Individualized precision control method for reducing blood pressure of renal hypertension

Abstract

The application discloses a precise regulation and control method for individual blood pressure reduction of a renal hypertension subject, and belongs to the technical field of medical treatment.The method collects full-dimension behavior data of the patient, such as sleep, diet and medication, in real time every day, carries out comprehensive evaluation in combination with physiological and genetic data, and formulates an initial individualized scheme; the data are processed through a cloud big data platform, abnormalities are identified, four-level alarms are triggered, and localized medical treatment and relative feedback are linked; in combination with a pre-door prompt light early warning and humanistic care, the scheme is dynamically fine-tuned every day, and a complete closed-loop management is formed.The application solves the problems of individualization deficiency, early warning lag and poor compliance of the prior art, realizes the combination of precise blood pressure reduction and humanistic care, is especially suitable for solitary old people, can improve the blood pressure control effect and protect the kidney function, and has remarkable clinical value.

Description

Technical Field

[0001] This invention relates to the field of medical and nursing technology, specifically to a precise method for individualized blood pressure control in patients with renal hypertension. Background Technology

[0002] Renal hypertension is a secondary hypertension caused by parenchymal and renal vascular lesions. It is characterized by high incidence, difficulty in control, dangerous complications, and rapid deterioration of renal function. Clinically, patients with renal hypertension commonly exhibit large blood pressure fluctuations, nocturnal hypertension, morning hypertension, salt sensitivity, and sympathetic nervous system excitation. These characteristics are highly dependent on the patient's daily behavior. Furthermore, elderly individuals living alone, due to a lack of supervision and weak self-management abilities, are prone to non-compliance with medical advice, missed medications, and neglect of behavioral interventions, leading to uncontrolled blood pressure and worsening renal function. This poses a significant challenge and focus in the diagnosis and treatment of renal hypertension.

[0003] Current technologies and clinical guidelines have the following insurmountable shortcomings: Focusing only on physiological indicators such as blood pressure, kidney function, and electrolytes, completely ignoring the real-time impact of individual behavior on blood pressure, and failing to establish a deep correlation between behavioral data and blood pressure fluctuations; The assessment cycle is based on weeks or months, which makes it impossible to achieve daily real-time monitoring and same-day early warning, and makes it difficult to detect abnormal behavior and blood pressure fluctuations in patients in a timely manner. Blood pressure reduction plans are mostly fixed prescriptions and cannot be adjusted in real time according to behaviors such as lack of sleep, high-salt diet, missed medication, staying up late, and emotional fluctuations, resulting in insufficient individualization. The lack of a behavior-blood pressure-renal function linkage model makes it impossible to predict the risk of blood pressure runaway in advance, and the absence of an effective monitoring mechanism results in poor patient compliance. The lack of centralized cloud-based data processing makes it impossible to quickly identify abnormal data and behaviors, and it also lacks integration with localized medical services, making it impossible to provide timely on-site intervention when abnormal situations occur. The importance of family supervision has been neglected, a family linkage mechanism has not been established, and a dual supervision system of "medical staff + family members" cannot be formed; The lack of intuitive and easily identifiable on-site early warning methods makes it difficult to remind elderly people living alone and other groups to follow medical advice in a simple and easy-to-understand way. This lack of humanistic care can easily lead to delays in treatment.

[0004] Currently, no existing technology or patent has achieved: daily real-time collection, aggregation, and analysis of behavioral data from all scenarios of patients with renal hypertension; real-time feedback on blood pressure status; tiered early warning; and daily adjustments to treatment plans. This is combined with cloud-based big data processing, localized medical service linkage, family monitoring, and door-to-door warning lights to form a complete system of "data collection - cloud analysis - early warning push - multi-party intervention - humanistic care." This invention fills this gap, possessing outstanding substantive features and significant progress, especially addressing the pain points of managing renal hypertension in elderly people living alone.

[0005] Therefore, we propose a precise regulation method for individualized blood pressure reduction in renal hypertension. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a precise regulation method for individualized blood pressure reduction in renal hypertension, thus solving the problems mentioned in the background art.

[0007] 1. A precise regulation method for individualized blood pressure reduction in renal hypertension, characterized by the following steps: S0: Daily real-time collection and summarization of comprehensive personal behavioral data, including patients' sleep, rest, activities, diet, water intake, medication, emotions, excretion and compliance with medical orders, summarizing daily and archiving with timestamps; S1: Multidimensional individualized comprehensive assessment, combining behavioral data, etiological classification, renal function classification, gene polymorphism, RAAS system activity, comorbidities and behavioral compliance to comprehensively classify and risk-stratify patients; S2: Develop an initial individualized blood pressure reduction plan, combining comprehensive classification, risk stratification, genetic polymorphism and behavioral habits to develop drug treatment and non-drug intervention plans; S3: Real-time monitoring of behavioral and physiological data and cloud-based big data processing. Uploads behavioral and physiological data to the cloud-based big data platform in real time. Uses intelligent algorithms for data preprocessing, linkage analysis, trend prediction and anomaly identification to identify abnormal data, abnormal behavior and compliance abnormalities. S4: Abnormal alarm and multi-party linkage feedback. When an alarm of the corresponding level is triggered, the alarm information is pushed to the local service center, the patient's relatives and the patient himself. The local service center includes community health service centers and local health centers. Local medical staff provide door-to-door service according to the alarm level. S5: Doorway indicator light warning and humanistic care. Install smart indicator lights in front of the patient's door. The corresponding light status is triggered according to the patient's compliance and alarm level. Ordinary people can also use the light status to provide psychological counseling and humanistic care to the patient and urge the patient to follow the doctor's advice. Especially for elderly people living alone, it can effectively monitor the physical and mental health of the elderly. S6: Real-time feedback and multi-level intelligent early warning, daily feedback on the patient's blood pressure control status and behavior compliance rate, and activation of four levels of early warning: yellow, orange, red, and critical, which are linked with alarm mechanisms and indicator light warnings; S7: Dynamic and precise adjustment based on daily behavioral data. The blood pressure reduction plan is fine-tuned on the same day based on the daily behavioral data, cloud analysis results, warning level and on-site service feedback. S8: Long-term follow-up and closed-loop management, develop personalized follow-up plans, and form a complete closed loop of "data collection → comprehensive evaluation → plan development → cloud processing → anomaly alarm → multi-party intervention → dynamic adjustment → follow-up optimization".

