Intelligent monitoring method and system for patient status based on vehicle-mounted blood pressure monitor

Abstract

The present invention relates to the field of artificial intelligence technology and relates to an intelligent patient status monitoring method and system based on an on-board blood pressure monitor. The method comprises: obtaining a steering wheel pressure matrix to identify palm position information; performing an infrared irradiation operation on the palm position based on a preset initial infrared wave to obtain a palm infrared reflection wave; obtaining a vibration wave on the steering wheel, filtering the palm infrared reflection wave according to the vibration wave to obtain a de-noised palm reflection wave, and performing a difference calculation between the de-noised palm reflection wave and the initial infrared wave to obtain an absorbed wave; utilizing a pre-trained variable pressure blood pressure recognition model to obtain a primary blood pressure recognition result based on the absorbed wave, and performing a pressure correction operation on the primary blood pressure recognition result according to the steering wheel pressure matrix to obtain a blood pressure recognition result; and monitoring the blood pressure recognition result using a preset health threshold. The present invention enables barrier-free blood pressure monitoring of drivers, improving driving safety.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and system for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor. Background Art

[0002] The continuous improvement of society's industrial level has led to the increasing popularity of automobiles. This increase in the number of vehicles has led to an increase in traffic accidents. Accident investigation data has revealed that driver error accounts for a much larger proportion of accidents than vehicle malfunction. A significant factor in driver error is physical problems that lead to a loss of concentration or control.

[0003] Due to prolonged sitting in work environments, drivers are now prone to high blood pressure. Drivers' health is crucial for the safety of drivers, passengers, and pedestrians. While some in-car blood pressure monitors are connected to the car's central control panel, they still require manual operation, such as attaching the airbag and pressing the test button. This not only wastes patients' time but also disrupts driving. Effectively monitoring drivers' health without disrupting driving has become a pressing issue. Summary of the Invention

[0004] The present invention provides an intelligent monitoring method for a patient's condition based on a vehicle-mounted blood pressure monitor, the main purpose of which is to perform barrier-free blood pressure monitoring on the driver and improve driving safety.

[0005] To achieve the above objectives, the present invention provides an intelligent monitoring method for a patient's condition based on a vehicle-mounted blood pressure monitor, comprising:

[0006] Using a pre-built pressure sensor, a steering wheel pressure matrix is ​​obtained, and based on the steering wheel pressure matrix, the palm position of the patient is identified to obtain palm position information;

[0007] Using a pre-built infrared sensor, according to the palm position information, an infrared irradiation operation based on a preset initial infrared wave is performed on the palm position, and a reflection signal of the infrared irradiation operation is received to obtain a palm infrared reflection wave;

[0008] Using a pre-built vibration sensor, a vibration wave on a pre-built steering wheel is obtained. The palm infrared reflected wave is filtered according to the vibration wave to obtain a de-noised palm reflected wave. The de-noised palm reflected wave and the initial infrared wave are then differenced to obtain an absorbed wave.

[0009] Using a pre-trained variable pressure blood pressure recognition model, performing a time-frequency analysis on the absorbed wave to obtain an absorbed wave feature set, performing a classification operation on the absorbed feature set based on preset red blood cell peak characteristics to obtain a red blood cell absorption wave feature set, and performing a red blood cell flow velocity and width distribution recognition operation on the red blood cell absorption wave feature set to obtain a red blood cell flow distribution;

[0010] performing a hand blood pressure recognition operation on the red blood cell flow distribution to obtain a primary blood pressure recognition result;

[0011] performing a feature extraction operation on the steering wheel pressure matrix to obtain a palm pressure distribution feature set, and performing a pressure correction operation on the primary blood pressure recognition result according to the palm pressure distribution feature set to obtain a blood pressure recognition result;

[0012] Obtaining a health threshold, and determining whether the blood pressure recognition result is greater than the health threshold;

[0013] When the blood pressure recognition result is greater than the health threshold, the patient is determined to be driving dangerously and an alarm prompt message is generated.

[0014] Optionally, before obtaining the steering wheel pressure matrix using the pre-built pressure sensor, the method further includes:

[0015] Leverage pre-built in-car navigation to obtain the patient’s real-time location;

[0016] Obtaining the patient's historical common routes, and determining whether the patient is in a straight driving section based on the historical common routes and the real-time location;

[0017] When the patient is in a straight driving section, it is determined that the patient is in a stable driving stage;

[0018] When the patient is not in a straight driving section, it is determined that the patient is in an unstable driving stage;

[0019] If the patient is in a stable driving stage, the operation of obtaining the steering wheel pressure matrix by using the pre-built pressure sensor is performed.

[0020] Optionally, identifying the patient's palm position according to the steering wheel pressure matrix to obtain palm position information includes:

[0021] performing a masking operation of a preset pressure on the steering wheel pressure matrix to obtain a pressure masking result;

[0022] Performing an outermost contour edge recognition operation on the pressure masking result to obtain a hand shape distribution of the user;

[0023] Performing a key node recognition operation on the hand shape distribution to obtain a key node set;

[0024] The palm position is predicted according to the relative position of each key node in the key node set to obtain palm position information.

[0025] Optionally, filtering the palm infrared reflected wave according to the vibration wave to obtain the de-noised palm reflected wave includes:

[0026] Using a pre-built high-pass filter to perform DC component filtering on the palm infrared reflected wave to obtain a DC component-free wave;

[0027] Performing a standardization process on the DC component-removed wave based on a mean of zero and a variance of one to obtain an infrared standard wave;

[0028] Using a pre-built fast Fourier transform algorithm, the vibration components of the vibration wave are identified to obtain the main vibration frequency;

[0029] The infrared standard wave is filtered according to the main vibration frequency to obtain a noise-reduced palm reflection wave.

[0030] Optionally, the variable pressure blood pressure identification model is expressed as:

[0031]

[0032]

[0033] Where, represents the blood pressure recognition result, represents the primary blood pressure recognition result, represents the initial weight of the primary blood pressure recognition result for the blood pressure recognition result, represents the steering wheel pressure matrix, Represents the steering wheel pressure matrix China The weight coefficient at the point position, Represents the steering wheel pressure matrix The horizontal axis in Represents the steering wheel pressure matrix The vertical coordinate in Indicates the bias of blood pressure recognition results, represents the bias of the primary blood pressure recognition result, Represents the waveform characteristics, represents the frequency characteristics, Represents the amplitude characteristics, 、 and All of these are characteristics of the red blood cell flow distribution. 、 and Represents the weight coefficient.

