensors. Although these devices can provide some basic monitoring data, their data acquisition range and accuracy are limited, and it is difficult to comprehensively and real-time reflect the dynamic characteristics of cerebrospinal fluid flow. For example, the existing pressure sensors can often only monitor the cerebrospinal fluid pressure changes at a single position and cannot accurately capture the changes in the entire cerebrospinal fluid system (including regions such as ventricles and subarachnoid space). In addition, although the flow sensors can provide flow rate data, their sensing accuracy and time resolution often cannot meet the requirements for detailed analysis of cerebrospinal fluid flow. Especially in high-speed or complex flow patterns, they are easily affected by noise interference and measurement errors.

[0003] Secondly, the existing methods lack comprehensive modeling and dynamic optimization processing of the multi-dimensional characteristics of cerebrospinal fluid flow. Most monitoring systems only rely on pressure and flow rate data for simple quantitative analysis, ignoring the non-linear changes during cerebrospinal fluid flow and the interaction with factors such as brain tissue deformation and blood flow, resulting in the inability to accurately reflect the complexity of cerebrospinal fluid flow state. These deficiencies make the traditional cerebrospinal fluid monitoring system have great limitations in identifying pathological abnormalities (such as hydrocephalus, cerebrospinal fluid leakage, etc.) and it is difficult to achieve early warning and precise intervention. Thirdly, most of the existing cerebrospinal fluid flow monitoring methods rely on static threshold judgments, such as setting a certain pressure or flow rate value as the abnormal judgment standard, and these thresholds are usually set based on statistical data or single-case experience, lacking personalized adjustment for individual differences. This makes it difficult to achieve precise personalized diagnosis and treatment when dealing with cerebrospinal fluid abnormalities, and it is easy to cause misjudgment and missed diagnosis. Especially in some complex pathological states, the traditional methods often cannot detect potential abnormal changes in a timely manner. More importantly, the real-time performance of many current cerebrospinal fluid monitoring systems is poor, and they cannot dynamically adjust the monitoring and intervention strategies. Since the cerebrospinal fluid flow state is affected by various factors, such as ventricular morphology, brain tissue elasticity, hemodynamics, etc., the flow state is a highly dynamic process, and most of the existing technologies rely on static physiological models and single detection methods, lacking sufficient flexibility and adaptability, and cannot track the changes in the patient's state in real time, resulting in the lag and insufficiency of treatment strategies.

[0004] On the other hand, in existing cerebrospinal fluid monitoring technologies, most systems lack sufficient data fusion and intelligent diagnosis functions, making it difficult to effectively integrate various data during multi-point and multi-dimensional monitoring. The state of cerebrospinal fluid flow is not only related to pressure and flow rate but also closely connected to multiple parameters such as temperature, density, and local resistance. These variables interact with each other to form complex flow patterns. However, existing sensors mostly rely on single monitoring data, such as pressure sensors or flow rate sensors, which makes the monitoring results lack comprehensiveness and depth and are difficult to capture the subtle changes under the interaction of multiple parameters. The lack of effective data fusion and intelligent analysis leads to limited ability of the system to identify abnormal patterns and unable to make comprehensive judgments like human experts. In addition, in traditional cerebrospinal fluid monitoring technologies, most devices still rely on manual intervention for data interpretation and diagnosis and lack sufficient intelligent diagnosis functions. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for dynamic monitoring and management of cerebrospinal fluid, so as to solve some of the drawbacks and deficiencies pointed out in the background technology.

[0006] The technical solutions adopted by the present invention to solve its above technical problems include the following steps:

[0007] S1. Abnormal pattern recognition algorithm based on cerebrospinal fluid pressure-flow characteristics:

[0008] S1.1. Use the dynamic characteristics of cerebrospinal fluid pressure and flow rate for pattern recognition; establish a comparison library of normal patterns and abnormal patterns by real-time monitoring of the flow velocity and pressure fluctuations of cerebrospinal fluid;

[0009] S1.2. Use the fluid mechanics model and neural network for deep learning to identify abnormal points with non-linear changes;

[0010] S1.3. Combine the pressure sensor array with the flow rate sensor to capture dynamic flow changes in different regions of the cerebrospinal fluid system including the ventricles and subarachnoid space, and identify abnormal fluctuations through continuous analysis of time-series data;

[0011] S2. Feedback regulation mechanism based on cerebrospinal fluid flow dynamics and brain tissue compression-expansion effect:

[0012] S2.1. By analyzing the interaction between the flow pattern of cerebrospinal fluid in the cranial cavity and the brain tissue compression-expansion effect, introduce an intelligent regulation mechanism based on the flow-pressure coupling effect;

[0013] S2.2. Under the influence of cerebrospinal fluid pressure changes, the deformation of brain tissue including ventricular dilation affects blood flow and tissue pressure in other regions of the cranial cavity;

[0014] S2.3. Predict the feedback effect of the compression and expansion changes of the brain tissue on the cerebrospinal fluid flow by modeling the coupling relationship between the cerebrospinal fluid flow and the brain tissue deformation;

[0015] S3. Cerebrospinal Fluid Dynamic Optimization and Intervention Strategy Based on Physiological and Pathological Dual Models:

[0016] S3.1. Conduct cerebrospinal fluid dynamic optimization and intervention by constructing a dual integration of the physiological model and the pathological model of the cerebrospinal fluid flow; the physiological model is based on the normal cerebrospinal fluid flow pattern in the healthy state, and the pathological model is based on the abnormal cerebrospinal fluid flow patterns of different brain diseases including hydrocephalus and cerebrospinal fluid leakage;

[0017] S3.2. The physiological model reflects the cerebrospinal fluid flow characteristics of healthy people, while the pathological model is adjusted according to the specific disease background of the patient; by combining these two, different types of cerebrospinal fluid abnormalities are identified;

[0018] S4. Non-invasive Sensing Technology of Intelligent Cerebrospinal Fluid Monitoring Equipment:

[0019] S4.1. Develop a non-invasive intelligent monitoring device, and use sensor technology to monitor the dynamic changes of cerebrospinal fluid without directly contacting the cerebrospinal fluid; measure the intracranial pressure, cerebrospinal fluid flow rate, and brain tissue deformation information through the sensor device.

[0020] Furthermore, the implementation method of the abnormal pattern recognition algorithm based on the cerebrospinal fluid pressure-flow characteristics includes:

[0021] Measure the pressure fluctuations in the cerebral ventricle through a pressure sensor, and at the same time combine the flow velocity sensor to detect the flow velocity of the cerebrospinal fluid in the same area, providing dynamic information about the flow state; through multi-dimensional and multi-point monitoring, calculate the changes in different regions and different times of the cerebrospinal fluid flow in real time; and describe the state of the cerebrospinal fluid flow, combine the pressure and flow velocity data, and use the composite flow state function f flow (t), where:

[0022] f flow (t) = ∫ Ω [ρ·v(t,x) + P(t,x)]dx

[0023] where:

[0024] f flow(t) represents the comprehensive state of cerebrospinal fluid (CSF) flow, which changes with time t, and calculates the combined effect of flow velocity and pressure; Ω represents the monitoring area, including different CSF flow regions in the ventricles and subarachnoid space; the integration area Ω spans spatial dimensions to capture multi-point data in the entire CSF flow system; ρ represents the density of CSF; v(t, x) represents the CSF flow velocity, which changes with time t and spatial position x, reflecting the flow velocity and direction of CSF; P(t, x) represents the CSF pressure at position x and time t, reflecting the local pressure difference under the flow state.

