Artificial intelligence-based health monitoring method, terminal and storage medium for patients with specific diseases

Abstract

The present invention relates to the field of health monitoring technology, and specifically to a health monitoring method, terminal and storage medium for special disease patients based on artificial intelligence, which specifically includes the following steps: step one: through a data acquisition unit, patient information of each monitoring point of the special disease patient corresponding to the monitoring period is collected, and energy intake data, body temperature data and blood pressure data are collected and transmitted to a health monitoring unit. The present invention obtains meal changes by analyzing the data of each meal of the special disease patient, analyzes the data fluctuations in energy intake, and combines them with relevant data such as exercise, analyzes the changes in body temperature and blood pressure of the special disease patient at the daily monitoring time point, determines the changes in temperature and blood pressure, and calculates and transforms multiple data through a preset analysis method, and combines them in multiple ways, thereby increasing the accuracy of data analysis, increasing the reliability of data, and accurately monitoring the health of the special disease patient.

Description

Technical Field

[0001] The present invention relates to the field of health monitoring technology, and specifically to a health monitoring method, terminal and storage medium for patients with specific diseases based on artificial intelligence. Background Art

[0002] Specialized diseases refer to diseases in a specific field that are caused by specific reasons or specific mechanisms. In the medical field, the classification of diseases is to better understand and study different types of diseases and provide patients with corresponding treatment and management measures. Specialized disease patient health monitoring refers to the comprehensive, continuous and dynamic evaluation and monitoring of the physiological and psychological conditions of patients with specific diseases through various examinations and tests. This monitoring aims to promptly detect possible changes in the patient's condition and provide doctors with accurate and comprehensive information so that they can make the right diagnosis and treatment plan.

[0003] However, existing health monitoring methods for patients with specialized diseases have limitations: they cannot fully reflect the health status of patients. For example, although traditional physiological parameter monitoring (such as blood pressure, heart rate, respiratory rate, etc.) can provide basic data, it cannot accurately reflect the health status of patients with specialized diseases, resulting in inaccurate or unreliable health monitoring results, which affects doctors' judgment of the physical health of patients with specialized diseases and further affects the treatment of patients with specialized diseases.

[0004] To this end, we propose an artificial intelligence-based health monitoring method, terminal and storage medium for patients with specific diseases. Summary of the invention

[0005] The purpose of the present invention is to provide a health monitoring method, terminal and storage medium for patients with specific diseases based on artificial intelligence to solve the problems raised by the above-mentioned background technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a health monitoring method, terminal and storage medium for patients with specific diseases based on artificial intelligence, and the method specifically comprises the following steps:

[0008] Step 1: The data collection unit collects patient information at each monitoring point during the corresponding monitoring period of the patient with a specific disease, collects energy intake data, body temperature data, and blood pressure data, and transmits them to the health monitoring unit;

[0009] Step 2: Through the health monitoring unit, the patient information collected from the special disease patients during the monitoring period is analyzed for health monitoring, so as to monitor and obtain the intake prompt signal, the meal sharing standard signal, the meal sharing violation signal, the first change rate abnormal signal and the second change rate abnormal signal, and transmit them to the health analysis unit;

[0010] Step 3: Through the health analysis unit, the movement conditions of the patient with a specific disease and the conversion signal of the supervision analysis are processed for health combination analysis to obtain the patient's health evaluation coefficient, and transmit it to the display warning terminal;

[0011] Step 4: Through the display warning terminal, the patient's physical health monitoring is carried out to provide health warning, and a continue observation signal, an inspection signal and a safety warning signal are obtained, and different warning alarms are issued on the display warning terminal according to the continue observation signal, the inspection signal and the safety warning signal.

[0012] Preferably, the energy intake data refers to the amount of food energy intake of the corresponding patient in the patient information, the body temperature data refers to the body temperature value of the corresponding patient at each monitoring point in the patient information, and the blood pressure data refers to the blood pressure value of the corresponding patient at each monitoring point in the patient information.

