CN111489806A - Intelligent diabetes heat management method and system - Google Patents
Intelligent diabetes heat management method and system Download PDFInfo
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- CN111489806A CN111489806A CN202010273245.7A CN202010273245A CN111489806A CN 111489806 A CN111489806 A CN 111489806A CN 202010273245 A CN202010273245 A CN 202010273245A CN 111489806 A CN111489806 A CN 111489806A
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
Abstract
The invention discloses an intelligent diabetes heat management method and system, comprising the following steps: s1, acquiring the calorie of each diet ingested by the patient within a certain time period; s2, acquiring the heat consumed by each movement of the patient in the time period; s3, obtaining the blood sugar value and the glycosylated hemoglobin value of the patient in the fasting state, the postprandial blood sugar value and the glycosylated hemoglobin value, and the 2-hour postprandial blood sugar value and the glycosylated hemoglobin value in the time period; s4, substituting the characteristics of the region to which the patient belongs, the calorie of each diet, the calorie consumed by each exercise, the blood sugar value and the glycosylated hemoglobin value in the fasting state, the blood sugar value and the glycosylated hemoglobin value after meal, and the blood sugar value and the glycosylated hemoglobin value after meal for 2 hours into the insert map model for training, substituting the training result of the insert map model into the graph convolution neural network for training to obtain the similar calorie control scheme of the patient marked as good blood sugar management to the patient.
Description
Technical Field
The invention relates to the technical field of diabetes heat management, in particular to an intelligent diabetes heat management method and system.
Background
Due to the accelerated aging process of the population and the influence of unhealthy life style caused by industrialization and urbanization, chronic diseases in China have a rapidly rising trend in recent years, the death caused by chronic diseases accounts for 86.6% of the total death of the whole country, the burden of the diseases accounts for nearly 70% of the total disease burden, diabetes is particularly prominent, the data published by the international diabetes union in 2015 show that the prevalence rate of global adults (20-79 years) is 10.6%, the prevalence rate of adult diabetes patients reaches 4.15 billion, the population of Chinese adult diabetes patients exceeds 1 and accounts for about one fourth of the total world, the eighth version of the global diabetes map published by the international diabetes alliance in 2017 shows that the prevalence of adult diabetes (20-79 years) reaches 1.14, the prevalence rate of type 2 diabetes reaches about 10%, the situation that about every 10 adults has no daily diabetes, one of type 2 diabetes patients becomes the most hygienic public health care, the dietary intake of insulin is not enough to meet the daily caloric control requirements of diabetes management requirements, the dietary requirements of general dietary requirements of people for dietary management of diabetes mellitus, the dietary management of insulin is not enough to satisfy the dietary requirements of dietary management of dietary conditions of diabetes mellitus, and dietary intake of dietary insulin, the dietary management of dietary factors of insulin, the dietary management of insulin is not enough to cause the dietary management of diabetes mellitus, the dietary management of dietary management.
The diabetes heat management APP on the market at present mostly depends on the active record of the patient and has no corresponding reminding function once the patient falls off, the food base of some heat calculation APPs is complete in data, can give out the heat of food, is matched with red and green lights of food, and is also attentive to the GI (blood glucose index) and G L (blood glucose load) values, when the food consumption is recorded, a measuring mode of spoon and the like is added, but not just how many grams of food is provided, but is matched with red and green lights of food, but is also attentive to the physical (physical food index) and the personalized value of G L (blood glucose load), when the food consumption is recorded, the food consumption is also attentive, a measuring mode of spoon and the like is also added, but not a simple diet is not matched with the red and green lights of food, but for all people or people with diabetes, the food is derived from ideal physical and protein, the personalized dietary intake ratio of the diabetes is also estimated based on the traditional dietary intake map, the heat management method can be used for developing a lower dietary intake of people, and the dietary intake of the dietary management is not based on the traditional dietary management technology.
Disclosure of Invention
Aiming at the problems and the defects in the prior art, the invention provides a novel intelligent diabetes heat management method and a novel intelligent diabetes heat management system.
The invention solves the technical problems through the following technical scheme:
the invention provides an intelligent diabetes heat management method which is characterized by comprising the following steps:
s1, acquiring the calorie of each diet ingested by the patient within a certain time period.
And S2, acquiring the heat consumed by each movement of the patient in the time period.
S3, obtaining the blood sugar value and the glycosylated hemoglobin value of the patient in the fasting state, the postprandial blood sugar value and the glycosylated hemoglobin value, and the 2-hour postprandial blood sugar value and the glycosylated hemoglobin value.
S4, substituting the characteristics of the region to which the patient belongs, the calorie of each diet, the calorie consumed by each exercise, the blood sugar value and the glycosylated hemoglobin value in the fasting state, the blood sugar value and the glycosylated hemoglobin value after meal, and the blood sugar value and the glycosylated hemoglobin value after meal for 2 hours into the insert map model for training, substituting the training result of the insert map model into the graph convolution neural network for training to obtain the similar calorie control scheme of the patient marked as good blood sugar management to the patient.