[0008] Furthermore, in step S0, the collection of behavioral data adopts a multi-terminal linkage collection mode of "wearable device + mobile APP + home smart monitoring device + smart terminal". Wearable device automatically collects sleep, activity and heart rate data, smart APP collects diet, mood and excretion data, smart pillbox collects medication data, and home monitoring device collects physiological data such as blood pressure, weight and urine output. All data is synchronized to the mobile APP in real time, automatically summarized, deduplicated and classified to generate "Behavioral-Physiological Daily Report", and pushed to patients, relatives and cloud big data platform. For groups with inconvenient operation, such as elderly people living alone, community medical staff and relatives can assist in entering some data.

[0009] Furthermore, in step S1, the multidimensional individualized comprehensive assessment specifically includes: classifying patients into three etiological types—renal parenchymal hypertension, renovascular hypertension, and hypertension associated with hereditary kidney disease—through medical history collection, physical examination, laboratory tests, and imaging examinations; classifying renal function into grades 1-5 based on estimated glomerular filtration rate, urinary albumin excretion rate, serum creatinine, and blood urea nitrogen levels; collecting peripheral blood samples from patients to detect polymorphisms in the ACE, AGTR1, CYP2D6, and CYP3A5 genes; assessing the degree of RAAS system activation by detecting renin, angiotensin II, and aldosterone levels in conjunction with the captopril test; assessing whether patients have complications such as diabetes or coronary heart disease and their severity; assessing patient behavioral compliance based on behavioral data collected in S0 and classifying it into high, medium, and low grades; and comprehensively analyzing the above data using analytic hierarchy process (AHP) to complete the comprehensive patient classification and risk stratification.

[0010] Furthermore, in step S2, the initial individualized antihypertensive regimen includes a drug treatment regimen and a non-drug intervention regimen. The drug treatment regimen follows the principles of "individualized drug selection, precise dosage, combination therapy, and kidney protection," selecting drugs with high sensitivity and few adverse reactions based on gene polymorphism, adjusting the initial dose based on renal function classification and risk classification, and selecting long-acting, intermediate- or short-acting, or fixed-drug formulations according to the patient's work-rest patterns and medication adherence, while avoiding drug contraindications. The non-drug intervention regimen includes interventions in diet, exercise, sleep, medication, emotions, and lifestyle habits, with clear specific implementation standards and monitoring requirements for each intervention item.

[0011] Furthermore, in step S3, the cloud-based big data platform is equipped with an intelligent algorithm that integrates BP neural networks and support vector machines. Data preprocessing includes cleaning, noise reduction, outlier removal, missing data supplementation, and standardization. Linkage analysis constructs a linkage model of "behavioral data-physiological data-blood pressure fluctuations" to analyze the correlation between various behaviors and blood pressure fluctuations and changes in renal function. Trend prediction predicts the patient's blood pressure change trend for the next 1-3 days based on historical and real-time data. Anomaly identification includes abnormal physiological data, abnormal behavior, and abnormal compliance. Abnormal physiological data includes sudden rises and falls in blood pressure, electrolyte imbalances, etc. Abnormal behavior includes consecutive missed medications, insufficient sleep, excessive sodium intake, etc. Abnormal compliance includes three consecutive failures to take blood pressure-lowering interventions as required and a compliance score that is consistently below 60.

[0012] Furthermore, in step S4, the alarm levels are divided into four levels: yellow, orange, red, and critical, which are linked to the warning levels in S6. A yellow alarm corresponds to a general abnormality, and local medical staff will arrive at the scene within 24 hours; an orange alarm corresponds to a moderate abnormality, and will arrive at the scene within 12 hours; a red alarm corresponds to a severe abnormality, and will arrive at the scene within 6 hours; a critical alarm corresponds to an extremely severe abnormality, and will arrive at the scene within 1 hour and contact the emergency center. The alarm information is simultaneously pushed to the local service center, the patient's relatives, and the patient. After the on-site service, the medical staff will upload the relevant records to the cloud big data platform and push them to the patient and relatives simultaneously.

[0013] Furthermore, in step S5, the intelligent indicator light is linked with the cloud-based big data platform and the patient's mobile app, and has three light states: green, yellow, and red. The green light corresponds to good patient compliance and stable blood pressure on the day; the yellow light corresponds to minor abnormal behavior or mild lack of compliance; and the red light corresponds to abnormal compliance or a red or critical alarm, flashing continuously for easy identification. For elderly people living alone, medical staff provide psychological counseling when they visit, community volunteers assist in completing relevant interventions, community medical staff conduct regular visits, and relatives are urged to visit regularly and monitor remotely.

[0014] Furthermore, in step S6, the real-time feedback includes today's blood pressure control status, behavioral target achievement rate, renal function load status, tomorrow's blood pressure prediction trend, and the current status of the warning light; the four-level warning is as follows: yellow warning corresponds to insufficient sleep + high sodium levels or a single missed medication dose; orange warning corresponds to missed medication dose + increased nocturia or two consecutive days of insufficient sleep and slightly elevated blood pressure; red warning corresponds to consecutive allergic reactions + high-salt diet + emotional excitement and elevated blood pressure or two consecutive failures to follow the intervention plan as required; and critical warning corresponds to blood pressure ≥180 / 120 mmHg accompanied by discomfort symptoms or serious complications.

[0015] Furthermore, the S9 quality control steps include: regularly calibrating various data acquisition devices and establishing a data audit mechanism; regularly optimizing cloud-based big data algorithm parameters and establishing a data security mechanism; clarifying alarm triggering standards and on-site service time limits at all levels, establishing an assessment mechanism for on-site services by medical staff, and regularly checking the operating status of indicator lights; and establishing a control plan review mechanism, with a multidisciplinary expert group reviewing the plan, regularly evaluating the control effect, and optimizing the strategy.

[0016] The present invention has the following beneficial effects: 1. A dual-driven assessment and regulation system based on "full-dimensional behavioral data + physiological data" has been constructed, breaking through the existing single assessment mode based solely on physiological indicators. For the first time, all behavioral data such as sleep, diet, activity, and medication are incorporated into the core of regulation. Combined with gene polymorphism and kidney function status, it achieves individualized and precise assessment, solving the problem of insufficient targeting in existing technologies.

[0017] 2. It pioneered a multi-party intervention system that combines cloud-based big data processing, localized service linkage, family supervision, and doorstep warning lights. This system fills the gap in existing technologies that lack multi-channel early warning and intervention mechanisms, enabling real-time identification, rapid alarm, and multi-party linkage intervention of abnormal data and behaviors. In particular, it addresses the pain point of "no supervision and no intervention" in the management of renal hypertension in elderly people living alone.