[0034] Optionally, before using the pre-trained variable pressure blood pressure recognition model, the method further includes:

[0035] Obtaining a pre-built variable pressure blood pressure recognition model and obtaining a real blood pressure measurement value sample, wherein the real blood pressure measurement value sample includes a measured hand pressure distribution, a hand blood pressure value label, and a real blood pressure value label;

[0036] Sequentially acquiring a target sample from the real blood pressure measurement value samples, performing hand blood pressure prediction on the target sample using the variable pressure blood pressure recognition model to obtain a hand blood pressure prediction result, and performing real blood pressure prediction on the hand blood pressure result based on the measured hand pressure distribution in the target sample to obtain a real blood pressure prediction result;

[0037] Using a pre-built cross entropy loss algorithm, the loss value between the hand blood pressure value label of the target sample and the hand blood pressure prediction result is calculated to obtain a first loss value, and the loss value between the true blood pressure value label of the target sample and the true blood pressure prediction result is calculated to obtain a second loss value;

[0038] Performing weighted calculation on the first loss value and the second loss value to obtain a comprehensive loss value;

[0039] Minimizing the comprehensive loss value according to the gradient descent algorithm to obtain the network model parameters when the comprehensive loss value is the minimum, and performing a reverse parameter update operation on the network model parameters to obtain an updated variable pressure blood pressure recognition model;

[0040] Return to the above step of obtaining a target sample from the real blood pressure measurement value samples in sequence, and iteratively train the updated variable-pressure blood pressure recognition model. When the sample traversal in the updated variable-pressure blood pressure recognition model is completed, a trained variable-pressure blood pressure recognition model is obtained.

[0041] Optionally, performing time-frequency analysis on the absorbed wave to obtain a set of absorbed wave characteristics includes:

[0042] Performing a waveform feature extraction operation on the absorbed wave to obtain a time domain signal feature set;

[0043] Using a pre-built short-time Fourier transform algorithm, the absorbed wave is subjected to a frequency domain conversion operation to obtain a frequency domain signal, and waveform feature extraction is performed on the frequency domain signal to obtain a frequency domain signal feature set;

[0044] The time domain signal feature set and the frequency domain signal feature set are output to obtain an absorbed wave feature set.

[0045] Optionally, after obtaining the blood pressure recognition result, the method further includes:

[0046] Recording the blood pressure recognition result to obtain a blood pressure change curve;

[0047] performing differential calculation on the blood pressure change curve to obtain a blood pressure change rate curve, and monitoring the blood pressure change rate curve according to a preset warning threshold;

[0048] When the blood pressure change in the blood pressure change rate curve is greater than the warning threshold, the patient is determined to be driving dangerously, and the vehicle is automatically braked according to a preset emergency braking strategy.

[0049] Optionally, obtaining the health threshold includes:

[0050] Use pre-built vehicle-mounted central control equipment to obtain patient identity information;

[0051] Querying a patient's specific information set based on the identity information, wherein the specific information set includes age, complications, and treatment history;

[0052] According to the specific information set, a weighted calculation is performed on the preset default hypertension threshold to obtain a healthy threshold.

[0053] To achieve the above objectives, the present invention further provides an intelligent monitoring system for patient status based on a vehicle-mounted blood pressure monitor, comprising:

[0054] an infrared monitoring module, configured to obtain a steering wheel pressure matrix using a pre-built pressure sensor, identify the patient's palm position based on the steering wheel pressure matrix, and obtain palm position information; and perform an infrared irradiation operation based on a preset initial infrared wave on the palm position using the pre-built infrared sensor based on the palm position information, and receive a reflection signal of the infrared irradiation operation to obtain a palm infrared reflection wave;

[0055] a blood absorption wave identification module, configured to utilize a pre-built vibration sensor to obtain a pre-built vibration wave on the steering wheel, filter the palm infrared reflection wave based on the vibration wave to obtain a de-noised palm reflection wave, and perform a difference calculation between the de-noised palm reflection wave and the initial infrared wave to obtain an absorbed wave;

[0056] a palm blood pressure recognition module, configured to use a pre-trained variable pressure blood pressure recognition model to perform time-frequency analysis on the absorbed wave to obtain an absorbed wave feature set, perform a classification operation on the absorbed feature set based on preset red blood cell peak characteristics to obtain a red blood cell absorption wave feature set, perform a red blood cell flow velocity and width distribution recognition operation on the red blood cell absorption wave feature set to obtain a red blood cell flow distribution, and perform a hand blood pressure recognition operation on the red blood cell flow distribution to obtain a primary blood pressure recognition result;

[0057] a blood pressure correction module, configured to perform a feature extraction operation on the steering wheel pressure matrix to obtain a palm pressure distribution feature set, and perform a pressure correction operation on the primary blood pressure recognition result based on the palm pressure distribution feature set to obtain a blood pressure recognition result;

[0058] The supervision and warning module is used to obtain a health threshold, determine whether the blood pressure recognition result is greater than the health threshold, and when the blood pressure recognition result is greater than the health threshold, determine that the patient is driving dangerously and generate an alarm prompt message.

[0059] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0060] a memory storing at least one instruction; and

[0061] The processor executes the instructions stored in the memory to implement the above-mentioned intelligent monitoring method for patient status based on the vehicle-mounted blood pressure monitor.

[0062] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned intelligent monitoring method of patient status based on a vehicle-mounted blood pressure monitor.

[0063] The present invention aims to solve the problems described in the background technology. First, the present invention obtains the blood flow pattern of the patient's palm through infrared scanning technology, thereby identifying the primary blood pressure recognition result of the patient's palm position. Then, the patient's grip on the steering wheel during driving is obtained to obtain the steering wheel pressure matrix. The present invention uses a pre-trained variable pressure blood pressure recognition model to explore the relationship between the grip of the human hand and the real blood pressure, thereby predicting the real blood pressure based on the steering wheel pressure matrix and the primary blood pressure recognition result to obtain the blood pressure recognition result. Among them, the sensor equipment in the present invention is all configured in the steering wheel, and the data processing process provides computing power resources through the car's central control to achieve no interference with the patient's driving process. Therefore, the present invention can perform barrier-free blood pressure monitoring on the driver and improve driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flow chart of a method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor according to an embodiment of the present invention;

[0065] Figure 2 This is a functional module diagram of an intelligent monitoring system for patient status based on a vehicle-mounted blood pressure monitor provided by one embodiment of the present invention;

[0066] Figure 3 A schematic structural diagram of an electronic device for implementing the method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor according to an embodiment of the present invention.

