Home nursing auxiliary system based on chronic patient

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a chronic disease patient-based home nursing auxiliary system. According to the system, physiological data of a chronic disease patient are collected through a remote health monitoring and diagnosis module, physiological function indexes of the chronic disease patient and body health indexes of the chronic disease patient are calculated to judge the abnormal body condition of the chronic disease patient, and health abnormal signals are sent out; a medicine taking reminding message is sent to a chronic disease patient through the smart home device and the smart phone terminal, health data, living habits, personal preferences and character characteristics of the chronic disease patient are recorded, and the risk prediction and intervention module analyzes the recorded data to predict the health risk faced by the patient in the future, so that the health risk of the patient in the future is predicted. And the intelligent nursing plan module performs intervention in advance and generates a health intervention signal, performs data analysis by applying an artificial intelligence algorithm according to the signal, and generates a personalized nursing plan meeting psychological and emotional requirements of the chronic disease patient.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a home care assistance system for patients with chronic diseases. Background Art

[0002] Chronic disease patients have the characteristics of limited mobility, lack of self-care ability and long bed rest time. The only child in society has great life pressure and it is difficult to take care of the elderly for a long time. These factors make it easy for chronic disease patients to have a reduced quality of life at home. In serious cases, they may suffer accidents such as falls and falling out of bed. At the same time, for the generation with elderly parents and young children, it is not realistic to accompany their parents every day. At this time, technological products that provide monitoring function become a solution: they can partially replace children to pay attention at all times, provide patients with more comfortable, safe and high-quality services, and reduce the workload of family caregivers. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the deficiencies in the prior art, the present invention provides a home care assistance system for chronic disease patients, which has the function of collecting physiological data of chronic disease patients through wearable bracelets and smart home sensors through remote health monitoring and diagnosis modules, calculating the average heart rate Pjxl, heart rate variability Xlby, average blood pressure Pjxy, average blood sugar Pjxt, average blood oxygen saturation Pjxy and average body temperature Pjtw based on the collected data using an algorithm formula, and comprehensively calculating the health index Mhzs of the chronic disease patients based on the above values ​​to judge the abnormal physical condition of the chronic disease patients, and sending a health abnormality signal Jkxh to the smart medication reminder module. When the smart medication reminder module receives the health abnormality signal Jkxh, it sends a medication reminder message to the chronic disease patients through smart home devices and smart phone terminals. At this time, the smart medicine box is used to realize automatic medication dispensing and medication record, and the smart phone terminal The health data, living habits, personal preferences and personality traits of chronic disease patients are recorded and sent to the risk prediction and intervention module. The risk prediction and intervention module analyzes the historical data of chronic disease patients through AI based on the health data, living habits, personal preferences and personality traits of chronic disease patients, predicts the health risks they will face in the future, intervenes in advance and generates a health intervention signal Gyxh to send to the intelligent care plan module. When the intelligent care plan module receives the health intervention signal Gyxh, it generates a personalized care plan that meets the psychological and emotional needs of chronic disease patients based on the historical data, living habits and health status information of chronic disease patients according to the AI ​​algorithm. Through this system, children can pay attention to chronic disease patients at all times, provide patients with more comfortable, safe and high-quality services, and at the same time reduce the workload of family caregivers, etc., to solve the above problems.

[0005] (II) Technical solution

[0006] To achieve the above-mentioned object, the present invention provides the following technical solutions: a home care assistance system for patients with chronic diseases, comprising a remote health monitoring and diagnosis module, an intelligent medication reminder module, a risk prediction and intervention module, and an intelligent care plan module;

[0007] The remote health monitoring and diagnosis module collects the heart rate data Mxxl, blood pressure data Mxxy, blood sugar data Xtsj, blood oxygen saturation data Xbhd and body temperature data Mbtw of chronic disease patients through wearable bracelets and smart home sensors, calculates the average heart rate Pjxl, heart rate variability Xlby, average blood pressure Pjxy, average blood sugar Pjxt, average blood oxygen saturation Pjxy and average body temperature Pjtw according to the algorithm formula, and calculates the physical health index Mhzs of chronic disease patients based on the above values ​​to judge the physical abnormality of chronic disease patients, and sends a health abnormality signal Jkxh to the smart medication reminder module;