Preferably, in step S1, when the food to be eaten by the patient is the finished product, the calorie two-dimensional code of the food to be eaten is scanned to obtain the calorie of the food to be eaten, and when the food to be eaten by the patient is cooked at home, the calorie of the food to be eaten is obtained by multiplying the unit calorie value of the raw material of the food to be eaten by the weight.
Preferably, in step S2, the calorie consumed by the patient under the exercise item is calculated according to the calorie consumed by the patient per unit time of the exercise item and the exercise time.
The invention also provides an intelligent diabetes heat management system which is characterized by comprising a diet heat acquisition module, a sports consumption heat acquisition module, a blood sugar acquisition module and a recommendation module.
The diet calorie acquisition module is used for acquiring calorie of each diet ingested by the patient within a certain time period.
The exercise consumption heat acquisition module is used for acquiring the heat consumed by each exercise in the time period of the patient.
The blood sugar obtaining module is used for obtaining the blood sugar value and the glycosylated hemoglobin value of the patient in the fasting state in the time period, the postprandial blood sugar value and the glycosylated hemoglobin value, and the 2-hour postprandial blood sugar value and the glycosylated hemoglobin value.
The recommending module is used for substituting the characteristics of the region to which the patient belongs, the calorie of each diet, the calorie consumed by each exercise, the blood sugar value and the glycosylated hemoglobin value in the fasting state, the blood sugar value and the glycosylated hemoglobin value after meal, and the blood sugar value and the glycosylated hemoglobin value after 2 hours of meal into the insert graph model for training, substituting the training result of the insert graph model into the graph convolution neural network for training to obtain the calorie control scheme similar to the patient and labeled as the patient with good blood sugar management, and recommending the heat control scheme to the patient.
Preferably, the diet calorie acquisition module is configured to scan the calorie two-dimensional code of the food to be eaten to obtain the calorie of the food to be eaten when the food to be eaten by the patient is a finished product, and multiply the unit calorie value by the weight according to the corresponding cooking mode of the raw material of the food to be eaten when the food to be eaten by the patient is cooked at home to obtain the calorie of the food to be eaten.
Preferably, the exercise consumption calorie acquisition module is configured to calculate the calorie consumed by the patient under the exercise item according to the calorie consumed by the patient per unit time of the exercise item and the exercise time.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows:
the invention recommends to the patient the dietary condition, the exercise condition and the blood sugar condition at present, and recommends to the patient the dietary and exercise calorie control scheme which is good for blood sugar management with similar conditions. The invention feeds back the calculated result of the managed calorie intake and consumption habits to other similar users with poor management according to the similarity of the users, thereby establishing a high-compliance diabetes calorie management scheme and realizing better calorie management.
Drawings
Fig. 1 is a setting interface of the diet calorie two-dimensional code in this embodiment.
Fig. 2 is a setting interface of the motion heat two-dimensional code in the embodiment.
FIG. 3 is a schematic diagram of an embedded graph model for generating node A from two layers of propagation.
FIG. 4 is a flowchart illustrating a method for intelligent diabetes heat management in accordance with the present invention.
FIG. 5 is a block diagram of the intelligent diabetes heat management system of the present embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 4, the present embodiment provides an intelligent diabetes heat management method, which includes the following steps:
In step 101, when the food to be eaten by the patient is a finished product, the calorie two-dimensional code of the food to be eaten is scanned to obtain the calorie of the food to be eaten (see fig. 1), and when the food to be eaten by the patient is cooked at home, the calorie of the food to be eaten is obtained by multiplying the unit calorie value of the raw material of the food to be eaten by the weight in the corresponding cooking mode.
In step 102, the calorie consumed by the patient under the exercise item is calculated according to the calorie consumed by the unit time of the exercise item and the exercise time of the patient (see fig. 2).
103, obtaining the blood sugar value and the glycosylated hemoglobin value of the patient in the fasting state, the postprandial blood sugar value and the glycosylated hemoglobin value, and the 2-hour postprandial blood sugar value and the glycosylated hemoglobin value.
And 104, substituting the characteristics of the region to which the patient belongs, the calorie of each diet, the calorie consumed by each exercise, the blood sugar value and the glycosylated hemoglobin value in the fasting state, the blood sugar value and the glycosylated hemoglobin value after meal, and the blood sugar value and the glycosylated hemoglobin value after meal for 2 hours into an insert map model (see figure 3) for training, and substituting the training result of the insert map model into a graph convolution neural network for training to obtain the similar calorie control scheme of the patient marked as good blood sugar management to the patient.
As shown in fig. 5, the present embodiment further provides an intelligent diabetes heat management system, which includes a diet heat acquiring module 1, an exercise consumption heat acquiring module 2, a blood sugar acquiring module 3, and a recommending module 4.
The diet calorie acquisition module 1 is used for acquiring calorie of each diet ingested by a patient within a certain period of time.
