Blood pressure data management method and system for face recognition blood pressure instrument

Abstract

The invention relates to the field of medical health, and provides a blood pressure data management method and system for a face recognition blood pressure meter, and the method comprises the steps: firstly obtaining and detecting a face image of a user, recognizing features, matching the features with a user face database to obtain identity data, extracting detailed physical sign parameters, calculating a health index, and measuring blood pressure based on the health index. Comprising the following steps: calculating a dynamic blood pressure change value and a resting blood pressure value, calculating a pulse pressure value, performing state analysis and risk detection on a detected user, determining a blood pressure index by grouping data, searching an index range to find abnormal blood pressure data, marking an abnormal blood pressure label, triggering a signal device to generate a blood pressure alarm signal, and sending the blood pressure alarm signal to a remote monitoring terminal. The analysis unit analyzes the abnormal blood pressure data to generate a blood pressure data report. According to the invention, the coordination management of blood pressure data can be improved.

Description

Technical Field

[0001] The present invention relates to the field of medical health, and in particular to a blood pressure data management method and system for a face recognition blood pressure monitor. Background Art

[0002] In the field of medical health, the face recognition blood pressure monitor is a smart medical device that combines face recognition technology with blood pressure monitoring functions. It can effectively identify the user's identity and monitor their blood pressure data in real time. However, there are some challenges in the current management of blood pressure data, such as inconvenient data transmission and difficulty in remote monitoring.

[0003] Traditional blood pressure data management methods usually rely on manual recording or simple local storage, which can easily lead to data loss and is not convenient for long-term tracking and analysis. In addition, due to the lack of real-time data sharing and collaborative processing mechanisms, it often cannot meet the needs of multi-party collaboration and remote monitoring, thus affecting the coordinated management of blood pressure data. Therefore, a blood pressure data management method for face recognition blood pressure monitors is needed to improve the coordinated management of blood pressure data. Summary of the invention

[0004] The present invention provides a blood pressure data management method and system for a face recognition blood pressure meter, the main purpose of which is to improve the coordinated management of blood pressure data.

[0005] Acquire a facial image corresponding to the detected user, identify facial features in the facial image, match the facial features with a preset user face library, obtain user identity data, extract detailed physical sign parameters corresponding to the user identity data, and calculate a health index corresponding to the detected user based on the detailed physical sign parameters;

[0006] Based on the health index, measuring the blood pressure of the detected user to obtain a blood pressure value of the user, wherein the blood pressure value of the user includes: a dynamic blood pressure change value and a resting blood pressure value, and calculating a pulse pressure value corresponding to the detected user based on the dynamic blood pressure change value and the resting blood pressure value;

[0007] Based on the pulse pressure value, performing status analysis on the detected user to obtain user status data, performing risk detection on the user status data to obtain user risk data, grouping the user risk data to obtain risk grouping data, and determining a blood pressure index corresponding to the detected user based on the risk grouping data;

[0008] querying an indicator range corresponding to the blood pressure indicator, determining abnormal blood pressure data corresponding to the detected user based on the indicator range, and labeling the abnormal blood pressure data to obtain an abnormal blood pressure label;

[0009] Based on the abnormal blood pressure label, a preset signal device is triggered to generate a blood pressure alarm signal. After the blood pressure alarm signal is sent to a preset remote monitoring terminal, the abnormal blood pressure data is analyzed by the analysis unit in the remote monitoring terminal to generate a blood pressure data report corresponding to the detected user.

[0010] Optionally, the calculating the health index corresponding to the detected user based on the detailed physical sign parameters includes:

[0011] The health index corresponding to the detected user is calculated using the following formula:

[0012]

[0013] Among them, JKS represents the health index corresponding to the detected user, Wt represents the weight corresponding to the detected user, Hs represents the height corresponding to the detected user, BMR represents the basal metabolic rate in the detailed physical sign parameters, Ta represents the body surface area corresponding to the detected user, Jr represents the resting heart rate in the detailed physical sign parameters, and Ps represents the skin relaxation index in the detailed physical sign parameters.

[0014] Optionally, measuring the blood pressure of the detected user based on the health index to obtain the blood pressure value of the user includes:

[0015] Based on the health index, determining the health status corresponding to the detected user;

[0016] Based on the health condition, formulate a blood pressure measurement plan corresponding to the detected user;

[0017] Based on the blood pressure measurement plan, the blood pressure of the detected user is measured using a preset blood pressure measurement device to obtain the user's blood pressure value.

[0018] Optionally, the calculating the pulse pressure value corresponding to the detected user based on the dynamic blood pressure change value and the resting blood pressure value includes:

[0019] The pulse pressure value corresponding to the detected user is calculated using the following formula:

[0020]

[0021] Wherein, MP represents the pulse pressure value corresponding to the detected user, DT d Indicates the dynamic blood pressure value corresponding to the detected user, ΔDP d represents the dynamic blood pressure change value, JP r represents the resting blood pressure value, and t1 and t2 represent the measurement time intervals corresponding to the detected user.

[0022] Optionally, performing risk detection on the user status data to obtain user risk data includes:

[0023] Removing data noise from the user status data to obtain denoised status data;

[0024] Extracting state features from the denoised state data, and querying risk coefficients corresponding to the state features;

[0025] Based on the risk coefficient, construct risk control measures corresponding to the user status data;

[0026] Based on the risk control measures, risk detection is performed on the user status data to obtain user risk data.