[0013] Preferably, the specific process of the health monitoring unit performing health monitoring analysis on the patient information collected from the patient with a specific disease during the monitoring period is as follows:

[0014] Extract corresponding energy intake data according to the meal times of three meals a day, sum and calculate the three energy intake data corresponding to the meal times of three meals a day, calculate the total energy value of the energy intake plane diagram, and mark it as the total energy value;

[0015] Repeat the total energy value calculation method, calculate the total energy value of meals every day, calculate several total energy values ​​according to the mean calculation formula, calculate the total energy mean, calculate the difference between each total energy value and the total energy mean, calculate several total energy differences, and compare the several total energy differences with thresholds. When the total energy difference falls within the total energy difference threshold range, a normal intake signal is generated, and when the total energy difference does not fall within the total energy difference threshold range, an abnormal intake signal is generated;

[0016] Among them, the energy difference threshold range is the preset value; the intake standard signal and the intake abnormal signal are uniformly calibrated as the intake prompt signal , the value of h is 1, 2;

[0017] Perform individual meal analysis on the first, second and third meals of the three meals a day to obtain meal separation standard signals and meal separation violation signals;

[0018] Extract body temperature data and blood pressure data and mark them once , , g represents the day, l represents the monitoring point, It is represented by the body temperature data corresponding to the lth monitoring point on the gth day, It is represented as the blood pressure data corresponding to the lth monitoring point on the gth day. The values ​​of g and l are positive integers. The temperature data of one monitoring point is selected from each monitoring point in the monitoring period as the initial body temperature, which is recorded as ;

[0019] Substitute the initial body temperature and body temperature data into the calculation formula: , calculate the temperature change of patients with special diseases at each monitoring point and calibrate it as the temperature change rate , when l=1, Initial body temperature , n is a positive integer, It is expressed as the deviation adjustment factor of the temperature variation of patients with special diseases;

[0020] The blood pressure data of one monitoring point is selected from each monitoring point in the monitoring period as the initial blood pressure, which is recorded as ;

[0021] Substitute the initial blood pressure and blood pressure data into the calculation formula: , calculate the blood pressure changes of patients with special diseases at various monitoring points and calibrate them as pressure change rate , when l=1, Initial blood pressure , n is a positive integer, It is expressed as the deviation adjustment factor of blood pressure changes in patients with special diseases;

[0022] Extract daily temperature change rate And the pressure change rate , the temperature change rate of the next day , pressure change rate Subtract the temperature change rate of the previous day And the pressure change rate , calculate the temperature change rate difference and the pressure change rate difference, mark the temperature change rate difference and the pressure change rate difference as positive or negative, when the temperature change rate difference is negative, it is determined that the temperature change rate is decreasing, and a temperature change rate decreasing signal is generated; when the temperature change rate difference is positive, it is determined that the temperature change rate is increasing, and a temperature change rate increasing signal is generated; when the pressure change rate difference is negative, it is determined that the pressure change rate is decreasing, and a pressure change rate decreasing signal is generated; when the pressure change rate difference is positive, it is determined that the pressure change rate is increasing, and a pressure change rate increasing signal is generated;

[0023] When the temperature change rate decrease signal M1 or the temperature change rate increase signal M2 appears continuously, it is determined that the patient's body temperature is abnormal and a temperature change rate abnormality signal is generated. , r1 takes values ​​of 1, 2. When r1 takes value of 1, It is indicated as an abnormal temperature drop signal. When the value of r1 is 2, It is expressed as a temperature increase abnormal signal. When the pressure change rate decrease signal M3 times or the pressure change rate increase signal M4 times appears continuously, it is determined that the blood pressure of the patient is abnormal, and a second rate abnormal signal is generated. , r2 takes values ​​of 1, 2. When r2 takes value of 1, It is indicated as an abnormal voltage drop signal. When the value of r2 is 2, Indicates an abnormal pressure increase signal.

