Blood pressure data management method and system based on digital twinning

Abstract

The invention discloses a blood pressure data management method and system based on digital twinning, and relates to the technical field of medical data management.The method comprises the steps that when a to-be-tested user is in a quiet state, after the state of the to-be-tested user is adjusted, a user ready signal is output, and after the user ready signal is received, the to-be-tested user is started; after the electronic sphygmomanometer is obtained and adjusted, an equipment ready signal is output, and after the equipment ready signal is received, primary blood pressure detection is conducted on a user to be detected to obtain user blood pressure data information; basic information of a to-be-tested user is collected, and a user digital twin model is created and stored based on the basic information of the to-be-tested user; analyzing the influence of the daily living habits of the to-be-tested user on the blood pressure of the to-be-tested user to obtain a living habit influence coefficient, and judging the influence of the medical history and the medicine history of the to-be-tested user on the blood pressure of the to-be-tested user to obtain a historical disease influence coefficient. The method has the effect of improving the blood pressure data management efficiency based on digital twinning.

Description

Technical Field

[0001] This invention relates to the field of medical data management technology, and in particular to a blood pressure data management method and system based on digital twins. Background Technology

[0002] Currently, hypertension (also known as high blood pressure) is a condition where the pressure exerted by blood on the walls of blood vessels is consistently higher than normal. Typical symptoms of hypertension include headache, fatigue or restlessness, arrhythmia, palpitations, and tinnitus. However, many people with hypertension may develop other complications without any symptoms, such as stroke, blurred vision, loss of consciousness, and memory loss. Therefore, hypertension is often called a "silent killer." Hypertension is the leading risk factor for death and disability worldwide. China has a high prevalence of hypertension, exceeding 50% in people over 60 years of age, and its control rate is low. Therefore, managing blood pressure data is crucial.

[0003] Current blood pressure data management methods involve measuring a user's blood pressure in their upper arm using an electronic blood pressure monitor, then comparing this data with blood pressure diagnostic standards to determine if the user's blood pressure is abnormal. However, these methods do not consider that human blood pressure is affected by physiological mechanisms such as heart function, vascular resistance, blood volume, and neurohumoral regulation, and is also closely related to lifestyle factors such as diet, exercise, weight, smoking, and sleep. Furthermore, individual blood pressure diagnostic standards vary, resulting in low accuracy and inefficient blood pressure data management, which warrants improvement. Summary of the Invention

[0004] To improve the efficiency of blood pressure data management based on digital twins, this invention provides a method and system for blood pressure data management based on digital twins.

[0005] In a first aspect, the present invention provides a blood pressure data management method based on digital twins, which adopts the following technical solution: A method for managing blood pressure data based on digital twins includes the following steps: Step S1: When the user to be tested is in a quiet state, after the user's state is adjusted, the user's own ready signal is output. After receiving the user's own ready signal, the electronic blood pressure monitor is acquired and adjusted, and then the device ready signal is output. After receiving the device ready signal, the blood pressure of the user to be tested is detected to obtain the user's blood pressure data information. Step S2: Collect basic information of the user to be tested, create a digital twin model of the user based on the basic information of the user to be tested, and store it. Step S3: Analyze the impact of the user's daily habits on the user's blood pressure to obtain the lifestyle habit impact coefficient, and determine the impact of the user's medical history and medication history on the user's blood pressure to obtain the historical disease impact coefficient; Step S4: Obtain standard blood pressure data information, and adjust the standard blood pressure data information based on the influence coefficient of lifestyle habits and the influence coefficient of historical diseases to obtain characteristic blood pressure threshold data information; Step S5: Compare the user's blood pressure data with the characteristic blood pressure threshold data, analyze the blood pressure status of the user to be tested, and output the user's blood pressure monitoring results; Step S6: Send the user's blood pressure monitoring results and the user's digital twin model to the background monitoring system and the user terminal, and upload the user's blood pressure monitoring results to the blockchain system for storage.

[0006] Preferably, the heart rate of the user to be tested is detected to obtain the user's heart rate information. If the user's heart rate information is within the preset resting heart rate standard range, then the first self-preparation completion signal is output. The respiratory rate of the user under test is detected to obtain the user's respiratory rate information. If the user's respiratory rate information is within the preset resting respiratory rate standard range, a second self-preparation completion signal is output. The system detects whether the user's posture is correct before blood pressure measurement. If it is incorrect, it adjusts the posture to a suitable position and outputs a third self-preparation completion signal. Once the first self-preparation completion signal, the second self-preparation completion signal, and the third self-preparation completion signal are received, it indicates that the user to be tested is ready for blood pressure measurement, and the user self-ready signal is output.

