Intelligent health management system and method, electronic equipment and storage medium

Abstract

The invention discloses an intelligent health management system and method, electronic equipment and a storage medium, and relates to the technical field of intelligent health management, and the system comprises a health data acquisition module and an intelligent recommendation module; the health data acquisition module is used for receiving health data input by a patient in real time, wherein the mode of inputting the health data comprises at least one of a voice mode, a text mode and an image mode; and the intelligent recommendation module is used for generating personalized health management suggestions by using the existing health data of the patient and providing the personalized health management suggestions to the patient. According to the invention, health data input by a patient in different modes can be received in real time, and real-time acquisition of the health data is realized; moreover, corresponding personalized health management suggestions can be generated by using the existing health data of the patient, and the method is more targeted and more suitable for the patient.

Description

Background Art

[0002] With the change of lifestyle and the aging of the population, the number of patients, especially those with chronic diseases, has increased year by year. Traditional health management methods usually rely on patients to visit the hospital regularly, which cannot achieve real-time collection of health data and has the problem of lagging health data monitoring. Moreover, the current health management plan fails to take individual differences into consideration and lacks specificity. Summary of the invention

[0003] The technical problem to be solved by the present invention is to address the deficiencies of the prior art, specifically the problem that "traditional health management methods are difficult to achieve real-time monitoring and personalized management of patients' health conditions", and specifically provide an intelligent health management system, method, electronic device and storage medium, as follows:

[0004] 1) In the first aspect, the present invention provides an intelligent health management system, and the specific technical solution is as follows:

[0005] Including health data acquisition module and intelligent recommendation module;

[0006] The health data acquisition module is used to: receive health data input by the patient in real time, where the health data input includes at least one of voice, text and image;

[0007] The intelligent recommendation module is used to: utilize the patient's existing health data to generate personalized health management suggestions and provide them to the patient.

[0008] The beneficial effects of an intelligent health management system provided by the present invention are as follows:

[0009] On the one hand, it can receive health data input by patients in different ways in real time and realize real-time collection of health data; on the other hand, by using the patient's existing health data, it can generate corresponding personalized health management suggestions, which are more targeted and more suitable for patients.

[0010] Based on the above solution, the intelligent health management system of the present invention can also be improved as follows.

[0011] Further, it also includes a data encryption module and a data storage module;

[0012] The data encryption module is used to: encrypt the health data of patients received in real time;

[0013] The data storage module is used to store the patient's health data in a pre-built cloud database.

[0014] The beneficial effect of adopting the above further solution is that the security of the patient's health data can be guaranteed to the greatest extent through data encryption.

[0015] Furthermore, it also includes an interactive feedback module, which is used to: push a health feedback questionnaire to the patient, receive the patient's reply to the health feedback questionnaire, and obtain the patient's self-assessment data.

[0016] The beneficial effect of adopting the above further scheme is: At present, there is a lack of effective communication and feedback mechanism between patients and doctors, which affects the effect of health management. The present invention pushes a health feedback questionnaire to patients through an interactive feedback module, and receives the patients' responses to the health feedback questionnaire, obtains the patients' self-assessment data, and can ensure the effect of health management.

[0017] Furthermore, the health data input by the patient includes at least one of: physiological parameters, diet, exercise status and emotions.

[0018] The beneficial effect of adopting the above further scheme is that the present invention can collect and manage data on multiple health parameters more comprehensively, making the generated personalized health management suggestions for each patient more applicable and practical.

[0019] 2) In the second aspect, the present invention also provides an intelligent health management method, and the specific technical solution is as follows:

[0020] Receiving health data input by the patient in real time, wherein the health data input includes at least one of voice, text and image;

[0021] Utilize the patient's existing health data to generate personalized health management recommendations and provide them to the patient.

[0022] Based on the above solution, the intelligent health management method of the present invention can also be improved as follows.

[0023] Furthermore, it also includes:

[0024] Encrypting patients’ health data received in real time;

[0025] Store patients’ health data in a pre-built cloud database.

[0026] Furthermore, it also includes:

[0027] Push the health feedback questionnaire to the patient, receive the patient's response to the health feedback questionnaire, and obtain the patient's self-assessment data.

[0028] Furthermore, the health data input by the patient includes at least one of: physiological parameters, diet, exercise status and emotions.