[0027] Optionally, grouping the user risk data to obtain risk grouping data includes:

[0028] Performing interpolation processing on the user risk data to obtain risk interpolation data;

[0029] extracting risk key features from the interpolated data;

[0030] Performing cluster analysis on the key risk characteristics to obtain risk analysis indicators;

[0031] Based on the risk analysis indicator, the user risk data is grouped to obtain risk grouping data.

[0032] Optionally, determining the blood pressure index corresponding to the detected user based on the risk grouping data includes:

[0033] Classifying the risk group data to obtain data risk levels;

[0034] Setting the blood pressure value range corresponding to the data risk level;

[0035] Based on the blood pressure value range, extracting a blood pressure index matching the risk level;

[0036] Based on the blood pressure index, a blood pressure index corresponding to the detected user is determined.

[0037] Optionally, determining the abnormal blood pressure data corresponding to the detected user based on the indicator range includes:

[0038] Dividing the indicator range into abnormal stages corresponding to the abnormal stages, and setting the blood pressure thresholds corresponding to the abnormal stages;

[0039] Screening for abnormal blood pressure points that exceed the blood pressure threshold value and match the detected user;

[0040] Based on the abnormal blood pressure points, construct an abnormal blood pressure curve graph corresponding to the detected user;

[0041] The abnormal blood pressure data in the abnormal blood pressure curve graph is extracted.

[0042] Optionally, labeling the abnormal blood pressure data to obtain an abnormal blood pressure label includes:

[0043] Removing duplicate records from the abnormal blood pressure data to obtain deduplicated data;

[0044] Querying the outliers in the deduplicated data; generating an identifier corresponding to the outlier;

[0045] Constructing a mapping relationship diagram corresponding to the identifier;

[0046] Based on the mapping relationship table, the abnormal blood pressure data is labeled to obtain an abnormal blood pressure label.

[0047] In order to solve the above problems, the present invention also provides a blood pressure data management system for a face recognition blood pressure meter, the system comprising:

[0048] An index calculation module is used to obtain a facial image corresponding to the detected user, identify facial features in the facial image, match the facial features with a preset user face library, obtain user identity data, extract detailed physical sign parameters corresponding to the user identity data, and calculate the health index corresponding to the detected user based on the detailed physical sign parameters;

[0049] A pulse pressure value calculation module, used to measure the blood pressure of the detected user based on the health index to obtain the user's blood pressure value, the user's blood pressure value includes: dynamic blood pressure change value, resting blood pressure value, based on the dynamic blood pressure change value and resting blood pressure value, calculate the pulse pressure value corresponding to the detected user;

[0050] a risk grouping module, configured to perform a status analysis on the detected user based on the pulse pressure value to obtain user status data, perform risk detection on the user status data to obtain user risk data, perform data grouping on the user risk data to obtain risk grouping data, and determine a blood pressure index corresponding to the detected user based on the risk grouping data;

[0051] A label marking module, used to query the indicator range corresponding to the blood pressure indicator, determine the abnormal blood pressure data corresponding to the detected user based on the indicator range, label the abnormal blood pressure data, and obtain an abnormal blood pressure label;

[0052] The report generation module is used to trigger a preset signal device to generate a blood pressure alarm signal based on the abnormal blood pressure label, and after sending the blood pressure alarm signal to a preset remote monitoring terminal, use the analysis unit in the remote monitoring terminal to analyze the abnormal blood pressure data to generate a blood pressure data report corresponding to the detected user.

[0053] Firstly, the present invention can significantly improve security by acquiring the facial image corresponding to the detected user and identifying the facial features in the facial image, while providing personalized services and data analysis for the user, which is helpful to establish and maintain a detailed health record database and improve the accuracy and efficiency of medical services. At the same time, the present invention can obtain the user's blood pressure value by measuring the blood pressure of the detected user based on the health index, so as to have a more comprehensive understanding of the user's physical condition, and can obtain more accurate and targeted blood pressure values ​​by measuring the blood pressure on this basis, which is of great significance for early detection of diseases such as hypertension, assessment of cardiovascular health status and formulation of personalized health management plans. The present invention performs status analysis on the detected user based on the pulse pressure value to obtain the user's blood pressure value. The status data can obtain the user's current cardiovascular health status and potential risks, so as to timely handle and intervene, and at the same time significantly reduce the incidence and severity of these diseases. The present invention can quickly diagnose potential blood pressure problems by querying the indicator range corresponding to the blood pressure indicator, which is helpful for early prevention and intervention of hypertension and its complications, and improves the patient's health status and quality of life. Furthermore, the present invention triggers a preset signal device to generate a blood pressure alarm signal based on the abnormal blood pressure label, and sends the blood pressure alarm signal to a preset remote monitoring terminal, so as to timely detect abnormal blood pressure conditions and send the alarm signal to the preset remote monitoring terminal, so as to take timely measures to ensure the health and safety of patients, and improve medical efficiency and quality. Therefore, the blood pressure data management method and system for face recognition blood pressure meter proposed by the present invention can improve the coordinated management of blood pressure data. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of a blood pressure data management method for a face recognition blood pressure monitor provided by an embodiment of the present invention;

[0055] Figure 2 A module diagram of a blood pressure data management system for a face recognition blood pressure monitor provided by an embodiment of the present invention.