[0067] Description of the figure mark:

[0068] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0069] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0070] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0071] The embodiment of the present application provides an intelligent monitoring method for patient status based on a vehicle-mounted blood pressure monitor. The execution subject of the intelligent monitoring method for patient status based on a vehicle-mounted blood pressure monitor includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the intelligent monitoring method for patient status based on a vehicle-mounted blood pressure monitor can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0072] Reference Figure 1 FIG. 1 is a flow chart of a method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor according to an embodiment of the present invention. In this embodiment, the method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor includes:

[0073] S1. Using a pre-built pressure sensor, obtain a steering wheel pressure matrix, and identify the patient's palm position based on the steering wheel pressure matrix to obtain palm position information.

[0074] In the embodiment of the present invention, the pressure sensor may be placed on the outside of the steering wheel in the form of a film-type sensor, or may be placed inside the steering wheel in the form of a built-in sensor.

[0075] The patient is a patient with hypertension, and in the embodiment, the patient plays the role of a driver.

[0076] Specifically, in the embodiment of the present invention, a film-type sensor is used to collect the pressure distribution of the palm of a hypertensive patient on the steering wheel to obtain a steering wheel pressure matrix.

[0077] It's important to note that during regular blood pressure measurements, patients are typically asked to keep their hands relaxed. However, while driving, they often grip the steering wheel tightly, causing hand blood pressure to deviate from normal blood pressure. Therefore, collecting a steering wheel pressure matrix not only reveals the patient's hand position but also facilitates research into the relationship between hand blood pressure and normal blood pressure.

[0078] Specifically, in an embodiment of the present invention, before obtaining the steering wheel pressure matrix using the pre-built pressure sensor, the method further includes:

[0079] Leverage pre-built in-car navigation to obtain the patient’s real-time location;

[0080] Obtaining the patient's historical common routes, and determining whether the patient is in a straight driving section based on the historical common routes and the real-time location;

[0081] When the patient is in a straight driving section, it is determined that the patient is in a stable driving stage;

[0082] When the patient is not in a straight driving section, it is determined that the patient is in an unstable driving stage;

[0083] If the patient is in a stable driving stage, the operation of obtaining the steering wheel pressure matrix by using the pre-built pressure sensor is performed.

[0084] The vehicle navigation system can obtain the real-time location of the vehicle driven by the patient based on a satellite positioning system or an offline map.

[0085] Wherein, the historical commonly used route is the route that the patient often passes by, such as the route to and from get off work. In addition, when the patient walks to a new route, the navigation route can be used as the historical commonly used data.

[0086] Furthermore, the straight driving section refers to a section where the vehicle can move forward stably, and the reliance on steering wheel control is relatively reduced.

[0087] The stable driving stage means that during the straight driving section, even if it is necessary to change lanes, the patient does not need to take his hands off the steering wheel to achieve control.

[0088] Testing and statistics show that during the stable driving phase, patients are less able to concentrate, making AI-assisted monitoring more necessary. Furthermore, during this phase, the patient's hands are stable on the steering wheel, making infrared scanning easier. Therefore, in this embodiment of the present invention, a monitoring activation strategy is configured to execute the operation of acquiring the steering wheel pressure matrix using the pre-built pressure sensor when the patient is in the stable driving phase.

[0089] Specifically, in an embodiment of the present invention, identifying the patient's palm position based on the steering wheel pressure matrix to obtain palm position information includes:

[0090] performing a masking operation of a preset pressure on the steering wheel pressure matrix to obtain a pressure masking result;

[0091] Performing an outermost contour edge recognition operation on the pressure masking result to obtain a hand shape distribution of the user;

[0092] Performing a key node recognition operation on the hand shape distribution to obtain a key node set;

[0093] The palm position is predicted according to the relative position of each key node in the key node set to obtain palm position information.

[0094] The mask represents an operation of selecting or excluding a specific part.

[0095] In an embodiment of the present invention, a masking operation is performed to mask values ​​less than 500Pa in the steering wheel pressure matrix, retaining the shape of the main force, which generally appears as an image of the palm. Then, the present invention uses an edge segmentation algorithm to identify the outermost contour edge to obtain the hand shape distribution, and then performs a key node identification operation, wherein the key nodes generally include the five fingers and the base of the thumb. When three or more key nodes are obtained, the palm position information can be inferred based on the positional relationship between the key nodes, for example, the palm position is above the 10 o'clock position of the steering wheel.

[0096] Among them, the edge segmentation algorithm refers to an algorithm used to detect edges in an image and segment different objects or areas. It usually represents the boundary or structure of an object based on the areas in the image where the brightness and color changes greatly. The present invention uses the values ​​in the steering wheel pressure matrix as brightness for segmentation, and can easily obtain the hand shape distribution.

[0097] S2. Using a pre-built infrared sensor, according to the palm position information, perform an infrared irradiation operation based on a preset initial infrared wave on the palm position, and receive a reflection signal of the infrared irradiation operation to obtain a palm infrared reflection wave.

[0098] In an embodiment of the present invention, the infrared sensor has the function of emitting and receiving near-infrared light. In addition, the infrared sensor is installed in the steering wheel and can be moved in the steering wheel by a motor or magnetic force.

[0099] Specifically, in this embodiment of the present invention, based on the palm position being above the 10 o'clock position on the steering wheel, the infrared sensor is moved to the palm position via a motor pull or electromagnetic track, and then infrared light scanning is performed. In this embodiment of the present invention, near-infrared light with a wavelength of 700nm is used as the initial infrared wave for irradiation. After being absorbed by the human skeleton, muscles, blood vessels, and blood, the remaining portion is returned to the infrared sensor, generating an infrared reflected wave from the palm.

[0100] S3. Using a pre-built vibration sensor, obtain a vibration wave on a pre-built steering wheel, perform a filtering operation on the palm infrared reflection wave according to the vibration wave to obtain a de-noised palm reflection wave, and perform a difference calculation between the de-noised palm reflection wave and the initial infrared wave to obtain an absorbed wave.