[0025] Furthermore, the implementation method of the abnormal pattern recognition algorithm based on cerebrospinal fluid pressure-flow characteristics includes:

[0026] Adopt dynamic modeling technology to analyze continuous pressure and flow velocity data, and calculate the regularity of CSF flow from it; for the dynamic modeling of CSF flow and the identification of abnormal fluctuations, an adaptive signal processing formula is used:

[0027]

[0028] Where:

[0029] represents the abnormality index of the CSF flow state, and identifies abnormal fluctuations in pressure or flow velocity in the continuous data at time t; P(t i ) and v(t i ) respectively represent the pressure and flow velocity data at the i-th moment; each t i corresponds to a unit time point, and records the monitoring data at that moment; α is an adaptive parameter used to adjust the weights of data at different time steps, dynamically adjusted according to the volatility of the data to avoid noise interference; |P(t i ) - P(t i-1 )| and |v(t i ) - v(t i-1 )| respectively represent the absolute changes in pressure and flow velocity within consecutive time steps; by calculating the differences, the sharp fluctuations in the flow state are captured to identify abnormal situations.

[0030] Furthermore, the implementation method of the abnormal pattern recognition algorithm based on cerebrospinal fluid pressure-flow characteristics includes:

[0031] By introducing an intelligent diagnosis algorithm, it can detect abnormal fluctuations in CSF flow in real time and give a diagnosis result; combining real-time flow velocity and pressure data, it judges whether there is hydrocephalus or other CSF circulation disorders, and according to the identified abnormal patterns, further intervention measures are proposed; and the data fusion process is represented by the following formula:

[0032]

[0033] Wherein:

[0034] D fusion (t) represents the comprehensive state assessment obtained through data fusion, reflecting the comprehensive state of cerebrospinal fluid flow in multiple monitoring areas; λ k represents the weight coefficient of the k-th sensor data, used to represent the importance of each sensor in the overall diagnosis; f k (t) represents the monitoring data output by the k-th sensor at time t, including pressure data and flow rate data, reflecting the local cerebrospinal fluid flow state at the location of each sensor; K represents the total number of sensors, covering data from all monitoring points.

[0035] Furthermore, the method for cerebrospinal fluid dynamic optimization and intervention strategy based on the dual physiological and pathological model includes:

[0036] Introduce multiple variables through multi-dimensional modeling and optimize using deep learning algorithms to simulate the flow pattern in the healthy state and adaptively adjust to cope with pathological states including hydrocephalus and cerebrospinal fluid leakage; calculate the characteristics of cerebrospinal fluid flow, using the improved Navier-Stokes equation, by considering the flow rate, pressure, density of cerebrospinal fluid, and local resistance factors to comprehensively simulate the flow process;

[0037]

[0038] Wherein:

[0039] u(x,t) represents the vector of cerebrospinal fluid flow velocity, x is the spatial position, and t is the time; the magnitude and direction of the flow velocity determine the motion state of cerebrospinal fluid; p is the pressure of cerebrospinal fluid, describing the mechanical changes during flow; ρ is the density of cerebrospinal fluid, affecting the inertial characteristics of flow; μ is the viscosity of cerebrospinal fluid, controlling the flow resistance; f(x,t) is the external force, reflecting the physiological processes of cerebrospinal fluid generation and absorption.

[0040] Furthermore, the method for cerebrospinal fluid dynamic optimization and intervention strategy based on the dual physiological and pathological model includes:

[0041] By combining the physiological model and the pathological model, switch between the healthy state and the pathological state, and optimize the intervention of the cerebrospinal fluid flow state in real time; the physiological model is based on the cerebrospinal fluid flow state of normal individuals, while the pathological model is built for abnormal states such as hydrocephalus and cerebrospinal fluid leakage; the real-time fusion of the physiological and pathological models uses the variational method to optimize the model, by minimizing the difference between the model prediction error and the reference model, and dynamically adjusting the intervention strategy according to real-time data:

[0042]

[0043] Wherein:

[0044] M t represents the cerebrospinal fluid state model at time t, including flow rate and pressure information; is the predicted flow rate value, is the actually measured flow rate value; is the second-order spatial derivative of the model, describing the curvature change of the flow, used to capture the minute changes in cerebrospinal fluid flow; M ref is the reference model in the healthy state, serving as the benchmark for model adjustment; λ1 and λ2 are regularization coefficients, used to balance the prediction error and model complexity.

[0045] Furthermore, the cerebrospinal fluid dynamic optimization and intervention strategy method based on the dual physiological and pathological models includes:

[0046] Through the data fusion of multiple sensors, including pressure sensors, flow rate sensors, and temperature sensors, comprehensively obtain the omni-directional information of cerebrospinal fluid flow; through the improved Kalman filtering algorithm, eliminate noise and extract features, and provide intelligent feedback based on real-time data; the improved Kalman filtering continuously optimizes the state estimation through the following recurrence formula;

[0047] x t+1 = Ax t + Bu t + w t

[0048] y t = Hx t + v t

[0049] where:

[0050] x t is the cerebrospinal fluid state vector at time t, including flow rate and pressure information; A is the state transition matrix, representing the state change from time t to t + 1, reflecting the physical law of cerebrospinal fluid flow; B is the control input matrix, u t is the control input; w t is the process noise, representing the random perturbation during the flow process; y t is the observed data from the sensor; H is the measurement matrix, representing the sensor's reflection of the cerebrospinal fluid state, v t is the observation noise.

[0051] The cerebrospinal fluid dynamic monitoring and management method of the present invention, by comprehensively applying means such as sensor technology, fluid mechanics modeling, intelligent algorithms, and deep learning, has the following remarkable beneficial effects:

[0052] Monitor the dynamic changes of cerebrospinal fluid flow rate, pressure, temperature, etc. in real time and comprehensively. Through the multi-dimensional data fusion of sensors, eliminate the noise interference of a single sensor, and ensure the accuracy and reliability of the data. This precise monitoring provides doctors with a comprehensive cerebrospinal fluid flow state, helps to detect potential abnormal fluctuations in a timely manner, and avoids the further deterioration of the condition. Through an abnormal pattern recognition algorithm based on the characteristics of cerebrospinal fluid pressure and flow rate, the non-linear changes in cerebrospinal fluid flow can be captured during dynamic monitoring, and pathological states such as hydrocephalus and cerebrospinal fluid leakage can be identified. This method accurately identifies the abnormal patterns of cerebrospinal fluid flow through multi-point monitoring and time-series data analysis, achieves early warning, and provides opportunities for timely intervention for patients.