[0056] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0058] The embodiment of the present application provides a blood pressure data management method for a face recognition blood pressure monitor. The execution subject of the blood pressure data management method for a face recognition blood pressure monitor includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the blood pressure data management method for a face recognition 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. The server can be an independent server, or it can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0059] Reference Figure 1 FIG. 1 is a flow chart of a blood pressure data management method for a face recognition blood pressure monitor provided by an embodiment of the present invention. In this embodiment, the blood pressure data management method for a face recognition blood pressure monitor includes:

[0060] S1. Obtain a facial image corresponding to a detected user, identify facial features in the facial image, match the facial features with a preset user face library, obtain user identity data, extract detailed vital sign parameters corresponding to the user identity data, and calculate a health index corresponding to the detected user based on the detailed vital sign parameters.

[0061] The present invention can significantly improve security by acquiring and detecting facial images corresponding to the user and identifying facial features in the facial images. At the same time, it can provide personalized services and data analysis for the user, help establish and maintain a detailed health record database, and improve the accuracy and efficiency of medical services.

[0062] Among them, the facial image refers to a facial photo of the detected user obtained by a camera or other image acquisition device; the facial features refer to feature information extracted from the facial image that can be used to identify personal identity, such as the shape, position and proportional relationship of facial organs such as eyes, nose and mouth, etc. Optionally, the facial image corresponding to the detected user can be obtained through a pre-trained CNN model, such as FaceNet, DeepFace and other models; the identification of facial features in the facial image can be obtained through a feature point detection algorithm, such as SIFT, SURF, ORB and other algorithms.

[0063] Furthermore, the present invention obtains user identity data by matching the facial features with a preset user face library, which can not only improve data security, privacy protection and user experience, but also help optimize resource allocation, personalized services and enhance the pertinence and effectiveness of health monitoring.

[0064] Among them, the preset user face library refers to a pre-created and stored database containing multiple user facial images and their associated identity information, which is used for subsequent face recognition and identity authentication operations; the user identity data refers to the identity information extracted from the facial image for uniquely identifying a specific user, which information includes name, age, gender, contact information and other relevant health history records, etc. Optionally, the user identity data can be obtained by matching using the preset user face library.

[0065] Furthermore, the present invention enhances the pertinence and accuracy of health monitoring by extracting detailed vital sign parameters corresponding to the user identity data, customizes personalized health management plans for users, and improves the quality and efficiency of medical services.

[0066] Among them, the detailed vital sign parameters refer to a series of quantitative health indicators associated with the user identity data, including physiological data such as height, weight, basal metabolic rate, skin relaxation index, resting heart rate, etc. Optionally, the detailed vital sign parameters corresponding to the user identity data can be extracted through a multimodal sensor network, such as: environmental sensors, biosensors, etc.

[0067] Furthermore, the present invention calculates the health index corresponding to the detected user based on the detailed vital sign parameters, which helps to understand the user's physical condition and provide targeted health advice and intervention measures, thereby promoting the improvement of overall health level and quality of life.

[0068] Among them, the health index refers to an indicator for comprehensively evaluating and detecting the user's health status, which is calculated by considering parameters such as weight, height, basal metabolic rate, body surface area, resting heart rate and skin relaxation index.

[0069] As an embodiment of the present invention, the calculating the health index corresponding to the detected user based on the detailed physical sign parameters includes:

[0070] The health index corresponding to the detected user is calculated using the following formula:

[0071]

[0072] Among them, JKS represents the health index corresponding to the detected user, Wt represents the weight corresponding to the detected user, Hs represents the height corresponding to the detected user, BMR represents the basal metabolic rate in the detailed physical sign parameters, Ta represents the body surface area corresponding to the detected user, Jr represents the resting heart rate in the detailed physical sign parameters, and Ps represents the skin relaxation index in the detailed physical sign parameters.

[0073] S2. Based on the health index, measure the blood pressure of the detected user to obtain the user's blood pressure value, which includes: a dynamic blood pressure change value and a resting blood pressure value. Based on the dynamic blood pressure change value and the resting blood pressure value, calculate the pulse pressure value corresponding to the detected user.

[0074] The present invention measures the blood pressure of the detected user based on the health index to obtain the user's blood pressure value, thereby providing a more comprehensive understanding of the user's physical condition. Blood pressure measurement is performed on this basis to obtain more accurate and targeted blood pressure values, which is of great significance for early detection of diseases such as hypertension, assessment of cardiovascular health status, and formulation of personalized health management plans.

[0075] Among them, the user's blood pressure value refers to the user's systolic and diastolic blood pressure data at different time points, which can be used to assess the user's vascular health and cardiovascular risk. The user's blood pressure value includes: dynamic blood pressure change value and resting blood pressure value.

[0076] As an embodiment of the present invention, the blood pressure of the detected user is measured based on the health index to obtain the user's blood pressure value, including: determining the health condition corresponding to the detected user based on the health index; formulating a blood pressure measurement plan corresponding to the detected user based on the health condition; based on the blood pressure measurement plan, measuring the blood pressure of the detected user using a preset blood pressure measurement device to obtain the user's blood pressure value.