[0101] The vibration sensor is a device that detects and measures the vibration or movement of an object. In the embodiment of the present invention, the vibration sensor is also installed in the steering wheel and is close to the infrared sensor.

[0102] In an embodiment of the present invention, the infrared sensor and the palm are both on the steering wheel. The vibration signal during vehicle driving will interfere with the collected palm infrared reflection wave. Therefore, a vibration sensor is required to collect the vibration wave and eliminate the part of the signal involving vibration in the palm infrared reflection wave.

[0103] Specifically, in an embodiment of the present invention, filtering the palm infrared reflected wave according to the vibration wave to obtain the de-noised palm reflected wave includes:

[0104] Using a pre-built high-pass filter to perform DC component filtering on the palm infrared reflected wave to obtain a DC component-free wave;

[0105] Performing a standardization process on the DC component-removed wave based on a mean of zero and a variance of one to obtain an infrared standard wave;

[0106] Using a pre-built fast Fourier transform algorithm, the vibration components of the vibration wave are identified to obtain the main vibration frequency;

[0107] The infrared standard wave is filtered according to the main vibration frequency to obtain a noise-reduced palm reflection wave.

[0108] The high-pass filter (HPF) is an electronic filter that allows high-frequency signals to pass through while suppressing low-frequency signals. In this embodiment of the present invention, the high-pass filter removes the DC component from the signal to better observe the AC component.

[0109] The Fast Fourier Transform (FFT) algorithm is an efficient way to calculate the Discrete Fourier Transform (DFT) and its inverse transform. The DFT is a mathematical tool that converts signals from the time domain to the frequency domain and is widely used in signal processing, image processing, audio analysis, communication systems, and other fields. The FFT significantly improves computational efficiency by breaking down the DFT calculation into multiple smaller DFT calculations.

[0110] Specifically, in an embodiment of the present invention, the DC component is first filtered through a high-pass filter to obtain a DC component-free wave, and then the DC component-free wave is processed through a standardized normal distribution to obtain an infrared standard wave. The infrared standard wave can make different features have the same scale, thereby improving the effect and convergence speed of the algorithm when performing machine learning or statistical analysis.

[0111] The present invention then uses a fast Fourier transform algorithm to convert the vibration wave of the time domain signal into a frequency domain signal. The main peak components in the frequency domain signal are then identified to obtain the main vibration frequency. Finally, the signal of the active vibration frequency in the infrared standard wave is precisely filtered to obtain the noise-reduced palm reflection wave.

[0112] Furthermore, in an embodiment of the present invention, the absorbed wave is obtained by calculating the difference between the noise-reduced palm reflected wave and the initial infrared wave, wherein the absorbed wave is the wave absorbed by the muscles, bones, blood vessels and blood of the hand.

[0113] S4. Using a pre-trained variable-pressure blood pressure recognition model, perform time-frequency analysis on the absorbed wave to obtain an absorbed wave feature set, perform a classification operation on the absorbed feature set based on preset red blood cell peak characteristics to obtain a red blood cell absorption wave feature set, and perform a red blood cell flow velocity and width distribution recognition operation on the red blood cell absorption wave feature set to obtain a red blood cell flow distribution.

[0114] The variable-pressure blood pressure recognition model is a neural network model used to predict a patient's actual blood pressure (blood pressure recognition result) based on hand pressure (steering wheel pressure matrix) and hand blood pressure (primary blood pressure recognition result). The variable-pressure blood pressure recognition model includes a blood pressure recognition network and a blood pressure correction network. The blood pressure recognition network identifies blood pressure based on the distribution and flow rate of hemoglobin in blood vessels, while the blood pressure correction network corrects hand blood pressure based on pressure at different hand locations to obtain the normal blood pressure measurement.

[0115] In detail, in the embodiment of the present invention, the variable pressure blood pressure recognition model is expressed as:

[0116]

[0117]

[0118] Where, represents the blood pressure recognition result, represents the primary blood pressure recognition result, represents the initial weight of the primary blood pressure recognition result for the blood pressure recognition result, represents the steering wheel pressure matrix, Represents the steering wheel pressure matrix China The weight coefficient at the point position, Represents the steering wheel pressure matrix The horizontal axis in Represents the steering wheel pressure matrix The vertical coordinate in Indicates the bias of blood pressure recognition results, represents the bias of the primary blood pressure recognition result, Represents the waveform characteristics, represents the frequency characteristics, Represents the amplitude characteristics, 、 and All of these are characteristics of the red blood cell flow distribution. 、 and Represents the weight coefficient.

[0119] Specifically, in the embodiment of the present invention, the red blood cell flow distribution is first feature extracted to obtain waveform, frequency, amplitude and other features for extraction, and then the network parameter relationship pre-trained in the variable pressure blood pressure recognition model is used to establish the relationship between the red blood cell flow characteristics and blood pressure, thereby realizing full connection calculation of waveform, frequency, amplitude and other features to obtain the primary blood pressure recognition result. Then, the steering wheel pressure matrix is ​​obtained, and the blood pressure value of the primary blood pressure recognition result is corrected through the regression relationship in the pre-trained network to obtain the final blood pressure recognition result.

[0120] In detail, in an embodiment of the present invention, before using the pre-trained variable pressure blood pressure recognition model, the method further includes:

[0121] Obtaining a pre-built variable pressure blood pressure recognition model and obtaining a real blood pressure measurement value sample, wherein the real blood pressure measurement value sample includes a measured hand pressure distribution, a hand blood pressure value label, and a real blood pressure value label;

[0122] Sequentially acquiring a target sample from the real blood pressure measurement value samples, performing hand blood pressure prediction on the target sample using the variable pressure blood pressure recognition model to obtain a hand blood pressure prediction result, and performing real blood pressure prediction on the hand blood pressure result based on the measured hand pressure distribution in the target sample to obtain a real blood pressure prediction result;

[0123] Using a pre-built cross entropy loss algorithm, the loss value between the hand blood pressure value label of the target sample and the hand blood pressure prediction result is calculated to obtain a first loss value, and the loss value between the true blood pressure value label of the target sample and the true blood pressure prediction result is calculated to obtain a second loss value;

[0124] Performing weighted calculation on the first loss value and the second loss value to obtain a comprehensive loss value;

[0125] Minimizing the comprehensive loss value according to the gradient descent algorithm to obtain the network model parameters when the comprehensive loss value is the minimum, and performing a reverse parameter update operation on the network model parameters to obtain an updated variable pressure blood pressure recognition model;

[0126] Return to the above step of obtaining a target sample from the real blood pressure measurement value samples in sequence, and iteratively train the updated variable-pressure blood pressure recognition model. When the sample traversal in the updated variable-pressure blood pressure recognition model is completed, a trained variable-pressure blood pressure recognition model is obtained.