[0029] 3) In a third aspect, the present invention further provides an electronic device, comprising a processor, the processor being coupled to a memory, the memory storing at least one computer program, and the at least one computer program being loaded and executed by the processor so that the electronic device implements any one of the above-mentioned intelligent health management methods.

[0030] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, any of the above-mentioned intelligent health management methods is implemented.

[0031] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments of the present invention:

[0033] Figure 1 A schematic diagram of the structure of an intelligent health management system according to an embodiment of the present invention;

[0034] Figure 2 A schematic diagram of a flow chart of an intelligent health management method according to an embodiment of the present invention;

[0035] Figure 3 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0037] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0038] like Figure 1 As shown, an intelligent health management system 200 according to an embodiment of the present invention includes a health data acquisition module 201 and an intelligent recommendation module 202;

[0039] The health data acquisition module 201 is used to: receive health data input by the patient in real time, where the health data input includes at least one of voice, text and image;

[0040] Among them, the health data input by the patient includes: at least one of physiological parameters, diet, exercise status and emotions. The health data input by the patient may also include medication status. Physiological parameters include blood sugar value, blood pressure value and heart rate value, etc.

[0041] Among them, an interface is set up to facilitate patients to input health data. The interface has click boxes for voice, text and image modes. Patients use clicks to select voice, text or image mode to input health data.

[0042] The intelligent recommendation module 202 is used to utilize the patient's existing health data to generate personalized health management suggestions and provide them to the patient.

[0043] Optionally, in the above technical solution, a data encryption module and a data storage module are also included;

[0044] The data encryption module is used to: encrypt the health data of patients received in real time;

[0045] The data storage module is used to store the patient's health data in a pre-built cloud database.

[0046] Optionally, the above technical solution also includes a health data analysis module, which is used to: use a data analysis algorithm to analyze the patient's existing health data, identify health risks and disease patterns, and provide them to the patient.

[0047] Optionally, in the above technical scheme, the health data analysis module is also used to set a threshold value corresponding to the blood sugar value (which may be called: blood sugar monitoring threshold), a threshold value corresponding to the blood pressure value (which may be called: blood pressure monitoring threshold) and a threshold value corresponding to the heart rate value (which may be called: heart rate monitoring threshold). When the blood sugar value input by the patient exceeds the blood sugar monitoring threshold, a warning notification is provided to the patient to remind that "blood sugar is high". When the blood pressure value input by the patient exceeds the blood pressure monitoring threshold, a warning notification is provided to the patient to remind that "blood pressure is high". When the heart rate value input by the patient exceeds the heart rate monitoring threshold, a warning notification is provided to the patient to remind that "heart rate is fast".

[0048] Optionally, in the above technical solution, the intelligent recommendation module 202 will also take into account the correlation between diet and blood sugar fluctuations when generating personalized health management suggestions.

[0049] Optionally, in the above technical solution, the intelligent recommendation module 202 is also used to generate personalized emotion management suggestions for the patient based on the correlation between the emotion input by the patient and the change in heart rate value, and provide the patient with the suggestions.

[0050] Use wearable devices (such as smart watches, heart rate monitors) or mobile applications to collect patients' heart rate and emotions in real time. The data may include heart rate values, heart rate variability, emotion labels (such as happiness, sadness, anger, etc.), and the patient's self-reported emotion intensity. Clean and organize the collected data, remove outliers, and ensure the accuracy and consistency of the data. At the same time, synchronize the heart rate data and emotional state in time for subsequent analysis. Extract key features from the heart rate data, such as average heart rate, heart rate variability, etc., which may be closely related to emotional state. Use machine learning or deep learning algorithms to train a heart rate model that can predict emotional state. This model can predict the emotional state that the patient may currently experience based on the heart rate data. Analyze the correlation between heart rate features and emotional state through statistical analysis or machine learning algorithms, and build the patient's first portrait based on the patient's personal characteristics (such as age, gender, health status, etc.) and emotional history data. This helps to understand the patient's emotional needs and preferences more accurately, and generate personalized emotional management suggestions based on the patient's current heart rate data and emotional state, as well as the patient's first portrait, and through the trained first deep learning model. Personalized emotion management suggestions include deep breathing exercises, meditation, relaxing music recommendations, exercise plans, etc. Based on patients' feedback and emotional changes, the trained first deep learning model is continuously optimized to improve the accuracy and effectiveness of the suggestions.