[0077] Among them, the health status refers to comprehensive indicators such as the user's physical function and metabolic level; the blood pressure measurement plan refers to a preliminary blood pressure measurement plan formulated based on the user's health status, including information such as measurement time, frequency, and location; the preset blood pressure measurement device refers to a calibrated sphygmomanometer or other measuring device that can accurately measure the user's blood pressure value.

[0078] Furthermore, the determination of the health status corresponding to the detected user can be obtained through a health risk assessment tool, such as a physical examination report, etc.; the formulation of a blood pressure measurement plan corresponding to the detected user can be obtained through an electronic medical record system, such as HIS, CIS, EHR, etc.; the blood pressure measurement of the detected user using a preset blood pressure measurement device can be achieved through an automated sphygmomanometer, such as using an electronic sensor to detect blood pressure, and displaying the measurement results through a display screen or other means.

[0079] The present invention calculates the pulse pressure value corresponding to the detected user based on the dynamic blood pressure change value and the resting blood pressure value, which helps to timely discover potential risks such as hypertension and arteriosclerosis, thereby promoting early intervention and prevention.

[0080] The pulse pressure value is also called pulse wave amplitude, which refers to the difference between systolic blood pressure and diastolic blood pressure.

[0081] As an embodiment of the present invention, the step of calculating the pulse pressure value corresponding to the detected user based on the dynamic blood pressure change value and the resting blood pressure value includes:

[0082] The pulse pressure value corresponding to the detected user is calculated using the following formula:

[0083]

[0084] Wherein, MP represents the pulse pressure value corresponding to the detected user, DT d Indicates the dynamic blood pressure value corresponding to the detected user, ΔDP d represents the dynamic blood pressure change value, JP r represents the resting blood pressure value, and t1 and t2 represent the measurement time intervals corresponding to the detected user.

[0085] S3. Based on the pulse pressure value, perform status analysis on the detected user to obtain user status data, perform risk detection on the user status data to obtain user risk data, group the user risk data to obtain risk grouping data, and determine the blood pressure index corresponding to the detected user based on the risk grouping data.

[0086] The present invention performs status analysis on the detected user based on the pulse pressure value to obtain user status data, which can obtain the user's current cardiovascular health status and potential risks, thereby providing timely treatment and intervention, and significantly reducing the incidence and severity of these diseases.

[0087] Among them, the user status data refers to the comprehensive information about the user's cardiovascular health status obtained by analyzing the pulse pressure value, which usually includes the user's pulse pressure value itself, and other related physiological indicators and evaluation results, such as blood pressure average, blood pressure volatility, blood pressure circadian rhythm, etc. Optionally, the status analysis of the detected user can be obtained through a machine learning algorithm, such as: decision tree, random forest, support vector machine and other algorithms.

[0088] Furthermore, the present invention obtains user risk data by performing risk detection on the user status data, and can timely identify potential pathological changes and adverse health risks, so as to take necessary preventive measures, reduce the possibility of disease occurrence, and improve overall health status, which helps to achieve early intervention and improve subsequent treatment effects.

[0089] The user risk data refers to the comprehensive assessment results obtained according to the above steps, including the user's current risk level and corresponding risk control measures.

[0090] As an embodiment of the present invention, the risk detection of the user status data to obtain user risk data includes: removing data noise in the user status data to obtain denoised status data; extracting status features in the denoised status data; based on the status features, calculating the risk coefficient corresponding to the user status data; based on the risk coefficient, constructing risk control measures corresponding to the user status data; based on the risk control measures, performing risk detection on the user status data to obtain user risk data.

[0091] Among them, the denoised state data refers to the data that has been processed to reduce abnormal interference and make the data more stable and reliable; the state characteristics refer to the key indicators and data points extracted from the denoised state data, which are used to describe the user's health status; the risk coefficient refers to the variables or factors directly related to the user's risk data; the risk control measures refer to a series of strategies aimed at reducing or avoiding risks.

[0092] Furthermore, the removal of data noise in the user status data can be achieved through a denoising method, such as: moving average method, wavelet transform, interpolation method and other methods; the extraction of state features in the denoised state data can be achieved through a feature extraction method, such as: SIFT, SURF, HOG and other algorithms; the calculation of the risk coefficient corresponding to the user status data can be achieved through the following calculation formula; the construction of risk control measures corresponding to the user status data can be achieved through a decision tree algorithm; the risk detection of the user status data can be achieved through a risk assessment model, such as: Kaplan-Meier estimation, Cox proportional risk model and other models.

[0093] In detail, as an embodiment of the present invention, the calculating the risk coefficient corresponding to the user status data based on the status feature includes:

[0094] The risk coefficient corresponding to the user status data is calculated using the following formula:

[0095]

[0096] Wherein, FXz represents the risk factor corresponding to the user status data, F(t) represents the status function of the user at time t, M(t) represents the time function of the user's baseline or expected status, t'1 and t'2 represent the time range, which is a time period in the user status data, represents the cumulative deviation of the state function from its expected value during the observation period, i represents the index of the data point, n represents the number of data points, and x i represents the specific value of the i-th data point in the state feature, Represents x i The average value of the data.

