Blood flow critical closing pressure acquisition device, equipment, medium and system based on Doppler technology

Abstract

The invention relates to a blood flow critical closing pressure obtaining device, equipment, a medium and a system based on the Doppler technology, and belongs to the field of medical instruments. The device comprises a first acquisition module used for acquiring arterial blood flow of a preset part of a target object through an ultrasonic Doppler blood flow measurement technology; the second acquisition module is used for acquiring the average blood pressure MAP of the preset part corresponding to the arterial blood flow within the same time of acquiring the arterial blood flow; the determining module is used for fitting a linear function which takes the arterial blood flow as an independent variable and MAP as a dependent variable according to the arterial blood flow and the MAP corresponding to the arterial blood flow; and then calculating a first value of the MAP when the arterial blood flow is zero according to the linear function, and determining the first value as the critical closing pressure of the preset part of the target object. According to the technical scheme, the problem that no reliable method for measuring the critical closing pressure exists clinically at present can be solved.

Description

Technical Field

[0001] This application relates to the field of medical devices, and in particular to a blood flow critical closure pressure acquisition device, computing device, computer-readable storage medium and system based on Doppler technology. Background Technology

[0002] In modern medical practice, clinicians rely on various information collection systems and methods to acquire patient data for accurate diagnosis and effective treatment. However, ensuring that the information provided to clinicians not only meets diagnostic and treatment needs but also possesses high accuracy and consistency is a challenging task. This problem is particularly pronounced in emergency situations where analysis and decision-making time are extremely limited, or where decisions made may have a significant impact on patient outcomes.

[0003] Critical closure pressure (Pcrit) refers to the arterial pressure at which blood flow ceases. It provides a basic measurement of vascular tone in response to disease and treatment, allowing for the assessment of perfusion pressure differences between different organs. This enables the optimization of resuscitation blood pressure targets during shock treatment, ensuring adequate perfusion of systemic organs and optimizing hemodynamic management during shock. However, there is currently no reliable method in clinical practice to measure this parameter.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] In view of this, in order to solve at least one problem existing in the prior art, this application provides a blood flow critical closure pressure acquisition device, computing device, computer-readable storage medium and system based on Doppler technology.

[0006] To achieve the above objectives, the technical solution of this application is implemented as follows:

[0007] In a first aspect, embodiments of this application provide a blood flow critical closure pressure acquisition device based on Doppler technology, the device comprising:

[0008] The first acquisition module is used to acquire arterial blood flow at a preset location of the target object using ultrasound Doppler blood flow measurement technology;

[0009] The second acquisition module is used to acquire the average blood pressure MAP of the preset site corresponding to the arterial blood flow within the same time period of acquiring the arterial blood flow.

[0010] The determination module is used to fit a linear function with arterial blood flow as the independent variable and MAP as the dependent variable based on multiple arterial blood flow rates and multiple MAPs corresponding to the arterial blood flow rates; then calculate the first value of MAP when the arterial blood flow rate is zero based on the linear function, and determine the first value as the critical closure pressure of the preset part of the target object.

[0011] In an optional implementation, the determining module is further configured to:

[0012] Dynamic time series data are constructed based on multiple arterial blood flow rates and multiple corresponding arterial blood flow maps;

[0013] The linear function is updated using a sliding window technique, which in turn updates the critical closure pressure; the size of the sliding window is N data points, where N is greater than or equal to 2.

[0014] In an optional implementation, the determining module is further configured to:

[0015] After the sliding window slides, the historical data is weighted using a forgetting factor, so that recent data has a greater impact on the update of the linear function.

[0016] In an optional implementation, the determining module is further configured to:

[0017] The linear function is fitted and updated using the K-nearest neighbor algorithm and an improved forgetting factor fusion method.

[0018] In an optional implementation, the determining module is further configured to:

[0019] Count the number of outlier data pairs within the sliding window;

[0020] The size of the sliding window is adjusted based on the number of abnormal data pairs within the sliding window.

[0021] In an optional implementation, the determining module is further configured to:

[0022] Based on the multiple arterial blood flow rates and the multiple MAPs corresponding to the arterial blood flow rates, a scatter plot in a rectangular coordinate system is drawn with the arterial blood flow rate on the horizontal axis and the MAP on the vertical axis; then, based on the scatter plot in the rectangular coordinate system, a linear regression line is fitted, and the MAP at the intersection of the linear regression line and the vertical axis is determined as the critical closure pressure of the preset part of the target object.

[0023] In an optional implementation, the determining module is further configured to:

[0024] The scatter plot in the rectangular coordinate system is sent to a display device for display.

[0025] In the Cartesian coordinate scatter plot, the region displaying the critical closure pressure of the target object is divided into a safe zone and a risk zone; the safe zone and the risk zone are distinguished based on the value of the critical closure pressure of the target object and its individual characteristics.

[0026] In an optional implementation, the determining module is further configured to:

[0027] The regions displaying the arterial blood flow and the MAP in the Cartesian coordinate system are divided into a safe zone and a risk zone; the safe zone and the risk zone are distinguished based on the values ​​of the arterial blood flow and the MAP of the target object and individual characteristics.

[0028] In an optional embodiment, the second acquisition module is further configured to:

[0029] The first MAP is obtained by invasively measuring the blood pressure corresponding to the arterial blood flow.

[0030] A second MAP is obtained by measuring the blood pressure corresponding to the arterial blood flow using a wearable device;

[0031] The first MAP and the second MAP are weighted and averaged to obtain the MAP.

[0032] In an optional embodiment, the apparatus further includes a calibration module, the calibration module being used for:

[0033] Upon first use, a personalized calibration curve is established using baseline data of the target object; the baseline data consists of arterial blood flow and arterial pressure (MAP) at rest.

[0034] In subsequent monitoring, if the fluctuation of the critical closure pressure is detected to exceed a preset threshold, a recalibration process is automatically triggered, and the coefficients of the linear function are updated.

[0035] In an optional embodiment, the apparatus further includes a third acquisition module, the third acquisition module being used for:

[0036] The auxiliary physiological parameters for determining the critical closure pressure are monitored and obtained, and the auxiliary parameter values ​​are dynamically correlated with the arterial blood flow and the arterial blood flow rate (MAP). The auxiliary physiological parameters include heart rate, blood oxygen saturation, and body temperature.