[0053] Combining the dual physiological and pathological models, the present invention can dynamically adjust the intervention strategy according to real-time data, thereby optimizing the cerebrospinal fluid flow state. By using the variational method to optimize the model and the adaptive adjustment function of the deep learning algorithm, it can accurately simulate and predict the cerebrospinal fluid flow patterns in healthy and pathological states, and take personalized intervention measures for different pathological states (such as hydrocephalus and cerebrospinal fluid leakage) to improve the treatment effect. Adopt a non-invasive intelligent monitoring device to obtain the cerebrospinal fluid flow state in real time through sensors without performing invasive operations on patients. Combined with the intelligent diagnosis algorithm, the system can analyze the data and provide diagnostic suggestions, reducing the workload of doctors, improving the diagnostic efficiency, and reducing human errors. Brief Description of the Drawings

[0054] Figure 1 It is a flowchart of the cerebrospinal fluid dynamic monitoring and management method of the present invention.

[0055] Figure 2 It is a flowchart of the implementation method of the abnormal pattern recognition algorithm based on the cerebrospinal fluid pressure and flow characteristics of the present invention.

[0056] Figure 3 It is a flowchart of the cerebrospinal fluid dynamic optimization and intervention strategy method based on the dual physiological and pathological models of the present invention. Detailed Embodiments

[0057] The following will give a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings.

[0058] The cerebrospinal fluid (CSF) dynamic monitoring and management method comprehensively combines multi-dimensional characteristics of CSF flow, advanced sensing technologies, and deep learning algorithms, aiming to identify and predict abnormal fluctuations in CSF flow in real time, and improve the accuracy and personalization of CSF management through dynamically optimizing intervention measures. First of all, the core of the method is to monitor the dynamic changes in the pressure and flow rate of CSF, and these two physical quantities can fully reflect the flow state of CSF in different regions. Traditional CSF monitoring mainly relies on pressure sensors, and judges the normality and abnormality of CSF flow through simple pressure value monitoring. However, this method often fails to provide comprehensive information about flow velocity, direction, and flow pattern. Therefore, there are certain limitations in the existing technologies. This method can capture the real-time changes in both CSF flow velocity and pressure by combining a pressure sensor array with a flow velocity sensor array, providing more three-dimensional and detailed data support for monitoring.

[0059] To achieve accurate dynamic monitoring of CSF, it is first necessary to establish a comparison library of normal and abnormal patterns. In this comparison library, the normal pattern includes the CSF flow states of healthy individuals under different physiological conditions, while the abnormal pattern mainly focuses on situations where there are abnormal fluctuations in CSF flow, such as pathological changes like hydrocephalus and CSF leakage. By establishing the comparison library, a benchmark can be provided for the system to help distinguish which flow patterns are within the normal range and which are potentially abnormal. On this basis, through the combination of a fluid mechanics model and a neural network, the understanding and prediction ability of the CSF flow state can be further improved. The fluid mechanics model can provide a basic physical model for CSF flow, describing the flow laws of the fluid in the CSF system, including factors such as pressure distribution, flow velocity, and local resistance. These physical factors provide a basic framework for identifying abnormal fluctuations in CSF. The neural network, through deep learning algorithms, can learn the complex patterns in CSF flow, especially non-linear changes, and identify abnormal points that are difficult to capture by conventional physical models. For example, through learning a large amount of historical data, the neural network can detect some minor changes, such as sudden mutations in local pressure and sudden decreases in flow velocity, thus providing a basis for early warning.

[0060] By combining various sensor data for dynamic monitoring, the method further enhances the capture accuracy of the CSF flow state. Through the joint deployment of a pressure sensor array and a flow velocity sensor, the changes in the pressure and flow rate of CSF can be simultaneously monitored in different regions such as the ventricles and subarachnoid space, and then multi-dimensional flow information of the CSF system in different parts can be obtained. This multi-point and multi-dimensional monitoring method can more comprehensively reflect the state of CSF flow, not only identifying the changes in the overall flow, but also capturing local abnormal fluctuations. These data are further processed through time series analysis to provide accurate change trends for real-time monitoring, and by accumulating historical data, the long-term evolution pattern of CSF flow is analyzed to effectively identify abnormal fluctuations.

[0061] Cerebrospinal fluid is not only a liquid that acts as a buffer and nutrient transporter between the ventricular system and the spinal cavity. Its flow pattern also directly affects the pressure distribution within the skull, the deformation of brain tissue, and the state of blood flow. When the flow state of cerebrospinal fluid changes, the pressure fluctuations of cerebrospinal fluid not only affect the morphology of brain tissue (such as the dilation or contraction of ventricles), but also affect the compression or expansion of the surrounding brain tissue, thereby changing the hemodynamic characteristics of cerebral blood flow and the local pressure within the tissue. Therefore, there is a dynamically coupled relationship between cerebrospinal fluid and brain tissue, and this relationship needs to be captured and regulated through precise modeling and real-time monitoring. Traditional cerebrospinal fluid monitoring techniques usually only focus on the flow rate and pressure of cerebrospinal fluid, while ignoring the deformation effect of brain tissue, which has led to insufficient monitoring effects and delayed pathological diagnosis in some cases.

[0062] In this context, the change in cerebrospinal fluid pressure becomes a key factor. With the fluctuation of cerebrospinal fluid pressure, the brain tissue, especially the ventricular system, undergoes dilation or contraction. This deformation not only affects the geometric structure of local brain tissue, but also affects the blood flow and tissue pressure in other regions of the skull. For example, in pathological conditions such as hydrocephalus, the dilation of ventricles leads to the compression of the surrounding brain tissue, which in turn causes changes in the compression or resistance of intracranial blood flow. The interaction between cerebrospinal fluid flow pressure and brain tissue deformation will feedback to the cerebrospinal fluid flow itself through complex physical and physiological mechanisms. This bidirectional feedback effect means that the flow state of cerebrospinal fluid not only affects brain tissue deformation, but also the compression and expansion of brain tissue will have a reverse impact on the flow of cerebrospinal fluid, resulting in changes in the flow characteristics of cerebrospinal fluid. Therefore, understanding this coupled relationship and making intelligent adjustments based on it have become the core of precise cerebrospinal fluid management.

[0063] To this end, this method has achieved a comprehensive understanding of the complex dynamic state within the skull by modeling the coupled relationship between cerebrospinal fluid flow and brain tissue deformation. First, the cerebrospinal fluid flow process can be described by a fluid mechanics model, considering factors such as the flow rate, pressure, and flow direction of cerebrospinal fluid. In contrast, brain tissue deformation involves a biomechanical model that focuses on describing the elasticity and plasticity of brain tissue under different pressure conditions. By combining these two, a comprehensive dynamic model including flow, deformation, blood flow, and tissue pressure can be established. This model can predict the feedback effect of brain tissue compression and expansion on cerebrospinal fluid flow, thereby accurately simulating the pressure changes within the skull. For example, if the dilation of ventricles in a certain area leads to local compression of brain tissue, this deformation effect will directly affect the flow of cerebrospinal fluid in this area, causing changes in local flow rate or fluctuations in cerebrospinal fluid pressure. Through real-time monitoring and calculation of these factors, the system can identify the complex interactions between cerebrospinal fluid flow and brain tissue deformation, and accordingly adjust the flow state of cerebrospinal fluid to avoid the further deterioration of pathological changes caused by pressure imbalance.