[0127] Among them, the real blood pressure measurement value sample refers to a set of values ​​of blood pressure measured by the test person through various grip strengths and grip postures. The content of the sample in the real blood pressure measurement value sample is the measured hand pressure distribution, and the hand blood pressure value label and the real blood pressure value label are marks.

[0128] In an embodiment of the present invention, during the forward network calculation process of the variable-pressure blood pressure recognition model, the hand blood pressure value is first predicted to obtain a hand blood pressure prediction result, and then the hand blood pressure prediction result is used as input to predict the true blood pressure together with the measured hand pressure distribution of the target sample to obtain a true blood pressure prediction result.

[0129] In the embodiment of the present invention, both parts of the network are regression predictions, so the same cross entropy loss algorithm can be used to calculate the loss value, and finally a comprehensive loss value can be calculated, and the overall model training can be achieved through the comprehensive loss value. The present invention uses a gradient descent algorithm to control the direction of model training, thereby obtaining an updated variable pressure blood pressure recognition model. Among them, the cross entropy loss algorithm is a method of measuring the difference between two probability distributions, which is often used to evaluate the similarity between the probability distribution output by the model and the true label distribution. The gradient descent algorithm is an iterative algorithm for optimizing model parameters, which is widely used in machine learning and deep learning. The main goal is to minimize the loss function so that the model can better fit the training data.

[0130] In the embodiment of the present invention, it is convenient to obtain the real blood pressure measurement value samples. After a real blood pressure measurement value sample traversal is completed, if the output result of the model still does not meet the standard, a new real blood pressure measurement value sample can be obtained for re-training.

[0131] After the trained variable pressure blood pressure recognition model is obtained, the feature extraction calculation is performed on the absorbed wave to obtain the feature set of the absorbed wave.

[0132] In detail, in an embodiment of the present invention, performing time-frequency analysis on the absorbed wave to obtain a set of absorbed wave characteristics includes:

[0133] Performing a waveform feature extraction operation on the absorbed wave to obtain a time domain signal feature set;

[0134] Using a pre-built short-time Fourier transform algorithm, the absorbed wave is subjected to a frequency domain conversion operation to obtain a frequency domain signal, and waveform feature extraction is performed on the frequency domain signal to obtain a frequency domain signal feature set;

[0135] The time domain signal feature set and the frequency domain signal feature set are output to obtain an absorbed wave feature set.

[0136] The Short-Time Fourier Transform (STFT) algorithm is a tool for analyzing the frequency components of non-stationary signals. It combines the advantages of time and frequency analysis and can be used for time-frequency analysis of signals.

[0137] Specifically, in the embodiment of the present invention, a waveform feature extraction operation is first performed directly on the absorbed wave, and then the time domain signal is converted into a frequency domain signal, and then waveform feature recognition is performed again. Finally, the two recognition results are comprehensively output to obtain a set of absorbed wave features.

[0138] The network process then continues, calculating the red blood cell absorption wave feature set and red blood cell flow distribution. This is the forward operation process of the neural network model. Its specific structure is varied and flexible, so it will not be detailed here.

[0139] S5. Perform a hand blood pressure recognition operation on the red blood cell flow distribution to obtain a primary blood pressure recognition result.

[0140] Specifically, in an embodiment of the present invention, through the hand blood pressure recognition operation, the red blood cell flow distribution is first convolved, pooled, and flattened to extract features, and then the weight parameters obtained according to model training are fully connected to obtain the primary blood pressure recognition result.

[0141] S6. Perform a feature extraction operation on the steering wheel pressure matrix to obtain a palm pressure distribution feature set, and perform a pressure correction operation on the primary blood pressure recognition result according to the palm pressure distribution feature set to obtain a blood pressure recognition result.

[0142] Specifically, in an embodiment of the present invention, similar to the steps in S5 above, this time the steering wheel pressure matrix and the primary blood pressure recognition results are used as input, and convolution, pooling and other operations are re-performed. Through the network model parameters corrected during the training process, full connection prediction is performed on each feature to obtain the blood pressure recognition result.

[0143] In detail, in the embodiment of the present invention, after obtaining the blood pressure recognition result, the method further includes:

[0144] Recording the blood pressure recognition result to obtain a blood pressure change curve;

[0145] performing differential calculation on the blood pressure change curve to obtain a blood pressure change rate curve, and monitoring the blood pressure change rate curve according to a preset warning threshold;

[0146] When the blood pressure change in the blood pressure change rate curve is greater than the warning threshold, the patient is determined to be driving dangerously, and the vehicle is automatically braked according to a preset emergency braking strategy.

[0147] In an embodiment of the present invention, when blood pressure exceeds a certain threshold, such as 160 mmHg, dizziness, fainting, etc. may occur. However, if blood pressure starts out normal and then suddenly increases, this situation is more dangerous and may be an acute hypertensive crisis, cardiac event, stress reaction, etc.

[0148] In an embodiment of the present invention, the blood pressure change curve is converted into a blood pressure change rate curve through a differential operation, and then a warning threshold is configured, for example, 20 mmHg per second. When the blood pressure change in the blood pressure change rate curve is greater than the warning threshold, this situation requires emergency avoidance, and the car can be pulled over through the car's central control system or even automatically alarmed.

[0149] S7. Obtain a health threshold, and determine whether the blood pressure recognition result is greater than the health threshold.

[0150] In detail, in the embodiment of the present invention, obtaining the health threshold includes:

[0151] Use pre-built vehicle-mounted central control equipment to obtain patient identity information;

[0152] Querying a patient's specific information set based on the identity information, wherein the specific information set includes age, complications, and treatment history;

[0153] According to the specific information set, a weighted calculation is performed on the preset default hypertension threshold to obtain a healthy threshold.