[0037] In an optional implementation, the determining module is further configured to:

[0038] The arterial blood flow, the MAP, and the auxiliary parameter values ​​are input into a pre-trained machine learning model to obtain the critical closure pressure at a preset location of the target object;

[0039] The training data for the machine learning model includes: arterial blood flow, MAP, auxiliary parameter values, and clinically diagnosed critical closure pressure of target subjects of different ages, genders, and medical histories.

[0040] Secondly, embodiments of this application provide a computing device, the computing device comprising: a storage component, a communication bus, and a processing component, wherein:

[0041] The storage component is used to store the operating program of the blood flow critical closure pressure acquisition device based on Doppler technology;

[0042] The communication bus is used to enable communication between the storage component and the processing component;

[0043] The processing unit is used to perform the work of each module in any of the blood flow critical closure pressure acquisition devices based on Doppler technology described above.

[0044] Thirdly, embodiments of this application provide a computer-readable storage medium storing an executable program, which, when executed by a processor, enables the operation of each module in any of the Doppler-based blood flow critical closure pressure acquisition devices described above.

[0045] Fourthly, embodiments of this application provide a blood flow critical closure pressure acquisition system, comprising:

[0046] Any of the above-mentioned Doppler-based blood flow critical closure pressure acquisition devices;

[0047] An ultrasonic Doppler blood flow monitoring device is used to monitor arterial blood flow at a preset location on a target object and transmit the data to the critical closure pressure acquisition device.

[0048] A blood pressure monitoring device is used to monitor the blood pressure at a preset location on a target object and send the data to the critical closure pressure acquisition device.

[0049] This application provides a Doppler-based critical closure pressure acquisition device, computing device, computer-readable storage medium, and system. The device includes: a first acquisition module for acquiring arterial blood flow at a preset location of a target object using ultrasound Doppler blood flow measurement technology; a second acquisition module for acquiring the mean arterial blood flow (MAP) at the preset location corresponding to the arterial blood flow within the same time frame; and a determination module for fitting a linear function with arterial blood flow as the independent variable and MAP as the dependent variable based on multiple arterial blood flow rates and multiple MAP values ​​corresponding to those arterial blood flow rates; then calculating a first value of MAP when the arterial blood flow is zero based on the linear function, and determining this first value as the critical closure pressure at the preset location of the target object. Therefore, the Doppler-based critical closure pressure acquisition device, computing device, and storage medium provided in this application fit a linear function to the acquired arterial blood flow and MAP, and calculate the critical closure pressure based on the linear function, eliminating the need for direct measurement and providing relatively high accuracy. Thus, the Doppler-based critical closure pressure acquisition device, computing device, and storage medium provided in this application can solve the problem that there is currently no reliable method for measuring critical closure pressure in clinical practice.

[0050] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0051] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0052] Figure 1 A schematic diagram of the structure of the Doppler-based critical closure pressure acquisition device provided in this application embodiment. Figure 1 ;

[0053] Figure 2 A schematic diagram comparing the weight settings of the forgetting factor sequence used in the prior art and this application;

[0054] Figure 3 A schematic diagram of a scatter plot drawn in the determination module of the Doppler-based blood flow critical closure pressure acquisition device provided in the embodiments of this application;

[0055] Figure 4 A schematic diagram of the structure of the Doppler-based critical closure pressure acquisition device provided in this application embodiment. Figure 2 ;

[0056] Figure 5A flowchart illustrating the execution process of the Doppler-based blood flow critical closure pressure acquisition device provided in this application embodiment;

[0057] Figure 6 A detailed flowchart illustrating the execution process of the Doppler-based blood flow critical closure pressure acquisition device provided in this application embodiment;

[0058] Figure 7 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application;

[0059] Figure 8 This is a schematic diagram of a blood flow critical closure pressure acquisition system provided in an embodiment of this application.

[0060] Explanation of reference numerals in the attached figures:

[0061] 100. Closure pressure acquisition device; 101. First acquisition module; 102. Second acquisition module; 103. Determination module; 104. Calibration module; 105. Third acquisition module; 600. Computing device; 601. Storage component; 602. Communication bus; 603. Processing component; 604. Input device; 605. Output device; 606. External communication interface; 802. Ultrasonic Doppler blood flow monitoring device; 803. Blood pressure monitoring device. Detailed Implementation

[0062] To make the technical solutions and beneficial effects of this application more obvious and understandable, the technical solutions in the embodiments of this application are clearly and completely described below by listing specific embodiments. Obviously, the embodiments of this application are not exhaustive, and the described embodiments are only some embodiments of this application, not all embodiments.

[0063] The exemplary embodiments disclosed in this application will now be described in more detail with reference to the accompanying drawings, providing detailed structures and steps to illustrate the technical solution of this application. Note that the drawings are not necessarily drawn to scale, and local features may be enlarged or reduced to more clearly show the details of the local features.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and should not be construed as limiting the technical solutions of this application.

[0065] The following description provides numerous specific details to offer a more thorough understanding of this application. However, it will be apparent to those skilled in the art that this application can be practiced without one or more of these details. To clearly define the inventive concept of this application and avoid confusion with its content, technical features well-known in the art and conventionally understood by those skilled in the art are not elaborated upon. Specifically, this document does not fully list all features of actual embodiments, nor does it provide a detailed description of well-known functions and structures.

[0066] In view of the lack of a reliable method for measuring Pcrit in the existing technology, the inventors of this application, after years of research, have derived a feasible technical approach: to estimate Pcrit by analyzing the relationship between arterial blood pressure waveform and blood flow parameters within each stroke cycle and constructing a fitting model. Therefore, the following embodiments are obtained.

[0067] Example 1

[0068] This application provides a Doppler-based blood flow critical closure pressure acquisition device (hereinafter referred to as closure pressure acquisition device 100), referencing... Figure 1 The closure pressure acquisition device 100 includes:

[0069] The first acquisition module 101 is used to acquire the arterial blood flow at a preset location of the target object using ultrasound Doppler blood flow measurement technology;

[0070] The second acquisition module 102 is used to acquire the average blood pressure MAP of the preset site corresponding to the arterial blood flow within the same time period of acquiring the arterial blood flow.

