Personalized health management suggestion recommendation method, system and device and storage medium

Abstract

The invention discloses a personalized health management suggestion recommendation method, system and device and a storage medium, and relates to the technical field of intelligent health management, and the method comprises the steps: obtaining current health data, current economic conditions and current health effect appeal data inputted by a target user; generating personalized health management suggestions according to the current health data, the historical health data, the current economic condition and the current health effect appeal data of the target user; and recommending the personalized health management suggestions to the target user. In the invention, when the personalized health management suggestion is generated, the current health data, the historical health data, the current economic condition and the current health effect appeal data of the target user can be comprehensively considered, so that the generated personalized health management suggestion can conform to the economic ability of the target user, thereby reducing the economic pressure of the target user and improving the user experience. And the satisfaction degree and the trust sense of the target user are improved.

Description

Background Art

[0002] Currently, when some institutions such as traditional medical systems or health management institutions provide health management advice to patients, they mainly rely on doctors' experience and standardized treatment processes, lacking sufficient consideration of patients' individual differences. Especially in grass-roots medical institutions and health management institutions, although doctors have a certain understanding of patients' family backgrounds and economic conditions, they lack a systematic method to convert this information into effective health management advice. In addition, existing medical recommendation systems often fail to fully consider patients' economic affordability and their demands for treatment effects, resulting in the problem that patients face a mismatch between economic burden and treatment effects. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a personalized health management advice recommendation method, system, device and storage medium in view of the deficiencies of the prior art, specifically as follows:

[0004] 1) In the first aspect, the present invention provides a personalized health management advice recommendation method, and the specific technical solution is as follows:

[0005] Obtain the current health data, current economic status and current health effect demand data input by the target user;

[0006] Generate personalized health management advice according to the target user's current health data, historical health data, current economic status and current health effect demand data;

[0007] Recommend the personalized health management advice to the target user.

[0008] The beneficial effects of a personalized health management advice recommendation method provided by the present invention are as follows:

[0009] When generating personalized health management advice, it can comprehensively consider the target user's current health data, historical health data, current economic status and current health effect demand data, so that the generated personalized health management advice can meet the economic ability of the target user, reduce the economic pressure of the target user, and improve the satisfaction and trust of the target user.

[0010] On the basis of the above solution, a personalized health management advice recommendation method of the present invention can also be improved as follows.

[0011] Further, it further includes:

[0012] When the target user is implementing the personalized health management advice, regularly push an execution feedback questionnaire to the target user;

[0013] Receive the responses made by the target user to the execution feedback questionnaire and process the data to obtain the self-assessment data of the target user when implementing the personalized health management suggestions;

[0014] Modify the personalized health management suggestions according to the self-assessment data.

[0015] The beneficial effect of adopting the above further solution is that by modifying the personalized health management suggestions, the health management effect can be guaranteed.

[0016] Furthermore, the personalized health management suggestions include at least one of a diet combination plan, an exercise and fitness plan, and an emotion management plan.

[0017] Furthermore, the ways of inputting the current health data, the current economic status, and the current health effect requirement data include at least one of a voice way, a text way, and an image way.

[0018] 2) In the second aspect, the present invention also provides a personalized health management suggestion recommendation system, and the specific technical solution is as follows:

[0019] It includes a data acquisition module, a suggestion generation module, and a recommendation module;

[0020] The data acquisition module is used to: acquire the current health data, the current economic status, and the current health effect requirement data input by the target user;

[0021] The suggestion generation module is used to: generate personalized health management suggestions according to the current health data, the historical health data, the current economic status, and the current health effect requirement data of the target user;

[0022] The recommendation module is used to: recommend the personalized health management suggestions to the target user.

[0023] On the basis of the above solution, a personalized health management suggestion recommendation system of the present invention can also be improved as follows.

[0024] Furthermore, it further includes a questionnaire sending module, a self-assessment data acquisition module, and a correction module;

[0025] The questionnaire sending module is used to: when the target user is implementing the personalized health management suggestions, regularly push an execution feedback questionnaire to the target user;

[0026] The self-assessment data acquisition module is used to: receive the responses made by the target user to the execution feedback questionnaire and process the data to obtain the self-assessment data of the target user when implementing the personalized health management suggestions;

[0027] The correction module is used to: modify the personalized health management suggestions according to the self-assessment data.

