A data processing method and system for improving blood pressure measurement accuracy

Abstract

The present application relates to the field of data processing technology, and provides a data processing method and system for improving the accuracy of blood pressure measurement. The method comprises: sensing and collecting the dynamics of the target user through the dynamic sensing device; determining the initial measurement status of the target user; collecting the environmental information when the target user measures the blood pressure, and obtaining comprehensive environmental data; performing real-time measurement on the target user, and obtaining the single-moment blood pressure measurement result of the target user; determining the influence of the single-moment blood pressure measurement result of the target user, and determining the influence coefficient; performing two-way adjustment and optimization of the blood pressure accuracy of the single-moment blood pressure measurement result of the target user, and obtaining the single-moment blood pressure measurement optimization result of the target user. The method solves the technical problem in the prior art that the initial measurement status of the user is not determined, resulting in low blood pressure measurement accuracy, and achieves the technical effect of improving the accuracy of blood pressure measurement.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data processing method and system for improving blood pressure measurement accuracy. Background Art

[0002] Hypertension is the most common chronic disease in my country, characterized by elevated systemic arterial pressure. It is a cardiovascular syndrome and the most important risk factor for cardiovascular and cerebrovascular disease. It often coexists with other cardiovascular risk factors, damaging the structure and function of vital organs such as the heart, brain, and kidneys, ultimately leading to organ failure. To protect cardiovascular health, understand blood pressure fluctuations, and prevent the development of hypertensive complications, preventive measures and daily blood pressure monitoring are essential. Existing hypertension diagnosis, detection, and control standards vary depending on the blood pressure measurement method, including home self-measurement, office blood pressure, and 24-hour ambulatory blood pressure. Measurements can be distorted by environmental or posture variations, making the acquisition of these data crucial. Furthermore, blood pressure fluctuations in some hypertensive patients are significantly affected by environmental factors, eating, and body position, resulting in stress-induced hypertension, postprandial hypotension, orthostatic hypotension, and postural hypertension. Failure to clearly define the blood pressure state at the time of measurement can significantly impact blood pressure measurement accuracy. At this time, in order to better prevent hypertension and understand the precise blood pressure status, this application proposes a data processing method to improve the accuracy of blood pressure measurement, which is used to solve the technical problem in the existing technology that the user's measurement status is not determined, resulting in low blood pressure measurement accuracy. Summary of the Invention

[0003] Based on this, it is necessary to provide a data processing method and system for improving blood pressure measurement accuracy to address the above technical issues, so as to solve the problem of low blood pressure measurement accuracy in the prior art.

[0004] In a first aspect, an embodiment of the present application provides a data processing method for improving blood pressure measurement accuracy, the method being applied to a data processing system, the data processing system being communicatively connected to a dynamic sensing device and a blood pressure measuring instrument, the method comprising: sensing and collecting the dynamics of a target user through the dynamic sensing device to obtain N pieces of dynamic information; determining the initial measurement condition of the target user based on the N pieces of dynamic information; collecting environmental information when the target user performs blood pressure measurement to obtain comprehensive environmental data; performing real-time measurement of the target user through the blood pressure measuring instrument to obtain a single-moment blood pressure measurement result of the target user; determining the degree of influence of the single-moment blood pressure measurement result of the target user based on the initial measurement condition of the target user and the comprehensive environmental data to determine an influence coefficient; performing bidirectional adjustment and optimization of the blood pressure accuracy of the single-moment blood pressure measurement result of the target user based on the influence coefficient to obtain the optimized single-moment blood pressure measurement result of the target user.

[0005] In a second aspect, an embodiment of the present application further provides a data processing system for improving the accuracy of blood pressure measurement, wherein the system is applied to a data processing system, wherein the data processing system is communicatively connected to a dynamic sensing device and a blood pressure measuring instrument, and the system comprises: a dynamic information acquisition module, wherein the dynamic information acquisition module is used to sense and collect the dynamic state of a target user through the dynamic sensing device, and obtain N dynamic information; a measurement initial condition determination module, wherein the measurement initial condition determination module is used to determine the measurement initial state of the target user based on the N dynamic information; an environmental comprehensive data acquisition module, wherein the environmental comprehensive data acquisition module is used to collect environmental information when the target user performs blood pressure measurement, and obtain environmental comprehensive data; a single A module for acquiring blood pressure measurement results at a moment, wherein the module is used to perform real-time measurement of the target user through the blood pressure measuring instrument to obtain the single-moment blood pressure measurement result of the target user; an influence coefficient determination module, wherein the module is used to determine the influence of the single-moment blood pressure measurement result of the target user based on the initial measurement condition of the target user and the comprehensive environmental data, and determine the influence coefficient; a module for acquiring blood pressure measurement optimization results, wherein the module is used to perform bidirectional adjustment and optimization of the blood pressure accuracy of the single-moment blood pressure measurement result of the target user based on the influence coefficient, and obtain the single-moment blood pressure measurement optimization result of the target user.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] First, the dynamic sensing device senses and collects the dynamics of the target user to obtain N dynamic information; secondly, the initial measurement status of the target user is determined based on the N dynamic information; then, the environmental information when the target user measures blood pressure is collected to obtain comprehensive environmental data; then, the target user is measured in real time by the blood pressure measuring instrument to obtain the single-moment blood pressure measurement result of the target user; next, the influence of the single-moment blood pressure measurement result of the target user is determined based on the initial measurement status of the target user and the comprehensive environmental data to determine the influence coefficient; finally, the single-moment blood pressure measurement result of the target user is bidirectionally adjusted and optimized based on the influence coefficient to obtain the single-moment blood pressure measurement optimization result of the target user. This application solves the technical problem in the prior art that the initial measurement status of the user is not determined, resulting in low blood pressure measurement accuracy, and achieves the technical effect of improving blood pressure measurement accuracy.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 1 is a flow chart of a data processing method for improving blood pressure measurement accuracy in one embodiment;

