Cerebral oxygen entropy-based blood flow dynamic simulation system

Abstract

The application relates to the technical field of medical signal processing, in particular to a blood flow dynamic simulation system based on cerebral oxygen entropy, which comprises a data acquisition module, a structure modeling module, a state monitoring module, a simulation calculation module and an abnormality prediction module.In the application, through synchronous utilization of cerebral oxygen saturation and blood flow velocity signals, accurate capture of the dynamic correlation between cerebral oxygen supply and blood flow dynamics is realized, the sensitivity of abnormal fluctuation identification is enhanced, individualization and accuracy of blood flow simulation are improved in combination with cerebral vascular geometric structure and vascular elasticity and compliance parameters, abnormal state recognition of blood supply is strengthened by utilizing multi-point dynamic coupling, cerebral blood supply stability evaluation is optimized, the degree of restoration of local blood flow microdynamic characteristics is improved through dynamic updating of inertia coefficients and shear stress, the nonlinear coupling relationship is extracted in combination with oxygen entropy fluctuation, shear stress and flow velocity gradient, the accuracy of abnormal event prediction is improved, and the early warning capability of cerebral blood supply abnormality is enhanced.

Description

Technical Field

[0001] This invention relates to the field of medical signal processing technology, and in particular to a blood flow dynamic simulation system based on brain oxygen entropy. Background Technology

[0002] The field of medical signal processing technology encompasses technologies related to the acquisition, analysis, processing, and utilization of biomedical signals. The core of this technology lies in extracting valuable information from human physiological signals to assist in medical diagnosis, disease monitoring, and treatment evaluation. Medical signal processing mainly includes the processing of electrophysiological signals such as electroencephalogram (EEG), electrocardiogram (ECG), and electromyography (EMG) signals, as well as signal processing obtained through optical and biomechanical methods. It covers the use of signal acquisition equipment and sensors, signal preprocessing such as noise reduction and filtering, feature extraction methods such as wavelet transform and principal component analysis, and pattern recognition methods such as machine learning and neural networks. These technologies play a role in clinical monitoring, disease prediction, and rehabilitation training.

[0003] Among them, the blood flow dynamic simulation system based on brain oxygen entropy refers to a system that uses brain oxygen-related parameters and entropy analysis methods to model and simulate the dynamic characteristics of brain blood flow. The system acquires brain oxygen parameters such as brain tissue oxygen saturation and hemoglobin concentration change data monitored by near-infrared spectroscopy, and extracts characteristic signals of brain oxygen supply and demand by combining signal processing methods such as wavelet decomposition and multi-scale entropy analysis. It uses a hemodynamic model and nonlinear dynamic analysis methods to simulate and model the changing laws of brain tissue blood flow, including data acquisition, data preprocessing, signal feature extraction, and hemodynamic simulation. The aim is to describe the correlation characteristics between brain oxygen level and brain blood flow dynamics in the form of a parametric model.

[0004] Traditional blood flow dynamic simulation techniques lack dynamic coupling analysis of cerebral oxygen saturation and blood flow velocity signals, resulting in an inability to accurately reveal the complex interaction between cerebral blood flow and oxygen supply. This reduces sensitivity to abnormal blood supply events. Signal processing fails to fully utilize the correlation between cerebral oxygen-related parameters, making it difficult to reflect real-time dynamic changes in cerebral oxygen supply status and affecting the comprehensiveness of abnormal state identification. Cerebral vascular modeling often relies on standard models, neglecting individual differences in vascular structure, compliance, and elasticity parameters, making blood flow simulations lack specificity and failing to accurately correspond to the patient's actual physiological state. Blood supply stability assessment lacks multi-dimensional parameter joint analysis, making early identification of abnormal blood supply areas difficult and affecting timely management of disease risks. The blood flow simulation process ignores the regulatory effect of local microstructural parameters on hemodynamics, reducing the realism of simulation results and affecting the accurate prediction of abnormal blood flow behaviors. Abnormal event prediction fails to establish a deep correlation between cerebral oxygen fluctuations and hemodynamics, lacking analysis of complex features such as nonlinear coupling and phase shift, leading to delayed warnings of events such as ischemia and hyperperfusion, reducing the timeliness and effectiveness of interventions, and hindering the effective implementation of clinical risk management. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a blood flow dynamic simulation system based on brain oxygen entropy.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a blood flow dynamic simulation system based on cerebral oxygen entropy includes:

[0007] The data acquisition module acquires blood flow velocity monitoring signals simultaneously based on brain oxygen saturation monitoring data. By calculating the correlation between oxygen saturation signals at adjacent monitoring points, it constructs a brain oxygen entropy time-series fluctuation curve. Combined with blood flow velocity signals, it extracts the correlation features between oxygen entropy fluctuation rate and flow velocity changes, generating correlation feature data.

[0008] The structural modeling module calls the associated feature data, combines it with medical imaging data, analyzes the three-dimensional geometric structure of the patient's cerebral blood vessels, calculates the flow resistance of multiple vascular branch nodes, analyzes the elastic coefficient and compliance parameters of the vascular wall, and generates a set of cerebral blood vessel parameters.

[0009] The status monitoring module calls the cerebrovascular parameter set to calculate the dynamic correlation between oxygen saturation and blood flow velocity, identify abnormal locations of the correlation, and calculate the blood supply stability of multiple locations in the patient's brain by assessing the impact of vascular elasticity and oxygen metabolism rate on blood supply, and identify abnormal locations of blood supply.

[0010] The simulation calculation module calls the abnormal blood supply location, combines the geometric structure and compliance parameters of the blood vessel to construct a dynamic blood flow simulation mesh, iteratively updates the inertia coefficient and shear stress distribution of the mesh nodes, calculates the local eddy current intensity and blood kinetic energy transfer efficiency, and generates a dynamic blood flow parameter field.

[0011] As a further aspect of the present invention, the associated feature data includes oxygen entropy fluctuation rate, flow velocity change trend, and oxygen saturation signal correlation; the cerebral vascular parameter set includes vascular three-dimensional geometry, flow resistance dataset, and compliance parameters; the blood supply stability includes blood supply fluctuation rate, abnormal location of vascular elasticity, and oxygen metabolism rate offset data; and the blood flow dynamic parameter field includes shear stress distribution data, local eddy current intensity, and blood flow kinetic energy transfer efficiency information.

[0012] As a further aspect of the present invention, the data acquisition module includes:

[0013] The oxygen saturation monitoring submodule calculates the correlation of oxygen saturation signals between adjacent monitoring points based on brain oxygen saturation monitoring data, extracts the rate of change of oxygen saturation in the time series, analyzes the trend of oxygen saturation fluctuation, and uses the change amplitude data to analyze the trend of signal change and constructs the brain oxygen entropy time series fluctuation curve.