[0028] Furthermore, the personalized health management suggestions include at least one of a diet plan, an exercise plan, and an emotion management plan.

[0029] Furthermore, the ways of inputting the current health data, the current economic status, and the current health effect requirement data include at least one of voice, text, and image.

[0030] 3) In a third aspect, the present invention also provides an electronic device, which includes a processor coupled to a memory. At least one computer program is stored in the memory and is loaded and executed by the processor to enable the electronic device to implement any one of the above-mentioned personalized health management suggestion recommendation methods.

[0031] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements any one of the above-mentioned personalized health management suggestion recommendation methods.

[0032] It should be noted that for the beneficial effects obtained by the technical solutions and corresponding possible implementation manners of the second to fourth aspects of the present invention, reference may be made to the technical effects of the first aspect and its corresponding possible implementation manners described above, which will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments of the present invention:

[0034] Figure 1 It is a schematic flowchart of a method for recommending personalized health management suggestions according to an embodiment of the present invention;

[0035] Figure 2 It is a schematic structural diagram of a system for recommending personalized health management suggestions according to an embodiment of the present invention;

[0036] Figure 3 It is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The principles and features of the present invention are described below, and the examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0038] The technical solutions of the present invention and how the technical solutions of the present invention solve the above technical problems are described in detail below with specific embodiments. These 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 with reference to the drawings.

[0039] As Figure 1 shown, a personalized health management advice recommendation method according to an embodiment of the present invention includes the following steps:

[0040] S1. Obtain the current health data, current economic status, and current health effect requirement data input by the target user;

[0041] Among them, the current health data, current economic status, and current health effect requirement data input by the target user are obtained through an interaction platform.

[0042] Among them, the interaction platform is divided into a management end and a user end. The management end can be an executable program in exe format and can be installed on a computer device. The management end can also be an APP program and can be installed on the smart phone or tablet computer of the management personnel. The user end can be an APP program and is convenient to be installed on the smart phone or tablet computer of the target user, so that each target user can input the current health data, current economic status, and current health effect requirement data in real time, and can also have a real-time conversation with the management end through a dialog box.

[0043] Among them, the current health data, current economic status, and current health effect requirement data input by the target user can be obtained through at least one of voice mode, text mode, and image mode.

[0044] Among them, on the display interface of the user end, click boxes for voice mode, text mode, and image mode are provided, so as to facilitate the target user to input the current health data, current economic status, and current health effect requirement data.

[0045] Among them, the current health data includes: current physiological parameters, current diet collocation, current exercise situation, etc., which can be set according to the actual situation. The current health data input by the target user can also include the current medication situation. The physiological parameters include blood glucose value, blood pressure value, heart rate value, etc.

[0046] Among them, the current economic status includes: current monthly income, disposable amount, family member situation, etc., which can be set according to the actual situation.

[0047] Among them, the personalized health management advice includes at least one of a diet collocation plan, an exercise and fitness plan, and an emotion management plan.

[0048] Among them, the personalized health management advice can also include a treatment plan.

[0049] Among them, the current health effect requirement data can be set according to different personalized health management suggestions. For example, when the personalized health management suggestion is a diet plan, the current health effect requirement data can be: control the weight to 65 kg, etc. When the personalized health management suggestion is an exercise plan, the current health effect requirement data can be: obvious muscle gain and weight loss of 5 kg, etc. When the personalized health management suggestion is an emotion management plan, the current health effect requirement data can be: not getting angry within a day, or not being pessimistic within 6 hours, etc. When the personalized health management suggestion is a treatment plan, the current health effect requirement data can be: significant treatment effect, pain relief, blood sugar control, blood pressure control, etc.

[0050] Among them, the target user can be any preset user, and the preset user is: a user registered on the interaction platform.