[0008] When the intelligent medication reminder module receives the abnormal health signal Jkxh, it sends a medication reminder message to the chronic disease patient through the smart home device and the smart phone terminal. At this time, the smart medicine box is used to realize automatic medication and medication record, and the health data, living habits, personal preferences and personality characteristics of the chronic disease patient are recorded through the smart phone terminal and sent to the risk prediction and intervention module;

[0009] The risk prediction and intervention module uses AI to analyze the historical data of chronic disease patients based on their health data, living habits, personal preferences and personality traits, predicts their future health risks, intervenes in advance and generates a health intervention signal Gyxh to send to the intelligent care plan module;

[0010] When the intelligent care planning module receives the health intervention signal Gyxh, it generates a personalized care plan that meets the psychological and emotional needs of chronic disease patients based on the historical data, living habits and health status information of chronic disease patients according to the AI ​​algorithm.

[0011] Preferably, the remote health monitoring and diagnosis module calculates the average heart rate Pjxl according to an algorithm formula, and the calculation formula is as follows:

[0012] Pj t =α*Mxxl t +(1-α)*Mxxl t-1

[0013] In the formula, Pjxl t represents the average heart rate at time t, α represents the smoothing factor, which is between 0.1 and 0.3, and Mxxl trepresents the heart rate of patients with chronic diseases at time t, Mxxl t-1 Represents the heart rate of patients with chronic diseases at time t-1.

[0014] Preferably, the remote health monitoring and diagnosis module calculates the heart rate variability Xlby according to an algorithm formula, and the calculation formula is as follows:

[0015]

[0016] In the formula, Xlby represents heart rate variability, Rx i represents the i-th heartbeat interval of a patient with chronic heart disease, Rx i+1 represents the i+1th heartbeat interval of a chronic heart disease patient, n represents the total number of heartbeat intervals, i represents the index variable, which is used to refer to the data of each heartbeat interval, (Rx i+1 -Rx i ) means calculating the difference between two consecutive heartbeat intervals. The summation formula represents the summation of the squares of all adjacent heartbeat interval differences.

[0017] Preferably, the remote health monitoring and diagnosis module calculates the average blood pressure Pjxy according to an algorithm formula, and the calculation formula is as follows:

[0018]

[0019] In the formula, Pjxy represents the average blood pressure, DBP represents the diastolic pressure, which is the lowest pressure in the blood vessels when the heart is relaxed, and SBP represents the systolic pressure, which is the highest pressure in the blood vessels when the heart is contracted.

[0020] Preferably, the remote health monitoring and diagnosis module calculates the average blood glucose Pjxt according to an algorithm formula, and the calculation formula is as follows:

[0021]

[0022] In the formula, Pjxt represents the average blood sugar, n represents the total number of blood sugar measurements, and Xtsj i Denotes the i-th blood glucose measurement, and this formula calculates the patient's average blood glucose level by summing all measurements and then dividing by the number of measurements.

[0023] Preferably, the remote health monitoring and diagnosis module calculates the average blood oxygen saturation Pjxy according to an algorithm formula, and the calculation formula is as follows:

[0024]

[0025] In the formula, Pjxy represents the average blood oxygen saturation, n represents the total number of blood oxygen saturation measurements, and Xbhd irepresents the blood oxygen saturation measured for the i-th time. The formula calculates the average level of blood oxygen saturation by summing and averaging, reflecting the patient's respiratory function and oxygenation status.

[0026] Preferably, the remote health monitoring and diagnosis module calculates the average body temperature Pjtw according to an algorithm formula, and the calculation formula is as follows:

[0027]

[0028] In the formula, Pjtw represents the average body temperature, n represents the total number of temperature measurements, and Mbtw i Represents the i-th temperature measurement value. The formula calculates the patient's average temperature level and evaluates his health status by summing up all measurements and dividing by the number of measurements.

