An intelligent mobile platform for personalized medical and health management

Abstract

The present invention discloses an intelligent mobile platform for personalized medical health management, which relates to the field of medical information technology. The method includes: a personal health record management module, which is used to collect the patient's personal information. When the patient uses the mobile platform to log in and register, a personal information questionnaire is set, including medical history, allergy history, family genetic history and nutritional intake information; a health monitoring module, which is used to collect the patient's physiological data through a wearable device, including heart rate, blood pressure, blood sugar, and exercise habits; a health assessment module, which is used to assess the patient's health status based on monitoring data and health record data, and provide health risk assessment, lifestyle improvement suggestions, and disease prediction services; a nutrition management module, which is used to provide nutritional intake guidance and diet plans based on the patient's health assessment and dietary preferences. The present invention reduces the cost of medical services and improves the utilization efficiency of medical resources by providing personalized medical services.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to an intelligent mobile platform for personalized medical health management. Background Art

[0002] With the rapid development of society, people's lifestyles and health conditions are constantly changing, and people's demand for health is getting higher and higher. Personalized medical and health management has become a new trend in current health services. The core of personalized medical and health management is to provide personalized medical and health services to individuals based on factors such as individual genotype, physiological characteristics, environmental conditions and living habits.

[0003] By analyzing and evaluating an individual's physiological and health data, we can more accurately understand the individual's health status and potential risks, provide personalized health management recommendations, and achieve continuous monitoring and management of patients through automated and intelligent means, thereby improving user satisfaction and experience and increasing resource utilization efficiency.

[0004] In summary, the intelligent mobile platform for personalized medical and health management is of great significance in improving the accuracy of health management, improving the efficiency of medical services, improving user experience and reducing medical costs.

[0005] For example, the existing Chinese patent with publication number CN114360724A discloses a method for constructing a health management knowledge graph, a service provision method and related devices, which obtain medical and health data; establish an entity recognition model; bring the medical and health data into the entity recognition model for classification, and establish basic instances of various types of data; obtain health management service information; establish the relationship between health management service information and basic instances; and construct a health management knowledge graph containing basic instances, health management services and the relationship between the two. By constructing a health management knowledge graph, it is possible to accurately match individual health needs with health management service supply, so that health management is more in line with human needs rather than the demands of disease management. At the same time, it reduces the requirements for user health management knowledge, improves the efficiency of health management services, and achieves precision and personalization of services. However, this design does not take into account the impact of eating habits and exercise habits on health conditions. For this reason, the present invention provides an intelligent mobile platform for personalized medical and health management. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent mobile platform for personalized medical health management to solve the existing problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: an intelligent mobile platform for personalized medical and health management, comprising:

[0008] The personal health record management module is used to collect the patient's personal information. When the patient logs in and registers using the mobile platform, a personal information questionnaire is set up, including medical history, allergy history, family genetic history, and nutritional intake information;

[0009] The health monitoring module is used to collect the patient's physiological data through wearable devices, including heart rate, blood pressure, blood sugar, and exercise habits;

[0010] The health assessment module is used to assess the patient's health status based on monitoring data and health record data, and provide health risk assessment, lifestyle improvement suggestions, and disease prediction services;

[0011] Nutrition management module, used to provide nutrition intake guidance and diet plan based on the patient's health assessment and dietary preferences.

[0012] A further improvement of the present invention is that the health monitoring module includes a data acquisition unit and a disease risk assessment unit. The data acquisition unit is used to collect the patient's exercise habits and nutritional intake information, and is also used to collect the patient's physiological data. The physiological data includes gender, age, heart rate, blood pressure, and blood sugar. After collecting the medical history information, the mobile platform will integrate the patient's medical data through an API interface. The medical data includes the patient's past medical records, examination results, laboratory reports, and drug prescriptions; the disease risk assessment unit is used to calculate the patient's disease risk index based on the information obtained by the data acquisition unit.

