Health management method and system, computer equipment and storage medium thereof

Abstract

The embodiment of the invention provides a health management method and system, computer equipment and a storage medium thereof. The method comprises the steps that food data of food and medicine data of medicine are acquired; collecting health information of a to-be-managed user; constructing a knowledge graph based on the food data, the medicine data and the health information of the to-be-managed user, and evaluating the matching degree of the medicine and the food based on the knowledge graph; and generating reminding information according to the matching degree. According to the embodiment of the invention, the health information of the user is obtained through the food data and the medicine data of the medicine, then the knowledge graph is constructed to obtain the matching degree of the medicine and the food, the reminding information is generated according to the matching degree, and the accuracy and efficiency of health management are improved.

Description

Technical Field

[0001] This application relates to the technical field of health management, and particularly to a health management method, system, computer device, and its storage medium. Background Art

[0002] In the related art, there are many deficiencies in traditional health management methods. For example, diet monitoring relies on manual records, the data is inaccurate and it is difficult to identify food ingredients. The health reminder system has a fixed push frequency and does not consider the real-time health status changes of users. The medication management and diet analysis systems are independent of each other, lacking a collaborative intervention mechanism, and no correlation analysis model between medication records and diet data is established, there is a risk of interaction between drugs and foods. Summary of the Invention

[0003] This application aims to solve at least one of the technical problems existing in the prior art. For this purpose, this application proposes a health management method, system, computer device, and its storage medium, aiming to improve the accuracy and efficiency of health management.

[0004] In a first aspect, an embodiment of this application provides a health management method, including:

[0005] Obtain food data of food and drug data of drugs;

[0006] Collect health information of the user to be managed;

[0007] Construct a knowledge graph based on the food data, the drug data, and the health information of the user to be managed, and evaluate the matching degree between the drug and the food based on the knowledge graph;

[0008] Generate a reminder message according to the matching degree.

[0009] According to some embodiments of this application, the food data is obtained through the following steps:

[0010] Based on an RGB-D camera and a millimeter-wave radar, obtain the volume data and surface data of the food through a three-dimensional reconstruction algorithm;

[0011] Obtain the mass data of the food according to a weighing sensor;

[0012] Obtain a mapping table between the mass and volume of the food according to the volume data, the surface data, and the mass data;

[0013] Obtain the food data according to the mapping table.

[0014] According to some embodiments of this application, the drug data is obtained through the following steps:

[0015] Based on the RFID tag and the pressure sensor, obtain the dosage and administration frequency of the drug;

[0016] Based on LSTM, analyze the time series data of the opening and closing state of the medicine box where the drug is placed to obtain the number of times the drug is taken;

[0017] Obtain the drug data according to the dosage, the administration frequency, and the number of times the drug is taken.

[0018] According to some embodiments of the present application, the matching degree is calculated by the following formula:

[0019] Match_Score = φ + ψ + ω

[0020] Wherein, the Match_Score represents the matching degree, the φ represents the text embedding of the knowledge graph, the ψ represents the surface data of the food, and the ω represents the temporal correlation degree of the health information.

[0021] According to some embodiments of the present application, the method further includes:

[0022] Obtain the weight change amount and blood glucose index of the user to be managed according to the health information;

[0023] Obtain the medication error value of the user to be managed according to the drug data;

[0024] Obtain the health status evaluation value of the user to be managed according to the weight change amount, the blood glucose index, and the medication error value;

[0025] When the health status evaluation value is greater than the first preset value, send a health reminder message to the user to be managed.

[0026] According to some embodiments of the present application, the health status evaluation value is calculated by the following formula:

[0027] H(t) = α·ΔW + β·GMI + γ·Med_Error

[0028] Wherein, the ΔW represents the weight change amount, the GMI represents the blood glucose index, the Med_Error represents the medication error value, and the H(t) represents the health status evaluation value.

[0029] According to some embodiments of the present application, the method further includes:

[0030] Obtain feedback information based on the health information by reinforcement learning, wherein the health information includes health status, eating habits, and medication conditions;

[0031] Generate a reminder strategy according to the feedback information.