[0051] S2. Generate personalized health management suggestions according to the target user's current health data, historical health data, current economic status, and current health effect requirement data. Specifically:

[0052] 1) When the personalized health management suggestion is a diet plan, collect the historical health data, historical economic status, and historical health effect requirement data of multiple preset users in advance, and set the diet reference plan for each preset user. Among them, the diet reference plan can be a diet plan determined after being demonstrated by multiple experts. Construct the first sample set. Based on the first sample set and under the constraint of the economic status, train the first preset deep learning model to obtain the trained first preset deep learning model, and use the trained first preset deep learning model to generate a diet plan.

[0053] Among them, the first preset deep learning model can be a convolutional neural network or a long short-term memory neural network, etc., which can be set according to the actual situation.

[0054] 2) When the personalized health management suggestion is an exercise plan, collect the historical health data, historical economic status, and historical health effect requirement data of multiple preset users in advance, and set the exercise reference plan for each preset user. Among them, the exercise reference plan can be an exercise plan determined after being demonstrated by multiple experts. Construct the second sample set. Based on the second sample set and under the constraint of the economic status, train the second preset deep learning model to obtain the trained second preset deep learning model, and use the trained second preset deep learning model to generate an exercise plan.

[0055] Among them, the second preset deep learning model can be a convolutional neural network or a long short-term memory neural network, etc., which can be set according to the actual situation.

[0056] 3) When the personalized health management advice is an emotion management plan, collect the historical health data, historical economic status, and historical health effect requirement data of multiple preset users in advance, and set the emotion management reference plan for each preset user. The emotion management reference plan can be an emotion management plan determined after being demonstrated by multiple experts. Construct a third sample set. Based on the third sample set and under the constraint of the economic status, train the third preset deep learning model to obtain a trained third preset deep learning model, and use the trained third preset deep learning model to generate an emotion management plan.

[0057] Among them, the third preset deep learning model can be a convolutional neural network or a long short-term memory neural network, etc., which can be set according to the actual situation.

[0058] 4) When the personalized health management advice is a treatment plan, collect the historical health data, historical economic status, and historical health effect requirement data of multiple preset users in advance, and set the treatment reference plan for each preset user. The treatment reference plan can be a treatment plan determined after being demonstrated by multiple experts. Construct a fourth sample set. Based on the fourth sample set and under the constraint of the economic status, train the fourth preset deep learning model to obtain a trained fourth preset deep learning model, and use the trained fourth preset deep learning model to generate a treatment plan.

[0059] Among them, the fourth preset deep learning model can be a convolutional neural network or a long short-term memory neural network, etc., which can be set according to the actual situation.

[0060] S3. Recommend the personalized health management advice to the target user.

[0061] Optionally, in the above technical solution, it further includes:

[0062] S4. When the target user is executing the personalized health management advice, regularly push an execution feedback questionnaire to the target user;

[0063] S5. Receive the reply made by the target user to the execution feedback questionnaire and perform data processing to obtain the self-assessment data of the target user when executing the personalized health management advice;

[0064] S6. Amend the personalized health management advice according to the self-assessment data.

[0065] Among them, the trained first preset deep learning model, the trained second preset deep learning module, the trained third preset deep learning model, and the trained fourth preset deep learning model are used to predict the self-assessment data of the target user after a preset duration. The preset duration can be 1 week, 1 month, etc., to obtain the assessment data of the target user. According to the predicted self-assessment data, a first portrait of the target user is established. According to the real assessment data of the target user, a second portrait of the target user is established. The similarity between the first portrait and the second portrait is calculated. When the similarity is less than the first preset similarity threshold, the target user is asked to confirm whether to strictly execute the personalized health management advice. When the target user confirms to strictly execute the personalized health management advice, the process data of the target user when executing the personalized health management advice is received, and the personalized health management advice is corrected by means of expert demonstration, etc., in combination with the self-assessment data of the target user on the personalized health management advice.

[0066] When the target user confirms that the personalized health management advice is not strictly executed, the predicted self-assessment data is provided to the target user to encourage the target user to strictly execute the personalized health management advice.

[0067] Optionally, in the above technical solution, it further includes:

[0068] S10. Generate personalized health management advice for multiple target users, and calculate the semantic similarity between the personalized health management advice of every two target users;

[0069] S11. Group the multiple target users according to the semantic similarity to obtain multiple first groups, and the semantic similarity between every two target users in each first group exceeds the second preset similarity threshold. Among them, the first preset similarity threshold can be set according to the actual situation.