[0097] The present invention can more effectively manage the health risks of users by grouping the user risk data to obtain risk grouping data. Grouping data can better identify groups with different risk levels, which helps to improve the overall health level and management effect.

[0098] The risk grouping data refers to the different groups into which user risk data is divided based on risk analysis indicators, and each group represents a user group with a similar risk level.

[0099] As an embodiment of the present invention, the data grouping of the user risk data to obtain risk grouping data includes: interpolating the user risk data to obtain risk interpolation data; extracting key risk features in the interpolation data; clustering the key risk features to obtain risk analysis indicators; and grouping the user risk data based on the risk analysis indicators to obtain risk grouping data.

[0100] Among them, the risk interpolation data refers to more continuous risk data obtained through interpolation processing; the risk key features refer to information extracted from the risk interpolation data that can distinguish different risk levels; the risk analysis indicators refer to the center points obtained through cluster analysis that can reflect the characteristics of different risk groups.

[0101] Furthermore, the interpolation processing of the user risk data can be achieved through Python libraries, such as SciPy, pandas, NumPy, etc.; the extraction of key risk features in the interpolation data can be achieved through feature selection tools, such as WEKA, Scikit-learn and other tools; the clustering analysis of the key risk features can be achieved through clustering algorithms, such as K-means, DBSCAN, hierarchical clustering and other algorithms; the data grouping of the user risk data can be achieved through data analysis tools, such as Excel, R or SAS and other tools.

[0102] The present invention determines the blood pressure index corresponding to the detected user based on the risk grouping data, and can provide the detected user with more targeted blood pressure monitoring and treatment suggestions, help reduce the health risks brought by hypertension, and optimize the allocation and utilization efficiency of medical resources.

[0103] The blood pressure indicator refers to a specific indicator determined based on the blood pressure index and used to reflect the user's blood pressure risk and condition.

[0104] As an embodiment of the present invention, determining the blood pressure index corresponding to the detected user based on the risk grouping data includes: classifying the risk grouping data to obtain a data risk level; setting a blood pressure value range corresponding to the data risk level; based on the blood pressure value range, extracting a blood pressure index matching the risk level; based on the blood pressure index, determining the blood pressure index corresponding to the detected user.

[0105] Among them, the data risk level refers to different levels of user risks divided according to risk grouping data; the blood pressure value range refers to the blood pressure value interval set for each risk level; and the blood pressure index refers to the blood pressure measurement value within a specific blood pressure value range.

[0106] Furthermore, the classification of the risk group data can be achieved through a data analysis library in Python, such as Pandas; the setting of the blood pressure value range corresponding to the data risk level can be achieved by consulting medical literature and guidelines; the extraction of the blood pressure index matching the risk level can be achieved through statistical analysis tools, such as SPSS, SAS and other tools; the determination of the blood pressure index corresponding to the detected user can be achieved through a blood pressure measurement tool, such as an electronic sphygmomanometer, a mercury sphygmomanometer and other tools.

[0107] S4. Query the indicator range corresponding to the blood pressure indicator, determine the abnormal blood pressure data corresponding to the detected user based on the indicator range, label the abnormal blood pressure data, and obtain an abnormal blood pressure label.

[0108] The present invention can quickly diagnose potential blood pressure problems by querying the indicator range corresponding to the blood pressure indicator, which is helpful for early prevention and intervention of the occurrence of hypertension and its complications, and improves the health status and quality of life of patients.

[0109] Among them, the indicator range refers to the normal value range, abnormal range and other relevant medical reference points set for the blood pressure index. Optionally, the query of the indicator range corresponding to the blood pressure index can be obtained through an online medical database, such as: PubMed, Cochrane Library, Uptodate, etc.

[0110] The present invention determines the abnormal blood pressure data corresponding to the detected user based on the indicator range, so that the user can better manage his or her own health, take appropriate measures to control blood pressure, and prevent the risks brought by high blood pressure or low blood pressure, while helping to improve health awareness and promote positive health behaviors.

[0111] The abnormal blood pressure data refers to specific blood pressure measurement values ​​or changes in blood pressure values ​​extracted from the abnormal blood pressure curve.

[0112] As an embodiment of the present invention, determining the abnormal blood pressure data corresponding to the detected user based on the indicator range includes: dividing the abnormal stages corresponding to the indicator range; setting the blood pressure threshold corresponding to the abnormal stage; screening the abnormal blood pressure points that match the detected user and exceed the blood pressure threshold; constructing an abnormal blood pressure curve graph corresponding to the detected user based on the abnormal blood pressure points; and extracting the abnormal blood pressure data in the abnormal blood pressure curve graph.

[0113] Among them, the abnormal stage refers to the classification of different blood pressure conditions defined according to the blood pressure index range; the blood pressure threshold refers to the boundary value for distinguishing different abnormal stages; the blood pressure abnormal point refers to an individual data point or a continuous data point sequence when the user is detected outside the blood pressure index range; the abnormal blood pressure curve chart refers to a visual expression showing the changes of blood pressure abnormal points over time.