[0013] A further improvement of the present invention is that the patient's disease risk index is obtained by establishing a disease risk assessment model, which uses the physiological data and medical data of historical patients in the hospital as training data for the model, and calculates the disease risk index of the examination results of historical patients in the hospital according to different disease types and severities. The disease risk index is calculated by the doctor assigning a disease risk value according to different disease types in the examination results, and multiplying it by the patient's cure time. The relationship between the disease risk index and the physiological data is trained, and a relationship data set between the disease risk index and the disease type is established. The data obtained by the data acquisition unit is used as the input of the disease risk assessment model to obtain the patient's disease risk index dri.

[0014] A further improvement of the present invention is that the health assessment module includes a diet and exercise risk assessment unit and a health service unit. The diet and exercise risk assessment unit is used to calculate the patient's diet and exercise risk index based on the exercise habits and nutritional intake information collected by the health monitoring module as input to the diet and exercise monitoring strategy; the health service unit is used to provide health risk assessment, lifestyle improvement suggestions and disease prediction services based on the data obtained by the diet and exercise risk assessment unit and the disease risk assessment unit.

[0015] A further improvement of the present invention is that the specific steps of the diet and exercise monitoring strategy include: using the patient's nutritional intake information as the input of the nutritional intake assessment strategy to obtain the nutritional intake assessment value nia; using the patient's exercise habits as the input of the exercise risk assessment strategy to obtain the exercise risk value Srv; combining the nutritional intake assessment value and the exercise risk value to obtain the diet and exercise risk index der = α1·nia + α2·Srv, wherein α1 represents the nutritional intake assessment value weight, and α2 represents the exercise risk value weight.

[0016] The present invention is further improved in that the nutritional intake information is obtained by patients uploading daily food photos, a food recognition and matching model is configured within the system, and the food types are obtained through the food recognition and matching model. The nutritional intake assessment strategy divides the food photos into food types through an edge detection algorithm and calculates the food size to obtain a food content data set (sf1, sf2, ..., sf n ), where sf n Represents the content of the nth food, n represents the total number of food types, and matches the food types with the nutritional composition table to obtain the food nutritional composition data set in, represents the jth nutrient component of the i-th food, mi represents the number of nutrients in the f-th food, and the nutritional intake assessment value is obtained

[0017] A further improvement of the present invention is that the exercise habit includes exercise patterns and exercise behaviors, the exercise pattern includes establishing an exercise type data set, including aerobic exercise aee, anaerobic exercise ane, strength training stt, flexibility training flt, circuit training cit and leisure exercise lea, and assigning exercise intensity values ​​in turn and sorting them from large to small to obtain an exercise type intensity data set (ane=6, aee=5, stt=4, cit=3, flt=2, lea=1); the exercise behavior includes exercise duration st, the patient's exercise type is collected after the patient exercises, and the patient's exercise type is sent to the exercise type intensity data set for comparison to obtain the patient's exercise intensity exi; the exercise risk assessment strategy is obtained by combining exercise patterns and exercise behaviors, and the exercise risk numerical calculation formula is srv=st·exi.

[0018] A further improvement of the present invention is that the disease prediction service includes uploading the disease risk index to a relationship data set between the disease risk index and the disease type, obtaining the predicted type of the patient's disease, retrieving the patient's medical history and family genetic history, and if the predicted type of the patient's disease matches the patient's medical history or family genetic history, the platform will recommend the patient to go to the hospital for medical treatment; if the predicted type of the patient's disease does not match the patient's medical history and family genetic history, a health risk assessment will be performed.

[0019] The present invention is further improved in that the health risk assessment includes setting disease risk thresholds Tdr1, Tdr2 and diet and exercise risk thresholds Tde1, Tde2, and setting judgment functions F1, F2, F3, F4, F5;

[0020] When the judgment function F1 = {dri∈(0, Tdr1)∩der∈[Tde1, Tde2]} is satisfied, the platform determines that the patient's diet, exercise and health status are safe;

[0021] When the judgment function is satisfied When the patient is diagnosed with a dangerous diet and exercise status, the platform will provide diet and exercise recommendations;

[0022] When the judgment function F3 = {dri∈[Tdr1, Tdr2]∩der∈[Tde1, Tde2]} is satisfied, the platform determines that the patient's health status is dangerous and recommends that the patient consult a doctor on the platform;

[0023] When the judgment function is satisfied When the patient's diet and exercise status is dangerous, the platform determines that the patient's diet and exercise status is dangerous and provides diet and exercise suggestions. If the judgment function F3 is met for three consecutive times, the patient is advised to consult a doctor on the platform;

[0024] When the judgment function is satisfied When the platform determines that the patient's diet and exercise status are in danger, it recommends that the patient go to the hospital for treatment.