The diet calorie acquisition module 1 is configured to scan a calorie two-dimensional code of food to be eaten to obtain a calorie of the food to be eaten when the food to be eaten by the patient is a finished product, and multiply the unit calorie value by the weight according to the corresponding cooking mode of the raw material of the food to be eaten when the food to be eaten by the patient is cooked at home to obtain the calorie of the food to be eaten.
The exercise consumption heat acquisition module 2 is used for acquiring the heat consumed by each exercise in the time period of the patient.
The exercise consumption calorie acquisition module 2 is configured to calculate the calorie consumed by the patient under the exercise item according to the calorie consumed by the patient in unit time of the exercise item and the exercise time.
The blood sugar obtaining module 3 is used for obtaining the blood sugar value and the glycosylated hemoglobin value of the patient in the fasting state, the postprandial blood sugar value and the glycosylated hemoglobin value, and the 2-hour postprandial blood sugar value and the glycosylated hemoglobin value.
The recommending module 4 is configured to substitute the characteristics of the region to which the patient belongs, the calorie of each diet, the calorie consumed by each exercise, the fasting blood glucose value and the glycated hemoglobin value, the postprandial blood glucose value and the glycated hemoglobin value, and the 2-hour postprandial blood glucose value and the glycated hemoglobin value into the inset model for training, substitute the inset model training result into the graph convolution neural network for training to obtain a calorie control scheme similar to that of the patient and labeled as a patient with good blood glucose management, and recommend the calorie control scheme to the patient.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (6)
1. An intelligent diabetes heat management method is characterized by comprising the following steps:
s1, acquiring the calorie of each diet ingested by the patient within a certain time period;
s2, acquiring the heat consumed by each movement of the patient in the time period;
s3, obtaining the blood sugar value and the glycosylated hemoglobin value of the patient in the fasting state, the postprandial blood sugar value and the glycosylated hemoglobin value, and the 2-hour postprandial blood sugar value and the glycosylated hemoglobin value in the time period;
s4, substituting the characteristics of the region to which the patient belongs, the calorie of each diet, the calorie consumed by each exercise, the blood sugar value and the glycosylated hemoglobin value in the fasting state, the blood sugar value and the glycosylated hemoglobin value after meal, and the blood sugar value and the glycosylated hemoglobin value after meal for 2 hours into the insert map model for training, substituting the training result of the insert map model into the graph convolution neural network for training to obtain the similar calorie control scheme of the patient marked as good blood sugar management to the patient.
2. The intelligent diabetes heat management method according to claim 1, wherein in step S1, when the food to be eaten by the patient is a finished product, the two-dimensional code of the heat of the food to be eaten is scanned to obtain the heat of the food to be eaten, and when the food to be eaten by the patient is cooked at home, the heat of the food to be eaten is obtained by multiplying the unit heat value by the weight according to the corresponding cooking style of the raw material of the food to be eaten.
3. The intelligent diabetes heat management method according to claim 1, wherein in step S2, the heat consumed by the patient under the exercise item is calculated according to the heat consumed per unit time and the exercise time of the exercise item of the patient.
4. An intelligent diabetes heat management system is characterized by comprising a diet heat acquisition module, a sports consumption heat acquisition module, a blood sugar acquisition module and a recommendation module;
the diet calorie acquisition module is used for acquiring the calorie of each diet ingested by the patient within a certain time period;
the exercise consumption heat acquisition module is used for acquiring the heat consumed by each exercise in the time period of the patient;
the blood sugar obtaining module is used for obtaining the blood sugar value and the glycosylated hemoglobin value of the patient in the fasting state in the time period, the postprandial blood sugar value and the glycosylated hemoglobin value, and the 2-hour postprandial blood sugar value and the glycosylated hemoglobin value;
the recommending module is used for substituting the characteristics of the region to which the patient belongs, the calorie of each diet, the calorie consumed by each exercise, the blood sugar value and the glycosylated hemoglobin value in the fasting state, the blood sugar value and the glycosylated hemoglobin value after meal, and the blood sugar value and the glycosylated hemoglobin value after 2 hours of meal into the insert graph model for training, substituting the training result of the insert graph model into the graph convolution neural network for training to obtain the calorie control scheme similar to the patient and labeled as the patient with good blood sugar management, and recommending the heat control scheme to the patient.
5. The intelligent diabetes heat management system of claim 4, wherein the diet heat acquiring module is configured to scan the heat two-dimensional code of the food to be eaten to acquire the heat of the food to be eaten when the food to be eaten by the patient is a finished product, and to multiply the unit heat value by the weight according to the corresponding cooking mode of the raw material of the food to be eaten when the food to be eaten by the patient is cooked at home to acquire the heat of the food to be eaten.
6. The intelligent diabetes heat management system of claim 4, wherein the exercise consumption heat acquisition module is configured to calculate the heat consumed by the patient under the exercise item according to the heat consumed by the patient per unit time and the exercise time of the exercise item.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113160931A (en) * | 2021-04-12 | 2021-07-23 | 深圳英鸿骏智能科技有限公司 | Fitness action energy consumption evaluation method, device, equipment and storage medium |
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