[0029] Preferably, the remote health monitoring and diagnosis module comprehensively calculates the physical health index Mhzs of chronic disease patients according to the average heart rate Pjxl, heart rate variability Xlby, average blood pressure Pjxy, average blood sugar Pjxt, average blood oxygen saturation Pjxy and average body temperature Pjtw. The calculation formula is as follows:

[0030] Mhzs=n1*Pjxl+n2*Xlby+n3*Pjxy+n4*Pjxt+n5*Pjxy+n6*Pjtw

[0031] In the formula, n1 represents the weight of the average heart rate, n2 represents the weight of the heart rate variability, n3 represents the weight of the average blood pressure, n4 represents the weight of the average blood sugar, n5 represents the weight of the average blood oxygen saturation, and n6 represents the weight of the average body temperature. The above weights are automatically assigned by the system.

[0032] Preferably, the remote health monitoring and diagnosis module determines the abnormal physical condition of the chronic disease patient according to the chronic disease patient's physical health index Mhzs, and the determination method is as follows:

[0033] When the value of the physical health index Mhzs of a chronic disease patient is greater than or equal to 0.1 and less than 0.4, it means that the physical health of the chronic disease patient is stable;

[0034] When the value of the health index Mhzs of a chronic disease patient is greater than or equal to 0.4 and less than 0.7, it means that the health status of the chronic disease patient is fluctuating and needs constant attention;

[0035] When the value of the health index Mhzs of a chronic disease patient is greater than or equal to 0.7, it indicates that the health status of the chronic disease patient is abnormal, and a health abnormality signal Jkxh is sent to the intelligent medication reminder module.

[0036] Preferably, the risk prediction and intervention module uses historical data and health records, applies the random forest algorithm for pattern recognition, constructs a multi-factor risk scoring model, generates a comprehensive risk score based on the patient's comprehensive data, and predicts the probability of occurrence of hypertension complications, cardiovascular diseases, and diabetes complications. When it is detected that the patient's health risk is increased and the intervention measures are not followed, the system will trigger an intelligent message notification operation.

[0037] Compared with the prior art, the present invention provides a home care auxiliary system for chronic disease patients, which has the following beneficial effects:

[0038] The present invention uses a wearable bracelet and a smart home sensor to collect the physiological data of chronic disease patients through a remote health monitoring and diagnosis module, and uses an algorithm formula to calculate the average heart rate Pjxl, heart rate variability Xlby, average blood pressure Pjxy, average blood sugar Pjxt, average blood oxygen saturation Pjxy and average body temperature Pjtw based on the collected data, and comprehensively calculates the physical health index Mhzs of the chronic disease patient based on the above values ​​to judge the physical abnormality of the chronic disease patient, and sends a health abnormality signal Jkxh to the smart medication reminder module. When the smart medication reminder module receives the health abnormality signal Jkxh, it sends a medication reminder message to the chronic disease patient through the smart home device and the smart phone terminal. At this time, the smart medicine box is used to realize automatic medication and medication records, and the health of the chronic disease patient is recorded through the smart phone terminal. The data, living habits, personal preferences and personality traits of chronic disease patients are sent to the risk prediction and intervention module. The risk prediction and intervention module analyzes the historical data of chronic disease patients through AI based on the health data, living habits, personal preferences and personality traits of chronic disease patients, predicts the health risks they will face in the future, intervenes in advance and generates a health intervention signal Gyxh to be sent to the intelligent care plan module. When the intelligent care plan module receives the health intervention signal Gyxh, it generates a personalized care plan that meets the psychological and emotional needs of chronic disease patients based on the historical data, living habits and health status information of chronic disease patients according to the AI ​​algorithm. Through this system, children can partially replace their children to pay attention to chronic disease patients at all times, provide patients with more comfortable, safe and high-quality services, and reduce the workload of family caregivers. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] In view of the current characteristics of chronic disease patients such as mobility difficulties, lack of self-care ability and long bed rest time, the high life pressure of only children in society and the difficulty of long-term care for the elderly, it is easy for chronic disease patients to have a lower quality of life at home, and in serious cases, they may fall or fall out of bed. At the same time, for the generation with elderly parents and young children, it is not realistic to accompany their parents every day. Therefore, a home care auxiliary system for chronic disease patients is proposed. Please refer to Figure 1 ,The system includes remote health monitoring and diagnosis module, intelligent medication reminder module, risk prediction and intervention module, and intelligent care planning module;

[0042] The remote health monitoring and diagnosis module collects physiological data such as heart rate, blood pressure, blood oxygen saturation and body temperature of patients with chronic diseases through wearable bracelets and smart home sensors. The bracelets are equipped with high-precision sensors, which monitor data in real time and transmit them to the cloud platform via Wi-Fi. Patients can input information such as living habits, medication records and self-perception through the bracelet interface to enhance the richness of data. Smart home sensors include smart weight scales, smart blood glucose meters, etc., which synchronize data through the Internet of Things. The sensors can automatically record data and interoperate with other health monitoring devices.