[0032] In a second aspect, an embodiment of the present application provides a health management system, including:

[0033] A data acquisition module for acquiring food data of food and drug data of drugs;

[0034] A health information collection module for collecting health information of the user to be managed;

[0035] A knowledge graph evaluation module for constructing a knowledge graph based on the food data, the drug data, and the health information of the user to be managed, and evaluating the matching degree between the drug and the food based on the knowledge graph;

[0036] A health reminder module for generating reminder information based on the matching degree.

[0037] In a third aspect, an embodiment of the present application provides a computer device, including:

[0038] At least one memory;

[0039] At least one processor;

[0040] At least one computer program;

[0041] The at least one computer program is stored in the at least one memory, and the at least one processor executes the at least one computer program to implement the health management method described in the first aspect above.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program for causing a computer to execute the health management method described in the first aspect above.

[0043] According to the technical solution of the embodiment of the present application, there are at least the following beneficial effects: The health management method of the embodiment of the present application includes: acquiring food data of food and drug data of drugs; collecting health information of the user to be managed; constructing a knowledge graph based on the food data, the drug data, and the health information of the user to be managed, and evaluating the matching degree between the drug and the food based on the knowledge graph; generating reminder information according to the matching degree. The embodiment of the present application obtains the health information of the user through the food data and the drug data of the drug, then constructs a knowledge graph to obtain the matching degree between the drug and the food, and generates reminder information according to the matching degree, improving the accuracy and efficiency of health management.

[0044] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings

[0045] The accompanying drawings are used to provide a further understanding of the technical solution of the present application, and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application, and do not constitute a limitation to the technical solution of the present application.

[0046] Figure 1 It is a schematic flowchart of a health management method provided by an embodiment of the present application;

[0047] Figure 2 It is a schematic flowchart of a method for obtaining food data provided by an embodiment of the present application;

[0048] Figure 3 It is a schematic flowchart of a method for obtaining drug data provided by an embodiment of the present application;

[0049] Figure 4 It is a schematic flowchart of a method for sending health reminder information according to health information provided by an embodiment of the present application;

[0050] Figure 5 It is a schematic flowchart of a method for generating a reminder strategy according to health information provided by an embodiment of the present application;

[0051] Figure 6 It is a schematic diagram of a health management system provided by an embodiment of the present application;

[0052] Figure 7 It is a schematic diagram of the hardware structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0053] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present application, and should not be construed as a limitation to the present application.

[0054] In the description of the present application, it should be understood that the orientation or positional relationship indicated by terms such as up, down, front, back, left, right, etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0055] In the description of this application, "several" means one or more, "multiple" means more than two, "greater than", "less than", "exceeding", etc. are understood as not including the corresponding number, and "above", "below", "within", etc. are understood as including the corresponding number. If "first" and "second" are described, they are only used to distinguish technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or the sequence relationship of the indicated technical features.

[0056] In the description of this application, unless otherwise clearly defined, terms such as "set", "installed", "connected", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in this application in combination with the specific content of the technical solution.

[0057] First, analyze several nouns involved in this application:

[0058] RGB-D camera: A multi-modal sensing device that combines an RGB color camera and depth information acquisition technology. It can not only capture the color information (RGB) of an object, but also measure the distance from the object to the camera (depth information D) in some way, thereby generating an image containing color and depth information.

[0059] Millimeter-wave radar: A technology that uses electromagnetic waves in the millimeter-wave band (wavelength 1 - 10 mm, frequency 30 - 300 GHz) for target detection and measurement. Its working principle is to emit millimeter-wave signals and receive the signals reflected by the target, and use information such as the time delay and frequency change of the signals to measure the distance, speed, and angle of the target.