[0010] Figure 2 A schematic diagram of a flow chart of constructing an initial condition judgment model in a data processing method for improving blood pressure measurement accuracy in one embodiment;

[0011] Figure 3 The figure is a structural block diagram of a data processing system for improving blood pressure measurement accuracy in one embodiment.

[0012] Explanation of the reference numerals: dynamic information acquisition module 11, measurement initial condition determination module 12, environmental comprehensive data acquisition module 13, single-moment blood pressure measurement result acquisition module 14, influence coefficient determination module 15, blood pressure measurement optimization result acquisition module 16. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0014] After introducing the basic principles of the present application, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the sake of ease of description, only the parts related to the present application, not all, are shown in the accompanying drawings.

[0015] Example 1

[0016] like Figure 1 As shown, the present application provides a data processing method for improving blood pressure measurement accuracy. The method is applied to a data processing system, the data processing system is communicatively connected to a dynamic sensing device and a blood pressure measuring instrument, and the method includes:

[0017] S100: Detecting and collecting the target user's dynamic state through the dynamic sensing device to obtain N dynamic information;

[0018] Specifically, blood pressure measurement is a test that measures the pressure in the arteries when the heart pumps blood. Each time the heart beats, it pumps blood into the arteries. The accuracy of blood pressure measurements can be affected by the user's posture, eating habits, dynamic information, and surroundings.

[0019] In this embodiment, the present application provides a data processing method for improving the accuracy of blood pressure measurement. The method is applied to a data processing system, and the data processing system is communicatively connected to a dynamic sensing device and a blood pressure measuring instrument. The user data and environmental data are collected and processed by the dynamic sensing device and the blood pressure measuring instrument, and the data are communicated to the data processing system, thereby achieving the technical effect of improving the accuracy of blood pressure measurement.

[0020] The dynamic sensing devices can be multiple devices that collect dynamic data of target users, such as cameras and thermal imagers. The target users are those whose blood pressure needs to be measured. The dynamic sensing devices collect dynamic data of the target users, such as by using cameras to capture dynamic sensing, to obtain data on N target users, such as their posture and whether they are dining. Where N is an arbitrary natural number, ≥ 1. By collecting dynamic data on the target users' status and transmitting it to a data processing system, the accuracy of blood pressure measurements can be improved.

[0021] Furthermore, the application steps include:

[0022] S110: sensing and collecting the target user's posture through the dynamic sensing device to determine the target user's posture dynamic information;

[0023] S120: Determine the target user's dining dynamic time information through intelligent query between the dynamic sensing device and the target user;

[0024] S130: sensing and collecting the state of the target user through the dynamic sensing device to determine the dynamic state information of the target user;

[0025] S140: Add the posture dynamic information, the dining dynamic time information, and the dynamic state information to the N dynamic information.

[0026] Specifically, the dynamic sensing device is a plurality of devices for collecting dynamic data of the target user. First, the dynamic sensing of the target user can be captured by a camera, and the posture of the target user can be photographed to determine the posture of the target user, wherein the blood pressure value when standing is usually lower than that when sitting or lying down; secondly, the robot can be used to inquire whether the target user is having a meal. If so, the meal time of the target user is obtained, and the time from the meal time of the target user to the blood pressure measurement time of the target user is calculated; then, the user's heartbeat and other data are collected through the dynamic bed device to judge the user's current mood, etc. Since the data is collected in real time and changes continuously, the target user's posture dynamic information, meal dynamic time information and dynamic status information can be obtained, and the posture dynamic information, the meal dynamic time information and the dynamic status information are added to the N dynamic information.

[0027] S200: Determine an initial measurement status of the target user according to the N pieces of dynamic information;

[0028] Furthermore, the application steps include:

[0029] S210: extracting the target user's posture based on the posture dynamic information and determining the initial posture influence;

[0030] S220: Extracting the target user's dining time based on the dynamic dining time information, and determining the initial dining time influence;

[0031] S230: extracting the target user's status based on the dynamic status information and determining the initial status influence;

[0032] S240: Input the initial posture influence, the initial meal time influence, and the initial state influence into an initial condition judgment model to determine the condition of the target user before blood pressure measurement, and output the measurement initial condition of the target user.

[0033] Specifically, the target user's initial measurement status is determined based on the N dynamic information, wherein the target user's initial measurement status includes posture dynamic information, dining dynamic time information, and dynamic state information communicated and transmitted by the dynamic sensing device.