[0071] The determining module 103 is used to fit a linear function with arterial blood flow as the independent variable and MAP as the dependent variable based on the multiple arterial blood flow rates and the multiple MAPs corresponding to the arterial blood flow rates; then calculate the first value of MAP when the arterial blood flow rate is zero based on the linear function, and determine the first value as the critical closure pressure of the preset part of the target object.

[0072] Understandably, Doppler ultrasound blood flow measurement technology is based on the Doppler effect. It emits ultrasound waves and receives their reflected signals, detecting the frequency shift (frequency shift) caused by red blood cell movement, thereby calculating blood flow velocity, direction, and properties. Doppler ultrasound blood flow measurement technology not only offers high resolution and precise quantification, but also boasts advantages such as being radiation-free, non-invasive, providing real-time dynamic monitoring, and being cost-effective.

[0073] MAP can be obtained through direct measurement methods, such as arterial catheterization or non-invasive continuous monitoring, or through indirect calculation methods, such as estimating MAP from systolic blood pressure (SYS) and diastolic blood pressure (DIA) using formulas. These methods are well known to those skilled in the art and will not be described in detail.

[0074] As mentioned earlier, the critical closure pressure can be estimated by analyzing the relationship between arterial blood pressure waveform and blood flow parameters. Furthermore, the study found that the relationship between the two is close to linear, thus it can be simplified to a linear function. Then, the first value of the MAP when the arterial blood flow is zero is determined as the critical closure pressure at the preset location of the target object.

[0075] In contrast to existing technologies, there is no reliable method to measure the critical closure pressure. The embodiments of this application obtain a more accurate and reliable result by fitting a linear function to obtain the critical closure pressure.

[0076] The critical closure pressure can be Pcrit, also known as critical closing pressure (CCP). It can also include arterial critical closure pressure (Pcc).

[0077] In some embodiments of this application, the first acquisition module 101 is specifically used for:

[0078] Obtain the renal artery minute distance (MD) of the target object's kidney;

[0079] The MD was modified by combining the anatomical structure and hemodynamic model of the renal artery to more accurately reflect the renal artery blood flow.

[0080] Understandably, when obtaining the systemic critical closure pressure, arterial blood flow can be used as cardiac output (CO). However, for patients requiring treatment of kidney-related diseases, molecular weight (MD) can be obtained to better capture the renal critical closure pressure. Based on the commonly used MD method, the MD can be modified by incorporating the renal artery's anatomy and hemodynamic models to obtain a more accurate renal artery blood flow.

[0081] MD is a flow velocity-derived parameter that represents the cumulative distance blood travels per minute. The calculation formula is MD = VTI (velocity-time integral) * HR (heart rate), with the unit being cm / min.

[0082] The renal artery has a complex anatomical structure, and its hemodynamics are influenced by various factors. In-depth analysis of these factors allows for a more accurate reflection of renal artery blood flow. For example, considering the effects of factors such as the branching angle of the renal artery, the elasticity of the vessel wall, and blood viscosity on blood flow, appropriate mathematical models can be used to correct for the mean closure pressure (MD), thus providing reliable data support for the subsequent accurate calculation of the critical closure pressure of the kidney. How to combine the anatomical structure and hemodynamic model of the renal artery is understood by those skilled in the art and will not be elaborated further.

[0083] In some embodiments of this application, the determining module 103 is further configured to:

[0084] Dynamic time series data are constructed based on multiple arterial blood flow rates and multiple corresponding arterial blood flow maps;

[0085] The linear function is updated using a sliding window technique, which in turn updates the critical closure pressure; the window size is N data points, where N is greater than or equal to 2.

[0086] Dynamic time series data refers to data that changes sequentially over time. Because a patient's physiological and pathological state is constantly changing, a sliding window technique is needed to continuously update the linear function. For example, with a window size of 5 data points, numbered 1, 2, 3, 4, and 5 by time sequence, the linear function is fitted to the 5 arterial blood flow rates and their corresponding mean arterial pressures (MAPs). Then, the window slides forward, say by 2 data points, resulting in 5 data points with time sequences of 3, 4, 5, 6, and 7. While still 5 data points, those originally numbered 1 and 2 are removed, and 6 and 7 are newly added, continuing the linear function fitting process. In the above example, the sliding distance is 2 data points, but it's understandable that other sliding lengths could be used, such as 1, 3, or more data points.

[0087] In some embodiments of this application, the determining module 103 is further configured to:

[0088] After the sliding window slides, the historical data is weighted using a forgetting factor, so that recent data has a greater impact on the update of the linear function.

[0089] The forgetting factor is used in recursive weighting algorithms (such as recursive least squares and Kalman filtering) to exponentially decay the weights of historical data. This causes the weights of historical data to decay exponentially over time, with earlier data receiving smaller weights. This allows the fitted function or model to respond more quickly to changes in new data, avoiding excessive interference from historical information with the current estimate.

[0090] In some embodiments of this application, the determining module 103 is further configured to:

[0091] The linear function is fitted and updated using the K-nearest neighbor algorithm and an improved forgetting factor fusion method.

[0092] The full name of the K-Nearest Neighbors algorithm is "K-Nearest Neighbors Algorithm", abbreviated as KNN.

[0093] The KNN algorithm is a supervised learning algorithm that can be used for both classification and regression problems. Its core principle is based on the existence of a dataset (training dataset) where each data point has a label (i.e., the correspondence between each data point and its class is known). When new, unlabeled data is input, the algorithm compares each feature of the new data with the corresponding features of the data in the training dataset, and then extracts the classification label of the most similar data (nearest neighbor) in the training dataset for prediction.

[0094] Understandably, traditional least squares methods assume that all samples have the same weight during model or function fitting, an assumption that is difficult to apply to real-world medical data. Furthermore, due to factors such as measurement errors, sensor anomalies, or pathological mutations, data often contains outliers, and ordinary fitting methods are too sensitive to these outliers, easily leading to fitting bias and distorted results.