[0060] RFID tag (Radio Frequency Identification Tag): A non-contact automatic identification tag based on radio frequency (RF) technology, used to store and transmit information. It communicates with a reader through radio waves to achieve the identification, tracking, and management of objects. An RFID tag mainly consists of an antenna and a chip. The working process is as follows: The reader emits a radio wave signal with a specific frequency through the antenna. After receiving the signal, the RFID tag converts the energy into electrical energy through its internal antenna, activates the chip, and modulates the information stored in the chip (such as ID number, data, etc.) into the reflected radio wave. The reader receives the signal reflected by the tag and decodes the information therein to complete the identification process.

[0061] LSTM (Long Short-Term Memory): A special type of recurrent neural network (RNN) specifically designed to address the vanishing gradient or exploding gradient problems in traditional RNNs when dealing with long sequence data. It controls the flow of information by introducing a "gating mechanism" and can thus effectively learn and remember long-term dependencies in long sequences.

[0062] Reinforcement Learning (RL) is a machine learning paradigm aimed at learning the optimal behavior policy through the interaction between an agent and an environment to maximize the cumulative reward. The core of reinforcement learning is the interaction between the agent and the environment. The agent takes actions in the environment, and the environment gives a reward based on the action and transitions to a new state. The goal of the agent is to learn a policy that maximizes the cumulative reward obtained during long-term interaction.

[0063] The health management method provided in the embodiments of this application will be specifically described through the following embodiments. First, the health management method in the embodiments of this application will be described.

[0064] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results in theory, methods, technologies, and application systems.

[0065] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0066] The health management method provided by the embodiments of the present application relates to the technical field of health management. The health management method provided by the embodiments of the present application can be applied to a terminal, or to a server side, or can also be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the health management method, etc., but is not limited to the above forms.

[0067] The present application can be used in many general or specific computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet-type devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0068] It should be noted that in each specific embodiment of the present application, when it comes to performing relevant processing based on data related to the user's identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first. Moreover, the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, the user's separate permission or separate consent will be obtained through methods such as pop-up windows or redirecting to a confirmation page. After clearly obtaining the user's separate permission or separate consent, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0069] In the related art, there are many deficiencies in traditional health management methods. For example, diet monitoring relies on manual records, the data is inaccurate and it is difficult to identify food ingredients. The health reminder system has a fixed push frequency and does not consider the real-time health status changes of users. The medication management system and the diet analysis system are independent of each other, lacking a collaborative intervention mechanism, and no correlation analysis model between medication records and diet data is established, resulting in the risk of drug-food interactions.

[0070] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a health management method provided by an embodiment of the present application; as Figure 1 shown, a health management method provided by an embodiment of the present application includes, but is not limited to, steps S110-S140, and each step will be introduced in turn below.

[0071] Step S110: Obtain the food data of food and the drug data of drugs;

[0072] Step S120: Collect the health information of the user to be managed;

[0073] Step S130: Construct a knowledge graph based on the food data, the drug data, and the health information of the user to be managed, and evaluate the matching degree between the drug and the food based on the knowledge graph;

[0074] Step S140: Generate a reminder message according to the matching degree.

[0075] It should be noted that the food data includes the quality data and volume data of food, the drug data includes the dosage, dosing frequency, and number of doses, the health information of the user includes personal information, health indicators, eating habits, and lifestyle. The personal information includes, but is not limited to, age, gender, weight, height, and allergy history. The health indicators of the user include, but are not limited to, blood pressure, blood sugar, liver and kidney functions. The eating habits and lifestyle of the user include, but are not limited to, the daily diet structure and exercise frequency.

[0076] In one embodiment, the interaction relationship between food and drugs is extracted, including but not limited to the impact of food on drug absorption and the impact of drugs on food digestion. Combining with the user's health information, the impact of the user's physical condition on the interaction between drugs and food is analyzed. The knowledge graph technology is used to integrate the above information to form a network containing food, drugs, and user health information. Based on the relationships and attributes in the knowledge graph, the matching degree between drugs and food is evaluated, and whether the food will affect the absorption or metabolism of the drug is analyzed. Considering user health indicators, for example, users with poor liver and kidney functions may need to avoid certain foods that burden the liver or kidneys. According to the evaluation result of the matching degree, personalized reminder information is generated. For example, when a certain food significantly reduces the absorption of a drug, the user is reminded to avoid eating the food before and after taking the medicine. When the user's health indicators are abnormal, the user is reminded to pay attention to the interaction between diet and drugs. The reminder information can be pushed through a mobile application, text message, or smart device for the user to view at any time.

