Exercise prescription service method and device, electronic equipment and storage medium

Abstract

The invention discloses an exercise prescription service method and device, electronic equipment and a storage medium, the method and device are applied to the electronic equipment, and specifically, feature extraction is performed on symptom information input by a user through one or more input modes based on an information input request of the user to obtain feature parameters; performing symptom classification based on the problem classifier feature parameters to obtain classification information; and based on the exercise health knowledge base, processing the symptom information and the classification information by using a large language model, and feeding back an exercise prescription to the user based on an obtained processing result. According to the scheme, the user can obtain the exercise prescription suitable for himself / herself without depending on a doctor or a therapist on the basis of an artificial intelligence mode, so that the problem that exercise diagnosis and treatment services cannot be provided for the user in time due to finiteness of medical resources is solved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and more specifically, to an exercise prescription service method, device, electronic device and storage medium. Background Art

[0002] Exercise prescription is formulated by rehabilitation physicians, rehabilitation therapists or physical education teachers, social sports instructors, personal fitness trainers, etc. based on the patient's or gym-goer's age, gender, general medical examination, rehabilitation medicine examination, exercise test, physical fitness / fitness test results, and their age, gender, health status, physical fitness, and cardiovascular and motor organ function conditions, combined with subjective and objective conditions, in the form of a prescription for the patient or gym-goer's appropriate exercise content, intensity, time and frequency, and points out precautions during exercise, so as to achieve the purpose of scientific and planned rehabilitation treatment or preventive fitness.

[0003] Currently, users can already communicate with doctors and therapists conveniently and quickly in real time through corresponding electronic application platforms. Doctors or therapists can formulate exercise prescriptions based on the user's specific situation and provide real-time feedback to the user, so that the user can carry out effective rehabilitation training according to the exercise prescription.

[0004] From the above description, it can be seen that the current solution is highly dependent on the expertise of doctors and therapists and real-time diagnosis based on expertise, that is, deep human involvement is required to obtain and feedback complete personalized exercise prescriptions to users. However, the limited medical resources, especially the time and energy of doctors and therapists, cannot provide users with exercise diagnosis and treatment services in a timely manner. Summary of the invention

[0005] In view of this, the present application provides an exercise prescription service method, device, electronic device and storage medium, which are used to solve the problem of being unable to provide users with exercise diagnosis and treatment services in a timely manner due to limited medical resources based on artificial intelligence means.

[0006] In order to achieve the above objectives, the proposed solution is as follows:

[0007] An exercise prescription service method is applied to an electronic device, and the exercise prescription service method comprises the following steps:

[0008] Based on the user's information input request, feature extraction is performed on the symptom information input by the user through one or more input methods to obtain feature parameters;

[0009] Classify the symptoms based on the characteristic parameters of the problem classifier to obtain classification information;

[0010] Based on a sports health knowledge base, the symptom information and the classification information are processed using a large language model, and exercise prescriptions are fed back to the user based on the obtained processing results.

[0011] Optionally, based on the user's information input request, feature extraction is performed on the symptom information input by the user through one or more input methods to obtain feature parameters, including the steps of:

[0012] receiving symptom information input by a user;

[0013] Feature extraction is performed on the symptom information to obtain the feature information.

[0014] Optionally, the input method is a text input method, a voice input method, and a graphical interface input method;

[0015] The symptom information includes part or all of symptom description information, medical history information and current health status information.

[0016] Optionally, the method is based on a sports health knowledge base and uses a large language model to process the symptom information and the classification information, and provides the user with exercise prescription feedback based on the obtained processing results, including the steps of:

[0017] Acquire the sports health knowledge base, the symptom information and the classification information;

[0018] Processing the sports health knowledge base, the symptom information and the classification information based on the large language model to obtain the exercise prescription;

[0019] The exercise prescription is outputted in a format specified by output prompt information of the user.

[0020] Optionally, the sports health knowledge base includes part or all of medical expertise, disease diagnosis criteria, treatment plans, drug information, and medical literature.

[0021] Optionally, the following steps are also included:

[0022] Accept feedback information from the user, optimize the exercise prescription based on the feedback information, and feed back the optimized exercise prescription to the user.