[0154] It should be noted that different people have different physical conditions, and the degree to which age affects blood pressure changes varies. In this embodiment of the present invention, big data is collected to analyze the impact of age, complications, and treatment history on hypertension resistance, thereby fine-tuning the patient's hypertension threshold. While the default hypertension threshold is 160 mmHg, some people may be configured to 150 or 170 mmHg.

[0155] The present invention can monitor the patient's condition by comparing whether the blood pressure identification result is greater than a healthy threshold.

[0156] S8. When the blood pressure recognition result is greater than the health threshold, the patient is determined to be driving dangerously and an alarm prompt message is generated.

[0157] The determination process of the embodiment of the present invention is similar to that of the above-mentioned blood pressure change rate curve. In the embodiment of the present invention, when the user's blood pressure slowly increases due to poor blood circulation caused by long-term driving and exceeds the health threshold, an alarm prompt message can be generated, such as a text such as "Pay attention to rest", to improve the patient's driving vigilance.

[0158] The present invention aims to solve the problems described in the background technology. First, the present invention obtains the blood flow pattern of the patient's palm through infrared scanning technology, thereby identifying the primary blood pressure recognition result of the patient's palm position. Then, the patient's grip on the steering wheel during driving is obtained to obtain the steering wheel pressure matrix. The present invention uses a pre-trained variable pressure blood pressure recognition model to explore the relationship between the grip of the human hand and the real blood pressure, thereby predicting the real blood pressure based on the steering wheel pressure matrix and the primary blood pressure recognition result to obtain the blood pressure recognition result. Among them, the sensor equipment in the present invention is all configured in the steering wheel, and the data processing process provides computing power resources through the car's central control to achieve no interference with the patient's driving process. Therefore, the present invention can perform barrier-free blood pressure monitoring on the driver and improve driving safety.

[0159] like Figure 2 , which is a functional module diagram of an intelligent monitoring system for patient status based on a vehicle-mounted blood pressure monitor provided by one embodiment of the present invention.

[0160] The intelligent patient status monitoring system 100 based on a vehicle-mounted blood pressure monitor described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the intelligent patient status monitoring system 100 based on a vehicle-mounted blood pressure monitor can include an infrared monitoring module 101, a blood absorption wave recognition module 102, a palm blood pressure recognition module 103, a blood pressure correction module 104, and a monitoring and warning module 105. A module, also referred to as a unit, refers to a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and is stored in the electronic device's memory.

[0161] The infrared monitoring module 101 is configured to obtain a steering wheel pressure matrix using a pre-built pressure sensor, identify the patient's palm position based on the steering wheel pressure matrix, and obtain palm position information; perform an infrared irradiation operation based on a preset initial infrared wave on the palm position using the pre-built infrared sensor based on the palm position information, and receive a reflection signal of the infrared irradiation operation to obtain a palm infrared reflection wave;

[0162] The blood absorption wave identification module 102 is configured to use a pre-built vibration sensor to obtain a pre-built vibration wave on the steering wheel, filter the palm infrared reflection wave according to the vibration wave to obtain a de-noised palm reflection wave, and perform a difference calculation between the de-noised palm reflection wave and the initial infrared wave to obtain an absorbed wave;

[0163] The palm blood pressure recognition module 103 is configured to perform time-frequency analysis on the absorbed wave using a pre-trained variable pressure blood pressure recognition model to obtain an absorbed wave feature set, perform a classification operation on the absorbed feature set based on preset red blood cell peak characteristics to obtain a red blood cell absorption wave feature set, perform a red blood cell flow velocity and width distribution recognition operation on the red blood cell absorption wave feature set to obtain a red blood cell flow distribution, and perform a hand blood pressure recognition operation on the red blood cell flow distribution to obtain a primary blood pressure recognition result.

[0164] The blood pressure correction module 104 is configured to perform a feature extraction operation on the steering wheel pressure matrix to obtain a palm pressure distribution feature set, and perform a pressure correction operation on the primary blood pressure recognition result based on the palm pressure distribution feature set to obtain a blood pressure recognition result;

[0165] The supervision and warning module 105 is used to obtain a health threshold, determine whether the blood pressure identification result is greater than the health threshold, and when the blood pressure identification result is greater than the health threshold, determine that the patient is driving dangerously and generate an alarm prompt message.

[0166] In detail, the modules in the intelligent monitoring system 100 for realizing the patient's status based on the vehicle-mounted blood pressure monitor in the embodiment of the present invention are used in the same manner as above. Figure 1 The same technical means as the method for realizing intelligent monitoring of patient status based on vehicle-mounted blood pressure monitor described in the specification and the like are used and can produce the same technical effects, so they will not be repeated here.

[0167] like Figure 3 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing an intelligent monitoring method for a patient's status based on a vehicle-mounted blood pressure monitor according to an embodiment of the present invention.

[0168] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for an intelligent monitoring method for a patient's condition based on a vehicle-mounted blood pressure monitor.

[0169] The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the electronic device 1. Furthermore, the memory 11 includes both the internal storage unit of the electronic device 1 and an external storage device. The memory 11 can be used not only to store application software installed in the electronic device 1 and various types of data, such as the code of a program for an intelligent patient status monitoring method based on a vehicle-mounted blood pressure monitor, but also to temporarily store data that has been output or is about to be output.

[0170] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a combination of multiple packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (control unit) of the electronic device, connecting the various components of the electronic device using various interfaces and circuits. It executes programs or modules stored in the memory 11 (e.g., a program for intelligent patient status monitoring based on an on-board blood pressure monitor) and accesses data stored in the memory 11 to execute various functions and process data.

[0171] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0172] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0173] For example, although not shown, the electronic device 1 may further include a power supply (e.g., a battery) to power various components. Preferably, the power supply may be logically connected to the at least one processor 10 via a power management device, thereby enabling functions such as charge management, discharge management, and power consumption management via the power management device. The power supply may further include any components such as one or more DC or AC power supplies, a recharging device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not further described here.

[0174] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0175] Optionally, the electronic device 1 may further include a patient interface, which may be a display or an input unit (such as a keyboard). Optionally, the patient interface may also be a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a display screen or display unit, and is used to display information processed by the electronic device 1 and to display a visual patient interface.