[0064] On this basis, an intelligent regulation mechanism based on flow-pressure coupling effect was proposed. This mechanism collects cerebrospinal fluid pressure and flow rate data in real time, monitors the deformation effect of brain tissue and local blood flow status, and inputs all data into the intelligent algorithm for analysis. The system can not only identify abnormal fluctuations in cerebrospinal fluid flow, but also regulate the flow state of cerebrospinal fluid through feedback control. For example, when ventricular expansion causes excessive cerebrospinal fluid pressure in a certain area, the intelligent regulation mechanism can adjust the flow rate, reduce the area with excessive cerebrospinal fluid pressure, and thus restore pressure balance and avoid further compression or damage to brain tissue. Through this regulation mechanism based on coupling effect, the system can achieve real-time and precise intervention under different physiological or pathological conditions to ensure the flow of cerebrospinal fluid and the health of brain tissue.

[0065] The physiological model reflects the normal pattern of cerebrospinal fluid flow in healthy individuals. It mainly includes the flow velocity, pressure distribution, generation and absorption rate of cerebrospinal fluid, etc. These characteristics show a stable and regular pattern in healthy people. The physiological model simulates the flow state of cerebrospinal fluid in the ventricles, subarachnoid space and spinal cord under normal conditions through mathematical modeling and fluid mechanics principles. This model provides baseline data for cerebrospinal fluid flow to monitor the changing trend under healthy conditions. The pathological model is a flow model that is adjusted and reconstructed for different types of brain diseases, such as hydrocephalus, cerebrospinal fluid leakage, and increased intracranial pressure. The pathological model reflects the abnormal characteristics of cerebrospinal fluid flow under disease conditions, such as cerebrospinal fluid retention caused by ventricular dilation in hydrocephalus, or flow abnormalities caused by cerebrospinal fluid leakage. These abnormalities can cause the cerebrospinal fluid flow pattern to deviate from the normal state. By establishing and optimizing these pathological models, changes in cerebrospinal fluid flow under different disease states can be identified, and data support can be provided for corresponding interventions. The dual fusion of physiological models and pathological models can not only accurately reflect the differences between normal and abnormal cerebrospinal fluid flow patterns, but also help identify the patient's specific cerebrospinal fluid abnormalities through comparative analysis. Through this combination of dual models, the optimization strategy can be continuously adjusted according to the patient's real-time data during dynamic monitoring to accurately intervene in different types of cerebrospinal fluid abnormalities. For example, when abnormal fluctuations in cerebrospinal fluid flow occur, the system can determine whether it is hydrocephalus, cerebrospinal fluid leakage or other pathological changes based on the predicted results of the pathological model, and adjust the optimization plan to restore normal cerebrospinal fluid flow, thereby improving the patient's health.

[0066] Traditional cerebrospinal fluid (CSF) monitoring methods often require invasive procedures, such as ventricular drainage tubes or lumbar punctures, to directly obtain CSF data. Although these methods are necessary in certain clinical scenarios, they also have problems such as infection risk, patient discomfort, and operational difficulty. Non-invasive monitoring techniques break through this limitation and use high-precision sensor devices to monitor CSF flow and pressure through surface measurements, thus providing a safer and more convenient solution. Sensor technology collects data from different locations in the skull to reflect the flow state of CSF, pressure fluctuations, and deformation of brain tissue in real time. Specifically, sensors can be deployed on the scalp surface or near the skull, using non-invasive techniques such as ultrasound, magnetic resonance imaging (MRI), near-infrared spectroscopy (NIRS), electromagnetic induction, or optical sensors to obtain data by penetrating the skull. These sensors can monitor intracranial pressure changes and infer the CSF flow pattern by analyzing the temporal data of pressure. The flow rate is another key indicator. By measuring the CSF flow rate, the flow conditions of CSF in the ventricles and subarachnoid space can be obtained, and then key information such as CSF flow resistance and circulation efficiency can be revealed. In addition, deformation information of brain tissue can also be obtained through non-invasive sensor technology. For example, using strain sensors or ultrasonic technology, by measuring the tiny displacement or deformation on the surface of brain tissue, the compression or expansion effect of CSF flow on brain tissue can be inferred. These deformation data can be combined with pressure and flow rate data to jointly depict the dynamic interaction between CSF and brain tissue. By real-time analyzing and processing the data collected by these sensors, the intelligent monitoring system can perform multi-dimensional data fusion through algorithm models to identify potential risks of abnormal CSF flow, such as pathological conditions like hydrocephalus and CSF leakage. This information not only helps monitor the health status of CSF flow but also provides accurate diagnostic basis for doctors, and then appropriate intervention measures can be taken. Therefore, non-invasive intelligent monitoring devices can provide real-time monitoring of dynamic changes in CSF without directly contacting CSF, providing strong support for CSF management and brain disease diagnosis.

[0067] Example 1:

[0068] The example is monitoring a patient with hydrocephalus. In this pathological state, CSF accumulates in the ventricles, affecting CSF flow. By installing non-invasive sensors on the patient's head, pressure and flow rate data can be collected in real time, and the flow state of the entire CSF system can be calculated through multi-point monitoring.

[0069] For ease of analysis, the ventricles are selected as the main monitoring area, and global modeling is carried out in combination with the flow conditions in different areas such as the subarachnoid space. First, the density ρ of the patient's CSF is set to be approximately 1.002 g / cm 3, this value is basically consistent among healthy people. In the case of hydrocephalus, due to ventricular dilation, the change in fluid density is relatively small. Next, a flow velocity sensor is combined with a pressure sensor to monitor the flow velocity and local pressure of cerebrospinal fluid in the ventricles in real time. It is set to use an ultrasonic flow velocity sensor. At a certain time point, the measured cerebrospinal fluid flow velocity is v(t,x) = 2 cm / s, that is, the flow velocity of cerebrospinal fluid at this position is 2 centimeters per second. At the same time, the measured cerebrospinal fluid pressure at the same position is P(t,x) = 150 mmHg, indicating the local cerebrospinal fluid pressure. According to the above formula, the comprehensive function f flow (t) can be obtained by integration:

[0070] f flow (t) = ∫ Ω [ρ·v(t,x) + P(t,x)]dx

[0071] It is set that the selected monitoring area Ω is a small area inside the ventricle, with an area of ΔA = 3 cm 2 , and the integral form is used to approximate the flow state in this area. By substituting the data of flow velocity and pressure into the formula, the flow state in this area is calculated:

[0072] f flow (t) = ∫ Ω [1.002·2 + 150]dx = (1.002·2 + 150)×3 cm 2 = (2.004 + 150)×3

[0073] = 456.012 g·cm / s 2

[0074] This comprehensive value f flow (t) = 456.012 g·cm / s 2 is the comprehensive index of the current cerebrospinal fluid flow state, indicating the influence of the flow velocity, pressure and its density of cerebrospinal fluid on the flow state at this time and place.