[0043] The remote health monitoring and diagnosis module calculates the average heart rate Pjxl, heart rate variability Xlby, average blood pressure Pjxy, average blood sugar Pjxt, average blood oxygen saturation Pjxy and average body temperature Pjtw based on the collected data, and calculates the health index Mhzs of chronic disease patients based on the above values ​​to determine the abnormal physical condition of chronic disease patients, and sends a health abnormality signal Jkxh to the intelligent medication reminder module, where:

[0044] The average heart rate calculation formula is as follows:

[0045] Pj t =α*Mxxl t +(1-α)*Mxxl t-1

[0046] By continuously or regularly monitoring the average heart rate of patients, it is possible to identify health trends in a timely manner. This is especially important for patients with chronic diseases because changes in heart rate may be associated with worsening or improvement of the disease. In the formula, Pjxl trepresents the average heart rate at time t, α represents the smoothing factor, which is between 0.1 and 0.3, and Mxxl t represents the heart rate of patients with chronic diseases at time t, Mxxl t-1 It indicates the heart rate of patients with chronic diseases at time t-1. If the average heart rate shows abnormal fluctuations, such as continuous acceleration or deceleration, it can be used as an early signal of potential health problems, prompting the medical team or family members to take appropriate intervention measures;

[0047] The formula for calculating heart rate variability is as follows:

[0048]

[0049] Heart rate variability is widely considered to be an important indicator of the balance of the autonomic nervous system. High heart rate variability usually indicates good physiological adaptability and health, while low heart rate variability may reflect negative health conditions such as stress and fatigue. In the formula, Xlby represents heart rate variability, Rx i represents the i-th heartbeat interval of a patient with chronic heart disease, Rx i+1 represents the i+1th heartbeat interval of a chronic heart disease patient, n represents the total number of heartbeat intervals, i represents the index variable, which is used to refer to the data of each heartbeat interval, (Rx i+1 -Rx i ) means calculating the difference between two consecutive heartbeat intervals. The summation formula represents the sum of the squares of all adjacent heartbeat interval differences. By observing the changes in heart rate variability after different interventions, medical personnel can effectively evaluate the success of these measures and adjust treatment plans accordingly.

[0050] The average blood pressure is calculated as follows:

[0051]

[0052] By regularly measuring and calculating average blood pressure, the cardiovascular health of patients can be effectively monitored. This is especially important for patients with hypertension and can reflect changes in their condition in real time. In the formula, Pjxy represents average blood pressure, DBP represents diastolic pressure, which is the lowest pressure in the blood vessels when the heart is relaxed, and SBP represents systolic pressure, which is the highest pressure in the blood vessels when the heart is contracted. By long-term monitoring of average blood pressure, patients can identify and adjust lifestyle factors that affect blood pressure early, reduce the risk of complications caused by hypertension, and thus improve their quality of life.

[0053] The average blood sugar calculation formula is as follows:

[0054]

[0055] The calculation of average blood sugar can help medical providers adjust drug dosage, diet and exercise plan according to the actual situation of the patient to achieve optimal blood sugar control. In the formula, Pjxt represents the average blood sugar, n represents the total number of blood sugar measurements, and Xtsj represents the average blood sugar. i represents the i-th blood glucose measurement. This formula calculates the patient's average blood glucose level by summing all the measurements and then dividing by the number of measurements. By analyzing the average blood glucose data, medical professionals can develop a diet plan for patients that is tailored to their individual circumstances to optimize blood glucose management.