[0023] An exercise prescription service device is applied to an electronic device, and the exercise prescription service device comprises:

[0024] An input and feature extraction module is configured to extract features of symptom information input by the user through one or more input methods based on the user's information input request to obtain feature parameters;

[0025] An information classification module is configured to classify the characteristic parameters according to the problem classifier to obtain classification information;

[0026] A sports health knowledge module, configured to store the sports health knowledge base;

[0027] The exercise prescription generation module is configured to process the symptom information and the classification information based on the exercise health knowledge base and utilize a large language model, and to provide the user with exercise prescription feedback based on the obtained processing results.

[0028] Optionally, also include:

[0029] The feedback optimization module is configured to receive feedback information from the user, optimize the exercise prescription based on the feedback information, and feed back the optimized exercise prescription to the user.

[0030] An electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0031] The memory is used to store computer programs or instructions;

[0032] The processor is used to execute the computer program or instructions so that the electronic device implements the exercise prescription service method as described above.

[0033] A computer-readable storage medium is applied to an electronic device, wherein the storage medium carries one or more computer programs, and the one or more computer programs can be executed by the electronic device, so that the electronic device implements the exercise prescription service method as described above.

[0034] It can be seen from the above technical solutions that the present application discloses an exercise prescription service method, device, electronic device and storage medium. The method and device are applied to electronic devices, specifically, based on the user's information input request, feature extraction is performed on the symptom information input by the user through one or more input methods to obtain feature parameters; based on the problem classifier feature parameters, symptom classification is performed to obtain classification information; based on the sports health knowledge base, and using a large language model to process the symptom information and classification information, and based on the obtained processing results, the exercise prescription is fed back to the user. This solution enables users to obtain exercise prescriptions suitable for themselves without relying on doctors or therapists based on artificial intelligence, thereby solving the problem of being unable to provide users with exercise diagnosis and treatment services in a timely manner due to limited medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 The circulation of an exercise prescription service method according to an embodiment of the present application;

[0037] Figure 2 A schematic diagram of information input and parameter extraction of an embodiment is provided for this application;

[0038] Figure 3 A flowchart of another exercise prescription service method according to an embodiment of the present application;

[0039] Figure 4 A block diagram of an exercise prescription service device according to an embodiment of the present application;

[0040] Figure 5 A block diagram of another exercise prescription service device according to an embodiment of the present application;

[0041] Figure 6 A block diagram of another exercise prescription service device according to an embodiment of the present application;

[0042] Figure 7 A schematic diagram of the execution principle of the feedback optimization module of an embodiment of the present application;

[0043] Figure 8 A block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] Figure 1 This is a flow chart of an exercise prescription service method according to an embodiment of the present application.

[0046] like Figure 1As shown, the exercise prescription service method provided in this embodiment is applied to an electronic device, and is used to tailor an exercise prescription for a user based on the user's needs, without the help of a doctor or therapist. The electronic device can be understood as a computer, server or cloud platform with data computing and information processing capabilities, including a corresponding client and server. The exercise prescription service method includes the following steps:

[0047] S1. Receive symptom information based on the user's information input request.

[0048] That is, if the user sends an information input request through the corresponding client, the symptom information input by the user based on one or more input methods is received by responding to the request, and the feature parameters of the symptom information are obtained by extracting the features of the symptom information, such as Figure 2 The input method here can be text input, voice input and image interface input. The so-called graphic interface input method can be a user providing an image interface including multiple contents, and the user inputs the corresponding symptom information by clicking or dragging. The symptom information here includes but is not limited to symptom description information, medical history information and current health status information.

[0049] Accurately and comprehensively collecting the patient's chief complaint is the basis for subsequent diagnosis and treatment, directly affecting the quality and effectiveness of medical services, and extracting key information for subsequent steps. The specific process includes:

[0050] First, symptom information input by the user through one or more input methods is received.

[0051] Then, feature extraction is performed on the symptom information to obtain feature information in the symptom information.

[0052] S2. Classify the symptoms of the characteristic parameters based on the information classifier to obtain classification information.

[0053] After receiving the characteristic parameters of the symptom information input by the user, the characteristic parameters are classified based on the information classifier to obtain the classification information of the symptoms. The extracted parameters are classified and sorted, for example, by disease type, symptom category, patient age group, etc. The purpose of classification is to organize information more efficiently, which is convenient for subsequent diagnosis analysis and treatment plan recommendation.

[0054] Classification of characteristic parameters helps reduce information redundancy and improve processing efficiency, while also helping to discover potential disease patterns or trends.