[0024] Preferably, the specific process of performing single meal analysis is:

[0025] Calculate the average energy intake of the first, second, and third meals of the day, and compare them with the energy intake data of the first, second, and third meals:

[0026] When the difference between the energy intake value of each meal and the corresponding average energy intake value of the first meal, the average energy intake value of the second meal, and the average energy intake value of the third meal is within the floating threshold range, a meal distribution standard signal is generated. , k1 takes the value of 1, 2, or 3. When k1 takes the value of 1, 2, or 3, it corresponds to the standard signal 1, standard signal 2, or standard signal 3. When the difference between the energy intake value of each meal and the corresponding average energy intake value of the first meal, the average energy intake value of the second meal, and the average energy intake value of the third meal is outside the floating threshold range, a meal sharing violation signal is generated. , when the value of k2 is 1, 2, or 3, it corresponds to violation signal one, violation signal two, or violation signal three, respectively, where the floating threshold range is the preset value.

[0027] Preferably, the specific process of the health analysis unit performing health combined analysis on the movement conditions of the patient with a specific disease and the conversion signal of the supervision analysis is as follows:

[0028] Collect the total length of exercise and the number of steps taken by the patients during exercise every day, and substitute the total length of exercise and the corresponding number of steps into the calculation formula: exercise consumption = total length of exercise / number of steps The preset conversion factor is a preset value. The difference between the exercise consumption of two consecutive days is calculated to calculate the difference in consumption. The daily body weight of the patient with a special disease is collected and calibrated as the weight data. The difference between the weight data of two consecutive days is calculated to calculate the difference in weight. According to the preset formula: Exercise consumption coefficient = patient weight difference, reverse deduction of exercise consumption coefficient;

[0029] According to the preset patient health evaluation formula:

[0030]

[0031] Calculate the patient's health evaluation coefficient JK.

[0032] Preferably, the specific process of performing health warning on the patient's physical health monitoring is:

[0033] The patient's health evaluation coefficient JK is compared with the health preset value MN of the special disease patient: when JK≥MN, a continue observation signal is generated; when JK<MN, a health abnormality signal is generated, and a secondary comparison is performed based on the health abnormality signal; when MN>JK>MN / 2, an inspection signal is generated; when MN / 2>JK, a safety warning signal is generated.

[0034] In a second aspect, the present invention provides an artificial intelligence-based health monitoring terminal for patients with specific diseases, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the artificial intelligence-based health monitoring terminal for patients with specific diseases executes the above-mentioned artificial intelligence-based health monitoring method for patients with specific diseases.

[0035] In a third aspect, the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, implement the above-mentioned artificial intelligence-based health monitoring method for specific disease patients.

[0036] Beneficial effects of the present invention:

[0037] The present invention performs data analysis on the daily food energy intake of patients with special diseases, obtains meal changes based on the data analysis of each meal, thereby analyzing the data fluctuations in energy intake, and combines relevant data such as exercise of patients with special diseases to analyze the comprehensive changes in energy. At the same time, the changes in body temperature and blood pressure of patients with special diseases at daily monitoring time points are analyzed to determine whether the temperature change rate and blood pressure change rate are normal. Through a preset analysis method, multiple data are calculated and converted, and multiple combinations are performed to increase the accuracy of data analysis, increase the reliability of data, and accurately monitor the health of patients with special diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] Figure 1 It is a flow chart 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] See also Figure 1 As shown, the present invention is a health monitoring method, terminal and storage medium for patients with special diseases based on artificial intelligence, including: a data entry unit, an identification unit, a data acquisition unit, a health supervision unit, a health analysis unit, a display and early warning terminal and a server, and the server is respectively connected to the data entry unit, the identification unit, the data acquisition unit, the health supervision unit, the health analysis unit, and the display and early warning terminal in communication;