[0018] 3. It has achieved real-time management of the entire process, including "daily real-time data collection, real-time cloud processing, real-time alarm for anomalies, real-time intervention by multiple parties, and real-time fine-tuning of the plan". It has broken through the static evaluation and control mode of traditional technology based on weeks and months, and achieved "same-day behavior, same-day analysis, same-day warning, and same-day adjustment", which has significantly improved the accuracy and timeliness of pressure reduction control.

[0019] 4. An innovative door-to-door warning light mechanism is introduced, using intuitive and easily identifiable light signals to achieve on-site early warning, making it easier for ordinary people, relatives, and community workers to promptly detect abnormalities in patients. At the same time, combined with humanistic care and guidance, it strengthens attention and support for elderly people living alone, achieving the dual goal of "medical treatment + humanistic care". This is an innovative point that has not been addressed by existing technologies.

[0020] 5. A triple supervision system of "medical staff + community + relatives" has been established. Through cloud data feedback, home visits, and family linkage, a comprehensive supervision network is formed, which effectively improves patients' treatment compliance and solves the core problems of patients not following medical advice, missing medications, and ignoring behavioral interventions in clinical practice.

[0021] 6. A "behavior-physiology-blood pressure" linkage model was constructed, which uses cloud-based big data algorithms to predict blood pressure trends and provide early warnings of abnormalities. It can identify the risk of blood pressure loss of control in advance, achieve "proactive prediction, early detection, and early intervention", avoid the deterioration of the condition, and reduce the incidence of cardiovascular and cerebrovascular complications and renal failure, which has significant clinical value.

[0022] 7. It has achieved centralized data management and multi-party sharing. The cloud-based big data platform integrates patients' behavioral data, physiological data, treatment plans, and home service records, enabling centralized storage and on-demand access to data. This facilitates medical staff to fully understand patients' conditions, optimize treatment plans, and synchronize information among patients, relatives, and medical staff, thereby improving management efficiency. Detailed Implementation

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] S0 Comprehensive Personal Behavioral Data Collection and Summary (Daily Real-Time) The system automatically / semi-automatically collects all behavioral data of patients that may affect fluctuations in renal hypertension, summarizes them daily, adds timestamps, and permanently archives them, providing basic data support for subsequent cloud processing, early warning, and control.

[0025] S01 Content Collection Sleep behavior data: sleep onset time, wake-up time, total sleep duration, sleep depth, number of awakenings during the night, snoring, whether you stay up late, and nap duration; Daily activity and exercise data: daily steps, sedentary time, type of exercise, exercise intensity, start and end time of exercise, level of fatigue, peak physical load; Dietary behavior data: meal times, eating speed, sodium intake, water intake, water drinking time, protein intake, intake of high-potassium / high-phosphorus foods, alcohol, coffee, strong tea, snacks, binge eating, and nighttime eating; Medication behavior data: medication time, whether it is on time, whether it is missed, whether it is made up, diet before and after medication, changes in body position after medication, and compliance score; Emotional and Stress Data: Periods of emotional fluctuation, tension, anxiety, excitement, anger, work stress, and stressful events; Excretion behavior data: 24-hour urine volume, daytime urination frequency, nighttime urination frequency, urine color, and foamy urine. Environmental and emergency data: temperature changes, sudden changes in body position, long-distance travel, fatigue, colds, pain and other stimuli; Follow the doctor's orders for data: whether you adjust your diet, exercise, and medication as required by medical staff, and whether you complete blood pressure monitoring on time.

[0026] S02 Data Acquisition Method The system employs a multi-terminal collaborative data collection model, combining wearable devices, a mobile app, home smart monitoring equipment, and smart terminals, to ensure real-time, accurate, and convenient data collection. Wearable devices: smart bracelets, sleep belts, which automatically collect data such as sleep, activity, and heart rate and upload it to a mobile app in real time; Smart terminal APP: Supports voice input, image recognition, and quick selection of preset food databases for collecting data on diet, mood, excretion, etc., and also supports manual supplementation of abnormal situations. Smart pillbox: Features timed reminders, QR code confirmation, automatically records medication time and missed doses, and uploads the data to the app. Home monitoring devices: smart blood pressure monitors, weight scales, urine analyzers, which automatically collect physiological data such as blood pressure, weight, and urine output, and upload them synchronously with behavioral data; Assisted data collection: For groups with limited operational experience, such as elderly people living alone, community medical staff or relatives can assist in entering some data to ensure comprehensive data collection. All data is synchronized to the mobile app in real time, automatically summarized, deduplicated, and categorized, generating a daily "Behavioral-Physiological Daily Report," which is simultaneously pushed to patients, relatives, and the cloud-based big data platform.

[0027] S1 Multidimensional Individualized Comprehensive Assessment System Based on behavioral data, further multi-dimensional data on patients' physiology, pathology, and genes are collected and combined with behavioral data for comprehensive evaluation, providing a basis for initial treatment plan development and subsequent regulation. Etiological classification assessment: Based on medical history collection, physical examination, laboratory tests, and imaging examinations, patients are classified into three types: renal parenchymal hypertension, renovascular hypertension, and hypertension associated with hereditary kidney disease. Renal function classification assessment: Based on the estimated glomerular filtration rate, urinary albumin excretion rate, serum creatinine, and blood urea nitrogen levels, the patient's renal function is classified into grades 1-5 to assess the degree of kidney damage; Gene polymorphism assessment: Peripheral blood samples were collected from patients to detect gene polymorphisms related to the pathogenesis and drug metabolism of renal hypertension, such as ACE gene, AGTR1 gene, CYP2D6 gene, and CYP3A5 gene. RAAS system activity assessment: The degree of RAAS system activation was assessed by detecting renin, angiotensin II, and aldosterone levels, combined with the captopril test. Comorbidity assessment: Assess whether the patient has complications such as diabetes, coronary heart disease, heart failure, electrolyte imbalance, cerebrovascular disease, etc., and determine the type and severity of the comorbidities; Behavioral adherence assessment: Based on behavioral data collected at S0, the patient's adherence to previous medical orders is assessed and categorized as high, moderate, or low adherence. The analytic hierarchy process (AHP) is used to comprehensively analyze the multi-dimensional data to classify and stratify the patient's risk.

[0028] S2 Initial Personalized Blood Pressure Reduction Protocol Based on Behavior and Genetics Based on the patient's comprehensive classification, risk stratification, genetic polymorphism, behavioral habits, and compliance, an initial individualized antihypertensive treatment plan is developed, which is divided into drug treatment plan and non-drug intervention plan to ensure the plan is targeted and feasible.