[0025] The present invention is further improved in that the nutrition management module obtains the patient's allergic foods through the patient's allergy history, retrieves the family genetic adverse food sequence through the patient's family genetic history, retrieves the medical history adverse food sequence through the patient's medical history, extracts the overlapping data between the family genetic adverse food sequence and the medical history adverse food sequence as the patient's taboo food sequence, and sorts the patient's food types obtained by the health assessment module according to the size of the intake to obtain the patient's dietary preference sequence (asf1, asf2, ..., asf n ), where asf nRepresents the nth kind of food, n represents the total number of food types in the sequence, and excludes foods in the patient's contraindicated food sequence and foods that the patient is allergic to. When the platform detects der∈(Tde2, +∞), the system will exclude high-calorie and high-fat foods from the patient's dietary preference sequence, and include fruits and vegetables with corresponding nutrients in front of asf1 in the patient's dietary preference sequence after excluding allergic foods and contraindicated foods; when the platform detects der∈(0, Tde1), the system will include high-calorie and high-egg foods in front of asf1 in the patient's dietary preference sequence after excluding allergic foods and contraindicated foods. The platform will provide food combination recommendations according to the patient's standard nutrient intake in accordance with the patient's dietary preference sequence.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. The present invention first sets a disease risk assessment model in the health monitoring module, trains the relationship between the disease risk index and the physiological data, and then uses the data obtained by the data acquisition unit as the input of the disease risk assessment model to obtain the patient's disease risk index. This can detect the patient's potential health problems, take preventive measures in advance, reduce the risk of disease, and provide disease prevention services for patients.

[0028] 2. Obtain the patient's dietary preference sequence through the nutrition management module, formulate a personalized dietary management plan based on the patient's health status and eating and exercise habits, provide specific nutrient intake guidance based on the patient's nutritional intake, medical history and allergy history, reduce the risk of disease, ensure the balance of the patient's daily intake of nutrients and energy, and reduce the cost of medical services and improve the utilization efficiency of medical resources by providing personalized medical services. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a framework diagram of an intelligent mobile platform for personalized medical and health management according to the present invention; DETAILED DESCRIPTION

[0030] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0031] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship.

[0032] Example 1

[0033] Figure 1 The framework diagram of a smart mobile platform for personalized medical and health management disclosed in this embodiment is shown, including:

[0034] The personal health record management module is used to collect patients' personal information. When patients log in and register using the mobile platform, a personal information questionnaire is set up, including medical history, allergy history, family genetic history and nutritional intake information. The platform develops a user-friendly interface that allows patients to enter and update personal health information on mobile devices. The interface contains necessary form fields and options, allowing users to easily enter their own health information, and establishes an exclusive database for each patient to store the patient's health information.

[0035] The health monitoring module is used to collect the patient's physiological data through wearable devices, including heart rate, blood pressure, blood sugar, and exercise habits; it includes a data collection unit and a disease risk assessment unit. The data collection unit is used to collect the patient's exercise habits and nutritional intake information, and is also used to collect the patient's physiological data. The physiological data includes gender, age, heart rate, blood pressure, and blood sugar. After collecting the medical history information, the mobile platform will integrate the patient's medical data through the API interface. The medical data includes the patient's past medical records, examination results, laboratory reports, and drug prescriptions; the disease risk assessment unit is used to establish a disease risk assessment model to obtain the patient's disease risk index. The disease risk assessment model uses the physiological data and medical data of historical patients in the hospital as training data for the model. The hospital's historical patient examination results are used to calculate the disease risk index according to different disease types and severities. The disease risk index is calculated by the doctor assigning a disease risk value according to different disease types in the examination results, and multiplying it with the patient's recovery time. The relationship between the disease risk index and physiological data is trained, and a relationship data set between the disease risk index and the disease type is established. The data obtained by the data acquisition unit is used as the input of the disease risk assessment model to obtain the patient's disease risk index dri. In this module, the system develops a user-friendly interface that allows patients to view and analyze their own physiological data on mobile devices. The interface can provide different charts and statistical data according to the needs of patients to help patients better understand their health status.