[0051] Among them, the patient's heart rate data and emotional state are continuously monitored to ensure that emotional changes are captured in a timely manner. When the patient's mood changes significantly, the system should be able to provide immediate feedback and adjust the emotional management suggestions to suit the current situation.

[0052] Among them, the first deep learning model can be a neural network, or a large language model, etc., which can be set according to actual conditions.

[0053] Optionally, the above technical solution further includes an interactive feedback module, which is used to: push a health feedback questionnaire to the patient, receive the patient's response to the health feedback questionnaire, and obtain the patient's self-assessment data.

[0054] Optionally, the health data analysis module is also used to collect existing health data of multiple patients, and set different weights for different physiological parameters, different diet combinations, different exercise conditions and different emotions according to preset weight rules, so as to obtain the weight corresponding to each health parameter of each patient. Among them, the preset weight rules may be: setting the weight of jogging for 0 to 30 minutes to 1, setting the weight of eggs to 0.1, etc., which can be set according to actual conditions. All weights are normalized using the maximum and minimum method. According to all normalized weights and preset composition rules, a healthy image corresponding to each patient is constructed. The preset composition rules are as follows: a layer area of ​​a fixed size is set, and the layer area is divided into sub-areas with the same health parameters. Each health parameter corresponds to a sub-area, and the normalized weight corresponding to each health parameter is used as the proportion. The pixel values ​​of the corresponding proportion of pixels in each sub-area are set to the same size, and the pixel values ​​R, G, and B of the remaining pixels are all 0. In this way, the healthy image corresponding to each patient is obtained, and combined with the personalized health management suggestions for each patient and the self-assessment data of each patient after a certain period of time after strict health management in accordance with the corresponding personalized health management suggestions (specifically using the word vector of each patient's personalized health management suggestions and the word vector in each patient's self-assessment data), the quantum convolutional neural network is trained to obtain a trained quantum convolutional neural network.

[0055] Among them, based on the preset weight rules and preset composition rules, the patient's existing health data will be converted into a health image. Even if someone obtains the health image, due to not knowing the preset composition rules and preset weight rules, the patient's existing health data will not be directly obtained, which can ensure the security of the patient's existing health data. In addition, the preset weight rules and preset composition rules can be updated regularly or randomly to further ensure the security of the data.

[0056] In a healthy image, "the pixel values ​​of the corresponding proportion of pixels in each sub-area are set to the same size, and the R value, G value and B value of the remaining pixels are all 0" can greatly reduce the amount of data processing and improve the data processing effect.

[0057] The health data analysis module is also used for: based on preset weight rules and preset composition rules, converting the patient's existing health data at the current moment into a health image, and providing personalized health management suggestions to the patient at the current moment, using the trained quantum convolutional neural network to predict the patient's self-assessment data after a preset period of time, the preset period can be 1 week or 1 month, etc., which can be set according to actual conditions. After the preset period of time, a health feedback questionnaire is pushed to the patient, and the patient's response to the health feedback questionnaire is received to obtain the patient's self-assessment data, and the predicted self-assessment data and the self-assessment data actually filled in by the patient are calculated for similarity. Specifically, the predicted self-assessment data and the self-assessment data actually filled in by the patient are converted into word vectors, and then similarity calculations are performed. When the similarity is lower than the similarity threshold, a warning notification is issued to the patient that health management is not strictly carried out in accordance with the personalized health management suggestions, and the predicted self-assessment data is provided to the patient. The similarity threshold can be set according to actual conditions.

[0058] Optionally, the trained quantum convolutional neural network is updated regularly to ensure the prediction accuracy of the trained quantum convolutional neural network.

[0059] Optionally, it also includes: a patient form design module, which is used to: list all data points that need to be collected according to the research objectives, such as the patient's basic information, the patient's medical history, examination and test results, treatment information, follow-up records, health data, personalized health management suggestions and personalized emotional management suggestions, etc., and design corresponding fields for each data point, including field name, data type (such as text, number, date, etc.), field length and format, etc. Use pre-coded answers or drop-down menus to reduce data entry errors, build a preliminary patient form, conduct internal testing on the preliminary patient form in a small range, collect feedback, revise and improve the preliminary patient form based on the feedback, and then conduct external testing on a larger scale, including researchers and data entry personnel, to determine the final patient form for patients to view.