[0077] In one embodiment, food data of food and drug data of drugs are obtained, the health information of the user to be managed is collected, a knowledge graph is constructed based on the food data, the drug data, and the health information of the user to be managed, the matching degree between drugs and food is evaluated based on the knowledge graph, and reminder information is generated according to the matching degree. In the embodiment of the present application, the health information of the user is obtained through the food data and the drug data of the drugs, and then the knowledge graph is constructed to obtain the matching degree between drugs and food, and reminder information is generated according to the matching degree, improving the accuracy and efficiency of health management.

[0078] Please refer to Figure 2 , Figure 2 which is a schematic flow chart of obtaining food data provided by an embodiment of the present application; as Figure 2 shown, a health management method provided by an embodiment of the present application includes but is not limited to steps S210-S240, and each step will be introduced in turn below.

[0079] Step S210: Based on an RGB-D camera and a millimeter-wave radar, volume data and surface data of food are obtained through a three-dimensional reconstruction algorithm;

[0080] Step S220: Mass data of food is obtained according to a weighing sensor;

[0081] Step S230: A mapping table between the mass and volume of food is obtained according to the volume data, the surface data, and the mass data;

[0082] Step S240: Food data is obtained according to the mapping table.

[0083] In one embodiment, an RGB-D camera can simultaneously acquire the color information (RGB) and depth information (D) of food. Through a three-dimensional reconstruction algorithm, a three-dimensional model of the food can be reconstructed, including volume and surface details. A millimeter-wave radar can be used to assist in measuring the volume of food, especially in dynamic scenarios, and can provide more accurate volume change data. A high-precision weighing sensor can be used to accurately measure the mass of food. The accuracy of the weighing sensor is generally 0.1 gram or higher, which can meet the requirements of food mass measurement. By combining the food volume data obtained by the RGB-D camera and the millimeter-wave radar with the mass data obtained by the weighing sensor, a mapping relationship between mass and volume can be established.

[0084] In one embodiment, by measuring different types of food multiple times, a mapping table can be generated. Using the generated mapping table and combining the real-time acquired volume and mass data, the food type can be quickly identified and its relevant data can be obtained. Record the mass range of each food at different volumes, so as to achieve the rapid identification and quantification of food. The mapping table can be used for subsequent food data acquisition and analysis, helping users to more accurately understand the nutritional components and calories of food.

[0085] Please refer to Figure 3 , Figure 3 is a schematic flow chart of obtaining drug data provided by an embodiment of the present application; as Figure 3 shown, a health management method provided by an embodiment of the present application includes but is not limited to steps S310 - S330. Each step will be introduced in turn below.

[0086] Step S310: Based on the RFID tag and the pressure sensor, obtain the dosage and administration frequency of the drug;

[0087] Step S320: Based on LSTM, analyze the time-series data of the opening and closing state of the medicine box where the drug is placed to obtain the administration times of the drug;

[0088] Step S330: Obtain drug data according to the dosage, administration frequency, and administration times.

[0089] In one embodiment, an RFID tag is pasted on the drug package or medicine box, and the basic information of the drug (name, dosage, administration instructions) is stored in the tag. Through an RFID reader / writer, the tag information can be read in real time. A pressure sensor is embedded in the medicine box to detect the pressure change when the drug is taken out. When the drug is taken out, the pressure sensor records an event, and the data is transmitted to the system through a single-chip microcomputer. The system calculates the administration frequency of the drug according to the number of events recorded by the pressure sensor and the time interval.