[0042] The data entry unit is used to enter the identity information corresponding to the special disease patient, the identity information includes the facial data and name data of the special disease patient, and transmit it to the server for information storage;

[0043] The data collection unit is used to collect patient information at each monitoring point in the corresponding monitoring period of the patient with a specific disease, and transmit the patient information to the identification unit. The identification unit extracts the corresponding identity information from the server and performs identification operations on the identity information and the patient information. The specific identification process is as follows:

[0044] The images of the monitored persons in the patient information at each monitoring point within the corresponding monitoring period are calibrated as monitoring image data, and the monitoring image data are matched with the facial data of the special disease patient. When the matching results are consistent, it is determined that the person monitored in the monitoring image data is a special disease patient, and the name data of the special disease patient corresponding to the facial data of the special disease patient is extracted, and the monitoring image data corresponding to the name data of the special disease patient is calibrated as patient image data, and the food intake energy of the corresponding patient in the patient information is calibrated as energy intake data based on the patient image data, and the body temperature value of the corresponding patient in the patient information at each monitoring point is calibrated as body temperature data based on the patient image data, and the blood pressure value of the corresponding patient in the patient information at each monitoring point is calibrated as blood pressure data based on the patient image data;

[0045] Transmitting patient imaging data, energy intake data, temperature data, and blood pressure data to a health monitoring unit;

[0046] The health monitoring unit is used to perform health monitoring processing operations on the patient's image data and the energy intake data, body temperature data, and blood pressure data corresponding to the patient's image data. The specific operation process is as follows:

[0047] The corresponding energy intake data is extracted based on the patient's imaging data, and the energy intake data is divided into a group of energy intake data for three meals a day for patients with special diseases. A plane rectangular coordinate system is established, and the energy intake data of three meals a day for each group of patients with special diseases are marked in the plane rectangular coordinate system, and the corresponding coordinate points are marked as energy value coordinate points , where j represents the number of days, i represents the number of meals corresponding to the group number, It is represented as the i-th meal on the j-th day, the value range of j is a positive integer greater than 1, and the value of i is 1, 2, 3. Each energy coordinate point is connected by a straight line, and the connection diagram of each group of three coordinate points is marked as the energy intake plane diagram;

[0048] Select one of the energy intake plane diagrams, and sum the Y-axis values ​​of the three energy value coordinate points in one of the energy intake plane diagrams to calculate the total energy value of the energy intake plane diagram, and mark it as the total energy value;

[0049] Repeat the calculation method of the total energy value, process each energy intake plane diagram, calculate several corresponding total energy values, calculate the total energy mean according to the mean calculation formula, perform difference calculation between each total energy value and the total energy mean, calculate several total energy differences, and perform threshold comparison on several total energy differences. When the total energy difference falls within the total energy difference threshold range, it is determined that the total energy acquisition value meets the energy intake requirement specification, and an intake specification signal is generated. When the total energy difference is greater than the maximum value of the total energy difference threshold range or less than the minimum value of the total energy difference threshold range, it is determined that the total energy acquisition value does not meet the energy intake requirement specification, and an intake abnormality signal is generated; wherein the total energy difference threshold range is a preset value; the intake specification signal and the intake abnormality signal are uniformly calibrated as the intake prompt signal , the value of h is 1, 2, when h=1, the intake prompt signal It is expressed as the intake standard signal. When h=2, the intake prompt signal It indicates an abnormal intake signal;