[0095] Furthermore, medical data often exhibits significant time-series characteristics. The statistical properties of the data may change over time after rapid disease progression or external interventions (such as medication or surgery). In this context, fixed-weight fitting methods struggle to adapt to the dynamic changes in the data.

[0096] To address these issues, some studies have proposed introducing time-weighted mechanisms, such as the exponentially weighted decay method. This approach assigns exponentially decaying weights to historical samples, allowing the model to focus more on recent data and thus improve dynamic adaptability. However, in practical medical applications, the exponential decay in this method is often too rapid, potentially ignoring still valuable recent data and affecting model stability and generalization ability.

[0097] Therefore, there is an urgent need for a dynamic weighted fitting method that balances time sensitivity and outlier robustness to improve the accuracy and real-time performance of modeling dynamic clinical data. After discovering the aforementioned technical problem, the inventors of this application, through extensive research and experimentation, developed a dynamic adaptive linear fitting algorithm that fuses KNN with an improved forgetting factor, achieving accurate calculation of the critical closure pressure. The specific steps include:

[0098] 1) Sliding window construction. A sliding window is constructed using N consecutive data pairs (where N is greater than or equal to 2), and the window is updated by sliding according to the data acquisition frequency.

[0099] Specifically, the continuous data pairs are {(Q,MAP)}, the window size N is 60, and the window slides every M new data points (M=10).

[0100] 2) KNN Outlier Detection and Suppression. For each data pair within the window, calculate its Euclidean distance to its k nearest neighbor data pairs. Calculate the average nearest neighbor distance as an indicator of the outlier degree of the current data pair. Data pairs with a distance greater than a certain multiple of the average nearest neighbor distance are considered outlier data pairs and are subject to a decay weight, i.e., anomaly weight.

[0101] Specifically, data pairs can include: i = 1, 2, ... N. Calculate the Euclidean distance to k = 5 nearest neighbor data pairs:

[0102]

[0103] j = 1, 2, ..., k.

[0104] Next, calculate the average distance to nearest neighbors:

[0105]

[0106] Set an abnormal threshold:

[0107]

[0108] That is, the exception threshold is all within the window. The mean plus 1.5 times the standard deviation. For all within the window The mean, Its standard deviation; for and For example, if the current window contains 60 data pairs, the Euclidean distance d between each data pair and its 5 nearest points will be calculated. ij That is, there are 5 d. ij For 5 d ij Take the average value Since there are 60 data pairs, there are 60 Then for 60 Take the average value For 60 Calculate the standard deviation to obtain

[0109] To satisfy Apply decay weights to outlier data:

[0110]

[0111] Where α>0 is the abnormal attenuation coefficient, and That is, the higher the degree of abnormality, the smaller the weight.

[0112] 3) Dynamic window size adjustment. Adjust the window size according to the proportion of abnormal data pairs within the window.

[0113] Specifically, the number of abnormal data pairs within the window is N out , then the abnormal rate is:

[0114]

[0115] When r > r0 = 5%, the window size is adjusted to:

[0116] N new = N·(1 - β·(r - r0)) (6)

[0117] When r < r0, the window size is adjusted to:

[0118] N new = N·(1 + γ·(r0 - r)) (7)

[0119] Where β and γ are constants, controlling the amplitude of window contraction and expansion respectively, β ∈ (0.4, 0.7), γ ∈ (0.1, 0.4); r0 is the preset abnormal rate threshold; N min ≤ N new ≤ N max N min and N max are preset to 40 and 120 respectively. Note that the window size adjustment in this step is for the next time and will not be returned for execution this time.

[0120] 4) Improved forgetting factor function. Adopt an improved forgetting factor to assign time weights to the data within the window according to the timestamp t.

[0121] Specifically, the improved forgetting factor is:

[0122]

[0123] Where h is the decay steepness parameter, with a value of h = 0.1, and p is the forgetting inflection point (window center), with a value of p = N / 2.

[0124] Normalize the forgetting factor sequence:

[0125]

[0126] Where max(f(t)) is the maximum value in the forgetting factor sequence, making the time weight of the latest data 1. The forgetting factor sequence is a curve drawn according to the forgetting factor function.

[0127] Figure 2 is a comparison chart of the weight settings for two forgetting factor sequences. From Figure 2 As can be seen, regarding the weighting of historical data (0 on the x-axis represents the oldest data, and 60 represents the most recent data), the exponential forgetting factor sequence is set to decay rapidly, while the improved forgetting factor sequence in this application exhibits a more gradual decay. This 'slow forgetting' characteristic can effectively preserve historical trends and improve prediction accuracy during patient monitoring.

[0128] 5) Weighted least squares fitting. By combining outlier weights and time weights, a composite weighting factor is constructed, and the weighted sum of squared residuals is minimized.

[0129] Specifically, the comprehensive anomaly weight and time weight f(t) i Construct a composite weighted factor:

[0130]

[0131] And minimize the weighted sum of squared residuals:

[0132] min k,b ∑w i ·(MAP i -kQ i -b) 2 (11)

[0133] Solve for the linear fitting parameters k and b, where k is the slope of the linear function and b is the intercept of the linear function.

[0134] 6) Solve for the linear fitting parameters. By minimizing the partial derivatives of the weighted sum of squared residuals with respect to the fitting parameters k and b, and setting the derivatives to zero, the normal equations are obtained. After simplification, a system of linear equations is obtained. By solving the above system of linear equations, the weighted least squares solutions k and b can be obtained, which are used for subsequent calculation of the critical closure pressure.

[0135] Specifically, minimize the weighted sum of squared residuals:

[0136]

[0137] Taking the partial derivatives with respect to k and b, and setting the derivatives to zero, we obtain the normal equation:

[0138]

[0139] After simplification, we obtain the following system of linear equations:

[0140]

[0141] By solving the above system of linear equations, we can obtain the weighted least squares solutions k and b, which can be used for the subsequent calculation of the critical closure pressure.

[0142] 7) Calculation and update of critical closure pressure. Based on the weighted least squares fitting model obtained in step 6: according to the definition, when blood flow Q = 0, the arterial pressure MAP is the critical closure pressure. Therefore, the critical closure pressure can be directly obtained from the intercept term of the model.