[0007] Preferably, an electronic blood pressure monitor is obtained and placed next to the user to be tested. The cuff of the electronic blood pressure monitor is directly attached to the upper arm skin of the user to be tested, and the banding range of the electronic blood pressure monitor is determined based on the upper arm circumference of the user to be tested. Based on the convergence range of the electronic blood pressure monitor, the upper arm circumference of the user to be tested is converged, and a first device preparation completion signal is output. The system detects whether the cuff of the electronic blood pressure monitor is at the same level as the heart of the user being tested. If they are not at the same level, the system adjusts the cuff of the electronic blood pressure monitor to be at the same level as the heart of the user being tested and outputs a second device ready signal. When the first device ready signal and the second device ready signal are received, it indicates that the device for measuring the blood pressure of the user is ready and outputs a device ready signal. Determine whether the user's arm posture is correct; if correct, output a signal indicating that the current posture is correct. Upon receiving the user's own readiness signal, the device's readiness signal, and the signal indicating that the current posture is correct, the measurement readiness signal is output. Upon receiving the measurement ready signal, the electronic blood pressure monitor is used to detect the blood pressure of the user and obtain the user's blood pressure data.

[0008] Preferably, the basic information of the user to be tested is obtained, including age, weight, gender, body shape dimensions, and vascular status. Based on the age, weight, gender, body shape, and vascular status information of the user under test, a digital twin model of the user is created, resulting in the user's digital twin model. The user digital twin model is stored, and the basic information of the user to be tested is updated regularly, and the user digital twin model is updated regularly.

[0009] Preferably, the basic information of the user to be tested is obtained, including age, weight, gender, body shape dimensions, and vascular status. Based on the age, weight, gender, body shape, and vascular status information of the user under test, a digital twin model of the user is created, resulting in the user's digital twin model. The user digital twin model is stored, and the basic information of the user to be tested is updated regularly, and the user digital twin model is updated regularly.

[0010] Preferably, the historical medical information of the user to be tested is obtained, including historical disease type information, historical disease severity information, historical disease duration information, historical medication type information, historical medication dosage information, and historical medication duration information; Based on historical disease type information, determine whether the medical history of the test user has an impact on the blood vessels or heart of the test user. If there is an impact, determine the degree of impact on the blood vessels or heart based on the historical disease type to obtain the disease type impact weight. Based on the influence weight of disease type, information on the severity of historical disease, and information on the duration of historical disease, the influence coefficient of medical history on the blood pressure detection of the test user is obtained. Based on historical medication type information, it is determined whether the user's medication history has caused damage to the user's blood vessels or heart. If the user's blood vessels or heart have caused damage, the degree of damage to the blood vessels or heart is determined based on historical medication type information to obtain the weight of the influence of medication type. Based on the influence weight of medication type, historical medication amount information, and historical medication duration information, the influence coefficient of medication history on blood pressure detection of the test user is obtained by judging the influence of the test user's medication history. The influence coefficient of medical history is calculated by superimposing the influence coefficient of medication history to determine the impact of the test user's historical illness on the test user's blood pressure, thus obtaining the influence coefficient of the test user's historical illness.

[0011] Preferably, blood pressure standard data information is obtained, which includes normal blood pressure value, high normal value, and high blood pressure value; Based on the influence coefficient of the user's lifestyle habits, the adjustment value of the blood pressure standard data is used to obtain the first blood pressure threshold compensation value. Based on the historical disease impact coefficient of the user under test, the adjustment value of the blood pressure standard data is used to obtain the second blood pressure threshold compensation value; Based on the first blood pressure threshold compensation value and the second blood pressure threshold compensation value, the blood pressure standard data information is adjusted to obtain characteristic blood pressure threshold data information, which includes characteristic normal blood pressure value, characteristic high normal value and characteristic high blood pressure value.

[0012] Preferably, the user's blood pressure data is compared with the characteristic hypertension value in the characteristic blood pressure threshold data. If the user's blood pressure data is higher than the characteristic hypertension value, the user is determined to be in a hypertensive state, and a hypertension alarm result is output. If the user's blood pressure data is lower than the characteristic hypertension value, the user's blood pressure data is compared with the characteristic high normal value in the characteristic blood pressure threshold data. If the user's blood pressure data is higher than the characteristic high normal value in the characteristic blood pressure threshold data, the user is judged to have a high risk of hypertension, and a level 1 hypertension risk warning result is output. If the user's blood pressure data is lower than the characteristic high normal value, the user's blood pressure data is compared with the characteristic normal blood pressure value in the characteristic blood pressure threshold data. If the user's blood pressure data is higher than the characteristic normal blood pressure value in the characteristic blood pressure threshold data, the user is determined to have a risk of hypertension, and a level 2 hypertension risk warning result is output. If the user's blood pressure data is lower than the characteristic normal blood pressure value, it is determined that the user does not have an abnormal blood pressure, and the result of normal blood pressure is output. The user's hypertension alarm result, the user's hypertension level 1 risk warning result, the user's hypertension level 2 risk warning result, and the user's normal blood pressure result are combined to form the user's blood pressure monitoring result; The user's blood pressure monitoring results are displayed on the user's digital twin model.

[0013] Preferably, a wireless communication module is acquired and a signal connection link is established between the wireless communication module and the user under test; The user's blood pressure monitoring results and the user's digital twin model are sent to the background monitoring system and the user terminal based on the wireless communication module; The user's blood pressure monitoring results and the user's digital twin model are uploaded to the blockchain system for storage.