[0056] The average blood oxygen saturation calculation formula is as follows:

[0057]

[0058] By calculating the average blood oxygen saturation, patients and medical teams can monitor the condition of the respiratory system and promptly identify potential respiratory problems, such as acute attacks of chronic obstructive pulmonary disease or asthma. In the formula, Pjxy represents the average blood oxygen saturation, n represents the total number of blood oxygen saturation measurements, and Xbhd i represents the blood oxygen saturation measured for the ith time. This formula calculates the average blood oxygen saturation level by summing and averaging, which reflects the patient's respiratory function and oxygenation status. For patients with cardiopulmonary insufficiency or other chronic diseases, monitoring the average blood oxygen saturation level helps to assess the stability and changing trend of the disease;

[0059] The average body temperature is calculated as follows:

[0060]

[0061] For some chronic diseases, temperature fluctuations can be used as an important indicator of disease activity. In the formula, Pjtw represents the average body temperature, n represents the total number of temperature measurements, and Mbtw represents the average body temperature. i represents the i-th temperature measurement value. This formula calculates the average temperature level of the patient by summing up all the measured values ​​and dividing by the number of measurements to assess their health status. Calculating the average temperature can help monitor health changes in patients with chronic diseases and identify potential infections or inflammation conditions early, because abnormal body temperature is often an early sign of infection.

[0062] The formula for calculating the physical health index of patients with chronic diseases is as follows:

[0063] Mhzs=n1*Pjxl+n2*Xlby+n3*Pjxy+n4*Pjxt+n5*Pjxy+n6*Pjtw

[0064] By monitoring the physical health index, doctors can promptly identify the risk of complications that chronic disease patients may face, especially in obesity and metabolic syndrome. In the formula, n1 represents the weight of average heart rate, n2 represents the weight of heart rate variability, n3 represents the weight of average blood pressure, n4 represents the weight of average blood sugar, n5 represents the weight of average blood oxygen saturation, and n6 represents the weight of average body temperature. The above weights are automatically assigned by the system. As a quantitative health indicator, the physical health index can provide strong data support for medical decision-making and reduce the bias of subjective judgment.

[0065] The remote health monitoring and diagnosis module determines the abnormal physical condition of chronic disease patients according to their physical health index Mhzs. The judgment method is as follows:

[0066] When the value of the physical health index Mhzs of a chronic disease patient is greater than or equal to 0.1 and less than 0.4, it means that the physical health of the chronic disease patient is stable;

[0067] When the value of the health index Mhzs of a chronic disease patient is greater than or equal to 0.4 and less than 0.7, it means that the health status of the chronic disease patient is fluctuating and needs constant attention;

[0068] When the value of the health index Mhzs of a chronic disease patient is greater than or equal to 0.7, it indicates that the health status of the chronic disease patient is abnormal, and a health abnormality signal Jkxh is sent to the intelligent medication reminder module;

[0069] When the intelligent medication reminder module receives abnormal health signal Jkxh, the system will automatically send out medication reminder messages through seamless connection with smart home devices and smart phone terminals. This mechanism closely integrates medical services with daily life and provides patients with timely and personalized medication guidance;

[0070] After receiving the reminder, patients can automatically dispense medicines through the smart medicine box. The smart medicine box can not only accurately release medicines according to the preset schedule, but also ensure that patients take medicines on time and in the right amount according to their specific needs and the dosage of the medicines. This process reduces the adverse reactions caused by patients remembering wrong or missing medicines, and improves patients' medication compliance. In addition, the smart medicine box also has a medication record function, which can record the time and dosage of each medication in detail, and transmit these data to the patient's smartphone terminal in real time, ensuring that patients and medical teams are aware of medication status at any time;

[0071] At the same time, the smartphone terminal will record the health data of patients with chronic diseases, including physiological indicators, living habits, and personal preferences and personality traits of patients. These data can not only help patients better manage their health, but also provide doctors with detailed reference information to help them develop more personalized treatment plans. All the above collected data will be integrated and sent to the risk prediction and intervention module;

[0072] The risk prediction and intervention module uses AI to analyze the historical data of chronic disease patients based on their health data, living habits, personal preferences and personality traits, predicts their future health risks, intervenes in advance and generates health intervention signals Gyxh to send to the intelligent care plan, including:

[0073] This module uses historical data and health records, applies the random forest algorithm for pattern recognition, and builds a multi-factor risk scoring model. It generates a comprehensive risk score based on the patient's comprehensive data to predict the probability of complications of hypertension, cardiovascular disease, and diabetes. When it detects that the patient's health risk is rising and he fails to follow the intervention measures, the system will trigger an intelligent message notification operation;