[0014] The velocity signal processing submodule calls the brain oxygen entropy time-series fluctuation curve, synchronously acquires blood flow velocity monitoring signals, calculates the rate of change of blood flow velocity signals, analyzes signal stability, and acquires blood flow velocity change data.

[0015] The associated feature extraction submodule calls the blood flow velocity change data to calculate the oxygen entropy fluctuation rate at multiple monitoring points using the formula:

[0016] ;

[0017] Calculate the correlation coefficient between oxygen saturation signal and blood flow velocity signal, obtain the correlation characteristics between oxygen entropy fluctuation rate and blood flow velocity change, and generate correlation feature data;

[0018] in, The correlation coefficient between oxygen saturation signal and blood flow velocity signal is denoted as . The index representing the time window. Represents the total number of time windows. Representing the Oxygen saturation signal within a time window This represents the mean of the oxygen saturation signal across all time windows. Representing the Blood flow velocity signal within a time window This represents the mean of blood flow velocity signals across all time windows.

[0019] As a further aspect of the present invention, the structural modeling module includes:

[0020] The 3D vascular reconstruction submodule acquires the associated feature data, combines it with medical imaging data, extracts the vascular contour boundary, identifies the structure of the patient's cerebral blood vessels, calculates the trajectory of the vascular centerline, constructs a 3D vascular geometric model, and obtains the 3D geometric structure of the blood vessels.

[0021] The blood flow resistance calculation submodule calls the three-dimensional geometry of the blood vessel, extracts each blood vessel branch node from the structure, obtains the blood vessel diameter, branch angle, and blood vessel length, and calculates the flow cross-sectional area of ​​each blood vessel branch using the formula:

[0022] ;

[0023] Calculate the flow resistance parameters, obtain the flow resistance of each vascular node, and generate the vascular flow resistance parameters;

[0024] in, Represents the resistance to flow in blood vessel branches. Represents blood viscosity. Represents the length of the blood vessel. Represents the diameter of the blood vessel. The local flow loss coefficient representing the vascular branch. Pi This represents the currently calculated vessel branch number. Represents the total number of blood vessel branches;

[0025] The elastic parameter analysis submodule calls the vascular flow resistance parameters, calculates the elastic modulus at multiple locations of the blood vessel based on the flow resistance and the patient's real-time blood pressure data, and calculates the compliance parameters using the blood vessel radius to generate a set of vascular elastic parameters.

[0026] As a further aspect of the present invention, the status monitoring module includes:

[0027] The dynamic correlation analysis submodule calls the cerebrovascular parameter set, aligns the cerebral oxygen saturation signal and blood flow velocity signal in spatial coordinates, maps the signals to the cerebrovascular structure, calculates the degree of synchronous change of oxygen saturation and blood flow velocity at multiple locations, identifies abnormal correlation locations, and generates correlation calculation results.

[0028] The blood supply stability quantification submodule utilizes the aforementioned correlation calculation results to assess the impact of vascular elasticity and oxygen metabolism rate on blood supply, combining the elasticity coefficient and compliance parameters of the patient's cerebral blood vessels, using the following formula:

[0029] ;

[0030] Calculate the stability index value of blood vessels at each location to identify locations with abnormal stability;

[0031] in, This represents an indicator of blood supply stability. Representing the The elastic modulus value at each blood vessel location. Representing the A blood vessel location compliance parameter, Representing the Oxygen metabolism rate at each vascular location Representing the Real-time pressure measured at each blood vessel location This is the reference value for blood pressure. This represents the total number of blood vessel locations. An index for the location of blood vessels;

[0032] The abnormal location extraction submodule calls the stable abnormal location, and based on the identified correlated abnormal location and stable abnormal location, identifies the area of ​​abnormal blood supply to the patient's brain and generates the abnormal blood supply location of the brain region.

[0033] As a further aspect of the present invention, the simulation calculation module includes:

[0034] The mesh generation submodule calls the abnormal blood supply location, combines the geometric structure and compliance parameters of the blood vessel, extracts the geometric features and fluid boundary conditions of the blood vessel, sets the scale of mesh division, adjusts the mesh density according to the blood vessel diameter, branch angle, and wall thickness, optimizes the connection relationship between mesh units, constructs the mesh topology, and generates a dynamic blood flow simulation mesh.

[0035] The fluid inertial analysis submodule calls the dynamic blood flow simulation mesh to simulate and extract blood flow velocity and cross-sectional area data within the simulation mesh, analyze the accumulation of local blood flow kinetic energy, calculate the changes in inertial force in vascular branches and tortuous areas, identify abnormal accumulation points of inertial force, assess the degree of influence of inertia on local blood flow, and obtain the inertial parameters of the mesh nodes.

[0036] The blood flow energy transfer submodule calls the inertial parameters of the grid nodes to extract the velocity changes at inertial anomaly points, analyzes the distribution pattern of blood flow energy, and uses the following formula:

[0037] ;

[0038] Calculate blood flow energy loss and obtain blood flow energy transfer efficiency;

[0039] in, Represents blood flow energy loss. Representing the Fluid density of each grid cell This represents the flow cross-sectional area of ​​the grid cell. The blood flow velocity at the grid cell inlet, The blood flow velocity at the grid cell outlet, The total number of grid cells. This is the index of the grid cell.

[0040] As a further aspect of the present invention, the system further includes:

[0041] The abnormal prediction module calls the blood flow dynamic parameter field, utilizes the shear stress distribution and kinetic energy transfer characteristics at multiple locations, combines the brain oxygen entropy fluctuation rate and flow velocity gradient, analyzes the phase shift relationship between oxygen entropy fluctuation and shear stress, extracts coupling features, decomposes the evolution trend and fluctuation law of phase shift, predicts abnormal blood flow events in the patient's brain, including ischemic and hyperperfusion events, and generates event prediction results.

[0042] The event prediction results include ischemic event prediction results, hyperperfusion event prediction results, and coupling feature parameters.

[0043] As a further aspect of the present invention, the anomaly prediction module includes:

[0044] The shear stress analysis submodule calls the blood flow dynamic parameter field, simulates and analyzes the shear stress of the local blood vessel wall, calculates the shear stress gradient of each region, analyzes the distribution of shear stress on the blood vessel wall, and obtains the shear stress distribution characteristics.