[0014] Preferably, the user blood pressure detection module is configured to output a user ready signal after the user is in a quiet state and the user's state is adjusted. After receiving the user ready signal, the module acquires and adjusts the electronic blood pressure monitor and outputs a device ready signal. After receiving the device ready signal, the module performs blood pressure detection on the user to obtain the user's blood pressure data information. The digital twin model creation module is configured to collect basic information about the user under test, create a digital twin model of the user based on the basic information of the user under test, and store it. The impact factor analysis module is configured to analyze the impact of the daily habits of the test user on the blood pressure of the test user to obtain the impact coefficient of the daily habits, and to determine the impact of the test user's medical history and medication history on the blood pressure of the test user to obtain the impact coefficient of the history of disease. The blood pressure threshold analysis module is configured to obtain standard blood pressure data information, and adjust the standard blood pressure data information based on the influence coefficient of lifestyle habits and the influence coefficient of historical diseases to obtain characteristic blood pressure threshold data information; The blood pressure abnormality judgment module is configured to compare the user's blood pressure data with the characteristic blood pressure threshold data, analyze the blood pressure status of the user under test, and output the user's blood pressure monitoring results. The communication and storage module is configured to send the user's blood pressure monitoring results and the user's digital twin model to the background monitoring system and the user terminal, and to upload the user's blood pressure monitoring results to the blockchain system for storage.

[0015] Secondly, the present invention provides a blood pressure data management system based on digital twins, which adopts the following technical solution: A blood pressure data management system based on digital twins includes: The user blood pressure detection module is configured to output a user ready signal after the user is in a quiet state and the user's state is adjusted. After receiving the user ready signal, the module acquires and adjusts the electronic blood pressure monitor and outputs a device ready signal. After receiving the device ready signal, the module detects the user's blood pressure and obtains the user's blood pressure data. The digital twin model creation module is configured to collect basic information about the user under test, create a digital twin model of the user based on the basic information of the user under test, and store it. The impact factor analysis module is configured to analyze the impact of the daily habits of the test user on the blood pressure of the test user to obtain the impact coefficient of the daily habits, and to determine the impact of the test user's medical history and medication history on the blood pressure of the test user to obtain the impact coefficient of the history of disease. The blood pressure threshold analysis module is configured to obtain standard blood pressure data information, and adjust the standard blood pressure data information based on the influence coefficient of lifestyle habits and the influence coefficient of historical diseases to obtain characteristic blood pressure threshold data information; The blood pressure abnormality judgment module is configured to compare the user's blood pressure data with the characteristic blood pressure threshold data, analyze the blood pressure status of the user under test, and output the user's blood pressure monitoring results. The communication and storage module is configured to send the user's blood pressure monitoring results and the user's digital twin model to the background monitoring system and the user terminal, and to upload the user's blood pressure monitoring results to the blockchain system for storage.

[0016] In summary, the present invention has at least one of the following beneficial technical effects: By measuring blood pressure data after both the user and the blood pressure monitoring equipment are ready, and by analyzing the pre-test preparation from multiple dimensions, the accuracy of blood pressure data detection based on the digital twin model is improved, thereby enhancing the efficiency of blood pressure data management based on digital twins. A digital twin model of the user is created based on the user's basic information. The influence of various factors on the user's blood pressure is analyzed from multiple dimensions, including diet, sleep, smoking and alcohol consumption, medical history, and medication history. The standard blood pressure data is adjusted to obtain characteristic blood pressure threshold data. The user's blood pressure data is compared with the characteristic blood pressure threshold data to analyze the user's blood pressure status and output the user's blood pressure monitoring results. This further improves the accuracy of blood pressure data detection based on the digital twin model, and consequently, the efficiency of blood pressure data management based on digital twins. Attached Figure Description

[0017] Figure 1 This embodiment mainly illustrates the process of blood pressure data management based on digital twins; Figure 2 This embodiment mainly illustrates the module diagram of a blood pressure data management system based on digital twins.

[0018] Attached reference numerals: 1. User blood pressure monitoring module; 2. Digital twin model creation module; 3. Influence factor analysis module; 4. Blood pressure threshold analysis module; 5. Blood pressure abnormality judgment module; 6. Communication and storage module. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to the accompanying drawings.

[0020] This invention discloses a blood pressure data management method based on digital twins.

[0021] A method for managing blood pressure data based on digital twins includes the following steps: Reference Figure 1 Step S1: When the user is in a quiet state, after the user's state is adjusted, a user ready signal is output. Upon receiving the user ready signal, the electronic blood pressure monitor is acquired and adjusted, and then a device ready signal is output. Upon receiving the device ready signal, the user's blood pressure is measured to obtain the user's blood pressure data. Step S1 specifically includes the following sub-steps: The system detects the user's heart rate to obtain the user's heart rate information. If the user's heart rate is within the preset resting heart rate standard range, it outputs the first self-preparation completion signal. In actual use, the standard resting heart rate range for normal adults is 60-100 beats per minute.

[0022] The system detects the user's respiratory rate to obtain the user's respiratory rate information. If the user's respiratory rate information is within the preset resting respiratory rate standard range, a second self-preparation completion signal is output. In practical applications, the standard range of resting respiratory rate for normal adults is 12-20 breaths per minute.