[0070] S12. Group all the target users in any first group according to the location addresses of each target user in the first group to obtain multiple corresponding second groups in the first group until each first group is grouped to obtain multiple corresponding second groups in each first group. Among them, the distance between the location addresses of every two target users in each second group is not greater than the preset distance threshold. The preset distance threshold can be 5 km, 10 km, etc., and can be set according to the actual situation.

[0071] S13. Obtain multiple health management intervention institutions within the address range corresponding to any second group, sort the multiple health management intervention institutions according to the scores of each health management intervention institution to form a recommended list, and recommend the recommended list to each target user in the second group until the recommended list is pushed to each target user in each second group.

[0072] Among them, when the personalized health management advice is a diet plan, the health management intervention institution can be: a nutrition and health consultation center with a nutritionist or a professional nutrition meal preparation company; when the personalized health management advice is an exercise and fitness plan, the health management intervention institution can be: a gym or beauty salon with a fitness coach, etc.; when the personalized health management advice is an emotion management plan, the health management intervention institution can be: a service institution with a psychologist; when the personalized health management advice is a treatment plan, the health management intervention institution can be: a hospital.

[0073] Compared with the prior art, which needs to push information to each target user separately, having the disadvantages of low recommendation efficiency and large data calculation amount, in the present invention, through S10 to S13, multiple target users with high similarity are analyzed, and then a recommendation list is pushed, with high recommendation accuracy, high recommendation efficiency, and small data calculation amount.

[0074] Among them, in S11, calculating the semantic similarity between the personalized health management advice of every two target users can be achieved in the following ways:

[0075] 1) The first way includes the following steps:

[0076] S110: Preset a structured form for the personalized health management advice. The structured form includes multiple items to be filled. The meanings of the multiple items to be filled can be set according to the actual situation. For example, when the personalized health management advice is a diet plan, the multiple items to be filled can be: vegetables, eggs and milk, fruits, meats, and water, etc. The amount of each vegetable and the amount of eggs and milk can also be set according to the situation.

[0077] Fill the generated personalized health management advice into the corresponding structured form.

[0078] S111: Extract the content filled in each item to be filled from the filled structured form of each target user, perform one-hot encoding, and splice the multiple one-hot encodings corresponding to each target user in the same order to obtain multiple spliced one-hot encodings.

[0079] S112: Use the same hash function to perform hash calculation on each spliced one-hot encoding to obtain the first hash calculation result of each spliced one-hot encoding.

[0080] S113: Calculate and take the first similarity between the first hash calculation results corresponding to any two target users as the semantic similarity between the personalized health management advice of these two target users, until the semantic similarity between the personalized health management advice of every two target users is calculated.

[0081] Through S110 to S113, based on the content filled in each item to be filled, the semantic similarity between the first hash calculation results corresponding to every two target users can be accurately calculated. And by calculating the similarity between different hash calculation results, compared with directly calculating the similarity between data, the data calculation amount will be reduced.

[0082] 2) The second method includes the following steps:

[0083] S210. Preset a structured form for personalized health management suggestions. The structured form includes multiple items to be filled. The meanings of multiple items to be filled can be set according to the actual situation. For example, when the personalized health management suggestion is a diet combination plan, the multiple items to be filled can be: vegetables, eggs and milk, fruits, meats, and water, etc. The amount of each vegetable and the amount of eggs and milk can also be set according to the situation.

[0084] Fill the generated personalized health management suggestions into the corresponding structured form.

[0085] S211. Extract the content filled in each item to be filled from the filled structured form of each target user, perform one-hot encoding, and splice the multiple one-hot encodings corresponding to each target user in the same order to obtain multiple spliced one-hot encodings.

[0086] S212. Use the same hash function to perform hash calculation on each spliced one-hot encoding to obtain the first hash calculation result of each spliced one-hot encoding. And randomly assign different weights to each item to be filled. Use the same hash function and combine the weights corresponding to each item to be filled to perform hash calculation on each spliced one-hot encoding to obtain the second hash calculation result of each spliced one-hot encoding.