[0045] The phase offset extraction submodule calls upon the shear stress distribution characteristics to extract the brain oxygen entropy fluctuation rate and blood flow velocity gradient, using the formula:

[0046] ;

[0047] The average phase shift between the oxygen entropy fluctuation signal and the shear stress signal is calculated, and the dynamic coupling degree of the signals is evaluated by combining the difference in phase change rate, and the coupling feature extraction results are obtained.

[0048] in, This represents the average phase offset. This represents the total number of data points within the calculation window. Representing the The phase value of the oxygen entropy fluctuation signal within a time window. Representing the The phase value of the shear stress signal within a time window. This represents the instantaneous rate of change of the oxygen entropy fluctuation signal within that time window. This represents the instantaneous rate of change of the shear stress signal within that time window. Represents the time interval between adjacent time points. The index representing the current time window for calculation;

[0049] The blood flow event prediction submodule calls the coupling feature extraction results, combines them with kinetic energy transfer features, calculates the deviation of the phase offset value from the preset threshold, quantifies the trigger probability of abnormal events, simulates and predicts abnormal blood flow events in the patient's brain, including ischemia and hyperperfusion events, and generates event prediction results.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0051] By synchronously utilizing cerebral oxygen saturation and blood flow velocity signals, precise capture of the dynamic correlation between cerebral oxygen supply and hemodynamics is achieved, enhancing the sensitivity of abnormal fluctuation identification. Combining cerebral vascular geometry with vascular elasticity and compliance parameters improves the individualization and accuracy of blood flow simulation. Utilizing multi-point dynamic coupling strengthens the identification of abnormal blood supply states and optimizes the assessment of cerebral blood supply stability. Through dynamic updates of inertia coefficient and shear stress, the fidelity of local blood flow micro-dynamic features is improved. Combining oxygen entropy fluctuations, shear stress, and flow velocity gradients, nonlinear coupling relationships are extracted, improving the accuracy of abnormal event prediction and enhancing the early warning capability for cerebral blood supply abnormalities. Attached Figure Description

[0052] Figure 1 This is a system flowchart of the present invention;

[0053] Figure 2 This is a flowchart of the data acquisition module of the present invention;

[0054] Figure 3 This is a flowchart of the structural modeling module of the present invention;

[0055] Figure 4 This is a flowchart of the status monitoring module of the present invention;

[0056] Figure 5 This is a flowchart of the simulation calculation module of the present invention;

[0057] Figure 6 This is a flowchart of the anomaly prediction module of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0059] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0060] Please see Figure 1 The blood flow dynamic simulation system based on cerebral oxygen entropy includes:

[0061] The data acquisition module acquires blood flow velocity monitoring signals simultaneously based on brain oxygen saturation monitoring data. By calculating the correlation between oxygen saturation signals at adjacent monitoring points, it constructs a brain oxygen entropy time-series fluctuation curve. Combined with blood flow velocity signals, it extracts the correlation features between oxygen entropy fluctuation rate and flow velocity changes, generating correlation feature data.

[0062] The structural modeling module calls the associated feature data and combines it with medical imaging data. By analyzing the three-dimensional geometric structure of the patient's cerebral blood vessels, including vascular branches, diameter, and wall thickness, it calculates the flow resistance of multiple vascular branch nodes and uses the relationship between oxygen entropy fluctuation rate and wall thickness parameters to adjust the vascular wall elasticity coefficient and compliance parameters to generate a set of cerebral blood vessel parameters.

[0063] The status monitoring module calls the cerebrovascular parameter set, uses the structure and elasticity coefficient of vascular geometry to align the cerebral oxygen saturation and blood flow velocity signals in spatial coordinates, identifies abnormal locations of correlation by quantifying the dynamic correlation between oxygen saturation and blood flow velocity, and assesses the impact of vascular elasticity and oxygen metabolism rate on blood supply by combining vascular elasticity parameters and oxygen entropy fluctuation rate. By calculating the blood supply stability of multiple locations in the patient's brain, it identifies the location of abnormal blood supply, records the coordinates of abnormal locations, the degree of abnormality, and the fluctuation pattern of abnormal data, and generates abnormal blood supply locations.

[0064] The simulation calculation module calls the abnormal blood supply location, uses the abnormal location coordinates and blood flow fluctuation mode, and combines the geometric structure and compliance parameters of the blood vessel to construct a dynamic blood flow simulation mesh. By iteratively updating the inertia coefficient and shear stress distribution of the mesh nodes, it calculates the local eddy current intensity and blood flow kinetic energy transfer efficiency, and generates a dynamic blood flow parameter field.

[0065] The abnormality prediction module calls upon the blood flow dynamic parameter field, utilizes the shear stress distribution and kinetic energy transfer characteristics at multiple locations, and combines the brain oxygen entropy fluctuation rate and flow velocity gradient. By analyzing the phase shift relationship between oxygen entropy fluctuation and shear stress, it extracts coupling features, decomposes the evolution trend and fluctuation law of phase shift, and predicts abnormal blood flow events in the patient's brain, including ischemic and hyperperfusion events, and generates event prediction results.

[0066] The associated feature data includes oxygen entropy fluctuation rate, flow velocity change trend, and oxygen saturation signal correlation; the cerebral vascular parameter set includes vascular three-dimensional geometry, flow resistance dataset, and compliance parameters; blood supply stability includes blood supply fluctuation rate, abnormal location of vascular elasticity, and oxygen metabolism rate offset data; the blood flow dynamic parameter field includes shear stress distribution data, local eddy current intensity, and blood flow kinetic energy transfer efficiency information; and the event prediction results include ischemic event prediction results, hyperperfusion event prediction results, and coupled feature parameters.

[0067] Please see Figure 2 The data acquisition module includes:

[0068] The oxygen saturation monitoring submodule calculates the correlation of oxygen saturation signals between adjacent monitoring points based on brain oxygen saturation monitoring data, extracts the rate of change of oxygen saturation in the time series, analyzes the trend of oxygen saturation fluctuation, and uses the change amplitude data to analyze the trend of signal change and constructs the brain oxygen entropy time series fluctuation curve.

[0069] Based on brain oxygen saturation monitoring data, multiple monitoring points were selected, and oxygen saturation signals were measured at fixed time intervals. The correlation between oxygen saturation signals from adjacent monitoring points was calculated. Initial oxygen saturation values ​​for each monitoring point were set, and the signal changes at subsequent time points were calculated to obtain the rate of change of oxygen saturation in the time series. The rate of change of oxygen saturation can be calculated using the following formula:

[0070] ;

[0071] in, Indicates the first The rate of change of oxygen saturation within a time window and Time windows and Oxygen saturation value at time [time]. and For the corresponding time point.