[0114] Furthermore, the abnormal stages corresponding to the division of the indicator range can be obtained through a decision tree model, such as: CART decision tree and other models; the setting of the blood pressure threshold corresponding to the abnormal stage can be obtained through statistical methods, such as: mean and standard deviation and other methods; the screening of blood pressure abnormal points that exceed the blood pressure threshold and match the detected user can be obtained through an abnormality detection algorithm, such as: 3σ rule, etc.; the construction of the abnormal blood pressure curve corresponding to the detected user can be obtained through data visualization tools, such as: Tableau and other tools; the extraction of abnormal blood pressure data in the abnormal blood pressure curve can be obtained through an abnormality detection algorithm, such as: isolation forest algorithm and other algorithms.

[0115] The present invention labels the abnormal blood pressure data to obtain abnormal blood pressure labels, which can help quickly identify key content in the blood pressure data, improve analysis efficiency, facilitate tracking and management of patients' blood pressure conditions, and evaluate the effects of different treatment strategies.

[0116] Among them, the abnormal blood pressure label refers to an identifier or category generated by marking abnormal blood pressure data. These labels can be used to distinguish and describe different types of abnormal blood pressure states, such as prehypertension, hypertension, etc.

[0117] As an embodiment of the present invention, the abnormal blood pressure data is labeled to obtain an abnormal blood pressure label, including: removing duplicate records in the abnormal blood pressure data to obtain deduplicated data; querying abnormal values ​​in the deduplicated data; generating identifiers corresponding to the abnormal values; constructing a mapping relationship graph corresponding to the identifiers; and based on the mapping relationship table, labeling the abnormal blood pressure data to obtain an abnormal blood pressure label.

[0118] Among them, the deduplicated data refers to the data remaining after removing duplicate entries from the original data set; the outlier refers to a numerical value in the data set that deviates significantly from the normal value; the identifier refers to a set of characters or numbers used to uniquely identify an element in a data set (such as a data record or data point); the mapping relationship diagram refers to a diagram used to establish relationships between different entities, which can convert identifiers or codes from different sources into a unified format.

[0119] Furthermore, the removal of duplicate records in the abnormal blood pressure data can be achieved through a deduplication algorithm, such as hash deduplication, sorting deduplication, set deduplication and other algorithms; the query of outliers in the deduplicated data can be achieved through a clustering algorithm, such as K-Means algorithm, DBSCAN algorithm, etc.; the generation of identifiers corresponding to the outliers can be achieved through a scripting programming language, such as Python, R and other programming languages; the construction of a mapping relationship graph corresponding to the identifier can be achieved through a graph algorithm, such as BFS, DFS, MST and other algorithms; the labeling of the abnormal blood pressure data can be achieved through a supervised learning model, such as a decision tree model, etc.

[0120] S5. Based on the abnormal blood pressure tag, a preset signal device is triggered to generate a blood pressure alarm signal. After the blood pressure alarm signal is sent to a preset remote monitoring terminal, the abnormal blood pressure data is analyzed using an analysis unit in the remote monitoring terminal to generate a blood pressure data report corresponding to the detected user.

[0121] The present invention is based on the abnormal blood pressure tag, triggers a preset signal device to generate a blood pressure alarm signal, and sends the blood pressure alarm signal to a preset remote monitoring terminal. Abnormal blood pressure conditions can be discovered in time, and the alarm signal can be sent to the preset remote monitoring terminal so that measures can be taken in time to ensure the health and safety of the patient, and to improve medical efficiency and quality.

[0122] Among them, the preset signal device refers to a pre-set device or mechanism for generating a blood pressure alarm signal; the blood pressure alarm signal refers to a specific signal generated by the signal device for reminding or alerting relevant personnel; the preset remote monitoring terminal refers to a pre-designated remote device or system for receiving a blood pressure alarm signal. Optionally, the generation of the blood pressure alarm signal can be obtained through a preset signal device.

[0123] The present invention utilizes the analysis unit in the remote monitoring terminal to analyze the abnormal blood pressure data and generate a blood pressure data report corresponding to the detected user, so as to timely detect abnormal blood pressure conditions so that timely measures can be taken to ensure the health and safety of patients, improve medical efficiency and quality, reduce the occurrence of medical accidents, and also improve patients' satisfaction and trust in abnormal blood pressure conditions.

[0124] Among them, the analysis unit refers to a module or component in the remote monitoring terminal for analyzing abnormal blood pressure data, which includes data analysis algorithms, software programs or other related functions to process and interpret blood pressure data; the blood pressure data report refers to a report generated after analyzing the blood pressure data of the detected user. Optionally, the analysis of the abnormal blood pressure data can be obtained by utilizing the analysis unit in the remote monitoring terminal; the generation of the blood pressure data report corresponding to the detected user can be obtained through visualization tools, such as: matplotlib, Seaborn and other tools.