[0060] Optionally, a visual dashboard is provided to display the patient's health status trends and personalized health management suggestions and personalized emotion management suggestions, etc., and health reports are generated regularly.

[0061] In another embodiment, an intelligent health management system 200 of the present invention is divided into a management end and a patient end. The management end and the patient end are respectively an executable program. The management end can be an executable program in exe format and can be installed on a computer device. In order to make it easier for patients to input health data, the patient end can be an APP program that is easy to install on the patient's smartphone or tablet computer. Then:

[0062] 1) On the display interface of the patient side, there are click boxes for voice mode, text mode and image mode. The patient selects voice mode, text mode or image mode by clicking to input health data, as follows:

[0063] ① When the patient selects the voice method, the recording function of the smartphone or tablet is activated to record the audio input by the patient, and then the audio is subjected to voice recognition to obtain at least one of the physiological parameters, diet, exercise conditions, medication conditions and emotions, and then the data are filled into the data boxes corresponding to the physiological parameters, diet, exercise conditions, medication conditions and emotions, respectively, and displayed one by one. On the display interface, a click box is set for determining whether it is correct. When the patient confirms that it is correct, a display interface containing the next health parameter is displayed. When the patient confirms that it is wrong, the patient is asked to make modifications until each health parameter is confirmed to be correct.

[0064] ②When the patient selects the image mode, a click box for adding images and a click box for taking photos pop up. When the patient clicks the click box for adding images, the patient can add images according to the actual situation, such as diet, medication, and facial images, etc. By identifying the diet and medication, the patient's medication combination and medication situation can be obtained. By performing emotion recognition on facial images, the patient's emotions can be determined and displayed one by one. On the display interface, a click box is set to determine whether it is correct. When the patient confirms that it is correct, the display interface containing the next health parameter is displayed. When the patient confirms that it is wrong, the patient is asked to make modifications until each health parameter is confirmed to be correct.

[0065] ③When the patient chooses the text mode, a data box for inputting physiological parameters, diet, exercise status, medication status and emotions will pop up to facilitate the patient to input.

[0066] After the health data acquisition module 201 of the management end receives the health data input by the patient, the intelligent recommendation module 202 of the management end generates personalized health management suggestions using the patient's existing health data and provides them to the patient. Specifically:

[0067] Based on the patient's existing health data, a second portrait reflecting the patient's preferences is constructed. For example, the patient's preferences include: high blood sugar, high blood pressure, fast heart rate, dietary preferences (for example, eating more beef, eggs, and spinach, etc.), exercise preferences (for example, more jogging and more push-ups, etc.), and emotional preferences (such as more positive attitudes and fewer negative attitudes, etc.).

[0068] The patient's second portrait is input into the trained second deep learning model to generate personalized health management recommendations.

[0069] Among them, the second deep learning model can be a neural network or a large language model, etc., and can be set according to actual conditions. When training the second deep learning model, the pre-obtained correlation between diet and blood sugar fluctuations is used to obtain blood sugar fluctuations corresponding to dietary preferences, and the pre-obtained correlation between emotions and heart rate value changes is used to obtain heart rate changes corresponding to emotions. Blood sugar fluctuations and heart rate changes are also used as input data for training the second deep learning model, and then the second deep learning model is trained to obtain a trained second deep learning model. Then, when the trained second deep learning model generates personalized health management recommendations, blood sugar fluctuations and heart rate changes will be taken into account, making the generated personalized health management recommendations more accurate.

[0070] Among them, the trained second deep learning model has a dynamic adjustment mechanism, that is, when more health data is obtained, the second deep learning model can be retrained.

[0071] The intelligent recommendation module 202 of the management end uses the patient's existing health data to generate personalized health management suggestions and provide them to the patient, specifically to the patient end, so that the patient can view them.

[0072] like Figure 2 As shown, an intelligent health management method according to an embodiment of the present invention comprises the following steps:

[0073] S1. Receive health data input by the patient in real time, where the health data is input in at least one of voice, text and image formats;

[0074] S2. Use the patient's existing health data to generate personalized health management recommendations and provide them to the patient.