[0072] Set a monitoring point at time Oxygen saturation at seconds In time Oxygen saturation at seconds The rate of change of oxygen saturation is:

[0073] ;

[0074] The calculations showed that the oxygen saturation at this monitoring point decreased at a rate of 0.2% per second. Finally, by integrating the fluctuation trend data from all time windows, a temporal fluctuation curve of brain oxygen entropy was constructed.

[0075] The velocity signal processing submodule calls the brain oxygen entropy time-series fluctuation curve, synchronously acquires blood flow velocity monitoring signals, calculates the rate of change of blood flow velocity signals, analyzes signal stability, and acquires blood flow velocity change data.

[0076] By calling the brain oxygen entropy time-series fluctuation curve, the signal fluctuation range within different time windows is determined, blood flow velocity monitoring signals are acquired, and the blood flow velocity data is sorted by time window. Segmentation, blood flow velocity in each time window Measured by a blood flow velocity sensor. The rate of change of blood flow velocity can be calculated using the following formula:

[0077] ;

[0078] in, Indicates the first Rate of change of blood flow velocity within a time window and Time windows and Blood flow velocity value at any given time. and For the corresponding time point.

[0079] Set a monitoring point at time Blood flow velocity per second cm / s, in time Blood flow velocity per second cm / s, then the rate of change of blood flow velocity is:

[0080] ;

[0081] The calculations showed that the blood flow velocity at this monitoring point decreased at a rate of 0.5 cm / s². Finally, the data on the change in blood flow velocity were obtained.

[0082] The associated feature extraction submodule calls blood flow velocity change data to calculate the oxygen entropy fluctuation rate at multiple monitoring points using the formula:

[0083] ;

[0084] Calculate the correlation coefficient between oxygen saturation signal and blood flow velocity signal, obtain the correlation characteristics between oxygen entropy fluctuation rate and blood flow velocity change, and generate correlation feature data;

[0085] in, The correlation coefficient between oxygen saturation signal and blood flow velocity signal is denoted as . The index representing the time window. Represents the total number of time windows. Representing the Oxygen saturation signal within a time window This represents the mean of the oxygen saturation signal across all time windows. Representing the Blood flow velocity signal within a time window This represents the mean of blood flow velocity signals across all time windows.

[0086] By retrieving blood flow velocity change data, the oxygen entropy fluctuation rate at multiple monitoring points is calculated, and the correlation between the oxygen saturation signal and the blood flow velocity signal is calculated using the following formula:

[0087] ;

[0088] in, The correlation coefficient between oxygen saturation signal and blood flow velocity signal is denoted as . For the first Oxygen saturation over a time window, This represents the average oxygen saturation over all time windows. For the first Blood flow velocity within a time window This represents the average blood flow velocity over all time windows. This represents the total number of time windows.

[0089] Table 1. Example data on oxygen saturation and blood flow velocity at monitoring points.

[0090]

[0091] As shown in Table 1, the parameters are set as follows: The total number of time windows is set to... The average oxygen saturation was The average blood flow velocity is Calculate the correlation coefficient between oxygen saturation and blood flow velocity:

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] The results indicate that, within the selected time window, the rate of oxygen saturation fluctuation is strongly positively correlated with changes in blood flow velocity.

[0111] Please see Figure 3 The structural modeling module includes:

[0112] The 3D vascular reconstruction submodule acquires associated feature data, combines it with medical imaging data, extracts vascular contour boundaries, identifies the structure of the patient's cerebral blood vessels, calculates the trajectory of the vascular centerline, constructs a 3D vascular geometric model, and obtains the 3D geometric structure of the blood vessels.

[0113] The process involves acquiring associated feature data and extracting multi-layered two-dimensional slices from medical imaging data. Each slice is obtained from high-resolution CT or MRI scans. Edge detection methods are used to identify vascular structures, and regions with pixel gradient changes exceeding a set threshold are selected. The overlap of consecutive slice boundaries is compared to determine the vascular contour boundaries. The centerline trajectory of the identified vessels is calculated, and the path curve of the vessels is fitted by analyzing the pixel density distribution point by point. Spline interpolation is used to smooth the centerline, improving the continuity and stability of the curve. Based on the reconstructed centerline trajectory, the vessel cross-section is segmented, and the vessel diameter and cross-sectional area are calculated. A three-dimensional surface model of the vessels is constructed, and all vessel segments are merged to form a complete vascular network structure. Voxel information is used to optimize the surface smoothness of the vessel wall, thus obtaining the three-dimensional geometric structure of the vessels.

[0114] The blood flow resistance calculation submodule calls upon the three-dimensional geometry of the blood vessel, extracts each branch node from the structure, obtains the vessel diameter, branch angle, and vessel length, and calculates the flow cross-sectional area of ​​each branch using the following formula:

[0115] ;

[0116] Calculate the flow resistance parameters, obtain the flow resistance of each vascular node, and generate the vascular flow resistance parameters;

[0117] in, Represents the resistance to flow in blood vessel branches. Represents blood viscosity. Represents the length of the blood vessel. Represents the diameter of the blood vessel. The local flow loss coefficient representing the vascular branch. Pi This represents the currently calculated vessel branch number. Represents the total number of blood vessel branches;

[0118] The three-dimensional geometry of the blood vessel is used to extract the nodes of each branch, recording the vessel diameter, branch angle, and vessel length. The flow cross-sectional area of ​​the blood flow channel is calculated based on the vessel diameter, and the blood flow resistance is calculated by combining this with the physical properties of blood. The flow resistance of each vessel branch is calculated using the following formula:

[0119] ;

[0120] in, This indicates blood viscosity (set value is 3.5 mPa·s). The length of the blood vessel. The diameter of the blood vessel. This represents the local flow loss coefficient at the branch point. This represents the number of blood vessel branches.

[0121] Set the length of a blood vessel branch mm, diameter mm, blood viscosity mPa·s, number of branches The local flow loss coefficients are respectively and The calculation is as follows:

[0122] ;

[0123] ;

[0124] ;

[0125] The results indicate that the flow resistance value of this vascular branch is 8.435 (Pa·s / mm³), and its value can be used to further analyze the hydrodynamic characteristics of different branches, ultimately generating vascular flow resistance parameters.

[0126] The elastic parameter analysis submodule calls the vascular flow resistance parameter, calculates the elastic modulus at multiple locations of the blood vessel based on the flow resistance and the patient's real-time blood pressure data, and calculates the compliance parameter using the blood vessel radius to generate a set of vascular elastic parameters.