[0050] Select energy value coordinate point The Y-axis value when i=1 is calculated, and the average energy intake value of the first meal is calculated. The energy value coordinate point is selected The Y-axis value when i=2 is calculated, and the average energy intake value of the second meal is calculated. The energy value coordinate point is selected The Y-axis value when i=3 in the figure is calculated, and the average energy intake value of the third meal is calculated. The energy intake data of the three meals per day are compared with the corresponding average energy intake value of the first meal, the average energy intake value of the second meal, and the average energy intake value of the third meal. When the difference between the energy intake value of each meal and the corresponding average energy intake value of the first meal, the average energy intake value of the second meal, and the average energy intake value of the third meal is within the floating threshold range, the corresponding meal is determined to be a standard meal, and a corresponding standard signal 1, standard signal 2, or standard signal 3 is generated. When the difference between the energy intake value of each meal and the corresponding average energy intake value of the first meal, the average energy intake value of the second meal, and the average energy intake value of the third meal is outside the floating threshold range, the corresponding meal is determined to be an irregular meal, and a corresponding violation signal 1, violation signal 2, or violation signal 3 is generated, wherein the floating threshold range is a preset value;

[0051] Standard signal 1, standard signal 2 or standard signal 3 are uniformly marked as standard signal for meal sharing , k1 takes values ​​of 1, 2, and 3; when k1 takes values ​​of 1, 2, and 3, the meal distribution standard signal They are respectively represented as standard signal 1, standard signal 2, and standard signal 3; the violation signal 1, violation signal 2, or violation signal 3 are uniformly marked as the meal separation violation signal , the value of k2 is 1, 2, 3; when the value of k2 is 1, 2, 3, the meal sharing violation signal They are respectively indicated as violation one signal, violation two signal or violation three signal;

[0052] Extract body temperature data and blood pressure data and mark them once , , g represents the day, l represents the monitoring point, It is represented by the body temperature data corresponding to the lth monitoring point on the gth day, It is represented as the blood pressure data corresponding to the lth monitoring point on the gth day. The values ​​of g and l are positive integers. The temperature data of one monitoring point is selected from each monitoring point in the monitoring period as the initial body temperature, which is recorded as ;

[0053] Substitute the initial body temperature and body temperature data into the calculation formula: , calculate the temperature change of patients with special diseases at each monitoring point and calibrate it as the temperature change rate , when l=1, Initial body temperature , n is a positive integer, It is expressed as the deviation adjustment factor of the temperature variation of patients with special diseases;

[0054] The blood pressure data of one monitoring point is selected from each monitoring point in the monitoring period as the initial blood pressure, which is recorded as ;

[0055] Substitute the initial blood pressure and blood pressure data into the calculation formula: , calculate the blood pressure changes of patients with special diseases at various monitoring points and calibrate them as pressure change rate , when l=1, Initial blood pressure , n is a positive integer, It is expressed as the deviation adjustment factor of blood pressure changes in patients with special diseases;

[0056] Extract daily temperature change rate And the pressure change rate , the temperature change rate of the next day , pressure change rate Subtract the temperature change rate of the previous day And the pressure change rate , calculate the temperature change rate difference and the pressure change rate difference, mark the temperature change rate difference and the pressure change rate difference as positive or negative, when the temperature change rate difference is negative, it is determined that the temperature change rate is decreasing, and a temperature change rate decreasing signal is generated; when the temperature change rate difference is positive, it is determined that the temperature change rate is increasing, and a temperature change rate increasing signal is generated; when the pressure change rate difference is negative, it is determined that the pressure change rate is decreasing, and a pressure change rate decreasing signal is generated; when the pressure change rate difference is positive, it is determined that the pressure change rate is increasing, and a pressure change rate increasing signal is generated;

[0057] When the temperature change rate decrease signal M1 or the temperature change rate increase signal M2 appears continuously, it is determined that the patient's body temperature is abnormal and a temperature change rate abnormality signal is generated. , r1 takes values ​​of 1, 2. When r1 takes value of 1, It is indicated as an abnormal temperature drop signal. When the value of r1 is 2, It is expressed as a temperature increase abnormal signal. When the pressure change rate decrease signal M3 times or the pressure change rate increase signal M4 times appears continuously, it is determined that the blood pressure of the patient is abnormal, and a second rate abnormal signal is generated. , r2 takes values ​​of 1, 2. When r2 takes value of 1, It is indicated as an abnormal voltage drop signal. When the value of r2 is 2, It is expressed as an abnormal pressure increase signal, and the period is defined as the first day of the previous week to the last day of the previous week;