[0075] Optionally, in the above technical solution, it also includes:

[0076] Encrypting patients’ health data received in real time;

[0077] Store patients’ health data in a pre-built cloud database.

[0078] Optionally, in the above technical solution, it also includes:

[0079] S3. Push a health feedback questionnaire to the patient, receive the patient's response to the health feedback questionnaire, and obtain the patient's self-assessment data.

[0080] Optionally, in the above technical solution, the health data input by the patient includes at least one of: physiological parameters, diet, exercise status and emotions.

[0081] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0082] It should be noted that the beneficial effects of the intelligent health management method provided in the above embodiment are the same as the beneficial effects of the above intelligent health management system, which will not be repeated here. In addition, when the system provided in the above embodiment realizes its functions, it only takes the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0083] Among them, the intelligent health management system of the present invention can be a computer program (including program code) running in a computer device. For example, the intelligent health management system of the present invention is an application software that can be used to execute the corresponding steps in the intelligent health management method of the present invention.

[0084] In some embodiments, the intelligent health management system of the present invention can be implemented by a combination of software and hardware. As an example, the intelligent health management system of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the intelligent health management method of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs) or other electronic components.

[0085] The modules involved in the embodiments of the present invention may be implemented in software or hardware. The name of a module does not limit the module itself in some cases.

[0086] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any one of the above-mentioned intelligent health management methods is implemented. That is to say, an electronic device according to an embodiment of the present invention may include but is not limited to: a processor and a memory; a memory for storing a computer program; a processor for executing the intelligent health management method shown in any embodiment of the present invention by calling a computer program.

[0087] In an alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0088] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0089] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3In the figure, only one thick line is used to represent the bus 4002, but this does not mean that there is only one bus or one type of bus.

[0090] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0091] The memory 4003 is used to store the application code (computer program) for executing the solution of the present invention, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0092] Among them, the electronic device can also be a terminal device, and the terminal device can be any device that can install applications, including at least one of a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart car device.

[0093] It should be noted that Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0094] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, and when the computer program is executed by a processor, any of the above-mentioned intelligent health management methods is implemented.

[0095] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0096] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs any of the above-mentioned intelligent health management methods.

[0097] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the computer, partially on the computer, as a separate software package, partially on the computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0098] It should be understood that the flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the module, the program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0099] The computer-readable storage medium provided by the embodiment of the present invention may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or component.

[0100] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0101] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) by each other.

[0102] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0103] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0104] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An intelligent health management system, characterized in that: Including health data acquisition module and intelligent recommendation module; The health data acquisition module is used to: receive health data input by the patient in real time, where the health data input includes at least one of voice, text and image; The intelligent recommendation module is used to generate personalized health management suggestions using the patient's existing health data and provide them to the patient.

2. An intelligent health management system according to claim 1, characterized in that: It also includes a data encryption module and a data storage module; The data encryption module is used to: encrypt the health data of the patient received in real time; The data storage module is used to store the patient's health data in a pre-built cloud database.

3. An intelligent health management system according to claim 2, characterized in that: It also includes an interactive feedback module, which is used to: push a health feedback questionnaire to the patient, receive the patient's reply to the health feedback questionnaire, and obtain the patient's self-assessment data.

4. An intelligent health management system according to any one of claims 1 to 3, characterized in that: The health data input by the patient includes at least one of physiological parameters, diet, exercise status and emotions.

5. An intelligent health management method, characterized in that: include: Receiving health data input by the patient in real time, wherein the health data input includes at least one of voice, text and image; The patient's existing health data is used to generate personalized health management recommendations and provide them to the patient.

6. The intelligent health management method according to claim 5, characterized in that: Also includes: Encrypting patients’ health data received in real time; Store patients’ health data in a pre-built cloud database.

7. The intelligent health management method according to claim 6, characterized in that: Also includes: A health feedback questionnaire is pushed to the patient, and a response to the health feedback questionnaire made by the patient is received to obtain the self-assessment data of the patient.

8. An intelligent health management method according to any one of claims 5 to 7, characterized in that: The health data input by the patient includes at least one of physiological parameters, diet, exercise status and emotions.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, an intelligent health management method as claimed in any one of claims 5 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the intelligent health management method according to any one of claims 5 to 8 is implemented.

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