[0127] By calling vascular flow resistance parameters and combining them with the patient's real-time blood pressure data, the elastic modulus of different vascular sites is calculated. The vessel radius is selected as one of the main parameters, and based on the relationship between vessel wall thickness and internal blood pressure, the Laplace equation is used to calculate the vessel compliance parameters. The patient's current blood pressure data is then obtained. Determine the thickness of the blood vessel wall and initial radius Calculate compliance parameters :

[0128] ;

[0129] Set the initial radius of a certain blood vessel segment. mm, wall thickness mm, real-time measured blood pressure mmHg, calculated as follows:

[0130] ;

[0131] ;

[0132] The results indicate that the compliance parameter of this vascular segment is 0.1042 mm / mmHg, representing its responsiveness to changes in blood flow pressure, and a set of vascular elasticity parameters is generated.

[0133] Table 2. Example data on geometric parameters of vascular branches

[0134]

[0135] As shown in Table 2, the flow resistance of different vascular branches is determined by their geometric characteristics. The thinner the blood vessel, the greater the flow resistance. Increasing the length of the blood vessel will also affect the calculation results of the resistance. These parameters are used to further optimize the hemodynamic analysis.

[0136] Please see Figure 4 The status monitoring module includes:

[0137] The dynamic correlation analysis submodule calls the cerebrovascular parameter set, aligns the cerebral oxygen saturation signal and blood flow velocity signal in spatial coordinates, maps the signals to the cerebral vascular structure, calculates the degree of synchronous change of oxygen saturation and blood flow velocity at multiple locations, identifies abnormal correlation locations, and generates correlation calculation results.

[0138] The system utilizes a cerebrovascular parameter set to extract key structural information from the patient's cerebrovascular imaging data, including vascular branch points, vessel length, and diameter. This data is then spatially aligned with oxygen saturation and blood flow velocity signals. A three-dimensional coordinate transformation method is used to map the oxygen saturation signal onto the patient's cerebrovascular structure, ensuring that the signal at each measurement point matches its corresponding anatomical location. After acquiring synchronous change information at multiple locations, the system calculates the trends in blood oxygen saturation and blood flow velocity at each location. By calculating the dynamic correlation between oxygen saturation and blood flow velocity signals, locations with abnormal correlations are identified. If a region has a high blood flow velocity but a low oxygen saturation level, or a low blood flow velocity but an high oxygen saturation level, then that region may have an abnormal blood supply. An abnormal point is screened by setting a correlation threshold. When the correlation coefficient is lower than the set threshold (e.g., 0.3), the region is marked as an abnormal region, ultimately yielding the correlation calculation results.

[0139] The blood supply stability quantification submodule utilizes correlation calculations to assess the impact of vascular elasticity and oxygen metabolism rate on blood supply, combining the elasticity coefficient and compliance parameters of the patient's cerebral blood vessels, using the following formula:

[0140] ;

[0141] Calculate the stability index value of blood vessels at each location to identify locations with abnormal stability;

[0142] in, This represents an indicator of blood supply stability. Representing the The elastic modulus value at each blood vessel location. Representing the A blood vessel location compliance parameter, Representing the Oxygen metabolism rate at each vascular location Representing the Real-time pressure measured at each blood vessel location This is the reference value for blood pressure. This represents the total number of blood vessel locations. An index for the location of blood vessels;

[0143] Using the correlation calculation results, based on the influence of vascular elasticity and oxygen metabolism rate, the vascular elastic modulus, compliance parameters, and oxygen metabolism rate of the target area are extracted. Combined with the patient's blood pressure data, local vascular pressure fluctuations are calculated using the following formula:

[0144] ;

[0145] in, This represents an indicator of blood supply stability. Representing the The elastic modulus value at each blood vessel location. Representing the A blood vessel location compliance parameter, Representing the Oxygen metabolism rate at each vascular location Representing the Real-time pressure measured at each blood vessel location This is the reference value for blood pressure. This represents the total number of blood vessel locations. An index for the location of blood vessels;

[0146] Let the elastic modulus of three blood vessel locations in a patient be respectively , , (Unit: MPa), compliance parameters are respectively , , The oxygen metabolism rates are respectively , , (Unit: mL O2 / min), real-time blood pressure measurements were as follows: , , (Unit: mmHg), blood pressure baseline value is set to (Unit: mmHg), substitute into the formula to calculate:

[0147] ;

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] The results indicate that the blood supply stability in this area is low, and there may be decreased vascular compliance or local ischemia, resulting in an unstable location.

[0153] The abnormal location extraction submodule calls the stable abnormal location function. Based on the identified correlated and stable abnormal locations, it identifies the areas of abnormal blood supply to the patient's brain and generates abnormal blood supply locations in the brain region.

[0154] By calling upon locations of stability anomalies and comparing key parameters such as vascular elastic modulus, compliance parameters, and oxygen metabolism rate based on correlation calculation results, the areas of vascular compression or abnormal dilation are analyzed to determine whether there is local insufficient blood supply or hyperperfusion. First, blood supply stability indicators are extracted. For blood vessel locations below a threshold, the threshold is set to 0.75. When a certain location... Below this threshold, it indicates a possible blood supply abnormality in the area. Subsequently, the standard deviation of local blood flow velocity is calculated to screen areas with abnormal fluctuations in blood flow velocity over a short period. If the standard deviation of blood flow velocity changes in a certain vessel segment exceeds a set range (e.g., exceeding 10%) across multiple consecutive time windows, then hemodynamic instability may exist in that area. Furthermore, combined with the patient's blood pressure data, the risk of local hyperperfusion is assessed. If a region has low vascular compliance and an abnormally elevated oxygen metabolism rate, local cerebral hyperperfusion may occur, and this region is marked as an abnormal location. To more intuitively display the distribution of vascular blood supply stability, a stability anomaly distribution table can be constructed, extracting blood supply stability index values, blood flow velocity standard deviation, and oxygen metabolism rate for each vessel location, and setting abnormal location identification criteria, such as...

[0155] Table 3. Identification Table of Abnormal Locations in Blood Supply Stability

[0156]

[0157] As shown in Table 3, locations numbered 2 and 4 had a blood supply stability index below 0.75, large fluctuations in blood flow velocity (standard deviation exceeding 10%), and high oxygen metabolism rates; therefore, they were marked as abnormal locations. These data will be further used to identify areas of abnormal brain blood supply and, combined with the patient's clinical data, analyze possible pathological changes. Ultimately, abnormal brain blood supply areas will be extracted from the patient's brain to generate abnormal brain blood supply locations.