[0034] Since standing blood pressure values ​​are usually lower than those in sitting or lying down, the posture of the target user is extracted based on the posture dynamic information to determine whether the target user is sitting, standing or lying down; measuring blood pressure directly after a meal will lead to a higher measurement result, and the meal time of the target user is extracted based on the meal dynamic time information to determine whether the target user has eaten; whether the target user is anxious, nervous, smoking or drinking before measuring blood pressure, all of which will affect the accuracy of blood pressure measurement, and the state of the target user is extracted based on the dynamic state information to determine whether the target user is anxious, nervous, smoking or drinking before measuring blood pressure; the initial posture influence, the initial meal time influence, and the initial state influence are input into the initial condition judgment model to determine the condition of the target user before blood pressure measurement, and the initial measurement condition of the target user is output.

[0035] like Figure 2 As shown, further, the application steps also include:

[0036] S241: constructing the initial condition judgment model based on the BP neural network, wherein the input data of the initial condition judgment model includes the initial posture influence, the initial dining time influence, and the initial state influence, and the output data includes the measured initial condition of the target user;

[0037] S242: The initial state judgment model includes a data input layer, a state judgment layer, and an initial state output layer;

[0038] S243: Annotate the N dynamic information to obtain a first constructed data set, wherein the first constructed data set includes a first training set and a first validation set;

[0039] S244: Using the first training set and the first validation set to perform supervised training and validation on the initial condition judgment model until the initial condition judgment model converges or the accuracy reaches a preset requirement.

[0040] Specifically, based on multiple historical device control parameters and multiple historical device energy consumption information, an initial condition judgment model is constructed. The process of constructing the initial condition judgment model is as follows: based on the BP neural network, the network structure of the initial condition judgment model is constructed. The initial condition judgment model includes multiple simple units that simulate human brain neurons. The initial condition judgment model can form parameters such as weights and thresholds for connections between simple units during supervised training. After training, the initial condition judgment model can perform complex nonlinear logical operations based on input data and output the initial condition judgment situation. The input data of the initial condition judgment model are the initial posture influence, the initial dining time influence, and the initial state influence, and the output data is the measured initial condition of the target user; the initial condition judgment model includes a data input layer, a condition judgment layer, and an initial condition output layer. The N dynamic information are obtained and annotated to obtain a first constructed data set, and an initial condition judgment model is constructed, wherein the first constructed data set includes a plurality of historical initial posture influences, historical initial dining time influences, historical initial state influences, and the historical measured initial conditions of the target user. The first constructed data set is further annotated and divided according to a certain ratio to obtain a first training set and a first validation set. The initial condition judgment model is trained and validated using the first constructed data set to obtain the initial condition judgment model.

[0041] An initial condition determination model is constructed based on a BP neural network. The initial condition determination model is a neural network model that can be iteratively optimized in machine learning and is obtained through supervised training on a first constructed dataset. The first constructed dataset is divided into a first validation set and a first test set according to a preset data partitioning rule. The preset data partitioning ratio can be customized by those skilled in the art based on actual circumstances, for example, 85% and 15%. The initial condition determination model is supervised trained using the first training set. When the model output results tend to converge, the accuracy of the output results of the initial condition determination model is verified using the first validation set to obtain a preset verification accuracy index. The preset verification accuracy index can be customized by those skilled in the art based on actual circumstances, for example, 90%. When the accuracy of the output results of the initial condition determination model is greater than or equal to the preset verification accuracy index, the initial condition determination model is obtained. By constructing an initial condition determination model based on a BP neural network, the efficiency and accuracy of the initial condition of the target user can be improved.

[0042] S300: Collecting environmental information when the target user's blood pressure is measured to obtain comprehensive environmental data;

[0043] Furthermore, the application steps include:

[0044] S310: extracting the ambient temperature of the target user from the comprehensive environmental data through a temperature sensing device to determine a temperature factor;

[0045] S320: Extracting the sound around the target user from the comprehensive environmental data using a vibration sensor device to determine a sound factor;

[0046] S330: Preset the ambient temperature measurement interval and the ambient sound measurement interval;

[0047] S340: Determine whether the temperature factor and / or the sound factor is greater than the preset ambient temperature measurement interval and the preset ambient sound measurement interval;

[0048] S350: If the temperature factor and the sound factor are both not within the preset ambient temperature measurement interval and the preset ambient sound measurement interval, generating a waiting instruction;

[0049] S360: If the sound factor is not within the preset ambient sound measurement range, and the temperature factor is within the preset ambient temperature measurement range, tracing the sound source to determine the sound source;

[0050] S370: If the temperature factor is not within the preset ambient temperature measurement zone and the sound factor is within the preset ambient sound measurement interval, acquiring temperature anomaly data and generating a replacement instruction according to the temperature anomaly data;

[0051] S380: If the temperature factor and the sound factor are both within the preset ambient temperature measurement interval and the preset ambient sound measurement interval, performing real-time measurement on the target user using the blood pressure measuring instrument.

[0052] Specifically, since both the environment and sound will affect the blood pressure measurement data, when the environment is noisy, or the target user is emotionally excited and talking loudly, the human body will speed up the heartbeat, constrict blood vessels, increase blood flow rate, and cause a sudden increase in blood pressure; high temperature will cause blood vessels to dilate and body fluids to accumulate in the periphery, causing blood pressure to rise, and low temperature will cause blood space to contract, causing blood pressure to rise. Therefore, the environmental information of the target user during blood pressure measurement is collected to obtain comprehensive environmental data, where the comprehensive environmental data includes environmental sound data and environmental temperature data.