[0074] When the intelligent care plan module receives the health intervention signal Gyxh, it generates a personalized care plan that meets the psychological and emotional needs of chronic disease patients based on the historical data, living habits and health status information of chronic disease patients according to the AI ​​algorithm. After the care plan is generated, the system will provide the care plan to the patient in an easy-to-understand and easy-to-operate way through smart devices. For example, the system can regularly push dietary suggestions, exercise plans or psychological counseling content through the calendar reminder function to ensure that patients can effectively implement the care plan in their daily lives. At the same time, patients can also use these devices to feedback their feelings, such as the implementation of the care plan and their feelings. These feedbacks will be fed back to the system in a timely manner to further optimize and adjust the care plan to make it more targeted and flexible;

[0075] The system also includes the following functions:

[0076] Online consultation and medical advice: Through natural language processing technology, AI can analyze patients’ symptoms, medical history and other information, provide doctors with diagnostic advice, and conduct online consultation and medical advice with patients;

[0077] Life-assistance functions for the elderly: For the elderly and patients with limited mobility, AI can provide life-assistance services. The intelligent voice system can help the elderly communicate by phone, check the weather, set reminders, etc. Smart home devices can help them control lights, televisions and other home appliances.

[0078] Through the application of the above system, children can partially replace their parents in paying constant attention to patients with chronic diseases, providing patients with more comfortable, safe and high-quality services, while reducing the workload of family caregivers.

[0079] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A home care assistance system for patients with chronic diseases, characterized by: It includes remote health monitoring and diagnosis module, intelligent medication reminder module, risk prediction and intervention module and intelligent care plan module; The remote health monitoring and diagnosis module collects the heart rate data Mxxl, blood pressure data Mxxy, blood sugar data Xtsj, blood oxygen saturation data Xbhd and body temperature data Mbtw of chronic disease patients through wearable bracelets and smart home sensors, calculates the average heart rate Pjxl, heart rate variability Xlby, average blood pressure Pjxy, average blood sugar Pjxt, average blood oxygen saturation Pjxy and average body temperature Pjtw according to the algorithm formula, and comprehensively calculates the physical health index Mhzs of chronic disease patients based on the above values ​​to judge the physical abnormality of chronic disease patients, and sends out health abnormality signals, kxh to the smart medication reminder module; When the intelligent medication reminder module receives the abnormal health signal, kxh, it sends a medication reminder message to the chronic disease patient through the smart home device and the smart phone terminal. At this time, the smart medicine box is used to realize automatic medication and medication record, and the health data, living habits, personal preferences and personality characteristics of the chronic disease patient are recorded through the smart phone terminal and sent to the risk prediction and intervention module; The risk prediction and intervention module uses AI to analyze the historical data of chronic disease patients based on their health data, living habits, personal preferences and personality traits, predicts their future health risks, intervenes in advance and generates a health intervention signal Gyxh to send to the intelligent care plan module; When the intelligent care planning module receives the health intervention signal Gyxh, it generates a personalized care plan that meets the psychological and emotional needs of chronic disease patients based on the historical data, living habits and health status information of chronic disease patients according to the AI ​​algorithm.

2. The home care assistance system for chronic disease patients according to claim 1 is characterized in that: The remote health monitoring and diagnosis module calculates the average heart rate Pjxl according to the algorithm formula, and the calculation formula is as follows: Pjxl t =α*Mxxl t +(1-α)*Mxxl t-1 In the formula, Pjxl t represents the average heart rate at time t, α represents the smoothing factor, which is between 0.1 and 0.3, and Mxxl t represents the heart rate of patients with chronic diseases at time t, Mxxl t-1 Represents the heart rate of patients with chronic diseases at time t-1.

3. A home care assistance system for chronic disease patients according to claim 2, characterized in that: The remote health monitoring and diagnosis module calculates the heart rate variability Xlby according to the algorithm formula, and the calculation formula is as follows: In the formula, Xlby represents heart rate variability, Rx i represents the i-th heartbeat interval of a patient with chronic heart disease, Rx i+1 represents the i+1th heartbeat interval of a chronic heart disease patient, n represents the total number of heartbeat intervals, i represents the index variable, which is used to refer to the data of each heartbeat interval, (Rx i+1 -Rx i ) means calculating the difference between two consecutive heartbeat intervals. The summation formula represents the summation of the squares of all adjacent heartbeat interval differences.