[0158] Please see Figure 5 The simulation calculation module includes:

[0159] The mesh generation submodule calls the location of blood supply abnormality, combines the geometric structure and compliance parameters of the blood vessel, extracts the geometric features and fluid boundary conditions of the blood vessel, sets the scale of mesh generation, adjusts the mesh density according to the blood vessel diameter, branch angle and wall thickness, optimizes the connection relationship between mesh units, constructs the mesh topology, and generates a dynamic blood flow simulation mesh.

[0160] Access the location of the blood supply abnormality and obtain blood flow parameter data for the abnormal area, including flow velocity. Blood viscosity Pressure distribution By combining the patient's vascular geometric data, the diameter of the blood vessels is extracted. curvature Branch angle Wall thickness Based on characteristics such as [specific features], a mesh generation scale is set to make the mesh denser in areas with smaller diameters or high curvature, while the mesh of relatively straight main blood vessels is relatively sparse. Flow parameters at the interfaces of blood vessel branch points are calculated to maintain continuity with adjacent elements. The connectivity between mesh elements is optimized, and the mesh density is adjusted to maintain numerical stability in the fluid computation process, thus constructing the mesh topology. During the mesh density optimization process, the local mesh size is calculated using the following formula:

[0161] ;

[0162] in, The side length of the grid cell. Local blood vessel diameter (unit: mm). This refers to the curvature of the blood vessel (dimensionless, typically ranging from 0.1 to 1.5). The angle of a blood vessel branch (unit: degree, usually ranging from 0° to 120°).

[0163] set up mm, curvature Branch angle Calculate the mesh size:

[0164] ;

[0165] The calculations show that, in this region, the side length of the grid cells should be set to 1.25 mm to suit the geometry of the blood vessels.

[0166] The fluid inertial analysis submodule calls the dynamic blood flow simulation mesh to simulate and extract blood flow velocity and cross-sectional area data within the simulation mesh, analyze the accumulation of local blood flow kinetic energy, calculate the changes in inertial force in vascular branches and tortuous regions, identify abnormal accumulation points of inertial force, assess the degree of influence of inertia on local blood flow, and obtain the inertial parameters of the mesh nodes.

[0167] Call the dynamic blood flow simulation mesh to extract the blood flow velocity within the mesh cells. Flow cross-sectional area Blood pressure distribution The calculation involves assessing the effect of fluid inertial forces on each grid cell, determining the rate of change of inertial forces, and utilizing the inertial force gradient. Calculate the local impact of inertial forces on blood flow, identify abnormal accumulation points of inertial forces, assess the extent of the effect of inertial forces in fluid transmission, and determine whether the accumulation of inertial forces may lead to abnormal fluctuations in local flow velocity.

[0168] The inertial force is calculated using the following formula:

[0169] ;

[0170] in, It is an inertial force. Blood density, The cross-sectional area of ​​blood flow. Local blood flow velocity, This represents the velocity gradient.

[0171] Set blood density kg / m3, blood flow cross-sectional area m2, local blood flow velocity changes m / s, at the grid cell length If the value changes within m, the inertial force is calculated as follows:

[0172] ;

[0173] ;

[0174] ;

[0175] The calculation shows that the inertial force within this grid cell is 140.67 N, which can be used to assess the impact of inertial force on local blood flow stability.

[0176] The blood flow energy transfer submodule calls the inertial parameters of the grid nodes to extract the velocity changes at inertial anomaly points, analyzes the distribution pattern of blood flow energy, and uses the following formula:

[0177] ;

[0178] Calculate blood flow energy loss and obtain blood flow energy transfer efficiency;

[0179] in, Represents blood flow energy loss. Representing the Fluid density of each grid cell This represents the flow cross-sectional area of ​​the grid cell. The blood flow velocity at the grid cell inlet, The blood flow velocity at the grid cell outlet, The total number of grid cells. For the index of the grid cell;

[0180] By calling the inertial parameters of the grid nodes, the flow velocity changes at inertial anomaly points are filtered, and the inlet and outlet flow velocity data of the inertial anomaly points are extracted. and Calculate the energy loss of blood flow in this region. The distribution pattern of energy in this region is analyzed using the following formula:

[0181] ;

[0182] Calculate blood flow energy loss and obtain blood flow energy transfer efficiency;

[0183] in, Represents blood flow energy loss. Representing the Fluid density of each grid cell This represents the flow cross-sectional area of ​​the grid cell. The blood flow velocity at the grid cell inlet, The blood flow velocity at the grid cell outlet, The total number of grid cells. This is the index of the grid cell.

[0184] Given a region with 5 grid cells, the blood density... kg / m3, flow cross-sectional area The values ​​are [0.002, 0.0025, 0.002, 0.0018, 0.0022] m², and the inlet flow rate is [0.002, 0.0025, 0.002, 0.0018, 0.0022]. The outlet flow rates are [0.5, 0.48, 0.52, 0.49, 0.47] m / s respectively. The values ​​are [0.48, 0.46, 0.5, 0.47, 0.45] m / s respectively.

[0185] Calculate the blood flow energy loss in each grid cell:

[0186] ;

[0187] ;

[0188] ;

[0189] ;

[0190] ;

[0191] Total blood flow energy loss:

[0192] ;

[0193] The calculation shows that the total blood flow energy loss in the five grid cells of the blood flow region is 5.18 J, which can be used to evaluate blood flow transmission efficiency and determine whether there is abnormal kinetic energy dissipation in the region.

[0194] Please see Figure 6 The anomaly prediction module includes:

[0195] The shear stress analysis submodule calls the blood flow dynamic parameter field to simulate and analyze the shear stress of the local blood vessel wall, calculate the shear stress gradient of each region, analyze the distribution of shear stress on the blood vessel wall, and obtain the shear stress distribution characteristics.

[0196] By invoking the blood flow dynamic parameter field and simulating and analyzing the shear stress of the local blood vessel wall, the following steps are taken: First, fluid dynamic parameters inside the blood vessel are collected, including variables such as blood flow velocity, vessel wall stress, and blood viscosity. Key data are then extracted using numerical calculation methods. The distribution of fluid shear force at different locations on the blood vessel wall is calculated. During the calculation, blood flow velocity, vessel radius, and fluid viscosity characteristics must be considered. The shear stress is then calculated using fluid mechanics formulas. :

[0197] ;

[0198] in, Represents shear stress. Represents the dynamic viscosity of blood. Represents blood flow velocity. Represents the radius of the blood vessel.

[0199] Setting the dynamic viscosity of blood Blood flow velocity Blood vessel radius The shear stress is calculated as follows:

[0200] ;

[0201] This result indicates that in this vascular region, the shear stress of blood on the vessel wall is... This can be used to assess the stress on local blood vessels and ultimately obtain the shear stress distribution characteristics.