[0087] S213. Calculate the first similarity between the first hash calculation results corresponding to any two target users, and calculate the second similarity between the second hash calculation results corresponding to the any two target users.

[0088] Pre-establish a functional relationship between the weight corresponding to each item to be filled and the similarity deviation through data simulation. The similarity deviation refers to the deviation between the similarity obtained when the weight corresponding to each item to be filled participates in the calculation and the similarity obtained when the weight corresponding to each item to be filled does not participate in the calculation.

[0089] S214. Substitute the weight assigned to each item to be filled into the functional relationship, calculate the target deviation, and judge the deviation between the first similarity and the second similarity of the any two target users. When the deviation exceeds the target deviation, send a reminder.

[0090] In the prior art, different personalized health management suggestions may be calculated to obtain hash calculation results with extremely small deviations, which may easily lead to inaccurate grouping. By randomly assigning different weights to each item to be filled in, the differences between the calculated different personalized health management suggestions are made obvious. However, when the deviation between the first similarity and the second similarity of any two target users exceeds the target deviation, it indicates that there may be a problem. After issuing a reminder, the difference between the two personalized health management suggestions can be manually judged, and the first similarity can be manually assigned a value.

[0091] S215. Using the first similarity between the first hash calculation results corresponding to any two target users as the semantic similarity between the personalized health management suggestions for the two target users, until the semantic similarity between the personalized health management suggestions for every two target users is calculated.

[0092] Through S214, the first similarity that may have problems can be eliminated, thereby improving the accuracy of grouping.

[0093] In another embodiment, the steps include:

[0094] S101. Collect basic information about the patient, including family background, economic status, medical history, and expectations for treatment outcomes;

[0095] S102. Based on machine learning and big data analysis technology, build a personalized medical recommendation algorithm to generate a treatment plan for each patient by analyzing the collected data.

[0096] S103. The algorithm model is combined with the user interface to facilitate use by doctors and patients. Doctors can make adjustments based on the recommended plan, and patients can understand the reasons for the recommendation.

[0097] S104. Establish a patient feedback system to collect patient feedback on recommended plans, continuously optimize the algorithm model, and improve the accuracy and practicality of recommendations.

[0098] In this embodiment, a personalized medical recommendation algorithm is established that comprehensively considers the patient's family economic status, personal medical history, and treatment effect requirements to provide a more accurate and effective treatment plan. The following technical effects are achieved:

[0099] 1) Personalization: Customize the treatment plan according to the patient's specific situation to improve the targetedness and effectiveness of the treatment.

[0100] 2) Economical and applicable: Fully consider the financial burden of the patient and recommend treatment plans that are in line with their financial capabilities to reduce the financial pressure on the patient.

[0101] 3) Improve satisfaction: By meeting patients' demands for treatment effects, improve patients' treatment satisfaction and trust.

[0102] In the above embodiments, although the steps are numbered as S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, and this is also within the protection scope of the present invention. It can be understood that in some embodiments, it may include some or all of the above embodiments.

[0103] Such as Figure 2 As shown, a personalized health management advice recommendation system 200 according to an embodiment of the present invention includes a data acquisition module 201, an advice generation module 202, and a recommendation module 203;

[0104] The data acquisition module 201 is configured to: acquire the current health data, current economic status, and current health effect demand data input by the target user;

[0105] The advice generation module 202 is configured to: generate personalized health management advice according to the current health data, historical health data, current economic status, and current health effect demand data of the target user;

[0106] The recommendation module 203 is configured to: recommend the personalized health management advice to the target user.

[0107] Optionally, in the above technical solution, it further includes a questionnaire sending module, a self-assessment data acquisition module, and a correction module;

[0108] The questionnaire sending module is configured to: when the target user is executing the personalized health management advice, regularly push an execution feedback questionnaire to the target user;

[0109] The self-assessment data acquisition module is configured to: receive the reply made by the target user to the execution feedback questionnaire and perform data processing to obtain the self-assessment data of the target user when executing the personalized health management advice;

[0110] The correction module is configured to: correct the personalized health management advice according to the self-assessment data.

[0111] Optionally, in the above technical solution, the personalized health management advice includes at least one of a diet matching plan, an exercise and fitness plan, and an emotion management plan.