[0176] The program for the intelligent monitoring method for a patient's condition based on a vehicle-mounted blood pressure monitor stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When executed in the processor 10, the following can be achieved:

[0177] Using a pre-built pressure sensor, a steering wheel pressure matrix is ​​obtained, and based on the steering wheel pressure matrix, the palm position of the patient is identified to obtain palm position information;

[0178] Using a pre-built infrared sensor, according to the palm position information, an infrared irradiation operation based on a preset initial infrared wave is performed on the palm position, and a reflection signal of the infrared irradiation operation is received to obtain a palm infrared reflection wave;

[0179] Using a pre-built vibration sensor, a vibration wave on a pre-built steering wheel is obtained. The palm infrared reflected wave is filtered according to the vibration wave to obtain a de-noised palm reflected wave. The de-noised palm reflected wave and the initial infrared wave are then differenced to obtain an absorbed wave.

[0180] Using a pre-trained variable pressure blood pressure recognition model, performing a time-frequency analysis on the absorbed wave to obtain an absorbed wave feature set, performing a classification operation on the absorbed feature set based on preset red blood cell peak characteristics to obtain a red blood cell absorption wave feature set, and performing a red blood cell flow velocity and width distribution recognition operation on the red blood cell absorption wave feature set to obtain a red blood cell flow distribution;

[0181] performing a hand blood pressure recognition operation on the red blood cell flow distribution to obtain a primary blood pressure recognition result;

[0182] performing a feature extraction operation on the steering wheel pressure matrix to obtain a palm pressure distribution feature set, and performing a pressure correction operation on the primary blood pressure recognition result according to the palm pressure distribution feature set to obtain a blood pressure recognition result;

[0183] Obtaining a health threshold, and determining whether the blood pressure recognition result is greater than the health threshold;

[0184] When the blood pressure recognition result is greater than the health threshold, the patient is determined to be driving dangerously and an alarm prompt message is generated.

[0185] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0186] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0187] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0188] Using a pre-built pressure sensor, a steering wheel pressure matrix is ​​obtained, and based on the steering wheel pressure matrix, the palm position of the patient is identified to obtain palm position information;

[0189] Using a pre-built infrared sensor, according to the palm position information, an infrared irradiation operation based on a preset initial infrared wave is performed on the palm position, and a reflection signal of the infrared irradiation operation is received to obtain a palm infrared reflection wave;

[0190] Using a pre-built vibration sensor, a vibration wave on a pre-built steering wheel is obtained. The palm infrared reflected wave is filtered according to the vibration wave to obtain a de-noised palm reflected wave. The de-noised palm reflected wave and the initial infrared wave are then differenced to obtain an absorbed wave.

[0191] Using a pre-trained variable pressure blood pressure recognition model, performing a time-frequency analysis on the absorbed wave to obtain an absorbed wave feature set, performing a classification operation on the absorbed feature set based on preset red blood cell peak characteristics to obtain a red blood cell absorption wave feature set, and performing a red blood cell flow velocity and width distribution recognition operation on the red blood cell absorption wave feature set to obtain a red blood cell flow distribution;

[0192] performing a hand blood pressure recognition operation on the red blood cell flow distribution to obtain a primary blood pressure recognition result;

[0193] performing a feature extraction operation on the steering wheel pressure matrix to obtain a palm pressure distribution feature set, and performing a pressure correction operation on the primary blood pressure recognition result according to the palm pressure distribution feature set to obtain a blood pressure recognition result;

[0194] Obtaining a health threshold, and determining whether the blood pressure recognition result is greater than the health threshold;

[0195] When the blood pressure recognition result is greater than the health threshold, the patient is determined to be driving dangerously and an alarm prompt message is generated.

[0196] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0197] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0198] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0199] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0200] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent monitoring method for patient status based on a vehicle-mounted blood pressure monitor, characterized in that: The method comprises: Using a pre-built pressure sensor, a steering wheel pressure matrix is ​​obtained, and based on the steering wheel pressure matrix, the palm position of the patient is identified to obtain palm position information; Using a pre-built infrared sensor, according to the palm position information, an infrared irradiation operation based on a preset initial infrared wave is performed on the palm position, and a reflection signal of the infrared irradiation operation is received to obtain a palm infrared reflection wave; Using a pre-built vibration sensor, a vibration wave on a pre-built steering wheel is obtained. The palm infrared reflected wave is filtered according to the vibration wave to obtain a de-noised palm reflected wave. The de-noised palm reflected wave and the initial infrared wave are then differenced to obtain an absorbed wave. Using a pre-trained variable pressure blood pressure recognition model, performing a time-frequency analysis on the absorbed wave to obtain an absorbed wave feature set, performing a classification operation on the absorbed wave feature set based on preset red blood cell peak characteristics to obtain a red blood cell absorption wave feature set, and performing a red blood cell flow velocity and width distribution recognition operation on the red blood cell absorption wave feature set to obtain a red blood cell flow distribution; performing a hand blood pressure recognition operation on the red blood cell flow distribution to obtain a primary blood pressure recognition result; performing a feature extraction operation on the steering wheel pressure matrix to obtain a palm pressure distribution feature set, and performing a pressure correction operation on the primary blood pressure recognition result according to the palm pressure distribution feature set to obtain a blood pressure recognition result; Obtaining a health threshold, and determining whether the blood pressure recognition result is greater than the health threshold; When the blood pressure recognition result is greater than the health threshold, the patient is determined to be driving dangerously and an alarm prompt message is generated.

2. The method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor according to claim 1, wherein: Before obtaining the steering wheel pressure matrix using the pre-built pressure sensor, the method further includes: Leverage pre-built in-car navigation to obtain the patient’s real-time location; Obtaining the patient's historical common routes, and determining whether the patient is in a straight driving section based on the historical common routes and the real-time location; When the patient is in a straight driving section, it is determined that the patient is in a stable driving stage; When the patient is not in a straight driving section, it is determined that the patient is in an unstable driving stage; If the patient is in a stable driving stage, the operation of using the pre-built pressure sensor to obtain the steering wheel pressure matrix is ​​performed.

3. The method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor according to claim 2, wherein: The step of identifying the patient's palm position according to the steering wheel pressure matrix to obtain palm position information includes: performing a masking operation of a preset pressure on the steering wheel pressure matrix to obtain a pressure masking result; Performing an outermost contour edge recognition operation on the pressure masking result to obtain a hand shape distribution of the user; Performing a key node recognition operation on the hand shape distribution to obtain a key node set; The palm position is predicted according to the relative position of each key node in the key node set to obtain palm position information.