[0023] The system checks whether the user's posture is correct before blood pressure measurement. If not, it adjusts the user to a suitable posture and outputs a third self-preparation signal. In practical applications, the appropriate posture for blood pressure measurement is a seated position with both feet flat on the ground.

[0024] Once the first self-preparation completion signal, the second self-preparation completion signal, and the third self-preparation completion signal are received, it indicates that the user to be tested is ready for blood pressure measurement, and the user self-ready signal is output.

[0025] Step S1 also includes the following sub-steps: Obtain an electronic blood pressure monitor and place it next to the user. Place the cuff directly onto the user's upper arm skin. Measure and determine the monitor's coverage area based on the user's upper arm circumference. The smaller the user's upper arm circumference, the larger the monitor's coverage area.

[0026] Based on the convergence range of the electronic blood pressure monitor, the upper arm circumference of the user to be tested is converged, and the first device preparation signal is output.

[0027] The system detects whether the cuff of the electronic blood pressure monitor is at the same level as the heart of the user being tested. If they are not at the same level, the system adjusts the cuff of the electronic blood pressure monitor to be at the same level as the heart of the user being tested, and outputs a signal indicating that the second device is ready.

[0028] Upon receiving both the first and second device readiness signals, the device for measuring the blood pressure of the user is ready, and a device ready signal is output.

[0029] The system determines whether the user's arm posture is correct. If correct, it outputs a signal indicating that the current posture is correct. It should be noted that, in this embodiment, the correct arm posture for the user during measurement is an upper arm extended slightly outward with the palm facing upward.

[0030] Upon receiving the user's own ready signal, the device ready signal, and the signal indicating that the current posture is correct, the device outputs a measurement ready signal.

[0031] Upon receiving the measurement ready signal, the system uses an electronic blood pressure monitor to measure the blood pressure of the user and obtain the user's blood pressure data.

[0032] Reference Figure 1 Step S2 involves collecting basic information about the user to be tested, creating a digital twin model of the user based on this information, and storing it. Step S2 specifically includes the following sub-steps: The basic information of the user to be tested is obtained, including age, weight, gender, body size, and vascular status.

[0033] Based on the age, weight, gender, body shape, and vascular status information of the user to be tested, a digital twin model of the user is created, resulting in the user's digital twin model.

[0034] Store the user's digital twin model, and regularly update the user's basic information and the user's digital twin model.

[0035] Reference Figure 1 Step S3 involves analyzing the impact of the user's daily habits on their blood pressure to obtain the lifestyle habit impact coefficient, and determining the impact of the user's medical history and medication history on their blood pressure to obtain the historical disease impact coefficient. Step S3 specifically includes the following sub-steps: Create a lifestyle habits survey questionnaire, send it to the user's terminal for completion, and submit and summarize the results to obtain the survey feedback questionnaire.

[0036] The influence coefficient of dietary habits on the blood pressure of the test subjects was obtained by judging the impact of the test subjects' dietary habits on the blood pressure of the test subjects based on the survey feedback questionnaire.

[0037] Specifically, based on the survey feedback questionnaire, the sodium and potassium intake information of the test users was collected. The sodium and potassium intake information includes sodium intake information and potassium intake information. The sodium intake information was compared with the daily sodium intake threshold. If the sodium intake information is greater than the daily sodium intake threshold, it is determined that the test user has a high salt diet. The difference between the sodium intake information and the daily sodium intake threshold is calculated to obtain the sodium intake difference value. Based on the sodium intake difference value, the influence of the test user's dietary sodium intake on blood pressure is determined to obtain the sodium influence coefficient.

[0038] In practical applications, the daily sodium intake threshold is 5 grams. A high-salt diet (i.e., daily sodium intake > 5 grams) leads to water and sodium retention, increases blood volume, and causes high blood pressure.

[0039] Potassium intake information is compared with the daily potassium intake threshold. If the potassium intake information is lower than the daily potassium intake threshold, it is determined that the user's daily diet is low in vegetables and fruits. The difference between the daily potassium intake threshold and the potassium intake information is calculated to obtain the potassium intake difference value. Based on the potassium intake difference value, the impact of the user's dietary potassium intake on blood pressure is determined to obtain the potassium influence coefficient.

[0040] In practical applications, potassium promotes sodium excretion, and a low-potassium diet (such as long-term consumption of few fruits and vegetables) can further raise blood pressure.

[0041] Based on the survey feedback questionnaire, the alcohol intake information of the test users is collected and compared with the preset alcohol intake threshold information to determine whether the test users drink excessively. If excessive drinking is found, the difference between the test users' alcohol intake information and the preset alcohol intake threshold information is calculated to obtain the alcohol intake difference value. Based on the alcohol intake difference value, the influence of the test users' dietary alcohol intake on blood pressure is determined to obtain the alcohol influence coefficient.

[0042] In practice, excessive alcohol consumption (more than 30 grams per day for men and more than 15 grams per day for women) can cause elevated blood pressure.