[0202] The phase offset extraction submodule utilizes shear stress distribution features to extract the brain oxygen entropy fluctuation rate and blood flow velocity gradient, using the following formula:

[0203] ;

[0204] The average phase shift between the oxygen entropy fluctuation signal and the shear stress signal is calculated, and the dynamic coupling degree of the signals is evaluated by combining the difference in phase change rate, and the coupling feature extraction results are obtained.

[0205] in, This represents the average phase offset. This represents the total number of data points within the calculation window. Representing the The phase value of the oxygen entropy fluctuation signal within a time window. Representing the The phase value of the shear stress signal within a time window. This represents the instantaneous rate of change of the oxygen entropy fluctuation signal within that time window. This represents the instantaneous rate of change of the shear stress signal within that time window. Represents the time interval between adjacent time points. The index representing the current time window for calculation;

[0206] By utilizing shear stress distribution characteristics, the brain oxygen entropy fluctuation rate and blood flow velocity gradient are extracted. First, the brain oxygen entropy fluctuation signal and shear stress signal within adjacent time windows are sampled in segments, and the time window length is set. The time axis is divided into discrete time points, and Fourier transform is performed on the data in each time window to extract phase information, and the phase difference between signals is calculated. And further calculate its time-series rate of change. and To capture the dynamic characteristics of the signal, the formula is:

[0207] ;

[0208] Set the total number of data points in the calculation window The phase value and instantaneous rate of change are set as follows: Time window 1: rad, rad, rad / s, rad / s, time window 2: rad, rad, rad / s, rad / s, time window 3: rad, rad, rad / s, rad / s, time window 4: rad, rad, rad / s, rad / s, time window 5: rad, rad, rad / s, rad / s, substitute into the formula to calculate:

[0209] ;

[0210] ;

[0211] ;

[0212] ;

[0213] ;

[0214] ;

[0215] ;

[0216] Calculation results Rad, representing the average phase offset between signals, can be used to determine the dynamic coupling degree between brain oxygen entropy fluctuation signals and shear stress signals, and to obtain coupling feature extraction results.

[0217] The blood flow event prediction submodule calls the coupling feature extraction results, combines them with kinetic energy transfer features, calculates the deviation of the phase offset value from the preset threshold, quantifies the trigger probability of abnormal events, simulates and predicts abnormal blood flow events in the patient's brain, including ischemia and hyperperfusion events, and generates event prediction results.

[0218] By calling the coupling feature extraction results and combining them with the kinetic energy transfer features, the phase shift value in the coupling features is analyzed. Perform statistical analysis, calculate the time mean and standard deviation of the phase offset values, and compare them with the set warning threshold. Compare and calculate the offset magnitude To quantify the probability of abnormal events, a probability calculation model is established, using the following formula:

[0219] ;

[0220] in, Represents the probability of an abnormal event being triggered. The standard deviation represents the phase offset. This represents the preset phase offset threshold. This represents the calculated average phase offset.

[0221] Set the calculated average phase offset Preset threshold Standard deviation ,but:

[0222] ;

[0223] The results indicate that the phase offset in this region deviates significantly from the preset threshold, corresponding to an abnormal event trigger probability of 1.0, meaning that the risk of abnormal blood flow events is relatively high. This data can be used to further predict possible ischemic or hyperperfusion events in the patient's brain, ultimately generating event prediction results.

[0224] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A blood flow dynamic simulation system based on cerebral oxygen entropy, characterized in that, The system includes: The data acquisition module acquires blood flow velocity monitoring signals simultaneously based on brain oxygen saturation monitoring data. By calculating the correlation between oxygen saturation signals at adjacent monitoring points, it constructs a brain oxygen entropy time-series fluctuation curve. Combined with blood flow velocity signals, it extracts the correlation features between oxygen entropy fluctuation rate and flow velocity changes, generating correlation feature data. The data acquisition module includes: The oxygen saturation monitoring submodule calculates the correlation of oxygen saturation signals between adjacent monitoring points based on brain oxygen saturation monitoring data, extracts the rate of change of oxygen saturation in the time series, analyzes the trend of oxygen saturation fluctuation, and uses the change amplitude data to analyze the trend of signal change and constructs the brain oxygen entropy time series fluctuation curve. The velocity signal processing submodule calls the brain oxygen entropy time-series fluctuation curve, synchronously acquires blood flow velocity monitoring signals, calculates the rate of change of blood flow velocity signals, analyzes signal stability, and acquires blood flow velocity change data. The associated feature extraction submodule calls the blood flow velocity change data to calculate the oxygen entropy fluctuation rate at multiple monitoring points using the formula: ； Calculate the correlation coefficient between oxygen saturation signal and blood flow velocity signal, obtain the correlation characteristics between oxygen entropy fluctuation rate and blood flow velocity change, and generate correlation feature data; in, The correlation coefficient between oxygen saturation signal and blood flow velocity signal is denoted as . The index representing the time window. Represents the total number of time windows. Representing the Oxygen saturation signal within a time window This represents the mean of the oxygen saturation signal across all time windows. Representing the Blood flow velocity signal within a time window This represents the mean of blood flow velocity signals across all time windows. The structural modeling module calls the associated feature data, combines it with medical imaging data, analyzes the three-dimensional geometric structure of the patient's cerebral blood vessels, calculates the flow resistance of multiple vascular branch nodes, analyzes the elastic coefficient and compliance parameters of the vascular wall, and generates a set of cerebral blood vessel parameters. The status monitoring module calls the cerebrovascular parameter set to calculate the dynamic correlation between oxygen saturation and blood flow velocity, identify abnormal locations of the correlation, and calculate the blood supply stability of multiple locations in the patient's brain by assessing the impact of vascular elasticity and oxygen metabolism rate on blood supply, and identify abnormal locations of blood supply. The status monitoring module includes: The dynamic correlation analysis submodule calls the cerebrovascular parameter set, aligns the cerebral oxygen saturation signal and blood flow velocity signal in spatial coordinates, maps the signals to the cerebrovascular structure, calculates the degree of synchronous change of oxygen saturation and blood flow velocity at multiple locations, identifies abnormal correlation locations, and generates correlation calculation results. The blood supply stability quantification submodule utilizes the aforementioned correlation calculation results to assess the impact of vascular elasticity and oxygen metabolism rate on blood supply, combining the elasticity coefficient and compliance parameters of the patient's cerebral blood vessels, using the following formula: ； Calculate the stability index value of blood vessels at each location to identify locations with abnormal stability; in, This represents an indicator of blood supply stability. Representing the The elastic modulus value at each blood vessel location. Representing the A blood vessel location compliance parameter, Representing the Oxygen metabolism rate at each vascular location Representing the Real-time pressure measured at each blood vessel location This is the reference value for blood pressure. This represents the total number of blood vessel locations. An index for the location of blood vessels; The abnormal location extraction submodule calls the stable abnormal location, and based on the identified correlated abnormal location and stable abnormal location, identifies the area of ​​abnormal blood supply to the patient's brain and generates the abnormal blood supply location of the brain region. The simulation calculation module calls the abnormal blood supply location, combines the geometric structure and compliance parameters of the blood vessel to construct a dynamic blood flow simulation mesh, iteratively updates the inertia coefficient and shear stress distribution of the mesh nodes, calculates the local eddy current intensity and blood kinetic energy transfer efficiency, and generates a dynamic blood flow parameter field.