[0055] S3. Use a large language model to process the above information and obtain an exercise prescription.

[0056] After obtaining the above symptom information and characteristic parameters, a large language model is used to process the symptom information and characteristic parameters as well as the pre-obtained sports health knowledge base to obtain an exercise prescription, and the exercise prescription is fed back to the user according to the user's needs. Specifically, the generation and feedback of the exercise prescription is realized through the following steps:

[0057] First, obtain the sports health knowledge base and the above-mentioned symptom information and classification parameters. Among them, the knowledge base stores medical expertise, disease diagnosis standards, treatment plans, drug information and medical literature. These are important sources of knowledge for the system to make intelligent diagnosis and recommend treatment plans. In the specific implementation, a database management system (DBMS) can be used to store and manage knowledge data, while providing efficient query and retrieval functions.

[0058] Then, based on the large language model, the sports health knowledge base, the symptom information and the classification information are processed to obtain the exercise prescription. This application uses advanced natural language processing technology and large language models LLM (such as GPT series, BERT, etc.), combined with the patient's symptom information, classification information and knowledge in the professional knowledge base, to generate personalized exercise prescriptions and treatment plan recommendations for users. The prescription content includes exercise type, exercise intensity, exercise duration, exercise frequency, etc.; these suggestions may include further examination suggestions, lifestyle adjustments, etc. At the same time, doctors can also personalize the recommended template prescriptions formed according to actual conditions to meet the specific needs and health conditions of different patients.

[0059] The introduction of LLM in this application enables the system to understand and handle the patient's complex conditions more intelligently and provide more personalized and accurate suggestions. The exercise prescription generated by the AI ​​exercise prescription assistant can be sent directly to the system console through the API interface for further editing by the physician or therapist. At the same time, it can also continue to learn and evolve, and continuously improve its accuracy and reliability.

[0060] Finally, after obtaining the exercise prescription, the specific content and structure of the exercise prescription are processed based on the output format prompt information prompted by the user, and finally the exercise prescription whose format meets the user's needs is output.

[0061] It can be seen from the above technical solution that this embodiment provides an exercise prescription service method, which is applied to electronic devices, specifically, based on the user's information input request, feature extraction is performed on the symptom information input by the user through one or more input methods to obtain feature parameters; based on the problem classifier feature parameters, symptom classification is performed to obtain classification information; based on the sports health knowledge base, and using a large language model to process the symptom information and classification information, and based on the obtained processing results, the exercise prescription is fed back to the user. This solution enables users to obtain exercise prescriptions suitable for themselves without relying on doctors or therapists based on artificial intelligence, thereby solving the problem of being unable to provide users with exercise diagnosis and treatment services in a timely manner due to limited medical resources.

[0062] In a specific implementation of this embodiment, the following steps are also included: Figure 3 shown.

[0063] S4. Optimize the exercise prescription based on user feedback information.

[0064] That is, after receiving the exercise prescription sent by the system, the user can provide feedback on the exercise effect or other opinions and suggestions based on his or her own experience. After obtaining the exercise effect, opinions or suggestions, the exercise prescription can be optimized and the optimized exercise prescription can be fed back to the user so that the user can get better recovery.

[0065] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square 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 implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square 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.

[0066] Although operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0067] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0068] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, 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 user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving 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.

[0069] Figure 4 This is a block diagram of an exercise prescription service device according to an embodiment of the present application.

[0070] like Figure 4 As shown, the exercise prescription service device provided in this embodiment is applied to an electronic device, and is used to tailor an exercise prescription for a user based on the needs of the user, without the help of a doctor or therapist. The electronic device can be understood as a computer, server or cloud platform with data computing and information processing capabilities, including a corresponding client and server. The exercise prescription service device includes an input and feature extraction module 10, an information classification module 20, an exercise health knowledge module 30 and an exercise prescription generation module 40.

[0071] The input and feature extraction module can also receive symptom information based on the user's information input request.

[0072] That is, if the user sends an information input request through the corresponding client, the symptom information input by the user based on one or more input methods is received by responding to the request, and the feature parameters of the symptom information are obtained by extracting the features of the symptom information, such as Figure 2 The input method here can be text input, voice input and image interface input. The so-called graphic interface input method can be a user providing an image interface including multiple contents, and the user inputs the corresponding symptom information by clicking or dragging. The symptom information here includes but is not limited to symptom description information, medical history information and current health status information.