[0053] The temperature of the target user's surroundings is extracted from the comprehensive environmental data using a temperature sensor device to determine a temperature factor, where the temperature factor is a temperature average value determined based on multiple values ​​of the target user's surrounding temperature extracted, and the target user's surrounding temperature before or during blood pressure measurement. The vibration sensor device is used to extract the sound surrounding the target user from the comprehensive environmental data to determine a sound factor, where the sound factor is a sound average value determined based on multiple values ​​of the sound surrounding the target user extracted, and the target user's surrounding sound before or during blood pressure measurement. Preset environmental temperature measurement intervals and environmental sound measurement intervals are provided. When measuring blood pressure, the ambient temperature is preferably maintained at approximately 20°C, not too low or too high. Simultaneously, the ambient environment should be kept quiet and not too noisy. A noise level of 30 to 40 decibels is an ideal quiet environment. Based on the above conclusions, the preset ambient temperature measurement interval is between 18°C ​​and 22°C, and the preset ambient sound measurement interval is between 30 and 40 decibels.

[0054] Determine whether the temperature factor and / or the sound factor are within the preset ambient temperature measurement interval and the preset ambient sound measurement interval, and determine whether the current environment is suitable for the target user to perform blood pressure measurement by judging the environmental conditions; if neither the temperature factor nor the sound factor is within the preset ambient temperature measurement interval and the preset ambient sound measurement interval, generate a waiting instruction, that is, if neither the temperature factor nor the sound factor meets the preset ambient temperature measurement interval and the preset ambient sound measurement interval, do not perform blood pressure measurement on the target user.

[0055] If the sound factor is not within the preset environmental sound measurement range, and the temperature factor is within the preset environmental temperature measurement range, the sound source is traced, and the vibration source is found according to the vibration sensor source. The vibration source is the sound source, such as TV playback, music playback, etc. Due to the noisy environment, or emotional excitement and loud talking, the human body will speed up the heartbeat, constrict blood vessels, increase blood flow rate, and cause a sudden increase in blood pressure. At this time, the sound source can be turned off, such as turning off the player, and the target user can sit and rest for 5 minutes. Then, the environmental comprehensive data of the target user's surrounding sound and temperature can be extracted again through the sensor device to determine a new sound factor and a new temperature factor. It is judged whether the new sound factor and the new temperature factor are within the preset environmental sound measurement range and the preset environmental temperature measurement range. If both are within, the target user's blood pressure can be measured.

[0056] If the temperature factor is not within the preset ambient temperature measurement area, and the sound factor is within the preset ambient sound measurement interval, temperature anomaly data is acquired. High temperatures can cause blood vessels to dilate and body fluids to accumulate in the periphery, increasing blood pressure, while low temperatures can cause blood volume to contract, increasing blood pressure. Based on the temperature anomaly data, a change instruction is generated, i.e., measurement is performed in a room temperature indoor area. For example, the air conditioning temperature is adjusted to maintain the ambient temperature at approximately 20°C. After the target user sits and rests for 5 minutes, the sensor device is used to extract the ambient sound and temperature surrounding the target user again, determine a new sound factor and a new temperature factor, and determine whether the new sound factor and the new temperature factor are within the preset ambient sound measurement interval and the preset ambient temperature measurement interval. If so, the target user's blood pressure can be measured.

[0057] S400: measuring the target user in real time using the blood pressure measuring instrument to obtain a single-moment blood pressure measurement result of the target user;

[0058] Furthermore, the application steps include:

[0059] S410: Extracting basic data of the blood pressure measuring instrument based on the blood pressure measuring instrument, wherein the basic data includes air pressure data and deflation data;

[0060] S420: Based on the basic data of the blood pressure measuring instrument, the target user is measured multiple times by the blood pressure measuring instrument to obtain the measured air pressure extreme value data and the deflation time data of the blood pressure measuring instrument during the measurement;

[0061] S430: Based on the measured air pressure extreme value data and the deflation time data, obtain the single-moment blood pressure measurement result of the target user.

[0062] Specifically, when the user's own data and environmental data meet the standards, the target user is measured in real time through the blood pressure measuring instrument. The multiple blood pressure measurements of the target user correspond to a unique moment, and one of them is extracted as the single-moment blood pressure measurement result.