[0143] Specifically, the weighted least squares fitting model is as follows:

[0144] MAP=kQ+b (15)

[0145] According to the definition, when blood flow Q = 0, arterial pressure MAP is the critical closure pressure. Therefore, the critical closure pressure can be directly obtained from the intercept term of the model.

[0146] In some embodiments of this application, reference is made to Figure 3 The determining module 103 is further configured to:

[0147] Based on the multiple arterial blood flow rates and the multiple MAPs corresponding to the arterial blood flow rates, a scatter plot in a rectangular coordinate system is drawn with the arterial blood flow rate on the horizontal axis and the MAP on the vertical axis; then, based on the scatter plot in the rectangular coordinate system, a linear regression line is fitted, and the MAP at the intersection of the linear regression line and the vertical axis is determined as the critical closure pressure of the preset part of the target object.

[0148] A scatter plot provides a more intuitive way to fit a straight line, and the linear regression line can be extended to the vertical axis; the MAP at the intersection with the vertical axis is the critical closure pressure. In a rectangular coordinate scatter plot, the horizontal axis represents blood flow (CO) in L / min, and the vertical axis represents MAP in mmHg.

[0149] Understood, the above is just an example. If you need to obtain the critical closure pressure at a specific site on the patient's body, the horizontal axis does not have to be CO, but rather the blood flow parameters at that specific site, such as the renal artery distance-per-minute (MD).

[0150] In some embodiments of this application, the determining module 103 is further configured to:

[0151] The scatter plot in the rectangular coordinate system is sent to a display device for display.

[0152] In the Cartesian coordinate scatter plot, the region displaying the critical closure pressure of the target object is divided into a safe zone and a risk zone; the safe zone and the risk zone are distinguished based on the value of the critical closure pressure of the target object and its individual characteristics.

[0153] A Cartesian coordinate scatter plot can be directly displayed on the screen of the relevant device, allowing users, such as doctors, to obtain information more intuitively.

[0154] Furthermore, the critical closure pressure area can be divided into a safe zone and a risk zone, that is, the vertical axis can be divided into regions. The method of partitioning can be to set different colors, for example, the safe zone is green and the risk zone is yellow or red.

[0155] In some embodiments of this application, the determining module 103 is further configured to:

[0156] The regions displaying the arterial blood flow and the MAP in the Cartesian coordinate system are divided into a safe zone and a risk zone; the safe zone and the risk zone are distinguished based on the values ​​of the arterial blood flow and the MAP of the target object and individual characteristics.

[0157] This involves dividing the area outside the vertical axis into zones, specifically safe and risk zones. Similarly, different zones can be distinguished by color.

[0158] Specifically, for patients with hypertension, the lower limit of arterial blood flow in the safe zone is increased by 5 mmHg; for patients with diabetes, the lower limit of arterial blood flow in the safe zone is increased by 10% to compensate for microcirculatory disturbances.

[0159] In some embodiments of this application, the second acquisition module 102 is further configured to:

[0160] The first MAP is obtained by invasively measuring the blood pressure corresponding to the arterial blood flow.

[0161] A second MAP is obtained by measuring the blood pressure corresponding to the arterial blood flow using a wearable device;

[0162] The first MAP and the second MAP are weighted and averaged to obtain the MAP.

[0163] This approach reduces the measurement errors and limitations of a single measurement method. For example, invasive methods for measuring arterial blood flow (blood pressure) are more accurate, but inconvenient for long-term monitoring. Wearable devices, on the other hand, allow for extended monitoring and generally do not cause significant side effects for patients. The weighting of the two measurement methods can be adjusted based on the patient's specific circumstances.

[0164] It is understandable that more measurement methods can be set for weighted averaging, but this embodiment does not limit it.

[0165] In some embodiments of this application, reference is made to Figure 4 The device further includes a calibration module 104, which is used for:

[0166] Upon first use, a personalized calibration curve is established using baseline data of the target object; the baseline data consists of arterial blood flow and arterial pressure (MAP) at rest.

[0167] In subsequent monitoring, if the fluctuation of the critical closure pressure is detected to exceed a preset threshold, a recalibration process is automatically triggered, and the coefficients of the linear function are updated.

[0168] This means that a preset threshold for fluctuation can be set based on the personalized calibration curve of the target object. The personalized calibration curve is formed by fitting a regression line based on baseline data plus a reasonable fluctuation range. In this way, abnormalities in the obtained critical closure pressure can be reduced due to factors such as measurement errors, sensor malfunctions, or pathological mutations.

[0169] Specifically, the baseline establishment process involves collecting resting state data from patients within 24 hours of admission, averaging the data every 10 minutes to generate an initial calibration curve.

[0170] Pcrit_base=0.85×MAP+0.3Q (16)

[0171] Where Q represents arterial blood flow.

[0172] In some embodiments of this application, the apparatus further includes a third acquisition module 105, the third acquisition module 105 being used for:

[0173] The auxiliary physiological parameters for determining the critical closure pressure are monitored and obtained, and the auxiliary parameter values ​​are dynamically correlated with the arterial blood flow and the arterial blood flow rate (MAP). The auxiliary physiological parameters include heart rate, blood oxygen saturation, and body temperature.

[0174] In this way, in addition to obtaining the critical closure pressure through linear regression analysis based on arterial blood flow and MAP, more physiological parameters can be combined to assist in the regression analysis.

[0175] In some embodiments of this application, the determining module 103 is further configured to:

[0176] The arterial blood flow, the MAP, and the auxiliary parameter values ​​are input into a pre-trained machine learning model to obtain the critical closure pressure at a preset location of the target object;

[0177] The training data for the machine learning model includes: arterial blood flow, MAP, auxiliary parameter values, and clinically diagnosed critical closure pressure of target subjects of different ages, genders, and medical histories.

[0178] In this way, more realistic mathematical models or functions can be built using more auxiliary parameters, rather than simple linear regression models or functions. Understandably, existing technologies offer many mature and efficient machine learning models that can overcome the surge in computational complexity caused by multi-parameter and nonlinear models.