[0029] The S21 drug treatment regimen follows the principles of "individualized drug selection, precise dosage, combination therapy, and kidney protection." It selects appropriate antihypertensive drugs, dosages, and administration methods based on the patient's comprehensive classification, gene polymorphism, RAAS activity, comorbidities, and behavioral habits. Drug selection: Prioritize commonly used antihypertensive drugs such as ACEI / ARBs, CCBs, beta-blockers, and diuretics. Combine gene polymorphism results to select drugs with high sensitivity and few adverse reactions. Dosage adjustment: The initial dose is adjusted based on renal function classification, gene polymorphism, and risk classification. Low-risk and high-compliance patients use the conventional dose, while medium- and high-risk and low-compliance patients start with a small dose and gradually adjust it. Administration method: Based on the patient's daily routine and medication adherence, choose long-acting, intermediate- or short-acting, or fixed-dose combination preparations to reduce the frequency of medication and improve adherence; Avoidance of contraindications: Clearly identify drug contraindications, such as ACEI / ARB drugs should not be used in patients with hyperkalemia or bilateral renal artery stenosis, and beta-blockers that affect blood glucose should be avoided in diabetic patients.

[0030] The S22 non-pharmacological intervention program, based on behavioral data and comprehensive assessment results, develops personalized non-pharmacological intervention plans that work synergistically with drug therapy. It also clearly defines specific implementation standards and monitoring requirements to facilitate subsequent evaluation and early warning. Dietary intervention: Based on the renal function classification and comorbidities, set daily sodium intake targets, water intake targets, and protein intake targets, and clearly define the types of foods that are prohibited or restricted; Exercise intervention: Based on the patient's renal function, exercise capacity, and daily routine, determine the number of exercise sessions per week, exercise duration, and exercise intensity, and clearly define the types of strenuous exercise that should be prohibited; Sleep intervention: Define the target for bedtime, wake-up time, and total sleep duration, and develop specific measures to improve sleep quality; Medication intervention: Clearly define the medication time, dosage, and precautions before and after medication; set medication reminders; and clarify the rules for handling missed doses and make-up doses. Emotional and lifestyle intervention: Develop an emotion regulation plan, clarify requirements for quitting smoking and limiting alcohol consumption, and avoid fatigue and sudden changes in body position.

[0031] S3 Real-time Monitoring of Behavioral and Physiological Data and Cloud-based Big Data Processing This step involves adding a core technical solution to achieve centralized data processing and anomaly identification, providing technical support for subsequent early warning and intervention. Specifically, it includes three stages: data uploading, cloud processing, and anomaly identification. S31 data is uploaded in real time. It integrates the comprehensive behavioral data collected in S0, the comprehensive assessment data collected in S1, the initial treatment plan data developed in S2, and real-time physiological data such as blood pressure, kidney function, electrolytes, and drug concentrations. This data is wirelessly transmitted to a cloud-based big data platform to create a digital twin health record for each patient, enabling centralized storage, permanent archiving, and on-demand access to the data. Simultaneously, the data is also uploaded to the patient's mobile app, related family member terminals, and local service center data terminals.

[0032] S32 cloud-based big data processing: The cloud-based big data platform is equipped with intelligent algorithms that integrate BP neural networks and support vector machines to perform real-time processing and in-depth analysis of uploaded behavioral and physiological data, specifically including: Data preprocessing: Cleaning and denoising the collected data, removing outliers such as measurement errors and data entry errors, supplementing missing data, standardizing the data, and converting it into a format that can be used for algorithm analysis; Linkage analysis: Construct a linkage model of "behavioral data-physiological data-blood pressure fluctuations" to analyze the correlation between various behavioral data and blood pressure fluctuations and changes in renal function; Trend prediction: Based on historical and real-time data, predict the patient's blood pressure change trend in the next 1-3 days and identify the risk of blood pressure loss of control in advance; Adherence assessment: Real-time analysis of patients' adherence to the treatment plan developed by S2, automatic generation of daily adherence scores, and determination of whether there are any cases of non-compliance.

[0033] S33 Abnormal Data and Behavior Recognition: The cloud-based big data platform identifies abnormal data and behaviors in real time based on preset anomaly judgment criteria, triggering an alarm mechanism, specifically including: Abnormal data identification: sudden rise in blood pressure, sudden drop in blood pressure, Scr increase >20%, eGFR decrease >10%, hyperkalemia, hypokalemia, and other abnormal physiological data; Abnormal behavior identification: missed medication ≥2 times, sleep <6 hours for 3 consecutive days, excessive sodium intake for 3 consecutive days, failure to exercise as required for 2 consecutive days, severe emotional fluctuations lasting more than 2 hours, sudden increase in nocturia ≥3 times, etc. Identification of abnormal compliance: failure to perform blood pressure reduction intervention as required by medical staff for 3 consecutive times, and compliance score consistently below 60.

[0034] The S4 anomaly alarm and multi-party collaborative feedback system is a new core technology solution that enables multi-channel push notifications for anomaly alarms, linking local service centers and patients' relatives to form a multi-party intervention system. This system specifically includes three stages: alarm triggering, multi-party feedback, and home service. When the S41 abnormal alarm is triggered, the cloud big data platform will immediately trigger the corresponding alarm level when it identifies abnormal data, abnormal behavior or abnormal compliance as described in S33. The alarm level is linked to the warning level of S5 and is divided into yellow alarm, orange alarm, red alarm and critical alarm. Different levels correspond to different feedback and intervention methods.

[0035] S42 multi-party linkage feedback: alarm information is pushed synchronously through multiple channels to ensure that relevant personnel receive it in a timely manner, achieving multi-party linkage. Local service center feedback: The alarm information is pushed to the service terminals of the patient's community health service center and local health center in real time, and a text message is sent to remind medical staff, clearly indicating the urgency of the need for home visit service; Patient's family feedback: Alarm information is simultaneously pushed to the pre-bound terminal of the patient's family member, informing the family member of the patient's abnormal condition and current risks, urging the family member to communicate with the patient in a timely manner, and supervising the patient to follow the doctor's advice to carry out antihypertensive intervention; Patient feedback: Alarm information is pushed to patients via mobile APP pop-ups and voice reminders, clearly informing them of the abnormal content, risk warnings, and preliminary treatment suggestions.