[0063] As we all know, blood pressure is currently measured indirectly. The sphygmomanometer used consists of a balloon, a cuff, and a manometer. The cuff's rubber tubes are connected to the balloon and manometer, forming a closed piping system. Manometers come in two types: mercury column and spring. For example, when measuring blood pressure, the balloon is first used to inflate the cuff, which is wrapped around the upper arm. This pressure acts on the brachial artery through soft tissue. When the applied pressure exceeds systolic pressure, the balloon is slowly deflated, causing the pressure within the cuff to drop. When the pressure within the cuff is equal to or slightly below systolic pressure, the heartbeat breaks free of the blocked blood vessels, forming a vortex. The stethoscope then detects the pulsating sound, and the pressure indicated by the manometer at this point corresponds to the systolic pressure. Continued slow deflation gradually reduces the pressure within the cuff. While the pressure within the cuff is below systolic pressure but above diastolic pressure, a heartbeat is heard with each contraction. When the cuff pressure drops to or slightly below the diastolic pressure, blood flow resumes, and the heartbeat sound suddenly weakens or disappears. The pressure indicated by the manometer now corresponds to the diastolic pressure. Blood pressure measured using the indirect method is an approximation, and its accuracy depends on the measurement technique. When measuring, keep the cuff flat, ensuring that the upper arm, heart, and the zero point of the mercury manometer (or spring manometer) are aligned as closely as possible. Do not deflate the cuff too quickly, as this can result in significant errors.

[0064] Based on the blood pressure measuring instrument, basic data of the blood pressure measuring instrument is extracted, wherein the basic data includes air pressure data and deflation data; according to the basic data of the blood pressure measuring instrument, the target user is measured multiple times by the blood pressure measuring instrument to obtain the measured air pressure extreme value data and deflation time data of the blood pressure measuring instrument when measuring, the air pressure extreme value data is the maximum air pressure and the minimum air pressure in the blood pressure measuring instrument when measuring, and the deflation time data means that the blood pressure measuring instrument cannot deflate too early, otherwise a large error will occur. According to the measured air pressure extreme value data and the deflation time data, the single-moment blood pressure measurement result of the target user is obtained.

[0065] S500: performing an influence determination on the single-moment blood pressure measurement result of the target user based on the initial measurement condition of the target user and the comprehensive environmental data, and determining an influence coefficient;

[0066] Furthermore, the application steps include:

[0067] S510: Obtaining a correlation influence factor by correlating the measured initial condition of the target user with the comprehensive environmental data;

[0068] S520: Obtaining a mapping relationship between the associated influencing factor and the single-moment blood pressure measurement result of the target user;

[0069] S530: extracting the number of associations and the degree of association in the mapping relationship, and determining the accuracy of the single-moment blood pressure measurement result of the target user;

[0070] S540: Determine the influence of the associated influencing factor on the single-moment blood pressure measurement result of the target user according to the accuracy of the single-moment blood pressure measurement result of the target user;

[0071] S550: Determine the influence coefficient based on the influence of the single-moment blood pressure measurement result of the target user.

[0072] Specifically, based on the initial measurement conditions of the target user and the comprehensive environmental data, the influence of the target user's single-moment blood pressure measurement result is determined, and the influence coefficient is determined, where the influence degree is the degree of influence of the initial measurement conditions and the comprehensive environmental data on the single-moment blood pressure measurement result, and the influence coefficient is determined.

[0073] By correlating the initial measurement condition of the target user with the comprehensive environmental data, an associated influence factor is obtained. For example, in the comprehensive environmental data, a louder sound is likely to cause the target user to feel anxious or nervous before measuring blood pressure, thereby affecting the accuracy of the blood pressure measurement. A mapping relationship between the associated influence factor and the single-moment blood pressure measurement result of the target user is obtained. The associated influence factor can be used to determine the degree of influence. The two are in a proportional relationship. The number of associations and the degree of association in the mapping relationship are extracted to determine the accuracy of the single-moment blood pressure measurement result of the target user. The smaller the number of associations and the degree of association in the mapping relationship, the higher the accuracy of the single-moment blood pressure measurement result of the target user, because the smaller the external factors that cause the blood pressure measurement result, the more stable the blood pressure measurement result. The degree of influence of the associated influence factor on the single-moment blood pressure measurement result of the target user is determined based on the accuracy of the single-moment blood pressure measurement result of the target user. The two are in a proportional relationship. The higher the associated influence factor, the higher the degree of influence. The influence coefficient is determined based on the degree of influence of the single-moment blood pressure measurement result of the target user.

[0074] S600: Performing bidirectional adjustment optimization of the blood pressure accuracy of the single-moment blood pressure measurement result of the target user based on the influence coefficient to obtain the single-moment blood pressure measurement optimization result of the target user.

[0075] Specifically, based on the influence coefficient, the blood pressure measurement result of the target user at a single moment is subjected to a two-way adjustment and optimization of the blood pressure accuracy. For example, when the influence coefficient is large, the blood pressure measurement result will be unstable. At this time, the stability of the blood pressure measurement result is improved by optimizing the initial measurement condition and the comprehensive environmental data. When the influence coefficient is small, the initial measurement condition and the comprehensive environmental data have little impact on the blood pressure measurement result, and the blood pressure measurement result is stable. At this time, the blood pressure measurement result is optimized and the initial measurement condition and the comprehensive environmental data during the blood pressure measurement of the target user are analyzed. By performing a two-way adjustment and optimization of the blood pressure accuracy of the single-moment blood pressure measurement result, the optimized single-moment blood pressure measurement result of the target user is obtained. This solves the technical problem in the prior art of not determining the initial measurement state of the user, resulting in low blood pressure measurement accuracy, and achieves the technical effect of improving blood pressure measurement accuracy.