[0058] Will take in the prompt signal , Standard signal for meal sharing , Signals of illegal meal sharing 1. Abnormal rate signal And the second rate abnormal signal Transmitted to a health analysis unit;

[0059] The health analysis unit is used to monitor the patient's exercise status and the intake prompt signals received. , Standard signal for meal sharing , Signals of illegal meal sharing 1. Abnormal rate signal And the second rate abnormal signal Conduct a health analysis of physical condition, exercise, and energy intake and consumption. The specific analysis process is as follows:

[0060] Collect the daily exercise-related data of the patients with special diseases, including the total daily exercise length and the number of steps taken by the patients during exercise, and collect the daily body weight of the patients with special diseases and mark it as the patient weight data;

[0061] Extract the total length of movement and the number of steps, and substitute them into the calculation formula: Movement consumption = total length of movement / number of steps The conversion preset factor is a preset value. The difference between the exercise consumption of two consecutive days is calculated to calculate the difference between the weight data of two consecutive days. The difference between the weight data of two consecutive days is calculated to calculate the difference between the weight data of two consecutive days. According to the preset formula: Exercise consumption coefficient = patient weight difference, reverse deduction of exercise consumption coefficient;

[0062] Extracting intake cues , Standard signal for meal sharing , Signals of illegal meal sharing 1. Abnormal rate signal And the second rate abnormal signal And substitute it into the preset calculation formula together with the energy consumption coefficient:

[0063]

[0064] Calculate the patient's health evaluation coefficient JK, They are respectively represented as the evaluation conversion preset factors of the intake prompt signal, the meal-sharing standard signal, the meal-sharing violation signal, the one-rate abnormal signal and the two-rate abnormal signal, and xh is represented as the exercise consumption coefficient;

[0065] Transmit the patient's health evaluation coefficient JK to the display and early warning terminal;

[0066] The display warning terminal is used to receive the patient's health evaluation coefficient JK and perform health warning operations based on the patient's health evaluation coefficient JK. The specific operation process is as follows:

[0067] Extract the patient's health evaluation coefficient JK, and compare the patient's health evaluation coefficient JK with the health preset value MN of the special disease patient. When the patient's health evaluation coefficient JK is greater than or equal to the health preset value MN of the special disease patient, it is determined that the physical state of the special disease patient is normal, and the monitoring and observation state is maintained, and a continued observation signal is generated. When the patient's health evaluation coefficient JK is less than the health preset value MN of the special disease patient, it is determined that the physical state of the special disease patient is abnormal, and a health abnormality signal is generated. A secondary comparison is performed based on the health abnormality signal. When the patient's health evaluation coefficient JK is less than the health preset value MN of the special disease patient, and greater than 1 / 2 of the health preset value MN of the special disease patient, it is determined that the health state of the special disease patient is poor and needs to be checked, and an inspection signal is generated. When the patient's health evaluation coefficient JK is less than 1 / 2 of the health preset value MN of the special disease patient, it is determined that the health state of the special disease patient is poor, and a comprehensive examination is immediately carried out, and a response plan is arranged in time to generate a safety warning signal;

[0068] Different warning prompts are sent based on the continued observation signal, inspection signal and safety warning signal.

[0069] The present invention provides an artificial intelligence-based health monitoring terminal for patients with specific diseases, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the artificial intelligence-based health monitoring terminal for patients with specific diseases executes the above-mentioned artificial intelligence-based health monitoring method for patients with specific diseases.

[0070] The present invention provides a computer-readable storage medium having instructions stored thereon, and when the instructions are executed by a processor, the above-mentioned artificial intelligence-based health monitoring method for patients with specific diseases is implemented.