[0036] S43 Localized Healthcare Personnel Home Visit Service: Upon receiving an alarm message, healthcare personnel from the local service center will provide home visit service within a specified timeframe, based on the alarm level. Specific service content includes: Yellow Alert: We will visit the patient within 24 hours to verify the patient's abnormal condition, remind the patient to correct the abnormal behavior, reiterate the doctor's orders, and assist the patient in adjusting the intervention plan for the day. Orange Alert: We will arrive at your home within 12 hours to verify abnormal data and behavior, measure the patient's blood pressure, check their physical condition, guide the patient in emergency treatment, and adjust the short-term treatment plan. Red Alert: We will arrive at your home within 6 hours to comprehensively assess your condition, monitor indicators such as blood pressure and kidney function, take emergency blood pressure reduction interventions, and contact a higher-level hospital for referral if necessary. Emergency Alert: We will arrive at your home within one hour to provide emergency treatment and simultaneously call for ambulance to assist in transferring the patient to a higher-level hospital to prevent the condition from worsening. After the home visit, medical staff will upload the service record, the patient's current status, and the adjusted intervention plan to the cloud-based big data platform in real time, update the patient's health record, and simultaneously push the information to the patient and their relatives.

[0037] The S5 door-to-door warning light and humanistic care: This step involves a new core technology solution that provides intuitive and easily identifiable on-site warnings for groups such as elderly people living alone, enhancing humanistic care and urging patients to follow medical advice. Specifically, it includes three stages: warning light installation, warning triggering, and humanistic guidance. S51 Indicator Light Installation and Binding A smart alert light was installed in front of the patient's residence. This light is linked to a cloud-based big data platform and the patient's mobile app, offering three lighting modes with moderate brightness and highly visible colors for easy identification by the average person while minimizing disruption to nearby residents. The light is also linked to the patient's personal information, responding only to any abnormal activity occurring in that specific patient, ensuring targeted alerts.

[0038] The S52 alert light triggering rules are as follows: the alert light status is triggered based on patient compliance and alarm level, and the specific rules are as follows: Green light: The patient's compliance is good on the day, with no abnormal data or behaviors, and blood pressure is stable, indicating that "the patient's current blood pressure is under normal control and compliance is good"; Yellow light: The patient exhibits mild abnormal behavior or slight lack of compliance, indicating that "the patient needs to adjust their behavior and follow the doctor's instructions to implement antihypertensive intervention." Red light: When a patient exhibits the compliance abnormality described in S33, i.e., fails to perform blood pressure reduction intervention as required by medical staff three times in a row, or triggers a red or emergency alarm, the indicator light will flash red continuously to facilitate identification by passing relatives, neighbors, and community workers, so as to promptly remind the patient or contact medical staff.

[0039] The warning light's status is synchronized with the cloud-based big data platform, allowing healthcare professionals and family members to view the light's status in real time and understand the patient's current compliance. Once the patient corrects abnormal behavior and restores good compliance, the warning light automatically switches to green.

[0040] S53 Humanistic care and guidance for elderly people living alone: ​​Targeting this group, this approach combines alert lights and home visits to enhance humanistic care and improve patient compliance. Counseling services: When medical staff visit, they not only provide medical intervention, but also provide psychological counseling to elderly people living alone, explaining the dangers of renal hypertension and the importance of following medical advice, relieving the elderly's anxiety and loneliness, and enhancing their confidence in treatment. Assistance in implementation: For elderly people living alone with limited mobility, medical staff and community volunteers assist them in taking medication, monitoring blood pressure, preparing meals, and other tasks to ensure that the intervention plan is implemented. Regular visits: Even if no alarm is triggered, community medical staff should still make regular visits to elderly people living alone to understand their living conditions and treatment status, and to identify potential problems in a timely manner. Family linkage: Encourage relatives to visit elderly people living alone regularly, view the elderly's behavior data and indicator light status in real time through connected terminals, and remotely remind the elderly to follow medical advice, forming a humanistic care system of "medical care + community + relatives".

[0041] S6 Real-time Feedback and Multi-level Intelligent Early Warning The system automatically calculates the blood pressure risk index and compliance score daily, and combines the results of cloud-based big data analysis to provide real-time feedback on the patient's current blood pressure status. It also activates tiered alerts, linking with the alarm mechanism of S4 and the indicator light warning of S5 to achieve multi-dimensional early warning: S61 real-time feedback includes: Today's blood pressure control status: Excellent, Good, Average, Hazardous; Today's behavioral target achievement rate: scores for the implementation of various behaviors such as sleep, diet, exercise, and medication; Today's renal function load status: assessing the current renal load based on renal function indicators and behavioral data, and providing kidney protection suggestions; Tomorrow's blood pressure prediction trend: predicting tomorrow's blood pressure changes based on today's behavioral data and historical data, and providing targeted intervention suggestions; Current status of the indicator light: synchronously displaying the status of the indicator light in front of the door to remind the patient and relatives to pay attention.

[0042] The S62 multi-level early warning mechanism has early warning levels that correspond to the alarm levels of S4, as detailed below: Yellow alert: Sleep <6 hours + high sodium levels, or missed medication dose, with no obvious blood pressure abnormalities; This indicates that the patient's blood pressure is likely to rise tonight. It is recommended to go to bed early, eat a light diet, increase water intake, and have family members help remind the patient; The alert light should remain yellow. Orange alert: Missed medication + increased urination at night, or two consecutive days of sleep deprivation, with a slight increase in blood pressure; This indicates that the patient may experience morning hypertension tomorrow morning. It is recommended to take the missed medication and strengthen blood pressure monitoring. Medical staff are preparing to visit the patient to verify the situation. The warning light will remain yellow. If the condition is not corrected within 24 hours, it will switch to flashing red. Red alert: Consecutive sleep deprivation + high-salt diet + emotional excitement + elevated blood pressure, or failure to follow the intervention plan twice in a row; alert information will be sent to medical staff and relatives, and medical staff will provide home intervention within 6 hours; the indicator light will continue to flash red. Critical warning: Blood pressure ≥180 / 120 mmHg + headache, chest tightness + sudden increase in urination at night, or serious complications such as hyperkalemia; immediately send the critical warning to medical staff and relatives, medical staff will provide emergency care within 1 hour, and contact the emergency center at the same time; the indicator light will flash red continuously and emit a slight warning sound.