[0036] The health assessment module is used to assess the health status of patients based on monitoring data and health file data, and provide health risk assessment, lifestyle improvement suggestions, and disease prediction services; it includes a diet and exercise risk assessment unit and a health service unit. The diet and exercise risk assessment unit is used to calculate the diet and exercise risk index of patients based on the exercise habits and nutritional intake information collected by the health monitoring module as input to the diet and exercise monitoring strategy. At the same time, the hospital's health management system can integrate the health assessment module to provide services for patients;

[0037] The specific steps of the diet and exercise monitoring strategy include: using the patient's nutritional intake information as the input of the nutritional intake assessment strategy to obtain a nutritional intake assessment value, the nutritional intake information is obtained by the patient uploading daily food photos, the system configures a food recognition matching model internally, and obtains the food type through the food recognition matching model, the nutritional intake assessment strategy divides the food photos into food types through an edge detection algorithm, and calculates the food size to obtain a food content data set (sf1, sf2, ..., sf n ), where sf n Represents the content of the nth food, n represents the total number of food types, and matches the food types with the nutritional composition table to obtain the food nutritional composition data set in, represents the jth nutrient component of the ith food, mi represents the number of nutrients in the ith food, and the nutritional intake assessment value is obtained Exercise habits include exercise patterns and exercise behaviors. The exercise patterns include establishing an exercise type data set, including aerobic exercise aee, anaerobic exercise ane, strength training stt, flexibility training flt, circuit training cit and leisure exercise lea, assigning exercise intensity values ​​in turn and sorting them from large to small to obtain an exercise type intensity data set (ane=6, aee=5, stt=4, cit=3, flt=2, lea=1); the exercise behavior includes exercise duration st, and the patient's exercise type is collected after the patient exercises, and the patient's exercise type is sent to the exercise type intensity data set for comparison to obtain the patient's exercise intensity exi; the exercise risk assessment strategy is obtained by combining exercise patterns and exercise behaviors, and the exercise risk numerical calculation formula is srv=st·exi.

[0038] The patient's exercise habits are used as the input of the exercise risk assessment strategy to obtain the exercise risk value srv; the nutritional intake assessment value and the exercise risk value are combined to obtain the diet exercise risk index der = α1·nia + α2·srv, wherein α1 represents the nutritional intake assessment value weight, and α2 represents the exercise risk value weight.

[0039] The health service unit is used to provide health risk assessment, lifestyle improvement suggestions and disease prediction services based on the data obtained by the diet and exercise risk assessment unit and the disease risk assessment unit. The health risk assessment includes setting disease risk thresholds Tdr1, Tdr2 and diet and exercise risk thresholds Tde1, Tde2, and setting judgment functions F1, F2, F3, F4, F5;

[0040] When the judgment function F1 = {dri∈(0, Tdr1)∩der∈[Tde1, Tde2]} is satisfied, the platform determines that the patient's diet, exercise and health status are safe;

[0041] When the judgment function is satisfied When the patient is diagnosed with a dangerous diet and exercise status, the platform will provide diet and exercise recommendations;

[0042] When the judgment function F3 = {dri∈[Tdr1, Tdr2]∩der∈[Tde1, Tde2]} is satisfied, the platform determines that the patient's health status is dangerous and recommends that the patient consult a doctor on the platform;

[0043] When the judgment function is satisfied When the patient's diet and exercise status is dangerous, the platform determines that the patient's diet and exercise status is dangerous and provides diet and exercise suggestions. If the judgment function F3 is met for three consecutive times, the patient is advised to consult a doctor on the platform;

[0044] When the judgment function is satisfied When the platform determines that the patient's diet and exercise status are in danger, it recommends that the patient go to the hospital for treatment.

[0045] The disease prediction service includes uploading the disease risk index to the relationship data set between the disease risk index and the disease type, obtaining the predicted type of disease the patient suffers from, and retrieving the patient's medical history and family genetic history. If the predicted type of disease the patient suffers from matches the patient's medical history or family genetic history, the platform will recommend the patient to go to the hospital for medical treatment. If the predicted type of disease the patient suffers from does not match the patient's medical history and family genetic history, a health risk assessment will be performed.