[0043] Based on the survey feedback questionnaire, the smoking information of the test users was statistically analyzed. The smoking information included the duration of smoking history and the frequency of smoking. The smoking influence coefficient was obtained by judging the impact of the test users' smoking on blood pressure based on the duration of smoking history and the frequency of smoking. The longer the smoking history, the greater the smoking influence coefficient, and the higher the smoking frequency, the greater the smoking influence.

[0044] In practice, nicotine in cigarettes stimulates the sympathetic nervous system, causing vasoconstriction, increased heart rate, and elevated blood pressure. Furthermore, smoking damages the vascular endothelium, accelerates arteriosclerosis, and further increases blood pressure.

[0045] The influence coefficient of a user's diet on their blood pressure is determined by superimposing the influence coefficients of sodium, potassium, alcohol, and smoking.

[0046] The influence coefficient of exercise habits on the blood pressure of the test subjects was obtained by judging the impact of the test subjects' exercise habits on the blood pressure.

[0047] Specifically, based on the survey feedback questionnaire, it is determined whether the test users engage in regular aerobic exercise. If the test users engage in regular aerobic exercise, the influence coefficient of exercise habits is 0; if the test users do not have exercise habits, the influence coefficient of exercise habits is taken as the maximum value.

[0048] The influence coefficient of sleep habits on the blood pressure of the test subjects was obtained by judging the impact of the test subjects' sleep habits on the test subjects' blood pressure.

[0049] Specifically, based on the survey feedback questionnaire, the sleep information of the users to be tested was statistically analyzed. The sleep information included sleep duration information and information on the diagnosis of sleep apnea-hypopnea syndrome.

[0050] The sleep duration information is compared with a preset sleep duration threshold to determine whether the user is sleep-deprived. If the sleep duration information is less than the preset sleep duration threshold, then there is sleep deprivation. The difference between the sleep duration information and the preset sleep duration threshold is calculated to obtain the sleep duration difference value. Based on the sleep duration difference value, the impact of the user's sleep duration on the user's blood pressure is determined to obtain the first sleep influence coefficient.

[0051] Based on the information regarding sleep apnea-hypopnea syndrome, if the user does not have sleep apnea-hypopnea syndrome, a second sleep impact coefficient is output. If the user does have sleep apnea-hypopnea syndrome, the impact of apnea during sleep on the user's blood pressure is determined based on the severity of the user's sleep apnea-hypopnea syndrome, and a second sleep impact coefficient is output.

[0052] The first sleep influence coefficient and the second sleep influence coefficient are superimposed to calculate the effect of the test user's sleep habits on the test user's blood pressure, and the sleep habit influence coefficient is obtained.

[0053] The influence coefficients of dietary habits, exercise habits, and sleep habits are combined and calculated to determine the impact of the test user's daily life habits on the test user's blood pressure, thus obtaining the influence coefficient of the test user's daily life habits.

[0054] Step S3 specifically includes the following sub-steps: Obtain the historical medical information of the user to be tested. The historical medical information includes the type of historical illness, the severity of historical illness, the duration of historical illness, the type of medication used, the amount of medication used, and the duration of medication used.

[0055] Based on historical disease type information, it is determined whether the medical history of the user under test has an impact on the user's blood vessels or heart. If there is an impact, the degree of impact on blood vessels or heart is determined based on the historical disease type to obtain the disease type impact weight.

[0056] Based on the influence weight of disease type, information on the severity of historical diseases, and information on the duration of historical diseases, the impact of the user's medical history on blood pressure monitoring is determined to obtain the medical history influence coefficient. Specifically, a higher influence weight for disease type, higher information on the severity of historical diseases, and a longer duration of historical diseases all result in a larger medical history influence coefficient. If the user has no historical diseases, the medical history influence coefficient is 0.

[0057] Based on historical medication type information, it is determined whether the user's medication history indicates damage to the user's blood vessels or heart. If damage to the user's blood vessels or heart is found, the degree of damage to the blood vessels or heart is determined based on historical medication type information to obtain the weight of the influence of medication type.

[0058] Based on the influence weight of medication type, historical medication dosage information, and historical medication duration information, the impact of the user's medication history on blood pressure monitoring is determined to obtain the medication history influence coefficient. Specifically, a higher medication type influence weight results in a larger medication history influence coefficient; more information on historical medication dosage also results in a larger coefficient; and a longer historical medication duration results in a larger coefficient. If the user has no historical medication history, the medication history influence coefficient is 0.

[0059] The influence coefficient of medical history and the influence coefficient of medication history are superimposed to determine the impact of the test user's historical illness on the test user's blood pressure, thus obtaining the historical illness influence coefficient of the test user.

[0060] Reference Figure 1 Step S4 involves obtaining standard blood pressure data and adjusting it based on the influence coefficients of lifestyle habits and historical disease patterns to obtain characteristic blood pressure threshold data. Step S4 specifically includes the following sub-steps: Obtain standard blood pressure data, which includes normal blood pressure, high-normal blood pressure, and high blood pressure.

[0061] The first blood pressure threshold compensation value is obtained by adjusting the standard blood pressure data based on the influence coefficient of the user's lifestyle.

[0062] The second blood pressure threshold compensation value is obtained by adjusting the standard blood pressure data based on the historical disease impact coefficient of the user being tested.