[0090] In one embodiment, the pressure change value is compared with a preset pressure value. When the pressure change exceeds the threshold, a taking behavior is recorded. It should be noted that the magnitude of the preset pressure value in the embodiments of the present application can be set by the user or by the system. The embodiments of the present application do not make specific limitations on the method of setting the magnitude of the preset pressure value.

[0091] In one embodiment, a sensor (such as a magnetic switch or a microswitch) is installed on the medicine box to record the opening and closing state of the medicine box. Each time the medicine box is opened, the sensor records an event, and the time series data of the opening and closing state of the medicine box is input into the LSTM model. LSTM can learn long-term dependencies and identify which opening and closing events correspond to actual drug taking behaviors. By analyzing the output of the LSTM model, the number of drug takings within a certain period of time is counted. For example, the model can identify that multiple consecutive openings and closings may be one taking behavior, thus accurately counting the number of takings.

[0092] It should be noted that the type of the sensor in the embodiments of the present application can be a magnetic switch or a microswitch. The embodiments of the present application do not make specific limitations on the type of the sensor switch.

[0093] In one embodiment, the drug name and dosage are read from the RFID tag, the taking frequency is calculated according to the time interval recorded by the pressure sensor, the number of takings is obtained by analyzing through the LSTM model, the total taking amount is calculated according to the amount taken each time and the number of takings, and the basic drug information recorded by the RFID tag, the taking amount and frequency recorded by the pressure sensor, and the number of takings analyzed by the LSTM are integrated.

[0094] In one embodiment, the matching degree is calculated by the following formula:

[0095] Match_Score = φ + ψ + ω;

[0096] where Match_Score represents the matching degree, φ represents the text embedding of the knowledge graph, ψ represents the surface data of the food, and ω represents the temporal correlation degree of the health information.

[0097] It should be noted that the matching degree is obtained by adding the text embedding of the knowledge graph, the surface data of the food, and the temporal correlation degree of the health information.

[0098] Please refer to Figure 4 , Figure 4 which is a schematic flowchart of the process of sending health reminder information according to health information provided by an embodiment of the present application; as Figure 4 shown, a health management method provided by an embodiment of the present application includes but is not limited to steps S410 - S440, and each step will be introduced in turn below.

[0099] Step S410: Obtain the weight change amount and blood glucose index of the user to be managed based on the health information;

[0100] Step S420: Obtain the medication error value of the user to be managed based on the medication data;

[0101] Step S430: Obtain the health status evaluation value of the user to be managed based on the weight change amount, blood glucose index, and medication error value;

[0102] Step S440: When the health status evaluation value is greater than the first preset value, send a health reminder message to the user to be managed.

[0103] In one embodiment, the health data of the user is collected in real time through an intelligent health monitoring device. The intelligent medicine box can bind user information and record the medication plan. At the same time, the weight and blood glucose of the user are monitored in real time through the health monitoring module to calculate the weight change amount, which is the difference between the current weight and the last recorded weight. Calculate the blood glucose index. For example, the average value or fluctuation range of the blood glucose level. Use the intelligent medicine box to record the user's medication behavior, including the medication time, dose, and frequency. Analyze the timing data of the opening and closing state of the medicine box through the LSTM model to identify whether the user's medication behavior conforms to the preset medication plan. The medication error value can be calculated by comparing the deviation between the actual medication behavior and the planned medication behavior, such as the number of missed doses, wrong doses, or overdosages.

[0104] In one embodiment, combining the weight change amount, blood glucose index, and medication error value, a multi-index comprehensive evaluation method is used to calculate the health status evaluation value. For example, the normalization method can be used to convert each index into a standardized value, and then the health status evaluation value is calculated by weighted summation.

[0105] In one embodiment, set the threshold value (the first preset value) of the health status evaluation value. When the evaluation value exceeds this threshold, trigger the reminder mechanism. The reminder message can be sent to the user through the alarm module of the intelligent medicine box, the mobile terminal, or the Bluetooth watch. The reminder content can include medication reminders (such as "You have missed one dose of medicine. Please take it as soon as possible"), health index abnormality reminders (such as "Your blood glucose value is on the high side. Please pay attention to your diet"), and medication behavior correction prompts (such as "Your medication dose exceeds the recommended range. Please consult a doctor").