4. The home care assistance system for chronic disease patients according to claim 3 is characterized by: The remote health monitoring and diagnosis module calculates the average blood pressure Pjxy according to the algorithm formula, and the calculation formula is as follows: In the formula, Pjxy represents the average blood pressure, DBP represents the diastolic pressure, which is the lowest pressure in the blood vessels when the heart is relaxed, and SBP represents the systolic pressure, which is the highest pressure in the blood vessels when the heart is relaxed.

5. The home care assistance system for chronic disease patients according to claim 4 is characterized in that: The remote health monitoring and diagnosis module calculates the average blood glucose Pjxt according to the algorithm formula, and the calculation formula is as follows: In the formula, Pjxt represents the average blood sugar, n represents the total number of blood sugar measurements, and Xtsj i Denotes the i-th blood glucose measurement, and this formula calculates the patient's average blood glucose level by summing all measurements and then dividing by the number of measurements.

6. The home care assistance system for chronic disease patients according to claim 5 is characterized in that: The remote health monitoring and diagnosis module calculates the average blood oxygen saturation Pjxy according to the algorithm formula, and the calculation formula is as follows: In the formula, Pjxy represents the average blood oxygen saturation, n represents the total number of blood oxygen saturation measurements, and Xbhd i represents the blood oxygen saturation measured for the i-th time. The formula calculates the average level of blood oxygen saturation by summing and averaging, reflecting the patient's respiratory function and oxygenation status.

7. The home care assistance system for chronic disease patients according to claim 6 is characterized by: The remote health monitoring and diagnosis module calculates the average body temperature Pjtw according to the algorithm formula, and the calculation formula is as follows: In the formula, Pjtw represents the average body temperature, n represents the total number of temperature measurements, and Mbtw i Represents the i-th temperature measurement value. The formula calculates the patient's average temperature level and evaluates his health status by summing up all measurements and dividing by the number of measurements.

8. The home care assistance system for chronic disease patients according to claim 7 is characterized by: The remote health monitoring and diagnosis module comprehensively calculates the physical health index Mhzs of chronic disease patients according to the average heart rate Pjxl, heart rate variability Xlby, average blood pressure Pjxy, average blood sugar Pjxt, average blood oxygen saturation Pjxy and average body temperature Pjtw. The calculation formula is as follows: Mhzs=n1*Pjxl+n2*Xlby+n3*Pjxy+n4*Pjxt+n5*Pjxy+n6*Pjtw In the formula, n1 represents the weight of the average heart rate, n2 represents the weight of the heart rate variability, n3 represents the weight of the average blood pressure, n4 represents the weight of the average blood sugar, n5 represents the weight of the average blood oxygen saturation, and n6 represents the weight of the average body temperature. The above weights are automatically assigned by the system.

9. The home care assistance system for chronic disease patients according to claim 8, characterized in that: The remote health monitoring and diagnosis module determines the abnormal physical condition of chronic disease patients according to the chronic disease patient's physical health index Mhzs, and the determination method is as follows: When the value of the physical health index Mhzs of a chronic disease patient is greater than or equal to 0.1 and less than 0.4, it means that the physical health of the chronic disease patient is stable; When the value of the health index Mhzs of a chronic disease patient is greater than or equal to 0.4 and less than 0.7, it means that the health status of the chronic disease patient is fluctuating and needs constant attention; When the value of the health index Mhzs of a chronic disease patient is greater than or equal to 0.7, it indicates that the health status of the chronic disease patient is abnormal, and a health abnormality signal Jkxh is sent to the intelligent medication reminder module.

10. A home care assistance system for chronic disease patients according to claim 9, characterized in that: The risk prediction and intervention module uses historical data and health records, applies the random forest algorithm for pattern recognition, and constructs a multi-factor risk scoring model. It generates a comprehensive risk score based on the patient's comprehensive data to predict the probability of hypertension complications, cardiovascular disease, and diabetes complications. When it is detected that the patient's health risk is increased and the intervention measures are not followed, the system will trigger an intelligent message notification operation.

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