[0046] The nutrition management module is used to provide nutrition intake guidance and diet plans according to the patient's health assessment and dietary preferences, obtain the patient's allergic foods through the patient's allergy history, retrieve the family genetic adverse food sequence through the patient's family genetic history, retrieve the medical history adverse food sequence through the patient's medical history, extract the overlapping data between the family genetic adverse food sequence and the medical history adverse food sequence as the patient's taboo food sequence, sort the patient's food types obtained by the health assessment module according to the size of the intake, and obtain the patient's dietary preference sequence (asf1, asf2, ..., asf n ), where asf nRepresents the nth kind of food, n represents the total number of food types in the sequence, and excludes foods in the patient's contraindicated food sequence and foods that the patient is allergic to. When the platform detects der∈(Tde2, +∞), the system will exclude high-calorie and high-fat foods from the patient's dietary preference sequence, and include fruits and vegetables with corresponding nutrients in front of asf1 in the patient's dietary preference sequence after excluding allergic foods and contraindicated foods; when the platform detects der∈(0, Tde1), the system will include high-calorie and high-egg foods in front of asf1 in the patient's dietary preference sequence after excluding allergic foods and contraindicated foods. The platform will provide food combination recommendations according to the patient's standard nutrient intake in accordance with the patient's dietary preference sequence.

[0047] The threshold and weight are set by professionals according to specific needs.