[0073] Accurately and comprehensively collecting the patient's chief complaint is the basis for subsequent diagnosis and treatment, directly affecting the quality and effectiveness of medical services, and extracting key information for subsequent steps. The specific process includes:

[0074] First, symptom information input by the user through one or more input methods is received.

[0075] Then, feature extraction is performed on the symptom information to obtain feature information in the symptom information.

[0076] The information classification module is used to classify symptoms of characteristic parameters based on the information classifier to obtain classification information.

[0077] After receiving the characteristic parameters of the symptom information input by the user, the characteristic parameters are classified based on the information classifier to obtain the classification information of the symptoms. The extracted parameters are classified and sorted, for example, by disease type, symptom category, patient age group, etc. The purpose of classification is to organize information more efficiently, which is convenient for subsequent diagnosis analysis and treatment plan recommendation.

[0078] Classification of characteristic parameters helps reduce information redundancy and improve processing efficiency, while also helping to discover potential disease patterns or trends.

[0079] The sports health knowledge module is used to store the sports health knowledge base. Among them, the knowledge base stores medical expertise, disease diagnosis standards, treatment plans, drug information and medical literature. These are important sources of knowledge for the system to make intelligent diagnosis and recommend treatment plans. In specific implementation, a database management system (DBMS) can be used to store and manage knowledge data, while providing efficient query and retrieval functions.

[0080] The exercise prescription generation module is used to process the above information using a large language model to obtain an exercise prescription.

[0081] After obtaining the above symptom information and characteristic parameters, the symptom information and characteristic parameters and the pre-obtained sports health knowledge base are processed using a large language model to obtain a sports prescription, and the sports prescription is fed back to the user according to the user's needs. Specifically, the sports prescription generation module includes an information input unit 41, a generation execution unit 42 and an output execution unit 43, such as Figure 5 shown.

[0082] The information input unit is used to obtain the sports health knowledge base and the above-mentioned symptom information and classification parameters.

[0083] The generation execution unit is used to process the sports health knowledge base, the symptom information and the classification information based on the large language model to obtain the exercise prescription. This application uses advanced natural language processing technology and large language models LLM (such as GPT series, BERT, etc.), combined with the patient's symptom information, classification information and knowledge in the professional knowledge base, to generate personalized exercise prescriptions and treatment plan recommendations for users. The prescription content includes exercise type, exercise intensity, exercise duration, exercise frequency, etc.; these suggestions may include further examination suggestions, lifestyle adjustments, etc. At the same time, doctors can also personalize the recommended template prescriptions formed according to actual conditions to meet the specific needs and health conditions of different patients.

[0084] The introduction of LLM in this application enables the system to understand and handle the patient's complex conditions more intelligently and provide more personalized and accurate suggestions. The exercise prescription generated by the AI ​​exercise prescription assistant can be sent directly to the system console through the API interface for further editing by the physician or therapist. At the same time, it can also continue to learn and evolve, and continuously improve its accuracy and reliability.

[0085] The output execution unit is used to process the specific content and structure of the exercise prescription based on the output format prompt information prompted by the user after the generation execution unit generates the exercise prescription, and finally output the exercise prescription whose format meets the user's needs.

[0086] It can be seen from the above technical solution that this embodiment provides an exercise prescription service device, which is applied to electronic devices, specifically based on the user's information input request, extracting features from the symptom information input by the user through one or more input methods to obtain feature parameters; classifying symptoms based on the problem classifier feature parameters to obtain classification information; based on the sports health knowledge base, and using a large language model to process the symptom information and classification information, and based on the obtained processing results, feedback the exercise prescription to the user. This solution enables users to obtain exercise prescriptions suitable for themselves without relying on doctors or therapists based on artificial intelligence, thereby solving the problem of being unable to provide users with exercise diagnosis and treatment services in a timely manner due to limited medical resources.

[0087] In a specific implementation of this embodiment, a feedback optimization module 50 is also included, such as Figure 6 shown.

[0088] The feedback optimization module is used to optimize the exercise prescription based on the user's feedback information.

[0089] That is, after receiving the exercise prescription sent by the system, the user can give feedback on the exercise effect or other opinions and suggestions based on his or her own experience. After obtaining the exercise effect, opinions or suggestions, the exercise prescription can be optimized and fed back to the user after the optimization, so that the user can get better rehabilitation. The specific execution principle of this module is as follows Figure 7 shown.