[0179] To better understand the blood flow critical closure pressure acquisition device based on Doppler technology provided in this application embodiment, the execution process of the blood flow critical closure pressure acquisition device based on Doppler technology provided in this application embodiment will be described below. Figure 5 This is a schematic flowchart illustrating the execution process of the Doppler-based blood flow critical closure pressure acquisition device provided in this application embodiment. (Refer to...) Figure 5 As shown, the execution process may include:

[0180] Step 201: Obtain arterial blood flow at a predetermined location on the target subject using ultrasound Doppler blood flow measurement technology;

[0181] Step 202: Within the same time frame of acquiring the arterial blood flow, acquire the average blood pressure MAP at the preset site corresponding to the arterial blood flow;

[0182] Step 203: Based on the multiple arterial blood flow rates and the multiple MAPs corresponding to the arterial blood flow rates, fit a linear function with the arterial blood flow rate as the independent variable and the MAP as the dependent variable; then calculate the first value of the MAP when the arterial blood flow rate is zero based on the linear function, and determine the first value as the critical closure pressure of the preset part of the target object.

[0183] Figure 6 A detailed flowchart illustrating the execution process of the Doppler-based blood flow critical closure pressure acquisition device provided in this application embodiment is shown below. Figure 6 As shown, the execution process may include:

[0184] Step 301: Obtain the Pcrit calibration curve. Resting state data are collected within 24 hours of patient admission, and the average value is taken every 10 minutes to generate the initial calibration curve, referring to expression (16).

[0185] Step 302: Obtain arterial blood flow. This is achieved through Doppler ultrasound blood flow measurement technology.

[0186] Step 303: Obtain the MAP. This includes obtaining the first MAP and the second MAP.

[0187] Step 304: Obtain data pairs. This involves obtaining a corresponding number of data pairs based on a preset sliding window size. The sliding window can slide periodically or irregularly to update the data pairs.

[0188] Step 305: Obtain the anomaly weights. Refer to expression (4).

[0189] Step 306: Obtain the time weight. Refer to expression (9).

[0190] Step 307: Obtain the composite weights. Refer to expression (10).

[0191] Step 308: Obtain a scatter plot. This involves visually representing the linear function graphically for a more intuitive understanding. After completing this step, proceed to step 311.

[0192] Step 309: Obtain auxiliary physiological parameters.

[0193] Step 310: Input into the machine learning model. Input the arterial blood flow, MAP, and auxiliary physiological parameters obtained above into the machine learning model.

[0194] Step 311: Obtain the critical closure pressure.

[0195] Step 312: Set up safe zones and risk zones.

[0196] Step 313: Trigger calibration? If yes, return to step 302; otherwise, proceed to step 312.

[0197] Step 314: End.

[0198] The modules included in this embodiment can be implemented using a processor in a computer; alternatively, they can be implemented using logic circuits in a computer. The processor can be a general-purpose processor, a digital signal processor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a central processing unit (CPU), a microprocessor (MPU), or any other conventional processor.

[0199] Example 2

[0200] This application provides a computing device 600, with reference to... Figure 7 The computing device 600 includes: a storage unit 601, a communication bus 602, and a processing unit 603, wherein:

[0201] The storage component 601 is used to store the operating program of the blood flow critical closure pressure acquisition device based on Doppler technology;

[0202] The communication bus 602 is used to realize the connection and communication between the storage component 601 and the processing component 603.

[0203] The processing unit 603 is used to perform the work of each module in the blood flow critical closure pressure acquisition device based on Doppler technology described in Embodiment 1.

[0204] The type or structure of the storage component 601 can be found in the storage medium section below, and will not be repeated here.

[0205] The processing unit 603 can be a general-purpose processor, a digital signal processor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a central processing unit (CPU), a microprocessor (MPU), or any other conventional processor.

[0206] In some embodiments, the computing device 600 may further include an input device 604, an output device 605, and an external communication interface 606, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0207] In some embodiments, input device 604 may include, for example, a keyboard, mouse, microphone, etc. Output device 605 may output various information to the outside, including a display, speaker, printer, projector, and communication network and its connected remote output devices, etc. External communication interface 606 may be wired, such as a standard serial port (RS232), a General-Purpose Interface Bus (GPIB) interface, an Ethernet interface, or a Universal Serial Bus (USB) interface, or it may be wireless, such as wireless network communication technology (WiFi), Bluetooth, etc.

[0208] The descriptions of the above device embodiments are similar to those of the above apparatus embodiments, and have similar beneficial effects. For technical details not disclosed in the embodiments of this application, please refer to the descriptions of the apparatus embodiments in this application for understanding.

[0209] Example 3

[0210] This application provides a computer-readable storage medium storing an executable program. When the executable program is executed by a processor, it enables the operation of each module in the blood flow critical closure pressure acquisition device based on Doppler technology described in Embodiment 1.

[0211] Exemplary examples show that a computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A computer-readable storage medium is a tangible device capable of holding and storing instructions for use by an instruction execution device. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), flash memory, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combinations thereof.

[0212] The RAM includes: Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).

[0213] The ROM includes: Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), and Electrically Erasable Programmable Read-Only Memory (EEPROM).

[0214] The description of the computer-readable storage medium embodiments above is similar to the description of the device embodiments above, and has similar beneficial effects. For technical details not disclosed in the embodiments of this application, please refer to the description of the device embodiments in this application for understanding.

[0215] Example 4

[0216] This application provides a system for obtaining critical closure pressure of blood flow, as described in the following embodiments. Figure 8 The critical closure pressure acquisition system for blood flow includes:

[0217] The blood flow critical closure pressure acquisition device based on Doppler technology described in Example 1;

[0218] The ultrasound Doppler blood flow monitoring device 802 is used to monitor arterial blood flow at a preset location of the target object and send the data to the critical closure pressure acquisition device.

[0219] The blood pressure monitoring device 803 is used to monitor the blood pressure at a preset location of the target object and send it to the critical closure pressure acquisition device.

[0220] The descriptions of the system embodiments above are similar to those of the device embodiments above, and have similar beneficial effects. For technical details not disclosed in the embodiments of this application, please refer to the descriptions of the device embodiments in this application for understanding.

[0221] It should be noted that the various embodiments provided in this application belong to the same concept; the technical features in the technical solutions described in each embodiment can be arbitrarily combined to form new embodiments without conflict.