[0075] Next, by comparing the typical data of healthy people and pathological conditions (such as hydrocephalus), a pattern recognition algorithm can be used to determine whether there is an abnormality. It is set that under normal conditions, the flow velocity of cerebrospinal fluid is about v(t,x) = 3 cm / s, and the pressure is P(t,x) = 120 mmHg. In patients with hydrocephalus, the flow velocity decreases and the pressure increases, indicating an increase in flow resistance and an increased risk of cerebrospinal fluid retention. At this time, by analyzing this value in real time and comparing it with the reference value in the healthy state, the algorithm will find the abnormal pattern of decreased cerebrospinal fluid flow velocity and increased pressure and give an early warning.

[0076] Furthermore, combined with multi-dimensional monitoring, a global view can be formed based on the flow velocity and pressure data in different regions. By combining machine learning model training, different types of cerebrospinal fluid abnormal patterns can be identified. For example, in this case, it is set that through multi-point monitoring, the pressure and flow velocity at multiple positions in the cerebral ventricle deviate from the normal range. By comparing historical data with current monitoring data, potential hydrocephalus risks can be identified and early warnings can be issued.

[0077] In the patient's cerebral ventricle, the monitoring device continuously records pressure and flow velocity data. Based on the real-time sensor feedback system, pressure and flow velocity data are continuously collected and input into the algorithm for processing. It is set that in a certain monitoring period, the measured cerebrospinal fluid pressure data are: P(t0) = 150 mmHg, P(t1) = 153 mmHg, P(t2) = 158 mmHg, and the flow velocity data are: v(t0) = 2.1 cm / s, v(t1) = 2.4 cm / s, v(t2) = 2.8 cm / s. These data represent the pressure and flow velocity values measured at different time points.

[0078] To identify abnormal fluctuations, an adaptive signal processing formula will be used to analyze the continuous pressure and flow velocity data and calculate the abnormality index. The formula is as follows:

[0079]

[0080] Where, represents the abnormality index of the cerebrospinal fluid flow state, reflecting the degree of sharp change in pressure and flow velocity within a given time period. P(t i ) and v(t i ) in the formula are the pressure and flow velocity data at the i-th moment respectively. α is an adaptive parameter used to adjust the weights of data at different time steps to avoid misjudgment caused by noise or excessive volatility.

[0081] It is set that α = 0.5 is used as the initial value of the adaptive parameter. The selection of this value range is verified based on previous clinical data, and it can effectively adjust the error in the calculation and reduce the interference introduced by noise.

[0082] Next, substitute the data step by step to calculate the abnormality index.

[0083] Calculate the change amount in the first time step:

[0084] |P(t1) - P(t0)| = |153 - 150| = 3 mmHg

[0085] |v(t1) - v(t0)| = |2.4 - 2.1| = 0.3 cm / s

[0086] Calculate the change at the second time step:

[0087] |P(t2) - P(t1)| = |158 - 153| = 5 mmHg

[0088] |v(t2) - v(t1)| = |2.8 - 2.4| = 0.4 cm / s

[0089] Substitute all the data into the formula to calculate the abnormality index:

[0090]

[0091] In this case, the abnormality index indicates that there are significant changes in the cerebrospinal fluid flow state, with large fluctuations in pressure and flow velocity, reflecting potential abnormal fluctuations. At this time, based on the set threshold model, the system will judge that this value exceeds the normal range, issue an alarm, and prompt the risk of hydrocephalus deterioration. Doctors can further evaluate the condition or take corresponding intervention measures based on this warning. Through the above-mentioned adaptive signal processing analysis, combined with the dynamic changes of pressure and flow velocity, potential abnormalities in cerebrospinal fluid flow can be identified. Especially in diseases such as hydrocephalus, the fluctuations of cerebrospinal fluid flow are usually large, which is exactly the advantage of this algorithm. The adjustment of the adaptive parameter α in the algorithm also ensures flexibility in different clinical scenarios.

[0092] To achieve comprehensive monitoring of cerebrospinal fluid flow, the system will collect the monitoring data of these different regions in real time and combine intelligent diagnostic algorithms to detect abnormal fluctuations in cerebrospinal fluid flow. Suppose the system collects the following data at a certain moment:

[0093] In the ventricular region, the pressure data read by the pressure sensor is: P1(t) = 160 mmHg;

[0094] In the same region, the flow velocity data read by the flow velocity sensor is: v1(t) = 2.3 cm / s;

[0095] In the subarachnoid space region, the pressure data read by the pressure sensor is: P2(t) = 162 mmHg;

[0096] In the subarachnoid space region, the flow velocity data read by the flow velocity sensor is: v2(t) = 2.5 cm / s.

[0097] In addition, the system uses a data fusion algorithm to integrate the data of multiple sensors, so as to provide more accurate results for the final diagnosis. The data fusion process is represented by the following formula:

[0098]

[0099] where D fusion(t) represents the comprehensive state assessment obtained through data fusion, reflecting the comprehensive state of cerebrospinal fluid flow in multiple monitoring areas; λ k represents the weight coefficient of the k-th sensor data, used to indicate the importance of each sensor in the overall diagnosis; f k (t) represents the monitoring data output by the k-th sensor at time t, including pressure data and flow rate data, reflecting the local cerebrospinal fluid flow state at the location of each sensor; K represents the total number of sensors, covering the data of all monitoring points.

[0100] To illustrate how to apply this algorithm, it is assumed that two sensors are used in the system: the first sensor is located in the ventricular region, and the second sensor is located in the subarachnoid space region. It is also assumed that the weight coefficients of the sensors are:

[0101] λ1 = 0.6 (the weight of the sensor in the ventricular region is higher because its monitoring data accounts for a larger proportion in the overall diagnosis);

[0102] λ2 = 0.4 (the weight of the sensor in the subarachnoid space region is lower but still important).

[0103] Therefore, at this moment, the data fusion formula can be written as:

[0104] D fusion (t) = 0.6·f1(t) + 0.4·f2(t)

[0105] Here, f1(t) and f2(t) represent the monitoring data output by the first and second sensors respectively, including pressure and flow rate information. To further illustrate, these data can be substituted into the formula:

[0106] For the first sensor, f1(t) = P1(t) + v1(t) = 160 + 2.3 = 162.3;

[0107] For the second sensor, f2(t) = P2(t) + v2(t) = 162 + 2.5 = 164.5.