[0043] S7's dynamic and precise control based on daily behavioral data Based on daily behavioral data, cloud-based big data analysis results, warning levels, and feedback from on-site services, the blood pressure lowering plan is dynamically adjusted daily to achieve individualized and precise control, ensuring stable blood pressure control while protecting kidney function. High blood pressure but not at target level: If the patient has behavioral abnormalities such as high-salt diet or insufficient sleep, prioritize adjusting the non-pharmacological intervention plan; if the behavior is at target level but blood pressure is still high, slightly adjust the drug dosage or adjust the medication time. Large fluctuations in blood pressure: If the fluctuations are related to rest and medication time, adjust to a long-acting formulation or take medication at different times; if they are related to emotions and exercise, strengthen emotional management and adjust the exercise plan. After a high-salt diet: temporarily increase the dosage of diuretics or water intake to promote sodium excretion, while strengthening blood pressure monitoring to avoid blood pressure rise; After staying up all night: Strengthen blood pressure monitoring the next morning and appropriately increase kidney protection interventions. If morning hypertension occurs, temporarily adjust the medication time to an empty stomach in the morning. If medication is missed: The system intelligently determines the missed time and type of medication, provides suggestions for making up the missed dose, and reminds relatives to supervise medication administration. For poor adherence and repeated triggering of red alerts: The intervention plan is adjusted, the medication administration process is simplified, the frequency of warning lights is increased, home visits by medical staff and supervision by relatives are strengthened, and community volunteers assist in implementing the intervention plan when necessary. Through the above implementation methods, this invention achieves real-time perception, accurate calculation, rapid response, intelligent collaboration, and reliable management of building energy efficiency and carbon emissions, effectively supporting the green and low-carbon operation of buildings. S8 Long-term Follow-up and Closed-Loop Management Based on the patient's risk stratification and compliance, a personalized follow-up plan is developed. This plan, combined with cloud data feedback, home visits, and family supervision, forms a complete closed-loop management system: "data collection → comprehensive assessment → plan development → cloud processing → anomaly alerts → multi-party intervention → dynamic adjustment → follow-up optimization," ensuring long-term treatment effectiveness. Follow-up frequency: Low-risk, high-compliance patients are followed up once a month; medium-risk, medium-compliance patients are followed up once every two weeks; high-risk, low-compliance patients are followed up once a week; elderly people living alone will receive an additional monthly follow-up for humanistic care. Follow-up methods: A combination of in-person and online follow-up to comprehensively understand the patient's treatment progress, behavioral adherence, and symptom changes; Data feedback and plan optimization: During follow-up visits, patient monitoring data, treatment compliance data, and home service records are collected and fed back to the cloud big data platform. This data is then compared with previous assessment data and treatment plans to analyze treatment effectiveness and further optimize the assessment system, control plan, and early warning rules. Patient education and compliance improvement: During follow-up visits, health education is conducted for patients and their families, explaining the pathogenesis of renal hypertension, key points of treatment, identification and management of adverse drug reactions, and the importance of lifestyle intervention. For elderly people living alone, the warning significance of the indicator light is explained in detail to improve the awareness and compliance of patients and their families. Closed-loop optimization: Through continuous follow-up and data feedback, we continuously improve the system of "cloud big data processing - local service linkage - family supervision - warning light alert - dynamic control" to ensure the pertinence and effectiveness of the control plan and achieve long-term precise management of patients with renal hypertension.

[0044] S9 Quality Control Steps To ensure the accuracy and reliability of individualized assessment, data collection, cloud processing, early warning intervention, and dynamic regulation, quality control steps are established to cover the entire regulation process: Data collection quality control: Regularly calibrate wearable devices, home monitoring devices, and laboratory testing equipment to ensure the accuracy of collected data; establish a data review mechanism, with medical staff reviewing uploaded data, removing outliers, and supplementing missing data; Cloud-based processing quality control: Regularly optimize cloud-based big data algorithm parameters, evaluate the accuracy of anomaly identification and trend prediction, and ensure that abnormal data and behaviors can be identified in a timely and accurate manner; establish a data security mechanism to protect patient privacy and prevent data leakage; Early warning and intervention quality control: Clarify the triggering standards for alarms at all levels and the time limit for door-to-door service, establish an assessment mechanism for door-to-door service by medical staff to ensure that door-to-door service is timely and standardized; regularly check the operating status of the door-to-door indicator lights and repair or replace faulty equipment in a timely manner. Treatment plan quality control: Establish a review mechanism for treatment plans, with a panel of experts from nephrology, cardiology, and clinical pharmacy reviewing the optimized plans to ensure their safety and effectiveness; regularly evaluate the effects of treatment and continuously optimize the strategy based on clinical data. Example 1: Patients with renal parenchymal hypertension, high-salt diet, staying up late, and living alone. Patient basic information: Male, 72 years old, living alone, with a 5-year history of chronic glomerulonephritis and a 3-year history of renal hypertension. His blood pressure is poorly controlled. He has a history of staying up late, eating a salty diet, and occasionally missing medications. He has no comorbidities such as diabetes or coronary heart disease. His renal function is classified as grade 2, which is considered a medium-risk patient. His compliance is moderately low.

[0045] The precise control using the method of this invention is as follows: S0: Comprehensive behavioral data collection, which collects sleep data through smart bracelets, dietary data through smart apps, and medication data through smart pillboxes, and uploads them synchronously to the cloud big data platform to generate the "Behavioral-Physiological Daily Report".

[0046] S1: Comprehensive assessment confirmed as a patient with high RAAS activity in the renal parenchyma, intermediate risk, ACE gene DD genotype, RAAS activity, and compliance score of 55.

[0047] S2: Initial treatment plan: ACE inhibitors are selected for drug treatment, taken once daily on an empty stomach in the morning; Non-drug intervention plan: Daily sodium intake ≤3g, water intake 1500ml, 3 times a week, 30 minutes of slow walking each time, and going to bed before 22:30.

[0048] S3: Data upload and cloud processing. Upload behavioral data, blood pressure data, and kidney function data to the cloud. Algorithm analysis shows that: high salt diet + staying up late → high blood pressure, low compliance, failure to control diet and sleep as required for 2 consecutive days, which is identified as abnormal behavior and triggers an orange alarm.

[0049] S4: Abnormal alarm and multi-party feedback. The cloud pushes orange alarm information to community health service centers and patients' relatives. After receiving the reminder, community medical staff will provide home service within 12 hours. After receiving the information, relatives will call to remind the patient to follow the doctor's advice.

[0050] S5: Doorway warning light alert and humanistic care: If the patient fails to follow the intervention plan as required for two consecutive days, the doorway warning light will switch to yellow; after the medical staff visits the elderly, they will provide psychological counseling, explain the harm of high salt and staying up late to blood pressure and kidneys, assist the elderly in adjusting their diet, set a bedtime reminder, and check the operation of the warning light; relatives will increase the frequency of home visits to supervise the elderly's medication and rest.