2. The blood flow dynamic simulation system based on cerebral oxygen entropy according to claim 1, characterized in that, The associated feature data includes oxygen entropy fluctuation rate, flow velocity change trend, and oxygen saturation signal correlation. The cerebral vascular parameter set includes vascular three-dimensional geometry, flow resistance dataset, and compliance parameters. The blood supply stability includes blood supply fluctuation rate, abnormal location of vascular elasticity, and oxygen metabolism rate offset data. The blood flow dynamic parameter field includes shear stress distribution data, local eddy current intensity, and blood flow kinetic energy transfer efficiency information.

3. The blood flow dynamic simulation system based on cerebral oxygen entropy according to claim 1, characterized in that, The structural modeling module includes: The 3D vascular reconstruction submodule acquires the associated feature data, combines it with medical imaging data, extracts the vascular contour boundary, identifies the structure of the patient's cerebral blood vessels, calculates the trajectory of the vascular centerline, constructs a 3D vascular geometric model, and obtains the 3D geometric structure of the blood vessels. The blood flow resistance calculation submodule calls the three-dimensional geometry of the blood vessel, extracts each blood vessel branch node from the structure, obtains the blood vessel diameter, branch angle, and blood vessel length, and calculates the flow cross-sectional area of ​​each blood vessel branch using the formula: ； Calculate the flow resistance parameters, obtain the flow resistance of each vascular node, and generate the vascular flow resistance parameters; in, Represents the resistance to flow in blood vessel branches. Represents blood viscosity. Represents the length of the blood vessel. Represents the diameter of the blood vessel. The local flow loss coefficient representing the vascular branch. Pi This represents the currently calculated vessel branch number. Represents the total number of blood vessel branches; The elastic parameter analysis submodule calls the vascular flow resistance parameters, calculates the elastic modulus at multiple locations of the blood vessel based on the flow resistance and the patient's real-time blood pressure data, and calculates the compliance parameters using the blood vessel radius to generate a set of vascular elastic parameters.

4. The blood flow dynamic simulation system based on cerebral oxygen entropy according to claim 1, characterized in that, The simulation calculation module includes: The mesh generation submodule calls the abnormal blood supply location, combines the geometric structure and compliance parameters of the blood vessel, extracts the geometric features and fluid boundary conditions of the blood vessel, sets the scale of mesh division, adjusts the mesh density according to the blood vessel diameter, branch angle, and wall thickness, optimizes the connection relationship between mesh units, constructs the mesh topology, and generates a dynamic blood flow simulation mesh. The fluid inertial analysis submodule calls the dynamic blood flow simulation mesh to simulate and extract blood flow velocity and cross-sectional area data within the simulation mesh, analyze the accumulation of local blood flow kinetic energy, calculate the changes in inertial force in vascular branches and tortuous areas, identify abnormal accumulation points of inertial force, assess the degree of influence of inertia on local blood flow, and obtain the inertial parameters of the mesh nodes. The blood flow energy transfer submodule calls the inertial parameters of the grid nodes to extract the velocity changes at inertial anomaly points, analyzes the distribution pattern of blood flow energy, and uses the following formula: ； Calculate blood flow energy loss and obtain blood flow energy transfer efficiency; in, Represents blood flow energy loss. Representing the Fluid density of each grid cell This represents the flow cross-sectional area of ​​the grid cell. The blood flow velocity at the grid cell inlet, The blood flow velocity at the grid cell outlet, The total number of grid cells. This is the index of the grid cell.

5. The blood flow dynamic simulation system based on cerebral oxygen entropy according to claim 1, characterized in that, The system also includes: The abnormal prediction module calls the blood flow dynamic parameter field, utilizes the shear stress distribution and kinetic energy transfer characteristics at multiple locations, combines the brain oxygen entropy fluctuation rate and flow velocity gradient, analyzes the phase shift relationship between oxygen entropy fluctuation and shear stress, extracts coupling features, decomposes the evolution trend and fluctuation law of phase shift, predicts abnormal blood flow events in the patient's brain, including ischemic and hyperperfusion events, and generates event prediction results. The event prediction results include ischemic event prediction results, hyperperfusion event prediction results, and coupling feature parameters.

6. The blood flow dynamic simulation system based on cerebral oxygen entropy according to claim 5, characterized in that, The anomaly prediction module includes: The shear stress analysis submodule calls the blood flow dynamic parameter field, simulates and analyzes the shear stress of the local blood vessel wall, calculates the shear stress gradient of each region, analyzes the distribution of shear stress on the blood vessel wall, and obtains the shear stress distribution characteristics. The phase offset extraction submodule calls upon the shear stress distribution characteristics to extract the brain oxygen entropy fluctuation rate and blood flow velocity gradient, using the formula: ； The average phase shift between the oxygen entropy fluctuation signal and the shear stress signal is calculated, and the dynamic coupling degree of the signals is evaluated by combining the difference in phase change rate, and the coupling feature extraction results are obtained. in, This represents the average phase offset. This represents the total number of data points within the calculation window. Representing the The phase value of the oxygen entropy fluctuation signal within a time window. Representing the The phase value of the shear stress signal within a time window. This represents the instantaneous rate of change of the oxygen entropy fluctuation signal within that time window. This represents the instantaneous rate of change of the shear stress signal within that time window. Represents the time interval between adjacent time points. Represents the total length of the calculation window. The index representing the current time window for calculation; The blood flow event prediction submodule calls the coupling feature extraction results, combines them with kinetic energy transfer features, calculates the deviation of the phase offset value from the preset threshold, quantifies the trigger probability of abnormal events, simulates and predicts abnormal blood flow events in the patient's brain, including ischemia and hyperperfusion events, and generates event prediction results.

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