[0112] Optionally, in the above technical solution, the ways of inputting the current health data, current economic status, and current health effect demand data include at least one of a voice way, a text way, and an image way.

[0113] In another embodiment, it includes:

[0114] Data collection module: Collects the basic information of patients, including family background, economic status, medical history, and expectations for treatment effects.

[0115] Algorithm model: Based on machine learning and big data analysis technologies, constructs a personalized medical recommendation algorithm. By analyzing the collected data, generates treatment plans for each patient.

[0116] Recommendation system: Combines the algorithm model with the user interface for the convenience of doctors and patients. Doctors can adjust according to the recommended plan, and patients can understand the reasons for the recommendation.

[0117] Feedback mechanism: Establishes a patient feedback system to collect feedback information from patients on the recommended plan, continuously optimizes the algorithm model, and improves the accuracy and practicality of the recommendation.

[0118] For example, a certain hospital treats a 30-year-old female patient with a history of hypertension and average family economic status. Through the data collection module, the system obtains the patient's basic information, including family income, past medical history, and expectations for treatment effects (hoping for significant curative effects and less economic burden). Based on this information, the algorithm model recommends an affordable and effective drug after analysis and provides corresponding lifestyle adjustment suggestions. The doctor adjusts according to the recommended plan and finally formulates a personalized treatment plan.

[0119] For another example, a 45-year-old male patient seeks medical treatment for diabetes. The patient's family background is a single-income family with relatively large economic pressure. The system obtains the patient's medical history, economic status, and expectations for treatment effects (hoping to control blood sugar without increasing additional economic burden) through the data collection module. The algorithm model recommends several suitable drugs after analysis and provides relatively low-cost diet and exercise suggestions. The doctor refers to the recommended plan for treatment, and the patient gives relatively high satisfaction feedback during subsequent follow-up visits, and the blood sugar is well controlled.

[0120] It should be noted that the beneficial effects of the personalized health management advice recommendation system provided in the above embodiments are the same as those of the personalized health management advice recommendation method described above, and will not be elaborated here. In addition, when the system provided in the above embodiments realizes its functions, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiments, which will not be elaborated here.

[0121] Among them, the personalized health management advice recommendation system of the present invention can be a computer program (including program code) running on a computer device. For example, the personalized health management advice recommendation system of the present invention is an application software that can be used to execute the corresponding steps in the personalized health management advice recommendation method of the present invention.

[0122] In some embodiments, the personalized health management advice recommendation system of the present invention can be implemented in a combination of software and hardware. As an example, the personalized health management advice recommendation system of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the personalized health management advice recommendation method of the present invention. For example, the processor in the form of a hardware decoding processor can employ 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.

[0123] Among them, the modules involved in the embodiments of the present invention can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the module itself in some cases.

[0124] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements any one of the above-mentioned personalized health management advice recommendation methods. 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; the memory is used to store the computer program; the processor is used to execute the personalized health management advice recommendation method shown in any one of the embodiments of the present invention by calling the computer program.

[0125] In an alternative embodiment, an electronic device is provided, as Figure 3 shown Figure 3The electronic device 4000 shown includes: a processor 4001 and a memory 4003. Among them, the processor 4001 and the memory 4003 are connected, such as being connected through a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, and the transceiver 4004 can be used for data interaction between this electronic device and other electronic devices, such as data sending and / or data receiving, etc. It should be noted that in practical applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation to the embodiments of the present invention.

[0126] The processor 4001 can be a CPU (Central Processing Unit, central processing unit), a general-purpose processor, a DSP (Digital Signal Processor, data signal processor), an ASIC (Application Specific Integrated Circuit, application-specific integrated circuit), an FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in connection with the disclosure of the present invention. The processor 4001 can also be a combination that realizes computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0127] The bus 4002 may include a path for transmitting information between the above components. The bus 4002 can be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture, extended industry standard structure) bus, etc. The bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 3 only a thick line is used to represent the bus 4002 in the figure, but it does not mean that there is only one bus or one type of bus.

[0128] 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. It can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, 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 thereto.