[0076] Example 2

[0077] like Figure 3 As shown, a data processing system for improving blood pressure measurement accuracy is applied to a data processing system, the data processing system is communicatively connected to a dynamic sensing device and a blood pressure measuring instrument, and the system includes:

[0078] A dynamic information acquisition module 11 is configured to sense and collect the dynamic state of the target user through the dynamic sensing device to obtain N dynamic information;

[0079] a measurement initial status determination module 12, configured to determine the measurement initial status of the target user based on the N pieces of dynamic information;

[0080] An environmental comprehensive data acquisition module 13 is used to collect environmental information when the target user's blood pressure is measured, and obtain environmental comprehensive data;

[0081] A single-moment blood pressure measurement result acquisition module 14 is configured to perform real-time measurement of the target user using the blood pressure measuring instrument to obtain a single-moment blood pressure measurement result of the target user;

[0082] An influence coefficient determination module 15 is configured to determine an influence degree of the single-moment blood pressure measurement result of the target user based on the initial measurement condition of the target user and the comprehensive environmental data, and determine an influence coefficient;

[0083] The blood pressure measurement optimization result acquisition module 16 is used to perform bidirectional adjustment optimization of the blood pressure accuracy of the single-moment blood pressure measurement result of the target user based on the influence coefficient, and obtain the single-moment blood pressure measurement optimization result of the target user.

[0084] Furthermore, the embodiment of the present application also includes:

[0085] a posture dynamic information determination module, configured to sense and collect the posture of the target user through the dynamic sensing device to determine the posture dynamic information of the target user;

[0086] a dining dynamic time information determination module, which determines the dining dynamic time information of the target user through intelligent query between the dynamic sensing device and the target user;

[0087] a dynamic state information determination module, configured to sense and collect the state of the target user through the dynamic sensing device to determine the dynamic state information of the target user;

[0088] A dynamic information adding module is used to add the posture dynamic information, the dining dynamic time information, and the dynamic state information to the N dynamic information.

[0089] Furthermore, the embodiments of the present application include:

[0090] an initial posture influence determination module, configured to extract the target user's posture based on the posture dynamic information and determine an initial posture influence;

[0091] an initial dining time influence determination module, configured to extract the target user's dining time based on the dining dynamic time information and determine the initial dining time influence;

[0092] an initial state influence determination module, configured to extract the state of the target user based on the dynamic state information and determine the initial state influence;

[0093] The measurement initial condition output module is used to input the initial posture influence, the initial meal time influence, and the initial state influence into the initial condition judgment model to determine the condition of the target user before blood pressure measurement, and output the measurement initial condition of the target user.

[0094] Furthermore, the embodiments of the present application include:

[0095] an initial condition judgment model construction module, wherein the initial condition judgment model construction module constructs the initial condition judgment model based on a BP neural network, wherein the input data of the initial condition judgment model includes the initial posture influence, the initial dining time influence, and the initial state influence, and the output data includes the measured initial condition of the target user;

[0096] The initial condition judgment model includes a module, and the initial condition judgment model includes a module. The initial condition judgment model includes a data input layer, a condition judgment layer, and an initial condition output layer;

[0097] A data set acquisition module is constructed, and the data set acquisition module is used to perform data annotation on the N dynamic information to obtain a first constructed data set, wherein the first constructed data set includes a first training set and a first validation set;

[0098] An initial condition judgment model verification module is used to use the first training set and the first verification set to perform supervised training and verification on the initial condition judgment model until the initial condition judgment model converges or the accuracy reaches a preset requirement.

[0099] Furthermore, the embodiments of the present application include:

[0100] a temperature factor determination module, configured to extract the target user's ambient temperature from the comprehensive environmental data using a temperature sensing device to determine a temperature factor;

[0101] a sound factor determination module, configured to extract the sound surrounding the target user from the comprehensive environmental data using a vibration sensor device to determine a sound factor;

[0102] A measurement interval preset module, which is used to preset an ambient temperature measurement interval and an ambient sound measurement interval;

[0103] a factor determination module, configured to determine whether the temperature factor and / or the sound factor is greater than the preset ambient temperature measurement interval and the preset ambient sound measurement interval;

[0104] a waiting instruction generating module, configured to generate a waiting instruction if the temperature factor and the sound factor are both not within the preset ambient temperature measurement interval and the preset ambient sound measurement interval;

[0105] a sound source determination module, configured to perform sound tracing and determine the sound source if the sound factor is not within the preset ambient sound measurement interval, but the temperature factor is within the preset ambient temperature measurement interval;

[0106] a temperature anomaly data acquisition module, configured to acquire temperature anomaly data if the temperature factor is not within the preset ambient temperature measurement zone and the sound factor is within the preset ambient sound measurement interval, and generate a replacement instruction based on the temperature anomaly data;

[0107] A real-time measurement module is used to perform real-time measurement of the target user through the blood pressure measuring instrument if the temperature factor and the sound factor are both within the preset ambient temperature measurement interval and the preset ambient sound measurement interval.

[0108] Furthermore, the embodiments of the present application include:

[0109] A basic data extraction module, configured to extract basic data of the blood pressure measuring instrument based on the blood pressure measuring instrument, wherein the basic data includes air pressure data and deflation data;

[0110] a multiple measurement module, configured to perform multiple measurements on the target user using the blood pressure measuring instrument based on the basic data of the blood pressure measuring instrument, and obtain the measured air pressure extreme value data and deflation time data of the blood pressure measuring instrument during the measurements;

[0111] A blood pressure measurement result acquisition module is used to obtain the single-moment blood pressure measurement result of the target user based on the measured air pressure extreme value data and the deflation moment data.