4. The method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor according to claim 3, wherein: The filtering operation on the palm infrared reflected wave according to the vibration wave to obtain the noise-reduced palm reflected wave includes: Using a pre-built high-pass filter to perform DC component filtering on the palm infrared reflected wave to obtain a DC component-free wave; Performing a standardization process on the DC component-removed wave based on a mean of zero and a variance of one to obtain an infrared standard wave; Using a pre-built fast Fourier transform algorithm, the vibration components of the vibration wave are identified to obtain the main vibration frequency; The infrared standard wave is filtered according to the main vibration frequency to obtain a noise-reduced palm reflection wave.

5. The method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor according to claim 4, wherein: The variable pressure blood pressure identification model is expressed as: Where, represents the blood pressure recognition result, represents the primary blood pressure recognition result, represents the initial weight of the primary blood pressure recognition result for the blood pressure recognition result, represents the steering wheel pressure matrix, Represents the steering wheel pressure matrix China The weight coefficient at the point position, Represents the steering wheel pressure matrix The horizontal axis in Represents the steering wheel pressure matrix The vertical coordinate in Indicates the bias of blood pressure recognition results, represents the bias of the primary blood pressure recognition result, Represents the waveform characteristics, represents the frequency characteristics, Represents the amplitude characteristics, 、 and All of these are characteristics of the red blood cell flow distribution. 、 and Represents the weight coefficient.

6. The method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor according to claim 5, wherein: Before using the pre-trained variable pressure blood pressure recognition model, the method further includes: Obtaining a pre-built variable pressure blood pressure recognition model and obtaining a real blood pressure measurement value sample, wherein the real blood pressure measurement value sample includes a measured hand pressure distribution, a hand blood pressure value label, and a real blood pressure value label; Sequentially acquiring a target sample from the real blood pressure measurement value samples, performing hand blood pressure prediction on the target sample using the variable pressure blood pressure recognition model to obtain a hand blood pressure prediction result, and performing real blood pressure prediction on the hand blood pressure result based on the measured hand pressure distribution in the target sample to obtain a real blood pressure prediction result; Using a pre-built cross entropy loss algorithm, the loss value between the hand blood pressure value label of the target sample and the hand blood pressure prediction result is calculated to obtain a first loss value, and the loss value between the true blood pressure value label of the target sample and the true blood pressure prediction result is calculated to obtain a second loss value; Performing weighted calculation on the first loss value and the second loss value to obtain a comprehensive loss value; Minimizing the comprehensive loss value according to the gradient descent algorithm to obtain the network model parameters when the comprehensive loss value is the minimum, and performing a reverse parameter update operation on the network model parameters to obtain an updated variable pressure blood pressure recognition model; Return to the above step of obtaining a target sample from the real blood pressure measurement value samples in sequence, and iteratively train the updated variable-pressure blood pressure recognition model. When the sample traversal in the updated variable-pressure blood pressure recognition model is completed, a trained variable-pressure blood pressure recognition model is obtained.

7. The method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor according to claim 6, wherein: The performing time-frequency analysis on the absorbed wave to obtain a set of absorbed wave characteristics includes: Performing a waveform feature extraction operation on the absorbed wave to obtain a time domain signal feature set; Using a pre-built short-time Fourier transform algorithm, the absorbed wave is subjected to a frequency domain conversion operation to obtain a frequency domain signal, and waveform feature extraction is performed on the frequency domain signal to obtain a frequency domain signal feature set; The time domain signal feature set and the frequency domain signal feature set are output to obtain an absorbed wave feature set.

8. The method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor according to claim 7, wherein: After obtaining the blood pressure recognition result, the method further includes: Recording the blood pressure recognition result to obtain a blood pressure change curve; performing differential calculation on the blood pressure change curve to obtain a blood pressure change rate curve, and monitoring the blood pressure change rate curve according to a preset warning threshold; When the blood pressure change in the blood pressure change rate curve is greater than the warning threshold, the patient is determined to be driving dangerously, and the vehicle is automatically braked according to a preset emergency braking strategy.

9. The method for intelligently monitoring a patient's condition based on a vehicle-mounted blood pressure monitor according to claim 8, wherein: The obtaining of the health threshold value includes: Use pre-built vehicle-mounted central control equipment to obtain patient identity information; Querying a patient's specific information set based on the identity information, wherein the specific information set includes age, complications, and treatment history; According to the specific information set, a weighted calculation is performed on the preset default hypertension threshold to obtain a healthy threshold.

10. An intelligent monitoring system for patient status based on a vehicle-mounted blood pressure monitor, characterized in that: The system comprises: an infrared monitoring module, configured to obtain a steering wheel pressure matrix using a pre-built pressure sensor, identify the patient's palm position based on the steering wheel pressure matrix, and obtain palm position information; and perform an infrared irradiation operation based on a preset initial infrared wave on the palm position using the pre-built infrared sensor based on the palm position information, and receive a reflection signal of the infrared irradiation operation to obtain a palm infrared reflection wave; a blood absorption wave identification module, configured to utilize a pre-built vibration sensor to obtain a pre-built vibration wave on the steering wheel, filter the palm infrared reflection wave based on the vibration wave to obtain a de-noised palm reflection wave, and perform a difference calculation between the de-noised palm reflection wave and the initial infrared wave to obtain an absorbed wave; a palm blood pressure recognition module, configured to use a pre-trained variable pressure blood pressure recognition model to perform time-frequency analysis on the absorbed wave to obtain an absorbed wave feature set, perform a classification operation on the absorbed wave feature set based on preset red blood cell peak characteristics to obtain a red blood cell absorption wave feature set, perform a red blood cell flow velocity and width distribution recognition operation on the red blood cell absorption wave feature set to obtain a red blood cell flow distribution, and perform a hand blood pressure recognition operation on the red blood cell flow distribution to obtain a primary blood pressure recognition result; a blood pressure correction module, configured to perform a feature extraction operation on the steering wheel pressure matrix to obtain a palm pressure distribution feature set, and perform a pressure correction operation on the primary blood pressure recognition result based on the palm pressure distribution feature set to obtain a blood pressure recognition result; The supervision and warning module is used to obtain a health threshold, determine whether the blood pressure recognition result is greater than the health threshold, and when the blood pressure recognition result is greater than the health threshold, determine that the patient is driving dangerously and generate an alarm prompt message.

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