[0222] It should be noted that the various embodiments or implementation methods in this document can be described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to mutually. It should be understood that in the various embodiments of this application, the embodiment numbers are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments.

[0223] Understandably, without conflict, the technical features in the technical solutions described in each embodiment can be arbitrarily combined to form new embodiments. For example, each structure in each embodiment can be implemented as an independent embodiment, and the structures can be arbitrarily combined; some or all of the structures in different embodiments can be arbitrarily combined. Each step in each embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined; the order of the steps can be arbitrarily interchanged; some or all of the steps in different embodiments can be arbitrarily combined. Furthermore, regarding the table in the embodiments, each element, each row, or each column in the table can be implemented as an independent embodiment.

[0224] In this document, when the terms "embodiment," "implementation," or "example" are used, it means that the specific features described in connection with these implementations or examples are included in at least one implementation, embodiment, or example of this application. It should be noted that the illustrative expressions of the above terms do not necessarily refer to the same implementation, embodiment, or example. Furthermore, the specific features described, such as structures or steps, can be appropriately combined in any one or more implementations, embodiments, or examples.

[0225] In some embodiments, prefixes such as "first" and "second" are used merely to distinguish different descriptive objects and do not impose restrictions on the position, order, priority, or value of the descriptive objects. The description of the descriptive objects is based on the context of the claims or embodiments, and the use of prefixes does not constitute unnecessary limitations. For example, the numerical value of the descriptive object is not limited by ordinal numbers and can be one or more. For instance, in "first device," the numerical value of "device" can be one or more. Furthermore, objects modified by different prefixes can be the same or different. For example, if the descriptive object is "device," then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Describing "first" does not necessarily imply the existence of "second," and discussing "second" does not necessarily imply the existence of "first."

[0226] In some embodiments, unless otherwise stated, elements expressed in the singular form, such as “a,” “the,” “the,” “the,” “the,” “the,” etc., can mean “one and only one,” or “one or more,” “at least one,” etc. For example, in translations using articles such as “a,” “an,” “the,” etc. in English, the noun following the article can be understood as either a singular or a plural expression.

[0227] In some embodiments, "multiple" means two or more.

[0228] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0229] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "A in one case, B in another", etc., may include the following technical solutions depending on the situation: in some embodiments, A (A is executed regardless of B); in some embodiments, B (B is executed regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0230] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, C, etc.

[0231] In some embodiments, unless otherwise expressly defined, the terms "installation," "connection," "linking," "fixing," "setting," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection or an indirect connection through an intermediate medium; it can also refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this embodiment according to the specific circumstances.

[0232] In some embodiments, specific operational steps, such as flowcharts, are provided. However, it should be noted that these operational steps may be added or removed based on conventional or non-creative effort. The order of steps listed in the embodiments is only one of many possible orders and does not represent the only order. When executed in actual devices, systems, or server products, the steps can be executed either in the order shown in the embodiments or the accompanying drawings, or in parallel in a parallel processor or multi-threaded processing environment.

[0233] The embodiments of this application may be methods, apparatus (systems), and / or computer-readable storage media. The computer-readable storage medium may carry an executable program for causing a processor to implement various aspects of this application. The executable program may be program code written in any combination of one or more programming languages ​​for executing the embodiments of this application. Programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​or other programming languages ​​such as "C". The program code may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer. The network may be a wired network or a wireless network.

[0234] In some embodiments, electronic circuits, such as programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), are personalized by utilizing state information of an executable program. These electronic circuits can execute executable programs to implement various aspects of this application.

[0235] The executable program described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded via a network to an external computer or external storage device. The network may include copper cables, fiber optic cables, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the executable program from the network and forwards it for storage on a computer-readable storage medium within the respective computing / processing device.

[0236] Various aspects of this application are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and / or computer-readable storage media according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by an executable program.

[0237] These executable programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of a flowchart and / or block diagram. These executable programs can also be stored in a computer-readable storage medium, the instructions of which cause a computer, programmable data processing apparatus, and / or other device to operate in a particular manner, thereby the computer-readable storage medium storing the instructions comprising an article of manufacture including instructions for implementing aspects of the functions / actions specified in one or more blocks of a flowchart and / or block diagram. The executable programs can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions, which execute on the computer, other programmable data processing apparatus, or other device, to implement the functions / actions specified in one or more blocks of a flowchart and / or block diagram.

[0238] In some embodiments, the disclosed devices and methods can be implemented in a variety of other ways. The described device embodiments are merely illustrative; for example, module division represents only one logical functional division method. In actual implementation, multiple modules or components may be combined or integrated into another system, or certain features may be ignored or specific operations may not be performed. Coupling, direct coupling, or communication connections between components can be implemented indirectly through interfaces, devices, or modules, and the connection form can be electrical, mechanical, or other types.

[0239] In some embodiments, the modules described as separate components may or may not be physically separated; the components shown as modules may or may not be physical modules; these modules may or may not be concentrated in one place or distributed across multiple network modules. In practical applications, some or all of the modules can be selected to achieve the objectives of this embodiment, depending on the requirements.

[0240] In some embodiments, the integration of functional modules is flexible and diverse: they can all be integrated into one processing module, each can be an independent module, or two or more functional modules can be integrated into one module. These integrated modules can be implemented in pure hardware or in a combination of hardware and software functional modules.

[0241] In some embodiments, all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The relevant program can be stored in a computer-readable storage medium, such as ROM, RAM, magnetic disk, or optical disk, and implements the steps of the above method embodiments when executed. If the integrated modules of this application are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Therefore, the technical solutions of the embodiments of this application, in essence or contributing to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and contains several instructions for causing an electronic device (such as a personal computer, server, or network device) to execute all or part of the steps of the methods described in the various embodiments of this application. Therefore, the embodiments of this application are not limited to any specific hardware and software combination.