[0063] Based on the first blood pressure threshold compensation value and the second blood pressure threshold compensation value, the blood pressure standard data information is adjusted to obtain characteristic blood pressure threshold data information, which includes characteristic normal blood pressure value, characteristic high normal value, and characteristic high blood pressure value.

[0064] Reference Figure 1 Step S5 involves comparing the user's blood pressure data with the characteristic blood pressure threshold data to analyze the user's blood pressure status and output the user's blood pressure monitoring results. Step S5 specifically includes the following sub-steps: The system compares the user's blood pressure data with the characteristic hypertension value in the characteristic blood pressure threshold data. If the user's blood pressure data is higher than the characteristic hypertension value, the user is determined to be in a hypertensive state, and a hypertension alarm result is output.

[0065] If the user's blood pressure data is lower than the characteristic hypertension value, the user's blood pressure data is compared with the characteristic high normal value in the characteristic blood pressure threshold data. If the user's blood pressure data is higher than the characteristic high normal value in the characteristic blood pressure threshold data, the user is judged to have a high risk of hypertension, and a level 1 hypertension risk warning result is output.

[0066] If the user's blood pressure data is lower than the high value of the characteristic normal range, the user's blood pressure data is compared with the characteristic normal blood pressure value in the characteristic blood pressure threshold data. If the user's blood pressure data is higher than the characteristic normal blood pressure value in the characteristic blood pressure threshold data, the user is judged to have a risk of hypertension, and a level 2 hypertension risk warning result is output.

[0067] If the user's blood pressure data is lower than the characteristic normal blood pressure value, it is determined that the user does not have an abnormal blood pressure, and the result of normal blood pressure is output.

[0068] The user's blood pressure monitoring results are formed by combining the user's hypertension alarm results, the user's hypertension level 1 risk warning results, the user's hypertension level 2 risk warning results, and the user's normal blood pressure results.

[0069] Display the user's blood pressure monitoring results on the user's digital twin model.

[0070] Reference Figure 1 Step S6 involves sending the user's blood pressure monitoring results and the user's digital twin model to the backend monitoring system and the user's terminal, and uploading the user's blood pressure monitoring results to the blockchain system for storage. Step S6 specifically includes the following sub-steps: Obtain the wireless communication module and establish a signal connection link between the wireless communication module and the user under test.

[0071] The wireless communication module sends the user's blood pressure monitoring results and the user's digital twin model to the back-end monitoring system and the user terminal.

[0072] The user's blood pressure monitoring results and the user's digital twin model are uploaded to the blockchain system for storage.

[0073] This invention also discloses a blood pressure data management system based on digital twins.

[0074] Reference Figure 2 A blood pressure data management system based on digital twins includes: The user blood pressure detection module is configured to output a user ready signal after the user is in a quiet state and the user's state is adjusted. After receiving the user ready signal, the module acquires and adjusts the electronic blood pressure monitor and outputs a device ready signal. After receiving the device ready signal, the module performs blood pressure detection on the user to obtain the user's blood pressure data.

[0075] The digital twin model creation module is configured to collect basic information about the user under test, create a digital twin model of the user based on the basic information, and store it.

[0076] The influencing factor analysis module is configured to analyze the impact of the user's daily life habits on the user's blood pressure to obtain the lifestyle habit influence coefficient, and to determine the impact of the user's medical history and medication history on the user's blood pressure to obtain the historical disease influence coefficient.

[0077] The blood pressure threshold analysis module is configured to obtain standard blood pressure data and adjust the standard blood pressure data based on the influence coefficients of lifestyle habits and historical disease incidence to obtain characteristic blood pressure threshold data.

[0078] The blood pressure abnormality judgment module is configured to compare user blood pressure data with characteristic blood pressure threshold data, analyze the blood pressure status of the user under test, and output the user's blood pressure monitoring results.

[0079] The communication and storage module is configured to send the user's blood pressure monitoring results and the user's digital twin model to the backend monitoring system and the user terminal, and to upload the user's blood pressure monitoring results to the blockchain system for storage.

[0080] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or systems. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] This invention is described with reference to flowchart illustrations and structural diagrams of methods and systems according to embodiments of the invention. It should be understood that the combination of each process and module in the flowchart and structural diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 One or more processes and structures Figure 1 A device for a function specified in one or more modules.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and structures Figure 1 The function specified in one or more modules.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and structures Figure 1 The steps of a specified function in one or more modules.