[0090] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0091] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0092] Figure 8 A block diagram of an electronic device according to an embodiment of the present application.

[0093] Reference below Figure 8 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. The electronic device is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0094] The electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 802 or a program loaded from an input device 806 into a random access memory RAM 803. In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, ROM, and RAM are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0095] Typically, the following devices may be connected to the I / O interface: input devices including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 808 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 809. The communication device 809 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various devices, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0096] The present application also provides an embodiment of a computer-readable storage medium.

[0097] The computer-readable storage medium is applied to an electronic device and carries one or more computer programs. When the one or more computer programs are executed by the electronic device, the electronic device extracts features of the symptom information input by the user through one or more input methods based on the user's information input request to obtain feature parameters; classifies symptoms based on the problem classifier feature parameters to obtain classification information; processes the symptom information and classification information based on the sports health knowledge base and using a large language model, and feeds back exercise prescriptions to the user based on the obtained processing results. This solution enables users to obtain exercise prescriptions suitable for themselves without relying on doctors or therapists based on artificial intelligence, thereby solving the problem of being unable to provide users with sports diagnosis and treatment services in a timely manner due to limited medical resources.

[0098] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. 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 of the above.

[0099] In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0100] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0101] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0102] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0103] The technical solution provided by the present invention is introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for providing exercise prescription services, applied to electronic equipment, characterized in that: The exercise prescription service method comprises the steps of: Based on the user's information input request, feature extraction is performed on the symptom information input by the user through one or more input methods to obtain feature parameters; Classify the symptoms based on the characteristic parameters of the problem classifier to obtain classification information; Based on a sports health knowledge base, the symptom information and the classification information are processed using a large language model, and exercise prescriptions are fed back to the user based on the obtained processing results.

2. The exercise prescription service method according to claim 1, characterized in that: The method of extracting features of symptom information input by the user through one or more input methods based on the user's information input request to obtain feature parameters includes the following steps: receiving symptom information input by a user; Feature extraction is performed on the symptom information to obtain the feature information.

3. The exercise prescription service method according to claim 2, characterized in that: The input method includes text input method, voice input method and graphical interface input method; The symptom information includes part or all of symptom description information, medical history information and current health status information.

4. The exercise prescription service method according to claim 1, characterized in that: The method is based on a sports health knowledge base and uses a large language model to process the symptom information and the classification information, and based on the obtained processing results, feeds back an exercise prescription to the user, including the steps of: Acquire the sports health knowledge base, the symptom information and the classification information; Processing the sports health knowledge base, the symptom information and the classification information based on the large language model to obtain the exercise prescription; The exercise prescription is outputted in a format specified by output prompt information of the user.

5. The exercise prescription service method according to claim 4, characterized in that: The sports health knowledge base includes part or all of medical expertise, disease diagnosis standards, treatment plans, drug information and medical literature.

6. The exercise prescription service method according to any one of claims 1 to 5, characterized in that: Also includes the steps: Accept feedback information from the user, optimize the exercise prescription based on the feedback information, and feed back the optimized exercise prescription to the user.

7. An exercise prescription service device, applied to electronic equipment, characterized in that: The exercise prescription service device comprises: An input and feature extraction module is configured to extract features of symptom information input by the user through one or more input methods based on the user's information input request to obtain feature parameters; An information classification module is configured to classify the characteristic parameters according to the problem classifier to obtain classification information; A sports health knowledge module, configured to store the sports health knowledge base; The exercise prescription generation module is configured to process the symptom information and the classification information based on the exercise health knowledge base and utilize a large language model, and to provide the user with exercise prescription feedback based on the obtained processing results.

8. The exercise prescription service device according to claim 7, characterized in that: Also includes: The feedback optimization module is configured to receive feedback information from the user, optimize the exercise prescription based on the feedback information, and feed back the optimized exercise prescription to the user.

9. An electronic device, characterized in that: The electronic device comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is used to execute the computer program or instruction so that the electronic device implements the exercise prescription service method according to any one of claims 1 to 6.

10. A computer-readable storage medium, applied to an electronic device, characterized in that: The storage medium carries one or more computer programs, and the one or more computer programs can be executed by the electronic device, so that the electronic device implements the exercise prescription service method according to any one of claims 1 to 6.

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