[0242] It should be understood that the above embodiments are exemplary and are not intended to encompass all possible implementations of the technical solutions of this application. Various modifications and changes can be made to the above embodiments without departing from the scope of this application. Similarly, the various technical features of the above embodiments can be arbitrarily combined to form other embodiments of this application that may not be explicitly described. Therefore, the above embodiments only illustrate several implementations of this application and do not limit the scope of protection of this patent application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

Claims

1. A device for acquiring critical closure pressure of blood flow based on Doppler technology, characterized in that, The device includes: The first acquisition module is used to acquire arterial blood flow at a preset location of the target object using ultrasound Doppler blood flow measurement technology; The second acquisition module is used to acquire the average blood pressure MAP of the preset site corresponding to the arterial blood flow within the same time period of acquiring the arterial blood flow. The determination module is used to fit a linear function with arterial blood flow as the independent variable and MAP as the dependent variable based on multiple arterial blood flow rates and multiple MAPs corresponding to the arterial blood flow rates; then calculate the first value of MAP when the arterial blood flow rate is zero based on the linear function, and determine the first value as the critical closure pressure of the preset part of the target object.

2. The blood flow critical closure pressure acquisition device based on Doppler technology according to claim 1, characterized in that, The determining module is also used for: Dynamic time series data are constructed based on multiple arterial blood flow rates and multiple corresponding arterial blood flow maps; The linear function is updated using a sliding window technique, which in turn updates the critical closure pressure; the size of the sliding window is N data points, where N is greater than or equal to 2.

3. The blood flow critical closure pressure acquisition device based on Doppler technology according to claim 2, characterized in that, The determining module is also used for: After the sliding window slides, the historical data is weighted using a forgetting factor, so that recent data has a greater impact on the update of the linear function.

4. The blood flow critical closure pressure acquisition device based on Doppler technology according to claim 2, characterized in that, The determining module is also used for: The linear function is fitted and updated using the K-nearest neighbor algorithm and an improved forgetting factor fusion method.

5. The blood flow critical closure pressure acquisition device based on Doppler technology according to claim 2, characterized in that, The determining module is also used for: Count the number of outlier data pairs within the sliding window; The size of the sliding window is adjusted based on the number of abnormal data pairs within the sliding window.

6. The blood flow critical closure pressure acquisition device based on Doppler technology according to claim 1, characterized in that, The determining module is also used for: Based on the multiple arterial blood flow rates and the multiple MAPs corresponding to the arterial blood flow rates, a scatter plot in a rectangular coordinate system is drawn with the arterial blood flow rate on the horizontal axis and the MAP on the vertical axis; then, based on the scatter plot in the rectangular coordinate system, a linear regression line is fitted, and the MAP at the intersection of the linear regression line and the vertical axis is determined as the critical closure pressure of the preset part of the target object.

7. The blood flow critical closure pressure acquisition device based on Doppler technology according to claim 6, characterized in that, The determining module is also used for: The scatter plot in the rectangular coordinate system is sent to a display device for display. In the Cartesian coordinate scatter plot, the region displaying the critical closure pressure of the target object is divided into a safe zone and a risk zone; the safe zone and the risk zone are distinguished based on the value of the critical closure pressure of the target object and its individual characteristics.

8. The blood flow critical closure pressure acquisition device based on Doppler technology according to claim 7, characterized in that, The determining module is also used for: The regions displaying the arterial blood flow and the MAP in the Cartesian coordinate system are divided into a safe zone and a risk zone; the safe zone and the risk zone are distinguished based on the values ​​of the arterial blood flow and the MAP of the target object and individual characteristics.

9. The blood flow critical closure pressure acquisition device based on Doppler technology according to any one of claims 1-8, characterized in that, The second acquisition module is also used for: The first MAP is obtained by invasively measuring the blood pressure corresponding to the arterial blood flow. A second MAP is obtained by measuring the blood pressure corresponding to the arterial blood flow using a wearable device; The first MAP and the second MAP are weighted and averaged to obtain the MAP.

10. The blood flow critical closure pressure acquisition device based on Doppler technology according to any one of claims 1-8, characterized in that, The device further includes a calibration module, the calibration module being used for: Upon first use, a personalized calibration curve is established using baseline data of the target object; the baseline data consists of arterial blood flow and arterial pressure (MAP) at rest. In subsequent monitoring, if the fluctuation of the critical closure pressure is detected to exceed a preset threshold, a recalibration process is automatically triggered, and the coefficients of the linear function are updated.

11. The blood flow critical closure pressure acquisition device based on Doppler technology according to any one of claims 1-8, characterized in that, The device further includes a third acquisition module, the third acquisition module being used for: The auxiliary physiological parameters for determining the critical closure pressure are monitored, the auxiliary parameter values ​​are obtained, and the auxiliary parameter values ​​are dynamically correlated with the arterial blood flow and the arterial blood flow rate (MAP). The auxiliary physiological parameters include heart rate, blood oxygen saturation, and body temperature.

12. The blood flow critical closure pressure acquisition device based on Doppler technology according to claim 11, characterized in that, The determining module is also used for: The arterial blood flow, the MAP, and the auxiliary parameter values ​​are input into a pre-trained machine learning model to obtain the critical closure pressure at a preset location of the target object; The training data for the machine learning model includes: arterial blood flow, MAP, auxiliary parameter values, and clinically diagnosed critical closure pressure of target subjects of different ages, genders, and medical histories.

13. A computing device, characterized in that, The computing device includes: a storage component, a communication bus, and a processing component, wherein: The storage component is used to store the operating program of the blood flow critical closure pressure acquisition device based on Doppler technology; The communication bus is used to enable communication between the storage component and the processing component; The processing component is used to perform the operation of each module in the blood flow critical closure pressure acquisition device based on Doppler technology as described in any one of claims 1-12.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an executable program, which, when executed by a processor, enables the operation of each module in the Doppler-based blood flow critical closure pressure acquisition device according to any one of claims 1-12.

15. A blood flow critical closure pressure acquisition system, characterized in that, include: The blood flow critical closure pressure acquisition device based on Doppler technology according to any one of claims 1-12; An ultrasonic Doppler blood flow monitoring device is used to monitor arterial blood flow at a preset location on a target object and transmit the data to the critical closure pressure acquisition device. A blood pressure monitoring device is used to monitor the blood pressure at a preset location on a target object and send the data to the critical closure pressure acquisition device.

Images