[0084] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0085] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A digital-twin-based blood pressure data management method, characterized by, The method comprises the following steps: Step S1, when the to-be-tested user is in a quiet state, outputting a user self-prepared signal after the to-be-tested user state is adjusted, obtaining and adjusting an electronic sphygmomanometer after receiving the user self-prepared signal, and outputting a device prepared signal, and obtaining user blood pressure data information after receiving the device prepared signal; Step S2, collecting basic information of the to-be-tested user, creating a user digital twin model based on the basic information of the to-be-tested user, and storing the user digital twin model; Step S3, analyzing the influence of daily life habits of the to-be-tested user on the blood pressure of the to-be-tested user to obtain a life habit influence coefficient, and judging the influence of the medical history of the to-be-tested user on the blood pressure of the to-be-tested user to obtain a historical disease influence coefficient; Step S4, obtaining blood pressure standard data information, adjusting the blood pressure standard data information based on the life habit influence coefficient and the historical disease influence coefficient to obtain characteristic blood pressure threshold data information; Step S5, comparing the user blood pressure data information with the characteristic blood pressure threshold data information, analyzing the blood pressure of the to-be-tested user, and outputting a user blood pressure monitoring result; Step S6, sending the user blood pressure monitoring result and the user digital twin model to a background monitoring system and a user terminal, and uploading the user blood pressure monitoring result to a blockchain system for storage. 2.The digital-twin-based blood pressure data management method of claim 1, wherein, The step S1 specifically comprises the following sub-steps: detecting the heart rate of the to-be-tested user to obtain user heart rate information, and outputting a first self-prepared completion signal if the user heart rate information is within a preset resting heart rate standard range; detecting the respiration rate of the to-be-tested user to obtain user respiration rate information, and outputting a second self-prepared completion signal if the user respiration rate information is within a preset resting respiration rate standard range; detecting whether the preparation posture of the to-be-tested user before blood pressure detection is correct, and outputting a third self-prepared completion signal after adjusting to a suitable posture if the preparation posture is incorrect; outputting a user self-prepared signal when the first self-prepared completion signal is received, the second self-prepared completion signal is received, and the third self-prepared completion signal is received, indicating that the to-be-tested user has prepared for blood pressure detection. 3.The digital-twin-based blood pressure data management method of claim 2, wherein, The step S1 specifically further comprises the following sub-steps: obtaining an electronic sphygmomanometer, placing the electronic sphygmomanometer beside the to-be-tested user, directly attaching the cuff of the electronic sphygmomanometer to the skin of the upper arm of the to-be-tested user, detecting the upper arm circumference of the to-be-tested user, and determining the convergence range of the electronic sphygmomanometer based on the upper arm circumference of the to-be-tested user; converging the upper arm circumference of the to-be-tested user based on the convergence range of the electronic sphygmomanometer, and outputting a first device prepared completion signal; detecting whether the cuff of the electronic sphygmomanometer and the heart of the to-be-tested user are at the same level, adjusting the cuff of the electronic sphygmomanometer and the heart of the to-be-tested user to the same level if they are not at the same level, and outputting a second device prepared completion signal; outputting a device prepared signal when the first device prepared completion signal is received and the second device prepared completion signal is received, indicating that the device for measuring the blood pressure of the to-be-tested user is prepared; judging whether the arm posture of the to-be-tested user is correct, and outputting a correct posture signal if the arm posture is correct; output a measurement ready signal when receiving the user self ready signal, the device ready signal, and the current posture correct signal; detect blood pressure of the user to be measured based on the electronic sphygmomanometer to obtain user blood pressure data information when receiving the measurement ready signal.

4. The digital-twin-based blood pressure data management method according to claim 3, characterized in that, The step S2 specifically includes the following sub-steps: obtain basic information of the user to be measured, the basic information of the user to be measured including age information, weight information, gender information, body shape dimension information, and blood vessel state information; create a digital twin model of the user to be measured based on the age information, the weight information, the gender information, the body shape dimension information, and the blood vessel state information of the user to be measured to obtain a user digital twin model; store the user digital twin model, and regularly update the basic information of the user to be measured and regularly update the user digital twin model.

5. The digital-twin-based blood pressure data management method according to claim 4, characterized in that, The step S3 specifically includes the following sub-steps: create a life habit questionnaire, send the life habit questionnaire to a user terminal of the user to be measured for filling, and after completion, submit and aggregate to obtain a survey feedback questionnaire; judge the influence of the eating habit of the user to be measured on the blood pressure of the user to be measured based on the survey feedback questionnaire to obtain an eating habit influence coefficient; judge the influence of the exercise habit of the user to be measured on the blood pressure of the user to be measured based on the survey feedback questionnaire to obtain an exercise habit influence coefficient; judge the influence of the sleep habit of the user to be measured on the blood pressure of the user to be measured based on the survey feedback questionnaire to obtain a sleep habit influence coefficient; superimpose and calculate the eating habit influence coefficient, the exercise habit influence coefficient, and the sleep habit influence coefficient to judge the influence of the daily life habit of the user to be measured on the blood pressure of the user to be measured to obtain a life habit influence coefficient of the user to be measured.