[0112] Furthermore, the embodiments of the present application include:

[0113] a correlation influence factor obtaining module, configured to obtain a correlation influence factor by correlating the measured initial condition of the target user with the environmental comprehensive data;

[0114] A mapping relationship acquisition module, configured to acquire a mapping relationship between the associated influencing factor and the single-moment blood pressure measurement result of the target user;

[0115] an accuracy determination module, the accuracy determination module being configured to extract the number of associations and the degree of association in the mapping relationship, and determine the accuracy of the single-moment blood pressure measurement result of the target user;

[0116] an influence determination module, configured to determine the influence of the associated influence factor on the single-moment blood pressure measurement result of the target user based on the accuracy of the single-moment blood pressure measurement result of the target user;

[0117] An influence coefficient determination module is used to determine the influence coefficient based on the influence degree of the single-moment blood pressure measurement result of the target user.

[0118] For a specific embodiment of a data processing system for improving blood pressure measurement accuracy, please refer to the embodiment of a data processing method for improving blood pressure measurement accuracy described above, and will not be repeated here. The above modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0119] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0120] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A data processing method for improving blood pressure measurement accuracy, characterized in that: The method is applied to a data processing system, wherein the data processing system is communicatively connected with a dynamic sensing device and a blood pressure measuring instrument, and the method comprises: The dynamic sensing device senses and collects the dynamic state of the target user to obtain N dynamic information; Determining an initial measurement status of the target user according to the N pieces of dynamic information; Collecting environmental information when the target user measures blood pressure to obtain comprehensive environmental data; Performing real-time measurement on the target user by using the blood pressure measuring instrument to obtain a single-moment blood pressure measurement result of the target user; performing an influence determination on the single-moment blood pressure measurement result of the target user based on the initial measurement condition of the target user and the comprehensive environmental data, and determining an influence coefficient; performing bidirectional adjustment optimization of the blood pressure accuracy of the single-moment blood pressure measurement result of the target user based on the influence coefficient to obtain the single-moment blood pressure measurement optimization result of the target user; Determining the influence coefficient, the method further includes: Obtaining a correlation influence factor by correlating the measured initial condition of the target user with the comprehensive environmental data; Obtaining a mapping relationship between the associated influencing factor and the single-moment blood pressure measurement result of the target user; Extracting the number of associations and the degree of association in the mapping relationship to determine the accuracy of the single-moment blood pressure measurement result of the target user; Determining the influence of the associated influencing factor on the single-moment blood pressure measurement result of the target user according to the accuracy of the single-moment blood pressure measurement result of the target user; determining the influence coefficient based on the influence degree of the single-moment blood pressure measurement result of the target user; sensing and collecting the target user's posture by the dynamic sensing device to determine the target user's posture dynamic information; Determine the target user's dining dynamic time information through intelligent query between the dynamic sensing device and the target user; The dynamic sensing device senses and collects the state of the target user to determine the dynamic state information of the target user; Adding the posture dynamic information, the dining dynamic time information, and the dynamic state information to the N dynamic information; Determining an initial measurement condition of the target user, the method further includes: extracting the target user's posture based on the posture dynamic information and determining the initial posture influence; Extracting the target user's dining time based on the dynamic dining time information and determining the initial dining time influence; Based on the dynamic state information, extracting the state of the target user and determining the initial state influence; Inputting the initial posture influence, the initial meal time influence, and the initial state influence into an initial condition judgment model to determine the condition of the target user before blood pressure measurement, and outputting the measurement initial condition of the target user; Extracting the ambient temperature of the target user from the comprehensive environmental data through a temperature sensing device to determine a temperature factor; Extracting the sound around the target user from the comprehensive environmental data using a vibration sensing device to determine a sound factor; Preset ambient temperature measurement interval, preset ambient sound measurement interval; Determining whether the temperature factor and / or the sound factor is greater than the preset ambient temperature measurement interval and the preset ambient sound measurement interval; If the temperature factor and the sound factor are both not within the preset ambient temperature measurement interval and the preset ambient sound measurement interval, generating a waiting instruction; If the sound factor is not within the preset ambient sound measurement range, and the temperature factor is within the preset ambient temperature measurement range, tracing the sound source to determine the sound source; If the temperature factor is not within the preset ambient temperature measurement zone and the sound factor is within the preset ambient sound measurement interval, acquiring temperature anomaly data and generating a replacement instruction according to the temperature anomaly data; If the temperature factor and the sound factor are both within the preset ambient temperature measurement interval and the preset ambient sound measurement interval, the target user is measured in real time by the blood pressure measuring instrument.

2. The method according to claim 1, wherein The initial condition judgment model, the method further includes: Based on the BP neural network, the initial condition judgment model is constructed, wherein the input data of the initial condition judgment model includes the initial posture influence, the initial dining time influence, and the initial state influence, and the output data includes the measured initial condition of the target user; The initial state judgment model includes a data input layer, a state judgment layer, and an initial state output layer; Performing data annotation on the N dynamic information to obtain a first constructed data set, wherein the first constructed data set includes a first training set and a first validation set; The initial condition judgment model is supervisedly trained and verified using the first training set and the first validation set until the initial condition judgment model converges or the accuracy reaches a preset requirement.