[0106] In one embodiment, the health status evaluation value is calculated according to the following formula:

[0107] H(t) = α·ΔW + β·GMI + γ·Med_Error;

[0108] Where, ΔW represents the weight change amount, GMI represents the blood glucose index, Med_Error represents the medication error value, and H(t) represents the health status evaluation value.

[0109] It should be noted that the health status evaluation value is obtained by adding the coefficient corresponding to the weight change amount multiplied by the weight change amount, the coefficient corresponding to the blood glucose index multiplied by the blood glucose index, and the coefficient corresponding to the medication error value multiplied by the medication error value.

[0110] Please refer to Figure 5 , Figure 5 which is a schematic flowchart of generating a reminder strategy based on health information provided by an embodiment of the present application; as Figure 5 shown, a health management method provided by an embodiment of the present application includes, but is not limited to, steps S510 - S520, and each step will be introduced in turn below.

[0111] Step S510: Obtain feedback information based on reinforcement learning according to health information, where the health information includes health status, eating habits, and medication conditions.

[0112] Step S520: Generate a reminder strategy according to the feedback information.

[0113] In one embodiment, the user's goal is to control blood glucose levels. The reinforcement learning model can obtain the current blood glucose level, the diet record and medication conditions in the recent week, and generate feedback information, such as "It is recommended to reduce carbohydrate intake" or "It is recommended to increase physical activity". When the blood glucose level is close to the target range, a positive reward is given, otherwise a negative reward is given.

[0114] In one embodiment, according to the user's eating habits and health goals, the user is reminded to adjust the diet structure. For example, if the user intakes too much high - sugar food, the system can remind "It is recommended to reduce the intake of high - sugar food and increase vegetables and whole grains".

[0115] In one embodiment, according to the user's medication conditions, the user is reminded to take medicine on time or adjust the dosage. For example, if the user misses a dose of medicine, the system can remind "You have missed a dose of medicine. Please take it as soon as possible".

[0116] In one embodiment, according to the user's health status and activity record, the user is reminded to increase physical activity. For example, if the user sits still for a long time, the system can remind "It is recommended that you get up and move for 10 minutes to improve blood circulation.

[0117] Please refer to Figure 6 , Figure 6 which is a schematic diagram of a health management system provided by an embodiment of the present application; the health management system 600 includes:

[0118] A data acquisition module 610, configured to acquire food data of food and drug data of drugs;

[0119] A health information collection module 620, configured to collect health information of the user to be managed;

[0120] A knowledge graph evaluation module 630 is configured to construct a knowledge graph based on food data, drug data, and the health information of the user to be managed, and evaluate the matching degree between drugs and foods based on the knowledge graph;

[0121] A health reminder module 640 is configured to generate reminder information based on the matching degree.

[0122] It should be noted that the specific implementation manner of the health management system provided in the embodiments of the present application is basically the same as the specific embodiments of the above health management method, and will not be elaborated herein.

[0123] The embodiments of the present application further provide a computer device, which includes: at least one memory, at least one processor, at least one computer program, at least one computer program is stored in at least one memory, and at least one processor executes at least one computer program to implement the flash memory particle test method in any one of the above embodiments. The computer device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0124] See Figure 7 , Figure 7 is a schematic hardware structure diagram of a computer device provided in an embodiment of the present application. The computer device includes:

[0125] A processor 710, which can be implemented by using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0126] A memory 720, which can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 720 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 720, and the processor 710 is called to execute the flash memory particle test method of the embodiments of the present application;

[0127] An input / output interface 730 is configured to implement information input and output;

[0128] A communication interface 740 for implementing communication and interaction between this device and other devices, which can achieve communication through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0129] A bus 750 for transmitting information between various components of the device (such as a processor 710, a memory 720, an input / output interface 730, and a communication interface 740);

[0130] Among them, the processor 710, the memory 720, the input / output interface 730, and the communication interface 740 achieve communication connections with each other inside the device through the bus 750.