[0108] Substitute into the data fusion formula:

[0109] D fusion (t) = 0.6·162.3 + 0.4·164.5 = 97.38 + 65.8 = 163.18

[0110] Therefore, the comprehensive status evaluation result after data fusion is 163.18. This comprehensive status evaluation value reflects the overall health status of cerebrospinal fluid flow. The system will determine whether this value deviates from the normal value according to the set normal range and threshold. For example, if the normal threshold of the system is set to 150 and values exceeding this are considered abnormal, the diagnostic algorithm will identify this abnormality and send an alert to the medical team, indicating a hydrocephalus problem or other cerebrospinal fluid flow disorders.

[0111] Next, the system further executes an intelligent diagnostic algorithm based on this result to determine whether intervention is needed. Assuming the current patient has been diagnosed as a high-risk patient with hydrocephalus, the system will generate the next intervention suggestions based on the fused data and real-time feedback. For example, it is recommended to perform drug treatment, adjust the patient's body position, or consider surgical operations, etc.

[0112] Example 2:

[0113] In the case of a certain hydrocephalus patient, in order to deeply study the cerebrospinal fluid flow condition of the patient, the hospital combined the method of cerebrospinal fluid dynamic optimization and intervention strategy based on the dual physiological and pathological models proposed in the present invention, and used multi-dimensional modeling technology and deep learning algorithms to perform precise simulation and intervention of cerebrospinal fluid flow.

[0114] During this process, first, real-time pressure and flow rate data of the patient's ventricles and subarachnoid space were collected through sensors, and these data will be used to further establish a flow model in a healthy state and simulate the abnormal patterns of the patient's current cerebrospinal fluid flow.

[0115] Specifically, the cerebrospinal fluid flow state of the patient is first described by using the improved Navier-Stokes equation. The Navier-Stokes equation, as the basic equation for describing fluid dynamics, is also applicable in the simulation of cerebrospinal fluid flow. The improved Navier-Stokes equation is as follows:

[0116]

[0117] In this equation:

[0118] u(x,t) represents the vector of cerebrospinal fluid flow velocity, which determines the movement direction and speed of cerebrospinal fluid, x is the spatial position, and t is the time; p is the pressure of cerebrospinal fluid, which describes the mechanical changes during the flow process; ρ is the density of cerebrospinal fluid, and this variable affects the inertial characteristics of cerebrospinal fluid, with a value range of approximately 1000 kg / m 3 or so; μ is the viscosity of cerebrospinal fluid, which controls the resistance during the flow of cerebrospinal fluid, with a value of approximately 1.2×10 -3 Pa·s; f(x,t) represents the external force, which mainly reflects the generation and absorption processes of cerebrospinal fluid.

[0119] The flow rate of the patient's cerebrospinal fluid in the ventricular region is set to u1 = 3.5 cm / s, the pressure is p1 = 150 mmHg, and the density of the cerebrospinal fluid is ρ = 1000 kg / m 3 , and the viscosity is μ = 1.2×10 -3 Pa·s. These data will be substituted into the improved Navier-Stokes equation to calculate the flow characteristics of the cerebrospinal fluid in the healthy state.

[0120] Regarding the change in the flow rate, considering that there are certain pathological changes in the cerebrospinal fluid flow of the patient (such as ventricular dilation caused by hydrocephalus), the original healthy state model will be adaptively adjusted. It is set that in the pathological state (such as hydrocephalus), the cerebrospinal fluid flow rate will slow down. When the cerebrospinal fluid flow rate is u2 = 1.5 cm / s, the change relationship between the flow rate and the pressure will be simulated by the model. At this time, the difference between the flow rate and the pressure of the pathological state model and those of the healthy state model will be used to adjust the adaptive algorithm of the model in order to optimize the actual pathological state of the patient.

[0121] At this time, through the deep learning algorithm, the system can train the model with a large amount of data of healthy individuals and pathological states to identify potential abnormal patterns in the cerebrospinal fluid flow. At this stage, the system will calculate the characteristics of the cerebrospinal fluid flow in real time, and gradually train and optimize the model in combination with the above improved Navier-Stokes equation.

[0122] Through the simulation process, the following example data can be used for further verification. At a certain moment, the model predicts the change trend between the flow rate and the pressure in the patient's ventricular region as:

[0123]

[0124] It is set that at this time u1 = 3.5 cm / s, and the cerebrospinal fluid flow rate u2 = 1.5 cm / s deduced by the model indicates a slowdown in the flow rate, while the pressure fluctuation is p2 = 170 mmHg. The slowdown in the flow rate and the increase in the pressure indicate the occurrence of hydrocephalus. At this time, the deep learning model can identify this pattern and propose further intervention measures in combination with the training data, such as adjusting the patient's body position, drug intervention or surgical plan. Therefore, the application of the physiological and pathological dual model, combined with real-time data monitoring and deep learning algorithm, can achieve seamless switching between the healthy state and the pathological state, providing strong support for the accurate identification and timely intervention of abnormal cerebrospinal fluid flow.

[0125] In this embodiment, during the treatment of a patient with hydrocephalus, the hospital used the cerebrospinal fluid dynamic optimization and intervention strategy method based on the dual physiological and pathological model proposed in the present invention to achieve real-time monitoring and precise intervention of the cerebrospinal fluid flow state. This method combines physiological and pathological models, optimizes the model prediction error through the variational method, and dynamically adjusts the cerebrospinal fluid flow state. In this process, the flow rate and pressure data of the patient's cerebrospinal fluid were first collected, and a pathological model based on the patient's specific disease was constructed, and a physiological model was established using the data of normal healthy individuals.

[0126] The patient's pathological condition is due to abnormal cerebrospinal fluid flow caused by hydrocephalus, with significant ventricular dilation, a significant slowdown in cerebrospinal fluid flow, and pressure fluctuations in local areas. In order to accurately simulate and optimize the cerebrospinal fluid flow state, the system first benchmarks the patient's normal state through the cerebrospinal fluid flow state of healthy people (i.e., physiological model). Then, the pathological model (hydrocephalus model) is combined with the physiological model through the variational method for real-time fusion to optimize the intervention strategy for the cerebrospinal fluid flow state.

[0127] During the optimization process, the following variational method formula is used for model optimization:

[0128]

[0129] The explanation of this formula is as follows:

[0130] M t The cerebrospinal fluid state model at time t contains flow rate and pressure information. It is a multi-dimensional variable that changes over time. is the flow rate value predicted at the i-th moment, the cerebrospinal fluid flow rate predicted based on the current model and data;

[0131] is the flow rate value actually measured at the i-th moment, which comes from the cerebrospinal fluid flow rate sensor; is the second-order spatial derivative of the model, which reflects small changes in the CSF flow process, describes the change in flow curvature, and helps capture potential abnormal changes in CSF flow;

[0132] M refIt is a reference model in a healthy state, representing the standard pattern of normal cerebrospinal fluid (CSF) flow. It provides a benchmark for adjustments in all pathological states; λ1 and λ2 are regularization coefficients used to balance the prediction error and model complexity. Specifically, λ1 controls the smoothness of the model to avoid overfitting, while λ2 is used to balance the difference between the reference model and real-time data. By comparing the real-time monitored flow velocity and pressure data with the standard flow state of the physiological model, the system can calculate the error between the predicted flow velocity and the actual flow velocity, and adjust the CSF flow state based on this error. Further, by introducing spatial derivatives and time integral terms, the optimization algorithm can identify and correct minor changes in the CSF flow process to ensure that the patient's CSF flow returns to the optimal state.