[0048] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0052] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. An intelligent mobile platform for personalized medical and health management, characterized by: include: The personal health record management module is used to collect the patient's personal information. When the patient logs in and registers using the mobile platform, a personal information questionnaire is set up, including medical history, allergy history, family genetic history, and nutritional intake information; The health monitoring module is used to collect the patient's physiological data through wearable devices, including heart rate, blood pressure, blood sugar, and exercise habits; The health assessment module is used to assess the patient's health status based on monitoring data and health record data, and provide health risk assessment, lifestyle improvement suggestions, and disease prediction services; Nutrition management module, which is used to provide nutrition intake guidance and diet plans based on the patient's health assessment and dietary preferences; The health monitoring module includes a data collection unit and a disease risk assessment unit. The data collection unit is used to collect the patient's exercise habits and nutritional intake information, and is also used to collect the patient's physiological data, which includes gender, age, heart rate, blood pressure, and blood sugar. After collecting the medical history information, the mobile platform will integrate the patient's medical data through the API interface. The medical data includes the patient's past medical records, examination results, laboratory reports, and drug prescriptions; the disease risk assessment unit is used to calculate the patient's disease risk index based on the information obtained by the data collection unit; The patient's disease risk index is obtained by establishing a disease risk assessment model. The disease risk assessment model uses the physiological data and medical data of historical patients in the hospital as training data of the model, and calculates the disease risk index according to different disease types and severities based on the examination results of historical patients in the hospital. The disease risk index is calculated by the doctor assigning a disease risk value according to different disease types in the examination results, and multiplying it by the patient's cure time. The relationship between the disease risk index and the physiological data is trained, and a relationship data set between the disease risk index and the disease type is established. The data obtained by the data acquisition unit is used as the input of the disease risk assessment model to obtain the patient's disease risk index dri. The specific steps of the diet and exercise monitoring strategy include: using the patient's nutritional intake information as the input of the nutritional intake assessment strategy to obtain a nutritional intake assessment value nia; using the patient's exercise habits as the input of the exercise risk assessment strategy to obtain an exercise risk value srv; combining the nutritional intake assessment value and the exercise risk value to obtain a diet and exercise risk index der = α1·nia + α2·srv, wherein α1 represents the nutritional intake assessment value weight, and α2 represents the exercise risk value weight; The nutritional intake information is obtained by patients uploading daily food photos. The system configures a food recognition and matching model internally, and obtains the food types through the food recognition and matching model. The nutritional intake assessment strategy divides the food photos into food types through an edge detection algorithm and calculates the food size to obtain a food content data set (sf1, sf2, ..., sf n ), where sf n Represents the content of the nth food, n represents the total number of food types, and matches the food types with the nutritional composition table to obtain the food nutritional composition data set in, represents the jth nutrient component of the ith food, mi represents the number of nutrients in the ith food, and the nutritional intake assessment value is obtained The exercise habit includes exercise pattern and exercise behavior. The exercise pattern includes establishing an exercise type data set, including aerobic exercise aee, anaerobic exercise ane, strength training stt, flexibility training flt, circuit training cit and leisure exercise lea, assigning exercise intensity values ​​in turn and sorting them from large to small to obtain an exercise type intensity data set (ane=6, aee=5, stt=4, cit=3, flt=2, lea=1); the exercise behavior includes exercise duration st, the patient's exercise type is collected after the patient exercises, and the patient's exercise type is sent to the exercise type intensity data set for comparison to obtain the patient's exercise intensity exi; the exercise risk assessment strategy is obtained by combining exercise pattern and exercise behavior, and the exercise risk numerical calculation formula is srv=st·exi; The disease prediction service includes uploading the disease risk index to the relationship data set between the disease risk index and the disease type, obtaining the predicted disease type of the patient, retrieving the patient's medical history and family genetic history, and if the predicted disease type of the patient matches the patient's medical history or family genetic history, the platform will recommend the patient to go to the hospital for medical treatment. If the predicted disease type of the patient does not match the patient's medical history and family genetic history, a health risk assessment will be performed; The health risk assessment includes setting disease risk thresholds Tdr1, Tdr2 and diet and exercise risk thresholds Tde1, Tde2, and setting judgment functions F1, F2, F3, F4, F5; When the judgment function F1 = {dri∈(0,Tdr1)∩der∈[Tde1,Tde2]} is satisfied, the platform determines that the patient's diet, exercise and health status are safe; When the judgment function is satisfied When the patient is diagnosed with a dangerous diet and exercise status, the platform will provide diet and exercise recommendations; When the judgment function F3 = {dri∈[Tdr1, Tdr2]∩der∈[Tde1, Tde2]} is satisfied, the platform determines that the patient's health status is dangerous and recommends that the patient consult a doctor on the platform; When the judgment function is satisfied When the patient's diet and exercise status is dangerous, the platform determines that the patient's diet and exercise status is dangerous and provides diet and exercise suggestions. If the judgment function F3 is met for three consecutive times, the patient is advised to consult a doctor on the platform; When the judgment function is satisfied When the platform determines that the patient's diet and exercise status are dangerous, it recommends that the patient go to the hospital for treatment; 2. The intelligent mobile platform for personalized medical health management according to claim 1, characterized in that: The health assessment module includes a diet and exercise risk assessment unit and a health service unit. The diet and exercise risk assessment unit is used to calculate the patient's diet and exercise risk index based on the exercise habits and nutritional intake information collected by the health monitoring module as inputs to the diet and exercise monitoring strategy; The health service unit is used to provide health risk assessment, lifestyle improvement suggestions and disease prediction services based on the data obtained by the diet and exercise risk assessment unit and the disease risk assessment unit.

3. The intelligent mobile platform for personalized medical health management according to claim 2, characterized in that: The nutrition management module obtains the patient's allergic foods through the patient's allergy history, retrieves the family genetic adverse food sequence through the patient's family genetic history, retrieves the medical history adverse food sequence through the patient's medical history, extracts the overlapping data between the family genetic adverse food sequence and the medical history adverse food sequence as the patient's taboo food sequence, and sorts the patient's food types obtained by the health assessment module according to the size of the intake to obtain the patient's dietary preference sequence (asf1, asf2, ..., asf n ), where asf n Indicates the nth food, n indicates the total number of food types in the sequence, and excludes the food in the patient's taboo food sequence and the patient's allergic food. When the platform detects der∈(Tde2, +∞), the system will exclude high-calorie and high-fat foods from the patient's dietary preference sequence, and include fruits and vegetables with corresponding nutrients in the patient's dietary preference sequence in front of asf1 after excluding allergic foods and taboo foods; When the platform detects der∈(0,Tde1), the system will list high-calorie and high-protein foods in front of asf1 in the patient's dietary preference sequence after eliminating allergic foods and contraindicated foods. The platform will provide food combination recommendations according to the patient's standard nutritional intake in accordance with the patient's dietary preference sequence.

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