[0131] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned flash memory particle testing method is implemented.

[0132] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely provided relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0133] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0134] Those skilled in the art can understand that the technical solutions shown in the figure do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figure, or combine certain steps, or different steps.

[0135] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] Those of ordinary skill in the art will understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or a suitable combination thereof.

[0137] As used in the specification of this application and the above-mentioned drawings, the terms "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data may be interchanged under appropriate circumstances so that the embodiments of this application described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0138] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may mean: only A exists, only B exists, and both A and B exist at the same time. Here, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or similar expressions refer to any combination of these items, including any combination of single items (ones) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0139] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above-mentioned unit division is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0140] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0142] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present application. The aforementioned storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0143] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings. However, this does not limit the scope of the rights of the embodiments of the present application. Any modification, equivalent replacement, and improvement made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall fall within the scope of the rights of the embodiments of the present application.

Claims

1. A health management method, characterized in that, Including: Obtaining food data of food and drug data of drugs; Collecting the health information of the user to be managed; Constructing a knowledge graph based on the food data, the drug data, and the health information of the user to be managed, and evaluating the matching degree between the drug and the food based on the knowledge graph; Generating a reminder message according to the matching degree.

2. The health management system according to claim 1, characterized in that, The food data is obtained through the following steps: Based on an RGB-D camera and a millimeter-wave radar, obtaining the volume data and surface data of the food through a three-dimensional reconstruction algorithm; Obtaining the mass data of the food according to a weighing sensor; Obtaining a mapping table between the mass and volume of the food according to the volume data, the surface data, and the mass data; Obtaining the food data according to the mapping table.

3. The health management system according to claim 1, characterized in that, The drug data is obtained through the following steps: Based on an RFID tag and a pressure sensor, obtaining the dosage and administration frequency of the drug; Based on LSTM, analyzing the time-series data of the opening and closing state of the medicine box where the drug is placed to obtain the administration times of the drug; Obtaining the drug data according to the dosage, the administration frequency, and the administration times.

4. The health management system according to claim 1, wherein The matching degree is calculated through the following formula: Match_Score = φ + ψ + ω; Wherein, the Match_Score represents the matching degree, the φ represents the text embedding of the knowledge graph, the ψ represents the surface data of the food, and the ω represents the time-series correlation degree of the health information.

5. The method according to claim 1, wherein The method further includes: Obtaining the weight change amount and blood glucose index of the user to be managed according to the health information; Obtaining the medication error value of the user to be managed according to the drug data; Obtaining the health status evaluation value of the user to be managed according to the weight change amount, the blood glucose index, and the medication error value; When the health status evaluation value is greater than a first preset value, sending a health reminder message to the user to be managed.

6. The method according to claim 5, characterized in that, The health status evaluation value is calculated through the following formula: H(t) = α·ΔW + β·GMI + γ·Med_Error; Wherein, the ΔW represents the weight change amount, the GMI represents the blood glucose index, the Med_Error represents the medication error value, and the H(t) represents the health status evaluation value.

7. The method according to claim 1, wherein The method further includes: Obtaining feedback information based on the health information through reinforcement learning, wherein the health information includes health status, eating habits, and medication conditions; Generating a reminder strategy according to the feedback information.

8. A health management system, characterized in that, Including: A data acquisition module, configured to obtain food data of food and drug data of drugs; A health information collection module, configured to collect the health information of the user to be managed; A knowledge graph evaluation module, configured to construct a knowledge graph based on the food data, the drug data, and the health information of the user to be managed, and evaluate the matching degree between the drug and the food based on the knowledge graph; A health reminder module, configured to generate a reminder message based on the matching degree.

9. A computer device, characterized in that, Including: At least one memory; At least one processor; At least one computer program; The at least one computer program is stored in the at least one memory, and the at least one processor executes the at least one computer program to implement: the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is used to cause a computer to execute: the method according to any one of claims 1-7.

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