[0133] In the specific case of this patient, the predicted value of the CSF flow velocity at a certain moment is set as u pred = 2.0 cm / s, while the actually measured flow velocity is u true = 1.8 cm / s. Based on the prediction error, the system will calculate:

[0134] (u pred - u true ) 2 = (2.0 - 1.8) 2 = 0.04 cm 2 / s 2

[0135] Meanwhile, the calculated value of the second-order spatial derivative term is set as:

[0136]

[0137] The difference between the reference model and the patient's current state is set as:

[0138]

[0139] Finally, the calculated result of the optimization objective is:

[0140]

[0141] Setting λ1 = 1.0 and λ2 = 0.5, the optimization objective is:

[0142]

[0143] Through this optimization process, the system can adjust the patient's CSF flow state in real time to ensure it is close to the normal healthy state. After the implementation of the intervention strategy, the patient's CSF flow velocity has been effectively improved, and the flow pattern has gradually returned to the healthy state. Finally, through continuous real-time monitoring and feedback adjustment, the patient's condition has been significantly alleviated, and the hydrocephalus symptoms have been effectively controlled.

[0144] In this embodiment, the hospital then deployed a variety of sensors: a pressure sensor for real-time monitoring of the pressure changes in cerebrospinal fluid, a flow rate sensor for capturing cerebrospinal fluid flow rate data, and a temperature sensor for detecting temperature changes in the cerebrospinal fluid system. These sensors can not only obtain the flow state of cerebrospinal fluid at various moments, but also provide data such as temperature and pressure, comprehensively reflecting the flow characteristics of cerebrospinal fluid. To address this challenge, the team introduced an improved Kalman filtering algorithm, which can effectively remove noise in the sensor data and extract key information, thereby providing an accurate real-time state estimate. The recursive formula of the Kalman filter is as follows:

[0145] x t+1 = Ax t + Bu t + w t

[0146] y t = Hx t + v t

[0147] Where x t is the cerebrospinal fluid state vector at time t, which contains key parameters of cerebrospinal fluid such as flow rate and pressure; A is the state transition matrix, representing the state change from time t to t+1, reflecting the physical laws of cerebrospinal fluid flow; B is the control input matrix, u t is the control input, representing external factors affecting cerebrospinal fluid flow, such as treatment interventions; w t is the process noise, representing random disturbances existing in the flow process; y t is the observed data from the sensor; H is the measurement matrix, representing how the sensor reflects the state of cerebrospinal fluid; v t is the observation noise, representing errors in the measurement process. In implementation, first, the system obtains preliminary data of cerebrospinal fluid through sensors. For example, at a certain moment t0, the flow rate sensor measures the cerebrospinal fluid flow rate v0 = 0.8 cm / s, the pressure sensor measures the pressure value P0 = 120 Pa, and the temperature sensor records the temperature value as T0 = 37.1 °C. These data are directly input into the Kalman filter through the sensors, and the algorithm calculates the current moment's cerebrospinal fluid state estimate x0 based on the known state transition matrix A and control input matrix B, combined with external input signals (such as treatment measures like drug interventions).

[0148] The state transition matrix A is set as:

[0149]

[0150] Where Δt is the time interval, representing the time step for each update, for example, set to 0.1 seconds.

[0151] The control input matrix B is set as:

[0152]

[0153] The control input u t is set as the regulation input of the drug infusion to the cerebrospinal fluid flow rate (for example, set as 0.2), then the updated state estimation formula will become:

[0154] x1 = Ax0 + Bu0 + w0

[0155]

[0156] Through the Kalman filter recursion, the system will continuously update the state x of the cerebrospinal fluid flow t , and use the observed data y of the sensor t to correct the state estimation. For example, at a certain moment t1, the data provided by the sensor is y1 = [0.85 cm / s, 125 Pa, 37.2 °C]. The Kalman filter will combine the predicted flow rate and pressure states with the actually measured observed data to further optimize the cerebrospinal fluid flow state.

[0157] Meanwhile, by introducing the data of the temperature sensor, the temperature change can also reflect the physical properties of the cerebrospinal fluid. For example, when there is an abnormality in the cerebrospinal fluid circulation, the temperature will fluctuate, thus providing more comprehensive diagnostic information for doctors. Finally, through this series of precise sensor data fusion and real-time optimization of the Kalman filter algorithm, the hospital can comprehensively master the dynamics of the patient's cerebrospinal fluid, and timely detect and respond to the abnormal fluctuations of the cerebrospinal fluid flow. In the treatment of this patient's hydrocephalus, after a period of intervention, the flow rate and pressure gradually returned to the normal range, the ventricular dilation was effectively relieved, and the patient's symptoms were significantly improved.

[0158] For example, on the 7th day after applying this solution, the patient's flow rate increased from 0.8 cm / s at the initial stage to 1.2 cm / s, the pressure decreased from 120 Pa to 90 Pa, and the temperature also stabilized from 37.1 °C to 36.8 °C. This series of improvements indicates that the cerebrospinal fluid flow state is gradually restored, and the treatment intervention has achieved initial success.

[0159] Through this example, the effectiveness of the cerebrospinal fluid dynamics optimization and intervention strategy based on the dual physiological and pathological models is demonstrated. The improved Kalman filter algorithm successfully removes the noise of the sensor data, provides a more accurate estimation of the cerebrospinal fluid flow state, and real-time optimizes the intervention strategy. This method can not only deal with common diseases such as hydrocephalus, but also provide strong support for other diseases with abnormal cerebrospinal fluid flow.

Claims

1. A method for dynamic monitoring and management of cerebrospinal fluid, characterized in that The following steps are involved: S1. Abnormal pattern recognition algorithm based on cerebrospinal fluid flow characteristics: S1.

1. Use the dynamic characteristics of cerebrospinal fluid pressure and flow rate for pattern recognition; establish a comparison library of normal and abnormal patterns by real-time monitoring of cerebrospinal fluid flow rate and pressure fluctuations; S1.2, use fluid mechanics models and neural networks for deep learning to identify abnormal points of nonlinear changes; S1.3, by combining the pressure sensor array with the flow velocity sensor, the dynamic flow changes in different areas of the cerebrospinal fluid system, including the ventricles and subarachnoid space, are captured, and abnormal fluctuations are identified through continuous analysis of time series data; S2. Feedback regulation mechanism based on cerebrospinal fluid flow dynamics and brain tissue compression and expansion effects: S2.