[0125] Firstly, the present invention can significantly improve security by acquiring the facial image corresponding to the detected user and identifying the facial features in the facial image, while providing personalized services and data analysis for the user, which is helpful to establish and maintain a detailed health record database and improve the accuracy and efficiency of medical services. At the same time, the present invention can obtain the user's blood pressure value by measuring the blood pressure of the detected user based on the health index, so as to have a more comprehensive understanding of the user's physical condition, and can obtain more accurate and targeted blood pressure values ​​by measuring the blood pressure on this basis, which is of great significance for early detection of diseases such as hypertension, assessment of cardiovascular health status and formulation of personalized health management plans. The present invention performs status analysis on the detected user based on the pulse pressure value to obtain the user's blood pressure value. The status data can obtain the user's current cardiovascular health status and potential risks, so as to timely handle and intervene, and at the same time significantly reduce the incidence and severity of these diseases. The present invention can quickly diagnose potential blood pressure problems by querying the indicator range corresponding to the blood pressure indicator, which is helpful for early prevention and intervention of hypertension and its complications, and improves the patient's health status and quality of life. Furthermore, the present invention triggers a preset signal device to generate a blood pressure alarm signal based on the abnormal blood pressure label, and sends the blood pressure alarm signal to a preset remote monitoring terminal, so as to timely detect abnormal blood pressure conditions and send the alarm signal to the preset remote monitoring terminal, so as to take timely measures to ensure the health and safety of patients, and improve medical efficiency and quality. Therefore, the blood pressure data management method and system for face recognition blood pressure meter proposed by the present invention can improve the coordinated management of blood pressure data.

[0126] like Figure 2 1 is a functional module diagram of a blood pressure data management system for a face recognition blood pressure monitor provided by an embodiment of the present invention.

[0127] The blood pressure data management system 200 for a face recognition blood pressure monitor described in the present invention can be installed in an electronic device. According to the functions implemented, the blood pressure data management system 200 for a face recognition blood pressure monitor can include an index calculation module 201, a pulse pressure value calculation module 202, a risk grouping module 203, a label marking module 204, and a report generation module 205. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0128] In this embodiment, the functions of each module / unit are as follows:

[0129] The index calculation module 201 is used to obtain a facial image corresponding to the detected user, identify facial features in the facial image, match the facial features with a preset user face library, obtain user identity data, extract detailed physical sign parameters corresponding to the user identity data, and calculate the health index corresponding to the detected user based on the detailed physical sign parameters;

[0130] The pulse pressure value calculation module 202 is used to measure the blood pressure of the detected user based on the health index to obtain the user's blood pressure value, the user's blood pressure value includes: dynamic blood pressure change value, resting blood pressure value, and calculate the pulse pressure value corresponding to the detected user based on the dynamic blood pressure change value and resting blood pressure value;

[0131] The risk grouping module 203 is used to perform status analysis on the detected user based on the pulse pressure value to obtain user status data, perform risk detection on the user status data to obtain user risk data, perform data grouping on the user risk data to obtain risk grouping data, and determine the blood pressure index corresponding to the detected user based on the risk grouping data;

[0132] The label marking module 204 is used to query the indicator range corresponding to the blood pressure indicator, determine the abnormal blood pressure data corresponding to the detected user based on the indicator range, and label the abnormal blood pressure data to obtain an abnormal blood pressure label;

[0133] The report generation module 205 is used to trigger a preset signal device to generate a blood pressure alarm signal based on the abnormal blood pressure label, and after sending the blood pressure alarm signal to a preset remote monitoring terminal, use the analysis unit in the remote monitoring terminal to analyze the abnormal blood pressure data to generate a blood pressure data report corresponding to the detected user.

[0134] In detail, the modules described in the blood pressure data management system 200 for a face recognition blood pressure monitor described in an embodiment of the present invention adopt the same technical means as the blood pressure data management method for a face recognition blood pressure monitor described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0135] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A blood pressure data management method for a face recognition blood pressure meter, characterized in that: The method comprises: Acquire a facial image corresponding to the detected user, identify facial features in the facial image, match the facial features with a preset user face library, obtain user identity data, extract detailed physical sign parameters corresponding to the user identity data, and calculate a health index corresponding to the detected user based on the detailed physical sign parameters; Based on the health index, measuring the blood pressure of the detected user to obtain a blood pressure value of the user, wherein the blood pressure value of the user includes: a dynamic blood pressure change value and a resting blood pressure value, and calculating a pulse pressure value corresponding to the detected user based on the dynamic blood pressure change value and the resting blood pressure value; Based on the pulse pressure value, performing status analysis on the detected user to obtain user status data, performing risk detection on the user status data to obtain user risk data, grouping the user risk data to obtain risk grouping data, and determining a blood pressure index corresponding to the detected user based on the risk grouping data; querying an indicator range corresponding to the blood pressure indicator, determining abnormal blood pressure data corresponding to the detected user based on the indicator range, and labeling the abnormal blood pressure data to obtain an abnormal blood pressure label; Based on the abnormal blood pressure label, a preset signal device is triggered to generate a blood pressure alarm signal. After the blood pressure alarm signal is sent to a preset remote monitoring terminal, the abnormal blood pressure data is analyzed by the analysis unit in the remote monitoring terminal to generate a blood pressure data report corresponding to the detected user.

2. A blood pressure data management method for a face recognition blood pressure monitor as claimed in claim 1, characterized in that: The calculating the health index corresponding to the detected user based on the detailed physical sign parameters includes: The health index corresponding to the detected user is calculated using the following formula: Among them, JKS represents the health index corresponding to the detected user, Wt represents the weight corresponding to the detected user, Hs represents the height corresponding to the detected user, BMR represents the basal metabolic rate in the detailed physical sign parameters, Ta represents the body surface area corresponding to the detected user, Jr represents the resting heart rate in the detailed physical sign parameters, and Ps represents the skin relaxation index in the detailed physical sign parameters.