6. The digital-twin-based blood pressure data management method according to claim 5, characterized in that, The step S3 specifically includes the following sub-steps: obtain historical disease information of the user to be measured, the historical disease information including historical disease type information, historical disease severity information, historical disease duration information, historical medication type information, historical medication amount information, and historical medication duration information; judge whether the medical history of the user to be measured has an influence on the blood vessels or heart of the user to be measured based on the historical disease type information, and if there is an influence, judge the influence degree on the blood vessels or heart based on the historical disease type to obtain a disease type influence weight; judge the influence of the medical history of the user to be measured on the blood pressure detection of the user to be measured based on the disease type influence weight, the historical disease severity information, and the historical disease duration information to obtain a medical history influence coefficient; judge whether the medication history of the user to be measured has an injury to the blood vessels or heart of the user to be measured based on the historical medication type information, and if there is an injury to the blood vessels or heart of the user to be measured, judge the injury degree of the blood vessels or heart based on the historical medication type information to obtain a medication type influence weight; judge the influence of the medication history of the user to be measured on the blood pressure detection of the user to be measured based on the medication type influence weight, the historical medication amount information, and the historical medication duration information to obtain a medication history influence coefficient; Superimpose the disease history influence coefficient and the medication history influence coefficient to determine the influence of the history of the user to be measured suffering from a disease on the blood pressure of the user to be measured, and obtain a history of the user to be measured suffering from a disease influence coefficient.

7. The digital-twin-based blood pressure data management method according to claim 6, characterized in that, The step S4 specifically comprises the following sub-steps: Obtain blood pressure standard data information, wherein the blood pressure standard data information comprises a normal blood pressure value, a normal high value, and a high blood pressure value; Determine an adjustment value of the blood pressure standard data based on the life habit influence coefficient of the user to be measured, and obtain a first blood pressure threshold compensation value; Determine an adjustment value of the blood pressure standard data based on the history of the user to be measured suffering from a disease influence coefficient, and obtain a second blood pressure threshold compensation value; Adjust the blood pressure standard data information based on the first blood pressure threshold compensation value and the second blood pressure threshold compensation value, and obtain characteristic blood pressure threshold data information, wherein the characteristic blood pressure threshold data information comprises a characteristic normal blood pressure value, a characteristic normal high value, and a characteristic high blood pressure value. 8.The digital-twin-based blood pressure data management method of claim 7, wherein, The step S5 specifically comprises the following sub-steps: Compare the user blood pressure data information with the characteristic high blood pressure value in the characteristic blood pressure threshold data information, if the user blood pressure data information is higher than the characteristic high blood pressure value, determine that the user to be measured is in a high blood pressure state, and output a user high blood pressure alarm result; If the user blood pressure data information is lower than the characteristic high blood pressure value, compare the user blood pressure data information with the characteristic normal high value in the characteristic blood pressure threshold data information, if the user blood pressure data information is higher than the characteristic normal high value in the characteristic blood pressure threshold data information, determine that the user to be measured has a higher high blood pressure risk, and output a user high blood pressure first risk early warning result; If the user blood pressure data information is lower than the characteristic normal high value, compare the user blood pressure data information with the characteristic normal blood pressure value in the characteristic blood pressure threshold data information, if the user blood pressure data information is higher than the characteristic normal blood pressure value in the characteristic blood pressure threshold data information, determine that the user to be measured has a high blood pressure risk, and output a user high blood pressure second risk early warning result; If the user blood pressure data information is lower than the characteristic normal blood pressure value, determine that the user to be measured does not have a blood pressure abnormality, and output a user blood pressure normal result; Combine the user high blood pressure alarm result, the user high blood pressure first risk early warning result, the user high blood pressure second risk early warning result, and the user blood pressure normal result to form a user blood pressure monitoring result; Display the user blood pressure monitoring result on the user digital twin model. 9.The digital-twin-based blood pressure data management method of claim 8, wherein, The step S6 specifically comprises the following sub-steps: Obtain a wireless communication module and establish a signal connection link between the wireless communication module and the user to be measured; Send the user blood pressure monitoring result and the user digital twin model to a background monitoring system and a user terminal based on the wireless communication module; Upload the user blood pressure monitoring result and the user digital twin model to a blockchain system for storage. 10.A digital-twin-based blood pressure data management system, comprising: The blood pressure data management system based on digital twins is used to implement the blood pressure data management method based on digital twins in any one of claims 1-9, comprising: The user blood pressure detection module is configured to output a user self-ready signal after the state of the user to be detected is adjusted when the user to be detected is in a quiet state, output a device ready signal after an electronic sphygmomanometer is acquired and adjusted, and detect the blood pressure of the user to be detected to obtain user blood pressure data information after receiving the device ready signal; The digital twin model creation module is configured to collect basic information of the user to be detected, create a user digital twin model based on the basic information of the user to be detected, and store the user digital twin model; The influence factor analysis module is configured to analyze the influence of the daily life habits of the user to be detected on the blood pressure of the user to be detected to obtain a life habit influence coefficient, judge the influence of the medical history and medication history of the user to be detected on the blood pressure of the user to be detected to obtain a historical disease influence coefficient; The blood pressure threshold analysis module is configured to acquire blood pressure standard data information, adjust the blood pressure standard data information based on the life habit influence coefficient and the historical disease influence coefficient to obtain characteristic blood pressure threshold data information; The blood pressure abnormality judgment module is configured to compare the user blood pressure data information with the characteristic blood pressure threshold data information, analyze the blood pressure of the user to be detected, and output a user blood pressure monitoring result; The communication storage module is configured to send the user blood pressure monitoring result and the user digital twin model to a background monitoring system and a user terminal, and upload the user blood pressure monitoring result to a blockchain system for storage.

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