[0129] The memory 4003 is used to store the application program code (computer program) for executing the solution of the present invention and is controlled by the processor 4001 for execution. The processor 4001 is used to execute the application program code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0130] Among them, the electronic device can also be a terminal device, and the terminal device can be any device on which an application can be installed, 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 vehicle-mounted device.

[0131] It should be noted that Figure 3 The electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0132] A computer-readable storage medium according to an embodiment of the present invention has a computer program stored thereon, and when the computer program is executed by a processor, it implements any one of the above-mentioned personalized health management recommendation methods.

[0133] Optionally, the computer-readable storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0134] In an exemplary embodiment, a computer program product or a computer program is further provided. The computer program product or the computer program includes computer instructions that are 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 executes any one of the above-mentioned personalized health management recommendation methods.

[0135] Computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's 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 user's 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 (for example, by using an Internet service provider to connect through the Internet).

[0136] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0137] The computer-readable storage medium provided by the embodiments of the present invention may be, but is not limited to, a system, device, or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of the computer-readable storage medium 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 of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program may be used by or in combination with an instruction execution system, device, or component.

[0138] The above computer-readable storage medium carries one or more programs. When the above one or more programs are executed by the electronic device, the electronic device is caused to execute the method shown in the above embodiments.

[0139] The above description is only the preferred embodiments of the present invention and the description of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present invention.

[0140] 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 are used to limit a specific order or sequence. The order of use of similar objects may be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0141] 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: it can be completely hardware, can also be completely software (including firmware, resident software, microcode, etc.), or can also be in the form of a combination of hardware and software, which is generally referred to as "circuit", "module", or "system" in this article. 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 media contain computer-readable program code.

[0142] 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 should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A personalized health management suggestion recommendation method, characterized in that: include: Obtain the current health data, current economic status, and current health effect demand data input by the target user; Generate personalized health management suggestions based on the target user's current health data, historical health data, current economic status, and current health effect demand data; The personalized health management suggestion is recommended to the target user.

2. A personalized health management suggestion recommendation method according to claim 1, characterized in that: Also includes: When the target user is executing the personalized health management suggestion, a feedback questionnaire is pushed to the target user at regular intervals; Receiving the target user's response to the execution feedback questionnaire and performing data processing to obtain the target user's self-assessment data when executing the personalized health management suggestion; The personalized health management recommendations are modified based on the self-assessment data.

3. A personalized health management suggestion recommendation method according to claim 1 or 2, characterized in that: The personalized health management suggestions include: at least one of a diet plan, an exercise and fitness plan, and an emotion management plan.

4. A personalized health management suggestion recommendation method according to claim 1 or 2, characterized in that: The method of inputting current health data, current economic status and current health effect demand data includes at least one of voice, text and image.

5. A personalized health management recommendation system, characterized in that: It includes data acquisition module, suggestion generation module and recommendation module; The data acquisition module is used to: acquire the current health data, current economic status and current health effect demand data input by the target user; The suggestion generation module is used to generate personalized health management suggestions based on the current health data, historical health data, current economic status and current health effect demand data of the target user; The recommendation module is used to recommend the personalized health management suggestion to the target user.

6. A personalized health management recommendation system according to claim 5, characterized in that: It also includes a questionnaire sending module, a self-assessment data acquisition module and a correction module; The questionnaire sending module is used to: when the target user is executing the personalized health management suggestion, regularly push the execution feedback questionnaire to the target user; The self-assessment data acquisition module is used to: receive the target user's response to the execution feedback questionnaire and perform data processing to obtain the target user's self-assessment data when executing the personalized health management advice; The correction module is used to correct the personalized health management suggestion according to the self-assessment data.

7. A personalized health management recommendation system according to claim 5 or 6, characterized in that: The personalized health management suggestions include: at least one of a diet plan, an exercise and fitness plan, and an emotion management plan.

8. A personalized health management recommendation system according to claim 5 or 6, characterized in that: The method of inputting current health data, current economic status and current health effect demand data includes at least one of voice, text and image.

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, a personalized health management suggestion recommendation method as claimed in any one of claims 1 to 4 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 method for recommending personalized health management advice is implemented as described in any one of claims 1 to 4.

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