3. The method according to claim 1, wherein Obtaining a single-moment blood pressure measurement result of the target user, the method further includes: Based on the blood pressure measuring instrument, extracting basic data of the blood pressure measuring instrument, wherein the basic data includes air pressure data and deflation data; Based on the basic data of the blood pressure measuring instrument, the target user is measured multiple times by the blood pressure measuring instrument to obtain the measured air pressure extreme value data and the deflation time data of the blood pressure measuring instrument during the measurement; Based on the measured air pressure extreme value data and the deflation time data, the single-moment blood pressure measurement result of the target user is obtained.

4. A data processing system for improving blood pressure measurement accuracy, characterized in that: The system is applied to a data processing system, the data processing system is communicatively connected with a dynamic sensing device and a blood pressure measuring instrument, and the system includes: A dynamic information acquisition module, configured to sense and collect the dynamic state of the target user through the dynamic sensing device to obtain N dynamic information; a measurement initial status determination module, configured to determine an initial measurement status of the target user based on the N pieces of dynamic information; An environmental comprehensive data acquisition module, which is used to collect environmental information when the target user's blood pressure is measured, and obtain environmental comprehensive data; A single-moment blood pressure measurement result acquisition module, configured to perform real-time measurement of the target user using the blood pressure measuring instrument to obtain a single-moment blood pressure measurement result of the target user; an influence coefficient determination module, configured to determine an influence degree of the single-moment blood pressure measurement result of the target user based on the initial measurement condition of the target user and the comprehensive environmental data, and determine an influence coefficient; a blood pressure measurement optimization result acquisition module, configured to perform bidirectional adjustment optimization on the single-moment blood pressure measurement result of the target user based on the influence coefficient to obtain the single-moment blood pressure measurement optimization result of the target user; a correlation influence factor obtaining module, configured to obtain a correlation influence factor by correlating the measured initial condition of the target user with the environmental comprehensive data; A mapping relationship acquisition module, configured to acquire a mapping relationship between the associated influencing factor and the single-moment blood pressure measurement result of the target user; an accuracy determination module, the accuracy determination module being configured to extract the number of associations and the degree of association in the mapping relationship, and determine the accuracy of the single-moment blood pressure measurement result of the target user; an influence determination module, configured to determine the influence of the associated influence factor on the single-moment blood pressure measurement result of the target user based on the accuracy of the single-moment blood pressure measurement result of the target user; an influence coefficient determination module, configured to determine the influence coefficient based on the influence of the single-moment blood pressure measurement result of the target user; a posture dynamic information determination module, configured to sense and collect the posture of the target user through the dynamic sensing device to determine the posture dynamic information of the target user; a dining dynamic time information determination module, which determines the dining dynamic time information of the target user through intelligent query between the dynamic sensing device and the target user; a dynamic state information determination module, configured to sense and collect the state of the target user through the dynamic sensing device to determine the dynamic state information of the target user; a dynamic information adding module, configured to add the posture dynamic information, the dining dynamic time information, and the dynamic state information to the N dynamic information; an initial posture influence determination module, configured to extract the target user's posture based on the posture dynamic information and determine an initial posture influence; an initial dining time influence determination module, configured to extract the target user's dining time based on the dining dynamic time information and determine the initial dining time influence; an initial state influence determination module, configured to extract the state of the target user based on the dynamic state information and determine the initial state influence; an initial measurement condition output module, the initial measurement condition output module being configured to input the initial posture influence, the initial meal time influence, and the initial state influence into an initial condition judgment model to determine the condition of the target user before blood pressure measurement, and output the initial measurement condition of the target user; a temperature factor determination module, configured to extract the target user's ambient temperature from the comprehensive environmental data using a temperature sensing device to determine a temperature factor; a sound factor determination module, configured to extract the sound surrounding the target user from the comprehensive environmental data using a vibration sensor device to determine a sound factor; A measurement interval preset module, which is used to preset an ambient temperature measurement interval and an ambient sound measurement interval; a factor determination module, configured to determine whether the temperature factor and / or the sound factor is greater than the preset ambient temperature measurement interval and the preset ambient sound measurement interval; a waiting instruction generating module, configured to generate a waiting instruction if the temperature factor and the sound factor are both not within the preset ambient temperature measurement interval and the preset ambient sound measurement interval; a sound source determination module, configured to perform sound tracing and determine the sound source if the sound factor is not within the preset ambient sound measurement interval, but the temperature factor is within the preset ambient temperature measurement interval; a temperature anomaly data acquisition module, configured to acquire temperature anomaly data if the temperature factor is not within the preset ambient temperature measurement zone and the sound factor is within the preset ambient sound measurement interval, and generate a replacement instruction based on the temperature anomaly data; A real-time measurement module is used to perform real-time measurement of the target user through the blood pressure measuring instrument if the temperature factor and the sound factor are both within the preset ambient temperature measurement interval and the preset ambient sound measurement interval.

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