[0051] S6: Real-time feedback and early warning. The system provides daily feedback on blood pressure control status as "normal" and behavior compliance rate as 60%. The indicator light is yellow and the warning level is orange, suggesting that the elderly go to bed earlier and eat a light diet.

[0052] S7: Dynamic regulation, fine-tuning non-pharmacological intervention plans based on abnormal behavior and blood pressure, increasing daily home visits by community volunteers to remind seniors and assist them in recording their diets; keeping medication dosages unchanged, adjusting medication reminder times to ensure seniors take their medications on time.

[0053] S8: Long-term follow-up, with one follow-up visit per week and one additional humanistic care follow-up visit per month, continuously collecting behavioral and physiological data, analyzing them in real time in the cloud, and gradually optimizing the plan.

[0054] Effects of the treatment: After one week, the patient's sleep duration increased to 7.5 hours, daily sodium intake was controlled to less than 3g, medication was no longer missed, and blood pressure dropped to 135 / 85 mmHg; after two weeks, blood pressure stabilized at 130 / 80 mmHg, compliance score improved to 85 points, and the doorbell indicator light switched to green; after one month, renal function indicators stabilized, no abnormal alarms occurred, the patient developed good eating, sleeping and medication habits, and the family's supervision was significantly improved, achieving the dual goals of precise blood pressure reduction and humanistic care.

[0055] Example 2: Patients with renovascular hypertension, sudden rise in blood pressure after exertion, and poor compliance. Patient basic information: Female, 65 years old, not living alone, with a 4-year history of renal artery stenosis, diagnosed with renal hypertension 2 years ago, with extremely fluctuating blood pressure, daily housework is tiring, does not rest at noon, is easily irritable, frequently misses taking CCB medications, has mild coronary heart disease, renal function grade 3, belongs to high-risk patients, and has low compliance.

[0056] The precise control using the method of this invention is as follows: S0: Comprehensive behavioral data collection, including activity data collected by smart bracelets, emotion data collected by smart apps, and medication data collected by smart pillboxes, all synchronously uploaded to the cloud.

[0057] S1: Comprehensive assessment confirmed as a high-risk patient with renovascular-malignant hypertension, CYP3A5 gene *1 / *3 genotype, high RAAS system activity, coronary heart disease, and compliance score of 45.

[0058] S2: Initial treatment plan: drug therapy is selected from CCB drugs, combined with β-blockers, taken twice daily; non-drug intervention plan: 30-minute rest at noon, avoid high-intensity housework in the afternoon, mood regulation once a day, and low-intensity exercise twice a week.

[0059] S3: Data upload and cloud processing. Cloud algorithm analysis shows: fatigue + emotional excitement + missed medication → sudden rise in blood pressure. Three consecutive failures to take medication and rest as required are identified as abnormal compliance and abnormal behavior, triggering a red alarm.

[0060] S4: Abnormal alarm and multi-party feedback. The cloud pushes red alarm information to the local health center and the patient's relatives. Health center medical staff will provide home service within 6 hours, and relatives will return home in time to supervise the patient's medication and rest.

[0061] S5: Door warning light and humanistic care: If a patient fails to follow the intervention plan as required three times in a row, the door warning light will flash red continuously. After the medical staff arrives at the patient's home, they will measure the patient's blood pressure and implement emergency blood pressure reduction intervention. At the same time, they will provide emotional support to the patient, explain the impact of fatigue and emotional excitement on blood pressure, and guide the patient and their relatives on how to recognize the warning light signal. The relatives will assist the patient with housework and supervise the patient to take medication on time and rest at noon.

[0062] S6: Real-time feedback and early warning. The system reports that the blood pressure control status is "dangerous" and the behavior compliance rate is 40%. The warning level is red, and the indicator light flashes red continuously. It is recommended to rest immediately, take medication, and avoid emotional excitement.

[0063] S7: Dynamic regulation and adjustment of medication regimen, changing amlodipine to a long-acting formulation, once daily, and increasing the frequency of medication reminders; non-drug intervention plan is adjusted to: mandatory 30-minute rest at noon, supervised by relatives, reducing daily housework time, and increasing emotional regulation training; community medical staff visit the patient once every 3 days to assist in monitoring blood pressure and medication.

[0064] S8: Long-term follow-up, weekly follow-up, real-time cloud monitoring of data, timely adjustment of the plan, and strengthening of family supervision and community intervention.

[0065] Treatment Results: After 3 days, the patient's blood pressure dropped to 145 / 90 mmHg, and there were no more missed medications. The patient was able to rest on time at noon, and mood swings decreased. After 1 week, blood pressure stabilized at 135 / 85 mmHg, and the warning light turned green. After 2 weeks, blood pressure was controlled at 130 / 80 mmHg, the adherence score improved to 75, the sudden spikes in blood pressure after exertion no longer occurred, coronary heart disease symptoms were relieved, and renal function indicators stabilized. In addition, throughout this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

Claims

1. A precise regulation method for individualized blood pressure reduction in renal hypertension, characterized in that, Includes the following steps: S0: Daily real-time collection and summarization of comprehensive personal behavioral data, including patients' sleep, rest, activities, diet, water intake, medication, emotions, excretion and compliance with medical orders, summarizing daily and archiving with timestamps; S1: Multidimensional individualized comprehensive assessment, combining behavioral data, etiological classification, renal function classification, gene polymorphism, RAAS system activity, comorbidities and behavioral compliance to comprehensively classify and risk-stratify patients; S2: Develop an initial individualized blood pressure reduction plan, combining comprehensive classification, risk stratification, genetic polymorphism and behavioral habits to develop drug treatment and non-drug intervention plans; S3: Real-time monitoring of behavioral and physiological data and cloud-based big data processing. Uploads behavioral and physiological data to the cloud-based big data platform in real time. Uses intelligent algorithms for data preprocessing, linkage analysis, trend prediction and anomaly identification to identify abnormal data, abnormal behavior and compliance abnormalities. S4: Abnormal alarm and multi-party linkage feedback. When an alarm of the corresponding level is triggered, the alarm information is pushed to the local service center, the patient's relatives and the patient himself. The local service center includes community health service centers and local health centers. Local medical staff provide door-to-door service according to the alarm level. S5: Doorway indicator light warning and humanistic care. Install smart indicator lights in front of the patient's door. The corresponding light status is triggered according to the patient's compliance and alarm level. Ordinary people can also use the light status to provide psychological counseling and humanistic care to the patient and urge the patient to follow the doctor's advice. Especially for elderly people living alone, it can effectively monitor the physical and mental health of the elderly. S6: Real-time feedback and multi-level intelligent early warning, daily feedback on the patient's blood pressure control status and behavior compliance rate, and activation of four levels of early warning: yellow, orange, red, and critical, which are linked with alarm mechanisms and indicator light warnings; S7: Dynamic and precise adjustment based on daily behavioral data. The blood pressure reduction plan is fine-tuned on the same day based on the daily behavioral data, cloud analysis results, warning level and on-site service feedback. S8: Long-term follow-up and closed-loop management, develop personalized follow-up plans, and form a complete closed loop of "data collection → comprehensive evaluation → plan development → cloud processing → anomaly alarm → multi-party intervention → dynamic adjustment → follow-up optimization".