[0071] When the present invention is working, the data acquisition unit is used to collect patient information of each monitoring point in the corresponding monitoring period of the special disease patient, and the energy intake data, body temperature data and blood pressure data are collected and transmitted to the health monitoring unit. The health monitoring unit performs health monitoring analysis on the patient information collected from the special disease patient in the monitoring period, so as to monitor and obtain the intake prompt signal, the meal sharing standard signal, the meal sharing violation signal, the first rate abnormality signal and the second rate abnormality signal, and transmit them to the health analysis unit. The health analysis unit performs health combined analysis and processing on the exercise condition of the special disease patient and the conversion signal of the monitoring analysis, obtains the patient health evaluation coefficient, and transmits it to the display and warning terminal. Through the display and warning terminal, a health warning is performed on the patient's physical health monitoring, and a continued observation signal, an inspection signal and a safety warning signal are obtained. Different warning alarms are performed on the display and warning terminal according to the continued observation signal, the inspection signal and the safety warning signal.

[0072] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A health monitoring method for patients with specific diseases based on artificial intelligence, the method specifically comprises the following steps: Step 1: The data collection unit collects patient information of each monitoring point in the corresponding monitoring period of the patient with a specific disease, collects energy intake data, body temperature data and blood pressure data and transmits them to the health monitoring unit; it is characterized in that: Also includes: Step 2: Through the health monitoring unit, the patient information collected during the monitoring period of the special disease patients is analyzed for health monitoring, and the corresponding energy intake data is extracted according to the meal time of the three meals a day, and the three energy intake data corresponding to the meal time of the three meals a day are summed up and calculated to calculate the total energy value of the energy intake plane diagram, and marked as the total energy value; the calculation method of the total energy value is repeated to calculate the total energy value of the meals every day, and several total energy values ​​are calculated according to the mean calculation formula to calculate the total energy mean, and each total energy value is calculated with the total energy mean to calculate the difference, and several total energy differences are calculated, and threshold comparison is performed on several total energy differences. When the total energy difference falls within the total energy difference threshold range, an intake standard signal is generated, and when the total energy difference does not fall within the total energy difference threshold range, an intake abnormality signal is generated; The first, second and third meals of the three meals a day are analyzed and processed individually to obtain the meal separation standard signal and the meal separation violation signal; the body temperature data is extracted, and the temperature data of one monitoring point is selected from each monitoring point in the monitoring period as the initial body temperature, and the initial body temperature and the body temperature data are substituted into the calculation formula: , calculate the temperature change of patients with special diseases at each monitoring point and calibrate it as the temperature change rate , when l=1, Initial body temperature , n is a positive integer, It is expressed as the deviation adjustment factor of the temperature change of patients with special diseases; extract blood pressure data and select the blood pressure data of one monitoring point from each monitoring point in the monitoring period as the initial blood pressure, and substitute the initial blood pressure and blood pressure data into the calculation formula: , calculate the blood pressure changes of patients with special diseases at various monitoring points and calibrate them as pressure change rate , when l=1, Initial blood pressure , It is expressed as a deviation adjustment factor of the blood pressure change of the patient with a specific disease, and the temperature change rate and the pressure change rate are processed to obtain a first change rate abnormality signal and a second change rate abnormality signal, and transmit them to the health analysis unit; Step 3: Through the health analysis unit, the movement conditions of the patient with a specific disease and the conversion signal of the supervision analysis are processed for health combination analysis to obtain the patient's health evaluation coefficient, and transmit it to the display warning terminal; Step 4: Perform health warning on the patient's physical health monitoring through the display warning terminal, obtain a continued observation signal, an inspection signal and a safety warning signal, and issue different warning alarms on the display warning terminal according to the continued observation signal, the inspection signal and the safety warning signal; The energy intake data refers to the amount of food energy intake of the corresponding patient in the patient information, the body temperature data refers to the body temperature value of the corresponding patient at