1. By analyzing the interaction between the flow pattern of cerebrospinal fluid in the skull and the compression and expansion effect of brain tissue, an intelligent regulation mechanism based on flow-pressure coupling effect is introduced; S2.

2. Under the influence of changes in cerebrospinal fluid pressure, deformation of brain tissue, including ventricular expansion, affects blood flow and tissue pressure in other areas of the skull; S2.

3. Predict the feedback effects of changes in brain tissue compression and expansion on CSF flow by modeling the coupling relationship between CSF flow and brain tissue deformation. S3. Dynamic optimization and intervention strategy of cerebrospinal fluid based on the dual physiological and pathological model: S3.

1. Dynamic optimization and intervention of cerebrospinal fluid by constructing a dual fusion of physiological and pathological models of cerebrospinal fluid flow; The physiological model is based on the normal cerebrospinal fluid flow pattern in a healthy state, while the pathological model is based on the abnormal cerebrospinal fluid flow pattern in different brain diseases including hydrocephalus and cerebrospinal fluid leakage; S3.

2. The physiological model reflects the cerebrospinal fluid flow characteristics of healthy people, while the pathological model is adjusted according to the patient's specific disease background; through the combination of the two, different types of cerebrospinal fluid abnormalities can be identified.

2. The method for dynamic monitoring and management of cerebrospinal fluid according to claim 1, characterized in that The implementation method of the abnormal pattern recognition algorithm based on cerebrospinal fluid flow characteristics includes: The pressure sensor is used to measure the pressure fluctuation in the ventricle, and the flow velocity sensor is used to detect the flow velocity of cerebrospinal fluid in the same area, providing dynamic information about the flow state. Through multi-dimensional and multi-point monitoring, the changes of cerebrospinal fluid flow in different areas and at different times are calculated in real time. The composite flow state function f is used to describe the state of cerebrospinal fluid flow, combining pressure and flow velocity data. flow (t), where: f flow (t)=∫ Ω [ρ·v(t,x)+P(t,x)]dx in: f flow (t) represents the comprehensive state of cerebrospinal fluid flow, which changes with time t and calculates the comprehensive effect of flow velocity and pressure; Ω represents the monitoring area, including different cerebrospinal fluid flow areas in the ventricle and subarachnoid space; the integration area Ω spans the spatial dimension and captures multi-point data in the entire cerebrospinal fluid flow system; ρ represents the density of cerebrospinal fluid; v(t,x) represents the cerebrospinal fluid flow velocity, which changes with time t and spatial position x; it reflects the flow speed and direction of cerebrospinal fluid; P(t,x) represents the cerebrospinal fluid pressure at position x and time t, reflecting the local pressure difference under the flow state.

3. The method for dynamic monitoring and management of cerebrospinal fluid according to claim 2, characterized in that The implementation method of the abnormal pattern recognition algorithm based on cerebrospinal fluid flow characteristics includes: Dynamic modeling technology is used to analyze continuous pressure and flow rate data, from which the regularity of cerebrospinal fluid flow is calculated; and the dynamic modeling of cerebrospinal fluid flow and the identification of abnormal fluctuations use the adaptive signal processing formula: in: The abnormality index of cerebrospinal fluid flow status can identify abnormal fluctuations in pressure or flow rate in continuous data at time t; P(t i ) and v(t i ) represent the pressure and flow rate data at the i-th moment respectively; each t i Corresponding to the unit time point, the monitoring data at that moment is recorded; α is an adaptive parameter used to adjust the weight of data at different time steps; it is dynamically adjusted according to the volatility of the data to avoid noise interference; |P(t i )P(t i-1 )| and |v(t i )v(t i-1 )|represent the absolute changes of pressure and flow velocity in continuous time steps respectively; by calculating the difference, the sharp fluctuations of flow state can be captured and abnormal situations can be identified.

4. The method for dynamic monitoring and management of cerebrospinal fluid according to claim 3, characterized in that The implementation method of the abnormal pattern recognition algorithm based on cerebrospinal fluid flow characteristics includes: By introducing intelligent diagnostic algorithms, abnormal fluctuations in cerebrospinal fluid flow can be detected in real time and diagnostic results can be given. By combining real-time flow rate and pressure data, it can be determined whether hydrocephalus or other cerebrospinal fluid circulation disorders exist, and further intervention measures can be proposed based on the identified abnormal patterns.

5. The method for dynamic monitoring and management of cerebrospinal fluid according to claim 1, characterized in that The cerebrospinal fluid dynamic optimization and intervention strategy method based on the physiological and pathological dual model includes: By introducing multiple variables through multi-dimensional modeling and optimizing with deep learning algorithms, the flow pattern under healthy conditions is simulated, and adaptive adjustments are made to cope with pathological conditions including hydrocephalus and cerebrospinal fluid leakage. The characteristics of cerebrospinal fluid flow are calculated, and the flow process is fully simulated using the improved Navier-Stokes equation by considering flow velocity, pressure, density of cerebrospinal fluid, and local resistance factors. in: u(x,t) represents the vector of cerebrospinal fluid flow velocity, x is the spatial position, and t is the time; the magnitude and direction of the flow velocity determine the movement state of the cerebrospinal fluid; p is the pressure of the cerebrospinal fluid, which describes the mechanical changes in the flow; ρ is the density of the cerebrospinal fluid, which affects the inertial characteristics of the flow; μ is the viscosity of the cerebrospinal fluid, which controls the resistance to flow; f(x,t) is the external force, which reflects the physiological process of the generation and absorption of cerebrospinal fluid.

6. The method for dynamic monitoring and management of cerebrospinal fluid according to claim 5, characterized in that The method for dynamic optimization and intervention strategy of cerebrospinal fluid based on the dual physiological and pathological models includes: switching between healthy state and pathological state through the combination of physiological model and pathological model, and optimizing the intervention of cerebrospinal fluid flow state in real time; the physiological model is based on the cerebrospinal fluid flow state of normal individuals, while the pathological model is used to model abnormal states of hydrocephalus and cerebrospinal fluid leakage.

7. The method for dynamic monitoring and management of cerebrospinal fluid according to claim 6, characterized in that The cerebrospinal fluid dynamic optimization and intervention strategy method based on the physiological and pathological dual model includes: Through the data fusion of multiple sensors, including pressure sensors, flow rate sensors, and temperature sensors, comprehensive information on cerebrospinal fluid flow is obtained; through the improved Kalman filter algorithm, noise is eliminated and features are extracted, providing intelligent feedback based on real-time data; the improved Kalman filter continuously optimizes state estimation through the following recursive formula; x t+1 =Ax t +Bu t +w t y t =Hx t +v t in: x t is the cerebrospinal fluid state vector at time t, including flow rate and pressure information; A is the state transfer matrix, which represents the state change from time t to t+1, reflecting the physical law of cerebrospinal fluid flow; B is the control input matrix, u t is the control input; w t is process noise, which indicates random disturbances in the flow process; t is the observation data from the sensor; H is the measurement matrix, which means that the sensor reflects the state of cerebrospinal fluid, v t is the observation noise.

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