2. The method according to claim 1, characterized in that, In step S0, behavioral data collection adopts a multi-terminal linkage collection mode of "wearable device + mobile APP + home smart monitoring device + smart terminal". Wearable device automatically collects sleep, activity and heart rate data, smart APP collects diet, mood and excretion data, smart pillbox collects medication data, and home monitoring device collects physiological data such as blood pressure, weight and urine output. All data is synchronized to the mobile APP in real time, automatically summarized, deduplicated and classified to generate "Behavioral-Physiological Daily Report", and pushed to patients, relatives and cloud big data platform. For elderly people living alone and other groups with inconvenient operation, community medical staff and relatives can assist in entering some data.

3. The method according to claim 1, characterized in that, In step S1, the multidimensional individualized comprehensive assessment specifically includes: classifying patients into three etiological types—renal parenchymal hypertension, renovascular hypertension, and hypertension associated with hereditary kidney disease—through medical history collection, physical examination, laboratory tests, and imaging examinations; classifying renal function into grades 1-5 based on estimated glomerular filtration rate, urinary albumin excretion rate, serum creatinine, and blood urea nitrogen levels; collecting peripheral blood samples from patients to detect polymorphisms in the ACE, AGTR1, CYP2D6, and CYP3A5 genes; assessing the degree of RAAS system activation by detecting renin, angiotensin II, and aldosterone levels in conjunction with the captopril test; assessing whether patients have complications such as diabetes or coronary heart disease and their severity; evaluating patient behavioral compliance based on behavioral data collected in S0 and classifying it into high, medium, and low grades; and comprehensively analyzing the above data using analytic hierarchy process (AHP) to complete the comprehensive patient classification and risk stratification.

4. The method according to claim 1, characterized in that, In step S2, the initial individualized antihypertensive regimen includes a drug treatment regimen and a non-drug intervention regimen. The drug treatment regimen follows the principles of "individualized drug selection, precise dosage, combination therapy, and kidney protection." It selects drugs with high sensitivity and few adverse reactions based on gene polymorphism, adjusts the initial dose based on renal function classification and risk classification, and selects long-acting, intermediate- or short-acting, or fixed-drug combination preparations according to the patient's work-rest patterns and medication adherence, while avoiding drug contraindications. The non-drug intervention regimen includes interventions in diet, exercise, sleep, medication, emotions, and lifestyle habits, and clarifies the specific implementation standards and monitoring requirements for each intervention item.

5. The method according to claim 1, characterized in that, In step S3, the cloud-based big data platform is equipped with an intelligent algorithm that integrates BP neural network and support vector machine. Data preprocessing includes cleaning, noise reduction, outlier removal, missing data supplementation, and standardization. Linkage analysis constructs a linkage model of "behavioral data-physiological data-blood pressure fluctuations" to analyze the correlation between various behaviors and blood pressure fluctuations and changes in renal function; trend prediction predicts the trend of blood pressure changes in patients over the next 1-3 days based on historical and real-time data; anomaly identification includes abnormal physiological data, abnormal behavior, and abnormal compliance. Abnormal physiological data includes sudden rises and falls in blood pressure, electrolyte imbalances, etc.; abnormal behavior includes consecutive missed medications, insufficient sleep, excessive sodium intake, etc.; and abnormal compliance includes failure to perform blood pressure-lowering intervention as required for 3 consecutive times and a compliance score that is consistently below 60.

6. The method according to claim 1, characterized in that, In step S4, the alarm levels are divided into four levels: yellow, orange, red, and critical, which are linked to the warning levels in S6. A yellow alarm corresponds to a general abnormality, and local medical staff will arrive at the scene within 24 hours; an orange alarm corresponds to a moderate abnormality, and will arrive at the scene within 12 hours; a red alarm corresponds to a severe abnormality, and will arrive at the scene within 6 hours; a critical alarm corresponds to an extremely severe abnormality, and will arrive at the scene within 1 hour and contact the emergency center. The alarm information is simultaneously pushed to the local service center, the patient's relatives, and the patient. After the on-site service, the medical staff will upload the relevant records to the cloud big data platform and push them to the patient and relatives simultaneously.

7. The method according to claim 1, characterized in that, In step S5, the intelligent indicator light is linked with the cloud big data platform and the patient's mobile APP, and has three light states: green, yellow, and red. The green light corresponds to the patient's good compliance and stable blood pressure on the day; the yellow light corresponds to minor abnormal behavior or mild lack of compliance; the red light corresponds to abnormal compliance or red / critical alarm, and flashes continuously for easy identification. For elderly people living alone, medical staff provide psychological counseling when they visit, community volunteers assist in completing relevant interventions, community medical staff conduct regular visits, and relatives are urged to visit regularly and monitor remotely.

8. The method according to claim 1, characterized in that, In step S6, the real-time feedback includes today's blood pressure control status, behavioral target achievement rate, renal function load status, tomorrow's blood pressure prediction trend, and the current status of the warning light. The four warning levels are as follows: yellow warning corresponds to insufficient sleep + high sodium levels or a single missed medication dose; orange warning corresponds to missed medication dose + increased nocturia or two consecutive days of insufficient sleep and slightly elevated blood pressure; red warning corresponds to consecutive allergic reactions + high-salt diet + emotional excitement and elevated blood pressure or two consecutive failures to follow the intervention plan as required; and critical warning corresponds to blood pressure ≥180 / 120 mmHg with discomfort symptoms or serious complications.

9. The method according to claim 1, characterized in that, It also includes the S9 quality control steps, specifically: regularly calibrating various data acquisition devices and establishing a data audit mechanism; regularly optimizing cloud-based big data algorithm parameters and establishing a data security mechanism; clarifying alarm triggering standards and on-site service time limits at all levels, establishing an assessment mechanism for on-site services by medical staff, and regularly checking the operating status of indicator lights; and establishing a control plan review mechanism, with a multidisciplinary expert group reviewing the plan, regularly evaluating the control effect and optimizing the strategy.