each monitoring point in the patient information, and the blood pressure data refers to the blood pressure value of the corresponding patient at each monitoring point in the patient information; The energy difference threshold range is the preset value; the intake standard signal and the intake abnormal signal are uniformly calibrated as the intake prompt signal , the value of h is 1, 2; The specific process of single meal analysis is as follows; Calculate the average energy intake of the first, second, and third meals of the day, and compare them with the energy intake data of the first, second, and third meals: When the difference between the energy intake value of each meal and the corresponding average energy intake value of the first meal, the average energy intake value of the second meal, and the average energy intake value of the third meal is within the floating threshold range, a meal distribution standard signal is generated. , k1 takes the value of 1, 2, or 3. When k1 takes the value of 1, 2, or 3, it corresponds to the standard signal 1, standard signal 2, or standard signal 3. When the difference between the energy intake value of each meal and the corresponding average energy intake value of the first meal, the average energy intake value of the second meal, and the average energy intake value of the third meal is outside the floating threshold range, a meal sharing violation signal is generated. , when the value of k2 is 1, 2, or 3, it corresponds to violation signal 1, violation signal 2, or violation signal 3, respectively, where the floating threshold range is the preset value; The specific process of rate change processing is as follows: The temperature change rate and pressure change rate corresponding to two adjacent dates are calculated by difference, and the temperature change rate difference and the pressure change rate difference are calculated. The temperature change rate difference less than zero is marked as a decreasing temperature change rate, and a temperature change rate decreasing signal is generated. The temperature change rate difference greater than zero is marked as an increasing temperature change rate, and a temperature change rate increasing signal is generated. Similarly, the pressure change rate difference is marked as positive or negative, and a pressure change rate decreasing signal or a pressure change rate increasing signal is generated. If the temperature change rate decrease signal or the temperature change rate increase signal appears more than the preset number of times, a rate abnormality signal is generated. , r1 takes the value of 1, 2. If the number of occurrences of the pressure change rate drop signal or the pressure change rate increase signal exceeds the preset number, a second rate abnormality signal is generated. , the value of r2 is 1, 2; The specific process of the health analysis unit performing health combined analysis on the movement conditions of the patient with a specific disease and the conversion signal of the supervision analysis is as follows: Collect the total length of exercise and the number of steps taken by the patients during exercise every day, and substitute the total length of exercise and the corresponding number of steps into the calculation formula: exercise consumption = total length of exercise / number of steps The preset conversion factor is a preset value. The difference between the exercise consumption of two consecutive days is calculated to calculate the difference in consumption. The daily body weight of the patient with a special disease is collected and calibrated as the weight data. The difference between the weight data of two consecutive days is calculated to calculate the difference in weight. According to the preset formula: Exercise consumption coefficient = patient weight difference, reverse deduction of exercise consumption coefficient; Extracting intake cues , Standard signal for meal sharing , Signals of illegal meal sharing 1. Abnormal rate signal And the second rate abnormal signal And substitute it into the preset calculation formula together with the energy consumption coefficient: Calculate the patient's health evaluation coefficient JK, They are respectively represented as the evaluation conversion preset factors of the intake prompt signal, the meal-sharing standard signal, the meal-sharing violation signal, the one-rate abnormal signal and the two-rate abnormal signal, and xh is represented as the exercise consumption coefficient; The specific process of monitoring the patient's physical health for health warning is as follows: The patient's health evaluation coefficient JK is compared with the health preset value MN of the special disease patient: when JK≥MN, a continue observation signal is generated; when JK<MN, a health abnormality signal is generated, and a secondary comparison is performed based on the health abnormality signal; when MN>JK>MN / 2, an inspection signal is generated; when MN / 2>JK, a safety warning signal is generated.

2. The artificial intelligence-based health monitoring terminal for patients with specific diseases is characterized by: include: A memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the artificial intelligence-based health monitoring terminal for special disease patients executes the artificial intelligence-based health monitoring method for special disease patients as described in any one of claim 1.

3. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the artificial intelligence-based health monitoring method for patients with specific diseases as described in any one of claim 1 is implemented.

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