3. A blood pressure data management method for a face recognition blood pressure monitor as claimed in claim 1, characterized in that: The step of measuring the blood pressure of the detected user based on the health index to obtain the blood pressure value of the user includes: Based on the health index, determining the health status corresponding to the detected user; Based on the health condition, formulate a blood pressure measurement plan corresponding to the detected user; Based on the blood pressure measurement plan, the blood pressure of the detected user is measured using a preset blood pressure measurement device to obtain the user's blood pressure value.

4. A blood pressure data management method for a face recognition blood pressure monitor as claimed in claim 1, characterized in that: The calculating the pulse pressure value corresponding to the detected user based on the dynamic blood pressure change value and the resting blood pressure value includes: The pulse pressure value corresponding to the detected user is calculated using the following formula: Wherein, MP represents the pulse pressure value corresponding to the detected user, DT d Indicates the dynamic blood pressure value corresponding to the detected user, ΔDP d represents the dynamic blood pressure change value, JP r represents the resting blood pressure value, and t1 and t2 represent the measurement time intervals corresponding to the detected user.

5. The blood pressure data management method for a face recognition blood pressure monitor according to claim 1, characterized in that: The performing risk detection on the user status data to obtain user risk data includes: Removing data noise from the user status data to obtain denoised status data; Extracting state features from the denoised state data, and querying risk coefficients corresponding to the state features; Based on the risk coefficient, construct risk control measures corresponding to the user status data; Based on the risk control measures, risk detection is performed on the user status data to obtain user risk data.

6. A blood pressure data management method for a face recognition blood pressure monitor as claimed in claim 1, characterized in that: The step of grouping the user risk data to obtain risk grouping data includes: Performing interpolation processing on the user risk data to obtain risk interpolation data; extracting risk key features from the interpolated data; Performing cluster analysis on the key risk characteristics to obtain risk analysis indicators; Based on the risk analysis indicator, the user risk data is grouped to obtain risk grouping data.

7. A blood pressure data management method for a face recognition blood pressure monitor as claimed in claim 1, characterized in that: The step of determining the blood pressure index corresponding to the detected user based on the risk grouping data includes: Classifying the risk group data to obtain data risk levels; Setting the blood pressure value range corresponding to the data risk level; Based on the blood pressure value range, extracting a blood pressure index matching the risk level; Based on the blood pressure index, a blood pressure index corresponding to the detected user is determined.

8. The blood pressure data management method for a face recognition blood pressure monitor according to claim 1, characterized in that: The determining, based on the indicator range, the abnormal blood pressure data corresponding to the detected user includes: Dividing the indicator range into abnormal stages corresponding to the abnormal stages, and setting the blood pressure thresholds corresponding to the abnormal stages; Screening for abnormal blood pressure points that exceed the blood pressure threshold value and match the detected user; Based on the abnormal blood pressure points, construct an abnormal blood pressure curve graph corresponding to the detected user; The abnormal blood pressure data in the abnormal blood pressure curve graph is extracted.

9. The blood pressure data management method for a face recognition blood pressure meter according to claim 1, characterized in that: The step of labeling the abnormal blood pressure data to obtain an abnormal blood pressure label includes: Removing duplicate records from the abnormal blood pressure data to obtain deduplicated data; Querying the outliers in the deduplicated data; generating an identifier corresponding to the outlier; Constructing a mapping relationship diagram corresponding to the identifier; Based on the mapping relationship table, the abnormal blood pressure data is labeled to obtain an abnormal blood pressure label.

10. A blood pressure data management system for a face recognition blood pressure monitor, characterized in that: A method for managing blood pressure data for a face recognition blood pressure monitor according to any one of claims 1 to 9, the system comprising: An index calculation module is used to obtain a facial image corresponding to the detected user, identify facial features in the facial image, match the facial features with a preset user face library, obtain user identity data, extract detailed physical sign parameters corresponding to the user identity data, and calculate the health index corresponding to the detected user based on the detailed physical sign parameters; A pulse pressure value calculation module, used to measure the blood pressure of the detected user based on the health index to obtain the user's blood pressure value, the user's blood pressure value includes: dynamic blood pressure change value, resting blood pressure value, based on the dynamic blood pressure change value and resting blood pressure value, calculate the pulse pressure value corresponding to the detected user; a risk grouping module, configured to perform a status analysis on the detected user based on the pulse pressure value to obtain user status data, perform risk detection on the user status data to obtain user risk data, perform data grouping on the user risk data to obtain risk grouping data, and determine a blood pressure index corresponding to the detected user based on the risk grouping data; A label marking module, used to query the indicator range corresponding to the blood pressure indicator, determine the abnormal blood pressure data corresponding to the detected user based on the indicator range, label the abnormal blood pressure data, and obtain an abnormal blood pressure label; The report generation module is used to trigger a preset signal device to generate a blood pressure alarm signal based on the abnormal blood pressure label, and after sending the blood pressure alarm signal to a preset remote monitoring terminal, use the analysis unit in the remote monitoring terminal to analyze the abnormal blood pressure data to generate a blood pressure data report corresponding to the detected user.

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